feat(letsplot): implement timeseries-forecast-uncertainty

plots/timeseries-forecast-uncertainty/implementations/python/letsplot.py

@@ -1,7 +1,7 @@
 """ anyplot.ai
 timeseries-forecast-uncertainty: Time Series Forecast with Uncertainty Band
 Library: letsplot 4.9.0 | Python 3.13.13
-Quality: 88/100 | Updated: 2026-05-16
+Quality: 90/100 | Updated: 2026-05-19
 """
 # ruff: noqa: F405
 
@@ -14,41 +14,34 @@
 
 LetsPlot.setup_html()
 
-# Theme tokens (see prompts/default-style-guide.md)
+# 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"
+INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
 INK_GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
 
-# Okabe-Ito palette for categorical data
 OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
 
-# Data - Stock price with 36 months history + 12 month forecast
+# Monthly energy demand: 36 months history + 12 month forecast
 np.random.seed(42)
 
-# Historical period (36 months)
 dates_hist = pd.date_range("2023-01-01", periods=36, freq="MS")
-# Trend + volatility + noise
-trend = np.linspace(120, 185, 36)
-volatility = 8 * np.sin(np.linspace(0, 4 * np.pi, 36))
-noise = np.random.normal(0, 6, 36)
-actual = trend + volatility + noise
+trend = np.linspace(420, 510, 36)
+seasonal = 40 * np.sin(np.linspace(0, 6 * np.pi, 36))
+noise = np.random.normal(0, 12, 36)
+actual = trend + seasonal + noise
 
-# Forecast period (12 months)
 dates_forecast = pd.date_range("2026-01-01", periods=12, freq="MS")
-trend_fc = np.linspace(185, 210, 12)
-volatility_fc = 8 * np.sin(np.linspace(4 * np.pi, 5 * np.pi, 12))
-forecast = trend_fc + volatility_fc
+trend_fc = np.linspace(510, 545, 12)
+seasonal_fc = 40 * np.sin(np.linspace(6 * np.pi, 8 * np.pi, 12))
+forecast = trend_fc + seasonal_fc
+uncertainty_80 = np.linspace(18, 45, 12)
+uncertainty_95 = np.linspace(28, 68, 12)
 
-# Uncertainty grows with forecast horizon (realistic for financial predictions)
-uncertainty_80 = np.linspace(12, 35, 12)
-uncertainty_95 = np.linspace(18, 55, 12)
-
-# Build DataFrames
 df_hist = pd.DataFrame({"date": dates_hist, "value": actual, "series": "Historical"})
-
 df_fc = pd.DataFrame(
     {
         "date": dates_forecast,
@@ -61,54 +54,50 @@
     }
 )
 
-# Forecast start date for vertical line
 forecast_start = dates_forecast[0]
 
-# Combine line data for legend
-df_lines = pd.concat([df_hist[["date", "value", "series"]], df_fc[["date", "value", "series"]]], ignore_index=True)
-
-# Theme-adaptive theme
-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_GRID, size=0.3),
-    axis_title=element_text(color=INK, size=20),
-    axis_text=element_text(color=INK_SOFT, size=16),
-    axis_line=element_line(color=INK_SOFT, size=0.5),
-    plot_title=element_text(color=INK, size=24),
-    legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
-    legend_text=element_text(color=INK_SOFT, size=16),
-    legend_title=element_text(color=INK, size=18),
-    plot_caption=element_text(color=INK_SOFT, size=14),
-    legend_position="right",
-)
-
-# Plot
+# Plot — theme_classic gives L-shaped spines; theme() overrides specific elements
 plot = (
     ggplot()
-    # 95% confidence band (lighter, using position 5 - orange)
-    + geom_ribbon(aes(x="date", ymin="lower_95", ymax="upper_95"), data=df_fc, fill=OKABE_ITO[4], alpha=0.2)
-    # 80% confidence band (darker, using position 5 - orange)
-    + geom_ribbon(aes(x="date", ymin="lower_80", ymax="upper_80"), data=df_fc, fill=OKABE_ITO[4], alpha=0.35)
-    # Historical and Forecast lines with legend
-    + geom_line(aes(x="date", y="value", color="series"), data=df_lines, size=1.5)
-    # Vertical line at forecast start
-    + geom_vline(xintercept=forecast_start.timestamp() * 1000, color=INK_SOFT, size=0.8, linetype="dotted")
-    # Manual color scale: historical using brand green, forecast using orange
-    + scale_color_manual(values={"Historical": OKABE_ITO[0], "Forecast": OKABE_ITO[4]}, name="Series")
-    # Labels
+    # 95% CI (outer, lighter)
+    + geom_ribbon(aes(x="date", ymin="lower_95", ymax="upper_95"), data=df_fc, fill=OKABE_ITO[4], alpha=0.18)
+    # 80% CI (inner, darker)
+    + geom_ribbon(aes(x="date", ymin="lower_80", ymax="upper_80"), data=df_fc, fill=OKABE_ITO[4], alpha=0.38)
+    # Historical solid line (brand green)
+    + geom_line(
+        aes(x="date", y="value", color="series"), data=df_hist[["date", "value", "series"]], size=1.2, linetype="solid"
+    )
+    # Forecast dashed line (orange)
+    + geom_line(
+        aes(x="date", y="value", color="series"), data=df_fc[["date", "value", "series"]], size=1.2, linetype="dashed"
+    )
+    # Vertical marker at forecast boundary
+    + geom_vline(xintercept=forecast_start.timestamp() * 1000, color=INK_MUTED, size=0.6, linetype="dotted")
+    + scale_color_manual(values={"Historical": OKABE_ITO[0], "Forecast": OKABE_ITO[4]}, name="")
     + labs(
         x="Date",
-        y="Stock Price ($)",
-        title="timeseries-forecast-uncertainty · letsplot · anyplot.ai",
-        caption="Shaded bands: 80% confidence interval (darker) and 95% CI (lighter)",
+        y="Energy Demand (MWh)",
+        title="timeseries-forecast-uncertainty · python · letsplot · anyplot.ai",
+        caption="Bands: 80% CI (darker)  ·  95% CI (lighter)",
+    )
+    + ggsize(800, 450)
+    + theme_classic()
+    + theme(
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG),
+        panel_grid_major_y=element_line(color=INK_GRID, size=0.5),
+        axis_title=element_text(color=INK, size=14),
+        axis_text=element_text(color=INK_SOFT, size=12),
+        axis_line=element_line(color=INK_SOFT, size=0.5),
+        plot_title=element_text(color=INK, size=18),
+        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
+        legend_text=element_text(color=INK_SOFT, size=12),
+        legend_title=element_blank(),
+        plot_caption=element_text(color=INK_MUTED, size=11),
+        legend_position="bottom",
     )
-    # Base size scaled 3x on export = 4800 × 2700 px
-    + ggsize(1600, 900)
-    # Apply custom theme
-    + anyplot_theme
 )
 
 # Save PNG and HTML with theme suffix
-ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
 ggsave(plot, f"plot-{THEME}.html", path=".")