feat(letsplot): implement acf-pacf

plots/acf-pacf/implementations/python/letsplot.py

@@ -1,119 +1,116 @@
-""" pyplots.ai
+""" anyplot.ai
 acf-pacf: Autocorrelation and Partial Autocorrelation (ACF/PACF) Plot
-Library: letsplot 4.9.0 | Python 3.14.3
-Quality: 90/100 | Created: 2026-03-14
+Library: letsplot 4.10.1 | Python 3.13.13
+Quality: 89/100 | Updated: 2026-06-10
 """
 
+import os
+
 import numpy as np
 import pandas as pd
-from lets_plot import *  # noqa: F403
-from lets_plot.export import ggsave as export_ggsave
+from lets_plot import (
+    LetsPlot,
+    aes,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    facet_wrap,
+    geom_hline,
+    geom_point,
+    geom_ribbon,
+    geom_segment,
+    ggplot,
+    ggsave,
+    ggsize,
+    labs,
+    scale_color_manual,
+    scale_x_continuous,
+    theme,
+    theme_minimal,
+)
 from statsmodels.tsa.stattools import acf, pacf
 
 
-LetsPlot.setup_html()  # noqa: F405
+LetsPlot.setup_html()
+
+# Theme tokens — Imprint palette
+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"
+GRID = "rgba(26,26,23,0.15)" if THEME == "light" else "rgba(240,239,232,0.15)"
+BRAND = "#009E73"  # Imprint position 1 — always first series
 
-# Data - Simulate monthly airline-style passenger data with trend and seasonality
+# Data — monthly airline-style passenger series with trend and seasonality
 np.random.seed(42)
 n = 200
-time = np.arange(n)
-trend = 0.05 * time
-seasonal = 10 * np.sin(2 * np.pi * time / 12)
-noise = np.random.normal(0, 2, n)
-passengers = 100 + trend + seasonal + noise
+t = np.arange(n)
+series = 100 + 0.05 * t + 10 * np.sin(2 * np.pi * t / 12) + np.random.normal(0, 2, n)
 
-# Compute ACF and PACF
 n_lags = 36
-acf_values = acf(passengers, nlags=n_lags)
-pacf_values = pacf(passengers, nlags=n_lags)
+acf_vals = acf(series, nlags=n_lags)
+pacf_vals = pacf(series, nlags=n_lags)
+ci = 1.96 / np.sqrt(n)
 
-# 95% confidence interval
-ci_bound = 1.96 / np.sqrt(n)
+acf_df = pd.DataFrame({"lag": np.arange(n_lags + 1), "value": acf_vals, "zero": 0.0, "panel": "ACF"})
+acf_df["sig"] = (acf_df["lag"] > 0) & (acf_df["value"].abs() > ci)
 
-# Prepare ACF data (include lag 0)
-acf_lags = list(range(0, n_lags + 1))
-acf_df = pd.DataFrame(
-    {
-        "lag": acf_lags,
-        "value": acf_values.tolist(),
-        "zero": 0.0,
-        "significant": [abs(v) > ci_bound and i > 0 for i, v in enumerate(acf_values)],
-    }
-)
-acf_df["color"] = acf_df["significant"].map({True: "Significant", False: "Non-significant"})
+pacf_df = pd.DataFrame({"lag": np.arange(1, n_lags + 1), "value": pacf_vals[1:], "zero": 0.0, "panel": "PACF"})
+pacf_df["sig"] = pacf_df["value"].abs() > ci
 
-# Prepare PACF data (start from lag 1)
-pacf_vals = pacf_values[1:].tolist()
-pacf_lags = list(range(1, n_lags + 1))
-pacf_df = pd.DataFrame(
-    {"lag": pacf_lags, "value": pacf_vals, "zero": 0.0, "significant": [abs(v) > ci_bound for v in pacf_vals]}
-)
-pacf_df["color"] = pacf_df["significant"].map({True: "Significant", False: "Non-significant"})
+df = pd.concat([acf_df, pacf_df], ignore_index=True)
+df["label"] = df["sig"].map({True: "Significant", False: "Non-significant"})
 
-# Color palette
-sig_colors = ["#306998", "#C0392B"]  # Non-significant (blue), Significant (red)
-sig_labels = ["Non-significant", "Significant"]
+# CI band columns — semi-transparent shaded confidence zone behind the stems
+df["ci_ymin"] = -ci
+df["ci_ymax"] = ci
 
-# Common theme
-x_breaks = list(range(0, n_lags + 1, 6))
-common_theme = (
-    flavor_high_contrast_light()  # noqa: F405
-    + theme(  # noqa: F405
-        axis_text=element_text(size=16),  # noqa: F405
-        axis_title=element_text(size=20),  # noqa: F405
-        panel_grid_major_x=element_blank(),  # noqa: F405
-        panel_grid_minor=element_blank(),  # noqa: F405
-        panel_grid_major_y=element_line(color="#E0E0E0", size=0.4),  # noqa: F405
-        legend_text=element_text(size=16),  # noqa: F405
-        legend_title=element_blank(),  # noqa: F405
-        legend_position=[0.85, 0.85],  # noqa: F405
-        legend_background=element_rect(fill="white", color="#CCCCCC", size=0.5),  # noqa: F405
-    )
-)
+# Separate datasets for visual hierarchy — significant stems are drawn thicker
+sig_df = df[df["sig"]].copy()
+nonsig_df = df[~df["sig"]].copy()
 
-# ACF plot
-acf_plot = (
-    ggplot(acf_df, aes(x="lag", y="value", color="color"))  # noqa: F405
-    + geom_segment(  # noqa: F405
-        aes(x="lag", y="zero", xend="lag", yend="value", color="color"),  # noqa: F405
-        size=2.5,
-    )
-    + geom_point(aes(x="lag", y="value", color="color"), size=4.5)  # noqa: F405
-    + geom_hline(yintercept=0, color="#333333", size=0.5)  # noqa: F405
-    + geom_hline(yintercept=ci_bound, color="#999999", size=0.7, linetype="dashed")  # noqa: F405
-    + geom_hline(yintercept=-ci_bound, color="#999999", size=0.7, linetype="dashed")  # noqa: F405
-    + scale_color_manual(values=sig_colors, limits=sig_labels)  # noqa: F405
-    + scale_x_continuous(breaks=x_breaks)  # noqa: F405
-    + labs(x="", y="ACF", title="acf-pacf · letsplot · pyplots.ai")  # noqa: F405
-    + theme(plot_title=element_text(size=24, hjust=0.5, face="bold"))  # noqa: F405
-    + common_theme
-    + ggsize(1600, 500)  # noqa: F405
-)
+color_order = ["Significant", "Non-significant"]
+color_values = [BRAND, INK_MUTED]
 
-# PACF plot (no legend - shared with ACF)
-pacf_plot = (
-    ggplot(pacf_df, aes(x="lag", y="value", color="color"))  # noqa: F405
-    + geom_segment(  # noqa: F405
-        aes(x="lag", y="zero", xend="lag", yend="value", color="color"),  # noqa: F405
-        size=2.5,
+# Plot — faceted ACF / PACF panels; theme_minimal() as lets-plot built-in base preset
+plot = (
+    ggplot(df, aes(x="lag", y="value"))
+    + geom_ribbon(aes(x="lag", ymin="ci_ymin", ymax="ci_ymax"), fill=INK_SOFT, alpha=0.1)
+    + geom_hline(yintercept=0, color=INK_SOFT, size=0.5)
+    + geom_hline(yintercept=ci, color=INK_MUTED, size=0.7, linetype="dashed")
+    + geom_hline(yintercept=-ci, color=INK_MUTED, size=0.7, linetype="dashed")
+    + geom_segment(aes(x="lag", y="zero", xend="lag", yend="value", color="label"), data=nonsig_df, size=0.8)
+    + geom_segment(aes(x="lag", y="zero", xend="lag", yend="value", color="label"), data=sig_df, size=2.0)
+    + geom_point(aes(x="lag", y="value", color="label"), data=nonsig_df, size=1.5)
+    + geom_point(aes(x="lag", y="value", color="label"), data=sig_df, size=3.5)
+    + scale_color_manual(values=color_values, limits=color_order, name="")
+    + scale_x_continuous(breaks=list(range(0, n_lags + 1, 6)))
+    + facet_wrap("panel", ncol=1, scales="free_y")
+    + labs(x="Lag", y="Correlation", title="acf-pacf · python · letsplot · anyplot.ai")
+    + theme_minimal()
+    + theme(
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_border=element_blank(),
+        strip_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.5),
+        strip_text=element_text(color=INK, size=14, face="bold"),
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+        panel_grid_major_y=element_line(color=GRID, size=0.5),
+        axis_title=element_text(color=INK, size=12),
+        axis_text=element_text(color=INK_SOFT, size=10),
+        axis_line=element_line(color=INK_SOFT, size=0.5),
+        plot_title=element_text(color=INK, size=16, hjust=0.5),
+        legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.5),
+        legend_text=element_text(color=INK_SOFT, size=10),
+        legend_title=element_blank(),
+        legend_position="bottom",
     )
-    + geom_point(aes(x="lag", y="value", color="color"), size=4.5)  # noqa: F405
-    + geom_hline(yintercept=0, color="#333333", size=0.5)  # noqa: F405
-    + geom_hline(yintercept=ci_bound, color="#999999", size=0.7, linetype="dashed")  # noqa: F405
-    + geom_hline(yintercept=-ci_bound, color="#999999", size=0.7, linetype="dashed")  # noqa: F405
-    + scale_color_manual(values=sig_colors, limits=sig_labels)  # noqa: F405
-    + scale_x_continuous(breaks=x_breaks)  # noqa: F405
-    + labs(x="Lag", y="PACF")  # noqa: F405
-    + common_theme
-    + theme(legend_position="none")  # noqa: F405
-    + ggsize(1600, 470)  # noqa: F405
-)
-
-# Combine vertically
-combined = ggbunch(  # noqa: F405
-    plots=[acf_plot, pacf_plot], regions=[(0, 0, 1, 0.54, 0, 0), (0, 0.48, 1, 0.54, 0, 0)]
+    + ggsize(800, 450)
 )
 
-# Save
-export_ggsave(combined, filename="plot.png", path=".", scale=3)
-export_ggsave(combined, filename="plot.html", path=".")
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
+ggsave(plot, f"plot-{THEME}.html", path=".")