feat(plotnine): implement acf-pacf

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

@@ -1,12 +1,26 @@
-""" pyplots.ai
+""" anyplot.ai
 acf-pacf: Autocorrelation and Partial Autocorrelation (ACF/PACF) Plot
-Library: plotnine 0.15.3 | Python 3.14.3
-Quality: 90/100 | Created: 2026-03-14
+Library: plotnine 0.15.5 | Python 3.13.13
+Quality: 91/100 | Updated: 2026-06-10
 """
 
+import os
+import sys
+
 import numpy as np
 import pandas as pd
-from plotnine import (
+
+
+# Work around naming conflict with plotnine.py script and plotnine package
+script_dir = os.path.dirname(os.path.abspath(__file__))
+if script_dir in sys.path:
+    sys.path.remove(script_dir)
+if "" in sys.path:
+    sys.path.remove("")
+if "." in sys.path:
+    sys.path.remove(".")
+
+from plotnine import (  # noqa: E402
     aes,
     element_blank,
     element_line,
@@ -16,18 +30,32 @@
     geom_hline,
     geom_point,
     geom_segment,
+    geom_vline,
     ggplot,
     guides,
     labs,
     scale_color_manual,
     scale_x_continuous,
+    scale_y_continuous,
     theme,
     theme_minimal,
 )
 from statsmodels.tsa.stattools import acf, pacf
 
 
-# Data - Simulated monthly temperature with seasonality and AR(1) component
+# Theme tokens — Imprint palette, theme-adaptive chrome
+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"
+
+# Imprint palette positions used
+BRAND = "#009E73"  # position 1 — significant lags (brand green, first series)
+ALARM = "#AE3030"  # position 5 — confidence bounds (semantic: alert/threshold)
+
+# Data — simulated monthly temperature with seasonality and AR(1) component
 np.random.seed(42)
 n_obs = 240
 time = np.arange(n_obs)
@@ -51,43 +79,55 @@
 
 # Mark significance: lags outside confidence bounds
 df["significant"] = np.where(np.abs(df["correlation"]) > confidence_bound, "Significant", "Non-significant")
-# Lag 0 in ACF is always 1.0 by definition - not a meaningful significant lag
+# Lag 0 in ACF is always 1.0 by definition — not a meaningful significant lag
 df.loc[(df["panel"] == "ACF") & (df["lag"] == 0), "significant"] = "Non-significant"
 
-# Colors
-PYTHON_BLUE = "#306998"
-MUTED_SILVER = "#B0BEC5"
-CONF_RED = "#C0392B"
+# Seasonal lag markers restricted to ACF panel — period-12 structure at lags 12, 24, 36
+seasonal_ann_df = pd.DataFrame(
+    {
+        "xintercept": [12, 24, 36],
+        "panel": pd.Categorical(["ACF", "ACF", "ACF"], categories=["ACF", "PACF"], ordered=True),
+    }
+)
+
+# Title — 41 chars, within 67-char baseline, no font scaling needed
+title = "acf-pacf · python · plotnine · anyplot.ai"
 
-# Plot with significance coloring for visual storytelling
+# Plot — strip labels "ACF" / "PACF" serve as per-panel y-axis identifiers per spec
 plot = (
     ggplot(df, aes(x="lag", y="correlation", color="significant"))
-    + geom_hline(yintercept=0, color="#9E9E9E", size=0.7)
-    + geom_hline(yintercept=confidence_bound, linetype="dashed", color=CONF_RED, size=0.8, alpha=0.5)
-    + geom_hline(yintercept=-confidence_bound, linetype="dashed", color=CONF_RED, size=0.8, alpha=0.5)
-    + geom_segment(aes(x="lag", xend="lag", y=0, yend="correlation"), size=1.3)
-    + geom_point(size=3.2)
-    + scale_color_manual(values={"Significant": PYTHON_BLUE, "Non-significant": MUTED_SILVER})
+    + geom_hline(yintercept=0, color=INK_SOFT, size=0.6, alpha=0.8)
+    + geom_vline(
+        data=seasonal_ann_df, mapping=aes(xintercept="xintercept"), color=BRAND, alpha=0.14, size=0.8, linetype="dotted"
+    )
+    + geom_hline(yintercept=confidence_bound, linetype="dashed", color=ALARM, size=0.7, alpha=0.65)
+    + geom_hline(yintercept=-confidence_bound, linetype="dashed", color=ALARM, size=0.7, alpha=0.65)
+    + geom_segment(aes(x="lag", xend="lag", y=0, yend="correlation"), size=1.2)
+    + geom_point(size=3.0)
+    + scale_color_manual(values={"Significant": BRAND, "Non-significant": INK_MUTED})
     + guides(color="none")
     + facet_wrap("~panel", ncol=1, scales="free_y")
-    + scale_x_continuous(breaks=range(0, n_lags + 1, 6))
-    + labs(x="Lag", y="Correlation", title="acf-pacf · plotnine · pyplots.ai")
+    + scale_x_continuous(breaks=list(range(0, n_lags + 1, 6)))
+    + scale_y_continuous(expand=(0.04, 0))
+    + labs(x="Lag", y="", title=title)
     + theme_minimal()
     + theme(
-        figure_size=(16, 10),
-        text=element_text(size=14, color="#2C3E50"),
-        axis_title=element_text(size=20, face="bold"),
-        axis_text=element_text(size=16, color="#546E7A"),
-        plot_title=element_text(size=24, face="bold", color="#1A237E"),
-        strip_text=element_text(size=20, face="bold", color="#263238"),
-        strip_background=element_rect(fill="#F5F5F5", color="none"),
+        figure_size=(8, 4.5),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG),
         panel_grid_major_x=element_blank(),
         panel_grid_minor_x=element_blank(),
-        panel_grid_major_y=element_line(color="#ECEFF1", size=0.4),
+        panel_grid_major_y=element_line(color=INK, size=0.2, alpha=0.12),
         panel_grid_minor_y=element_blank(),
-        panel_spacing_y=0.15,
+        axis_title_x=element_text(color=INK, size=10),
+        axis_title_y=element_blank(),
+        axis_text=element_text(color=INK_SOFT, size=8),
+        plot_title=element_text(color=INK, size=12, face="bold"),
+        strip_background=element_rect(fill=PAGE_BG, color="none"),
+        strip_text=element_text(color=INK, size=10, face="bold"),
+        panel_spacing_y=0.08,
     )
 )
 
-# Save
-plot.save("plot.png", dpi=300, verbose=False)
+# Save — canvas: 8×4.5 in × 400 dpi = 3200×1800 px
+plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)