feat(letsplot): implement box-horizontal

plots/box-horizontal/implementations/python/letsplot.py

@@ -1,40 +1,47 @@
-""" pyplots.ai
+""" anyplot.ai
 box-horizontal: Horizontal Box Plot
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-30
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 87/100 | Updated: 2026-05-12
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from lets_plot import *
 
 
 LetsPlot.setup_html()
 
-# Data - Response times by service type (realistic scenario with varied distributions)
+# 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"
+BRAND = "#009E73"  # Okabe-Ito position 1
+
+# Data - Response times by service type
 np.random.seed(42)
 
 services = ["API Gateway", "Database Query", "Authentication", "File Storage", "Cache Lookup", "Email Service"]
 
 data = []
-# Different distributions to showcase boxplot features (medians, spreads, outliers)
 distributions = {
-    "API Gateway": (120, 40, 3),  # Medium response, moderate spread, some outliers
-    "Database Query": (250, 100, 5),  # Slower, high variability, more outliers
-    "Authentication": (80, 25, 2),  # Fast, consistent
-    "File Storage": (300, 80, 4),  # Slowest, variable
-    "Cache Lookup": (15, 8, 2),  # Very fast, tight distribution
-    "Email Service": (180, 60, 6),  # Medium, some outliers
+    "API Gateway": (120, 40, 3),
+    "Database Query": (250, 100, 5),
+    "Authentication": (80, 25, 2),
+    "File Storage": (300, 80, 4),
+    "Cache Lookup": (15, 8, 2),
+    "Email Service": (180, 60, 6),
 }
 
 for service in services:
     mean, std, n_outliers = distributions[service]
-    # Main distribution
     values = np.random.normal(mean, std, 80)
-    # Add some outliers
     outliers = np.random.normal(mean + 4 * std, std / 2, n_outliers)
     all_values = np.concatenate([values, outliers])
-    all_values = np.maximum(all_values, 5)  # Ensure positive values
+    all_values = np.maximum(all_values, 5)
 
     for val in all_values:
         data.append({"Service": service, "Response Time (ms)": val})
@@ -45,23 +52,27 @@
 plot = (
     ggplot(df, aes(x="Response Time (ms)", y="Service"))
     + geom_boxplot(
-        fill="#306998", color="#1a3a4f", alpha=0.7, outlier_color="#FFD43B", outlier_size=4, outlier_alpha=0.8, size=1.2
+        fill=BRAND, color=INK_SOFT, alpha=0.7, outlier_color=INK_SOFT, outlier_size=4, outlier_alpha=0.6, size=1.0
     )
-    + labs(x="Response Time (ms)", y="Service Type", title="box-horizontal · letsplot · pyplots.ai")
+    + labs(x="Response Time (ms)", y="Service Type", title="box-horizontal · letsplot · anyplot.ai")
     + theme_minimal()
     + theme(
-        axis_title=element_text(size=20),
-        axis_text=element_text(size=16),
-        axis_text_y=element_text(size=16),
-        plot_title=element_text(size=24),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        axis_title=element_text(size=20, color=INK),
+        axis_text=element_text(size=16, color=INK_SOFT),
+        axis_text_y=element_text(size=16, color=INK_SOFT),
+        plot_title=element_text(size=24, color=INK),
+        panel_grid_major_x=element_line(color=INK_SOFT, size=0.2),
         panel_grid_major_y=element_blank(),
         panel_grid_minor=element_blank(),
+        axis_line=element_line(color=INK_SOFT, size=0.5),
     )
     + ggsize(1600, 900)
 )
 
-# Save as PNG (scale 3x to get 4800 x 2700 px)
-ggsave(plot, "plot.png", path=".", scale=3)
+# Save PNG (scale 3x to get 4800 x 2700 px)
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
 
-# Save interactive HTML version
-ggsave(plot, "plot.html", path=".")
+# Save interactive HTML
+ggsave(plot, f"plot-{THEME}.html", path=".")