feat(plotnine): implement scatter-marginal

plots/scatter-marginal/implementations/python/plotnine.py

@@ -1,9 +1,11 @@
-""" pyplots.ai
+""" anyplot.ai
 scatter-marginal: Scatter Plot with Marginal Distributions
-Library: plotnine 0.15.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-26
+Library: plotnine 0.15.4 | Python 3.13.13
+Quality: 93/100 | Updated: 2026-05-09
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from plotnine import (
@@ -25,95 +27,106 @@
 from plotnine.composition import plot_spacer
 
 
-# Data - Bivariate data with moderate correlation (realistic scenario)
+# 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
+SCATTER_COLOR = "#009E73"  # Brand green (position 1)
+MARGINAL_COLOR = "#D55E00"  # Vermillion (position 2)
+
+# Data - Bivariate data with correlation
 np.random.seed(42)
 n = 200
-
-# Study hours vs exam score with positive correlation
 study_hours = np.random.normal(25, 8, n)
-study_hours = np.clip(study_hours, 5, 45)  # Realistic range
-noise = np.random.normal(0, 8, n)
-exam_score = 35 + 1.5 * study_hours + noise
+study_hours = np.clip(study_hours, 5, 45)
+exam_score = 35 + 1.5 * study_hours + np.random.normal(0, 8, n)
 exam_score = np.clip(exam_score, 30, 100)
-
 df = pd.DataFrame({"study_hours": study_hours, "exam_score": exam_score})
 
-# Shared axis limits (include all data with margin)
-x_min, x_max = 0, 50
-y_min, y_max = 30, 105
-
-# Figure sizes calculated for 4800x2700 final output at 300 DPI
-# Total: 16x9 inches = 4800x2700 px
-# Layout: scatter (12x6.5), top hist (12x2.5), right hist (4x6.5), spacer (4x2.5)
+# Layout dimensions for 4800x2700 output
 main_w, main_h = 12, 6.5
 marg_w, marg_h = 4, 2.5
 
-# Common theme elements for large canvas
+# Shared theme - L-shaped spine (left + bottom only)
 base_theme = theme_minimal() + theme(
-    text=element_text(size=14),
-    axis_title=element_text(size=20),
-    axis_text=element_text(size=16),
-    plot_title=element_text(size=24),
-    panel_grid_major=element_line(color="#cccccc", size=0.5, alpha=0.3),
+    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+    panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+    panel_grid_major=element_line(color=INK, size=0.3, alpha=0.10),
     panel_grid_minor=element_blank(),
-    plot_background=element_rect(fill="white"),
+    panel_border=element_blank(),
+    axis_title=element_text(color=INK, size=20, weight="bold"),
+    axis_text=element_text(color=INK_SOFT, size=16),
+    axis_line=element_line(color=INK_SOFT, size=0.5),
+    axis_ticks=element_line(color=INK_SOFT, size=0.4),
+    plot_title=element_text(color=INK, size=24, weight="bold", margin={"t": 10, "b": 10}),
 )
 
 # Top histogram (x distribution)
 top_hist = (
     ggplot(df, aes(x="study_hours"))
-    + geom_histogram(bins=15, fill="#306998", color="#1a3d5c", alpha=0.7, size=0.3)
-    + scale_x_continuous(limits=(x_min, x_max))
-    + labs(x="", y="", title="scatter-marginal · plotnine · pyplots.ai")
+    + geom_histogram(bins=15, fill=MARGINAL_COLOR, color=INK_SOFT, alpha=0.7, size=0.3)
+    + scale_x_continuous(limits=(0, 50))
+    + labs(x="", y="", title="scatter-marginal · plotnine · anyplot.ai")
     + base_theme
     + theme(
         figure_size=(main_w, marg_h),
         axis_text_x=element_blank(),
         axis_ticks_major_x=element_blank(),
+        axis_ticks_minor_x=element_blank(),
+        axis_line_x=element_blank(),
         axis_title_y=element_blank(),
         axis_text_y=element_blank(),
         axis_ticks_major_y=element_blank(),
+        axis_ticks_minor_y=element_blank(),
+        axis_line_y=element_blank(),
+        panel_grid_major=element_blank(),
     )
 )
 
-# Right histogram (y distribution, flipped)
+# Right histogram (y distribution)
 right_hist = (
     ggplot(df, aes(x="exam_score"))
-    + geom_histogram(bins=15, fill="#FFD43B", color="#d4a80a", alpha=0.7, size=0.3)
+    + geom_histogram(bins=15, fill=MARGINAL_COLOR, color=INK_SOFT, alpha=0.7, size=0.3)
     + coord_flip()
-    + scale_x_continuous(limits=(y_min, y_max))
+    + scale_x_continuous(limits=(30, 105))
     + labs(x="", y="")
     + base_theme
     + theme(
         figure_size=(marg_w, main_h),
         axis_text_y=element_blank(),
         axis_ticks_major_y=element_blank(),
+        axis_ticks_minor_y=element_blank(),
+        axis_line_y=element_blank(),
         axis_title_x=element_blank(),
         axis_text_x=element_blank(),
         axis_ticks_major_x=element_blank(),
+        axis_ticks_minor_x=element_blank(),
+        axis_line_x=element_blank(),
+        panel_grid_major=element_blank(),
     )
 )
 
 # Main scatter plot
 scatter_plot = (
     ggplot(df, aes(x="study_hours", y="exam_score"))
-    + geom_point(size=3.5, alpha=0.6, color="#306998")
-    + scale_x_continuous(limits=(x_min, x_max))
-    + scale_y_continuous(limits=(y_min, y_max))
+    + geom_point(size=3.5, alpha=0.6, color=SCATTER_COLOR)
+    + scale_x_continuous(limits=(0, 50))
+    + scale_y_continuous(limits=(30, 105))
     + labs(x="Study Hours per Week", y="Exam Score (%)")
     + base_theme
     + theme(figure_size=(main_w, main_h))
 )
 
-# Empty spacer for top-right corner
+# Spacer
 spacer = plot_spacer() + theme(figure_size=(marg_w, marg_h))
 
-# Compose: top row (histogram | spacer), bottom row (scatter | right histogram)
-top_row = top_hist | spacer
-bottom_row = scatter_plot | right_hist
-composed = top_row / bottom_row
+# Compose layout
+composed = (top_hist | spacer) / (scatter_plot | right_hist)
 
-# Draw to matplotlib figure and save with correct dimensions
+# Save
 fig = composed.draw()
 fig.set_size_inches(16, 9)
-fig.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")
+fig.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)