feat(seaborn): implement manhattan-gwas

plots/manhattan-gwas/implementations/python/seaborn.py

@@ -1,15 +1,28 @@
-""" pyplots.ai
+""" anyplot.ai
 manhattan-gwas: Manhattan Plot for GWAS
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-31
+Library: seaborn 0.13.2 | Python 3.13.13
+Quality: 94/100 | Updated: 2026-05-15
 """
 
+import os
+
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
 
 
+# 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"
+
+# Okabe-Ito palette for alternating chromosomes
+OKABE_ITO_1 = "#009E73"  # bluish green (brand)
+OKABE_ITO_2 = "#D55E00"  # vermillion
+
 # Data - Simulate GWAS data with realistic structure
 np.random.seed(42)
 
@@ -68,72 +81,82 @@
 df = pd.DataFrame(data)
 
 # Create alternating color groups for chromosomes
-df["color_group"] = df["chromosome"].apply(lambda x: int(x) % 2)
+df["color_group"] = df["chromosome"].apply(lambda x: OKABE_ITO_1 if int(x) % 2 == 1 else OKABE_ITO_2)
+
+# Configure theme-adaptive styling
+sns.set_theme(
+    style="ticks",
+    rc={
+        "figure.facecolor": PAGE_BG,
+        "axes.facecolor": PAGE_BG,
+        "axes.edgecolor": INK_SOFT,
+        "axes.labelcolor": INK,
+        "text.color": INK,
+        "xtick.color": INK_SOFT,
+        "ytick.color": INK_SOFT,
+        "grid.color": INK,
+        "grid.alpha": 0.10,
+        "legend.facecolor": ELEVATED_BG,
+        "legend.edgecolor": INK_SOFT,
+    },
+)
 
 # Plot
-fig, ax = plt.subplots(figsize=(16, 9))
-
-# Create custom palette - Python Blue and a muted gray
-palette = {0: "#306998", 1: "#8B9DC3"}
-
-# Plot points using seaborn scatterplot with hue for alternating colors
-sns.scatterplot(
-    data=df,
-    x="cumulative_position",
-    y="neg_log_p",
-    hue="color_group",
-    palette=palette,
-    s=15,
-    alpha=0.7,
-    edgecolor="none",
-    legend=False,
-    ax=ax,
-)
+fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
+
+# Plot non-significant SNPs with alternating colors
+for color in [OKABE_ITO_1, OKABE_ITO_2]:
+    subset = df[df["color_group"] == color]
+    ax.scatter(
+        subset["cumulative_position"], subset["neg_log_p"], c=color, s=20, alpha=0.7, edgecolor="none", rasterized=True
+    )
 
 # Highlight significant SNPs (above genome-wide threshold)
 significant = df[df["neg_log_p"] > 7.3]
 if len(significant) > 0:
     ax.scatter(
         significant["cumulative_position"],
         significant["neg_log_p"],
-        c="#FFD43B",  # Python Yellow for significant hits
-        s=40,
-        edgecolor="black",
-        linewidth=0.5,
+        c=OKABE_ITO_1,
+        s=60,
+        alpha=0.9,
+        edgecolor=INK,
+        linewidth=0.8,
         zorder=5,
     )
 
 # Genome-wide significance threshold
-ax.axhline(y=7.3, color="#D62728", linestyle="--", linewidth=2, label="Genome-wide significance (p < 5×10⁻⁸)")
+ax.axhline(y=7.3, color=INK_SOFT, linestyle="--", linewidth=2, alpha=0.6, label="Genome-wide significance (p < 5×10⁻⁸)")
 
 # Suggestive threshold
-ax.axhline(y=5, color="#7F7F7F", linestyle=":", linewidth=1.5, label="Suggestive (p < 1×10⁻⁵)")
+ax.axhline(y=5, color=INK_SOFT, linestyle=":", linewidth=1.5, alpha=0.4, label="Suggestive (p < 1×10⁻⁵)")
 
 # Set x-axis ticks at chromosome centers
 ax.set_xticks([chrom_centers[c] for c in chromosomes])
-ax.set_xticklabels(chromosomes)
+ax.set_xticklabels(chromosomes, fontsize=16)
 
 # Styling
-ax.set_xlabel("Chromosome", fontsize=20)
-ax.set_ylabel("-log₁₀(p-value)", fontsize=20)
-ax.set_title("manhattan-gwas · seaborn · pyplots.ai", fontsize=24)
-ax.tick_params(axis="both", labelsize=14)
-ax.tick_params(axis="x", labelsize=12)
+ax.set_xlabel("Chromosome", fontsize=20, color=INK)
+ax.set_ylabel("-log₁₀(p-value)", fontsize=20, color=INK)
+ax.set_title("manhattan-gwas · seaborn · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
+ax.tick_params(axis="both", labelsize=16)
 
 # Set axis limits
 ax.set_xlim(0, cumulative_pos)
 ax.set_ylim(0, max(df["neg_log_p"]) * 1.05)
 
-# Remove top and right spines for cleaner look
+# Remove top and right spines
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
+ax.spines["left"].set_color(INK_SOFT)
+ax.spines["bottom"].set_color(INK_SOFT)
 
 # Add legend
-ax.legend(loc="upper right", fontsize=14, framealpha=0.9)
+ax.legend(loc="upper right", fontsize=16, framealpha=0.95, edgecolor=INK_SOFT)
 
 # Subtle grid on y-axis only
-ax.yaxis.grid(True, alpha=0.3, linestyle="-")
+ax.yaxis.grid(True, alpha=0.10, linewidth=0.8)
 ax.xaxis.grid(False)
 
 plt.tight_layout()
-plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)