feat(bokeh): implement manhattan-gwas

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

@@ -1,20 +1,37 @@
-""" pyplots.ai
+""" anyplot.ai
 manhattan-gwas: Manhattan Plot for GWAS
-Library: bokeh 3.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-31
+Library: bokeh 3.9.0 | Python 3.13.13
+Quality: 85/100 | Updated: 2026-05-15
 """
 
+import os
+import time
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
-from bokeh.io import export_png, output_file, save
-from bokeh.models import ColumnDataSource, Label, Span
+from bokeh.io import output_file, save
+from bokeh.models import ColumnDataSource, HoverTool, Label, Span
 from bokeh.plotting import figure
+from selenium import webdriver
+from selenium.webdriver.chrome.options import Options
+
+
+# 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 data colors
+CHROM_COLOR_1 = "#009E73"  # Okabe-Ito position 1 (first series)
+CHROM_COLOR_2 = "#D55E00"  # Okabe-Ito position 2 (alternating)
+SIG_COLOR = "#F0E442"  # Okabe-Ito position 7 (significant SNPs highlight)
 
 # Data - Simulated GWAS data with significant peaks
 np.random.seed(42)
 
-# Chromosome sizes (simplified, in millions of base pairs)
 chrom_sizes = {
     "1": 248,
     "2": 242,
@@ -47,20 +64,14 @@
 chrom_centers = {}
 
 for chrom, size in chrom_sizes.items():
-    # Random positions within chromosome
     positions = np.sort(np.random.randint(1, size * 1_000_000, n_snps_per_chrom))
-
-    # Base p-values (mostly non-significant)
     p_values = np.random.uniform(0.001, 1.0, n_snps_per_chrom)
 
-    # Add some significant peaks for certain chromosomes
     if chrom in ["2", "6", "11", "17"]:
-        # Add 5-15 significant SNPs per peak chromosome
         n_significant = np.random.randint(5, 15)
         peak_indices = np.random.choice(n_snps_per_chrom, n_significant, replace=False)
         p_values[peak_indices] = 10 ** np.random.uniform(-12, -8, n_significant)
 
-    # Calculate cumulative position
     cumulative_positions = positions + cumulative_pos
     chrom_centers[chrom] = cumulative_pos + (size * 1_000_000) / 2
 
@@ -79,102 +90,149 @@
 
 df = pd.DataFrame(data)
 
-# Assign colors based on chromosome (alternating)
-colors = ["#306998", "#7BA3C9"]  # Python Blue and lighter shade
-df["color"] = df["chromosome"].apply(lambda x: colors[int(x) % 2])
+# Assign alternating colors based on chromosome parity
+chrom_int = df["chromosome"].astype(int)
+df["color"] = df["chromosome"].apply(lambda x: CHROM_COLOR_1 if int(x) % 2 == 1 else CHROM_COLOR_2)
+df["color_label"] = df["chromosome"].apply(lambda x: "Odd chromosome" if int(x) % 2 == 1 else "Even chromosome")
 
-# Highlight significant SNPs (above genome-wide threshold)
-significance_threshold = -np.log10(5e-8)  # ~7.3
+# Highlight significant SNPs
+significance_threshold = -np.log10(5e-8)
 significant_mask = df["neg_log_p"] >= significance_threshold
-df.loc[significant_mask, "color"] = "#FFD43B"  # Python Yellow for significant
+df.loc[significant_mask, "color"] = SIG_COLOR
+df.loc[significant_mask, "color_label"] = "Significant SNP (p < 5×10⁻⁸)"
 
-# Adjust point sizes - smaller for non-significant, larger for significant
+# Adjust point sizes
 df["size"] = 6
 df.loc[significant_mask, "size"] = 12
 
-# Create plot
+# Plot
 source = ColumnDataSource(df)
 
 p = figure(
     width=4800,
     height=2700,
-    title="manhattan-gwas · bokeh · pyplots.ai",
+    title="manhattan-gwas · bokeh · anyplot.ai",
     x_axis_label="Genomic Position",
     y_axis_label="-log₁₀(p-value)",
     tools="pan,wheel_zoom,box_zoom,reset,save",
 )
 
-# Scatter plot
-p.scatter(x="cumulative_pos", y="neg_log_p", source=source, size="size", color="color", alpha=0.7, line_color=None)
+# Scatter plot with legend
+p.scatter(
+    x="cumulative_pos",
+    y="neg_log_p",
+    source=source,
+    size="size",
+    color="color",
+    alpha=0.7,
+    line_color=None,
+    legend_field="color_label",
+)
 
-# Genome-wide significance threshold line (p < 5e-8)
+# Significance threshold line
 significance_line = Span(
-    location=significance_threshold, dimension="width", line_color="#E63946", line_dash="dashed", line_width=3
+    location=significance_threshold, dimension="width", line_color=INK_SOFT, line_dash="dashed", line_width=3
 )
 p.add_layout(significance_line)
 
-# Suggestive threshold line (p < 1e-5)
-suggestive_threshold = -np.log10(1e-5)  # = 5
+# Suggestive threshold line
+suggestive_threshold = -np.log10(1e-5)
 suggestive_line = Span(
-    location=suggestive_threshold, dimension="width", line_color="#2A9D8F", line_dash="dotted", line_width=2
+    location=suggestive_threshold, dimension="width", line_color=INK_SOFT, line_dash="dotted", line_width=2
 )
 p.add_layout(suggestive_line)
 
-# Add threshold labels
+# Threshold labels
 sig_label = Label(
     x=cumulative_pos * 0.98,
     y=significance_threshold + 0.3,
     text="p = 5×10⁻⁸",
-    text_font_size="16pt",
-    text_color="#E63946",
+    text_font_size="18pt",
+    text_color=INK_SOFT,
 )
 p.add_layout(sig_label)
 
 sug_label = Label(
-    x=cumulative_pos * 0.98,
-    y=suggestive_threshold + 0.3,
-    text="p = 1×10⁻⁵",
-    text_font_size="16pt",
-    text_color="#2A9D8F",
+    x=cumulative_pos * 0.98, y=suggestive_threshold + 0.3, text="p = 1×10⁻⁵", text_font_size="18pt", text_color=INK_SOFT
 )
 p.add_layout(sug_label)
 
+# Add chromosome labels
+for chrom, center in chrom_centers.items():
+    chrom_label = Label(x=center, y=-0.8, text=chrom, text_font_size="16pt", text_align="center", text_color=INK_SOFT)
+    p.add_layout(chrom_label)
+
+# Add HoverTool for interactivity
+hover = HoverTool(
+    tooltips=[
+        ("Chromosome", "@chromosome"),
+        ("Position", "@{position:0,0}"),
+        ("-log₁₀(p)", "@{neg_log_p:.2f}"),
+        ("Type", "@color_label"),
+    ]
+)
+p.add_tools(hover)
+
 # Style the plot
 p.title.text_font_size = "28pt"
+p.title.text_color = INK
 p.xaxis.axis_label_text_font_size = "22pt"
 p.yaxis.axis_label_text_font_size = "22pt"
-p.xaxis.major_label_text_font_size = "16pt"
+p.xaxis.axis_label_text_color = INK
+p.yaxis.axis_label_text_color = INK
+p.xaxis.major_label_text_font_size = "0pt"
 p.yaxis.major_label_text_font_size = "18pt"
+p.yaxis.major_label_text_color = INK_SOFT
 
-# Hide x-axis tick labels (we'll add chromosome labels instead)
-p.xaxis.major_label_text_font_size = "0pt"
+# Hide x-axis ticks (using chromosome labels instead)
 p.xaxis.major_tick_line_color = None
 p.xaxis.minor_tick_line_color = None
-
-# Add chromosome labels below the plot
-for chrom, center in chrom_centers.items():
-    chrom_label = Label(x=center, y=-0.8, text=chrom, text_font_size="14pt", text_align="center", text_color="#333333")
-    p.add_layout(chrom_label)
+p.xaxis.axis_line_color = INK_SOFT
+p.yaxis.axis_line_color = INK_SOFT
+p.yaxis.major_tick_line_color = INK_SOFT
 
 # Grid styling
-p.xgrid.grid_line_alpha = 0.3
-p.ygrid.grid_line_alpha = 0.3
-
-# Background
-p.background_fill_color = "#FAFAFA"
-p.border_fill_color = "#FFFFFF"
-
-# Axis styling
-p.yaxis.axis_line_width = 2
-p.xaxis.axis_line_width = 2
-
-# Set y-axis range to show chromosome labels
+p.xgrid.grid_line_alpha = 0.10
+p.ygrid.grid_line_alpha = 0.10
+p.xgrid.grid_line_color = INK
+p.ygrid.grid_line_color = INK
+
+# Background and borders
+p.background_fill_color = PAGE_BG
+p.border_fill_color = PAGE_BG
+
+# Legend styling
+if p.legend:
+    p.legend.background_fill_color = ELEVATED_BG
+    p.legend.border_line_color = INK_SOFT
+    p.legend.label_text_color = INK_SOFT
+    p.legend.label_text_font_size = "16pt"
+    p.legend.location = "top_right"
+
+# Set y-axis range to accommodate chromosome labels
 p.y_range.start = -1.5
 p.y_range.end = df["neg_log_p"].max() + 1
 
-# Save PNG
-export_png(p, filename="plot.png")
-
-# Save HTML for interactivity
-output_file("plot.html")
+# Save HTML
+output_file(f"plot-{THEME}.html")
 save(p)
+
+# Screenshot with headless Chrome via Selenium
+W, H = 4800, 2700
+opts = Options()
+for arg in (
+    "--headless=new",
+    "--no-sandbox",
+    "--disable-dev-shm-usage",
+    "--disable-gpu",
+    f"--window-size={W},{H}",
+    "--hide-scrollbars",
+):
+    opts.add_argument(arg)
+
+driver = webdriver.Chrome(options=opts)
+driver.set_window_size(W, H)
+driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
+time.sleep(3)
+driver.save_screenshot(f"plot-{THEME}.png")
+driver.quit()