feat(letsplot): implement manhattan-gwas

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

@@ -1,16 +1,30 @@
-""" pyplots.ai
+""" anyplot.ai
 manhattan-gwas: Manhattan Plot for GWAS
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 92/100 | Created: 2025-12-31
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 92/100 | Updated: 2026-05-15
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from lets_plot import *
 
 
 LetsPlot.setup_html()
 
+# Theme-adaptive colors
+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"
+RULE = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
+
+# Okabe-Ito palette for alternating chromosomes
+OKABE_ITO = ["#009E73", "#D55E00", "#0072B2", "#CC79A7", "#E69F00", "#56B4E9", "#F0E442"]
+
 # Set seed for reproducibility
 np.random.seed(42)
 
@@ -24,7 +38,7 @@
 
 for i, chrom in enumerate(chromosomes):
     # Random positions within chromosome (scaled by chromosome "size")
-    chrom_size = 250_000_000 - i * 5_000_000  # Varying chromosome sizes
+    chrom_size = 250_000_000 - i * 5_000_000
     positions = np.sort(np.random.randint(1, chrom_size, n_snps_per_chrom))
 
     # Generate p-values - mostly non-significant with some peaks
@@ -61,11 +75,9 @@
 
 df = pd.DataFrame(data)
 
-# Create chromosome index for coloring (alternating pattern)
+# Assign alternating colors using Okabe-Ito palette
 df["chrom_idx"] = df["chromosome"].astype(int) % 2
-
-# Assign colors based on alternating pattern
-df["color_group"] = df["chrom_idx"].map({0: "even", 1: "odd"})
+df["color_group"] = df["chrom_idx"].map({0: OKABE_ITO[0], 1: OKABE_ITO[1]})
 
 # Significance thresholds
 genome_wide_threshold = -np.log10(5e-8)  # ~7.3
@@ -80,33 +92,40 @@
 # Create the Manhattan plot
 plot = (
     ggplot(df, aes(x="cumulative_pos", y="neg_log10_p", color="color_group"))
-    + geom_point(size=1.5, alpha=0.7)
-    + scale_color_manual(values=["#306998", "#7A9BBD"], guide="none")
+    + geom_point(size=2, alpha=0.7)
+    + scale_color_identity()
     # Highlight significant points
     + geom_point(
-        data=df[df["significant"]], mapping=aes(x="cumulative_pos", y="neg_log10_p"), color="#DC2626", size=3, alpha=0.9
+        data=df[df["significant"]],
+        mapping=aes(x="cumulative_pos", y="neg_log10_p"),
+        color=OKABE_ITO[4],
+        size=3.5,
+        alpha=0.9,
     )
     # Genome-wide significance threshold line
-    + geom_hline(yintercept=genome_wide_threshold, linetype="dashed", color="#DC2626", size=0.8)
+    + geom_hline(yintercept=genome_wide_threshold, linetype="dashed", color=OKABE_ITO[4], size=1)
     # Suggestive threshold line
-    + geom_hline(yintercept=suggestive_threshold, linetype="dotted", color="#666666", size=0.6)
-    + labs(title="manhattan-gwas · letsplot · pyplots.ai", x="Chromosome", y="-log₁₀(p-value)")
+    + geom_hline(yintercept=suggestive_threshold, linetype="dotted", color=INK_MUTED, size=0.7)
+    + labs(title="manhattan-gwas · letsplot · anyplot.ai", x="Chromosome", y="-log₁₀(p-value)")
     + scale_x_continuous(breaks=tick_positions, labels=chromosomes)
     + theme_minimal()
     + theme(
-        plot_title=element_text(size=28, face="bold"),
-        axis_title_x=element_text(size=22),
-        axis_title_y=element_text(size=22),
-        axis_text_x=element_text(size=14),
-        axis_text_y=element_text(size=16),
+        plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+        panel_grid_major_y=element_line(color=RULE, size=0.3),
         panel_grid_major_x=element_blank(),
         panel_grid_minor=element_blank(),
+        plot_title=element_text(size=28, face="bold", color=INK),
+        axis_title_x=element_text(size=22, color=INK),
+        axis_title_y=element_text(size=22, color=INK),
+        axis_text_x=element_text(size=16, color=INK_SOFT),
+        axis_text_y=element_text(size=16, color=INK_SOFT),
+        axis_line_x=element_line(color=INK_SOFT, size=0.6),
+        axis_line_y=element_line(color=INK_SOFT, size=0.6),
     )
     + ggsize(1600, 900)
 )
 
-# Save as PNG (scale 3x for 4800x2700)
-ggsave(plot, "plot.png", path=".", scale=3)
-
-# Save interactive HTML version
-ggsave(plot, "plot.html", path=".")
+# Save as PNG and HTML with theme suffix
+ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
+ggsave(plot, f"plot-{THEME}.html", path=".")

plots/manhattan-gwas/metadata/python/letsplot.yaml

@@ -1,15 +1,43 @@
 library: letsplot
+language: python
 specification_id: manhattan-gwas
 created: '2025-12-31T05:34:36Z'
-updated: '2025-12-31T05:46:54Z'
-generated_by: claude-opus-4-5-20251101
-workflow_run: 20612795129
+updated: '2026-05-15T03:43:49Z'
+generated_by: claude-haiku
+workflow_run: 25898824897
 issue: 2925
-python_version: 3.13.11
-library_version: 4.8.2
-preview_url: https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/letsplot/plot.png
-preview_html: https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/letsplot/plot.html
+python_version: 3.13.13
+library_version: 4.9.0
+preview_url_light: https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/python/letsplot/plot-light.png
+preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/python/letsplot/plot-dark.png
+preview_html_light: https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/python/letsplot/plot-light.html
+preview_html_dark: https://storage.googleapis.com/anyplot-images/plots/manhattan-gwas/python/letsplot/plot-dark.html
 quality_score: 92
+review:
+  strengths:
+  - Perfect technical execution across all visual quality criteria
+  - Excellent theme-adaptive design with proper color token usage for both light and
+    dark renders
+  - Realistic, comprehensive GWAS data example with significant peaks and suggestive
+    signals
+  - Clean, reproducible, idiomatic letsplot code following grammar of graphics idioms
+  - All specification requirements met with attention to detail and polish
+  weaknesses:
+  - Design excellence relies on library defaults rather than unique aesthetic innovation—consider
+    exploring custom color harmonies or refined visual treatments in future implementations
+  image_description: |-
+    Light render (plot-light.png):
+      Background: Warm off-white (#FAF8F1) as specified
+      Chrome: Title "manhattan-gwas · letsplot · anyplot.ai" in dark bold text; x-axis label "Chromosome" and y-axis label "-log₁₀(p-value)" clearly visible in dark text (INK token); tick labels (chromosome 1-22) and y-axis values readable in secondary dark color (INK_SOFT token); horizontal grid lines subtle with proper RULE token opacity
+      Data: Alternating chromosome bands in Okabe-Ito colors—green (#009E73) and orange (#D55E00) alternate across 22 chromosomes; significant SNPs above y≈7.3 threshold highlighted in brighter orange (#E69F00); yellow/orange dashed line at genome-wide significance threshold; dotted line at suggestive threshold (y=5); ~44,000 data points with alpha=0.7 preventing overplotting
+      Legibility verdict: PASS - All text readable against light background; data colors distinct and clear
+
+    Dark render (plot-dark.png):
+      Background: Warm near-black (#1A1A17) as specified
+      Chrome: Title in light text (INK token #F0EFE8); axis labels clearly visible in light color; tick labels in secondary light color (INK_SOFT token #B8B7B0); grid lines visible with proper dark-theme opacity; NO dark-on-dark failures observed
+      Data: Identical colors to light render—same green (#009E73) and orange (#D55E00) alternating pattern, same orange highlights for significant SNPs, confirming only chrome has adapted to theme while data colors remain constant
+      Legibility verdict: PASS - All text readable against dark background; data colors identical to light render, proving correct theme-adaptive implementation; no dark text on dark background issues
+  verdict: APPROVED
 impl_tags:
   dependencies: []
   techniques:
@@ -18,189 +46,7 @@ impl_tags:
   - html-export
   patterns:
   - data-generation
-  - iteration-over-groups
   dataprep: []
-  styling: []
-review:
-  strengths:
-  - Excellent Manhattan plot structure with proper cumulative genomic positions and
-    chromosome labeling
-  - Effective use of alternating blue/gray colors for chromosome distinction
-  - Significant SNPs highlighted in red above genome-wide threshold line
-  - Both genome-wide (dashed red) and suggestive (dotted gray) threshold lines included
-  - Good data generation with realistic GWAS patterns including peaks on specific
-    chromosomes
-  weaknesses:
-  - Chromosome labels on x-axis could be slightly larger for better readability at
-    full resolution
-  image_description: The Manhattan plot displays GWAS results across 22 chromosomes
-    arranged along the x-axis with cumulative genomic positions. Points are colored
-    in alternating blue (#306998) and light blue (#7A9BBD) for adjacent chromosomes.
-    The y-axis shows -log₁₀(p-value) ranging from 0 to 12. A horizontal dashed red
-    line indicates the genome-wide significance threshold at approximately 7.3 (-log₁₀(5×10⁻⁸)),
-    and a dotted gray line marks the suggestive threshold at 5. Significant SNPs above
-    the genome-wide threshold are highlighted with larger red points. Clear peaks
-    are visible on chromosomes 2, 6, 11, and 17, with additional suggestive signals
-    on chromosomes 1, 5, 8, 14, and 19. The title "manhattan-gwas · letsplot · pyplots.ai"
-    is displayed at the top in bold. The overall layout is well-balanced with good
-    use of the canvas space.
-  criteria_checklist:
-    visual_quality:
-      score: 36
-      max: 40
-      items:
-      - id: VQ-01
-        name: Text Legibility
-        score: 9
-        max: 10
-        passed: true
-        comment: Title large and bold, axis labels clear, chromosome labels readable
-          but slightly small
-      - id: VQ-02
-        name: No Overlap
-        score: 8
-        max: 8
-        passed: true
-        comment: No overlapping text elements
-      - id: VQ-03
-        name: Element Visibility
-        score: 7
-        max: 8
-        passed: true
-        comment: Points well-sized with appropriate alpha for dense data, significant
-          points clearly highlighted
-      - id: VQ-04
-        name: Color Accessibility
-        score: 5
-        max: 5
-        passed: true
-        comment: Blue/gray alternating scheme is colorblind-safe, red highlights distinct
-      - id: VQ-05
-        name: Layout Balance
-        score: 5
-        max: 5
-        passed: true
-        comment: Good use of canvas, plot fills appropriate area
-      - id: VQ-06
-        name: Axis Labels
-        score: 1
-        max: 2
-        passed: true
-        comment: Y-axis has subscript notation (-log₁₀), but no units needed for this
-          type
-      - id: VQ-07
-        name: Grid & Legend
-        score: 1
-        max: 2
-        passed: true
-        comment: Minimal grid (major x removed), no legend needed (guide="none" appropriate)
-    spec_compliance:
-      score: 25
-      max: 25
-      items:
-      - id: SC-01
-        name: Plot Type
-        score: 8
-        max: 8
-        passed: true
-        comment: Correct Manhattan plot
-      - id: SC-02
-        name: Data Mapping
-        score: 5
-        max: 5
-        passed: true
-        comment: Cumulative position on X, -log₁₀(p) on Y
-      - id: SC-03
-        name: Required Features
-        score: 5
-        max: 5
-        passed: true
-        comment: 'All features: alternating colors, threshold lines, significant SNP
-          highlighting'
-      - id: SC-04
-        name: Data Range
-        score: 3
-        max: 3
-        passed: true
-        comment: All chromosomes 1-22 visible, y-axis shows full range
-      - id: SC-05
-        name: Legend Accuracy
-        score: 2
-        max: 2
-        passed: true
-        comment: Legend hidden appropriately (colors self-explanatory)
-      - id: SC-06
-        name: Title Format
-        score: 2
-        max: 2
-        passed: true
-        comment: 'Correct format: "manhattan-gwas · letsplot · pyplots.ai"'
-    data_quality:
-      score: 19
-      max: 20
-      items:
-      - id: DQ-01
-        name: Feature Coverage
-        score: 8
-        max: 8
-        passed: true
-        comment: Shows significant peaks, suggestive signals, and background noise
-      - id: DQ-02
-        name: Realistic Context
-        score: 7
-        max: 7
-        passed: true
-        comment: Simulated GWAS data with realistic patterns (44,000 SNPs across 22
-          chromosomes)
-      - id: DQ-03
-        name: Appropriate Scale
-        score: 4
-        max: 5
-        passed: true
-        comment: P-values span realistic range, chromosome sizes vary appropriately
-    code_quality:
-      score: 9
-      max: 10
-      items:
-      - id: CQ-01
-        name: KISS Structure
-        score: 3
-        max: 3
-        passed: true
-        comment: 'Clean linear flow: imports → data → plot → save'
-      - id: CQ-02
-        name: Reproducibility
-        score: 3
-        max: 3
-        passed: true
-        comment: np.random.seed(42) set
-      - id: CQ-03
-        name: Clean Imports
-        score: 2
-        max: 2
-        passed: true
-        comment: Only numpy, pandas, lets_plot used
-      - id: CQ-04
-        name: No Deprecated API
-        score: 1
-        max: 1
-        passed: true
-        comment: Current lets-plot API
-      - id: CQ-05
-        name: Output Correct
-        score: 0
-        max: 1
-        passed: false
-        comment: Saves to "plot.png" but uses path="." parameter
-    library_features:
-      score: 3
-      max: 5
-      items:
-      - id: LF-01
-        name: Uses distinctive library features
-        score: 3
-        max: 5
-        passed: true
-        comment: Good use of ggplot grammar, scale_x_continuous with custom breaks/labels,
-          theme customization, but could use more advanced lets-plot features
-  verdict: APPROVED
+  styling:
+  - grid-styling
+  - alpha-blending