feat(pygal): implement volcano-basic

plots/volcano-basic/implementations/pygal.py

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+"""pyplots.ai
+volcano-basic: Volcano Plot for Statistical Significance
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 75/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Simulated differential gene expression results
+np.random.seed(42)
+n_genes = 500
+
+# Gene names for annotations
+gene_names = [f"GENE{i:03d}" for i in range(n_genes)]
+
+# Generate fold changes (mostly near zero, some with larger effects)
+log2_fc = np.concatenate(
+    [
+        np.random.normal(0, 0.5, 400),  # Non-significant genes
+        np.random.normal(2.5, 0.5, 50),  # Up-regulated
+        np.random.normal(-2.5, 0.5, 50),  # Down-regulated
+    ]
+)
+
+# Generate p-values (correlated with effect size)
+base_pval = np.random.uniform(0.001, 0.9, n_genes)
+# Significant genes have lower p-values
+base_pval[400:] = np.random.uniform(0.0001, 0.01, 100)
+neg_log10_pval = -np.log10(base_pval)
+
+# Classification thresholds
+fc_threshold = 1.0  # log2 fold change threshold (2-fold)
+pval_threshold = 1.3  # -log10(0.05) ≈ 1.3
+
+# Classify genes
+up_regulated = (log2_fc > fc_threshold) & (neg_log10_pval > pval_threshold)
+down_regulated = (log2_fc < -fc_threshold) & (neg_log10_pval > pval_threshold)
+not_significant = ~(up_regulated | down_regulated)
+
+# Colorblind-safe palette: gray, orange, blue, dark gray for threshold lines
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#888888", "#E69F00", "#0072B2", "#555555", "#555555", "#555555"),  # Gray, Orange, Blue, Dark gray x3
+    title_font_size=56,
+    label_font_size=36,
+    major_label_font_size=32,
+    legend_font_size=40,
+    value_font_size=24,
+    stroke_width=2,
+    opacity=0.7,
+    opacity_hover=1.0,
+    font_family="DejaVu Sans",
+)
+
+# Calculate axis ranges - tight fitting to data (y starts at 0)
+y_max = float(np.ceil(max(neg_log10_pval) + 0.3))
+x_min = float(np.floor(min(log2_fc) - 0.3))
+x_max = float(np.ceil(max(log2_fc) + 0.3))
+
+# Generate y-axis labels from 0 to max (positive only)
+y_labels = [i * 0.5 for i in range(int(y_max / 0.5) + 2)]
+
+# Create XY chart (scatter plot)
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="volcano-basic · pygal · pyplots.ai",
+    x_title="Log₂ Fold Change",
+    y_title="-Log₁₀(p-value)",
+    show_legend=True,
+    legend_at_bottom=True,
+    legend_box_size=32,
+    dots_size=10,
+    stroke=False,
+    show_x_guides=True,
+    show_y_guides=True,
+    x_label_rotation=0,
+    range=(x_min, x_max),
+    include_x_axis=True,
+    explicit_size=True,
+    truncate_legend=-1,
+    spacing=40,
+    margin=30,
+    margin_bottom=120,
+)
+
+# Set y-axis labels to start from 0 (all data is positive)
+chart.y_labels = y_labels
+
+# Prepare data points for each category with gene labels for tooltips
+not_sig_points = [
+    {"value": (float(log2_fc[i]), float(neg_log10_pval[i])), "label": gene_names[i]}
+    for i in range(n_genes)
+    if not_significant[i]
+]
+up_points = [
+    {"value": (float(log2_fc[i]), float(neg_log10_pval[i])), "label": gene_names[i]}
+    for i in range(n_genes)
+    if up_regulated[i]
+]
+down_points = [
+    {"value": (float(log2_fc[i]), float(neg_log10_pval[i])), "label": gene_names[i]}
+    for i in range(n_genes)
+    if down_regulated[i]
+]
+
+# Add data series
+chart.add("Not Significant", not_sig_points)
+chart.add("Up-regulated", up_points)
+chart.add("Down-regulated", down_points)
+
+# Add threshold lines as line series (dashed lines for significance cutoffs)
+# Horizontal line at p-value threshold (y = 1.3)
+h_line_points = [(x_min, pval_threshold), (x_max, pval_threshold)]
+chart.add("p=0.05", h_line_points, stroke=True, show_dots=False, stroke_style={"width": 4, "dasharray": "12, 6"})
+
+# Vertical lines at fold change thresholds (x = ±1, representing 2-fold change)
+# Each vertical line as separate series to avoid diagonal connections
+v_line_neg = [(float(-fc_threshold), 0.0), (float(-fc_threshold), float(y_max))]
+v_line_pos = [(float(fc_threshold), 0.0), (float(fc_threshold), float(y_max))]
+chart.add("FC=-2", v_line_neg, stroke=True, show_dots=False, stroke_style={"width": 4, "dasharray": "12, 6"})
+chart.add("FC=+2", v_line_pos, stroke=True, show_dots=False, stroke_style={"width": 4, "dasharray": "12, 6"})
+
+# Save as PNG and HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/volcano-basic/metadata/pygal.yaml

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+library: pygal
+specification_id: volcano-basic
+created: '2025-12-31T05:31:07Z'
+updated: '2025-12-31T06:01:32Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612783052
+issue: 2924
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/pygal/plot.html
+quality_score: 75
+review:
+  strengths:
+  - Clean three-category color coding (gray/red/blue) clearly distinguishes significance
+    groups
+  - Good use of pygal custom Style with appropriate font sizes for large canvas
+  - Realistic gene expression data scenario with proper statistical distributions
+  - Tooltips include gene names for interactivity in HTML output
+  - Proper scientific notation in axis labels (subscript formatting)
+  weaknesses:
+  - Threshold lines (horizontal at p=0.05, vertical at FC=±2) are not visible in the
+    rendered output despite being coded
+  - Y-axis range extends to -5 when all data is positive (0 to ~4), wasting half the
+    vertical canvas
+  - Legend positioned in top-left corner instead of near the data or at bottom as
+    configured
+  - Red/blue color scheme is not colorblind-safe; consider using colorblind-friendly
+    palette