feat(bokeh): implement volcano-basic (#2940)

## Implementation: `volcano-basic` - bokeh

Implements the **bokeh** version of `volcano-basic`.

**File:** `plots/volcano-basic/implementations/bokeh.py`

**Parent Issue:** #2924

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20612781583)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/volcano-basic/implementations/bokeh.py

@@ -0,0 +1,113 @@
+""" pyplots.ai
+volcano-basic: Volcano Plot for Statistical Significance
+Library: bokeh 3.8.1 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource, Span
+from bokeh.plotting import figure
+
+
+# Data - Simulated differential expression results
+np.random.seed(42)
+n_genes = 2000
+
+# Generate log2 fold changes (effect sizes)
+log2_fc = np.random.normal(0, 1.5, n_genes)
+
+# Generate p-values: most genes non-significant, some highly significant
+# Use inverse relationship with fold change magnitude for realism
+base_pvals = np.random.uniform(0.001, 1, n_genes)
+# Genes with larger fold changes tend to have lower p-values
+fold_effect = np.abs(log2_fc) / np.max(np.abs(log2_fc))
+pvals = base_pvals * (1 - 0.7 * fold_effect) + 0.01 * np.random.random(n_genes)
+pvals = np.clip(pvals, 1e-50, 1)
+
+neg_log10_pval = -np.log10(pvals)
+
+# Significance thresholds
+pval_threshold = -np.log10(0.05)  # ~1.3
+fc_threshold = 1.0  # log2(2) = 1
+
+# Classify points by significance
+significant_up = (neg_log10_pval > pval_threshold) & (log2_fc > fc_threshold)
+significant_down = (neg_log10_pval > pval_threshold) & (log2_fc < -fc_threshold)
+
+# Create separate data sources for each category (enables proper legend)
+source_up = ColumnDataSource(data={"x": log2_fc[significant_up], "y": neg_log10_pval[significant_up]})
+
+source_down = ColumnDataSource(data={"x": log2_fc[significant_down], "y": neg_log10_pval[significant_down]})
+
+source_ns = ColumnDataSource(
+    data={"x": log2_fc[~(significant_up | significant_down)], "y": neg_log10_pval[~(significant_up | significant_down)]}
+)
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="volcano-basic · bokeh · pyplots.ai",
+    x_axis_label="Log₂ Fold Change",
+    y_axis_label="-Log₁₀ (P-value)",
+)
+
+# Plot points by category (non-significant first, then significant on top)
+p.scatter(x="x", y="y", source=source_ns, color="#AAAAAA", size=18, alpha=0.5, legend_label="Not significant")
+
+p.scatter(x="x", y="y", source=source_down, color="#306998", size=25, alpha=0.7, legend_label="Down-regulated")
+
+p.scatter(x="x", y="y", source=source_up, color="#D62728", size=25, alpha=0.7, legend_label="Up-regulated")
+
+# Add threshold lines
+# Horizontal line for p-value threshold
+hline = Span(
+    location=pval_threshold, dimension="width", line_color="#333333", line_dash="dashed", line_width=3, line_alpha=0.7
+)
+p.add_layout(hline)
+
+# Vertical lines for fold change thresholds
+vline_pos = Span(
+    location=fc_threshold, dimension="height", line_color="#333333", line_dash="dashed", line_width=3, line_alpha=0.7
+)
+p.add_layout(vline_pos)
+
+vline_neg = Span(
+    location=-fc_threshold, dimension="height", line_color="#333333", line_dash="dashed", line_width=3, line_alpha=0.7
+)
+p.add_layout(vline_neg)
+
+# Styling - scaled for 4800x2700 canvas
+p.title.text_font_size = "36pt"
+p.title.align = "center"
+p.xaxis.axis_label_text_font_size = "28pt"
+p.yaxis.axis_label_text_font_size = "28pt"
+p.xaxis.major_label_text_font_size = "22pt"
+p.yaxis.major_label_text_font_size = "22pt"
+p.xaxis.axis_line_width = 2
+p.yaxis.axis_line_width = 2
+p.xaxis.major_tick_line_width = 2
+p.yaxis.major_tick_line_width = 2
+
+# Grid styling
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = "dotted"
+
+# Background
+p.background_fill_color = "#FAFAFA"
+p.border_fill_color = "#FFFFFF"
+
+# Legend styling
+p.legend.location = "top_right"
+p.legend.label_text_font_size = "22pt"
+p.legend.glyph_height = 40
+p.legend.glyph_width = 40
+p.legend.spacing = 15
+p.legend.padding = 20
+p.legend.background_fill_alpha = 0.8
+p.legend.border_line_width = 2
+p.legend.border_line_color = "#CCCCCC"
+
+# Save
+export_png(p, filename="plot.png")

plots/volcano-basic/metadata/bokeh.yaml

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+library: bokeh
+specification_id: volcano-basic
+created: '2025-12-31T05:31:44Z'
+updated: '2025-12-31T05:38:22Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20612781583
+issue: 2924
+python_version: 3.13.11
+library_version: 3.8.1
+preview_url: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/bokeh/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/bokeh/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/volcano-basic/bokeh/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of volcano plot with all required threshold lines and
+    color coding
+  - Well-scaled for 4800x2700 canvas with appropriately sized text and markers
+  - Clean separation of data into three ColumnDataSources enables proper legend handling
+  - Realistic simulated gene expression data with correlation between fold change
+    magnitude and significance
+  - Proper use of alpha transparency for dense data visualization
+  - Clean, readable code following KISS principles
+  weaknesses:
+  - Red/blue color scheme not optimal for colorblind accessibility - consider using
+    colorblind-safe palette
+  - 'Missing optional feature: labeling of top significant genes by name as suggested
+    in spec notes'
+  - No hover tooltips to identify individual points - a key Bokeh interactive feature