feat(matplotlib): implement volcano-basic

[github-actions]

Implementation: volcano-basic - matplotlib

Implements the matplotlib version of volcano-basic.

File: plots/volcano-basic/implementations/matplotlib.py

Parent Issue: #2924

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a volcano plot displaying differential gene expression data. The x-axis shows "Log₂ Fold Change" ranging from approximately -6 to 6, and the y-axis shows "-Log₁₀ (p-value)" ranging from 0 to 6. Points are colored in three categories: gray for "Not significant" (centered in the middle below the threshold lines), blue (#306998, Python Blue) for "Down-regulated" genes (left side), and gold/yellow (#FFD43B, Python Yellow) for "Up-regulated" genes (right side). Three dashed threshold lines are present: one horizontal line at y≈1.3 (p-value significance cutoff) and two vertical lines at x=±1 (fold change cutoffs). Top significant genes are labeled on both sides (Gene_1971, Gene_262, Gene_668, Gene_1591, Gene_1539 on the left; Gene_1957, Gene_1615, Gene_209, Gene_179, Gene_478 on the right). The title follows the correct format "volcano-basic · matplotlib · pyplots.ai". Legend is positioned in the upper right. Grid lines are subtle with dashed style and low alpha.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, axis labels 20pt, ticks 16pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, gene labels well positioned with offsets
• VQ-03: Element Visibility (6/8) - Markers visible with appropriate alpha, but sizes could be slightly larger per guidelines (s=50/80 used, could be 100-200 for 2000 points)
• VQ-04: Color Accessibility (4/5) - Blue and gold provide good contrast, though not a standard colorblind-safe palette (blue-yellow is generally accessible but not optimal)
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, good margins, symmetric x-axis limits
• VQ-06: Axis Labels (2/2) - Descriptive labels with subscript formatting (Log₂, Log₁₀)
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), but legend could be better positioned to not overlap the data area

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct volcano plot with fold change vs significance
• SC-02: Data Mapping (5/5) - log2 fold change on x-axis, -log10(p-value) on y-axis as specified
• SC-03: Required Features (5/5) - All spec features present: threshold lines (horizontal at 1.3, vertical at ±1), color coding by significance, gene labels on top features
• SC-04: Data Range (3/3) - Axes show all data with proper padding (1.1x multiplier)
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies all three categories
• SC-06: Title Format (2/2) - Correct format: "volcano-basic · matplotlib · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows all aspects: non-significant cluster, up-regulated, down-regulated, high significance outliers, threshold regions
• DQ-02: Realistic Context (7/7) - Simulated differential gene expression data, scientifically plausible with realistic p-value distribution
• DQ-03: Appropriate Scale (5/5) - 2000 genes, log2 fold changes centered at 0 with realistic spread, p-values correlated with effect size

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) used
• CQ-03: Clean Imports (2/2) - Only matplotlib and numpy, both used
• CQ-04: No Deprecated API (1/1) - All APIs current
• CQ-05: Output Correct (1/1) - Saves as plot.png with dpi=300

Library Features (0/5 pts)

• LF-01: Uses distinctive library features (0/5) - Uses standard scatter and annotation, no distinctive matplotlib features like custom colormaps, PathEffects, or advanced annotations

Strengths

• Excellent implementation of all spec requirements including threshold lines, color coding, and gene annotations
• Scientifically accurate data simulation with p-values correlated to fold change magnitude
• Clean, readable code following KISS principles
• Proper axis formatting with subscript notation (Log₂, Log₁₀)
• Symmetric axis limits for better visual interpretation
• Good alpha transparency for handling overlapping points

Weaknesses

• Marker sizes could be larger (s=100-200 recommended for 2000 points density)
• No use of distinctive matplotlib features like PathEffects for label backgrounds or custom colormaps
• Legend placement in upper right could potentially overlap with high-significance up-regulated points

Verdict: APPROVED