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chore(matplotlib): update quality score 85 and review feedback for scatter-matrix-interactive
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‎plots/scatter-matrix-interactive/implementations/python/matplotlib.py‎

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"""anyplot.ai
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""" anyplot.ai
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scatter-matrix-interactive: Interactive Scatter Plot Matrix (SPLOM)
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Library: matplotlib 3.10.8 | Python 3.13
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Quality: pending | Created: 2026-05-18
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Library: matplotlib 3.10.9 | Python 3.13.13
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Quality: 85/100 | Updated: 2026-05-18
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"""
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import os

‎plots/scatter-matrix-interactive/metadata/python/matplotlib.yaml‎

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# Per-library metadata for matplotlib implementation of scatter-matrix-interactive
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# Auto-generated by impl-generate.yml
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library: matplotlib
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language: python
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specification_id: scatter-matrix-interactive
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created: '2026-01-10T01:57:21Z'
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updated: '2026-05-18T16:15:34Z'
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updated: '2026-05-18T16:18:53Z'
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generated_by: claude-haiku
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workflow_run: 26045487922
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issue: 3604
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preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/scatter-matrix-interactive/python/matplotlib/plot-dark.png
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preview_html_light: null
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preview_html_dark: null
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quality_score: null
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quality_score: 85
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review:
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strengths: []
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weaknesses: []
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strengths:
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- Perfect theme adaptation - both light and dark renders fully readable with correct
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color tokens
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- Correct color palette - Okabe-Ito positions 1-3 used in correct order for three
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species
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- Clear scatter matrix layout - proper 4x4 grid with pairwise relationships and
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diagonal univariate distributions
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- Honest acknowledgment of matplotlib's static limitations
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- Proper accessibility - all text legible with good contrast and CVD-safe colors
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weaknesses:
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- Generic design using standard matplotlib styling without custom visual refinement
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- Limited visual hierarchy - layout is functional but doesn't emphasize key relationships
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- Static-only limitation - matplotlib cannot provide interactive brushing (fundamental
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library constraint, not a code issue)
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image_description: |-
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Light render (plot-light.png):
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Background: Warm off-white #FAF8F1, correct theme color
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Chrome: Title "Iris Dataset · scatter-matrix-interactive" bold and clear. Axis labels show feature names with units (cm) in primary text #1A1A17. Tick labels in secondary text #4A4A44, fully readable. Grid subtle but visible. Spines appropriately colored in #4A4A44.
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Data: 4x4 scatter matrix with diagonal histograms. Three species shown in Okabe-Ito colors: green (#009E73), orange (#D55E00), blue (#0072B2). Scatter points have white edges for definition. Histograms show overlaid bars by species. Legend at top shows all three species clearly.
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Legibility verdict: PASS - All text and data elements are clearly readable against the light background. White edges on markers provide excellent definition. Grid is subtle without overwhelming the data.
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Dark render (plot-dark.png):
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Background: Warm near-black #1A1A17, correct theme color
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Chrome: Title in light text #F0EFE8, clearly visible. Axis labels in light text. Tick labels in light gray #B8B7B0 with good contrast. Grid lines subtle but visible. Spines in #B8B7B0.
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Data: Identical to light render - same three Okabe-Ito colors for species (green, orange, blue). Scatter points with white edges clearly visible. Histograms bars distinct and readable. Legend frame uses elevated dark background #242420 with light text.
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Legibility verdict: PASS - All text is readable with proper contrast. No dark-on-dark failures. Data colors are preserved across themes, confirming proper theme adaptation. Brand green #009E73 visible and distinguishable in both renders.
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criteria_checklist:
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visual_quality:
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score: 30
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: All text properly sized and colored; both themes fully readable
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 6
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passed: true
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comment: Clean scatter matrix grid with clear separation; no text or marker
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collisions
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- id: VQ-03
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name: Element Visibility
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score: 6
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max: 6
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passed: true
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comment: White-edged scatter points and histogram bars clearly visible with
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appropriate alpha blending
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- id: VQ-04
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name: Color Accessibility
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score: 2
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max: 2
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passed: true
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comment: Okabe-Ito palette is CVD-safe by design
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: Square 12x12 inch figure appropriate for matrix; proper margins;
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nothing cut off
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: Descriptive labels with units; clear title including spec ID
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- id: VQ-07
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name: Palette Compliance
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score: 2
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max: 2
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passed: true
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comment: 'First series is #009E73; multi-series follow Okabe-Ito; backgrounds
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correct; both themes theme-adaptive'
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design_excellence:
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score: 10
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 4
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max: 8
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passed: false
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comment: Clean professional design but uses standard matplotlib defaults without
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custom visual intent
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- id: DE-02
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name: Visual Refinement
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score: 3
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max: 6
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passed: false
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comment: Spines subtle and thin; grid appropriately understated; whitespace
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generous; but standard styling
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- id: DE-03
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name: Data Storytelling
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score: 3
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max: 6
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passed: false
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comment: Grid effectively shows pairwise relationships; diagonal histograms
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provide univariate context; no visual hierarchy or emphasis
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct 4x4 scatter matrix with diagonal univariate distributions
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: Pairwise scatter plots, diagonal histograms, consistent color encoding
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by species; honest note about static limitation
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: All four Iris features properly mapped; all 150 samples visible
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Title descriptive with spec context; legend clear with correct species
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and colors
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data_quality:
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score: 15
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 6
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max: 6
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passed: true
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comment: All four Iris features shown in pairwise combinations with diagonal
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univariates
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: Iris dataset is classic, real-world, and neutral; excellent for multivariate
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exploration
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Feature ranges sensible for domain; histogram bins appropriate
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: Procedural code with simple nested loops; no unnecessary functions
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or classes
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: Iris dataset deterministic; no random seeds needed; fully reproducible
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: 'Only necessary imports: os, matplotlib.pyplot, sklearn.datasets;
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all used'
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Appropriate complexity; no fake UI or simulated interactivity; honest
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about limitations
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves as plot-{THEME}.png; no bare plot.png; current API usage
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library_mastery:
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score: 5
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 4
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max: 5
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passed: true
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comment: Subplots API used correctly; axes iteration proper; theme tokens
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via os.getenv() idiomatic
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- id: LM-02
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name: Distinctive Features
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score: 1
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max: 5
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passed: false
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comment: Standard matplotlib usage; no distinctive library-specific techniques
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beyond basic API
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verdict: APPROVED
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impl_tags:
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dependencies:
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- sklearn
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techniques:
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- subplots
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patterns:
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- dataset-loading
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- iteration-over-groups
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dataprep: []
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styling:
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- edge-highlighting
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- alpha-blending

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