feat(matplotlib): implement waffle-basic

[github-actions]

Implementation: waffle-basic - python/matplotlib

Implements the python/matplotlib version of waffle-basic.

File: plots/waffle-basic/implementations/python/matplotlib.py

Parent Issue: #998

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Warm off-white background (#FAF8F1, not pure white). A 10x10 grid of 100 rounded squares: the bottom ~4.8 rows are brand green (#009E73, "Strongly Agree" 48%), then lavender (#C475FD, "Agree" 32%), then blue (#4467A3, "Neutral" 15%), and the top row is ochre (#BD8233, "Disagree" 5%). The dominant green block carries a dark ink-toned outline plus a subtle drop shadow; the other categories use background-blended borders. Title "waffle-basic · python · matplotlib · anyplot.ai" sits in dark ink at top, with a softer-ink subtitle "Survey Response Distribution" below it, and a two-column legend (with percentages) sits in an elevated cream box beneath the grid. All text is clearly readable against the light background — no light-on-light issues.

Dark render (plot-dark.png): Warm near-black background (#1A1A17, not pure black). Data colors are identical to the light render (verified: green/lavender/blue/ochre unchanged) — only chrome flipped. Title and subtitle are now light-toned and clearly legible against the dark background; the dominant block's emphasis outline flips to a light-grey tone; the legend box uses the darker elevated fill with light text. No dark-on-dark failures anywhere.

Both paragraphs describe both renders — theme adaptation is correct and legible in both.

Score: 89/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	89	100

Visual Quality (28/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicit, readable in both themes
• VQ-02: No Overlap (6/6) - No collisions between squares, legend, title/subtitle
• VQ-03: Element Visibility (6/6) - 100 squares at appropriate density with clear gaps
• VQ-04: Color Accessibility (2/2) - Good contrast, CVD-safe
• VQ-05: Layout & Canvas (3/4) - Balanced symmetric side margins, but vertical fill (~94%) noticeably exceeds horizontal fill (~74%)
• VQ-06: Axis Labels & Title (2/2) - Descriptive title + subtitle; axis intentionally hidden
• VQ-07: Palette Compliance (2/2) - First series #009E73, canonical order, theme-correct chrome in both renders

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Rounded squares + drop shadow + emphasis outline clearly above default styling
• DE-02: Visual Refinement (5/6) - Hidden axis, generous whitespace, subtle shadow detail; legend frame slightly heavy
• DE-03: Data Storytelling (4/6) - Ink-outline on dominant category creates a real focal point without extra text

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct 10x10 waffle grid, 1 square = 1%
• SC-02: Required Features (4/4) - Grid, legend w/ percentages, distinct colors, 4 categories
• SC-03: Data Mapping (3/3) - Values (48/32/15/5) correctly counted into squares, sums to 100
• SC-04: Title & Legend (3/3) - Title matches mandated format exactly; legend matches data

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Good spread from 48% down to 5%
• DQ-02: Realistic Context (5/5) - Neutral, plausible survey-response scenario
• DQ-03: Appropriate Scale (4/4) - Sums to exactly 100, realistic Likert-style distribution

Code Quality (10/10)

• CQ-01: KISS Structure (3/3)
• CQ-02: Reproducibility (2/2) - Fully deterministic, no randomness needed
• CQ-03: Clean Imports (2/2)
• CQ-04: Code Elegance (2/2) - Appropriate complexity for manual waffle construction
• CQ-05: Output & API (1/1)

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Axes-level patch API, fig.legend with custom handles
• LM-02: Distinctive Features (3/5) - patheffects shadow + FancyBboxPatch rounding are matplotlib-distinctive

Score Caps Applied

• None

Strengths

• Correct Imprint palette applied in canonical order, first series #009E73
• Excellent theme-adaptive chrome: verified pixel margins (69px top/bottom, 315px left/right, symmetric) and no dark-on-dark or light-on-light failures
• Creative use of matplotlib patches (FancyBboxPatch rounded corners + patheffects drop shadow) to build a waffle chart matplotlib has no native geometry for
• Thoughtful emphasis technique: dominant category gets an ink-toned outline instead of a background-blended one, creating visual hierarchy without overlapping text
• Clean KISS-structured, deterministic code with correct theme-driven output naming
• No edge clipping on either render (AR-09 clear) - confirmed via pixel-level margin analysis

Weaknesses

• Left/right margins (~315px each, ~26% of canvas width combined) are noticeably larger than top/bottom margins, leaving vertical content fill (~94%) well above horizontal fill (~74%) — widen the grid slightly or tighten side margins so both dimensions land closer together
• Consider reinforcing the dominant-category emphasis with a direct percentage callout near/on the green block to sharpen the data-storytelling focal point further
• Typography hierarchy is fairly flat between subtitle and legend text — a bolder or larger legend category label vs. percentage figure would add more editorial polish

Issues Found

1. VQ-05 MINOR: Vertical canvas fill (~94%) exceeds horizontal fill (~74%) by a wide margin
   • Fix: Slightly widen ax.set_position horizontal extent or reduce top/bottom whitespace so both axes are closer to the 50-80% target together
2. DE-03 LOW-MODERATE: Visual hierarchy exists (ink-outline emphasis) but no direct data callout
   • Fix: Add a subtle percentage annotation near the dominant block to strengthen the storytelling focal point

AI Feedback for Next Attempt

Solid implementation — correct palette, correct theme chrome, no clipping, and a genuinely creative custom-drawn waffle grid with shadow + emphasis styling. To push past 90: balance horizontal vs. vertical canvas fill more evenly, and add one more layer of visual hierarchy (e.g., a direct percentage callout on the dominant block) to elevate Design Excellence and Data Storytelling scores.

Verdict: REJECTED

[github-actions]

🔧 Repair Workflow Crashed (Attempt 1/4) — Auto-Retrying

The repair workflow failed (probably a transient Claude Code Action issue). Automatically re-triggering this attempt...

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🤖 impl-repair

[github-actions]

❌ Repair Workflow Crashed (Attempt 1/4, retry exhausted)

The repair workflow itself failed twice for this attempt — likely a persistent Claude Code Action issue.

Manual restart:

gh workflow run impl-repair.yml -f pr_number=9949 -f specification_id=waffle-basic -f library=matplotlib -f attempt=1 -f model=sonnet

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🤖 impl-repair