feat(letsplot): implement polar-bar

[github-actions]

Implementation: polar-bar - python/letsplot

Implements the python/letsplot version of polar-bar.

File: plots/polar-bar/implementations/python/letsplot.py

Parent Issue: #2693

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (): The polar bar chart displays 8 compass directions (N, NE, E, SE, S, SW, W, NW) arranged radially with bars extending outward proportional to their frequency values. The plot uses a warm off-white background (#FAF8F1) with concentric grid circles in subtle gray. The title "polar-bar · letsplot · anyplot.ai" is rendered in dark text (INK color, #1A1A17) at the top. The bars use Okabe-Ito colors in order: N (#009E73 brand green, small 3-unit bar), NE (#D55E00 vermillion), E (#0072B2 blue), SE (#CC79A7 reddish purple), S (#E69F00 orange, 28 units), SW (#56B4E9 sky blue, largest at 35 units), W (#F0E442 yellow), NW (#1A1A1A neutral dark, 5 units). Each bar has a subtle white edge for definition. The Y-axis is labeled "Frequency (%)" with tick labels (5, 10, 15, 20, 25, 30, 35) in gray. All text is clearly readable against the light background. The legend is hidden as appropriate for a categorical wind rose where directions are labeled around the perimeter.

Dark render (): The same plot renders on a warm near-black background (#1A1A17). The title, axis label, and tick labels are now rendered in light text (INK #F0EFE8 for title, INK_SOFT #B8B7B0 for other labels), maintaining excellent contrast and readability against the dark surface. The data bar colors are identical to the light render—Okabe-Ito positions 1–7 remain constant to preserve category identity. The neutral 8th bar (NW) correctly adapts to #E8E8E0 (light off-white) for visibility on the dark background. Grid lines remain subtle but visible. No "dark-on-dark" legibility failures observed. All chrome (title, labels, grid) has been properly adapted for the dark theme while data colors stay consistent.

Score: 85/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	10	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	5	10
Total	85	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All elements properly sized (24pt title, 20pt axis label, 16pt ticks). Fully readable in both themes. Theme-adaptive colors applied throughout.
• VQ-02: No Overlap (6/6) - Direction labels positioned around circle, data bars clearly distinguishable, no text collisions.
• VQ-03: Element Visibility (6/6) - Even small bars (3–6 units) are visible with alpha=0.85. White edges on bars aid definition without obscuring data.
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is CVD-safe. No problematic color pairs. Colorblind-friendly throughout.
• VQ-05: Layout & Canvas (4/4) - 1200×1200 base (→ 3600×3600 px final) appropriate for symmetric polar plot. Grid spacing proportionate, nothing cut off.
• VQ-06: Axis Labels & Title (2/2) - Title format correct ("polar-bar · letsplot · anyplot.ai"). Y-axis labeled "Frequency (%)". Directions labeled around perimeter.
• VQ-07: Palette Compliance (2/2) - First series (N) correctly uses #009E73 (brand green). All 8 categories follow Okabe-Ito order. Backgrounds are #FAF8F1 (light) / #1A1A17 (dark). Neutral (position 8) properly adapts: #1A1A1A (light) → #E8E8E0 (dark). Both renders theme-correct.

Design Excellence (10/20)

• DE-01: Aesthetic Sophistication (4/8) - Clean, minimal aesthetic using theme_minimal() as foundation. Adequate execution of basics (edges on bars, subtle grid), but lacks custom design touches or distinctive visual refinement beyond defaults.
• DE-02: Visual Refinement (3/6) - Theme_minimal() provides baseline; spines are implicit. Grid is subtly styled (size=0.3, adaptive color). Some refinement (bar edges), but mostly standard styling without extended customization.
• DE-03: Data Storytelling (3/6) - Plot effectively shows monsoon wind dominance (SW peak), but visual hierarchy is functional rather than intentional. Color naturally separates directions, but no additional emphasis or focal-point design.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct polar bar chart (wind rose). coord_polar() with appropriate angular offset (start=-π/8).
• SC-02: Required Features (4/4) - All 8 compass directions shown, frequency as bar magnitude, bars radiate outward, appropriate for meteorological/directional data.
• SC-03: Data Mapping (3/3) - X correctly maps to direction (categorical), Y to frequency (numeric). Axes show full range (0–35 frequency). All categories represented.
• SC-04: Title & Legend (3/3) - Title follows format. Legend hidden (appropriate; categories labeled around circle).

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Full compass rose (8 cardinal directions), frequency range 3–35 demonstrates plot-type versatility.
• DQ-02: Realistic Context (5/5) - Monsoon wind pattern (SW dominance, reduced activity N/E) is meteorologically plausible and neutral.
• DQ-03: Appropriate Scale (4/4) - Frequency values sensible for wind distribution. No outliers, good data range for visualization.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No unnecessary functions/classes. Straightforward procedural code, easy to read and understand.
• CQ-02: Reproducibility (2/2) - Data hardcoded (deterministic), no random generation. Fully reproducible.
• CQ-03: Clean Imports (2/2) - Only imports from lets_plot, numpy, pandas, os. All imports used. No extraneous dependencies.
• CQ-04: Code Elegance (2/2) - No fake UI, no simulated interactivity. Complexity appropriate for visualization. No AR-08 violations.
• CQ-05: Output & API (1/1) - Correctly saves as plot-{THEME}.png and plot-{THEME}.html. Theme-suffixed filenames. Current letsplot API (ggsave with scale parameter).

Library Mastery (5/10)

• LM-01: Idiomatic Usage (4/5) - Uses idiomatic ggplot2/letsplot pattern (ggplot + geom + coord + scale + labs + theme). High-level API throughout. coord_polar() applied correctly. Deduction: theme customization is functional but not particularly sophisticated.
• LM-02: Distinctive Features (1/5) - Standard use of letsplot features (ggsize, ggsave, theme system). No distinctive library-specific techniques beyond what's required for the plot type.

Score Caps Applied

• None — no criteria fell to 0.

Strengths

• Perfect text legibility in both light and dark themes; theme-adaptive colors applied throughout
• Correct and strict adherence to Okabe-Ito palette with proper brand-green-first ordering
• Clean, minimal code structure with no unnecessary complexity; fully reproducible
• Proper canvas sizing for symmetric plots (3600×3600 px final) with scale=3 export
• All background colors match spec (#FAF8F1 light / #1A1A17 dark); no pure white/black
• Accurate monsoon data pattern (realistic meteorological context)
• Bars have white edges and alpha=0.85 for visual definition without obscuring data

Weaknesses

• Design Excellence low: implementation is functional and clean but lacks distinctive aesthetic choices beyond the minimal theme
• Library Mastery limited: uses core letsplot features correctly but doesn't showcase library-specific capabilities or advanced customization
• Visual hierarchy: the plot communicates the data clearly, but doesn't intentionally guide the viewer or emphasize key patterns through color, size, or position emphasis

AI Feedback for Next Attempt

The implementation is technically sound and follows all palette/theme/output guidelines correctly. To improve Design Excellence, consider: (1) custom color focus on the dominant direction (SW) using a slightly bolder or contrasting treatment, or (2) adding subtle visual hierarchy through size variation or alpha gradients, or (3) refining grid opacity/style to be more intentional. Library Mastery could be raised by leveraging letsplot-specific features such as custom legend positioning with refined styling, or layer composition for stacked frequency ranges.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 1/4

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

Light render (plot-light.png): The plot displays a polar wind rose chart with a warm off-white background (#FAF8F1). The title "polar-bar · letsplot · anyplot.ai" is prominently displayed in bold at the top (24pt, dark text #1A1A17). Eight compass directions (N, NE, E, SE, S, SW, W, NW) are labeled around the circle (16pt, dark text). The Y-axis label "Frequency (%)" is positioned on the left (20pt, dark text). Bars radiate from the center using Okabe-Ito palette colors: the dominant SW direction shows a bright blue bar (freq=35), S displays yellow-gold (freq=28), SE is magenta (freq=12), and smaller bars in various colors represent the remaining directions. White bar edges provide clean definition. All grid lines are subtle and visible. Text has strong contrast against the light background with no legibility issues.

Dark render (plot-dark.png): The same polar wind rose rendered on a warm near-black background (#1A1A17). The title remains clearly visible in light text (#F0EFE8), as do the compass directions and Y-axis label—all properly adapted to dark theme. Critically, the data bar colors are identical to the light render: SW maintains its bright blue, S remains yellow-gold, SE is still magenta. This confirms proper theme separation—only chrome elements (background, text, grid) adapted to dark theme while Okabe-Ito data colors remained constant. Grid lines are appropriately visible. All text elements have strong contrast with no dark-on-dark failures. Both renders demonstrate excellent theme adaptation.

Score: 92/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	14	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	92	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - Title, axis labels, compass directions all readable in both themes with proper contrast and sizing
• VQ-02: No Overlap (6/6) - Compass directions spaced cleanly, no text collisions, radial arrangement prevents overlap
• VQ-03: Element Visibility (6/6) - All bars visible from largest (SW=35) to smallest (N=3), white edges provide definition
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is CVD-safe, good contrast, no red-green-only encoding
• VQ-05: Layout & Canvas (4/4) - Square canvas (1200x1200) appropriate for polar chart, balanced proportions
• VQ-06: Axis Labels & Title (2/2) - Title format correct, Y-axis includes units, compass directions descriptive
• VQ-07: Palette Compliance (2/2) - Okabe-Ito correctly applied, backgrounds correct (#FAF8F1 light / #1A1A17 dark), data colors identical across themes

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (6/8) - Professional Okabe-Ito palette with theme-aware styling, custom sizing for clarity. Could strengthen visual emphasis on dominant direction.
• DE-02: Visual Refinement (4/6) - Subtle grid, white bar edges, adequate whitespace. Could refine grid styling further.
• DE-03: Data Storytelling (4/6) - Wind rose format clearly shows directional distribution with SW dominance apparent. Could emphasize primary direction more visually.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct polar bar chart with proper radial arrangement
• SC-02: Required Features (4/4) - All 8 compass directions, frequency values correctly mapped
• SC-03: Data Mapping (3/3) - Direction → angle, frequency → height mapping correct
• SC-04: Title & Legend (3/3) - Title format correct, legend appropriately disabled

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Complete 8-direction wind rose showing all aspects of polar bar type
• DQ-02: Realistic Context (5/5) - Monsoon wind pattern with SW dominance is realistic meteorological data
• DQ-03: Appropriate Scale (4/4) - Frequency range 0-35 sensible, Y-axis scale appropriate

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Simple, direct ggplot chain with no unnecessary abstractions
• CQ-02: Reproducibility (2/2) - Hardcoded data is deterministic
• CQ-03: Clean Imports (2/2) - Only necessary imports used
• CQ-04: Code Elegance (2/2) - Appropriate complexity, no fake functionality
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png and plot-{THEME}.html

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Perfect ggplot grammar, coord_polar transformation, geom_bar stat='identity', scale_fill_manual, theme integration
• LM-02: Distinctive Features (3/5) - Leverages coord_polar and interactive HTML export (lets_plot strengths)

Score Caps Applied

• None

Strengths

• Proper polar coordinate system implementation showing clear directional distribution
• Theme-adaptive design correctly applies light (#FAF8F1 background, dark ink) and dark (#1A1A17 background, light ink) tokens in both renders
• Excellent text legibility in both themes with no contrast issues
• Clean, idiomatic lets_plot code using coord_polar and ggplot grammar patterns effectively
• Correct Okabe-Ito palette integration, maintains color consistency across themes
• Wind rose format effectively communicates frequency distribution with SW dominance visually apparent
• White bar edges provide subtle definition without clutter

Weaknesses

• Design Excellence could be enhanced: grid styling is functional but could use more sophisticated visual refinement
• Visual hierarchy of dominant direction (SW=35) could be strengthened through size emphasis or color placement

Verdict: APPROVED ✅