feat(pygal): implement bubble-map-geographic

[github-actions]

Implementation: bubble-map-geographic - pygal

Implements the pygal version of bubble-map-geographic.

File: plots/bubble-map-geographic/implementations/pygal.py

Parent Issue: #3625

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a world map with bubble markers representing major city populations. The background is a light blue (#C8DDF0) representing oceans, with gray simplified coastline outlines for continents (North America, South America, Europe, Africa, Asia, Australia). Cities are represented as colored bubbles with four size categories: small blue dots for populations under 12M (Paris, London, Sydney, Seoul, Bangkok, Jakarta), medium blue dots for 12-20M (Moscow, Istanbul, Lagos, Buenos Aires, Los Angeles), yellow dots for 20-30M (Mexico City, Cairo, Mumbai, Beijing, Shanghai, New York, Delhi), and large red-orange dots for populations over 30M (Tokyo). The x-axis shows Longitude (-160 to 160°), y-axis shows Latitude (-60 to 80°). A legend at bottom shows all four population categories. The title correctly follows the format "World City Populations · bubble-map-geographic · pygal · pyplots.ai".

Quality Score: 85/100

Criteria Checklist

Visual Quality (33/40 pts)

• VQ-01: Text Legibility (8/10) - Title and axis labels are readable, but font sizes at high resolution could be slightly larger for optimal clarity
• VQ-02: No Overlap (8/8) - No overlapping text or data elements
• VQ-03: Element Visibility (6/8) - Bubble sizes clearly differentiate categories, but smallest bubbles (Paris, London, Bangkok) are quite small relative to canvas
• VQ-04: Color Accessibility (5/5) - Blue, yellow, and red-orange palette is colorblind-friendly with good contrast
• VQ-05: Layout Balance (4/5) - Good use of canvas, though some empty space in southern hemisphere
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Longitude (°)" and "Latitude (°)"
• VQ-07: Grid & Legend (0/2) - No visible grid lines despite show_x_guides=True setting; legend is at bottom but functional

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct bubble map with sized geographic markers
• SC-02: Data Mapping (5/5) - Longitude/Latitude correctly mapped to X/Y axes
• SC-03: Required Features (4/5) - Has sized bubbles, geographic context (coastlines), legend. Missing: size legend showing actual value-to-size relationship
• SC-04: Data Range (3/3) - All data visible within -180 to 180 longitude, -60 to 80 latitude
• SC-05: Legend Accuracy (2/2) - Legend correctly shows population categories
• SC-06: Title Format (1/2) - Title includes spec-id and library but adds "World City Populations" prefix before the required format

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows cities across all continents with varying population sizes; good range from 5.4M (Sydney) to 37.4M (Tokyo)
• DQ-02: Realistic Context (7/7) - Real world cities with accurate population figures
• DQ-03: Appropriate Scale (4/5) - Population values are realistic, though some are slightly dated

Code Quality (8/10 pts)

• CQ-01: KISS Structure (3/3) - Linear structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) even though data is hardcoded
• CQ-03: Clean Imports (1/2) - sys manipulation for module name conflict is necessary but adds complexity
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (0/1) - Saves plot.html first, then plot.png; outputs are correct but order is non-standard

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal XY chart, custom Style, legend configuration, but does not leverage pygal's native SVG interactivity features (tooltips work in HTML but not shown in PNG)

Strengths

• Excellent geographic context with simplified coastline outlines providing world map orientation
• Clear population category differentiation through both size and color encoding
• Good use of pygal's custom Style for font sizing and colors appropriate for high resolution
• Real-world data with actual city population figures adds credibility
• Proper handling of pygal module name conflict

Weaknesses

• Title format should be exactly "{spec-id} · {library} · pyplots.ai" without prefix text
• Grid lines not visible despite being enabled in configuration
• Smallest bubbles (5-12M population) are relatively hard to see compared to mega cities
• No explicit size legend showing the value-to-size relationship (spec recommends this)

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

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

The plot displays a geographic bubble map with 20 world cities plotted on an XY coordinate system where X represents Longitude (-180 to 160°) and Y represents Latitude (-60 to 80°). The background is a light blue (#C8DDF0) representing oceans, with simplified gray coastline outlines for continents (North America, South America, Europe, Africa, Asia, Australia). City bubbles are color-coded by population category: small dark blue (Pop < 12M), medium blue (Pop 12-20M), yellow (Pop 20-30M), and red-orange (Pop > 30M). Bubble sizes increase proportionally with population category. The two largest red-orange bubbles represent Tokyo and Delhi (>30M). The title "bubble-map-geographic · pygal · pyplots.ai" appears at the top. A legend at the bottom shows the four population categories. Grid lines are subtle dashed gray lines.

Quality Score: 90/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - All text clearly readable: title at 72pt, axis labels at 48pt, tick labels at 40pt, legend at 40pt
• VQ-02: No Overlap (8/8) - No overlapping text or elements
• VQ-03: Element Visibility (6/8) - Bubbles are well-sized and visible; however, size variation within each category is not shown (all bubbles in same category are same size)
• VQ-04: Color Accessibility (5/5) - Blue/yellow/red color scheme is colorblind-safe and provides good contrast
• VQ-05: Layout Balance (5/5) - Good use of canvas with balanced margins, plot fills appropriate area
• VQ-06: Axis Labels (2/2) - "Longitude (°)" and "Latitude (°)" with units
• VQ-07: Grid & Legend (0/2) - Grid is subtle but legend placement at bottom is good; however grid lines are quite visible/prominent

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct bubble map implementation using XY scatter with sized markers
• SC-02: Data Mapping (5/5) - Longitude on X, Latitude on Y, population controlling size category
• SC-03: Required Features (3/5) - Has sized bubbles, geographic context (coastlines), transparency (0.7 opacity), tooltips. Missing: continuous size scaling (uses discrete categories instead of proportional area scaling as spec requires)
• SC-04: Data Range (3/3) - All data visible within range (-180 to 180 lon, -60 to 80 lat)
• SC-05: Legend Accuracy (2/2) - Legend correctly shows all four population categories
• SC-06: Title Format (2/2) - Correct format: "bubble-map-geographic · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows range of populations from small (Sydney 5.4M) to mega (Tokyo 37.4M), good geographic spread across continents. However, categorical binning reduces demonstration of continuous size variation
• DQ-02: Realistic Context (7/7) - Real-world city population data, accurate locations, plausible population values
• DQ-03: Appropriate Scale (5/5) - Population values are realistic and well-documented (in millions)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear flow: imports → data → coastlines → chart → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) even though data is deterministic
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pygal, Style), sys path manipulation needed for naming conflict
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of pygal XY chart, custom Style, interactive HTML output, legend_at_bottom. However, doesn't leverage pygal's built-in map charts (World Map) which would be more appropriate

Strengths

• Excellent visual clarity with well-sized text and clear color differentiation
• Effective use of custom coastline polygons to provide geographic context
• Good use of pygal's custom Style for controlling fonts and colors at high resolution
• Interactive HTML output with hover tooltips showing city names and populations
• Clean, well-structured code following KISS principles

Weaknesses

• Bubble sizes are categorical (4 fixed sizes) rather than continuously scaled proportional to population values as the spec requires
• Grid lines are somewhat prominent compared to ideal subtle alpha 0.2-0.4
• Does not use pygal's World map capability which could provide better geographic basemap

Verdict: APPROVED