feat(bokeh): implement choropleth-basic

[github-actions]

Implementation: choropleth-basic - bokeh

Implements the bokeh version of choropleth-basic.

File: plots/choropleth-basic/implementations/bokeh.py

Parent Issue: #3069

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a tile grid map of the United States showing population density by state. Each state is represented as a rectangular tile with a two-letter abbreviation (e.g., CA, TX, NY). The tiles are arranged to roughly approximate the geographic layout of the US. Colors range from very light blue/white (low density states like AK, WY, MT) to dark blue (high density states like NJ, RI, MA, CT, MD). DC appears in gray, demonstrating missing data handling. The title "US Population Density · choropleth-basic · bokeh · pyplots.ai" is centered at the top. A color bar legend on the right side shows the scale from 0 to 1200+ people per square mile with the label "Population Density (per sq mile)".

Quality Score: 88/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 32pt, state labels at 18pt bold are readable; color bar labels slightly small
• VQ-02: No Overlap (8/8) - No overlapping text, all state abbreviations clearly visible
• VQ-03: Element Visibility (7/8) - Tiles are well-sized and clearly visible; some low-density states hard to distinguish
• VQ-04: Color Accessibility (4/5) - Blues9 palette is colorblind-safe sequential; good choice
• VQ-05: Layout Balance (4/5) - Good use of canvas space; color bar positioned well but slightly isolated
• VQ-06: Axis Labels (2/2) - N/A for choropleth, but color bar has proper label with units
• VQ-07: Grid & Legend (1/2) - Color bar present and functional; could be larger for better readability

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct choropleth/tile grid map visualization
• SC-02: Data Mapping (5/5) - Values correctly mapped to colors
• SC-03: Required Features (4/5) - Has color legend, handles missing data (DC shown gray); not a true geographic choropleth but valid tile approach
• SC-04: Data Range (3/3) - All states displayed with appropriate color range
• SC-05: Legend Accuracy (2/2) - Color bar accurately represents the data range
• SC-06: Title Format (1/2) - Title format is close but uses "US Population Density · choropleth-basic · bokeh · pyplots.ai" instead of standard format

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows wide range from sparse (AK=1, WY=6) to dense (NJ=1263, RI=1061); missing data demonstrated
• DQ-02: Realistic Context (7/7) - US population density is a perfect, neutral real-world scenario
• DQ-03: Appropriate Scale (4/5) - Values are realistic; DC intentionally missing is good but actual DC density would add value

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → processing → plot → save
• CQ-02: Reproducibility (3/3) - Deterministic data, no random elements
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current Bokeh API
• CQ-05: Output Correct (0/1) - Saves as 'plot.png' but in current directory, should verify path

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ColumnDataSource, ColorBar, LabelSet, LinearColorMapper - good Bokeh features but could leverage more interactive capabilities

Strengths

• Excellent use of tile grid map approach which works well for Bokeh's rect glyph capabilities
• Smart handling of missing data with gray color and nan_color configuration
• Good text contrast logic - white text on dark backgrounds, blue text on light backgrounds
• Clean, well-organized code following KISS principles
• Realistic population density data with proper units in the legend

Weaknesses

• Title format should follow exact spec pattern "{spec-id} · {library} · pyplots.ai" not include data description
• Color bar is functional but relatively small compared to the main plot; could be more prominent
• Some very low density states (AK, WY, MT, etc.) are nearly indistinguishable from each other in the lightest color band

Verdict: REJECTED

The implementation is high quality at 88/100 but falls just short of the 90-point threshold for automatic approval. The main issues are minor: title format deviation and color bar sizing. These are straightforward fixes that should push the score above 90.

[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 tile grid map of US population density using rectangular tiles arranged to approximate the geographic positions of all 50 US states plus DC. Each state is represented by a colored rectangle with a 2-letter state abbreviation. The color scheme uses a sequential blue palette (Blues9) where lighter blues indicate lower population density and darker blues indicate higher density. States like NJ, RI, MA, CT, DE, MD, and FL appear in darker blue shades (high density), while states like AK, WY, MT, ND, SD appear in very light blue (low density). DC is shown in gray, demonstrating missing data handling. The title reads "US Population Density · choropleth-basic · bokeh · pyplots.ai" at the top. A color bar legend is positioned on the right side showing "Population Density (per sq mile)" with a logarithmic scale ranging from approximately 1 to 1200.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 32pt, labels at 18pt are readable; slightly smaller than ideal for state abbreviations
• VQ-02: No Overlap (8/8) - No overlapping text; each state tile is separate with clear spacing
• VQ-03: Element Visibility (8/8) - Tiles are well-sized and clearly visible with good spacing
• VQ-04: Color Accessibility (5/5) - Blues9 sequential palette is colorblind-safe
• VQ-05: Layout Balance (4/5) - Good use of canvas; slight imbalance with color bar placement creating some empty space in lower right
• VQ-06: Axis Labels (N/A) - Not applicable for map visualization
• VQ-07: Grid & Legend (2/2) - Color bar well-placed with clear title and tick labels

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct choropleth map representation using tile grid approach
• SC-02: Data Mapping (5/5) - Population density correctly mapped to color intensity
• SC-03: Required Features (4/5) - Has color legend, clear boundaries, missing data handling; tile grid approach is creative but not traditional geographic projection
• SC-04: Data Range (3/3) - Full range of density values displayed from 1 (AK) to 1263 (NJ)
• SC-05: Legend Accuracy (2/2) - Color bar accurately shows population density scale
• SC-06: Title Format (1/2) - Title includes extra "US Population Density" prefix; should be just "choropleth-basic · bokeh · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows wide range of density values, missing data handling; but tile grid loses some geographic context
• DQ-02: Realistic Context (7/7) - US population density is a real, neutral, educational topic with accurate data
• DQ-03: Appropriate Scale (5/5) - Realistic population density values (1-1263 people per sq mile)

Code Quality (8/10 pts)

• CQ-01: KISS Structure (2/3) - Mostly linear structure but has some complexity with manual color mapping logic
• CQ-02: Reproducibility (3/3) - Deterministic data, no random values
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current Bokeh API
• CQ-05: Output Correct (0/1) - Saves as plot.png correctly

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Excellent use of Bokeh's ColumnDataSource, LogColorMapper, ColorBar, LabelSet, and rect glyph

Strengths

• Excellent use of Bokeh-specific features including LogColorMapper for better differentiation of low-density states
• Smart contrast handling with dynamic text colors (white on dark, blue on light backgrounds)
• Good missing data handling demonstrated with DC shown in gray
• Clean tile grid approach that fits all 50 states + DC in a readable layout
• Well-implemented color bar with logarithmic scale and clear labeling

Weaknesses

• Title format should be exactly "choropleth-basic · bokeh · pyplots.ai" without the "US Population Density" prefix
• Tile grid approach, while creative and readable, differs from traditional geographic projection that spec mentions (Robinson for world, Albers for US)

Verdict: APPROVED