feat(bokeh): implement contour-filled

[github-actions]

Implementation: contour-filled - bokeh

Implements the bokeh version of contour-filled.

File: plots/contour-filled/implementations/bokeh.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a filled contour visualization of a 2D scalar field using Bokeh's image rendering. The visualization shows multiple Gaussian peaks on an 80x80 grid spanning from -3 to 3 on both axes. The Viridis colormap is used, with dark purple representing low values (around -0.2 to 0) and bright yellow representing high values (around 2.0). Three main bright yellow/green peaks are visible: a large dominant peak in the lower-left quadrant (around x=-1.5, y=-0.5), a medium peak in the upper-right (around x=1, y=1), and a smaller peak at the right edge. A dark purple depression is visible in the upper-center area. The colorbar on the right shows 'Surface Value' with range from approximately -0.2 to 2.0. The title reads 'contour-filled · bokeh · pyplots.ai' and axes are labeled 'X Coordinate' and 'Y Coordinate'.

Quality Score: 78/100

Criteria Checklist

Visual Quality (32/40 pts)

• VQ-01: Text Legibility (8/10) - Title and axis labels readable but could be slightly larger for the canvas size
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Surface visualization is clear with good color gradients
• VQ-04: Color Accessibility (5/5) - Viridis is colorblind-safe
• VQ-05: Layout Balance (3/5) - Good layout but colorbar title 'Surface Value' appears rotated/vertical and hard to read
• VQ-06: Axis Labels (0/2) - Labels say 'X Coordinate' and 'Y Coordinate' which are generic, not descriptive with units
• VQ-07: Grid & Legend (0/2) - Grid lines are not visible over the image, colorbar present but no contour lines overlaid as suggested in spec

Spec Compliance (20/25 pts)

• SC-01: Plot Type (8/8) - Correct filled contour/heatmap representation
• SC-02: Data Mapping (5/5) - X/Y grid correctly mapped to Z values
• SC-03: Required Features (2/5) - Missing overlaid contour lines for precise level identification (spec note: 'Consider overlaying contour lines')
• SC-04: Data Range (3/3) - Full data range visible
• SC-05: Legend Accuracy (0/2) - Colorbar present but title is rotated and difficult to read
• SC-06: Title Format (2/2) - Correct format: 'contour-filled · bokeh · pyplots.ai'

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple Gaussian peaks with varying heights and a depression, demonstrating range of values
• DQ-02: Realistic Context (7/7) - Mathematical function surface is appropriate for demonstrating filled contours
• DQ-03: Appropriate Scale (4/5) - Values are reasonable but context is abstract mathematical

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear structure with no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) present
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Modern Bokeh API used
• 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) - Uses Bokeh's image() with LinearColorMapper and ColorBar, but doesn't leverage Bokeh's contour capabilities

Strengths

• Excellent use of Viridis colormap for colorblind accessibility
• Clean, well-structured code following KISS principles
• Good mathematical surface with multiple peaks demonstrating the visualization type well
• Proper canvas sizing at 4800x2700 with appropriate font scaling
• Both PNG and HTML outputs generated for static and interactive viewing

Weaknesses

• Missing overlaid contour lines which would help with precise level identification (spec suggests this)
• Generic axis labels ('X Coordinate', 'Y Coordinate') without descriptive context or units
• Colorbar title appears vertical and is difficult to read
• Grid lines not visible (obscured by the image fill)
• Uses image() for a heatmap effect rather than true filled contours with discrete level bands

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 filled contour visualization using Bokeh's image glyph with the Viridis colormap (dark purple to yellow). The canvas shows multiple Gaussian peaks representing terrain elevation, with the brightest yellow peak in the lower-left quadrant around coordinates (-1.5, -0.5), a secondary bright peak in the upper-right around (1, 1), and a darker depression visible in the upper-center region. The title "contour-filled · bokeh · pyplots.ai" appears in the top-left but is quite small. Axis labels show generic "X Coordinate" and "Y Coordinate" rather than the intended "Distance East (km)" and "Distance North (km)" from the code. The colorbar on the right displays values from approximately -0.2 to 2 with a "Surface Value" label. Critically, no contour lines are overlaid on the filled surface, despite the specification requiring them for precise level identification. The grid lines are not visible.

Quality Score: 72/100

Criteria Checklist

Visual Quality (30/40)

• VQ-01: Text Legibility (7/10) - Title and labels are readable but smaller than optimal for 4800x2700; code specifies correct sizes but rendering shows smaller text
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (6/8) - Filled surface is clearly visible, but missing contour lines reduce precision
• VQ-04: Color Accessibility (5/5) - Viridis colormap is colorblind-safe
• VQ-05: Layout Balance (4/5) - Good layout but slight imbalance with colorbar spacing
• VQ-06: Axis Labels (0/2) - Generic "X Coordinate"/"Y Coordinate" displayed instead of descriptive labels with units
• VQ-07: Grid & Legend (0/2) - Grid lines not visible despite being configured

Spec Compliance (18/25)

• SC-01: Plot Type (6/8) - Filled surface shown but missing required contour line overlay
• SC-02: Data Mapping (5/5) - X/Y/Z correctly mapped to grid
• SC-03: Required Features (2/5) - Colorbar present but contour lines missing (spec says "Consider overlaying contour lines for precise level identification")
• SC-04: Data Range (3/3) - Full data range visible
• SC-05: Legend Accuracy (0/2) - Colorbar title shows "Elevation (m)" in code but axis labels are generic
• SC-06: Title Format (2/2) - Correct format "{spec-id} · {library} · pyplots.ai"

Data Quality (17/20)

• DQ-01: Feature Coverage (7/8) - Shows peaks, valleys, and gradient transitions well
• DQ-02: Realistic Context (5/7) - Terrain elevation is plausible but axis labels don't reinforce context
• DQ-03: Appropriate Scale (5/5) - Elevation values 0-2000m are realistic

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear structure, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: No Deprecated API (1/1) - Current Bokeh API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5)

• LF-01: Uses distinctive library features (3/5) - Uses Bokeh's image glyph and contourpy for line generation, but contour lines not rendering

Strengths

• Correct use of Bokeh's image glyph for filled surface visualization with Viridis palette
• Proper 4800x2700 canvas dimensions with appropriate text sizing in code
• Realistic terrain elevation data with multiple Gaussian peaks and a valley
• Clean KISS code structure with reproducibility (seed=42)
• Both PNG and HTML outputs generated

Weaknesses

• Contour lines are generated in code but not visible in output - the lines may need higher contrast or visibility
• Axis labels display generic "X Coordinate"/"Y Coordinate" instead of the configured "Distance East (km)"/"Distance North (km)"
• Grid lines not visible despite overlay configuration
• Colorbar label shows generic scale, not terrain context

Verdict: REJECTED

The implementation is close but has rendering issues: contour lines are coded but not visible, and axis labels are not displaying the configured values. The contour lines are a key feature mentioned in the specification for precise level identification.

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

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

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🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a filled contour visualization of terrain elevation data using the Viridis colormap. The image shows multiple Gaussian peaks representing terrain features - a large bright yellow-green peak in the lower-left quadrant (highest elevation ~2000m), a medium peak in the upper-right area (~1400m), a smaller peak in the lower-right (~1000m), and a dark purple depression/valley in the upper-center region (low elevation). White contour lines with dark outlines are overlaid on the colored regions for precise level identification. The axes are labeled "Distance East (km)" and "Distance North (km)", with a colorbar on the right showing "Terrain Elevation (m)" ranging from 0-2000. The title reads "contour-filled · bokeh · pyplots.ai". A subtle white dashed grid is visible over the plot area.

Quality Score: 86/100

Criteria Checklist

Visual Quality (35/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick marks are readable at full size. The colorbar title is slightly small but still legible.
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Contour lines are visible with good contrast (white with dark outline). The filled regions are clear and distinguishable.
• VQ-04: Color Accessibility (5/5) - Viridis is a colorblind-safe perceptually uniform colormap
• VQ-05: Layout Balance (4/5) - Good use of canvas space, plot fills the area well. Minor: colorbar could be slightly better integrated.
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Distance East (km)", "Distance North (km)"
• VQ-07: Grid & Legend (0/2) - Grid alpha is 0.6 which is too prominent/distracting. Recommended is 0.2-0.4.

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct filled contour plot
• SC-02: Data Mapping (5/5) - X/Y grid coordinates correctly mapped with Z values as colors
• SC-03: Required Features (5/5) - Has colorbar, overlaid contour lines, appropriate number of levels (12)
• SC-04: Data Range (3/3) - All data visible within axes
• SC-05: Legend Accuracy (0/2) - No legend needed for this plot type, but colorbar title could be more prominent
• SC-06: Title Format (2/2) - Correct format: "contour-filled · bokeh · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows peaks of different heights, a valley (negative contribution), and saddle points. Could show more varied contour line spacing.
• DQ-02: Realistic Context (7/7) - Terrain elevation is a perfect, neutral real-world application for filled contours
• DQ-03: Appropriate Scale (4/5) - 0-2000m elevation range is realistic. The -3 to 3 km distance scale works well.

Code Quality (8/10 pts)

• CQ-01: KISS Structure (3/3) - No functions or classes, straightforward linear code
• CQ-02: Reproducibility (3/3) - np.random.seed(42) is set (though data is deterministic)
• CQ-03: Clean Imports (1/2) - All imports are used, but minor: numpy seed is set when data is actually deterministic
• CQ-04: No Deprecated API (1/1) - Using current bokeh APIs
• CQ-05: Output Correct (0/1) - Saves as plot.png and plot.html (correct)

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses bokeh's image() for the filled surface and line() for contours. However, this approach uses contourpy for contour generation rather than leveraging bokeh's native capabilities. The implementation is functional but not showcasing bokeh's distinctive interactive features in a meaningful way.

Strengths

• Excellent visual quality with the Viridis colormap providing clear elevation gradients
• High-contrast contour lines (white with dark outline) that are visible across all elevation levels
• Realistic terrain elevation scenario with multiple peaks and a valley
• Proper axis labels with units (km, m)
• Colorbar is well-positioned with appropriate title

Weaknesses

• Grid lines are too prominent (alpha 0.6 should be reduced to 0.2-0.4 for subtlety)
• Does not leverage bokeh's distinctive interactive features - the HTML output would benefit from hover tooltips showing elevation values

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

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

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot shows a filled contour visualization of terrain elevation data using the Viridis colormap. The image displays a 2D surface with multiple Gaussian peaks representing elevation values from 0 to 2000 meters. There are three prominent bright yellow-green peaks (highest elevations around 1800-2000m) and one darker purple depression (lowest elevations). White contour lines with thin dark outlines are overlaid on the filled surface for precise level identification. The title "contour-filled · bokeh · pyplots.ai" appears in the top-left corner. The X-axis is labeled "Distance East (km)" and the Y-axis is labeled "Distance North (km)". A vertical colorbar on the right side shows "Terrain Elevation (m)" with tick marks from 0 to 2000. A subtle white dashed grid overlays the plot. The overall layout is well-balanced with the plot filling most of the canvas.

Quality Score: 90/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 28pt, axis labels at 22pt, tick labels at 18pt - all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Filled surface and contour lines are well-visible; contour lines use effective white+dark styling
• VQ-04: Color Accessibility (5/5) - Viridis colormap is colorblind-safe
• VQ-05: Layout Balance (4/5) - Good canvas utilization, slight margin imbalance with colorbar
• VQ-06: Axis Labels (2/2) - Descriptive labels with units ("Distance East (km)", "Distance North (km)")
• VQ-07: Grid & Legend (0/2) - Grid alpha at 0.3 with width 2 is still somewhat prominent; overlays the contour visualization more than ideal

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct filled contour plot
• SC-02: Data Mapping (5/5) - X/Y coordinates correctly mapped, Z values shown as color fill
• SC-03: Required Features (5/5) - Sequential colormap, colorbar, overlaid contour lines, smooth color transitions
• SC-04: Data Range (3/3) - All data visible within axes range
• SC-05: Legend Accuracy (2/2) - Colorbar correctly shows elevation mapping
• SC-06: Title Format (2/2) - Uses correct format "contour-filled · bokeh · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple peaks and one valley, demonstrating contour behavior well; could show more dramatic gradient variation
• DQ-02: Realistic Context (7/7) - Terrain elevation is a perfect, neutral real-world application
• DQ-03: Appropriate Scale (4/5) - 0-2000m elevation is realistic; 80x80 grid provides smooth transitions

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Linear script structure with no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• 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, but axis labels set via figure() params would be cleaner

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses bokeh's image, HoverTool for interactivity, and contourpy integration; however, hover functionality only works in HTML output, not visible in PNG. Interactive tools (pan, zoom) are appropriate for Bokeh's strengths.

Strengths

• Excellent use of Viridis colormap for colorblind accessibility and visual appeal
• Smart contour line styling with white base + dark outline for visibility across all elevation levels
• Well-implemented terrain elevation scenario with realistic 0-2000m scale
• Clean integration of contourpy for precise contour line generation
• Proper text sizing for 4800x2700 canvas (28pt title, 22pt labels, 18pt ticks)
• HoverTool implementation provides interactivity in HTML output

Weaknesses

• Grid styling still somewhat prominent (alpha 0.3 with width 2); could be reduced further for cleaner contour visualization
• Colorbar title "Terrain Elevation (m)" is slightly small relative to other text elements

Verdict: APPROVED