feat(letsplot): implement scatter-brush-zoom

[github-actions]

Implementation: scatter-brush-zoom - letsplot

Implements the letsplot version of scatter-brush-zoom.

File: plots/scatter-brush-zoom/implementations/letsplot.py

Parent Issue: #3295

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter plot with 400 data points distributed across 4 distinct clusters. The title "scatter-brush-zoom · letsplot · pyplots.ai" appears at the top left. The X-axis is labeled "X Value" (range -5 to 105) and Y-axis is labeled "Y Value" (range 0 to 100). Four clusters are color-coded: Cluster A (blue, ~80 points centered around x=20, y=60), Cluster B (yellow, ~120 points centered around x=50, y=30), Cluster C (red, ~100 points centered around x=70, y=70), and Cluster D (green, ~100 points centered around x=40, y=80). The legend is positioned on the right side with clear category labels. The plot uses a minimal theme with subtle grid lines and good spacing between elements.

Quality Score: 65/100

Criteria Checklist

Visual Quality (34/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, tick marks, and legend all clearly readable at appropriate sizes
• VQ-02: No Overlap (8/8) - No text overlap, all elements well-separated
• VQ-03: Element Visibility (6/8) - Markers visible but somewhat small for 400 points; alpha=0.7 helps with some overlap
• VQ-04: Color Accessibility (4/5) - Blue, yellow, red, green palette is mostly accessible; red-green distinction could be challenging for some colorblind users
• VQ-05: Layout Balance (4/5) - Good use of canvas, balanced margins, legend well-positioned
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("X Value", "Y Value") but lack units
• VQ-07: Grid & Legend (2/2) - Subtle grid, legend well-placed and clear

Spec Compliance (12/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot type
• SC-02: Data Mapping (5/5) - X/Y correctly mapped, color mapped to category
• SC-03: Required Features (0/5) - MISSING: No brush selection functionality, no visible selection rectangle, no highlight of selected points, no reset/clear button, no zoom toolbar buttons
• SC-04: Data Range (0/3) - While axes show all data, the spec requires interactive zoom to "zoom into specific areas" which is not demonstrated
• SC-05: Legend Accuracy (2/2) - Legend correctly shows all 4 clusters
• SC-06: Title Format (2/2) - Correct format "scatter-brush-zoom · letsplot · pyplots.ai"

Data Quality (16/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows 4 distinct clusters suitable for brush selection demo, but doesn't demonstrate the actual selection feature
• DQ-02: Realistic Context (5/7) - Plausible clustered data scenario for exploratory analysis
• DQ-03: Appropriate Scale (5/5) - Sensible numeric ranges (0-100 scale)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple imports → data → plot → save structure
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pandas, lets_plot)
• CQ-04: No Deprecated API (1/1) - Uses current lets-plot API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (3/5 pts)

• LF-01: Distinctive Features (3/5) - Uses ggplot grammar syntax and exports HTML with basic pan/zoom, but does NOT utilize lets-plot's selection/brush capabilities (if available) or demonstrate the interactive features the spec requires

Strengths

• Clean visual presentation with minimal theme and readable text
• Appropriate cluster data generation for demonstrating brush selection scenarios
• Correct color scheme with good distinction between clusters
• Proper use of lets-plot's ggplot-style grammar
• Code follows KISS principle with reproducible seed

Weaknesses

• Critical: Missing brush selection functionality - the core feature of the specification is not implemented
• No visual rectangle shown during brush dragging
• No highlighting mechanism for selected points
• No reset/clear selection button
• No demonstration of zoom toolbar or double-click zoom
• No status area showing count of selected points
• The HTML export provides basic pan/zoom but not brush selection capabilities as specified

Verdict: REJECTED

The implementation is a well-formatted basic scatter plot, but it fundamentally fails to implement the core interactive features specified: brush selection (click and drag to select a rectangular region), visual highlighting of selected points, and a reset/clear button. The specification is explicitly about "Interactive Scatter Plot with Brush Selection and Zoom" - without these features, this cannot be approved.

[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 scatter plot with 400 data points distributed across 4 distinct clusters. Blue points (Cluster A) are concentrated in the lower-left quadrant around (20, 60). Yellow points (Cluster B) form the largest cluster centered around (50, 30) in the lower-middle area. Red points (Cluster C) are positioned in the upper-right quadrant around (70, 70). Green points (Cluster D) occupy the upper-center area around (40, 80). The title "scatter-brush-zoom · letsplot · pyplots.ai" appears in the top-left. A helpful annotation in gray text near the top indicates "Interactive HTML: Use toolbar for pan, zoom, box-select, and reset". The legend on the right side clearly identifies all four clusters. Axes are labeled "X Value (units)" and "Y Value (units)". The plot uses a clean white background with no grid lines.

Quality Score: 78/100

Criteria Checklist

Visual Quality (32/40)

• VQ-01: Text Legibility (10/10) - Title, axis labels, tick marks, and legend all clearly readable at appropriate font sizes
• VQ-02: No Overlap (8/8) - No overlapping text elements; all labels and annotations are well-spaced
• VQ-03: Element Visibility (6/8) - Markers sized appropriately (size=5) with alpha=0.7 for 400 points; some overlap in dense cluster centers but generally visible
• VQ-04: Color Accessibility (4/5) - Blue, yellow, red, green provide reasonable differentiation; yellow/green may be slightly challenging for some colorblind users
• VQ-05: Layout Balance (3/5) - Plot fills canvas reasonably well, but annotation at top creates some asymmetry
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "X Value (units)", "Y Value (units)"
• VQ-07: Grid & Legend (0/2) - Grid completely removed (element_blank for panel_grid), which makes it harder to read precise values; legend placement is good

Spec Compliance (19/25)

• SC-01: Plot Type (8/8) - Correct scatter plot type
• SC-02: Data Mapping (5/5) - X/Y correctly mapped to numeric values, color to category
• SC-03: Required Features (2/5) - Interactive features (brush, zoom, pan) only available in HTML; PNG shows static plot with annotation explaining interactivity. Spec requires "visible rectangle while dragging" and "selected points should be visually highlighted" which cannot be demonstrated in static PNG
• SC-04: Data Range (3/3) - All data points visible within axes range
• SC-05: Legend Accuracy (2/2) - Legend correctly shows all 4 cluster categories
• SC-06: Title Format (0/2) - Title not using correct format: shows "scatter-brush-zoom · letsplot · pyplots.ai" but overlaps with y-axis 100 label

Data Quality (17/20)

• DQ-01: Feature Coverage (6/8) - Shows 4 distinct clusters demonstrating selection scenarios; however, no overlap between clusters limits brush selection demonstration
• DQ-02: Realistic Context (7/7) - Generic clustered data appropriate for demonstrating brush selection; neutral scientific context
• DQ-03: Appropriate Scale (4/5) - Values in 0-100 range are sensible; some cluster overlap at edges

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear imports → data → plot → save structure; no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) for reproducible data generation
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current lets_plot API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (0/5)

• LF-01: Uses distinctive library features (0/5) - While lets-plot has excellent interactive features (tooltips configured well), the static PNG cannot demonstrate brush selection which is the core spec requirement. The box-zoom in HTML toolbar is not the same as brush selection for data filtering.

Strengths

• Excellent tooltip configuration with layer_tooltips showing point details
• Clean code structure following KISS principle
• Good use of clustered data appropriate for brush selection scenarios
• HTML export enables interactive zoom and pan functionality
• Annotation helpfully explains interactive features in the static image

Weaknesses

• The spec requires brush selection with visible rectangle and highlighted selected points - lets-plot's built-in toolbar provides box-zoom but not true brush selection with point highlighting
• Grid completely removed makes it difficult to read precise coordinate values
• Title position slightly overlaps with y-axis maximum value (100)
• The implementation relies on HTML export for interactivity, but the core spec features (brush selection highlighting, selection count display) are not implemented

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

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 displays an interactive scatter plot demonstrating brush selection and zoom functionality. The canvas shows 400 data points distributed across 4 distinct clusters (Cluster A in blue, Cluster B in yellow, Cluster C in red, Cluster D in green). A prominent light blue rectangular brush selection area is visible with a dashed blue border spanning approximately x=[25,55] and y=[50,85]. Points within this selection region appear at full opacity while points outside are dimmed (alpha ~0.25). A bold blue annotation at the top reads "Brush Selection: 100 points selected". The axes are labeled "X Value (units)" and "Y Value (units)" with clear tick marks. The title correctly follows the format "scatter-brush-zoom · letsplot · pyplots.ai". A legend on the right identifies the four clusters. The grid is subtle with light gray lines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, annotation placed in clear area
• VQ-03: Element Visibility (6/8) - Points visible with good size, alpha differentiation works well but some dimmed points are quite faint
• VQ-04: Color Accessibility (5/5) - Four distinct colors (blue, yellow, red, green) are distinguishable, good contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas appropriately, balanced margins, legend well positioned
• VQ-06: Axis Labels (2/2) - Descriptive with units: "X Value (units)", "Y Value (units)"
• VQ-07: Grid & Legend (0/2) - Grid is subtle and good, BUT legend appears cut off on right edge

Spec Compliance (23/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot with brush selection visualization
• SC-02: Data Mapping (5/5) - X/Y correctly mapped to numeric axes
• SC-03: Required Features (4/5) - Brush rectangle visible, selected points highlighted, selection count shown. However, the static image cannot demonstrate actual interactivity (zoom, pan, reset)
• SC-04: Data Range (3/3) - All data points visible within axes range
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies all four clusters
• SC-06: Title Format (1/2) - Title uses correct format but uses middle dot (·) instead of standard separator

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows 4 distinct clusters with varying sizes (80-120 points each), demonstrates selection across multiple clusters
• DQ-02: Realistic Context (6/7) - Generic clustered data scenario, plausible for exploratory data analysis though context could be more specific (e.g., customer segments)
• DQ-03: Appropriate Scale (5/5) - Values in reasonable numeric range (0-100 for both axes)

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean script structure: imports → data generation → plot creation → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current lets_plot API
• CQ-05: Output Correct (0/1) - Saves to 'plot.png' but also 'plot.html'; the scale=3 should produce correct dimensions

Library Features (4/5 pts)

• LF-01: Distinctive Features (4/5) - Good use of lets-plot features: layer_tooltips for hover information, geom_rect for brush visualization, scale_alpha_identity, theme customization. HTML export with built-in zoom/pan toolbar is a good library-specific feature. Could have used more advanced lets-plot interactivity.

Strengths

• Excellent visualization of brush selection with clear visual distinction between selected and unselected points
• Proper use of lets-plot's tooltip system for hover information
• Good clustering data that demonstrates the utility of brush selection across multiple groups
• Clean, well-structured code following KISS principles
• Both PNG and HTML exports provided, showcasing lets-plot's dual-output capability
• Selection count annotation adds useful context

Weaknesses

• Legend appears slightly cut off on the right edge of the image
• The alpha=0.25 for unselected points may be too faint for some users to distinguish clusters
• Static PNG cannot demonstrate actual brush/zoom interactivity (inherent limitation)

Verdict: APPROVED