feat(matplotlib): implement line-markers

[github-actions]

Implementation: line-markers - matplotlib

Implements the matplotlib version of line-markers.

File: plots/line-markers/implementations/matplotlib.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a line chart showing temperature readings (°C) over 14 days from three weather stations. Three distinct lines with markers are shown:

• Coastal Station (blue circles): Highest temperatures, ranging from ~18°C to ~22°C, showing an upward trend mid-period then declining
• Inland Station (yellow squares): Mid-range temperatures, starting at ~13°C and declining to ~7°C over the period
• Mountain Station (red triangles): Lowest temperatures, ranging from ~6°C to ~10°C with a general downward trend

The title follows the required format "line-markers · matplotlib · pyplots.ai". Axis labels are "Day" (x-axis) and "Temperature (°C)" (y-axis). A legend is positioned in the upper left. Grid lines are visible with subtle alpha. Markers are clearly visible with white edge borders for contrast. The layout is well-balanced with good use of the 16:9 canvas.

Quality Score: 94/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, labels at 20pt, ticks at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements anywhere
• VQ-03: Element Visibility (8/8) - Markers are size 12 with linewidth 3, perfectly visible for 14 data points per series; white marker edges provide excellent contrast
• VQ-04: Color Accessibility (4/5) - Blue, yellow, and red are distinguishable; not ideal for red-green colorblind but shapes compensate
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins using tight_layout()
• VQ-06: Axis Labels (2/2) - "Temperature (°C)" includes units, "Day" is descriptive
• VQ-07: Grid & Legend (1/2) - Grid alpha 0.3 is good, legend placed well but could be positioned outside plot area to avoid any potential data overlap

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line plot with markers combining line and scatter features
• SC-02: Data Mapping (5/5) - X=days (continuous), Y=temperature values correctly assigned
• SC-03: Required Features (5/5) - Multiple series with different marker shapes (circle, square, triangle) as specified
• SC-04: Data Range (3/3) - All data points visible, axes show full range
• SC-05: Legend Accuracy (2/2) - Legend labels correctly identify each station
• SC-06: Title Format (2/2) - Uses exact format "line-markers · matplotlib · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows multiple series with different markers and line patterns; could benefit from showing filled vs unfilled markers as mentioned in spec notes
• DQ-02: Realistic Context (7/7) - Weather station temperature data is a real, comprehensible, neutral scientific scenario
• DQ-03: Appropriate Scale (5/5) - Temperature values are realistic (6-22°C over 14 days)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only matplotlib.pyplot and numpy imported, both used
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib APIs
• CQ-05: Output Correct (1/1) - Saves as 'plot.png'

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses basic matplotlib features well but doesn't leverage distinctive capabilities like annotations, custom tick formatters, or advanced styling

Strengths

• Excellent marker visibility with white edge borders creating strong contrast against lines
• Three distinct marker shapes (circle, square, triangle) for easy series differentiation
• Realistic weather station scenario with plausible temperature ranges
• Clean, readable code following KISS principles
• Perfect title format and axis labels with units

Weaknesses

• Could demonstrate filled vs unfilled markers as suggested in spec notes for additional distinction
• Legend placement inside plot area could potentially overlap with data in edge cases
• Does not leverage more distinctive matplotlib features like annotations or custom styling

Verdict: APPROVED