feat(pygal): implement line-styled

[github-actions]

Implementation: line-styled - pygal

Implements the pygal version of line-styled.

File: plots/line-styled/implementations/pygal.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a styled line chart showing temperature measurements from 4 sensors over 12 months (January to December). The title "line-styled · pygal · pyplots.ai" appears at the top. The y-axis displays "Temperature (°C)" ranging from approximately 3-28°C, and the x-axis shows "Month" with all 12 months labeled clearly. Four data series are shown with distinct line styles:

• Sensor A (Solid) - Blue solid line with circular markers
• Sensor B (Dashed) - Yellow dashed line with circular markers
• Sensor C (Dotted) - Red dotted line with circular markers
• Sensor D (Dash-Dot) - Green dash-dot line with circular markers

The legend is positioned in the upper left corner outside the plot area. All four line styles are clearly distinguishable from each other. The data shows a realistic seasonal temperature pattern (low in winter months, peaking in July). The plot has a clean white background with subtle horizontal grid lines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (9/10) - Title, axis labels, and tick labels are clearly readable at full size. Legend text is slightly small but still legible.
• VQ-02: No Overlap (8/8) - No overlapping text anywhere in the plot.
• VQ-03: Element Visibility (7/8) - Lines and markers are well-sized and clearly visible. Line thickness and marker size are appropriate.
• VQ-04: Color Accessibility (4/5) - Colors are reasonably distinguishable (blue, yellow, red, green), but red-green combination could be challenging for some colorblind viewers. However, line styles provide additional differentiation.
• VQ-05: Layout Balance (5/5) - Good proportions with plot filling most of the canvas. Balanced margins.
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels with units: "Temperature (°C)" and "Month".
• VQ-07: Grid & Legend (1/2) - Grid is subtle with only y-guides shown. Legend placement in upper left is acceptable but overlaps with plot area slightly.

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct line chart with multiple styled lines.
• SC-02: Data Mapping (5/5) - X-axis shows time (months), Y-axis shows continuous values (temperature).
• SC-03: Required Features (5/5) - All 4 standard line styles present: solid, dashed, dotted, dash-dot. Legend maps styles to series names.
• SC-04: Data Range (3/3) - All data points visible within the axis range.
• SC-05: Legend Accuracy (2/2) - Legend correctly identifies each series with its line style.
• SC-06: Title Format (2/2) - Uses correct format: "line-styled · pygal · pyplots.ai".

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows all 4 line styles with varying but similar trends. Could benefit from slightly more variation between series.
• DQ-02: Realistic Context (7/7) - Temperature sensor readings over a year is a realistic, neutral scenario applicable to climate monitoring.
• DQ-03: Appropriate Scale (4/5) - Temperature values are realistic for a temperate climate (5-28°C). Some values in Sensor C dip quite low (~1°C in December) which is plausible but at the edge.

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear structure: imports → data → plot → save. No functions or classes.
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42) for reproducibility.
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pygal, Style).
• CQ-04: No Deprecated API (1/1) - Uses current pygal API.
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html, which is correct for pygal.

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses pygal's stroke_style with dasharray for line styles, custom Style class for theming, and both PNG and HTML outputs. Could leverage more advanced features like tooltips or animations.

Strengths

• Excellent implementation of all 4 standard line styles (solid, dashed, dotted, dash-dot) using pygal's stroke_dasharray
• Clear, readable text with appropriate font sizes for the large canvas
• Realistic temperature sensor data scenario with seasonal variation
• Clean code structure following KISS principles
• Proper use of custom Style for large canvas sizing
• Both PNG and interactive HTML outputs generated

Weaknesses

• Legend placement in upper left could be moved to avoid any potential overlap with data
• Color palette includes red-green combination; while line styles provide differentiation, a more colorblind-friendly palette would be better
• Could showcase more pygal-specific features like tooltips or value labels

Verdict: APPROVED