feat: add scatter-basic implementation (9 libraries)

[MarkusNeusinger]

Summary

Adds scatter-basic plot implementation.

Libraries

• Merged: 9 (all libraries)
• Not Feasible: 0

Links

• Spec: specs/scatter-basic.md
• Parent Issue: [scatter-basic] [update] Regenerate with KISS style #207

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[github-actions]

✅ Plot Tests Passed

Python 3.13 (required): passed

Compatibility:

Note: Only Python 3.13 is required to pass. Python 3.12 is tested for compatibility.

[Copilot]

Pull request overview

This PR implements the scatter-basic specification across all 9 supported visualization libraries (matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, lets-plot). The implementation follows the project's specification-first design pattern, where a single library-agnostic spec in specs/scatter-basic.md generates corresponding implementations in each library's directory structure. All implementations generate scatter plots showing the relationship between two continuous variables with consistent styling (Python Blue #306998 color, 16:9 aspect ratio at 4800×2700px).

Key Changes:

• Replaces function-based matplotlib/seaborn implementations with simpler KISS-style scripts
• Adds 7 new library implementations (plotly, bokeh, altair, plotnine, pygal, highcharts, lets-plot)
• All implementations follow the modern gallery-style pattern: imports → data → plot → save

Reviewed changes

Copilot reviewed 9 out of 10 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
plots/matplotlib/scatter/scatter-basic/default.py	Simplified from function-based to script-based implementation; data generation parameters differ from other libraries
plots/seaborn/scatterplot/scatter-basic/default.py	Simplified from function-based to script-based implementation with consistent styling
plots/plotly/scatter/scatter-basic/default.py	New implementation using plotly graph objects with proper 4800×2700px output
plots/bokeh/scatter/scatter-basic/default.py	New implementation using bokeh scatter with export_png
plots/altair/point/scatter-basic/default.py	New declarative implementation with scale_factor for proper dimensions
plots/plotnine/point/scatter-basic/default.py	New ggplot2-style implementation following plotnine conventions
plots/pygal/xy/scatter-basic/default.py	New SVG-based implementation with custom styling
plots/highcharts/scatter/scatter-basic/default.py	New implementation using Selenium for rendering; uses different data than other libraries and downloads external JS
plots/letsplot/point/scatter-basic/default.py	New JetBrains lets-plot implementation with ggplot2 grammar

[Copilot]

Data generation is inconsistent with other libraries. Most implementations use x = np.random.randn(100) * 2 + 10, but this one uses + 5 instead of + 10. This will result in different x-axis ranges and make visual comparisons across libraries inconsistent.

Suggested change

	x = np.random.randn(100) * 2 + 5
	x = np.random.randn(100) * 2 + 10

[Copilot]

Data generation is inconsistent with other libraries. The y-value calculation uses * 1.5 for the random component, while most other implementations use * 2. This creates different data distributions and visual appearances across libraries.

Suggested change

	y = x * 0.8 + np.random.randn(100) * 1.5
	y = x * 0.8 + np.random.randn(100) * 2

[Copilot]

Hard-coded data is inconsistent with all other implementations. Other libraries generate 100 random points with correlation, but this uses only 8 manually specified points. This creates a significantly different visualization and doesn't properly demonstrate a scatter plot's utility for many data points. Use the same data generation pattern as other libraries: np.random.seed(42), x = np.random.randn(100) * 2 + 10, y = x * 0.8 + np.random.randn(100) * 2.

[Copilot]

Downloading external JavaScript from an untrusted source without integrity verification poses a security risk. If code.highcharts.com is compromised, malicious code could be executed. Consider either: (1) bundling the Highcharts library with the project, (2) verifying the downloaded content's hash against a known good value, or (3) adding a comment acknowledging this trade-off if it's an intentional design decision.

Suggested change

	# Download Highcharts JS for inline embedding
	highcharts_url = "https://code.highcharts.com/highcharts.js"
	with urllib.request.urlopen(highcharts_url, timeout=30) as response:
	highcharts_js = response.read().decode("utf-8")
	# Use bundled Highcharts JS for inline embedding to avoid runtime download and supply chain risk.
	# See: https://github.com/highcharts/highcharts for official source.
	highcharts_js_path = Path(__file__).parent.parent / "vendor" / "highcharts.js"
	with open(highcharts_js_path, "r", encoding="utf-8") as f:
	highcharts_js = f.read()