feat(downsample_cells_template): add downsample_cells_template functi…

[fangliu117]

…on and unit tests

[Copilot]

Pull Request Overview

This PR adds a new downsample_cells_template function that provides a platform-agnostic interface for cell downsampling operations, converted from NIDAP template functionality. The implementation includes comprehensive parameter handling, multiple input format support, and flexible output options.

• Implements run_from_json function with support for JSON, dict, and direct DataFrame inputs
• Adds comprehensive test coverage including I/O workflows, error validation, and function mocking
• Provides CLI interface and handles multiple file formats (CSV, pickle)

Reviewed Changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 5 comments.

File	Description
src/spac/templates/downsample_cells_template.py	Main template implementation with parameter parsing, data loading, and downsample execution
tests/templates/test_downsample_cells_template.py	Comprehensive unit tests covering various input formats, error cases, and function validation

[Copilot]

Modifying sys.path at runtime is an anti-pattern that can cause import issues. Consider using relative imports or proper package structure instead.

Suggested change

sys.path.append(str(Path(__file__).parent.parent.parent))

[Copilot]

Modifying sys.path in tests is an anti-pattern. Consider using proper test discovery mechanisms or PYTHONPATH configuration instead.

Suggested change

	sys.path.append(
	os.path.dirname(os.path.realpath(__file__)) + "/../../src"
	)

[Copilot]

[nitpick] Using print statements in library code reduces flexibility for users who may want to control logging. Consider using the logging module instead.

Suggested change

	print("Downsampled! Processed dataset info:")
	print(down_sampled_dataset.info())
	logging.info("Downsampled! Processed dataset info:")
	logging.info(down_sampled_dataset.info())

[Copilot]

[nitpick] Using print statements in library code reduces flexibility for users who may want to control logging. Consider using the logging module instead.

Suggested change

	print(
	f"Downsample Cells completed → {saved_files[output_file]}"
	)
	return saved_files
	else:
	# Return the dataframe directly for in-memory workflows
	print("Returning DataFrame (not saving to file)")
	return down_sampled_dataset
	# CLI interface
	if __name__ == "__main__":
	logging.info(
	f"Downsample Cells completed → {saved_files[output_file]}"
	)
	return saved_files
	else:
	# Return the dataframe directly for in-memory workflows
	logging.info("Returning DataFrame (not saving to file)")
	return down_sampled_dataset
	# CLI interface
	if __name__ == "__main__":
	logging.basicConfig(level=logging.INFO)

[Copilot]

[nitpick] Using print statements in library code reduces flexibility for users who may want to control logging. Consider using the logging module instead.