Add algorithm selection to feature grouping and peak picking methods

[Copilot]

Summary: Add Algorithm Selection Options to Hardcoded Methods

Problem Statement

The codebase had several methods with hardcoded algorithms. This PR adds flexibility to choose different algorithms while maintaining backward compatibility.

Changes Implemented

1. Feature Grouping Algorithms ✅

Files Modified:

• openms_python/py_consensusmap.py
• openms_python/workflows.py

New Functionality:

• Py_ConsensusMap.align_and_link(): Added grouping_method and grouping_params parameters
• link_features(): Added grouping_method parameter
• Supported algorithms: "qt" (default), "kd", "labeled", "unlabeled"

Example Usage:

# Use KD-tree based grouping
consensus = Py_ConsensusMap.align_and_link(
    feature_maps,
    alignment_method="identity",
    grouping_method="kd"
)

# Use QT clustering with custom parameters
consensus = link_features(
    feature_maps,
    grouping_method="qt",
    params={"distance_RT:max_difference": 100.0}
)

2. Peak Picking Algorithms ✅

Files Modified:

• openms_python/py_msexperiment.py

New Functionality:

• Added "iterative" to PEAK_PICKER_REGISTRY
• Now supports: "hires" (default), "cwt", "iterative"

Example Usage:

# Use iterative peak picker
picked = exp.pick_peaks(method="iterative", ms_levels=1)

3. Documentation ✅

Files Modified:

• README.md

Updates:

• Updated consensus alignment section to show grouping algorithm options
• Updated peak picking section to show all available algorithms
• Updated workflow helpers section to demonstrate parameter usage

Testing & Quality Assurance

• Added 5 new tests covering all new functionality
• All 83 tests pass
• No security vulnerabilities detected (CodeQL scan)
• No breaking changes to existing API
• README updated with usage examples

Backward Compatibility

All changes are fully backward compatible:

• Default values maintain existing behavior
• All existing tests pass without modification
• New parameters are optional and keyword-only

Files Changed

1. openms_python/py_consensusmap.py (+39/-2)
2. openms_python/py_msexperiment.py (+1)
3. openms_python/workflows.py (+38/-3)
4. tests/test_consensus_map.py (+42)
5. tests/test_py_msexperiment.py (+12)
6. tests/test_workflows.py (+47)
7. README.md (+16/-4) - documentation updates

Total: 190 insertions, 9 deletions

Security Summary

CodeQL security scan completed successfully with 0 alerts. No security vulnerabilities introduced.

Original prompt

• some of the methods have a lot of hardcoded algorithms in there. Decide were it makes sense to add an option to choose the algorithm and pass parameters

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[timosachsenberg]

@copilot test it if not already done and update readme

[Copilot]

@copilot test it if not already done and update readme

Tests completed successfully - all 83 tests pass. README updated with documentation for the new algorithm selection options in commit 2f42d7f.

The documentation now covers:

• Feature grouping algorithm options (qt, kd, labeled, unlabeled) in align_and_link() and link_features()
• Peak picking algorithm options (hires, cwt, iterative) in pick_peaks()
• Example usage with parameters in the workflow helpers section