Fix bruker issues.

[ypriverol]

This PR updates bruker support for parquet ms information.

Summary by CodeRabbit

• Refactor

  • Improved mzML statistics processing to ensure consistent data schema mapping and accurate output generation.

• Tests

  • Expanded test coverage with added validations for Bruker sample processing and enhanced checks to guarantee non-empty results.

[coderabbitai]

Walkthrough

The changes update the batch_write_bruker_d function to use tuples for mapping SQL expressions to schema constants, ensuring consistent construction of queries and DataFrame column naming. Additionally, a new test case for Bruker samples is introduced, and an existing test for local samples is enhanced with extra validations to ensure non-empty output.

Changes

File	Change Summary
quantmsutils/mzml/mzml_statistics.py	Modified allowed_columns to use tuples (SQL expression, schema constant); updated construction of safe_columns to use only the SQL expression; renamed DataFrame columns using the mapping; replaced string literal with ACQUISITION_DATETIME; casted SCAN to string.
tests/test_commands.py	Added a new test method test_mzml_statistics_bruker for testing Bruker samples (marked to be skipped); enhanced the existing test_mzml_statistics_local test with assertions ensuring non-empty output from the generated parquet file.

Sequence Diagram(s)

sequenceDiagram
    participant T as Test Suite
    participant CLI as mzmlstats Command
    participant B as batch_write_bruker_d Function
    participant DF as DataFrame Processing
    T->>CLI: Execute mzmlstats with Bruker args
    CLI->>B: Process Bruker data
    B->>DF: Apply allowed_columns mapping & update DataFrame
    DF-->>B: Return updated DataFrame with schema corrections
    B-->>CLI: Return processed data
    CLI-->>T: Exit with code 0 and show output file exists

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Poem

I'm a rabbit with a hop so light,
Tweaking code from morning to night.
Tuples and constants now dance in line,
Tests ensure data's perfectly fine.
Coding hops and joyful leaps, oh what a sight! 🐰✨

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📥 Commits

Reviewing files that changed from the base of the PR and between 66de992 and 4ef17ad.

📒 Files selected for processing (2)

• quantmsutils/mzml/mzml_statistics.py (2 hunks)
• tests/test_commands.py (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (2)

• GitHub Check: build
• GitHub Check: build

🔇 Additional comments (5)

tests/test_commands.py (2)

148-148: Good addition - validation of non-empty output.

Adding this assertion ensures that the test verifies not just the existence of the output file but also that it contains data, which is a good practice for comprehensive test validation.

---

150-160: Well-structured test for Bruker samples.

The new test method appropriately tests the mzML statistics functionality on Bruker samples, following the same pattern as existing tests. The skip marker is appropriate for a test that requires specific local files.

A few observations:

1. The test validates file creation and non-empty output, covering the basic functionality
2. The skip marker ensures the test won't fail in automated CI environments
3. The test complements existing tests to provide better coverage of different file formats

quantmsutils/mzml/mzml_statistics.py (3)

102-111: Improved structure using tuples for SQL-schema mapping.

Converting the allowed_columns dictionary to use tuples is a good design improvement. This change creates an explicit mapping between SQL expressions and schema constants, making the code more maintainable and reducing the risk of mismatches.

---

115-117: Consistent SQL expression extraction.

This change correctly extracts only the SQL expression part from the tuple while maintaining the association with schema constants. This ensures the SQL query is constructed properly while preserving the mapping information for later use.

---

128-131: Enhanced DataFrame column handling.

These changes improve the code by:

1. Using the constant ACQUISITION_DATETIME instead of a string literal
2. Properly renaming the columns to match the schema using the mapping from allowed_columns
3. Explicitly converting the SCAN column to a string type to match the schema requirements

This ensures consistency between the DataFrame's structure and the schema, reducing the likelihood of type mismatches or naming errors.

✨ Finishing Touches

• 📝 Generate Docstrings

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