Dev

[zprobot]

PR Type

enhancement, documentation, tests

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Description

• Implemented new classes and methods for handling PSM data, including MzTab and PsmInMemory.
• Defined schema for PSM fields using PyArrow.
• Added test cases for the new PSM processing functionality.
• Updated documentation and schema files to reflect new field names and mappings.

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Changes walkthrough 📝

	Relevant files
Enhancement	4 files common.py Define PSM mapping and additional attributes                          quantmsio/temp_core/common.py Defined PSM_MAP and PSM_USECOLS for mapping PSM data. Added ADDITIONS list for additional PSM attributes. +39/-0    format.py Define schema for PSM fields using PyArrow                              quantmsio/temp_core/format.py Defined PEPTIDE_FIELDS and PSM_UNIQUE_FIELDS using PyArrow. Combined fields into PSM_FIELDS. +165/-0  mzTab.py Implement MzTab class for mzTab file processing                    quantmsio/temp_core/mzTab.py Implemented MzTab class for handling mzTab files. Added methods for extracting modifications and MS runs. +224/-0  psm.py Create PsmInMemory class for PSM processing                            quantmsio/temp_core/psm.py Created PsmInMemory class extending MzTab. Added methods for generating reports and converting to Parquet. +105/-0
Tests	1 files test_new_psm.py Add test case for PsmInMemory class                                            tests/test_new_psm.py Added test case for PsmInMemory class. Tested conversion of mzTab to feature. +10/-0
Documentation	5 files README.adoc Update README with new PSM field descriptions                        docs/README.adoc Updated field descriptions for peptide-based views. Added mappings for new PSM fields. +15/-14  feature.avsc Update field names in feature schema                                          docs/feature.avsc Updated field names for gene accessions and names. +2/-2      peptide.avsc Update field names in peptide schema                                          docs/peptide.avsc Updated field names for gene accessions and names. +2/-2      protein.avsc Update field names in protein schema                                          docs/protein.avsc Updated field names for gene accessions and names. +2/-2      psm.avsc Update field names in PSM schema                                                  docs/psm.avsc Updated field names for gene accessions and names. +2/-2

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[qodo-merge-pro]

PR Reviewer Guide 🔍

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪

🧪 PR contains tests

🔒 No security concerns identified

⚡ Key issues to review Potential Performance Issue The generate_report method uses a for loop to process chunks of data, which might be inefficient for large datasets. Consider using a more memory-efficient approach or implementing parallel processing. Code Duplication The transform_psm method contains multiple similar operations for adding columns to the DataFrame. Consider refactoring to reduce duplication and improve maintainability. Error Handling The fetch_modifications_from_mztab_line function raises exceptions without proper error handling or logging. Consider implementing more robust error handling and providing informative error messages.

[qodo-merge-pro]

PR Code Suggestions ✨

Category	Suggestion	Score
Performance	Improve memory efficiency when writing large DataFrames to Parquet files Consider using a more memory-efficient approach when writing large DataFrames to Parquet files by using the pyarrow.parquet.ParquetWriter with chunked writing. quantmsio/temp_core/psm.py [80-83] -for p in self.generate_report(chunksize=chunksize,protein_str=protein_str): - if not pqwriter: - pqwriter = pq.ParquetWriter(output_path, p.schema) - pqwriter.write_table(p) +with pq.ParquetWriter(output_path, PSM_SCHEMA) as pqwriter: + for p in self.generate_report(chunksize=chunksize, protein_str=protein_str): + pqwriter.write_table(p) Apply this suggestion Suggestion importance[1-10]: 8 Why: The suggestion provides a significant improvement in memory efficiency by using a context manager for ParquetWriter, which is a good practice for handling large data. This change is beneficial for resource management, warranting a higher score.	8
	Vectorize the generation of 'pg_positions' for improved performance Use a more efficient method to generate the pg_positions column by vectorizing the operation instead of using apply. quantmsio/temp_core/psm.py [41-44] -df.loc[:, 'pg_positions'] = df[['start','end']].apply( - lambda row: self.generate_positions(row["start"], row["end"]), - axis=1 -) +df['pg_positions'] = df['start'].str.split(',').combine(df['end'].str.split(','), lambda x, y: [f'{s}:{e}' for s, e in zip(x, y)]) Apply this suggestion Suggestion importance[1-10]: 7 Why: The suggestion correctly identifies a potential performance improvement by vectorizing the operation, which can lead to faster execution for large DataFrames. However, the improvement is not critical, hence a moderate score.	7
	Vectorize the generation of 'additional_scores' for improved performance Use a more efficient method to generate the additional_scores column by applying the function to the entire DataFrame at once instead of row by row. quantmsio/temp_core/psm.py [48] -df.loc[:, "additional_scores"] = df[list(self._score_names.values())].apply(self._genarate_additional_scores,axis=1) +df["additional_scores"] = df[list(self._score_names.values())].apply(lambda x: [{"name": name, "value": x[score]} for name, score in self._score_names.items()], axis=1) Apply this suggestion Suggestion importance[1-10]: 7 Why: The suggestion offers a valid performance enhancement by vectorizing the operation, which can be beneficial for large datasets. The change is not essential but can improve efficiency, justifying a moderate score.	7
	Optimize the generation of 'peptidoform' for better performance Consider using a more efficient method to generate the peptidoform column by vectorizing the operation instead of using apply. quantmsio/temp_core/psm.py [49-52] -df.loc[:,'peptidoform'] = df[["modifications", "sequence"]].apply( - lambda row: get_peptidoform_proforma_version_in_mztab(row["sequence"], row["modifications"], self._modifications), - axis=1 -) +df['peptidoform'] = df.apply(lambda row: get_peptidoform_proforma_version_in_mztab(row["sequence"], row["modifications"], self._modifications), axis=1) Apply this suggestion Suggestion importance[1-10]: 5 Why: The suggestion proposes a vectorized approach, but the improved code does not fully vectorize the operation as it still uses apply. The impact on performance is limited, resulting in a lower score.	5