Polish CLI

[mobiusklein]

User description

This change set tries to polish the CLI, being more intrusive than the last batch of changes because of the increase in use of the logging module.

I've replaced calls to logging.info and print with dedicated module loggers. Additionally, I moved the logging initialization into the CLI. If the code is used as a library, it will be the responsibility of the calling code to initialize logging, as is convention.

I spent some time normalizing docstrings to use the Numpydoc style, but did not get through all of them at this point. I tried to make the failure modes of run_batch_correction clearer. Are you using an AI tool to generate some of your docstrings?

Separately, I removed the parameter documentation from the click.command functions because their docstrings are injected as part of the CLI help text, and the parameters will be described by their help text.

Finally, I made the anndata import late-binding to not delay starting up the program for the optional dependency.

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PR Type

Enhancement, Documentation, Bug fix

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Description

• Enhanced logging by replacing print and logging.info with dedicated loggers.

  • Added logger initialization with NullHandler for modules.
  • Introduced CLI options for log level and log file configuration.

• Updated docstrings across multiple files to adhere to Numpydoc style.

  • Improved parameter descriptions and added missing sections like Raises and Examples.
  • Standardized return type annotations and descriptions.

• Refactored error handling for better clarity and robustness.

  • Replaced SystemExit and print with exceptions like ValueError and custom exceptions.
  • Improved error messages for better debugging.

• Made anndata imports conditional to avoid unnecessary dependencies during startup.

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Changes walkthrough 📝

	Relevant files
Enhancement	5 files correct_batches.py Refactored logging and updated docstrings for batch correction +71/-53  file_utils.py Added logger and conditional `anndata` import                        +12/-4    ibaqpy_postprocessing.py Enhanced logging and standardized docstrings                          +79/-44  peptide_normalization.py Improved logging and error handling in peptide normalization +45/-30  ibaqpyc.py Added CLI logging configuration options                                    +31/-2
Documentation	4 files features2peptides.py Simplified docstrings for feature-to-parquet conversion    +1/-18    peptides2protein.py Simplified docstrings for peptide-to-protein computation  +1/-17    ibaqpy_commons.py Updated docstrings and improved type annotations                  +38/-12  utils.py Standardized docstrings and added custom exception              +146/-75

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PR Reviewer Guide 🔍

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⏱️ Estimated effort to review: 3 🔵🔵🔵⚪⚪

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⚡ Recommended focus areas for review Error Handling The new TooFewSamplesInBatch exception class has an incorrect super() call that will cause runtime errors. The correct syntax should be super().init(). class TooFewSamplesInBatch(ValueError): def __init__(self, batches): super.__init__( f"Batches must contain at least two samples, the following batch factors did not: {batches}" ) Exception Handling The error handling for pg_accessions parsing could be improved by adding more specific exception types and error messages to help with debugging. f_table.dropna(subset=["pg_accessions"], inplace=True) try: f_table["pg_accessions"] = f_table["pg_accessions"].apply(lambda x: x[0].split("|")[1]) except IndexError: f_table["pg_accessions"] = f_table["pg_accessions"].apply(lambda x: x[0]) except Exception as e: raise ValueError("Some errors occurred when parsing pg_accessions column in feature parquet!") from e f_table.set_index(["sequence", "pg_accessions"], inplace=True)

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PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
Possible issue	Fix incorrect filtering logic The filter_missing_value_by_group() function has incorrect logic in its return statement. The current condition keeps samples with FEWER proteins than the threshold, which is the opposite of what's intended. The lambda function needs to be changed to keep samples with MORE proteins than the threshold. ibaqpy/ibaq/utils.py [198-200] return df_input.groupby(col, as_index=False).filter( - lambda x: len(x) < non_missing_percent_to_keep * len(df_input) + lambda x: len(x) >= non_missing_percent_to_keep * len(df_input) ) Apply this suggestion Suggestion importance[1-10]: 9 __ Why: The current logic is reversed, keeping samples with fewer proteins than the threshold instead of more. This is a critical bug that would significantly impact the data filtering results.	High
	Fix incorrect super() call The super() call in TooFewSamplesInBatch.init() is incorrect - it's missing parentheses after super. This will cause an error when the exception is raised. The correct syntax should include parentheses. ibaqpy/ibaq/peptide_normalization.py [348-350] def __init__(self, batches): - super.__init__( + super().__init__( f"Batches must contain at least two samples, the following batch factors did not: {batches}" ) [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 8 __ Why: The missing parentheses in super() call is a syntax error that would prevent the custom exception from being properly initialized, causing runtime errors when the exception is raised.	Medium
General	Add missing log levels The log level mapping dictionary should include all standard Python logging levels for completeness. Add ERROR and CRITICAL levels. ibaqpy/ibaqpyc.py [14-19] -LOG_LEVELS = ["debug", "info", "warn"] +LOG_LEVELS = ["debug", "info", "warn", "error", "critical"] LOG_LEVELS_TO_LEVELS = { "debug": logging.DEBUG, "info": logging.INFO, "warn": logging.WARN, + "error": logging.ERROR, + "critical": logging.CRITICAL } Apply this suggestion Suggestion importance[1-10]: 7 __ Why: Adding ERROR and CRITICAL log levels is important for proper error handling and system monitoring, as these are standard logging levels that might be needed for critical issues and error conditions.	Medium
	Ensure consistent log formatting The log file handler should have the same formatter as the root logger. Extract the formatter into a variable and reuse it to ensure consistent log formatting across handlers. ibaqpy/ibaqpyc.py [35-49] +log_formatter = logging.Formatter("%(asctime)s [%(funcName)s] - %(message)s") logging.basicConfig( format="%(asctime)s [%(funcName)s] - %(message)s", level=LOG_LEVELS_TO_LEVELS[log_level.lower()] ) logging.captureWarnings() if log_file: if not log_file.exists(): if not log_file.parent.exists(): log_file.parent.mkdir(parents=True, exist_ok=True) handler = logging.FileHandler(log_file) handler.setLevel(LOG_LEVELS_TO_LEVELS[log_level.lower()]) - handler.setFormatter( - logging.Formatter("%(asctime)s [%(funcName)s] - %(message)s") - ) + handler.setFormatter(log_formatter) logging.getLogger().addHandler(handler) Apply this suggestion Suggestion importance[1-10]: 5 __ Why: The suggestion improves code maintainability by avoiding duplicate log format strings and making it easier to update the format consistently across handlers. While valid, it's a minor optimization with moderate impact.	Low

[ypriverol]

@mobiusklein This is a good point:

I spent some time normalizing docstrings to use the Numpydoc style but did not get through all of them at this point. I tried to make the failure modes of run_batch_correction clearer. Are you using an AI tool to generate some of your docstrings?

Because we have multiple people generating with AI but also with their Editor, we have multiple styles. I have tried to move to Numpy everything but also got stock in the middle. Do you know any tool that allows you to move all docstring to Numpy? Something like black?