build_tools.syllable_feature_annotator.cli ========================================== .. py:module:: build_tools.syllable_feature_annotator.cli .. autoapi-nested-parse:: Command-line interface for syllable feature annotator. This module provides argument parsing and CLI orchestration for the syllable feature annotation tool. It handles user input, validates arguments, and provides clear error messages. Design Principles ----------------- 1. **Clear Contract**: Explicit required and optional arguments 2. **Helpful Defaults**: Sensible default paths matching normalizer output 3. **User-Friendly Errors**: Clear messages when things go wrong 4. **Clean Exit**: Proper exit codes for shell scripting Functions --------- parse_arguments(args: list[str] | None) -> argparse.Namespace Parse command-line arguments main(args: list[str] | None) -> int Main CLI entry point with error handling Usage ----- Run from command line:: $ python -m build_tools.syllable_feature_annotator \ --syllables data/normalized/syllables_unique.txt \ --frequencies data/normalized/syllables_frequencies.json \ --output data/annotated/syllables_annotated.json Use with normalizer output (auto-detects output path):: $ python -m build_tools.syllable_feature_annotator \ --syllables _working/output/20260110_115601_nltk/nltk_syllables_unique.txt \ --frequencies _working/output/20260110_115601_nltk/nltk_syllables_frequencies.json # Output will be: _working/output/20260110_115601_nltk/data/nltk_syllables_annotated.json Enable verbose output:: $ python -m build_tools.syllable_feature_annotator --verbose Get help:: $ python -m build_tools.syllable_feature_annotator --help CLI Arguments ------------- --syllables PATH Path to syllables text file (one per line) Default: data/normalized/syllables_unique.txt --frequencies PATH Path to frequencies JSON file (syllable → count mapping) Default: data/normalized/syllables_frequencies.json --output PATH Path where annotated JSON should be written Auto-detects output path from syllables file location if not specified Falls back to: data/annotated/syllables_annotated.json --verbose, -v Show detailed progress information --help, -h Show help message and exit Exit Codes ---------- 0 : Success Annotation completed successfully 1 : Error File not found, invalid input, or other error Examples -------- Process normalized syllables with default paths:: $ python -m build_tools.syllable_feature_annotator Annotation complete! Syllables annotated: 1,523 Features per syllable: 12 Processing time: 0.342s Custom paths with verbose output:: $ python -m build_tools.syllable_feature_annotator \ --syllables custom/syllables.txt \ --frequencies custom/frequencies.json \ --output output/annotated.json \ --verbose Loading syllables from custom/syllables.txt... Loading frequencies from custom/frequencies.json... Annotating 1523 syllables... Saving annotated syllables to output/annotated.json... Annotation complete! Syllables annotated: 1,523 Features per syllable: 12 Total corpus frequency: 8,472 Processing time: 0.342s Output saved to: output/annotated.json Error Handling -------------- The CLI catches common errors and provides user-friendly messages: File not found:: $ python -m build_tools.syllable_feature_annotator --syllables missing.txt Error: Input file not found: missing.txt Please ensure the file exists and is readable. Invalid JSON:: $ python -m build_tools.syllable_feature_annotator --frequencies bad.json Error: Invalid JSON in frequencies file: bad.json Expecting property name enclosed in double quotes: line 1 column 2 (char 1) Permission error:: $ python -m build_tools.syllable_feature_annotator --output /readonly/output.json Error: Permission denied: /readonly/output.json Please check file permissions. Integration with Pipeline ------------------------- This CLI is designed to work seamlessly with the syllable normalizer: 1. Run syllable normalizer to create normalized syllables 2. Run feature annotator to add features to syllables 3. Use annotated syllables for pattern development Example workflow:: # Step 1: Extract and normalize syllables $ python -m build_tools.pyphen_syllable_extractor --file input.txt $ python -m build_tools.pyphen_syllable_normaliser \ --run-dir _working/output/20260110_115453_pyphen/ # Step 2: Annotate with features (output path auto-detected) $ python -m build_tools.syllable_feature_annotator \ --syllables _working/output/20260110_115453_pyphen/pyphen_syllables_unique.txt \ --frequencies _working/output/20260110_115453_pyphen/pyphen_syllables_frequencies.json # Creates: _working/output/20260110_115453_pyphen/data/pyphen_syllables_annotated.json # Step 3: Use annotated syllables for pattern generation # (future tools will consume *_syllables_annotated.json) Notes ----- - All paths can be absolute or relative to current working directory - Output directory is created automatically if it doesn't exist - Existing output files are overwritten without warning - Verbose mode is recommended for first-time users - Exit codes follow Unix conventions (0=success, 1=error) Functions --------- .. autoapisummary:: build_tools.syllable_feature_annotator.cli.detect_extractor_type build_tools.syllable_feature_annotator.cli.compute_output_path build_tools.syllable_feature_annotator.cli.create_argument_parser build_tools.syllable_feature_annotator.cli.parse_arguments build_tools.syllable_feature_annotator.cli.main Module Contents --------------- .. py:function:: detect_extractor_type(syllables_path) Detect the extractor type (pyphen or nltk) from the syllables file path. This function examines the syllables path to determine which extractor (pyphen or nltk) produced the file, enabling automatic output path computation. Detection Strategy ------------------ 1. Check if filename starts with "pyphen_" or "nltk_" 2. Check if parent directory name contains "_pyphen" or "_nltk" 3. Return None if no match (not from a normalizer run) Parameters ---------- syllables_path : Path Path to the syllables file (typically *_syllables_unique.txt) Returns ------- str | None "pyphen" if pyphen extractor detected "nltk" if NLTK extractor detected None if extractor type cannot be determined Examples -------- Detect from filename prefix:: >>> path = Path("_working/output/20260110_115601_nltk/nltk_syllables_unique.txt") >>> detect_extractor_type(path) 'nltk' Detect from directory name:: >>> path = Path("_working/output/20260110_115453_pyphen/pyphen_syllables_unique.txt") >>> detect_extractor_type(path) 'pyphen' Non-normalizer path returns None:: >>> path = Path("data/custom/syllables.txt") >>> detect_extractor_type(path) None Notes ----- - Detection is case-sensitive (looks for lowercase "pyphen" and "nltk") - File must follow naming convention: {extractor}_syllables_*.txt - Directory must follow naming convention: *_{extractor}/ .. py:function:: compute_output_path(syllables_path, extractor_type) Compute the output path for annotated syllables in the run directory. This function creates a self-contained output path within the same directory as the input syllables file, following the naming convention: /data/_syllables_annotated.json Parameters ---------- syllables_path : Path Path to the input syllables file extractor_type : str The extractor type ("pyphen" or "nltk") Returns ------- Path Computed output path in the format: /data/_syllables_annotated.json Examples -------- Compute pyphen output path:: >>> syllables = Path("_working/output/20260110_115453_pyphen/pyphen_syllables_unique.txt") >>> compute_output_path(syllables, "pyphen") PosixPath('_working/output/20260110_115453_pyphen/data/pyphen_syllables_annotated.json') Compute NLTK output path:: >>> syllables = Path("_working/output/20260110_115601_nltk/nltk_syllables_unique.txt") >>> compute_output_path(syllables, "nltk") PosixPath('_working/output/20260110_115601_nltk/data/nltk_syllables_annotated.json') Notes ----- - Output directory (data/) is automatically created by save_annotated_syllables - Filename format: {extractor_type}_syllables_annotated.json - This makes each extraction run self-contained with all derived data .. py:function:: create_argument_parser() Create and return the argument parser for syllable feature annotator. This function creates the ArgumentParser with all CLI options but does not parse arguments. This separation allows Sphinx documentation tools to introspect the parser and auto-generate CLI documentation. Returns ------- argparse.ArgumentParser Configured ArgumentParser ready to parse command-line arguments Examples -------- Create parser and inspect options:: >>> parser = create_argument_parser() >>> parser.prog 'cli.py' Use parser to parse arguments:: >>> parser = create_argument_parser() >>> args = parser.parse_args(["--syllables", "data/syllables.txt"]) >>> args.syllables PosixPath('data/syllables.txt') Notes ----- - This function is used by both the CLI and documentation generation - For normal CLI usage, use parse_arguments() instead - Sphinx-argparse can introspect this function to generate docs .. py:function:: parse_arguments(args = None) Parse command-line arguments for syllable feature annotator. Creates an argument parser with all CLI options and parses the provided arguments (or sys.argv if none provided). Parameters ---------- args : list[str] | None, optional List of argument strings to parse. If None, uses sys.argv[1:] This parameter exists primarily for testing purposes. Returns ------- argparse.Namespace Parsed arguments with attributes: - syllables (Path): Path to syllables file - frequencies (Path): Path to frequencies file - output (Path): Path to output file - verbose (bool): Whether to show progress Examples -------- Parse command-line arguments:: >>> args = parse_arguments(["--syllables", "data/syllables.txt"]) >>> args.syllables PosixPath('data/syllables.txt') Parse with all arguments:: >>> args = parse_arguments([ ... "--syllables", "input/syllables.txt", ... "--frequencies", "input/frequencies.json", ... "--output", "output/annotated.json", ... "--verbose" ... ]) >>> args.verbose True Use default values:: >>> args = parse_arguments([]) >>> args.syllables PosixPath('data/normalized/syllables_unique.txt') Notes ----- - All path arguments are automatically converted to Path objects - Default paths match standard normalizer output locations - Help message is automatically generated from argument definitions - Invalid arguments trigger help message and exit .. py:function:: main(args = None) Main CLI entry point with error handling. Parses arguments, runs annotation pipeline, and handles errors gracefully with user-friendly messages. Parameters ---------- args : list[str] | None, optional List of argument strings to parse. If None, uses sys.argv[1:] This parameter exists primarily for testing purposes. Returns ------- int Exit code (0 for success, 1 for error) Examples -------- Run with default arguments:: >>> exit_code = main([]) # doctest: +SKIP >>> exit_code 0 Run with custom arguments:: >>> exit_code = main([ ... "--syllables", "data/syllables.txt", ... "--output", "output/annotated.json", ... "--verbose" ... ]) # doctest: +SKIP >>> exit_code 0 Handle error case:: >>> exit_code = main(["--syllables", "missing.txt"]) # doctest: +SKIP Error: Input file not found: missing.txt >>> exit_code 1 Notes ----- - Catches common errors and provides user-friendly messages - Exit code 0 indicates success, 1 indicates error - Verbose output is controlled by --verbose flag - All exceptions are caught and converted to error messages