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* Expand test coverage with comprehensive test suites Add extensive test coverage for previously untested modules: - test_utils.py: Comprehensive tests for utility functions (setup_logger, log, RBF, parse_device, load_hdf5_parallel, PairedDataset, collate_paired_sequences) - test_glider.py: Complete test suite for graph-based link prediction module (get_dim, densify, compute_X_normalized, scoring functions, GLIDE algorithms) - test_loading.py: Tests for parallel HDF5 data loading with LoadingPool, including edge cases, error handling, and integration tests - test_language_model.py: Expanded from 2 to 13 test methods, adding coverage for lm_embed, embed_from_fasta with various edge cases and validations These additions significantly improve test coverage for: - dscript/utils.py (167 lines, previously untested) - dscript/glider.py (346 lines, previously untested) - dscript/loading.py (92 lines, previously untested) - dscript/language_model.py (minimal coverage expanded) Total new test methods: ~200+ assertions across 4 test modules * Add comprehensive tests for command modules and worker functions Create four new test modules to expand coverage of previously untested code: 1. test_extract_3di.py (19 test methods, ~370 lines) - Tests for 3Di sequence extraction from PDB/CIF files - Argument parsing, file filtering, FASTA output validation - Integration tests for full workflow - Covers dscript/commands/extract_3di.py (~58 lines) 2. test_par_writer.py (24 test methods, ~400 lines) - Tests for parallel prediction writer process - TSV output writing, threshold filtering, contact map storage - HDF5 contact map dataset handling - Progress tracking and data type validation - Covers dscript/commands/par_writer.py (~40 lines) 3. test_main.py (24 test methods, ~320 lines) - Tests for CLI entry point and argument parsing - CitationAction class testing - All subcommand registration and invocation - Version and help flag handling - Integration tests for command dispatch - Covers dscript/__main__.py (~87 lines, increasing from ~85% to ~95%) 4. test_load_worker.py (23 test methods, ~330 lines) - Direct unit tests for HDF5 loading worker function - Queue handling, data type conversion, memory sharing - Error handling for corrupted/missing files - Multi-dimensional array support - Covers dscript/load_worker.py (~25 lines, previously only indirect coverage) Total additions: - ~1,420 lines of new test code - 90+ test methods with comprehensive assertions - ~210 lines of source code now directly tested - Addresses high-priority gaps identified in coverage analysis These tests complement the existing suite and focus on command-line interface components and parallel processing infrastructure. * Fix linting issues and apply code formatting - Remove unused variables flagged by ruff - Apply ruff formatting to all test files - Ensure all pre-commit hooks pass Changes: - test_loading.py: Remove unused 'f' variable - test_main.py: Remove unused 'fake_out' and 'output' variables - test_utils.py: Remove unused 'log_file' variable and tmp_path param - Applied ruff formatting to maintain code style consistency * Fix test_load_worker.py hanging issue in CI Rewrote test_load_worker.py to prevent CI hangs that occurred when tests called the blocking worker function directly. The worker function _hdf5_load_partial_func runs in an infinite loop waiting on a queue, which caused tests to hang indefinitely. Changes: - Created run_worker_with_timeout() helper that wraps worker execution in a daemon thread with configurable timeout (default 5 seconds) - Modified all tests to use this helper and assert successful completion - Changed queue operations from blocking get() to non-blocking get_nowait() - Reduced test count from 23 to 16 focused tests - Added documentation noting worker is primarily tested via LoadingPool This should resolve the CI timeout issue where tests hung at 43% completion. * Rewrite test_language_model.py to use mocks instead of real model The original tests were calling the real language model which: - Downloads/loads pretrained model weights (slow, can fail) - Runs actual neural network inference (resource intensive) - Causes test failures when model files aren't available Changes: - Rewrote unit tests to mock get_pretrained() function - Mock model returns realistic tensor shapes but doesn't load weights - Tests are now fast, reliable, and don't require model files - Moved real model tests to TestLanguageModelIntegration class - Marked integration tests with @pytest.mark.slow so they can be skipped - Removed unnecessary loguru import that caused import errors - Removed problematic setup.py install step from setup_class This should fix the 4 failing tests reported by CI. * fix failing tests * Update .github/workflows/autorun-tests.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update .github/workflows/autorun-tests.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>