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Major redesign of how OME-NGFF metadata is stored in xarray objects. Metadata that can be represented in xarray's data model (coordinates, dimension names) is now stored there, not duplicated in attrs. Design Principles: - Coordinates represent data: scale/translation transforms → coord arrays, channel labels → channel coordinate values - Attrs for non-representable metadata only: axis types, colors, windows, version info - Full metadata dict preserved for perfect round-tripping - No backward compatibility cruft (no users yet) New Module: xarray_ome/metadata.py - metadata_to_xarray_attrs(): OME-NGFF → xarray attrs (non-coord metadata) - xarray_to_metadata(): xarray → OME-NGFF dict (for writing) - Comprehensive docstrings explaining what goes where - Designed for bidirectional conversion Tests: tests/test_metadata_conversion.py (18 new tests) - Test basic metadata extraction - Test axes types, units, orientations - Test multiscale info, channel colors/windows - Test round-trip fidelity (metadata → xarray → metadata) - Test preservation of unknown/future fields - Test both string and numeric channel coordinates Changes to reader.py: - Remove _break_out_metadata() (replaced by new module) - Simplify _ngff_image_to_dataset() - no backward compat attrs - Use metadata_to_xarray_attrs() for clean separation - Keep only ome_scale/ome_translation for efficient round-trip Changes to writer.py: - Import xarray_to_metadata (prepared for future use) - Writer currently relies on ngff-zarr defaults (works correctly) - Full metadata reconstruction will be added when needed Changes to docs/api.md: - Updated metadata attributes documentation - Reorganized into "Common Attributes" and "Dataset-Only Attributes" - Added all new attribute names with examples All 60 tests pass (42 original + 18 new metadata tests). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Document the zarr backend fallback feature as a TODO for now, as it needs more work to properly handle the interaction between backend detection and reader error handling. Changes: - Add TODO.md with detailed notes on zarr fallback implementation - Update reader.py to check store type before attempting to parse - Improve error messages to distinguish between: - Regular zarr files (not OME-NGFF) - HCS plate structures (not yet supported) - Failed metadata parsing - Backend already has fallback logic, but needs refactoring to work properly with reader's validation The fallback feature is partially implemented but not yet working end-to-end. Documented for future completion. All 60 tests still pass. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Create detailed reference documenting how each OME-NGFF metadata component is mapped to xarray structures (coordinates, dimensions, or attributes). Content: - Design philosophy: metadata that CAN be in xarray coords/dims goes there, not duplicated in attrs - Complete mapping tables for axes, coordinateTransformations, multiscales, omero, labels, and plate/well sections - Direct links to OME-NGFF v0.5 spec sections for each metadata type - Code examples showing both OME-NGFF and xarray representations - Complete attribute reference for DataTree and Dataset objects - Round-trip fidelity documentation - Implementation details with function references Added metadata_mapping page to docs/index.md toctree between usage and examples. This documentation provides a comprehensive reference for understanding the bidirectional metadata conversion implemented in the metadata.py module. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Set up automated testing, linting, and documentation building with uv package manager for fast, reliable CI/CD. Workflow Jobs: 1. Test Matrix: - Runs on: Ubuntu, macOS, Windows - Python versions: 3.11, 3.12, 3.13 - Uses uv for dependency installation - Runs pytest with coverage reporting - Uploads coverage to Codecov (Ubuntu + Python 3.11 only) 2. Lint: - Runs ruff linting and formatting checks - Runs mypy type checking - Ensures code quality standards 3. Docs: - Builds Sphinx documentation with warnings as errors - Uploads HTML documentation as artifact - Validates documentation builds successfully Benefits of using uv: - Fast dependency resolution and installation - Consistent environments across local dev and CI - Native Python version management - Works seamlessly with pyproject.toml The workflow triggers on: - Push to main and active feature branches - Pull requests to main Also fixed broken link in metadata_mapping.md documentation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Reduce CI complexity by testing only on Ubuntu across Python 3.11-3.13. This speeds up CI runs while maintaining Python version compatibility testing. Cross-platform testing can be added later if needed. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Simplified workflow to only run tests since: - Linting is handled by pre-commit.ci - Documentation builds are handled by Read the Docs This keeps CI focused on testing across Python versions (3.11-3.13) with coverage reporting. Final CI setup: - Test matrix: Ubuntu with Python 3.11, 3.12, 3.13 - Coverage upload to Codecov for Python 3.11 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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working to more methodically convert all ome-metadata to equiv xarray object