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Read/write/validate/query BIDS datasets

Project description

Documentation Status Latest Version PyPI - Python Version Test Status Codecov License

ancpBIDS Technical Overview

Key Features

  • BIDS Dataset Loading
    Load BIDS datasets of any size or complexity, with support for multiple BIDS schema versions.

  • Flexible Query Engine
    Query files, folders, and metadata using a powerful, Pythonic API. Supports entity-based, scope-based, and custom queries.

  • Validation
    Validate datasets against the BIDS specification and custom rules using a plugin-based validation system.

  • Writing and Derivatives
    Write and update BIDS datasets, including support for creating and saving BIDS derivatives.

  • Lazy Loading
    Efficiently handle large datasets with optional lazy loading, reducing memory usage and speeding up initial access.

  • Plugin Architecture
    Extend or customize core functionality (validation, file handling, schema, etc.) via a robust plugin system.

  • PyBIDS Compatibility Layer
    Drop-in compatibility for many pybids API calls, easing migration from or integration with existing codebases.

  • Synthetic and Real Data Support
    Works with both synthetic test datasets and real-world BIDS datasets.

  • CI/CD Ready
    Includes a comprehensive suite of automated and manual tests, with synthetic datasets for reproducibility.

  • Extensible and Versioned Schema
    Supports multiple BIDS schema versions and allows for easy extension as the BIDS standard evolves.

Architecture

ancpBIDS is a modular Python library for reading, querying, validating, and writing BIDS datasets. Its architecture is designed for extensibility and maintainability, with the following key components:

  • Core Models:
    The core BIDS data model is implemented in ancpbids/model_base.py and versioned model files (e.g., model_v1_8_0.py). These define the schema and object graph for BIDS datasets.

  • Plugin System:
    The plugin mechanism (see ancpbids/plugin.py) allows for dynamic extension of core functionality. Plugins can hook into schema modification, dataset processing, file handling, writing, and validation.

  • Query Engine:
    The query logic is implemented in ancpbids/query.py, providing flexible access to dataset contents and metadata.

  • Compatibility Layer:
    ancpbids/pybids_compat.py provides compatibility with the pybids API for easier migration and integration.

  • Utilities:
    Helper functions and utilities are in ancpbids/utils.py.

  • Testing:
    The tests/ directory is organized into auto (CI-safe) and manual (non-deterministic or performance) tests, with synthetic datasets under tests/data/.

Plugin System

The plugin system is a core feature for extensibility:

  • Plugin Types:

    • SchemaPlugin: Modify or extend the BIDS schema.
    • DatasetPlugin: Operate on in-memory dataset graphs.
    • FileHandlerPlugin: Register custom file readers/writers.
    • WritingPlugin: Add files/folders during dataset writing.
    • ValidationPlugin: Add custom validation rules.
  • Registration and Discovery:
    Plugins are registered via register_plugin or discovered with load_plugins_by_package. They are prioritized by a ranking value.

  • Execution:
    At key points (e.g., dataset load, write, validate), the system retrieves and executes all relevant plugins using get_plugins.

  • How to Add a Plugin:

    1. Subclass the appropriate plugin base class from plugin.py.
    2. Implement the required execute method.
    3. Register your plugin using register_plugin or by placing it in a discoverable package.

Versioning and Schema Evolution

  • The codebase supports multiple BIDS schema versions, with separate model files for each version.
  • The schema is loaded dynamically based on the dataset version, allowing for forward compatibility.

Testing and CI

  • Unit Tests:
    Located in tests/auto/, these are run automatically in CI.
  • Manual/Performance Tests:
    Located in tests/manual/, these are for benchmarking or non-deterministic checks.
  • Synthetic Data:
    All tests use synthetic datasets in tests/data/ to ensure reproducibility.

Developer Guidelines

  • Extending the Model:
    Add new schema versions as new files in ancpbids/, following the pattern of existing model files.
  • Adding Plugins:
    Follow the plugin system described above.
  • Testing:
    Add new tests to tests/auto/ for CI-safe code, and to tests/manual/ for performance or integration tests.
  • Documentation:
    Update README.md and docstrings for any new features or changes.

Code Quality

  • The codebase uses type hints and docstrings for clarity.
  • Contributions should follow PEP8 and include tests and documentation.

Further Reading

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