Socket
Book a DemoInstallSign in
Socket

ancpbids

Package Overview
Dependencies
Maintainers
1
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

ancpbids

Read/write/validate/query BIDS datasets

0.2.5
pipPyPI
Maintainers
1

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:

    • Subclass the appropriate plugin base class from plugin.py.
    • Implement the required execute method.
    • 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

FAQs

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

About

Packages

Stay in touch

Get open source security insights delivered straight into your inbox.

  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc

U.S. Patent No. 12,346,443 & 12,314,394. Other pending.