bids: interface with datasets conforming to BIDS
Project description
PyBIDS
PyBIDS is a Python library to centralize interactions with datasets conforming BIDS (Brain Imaging Data Structure) format. For more information about BIDS visit https://bids.neuroimaging.io.
PyBIDS currently supports Python 3 on POSIX operating systems (including Mac OS). Windows is not officially supported, though most PyBIDS functionality will probably work fine.
Installation
PyBIDS is most easily installed from pip. To install the latest official release:
pip install pybids
Or, alternatively via conda:
conda install -c conda-forge pybids
If you want to live on the bleeding edge, you can install from master:
pip install git+https://github.com/bids-standard/pybids.git
Dependencies
PyBIDS has a number of dependencies. The core querying functionality requires only the BIDS-Validator package. However, most other modules require the core Python neuroimaging stack: numpy, scipy, pandas, and nibabel. The reports module additionally requires num2words. By default, all dependencies will be installed with pybids (if they aren't already available).
Usage
Get started by checking out the documentation!
Or you can start at our tutorial! You can run it interactively without installing anything via binder. Click on the link and then navigate to doc/examples/pybids_tutorial.md to explore.
Development and testing
PyBIDS uses tox to manage testing environments and ensure that the same commands can easily be run locally and in CI.
Install tox with:
pip install tox
To run tests with standard, minimal, or pre-release dependencies, use:
tox run -e [full|min|pre]
To run with a specific version, also specify py3X:
tox run -e py311-full
See tox documentation for more detail.
How to cite
To credit PyBIDS in your work, please cite both the JOSS paper and the Zenodo archive. The former provides a high level description of the package, and the latter points to a permanent record of all PyBIDS versions (we encourage you to cite the specific version you used). Example citations (for PyBIDS 0.9.3):
Yarkoni et al., (2019). PyBIDS: Python tools for BIDS datasets. Journal of Open Source Software, 4(40), 1294, https://doi.org/10.21105/joss.01294
Yarkoni, Tal, Markiewicz, Christopher J., de la Vega, Alejandro, Gorgolewski, Krzysztof J., Halchenko, Yaroslav O., Salo, Taylor, … Blair, Ross. (2019, August 8). bids-standard/pybids: 0.9.3 (Version 0.9.3). Zenodo. https://doi.org/10.5281/zenodo.3363985
Community guidelines
Bug reports, feedback, and other contributions to PyBIDS are welcome. To report a bug, request a feature, or raise a technical (non-usage-related) question for discussion, please open an issue on GitHub. Requests for support and questions about usage (where no bug is suspected) should be posted on the NeuroStars forum. For questions about the BIDS specification or BIDS-Validator, please see the corresponding repositories. For general BIDS-related discussion, see the bids-discussion mailing list.
Pull requests are always welcome. If you have any doubts about the suitability of a PR, or want to discuss implementation-related issues, feel free to open an issue for discussion.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file pybids-0.16.5.tar.gz.
File metadata
- Download URL: pybids-0.16.5.tar.gz
- Upload date:
- Size: 13.2 MB
- Tags: Source
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | e4c029e426253a1d56c6c5ce13f2c754d9bf2b854615a2ca59a68a6302a34083 |
|
| MD5 | 17d666bb3cc983ffbeab421b21f59170 |
|
| BLAKE2b-256 | 1217063b9c143c380d9f237c198bfc5057e561c457e6cc251acbcedbea3ae6fe |
File details
Details for the file pybids-0.16.5-py3-none-any.whl.
File metadata
- Download URL: pybids-0.16.5-py3-none-any.whl
- Upload date:
- Size: 14.9 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? Yes
- Uploaded via: twine/5.0.0 CPython/3.12.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 7b3d4b8005644895fcff01d565cc163de9e2aa338f3ef238cec8b2289959eef5 |
|
| MD5 | c5a6e05b3c528d5b39fdb593ade87dba |
|
| BLAKE2b-256 | 4bc603cc0fcc0dd32328110a3775024dc6835ea0e9e531ecfc546605f758b0b0 |
