Skip to main content

FSL Python library

Project description

https://img.shields.io/pypi/v/fslpy.svg https://anaconda.org/conda-forge/fslpy/badges/version.svg https://zenodo.org/badge/DOI/10.5281/zenodo.1470750.svg https://git.fmrib.ox.ac.uk/fsl/fslpy/badges/master/coverage.svg

The fslpy project is a FSL programming library written in Python. It is used by FSLeyes.

fslpy is tested against Python versions 3.8, 3.9, 3.10, and 3.11.

Installation

Install fslpy and its core dependencies via pip:

pip install fslpy

fslpy is also available on conda-forge:

conda install -c conda-forge fslpy

Dependencies

All of the core dependencies of fslpy are listed in the pyproject.toml file.

Some optional dependencies (labelled extra in pyproject.toml) provide addditional functionality:

  • wxPython: The fsl.utils.idle module has functionality to schedule functions on the wx idle loop.

  • indexed_gzip: The fsl.data.image.Image class can use indexed_gzip to keep large compressed images on disk instead of decompressing and loading them into memory..

  • trimesh/rtree: The fsl.data.mesh.TriangleMesh class has some methods which use trimesh to perform geometric queries on the mesh.

  • Pillow: The fsl.data.bitmap.Bitmap class uses Pillow to load image files.

If you are using Linux, you need to install wxPython first, as binaries are not available on PyPI. Install wxPython like so, changing the URL for your specific platform:

pip install -f https://extras.wxpython.org/wxPython4/extras/linux/gtk2/ubuntu-16.04/ wxpython

Once wxPython has been installed, you can type the following to install the remaining optional dependencies:

pip install "fslpy[extra]"

Dependencies for testing and documentation are also listed in pyproject.toml, and are respectively labelled as test and doc.

Non-Python dependencies

The fsl.data.dicom module requires the presence of Chris Rorden’s dcm2niix program.

The rtree library assumes that libspatialindex is installed on your system.

The fsl.transform.x5 module uses h5py, which requires libhdf5.

Documentation

API documentation for fslpy is hosted at https://open.win.ox.ac.uk/pages/fsl/fslpy/.

fslpy is documented using sphinx. You can build the API documentation by running:

pip install ".[doc]"
sphinx-build doc html

The HTML documentation will be generated and saved in the html/ directory.

Tests

Run the test suite via:

pip install ".[test]"
pytest

Some tests will only pass if the test environment meets certain criteria - refer to the tool.pytest.init_options section of [pyproject.toml](pyproject.toml) for a list of [pytest marks](https://docs.pytest.org/en/7.1.x/example/markers.html) which can be selectively enabled or disabled.

Contributing

If you are interested in contributing to fslpy, check out the contributing guide.

Credits

The fsl.data.dicom module is little more than a thin wrapper around Chris Rorden’s dcm2niix program.

The example.mgz file, used for testing, originates from the nibabel test data set.

Project details


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

fslpy-3.21.1.tar.gz (4.8 MB view details)

Uploaded Source

Built Distribution

fslpy-3.21.1-py3-none-any.whl (4.8 MB view details)

Uploaded Python 3

File details

Details for the file fslpy-3.21.1.tar.gz.

File metadata

  • Download URL: fslpy-3.21.1.tar.gz
  • Upload date:
  • Size: 4.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.15

File hashes

Hashes for fslpy-3.21.1.tar.gz
Algorithm Hash digest
SHA256 c23c965d133a4bc1a254c30b52e9da84316bdfc4d366aacb785eb129c7c12762
MD5 cb306db5f039233a8fdd1a6fcf03a13d
BLAKE2b-256 5ac96957cdb0561182f246e56808772a405007f43cb4105e371dc1826312b440

See more details on using hashes here.

File details

Details for the file fslpy-3.21.1-py3-none-any.whl.

File metadata

  • Download URL: fslpy-3.21.1-py3-none-any.whl
  • Upload date:
  • Size: 4.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.10.15

File hashes

Hashes for fslpy-3.21.1-py3-none-any.whl
Algorithm Hash digest
SHA256 0daebf1cd9182a2547830d54cd5552311617dfd630624c1d4befc63e2b349201
MD5 83a133664691ed7779a8d8948ec4c6e7
BLAKE2b-256 666afc716c71f0285accfacd782a08c1f2126d94799edc36d1284b3add1e7fdf

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page