The fslpy project is a FSL programming library written in Python. It is used by FSLeyes.
Installation
Install fslpy and its core dependencies via pip:
pip install fslpy
Dependencies
All of the core dependencies of fslpy are listed in the requirements.txt file.
Some extra dependencies are listed in requirements.txt which 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.
To install these additional dependencies, you first need to install wxPython, which is still in pre-relaes.
macOS: pip install --pre wxPython
Linux (change the URL for your specific platform): pip install --only-binary wxpython -f https://extras.wxpython.org/wxPython4/extras/linux/gtk2/ubuntu-16.04/ wxpython
The rtree library also assumes that libspatialindex is installed on your system.
Once wxPython has been installed, you can simply type the following to install the rest of the extra dependencies:
pip install fslpy[extras]
Documentation
fslpy is documented using sphinx. You can build the API documentation by running:
python setup.py doc
The HTML documentation will be generated and saved in the doc/html/ directory.
If you are interested in contributing to fslpy, check out the contributing guide.
Tests
Run the test suite via:
python setup.py test
A test report will be generated at report.html, and a code coverage report will be generated in htmlcov/.
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.
1 maintainer
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