Init for Xibabel package
Project description
Xibabel
Xibabel is an experimental package for working with neuroimaging data formats.
It builds on the standard Nibabel package, but adds the extensions listed below.
The extensions allow code like this:
Quickstart
Here is a basic read and slice operation with Xibabel. Compare to the more manual labor that you would have to do with Nibabel.
To run this code, install Xibabel with:
pip install --pre xibabel[optional]
# Basic read and slice with Xibabel
import xibabel as xib
import matplotlib.pyplot as plt
plt.rc('image', cmap='gray')
# We can load NIfTI images directly from web URLs.
# This is a 4D (functional image)
ximg = xib.load('https://s3.amazonaws.com/openneuro.org/ds000105/sub-1/func/sub-1_task-objectviewing_run-01_bold.nii.gz')
# Slicing can now use axis labels:
mean_img = ximg.mean('time')
# Notice that we haven't yet fetched the data. We do so only when we need
# it - for example, when plotting the image data:
plt.imshow(mean_img.sel(k=32))
See the example notebooks in the experiments
directory for more.
Features
- Xibabel images are Xarrays. They have labeled
axes, with default labels for spatial axes of
i
,j
andk
; time istime
. The labels allow slicing operations such as selecting slices over time. - The labels allow concise and readable operations on named axes. See the
glm_in_xibabel
notebook in theexperiments
directory. - Xarrays have attributes that can be attached to the image or the axes of the image. We can load these attributes from BIDS format JSON files. This allows better transmission of metadata.
- The Xarrays have a Dask backend, to computations can be deferred and run at the point at which you need the data in memory.
- Xarrays and Dask allow new storage formats, including storage in Zarr and HDF5 / netCDF.
- You can optimize the on-disk format for memory and CPU by adjusting chunking. We are working on performance metrics for different processing steps.
- Xibabel uses fsspec for reading NIfTI and other files, allowing you to use many filesystems as the source for your data, including HTTP, Amazon, Google Cloud and others. See the FSSpec documentation for details, and the code above for an example using HTTP as a backing store.
Status
Xibabel is in development mode at the moment. We are still experimenting with the API. We'd love to hear from you if you are interested to help. Please do not rely on any particular features in this alpha version, including compatibility of file formats; prefer
Install
From Pip — the current pre-release:
pip install --pre xarray
From Pip — the development code:
pip install git+https://github.com/matthew-brett/xibabel@main
License
We release Xibabel under a BSD simplified 2-clause license (see LICENSE
).
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