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Efficient, backend-agnostic batching of NIfTI-MRS spectra

PyPI version Python License


nifti-mrs-plus wraps a list of NIfTI-MRS objects into a single batch-aware tensor, keeping the header, dimension tags, and provenance intact. It materializes in whatever backend your pipeline needs (NumPy, PyTorch, JAX, TensorFlow, or Keras) with no code changes.


Install

pip install nifti-mrs-plus                   # core (numpy + nifti-mrs)
pip install "nifti-mrs-plus[torch]"          # + PyTorch
pip install "nifti-mrs-plus[viz]"            # + plotting (matplotlib + fsl-mrs)
pip install "nifti-mrs-plus[all]"            # everything

Extras: torch · jax · tensorflow · keras · viz · hdf5 · all · dev


Quick start

from nifti_mrs_plus import NIfTI_MRS_Plus, Backend

batch = NIfTI_MRS_Plus(nifti_list)

batch.shape                          # (8, 1, 1, 1, 2048)
batch.numpy()                        # np.ndarray, cached
batch.get_data(Backend.PYTORCH)      # torch.Tensor  (if torch installed)
batch[0]                             # NIFTI_MRS  (single subject)
batch[0:4]                           # NIfTI_MRS_Plus  (sub-batch)

Backends

Extra Backend Returns
(none) NIFTI_LIST list[NIFTI_MRS]
(none) NUMPY numpy.ndarray
torch PYTORCH torch.Tensor
jax JAX jax.Array
tensorflow TENSORFLOW tf.Tensor
keras KERAS KerasTensor

Built with ❤️ for the MRS community

Metadata

Release files for nifti-mrs-plus 0.2.1

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