https://github.com/mikgroup/sigpy
Tip revision: 8d2030d98fe1d6199f430828f1d8c67c1502ad56 authored by Frank Ong on 04 April 2023, 05:34:03 UTC
Bump version: 0.1.24 → 0.1.25
Bump version: 0.1.24 → 0.1.25
Tip revision: 8d2030d
README.rst
SigPy
=====
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`Source Code <https://github.com/mikgroup/sigpy>`_ | `Documentation <https://sigpy.readthedocs.io>`_ | `MRI Recon Tutorial <https://github.com/mikgroup/sigpy-mri-tutorial>`_ | `MRI Pulse Design Tutorial <https://github.com/jonbmartin/open-source-pulse-design>`_
SigPy is a package for signal processing, with emphasis on iterative methods. It is built to operate directly on NumPy arrays on CPU and CuPy arrays on GPU. SigPy also provides several domain-specific submodules: ``sigpy.plot`` for multi-dimensional array plotting, ``sigpy.mri`` for MRI reconstruction, and ``sigpy.mri.rf`` for MRI pulse design.
Installation
------------
SigPy requires Python version >= 3.5. The core module depends on ``numba``, ``numpy``, ``PyWavelets``, ``scipy``, and ``tqdm``.
Additional features can be unlocked by installing the appropriate packages. To enable the plotting functions, you will need to install ``matplotlib``. To enable CUDA support, you will need to install ``cupy``. And to enable MPI support, you will need to install ``mpi4py``.
Via ``conda``
*************
We recommend installing SigPy through ``conda``::
conda install -c frankong sigpy
# (optional for plot support) conda install matplotlib
# (optional for CUDA support) conda install cupy
# (optional for MPI support) conda install mpi4py
Via ``pip``
***********
SigPy can also be installed through ``pip``::
pip install sigpy
# (optional for plot support) pip install matplotlib
# (optional for CUDA support) pip install cupy
# (optional for MPI support) pip install mpi4py
Installation for Developers
***************************
If you want to contribute to the SigPy source code, we recommend you install it with ``pip`` in editable mode::
cd /path/to/sigpy
pip install -e .
To run tests and contribute, we recommend installing the following packages::
pip install coverage flake8 sphinx
and run the script ``run_tests.sh``.
Features
--------
CPU/GPU Signal Processing Functions
***********************************
SigPy provides signal processing functions with a unified CPU/GPU interface. For example, the same code can perform a CPU or GPU convolution on the input array device:
.. code:: python
# CPU convolve
x = numpy.array([1, 2, 3, 4, 5])
y = numpy.array([1, 1, 1])
z = sigpy.convolve(x, y)
# GPU convolve
x = cupy.array([1, 2, 3, 4, 5])
y = cupy.array([1, 1, 1])
z = sigpy.convolve(x, y)
Iterative Algorithms
********************
SigPy also provides convenient abstractions and classes for iterative algorithms. A compressed sensing experiment can be implemented in four lines using SigPy:
.. code:: python
# Given some observation vector y, and measurement matrix mat
A = sigpy.linop.MatMul([n, 1], mat) # define forward linear operator
proxg = sigpy.prox.L1Reg([n, 1], lamda=0.001) # define proximal operator
x_hat = sigpy.app.LinearLeastSquares(A, y, proxg=proxg).run() # run iterative algorithm
PyTorch Interoperability
************************
Want to do machine learning without giving up signal processing? SigPy has convenient functions to convert arrays and linear operators into PyTorch Tensors and Functions. For example, given a cupy array ``x``, and a ``Linop`` ``A``, we can convert them to Pytorch:
.. code:: python
x_torch = sigpy.to_pytorch(x)
A_torch = sigpy.to_pytorch_function(A)