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 ruff sphinx sphinx_rtd_theme black isort

and run the script run_tests.sh.

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:

# 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:

# 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:

x_torch = sigpy.to_pytorch(x)
A_torch = sigpy.to_pytorch_function(A)