SigPy Documentation Status

Source Code | Documentation | MRI Recon Tutorial | MRI Pulse Design Tutorial

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.


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 ruff sphinx sphinx_rtd_theme black isort

and run the script


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 =, 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)