Overview

Introduction

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. Its main features include:

• A unified CPU/GPU interface to signal processing functions, including convolution, FFT, NUFFT, wavelet transform, and thresholding functions.
• Linear operator classes (Linop) that can do adjoint, addition, composing, and stacking.
• Proximal operator classes (Prox) that can do stacking, and conjugation.
• Iterative algorithm classes (Alg), including conjugate gradient, (accelerated/proximal) gradient method, and primal dual hybrid gradient.
• Application classes (App) that wrap Alg, Linop, and Prox to form a final deliverable for each application.

SigPy also provides a submodule sigpy.mri for MRI iterative reconstruction methods. Its main features include:

• Commonly used MRI reconstruction methods as an App: SENSE reconstruction, l1-wavelet reconstruction, total-variation reconstruction, and JSENSE reconstruction
• Convenient simulation and sampling functions, including poisson-disc sampling function, and shepp-logan phantom generation function.

Finally, SigPy provides a preliminary submodule sigpy.learn that implements convolutional sparse coding, and linear regression, using the core module.

Installation

The package can be installed via pip:

# (optional for CUDA support) pip install cupy
# (optional for MPI support) pip install mpi4py
pip install sigpy

Or via conda:

# (optional for CUDA support) conda install cupy
# (optional for MPI support) conda install mpi4py
conda install -c frankong sigpy

Alternatively, the package can be installed from source with the following requirements:

• python3
• numpy
• pywavelets
• numba

Documentation

Our documentation is hosted on Read the Docs: https://sigpy.readthedocs.io