Skip to content

jtamir/sigpy

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

https://travis-ci.com/mikgroup/sigpy.svg?branch=master Documentation Status

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

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 100.0%