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https://opensource.org/licenses/BSD-3-Clause\n\t   \n.. image:: https://travis-ci.com/mikgroup/sigpy.svg?branch=master\n\t:target: https://travis-ci.com/mikgroup/sigpy\n\t   \n.. image:: https://readthedocs.org/projects/sigpy/badge/?version=latest\n\t:target: https://sigpy.readthedocs.io/en/latest/?badge=latest\n\t:alt: Documentation Status\n\t\n.. image:: https://codecov.io/gh/mikgroup/sigpy/branch/master/graph/badge.svg\n\t:target: https://codecov.io/gh/mikgroup/sigpy\n\n.. image:: https://zenodo.org/badge/139635485.svg\n   :target: https://zenodo.org/badge/latestdoi/139635485\n\n\n`Source Code \u003chttps://github.com/mikgroup/sigpy\u003e`_ | `Documentation \u003chttps://sigpy.readthedocs.io\u003e`_ | `MRI Recon Tutorial \u003chttps://github.com/mikgroup/sigpy-mri-tutorial\u003e`_ | `MRI Pulse Design Tutorial \u003chttps://github.com/jonbmartin/open-source-pulse-design\u003e`_\n\nSigPy 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.\n\nInstallation\n------------\n\nSigPy requires Python version \u003e= 3.5. The core module depends on ``numba``, ``numpy``, ``PyWavelets``, ``scipy``, and ``tqdm``.\n\nAdditional 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``.\n\nVia ``conda``\n*************\n\nWe recommend installing SigPy through ``conda``::\n\n\tconda install -c frankong sigpy\n\t# (optional for plot support) conda install matplotlib\n\t# (optional for CUDA support) conda install cupy\n        # (optional for MPI support) conda install mpi4py\n\nVia ``pip``\n***********\n\nSigPy can also be installed through ``pip``::\n\n\tpip install sigpy\n\t# (optional for plot support) pip install matplotlib\n\t# (optional for CUDA support) pip install cupy\n        # (optional for MPI support) pip install mpi4py\n\t\nInstallation for Developers\n***************************\n\nIf you want to contribute to the SigPy source code, we recommend you install it with ``pip`` in editable mode::\n\n\tcd /path/to/sigpy\n\tpip install -e .\n\t\nTo run tests and contribute, we recommend installing the following packages::\n\n\tpip install coverage ruff sphinx sphinx_rtd_theme black isort\n\nand run the script ``run_tests.sh``.\n\nFeatures\n--------\n\nCPU/GPU Signal Processing Functions\n***********************************\nSigPy 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:\n\n.. code:: python\n\n\t  # CPU convolve\n\t  x = numpy.array([1, 2, 3, 4, 5])\n\t  y = numpy.array([1, 1, 1])\n\t  z = sigpy.convolve(x, y)\n\n\t  # GPU convolve\n\t  x = cupy.array([1, 2, 3, 4, 5])\n\t  y = cupy.array([1, 1, 1])\n\t  z = sigpy.convolve(x, y)\n\nIterative Algorithms\n********************\nSigPy also provides convenient abstractions and classes for iterative algorithms. A compressed sensing experiment can be implemented in four lines using SigPy:\n\n.. code:: python\n\n\t  # Given some observation vector y, and measurement matrix mat\n\t  A = sigpy.linop.MatMul([n, 1], mat)  # define forward linear operator\n\t  proxg = sigpy.prox.L1Reg([n, 1], lamda=0.001)  # define proximal operator\n\t  x_hat = sigpy.app.LinearLeastSquares(A, y, proxg=proxg).run()  # run iterative algorithm\n\nPyTorch Interoperability\n************************\nWant 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:\n\n.. code:: python\n\n\t  x_torch = sigpy.to_pytorch(x)\n\t  A_torch = sigpy.to_pytorch_function(A)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikgroup%2Fsigpy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmikgroup%2Fsigpy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikgroup%2Fsigpy/lists"}