A minimal "getting start" tutorial is available at https://pynufft.readthedocs.io/en/latest/index.html . This package reimplements the min-max interpolator (Fessler, Jeffrey A., and Bradley P. Sutton. "Nonuniform fast Fourier transforms using min-max interpolation." IEEE transactions on signal processing 51.2 (2003): 560-574.) for Python.
Add experimental support for cupy, PyTorch and TensorFlow Eager mode with test files.
https://ieeexplore.ieee.org/document/10012099/
https://iopscience.iop.org/article/10.1088/1361-6560/ab9358/meta (deep learning)
https://arxiv.org/abs/2103.09203 (deep learning)
https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.14809 (deep learning)
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8579232 (deep learning)
https://joss.theoj.org/papers/10.21105/joss.02825 (gravitational lens)
https://joss.theoj.org/papers/10.21105/joss.02578 (Off-resonance CorrecTion OPen soUrce Software)
$ pip3 install pynufft --user
$ python
Python 3.6.11 (default, Aug 23 2020, 18:05:39)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pynufft import NUFFT
>>> import numpy
>>> A = NUFFT()
>>> om = numpy.random.randn(10,2)
>>> Nd = (64,64)
>>> Kd = (128,128)
>>> Jd = (6,6)
>>> A.plan(om, Nd, Kd, Jd)
0
>>> x=numpy.random.randn(*Nd)
>>> y = A.forward(x)
>>> from pynufft import NUFFT, helper
>>> import numpy
>>> A2= NUFFT(helper.device_list()[0])
>>> A2.device
<reikna.cluda.cuda.Device object at 0x7f9ad99923b0>
>>> om = numpy.random.randn(10,2)
>>> Nd = (64,64)
>>> Kd = (128,128)
>>> Jd = (6,6)
>>> A2.plan(om, Nd, Kd, Jd)
0
>>> x=numpy.random.randn(*Nd)
>>> y = A2.forward(x)
>>> from pynufft import NUFFT_torch, helper
>>> import numpy
>>> A2= NUFFT_torch()
>>> A2= NUFFT_torch()
KeyboardInterrupt
>>> om = numpy.random.randn(10,2)
>>> Nd = (64,64)
>>> Kd = (128,128)
>>> Jd = (6,6)
>>> A2.plan(om, Nd, Kd, Jd)
0
>>> x=numpy.random.randn(*Nd)
>>> y = A2.forward(x)
>>> x2 = A2.adjoint(y)
>>> x2.shape
torch.Size([64, 64])
>>> y.shape
torch.Size([10])
>>> from pynufft import NUFFT_tf_eager, helper
>>> import numpy
>>> A2= NUFFT_tf_eager()
>>> om = numpy.random.randn(10,2)
>>> A2.plan(om, Nd, Kd, Jd)
2022-09-05 12:19:33.954058: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
0
>>> x=numpy.random.randn(*Nd)
>>> y = A2.forward(x)
>>> x2 = A2.adjoint(y)
>>> x2.shape
TensorShape([64, 64])
>>> y.shape
TensorShape([10])
Python 3.6.11 (default, Aug 23 2020, 18:05:39)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pynufft import tests
>>> tests.test_init(0)
device name = <reikna.cluda.cuda.Device object at 0x7f41d4098688>
0.06576069355010987
0.006289639472961426
error gx2= 2.0638987e-07
error gy= 1.0912560261408778e-07
acceleration= 10.455399523742015
17.97926664352417 2.710083246231079
acceleration in solver= 6.634211944790991
Python 3.10.4 (main, Jun 29 2022, 12:14:53) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pynufft import tests as t
>>> t.test_torch()
/home/sram/.local/lib/python3.10/site-packages/pynufft-2022.2.3rc1-py3.10.egg/pynufft/tests/test_torch.py:173: UserWarning: Module pynufft was already imported from /home/sram/.local/lib/python3.10/site-packages/pynufft-2022.2.3rc1-py3.10.egg/pynufft/__init__.py, but /home/sram/github/pynufft_online is being added to sys.path
import pkg_resources
Forward Error between torch and numpy 1.5022443208777513e-07
Adjoint Error between torch and numpy 2.091061e-07
>>> t.test_tf_eager()
2022-09-05 12:15:50.556015: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Forward error between tf and numpy 1.801203473553717e-06
Adjoint Error between tf and numpy 3.861161e-06
>>> t.test_cupy()
Forward Error between cupy and numpy 2.2403478444672691e-07
Adjoint Error between cupy and numpy 2.1125161308793866e-07
The comparison may not imply the clinical quality of third-party packages.
If you have professional requests related to the project, please contact email: pynufft@gmail.com
You can also find other very useful Python nufft/nfft functions at:
FAQs
Python non-uniform fast Fourier transform (PyNUFFT)
We found that pynufft demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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