Remove pynufft following its removal from PyAutoArray

autolens/__init__.py

@@ -27,7 +27,6 @@
 from autoarray.operators.convolver import Convolver
 from autoarray.operators.transformer import TransformerDFT
 from autoarray.operators.transformer import TransformerNUFFT
-from autoarray.operators.transformer import TransformerNUFFTPyNUFFT
 from autoarray.structures.arrays.uniform_1d import Array1D
 from autoarray.structures.arrays.uniform_2d import Array2D
 from autoarray.structures.arrays.rgb import Array2DRGB

docs/installation/conda.md

@@ -114,10 +114,10 @@ successful numba install working, with more information provided [at this readth
 
 ## Optional
 
-For interferometer analysis there are two optional dependencies that must be installed via the commands:
+For interferometer analysis there is one optional dependency that must be installed via the command:
 
 ```bash
-pip install pynufft
+pip install nufftax
 ```
 
 **PyAutoLens** will run without these libraries and it is recommended that you only install them if you intend to

docs/installation/pip.md

@@ -106,10 +106,10 @@ successful numba install working, with more information provided [at this readth
 
 ## Optional
 
-For interferometer analysis there are two optional dependencies that must be installed via the commands:
+For interferometer analysis there is one optional dependency that must be installed via the command:
 
 ```bash
-pip install pynufft
+pip install nufftax
 ```
 
 **PyAutoLens** will run without these libraries and it is recommended that you only install them if you intend to

docs/installation/source.md

@@ -69,7 +69,7 @@ pip install numba
 For unit tests to pass you will also need the following optional requirements:
 
 ```bash
-pip install pynufft
+pip install nufftax
 ```
 
 If you are using a `conda` environment, add the source repository as follows:

docs/overview/overview_3_features.md

@@ -101,7 +101,7 @@ Modeling of interferometer data from submillimeter (e.g. ALMA) and radio (e.g. L
 
 Visibilities data is fitted directly in the uv-plane, circumventing issues that arise when fitting a dirty image
 such as correlated noise. This uses the non-uniform fast fourier transform algorithm
-\[PyNUFFT\](<https://github.com/jyhmiinlin/pynufft>) to efficiently map the galaxy model images to the uv-plane.
+\[nufftax\](<https://github.com/GragasLab/nufftax>) to efficiently map the galaxy model images to the uv-plane.
 
 Checkout the `autolens_workspace/*/interferometer` package to get started.
 

files/citations.bib

@@ -188,20 +188,6 @@ @article{pyautolens
   author = {Nightingale, J. W. and Hayes, R. G. and Ashley Kelly and Aristeidis Amvrosiadis and Amy Etherington and Qiuhan He and Nan Li and XiaoYue Cao and Jonathan Frawley and Shaun Cole and Andrea Enia and Carlos S. Frenk and David R. Harvey and Ran Li and Richard J. Massey and Mattia Negrello and Andrew Robertson},
   title = {`PyAutoLens`: Open-Source Strong Gravitational Lensing},
   journal = {J. Open Source Softw.}
-}
-@article{pynufft,
-abstract = {A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms. Since scientific computing with Python encompasses a mature and integrated environment, the time efficiency of the NUFFT algorithm has been a major obstacle to real-time non-Cartesian image reconstruction with Python. The current PyNUFFT software enables multi-dimensional NUFFT accelerated on a heterogeneous platform, which yields an efficient solution to many non-Cartesian imaging problems. The PyNUFFT also provides several solvers, including the conjugate gradient method, 1 total variation regularized ordinary least square (L1TV-OLS), and 1 total variation regularized least absolute deviation (L1TV-LAD). Metaprogramming libraries have been employed to accelerate PyNUFFT. The PyNUFFT package has been tested on multi-core central processing units (CPUs) and graphic processing units (GPUs), with acceleration factors of 6.3–9.5× on a 32-thread CPU platform and 5.4–13× on a GPU.},
-author = {Lin, Jyh Miin},
-doi = {10.3390/jimaging4030051},
-file = {:home/jammy/Documents/Papers/Software/jimaging-04-00051-v2.pdf:pdf},
-issn = {2313433X},
-journal = {Journal of Imaging},
-keywords = {Graphic processing unit (GPU),Heterogeneous system architecture (HSA),Magnetic resonance imaging (MRI),Multi-core system,Total variation (TV)},
-number = {3},
-pages = {1--22},
-title = {{Python non-uniform fast fourier transform (PyNUFFT): An accelerated non-cartesian MRI package on a heterogeneous platform (CPU/GPU)}},
-volume = {4},
-year = {2018}
 }
  @book{python,
  author = {Van Rossum, Guido and Drake, Fred L.},

files/citations.md

@@ -29,7 +29,6 @@ This work uses the following software packages:
 - `PyAutoFit` https://github.com/PyAutoLabs/PyAutoFit [@pyautofit]
 - `PyAutoGalaxy` https://github.com/PyAutoLabs/PyAutoGalaxy [@Nightingale2018] [@pyautogalaxy]
 - `PyAutoLens` https://github.com/PyAutoLabs/PyAutoLens [@Nightingale2015] [@Nightingale2018] [@pyautolens]
-- `PyNUFFT` https://github.com/jyhmiinlin/pynufft [@pynufft]
 - `Python` https://www.python.org/ [@python]
 - `scikit-image` https://github.com/scikit-image/scikit-image [@scikit-image]
 - `scikit-learn` https://github.com/scikit-learn/scikit-learn [@scikit-learn]

files/citations.tex

@@ -94,10 +94,6 @@ \section*{Software Citations}
 \href{https://github.com/PyAutoLabs/PyAutoLens}{\texttt{PyAutoLens}}
 \citep{Nightingale2015, Nightingale2018, pyautolens}
 
-\item
-\href{https://github.com/jyhmiinlin/pynufft}{\texttt{PyNUFFT}}
-\citep{pynufft}
-
 \item
 \href{https://www.python.org/}{\texttt{Python}}
 \citep{python}

pyproject.toml

@@ -55,7 +55,6 @@ optional = [
     "autolens[jax]",
     "coolest",
     "numba",
-    "pynufft",
     "zeus-mcmc==2.5.4",
     "getdist==1.4"
 ]