Remove pynufft following its removal from PyAutoArray

autogalaxy/__init__.py

@@ -26,7 +26,6 @@
 from autoarray.mask.derive.zoom_2d import Zoom2D
 from autoarray.operators.transformer import TransformerDFT  # noqa
 from autoarray.operators.transformer import TransformerNUFFT  # noqa
-from autoarray.operators.transformer import TransformerNUFFTPyNUFFT  # noqa
 from autoarray.layout.layout import Layout2D  # noqa
 from autoarray.structures.arrays.uniform_1d import Array1D  # noqa
 from autoarray.structures.arrays.uniform_2d import Array2D  # noqa

docs/index.md

@@ -90,7 +90,7 @@ be passed through `Operator` objects to apply a 2D convolution or Fast Fourier T
 the `Galaxy`'s image to an imaging or interferometer dataset. The `inversion` package contains a range of non-parametric
 models which fit a galaxy's light using a Bayesian linear matrix inversion. The `astropy` cosmology module is
 used to handle unit conversions and calculations are optimized using the packages `NumPy` [@numpy], `numba` [@numba]
-and `PyNUFFT` [@pynufft].
+and `nufftax` [@nufftax].
 
 ```python
 import autogalaxy as ag

docs/installation/conda.md

@@ -96,10 +96,10 @@ 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
 ```
 
 **PyAutoGalaxy** will run without these libraries and it is recommended that you only install them if you intend to

docs/installation/overview.md

@@ -54,4 +54,4 @@ our [building from source installation guide](https://pyautogalaxy.readthedocs.i
 
 And the following optional dependencies:
 
-**pynufft**: <https://github.com/jyhmiinlin/pynufft>
+**nufftax**: <https://github.com/GragasLab/nufftax>

docs/installation/pip.md

@@ -84,10 +84,10 @@ 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
 ```
 
 **PyAutoGalaxy** will run without these libraries and it is recommended that you only install them if you intend to

docs/installation/source.md

@@ -52,7 +52,7 @@ 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

@@ -34,7 +34,7 @@ Modeling interferometer data from submillimeter (e.g. ALMA) and radio (e.g. LOFA
 
 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 `autogalaxy_workspace/*/interferometer` package to get started.
 

files/citations.bib

@@ -189,19 +189,20 @@ @article{pyautolens
   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}
+@software{nufftax,
+author = {Gragas and Oudoumanessah, Geoffroy and Iollo, Jacopo},
+title = {nufftax: Pure {JAX} implementation of the Non-Uniform Fast Fourier Transform},
+url = {https://github.com/GragasLab/nufftax},
+year = {2026}
+}
+@article{finufft,
+author = {Barnett, Alexander H. and Magland, Jeremy F. and af Klinteberg, Ludvig},
+title = {A parallel non-uniform fast {F}ourier transform library based on an 'exponential of semicircle' kernel},
+journal = {SIAM J. Sci. Comput.},
+volume = {41},
+number = {5},
+pages = {C479--C504},
+year = {2019}
 }
 @article{pyswarms,
     author  = {Lester James V. Miranda},

files/citations.md

@@ -14,11 +14,11 @@ This work uses the following software packages:
 - `dynesty` https://github.com/joshspeagle/dynesty [@dynesty] [@dynesty1] [@dynesty2] [@dynesty3] [@dynesty4]
 - `emcee` https://github.com/dfm/emcee [@emcee]
 - `matplotlib` https://github.com/matplotlib/matplotlib [@matplotlib]
+- `nufftax` https://github.com/GragasLab/nufftax [@nufftax] [@finufft]
 - `numba` https://github.com/numba/numba [@numba]
 - `NumPy` https://github.com/numpy/numpy [@numpy]
 - `PyAutoFit` https://github.com/PyAutoLabs/PyAutoFit [@pyautofit]
 - `PyAutoGalaxy` https://github.com/PyAutoLabs/PyAutoGalaxy [@Nightingale2018] [@pyautogalaxy]
-- `PyNUFFT` https://github.com/jyhmiinlin/pynufft [@pynufft]
 - `PySwarms` https://github.com/ljvmiranda921/pyswarms [@pyswarms]
 - `Python` https://www.python.org/ [@python]
 - `scikit-image` https://github.com/scikit-image/scikit-image [@scikit-image]

files/citations.tex

@@ -52,8 +52,8 @@ \section*{Software Citations}
 
 
 \item
-\href{https://github.com/jyhmiinlin/pynufft}{\textt{PyNUFFT}}
-\citep{pynufft}
+\href{https://github.com/GragasLab/nufftax}{\textt{nufftax}}
+\citep{nufftax, finufft}
 
 \item
 \href{https://github.com/ljvmiranda921/pyswarms}{\textt{PySwarms}}

pyproject.toml

@@ -57,7 +57,6 @@ jax = []
 optional = [
     "autogalaxy[jax]",
     "numba",
-    "pynufft",
     "zeus-mcmc==2.5.4",
     "getdist==1.4"
 ]