path: root/python-jax-finufft.spec

%global _empty_manifest_terminate_build 0
Name:		python-jax-finufft
Version:	0.0.3
Release:	1
Summary:	Unofficial JAX bindings for finufft
License:	MIT
URL:		https://github.com/dfm/jax-finufft
Source0:	https://mirrors.aliyun.com/pypi/web/packages/a5/7f/b511040193939f6cf1ae426796ad4338728638ce86bf60743027264ab320/jax-finufft-0.0.3.tar.gz
BuildArch:	noarch

Requires:	python3-jax
Requires:	python3-jaxlib
Requires:	python3-pytest

%description
# JAX bindings to FINUFFT

This package provides a [JAX](https://github.com/google/jax) interface to (a
subset of) the [Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT)
library](https://github.com/flatironinstitute/finufft). Take a look at the
[FINUFFT docs](https://finufft.readthedocs.io) for all the necessary
definitions, conventions, and more information about the algorithms and their
implementation. This package uses a low-level interface to directly expose the
FINUFFT library to JAX's XLA backend, as well as implementing differentiation
rules for the transforms.

## Included features

_This library is currently CPU-only, but GPU support is in the works using the
[cuFINUFFT library](https://github.com/flatironinstitute/cufinufft)._

[Type 1 and 2](https://finufft.readthedocs.io/en/latest/math.html) transforms
are supported in 1-, 2-, and 3-dimensions. All of these functions support
forward, reverse, and higher-order differentiation, as well as batching using
`vmap`.

## Installation

_For now, only a source build is supported._

For building, you should only need a recent version of Python (>3.6) and
[FFTW](https://www.fftw.org/). At runtime, you'll need `numpy`, `scipy`, and
`jax`. To set up such an environment, you can use `conda` (but you're welcome to
use whatever workflow works for you!):

```bash
conda create -n jax-finufft -c conda-forge python=3.9 numpy scipy fftw
python -m pip install "jax[cpu]"
```

Then you can install from source using (don't forget the `--recursive` flag
because FINUFFT is included as a submodule):

```bash
git clone --recursive https://github.com/dfm/jax-finufft
cd jax-finufft
python -m pip install .
```

## Usage

This library provides two high-level functions (and these should be all that you
generally need to interact with): `nufft1` and `nufft2` (for the two "types" of
transforms). If you're already familiar with the [Python
interface](https://finufft.readthedocs.io/en/latest/python.html) to FINUFFT,
_please note that the function signatures here are different_!

For example, here's how you can do a 1-dimensional type 1 transform:

```python
import numpy as np
from jax_finufft import nufft1

M = 100000
N = 200000

x = 2 * np.pi * np.random.uniform(size=M)
c = np.random.standard_normal(size=M) + 1j * np.random.standard_normal(size=M)
f = nufft1(N, c, x, eps=1e-6, iflag=1)
```

Noting that the `eps` and `iflag` are optional, and that (for good reason, I
promise!) the order of the positional arguments is reversed from the `finufft`
Python package.

The syntax for a 2-, or 3-dimensional transform is:

```python
f = nufft1((Nx, Ny), c, x, y)  # 2D
f = nufft1((Nx, Ny, Nz), c, x, y, z)  # 3D
```

The syntax for a type 2 transform is (also allowing optional `iflag` and `eps`
parameters):

```python
c = nufft2(f, x)  # 1D
c = nufft2(f, x, y)  # 2D
c = nufft2(f, x, y, z)  # 3D
```

## Similar libraries

- [finufft](https://finufft.readthedocs.io/en/latest/python.html): The
  "official" Python bindings to FINUFFT. A good choice if you're not already
  using JAX and if you don't need to differentiate through your transform.
- [mrphys/tensorflow-nufft](https://github.com/mrphys/tensorflow-nufft):
  TensorFlow bindings for FINUFFT and cuFINUFFT.

## License & attribution

This package, developed by Dan Foreman-Mackey is licensed under the Apache
License, Version 2.0, with the following copyright:

Copyright 2021 The Simons Foundation, Inc.

If you use this software, please cite the primary references listed on the
[FINUFFT docs](https://finufft.readthedocs.io/en/latest/refs.html).




%package -n python3-jax-finufft
Summary:	Unofficial JAX bindings for finufft
Provides:	python-jax-finufft
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-jax-finufft
# JAX bindings to FINUFFT

This package provides a [JAX](https://github.com/google/jax) interface to (a
subset of) the [Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT)
library](https://github.com/flatironinstitute/finufft). Take a look at the
[FINUFFT docs](https://finufft.readthedocs.io) for all the necessary
definitions, conventions, and more information about the algorithms and their
implementation. This package uses a low-level interface to directly expose the
FINUFFT library to JAX's XLA backend, as well as implementing differentiation
rules for the transforms.

## Included features

_This library is currently CPU-only, but GPU support is in the works using the
[cuFINUFFT library](https://github.com/flatironinstitute/cufinufft)._

[Type 1 and 2](https://finufft.readthedocs.io/en/latest/math.html) transforms
are supported in 1-, 2-, and 3-dimensions. All of these functions support
forward, reverse, and higher-order differentiation, as well as batching using
`vmap`.

## Installation

_For now, only a source build is supported._

For building, you should only need a recent version of Python (>3.6) and
[FFTW](https://www.fftw.org/). At runtime, you'll need `numpy`, `scipy`, and
`jax`. To set up such an environment, you can use `conda` (but you're welcome to
use whatever workflow works for you!):

```bash
conda create -n jax-finufft -c conda-forge python=3.9 numpy scipy fftw
python -m pip install "jax[cpu]"
```

Then you can install from source using (don't forget the `--recursive` flag
because FINUFFT is included as a submodule):

```bash
git clone --recursive https://github.com/dfm/jax-finufft
cd jax-finufft
python -m pip install .
```

## Usage

This library provides two high-level functions (and these should be all that you
generally need to interact with): `nufft1` and `nufft2` (for the two "types" of
transforms). If you're already familiar with the [Python
interface](https://finufft.readthedocs.io/en/latest/python.html) to FINUFFT,
_please note that the function signatures here are different_!

For example, here's how you can do a 1-dimensional type 1 transform:

```python
import numpy as np
from jax_finufft import nufft1

M = 100000
N = 200000

x = 2 * np.pi * np.random.uniform(size=M)
c = np.random.standard_normal(size=M) + 1j * np.random.standard_normal(size=M)
f = nufft1(N, c, x, eps=1e-6, iflag=1)
```

Noting that the `eps` and `iflag` are optional, and that (for good reason, I
promise!) the order of the positional arguments is reversed from the `finufft`
Python package.

The syntax for a 2-, or 3-dimensional transform is:

```python
f = nufft1((Nx, Ny), c, x, y)  # 2D
f = nufft1((Nx, Ny, Nz), c, x, y, z)  # 3D
```

The syntax for a type 2 transform is (also allowing optional `iflag` and `eps`
parameters):

```python
c = nufft2(f, x)  # 1D
c = nufft2(f, x, y)  # 2D
c = nufft2(f, x, y, z)  # 3D
```

## Similar libraries

- [finufft](https://finufft.readthedocs.io/en/latest/python.html): The
  "official" Python bindings to FINUFFT. A good choice if you're not already
  using JAX and if you don't need to differentiate through your transform.
- [mrphys/tensorflow-nufft](https://github.com/mrphys/tensorflow-nufft):
  TensorFlow bindings for FINUFFT and cuFINUFFT.

## License & attribution

This package, developed by Dan Foreman-Mackey is licensed under the Apache
License, Version 2.0, with the following copyright:

Copyright 2021 The Simons Foundation, Inc.

If you use this software, please cite the primary references listed on the
[FINUFFT docs](https://finufft.readthedocs.io/en/latest/refs.html).




%package help
Summary:	Development documents and examples for jax-finufft
Provides:	python3-jax-finufft-doc
%description help
# JAX bindings to FINUFFT

This package provides a [JAX](https://github.com/google/jax) interface to (a
subset of) the [Flatiron Institute Non-uniform Fast Fourier Transform (FINUFFT)
library](https://github.com/flatironinstitute/finufft). Take a look at the
[FINUFFT docs](https://finufft.readthedocs.io) for all the necessary
definitions, conventions, and more information about the algorithms and their
implementation. This package uses a low-level interface to directly expose the
FINUFFT library to JAX's XLA backend, as well as implementing differentiation
rules for the transforms.

## Included features

_This library is currently CPU-only, but GPU support is in the works using the
[cuFINUFFT library](https://github.com/flatironinstitute/cufinufft)._

[Type 1 and 2](https://finufft.readthedocs.io/en/latest/math.html) transforms
are supported in 1-, 2-, and 3-dimensions. All of these functions support
forward, reverse, and higher-order differentiation, as well as batching using
`vmap`.

## Installation

_For now, only a source build is supported._

For building, you should only need a recent version of Python (>3.6) and
[FFTW](https://www.fftw.org/). At runtime, you'll need `numpy`, `scipy`, and
`jax`. To set up such an environment, you can use `conda` (but you're welcome to
use whatever workflow works for you!):

```bash
conda create -n jax-finufft -c conda-forge python=3.9 numpy scipy fftw
python -m pip install "jax[cpu]"
```

Then you can install from source using (don't forget the `--recursive` flag
because FINUFFT is included as a submodule):

```bash
git clone --recursive https://github.com/dfm/jax-finufft
cd jax-finufft
python -m pip install .
```

## Usage

This library provides two high-level functions (and these should be all that you
generally need to interact with): `nufft1` and `nufft2` (for the two "types" of
transforms). If you're already familiar with the [Python
interface](https://finufft.readthedocs.io/en/latest/python.html) to FINUFFT,
_please note that the function signatures here are different_!

For example, here's how you can do a 1-dimensional type 1 transform:

```python
import numpy as np
from jax_finufft import nufft1

M = 100000
N = 200000

x = 2 * np.pi * np.random.uniform(size=M)
c = np.random.standard_normal(size=M) + 1j * np.random.standard_normal(size=M)
f = nufft1(N, c, x, eps=1e-6, iflag=1)
```

Noting that the `eps` and `iflag` are optional, and that (for good reason, I
promise!) the order of the positional arguments is reversed from the `finufft`
Python package.

The syntax for a 2-, or 3-dimensional transform is:

```python
f = nufft1((Nx, Ny), c, x, y)  # 2D
f = nufft1((Nx, Ny, Nz), c, x, y, z)  # 3D
```

The syntax for a type 2 transform is (also allowing optional `iflag` and `eps`
parameters):

```python
c = nufft2(f, x)  # 1D
c = nufft2(f, x, y)  # 2D
c = nufft2(f, x, y, z)  # 3D
```

## Similar libraries

- [finufft](https://finufft.readthedocs.io/en/latest/python.html): The
  "official" Python bindings to FINUFFT. A good choice if you're not already
  using JAX and if you don't need to differentiate through your transform.
- [mrphys/tensorflow-nufft](https://github.com/mrphys/tensorflow-nufft):
  TensorFlow bindings for FINUFFT and cuFINUFFT.

## License & attribution

This package, developed by Dan Foreman-Mackey is licensed under the Apache
License, Version 2.0, with the following copyright:

Copyright 2021 The Simons Foundation, Inc.

If you use this software, please cite the primary references listed on the
[FINUFFT docs](https://finufft.readthedocs.io/en/latest/refs.html).




%prep
%autosetup -n jax-finufft-0.0.3

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-jax-finufft -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.3-1
- Package Spec generated