automatic import of python-jax-finufft

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+/jax-finufft-0.0.3.tar.gz
diff --git a/python-jax-finufft.spec b/python-jax-finufft.spec
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+%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.nju.edu.cn/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
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..d6ec16b
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d8580be04d860cc2010ce5eab8f35221  jax-finufft-0.0.3.tar.gz