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| author | CoprDistGit <infra@openeuler.org> | 2023-05-29 10:27:16 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-29 10:27:16 +0000 |
| commit | ff8aba06bbb5518625f174bf7c76ef50267fcfca (patch) | |
| tree | c9d3ccf03848e5be2157451176bcf8dfcbd6b86d | |
| parent | 4bb38a33b4e2d65e4eef9ef9cf7fa55675452393 (diff) | |
automatic import of python-gpjax
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-gpjax.spec | 699 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 701 insertions, 0 deletions
@@ -0,0 +1 @@ +/gpjax-0.6.1.tar.gz diff --git a/python-gpjax.spec b/python-gpjax.spec new file mode 100644 index 0000000..227a1ca --- /dev/null +++ b/python-gpjax.spec @@ -0,0 +1,699 @@ +%global _empty_manifest_terminate_build 0 +Name: python-gpjax +Version: 0.6.1 +Release: 1 +Summary: Gaussian processes in JAX. +License: Apache-2.0 +URL: https://github.com/JaxGaussianProcesses/GPJax +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/39/0a/4296668bec6e4468018d9898615e84ce92abd76ba70cbe1b64d286049388/gpjax-0.6.1.tar.gz +BuildArch: noarch + +Requires: python3-jax +Requires: python3-optax +Requires: python3-jaxtyping +Requires: python3-tqdm +Requires: python3-simple-pytree +Requires: python3-tensorflow-probability +Requires: python3-orbax-checkpoint +Requires: python3-beartype +Requires: python3-jaxlib + +%description +<!-- <h1 align='center'>GPJax</h1> +<h2 align='center'>Gaussian processes in Jax.</h2> --> +<p align="center"> +<img width="700" height="300" src="https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/_static/gpjax_logo.svg" alt="GPJax's logo"> +</p> + +[](https://codecov.io/gh/JaxGaussianProcesses/GPJax) +[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax) +[](https://gpjax.readthedocs.io/en/latest/?badge=latest) +[](https://badge.fury.io/py/GPJax) +[](https://doi.org/10.21105/joss.04455) +[](https://pepy.tech/project/gpjax) +[](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw) + +[**Quickstart**](#simple-example) +| [**Install guide**](#installation) +| [**Documentation**](https://gpjax.readthedocs.io/en/latest/) +| [**Slack Community**](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw) + +GPJax aims to provide a low-level interface to Gaussian process (GP) models in +[Jax](https://github.com/google/jax), structured to give researchers maximum +flexibility in extending the code to suit their own needs. The idea is that the +code should be as close as possible to the maths we write on paper when working +with GP models. + +# Package support + +GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today, +the maintenance of GPJax is undertaken by [Thomas +Pinder](https://github.com/thomaspinder) and [Daniel +Dodd](https://github.com/Daniel-Dodd). + +We would be delighted to receive contributions from interested individuals and +groups. To learn how you can get involved, please read our [guide for +contributing](https://github.com/JaxGaussianProcesses/GPJax/blob/master/CONTRIBUTING.md). +If you have any questions, we encourage you to [open an +issue](https://github.com/JaxGaussianProcesses/GPJax/issues/new/choose). For +broader conversations, such as best GP fitting practices or questions about the +mathematics of GPs, we invite you to [open a +discussion](https://github.com/JaxGaussianProcesses/GPJax/discussions). + +Feel free to join our [Slack +Channel](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw), +where we can discuss the development of GPJax and broader support for Gaussian +process modelling. + +# Supported methods and interfaces + +## Notebook examples + +> - [**Conjugate Inference**](https://gpjax.readthedocs.io/en/latest/examples/regression.html) +> - [**Classification with MCMC**](https://gpjax.readthedocs.io/en/latest/examples/classification.html) +> - [**Sparse Variational Inference**](https://gpjax.readthedocs.io/en/latest/examples/uncollapsed_vi.html) +> - [**BlackJax Integration**](https://gpjax.readthedocs.io/en/latest/examples/classification.html) +> - [**Laplace Approximation**](https://gpjax.readthedocs.io/en/latest/examples/classification.html#Laplace-approximation) +> - [**Inference on Non-Euclidean Spaces**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel) +> - [**Inference on Graphs**](https://gpjax.readthedocs.io/en/latest/examples/graph_kernels.html) +> - [**Learning Gaussian Process Barycentres**](https://gpjax.readthedocs.io/en/latest/examples/barycentres.html) +> - [**Deep Kernel Regression**](https://gpjax.readthedocs.io/en/latest/examples/haiku.html) + +## Guides for customisation +> +> - [**Custom kernels**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel) +> - [**UCI regression**](https://gpjax.readthedocs.io/en/latest/examples/yacht.html) + +## Conversion between `.ipynb` and `.py` +Above examples are stored in [examples](examples) directory in the double +percent (`py:percent`) format. Checkout [jupytext +using-cli](https://jupytext.readthedocs.io/en/latest/using-cli.html) for more +info. + +* To convert `example.py` to `example.ipynb`, run: + +```bash +jupytext --to notebook example.py +``` + +* To convert `example.ipynb` to `example.py`, run: + +```bash +jupytext --to py:percent example.ipynb +``` + +# Simple example + +Let us import some dependencies and simulate a toy dataset $\mathcal{D}$. + +```python +import gpjax as gpx +from jax import grad, jit +import jax.numpy as jnp +import jax.random as jr +import optax as ox + +key = jr.PRNGKey(123) + +f = lambda x: 10 * jnp.sin(x) + +n = 50 +x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,1)).sort() +y = f(x) + jr.normal(key, shape=(n,1)) +D = gpx.Dataset(X=x, y=y) + +# Construct the prior +meanf = gpx.mean_functions.Zero() +kernel = gpx.kernels.RBF() +prior = gpx.Prior(mean_function=meanf, kernel = kernel) + +# Define a likelihood +likelihood = gpx.Gaussian(num_datapoints = n) + +# Construct the posterior +posterior = prior * likelihood + +# Define an optimiser +optimiser = ox.adam(learning_rate=1e-2) + +# Define the marginal log-likelihood +negative_mll = jit(gpx.objectives.ConjugateMLL(negative=True)) + +# Obtain Type 2 MLEs of the hyperparameters +opt_posterior, history = gpx.fit( + model=posterior, + objective=negative_mll, + train_data=D, + optim=optimiser, + num_iters=500, + safe=True, + key=key, +) + +# Infer the predictive posterior distribution +xtest = jnp.linspace(-3., 3., 100).reshape(-1, 1) +latent_dist = opt_posterior(xtest, D) +predictive_dist = opt_posterior.likelihood(latent_dist) + +# Obtain the predictive mean and standard deviation +pred_mean = predictive_dist.mean() +pred_std = predictive_dist.stddev() +``` + +# Installation + +## Stable version + +The latest stable version of GPJax can be installed via +pip: + +```bash +pip install gpjax +``` + +> **Note** +> +> We recommend you check your installation version: +> ```python +> python -c 'import gpjax; print(gpjax.__version__)' +> ``` + + + +## Development version +> **Warning** +> +> This version is possibly unstable and may contain bugs. + +Clone a copy of the repository to your local machine and run the setup +configuration in development mode. +```bash +git clone https://github.com/JaxGaussianProcesses/GPJax.git +cd GPJax +poetry install +``` + +> **Note** +> +> We advise you create virtual environment before installing: +> ``` +> conda create -n gpjax_experimental python=3.10.0 +> conda activate gpjax_experimental +> ``` +> +> and recommend you check your installation passes the supplied unit tests: +> +> ```python +> poetry run pytest +> ``` + +# Citing GPJax + +If you use GPJax in your research, please cite our [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.04455#). + +``` +@article{Pinder2022, + doi = {10.21105/joss.04455}, + url = {https://doi.org/10.21105/joss.04455}, + year = {2022}, + publisher = {The Open Journal}, + volume = {7}, + number = {75}, + pages = {4455}, + author = {Thomas Pinder and Daniel Dodd}, + title = {GPJax: A Gaussian Process Framework in JAX}, + journal = {Journal of Open Source Software} +} +``` + + +%package -n python3-gpjax +Summary: Gaussian processes in JAX. +Provides: python-gpjax +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-gpjax +<!-- <h1 align='center'>GPJax</h1> +<h2 align='center'>Gaussian processes in Jax.</h2> --> +<p align="center"> +<img width="700" height="300" src="https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/_static/gpjax_logo.svg" alt="GPJax's logo"> +</p> + +[](https://codecov.io/gh/JaxGaussianProcesses/GPJax) +[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax) +[](https://gpjax.readthedocs.io/en/latest/?badge=latest) +[](https://badge.fury.io/py/GPJax) +[](https://doi.org/10.21105/joss.04455) +[](https://pepy.tech/project/gpjax) +[](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw) + +[**Quickstart**](#simple-example) +| [**Install guide**](#installation) +| [**Documentation**](https://gpjax.readthedocs.io/en/latest/) +| [**Slack Community**](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw) + +GPJax aims to provide a low-level interface to Gaussian process (GP) models in +[Jax](https://github.com/google/jax), structured to give researchers maximum +flexibility in extending the code to suit their own needs. The idea is that the +code should be as close as possible to the maths we write on paper when working +with GP models. + +# Package support + +GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today, +the maintenance of GPJax is undertaken by [Thomas +Pinder](https://github.com/thomaspinder) and [Daniel +Dodd](https://github.com/Daniel-Dodd). + +We would be delighted to receive contributions from interested individuals and +groups. To learn how you can get involved, please read our [guide for +contributing](https://github.com/JaxGaussianProcesses/GPJax/blob/master/CONTRIBUTING.md). +If you have any questions, we encourage you to [open an +issue](https://github.com/JaxGaussianProcesses/GPJax/issues/new/choose). For +broader conversations, such as best GP fitting practices or questions about the +mathematics of GPs, we invite you to [open a +discussion](https://github.com/JaxGaussianProcesses/GPJax/discussions). + +Feel free to join our [Slack +Channel](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw), +where we can discuss the development of GPJax and broader support for Gaussian +process modelling. + +# Supported methods and interfaces + +## Notebook examples + +> - [**Conjugate Inference**](https://gpjax.readthedocs.io/en/latest/examples/regression.html) +> - [**Classification with MCMC**](https://gpjax.readthedocs.io/en/latest/examples/classification.html) +> - [**Sparse Variational Inference**](https://gpjax.readthedocs.io/en/latest/examples/uncollapsed_vi.html) +> - [**BlackJax Integration**](https://gpjax.readthedocs.io/en/latest/examples/classification.html) +> - [**Laplace Approximation**](https://gpjax.readthedocs.io/en/latest/examples/classification.html#Laplace-approximation) +> - [**Inference on Non-Euclidean Spaces**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel) +> - [**Inference on Graphs**](https://gpjax.readthedocs.io/en/latest/examples/graph_kernels.html) +> - [**Learning Gaussian Process Barycentres**](https://gpjax.readthedocs.io/en/latest/examples/barycentres.html) +> - [**Deep Kernel Regression**](https://gpjax.readthedocs.io/en/latest/examples/haiku.html) + +## Guides for customisation +> +> - [**Custom kernels**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel) +> - [**UCI regression**](https://gpjax.readthedocs.io/en/latest/examples/yacht.html) + +## Conversion between `.ipynb` and `.py` +Above examples are stored in [examples](examples) directory in the double +percent (`py:percent`) format. Checkout [jupytext +using-cli](https://jupytext.readthedocs.io/en/latest/using-cli.html) for more +info. + +* To convert `example.py` to `example.ipynb`, run: + +```bash +jupytext --to notebook example.py +``` + +* To convert `example.ipynb` to `example.py`, run: + +```bash +jupytext --to py:percent example.ipynb +``` + +# Simple example + +Let us import some dependencies and simulate a toy dataset $\mathcal{D}$. + +```python +import gpjax as gpx +from jax import grad, jit +import jax.numpy as jnp +import jax.random as jr +import optax as ox + +key = jr.PRNGKey(123) + +f = lambda x: 10 * jnp.sin(x) + +n = 50 +x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,1)).sort() +y = f(x) + jr.normal(key, shape=(n,1)) +D = gpx.Dataset(X=x, y=y) + +# Construct the prior +meanf = gpx.mean_functions.Zero() +kernel = gpx.kernels.RBF() +prior = gpx.Prior(mean_function=meanf, kernel = kernel) + +# Define a likelihood +likelihood = gpx.Gaussian(num_datapoints = n) + +# Construct the posterior +posterior = prior * likelihood + +# Define an optimiser +optimiser = ox.adam(learning_rate=1e-2) + +# Define the marginal log-likelihood +negative_mll = jit(gpx.objectives.ConjugateMLL(negative=True)) + +# Obtain Type 2 MLEs of the hyperparameters +opt_posterior, history = gpx.fit( + model=posterior, + objective=negative_mll, + train_data=D, + optim=optimiser, + num_iters=500, + safe=True, + key=key, +) + +# Infer the predictive posterior distribution +xtest = jnp.linspace(-3., 3., 100).reshape(-1, 1) +latent_dist = opt_posterior(xtest, D) +predictive_dist = opt_posterior.likelihood(latent_dist) + +# Obtain the predictive mean and standard deviation +pred_mean = predictive_dist.mean() +pred_std = predictive_dist.stddev() +``` + +# Installation + +## Stable version + +The latest stable version of GPJax can be installed via +pip: + +```bash +pip install gpjax +``` + +> **Note** +> +> We recommend you check your installation version: +> ```python +> python -c 'import gpjax; print(gpjax.__version__)' +> ``` + + + +## Development version +> **Warning** +> +> This version is possibly unstable and may contain bugs. + +Clone a copy of the repository to your local machine and run the setup +configuration in development mode. +```bash +git clone https://github.com/JaxGaussianProcesses/GPJax.git +cd GPJax +poetry install +``` + +> **Note** +> +> We advise you create virtual environment before installing: +> ``` +> conda create -n gpjax_experimental python=3.10.0 +> conda activate gpjax_experimental +> ``` +> +> and recommend you check your installation passes the supplied unit tests: +> +> ```python +> poetry run pytest +> ``` + +# Citing GPJax + +If you use GPJax in your research, please cite our [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.04455#). + +``` +@article{Pinder2022, + doi = {10.21105/joss.04455}, + url = {https://doi.org/10.21105/joss.04455}, + year = {2022}, + publisher = {The Open Journal}, + volume = {7}, + number = {75}, + pages = {4455}, + author = {Thomas Pinder and Daniel Dodd}, + title = {GPJax: A Gaussian Process Framework in JAX}, + journal = {Journal of Open Source Software} +} +``` + + +%package help +Summary: Development documents and examples for gpjax +Provides: python3-gpjax-doc +%description help +<!-- <h1 align='center'>GPJax</h1> +<h2 align='center'>Gaussian processes in Jax.</h2> --> +<p align="center"> +<img width="700" height="300" src="https://raw.githubusercontent.com/JaxGaussianProcesses/GPJax/main/docs/_static/gpjax_logo.svg" alt="GPJax's logo"> +</p> + +[](https://codecov.io/gh/JaxGaussianProcesses/GPJax) +[](https://www.codefactor.io/repository/github/jaxgaussianprocesses/gpjax) +[](https://gpjax.readthedocs.io/en/latest/?badge=latest) +[](https://badge.fury.io/py/GPJax) +[](https://doi.org/10.21105/joss.04455) +[](https://pepy.tech/project/gpjax) +[](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw) + +[**Quickstart**](#simple-example) +| [**Install guide**](#installation) +| [**Documentation**](https://gpjax.readthedocs.io/en/latest/) +| [**Slack Community**](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw) + +GPJax aims to provide a low-level interface to Gaussian process (GP) models in +[Jax](https://github.com/google/jax), structured to give researchers maximum +flexibility in extending the code to suit their own needs. The idea is that the +code should be as close as possible to the maths we write on paper when working +with GP models. + +# Package support + +GPJax was founded by [Thomas Pinder](https://github.com/thomaspinder). Today, +the maintenance of GPJax is undertaken by [Thomas +Pinder](https://github.com/thomaspinder) and [Daniel +Dodd](https://github.com/Daniel-Dodd). + +We would be delighted to receive contributions from interested individuals and +groups. To learn how you can get involved, please read our [guide for +contributing](https://github.com/JaxGaussianProcesses/GPJax/blob/master/CONTRIBUTING.md). +If you have any questions, we encourage you to [open an +issue](https://github.com/JaxGaussianProcesses/GPJax/issues/new/choose). For +broader conversations, such as best GP fitting practices or questions about the +mathematics of GPs, we invite you to [open a +discussion](https://github.com/JaxGaussianProcesses/GPJax/discussions). + +Feel free to join our [Slack +Channel](https://join.slack.com/t/gpjax/shared_invite/zt-1da57pmjn-rdBCVg9kApirEEn2E5Q2Zw), +where we can discuss the development of GPJax and broader support for Gaussian +process modelling. + +# Supported methods and interfaces + +## Notebook examples + +> - [**Conjugate Inference**](https://gpjax.readthedocs.io/en/latest/examples/regression.html) +> - [**Classification with MCMC**](https://gpjax.readthedocs.io/en/latest/examples/classification.html) +> - [**Sparse Variational Inference**](https://gpjax.readthedocs.io/en/latest/examples/uncollapsed_vi.html) +> - [**BlackJax Integration**](https://gpjax.readthedocs.io/en/latest/examples/classification.html) +> - [**Laplace Approximation**](https://gpjax.readthedocs.io/en/latest/examples/classification.html#Laplace-approximation) +> - [**Inference on Non-Euclidean Spaces**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel) +> - [**Inference on Graphs**](https://gpjax.readthedocs.io/en/latest/examples/graph_kernels.html) +> - [**Learning Gaussian Process Barycentres**](https://gpjax.readthedocs.io/en/latest/examples/barycentres.html) +> - [**Deep Kernel Regression**](https://gpjax.readthedocs.io/en/latest/examples/haiku.html) + +## Guides for customisation +> +> - [**Custom kernels**](https://gpjax.readthedocs.io/en/latest/examples/kernels.html#Custom-Kernel) +> - [**UCI regression**](https://gpjax.readthedocs.io/en/latest/examples/yacht.html) + +## Conversion between `.ipynb` and `.py` +Above examples are stored in [examples](examples) directory in the double +percent (`py:percent`) format. Checkout [jupytext +using-cli](https://jupytext.readthedocs.io/en/latest/using-cli.html) for more +info. + +* To convert `example.py` to `example.ipynb`, run: + +```bash +jupytext --to notebook example.py +``` + +* To convert `example.ipynb` to `example.py`, run: + +```bash +jupytext --to py:percent example.ipynb +``` + +# Simple example + +Let us import some dependencies and simulate a toy dataset $\mathcal{D}$. + +```python +import gpjax as gpx +from jax import grad, jit +import jax.numpy as jnp +import jax.random as jr +import optax as ox + +key = jr.PRNGKey(123) + +f = lambda x: 10 * jnp.sin(x) + +n = 50 +x = jr.uniform(key=key, minval=-3.0, maxval=3.0, shape=(n,1)).sort() +y = f(x) + jr.normal(key, shape=(n,1)) +D = gpx.Dataset(X=x, y=y) + +# Construct the prior +meanf = gpx.mean_functions.Zero() +kernel = gpx.kernels.RBF() +prior = gpx.Prior(mean_function=meanf, kernel = kernel) + +# Define a likelihood +likelihood = gpx.Gaussian(num_datapoints = n) + +# Construct the posterior +posterior = prior * likelihood + +# Define an optimiser +optimiser = ox.adam(learning_rate=1e-2) + +# Define the marginal log-likelihood +negative_mll = jit(gpx.objectives.ConjugateMLL(negative=True)) + +# Obtain Type 2 MLEs of the hyperparameters +opt_posterior, history = gpx.fit( + model=posterior, + objective=negative_mll, + train_data=D, + optim=optimiser, + num_iters=500, + safe=True, + key=key, +) + +# Infer the predictive posterior distribution +xtest = jnp.linspace(-3., 3., 100).reshape(-1, 1) +latent_dist = opt_posterior(xtest, D) +predictive_dist = opt_posterior.likelihood(latent_dist) + +# Obtain the predictive mean and standard deviation +pred_mean = predictive_dist.mean() +pred_std = predictive_dist.stddev() +``` + +# Installation + +## Stable version + +The latest stable version of GPJax can be installed via +pip: + +```bash +pip install gpjax +``` + +> **Note** +> +> We recommend you check your installation version: +> ```python +> python -c 'import gpjax; print(gpjax.__version__)' +> ``` + + + +## Development version +> **Warning** +> +> This version is possibly unstable and may contain bugs. + +Clone a copy of the repository to your local machine and run the setup +configuration in development mode. +```bash +git clone https://github.com/JaxGaussianProcesses/GPJax.git +cd GPJax +poetry install +``` + +> **Note** +> +> We advise you create virtual environment before installing: +> ``` +> conda create -n gpjax_experimental python=3.10.0 +> conda activate gpjax_experimental +> ``` +> +> and recommend you check your installation passes the supplied unit tests: +> +> ```python +> poetry run pytest +> ``` + +# Citing GPJax + +If you use GPJax in your research, please cite our [JOSS paper](https://joss.theoj.org/papers/10.21105/joss.04455#). + +``` +@article{Pinder2022, + doi = {10.21105/joss.04455}, + url = {https://doi.org/10.21105/joss.04455}, + year = {2022}, + publisher = {The Open Journal}, + volume = {7}, + number = {75}, + pages = {4455}, + author = {Thomas Pinder and Daniel Dodd}, + title = {GPJax: A Gaussian Process Framework in JAX}, + journal = {Journal of Open Source Software} +} +``` + + +%prep +%autosetup -n gpjax-0.6.1 + +%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-gpjax -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.1-1 +- Package Spec generated @@ -0,0 +1 @@ +9fbb475a687fd03f514b0fc19e9f30bf gpjax-0.6.1.tar.gz |