path: root/python-wgpu.spec

%global _empty_manifest_terminate_build 0
Name:		python-wgpu
Version:	0.9.4
Release:	1
Summary:	Next generation GPU API for Python
License:	BSD 2-Clause
URL:		https://github.com/pygfx/wgpu-py
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7b/b0/9b790143926ccfe1c66e56cc9d22e8767bb7eb9cbce83d3ed9a52ccb5bd0/wgpu-0.9.4.tar.gz

Requires:	python3-cffi
Requires:	python3-rubicon-objc
Requires:	python3-sphinx
Requires:	python3-glfw
Requires:	python3-jupyter-rfb

%description
[![CI](https://github.com/pygfx/wgpu-py/workflows/CI/badge.svg)](https://github.com/pygfx/wgpu-py/actions)
[![Documentation Status](https://readthedocs.org/projects/wgpu-py/badge/?version=latest)](https://wgpu-py.readthedocs.io)
[![PyPI version](https://badge.fury.io/py/wgpu.svg)](https://badge.fury.io/py/wgpu)

# wgpu-py

A Python implementation of WebGPU - the next generation GPU API.

<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/cube.png' />
<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/triangle_auto.png' />


## Introduction

In short, this is a Python lib wrapping
[wgpu-native](https://github.com/gfx-rs/wgpu) and exposing it with a Pythonic
API similar to the [WebGPU spec](https://gpuweb.github.io/gpuweb/).

The OpenGL API is old and showing it's cracks. New API's like Vulkan, Metal and
DX12 provide a modern way to control the GPU, but these API's are too low-level
for general use. The WebGPU API follows the same concepts, but with a simpler
(higher level) spelling. The Python `wgpu` library brings the WebGPU API to
Python.

To get an idea of what this API looks like have a look at
[triangle.py](https://github.com/pygfx/wgpu-py/blob/main/examples/triangle.py)
and the other [examples](https://github.com/pygfx/wgpu-py/blob/main/examples/).


## Status

> **Note**
>
> The wgpu-API has not settled yet, use with care!

* Coverage of the WebGPU spec is complete enough to build e.g.
  [pygfx](https://github.com/pygfx/pygfx).
* Test coverage of the API is 100%.
* Support for Windows, Linux, and MacOS (Intel and M1).
* Until WebGPU settles as a standard, its specification may change, and with
  that our API will probably too. Check the [changelog](CHANGELOG.md) when you
  upgrade!


## Installation


```
pip install wgpu glfw
```

Linux users should make sure that **pip >= 20.3**. That should do the
trick on most systems. See [getting started](https://wgpu-py.readthedocs.io/en/stable/start.html)
for details.


## Usage

Also see the [online documentation](https://wgpu-py.readthedocs.io) and the [examples](https://github.com/pygfx/wgpu-py/tree/main/examples).

The full API is accessable via the main namespace:
```py
import wgpu
```

But to use it, you need to select a backend first. You do this by importing it.
There is currently only one backend:
```py
import wgpu.backends.rs
```

To render to the screen you can use a variety of GUI toolkits:

```py
# The auto backend selects either the glfw, qt or jupyter backend
from wgpu.gui.auto import WgpuCanvas, run, call_later

# Visualizations can be embedded as a widget in a Qt application.
# Import PySide6, PyQt6, PySide2 or PyQt5 before running the line below.
# The code will detect and use the library that is imported.
from wgpu.gui.qt import WgpuCanvas

# Visualizations can be embedded as a widget in a wx application.
from wgpu.gui.wx import WgpuCanvas
```

Some functions in the original `wgpu-native` API are async. In the Python API,
the default functions are all sync (blocking), making things easy for general use.
Async versions of these functions are available, so wgpu can also work
well with Asyncio or Trio.


## License

This code is distributed under the 2-clause BSD license.


## Developers

* Clone the repo.
* Install devtools using `pip install -r dev-requirements.txt` (you can replace
  `pip` with `pipenv` to install to a virtualenv).
* Install wgpu-py in editable mode by running `pip install -e .`, this will also
  install runtime dependencies as needed.
* Run `python download-wgpu-native.py` to download the upstream wgpu-native
  binaries.
  * Or alternatively point the `WGPU_LIB_PATH` environment variable to a custom
    build.
* Use `black .` to apply autoformatting.
* Use `flake8 .` to check for flake errors.
* Use `pytest .` to run the tests.
* Use `pip wheel --no-deps .` to build a wheel.


### Changing the upstream wgpu-native version

* Use the optional arguments to `python download-wgpu-native.py --help` to
  download a different version of the upstream wgpu-native binaries.
* The file `wgpu/resources/wgpu_native-version` will be updated by the script to
  track which version we depend upon.

## Testing

The test suite is divided into multiple parts:

* `pytest -v tests` runs the core unit tests.
* `pytest -v examples` tests the examples.
* `pytest -v wgpu/__pyinstaller` tests if wgpu is properly supported by
  pyinstaller.
* `pytest -v codegen` lints the generated binding code.

There are two types of tests for examples included:

### Type 1: Checking if examples can run

When running the test suite, pytest will run every example in a subprocess, to
see if it can run and exit cleanly. You can opt out of this mechanism by
including the comment `# run_example = false` in the module.

### Type 2: Checking if examples output an image

You can also (independently) opt-in to output testing for examples, by including
the comment `# test_example = true` in the module. Output testing means the test
suite will attempt to import the `canvas` instance global from your example, and
call it to see if an image is produced.

To support this type of testing, ensure the following requirements are met:

* The `WgpuCanvas` class is imported from the `wgpu.gui.auto` module.
* The `canvas` instance is exposed as a global in the module.
* A rendering callback has been registered with `canvas.request_draw(fn)`.

Reference screenshots are stored in the `examples/screenshots` folder, the test
suite will compare the rendered image with the reference.

Note: this step will be skipped when not running on CI. Since images will have
subtle differences depending on the system on which they are rendered, that
would make the tests unreliable.

For every test that fails on screenshot verification, diffs will be generated
for the rgb and alpha channels and made available in the
`examples/screenshots/diffs` folder. On CI, the `examples/screenshots` folder
will be published as a build artifact so you can download and inspect the
differences.

If you want to update the reference screenshot for a given example, you can grab
those from the build artifacts as well and commit them to your branch.


%package -n python3-wgpu
Summary:	Next generation GPU API for Python
Provides:	python-wgpu
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-wgpu
[![CI](https://github.com/pygfx/wgpu-py/workflows/CI/badge.svg)](https://github.com/pygfx/wgpu-py/actions)
[![Documentation Status](https://readthedocs.org/projects/wgpu-py/badge/?version=latest)](https://wgpu-py.readthedocs.io)
[![PyPI version](https://badge.fury.io/py/wgpu.svg)](https://badge.fury.io/py/wgpu)

# wgpu-py

A Python implementation of WebGPU - the next generation GPU API.

<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/cube.png' />
<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/triangle_auto.png' />


## Introduction

In short, this is a Python lib wrapping
[wgpu-native](https://github.com/gfx-rs/wgpu) and exposing it with a Pythonic
API similar to the [WebGPU spec](https://gpuweb.github.io/gpuweb/).

The OpenGL API is old and showing it's cracks. New API's like Vulkan, Metal and
DX12 provide a modern way to control the GPU, but these API's are too low-level
for general use. The WebGPU API follows the same concepts, but with a simpler
(higher level) spelling. The Python `wgpu` library brings the WebGPU API to
Python.

To get an idea of what this API looks like have a look at
[triangle.py](https://github.com/pygfx/wgpu-py/blob/main/examples/triangle.py)
and the other [examples](https://github.com/pygfx/wgpu-py/blob/main/examples/).


## Status

> **Note**
>
> The wgpu-API has not settled yet, use with care!

* Coverage of the WebGPU spec is complete enough to build e.g.
  [pygfx](https://github.com/pygfx/pygfx).
* Test coverage of the API is 100%.
* Support for Windows, Linux, and MacOS (Intel and M1).
* Until WebGPU settles as a standard, its specification may change, and with
  that our API will probably too. Check the [changelog](CHANGELOG.md) when you
  upgrade!


## Installation


```
pip install wgpu glfw
```

Linux users should make sure that **pip >= 20.3**. That should do the
trick on most systems. See [getting started](https://wgpu-py.readthedocs.io/en/stable/start.html)
for details.


## Usage

Also see the [online documentation](https://wgpu-py.readthedocs.io) and the [examples](https://github.com/pygfx/wgpu-py/tree/main/examples).

The full API is accessable via the main namespace:
```py
import wgpu
```

But to use it, you need to select a backend first. You do this by importing it.
There is currently only one backend:
```py
import wgpu.backends.rs
```

To render to the screen you can use a variety of GUI toolkits:

```py
# The auto backend selects either the glfw, qt or jupyter backend
from wgpu.gui.auto import WgpuCanvas, run, call_later

# Visualizations can be embedded as a widget in a Qt application.
# Import PySide6, PyQt6, PySide2 or PyQt5 before running the line below.
# The code will detect and use the library that is imported.
from wgpu.gui.qt import WgpuCanvas

# Visualizations can be embedded as a widget in a wx application.
from wgpu.gui.wx import WgpuCanvas
```

Some functions in the original `wgpu-native` API are async. In the Python API,
the default functions are all sync (blocking), making things easy for general use.
Async versions of these functions are available, so wgpu can also work
well with Asyncio or Trio.


## License

This code is distributed under the 2-clause BSD license.


## Developers

* Clone the repo.
* Install devtools using `pip install -r dev-requirements.txt` (you can replace
  `pip` with `pipenv` to install to a virtualenv).
* Install wgpu-py in editable mode by running `pip install -e .`, this will also
  install runtime dependencies as needed.
* Run `python download-wgpu-native.py` to download the upstream wgpu-native
  binaries.
  * Or alternatively point the `WGPU_LIB_PATH` environment variable to a custom
    build.
* Use `black .` to apply autoformatting.
* Use `flake8 .` to check for flake errors.
* Use `pytest .` to run the tests.
* Use `pip wheel --no-deps .` to build a wheel.


### Changing the upstream wgpu-native version

* Use the optional arguments to `python download-wgpu-native.py --help` to
  download a different version of the upstream wgpu-native binaries.
* The file `wgpu/resources/wgpu_native-version` will be updated by the script to
  track which version we depend upon.

## Testing

The test suite is divided into multiple parts:

* `pytest -v tests` runs the core unit tests.
* `pytest -v examples` tests the examples.
* `pytest -v wgpu/__pyinstaller` tests if wgpu is properly supported by
  pyinstaller.
* `pytest -v codegen` lints the generated binding code.

There are two types of tests for examples included:

### Type 1: Checking if examples can run

When running the test suite, pytest will run every example in a subprocess, to
see if it can run and exit cleanly. You can opt out of this mechanism by
including the comment `# run_example = false` in the module.

### Type 2: Checking if examples output an image

You can also (independently) opt-in to output testing for examples, by including
the comment `# test_example = true` in the module. Output testing means the test
suite will attempt to import the `canvas` instance global from your example, and
call it to see if an image is produced.

To support this type of testing, ensure the following requirements are met:

* The `WgpuCanvas` class is imported from the `wgpu.gui.auto` module.
* The `canvas` instance is exposed as a global in the module.
* A rendering callback has been registered with `canvas.request_draw(fn)`.

Reference screenshots are stored in the `examples/screenshots` folder, the test
suite will compare the rendered image with the reference.

Note: this step will be skipped when not running on CI. Since images will have
subtle differences depending on the system on which they are rendered, that
would make the tests unreliable.

For every test that fails on screenshot verification, diffs will be generated
for the rgb and alpha channels and made available in the
`examples/screenshots/diffs` folder. On CI, the `examples/screenshots` folder
will be published as a build artifact so you can download and inspect the
differences.

If you want to update the reference screenshot for a given example, you can grab
those from the build artifacts as well and commit them to your branch.


%package help
Summary:	Development documents and examples for wgpu
Provides:	python3-wgpu-doc
%description help
[![CI](https://github.com/pygfx/wgpu-py/workflows/CI/badge.svg)](https://github.com/pygfx/wgpu-py/actions)
[![Documentation Status](https://readthedocs.org/projects/wgpu-py/badge/?version=latest)](https://wgpu-py.readthedocs.io)
[![PyPI version](https://badge.fury.io/py/wgpu.svg)](https://badge.fury.io/py/wgpu)

# wgpu-py

A Python implementation of WebGPU - the next generation GPU API.

<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/cube.png' />
<img width=300 src='https://raw.githubusercontent.com/pygfx/wgpu-py/main/examples/screenshots/triangle_auto.png' />


## Introduction

In short, this is a Python lib wrapping
[wgpu-native](https://github.com/gfx-rs/wgpu) and exposing it with a Pythonic
API similar to the [WebGPU spec](https://gpuweb.github.io/gpuweb/).

The OpenGL API is old and showing it's cracks. New API's like Vulkan, Metal and
DX12 provide a modern way to control the GPU, but these API's are too low-level
for general use. The WebGPU API follows the same concepts, but with a simpler
(higher level) spelling. The Python `wgpu` library brings the WebGPU API to
Python.

To get an idea of what this API looks like have a look at
[triangle.py](https://github.com/pygfx/wgpu-py/blob/main/examples/triangle.py)
and the other [examples](https://github.com/pygfx/wgpu-py/blob/main/examples/).


## Status

> **Note**
>
> The wgpu-API has not settled yet, use with care!

* Coverage of the WebGPU spec is complete enough to build e.g.
  [pygfx](https://github.com/pygfx/pygfx).
* Test coverage of the API is 100%.
* Support for Windows, Linux, and MacOS (Intel and M1).
* Until WebGPU settles as a standard, its specification may change, and with
  that our API will probably too. Check the [changelog](CHANGELOG.md) when you
  upgrade!


## Installation


```
pip install wgpu glfw
```

Linux users should make sure that **pip >= 20.3**. That should do the
trick on most systems. See [getting started](https://wgpu-py.readthedocs.io/en/stable/start.html)
for details.


## Usage

Also see the [online documentation](https://wgpu-py.readthedocs.io) and the [examples](https://github.com/pygfx/wgpu-py/tree/main/examples).

The full API is accessable via the main namespace:
```py
import wgpu
```

But to use it, you need to select a backend first. You do this by importing it.
There is currently only one backend:
```py
import wgpu.backends.rs
```

To render to the screen you can use a variety of GUI toolkits:

```py
# The auto backend selects either the glfw, qt or jupyter backend
from wgpu.gui.auto import WgpuCanvas, run, call_later

# Visualizations can be embedded as a widget in a Qt application.
# Import PySide6, PyQt6, PySide2 or PyQt5 before running the line below.
# The code will detect and use the library that is imported.
from wgpu.gui.qt import WgpuCanvas

# Visualizations can be embedded as a widget in a wx application.
from wgpu.gui.wx import WgpuCanvas
```

Some functions in the original `wgpu-native` API are async. In the Python API,
the default functions are all sync (blocking), making things easy for general use.
Async versions of these functions are available, so wgpu can also work
well with Asyncio or Trio.


## License

This code is distributed under the 2-clause BSD license.


## Developers

* Clone the repo.
* Install devtools using `pip install -r dev-requirements.txt` (you can replace
  `pip` with `pipenv` to install to a virtualenv).
* Install wgpu-py in editable mode by running `pip install -e .`, this will also
  install runtime dependencies as needed.
* Run `python download-wgpu-native.py` to download the upstream wgpu-native
  binaries.
  * Or alternatively point the `WGPU_LIB_PATH` environment variable to a custom
    build.
* Use `black .` to apply autoformatting.
* Use `flake8 .` to check for flake errors.
* Use `pytest .` to run the tests.
* Use `pip wheel --no-deps .` to build a wheel.


### Changing the upstream wgpu-native version

* Use the optional arguments to `python download-wgpu-native.py --help` to
  download a different version of the upstream wgpu-native binaries.
* The file `wgpu/resources/wgpu_native-version` will be updated by the script to
  track which version we depend upon.

## Testing

The test suite is divided into multiple parts:

* `pytest -v tests` runs the core unit tests.
* `pytest -v examples` tests the examples.
* `pytest -v wgpu/__pyinstaller` tests if wgpu is properly supported by
  pyinstaller.
* `pytest -v codegen` lints the generated binding code.

There are two types of tests for examples included:

### Type 1: Checking if examples can run

When running the test suite, pytest will run every example in a subprocess, to
see if it can run and exit cleanly. You can opt out of this mechanism by
including the comment `# run_example = false` in the module.

### Type 2: Checking if examples output an image

You can also (independently) opt-in to output testing for examples, by including
the comment `# test_example = true` in the module. Output testing means the test
suite will attempt to import the `canvas` instance global from your example, and
call it to see if an image is produced.

To support this type of testing, ensure the following requirements are met:

* The `WgpuCanvas` class is imported from the `wgpu.gui.auto` module.
* The `canvas` instance is exposed as a global in the module.
* A rendering callback has been registered with `canvas.request_draw(fn)`.

Reference screenshots are stored in the `examples/screenshots` folder, the test
suite will compare the rendered image with the reference.

Note: this step will be skipped when not running on CI. Since images will have
subtle differences depending on the system on which they are rendered, that
would make the tests unreliable.

For every test that fails on screenshot verification, diffs will be generated
for the rgb and alpha channels and made available in the
`examples/screenshots/diffs` folder. On CI, the `examples/screenshots` folder
will be published as a build artifact so you can download and inspect the
differences.

If you want to update the reference screenshot for a given example, you can grab
those from the build artifacts as well and commit them to your branch.


%prep
%autosetup -n wgpu-0.9.4

%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-wgpu -f filelist.lst
%dir %{python3_sitearch}/*

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

%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.4-1
- Package Spec generated