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datoviz

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⚡ Datoviz: high-performance GPU rendering for scientific data visualization

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# Datoviz

[![Build and test](https://github.com/datoviz/datoviz/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/datoviz/datoviz/actions/workflows/test.yml)
[![Documentation](https://img.shields.io/badge/docs-datoviz.org-2563eb)](https://datoviz.org/)
[![Python 3.10–3.14](https://img.shields.io/badge/python-3.10%E2%80%933.14-3776ab)](https://datoviz.org/start/install/)
[![License: MIT](https://img.shields.io/badge/license-MIT-22c55e)](https://raw.githubusercontent.com/datoviz/datoviz/main/LICENSE)

**Datoviz is a GPU-powered visualization engine for interactive scientific data.** Use it when
dense points, images, meshes, volumes, annotations, linked panels, or custom scientific scenes need
more control and performance than an ordinary plotting library provides.

[![Interactive 3D protein visualization rendered with Datoviz](https://datoviz.org/assets/gallery/v0.4/showcases/showcases_protein.poster.webp)](https://datoviz.org/examples/gallery/showcases/showcases_protein/)

| [![Allen mouse brain volume rendered with Datoviz](https://datoviz.org/assets/gallery/v0.4/showcases/showcases_brain_volume.poster.webp)](https://datoviz.org/examples/gallery/showcases/showcases_brain_volume/) | [![Large colorized LiDAR point cloud rendered with Datoviz](https://datoviz.org/assets/gallery/v0.4/showcases/showcases_point_cloud.poster.webp)](https://datoviz.org/examples/gallery/showcases/showcases_point_cloud/) | [![U.S. state population choropleth rendered with Datoviz](https://datoviz.org/assets/gallery/v0.4/showcases/showcases_choropleth.webp)](https://datoviz.org/examples/gallery/showcases/showcases_choropleth/) |
| --- | --- | --- |
| [Allen mouse brain](https://datoviz.org/examples/gallery/showcases/showcases_brain_volume/) | [Point cloud](https://datoviz.org/examples/gallery/showcases/showcases_point_cloud/) | [U.S. state choropleth](https://datoviz.org/examples/gallery/showcases/showcases_choropleth/) |

**[Browse the full gallery →](https://datoviz.org/examples/)**

Datoviz v0.4 provides a retained scene model, native Vulkan rendering, desktop and offscreen
presentation, reproducible capture, and direct use from C or Python. Python users call the generated
binding with `import datoviz as dvz` and pass NumPy arrays directly to supported data-upload APIs.

## Install

Datoviz v0.4.0rc2 is the active published release candidate. Install the exact package from PyPI with:

```sh
python -m pip install --pre datoviz==0.4.0rc2
```

RC2 supersedes RC1. In particular, macOS users should upgrade because the RC1 wheel has a native-window Vulkan-loader defect that RC2 fixes.

After the final v0.4 release, the normal command will be:

```sh
python -m pip install datoviz
```

Use the source build below for development or platforms without a published wheel. The full
[installation guide](https://datoviz.org/start/install/) covers macOS, Linux, Windows, Python, and
C/C++ integration.

## Minimal Python Example

This deterministic example creates the scatter plot shown in the documentation: 10,000 random
points with random colors and sizes.

```python
import numpy as np
import datoviz as dvz

# Create deterministic NumPy arrays for one point per row.
n = 10_000
rng = np.random.default_rng(12345)
positions = rng.uniform(-1, 1, (n, 3)).astype(np.float32)
positions[:, 2] = 0
colors = rng.integers(0, 256, (n, 4), dtype=np.uint8)
colors[:, 3] = 200
diameters = rng.uniform(4, 12, n).astype(np.float32)

# Create the retained scene, its output figure, and one full-size panel.
scene = dvz.dvz_scene()
figure = dvz.dvz_figure(scene, 1280, 720, 0)
panel = dvz.dvz_panel_full(figure)

# Attach the arrays to a point visual, then add it to the panel.
points = dvz.dvz_point(scene, 0)
dvz.dvz_visual_set_data_many(
    points,
    {"position": positions, "color": colors, "diameter_px": diameters},
)
dvz.dvz_panel_add_visual(panel, points, None)

# Bind 2D pan and zoom interaction, then open the native window.
panzoom = dvz.dvz_panzoom(scene, None)
dvz.dvz_panel_bind_controller(panel, panzoom, dvz.DVZ_DIM_MASK_XY)

dvz.run(scene, figure, title="Datoviz Quickstart")
```

Continue with the annotated [Quickstart](https://datoviz.org/start/quickstart/) or browse the
[example gallery](https://datoviz.org/examples/).

## v0.4 Release Status

The `main` branch is the active v0.4 release-candidate line. The native C and Python/NumPy
paths are release-facing; experimental and deferred surfaces remain explicitly labeled throughout
the documentation.

| Surface | v0.4 status |
| --- | --- |
| Native C scene/app API | supported, with feature-specific gaps |
| Python API with NumPy adaptation and exact raw calls | supported |
| Offscreen rendering and capture | supported |
| Qt/PyQt hosted rendering | supported, optional provider |
| Retained visual families | supported/experimental by family |
| WebGPU/WASM browser path | experimental |
| Scene compute-to-render integration | experimental |
| DRP2 command streams and runtime internals | advanced/unstable |
| High-level plotting API | external/GSP |

See the detailed [feature status](https://datoviz.org/reference/feature-status/),
[platform support](https://datoviz.org/reference/platform-support/), and
[v0.3 capability disposition](https://datoviz.org/reference/v03-visible-parity/) before relying on
an experimental or backend-specific feature.

## C And C++

Datoviz is a native C library. Installed packages expose headers and a CMake package for C and C++
applications:

```cmake
find_package(datoviz CONFIG REQUIRED)
target_link_libraries(my_app PRIVATE datoviz::datoviz)
```

The public API uses C linkage and can be called directly from C++. See
[C/C++ integration](https://datoviz.org/how-to/c-integration/) and the generated
[C API reference](https://datoviz.org/reference/c-api/).

## Build From Source

Source builds require Git, CMake 3.21+, Ninja, `just`, Python 3.10+, a supported C/C++ compiler,
shader tools, and a Vulkan-capable runtime. Clone the active branch with its submodules, then build
and test:

```sh
git clone --branch main --recursive https://github.com/datoviz/datoviz.git
cd datoviz
just build
just test
python -m pip install -e .
```

Platform packages, compiler versions, Vulkan/MoltenVK requirements, and native Windows guidance
are maintained in the [installation guide](https://datoviz.org/start/install/). Contributors should
also read [CONTRIBUTING.md](CONTRIBUTING.md) and [BUILD.md](BUILD.md).

## Which Layer Should I Use?

| Need | Use |
| --- | --- |
| Python with Datoviz visuals and NumPy arrays | `import datoviz as dvz` |
| Native application or C/C++ integration | C scene/app API |
| Exact pointer/count form of the Python binding | `datoviz.raw` |
| Browser rendering for promoted examples | experimental WebGPU/WASM subset |
| High-level functions such as `scatter()` or `imshow()` | Not in v0.4 — use the scene API today; GSP/VisPy2 later |

Datoviz v0.4 is the explicit engine layer: scenes, visuals, data uploads, controllers, windows,
captures, and integration surfaces. The old high-level Datoviz Python plotting API is not part of
v0.4; that role belongs to the developing GSP/VisPy2 layer. See
[Choose Your Layer](https://datoviz.org/start/choose-your-layer/) for the complete comparison.

## Documentation

- [Get Started](https://datoviz.org/start/)
- [Examples and Gallery](https://datoviz.org/examples/)
- [How-To Guides](https://datoviz.org/how-to/create-a-scene/)
- [Feature Status](https://datoviz.org/reference/feature-status/)
- [Platform Support](https://datoviz.org/reference/platform-support/)
- [Python API](https://datoviz.org/reference/ctypes/)
- [C API Reference](https://datoviz.org/reference/c-api/)
- [WebGPU/WASM Subset](https://datoviz.org/reference/webgpu-subset/)
- [Changelog](CHANGELOG.md)
- [Citation](https://datoviz.org/reference/citation/)

## License And Credits

Datoviz is released under the [MIT license](LICENSE). It is developed by
[Cyrille Rossant](https://cyrille.rossant.net/) at the
[International Brain Laboratory](https://www.internationalbrainlab.com/), with support from the
Wellcome Trust, Simons Foundation, and Chan Zuckerberg Initiative.

If you use Datoviz in research, follow the current [citation guidance](https://datoviz.org/reference/citation/)
and repository metadata in [CITATION.cff](CITATION.cff). The final v0.4.0 release is planned for
archival with Zenodo and a version-specific DOI.

Datoviz builds on earlier open-source GPU visualization work including VisPy, Glumpy, Galry, and
the Vulkan-based Datoviz releases. Development of v0.4 was assisted by
[OpenAI Codex](https://openai.com/codex/) for implementation, review, testing, documentation, and
release preparation. All changes were directed, reviewed, and validated by the project maintainer.

datoviz

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