fastplotlib 0.1.0a11

A fast plotting library built using the pygfx render engine

fastplotlib

Installation | GPU Drivers | Examples | Contributing

A fast plotting library built using the pygfx render engine utilizing Vulkan, DX12, or Metal via WGPU, so it is very fast! We also aim to be an expressive plotting library that enables rapid prototyping for large scale explorative scientific visualization.

Higher resolution demo: https://github.com/kushalkolar/fastplotlib/assets/9403332/1df06d4d-9a7e-4f0d-aad8-8d2e9b387647

Supported frameworks

fastplotlib can run on anything that pygfx can also run, this includes:

:heavy_check_mark: Jupyter lab, using jupyter_rfb
:heavy_check_mark: PyQt and PySide
:heavy_check_mark: glfw
:heavy_check_mark: wxPython

Notes:
:heavy_check_mark: You can use a non-blocking glfw canvas from a notebook, as long as you're working locally or have a way to forward the remote graphical desktop (such as X11 forwarding).
:grey_exclamation: We do not officially support jupyter notebook through jupyter_rfb, this may change with notebook v7
:disappointed: jupyter_rfb does not work in collab, for a detailed discussion see: https://github.com/vispy/jupyter_rfb/issues/57

Note

fastplotlib is currently in the early alpha stage with breaking changes every ~week, but you're welcome to try it out or contribute! See our Roadmap for 2023.

Documentation

http://fastplotlib.readthedocs.io/

The docs are not entirely thorough, we recommend the example notebooks to get started.

Questions, ideas? Post an issue or chat on gitter.

Installation

Install using pip.

Minimal, use with your own Qt or glfw applications

pip install fastplotlib

Notebook

pip install "fastplotlib[notebook]"

Optional: install simplejpeg for much faster notebook visualization, you will need C compilers and libjpeg-turbo to install it:

pip install simplejpeg

Note

fastplotlib and pygfx are fast evolving projects, the version available through pip might be outdated, you will need to follow the "For developers" instructions below if you want the latest features. You can find the release history on pypi here: https://pypi.org/project/fastplotlib/#history

For developers

git clone https://github.com/kushalkolar/fastplotlib.git
cd fastplotlib

# install all extras in place
pip install -e ".[notebook,docs,tests]"

Examples

Note

fastplotlib and pygfx are fast evolving, you may require the latest pygfx and fastplotlib from github to use the examples in the master branch.

Clone or download the repo to try the examples

# clone the repo
git clone https://github.com/kushalkolar/fastplotlib.git

# IMPORTANT: if you are using a specific version from pip, checkout that version to get the examples which work for that version
# example:
# git checkout git checkout v0.1.0.a9 # replace "v0.1.0.a9" with the version you have

# cd into notebooks and launch jupyter lab
cd fastplotlib/notebooks
jupyter lab

Start out with simple.ipynb.

Simple image plot

from fastplotlib import Plot
import numpy as np

plot = Plot()

data = np.random.rand(512, 512)
plot.add_image(data=data)

plot.show()

Fast animations

from fastplotlib import Plot
import numpy as np

plot = Plot()

data = np.random.rand(512, 512)
image = plot.image(data=data)

def update_data():
    new_data = np.random.rand(512, 512)
    image.data = new_data

plot.add_animations(update_data)

plot.show()

Image widget

Interactive visualization of large imaging datasets in the notebook.

Graphics drivers

You will need a relatively modern GPU (newer integrated GPUs in CPUs are usually fine). Generally if your GPU is from 2017 or later it should be fine.

For more information see: https://wgpu-py.readthedocs.io/en/stable/start.html#platform-requirements

Windows:

Vulkan drivers should be installed by default on Windows 11, but you will need to install your GPU manufacturer's driver package (Nvidia or AMD). If you have an integrated GPU within your CPU, you might still need to install a driver package too, check your CPU manufacturer's info.

We also recommend installing C compilers so that you can install simplejpeg which improves remote frame buffer performance in notebooks.

Linux:

Debian based distros:

sudo apt install mesa-vulkan-drivers
# for better performance with the remote frame buffer install libjpeg-turbo
sudo apt install libjpeg-turbo

For other distros install the appropriate vulkan driver package, and optionally the corresponding libjpeg-turbo package for better remote-frame-buffer performance in jupyter notebooks.

CPU/software rendering (Lavapipe)

If you do not have a GPU you can perform limited software rendering using lavapipe. This should get you everything you need for that on Debian or Ubuntu based distros:

sudo apt install llvm-dev libturbojpeg* libgl1-mesa-dev libgl1-mesa-glx libglapi-mesa libglx-mesa0 mesa-common-dev mesa-vulkan-drivers

Mac OSX:

As far as I know, WGPU uses Metal instead of Vulkan on Mac. You will need at least Mac OSX 10.13.

:heart: Contributing

We welcome contributions! See the contributing guide: https://github.com/kushalkolar/fastplotlib/blob/master/CONTRIBUTING.md

You can also take a look at our Roadmap for 2023 and Issues for ideas on how to contribute!