fastplotlib 0.1.0a8

A fast plotting library built using the pygfx render engine

fastplotlib

Installation | GPU Drivers | Examples

A fast plotting library built using the pygfx render engine utilizing Vulkan via WGPU, so it is very fast! fastplotlib is focussed on very fast interactive plotting in the notebook using an expressive API. It also works within desktop applications using glfw or Qt.

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.

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

Examples

See the examples directory. Start out with simple.ipynb.

Neuroscience usecase demonstrating some of fastplotlib's capabilities

https://user-images.githubusercontent.com/9403332/210304485-e554b648-50b4-4243-b292-a9ed30514a2d.mp4

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 image updates

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()

Interactivity

This is all in the notebook and non-blocking!

Image widget

Interactive visualization of large imaging datasets in the notebook.

Installation

Install using pip.

pip install fastplotlib

Installing simplejpeg is recommended for faster plotting in notebooks using remote frame buffer. You will need C compilers to install it:

pip install simplejpeg

Clone or download the repo to try the examples

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

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

Note: fastplotlib and pygfx are fast evolving projects, the version available through pip might be outdated, you will need to follow the "For Development" instructions below if you want the latest features.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

For Development

git clone https://github.com/kushalkolar/fastplotlib.git
cd fastplotlib
pip install -r requirements.txt
pip install -e .

# try the examples
cd examples
jupyter lab

Install Vulkan drivers

You will need a GPU that supports Vulkan (integrated GPUs in CPUs are usually fine). Generally if your GPU is from 2017 or later it should support Vulkan.

For more information see: https://github.com/pygfx/wgpu-py#platform-requirements

Windows:

Vulkan 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 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.

Mac OSX:

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

Gallery

Extremely fast image updates, 5 x 5 gridplot

https://www.youtube.com/embed/-_0Gp_EqepI

Very fast image updates with some synced controllers

https://user-images.githubusercontent.com/9403332/165678225-dcf3b401-86a5-4df5-a9e5-dc65bdb0443a.mp4

4x Grid of Scatter plots, 1.2 million points each

https://user-images.githubusercontent.com/9403332/165677576-a0aa2d0f-a201-4e0e-91bd-aed800f775ee.mp4

Lineplot

https://user-images.githubusercontent.com/9403332/165678270-aea65a83-6cc1-4edc-981c-4857eaf293c7.mp4