2025-12-10 –, Horace Mann
Fast interactive visualization remains a considerable barrier in analysis pipelines for large neuronal datasets. Here, we present fastplotlib, a scientific plotting library featuring an expressive API for very fast visualization of scientific data. Fastplotlib is built upon pygfx, which utilizes the GPU via WGPU, allowing it to interface with modern graphics APIs such as Vulkan for fast rendering of objects. Fastplotlib is non-blocking, allowing for interactivity with data after plot generation. Ultimately, fastplotlib is a general-purpose scientific plotting library that is useful for fast and live visualization and analysis of complex datasets.
Over the past decade, advanced pipelines have been developed for analyzing large datasets. However, fast visualization and live interactivity during data collection remain challenging. While current tools within the Python plotting ecosystem enable interactive data visualization, they often fail to efficiently leverage modern GPUs, lack intuitive APIs for rapid prototyping, or require users to write their own shaders. Additionally, other popular plotting libraries, such as bokeh and matplotlib, are not geared towards fast interactive visualization with millions of objects. Given these challenges with current visualization tools, the need for a modern GPU-driven interactive plotting library exists. In this presentation, we will go through the technical details, as well as a brief demo on how fastplotlib makes fast interactive visualization of complex datasets possible. We will demonstrate the broad applicability of fastplotlib as a fast, general-purpose plotting library.
Fastplotlib is built on top of pygfx, a cutting-edge Python rendering engine that utilizes WGPU, which can efficiently leverage modern GPU and CPU hardware. WGPU is the successor to OpenGL and features a low overhead with respect to the amount of code per-draw-per-object, allowing for speed even when rendering millions of objects. Pygfx is also non-blocking, which allows for interactivity and modification of already drawn objects. Fastplotlib utilizes the pygfx rendering library for fast visualization with an expressive API for scientific visualization. The benefits of fastplotlib are that it reduces boilerplate code, which allows users to focus on their data without having to manage the underlying rendering process. Additionally, fastplotlib enables animations as well as high-level interactivity among plots, which can be combined with lazy loading and lazy compute of very large datasets that are hundreds of gigabytes or terabytes in size. Furthermore, fastplotlib can be used in jupyter notebooks, allowing it to be used on cloud computing and other remote infrastructures for streaming visualizations of extremely large datasets. In total, these unique features and the underlying architecture create a plotting library that is fast, easy to use, and multifaceted.
PhD Candidate at NYU. 10+ years of experience using Python for data analysis and machine learning with neuroscience datasets. Core developer of fastplotlib and maintainer of several Python libraries in neuroscience with significant user bases, and a contributor to other libraries such as tslearn.
