Conference site ยป Proceedings

FigureFirst: A Layout-first Approach for Scientific Figures

Theodore Lindsay
Caltech Division of Biology and Biological Engineering

Peter T. Weir
Data Science at Yelp

Floris van Breugel
University of Washington


One major reason that Python has been widely adopted as a scientific computing platform is the availability of powerful visualization libraries. Although these tools facilitate discovery and data exploration, they are difficult to use when constructing the sometimes-intricate figures required to advance the narrative of a scientific manuscript. For this reason, figure creation often follows an inefficient serial process, where simple representations of raw data are constructed in analysis software and then imported into desktop publishing software to construct the final figure. Though the graphical user interface of publishing software is uniquely tailored to the production of publication quality layouts, once the data are imported, all edits must be re-applied if the analysis code or underlying dataset changes. Here we introduce a new Python package, FigureFirst, that allows users to design figures and analyze data in a parallel fashion, making it easy to generate and continuously update aesthetically pleasing and informative figures directly from raw data. To accomplish this, FigureFirst acts as a bridge between the Scalable Vector Graphics (SVG) format and Matplotlib Hunter08 plotting in Python. With FigureFirst, the user specifies the layout of a figure by drawing a set of rectangles on the page using a standard SVG editor such as Inkscape Altert13. In Python, FigureFirst uses this layout file to generate Matplotlib figures and axes in which the user can plot the data. Additionally, FigureFirst saves the populated figures back into the original SVG layout file. This functionality allows the user to adjust the layout in Inkscape, then run the script again, updating the data layers to match the new layout. Building on this architecture, we have implemented a number of features that make complex tasks remarkably easy including axis templates; changing attributes of standard SVG items such as their size, shape, color, and text; and an API for adding JessyInk Jagannathan12 extensions to Matplotlib objects for automatically generating animated slide presentations. We have used FigureFirst to generate figures for publications Lindsay17 and provide code and the layouts for the figures presented in this manuscript at our GitHub page:


plotting, figures, SVG, Matplotlib

Bibtex entry

Full text PDF