Figures are the first thing people look at when reading papers. If the figures are poorly prepared, it can raise questions about the competency of the work overall. We suggest matplotlib for preparing plots and keep a gallery of curated examples here. Here are our suggestions:
- Check the journal requirements for the figures. Make sure you follow their conventions including the labeling of subplots as either a, a), or (a).
- Vector graphics formats such as pdf are faster and zoom perfectly. This is usually preferable over bitmap formats such as jpeg. pdf is usually better supported than eps unless you have a special reason to use eps. If bitmap graphics are needed, choose a generous resolution setting e.g. 600 dpi or higher.
- Set the width of a figure to the real physical column (or page) width, such as 3.375”/6.75” for a full column/full page. This will mean that fonts, point-size, line-width, etc. appear on the page in the correct size in a way that is consistent throughout the manuscript.
- Using a uniform style is very important for an attractive manuscript. Try to avoid ad-hoc changes to the graph settings. If a change is desirable, consider applying it to the whole manuscript. Avoid making just one axis label smaller just to squeeze it in.
- Use color cautiously and consistently. Do use color to emphasize an important point, but avoid indiscriminate use of colors for every element of a plot. If you choose to represent, for example, temperature using a set of colors consider using the same set of colors to denote temperature throughout the manuscript and if you can easily avoid re-using the same colors with a different meaning do so.
- While colormaps are often the best (or least bad) way to represent 2D data, they have potential to be misleading. All else being equal you usually want to use a sequential colormap (e.g. viridis) for gradually vary data. If you want to emphasize deviation from a special value choose a diverging colormap (e.g. bwr).
- Make the effort to label all axes including units. A colorbar is needed if you use a colormap.
- Avoid overlap between different elements of the plot. If you cannot see some of the points, consider using smaller marker sizes, transparency or even re-binnig the data (assuming that makes sense).
- 1-4 minor ticks per major tick is usually good.