Colormaps

For visual inspection and presentation SMLM data is typically rendered in 2D or 3D images. Various binning algorithms are used to create a representation of localisation densities. Intensity values are represented by color according to a selected colormap. Various colormaps can be chosen that accentuate certain data structures.

For SMLM images we aim at using colormaps that SMLM users are used to but that perform well with respect to human perception. Therefore, we recommend using colormaps that are optimized for accurate perception as for instance provided by the colorcet library.

We recommend default use for various types of colormaps: All 2D one-channel plot functions use the fire colormap as default if colorcet is installed. Otherwise we use the matplotlib colormap viridis as default.

For categorical data we use glasbey_dark from colorcet as default colormap or alternatively tab20 from matplotlib. For diverging data we use coolwarm from colorcet as default colormap or alternatively coolwarm from matplotlib. For rainbow/jet-like data we use turbo as default colormap.

For details on how to use colormaps see API documentation for locan.visualize.colormap.