  Quality colormaps can help communicate important data patterns. However,
finding an aesthetically pleasing colormap that looks "just right" for a given
scenario requires significant design and technical expertise. We introduce
Cieran, a tool that allows any data analyst to rapidly find quality colormaps
while designing charts within Jupyter Notebooks. Our system employs an active
preference learning paradigm to rank expert-designed colormaps and create new
ones from pairwise comparisons, allowing analysts who are novices in color
design to tailor colormaps to their data context. We accomplish this by
treating colormap design as a path planning problem through the CIELAB
colorspace with a context-specific reward model. In an evaluation with twelve
scientists, we found that Cieran effectively modeled user preferences to rank
colormaps and leveraged this model to create new quality designs. Our work
shows the potential of active preference learning for supporting efficient
visualization design optimization.
