# Visualized Imputation of 2D Sample Space With Saturation Corresponding to Confidence?

I want to visualize a 2D sample space imputed from a 2D scatter of measurements. Something like a 2D hexbin color histogram would work if it were enhanced in the following ways:

• The number of samples in a bin determines the saturation of the color -- corresponding to the confidence obtained with more samples.
• The hue of the bin is the maximum likelihood or expected value measurement for that bin.
• Bins with no or very few samples would enjoy higher confidence, hence saturation, based on imputation from nearby bins with higher confidence (number of samples).

This would require a 2D color map with one dimension a range of hues and the other dimension a range of saturations.

Is there published work along these lines, preferrably with an existing software library?