Traditional approaches for inter- and extrapolation of scattered data work on a filled rectangular area surrounding the data points or in their filled convex hull. However, scattered data often consists of different clusters of irregular shapes and usually contains areas where there simply is no data. Forcing such data into a traditional inter- or extrapolation scheme often does not lead to the desired results.
Heatmaps, on the other hand, deal well with scattered data but often do not provide real interpolation: Instead they usually use raw sums of kernel functions which overestimate the target value in densely populated areas.
Clusterpolation is a hybrid inter- and extrapolation scheme to fix this. It uses kernel functions for a weighted inter- and extrapolation of local values, as well as for a density estimation of the data. The latter is used to assign a membership degree to clusterpolated points: Points with a low membership degree lie in an area where there's just not enough data.
Given scattered data like
the clusterpolate.image function produces
Note how the values are cleanly interpolated even within dense regions and how extrapolation only occurs close to existing data points.
Of course you can also use clusterpolation on your data without
generating any images: simply use the clusterpolate.clusterpolate
function.
pip install clusterpolate
The documentation is hosted on ReadTheDocs.
- 0.2.0: Support for multiprocessing
- 0.1.0: First release