https://github.com/torfsen/clusterpolate

Inter- and extrapolation for scattered data
https://github.com/torfsen/clusterpolate

Last synced: about 1 year ago
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Inter- and extrapolation for scattered data

• Host: GitHub
• URL: https://github.com/torfsen/clusterpolate
• Owner: torfsen
• License: mit
• Created: 2015-10-29T09:05:31.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T20:52:29.000Z (over 2 years ago)
• Last Synced: 2025-04-30T01:54:06.630Z (about 1 year ago)
• Language: Python
• Homepage: https://clusterpolate.readthedocs.org
• Size: 255 KB
• Stars: 4
• Watchers: 3
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Clusterpolate

## Inter- and extrapolation for clustered data

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.

## Example

Given scattered data like

![Plot of raw data](docs/plot.png "Raw data")

the `clusterpolate.image` function produces

![Image of clusterpolated data](docs/clusterpolated.png "Clusterpolated data")

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.

## Installation

    pip install clusterpolate

## Documentation

The documentation is [hosted on ReadTheDocs](https://clusterpolate.readthedocs.org).

## History

* *0.2.0:* Support for multiprocessing

* *0.1.0:* First release