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Filters a set of points in a 2D space.

Project Description

tinfiltering

This is the tinfiltering package, created to perform the automated filtering of 2D data points, based on the edges distance distribution of a Delaunay triangulation.

The methodology upon which this package is based on the initial steps of the algorithm proposed by Yang and Cui (2010) in the paper “A Novel Spatial Clustering Algorithm Based on Delaunay Triangulation”.

This package was developed while in my PhD research in the Department of Geography at University College London, supervised by Dr Mat Disney (Earth Observation/Remote Sensing).

If you have any problem, question or suggestion, please, don’t hesitate to contact the author.

Usage as module

>>> import tinfiltering
>>> tinfiltering.apply_filter(x, y)

Parameters

x, y: list or numpy.ndarray
Coordinates for the set of points to be filtered.

Returns

out_points: 2D numpy.ndarray
Coordinates for the filtered set of points.

Raises

ValueError
If input is in the wrong shape (the shapes of x and y should match).
TypeError
If the input is of the wrong type (the inputs must be a 1-D array or list)

Usage in console

>>> tinfiltering in_filename out_filename

Parameters

in_filename: str
Name of the file containing the numpy array point data to be filtered.
out_filename: str
Name of the file to save the numpy array of filtered point data.

Packages

  • tinfiltering
  • tinfiltering.test
  • tinfiltering.tin
  • Version 0.1.4

Dependencies

  • numpy
  • scipy

Author

  • Matheus Boni Vicari (@matt_bv)

Who do I talk to?

References

[1]X. Yang and W. Cui, “A Novel Spatial Clustering Algorithm Based on Delaunay Triangulation,” Journal of Software Engineering and Applications, Vol. 3 No. 2, 2010, pp. 141-149. doi: 10.4236/jsea.2010.32018.
Release History

Release History

This version
History Node

0.1.4

History Node

0.1.3

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0.1.2

History Node

0.1.1

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
tinfiltering-0.1.4.tar.gz (70.2 kB) Copy SHA256 Checksum SHA256 Source May 24, 2016

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