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VorBin: Voronoi Binning of Two Dimensional Data

Project description

Adaptive Voronoi Binning of Two Dimensional Data

This VorBin package is a Python implementation of the two-dimensional adaptive spatial binning method of Cappellari & Copin (2003). It uses Voronoi tessellations to bin data to a given minimum signal-to-noise ratio.


If you use this software for your research, please cite Cappellari & Copin (2003). The BibTeX entry for the paper is:

    author = {{Cappellari}, M. and {Copin}, Y.},
    title = "{Adaptive spatial binning of integral-field spectroscopic
        data using Voronoi tessellations}",
    journal = {MNRAS},
    eprint = {astro-ph/0302262},
    year = 2003,
    volume = 342,
    pages = {345-354},
    doi = {10.1046/j.1365-8711.2003.06541.x}


install with:

pip install vorbin

Without writing access to the global site-packages directory, use:

pip install --user vorbin


A usage example is provided by the procedure

Perform the following simple steps to bin you own 2D data with minimal Python interaction:

  1. Write your data vectors [X, Y, Signal, Noise] in the text file voronoi_2d_binning_example.txt, following the example provided;

  2. Change the line targetSN = 50.0 in the procedure, to specify the desired target S/N of your final bins;

  3. Run the program voronoi_2d_binning_example and wait for the final plot to appear. The output is saved in the text file voronoi_2d_binning_output.txt. The last column BIN_NUM in the file is all that is needed to actually bin the data;

  4. Read the documentation at the beginning of the file to fully understand the meaning of the various optional output parameters.

When some pixels have no signal

Binning should not be used blindly when some pixels contain significant noise but virtually no signal. This situation may happen e.g. when extracting the gas kinematics from observed galaxy spectra. One way of using voronoi_2d_binning consists of first selecting the pixels with S/N above a minimum threshold and then binning each set of connected pixels separately. Alternatively one may optimally weight the pixels before binning. For details, see Sec. 2.1 of Cappellari & Copin (2003).

Binning X-ray data

For X-ray data, or other data coming from photon-counting devices the noise is generally accurately Poissonian. In the Poissonian case, the S/N in a bin can never decrease by adding a pixel (see Sec.2.1 of Cappellari & Copin 2003), and it is preferable to bin the data without first removing the observed pixels with no signal.

Binning very big images

Computation time in voronoi_2d_binning scales nearly as npixels^1.5, so it may become a problem for large images (e.g. at the time of writing npixels > 1000x1000). Let’s assume that we really need to bin the image as a whole and that the S/N in a significant number of pixels is well above our target S/N. As for many other computational problems, a way to radically decrease the computation time consists of proceeding in a hierarchical manner. Suppose for example we have a 4000x4000 pixels image, we can do the following:

  1. Rebin the image regularly (e.g. in groups of 8x8 pixels) to a manageable size of 500x500 pixels;

  2. Apply the standard Voronoi 2D-binning procedure to the 500x500 image;

  3. Transform all unbinned pixels (which already have enough S/N) of the 500x500 Voronoi 2D-binned image back into their original individual full-resolution pixels;

  4. Now apply Voronoi 2D-binning only to the connected regions of full-resolution pixels;

  5. Merge the set of lower resolution bins with the higher resolution ones.


Copyright (c) 2001-2018 Michele Cappellari

This software is provided as is without any warranty whatsoever. Permission to use, for non-commercial purposes is granted. Permission to modify for personal or internal use is granted, provided this copyright and disclaimer are included in all copies of the software. All other rights are reserved. In particular, redistribution of the code is not allowed.

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