Cygrid is a cython-powered convolution-based gridding module for astronomy
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  • Version: 0.9
  • Authors: Benjamin Winkel, Lars Flöer, Daniel Lenz

PyPI version Build Status Build status Publication License


cygrid allows to resample a number of spectra (or data points) to a regular grid - a data cube - using any valid astronomical FITS/WCS projection (see

The method is a based on serialized convolution with finite gridding kernels. Currently, only Gaussian (radial-symmetric or elliptical) kernels are provided (which has the drawback of slight degradation of the effective resolution). The algorithm has very small memory footprint, allows easy parallelization, and is very fast.

A detailed description of the algorithm is given in Winkel, Lenz & Flöer (2016), which we kindly ask to be used as reference if you found cygrid useful for your research.


  • Supports any WCS projection system as target.
  • Conserves flux.
  • Low memory footprint.
  • Scales very well on multi-processor/core platforms.


The easiest way to install cygrid is via pip:

pip install cygrid

The installation is also possible from source. Download the tar.gz-file, extract (or clone from GitHub) and simply execute

python install

Note, for Windows machines only Python 3.5+ is supported.


We kept the dependencies as minimal as possible. The following packages are required:

  • numpy 1.10 or later
  • cython 0.23.4 or later
  • astropy 1.0 or later (Older versions of these libraries may work, but we didn't test this!)

If you want to run the notebooks yourself, you will also need the Jupyter server, matplotlib and wcsaxes packages. To run the tests, you'll need HealPy.

Note, for compiling the C-extension, openmp is used for parallelization and some C++11 language features are necessary. If you use gcc, for example, you need at least version 4.8 otherwise the setup-script will fail. (If you have absolutely no possibility to upgrade gcc, older version may work if you replace -std=c++11 with -std=c++0x in Thanks to bs538 for pointing this out.)

For Mac OS, it is required to use gcc-6 in order to install cygrid. We recommend to simply use the homebrew package manager and then use brew install gcc.


Minimal example

Using cygrid is extremely simple. Just define a FITS header (with valid WCS), define gridding kernel and run the grid function:

from import fits
import cygrid

# read-in data
glon, glat, signal = get_data(...)

# define target FITS/WCS header
header = {
    'NAXIS': 3,
    'NAXIS1': 101,
    'NAXIS2': 101,
    'NAXIS3': 1024,
    'CTYPE1': 'GLON-SFL',
    'CTYPE2': 'GLAT-SFL',
    'CDELT1': -0.1,
    'CDELT2': 0.1,
    'CRPIX1': 51,
    'CRPIX2': 51,
    'CRVAL1': 12.345,
    'CRVAL2': 3.14,

# prepare gridder
kernelsize_sigma = 0.2

kernel_type = 'gauss1d'
kernel_params = (kernelsize_sigma, )
kernel_support = 3 * kernelsize_sigma
hpx_maxres = kernelsize_sigma / 2

mygridder = cygrid.WcsGrid(header)

# do the gridding
mygridder.grid(glon, glat, signal)

# query result and store to disk
data_cube = mygridder.get_datacube()
    header=header, data=data_cube

More use-cases and tutorials

Check out the ipython notebooks in the repository for further examples of how to use cygrid. Note that you only view them on the nbviewer service, and will have to clone the repository or download the notebooks to run them on your machine.

Who do I talk to?

If you encounter any problems or have questions, do not hesitate to raise an issue or make a pull request. Moreover, you can contact the devs directly: