cygrid 2.0.4

Project Status

Cygrid is already used in several “production” systems, for example it was utilized for two major 21-cm HI surveys, EBHIS and HI4PI. Nevertheless, we cannot guarantee that it’s completely bug-free. We kindly invite you to use the library and we are grateful for feedback. Note, that work on the documentation is still ongoing.

Purpose

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 http://docs.astropy.org/en/stable/wcs/).

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.

Features

• Supports any WCS projection system as target.

• Conserves flux.

• Low memory footprint.

• Scales very well on multi-processor/core platforms.

Installation

We highly recommend to use cygrid with the Anaconda Python distribution, in which case installiation is as easy as

conda install -c conda-forge cygrid

Otherwise, you should install cygrid via pip:

python -m pip install cygrid

The installation is also possible from source, but you’ll need a C++ compiler. Download the tar.gz-file, extract (or clone from GitHub) and execute (in project directory):

python -m pip install .

Usage

from astropy.io 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)
mygridder.set_kernel(
    kernel_type,
    kernel_params,
    kernel_support,
    hpx_maxres
    )

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

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

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:

• <bwinkel@mpifr.de>

• <mail@daniellenz.org>

Preferred citation method

Please cite our paper if you use cygrid for your projects.

@ARTICLE{2016A&A...591A..12W,
    author = {{Winkel}, B. and {Lenz}, D. and {Fl{\"o}er}, L.},
    title = "{Cygrid: A fast Cython-powered convolution-based gridding module for Python}",
    journal = {\aap},
    archivePrefix = "arXiv",
    eprint = {1604.06667},
    primaryClass = "astro-ph.IM",
    keywords = {methods: numerical, techniques: image processing},
    year = 2016,
    month = jun,
    volume = 591,
    eid = {A12},
    pages = {A12},
    doi = {10.1051/0004-6361/201628475},
    adsurl = {http://adsabs.harvard.edu/abs/2016A%26A...591A..12W},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}