Welcome to cdshealpix’s documentation!¶
Contents:
- Install
- Examples
- API
- cdshealpix
- cdshealpix.nested
- cdshealpix.nested.lonlat_to_healpix
- cdshealpix.nested.skycoord_to_healpix
- cdshealpix.nested.healpix_to_lonlat
- cdshealpix.nested.healpix_to_skycoord
- cdshealpix.nested.healpix_to_xy
- cdshealpix.nested.lonlat_to_xy
- cdshealpix.nested.xy_to_lonlat
- cdshealpix.nested.vertices
- cdshealpix.nested.vertices_skycoord
- cdshealpix.nested.neighbours
- cdshealpix.nested.external_neighbours
- cdshealpix.nested.cone_search
- cdshealpix.nested.polygon_search
- cdshealpix.nested.elliptical_cone_search
- cdshealpix.nested.bilinear_interpolation
- cdshealpix.ring
- cdshealpix.skymap
- Contributing
cdshealpix is a Python wrapper around the cdshealpix Rust crate.
cdshealpix is deployed to the PyPI server as the cdshealpix named pip package and
is available for Python 3.5, 3.6 and 3.7 through the following architectures:
Linux i686/x86_64
Windows 64 bits
MacOS
What is HEALPix ?¶
HEALPix describes a partionning of the sky into several equal area cells. This partionning is hierarchical meaning that each cell has a depth associated to it. Possible depths are in \([0, 29]\). A cell of depth \(N\) can be subdivided into its four children of depth \(N+1\):
At the depth \(0\), the sky is fractionned into \(12\) cells of equal areas.
At the depth \(1\), the sky is fractionned into \(12 \times 4\) cells.
…
At the depth \(N\), the sky is fractionned into \(12 \times 4^{N}\) cells.
…
At the depth \(29\), the sky is fractionned into \(12 \times 4^{29}\) cells.
The HEALPix nested scheme relies on the following papers that you can check it out if you want to know more about HEALPix:
References¶
Mark R. Calabretta. Mapping on the HEALPix grid. Submitted to: Astron. Astrophys., 2004. arXiv:astro-ph/0412607.
K. M. Gorski, Eric Hivon, A. J. Banday, B. D. Wandelt, F. K. Hansen, M. Reinecke, and M. Bartelman. HEALPix - A Framework for high resolution discretization, and fast analysis of data distributed on the sphere. Astrophys. J., 622:759–771, 2005. arXiv:astro-ph/0409513, doi:10.1086/427976.
David J. Schlegel, Douglas P. Finkbeiner, and Marc Davis. Maps of dust infrared emission for use in estimation of reddening and cosmic microwave background radiation foregrounds. The Astrophysical Journal, 500(2):525, jun 1998. doi:10.1086/305772.
M. R. Calabretta and B. F. Roukema. Mapping on the HEALPix grid. \mnras , 381:865–872, October 2007. doi:10.1111/j.1365-2966.2007.12297.x.
M. Reinecke and E. Hivon. Efficient data structures for masks on 2D grids. \aap , 580:A132, Aug 2015. doi:10.1051/0004-6361/201526549.