Metadata-Version: 2.1
Name: healsparse
Version: 1.4.0
Summary: Sparse healpix maps and geometry library
Home-page: https://github.com/lsstdesc/healsparse
Author: Eli Rykoff, Javier Sanchez, and others
Author-email: erykoff@stanford.edu
License: UNKNOWN
Description: # `healsparse`
        Python implementation of sparse HEALPix maps.
        
        `HealSparse` is a sparse implementation of
        [HEALPix](https://healpix.jpl.nasa.gov/) in Python, written for the Rubin
        Observatory Legacy Survey of Space and Time Dark Energy Science Collaboration
        ([DESC](https://lsst-desc.org/)).  `HealSparse` is a pure python library that
        sits on top of [numpy](https://github.com/numpy/numpy) and
        [healpy](https://github.com/healpy/healpy/) and is designed to avoid storing
        full sky maps in case of partial coverage, including easy reading of sub-maps.
        This reduces the overall memory footprint allowing maps to be rendered at
        arcsecond resolution while keeping the familiarity and power of
        [healpy](https://github.com/healpy/healpy/).
        
        `HealSparse` expands on [healpy](https://github.com/healpy/healpy/) and
        straight [HEALPix](https://healpix.jpl.nasa.gov/) maps by allowing maps of
        different data types, including 32- and 64-bit floats; 8-, 16-, 32-, and 64-bit
        integers; "wide bit masks" of arbitrary width (allowing hundreds of bits to be
        efficiently and conveniently stored); and
        [numpy](https://github.com/numpy/numpy) record arrays.  Arithmetic operations
        between maps are supported, including sum, product, min/max, and and/or/xor
        bitwise operations for integer maps.  In addition, there is general support for
        any [numpy](https://github.com/numpy/numpy) universal function.
        
        `HealSparse` also includes a simple geometric primitive library, to render
        circles and convex polygons.
        
        ## Requirements:
        
        `healsparse` requires to have pre-installed the following packages:
        
        - [numpy](https://github.com/numpy/numpy)
        - [healpy](https://github.com/healpy/healpy)
        - [astropy](https://astropy.org)
        
        The following package is optional but recommended for all features:
        - [fitsio](https://github.com/esheldon/fitsio)
        
        ## Documentation:
        
        Read the full documentation at https://healsparse.readthedocs.io/en/latest/.
        
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