How to Install and Uninstall python3-pymoc Package on Kali Linux

Last updated: May 13,2024

1. Install "python3-pymoc" package

Learn how to install python3-pymoc on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                python3-pymoc
            
                    
    
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2. Uninstall "python3-pymoc" package

Please follow the instructions below to uninstall python3-pymoc on Kali Linux:


    
        
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                sudo apt remove
            
                        
                python3-pymoc
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the python3-pymoc package on Kali Linux

Package: python3-pymoc
Source: pymoc
Version: 0.5.0-5
Installed-Size: 85
Maintainer: Debian Astro Team
Architecture: all
Depends: python3-astropy, python3:any
Suggests: python3-healpy
Size: 18816
SHA256: 9b42680d91a7c92f692d24e24bb6a84b968bf8823ab758c94e78a5d49ae733f8
SHA1: ee2c2c1d8cb7fe3335a38d3fab86f03aade070ae
MD5sum: c7b6ee78f4aa722fe4f8b496152ca6aa
Description: Python Multi-Order Coverage maps for Virtual Observatory
PyMOC provides a Python compatible library for handling MOCs.
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Frequently astronomical survey catalogues or images are sparse and
cover only a small part of the sky. In a Multi-Order Coverage map
the extent of data in a particular dataset is cached as a
pre-calculated mask image. The hierarchical nature enables fast
boolean operations in image space, without needing to perform complex
geometrical calculations. Services such as VizieR generally offer the
MOC masks, allowing a faster experience in graphical applications
such as Aladin, or for researchers quickly needing to locate which
datasets may contain overlapping coverage.
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The MOC mask image itself is tessellated and stored in NASA HealPix
format, encoded inside a FITS image container. Using the HealPix
(Hierarchical Equal Area isoLatitude Pixelization) tessellation
method ensures that more precision (pixels) in the mask are available
when describing complex shapes such as approximating survey or
polygon edges, while only needing to store a single big cell/pixel
when an coverage is either completely inside, or outside of the mask.
Catalogues can be rendered on the mask as circles.
Description-md5:
Homepage: https://github.com/grahambell/pymoc
Section: python
Priority: optional
Filename: pool/main/p/pymoc/python3-pymoc_0.5.0-5_all.deb