How to Install and Uninstall pymoctool Package on Kali Linux

Last updated: November 26,2024

1. Install "pymoctool" package

In this section, we are going to explain the necessary steps to install pymoctool on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                pymoctool
            
                    
    
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2. Uninstall "pymoctool" package

Learn how to uninstall pymoctool on Kali Linux:


    
        
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                sudo apt remove
            
                        
                pymoctool
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the pymoctool package on Kali Linux

Package: pymoctool
Source: pymoc
Version: 0.5.0-5
Installed-Size: 17
Maintainer: Debian Astro Team
Architecture: all
Depends: python3-pymoc, python3-tk, python3-healpy, python3:any
Size: 5592
SHA256: c07cd2a1c398840a0e8314c2e7ce8c1d0d32ad40feb393b9989e4225007a96d4
SHA1: afa2ce603f80ca1b172259e10737572b1aa37a31
MD5sum: cfa3b3dd50f62743cb16971ff516ac21
Description: Python Multi-Order Coverage maps tool for Virtual Observatory
'pymoctool' is a command-line Python-based library for manipulating
Multi-Order Coverage maps (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.
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It is written in Python and uses the PyMOC library.
Description-md5:
Homepage: https://github.com/grahambell/pymoc
Section: science
Priority: optional
Filename: pool/main/p/pymoc/pymoctool_0.5.0-5_all.deb