Ubuntu
pymoc package

pymoc 0.5.0-5 source package in Ubuntu

Changelog

pymoc (0.5.0-5) unstable; urgency=medium

  * Depend python3-healpy and python3-tk since they are
    needed with pymoctools --plot flag (Closes: #950033)
  * Improve autopkgtests to catch pymoctool error if
    it happens in future
  * Add upstream/metadata
  * Drop compat, switch to debhelper-compat version 13
  * Declare compliance with policy 4.5.1
  * Add "Rules-Requires-Root:no"
  * Switch watch version to 4
  * Add gbp.conf so gbp operations to not conflict with default .gbp.conf

 -- Nilesh Patra <email address hidden>  Sat, 23 Jan 2021 18:12:51 +0530

Upload details

Uploaded by:
Debian Astronomy Maintainers
Uploaded to:
Sid
Original maintainer:
Debian Astronomy Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

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Builds

Hirsute: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
pymoc_0.5.0-5.dsc 2.1 KiB 127513305a0f6379fa59e8b74016c5c56f49f889826670200719cc3fc3f0d112
pymoc_0.5.0.orig.tar.gz 36.6 KiB afadf5aeadd4ac7055a89429ab4b4d10901638584519db030f8ca43f7c10f168
pymoc_0.5.0-5.debian.tar.xz 6.2 KiB 02c964395efe386f43c0cbd158178ab02f0a50dc3d7591ac35d1bf896a23fb19

Available diffs

No changes file available.

Binary packages built by this source

pymoctool: Python Multi-Order Coverage maps tool for Virtual Observatory

 'pymoctool' is a command-line Python-based library for manipulating
 Multi-Order Coverage maps (MOCs).
 .
 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.
 .
 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.
 .
 It is written in Python and uses the PyMOC library.

python3-pymoc: Python Multi-Order Coverage maps for Virtual Observatory

 PyMOC provides a Python compatible library for handling MOCs.
 .
 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.
 .
 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.