pymoc 0.4.2-1 source package in Ubuntu
Changelog
pymoc (0.4.2-1) unstable; urgency=low * Import upstream bugfix release (0.4.2) + Fix large FITS import on 32-bit architectures (Closes: #860629) -- Paul Sladen <email address hidden> Thu, 20 Apr 2017 15:21:00 +0200
Upload details
- Uploaded by:
- Debian Astronomy Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian Astronomy Maintainers
- Architectures:
- all
- Section:
- misc
- Urgency:
- Low Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| pymoc_0.4.2-1.dsc | 2.1 KiB | 8da10f9f9291825cb788cebd7ec44d1111e31c4a3b1d6a765695318147dbbb46 |
| pymoc_0.4.2.orig.tar.gz | 35.1 KiB | 4fdfd43c5dab8dc4aa2c52652f210dbb6643dad038015d7b746209f5952355fb |
| pymoc_0.4.2-1.debian.tar.xz | 3.7 KiB | 355d8e17cbe6c79f2fad56702ee1044a0f9bf67bbb3d8d5d5d91fc011c9ab2a6 |
Available diffs
- diff from 0.4.1-1 to 0.4.2-1 (1.5 KiB)
No changes file available.
Binary packages built by this source
- pymoctool: No summary available for pymoctool in ubuntu artful.
No description available for pymoctool in ubuntu artful.
- python-pymoc: Python 2 Multi-Order Coverage maps for Virtual Observatory
PyMOC provides a Python 2-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.
- python3-pymoc: Python 3 Multi-Order Coverage maps for Virtual Observatory
PyMOC provides a Python 3-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.
