dev-python/pymoc: initial import

Package-Manager: Portage-2.3.6, Repoman-2.3.2

1 files changed, 32 insertions, 0 deletions

diff --git a/dev-python/pymoc/metadata.xml b/dev-python/pymoc/metadata.xml
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+++ b/dev-python/pymoc/metadata.xml
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+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+  <maintainer type="project">
+    <email>sci-astronomy@gentoo.org</email>
+    <name>Gentoo Astronomy Project</name>
+  </maintainer>
+  <longdescription lang="en">
+    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.
+  </longdescription>
+  <upstream>
+    <remote-id type="pypi">pymoc</remote-id>
+    <remote-id type="github">grahambell/pymoc</remote-id>
+  </upstream>
+</pkgmetadata>