snaphu 2.0.6-2 source package in Ubuntu

Changelog

snaphu (2.0.6-2) unstable; urgency=medium

  [ Bas Couwenberg ]
  * Use execute_{before,after} instead of override in rules file.

 -- Antonio Valentino <email address hidden>  Sat, 09 Dec 2023 15:35:11 +0000

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Uploaded by:
Debian GIS Project
Uploaded to:
Sid
Original maintainer:
Debian GIS Project
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Noble release multiverse misc

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File Size SHA-256 Checksum
snaphu_2.0.6-2.dsc 2.0 KiB b9ca7295969d4c8edeaa9350db562e9dc377a988530618dbeb190e99d7619700
snaphu_2.0.6.orig.tar.gz 169.3 KiB ce481c8d8d4c1e8fffeb5a9052a68bf48e6dd955293a30f6769d5bbb4df7a998
snaphu_2.0.6-2.debian.tar.xz 6.3 KiB f28ded66ff854a225c45bdff8701e49107539ee879d42ce0575d4a242e5bf9c1

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Binary packages built by this source

snaphu: Statistical-Cost, Network-Flow Algorithm for 2D Phase Unwrapping

 Two-dimensional phase unwrapping is the process of recovering
 unambiguous phase data from a 2-D array of phase values known only
 modulo 2pi rad.
 .
 There are many applications, like Magnetic Resonance Imaging (MRI),
 Synthetic Aperture Radar (SAR), fringe pattern analysis, tomography
 and spectroscopy, which as part of their fundamental operation depend
 upon the extraction of a phase signal from their input image. Usually
 the phase is available in a form that suffers from 2-pi phase jumps
 due to the use of the mathematical arctangent function, which produces
 an inherently wrapped output. This wrapped phase is unusable until the
 phase discontinuities are removed.
 .
 SNAPHU is an implementation of the Statistical-cost, Network-flow
 Algorithm for Phase Unwrapping particularly suitable for SAR
 interferometry applications. This algorithm poses phase unwrapping as
 a maximum a posteriori probability (MAP) estimation problem, the
 objective of which is to compute the most likely unwrapped solution
 given the observable input data. Because the statistics relating the
 input data to the solution depend on the measured quantity, SNAPHU
 incorporates three built-in statistical models, for topography data,
 deformation data, and smooth generic data. The posed optimization
 problem is solved approximately with use of network-flow techniques.
 .
 As SNAPHU uses an iterative optimization procedure, its execution time
 depends on the difficulty of the interferogram. In single-tile mode
 the required memory is on the order of 100 MB per 1000000 pixels in
 the input interferogram.

snaphu-dbgsym: debug symbols for snaphu