yorick-mira 1.1.0+git20170124.3bd1c3~dfsg1-2 source package in Ubuntu


yorick-mira (1.1.0+git20170124.3bd1c3~dfsg1-2) unstable; urgency=low

  * Bug fix: "ymira crashes when trying to fit several wavelengths"
    (Closes: #856835).

 -- Thibaut Paumard <email address hidden>  Sun, 05 Mar 2017 11:43:23 +0100

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Uploaded by:
Debian Astronomy Maintainers on 2017-03-05
Uploaded to:
Original maintainer:
Debian Astronomy Maintainers
Low Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release on 2019-10-18 universe science
Eoan release on 2019-04-18 universe science
Disco release on 2018-10-30 universe science
Cosmic release on 2018-05-01 universe science
Bionic release on 2017-10-24 universe science
Artful release on 2017-06-22 universe science


Artful: [FULLYBUILT] amd64


File Size SHA-256 Checksum
yorick-mira_1.1.0+git20170124.3bd1c3~dfsg1-2.dsc 2.2 KiB 3129a92ec08701239627b8ef988ba643a860b514150f27600d27c23378b0db58
yorick-mira_1.1.0+git20170124.3bd1c3~dfsg1.orig.tar.xz 256.8 KiB d765bd4819d130983f72f8483283922dd068359298eb898735bbf8bb99440a3f
yorick-mira_1.1.0+git20170124.3bd1c3~dfsg1-2.debian.tar.xz 8.2 KiB ff57b0da222d091c3515748985af4ac78b58e9bb2c79a9eee4fe11087cb7205f

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Binary packages built by this source

yorick-mira: optical interferometry image reconstruction within Yorick

 MiRA is an algorithm for image reconstruction from data provided by
 optical interferometers. It is written in the Yorick language and
 operated through the Yorick interpreter.
 MiRA won the 2008' Interferometric Imaging Beauty Contest organized
 by International Astronomical Union (IAU) to compare the image
 synthesis algorithms designed for optical interferometry. In a
 nutshell, MiRA proceeds by direct minimization of a penalized
 likelihood. This penalty is the sum of two terms: a likelihood term
 (typically a χ2) which enforces agreement of the model with the data,
 plus a regularization term to account for priors. The priors are
 required to lever the many degeneracies due to the sparseness of the
 spatial frequency sampling. MiRA implements many different
 regularizations (quadratic or edge-preserving smoothness, total
 variation, maximum entropy, etc.) and let the user defines his own
 priors. The likelihood penalty is modular and designed to account for
 available data of any kind (complex visibilities, powerspectra and/or
 closure phase). One of the strength of MiRA is that it is purely
 based on an inverse problem approach and can therefore cope with
 incomplete data set; for instance, MiRA can build an image without
 any Fourier phase information. Input data must be in OI-FITS format.