NIFTY: A versatile Python library for signal inference

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NIFTY: A versatile Python library for signal inference

Post by owlice » Mon Feb 25, 2013 6:42 pm

[c]NIFTY: A versatile Python library for signal inference[/c][/b]
Abstract: NIFTY (Numerical Information Field TheorY) is a versatile library enables the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTY offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTY permits rapid prototyping of algorithms in 1D and then the application of the developed code in higher-dimensional settings of real world problems. NIFTY operates on point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those.

Credit: Selig, Marco; Bell, Michael R.; Junklewitz, Henrik; Oppermann, Niels; Reinecke, Martin; Greiner, Maksim; Pachajoa, Carlos; Ensslin, Torsten A.

Site: https://github.com/information-field-theory/nifty
http://adsabs.harvard.edu/abs/2013A%26A...554A..26S

Bibcode: 2013ascl.soft02013S

ID: ascl:1302.013
Last edited by Ada Coda on Sun Feb 24, 2019 6:43 pm, edited 1 time in total.
Reason: Updated code entry.
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NIFTY update

Post by Guest » Thu Mar 21, 2013 2:36 pm

Update: A new NIFTY update has been released, see the project hompage http://www.mpa-garching.mpg.de/ift/nifty/ for details.

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