NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference
Marco Selig, Michael R. Bell, Henrik Junklewitz, Niels Oppermann,, Martin Reinecke, Maksim Greiner, Carlos Pachajoa, Torsten A. En{\ss}lin

TL;DR
NIFTY is a versatile Python library that simplifies the development of signal inference algorithms across various spatial grids and resolutions, facilitating rapid prototyping and application in complex, real-world problems.
Contribution
It introduces an object-oriented framework that abstracts continuous spaces and operators, enabling flexible, grid-independent signal inference algorithm development.
Findings
Successfully implements a Wiener filter across different spaces without modification
Supports a wide range of spaces including point sets, grids, and spherical spaces
Automates normalization of operations for user convenience
Abstract
NIFTY, "Numerical Information Field Theory", is a software package designed to enable 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 without concerning the user. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTY permits its user to rapidly prototype algorithms in 1D, and then apply the developed code in higher-dimensional settings of real world…
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Taxonomy
TopicsScientific Research and Discoveries · Geophysical and Geoelectrical Methods · Statistical Mechanics and Entropy
