NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference

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 problems. The set of spaces on which NIFTY operates comprises point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those. The functionality and diversity of the package is demonstrated by a Wiener filter code example that successfully runs without modification regardless of the space on which the inference problem is defined.

9 pages, 3 tables, 4 figures, accepted by Astronomy & Astrophysics; refereed version, 1 figure added, results unchanged

References in corpus (3)

• Reconstruction of Gaussian and log-normal fields with spectral smoothness
• Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty
• Libpsht - algorithms for efficient spherical harmonic transforms

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