WARNING:

The code on this branch is not meant to be an official version of NIFTy. As a consequence, it does not install as package "nifty", but rather as "nifty2go", to allow parallel installation alongside the official NIFTy and avoid any conflicts.

NIFTY - Numerical Information Field Theory

NIFTY project homepage: http://www.mpa-garching.mpg.de/ift/nifty/

Summary

Description

NIFTY, "Numerical Information Field Theory", is a versatile library 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 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.

Class & Feature Overview

The NIFTY library features three main classes: spaces that represent certain grids, fields that are defined on spaces, and operators that apply to fields.

• Spaces
  • RGSpace - n-dimensional regular Euclidean grid
  • LMSpace - spherical harmonics
  • GLSpace - Gauss-Legendre grid on the 2-sphere
  • HPSpace - HEALPix grid on the 2-sphere
• Fields
  • Field - generic class for (discretized) fields

Field.conjugate     Field.dim          Field.norm
Field.vdot          Field.weight

• Operators
  • DiagonalOperator - purely diagonal matrices in a specified basis
  • FFTOperator - conversion between spaces and their harmonic counterparts
  • (and more)
• (and more)

Parts of this summary are taken from [1] without marking them explicitly as quotations.

Installation

Requirements

• Python (v2.7.x or 3.5.x)
  • NumPy

Sources

The current version of Nifty3 can be obtained by cloning the repository:

git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git

Installation via pip

It is possible to simply install NIFTy with all its dependencies via the command

pip install --user --process-dependency-links --egg git+https://gitlab.mpcdf.mpg.de/ift/NIFTy.git@nifty2go

Running the tests

In oder to run the tests one needs two additional packages:

pip install nose parameterized

Afterwards the tests (including a coverage report) are run using the following command in the repository root:

nosetests -x --with-coverage --cover-html --cover-package=nifty2go

First Steps

For a quick start, you can browse through the informal introduction or dive into NIFTY by running one of the demonstrations, e.g.:

python demos/wiener_filter_via_curvature.py

Acknowledgement

Please acknowledge the use of NIFTY in your publication(s) by using a phrase such as the following:

"Some of the results in this publication have been derived using the NIFTY package [Selig et al., 2013]."

References

Release Notes

The NIFTY package is licensed under the GPLv3 and is distributed without any warranty.

NIFTY project homepage:

[1] Selig et al., "NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference", A&A, vol. 554, id. A26, 2013; arXiv:1301.4499