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.dot           Field.set_val      Field.weight

• Operators
  • DiagonalOperator - purely diagonal matrices in a specified basis
  • ProjectionOperator - projections onto subsets of a specified basis
  • PropagatorOperator - information propagator in Wiener filter theory
  • (and more)
• (and more)

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

Installation

Requirements

• Python (v2.7.x)
  • NumPy

Download

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

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

Installation on Ubuntu

This is for you if you want to install NIFTy on your personal computer running with an Ubuntu-like linux system were you have root priviledges. Starting with a fresh Ubuntu installation move to a folder like ~/Downloads:

• Install basic packages like python, python-dev, gsl and others:

  sudo apt-get install curl git autoconf libtool python-dev python-pip python-numpy

• Install pyHealpix:

  git clone https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
  (cd pyHealpix && autoreconf -i && ./configure --prefix=$HOME/.local --enable-openmp --enable-native-optimizations && make -j4 install)

• Finally, NIFTy:

  git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
  (cd NIFTy && python setup.py install --user)

Installation on Linux systems in general

Since all the "unconventional" packages (i.e. pyHealpix and NIFTy) listed in the section above are installed within the home directory of the user, the installation instructions for these should also work on any Linux machine where you do not have root access. In this case you have to ensure with your system administrators that the "standard" dependencies (python, numpy, etc.) are installed system-wide.

Installation on OS X 10.11

We advise to install the following packages in the order as they appear below. We strongly recommend to install all needed packages via MacPorts. Please be aware that not all packages are available on MacPorts, missing ones need to be installed manually. It may also be mentioned that one should only use one package manager, as multiple ones may cause trouble.

• Install numpy:

  sudo port install py27-numpy

• Install pyHealpix:

  git clone https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git
  cd pyHealpix
  autoreconf -i && ./configure --prefix=`python-config --prefix` --enable-openmp --enable-native-optimizations && make -j4 && sudo make install
  cd ..

  (The third command installs the package system-wide. User-specific installation would be preferrable, but we haven't found a simple recipe yet how to determine the installation prefix ...)

• Install NIFTy:

  git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
  (cd NIFTy && python setup.py install --user)

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 --exe --cover-html

First Steps

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

python demos/wiener_filter.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