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 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.

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
  • point_space - unstructured list of points
  • rg_space - n-dimensional regular Euclidean grid
  • lm_space - spherical harmonics
  • gl_space - Gauss-Legendre grid on the 2-sphere
  • hp_space - HEALPix grid on the 2-sphere
  • nested_space - arbitrary product of grids
• Fields
  • field - generic class for (discretized) fields

field.cast_domain   field.hat           field.power        field.smooth
field.conjugate     field.inverse_hat   field.pseudo_dot   field.tensor_dot
field.dim           field.norm          field.set_target   field.transform
field.dot           field.plot          field.set_val      field.weight

• Operators
  • diagonal_operator - purely diagonal matrices in a specified basis
  • projection_operator - projections onto subsets of a specified basis
  • vecvec_operator - matrices derived from the outer product of a vector
  • response_operator - exemplary responses that include a convolution, masking and projection
  • propagator_operator - information propagator in Wiener filter theory
  • explicit_operator - linear operators with an explicit matrix representation
  • (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
  • SciPy
  • Cython
  • matplotlib
• GFFT (v0.1.0) - Generalized Fast Fourier Transformations for Python - optional
• HEALPy (v1.8.1 without openmp) - A Python wrapper for HEALPix -optional, only needed for spherical spaces
• libsharp-wrapper (v0.1.2 without openmp) - A Python wrapper for the libsharp library -optional, only needed for spherical spaces

Download

The latest release is tagged v1.0.7 and is available as a source package at . The current version 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 
  sudo apt-get install python-dev python-pip gsl-bin libgsl0-dev libfreetype6-dev libpng-dev  libatlas-base-dev gfortran 

• Install matplotlib:

  sudo apt-get install python-matplotlib

• Using pip install numpy, scipy, etc...:

  sudo pip install numpy scipy cython pyfits healpy

• Now install the 'non-standard' dependencies. First of all gfft:

  curl -LOk https://github.com/mrbell/gfft/tarball/master 
  tar -xzf master 
  cd mrbell-gfft* 
  sudo python setup.py install 
  cd ..

• Libsharp:

  git clone http://git.code.sf.net/p/libsharp/code libsharp-code 
  cd libsharp-code 
  sudo autoconf 
  ./configure --enable-pic --disable-openmp 
  sudo make 
  cd ..

• Libsharpwrapper:

  git clone http://github.com/mselig/libsharp-wrapper.git libsharp-wrapper 
  cd libsharp-wrapper 
  sudo python setup.py build_ext 
  sudo python setup.py install 
  cd ..

• Finally, NIFTy:

  git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
  cd nifty
  sudo python setup.py install 
  cd .. 

Installation on a linux cluster

This is for you if you want to install NIFTy on a HPC machine or cluster that is hosted by your university or institute. Most of the dependencies will most likely already be there, but you won't have superuser priviledges. In this case, instead:

sudo python setup.py install 

use:

python setup.py install --user

or:

python setup.py install --install-lib=/SOMEWHERE

in the instruction above. This will install the python packages into your local user directory.

Installation on OS X 10.11

We advice 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 basic packages python, scipy, matplotlib and cython:

  sudo port install py27-numpy
  sudo port install py27-scipy
  sudo port install py27-matplotlib
  sudo port install py27-cython

• Install gfft. Depending where you installed GSL you may need to change the path in setup.py!:

  sudo port install gsl
  git clone https://github.com/mrbell/gfft.git}{https://github.com/mrbell/gfft.git
  sudo python setup.py install

• Install healpy:

  sudo port install py27-pyfits
  git clone https://github.com/healpy/healpy.git
  cd healpy 
  sudo python setup.py install
  cd ..

• Install libsharp and therefore autoconf, automake and libtool. Installations instructions for libsharp may be found here: https://sourceforge.net/p/libsharp/code/ci/master/tree/:

  curl -OL http://ftpmirror.gnu.org/autoconf/autoconf-2.69.tar.gz
  tar -xzf autoconf-2.69.tar.gz 
  cd autoconf-2.69
  ./configure && make && sudo make install
  cd ..
  
  curl -OL http://ftpmirror.gnu.org/automake/automake-1.14.tar.gz
  tar -xzf automake-1.14.tar.gz
  cd automake-1.14
  ./configure && make && sudo make install
  cd ..
  
  curl -OL http://ftpmirror.gnu.org/libtool/libtool-2.4.2.tar.gz
  tar -xzf libtool-2.4.2.tar.gz
  cd libtool-2.4.2
  ./configure && make && sudo make install
  cd ..
  
  git clone http://git.code.sf.net/p/libsharp/code libsharp-code 
  cd libsharp-code 
  sudo autoconf 
  ./configure --enable-pic --disable-openmp 
  sudo make 
  cd ..

• Install libsharp-wrapper. Adopt the path of the libsharp installation in setup.py :

  sudo port install gcc
  sudo port select gcc  mp-gcc5
  git clone https://github.com/mselig/libsharp-wrapper.git
  cd libsharp-wrapper
  sudo python setup.py install
  cd ..

• Install NIFTy:

  git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git
  cd nifty
  sudo python setup.py install 
  cd .. 

Installation using pypi

NIFTY can be installed using PyPI and pip by running the following command:

pip install ift_nifty

Alternatively, a private or user specific installation can be done by:

pip install --user ift_nifty

Running the tests

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

pip install nose
pip install 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.:

>>> run -m nifty.demos.demo_wf1

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