Numerical Information Field Theory
Project description
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.
-
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
-
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
-
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)
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 https://gitlab.mpcdf.mpg.de/ift/NIFTy/tags. 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. Further install instructions for libsharp may be found here: https://sourceforge.net/p/libsharp/code/ci/master/tree/:
sudo port install autoconf 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
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: http://www.mpa-garching.mpg.de/ift/nifty/
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