NIFTY - Numerical Information Field Theory ========================================== [![build status](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/master/build.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/master) [![coverage report](https://gitlab.mpcdf.mpg.de/ift/NIFTy/badges/master/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/NIFTy/commits/master) **NIFTY** project homepage: [http://www.mpa-garching.mpg.de/ift/nifty/](http://www.mpa-garching.mpg.de/ift/nifty/) Summary ------- ### Description **NIFTY**, "**N**umerical **I**nformation **F**ield **T**heor**y**", 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](http://www.mpa-garching.mpg.de/ift/nifty/space.html) - `RGSpace` - *n*-dimensional regular Euclidean grid - `LMSpace` - spherical harmonics - `GLSpace` - Gauss-Legendre grid on the 2-sphere - `HPSpace` - [HEALPix](http://sourceforge.net/projects/healpix/) grid on the 2-sphere - [Fields](http://www.mpa-garching.mpg.de/ift/nifty/field.html) - `Field` - generic class for (discretized) fields Field.conjugate Field.dim Field.norm Field.dot Field.set_val Field.weight - [Operators](http://www.mpa-garching.mpg.de/ift/nifty/operator.html) - `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](http://www.python.org/) (v2.7.x) - [NumPy](http://www.numpy.org/) - [Cython](http://cython.org/) ### Download The current version of Nifty3 can be obtained by cloning the repository: git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git and switching to the "master" branch: cd NIFTy git checkout master ### 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 python-numpy cython (MR: do we really need all of those? I would have expected something like "git autoconf python-dev python-pip python-numpy cython") - Install pyHealpix: git clone https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git cd pyHealpix autoreconf -i && ./configure --prefix=$HOME/.local && make -j4 && make install cd .. - Finally, NIFTy: git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git cd NIFTy git checkout master python setup.py install --user cd .. ### 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 basic packages numpy and cython: sudo port install py27-numpy sudo port install py27-cython - Install pyHealpix: git clone https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git cd pyHealpix autoreconf -i && ./configure --prefix=`python-config --prefix` && 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 git checkout master python setup.py install --user cd .. ### Installation using pypi (MR: I'm not sure whether this approach works ... pyHealpix is not registered with pypi and I'm not sure it can be.) NIFTY can be installed using [PyPI](https://pypi.python.org/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](http://www.mpa-garching.mpg.de/ift/nifty/start.html) 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](http://www.gnu.org/licenses/gpl.html) and is distributed *without any warranty*. * * * * * **NIFTY** project homepage: [](http://www.mpa-garching.mpg.de/ift/nifty/) [1] Selig et al., "NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference", [A&A, vol. 554, id. A26](http://dx.doi.org/10.1051/0004-6361/201321236), 2013; [arXiv:1301.4499](http://www.arxiv.org/abs/1301.4499)