NIFTy - Numerical Information Field Theory ========================================== [![build status](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/badges/NIFTy_5/build.svg)](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/commits/NIFTy_5) [![coverage report](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/badges/NIFTy_5/coverage.svg)](https://gitlab.mpcdf.mpg.de/ift/nifty-dev/commits/NIFTy_5) **NIFTy** project homepage: [http://ift.pages.mpcdf.de/NIFTy](http://ift.pages.mpcdf.de/NIFTy) Summary ------- ### Description **NIFTy**, "**N**umerical **I**nformation **F**ield **T**heory", 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. 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. Installation ------------ ### Requirements - [Python 3](https://www.python.org/) (3.5.x or later) - [SciPy](https://www.scipy.org/) - [pyFFTW](https://pypi.python.org/pypi/pyFFTW) Optional dependencies: - [pyHealpix](https://gitlab.mpcdf.mpg.de/ift/pyHealpix) (for harmonic transforms involving domains on the sphere) - [mpi4py](https://mpi4py.scipy.org) (for MPI-parallel execution) - [matplotlib](https://matplotlib.org/) (for field plotting) ### Sources The current version of Nifty5 can be obtained by cloning the repository and switching to the NIFTy_5 branch: git clone https://gitlab.mpcdf.mpg.de/ift/NIFTy.git ### Installation In the following, we assume a Debian-based distribution. For other distributions, the "apt" lines will need slight changes. NIFTy5 and its mandatory dependencies can be installed via: sudo apt-get install git libfftw3-dev python3 python3-pip python3-dev pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/NIFTy.git@NIFTy_5 (Note: If you encounter problems related to `pyFFTW`, make sure that you are using a pip-installed `pyFFTW` package. Unfortunately, some distributions are shipping an incorrectly configured `pyFFTW` package, which does not cooperate with the installed `FFTW3` libraries.) Plotting support is added via: pip3 install --user matplotlib Support for spherical harmonic transforms is added via: pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git MPI support is added via: sudo apt-get install openmpi-bin libopenmpi-dev pip3 install --user mpi4py ### Running the tests In oder to run the tests one needs two additional packages: sudo apt-get install python3-coverage python3-parameterized python3-pytest python3-pytest-cov Afterwards the tests (including a coverage report) can be run using the following command in the repository root: pytest-3 --cov=nifty5 test ### First Steps For a quick start, you can browse through the [informal introduction](http://ift.pages.mpcdf.de/NIFTy/code.html) or dive into NIFTy by running one of the demonstrations, e.g.: python3 demos/getting_started_1.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 [(https://gitlab.mpcdf.mpg.de/ift/NIFTy)](https://gitlab.mpcdf.mpg.de/ift/NIFTy)" and a citation to one of the [publications](http://ift.pages.mpcdf.de/NIFTy/citations.html). ### Release Notes The NIFTy package is licensed under the terms of the [GPLv3](https://www.gnu.org/licenses/gpl.html) and is distributed *without any warranty*.