NIFTy - Numerical Information Field Theory
NIFTy project homepage: http://ift.pages.mpcdf.de/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 grids (spatial, spectral, temporal, …) and their resolutions. 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 these fields into classes. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. NIFTy's interface is designed to resemble IFT formulae in the sense that the user implements algorithms in NIFTy independent of the topology of the underlying spaces and the discretization scheme. Thus, the user can develop algorithms on subsets of problems and on spaces where the detailed performance of the algorithm can be properly evaluated and then easily generalize them to other, more complex spaces and the full problem, respectively.
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. NIFTy takes care of numerical subtleties like the normalization of operations on fields and the numerical representation of model components, allowing the user to focus on formulating the abstract inference procedures and process-specific model properties.
Installation
Requirements
Optional dependencies:
- pyFFTW for faster Fourier transforms
- pyHealpix (for harmonic transforms involving domains on the sphere)
- mpi4py (for MPI-parallel execution)
- matplotlib (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.gitInstallation
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 python3 python3-pip python3-dev
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/nifty.git@NIFTy_5Plotting support is added via:
sudo apt-get install python3-matplotlibNIFTy uses Numpy's FFT implementation by default. For large problems FFTW may be used because of its higher performance. It can be installed via:
sudo apt-get install libfftw3-dev
pip3 install --user pyfftwTo enable FFTW usage in NIFTy, call
nifty5.fft.enable_fftw()at the beginning of your code.
(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.)
Support for spherical harmonic transforms is added via:
pip3 install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.gitMPI support is added via:
sudo apt-get install python3-mpi4pyRunning the tests
To run the tests, additional packages are required:
sudo apt-get install python3-pytest-covAfterwards the tests (including a coverage report) can be run using the following command in the repository root:
pytest-3 --cov=nifty5 testFirst Steps
For a quick start, you can browse through the informal introduction or dive into NIFTy by running one of the demonstrations, e.g.:
python3 demos/getting_started_1.pyAcknowledgements
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)"
and a citation to one of the publications.
Licensing terms
The NIFTy package is licensed under the terms of the GPLv3 and is distributed without any warranty.