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 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 (v2.7.x or 3.5.x)
• NumPy
• pyFFTW

Optional dependencies:

• pyHealpix (for harmonic transforms involving domains on the sphere)
• mpi4py (for MPI-parallel execution)
• matplotlib (for field plotting)
• SciPy (for additional minimization algorithms)

Sources

The current version of Nifty4 can be obtained by cloning the repository and switching to the NIFTy_4 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.

NIFTy4 and its mandatory dependencies can be installed via:

sudo apt-get install git libfftw3-dev python python-pip python-dev
pip install --user git+https://gitlab.mpcdf.mpg.de/ift/NIFTy.git@NIFTy_4

(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:

pip install --user matplotlib

Support for spherical harmonic transforms is added via:

pip install --user git+https://gitlab.mpcdf.mpg.de/ift/pyHealpix.git

MPI support is added via:

sudo apt-get install openmpi-bin libopenmpi-dev
pip install --user mpi4py

Scipy-based minimizers are enabled via:

pip install --user scipy

Installation for Python 3

If you want to run NIFTy with Python 3, you need to make the following changes to the instructions above:

• in all apt-get commands, replace python-* by python3-*
• in all pip commands, replace pip by pip3

Running the tests

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

pip install --user nose parameterized coverage

Afterwards the tests (including a coverage report) can be run using the following command in the repository root:

nosetests -x --with-coverage --cover-html --cover-package=nifty4

First Steps

For a quick start, you can browse through the informal introduction or dive into NIFTy by running one of the demonstrations, e.g.:

python demos/wiener_filter_via_curvature.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 [Steininger et al., 2017]."[1]

References

Release Notes

The NIFTy package is licensed under the terms of the GPLv3 and is distributed without any warranty.

[1] Steininger et al., "NIFTy 3 - Numerical Information Field Theory - A Python framework for multicomponent signal inference on HPC clusters", 2017, submitted to PLOS One; arXiv:1708.01073