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NIFTy - Numerical Information Field Theory

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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

Optional dependencies:

  • 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.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

To run the tests, additional packages are required:

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 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)"

and a citation to one of the publications.

Release Notes

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