nifty7 7.5

Library for signal inference algorithms that operate regardless of the underlying grids and their resolutions.

NIFTy - Numerical Information Field Theory

NIFTy project homepage: https://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

• Python 3 (3.6.x or later)
• SciPy

Optional dependencies:

• ducc0 for faster FFTs, spherical harmonic transforms, and radio interferometry gridding support
• mpi4py (for MPI-parallel execution)
• matplotlib (for field plotting)

Sources

The current version of NIFTy7 can be obtained by cloning the repository and switching to the NIFTy_7 branch:

git clone https://gitlab.mpcdf.mpg.de/ift/nifty.git

Installation for users

If you only want to to use NIFTy in your projects, but not change its source code, the easiest way to install the package is the command:

pip install --user nifty7

Depending on your OS, you may have to use pip3 instead of pip. This approach should work on Linux, MacOS and Windows.

Installation for developers

In the following, we assume a Debian-based distribution. For other distributions, the "apt" lines will need slight changes.

NIFTy7 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_7

Plotting support is added via:

sudo apt-get install python3-matplotlib

The DUCC0 package is installed via:

pip3 install ducc0

If this library is present, NIFTy will detect it automatically and prefer ducc0.fft over SciPy's FFT. The underlying code is actually the same, but DUCC's FFT is compiled with optimizations for the host CPU and can provide significantly faster transforms.

MPI support is added via:

sudo apt-get install python3-mpi4py

Run the tests

To run the tests, additional packages are required:

sudo apt-get install python3-pytest-cov

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

pytest-3 --cov=nifty7 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

Acknowledgements

Please consider acknowledging 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.

Contributors

NIFTy7

• Andrija Kostic
• Gordian Edenhofer
• Jakob Knollmüller
• Jakob Roth
• Lukas Platz
• Matteo Guardiani
• Martin Reinecke
• Philipp Arras
• Philipp Frank
• Reimar Heinrich Leike
• Simon Ding
• Vincent Eberle

NIFTy6

• Andrija Kostic
• Gordian Edenhofer
• Jakob Knollmüller
• Lukas Platz
• Martin Reinecke
• Philipp Arras
• Philipp Frank
• Philipp Haim
• Reimar Heinrich Leike
• Rouven Lemmerz
• Torsten Enßlin
• Vincent Eberle

NIFTy5

• Christoph Lienhard
• Gordian Edenhofer
• Jakob Knollmüller
• Julia Stadler
• Julian Rüstig
• Lukas Platz
• Martin Reinecke
• Max-Niklas Newrzella
• Natalia
• Philipp Arras
• Philipp Frank
• Philipp Haim
• Reimar Heinrich Leike
• Sebastian Hutschenreuter
• Silvan Streit
• Torsten Enßlin

NIFTy4

• Christoph Lienhard
• Jakob Knollmüller
• Lukas Platz
• Martin Reinecke
• Mihai Baltac
• Philipp Arras
• Philipp Frank
• Reimar Heinrich Leike
• Silvan Streit
• Torsten Enßlin

NIFTy3

• Daniel Pumpe
• Jait Dixit
• Jakob Knollmüller
• Martin Reinecke
• Mihai Baltac
• Natalia
• Philipp Arras
• Philipp Frank
• Reimar Heinrich Leike
• Matevz Sraml
• Theo Steininger
• csongor

NIFTy2

• Jait Dixit
• Theo Steininger
• csongor

NIFTy1

• Johannes Buchner
• Marco Selig
• Theo Steininger