Skip to main content

NIFTy - Numerical Information Field Theory

pipeline status coverage report

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

Detailed installation instructions can be found in the NIFTy Documentation for:

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=nifty8 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

NIFTy8

NIFTy7

NIFTy6

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

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nifty8-8.1.tar.gz (291.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

nifty8-8.1-py3-none-any.whl (338.9 kB view details)

Uploaded Python 3

File details

Details for the file nifty8-8.1.tar.gz.

File metadata

  • Download URL: nifty8-8.1.tar.gz
  • Upload date:
  • Size: 291.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for nifty8-8.1.tar.gz
Algorithm Hash digest
SHA256 6e9852fae85e14235d88a5a9de6eb84d25353f934734b272b2b8449b34b8a34a
MD5 9ecb9653cca1b5148abf56b4641d3cff
BLAKE2b-256 4b80f73de4b780e4d86a63589c3e8cf9db0b38b7fc0639374ad648e272d3a115

See more details on using hashes here.

File details

Details for the file nifty8-8.1-py3-none-any.whl.

File metadata

  • Download URL: nifty8-8.1-py3-none-any.whl
  • Upload date:
  • Size: 338.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.6

File hashes

Hashes for nifty8-8.1-py3-none-any.whl
Algorithm Hash digest
SHA256 4618156399425c23cbd3ea425e02f663ac8915fc90b3271383a3ff4a7552571d
MD5 486d6c5fbd4f17ad95aff511f145ddea
BLAKE2b-256 93d899e4bd477fb3de6d52b59740df77d4dac3557744870a5ab234b44e18bdc3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page