NIFTY project homepage: http://www.mpa-garching.mpg.de/ift/nifty/
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 Cython, 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. Thereby, 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.
The NIFTY library features three main classes: spaces that represent certain grids, fields that are defined on spaces, and operators that apply to fields.
- Spaces
point_space
- unstructured list of pointsrg_space
- n-dimensional regular Euclidean gridlm_space
- spherical harmonicsgl_space
- Gauss-Legendre grid on the 2-spherehp_space
- HEALPix grid on the 2-spherenested_space
- arbitrary product of grids
- Fields
field
- generic class for (discretized) fields
field.cast_domain field.hat field.power field.smooth
field.conjugate field.inverse_hat field.pseudo_dot field.tensor_dot
field.dim field.norm field.set_target field.transform
field.dot field.plot field.set_val field.weight
- Operators
diagonal_operator
- purely diagonal matrices in a specified basisprojection_operator
- projections onto subsets of a specified basisvecvec_operator
- matrices derived from the outer product of a vectorresponse_operator
- exemplary responses that include a convolution, masking and projectionpropagator_operator
- information propagator in Wiener filter theoryexplicit_operator
- linear operators with an explicit matrix representation- (and more)
- (and more)
Parts of this summary are taken from1 without marking them explicitly as quotations.
- Python (v2.7.x)
- NumPy and SciPy
- matplotlib
- multiprocessing (standard library)
- GFFT (v0.1.0) - Generalized Fast Fourier Transformations for Python - optional
- HEALPy (v1.8.1 without openmp) - A Python wrapper for HEALPix -optional
- libsharp-wrapper (v0.1.2 without openmp) - A Python wrapper for the libsharp library -optional
The latest release is tagged v1.0.7 and is available as a source package at https://github.com/information-field-theory/nifty/tags. The current version can be obtained by cloning the repository:
git clone git://github.com/information-field-theory/nifty.git
NIFTY can be installed using PyPI and pip by running the following command:
pip install ift_nifty
Alternatively, a private or user specific installation can be done by:
pip install --user ift_nifty
NIFTY can be installed using Distutils by running the following command:
cd nifty python setup.py install
Alternatively, a private or user specific installation can be done by:
python setup.py install --user python setup.py install --install-lib=/SOMEWHERE
For a quickstart, you can browse through the informal introduction or dive into NIFTY by running one of the demonstrations, e.g.:
>>> run -m nifty.demos.demo_wf1
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 [Selig et al., 2013]."
The NIFTY package is licensed under the GPLv3 and is distributed without any warranty.
NIFTY project homepage: http://www.mpa-garching.mpg.de/ift/nifty/
Selig et al., "NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference", A&A, vol. 554, id. A26, 2013; arXiv:1301.4499↩