ift_nifty 1.0.7

Numerical Information Field Theory

NIFTY project homepage: http://www.mpa-garching.mpg.de/ift/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 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.

Class & Feature Overview

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 points

  • rg_space - n-dimensional regular Euclidean grid

  • lm_space - spherical harmonics

  • gl_space - Gauss-Legendre grid on the 2-sphere

  • hp_space - HEALPix grid on the 2-sphere

  • nested_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 basis

  • projection_operator - projections onto subsets of a specified basis

  • vecvec_operator - matrices derived from the outer product of a vector

  • response_operator - exemplary responses that include a convolution, masking and projection

  • propagator_operator - information propagator in Wiener filter theory

  • explicit_operator - linear operators with an explicit matrix representation

  • (and more)

• (and more)

Parts of this summary are taken from [1] without marking them explicitly as quotations.

Installation

Requirements

• 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

Download

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

Installation

• 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

First Steps

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

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 [Selig et al., 2013].”

References

[1]

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

Release Notes

The NIFTY package is licensed under the GPLv3 and is distributed without any warranty.

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NIFTY project homepage: http://www.mpa-garching.mpg.de/ift/nifty/