NIFTY - Numerical Information Field Theory ========================================== **NIFTY** project homepage: ``_ Summary ------- Description ........... **NIFTY**, "\ **N**\umerical **I**\nformation **F**\ield **T**\heor\ **y**\ ", 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 `_ (v1.4 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 **v1.0.6** and is available as a source package at ``_. The current version can be obtained by cloning the repository:: git clone git://github.com/mselig/nifty.git cd nifty Installation ............ * NIFTY can be installed using `PyPI `_ and **pip** by running the following command:: pip install nifty Alternatively, a private or user specific installation can be done by:: pip install --user nifty * NIFTY can be installed using **Distutils** by running the following command:: 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 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*. ---- **NIFTY** project homepage: ``_