Scheduled maintenance on Monday 2019-06-24 between 10:00-11:00 CEST

Commit 1c72283b authored by Philipp Arras's avatar Philipp Arras

Merge branch 'fix_readme' into 'NIFTy_5'

docs: sync intro/teaser text from docs to readme

See merge request !283
parents 1de0d9bc 91576453
Pipeline #43176 passed with stages
in 8 minutes and 8 seconds
......@@ -13,23 +13,31 @@ Summary
**NIFTy**, "**N**umerical **I**nformation **F**ield **T**heor<strong>y</strong>", 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 C++ and C for
efficiency.
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
fields into classes. 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
these fields into classes.
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.
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
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment