Commit 91576453 authored by Lukas Platz's avatar Lukas Platz
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docs: sync intro/teaser text from docs to readme

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...@@ -13,23 +13,31 @@ Summary ...@@ -13,23 +13,31 @@ Summary
**NIFTy**, "**N**umerical **I**nformation **F**ield **T**heor<strong>y</strong>", is **NIFTy**, "**N**umerical **I**nformation **F**ield **T**heor<strong>y</strong>", is
a versatile library designed to enable the development of signal a versatile library designed to enable the development of signal
inference algorithms that operate regardless of the underlying spatial inference algorithms that operate regardless of the underlying grids
grid and its resolution. Its object-oriented framework is written in (spatial, spectral, temporal, …) and their resolutions.
Python, although it accesses libraries written in C++ and C for Its object-oriented framework is written in Python, although it accesses
efficiency. libraries written in C++ and C for efficiency.
NIFTy offers a toolkit that abstracts discretized representations of NIFTy offers a toolkit that abstracts discretized representations of
continuous spaces, fields in these spaces, and operators acting on continuous spaces, fields in these spaces, and operators acting on
fields into classes. The correct normalization of operations on these fields into classes.
fields is taken care of automatically without concerning the user. This This allows for an abstract formulation and programming of inference
allows for an abstract formulation and programming of inference
algorithms, including those derived within information field theory. algorithms, including those derived within information field theory.
Thus, NIFTy permits its user to rapidly prototype algorithms in 1D, and NIFTy's interface is designed to resemble IFT formulae in the sense
then apply the developed code in higher-dimensional settings of real that the user implements algorithms in NIFTy independent of the topology
world problems. The set of spaces on which NIFTy operates comprises of the underlying spaces and the discretization scheme.
point sets, *n*-dimensional regular grids, spherical spaces, their Thus, the user can develop algorithms on subsets of problems and on
harmonic counterparts, and product spaces constructed as combinations of spaces where the detailed performance of the algorithm can be properly
those. 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 Installation
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