Merge branch 'fix_readme' into 'NIFTy_5'

docs: sync intro/teaser text from docs to readme

See merge request !283

README.md

@@ -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