tweak documentation

documentation/chapters/overview.rst

@@ -8,44 +8,37 @@ General comments
 
 The purpose of this code is to run pseudo-spectral DNS of turbulence,
 and integrate particle trajectories in the resulting fields.
-In brief, the main aim of the code is to simplify the launching of
+An important aim of the code is to simplify the launching of
 compute jobs and postprocessing, up to and including the generation of
 publication-ready figures.
 
 For research, people routinely write code from scratch because research
 goals change to a point where modifying the previous code is too
 expensive.
-With bfps, the desire is to identify core functionality that should be
+With TurTLE, the desire is to identify core functionality that should be
 implemented in a library.
 The core library can then be used by many problem-specific codes.
 
 In this sense, the structuring of the code-base is non-standard.
 The core functionality is implemented in C++ (classes useful for
-describing working with fields or sets of particles), while a python
+describing working with fields or sets of particles), while a Python3
 wrapper is used for generating "main" programmes to be linked against
 the core library.
-The core library uses MPI for parallelization, and the python wrapper
+The core library uses a hybrid MPI/OpenMP approach for parallelization, and the Python3 wrapper
 compiles this core library when being installed.
-The compilation environment can be configured for different
-machines as required.
 
 Python3 "wrapper"
 -----------------
 
-In principle, users of the code should only need to use python3 for
+In principle, users of the code should only need to use Python3 for
 launching jobs and postprocessing data.
-While python2 compatibility should not be too hard to maintain, the
-usage of strings makes it a bit cumbersome ---
-the code makes extensive usage of strings for `HDF5` I/O.
 
-Classes defined in the python package can be used to generate executable
+Classes defined in the Python3 package can be used to generate executable
 codes, compile/launch them, and then for accessing and postprocessing
-data.
-Obviously, postprocessing methods can be optimized with C extensions or
-otherwise, as needed.
+data with a full Python3 environment.
 
 Code generation is quite straightforward, with C++ code snippets handled
-as strings in the python code, such that they can be combined in
+as strings in the Python3 code, such that they can be combined in
 different ways.
 
 Once a "main" file has been written, it is compiled and linked against