Commit 610f4d2a authored by Martin Reinecke's avatar Martin Reinecke
Browse files

describe versioning concept

parent be637750
Pipeline #76118 failed with stages
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......@@ -16,7 +16,7 @@ Installation
### Sources
The current version of DUCC can be obtained by cloning the repository via
The latest version of DUCC can be obtained by cloning the repository via
git clone
......@@ -31,6 +31,26 @@ DUCC and its mandatory dependencies can be installed via:
pip3 install --user git+
Installing multiple versions simultaneously
The interfaces of the DUCC components are expected to evolve over time; whenever
an interface changes in a manner that is not backwards compatible, the DUCC
version number will increase. As a consequence it might happen that one part of
a Python code may use an older version of DUCC while at the same time another
part requires a newer version. Since DUCC's version number is included in the
module name itself (the module is not called "ducc", but rather "ducc_x_y"),
this is not a problem, as multiple DUCC versions can be installed
The latest patch levels of a given DUCC version will always be available at the
HEAD of the git branch with the respective name. In other words, if you need
the latest incarnation of DUCC 0.1, this will be in branch "ducc_0_1" of the
git repository, and it will be installed as the package "ducc_0_1".
Later versions (like ducc_0_2 or ducc_1_0) will be maintained on new branches
and will be installed as "ducc_0_2" and "ducc_1_0", so that there will be no
conflict with potentially installed older versions.
DUCC components
......@@ -80,7 +100,7 @@ total convolution data cube from a set of sky and beam `a_lm` and computes
interpolated values for a given list of detector pointings.
Algorithmic details:
- the code uses `ducc.sht` SHTs to compute the data cube
- the code uses `ducc.sht` SHTs and `ducc.fft` FFTs to compute the data cube
- shared-memory parallelization is provided via standard C++ threads.
- for interpolation, the algorithm and kernel described in are used. This allows very efficient
......@@ -93,7 +113,7 @@ ducc.wgridder
Library for high-accuracy gridding/degridding of radio interferometry datasets
Programming aspects
- written in C++11, fully portable
- written in C++17, fully portable
- shared-memory parallelization via and C++ threads.
- Python interface available
- kernel computation is performed on the fly, avoiding inaccuracies
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