github_url: | https://github.com/adc-connect/adcc/blob/master/docs/developers.rst |
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The adcc project consists of two main components,
namely the adcc python library and the hybrid
C++/Python libadcc
library.
The distribution of workload is such that libadcc
is responsible for:
- Interaction with the underlying linear algebra backend, i.e. the tensor library
- A unified interface to import Hartree-Fock results into the tensor library
In contrast the adcc Python module
- Implements iterative numerical solver schemes (e.g. the Davidson diagonalisation)
- Implements all working equations (Møller-Plesset perturbation theory and ADC matrix expressions)
- Interacts with Python-based SCF codes
- Provides high-level functionality and user interaction
- Orchestrates the workflow of an ADC calculation
- Implements analysis and visualisation of results.
The libadcc
library makes use of Pybind11
to expose the necessary C++ functionality to Python.
The functionality of adcc has already been described
in :ref:`performing-calculations` and :ref:`topics`.
In fact many of the functions and classes described
in these chapters are only partly implemented in adcc
and inherit from components defined in libadcc
,
which is discussed in more detail in :ref:`libadcc-layer`.
The entire source code of adcc can be obtained from github, simply by cloning
git clone https://code.adc-connect.org
Building and testing libadcc and adcc can be achieved by
./setup.py test
Afterwards modifications on the adcc python level can be done
at wish without re-running any build commands. If you modify source
files in libadcc
, make sure to re-run the ./setup.py test
such that your changes are being compiled into
the libadcc
shared library.
The setup.py
script of adcc is a largely a typical setuptools script,
but has a few additional commands and features worth knowing:
setup.py build_ext
: Build the C++ part of adcc in the current directory.setup.py cpptests
: Build and run the C++ tests forlibadcc
This very document is created with Sphinx and Doxygen extracting parts of the content directly from the source code documentation. Building the documentation locally thus requires both these tools and additionally and a few Sphinx plugins (e.g. breathe). This can be achieved using
pip install adcc[build_docs]
On the Python-side we follow the numpy docstring standard.
On the Python end, the repository contains a setup.cfg
file,
which largely defines the code conventions. Use your favourite flake8
-plugin
to ensure compliance. On the C++-end we provide .clang-format
files,
such that automatic formatting can be done with
your favourite tool based on clang-format
.
VIM: For setting up
vim
with this repository, you can use the following plugins: