Components of adcc¶
The adcc project consists of two main components,
namely the adcc python library and the hybrid
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.
libadcc library makes use of Pybind11
to expose the necessary C++ functionality to Python.
The functionality of adcc has already been described
in Performing calculations with adcc and Overview of adcc.
In fact many of the functions and classes described
in these chapters are only partly implemented in adcc
and inherit from components defined in
which is discussed in more detail in libadcc: C++ library.
Obtaining the adcc sources¶
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
Afterwards modifications on the adcc python level can be done
at wish without re-running any build commands. If you modify source
libadcc, make sure to re-run the
such that your changes are being compiled into
libadcc shared library.
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 test: Run the adcc unit tests via pytest. Implies
build_ext. This command has a few useful options:
-m full: Run the full test suite not only the fast tests
-s: Skip updating the testdata
-a: Pass additional arguments to
pytest(See pytest documentation). This is extremely valuable in combination with the
pytest. For example
./setup.py test -a "-k 'functionality and adc2'"
will run only the tests, which have the keywords “functionality” and “adc2” in their description.
setup.py build_docs: Build the documentation locally using Doxygen and Sphinx. See the section below for details.
setup.py cpptests: Build and run the C++ tests for
Documentation, documentation, documentation¶
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
which largely defines the code conventions. Use your favourite
to ensure compliance. On the C++-end we provide
such that automatic formatting can be done with
your favourite tool based on