PAUXY is a collection of Python implementations of AUXilliarY field quantum Monte Carlo algorithms with a focus on simplicity rather than speed.
PAUXY can currently:
- estimate ground state properties of real (ab-initio) and model (Hubbard + UEG) systems.
- perform phaseless and constrained path AFQMC.
- calculate expectation values and correlation functions using back propagation.
- calculate imaginary time correlation functions.
- perform simple data analysis.
Clone the repository
$ git clone https://github.com/pauxy-qmc/pauxy.git
and run the following in the top-level pauxy directory
$ pip install -r requirements.txt $ python setup.py build_ext --inplace $ python setup.py install
You may also need to set your PYTHONPATH appropriately.
- python (>= 3.6)
- numpy
- scipy
- h5py
- mpi4py
- cython
- pandas
Minimum versions are listed in the requirements.txt. To run the tests you will need pytest. To perform error analysis you will also need pyblock.
Pauxy contains unit tests and some longer driver tests that can be run using pytest by running:
$ pytest -v
in the base of the repo. Some longer parallel tests are also run through the CI. See travis.yml for more details.
Documentation and tutorials are available at readthedocs.