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Python Based Auxiliary-Field Quantum Monte Carlo

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PAUXY

PAUXY is a collection of Python implementations of AUXilliarY field quantum Monte Carlo algorithms with a focus on simplicity rather than speed.

https://travis-ci.com/pauxy-qmc/pauxy.svg?branch=master http://readthedocs.org/projects/pauxy/badge/?version=latest

Features

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.

Installation

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

You may also need to set your PYTHONPATH appropriately.

Requirements

  • 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.

Running the Test Suite

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.

https://travis-ci.com/pauxy-qmc/pauxy.svg?branch=master

Documentation

Documentation and tutorials are available at readthedocs.

http://readthedocs.org/projects/pauxy/badge/?version=latest

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