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.. image:: https://github.com/linusjoonho/ipie/blob/main/logo.png :width: 200
ipie stands for I\ntelligent P\ython-based I\maginary-time E\volution with a focus on simplicity and speed.
ipie inherits a lot of QMC features from pauxy.
.. image:: https://github.com/linusjoonho/ipie/workflows/CI/badge.svg :target: https://github.com/linusjoonho/ipie/workflows/CI/badge.svg
.. image:: http://readthedocs.org/projects/ipie/badge/?version=latest :target: http://ipie.readthedocs.io/en/latest/?badge=latest
.. image:: https://img.shields.io/badge/License-Apache%20v2-blue.svg :target: http://github.com/linusjoonho/ipie/blob/master/LICENSE
.. .. image:: https://codecov.io/gh/linusjoonho/ipie/branch/develop/graph/badge.svg .. :target: https://codecov.io/gh/linusjoonho/ipie
.. image:: https://img.shields.io/badge/paper%20%28v0%29-arXiv%3A2209.04015-B31B1B :target: https://arxiv.org/abs/2209.04015
Copyright by Joonho Lee (joonholee@g.harvard.edu) Our first release paper can be found at https://pubs.acs.org/doi/10.1021/acs.jctc.2c00934
ipie currently supports:
Linux and Mac OS wheels are available for installation via pip
::
$ pip install ipie
For develpment you can instead clone the repository
::
$ git clone https://github.com/linusjoonho/ipie.git
and run the following in the top-level ipie directory
::
$ pip install -r requirements.txt
$ pip install -e .
To build ipie with MPI support (via mpi4py) do:
::
$ pip install -e .[mpi]
Note that mpi4py requires a working MPI installation to be built on your machine. This it is often the trickiest dependency to setup correctly.
One of the easiest ways (if you are using pip to install ipie wheels) is via conda:
::
conda install openmpi
which will just install the OpenMPI library.
We refer users to the mpi4py
documentation <https://mpi4py.readthedocs.io/en/stable/install.html>
_ for
alternative ways of building mpi4py and the required MPI library.
Further requirements are listed in requirements.txt.
Cupy is is required when running calculations on GPUs which
can be install following the instructions here <https://cupy.dev/>
_ .
Cuda aware MPI may be installed via conda-forge.
ipie 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
.github/workflows/ci.yml
for more details.
.. image:: https://github.com/linusjoonho/ipie/workflows/CI/badge.svg :target: https://github.com/linusjoonho/ipie/workflows/CI/badge.svg
Documentation and tutorials are available at
readthedocs <https://ipie.readthedocs.org>
_.
.. image:: http://readthedocs.org/projects/ipie/badge/?version=latest :target: http://ipie.readthedocs.io/en/latest/?badge=latest
FAQs
Python implementations of Imaginary-time Evolution algorithms
We found that ipie demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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