Package: thermoextrap
thermoextrap: Thermodynamic Extrapolation/Interpolation LibraryThis repository contains code used and described in references [^fn1] [^fn2].[^fn1]:Extrapolation and Interpolation Strategies for Efficiently Estimating Structural Observables as a Function of Temperature and Density[^fn2]:
Leveraging Uncertainty Estimates and Derivative Information in Gaussian
Process Regression for Expedited Data Collection in Molecular Simulations.
In preparation.OverviewIf you find this code useful in producing published works, please provide an
appropriate citation. Note that the second citation is focused on adding
features that make use of GPR models based on derivative information produced by
the core code base. For now, the GPR code, along with more information, may be
found underhere. In a future release, we expect this to be fully
integrated into the code base rather than a standalone module.Code included here can be used to perform thermodynamic extrapolation and
interpolation of observables calculated from molecular simulations. This allows
for more efficient use of simulation data for calculating how observables change
with simulation conditions, including temperature, density, pressure, chemical
potential, or force field parameters. Users are highly encourage to work through
theJupyter Notebookspresenting examples for a variety of
different observable functional forms. We only guarantee that this code is
functional for the test cases we present here or for which it has previously
been applied Additionally, the code may be in continuous development at any
time. Use at your own risk and always check to make sure the produced results
make sense. If bugs are found, please report them. If specific features would be
helpful just let us know and we will be happy to work with you to come up with a
solution.FeaturesFast calculation of derivativesStatusThis package is actively used by the author. Please feel free to create a pull
request for wanted features and suggestions!Quick startthermoextrapmay be installed with either (recommended)condainstall-cconda-forgethermoextraporpipinstallthermoextrapIf you use pip, then you can include additional dependencies usingpipinstallthermoextrap[all]If you installthermoextrapwith conda, there are additional optional
dependencies that take some care for installation. We recommend installing the
following viapip, as the versions on the conda/conda-forge channels are often
a bit old.pipinstalltensorflowtensorflow-probabilitygpflowTo install from source do the following:gitclonegit@github.com:usnistgov/thermoextrap.gitcdthermoextrap
pipinstall.[-e]To (optionally) include the example data do the following:gitsubmoduleupdate--init--recursiveExample usageimportthermoextrapDocumentationSee thedocumentationfor a look atthermoextrapin action.To have a look at usingthermoextrapwith Gaussian process regression, look in
thegprandgpr_active_learningdirectories.LicenseThis is free software. SeeLICENSE.Related workThis package extensively uses thecmomypackage to handle central comoments.ContactQuestions may be addressed to Bill Krekelberg atwilliam.krekelberg@nist.govor
Jacob Monroe atjacob.monroe@uark.edu.CreditsThis package was created withCookiecutterand thewpk-nist-gov/cookiecutter-pypackageProject template forked fromaudreyr/cookiecutter-pypackage.ChangelogChangelog forthermoextrapUnreleasedSee the fragment files inchangelog.dv0.4.0 — 2023-06-15AddedPackage now available on conda-forgeNow support python3.11Bumped pymbar version to pymbar>=4.0ChangedSwitched from tox to nox for testing.DeprecatedNo longer support pymbar < 4.0v0.3.0 — 2023-05-03ChangedNew linters via pre-commitDevelopment env now handled by toxMovedmodesl, data, idealgasfromthermoextrap.coretothermoextrap.
These were imported at top level anyway. This fixes issues with doing things
likefrom thermoextrap.data import ..., etc.Movedcore._docstrings_todocstrings.Now usingcmomy.docstringsinstead of repeating them here.Full set of changes:v0.2.2...v0.3.0v0.2.2 - 2023-04-05Full set of changes:v0.2.1...v0.2.2v0.2.1 - 2023-03-30Full set of changes:v0.2.0...v0.2.1v0.2.0 - 2023-03-28Full set of changes:v0.1.9...v0.2.0v0.1.9 - 2023-02-15Full set of changes:v0.1.8...v0.1.9v0.1.8 - 2023-02-15Full set of changes:v0.1.7...v0.1.8v0.1.7 - 2023-02-14This software was developed by employees of the National Institute of Standards
and Technology (NIST), an agency of the Federal Government. Pursuant to title 17
United States Code Section 105, works of NIST employees are not subject to
copyright protection in the United States and are considered to be in the public
domain. Permission to freely use, copy, modify, and distribute this software and
its documentation without fee is hereby granted, provided that this notice and
disclaimer of warranty appears in all copies.THE SOFTWARE IS PROVIDED 'AS IS' WITHOUT ANY WARRANTY OF ANY KIND, EITHER
EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY
THAT THE SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND FREEDOM FROM
INFRINGEMENT, AND ANY WARRANTY THAT THE DOCUMENTATION WILL CONFORM TO THE
SOFTWARE, OR ANY WARRANTY THAT THE SOFTWARE WILL BE ERROR FREE. IN NO EVENT
SHALL NIST BE LIABLE FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO, DIRECT,
INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES, ARISING OUT OF, RESULTING FROM, OR
IN ANY WAY CONNECTED WITH THIS SOFTWARE, WHETHER OR NOT BASED UPON WARRANTY,
CONTRACT, TORT, OR OTHERWISE, WHETHER OR NOT INJURY WAS SUSTAINED BY PERSONS OR
PROPERTY OR OTHERWISE, AND WHETHER OR NOT LOSS WAS SUSTAINED FROM, OR AROSE OUT
OF THE RESULTS OF, OR USE OF, THE SOFTWARE OR SERVICES PROVIDED HEREUNDER.Distributions of NIST software should also include copyright and licensing
statements of any third-party software that are legally bundled with the code in
compliance with the conditions of those licenses.