Metadata-Version: 2.1
Name: tmmc-lnpy
Version: 0.1.0
Summary: Analysis of lnPi results from TMMC simulation
Home-page: https://github.com/wpk-nist-gov/tmmc-lnpy
Author: William P. Krekelberg
Author-email: wpk@nist.gov
License: NIST license
Keywords: TMMC
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: Public Domain
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.8
License-File: LICENSE

tmmc-lnpy
=========

Package to analyze :math:`\ln \Pi(N)` data from Transition Matrix Monte
Carlo simulation.

Installation
------------

From Source
~~~~~~~~~~~

.. code:: console

   # * From pip
   pip install tmmc-lnpy

   # * From conda/mamba
   conda install -c wpk-nist tmmc-lnpy

   # * From Source
   git clone {repo}
   cd {repo}

   # create needed environment
   conda env create -n {optional-name] -f environment.yaml

   # activate environment
   conda activate {optional-name/lnpy-env (default)}

   # install in development mode
   pip install -e . --no-deps

   # Optionally run tests.  This requires pytest
   conda install pytest

   pytest -x -v

Quick Intro
-----------

Take a look at the `basic usage <https://github.com/wpk-nist-gov/tmmc-lnpy/blob/master/docs/notebooks/Basic_usage.ipynb>`__
notebook for a quick introduction.

Note that the distrubution name `tmmc-lnpy` is different than the package name `lnpy`, due to name conflicts on pypi.  To load the package in python, do the following:

.. code:: python

    import lnpy
    import lnpy.examples

    ref = lnpy.examples("lj_sup")


Credits
~~~~~~~

This package was created with
`Cookiecutter <https://github.com/audreyr/cookiecutter>`__ and the
`wpk-nist-gov/cookiecutter-pypackage <https://github.com/wpk-nist-gov/cookiecutter-pypackage>`__
Project template forked from
`audreyr/cookiecutter-pypackage <https://github.com/audreyr/cookiecutter-pypackage>`__.

=======
History
=======

0.1.1 (2022-09-12)
------------------

* First release on PyPI.

This 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
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