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        "author": "Brandon Bocklund",
        "author_email": "brandonbocklund@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "=====\nESPEI\n=====\n\nESPEI, or Extensible Self-optimizing Phase Equilibria Infrastructure, is a tool for automated thermodynamic database development within the CALPHAD method.\n\nThe ESPEI package is based on a fork of `pycalphad-fitting`_ and uses `pycalphad`_ for calculating Gibbs free energies of thermodynamic models.\nThe implementation for ESPEI involves first fitting single-phase data by calculating parameters in thermodynamic models that are linearly described by the single-phase input data.\nThen Markov Chain Monte Carlo (MCMC) is used to optimize the candidate models from the single-phase fitting to multi-phase zero-phase fraction data.\nSingle-phase and multi-phase fitting methods are described in Chapter 3 of `Richard Otis's thesis`_.\n\nThe benefit of this approach is the automated, simultaneous fitting for many parameters that yields uncertainty quantification, as shown in Otis and Liu High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME. `Jom 69, (2017)`_.\n\nThe name and idea of ESPEI are originally based off of Shang, Wang, and Liu, ESPEI: Extensible, Self-optimizing Phase Equilibrium Infrastructure for Magnesium Alloys `Magnes. Technol. 2010 617-622 (2010)`_.\n\n.. _pycalphad-fitting: https://github.com/richardotis/pycalphad-fitting\n.. _pycalphad: http://pycalphad.org\n.. _Richard Otis's thesis: https://etda.libraries.psu.edu/catalog/s1784k73d\n.. _Jom 69, (2017): http://dx.doi.org/10.1007/s11837-017-2318-6\n.. _Magnes. Technol. 2010 617-622 (2010): http://www.phases.psu.edu/wp-content/uploads/2010-Shang-Shunli-MagTech-ESPEI-0617-1.pdf\n\n\n.. figure:: docs/_static/cu-mg-mcmc-phase-diagram.png\n    :alt: Cu-Mg phase diagram\n    :scale: 100%\n\n    Cu-Mg phase diagram from a database created with and optimized by ESPEI.\n    See the `Cu-Mg Example <http://espei.org/en/latest/cu-mg-example.html>`_.\n\n\nInstallation\n============\n\n\nAnaconda (recommended)\n----------------------\n\nESPEI does not require any special compiler, but several dependencies do.\nTherefore it is suggested to install ESPEI from conda-forge.\n\n.. code-block:: bash\n\n    conda install -c pycalphad -c msys2 -c conda-forge --yes espei\n\n\nPyPI\n----\n\nBefore you install ESPEI via PyPI, be aware that pycalphad and\nemcee must be compiled and pycalphad requires an external\ndependency of `Ipopt <https://projects.coin-or.org/Ipopt>`_.\n\n.. code-block:: bash\n\n    pip install espei\n\n\nDevelopment versions\n--------------------\n\nYou may install ESPEI however you like, but here we suggest using\nAnaconda to download all of the required dependencies. This\nmethod installs ESPEI with Anaconda, removes specifically the\nESPEI package, and replaces it with the package from GitHub.\n\n.. code-block:: bash\n\n    git clone https://github.com/phasesresearchlab/espei.git\n    cd espei\n    conda install espei\n    conda remove --force espei\n    pip install -e .\n\nUpgrading ESPEI later requires you to run ``git pull`` in this directory.\n\n\nUsage\n=====\n\nESPEI has two different fitting modes: single-phase and multi-phase fitting.\nYou can run either of these modes or both of them sequentially.\n\nTo run either of the modes, you need to have a phase models file that describes the phases in the system using the standard CALPHAD approach within the compound energy formalism.\nYou also need to describe the data that ESPEI should fit to.\nYou will need single-phase and multi-phase data for a full run.\nFit settings and all datasets are stored as JSON files and described in detail at the `Gathering input data <http://espei.org/en/latest/input_data.html>`_ page.\nAll of your input datasets should be validated by running ``espei --check-datasets my-input-datasets``, where ``my-input-datasets`` is a folder of all your JSON files.\n\nThe main output result is going to be a database (defaults to ``out.tdb``), an array of the steps in the MCMC chain (defaults to ``chain.npy``), and the an array of the log-probabilities for each iteration and chain (defaults to ``lnprob.npy``).\n\nSingle-phase only\n-----------------\n\nIf you have only heat capacity, entropy and enthalpy data and mixing data (e.g. from first-principles),\nyou may want to see the starting point for your MCMC calculation.\n\nCreate an input file called ``espei-in.yaml``.\n\n.. code-block:: yaml\n\n    system:\n      phase_models: my-phases.json\n      datasets: my-input-datasets\n    generate_parameters:\n      excess_model: linear\n      ref_state: SGTE91\n\nThen ESPEI can be run by running\n\n.. code-block:: bash\n\n    espei --input espei-in.yaml\n\n\nMulti-phase only\n----------------\n\nIf you have a database already and just want to do a multi-phase fitting, you can specify a starting TDB file (named ``my-tdb.tdb``) with\n\n.. code-block:: YAML\n\n    system:\n      phase_models: my-phases.json\n      datasets: my-input-data\n    mcmc:\n      mcmc_steps: 1000\n      input_db: my-tdb.tdb                \n\nThe TDB file you input must have all of the degrees of freedom you want as FUNCTIONs with names beginning with ``VV``.\n\nRestart from previous run-phase only\n------------------------------------\n\nIf you've run an MCMC fitting already in ESPEI and have a chain file called ``my-previous-chain.npy`` , then you can resume the calculation with the following input file\n\n.. code-block:: yaml\n\n    system:\n      phase_models: my-phases.json\n      datasets: my-input-data\n    mcmc:\n      mcmc_steps: 1000\n      input_db: my-tdb.tdb\n      restart_chain: my-previous-chain.npy\n\n\nFull run\n--------\n\nA minimal full run of ESPEI with single phase fitting and MCMC fitting is done by the following\n\n.. code-block:: yaml\n\n    system:\n      phase_models: my-phases.json\n      datasets: my-input-data\n    generate_parameters:\n      excess_model: linear\n      ref_state: SGTE91\n    mcmc:\n      mcmc_steps: 1000\n\n\nInput Customization\n-------------------\n\nESPEI lets you control many aspects of your calculations with the input files shown above.\nSee `Writing input files <http://espei.org/en/latest/writing_input.html>`_ for a full description of all possible inputs.\n\n\nFAQ\n---\n\nQ: There is an error in my JSON files\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nA: Common mistakes are using single quotes instead of the double quotes required by JSON files.\nAnother common source of errors is misaligned open/closing brackets.\n\nMany mistakes are found with ESPEI's ``check-datasets`` utility.\nRun ``espei check-datasets my-input-datasets`` on your directory ``my-input-datasets``. \n\nQ: How do I analyze my results?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nA: By default, ESPEI will create ``chain.npy`` and ``lnprob.npy`` for the MCMC chain at the end of your run and according to the save interval (defaults to every 20 iterations).\nThese are created from arrays via ``numpy.save()`` and can thus be loaded with ``numpy.load()``.\nNote that the arrays are preallocated with zeros.\nThese filenames and settings can be changed using in the input file.\nYou can then use these chains and corresponding log-probabilities to make corner plots, calculate autocorrelations, find optimal parameters for databases, etc..\nFinally, you can use py:mod:`espei.plot` functions such as ``multiplot`` to plot phase diagrams with your input equilibria data and ``plot_parameters`` to compare single-phase data (e.g. formation and mixing data) with the properties calculated with your database.\n\nQ: Can I run ESPEI on a supercomputer supporting MPI?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nA: Yes! ESPEI has MPI support.\nTo use ESPEI with MPI, you simply call ESPEI in the same way as above with `mpirun` or whichever MPI software you use.\nYou also must indicate to ESPEI that it should create an MPI scheduler by setting the input option ``scheduler: MPIPool`` in the ``mcmc`` heading.\nBe aware that ``mpi4py`` must be compiled with an MPI-enabled compiler, see the `mpi4py installation instructions <https://mpi4py.readthedocs.io/en/stable/install.html>`_.\n\nGetting Help\n============\n\nFor help on installing and using ESPEI, please join the `PhasesResearchLab/ESPEI Gitter room <https://gitter.im/PhasesResearchLab/ESPEI>`_.\n\nBugs and software issues should be reported on `GitHub <https://github.com/PhasesResearchLab/ESPEI/issues>`_.\n\nModule Hierarchy\n================\n\n* ``fit.py`` is the main entry point\n* ``paramselect.py`` is where all of the fitting happens. This is the core.\n* ``core_utils.py`` contains specialized utilities for ESPEI.\n* ``utils.py`` are utilities with reuse potential outside of ESPEI.\n* ``plot.py`` holds plotting functions\n\nLicense\n=======\n\nESPEI is MIT licensed. See LICENSE.",
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