| Name | tmmc-lnpy JSON |
| Version |
0.7.0
JSON |
| download |
| home_page | None |
| Summary | Analysis of lnPi results from TMMC simulation |
| upload_time | 2024-03-28 19:42:13 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <=3.12,>=3.8 |
| license | NIST-PD |
| keywords |
tmmc-lnpy
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- markdownlint-disable MD041 -->
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[license-link]: https://github.com/usnistgov/tmmc-lnpy/blob/main/LICENSE
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# `tmmc-lnpy`
## Overview
A package to analyze $\ln \Pi(N)$ data from Transition Matrix Monte Carlo
simulation. The main output from TMMC simulations, $\ln \Pi(N)$, provides a
means to calculate a host of thermodynamic properties. Moreover, if $\ln \Pi(N)$
is calculated at a specific chemical potential, it can be reweighted to provide
thermodynamic information at a different chemical potential
## Features
`tmmc-lnpy` provides a wide array of routines to analyze $\ln \Pi(N)$. These
include:
- Reweighting to arbitrary chemical potential
- Segmenting $\ln \Pi(N)$ (to identify unique phases)
- Containers for interacting with several values of $\ln \Pi(N)$ in a vectorized
way.
- Calculating thermodynamic properties from these containers
- Calculating limits of stability, and phase equilibrium
## Status
This package is actively used by the author. Please feel free to create a pull
request for wanted features and suggestions!
## Quick start
Use one of the following
```bash
pip install tmmc-lnpy
```
or
```bash
conda install -c conda-forge tmmc-lnpy
```
## Example usage
Note that the distribution name `tmmc-lnpy` is different than the import name
`lnpy` due to name clashing on pypi.
```pycon
>>> import numpy as np
>>> import lnpy
>>> import lnpy.examples
>>> ref = lnpy.examples.load_example_lnpimasked("lj_sub")
>>> phase_creator = lnpy.PhaseCreator(nmax=1, ref=ref)
>>> build_phases = phase_creator.build_phases_mu([None])
>>> collection = lnpy.lnPiCollection.from_builder(
... lnzs=np.linspace(-10, 3, 5), build_phases=build_phases
... )
# Collections are like pandas.Series
>>> collection
<class lnPiCollection>
lnz_0 phase
-10.00 0 [-10.0]
-6.75 0 [-6.75]
-3.50 0 [-3.5]
-0.25 0 [-0.25]
3.00 0 [3.0]
dtype: object
# Access xarray backend for Grand Canonical properties with `xge` accessor
>>> collection.xge.betaOmega()
<xarray.DataArray 'betaOmega' (lnz_0: 5, phase: 1)> Size: 40B
array([[-2.3245e-02],
[-6.0370e-01],
[-1.8552e+02],
[-1.5447e+03],
[-2.9580e+03]])
Coordinates:
* lnz_0 (lnz_0) float64 40B -10.0 -6.75 -3.5 -0.25 3.0
* phase (phase) int64 8B 0
beta float64 8B 1.372
volume float64 8B 512.0
Attributes:
dims_n: ['n_0']
dims_lnz: ['lnz_0']
dims_comp: ['component']
dims_state: ['lnz_0', 'beta', 'volume']
dims_rec: ['sample']
standard_name: grand_potential
long_name: $\beta \Omega(\mu,V,T)$
```
<!-- end-docs -->
## Documentation
See the [documentation][docs-link] for a look at `tmmc-lnpy` in action.
## License
This is free software. See [LICENSE][license-link].
## Related work
This package is used for with
[thermoextrap](https://github.com/usnistgov/thermo-extrap) to analyze
thermodynamically extrapolated macro state probability distributions.
## Contact
The author can be reached at <wpk@nist.gov>.
## Credits
This package was created using
[Cookiecutter](https://github.com/audreyr/cookiecutter) with the
[usnistgov/cookiecutter-nist-python](https://github.com/usnistgov/cookiecutter-nist-python)
template.
<!-- markdownlint-disable MD024 -->
<!-- markdownlint-disable MD013 -->
<!-- prettier-ignore-start -->
# Changelog
Changelog for `lnpy`
## Unreleased
[changelog.d]: https://github.com/usnistgov/tmmc-lnpy/tree/main/changelog.d
See the fragment files in [changelog.d]
<!-- prettier-ignore-end -->
<!-- markdownlint-enable MD013 -->
<!-- scriv-insert-here -->
## v0.7.0 — 2024-03-28
### Added
- Added submodule `lnpy.combine` to combine $\ln\Pi$ from multiple simulations.
## v0.6.0 — 2023-08-24
### Added
- Added type hints to most all code. Passing mypy (with strict) and pyright
(non-strict).
- Clean up doc strings in several places.
- Added nbval testing.
- Ran linters across all code and notebooks.
## v0.5.0 — 2023-07-06
### Added
- Now use [lazy_loader](https://github.com/scientific-python/lazy_loader) to
speed up initial load time.
Full set of changes:
[`v0.4.0...0.5.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.4.0...v0.5.0)
## v0.4.0 — 2023-05-12
### Added
- Package now available on conda-forge Full set of changes:
[`v0.3.0...0.4.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.3.0...v0.4.0)
### Changed
- Changed `examples.load_example_maskddata` to
`examples.load_example_lnpimasked` for consistency with other method names.
## v0.3.0 — 2023-05-02
### Added
- Added support for python3.11
- Moved `_docstrings` -> `docstrings` to make available
- Moved from local docfiller to module_utilities.docfiller
- Moved from local cached module to module-utilities.cached
- Add support for python3.11
### Changed
- Update package layout
- New linters via pre-commit
- Development env now handled by tox
Full set of changes:
[`v0.2.2...0.3.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.2.2...v0.3.0)
## v0.2.2 - 2023-04-05
Full set of changes:
[`v0.2.1...v0.2.2`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.2.1...v0.2.2)
## v0.2.1 - 2023-04-04
Full set of changes:
[`v0.2.0...v0.2.1`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.2.0...v0.2.1)
## v0.2.0 - 2023-04-04
Full set of changes:
[`v0.1.5...v0.2.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.5...v0.2.0)
## v0.1.5 - 2022-09-28
Full set of changes:
[`v0.1.4...v0.1.5`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.4...v0.1.5)
## v0.1.4 - 2022-09-26
Full set of changes:
[`v0.1.3...v0.1.4`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.3...v0.1.4)
## v0.1.3 - 2022-09-15
Full set of changes:
[`v0.1.2...v0.1.3`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.2...v0.1.3)
## v0.1.2 - 2022-09-14
Full set of changes:
[`v0.1.1...v0.1.2`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.1...v0.1.2)
## v0.1.1 - 2022-09-13
Full set of changes:
[`v0.1.0...v0.1.1`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.0...v0.1.1)
## v0.1.0 - 2022-09-13
Full set of changes:
[`v0.0.1...v0.1.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.0.1...v0.1.0)
## v0.0.1 - 2022-09-13
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
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.
Raw data
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The main output from TMMC simulations, $\\ln \\Pi(N)$, provides a\nmeans to calculate a host of thermodynamic properties. Moreover, if $\\ln \\Pi(N)$\nis calculated at a specific chemical potential, it can be reweighted to provide\nthermodynamic information at a different chemical potential\n\n## Features\n\n`tmmc-lnpy` provides a wide array of routines to analyze $\\ln \\Pi(N)$. These\ninclude:\n\n- Reweighting to arbitrary chemical potential\n- Segmenting $\\ln \\Pi(N)$ (to identify unique phases)\n- Containers for interacting with several values of $\\ln \\Pi(N)$ in a vectorized\n way.\n- Calculating thermodynamic properties from these containers\n- Calculating limits of stability, and phase equilibrium\n\n## Status\n\nThis package is actively used by the author. Please feel free to create a pull\nrequest for wanted features and suggestions!\n\n## Quick start\n\nUse one of the following\n\n```bash\npip install tmmc-lnpy\n```\n\nor\n\n```bash\nconda install -c conda-forge tmmc-lnpy\n```\n\n## Example usage\n\nNote that the distribution name `tmmc-lnpy` is different than the import name\n`lnpy` due to name clashing on pypi.\n\n```pycon\n>>> import numpy as np\n>>> import lnpy\n>>> import lnpy.examples\n\n>>> ref = lnpy.examples.load_example_lnpimasked(\"lj_sub\")\n\n>>> phase_creator = lnpy.PhaseCreator(nmax=1, ref=ref)\n>>> build_phases = phase_creator.build_phases_mu([None])\n>>> collection = lnpy.lnPiCollection.from_builder(\n... lnzs=np.linspace(-10, 3, 5), build_phases=build_phases\n... )\n\n\n# Collections are like pandas.Series\n>>> collection\n<class lnPiCollection>\nlnz_0 phase\n-10.00 0 [-10.0]\n-6.75 0 [-6.75]\n-3.50 0 [-3.5]\n-0.25 0 [-0.25]\n 3.00 0 [3.0]\ndtype: object\n\n\n# Access xarray backend for Grand Canonical properties with `xge` accessor\n>>> collection.xge.betaOmega()\n<xarray.DataArray 'betaOmega' (lnz_0: 5, phase: 1)> Size: 40B\narray([[-2.3245e-02],\n [-6.0370e-01],\n [-1.8552e+02],\n [-1.5447e+03],\n [-2.9580e+03]])\nCoordinates:\n * lnz_0 (lnz_0) float64 40B -10.0 -6.75 -3.5 -0.25 3.0\n * phase (phase) int64 8B 0\n beta float64 8B 1.372\n volume float64 8B 512.0\nAttributes:\n dims_n: ['n_0']\n dims_lnz: ['lnz_0']\n dims_comp: ['component']\n dims_state: ['lnz_0', 'beta', 'volume']\n dims_rec: ['sample']\n standard_name: grand_potential\n long_name: $\\beta \\Omega(\\mu,V,T)$\n\n\n```\n\n<!-- end-docs -->\n\n## Documentation\n\nSee the [documentation][docs-link] for a look at `tmmc-lnpy` in action.\n\n## License\n\nThis is free software. See [LICENSE][license-link].\n\n## Related work\n\nThis package is used for with\n[thermoextrap](https://github.com/usnistgov/thermo-extrap) to analyze\nthermodynamically extrapolated macro state probability distributions.\n\n## Contact\n\nThe author can be reached at <wpk@nist.gov>.\n\n## Credits\n\nThis package was created using\n[Cookiecutter](https://github.com/audreyr/cookiecutter) with the\n[usnistgov/cookiecutter-nist-python](https://github.com/usnistgov/cookiecutter-nist-python)\ntemplate.\n<!-- markdownlint-disable MD024 -->\n<!-- markdownlint-disable MD013 -->\n<!-- prettier-ignore-start -->\n\n# Changelog\n\nChangelog for `lnpy`\n\n## Unreleased\n\n[changelog.d]: https://github.com/usnistgov/tmmc-lnpy/tree/main/changelog.d\n\nSee the fragment files in [changelog.d]\n\n<!-- prettier-ignore-end -->\n\n<!-- markdownlint-enable MD013 -->\n\n<!-- scriv-insert-here -->\n\n## v0.7.0 \u2014 2024-03-28\n\n### Added\n\n- Added submodule `lnpy.combine` to combine $\\ln\\Pi$ from multiple simulations.\n\n## v0.6.0 \u2014 2023-08-24\n\n### Added\n\n- Added type hints to most all code. Passing mypy (with strict) and pyright\n (non-strict).\n- Clean up doc strings in several places.\n- Added nbval testing.\n- Ran linters across all code and notebooks.\n\n## v0.5.0 \u2014 2023-07-06\n\n### Added\n\n- Now use [lazy_loader](https://github.com/scientific-python/lazy_loader) to\n speed up initial load time.\n\nFull set of changes:\n[`v0.4.0...0.5.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.4.0...v0.5.0)\n\n## v0.4.0 \u2014 2023-05-12\n\n### Added\n\n- Package now available on conda-forge Full set of changes:\n [`v0.3.0...0.4.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.3.0...v0.4.0)\n\n### Changed\n\n- Changed `examples.load_example_maskddata` to\n `examples.load_example_lnpimasked` for consistency with other method names.\n\n## v0.3.0 \u2014 2023-05-02\n\n### Added\n\n- Added support for python3.11\n\n- Moved `_docstrings` -> `docstrings` to make available\n- Moved from local docfiller to module_utilities.docfiller\n- Moved from local cached module to module-utilities.cached\n- Add support for python3.11\n\n### Changed\n\n- Update package layout\n- New linters via pre-commit\n- Development env now handled by tox\n\nFull set of changes:\n[`v0.2.2...0.3.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.2.2...v0.3.0)\n\n## v0.2.2 - 2023-04-05\n\nFull set of changes:\n[`v0.2.1...v0.2.2`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.2.1...v0.2.2)\n\n## v0.2.1 - 2023-04-04\n\nFull set of changes:\n[`v0.2.0...v0.2.1`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.2.0...v0.2.1)\n\n## v0.2.0 - 2023-04-04\n\nFull set of changes:\n[`v0.1.5...v0.2.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.5...v0.2.0)\n\n## v0.1.5 - 2022-09-28\n\nFull set of changes:\n[`v0.1.4...v0.1.5`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.4...v0.1.5)\n\n## v0.1.4 - 2022-09-26\n\nFull set of changes:\n[`v0.1.3...v0.1.4`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.3...v0.1.4)\n\n## v0.1.3 - 2022-09-15\n\nFull set of changes:\n[`v0.1.2...v0.1.3`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.2...v0.1.3)\n\n## v0.1.2 - 2022-09-14\n\nFull set of changes:\n[`v0.1.1...v0.1.2`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.1...v0.1.2)\n\n## v0.1.1 - 2022-09-13\n\nFull set of changes:\n[`v0.1.0...v0.1.1`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.1.0...v0.1.1)\n\n## v0.1.0 - 2022-09-13\n\nFull set of changes:\n[`v0.0.1...v0.1.0`](https://github.com/usnistgov/tmmc-lnpy/compare/v0.0.1...v0.1.0)\n\n## v0.0.1 - 2022-09-13\nThis software was developed by employees of the National Institute of Standards\nand Technology (NIST), an agency of the Federal Government. Pursuant to title 17\nUnited States Code Section 105, works of NIST employees are not subject to\ncopyright protection in the United States and are considered to be in the public\ndomain. Permission to freely use, copy, modify, and distribute this software and\nits documentation without fee is hereby granted, provided that this notice and\ndisclaimer of warranty appears in all copies.\n\nTHE SOFTWARE IS PROVIDED 'AS IS' WITHOUT ANY WARRANTY OF ANY KIND, EITHER\nEXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY\nTHAT THE SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND FREEDOM FROM\nINFRINGEMENT, AND ANY WARRANTY THAT THE DOCUMENTATION WILL CONFORM TO THE\nSOFTWARE, OR ANY WARRANTY THAT THE SOFTWARE WILL BE ERROR FREE. IN NO EVENT\nSHALL NIST BE LIABLE FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO, DIRECT,\nINDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES, ARISING OUT OF, RESULTING FROM, OR\nIN ANY WAY CONNECTED WITH THIS SOFTWARE, WHETHER OR NOT BASED UPON WARRANTY,\nCONTRACT, TORT, OR OTHERWISE, WHETHER OR NOT INJURY WAS SUSTAINED BY PERSONS OR\nPROPERTY OR OTHERWISE, AND WHETHER OR NOT LOSS WAS SUSTAINED FROM, OR AROSE OUT\nOF THE RESULTS OF, OR USE OF, THE SOFTWARE OR SERVICES PROVIDED HEREUNDER.\n\nDistributions of NIST software should also include copyright and licensing\nstatements of any third-party software that are legally bundled with the code in\ncompliance with the conditions of those licenses.\n",
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