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ENPMDA-0.5.0
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مشاهده بیشترتوضیحات
parallel analysis for ensemble simulations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ENPMDA-0.5.0 |
| نام | ENPMDA |
| نسخه کتابخانه | 0.5.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Yuxuan Zhuang |
| ایمیل نویسنده | yuxuan.zhuang@dbb.su.se |
| آدرس صفحه اصلی | https://github.com/yuxuanzhuang/ENPMDA |
| آدرس اینترنتی | https://pypi.org/project/ENPMDA/ |
| مجوز | GNU General Public License v3 |
============================
Ensemble Parallel MDAnalysis
============================
**Warning: This package is still under constrution.**
|pypi| |travis| |readthedocs| |codecov|
|mdanalysis|
|colab|
ENPMDA is a parallel analysis package for ensemble simulations
powered by MDAnalysis.
It stores metadata in ``pandas.DataFrame``
and distributes computation jobs in ``dask.DataFrame``
so that the parallel analysis can be performed
not only for one single trajectory
but also across simulations and analyses.
It can be used as an initial inspection of
the raw trajectories as well as a framework for
extracting features from final production simulations
for further e.g. machine learning and markov
state modeling. It automatically fixes the PBC issue, and
align and center the protein inside the simulation box.
It also works for multimeric proteins!
The framework is intended to be adaptable by being able to
simply wrapping MDAnalysis analysis functions without worrying
about the parallel machinery behind.
* Free software: GNU General Public License v3
* Documentation: https://ENPMDA.readthedocs.io.
Features
--------
* Parallel analysis for ensemble simulations.
* Dataframe for storing and accessing results.
* dask-based task scheduler, suitable for both workstations and clusters.
* Expandable analysis library powered by MDAnalysis.
Example Code Snippet
--------------------
.. code:: python
from ENPMDA import MDDataFrame
from ENPMDA.preprocessing import TrajectoryEnsemble
from ENPMDA.analysis import get_backbonetorsion, rmsd_to_init
# construct trajectory ensemble
traj_ensembles = TrajectoryEnsemble(
ensemble_name='ensemble',
topology_list=ensemble_top_list,
trajectory_list=ensemble_traj_list
)
# initilize dataframe and add trajectory ensemble
md_dataframe = MDDataFrame(dataframe_name='dataframe')
md_dataframe.add_traj_ensemble(traj_ensembles, npartitions=16)
# add analyses
md_dataframe.add_analysis(get_backbonetorsion)
md_dataframe.add_analysis(rmsd_to_init)
# save dataframe
md_dataframe.save('results')
# retrieve feature
feature_dataframe = md_dataframe.get_feature([
'torsion',
'rmsd_to_init'
])
# plot analysis results
import seaborn as sns
sns.barplot(data=feature_dataframe,
x='system',
y='rmsd_to_init')
sns.lineplot(data=feature_dataframe,
x='traj_time',
y='0_phi_cos',
hue='system')
Workflow Illustration
---------------------
.. image:: /docs/source/_static/example.png
:width: 700
:alt: Illustration of the ensemble analysis workflow.
TODO
----
* option to add more than one ensemble
* more analysis functions.
* unit testing
* benchmarking
* documentation
* add functions to cancel running tasks
See Also
--------
* MDAnaysis: https://www.mdanalysis.org/
* pmda: https://github.com/mdAnalysis/pmda
* dask: https://dask.org/
Credits
-------
This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.
.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
.. |mdanalysis| image:: https://img.shields.io/badge/powered%20by-MDAnalysis-orange.svg?logoWidth=16&logo=data:image/x-icon;base64,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
:alt: Powered by MDAnalysis
:target: https://www.mdanalysis.org
.. |pypi| image:: https://img.shields.io/pypi/v/ENPMDA.svg
:target: https://pypi.python.org/pypi/ENPMDA
.. |travis| image:: https://img.shields.io/travis/yuxuanzhuang/ENPMDA.svg
:target: https://travis-ci.com/yuxuanzhuang/ENPMDA
.. |readthedocs| image:: https://readthedocs.org/projects/pip/badge/?version=latest&style=flat
.. |codecov| image:: https://codecov.io/gh/yuxuanzhuang/ENPMDA/branch/main/graph/badge.svg
:alt: Coverage Status
:target: https://codecov.io/gh/yuxuanzhuang/ENPMDA
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: open in colab
:target: https://colab.research.google.com/github/yuxuanzhuang/ENPMDA/blob/main/docs/source/examples/examples.ipynb
=======
History
=======
0.1.0 (2022-05-09)
------------------
* First release on PyPI.
نیازمندی
| مقدار | نام |
|---|---|
| >=2.0.0 | mdanalysis |
| - | dask[complete] |
| - | distributed |
| - | numpy |
| - | pandas |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl ENPMDA-0.5.0:
pip install ENPMDA-0.5.0.whl
نصب پکیج tar.gz ENPMDA-0.5.0:
pip install ENPMDA-0.5.0.tar.gz