|banner|
BluePyMM
========
+----------------+------------+
| Latest Release | |pypi| |
+----------------+------------+
| Documentation | |docs| |
+----------------+------------+
| License | |license| |
+----------------+------------+
| Build Status | |tests| |
+----------------+------------+
| Coverage | |coverage| |
+----------------+------------+
| Citation | |zenodo| |
+----------------+------------+
| Gitter | |gitter| |
+----------------+------------+
Introduction
------------
When building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit.
E-models can e.g. be obtained using `BluePyOpt <https://github.com/BlueBrain/BluePyOpt>`_ by data-driven model parameter optimisation.
Developing e-models can take a lot of time and computing resources. Therefore, these models are not reoptimized for every morphology in the network.
Instead we want to test if an existing e-model matches that particular morphology 'well enough'.
This process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information.
Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination.
Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches.
Citation
--------
When you use this BluePyMM software for your research, we ask you to cite the following publications (this includes poster presentations):
.. code-block::
@article{bluepymm,
title={BluePyMM},
DOI={10.5281/zenodo.8146238},
url={https://doi.org/10.5281/zenodo.8146238}
abstractNote={BluePyMM is a software built to do Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination. Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches.},
publisher={Zenodo},
author={Van Geit, Werner and
Vanherpe, Liesbeth and
Rössert, Christian and
Gevaert, Michael and
Courcol, Jean-Denis and
King, James Gonzalo and
Jaquier, Aurélien},
year={2023},
month={Jul}
}
Support
-------
We are providing support using a chat channel on `Gitter <https://gitter.im/BlueBrain/BluePyMM>`_.
Requirements
------------
* `Python 3.9+ <https://www.python.org/downloads/release/python-390/>`_
* `Neuron 7.4+ <http://neuron.yale.edu/>`_
* `eFEL eFeature Extraction Library <https://github.com/BlueBrain/eFEL>`_
* `BluePyOpt <https://github.com/BlueBrain/BluePyOpt>`_
* `NumPy <http://www.numpy.org>`_
* `pandas <http://pandas.pydata.org/>`_
* `matplotlib <https://matplotlib.org/>`_
* `sh <https://pypi.python.org/pypi/sh>`_
* `ipyparallel <https://pypi.python.org/pypi/ipyparallel>`_
* `lxml <https://pypi.python.org/pypi/lxml>`_
* `h5py <https://pypi.python.org/pypi/h5py>`_
* `pyyaml <https://pypi.python.org/pypi/pyyaml>`_
All of the requirements except for `Neuron` are automatically installed with bluepymm.
The decision on how to install `Neuron` is left to the user.
One simple way of installing Neuron is through pip
.. code-block:: bash
pip install NEURON
Neuron can also be installed from the source and used by bluepymm provided that it is compiled with Python support.
Installation
------------
.. code-block:: bash
pip install bluepymm
NOTES:
* Make sure you are using the latest version of pip (at least >9.0). Otherwise the ipython dependency will fail to install correctly.
* Make sure you are using a new version of git (at least >=1.8). Otherwise some exceptions might be raised by the versioneer module.
Quick Start
-----------
An IPython notebook with a simple test example can be found in:
https://github.com/BlueBrain/BluePyMM/blob/master/notebook/BluePyMM.ipynb
API documentation
-----------------
The API documentation can be found on `ReadTheDocs <http://bluepymm.readthedocs.io/en/latest/>`_.
License
-------
BluePyMM is licensed under the LGPL, unless noted otherwise, e.g., for external
dependencies. See file LGPL.txt for the full license.
Funding
-------
This work has been partially funded by the European Union Seventh Framework Program (FP7/20072013) under grant agreement no. 604102 (HBP),
the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907
(Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework
Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).
This project/research was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL),
from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.
Copyright (c) 2016-2024 Blue Brain Project/EPFL
.. |pypi| image:: https://img.shields.io/pypi/v/bluepymm.svg
:target: https://pypi.org/project/bluepymm/
:alt: latest release
.. |docs| image:: https://readthedocs.org/projects/bluepymm/badge/?version=latest
:target: https://bluepymm.readthedocs.io/en/latest/
:alt: latest documentation
.. |license| image:: https://img.shields.io/pypi/l/bluepymm.svg
:target: https://github.com/BlueBrain/bluepymm/blob/master/LICENSE.txt
:alt: license
.. |tests| image:: https://github.com/BlueBrain/BluePyMM/workflows/Build/badge.svg?branch=master
:target: https://github.com/BlueBrain/BluePyMM/actions
:alt: Actions build status
.. |coverage| image:: https://codecov.io/github/BlueBrain/BluePyMM/coverage.svg?branch=master
:target: https://codecov.io/gh/BlueBrain/bluepymm
:alt: coverage
.. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg
:target: https://gitter.im/bluebrain/bluepymm
:alt: gitter
.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8146238.svg
:target: https://doi.org/10.5281/zenodo.8146238
:alt: DOI
..
The following image is also defined in the index.rst file, as the relative path is
different, depending from where it is sourced.
The following location is used for the github README
The index.rst location is used for the docs README; index.rst also defined an end-marker,
to skip content after the marker 'substitutions'.
.. substitutions
.. |banner| image:: docs/source/logo/BluePyMMBanner.png
Raw data
{
"_id": null,
"home_page": null,
"name": "bluepymm",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": null,
"keywords": "optimisation, neuroscience, BlueBrainProject",
"author": null,
"author_email": "\"Blue Brain Project, EPFL\" <werner.vangeit@epfl.ch>",
"download_url": "https://files.pythonhosted.org/packages/c2/a2/079e0ebefdee707cbec34644f54ef03558744a5db6ea874e769c93fe49de/bluepymm-0.9.11.tar.gz",
"platform": null,
"description": "|banner|\n\nBluePyMM\n========\n\n+----------------+------------+\n| Latest Release | |pypi| |\n+----------------+------------+\n| Documentation | |docs| |\n+----------------+------------+\n| License | |license| |\n+----------------+------------+\n| Build Status \t | |tests| |\n+----------------+------------+\n| Coverage | |coverage| |\n+----------------+------------+\n| Citation | |zenodo| |\n+----------------+------------+\n| Gitter | |gitter| |\n+----------------+------------+\n\nIntroduction\n------------\n\n\nWhen building a network simulation, biophysically detailed electrical models (e-models) need to be tested for every morphology that is possibly used in the circuit.\n\nE-models can e.g. be obtained using `BluePyOpt <https://github.com/BlueBrain/BluePyOpt>`_ by data-driven model parameter optimisation.\nDeveloping e-models can take a lot of time and computing resources. Therefore, these models are not reoptimized for every morphology in the network.\nInstead we want to test if an existing e-model matches that particular morphology 'well enough'.\n\nThis process is called Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information.\nNext, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination.\nFinally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches.\n\nCitation\n--------\n\nWhen you use this BluePyMM software for your research, we ask you to cite the following publications (this includes poster presentations):\n\n.. code-block:: \n\n @article{bluepymm, \n title={BluePyMM}, \n DOI={10.5281/zenodo.8146238},\n url={https://doi.org/10.5281/zenodo.8146238} \n abstractNote={BluePyMM is a software built to do Cell Model Management (MM). It takes as input a morphology release, a circuit recipe and a set of e-models with some extra information. Next, it finds all possible (morphology, e-model)-combinations (me-combos) based on e-type, m-type, and layer as described by the circuit recipe, and calculates the scores for every combination. Finally, it writes out the resulting accepted me-combos to a database, and produces a report with information on the number of matches.}, \n publisher={Zenodo}, \n author={Van Geit, Werner and\n\t\t\t\tVanherpe, Liesbeth and\n\t\t\t\tR\u00f6ssert, Christian and\n\t\t\t\tGevaert, Michael and\n\t\t\t\tCourcol, Jean-Denis and\n\t\t\t\tKing, James Gonzalo and\n\t\t\t\tJaquier, Aur\u00e9lien},\n year={2023}, \n month={Jul} \n }\n\nSupport\n-------\n\nWe are providing support using a chat channel on `Gitter <https://gitter.im/BlueBrain/BluePyMM>`_.\n\nRequirements\n------------\n\n* `Python 3.9+ <https://www.python.org/downloads/release/python-390/>`_\n* `Neuron 7.4+ <http://neuron.yale.edu/>`_\n* `eFEL eFeature Extraction Library <https://github.com/BlueBrain/eFEL>`_\n* `BluePyOpt <https://github.com/BlueBrain/BluePyOpt>`_\n* `NumPy <http://www.numpy.org>`_\n* `pandas <http://pandas.pydata.org/>`_\n* `matplotlib <https://matplotlib.org/>`_\n* `sh <https://pypi.python.org/pypi/sh>`_\n* `ipyparallel <https://pypi.python.org/pypi/ipyparallel>`_\n* `lxml <https://pypi.python.org/pypi/lxml>`_\n* `h5py <https://pypi.python.org/pypi/h5py>`_\n* `pyyaml <https://pypi.python.org/pypi/pyyaml>`_\n\nAll of the requirements except for `Neuron` are automatically installed with bluepymm.\nThe decision on how to install `Neuron` is left to the user.\n\nOne simple way of installing Neuron is through pip\n\n.. code-block:: bash\n\n pip install NEURON\n\nNeuron can also be installed from the source and used by bluepymm provided that it is compiled with Python support.\n\n\nInstallation\n------------\n\n\n.. code-block:: bash\n\n pip install bluepymm\n\nNOTES: \n\n* Make sure you are using the latest version of pip (at least >9.0). Otherwise the ipython dependency will fail to install correctly.\n* Make sure you are using a new version of git (at least >=1.8). Otherwise some exceptions might be raised by the versioneer module.\n\nQuick Start\n-----------\n\nAn IPython notebook with a simple test example can be found in:\n\nhttps://github.com/BlueBrain/BluePyMM/blob/master/notebook/BluePyMM.ipynb\n\nAPI documentation\n-----------------\nThe API documentation can be found on `ReadTheDocs <http://bluepymm.readthedocs.io/en/latest/>`_.\n\nLicense\n-------\n\nBluePyMM is licensed under the LGPL, unless noted otherwise, e.g., for external \ndependencies. See file LGPL.txt for the full license.\n\nFunding\n-------\nThis work has been partially funded by the European Union Seventh Framework Program (FP7/2007\u00ad2013) under grant agreement no. 604102 (HBP), \nthe European Union\u2019s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907 \n(Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union\u2019s Horizon 2020 Framework \nProgramme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).\nThis project/research was supported by funding to the Blue Brain Project, a research center of the \u00c9cole polytechnique f\u00e9d\u00e9rale de Lausanne (EPFL), \nfrom the Swiss government\u2019s ETH Board of the Swiss Federal Institutes of Technology.\n\nCopyright (c) 2016-2024 Blue Brain Project/EPFL\n\n.. |pypi| image:: https://img.shields.io/pypi/v/bluepymm.svg\n :target: https://pypi.org/project/bluepymm/\n :alt: latest release\n.. |docs| image:: https://readthedocs.org/projects/bluepymm/badge/?version=latest\n :target: https://bluepymm.readthedocs.io/en/latest/\n :alt: latest documentation\n.. |license| image:: https://img.shields.io/pypi/l/bluepymm.svg\n :target: https://github.com/BlueBrain/bluepymm/blob/master/LICENSE.txt\n :alt: license\n.. |tests| image:: https://github.com/BlueBrain/BluePyMM/workflows/Build/badge.svg?branch=master\n :target: https://github.com/BlueBrain/BluePyMM/actions\n :alt: Actions build status\n.. |coverage| image:: https://codecov.io/github/BlueBrain/BluePyMM/coverage.svg?branch=master\n :target: https://codecov.io/gh/BlueBrain/bluepymm\n :alt: coverage\n.. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg\n :target: https://gitter.im/bluebrain/bluepymm\n :alt: gitter\n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8146238.svg\n :target: https://doi.org/10.5281/zenodo.8146238\n :alt: DOI\n\n..\n The following image is also defined in the index.rst file, as the relative path is \n different, depending from where it is sourced.\n The following location is used for the github README\n The index.rst location is used for the docs README; index.rst also defined an end-marker, \n to skip content after the marker 'substitutions'.\n\n.. substitutions\n.. |banner| image:: docs/source/logo/BluePyMMBanner.png\n",
"bugtrack_url": null,
"license": "BluePyMM - Bluebrain Python Optimisation Library BluePyMM is licensed under the LGPL, unless noted otherwise, e.g., for external dependencies. See files COPYING and COPYING.lesser for the full license. Examples and test are BSD-licensed. External dependencies are either LGPL or BSD-licensed. See file ACKNOWLEDGEMENTS.txt and AUTHORS.txt for further details. Copyright (C) 2005-2018, Blue Brain Project/EPFL. This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. ",
"summary": "Model Management Python Library (bluepymm)",
"version": "0.9.11",
"project_urls": {
"Documentation": "https://bluepymm.readthedocs.io/en/latest",
"Homepage": "https://github.com/BlueBrain/BluePyMM",
"Repository": "https://github.com/BlueBrain/BluePyMM.git",
"Source": "https://github.com/BlueBrain/BluePyMM",
"Tracker": "https://github.com/BlueBrain/BluePyMM/issues"
},
"split_keywords": [
"optimisation",
" neuroscience",
" bluebrainproject"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "bf6f93dbe07f87fac837da50748b1908fe837f4b3930479cdd0bf7258a704482",
"md5": "da2f3a4b2aef5b21fa4d7566bbc307c8",
"sha256": "dc82bda1beac161aaa97aa1599c672772694dd7b6c1fb6cbc2d97dabae1dc5ea"
},
"downloads": -1,
"filename": "bluepymm-0.9.11-py3-none-any.whl",
"has_sig": false,
"md5_digest": "da2f3a4b2aef5b21fa4d7566bbc307c8",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 298080,
"upload_time": "2024-11-01T09:58:21",
"upload_time_iso_8601": "2024-11-01T09:58:21.938055Z",
"url": "https://files.pythonhosted.org/packages/bf/6f/93dbe07f87fac837da50748b1908fe837f4b3930479cdd0bf7258a704482/bluepymm-0.9.11-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c2a2079e0ebefdee707cbec34644f54ef03558744a5db6ea874e769c93fe49de",
"md5": "1e8b3aedfd9acd31bcaa0f8828646fe4",
"sha256": "8514b5ce413c553e0ba6d16138c98a17c534bc7fb0f0c3cdce701a62e44b1d54"
},
"downloads": -1,
"filename": "bluepymm-0.9.11.tar.gz",
"has_sig": false,
"md5_digest": "1e8b3aedfd9acd31bcaa0f8828646fe4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 289315,
"upload_time": "2024-11-01T09:58:24",
"upload_time_iso_8601": "2024-11-01T09:58:24.688478Z",
"url": "https://files.pythonhosted.org/packages/c2/a2/079e0ebefdee707cbec34644f54ef03558744a5db6ea874e769c93fe49de/bluepymm-0.9.11.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-01 09:58:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "BlueBrain",
"github_project": "BluePyMM",
"travis_ci": false,
"coveralls": true,
"github_actions": true,
"requirements": [],
"tox": true,
"lcname": "bluepymm"
}
