|banner|
BluePyOpt
=========
+----------------+------------+
| Latest Release | |pypi| |
+----------------+------------+
| Documentation | |docs| |
+----------------+------------+
| License | |license| |
+----------------+------------+
| Build Status | |build| |
+----------------+------------+
| Coverage | |coverage| |
+----------------+------------+
| Gitter | |gitter| |
+----------------+------------+
| Zenodo | |zenodo| |
+----------------+------------+
Introduction
============
The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible
framework for data-driven model parameter optimisation that wraps and
standardises several existing open-source tools.
It simplifies the task of creating and sharing these optimisations,
and the associated techniques and knowledge.
This is achieved by abstracting the optimisation and evaluation tasks
into various reusable and flexible discrete elements according to established
best-practices.
Further, BluePyOpt provides methods for setting up both small- and large-scale
optimisations on a variety of platforms,
ranging from laptops to Linux clusters and cloud-based compute infrastructures.
Citation
========
When you use the BluePyOpt software or method for your research, we ask you to cite the following publication (**this includes poster presentations**):
`Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J, Muller EB, Schürmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017 <http://journal.frontiersin.org/article/10.3389/fninf.2016.00017>`_.
.. code-block::
@ARTICLE{bluepyopt,
AUTHOR={Van Geit, Werner and Gevaert, Michael and Chindemi, Giuseppe and Rössert, Christian and Courcol, Jean-Denis and Muller, Eilif Benjamin and Schürmann, Felix and Segev, Idan and Markram, Henry},
TITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience},
JOURNAL={Frontiers in Neuroinformatics},
VOLUME={10},
YEAR={2016},
NUMBER={17},
URL={http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2016.00017/abstract},
DOI={10.3389/fninf.2016.00017},
ISSN={1662-5196}
}
Publications that use or mention BluePyOpt
==========================================
The list of publications that use or mention BluePyOpt can be found on `the github wiki page <https://github.com/BlueBrain/BluePyOpt/wiki/Publications-that-use-or-mention-BluePyOpt>`_.
Support
=======
We are providing support using a chat channel on `Gitter <https://gitter.im/BlueBrain/BluePyOpt>`_, or the `Github discussion page <https://github.com/BlueBrain/BluePyOpt/discussions>`_.
News
====
- 2023/01: BluePyOpt now supports the Arbor simulator.
- 2022/12: Support for LFPy models merged into master. Examples and preprint: https://github.com/alejoe91/multimodalfitting, https://www.biorxiv.org/content/10.1101/2022.08.03.502468v1.full
- 2022/12: BluePyOpt now has the ability to write out NeuroML files: https://github.com/BlueBrain/BluePyOpt/tree/master/bluepyopt/neuroml
- 2021/08/30: BluePyOpt dropped Python 2.7 support.
- 2017/01/04: BluePyOpt is now considered compatible with Python 3.6+.
- 2016/11/10: BluePyOpt now supports NEURON point processes. This means we can fit parameters of Adex/GIF/Izhikevich models, and also synapse models.
- 2016/06/14: Started a wiki: https://github.com/BlueBrain/BluePyOpt/wiki
- 2016/06/07: The BluePyOpt paper was published in Frontiers in Neuroinformatics (for link, see above)
- 2016/05/03: The API documentation was moved to `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_
- 2016/04/20: BluePyOpt now contains the code of the IBEA selector, no need to install a BBP-specific version of DEAP anymore
- 2016/03/24: Released version 1.0
Requirements
============
* `Python 3.9+ <https://www.python.org/downloads/release/python-390/>`_
* `Pip <https://pip.pypa.io>`_ (installed by default in newer versions of Python)
* `Neuron 7.4+ <http://neuron.yale.edu/>`_ (compiled with Python support)
* `eFEL eFeature Extraction Library <https://github.com/BlueBrain/eFEL>`_ (automatically installed by pip)
* `Numpy <http://www.numpy.org>`_ (automatically installed by pip)
* `Pandas <http://pandas.pydata.org/>`_ (automatically installed by pip)
* The instruction below are written assuming you have access to a command shell on Linux / UNIX / MacOSX / Cygwin
Installation
============
If you want to use the ephys module of BluePyOpt, you first need to install NEURON with Python support on your machine.
And then bluepyopt itself:
.. code-block:: bash
pip install bluepyopt
Support for simulators other than NEURON is optional and not installed by default. If you want to use [Arbor](https://arbor-sim.org/) to run your models, use the following line instead to install bluepyopt.
.. code-block:: bash
pip install bluepyopt[arbor]
Cloud infrastructure
====================
We provide instructions on how to set up an optimisation environment on cloud
infrastructure or cluster computers
`here <https://github.com/BlueBrain/BluePyOpt/tree/master/cloud-config>`_
Quick Start
===========
Single compartmental model
--------------------------
An iPython notebook with an introductory optimisation of a one compartmental
model with 2 HH channels can be found at
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb (NEURON)
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell_arbor.ipynb (Arbor)
|landscape_example|
**Figure**: The solution space of a single compartmental model with two parameters: the maximal conductance of Na and K ion channels. The color represents how well the model fits two objectives: when injected with two different currents, the model has to fire 1 and 4 action potential respectively during the stimuli. Dark blue is the best fitness. The blue circles represent solutions with a perfect score.
Neocortical Layer 5 Pyramidal Cell
----------------------------------
Scripts for a more complex neocortical L5PC are in
`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc>`__
With a notebook:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb (NEURON)
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC_arbor.ipynb (Arbor)
Thalamocortical Cells
---------------------
Scripts for 2 thalamocortical cell types are in
`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/thalamocortical-cell>`__
With a notebook:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb
Tsodyks-Markram Model of Short-Term Plasticity
----------------------------------------------
Scripts for 2 version of fitting the Tsodyks-Markram model to synaptic traces are in
`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/tsodyksmarkramstp>`__
With 2 notebooks:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb
Exporting cell in neuroml format
--------------------------------
An iPython notebook showing how to export a BluePyOpt cell in the neuroml format, how to create a LEMS simulation,
and how to run the LEMS simulation with the neuroml cell can be found at:
https://github.com/BlueBrain/BluePyOpt/blob/master/examples/neuroml/neuroml.ipynb
API documentation
=================
The API documentation can be found on `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_.
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
..
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'.
.. |pypi| image:: https://img.shields.io/pypi/v/bluepyopt.svg
:target: https://pypi.org/project/bluepyopt/
:alt: latest release
.. |docs| image:: https://readthedocs.org/projects/bluepyopt/badge/?version=latest
:target: https://bluepyopt.readthedocs.io/
:alt: latest documentation
.. |license| image:: https://img.shields.io/pypi/l/bluepyopt.svg
:target: https://github.com/BlueBrain/bluepyopt/blob/master/LICENSE.txt
:alt: license
.. |build| image:: https://github.com/BlueBrain/BluePyOpt/workflows/Build/badge.svg?branch=master
:target: https://github.com/BlueBrain/BluePyOpt/actions
:alt: actions build status
.. |coverage| image:: https://codecov.io/github/BlueBrain/BluePyOpt/coverage.svg?branch=master
:target: https://codecov.io/gh/BlueBrain/bluepyopt
:alt: coverage
.. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg
:target: https://gitter.im/BlueBrain/blueptopt
:alt: Join the chat at https://gitter.im/BlueBrain/BluePyOpt
.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8135890.svg
:target: https://doi.org/10.5281/zenodo.8135890
.. substitutions
.. |banner| image:: docs/source/logo/BluePyOptBanner.png
.. |landscape_example| image:: examples/simplecell/figures/landscape_example.png
Raw data
{
"_id": null,
"home_page": null,
"name": "bluepyopt",
"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/db/1e/8026e4bc5906814d2b2a0457e8ea267524be9b526bb5b04a27f0d8a791a7/bluepyopt-1.14.17.tar.gz",
"platform": null,
"description": "|banner|\n\nBluePyOpt\n=========\n\n\n+----------------+------------+\n| Latest Release | |pypi| |\n+----------------+------------+\n| Documentation | |docs| |\n+----------------+------------+\n| License | |license| |\n+----------------+------------+\n| Build Status \t | |build| |\n+----------------+------------+\n| Coverage | |coverage| |\n+----------------+------------+\n| Gitter | |gitter| |\n+----------------+------------+\n| Zenodo | |zenodo| |\n+----------------+------------+\n\n\nIntroduction\n============\n\nThe Blue Brain Python Optimisation Library (BluePyOpt) is an extensible\nframework for data-driven model parameter optimisation that wraps and\nstandardises several existing open-source tools.\n\nIt simplifies the task of creating and sharing these optimisations,\nand the associated techniques and knowledge.\nThis is achieved by abstracting the optimisation and evaluation tasks\ninto various reusable and flexible discrete elements according to established\nbest-practices.\n\nFurther, BluePyOpt provides methods for setting up both small- and large-scale\noptimisations on a variety of platforms,\nranging from laptops to Linux clusters and cloud-based compute infrastructures.\n\nCitation\n========\n\nWhen you use the BluePyOpt software or method for your research, we ask you to cite the following publication (**this includes poster presentations**):\n\n`Van Geit W, Gevaert M, Chindemi G, R\u00f6ssert C, Courcol J, Muller EB, Sch\u00fcrmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017 <http://journal.frontiersin.org/article/10.3389/fninf.2016.00017>`_.\n\n.. code-block:: \n\n\t@ARTICLE{bluepyopt,\n\tAUTHOR={Van Geit, Werner and Gevaert, Michael and Chindemi, Giuseppe and R\u00f6ssert, Christian and Courcol, Jean-Denis and Muller, Eilif Benjamin and Sch\u00fcrmann, Felix and Segev, Idan and Markram, Henry}, \n\tTITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience},\n\tJOURNAL={Frontiers in Neuroinformatics},\n\tVOLUME={10},\n\tYEAR={2016},\n\tNUMBER={17},\n\tURL={http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2016.00017/abstract},\n\tDOI={10.3389/fninf.2016.00017},\n\tISSN={1662-5196}\n\t}\n\n\nPublications that use or mention BluePyOpt\n==========================================\nThe list of publications that use or mention BluePyOpt can be found on `the github wiki page <https://github.com/BlueBrain/BluePyOpt/wiki/Publications-that-use-or-mention-BluePyOpt>`_.\n\nSupport\n=======\nWe are providing support using a chat channel on `Gitter <https://gitter.im/BlueBrain/BluePyOpt>`_, or the `Github discussion page <https://github.com/BlueBrain/BluePyOpt/discussions>`_.\n\nNews\n====\n- 2023/01: BluePyOpt now supports the Arbor simulator.\n- 2022/12: Support for LFPy models merged into master. Examples and preprint: https://github.com/alejoe91/multimodalfitting, https://www.biorxiv.org/content/10.1101/2022.08.03.502468v1.full\n- 2022/12: BluePyOpt now has the ability to write out NeuroML files: https://github.com/BlueBrain/BluePyOpt/tree/master/bluepyopt/neuroml\n- 2021/08/30: BluePyOpt dropped Python 2.7 support.\n- 2017/01/04: BluePyOpt is now considered compatible with Python 3.6+.\n- 2016/11/10: BluePyOpt now supports NEURON point processes. This means we can fit parameters of Adex/GIF/Izhikevich models, and also synapse models.\n- 2016/06/14: Started a wiki: https://github.com/BlueBrain/BluePyOpt/wiki\n- 2016/06/07: The BluePyOpt paper was published in Frontiers in Neuroinformatics (for link, see above)\n- 2016/05/03: The API documentation was moved to `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_\n- 2016/04/20: BluePyOpt now contains the code of the IBEA selector, no need to install a BBP-specific version of DEAP anymore\n- 2016/03/24: Released version 1.0\n\nRequirements\n============\n\n* `Python 3.9+ <https://www.python.org/downloads/release/python-390/>`_\n* `Pip <https://pip.pypa.io>`_ (installed by default in newer versions of Python)\n* `Neuron 7.4+ <http://neuron.yale.edu/>`_ (compiled with Python support)\n* `eFEL eFeature Extraction Library <https://github.com/BlueBrain/eFEL>`_ (automatically installed by pip)\n* `Numpy <http://www.numpy.org>`_ (automatically installed by pip)\n* `Pandas <http://pandas.pydata.org/>`_ (automatically installed by pip)\n* The instruction below are written assuming you have access to a command shell on Linux / UNIX / MacOSX / Cygwin\n\nInstallation\n============\n\nIf you want to use the ephys module of BluePyOpt, you first need to install NEURON with Python support on your machine.\n\nAnd then bluepyopt itself:\n\n\n.. code-block:: bash\n\n pip install bluepyopt\n\nSupport for simulators other than NEURON is optional and not installed by default. If you want to use [Arbor](https://arbor-sim.org/) to run your models, use the following line instead to install bluepyopt.\n\n.. code-block:: bash\n\n pip install bluepyopt[arbor]\n\nCloud infrastructure\n====================\n\nWe provide instructions on how to set up an optimisation environment on cloud\ninfrastructure or cluster computers\n`here <https://github.com/BlueBrain/BluePyOpt/tree/master/cloud-config>`_\n\nQuick Start\n===========\n\nSingle compartmental model\n--------------------------\n\nAn iPython notebook with an introductory optimisation of a one compartmental\nmodel with 2 HH channels can be found at\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb (NEURON)\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell_arbor.ipynb (Arbor)\n\n\n|landscape_example|\n\n\n**Figure**: The solution space of a single compartmental model with two parameters: the maximal conductance of Na and K ion channels. The color represents how well the model fits two objectives: when injected with two different currents, the model has to fire 1 and 4 action potential respectively during the stimuli. Dark blue is the best fitness. The blue circles represent solutions with a perfect score.\n\nNeocortical Layer 5 Pyramidal Cell\n----------------------------------\nScripts for a more complex neocortical L5PC are in\n`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc>`__\n\nWith a notebook:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb (NEURON)\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC_arbor.ipynb (Arbor)\n\nThalamocortical Cells\n---------------------\nScripts for 2 thalamocortical cell types are in\n`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/thalamocortical-cell>`__\n\nWith a notebook:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb\n\n\nTsodyks-Markram Model of Short-Term Plasticity\n----------------------------------------------\nScripts for 2 version of fitting the Tsodyks-Markram model to synaptic traces are in\n`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/tsodyksmarkramstp>`__\n\nWith 2 notebooks:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb\n\nExporting cell in neuroml format\n--------------------------------\nAn iPython notebook showing how to export a BluePyOpt cell in the neuroml format, how to create a LEMS simulation,\nand how to run the LEMS simulation with the neuroml cell can be found at:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/neuroml/neuroml.ipynb\n\n\nAPI documentation\n=================\nThe API documentation can be found on `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_.\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), the European Union\u2019s 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\u2019s Horizon 2020 Framework Programme 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), from the\u00a0Swiss government\u2019s\u00a0ETH Board\u00a0of the Swiss Federal Institutes of Technology.\n\nCopyright (c) 2016-2024 Blue Brain Project/EPFL\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.. |pypi| image:: https://img.shields.io/pypi/v/bluepyopt.svg\n :target: https://pypi.org/project/bluepyopt/\n :alt: latest release\n\n.. |docs| image:: https://readthedocs.org/projects/bluepyopt/badge/?version=latest\n :target: https://bluepyopt.readthedocs.io/\n :alt: latest documentation\n\n.. |license| image:: https://img.shields.io/pypi/l/bluepyopt.svg\n :target: https://github.com/BlueBrain/bluepyopt/blob/master/LICENSE.txt\n :alt: license\n\n.. |build| image:: https://github.com/BlueBrain/BluePyOpt/workflows/Build/badge.svg?branch=master\n :target: https://github.com/BlueBrain/BluePyOpt/actions\n :alt: actions build status\n\n.. |coverage| image:: https://codecov.io/github/BlueBrain/BluePyOpt/coverage.svg?branch=master\n :target: https://codecov.io/gh/BlueBrain/bluepyopt\n :alt: coverage\n\n.. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg\n :target: https://gitter.im/BlueBrain/blueptopt\n :alt: Join the chat at https://gitter.im/BlueBrain/BluePyOpt\n\n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8135890.svg\n :target: https://doi.org/10.5281/zenodo.8135890\n\n.. substitutions\n.. |banner| image:: docs/source/logo/BluePyOptBanner.png\n.. |landscape_example| image:: examples/simplecell/figures/landscape_example.png\n",
"bugtrack_url": null,
"license": "BluePyOpt - Bluebrain Python Optimisation Library BluePyOpt 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) Blue Brain Project/EPFL 2016-2022. 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": "Blue Brain Python Optimisation Library (bluepyopt)",
"version": "1.14.17",
"project_urls": {
"Documentation": "https://bluepyopt.readthedocs.io/en/latest",
"Homepage": "https://github.com/BlueBrain/BluePyOpt",
"Repository": "https://github.com/BlueBrain/BluePyOpt.git",
"Source": "https://github.com/BlueBrain/BluePyOpt",
"Tracker": "https://github.com/BlueBrain/BluePyOpt/issues"
},
"split_keywords": [
"optimisation",
" neuroscience",
" bluebrainproject"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0b38c36b032e0f197f15134d71f1c57d8f5a33f5bf3c24cba961a415b4c79936",
"md5": "d04c7653f1a354f7c58a544a8bb075ed",
"sha256": "a94fe300ffaeda6ad2cd50e973e7bafdfc3930f92a95055444243c8cb30dc2a9"
},
"downloads": -1,
"filename": "bluepyopt-1.14.17-py3-none-any.whl",
"has_sig": false,
"md5_digest": "d04c7653f1a354f7c58a544a8bb075ed",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 21749961,
"upload_time": "2024-11-01T10:13:35",
"upload_time_iso_8601": "2024-11-01T10:13:35.246726Z",
"url": "https://files.pythonhosted.org/packages/0b/38/c36b032e0f197f15134d71f1c57d8f5a33f5bf3c24cba961a415b4c79936/bluepyopt-1.14.17-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "db1e8026e4bc5906814d2b2a0457e8ea267524be9b526bb5b04a27f0d8a791a7",
"md5": "eaa8fecc3e10ae8f4b6a97ad504920c4",
"sha256": "4b1b2fcaa4705dcdc40a52f381024baca49ce66f7d0fd2add51b948b6cd98f1f"
},
"downloads": -1,
"filename": "bluepyopt-1.14.17.tar.gz",
"has_sig": false,
"md5_digest": "eaa8fecc3e10ae8f4b6a97ad504920c4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 29595053,
"upload_time": "2024-11-01T10:13:39",
"upload_time_iso_8601": "2024-11-01T10:13:39.655987Z",
"url": "https://files.pythonhosted.org/packages/db/1e/8026e4bc5906814d2b2a0457e8ea267524be9b526bb5b04a27f0d8a791a7/bluepyopt-1.14.17.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-01 10:13:39",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "BlueBrain",
"github_project": "BluePyOpt",
"travis_ci": false,
"coveralls": true,
"github_actions": true,
"requirements": [],
"tox": true,
"lcname": "bluepyopt"
}
