Neuronunit reduced cells

.circleci/config.yml

@@ -0,0 +1,59 @@
+defaults: &defaults
+  working_directory: ~/markovmodel/PyEMMA
+  docker:
+    - image: continuumio/miniconda3
+
+inst_conda_bld: &inst_conda_bld
+  - run: conda config --add channels conda-forge
+  - run: conda config --set always_yes true
+  - run: conda config --set quiet true
+  - run: conda install conda-build
+
+version: 2
+
+jobs:
+  build:
+    <<: *defaults
+    parallelism: 1
+    steps:
+      - checkout
+      - run: git fetch --unshallow || true
+      - run: apt-get install -y cpp gcc
+      - run: apt-get install -y libx11-6 python-dev git build-essential
+      - run: apt-get install -y autoconf automake gcc g++ make gfortran
+      - run: apt-get install -y python-tables
+      - run: apt-get install -y libhdf5-serial-dev
+
+      - run: conda config --add channels conda-forge
+      - run: conda config --set always_yes true
+      - run: conda config --set quiet true
+      - run: conda install conda-build
+      - run: conda install scipy;
+      - run: conda install numpy;
+      - run: conda install numba;
+      - run: conda install dask;
+      - run: pip install tables
+      - run: pip install pip --upgrade; pip install -r requirements.txt;
+      - run: pip install -e .
+      - run: git clone -b neuronunit https://github.com/russelljjarvis/jit_hub.git
+      - run: cd jit_hub; pip install -e .; cd ..;
+      - run: git clone -b optimization https://github.com/russelljjarvis/neuronunit
+      - run: cd neuronunit; pip install -e .; cd ..;
+      - run: git clone https://github.com/russelljjarvis/eFEL
+      - run: cd eFEL; pip install -e .; cd ..;
+      - run: pip install cython
+      - run: pip install streamlit;
+      - run: pip install seaborn;
+      - run: pip install sklearn
+      - run: pip install allensdk==0.16.3
+      - run: pip install frozendict
+      - run: pip install nose
+      - run: rm -rf /opt/conda/lib/python3.8/site-packages/sciunit
+      - run: git clone -b dev https://github.com/russelljjarvis/sciunit.git
+      - run: cd sciunit; pip install -e .; cd ..;
+      - run: cd bluepyopt/tests; nosetests test_evaluators.py
+      - run: cd bluepyopt/tests; nosetests scores_unit_test.py
+      - run: cd bluepyopt/tests; python rheobase_model_test.py
+
+      # causes time out
+      #- run: cd bluepyopt/tests; python izhi_opt.py

.readthedocs.yaml

@@ -0,0 +1,16 @@
+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+sphinx:
+  configuration: docs/source/conf.py
+  fail_on_warning: true
+
+python:
+  install:
+    - method: pip
+      path: .
+    - requirements: requirements_docs.txt

README.rst

@@ -0,0 +1,273 @@
+<<<<<<< HEAD:README.rst
+|banner|
+
+BluePyOpt
+=========
+
+.. raw:: html
+
+	<table>
+	<tr>
+	  <td>Latest Release</td>
+	  <td>
+	    <a href="https://pypi.org/project/bluepyopt/">
+	    <img src="https://img.shields.io/pypi/v/bluepyopt.svg" alt="latest release" />
+	    </a>
+	  </td>
+	</tr>
+	<tr>
+	  <td>Documentation</td>
+	  <td>
+	    <a href="https://bluepyopt.readthedocs.io/">
+	    <img src="https://readthedocs.org/projects/bluepyopt/badge/?version=latest" alt="latest documentation" />
+	    </a>
+	  </td>
+	</tr>
+	<tr>
+	  <td>License</td>
+	  <td>
+	    <a href="https://github.com/BlueBrain/bluepyopt/blob/master/LICENSE.txt">
+	    <img src="https://img.shields.io/pypi/l/bluepyopt.svg" alt="license" />
+	    </a>
+	</td>
+	</tr>
+	<tr>
+	  <td>Build Status</td>
+	  <td>
+	    <a href="https://github.com/BlueBrain/BluePyOpt/actions">
+	    <img src="https://github.com/BlueBrain/BluePyOpt/workflows/Build/badge.svg?branch=master" alt="Actions build status" />
+	    </a>
+	  </td>
+	</tr>
+	<tr>
+	  <td>Coverage</td>
+	  <td>
+	    <a href="https://codecov.io/gh/BlueBrain/bluepyopt">
+	    <img src="https://codecov.io/github/BlueBrain/BluePyOpt/coverage.svg?branch=master" alt="coverage" />
+	    </a>
+	  </td>
+	</tr>
+	<tr>
+		<td>Gitter</td>
+		<td>
+			<a href="https://gitter.im/bluebrain/bluepyopt">
+			<img src="https://badges.gitter.im/Join%20Chat.svg"
+		</a>
+		</td>
+	</tr>
+	</table>
+
+
+
+=======
+<img src="docs/source/logo/BluePyOptBanner.png"/>
+
+-----------------
+# BluePyOpt: Blue Brain Python Optimisation Library
+
+<table>
+<tr>
+  <td>Latest Release</td>
+  <td>
+    <a href="https://pypi.org/project/bluepyopt/">
+    <img src="https://img.shields.io/pypi/v/bluepyopt.svg" alt="latest release" />
+    </a>
+  </td>
+</tr>
+<tr>
+  <td>Documentation</td>
+  <td>
+    <a href="https://bluepyopt.readthedocs.io/">
+    <img src="https://readthedocs.org/projects/bluepyopt/badge/?version=latest" alt="latest documentation" />
+    </a>
+  </td>
+</tr>
+<tr>
+  <td>License</td>
+  <td>
+    <a href="https://github.com/BlueBrain/bluepyopt/blob/master/LICENSE.txt">
+    <img src="https://img.shields.io/pypi/l/bluepyopt.svg" alt="license" />
+    </a>
+</td>
+</tr>
+<tr>
+  <td>Build Status</td>
+  <td>
+    <a href="https://github.com/BlueBrain/BluePyOpt/actions">
+    <img src="https://github.com/BlueBrain/BluePyOpt/workflows/Build/badge.svg?branch=master" alt="Actions build status" />
+    </a>
+  </td>
+</tr>
+<tr>
+  <td>Coverage</td>
+  <td>
+    <a href="https://codecov.io/gh/BlueBrain/bluepyopt">
+    <img src="https://codecov.io/github/BlueBrain/BluePyOpt/coverage.svg?branch=master" alt="coverage" />
+    </a>
+  </td>
+</tr>
+<tr>
+	<td>Gitter</td>
+	<td>
+		<a href="https://gitter.im/bluebrain/bluepyopt">
+		<img src="https://badges.gitter.im/Join%20Chat.svg"
+	</a>
+	</td>
+</tr>
+</table>
+>>>>>>> neuronunit_reduced_cells:README.md
+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}
+	}
+
+
+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
+====
+- 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 2.7+ <https://www.python.org/download/releases/2.7/>`_ or `Python 3.6+ <https://www.python.org/downloads/release/python-360/>`_
+* `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
+
+
+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
+
+
+.. image:: examples/simplecell/figures/landscape_example.png
+
+
+**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
+
+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
+
+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).
+<<<<<<< HEAD:README.rst
+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.
+
+© 2016-2021, 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'.
+
+.. substitutions
+.. |banner| image:: docs/source/logo/BluePyOptBanner.png
+.. |landscape_example| image:: examples/simplecell/figures/landscape_example.png
+=======
+- 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.
+>>>>>>> neuronunit_reduced_cells:README.md

bluepyopt/deapext/optimisations.py

@@ -101,7 +101,9 @@ def __init__(self, evaluator=None,
                  cxpb=1.0,
                  map_function=None,
                  hof=None,
-                 selector_name=None):
+                 selector_name=None,
+                 neuronunit=False,
+                 ):
         """Constructor
 
         Args:
@@ -110,7 +112,7 @@ def __init__(self, evaluator=None,
             offspring_size (int): Number of offspring individuals in each
                 generation
             eta (float): Parameter that controls how far the crossover and
-                mutation operator disturbe the original individuals
+                mutation operator perturb the original individuals
             mutpb (float): Mutation probability
             cxpb (float): Crossover probability
             map_function (function): Function used to map (parallelise) the
@@ -129,7 +131,7 @@ def __init__(self, evaluator=None,
         self.cxpb = cxpb
         self.mutpb = mutpb
         self.map_function = map_function
-
+        self.neuronunit = neuronunit
         self.selector_name = selector_name
         if self.selector_name is None:
             self.selector_name = 'IBEA'
@@ -302,7 +304,12 @@ def run(self,
         stats.register("std", numpy.std)
         stats.register("min", numpy.min)
         stats.register("max", numpy.max)
-
+        if self.neuronunit:
+            # if neuronunit overwrite path of optimization algorithms
+            # to get some reduced model relevant customizations.
+            from neuronunit.optimization import algorithms
+        else:
+            from bluepyopt.deapext import algorithms
         pop, hof, log, history = algorithms.eaAlphaMuPlusLambdaCheckpoint(
             pop,
             self.toolbox,