A single package in Python unifying scripts and modules for reading, writing, simulating and analysing NeuroML2/LEMS models.

Builds on: libNeuroML & PyLEMS and wraps functionality from jNeuroML.

pyNeuroML relies on additional software to carry out its functions:

• Java Runtime environment (JRE)
• dot (from Graphviz)
• lxml

On most Linux systems, these can be installed using the default package manager. On Ubuntu based distributions:

sudo apt-get install python-lxml graphviz openjdk-11-jdk

pyNeuroML can be installed with pip (preferably in a virtual environment):

pip install pyneuroml

The NeuroFedora community initiative provides pyNeuroML for use on the Fedora Linux Distribution. Fedora users can install pyNeuroML using the following commands:

sudo dnf copr enable @neurofedora/neurofedora-extra
sudo dnf install python3-pyneuroml

This will also pull in all the necessary dependencies (Java, lxml, graphviz). Please see the project documentation for more information.

Clone the repository:

git clone https://github.com/NeuroML/pyNeuroML.git
cd pyNeuroML

It should be possible to install pyNeuroML using just:

sudo pip install .

You may have to install lxml and the development version of libNeuroML manually:

apt-get install python-lxml
cd ..
git clone https://github.com/NeuralEnsemble/libNeuroML.git
cd libNeuroML
git checkout development
sudo python setup.py install

1) Single Python package for NeuroML2/LEMS

One Python package which can be installed using pip & a user has everything they need to work with NeuroML2/LEMS files:

• libNeuroML
• PyLEMS
• A bundled version of jNeuroML which can be used to run simulations

2) Run models using jNeuroML or PyLEMS

Ability to run NeuroML2/LEMS models using jLEMS/jNeuroML (with bundled jar) or PyLEMS (todo...)

Uses similar command line interface to jNeuroML, i.e. based on jnml

Try:

pynml -h

to list current options.

3) Access to export & import options of jNeuroML

All export & import options of jNeuroML available through easy command line interface (see here for progress) & through Python methods.

Example of export of NeuroML2/LEMS to NEURON and execution of generated code using single method is here.

4) Helper Python scripts

Lots of helper scripts for commonly used functions, e.g. generating a firing frequency vs injected current plot, generating a LEMS file for use with a NeuroML2 file,

5) Analysis of ion channels

Generation of plots of activation rates for ion channels from NeuroML2 channel file (example):

pynml-channelanalysis NaConductance.channel.nml

Generation of plots of activation rates for ion channels from NEURON mod file (example):

pynml-modchananalysis NaConductance -modFile NaConductance.mod

See here for more.

6) Home for existing functionality distributed in various places

Incorporate ChannelML2NeuroML2beta.xsl for updating ChannelML (coming soon...)

7) NEURON to NeuroML2

Scripts for converting NEURON to NeuroML2

• Export morphologies (plus channels, soon). See here.

• mod files - make best guess at initial NeuroML2 form (todo)

8) Export of images/movies from cell/networks

Files can be generated for POV-Ray which can be used to generate high resolution images and even sequences of images for creating movies. try:

pynml-povray -h

9) Tuning cell models in NeuroML 2

Builds on Neurotune and pyelectro. See here for example.

9) Planned functionality

Built in viewer of cells in 3D? Mayavi? More closely tied to PyNN?