pyNeuroML

pyNeuroML is a Python package that allows you to work with NeuroML models using the Python programming language. It includes all the API functions provided by libNeuroML and pyLEMS, and also wraps all the functions that jNeuroML provides, which can therefore be used from within Python itself.

With pyNeuroML you can:

  • Create NeuroML models and simulations

  • Validate NeuroML v1.8.1 and v2.x files

  • Simulate NeuroML 2 models

  • Export NeuroML 2 and LEMS files to many formats such as Neuron, Brian, Matlab, etc.

  • Import other languages into LEMS (e.g. SBML)

  • Visualise NeuroML models and simulations

jNeuroML and pyNeuroML

Fig. 37 Relationship between jLEMS, jNeuroML, the NeuroML 2 LEMS definitions, libNeuroML, pyLEMS and pyNeuroML.

Quick start

Install Python and the Java Runtime Environment

Python is generally pre-installed on all computers nowadays. However, if you do not have Python installed on your system, please follow the official installation instructions to install Python on your computer. A number of Free/Open source Integrated Development Environments (IDEs) are also available that make working with Python (even) easier. An example list is here.

Since pyNeuroML wraps around jNeuroML which is written in Java, you will need a Java Runtime Environment (JRE) installed on your system. On most Linux systems Free/Open source OpenJDK runtime environment is already pre-installed. You can also install Oracle’s proprietary Java platform from their download page if you prefer. Please refer to your operating system’s documentation to install a JRE.

Install pyNeuroML with pip

The easiest way to install the latest version of pyNeuroML is using the default Python package manager, pip:

pip install pyneuroml

Installation on Fedora Linux

On Fedora Linux systems, the NeuroFedora community provides pyNeuroML as a package in their extras repository and can be installed using the following commands:

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

Documentation

pyNeuroML provides a set of command line utilities along with an API to use from within Python scripts:

  • pynml

  • pynml-channelanalysis

  • pynml-modchananalysis

  • pynml-plotspikes

  • pynml-povray

  • pynml-sonata

  • pynml-summary

  • pynml-tune

These utilities are self-documented. So, to learn how these utilities are to be used, run them with the -h flag. For example:

pynml -h
usage: pynml [-h|--help] [<shared options>] <one of the mutually-exclusive options>

pyNeuroML v0.5.9: Python utilities for NeuroML2
    libNeuroML v0.2.54
    jNeuroML v0.10.2

optional arguments:
  -h, --help            show this help message and exit

Shared options:
  These options can be added to any of the mutually-exclusive options

  -verbose              Verbose output
  -java_max_memory MAX  Java memory for jNeuroML, e.g. 400M, 2G (used in
                        -Xmx argument to java)
  -nogui                Suppress GUI,
                        i.e. show no plots, just save results
  <LEMS/NeuroML 2 file>
                        LEMS/NeuroML 2 file to process

...

API documentation

Detailed API documentation for pyNeuroML can be found here.

The pyNeuroML API is also self documented. You can use Python’s in-built documentation viewer pydoc to view the documentation for any of the package’s modules and their functions:

pydoc pyneuroml
Help on package pyneuroml:

NAME
    pyneuroml

PACKAGE CONTENTS
    analysis (package)
    lems (package)
    neuron (package)
    plot (package)
    povray (package)
    pynml
    swc (package)
    tune (package)

DATA
    JNEUROML_VERSION = '0.10.2'

VERSION
    0.5.9

FILE
    /usr/lib/python3.9/site-packages/pyneuroml/__init__.py
pydoc pyneuroml.analysis

Help on package pyneuroml.analysis in pyneuroml:

NAME
    pyneuroml.analysis

PACKAGE CONTENTS
    ChannelDensityPlot
    ChannelHelper
    NML2ChannelAnalysis

FUNCTIONS
    analyse_spiketime_vs_dt(nml2_file, target, duration, simulator, cell_v_path, dts, verbose=False, spike_threshold_mV=0, show_plot_already=True, save_figure_to=None, num_of_last_spikes=None)

    generate_current_vs_frequency_curve(nml2_file, cell_id, start_amp_nA=-0.1, end_amp_nA=0.1, step_nA=0.01, custom_amps_nA=[], analysis_duration=1000, analysis_delay=0, pre_zero_pulse=0, post_zero_pulse=0, dt=0.05, temperature='32degC', spike_threshold_mV=0.0, plot_voltage_traces=False, plot_if=True, plot_iv=False, xlim_if=None, ylim_if=None, xlim_iv=None, ylim_iv=None, label_xaxis=True, label_yaxis=True, show_volts_label=True, grid=True, font_size=12, if_iv_color='k', linewidth=1, bottom_left_spines_only=False, show_plot_already=True, save_voltage_traces_to=None, save_if_figure_to=None, save_iv_figure_to=None, save_if_data_to=None, save_iv_data_to=None, simulator='jNeuroML', num_processors=1, include_included=True, title_above_plot=False, return_axes=False, verbose=False)

FILE
    /usr/lib/python3.9/site-packages/pyneuroml/analysis/__init__.py

Most IDEs are able to show you this information as you use them in your Python scripts.

Getting help

For any questions regarding pyNeuroML, please open an issue on the GitHub issue tracker here. Any bugs and feature requests can also be filed there.

You can also use any of the communication channels of the NeuroML community.

Development

pyNeuroML is developed on GitHub at https://github.com/NeuroML/pyNeuroML under the LPGL-3.0 license. The repository contains the complete source code along with instructions on building/installing pyNeuroML. Please follow the instructions there to build pyNeuroML from source.