PYNML(1)

NAME
pynml - manual page for pynml v1.3.21

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

Python utilities for NeuroML2

  options:

-h, --help
    show this help message and exit

-version
    Print version and exit

  Shared options:

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

-verbose {DEBUG,INFO,WARNING,ERROR,CRITICAL}
    Verbose output (default: WARNING)

-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/SBML/SEDML file(s)>
    LEMS/NeuroML 2/SBML/SEDML file(s) to process

  Mutually-exclusive options:

    Only one of these options can be selected

-sedml
    (Via jNeuroML) Load a LEMS file, and convert simulation settings (duration, dt, what to save) to SED-ML format

-neuron ...
    (Via jNeuroML) Load a LEMS file, and convert it to NEURON format. The full format of the '-neuron' option is: -neuron [-nogui] [-run] [-outputdir dir] <LEMS file>

-nogui
    do not generate graphical elements in NEURON, just run, save data, and quit

-run
    compile NMODL files and run the main NEURON hoc file (Linux only currently)

-outputdir <dir>
    generate NEURON files in directory <dir>

<LEMS file>
    the LEMS file to use

-run-tellurium ...
    Load a SEDML file, and run it using tellurium: <SEDML file> -run-tellurium [-outputdir dir]

<SEDML file>
    the SEDML file to use

-outputdir <dir>
    save any output reports in directory <dir> default is current directory ie '.' use 'none' to disable output altogether

-netpyne ...
    (Via jNeuroML) Load a LEMS file, and convert it to NetPyNE format. The full format of the '-netpyne' option is: -netpyne [-run] [-outputdir dir] [-np cores] <LEMS file>

-run
    compile NMODL files and run the NetPyNE simulation (Linux only currently)

-outputdir <dir>
    generate NEURON files in directory <dir>

-np <cores>
    number of cores to run with (if using MPI)

-json
    generate network as NetPyNE JSON

<LEMS file>
    the LEMS file to use

-eden ...
    Load a LEMS file, and generate a Python script to load and execute it in EDEN

-svg
    (Via jNeuroML) Convert NeuroML2 file (network & cells) to SVG format view of 3D structure

-png
    (Via jNeuroML) Convert NeuroML2 file (network & cells) to PNG format view of 3D structure

-dlems
    (Via jNeuroML) Load a LEMS file, and convert it to dLEMS format, a distilled form of LEMS in JSON

-vertex
    (Via jNeuroML) Load a LEMS file, and convert it to VERTEX format

-xpp
    (Via jNeuroML) Load a LEMS file, and convert it to XPPAUT format

-dnsim
    (Via jNeuroML) Load a LEMS file, and convert it to DNsim format

-brian
    (Via jNeuroML) Load a LEMS file, and convert it to Brian format

-brian2
    (Via jNeuroML) Load a LEMS file, and convert it to Brian2 format

-moose
    (Via jNeuroML) Load a LEMS file, and convert it to Moose format

-sbml
    (Via jNeuroML) Load a LEMS file, and convert it to SBML format

-sbml-sedml
    (Via jNeuroML) Load a LEMS file, and convert it to SBML format with a SED-ML file describing the experiment to run

-matlab
    (Via jNeuroML) Load a LEMS file, and convert it to MATLAB format

-cvode
    (Via jNeuroML) Load a LEMS file, and convert it to C format using CVODE package

-nineml
    (Via jNeuroML) Load a LEMS file, and convert it to NineML format

-spineml
    (Via jNeuroML) Load a LEMS file, and convert it to SpineML format

-sbml-import <SBML file> duration dt
    (Via jNeuroML) Load a SBML file, and convert it to LEMS format using values for duration & dt in ms (ignoring SBML units)

-sbml-import-units <SBML file> duration dt
    (Via jNeuroML) Load a SBML file, and convert it to LEMS format using values for duration & dt in ms (attempt to extract SBML units; ensure units are valid in the SBML!)

-vhdl neuronid <LEMS file>
    (Via jNeuroML) Load a LEMS file, and convert it to VHDL format

-graph level
    Load a NeuroML file, and convert it to a graph using GraphViz. Detail is set by level (min20..0..20, where min implies negative) An optional single letter suffix can be used to select engine Example: 1d for level 1, using the dot engine Available engines: {'d': 'dot', 'c': 'circo', 'n': 'neato', 't': 'twopi', 'f': 'fdp', 's': 'sfdp', 'p': 'patchwork'}

-lems-graph
    (Via jNeuroML) Load LEMS file, and convert it to a graph using GraphViz.

-matrix level
    Load a NeuroML file, and convert it to a matrix displaying connectivity. Detail is set by level (1, 2, etc.)

-validate
    (Via jNeuroML) Validate NeuroML2 file(s) against the latest Schema

-validatev1
    (Via jNeuroML) Validate NeuroML file(s) against the v1.8.1 Schema

-validate-sbml
    Validate SBML file(s), unit consistency failure generates a warning

-validate-sbml-units
    Validate SBML file(s), unit consistency failure generates an error

-validate-sedml
    Validate SEDML file(s)

-swc
    Load NeuroML file(s), and convert it to swc format

SEE-ALSO
pynml(1), pynml-archive(1), pynml-channelanalysis(1), pynml-channelml2nml(1), pynml-modchananalysis(1), pynml-plotchan(1), pynml-plotmorph(1), pynml-plotspikes(1), pynml-plottimeseries(1), pynml-povray(1), pynml-sonata(1), pynml-summary(1), pynml-swc2nml(1), pynml-tune(1), pynml-xpp(1),

Please see https://docs.neuroml.org for complete documentation on the NeuroML standard and the software ecosystem.

ENVIRONMENT

pyNeuroML v1.3.21 (libNeuroML v0.6.7, jNeuroML v0.14.0)