netpyne.conversion.neuromlFormat

Module for importing and exporting NeuroML 2

Functions:

H(x)	Short description of netpyne.conversion.neuromlFormat.H
exportNeuroML2(reference[, connections, ...])	Exports the current NetPyNE network to NeuroML format
importNeuroML2(fileName, simConfig[, ...])	Import network from NeuroML2 and convert internally to NetPyNE format

Classes:

NetPyNEBuilder(netParams[, simConfig, verbose])

Works with libNeuroML's NeuroMLXMLParser and/or NeuroMLHdf5Parser to parse the NML & build equivalent in NetPyNE

netpyne.conversion.neuromlFormat.H(x)

Short description of netpyne.conversion.neuromlFormat.H

Parameters:

x –

Short description of x

Default: Required

Options:

netpyne.conversion.neuromlFormat.exportNeuroML2(reference, connections=True, stimulations=True, format='xml', default_cell_radius=5)

Exports the current NetPyNE network to NeuroML format

Parameters:

• reference (str) – Will be used for id of the network

• connections (bool) – Should connections also be exported? Default: True

• stimulations (bool) – Should stimulations (current clamps etc) also be exported? Default: True

• format (str) – Which format, xml or hdf5 Default: 'xml' Options: 'xml' Export as XML format 'hdf5' Export as binary HDF5 format

default_cell_radiusint

For abstract cells, e.g. izhikevich, what value should be used in the optional radius property of a population, which can be used in 3D visualizations, etc. Default: 5

class netpyne.conversion.neuromlFormat.NetPyNEBuilder(netParams, simConfig=None, verbose=False)

Bases: DefaultNetworkHandler

Works with libNeuroML’s NeuroMLXMLParser and/or NeuroMLHdf5Parser to parse the NML & build equivalent in NetPyNE

Attributes:

cellParams
popParams
pop_ids_vs_seg_ids_vs_segs
pop_ids_vs_components
pop_ids_vs_use_segment_groups_for_neuron
pop_ids_vs_ordered_segs
pop_ids_vs_cumulative_lengths
projection_infos
connections
popStimSources
stimSources
popStimLists
stimLists
gids
next_gid
stochastic_input_count

Methods:

finalise()
handle_network(network_id, notes[, temperature])
handle_population(population_id, component, ...)
handle_location(id, population_id, ...)
handle_projection(projName, prePop, postPop, ...)
handle_connection(projName, id, prePop, ...)
handle_input_list(inputListId, ...[, ...])
handle_single_input(inputListId, id, cellId)

cellParams = {}

popParams = {}

pop_ids_vs_seg_ids_vs_segs = {}

pop_ids_vs_components = {}

pop_ids_vs_use_segment_groups_for_neuron = {}

pop_ids_vs_ordered_segs = {}

pop_ids_vs_cumulative_lengths = {}

projection_infos = {}

connections = {}

popStimSources = {}

stimSources = {}

popStimLists = {}

stimLists = {}

gids = {}

next_gid = 0

stochastic_input_count = 0

finalise()

handle_network(network_id, notes, temperature=None)

handle_population(population_id, component, size, component_obj, properties={})

handle_location(id, population_id, component, x, y, z)

handle_projection(projName, prePop, postPop, synapse, hasWeights=False, hasDelays=False, type='projection', synapse_obj=None, pre_synapse_obj=None)

handle_connection(projName, id, prePop, postPop, synapseType, preCellId, postCellId, preSegId=0, preFract=0.5, postSegId=0, postFract=0.5, delay=0, weight=1)

handle_input_list(inputListId, population_id, component, size, input_comp_obj=None)

handle_single_input(inputListId, id, cellId, segId=0, fract=0.5, weight=1.0)

netpyne.conversion.neuromlFormat.importNeuroML2(fileName, simConfig, simulate=True, analyze=True, return_net_params_also=False)

Import network from NeuroML2 and convert internally to NetPyNE format

Parameters:

• fileName (str) – The filename of the NeuroML file Default: Required

• simConfig (simConfig object) – NetPyNE simConfig object specifying simulation configuration. Default: Required

• simulate (bool) – Go ahead and run a simulation of it already Default: True

• analyze (bool) – Run sim.saveData() and sim.analysis.plotData() Default: True