netpyne.conversion.sonataImport module

Module with functions to import from and export to SONATA format

netpyne.conversion.sonataImport.load_csv_props(info_file)[source]: Load a generic csv file as used in Sonata

netpyne.conversion.sonataImport.ascii_encode_dict(data)[source]

netpyne.conversion.sonataImport.load_json(filename)[source]

class netpyne.conversion.sonataImport.EmptyCell[source]: Bases: object

netpyne.conversion.sonataImport.fix_axon_peri(hobj)[source]: Replace reconstructed axon with a stub :param hobj: hoc object

netpyne.conversion.sonataImport.fix_axon_peri_v2(hobj)[source]: Replace reconstructed axon with a stub (keep order); BBP version :param hobj: hoc object

netpyne.conversion.sonataImport.fix_sec_nseg(secs, dL)[source]: Set nseg of sections based on dL param: section.nseg = 1 + 2 * int(section.L / (2*dL)) :param secs: netpyne dictionary with all sections :param dL: dL from config file

netpyne.conversion.sonataImport.swap_soma_xy(secs)[source]: Swap soma x and y coords so cylinder is vertical instead of horizontal

class netpyne.conversion.sonataImport.SONATAImporter(**parameters)[source]

Bases: object

importNet(configFile, replaceAxon=True, setdLNseg=True, swapSomaXY=True)[source]

createSimulationConfig()[source]

createPops()[source]

createCells()[source]

createConns()[source]: SONATA method - works but same results as NeuroMLlite

createNetStims()[source]

createIClamps()[source]

setCellRuleDynamicParamsFromNeuroml(nml_params, cellRule)[source]

setCellRuleDynamicParamsFromNeuroml_old(cell, cellRule)[source]

setCellRuleDynamicParamsFromJson(cell_dynamic_params, cellRule)[source]

parse_group(g)[source]

parse_dataset(d)[source]

subs(path)[source]: Search the strings in a config file for a substitutable value, e.g. “morphologies_dir”: “$COMPONENT_DIR/morphologies”,