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Directed graph containing the pattern's ports (nodes) and connections
(edges).
id : str
Pattern identifier.
Returns
-------
pattern : neurokernel.neuroml.Pattern
pattern instance.
"""
pattern = Pattern(id=id)
interface = Interface()
for p in g.nodes():
attr_dict = g.node[p]
port = Port(identifier=attr_dict['identifier'],
interface=attr_dict['interface'],
io=attr_dict['io'],
type=attr_dict['type'])
interface.ports.append(port)
pattern.interface = interface
for c in g.edges():
connection = PatternConnection(from_=c[0], to=c[1])
pattern.connections.append(connection)
return pattern
elif attr_dict['model'] == 'power_gpot_gpot':
pgg = PGGSynapse(id=attr_dict['name'],
class_=attr_dict['class'],
slope=attr_dict['slope'],
threshold=attr_dict['threshold'],
power=attr_dict['power'],
saturation=attr_dict['saturation'],
delay=attr_dict['delay'],
reverse=attr_dict['reverse'],
conductance=attr_dict['conductance'])
module.pgg_synapses.append(pgg)
interface = Interface()
for p in i.nodes():
attr_dict = i.node[p]
port = Port(identifier=p,
interface=attr_dict['interface'],
io=attr_dict['io'],
type=attr_dict['type'])
interface.ports.append(port)
module.interface = interface
return module