def __new__(cls, name, bases, dct):
import nineml.abstraction as al
from nineml.abstraction import flattening, writers, component_modifiers
import nest
# Extract Parameters Back out from Dict:
nineml_model = dct['nineml_model']
synapse_components = dct['synapse_components']
# Flatten the model:
assert isinstance(nineml_model, al.ComponentClass)
if nineml_model.is_flat():
flat_component = nineml_model
else:
flat_component = flattening.flatten(nineml_model, name)
# Make the substitutions:
flat_component.backsub_all()
#flat_component.backsub_aliases()
#flat_component.backsub_equations()
# Close any open reduce ports:
component_modifiers.ComponentModifier.close_all_reduce_ports(component=flat_component)
flat_component.short_description = "Auto-generated 9ML neuron model for PyNN.nest"
flat_component.long_description = "Auto-generated 9ML neuron model for PyNN.nest"
# Close any open reduce ports:
component_modifiers.ComponentModifier.close_all_reduce_ports(component=flat_component)
# synapse ports:
synapse_ports = []
for syn in synapse_components:
# get recv event ports
# TODO: model namespace look
#syn_component = nineml_model[syn.namespace]
syn_component = nineml_model.subnodes[syn.namespace]
recv_event_ports = list(syn_component.query.event_recv_ports)
# check there's only one
if len(recv_event_ports) != 1:
raise ValueError("A synapse component has multiple recv ports. Cannot dis-ambiguate")
synapse_ports.append(syn.namespace + '_' + recv_event_ports[0].name)
# New:
dct["combined_model"] = flat_component
# TODO: Override this with user layer
#default_values = ModelToSingleComponentReducer.flatten_namespace_dict( parameters )
dct["default_parameters"] = dict((p.name, 1.0) for p in flat_component.parameters)
dct["default_initial_values"] = dict((s.name, 0.0) for s in flat_component.state_variables)
dct["synapse_types"] = [syn.namespace for syn in synapse_components]
dct["standard_receptor_type"] = (dct["synapse_types"] == ('excitatory', 'inhibitory'))
dct["injectable"] = True # need to determine this. How??
dct["conductance_based"] = True # how to determine this??
dct["model_name"] = name
dct["nest_model"] = name
# Recording from bindings:
dct["recordable"] = [port.name for port in flat_component.analog_ports] + ['spikes', 'regime']
# TODO bindings -> alias and support recording of them in nest template
#+ [binding.name for binding in flat_component.bindings]
dct["weight_variables"] = dict([(syn.namespace, syn.namespace + '_' + syn.weight_connector)
for syn in synapse_components])
logger.debug("Creating class '%s' with bases %s and dictionary %s" % (name, bases, dct))
# TODO: UL configuration of initial regime.
initial_regime = flat_component.regimes_map.keys()[0]
from nestbuilder import NestFileBuilder
nfb = NestFileBuilder(nest_classname=name,
component=flat_component,
synapse_ports=synapse_ports,
initial_regime=initial_regime,
initial_values=dct["default_initial_values"],
default_values=dct["default_parameters"],
)
nfb.compile_files()
nest.Install('mymodule')
return type.__new__(cls, name, bases, dct)
def __new__(cls, name, bases, dct):
import nineml.abstraction as al
from nineml.abstraction import flattening, writers, component_modifiers
import nest
#Extract Parameters Back out from Dict:
nineml_model = dct['nineml_model']
synapse_components = dct['synapse_components']
# Flatten the model:
assert isinstance(nineml_model, al.ComponentClass)
if nineml_model.is_flat():
flat_component = nineml_model
else:
flat_component = flattening.flatten( nineml_model,name )
# Make the substitutions:
flat_component.backsub_all()
#flat_component.backsub_aliases()
#flat_component.backsub_equations()
# Close any open reduce ports:
component_modifiers.ComponentModifier.close_all_reduce_ports(component = flat_component)
flat_component.short_description = "Auto-generated 9ML neuron model for PyNN.nest"
flat_component.long_description = "Auto-generated 9ML neuron model for PyNN.nest"
# Close any open reduce ports:
component_modifiers.ComponentModifier.close_all_reduce_ports(component = flat_component)
# synapse ports:
synapse_ports = []
for syn in synapse_components:
# get recv event ports
# TODO: model namespace look
#syn_component = nineml_model[syn.namespace]
syn_component = nineml_model.subnodes[syn.namespace]
recv_event_ports = list(syn_component.query.event_recv_ports)
# check there's only one
if len(recv_event_ports)!=1:
raise ValueError("A synapse component has multiple recv ports. Cannot dis-ambiguate")
synapse_ports.append(syn.namespace+'_'+recv_event_ports[0].name)
# New:
dct["combined_model"] = flat_component
# TODO: Override this with user layer
#default_values = ModelToSingleComponentReducer.flatten_namespace_dict( parameters )
dct["default_parameters"] = dict( (p.name, 1.0) for p in flat_component.parameters )
dct["default_initial_values"] = dict((s.name, 0.0) for s in flat_component.state_variables)
dct["synapse_types"] = [syn.namespace for syn in synapse_components]
dct["standard_receptor_type"] = (dct["synapse_types"] == ('excitatory', 'inhibitory'))
dct["injectable"] = True # need to determine this. How??
dct["conductance_based"] = True # how to determine this??
dct["model_name"] = name
dct["nest_model"] = name
# Recording from bindings:
dct["recordable"] = [port.name for port in flat_component.analog_ports] + ['spikes', 'regime']
# TODO bindings -> alias and support recording of them in nest template
#+ [binding.name for binding in flat_component.bindings]
dct["weight_variables"] = dict([ (syn.namespace,syn.namespace+'_'+syn.weight_connector )
for syn in synapse_components ])
logger.debug("Creating class '%s' with bases %s and dictionary %s" % (name, bases, dct))
# TODO: UL configuration of initial regime.
initial_regime = flat_component.regimes_map.keys()[0]
from nestbuilder import NestFileBuilder
nfb = NestFileBuilder( nest_classname = name,
component = flat_component,
synapse_ports = synapse_ports,
initial_regime = initial_regime,
initial_values = dct["default_initial_values"],
default_values = dct["default_parameters"],
)
nfb.compile_files()
nest.Install('mymodule')
return type.__new__(cls, name, bases, dct)