def __new__(cls, name, bases, dct):
import nineml.abstraction_layer as al
from nineml.abstraction_layer 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)
comp_data = TestableComponent('nestequivalent_iaf_cond_exp')
# Build the component:
component = comp_data()
component = flatten(component)
component.backsub_all()
ComponentModifier.close_all_reduce_ports(component=component)
# Copy the descriptive strings:
component.short_description = comp_data.metadata.short_description
component.long_description = comp_data.metadata.long_description
# Get the initial regime. If this component comes from an flattened component,
# then we should look up the new regime from the locations in the old
# components, hence the nedd for this code:
initial_regime = comp_data.metadata.initial_regime
if component.was_flattened():
new_regime = component.flattener.get_new_regime(initial_regime)
initial_regime = new_regime.name
from nestbuilder import NestFileBuilder
nfb = NestFileBuilder(
nest_classname=comp_data.metadata.nest_classname,
component=component,
synapse_ports=comp_data.metadata.synapse_ports,
initial_regime=initial_regime, # new_regime.name,
initial_values=comp_data.metadata.initial_values,
default_values=comp_data.metadata.parameters,
)
nfb.compile_files()
def __new__(cls, name, bases, dct):
import nineml.abstraction_layer as al
from nineml.abstraction_layer 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)
'iaf.vrest': -65.0,
'iaf.vreset': -65.0,
'iaf.vthresh': -50.0,
'cobaExcit.tau': 2.0,
'cobaInhib.tau': 5.0,
'cobaExcit.vrev': 0.0,
'cobaInhib.vrev': -70.0,
'cobaExcit.q': 2.0,
'cobaInhib.q': 2.0,
'iaf.ISyn':0.0,
'cobaExcit.gl': 0.0,
}
default_values = ComponentFlattener.flatten_namespace_dict( parameters )
from nestbuilder import NestFileBuilder
nfb = NestFileBuilder( nest_classname = nest_classname,
component = iaf_cond_exp_9ML_reduced,
synapse_ports = synapse_ports,
initial_regime = initial_regime,
initial_values = initial_values,
default_values = default_values,
)
nfb.compile_files()
initial_values = {'V_m': -70.0, 'g_ex': 0.0, 'g_in': 0.0, 't_spike': 0.0}
default_values = {
'tau_syn_ex': 1.5,
'tau_syn_in': 10.0,
'E_ex': 0.0,
'E_in': -80.0,
'Isyn': 0.0,
'C_m': 1.0,
'g_L': 10.0,
'q_ex': 2.0,
'q_in': 2.0,
't_ref': 5.0,
'V_th': -55.0,
'E_L': -70.0,
'V_reset': -70.0
}
# CG specific stuff which needs to be determined somehow
nest_classname = "iaf_cond_exp_9ml"
from nestbuilder import NestFileBuilder
nfb = NestFileBuilder(nest_classname=nest_classname,
component=iaf_cond_exp_9ML,
synapse_ports=synapse_ports,
initial_regime=initial_regime,
initial_values=initial_values,
default_values=default_values)
nfb.compile_files()
