def __new__(cls, name, bases, dct):
import nineml.abstraction as al
from nineml.abstraction.dynamics.utils import (flattener, xml,
modifiers)
# Extract Parameters Back out from Dict:
combined_model = dct['combined_model']
weight_vars = dct['weight_variables']
# Flatten the model:
assert isinstance(combined_model, al.ComponentClass)
if combined_model.is_flat():
flat_component = combined_model
else:
flat_component = flattener.flatten(combined_model, name)
# Make the substitutions:
flat_component.backsub_all()
# flat_component.backsub_aliases()
# flat_component.backsub_equations()
# Close any open reduce ports:
modifiers.DynamicPortModifier.close_all_reduce_ports(
componentclass=flat_component)
# New:
dct["combined_model"] = flat_component
dct["default_parameters"] = dict(
(param.name, 1.0) for param in flat_component.parameters)
dct["default_initial_values"] = dict(
(statevar.name, 0.0)
for statevar in chain(flat_component.state_variables))
dct["receptor_types"] = list(weight_vars.keys())
dct["standard_receptor_type"] = (dct["receptor_types"] == (
'excitatory', 'inhibitory'))
# how to determine this? neuron component has a receive analog port with dimension current, that is not connected to a synapse port?
dct["injectable"] = False
# how to determine this? synapse component has a receive analog port with dimension voltage?
dct["conductance_based"] = True
dct["model_name"] = name
dct["units"] = dict(
(statevar.name, _default_units[statevar.dimension.name])
for statevar in chain(flat_component.state_variables))
# Recording from bindings:
dct["recordable"] = (
[port.name
for port in flat_component.analog_ports] + ['spikes', 'regime'] +
[alias.lhs for alias in flat_component.aliases] +
[statevar.name for statevar in flat_component.state_variables])
logger.debug("Creating class '%s' with bases %s and dictionary %s" %
(name, bases, dct))
dct["builder"](flat_component,
dct["weight_variables"],
hierarchical_mode=True)
return type.__new__(cls, name, bases, dct)
def __new__(cls, name, bases, dct):
import nineml.abstraction as al
from nineml.abstraction.dynamics.utils import (
flattener, xml, modifiers)
# Extract Parameters Back out from Dict:
combined_model = dct['combined_model']
weight_vars = dct['weight_variables']
# Flatten the model:
assert isinstance(combined_model, al.ComponentClass)
if combined_model.is_flat():
flat_component = combined_model
else:
flat_component = flattener.flatten(combined_model, name)
# Make the substitutions:
flat_component.backsub_all()
#flat_component.backsub_aliases()
#flat_component.backsub_equations()
# Close any open reduce ports:
modifiers.DynamicPortModifier.close_all_reduce_ports(componentclass=flat_component)
# New:
dct["combined_model"] = flat_component
dct["default_parameters"] = dict((param.name, 1.0) for param in flat_component.parameters)
dct["default_initial_values"] = dict((statevar.name, 0.0) for statevar in chain(flat_component.state_variables))
dct["receptor_types"] = list(weight_vars.keys())
dct["standard_receptor_type"] = (dct["receptor_types"] == ('excitatory', 'inhibitory'))
dct["injectable"] = False # how to determine this? neuron component has a receive analog port with dimension current, that is not connected to a synapse port?
dct["conductance_based"] = True # how to determine this? synapse component has a receive analog port with dimension voltage?
dct["model_name"] = name
dct["units"] = dict((statevar.name, _default_units[statevar.dimension.name]) for statevar in chain(flat_component.state_variables))
# Recording from bindings:
dct["recordable"] = ([port.name for port in flat_component.analog_ports]
+ ['spikes', 'regime']
+ [alias.lhs for alias in flat_component.aliases]
+ [statevar.name for statevar in flat_component.state_variables])
logger.debug("Creating class '%s' with bases %s and dictionary %s" % (name, bases, dct))
dct["builder"](flat_component, dct["weight_variables"], hierarchical_mode=True)
return type.__new__(cls, name, bases, dct)