def _create_replacement_connection(c_in, c_out):
"""Generate a new Connection to replace two through a passthrough Node"""
assert c_in.post_obj is c_out.pre_obj
assert c_in.post_obj.output is None
# determine the filter for the new Connection
if c_in.synapse is None:
synapse = c_out.synapse
elif c_out.synapse is None:
synapse = c_in.synapse
else:
raise Unconvertible("Cannot merge two filters")
# Note: the algorithm below is in the right ballpark,
# but isn't exactly the same as two low-pass filters
# filter = c_out.filter + c_in.filter
function = c_in.function
if c_out.function is not None:
raise Unconvertible("Cannot remove a connection with a function")
# compute the combined transform
transform = np.dot(full_transform(c_out), full_transform(c_in))
# check if the transform is 0 (this happens a lot
# with things like identity transforms)
if np.all(transform == 0):
return None
c = nengo.Connection(c_in.pre_obj,
c_out.post_obj,
synapse=synapse,
transform=transform,
function=function,
add_to_container=False)
return c
def remove_passthrough_nodes( # noqa: C901
objs, connections, create_connection_fn=None):
"""Returns a version of the model without passthrough Nodes.
For some backends (such as SpiNNaker), it is useful to remove Nodes that
have 'None' as their output. These nodes simply sum their inputs and
use that as their output. These nodes are defined purely for organizational
purposes and should not affect the behaviour of the model. For example,
the 'input' and 'output' Nodes in an EnsembleArray, which are just meant to
aggregate data.
Note that removing passthrough nodes can simplify a model and may be useful
for other backends as well. For example, an EnsembleArray connected to
another EnsembleArray with an identity matrix as the transform
should collapse down to D Connections between the corresponding Ensembles
inside the EnsembleArrays.
Parameters
----------
objs : list of Nodes and Ensembles
All the objects in the model
connections : list of Connections
All the Connections in the model
Returns the objs and connections of the resulting model. The passthrough
Nodes will be removed, and the Connections that interact with those Nodes
will be replaced with equivalent Connections that don't interact with those
Nodes.
"""
# imported here to avoid circular imports
from nengo import Node # pylint: disable=import-outside-toplevel
if create_connection_fn is None:
create_connection_fn = _create_replacement_connection
inputs, outputs = find_all_io(connections)
result_conn = list(connections)
result_objs = list(objs)
# look for passthrough Nodes to remove
for obj in objs:
if isinstance(obj, Node) and obj.output is None:
result_objs.remove(obj)
# get rid of the connections to and from this Node
for c in inputs[obj]:
result_conn.remove(c)
outputs[c.pre_obj].remove(c)
for c in outputs[obj]:
result_conn.remove(c)
inputs[c.post_obj].remove(c)
# replace those connections with equivalent ones
for c_in in inputs[obj]:
if c_in.pre_obj is obj:
raise Unconvertible(
"Cannot remove a Node with a feedback connection")
for c_out in outputs[obj]:
c = create_connection_fn(c_in, c_out)
if c is not None:
result_conn.append(c)
# put this in the list, since it might be used
# another time through the loop
outputs[c.pre_obj].append(c)
inputs[c.post_obj].append(c)
return result_objs, result_conn