def get_inner_edges(graph, srcs, dests, wflow):
graph = graph.subgraph(graph.nodes())
# add @in and @out nodes, rewire input srcs, etc.
mod_for_derivs(graph, srcs, dests, wflow)
# sort edges by src so that basevars occur before subvars
edges = sorted(graph.list_connections(), key=lambda e: e[0])
return edges_to_dict(edges)
def get_inner_edges(graph, srcs, dests, wflow):
graph = graph.subgraph(graph.nodes())
# add @in and @out nodes, rewire input srcs, etc.
graph = mod_for_derivs(graph, srcs, dests, wflow)
# sort edges by src so that basevars occur before subvars
edges = sorted(graph.list_connections(), key=lambda e: e[0])
return edges_to_dict(edges)
def derivative_graph(self,
inputs=None,
outputs=None,
fd=False,
severed=None,
group_nondif=True):
"""Returns the local graph that we use for derivatives.
inputs: list of strings or tuples of strings
List of input variables that we are taking derivatives with respect
to. They must be within this workflow's scope. If no inputs are
given, the parent driver's parameters are used. A tuple can be used
to link inputs together.
outputs: list of strings
List of output variables that we are taking derivatives of.
They must be within this workflow's scope. If no outputs are
given, the parent driver's objectives and constraints are used.
fd: boolean
set to True to finite difference the whole model together with
fake finite difference turned off. This is mainly for checking
your model's analytic derivatives.
severed: list
If a workflow has a cylic connection, some edges must be severed.
When a cyclic workflow calls this function, it passes a list of
edges so that they can be severed prior to the topological sort.
group_nondif: bool
If True, collapse parts of the graph into PseudoAssemblies when
necessary.
"""
if self._derivative_graph is None or group_nondif is False:
# when we call with group_nondif = False, we want the union of the
# passed inputs/outputs plus the inputs/outputs from the solver
if group_nondif is False:
tmp_inputs = [] if inputs is None else inputs
tmp_outputs = [] if outputs is None else outputs
inputs = None
outputs = None
# If inputs aren't specified, use the parameters
parent_deriv_vars = list_deriv_vars(self._parent.parent)
if inputs is None:
if hasattr(self._parent, 'list_param_group_targets'):
inputs = self._parent.list_param_group_targets()
elif parent_deriv_vars[0]:
inputs = parent_deriv_vars[0]
else:
msg = "No inputs given for derivatives."
self.scope.raise_exception(msg, RuntimeError)
if group_nondif is False:
inputs = list(set(tmp_inputs).union(inputs))
# If outputs aren't specified, use the objectives and constraints
if outputs is None:
outputs = []
if hasattr(self._parent, 'get_objectives'):
outputs.extend([
"%s.out0" % item.pcomp_name
for item in self._parent.get_objectives().values()
])
if hasattr(self._parent, 'get_constraints'):
outputs.extend([
"%s.out0" % item.pcomp_name
for item in self._parent.get_constraints().values()
])
if group_nondif is False:
outputs = list(set(tmp_outputs).union(outputs))
if len(outputs) == 0:
if parent_deriv_vars[1]:
outputs = parent_deriv_vars[1]
else:
msg = "No outputs given for derivatives."
self.scope.raise_exception(msg, RuntimeError)
graph = self.scope._depgraph
# make a copy of the graph because it will be
# modified by mod_for_derivs
dgraph = graph.subgraph(graph.nodes())
dgraph = mod_for_derivs(dgraph, inputs, outputs, self, fd)
if group_nondif:
self._derivative_graph = dgraph
self._group_nondifferentiables(fd, severed)
else:
# we're being called to determine the deriv graph
# for a subsolver, so get rid of @in and @out nodes
dgraph.remove_nodes_from(
['@in%d' % i for i in range(len(inputs))])
dgraph.remove_nodes_from(
['@out%d' % i for i in range(len(outputs))])
dgraph.graph['inputs'] = inputs[:]
dgraph.graph['outputs'] = outputs[:]
return dgraph
return self._derivative_graph
def derivative_graph(self, inputs=None, outputs=None, fd=False,
severed=None, group_nondif=True):
"""Returns the local graph that we use for derivatives.
inputs: list of strings or tuples of strings
List of input variables that we are taking derivatives with respect
to. They must be within this workflow's scope. If no inputs are
given, the parent driver's parameters are used. A tuple can be used
to link inputs together.
outputs: list of strings
List of output variables that we are taking derivatives of.
They must be within this workflow's scope. If no outputs are
given, the parent driver's objectives and constraints are used.
fd: boolean
set to True to finite difference the whole model together with
fake finite difference turned off. This is mainly for checking
your model's analytic derivatives.
severed: list
If a workflow has a cylic connection, some edges must be severed.
When a cyclic workflow calls this function, it passes a list of
edges so that they can be severed prior to the topological sort.
group_nondif: bool
If True, collapse parts of the graph into PseudoAssemblies when
necessary.
"""
if self._derivative_graph is None or group_nondif is False:
# when we call with group_nondif = False, we want the union of the
# passed inputs/outputs plus the inputs/outputs from the solver
if group_nondif is False:
tmp_inputs = [] if inputs is None else inputs
tmp_outputs = [] if outputs is None else outputs
inputs = None
outputs = None
# If inputs aren't specified, use the parameters
if inputs is None:
if hasattr(self._parent, 'list_param_group_targets'):
inputs = self._parent.list_param_group_targets()
else:
msg = "No inputs given for derivatives."
self.scope.raise_exception(msg, RuntimeError)
if group_nondif is False:
inputs = list(set(tmp_inputs).union(inputs))
# If outputs aren't specified, use the objectives and constraints
if outputs is None:
outputs = []
if hasattr(self._parent, 'get_objectives'):
outputs.extend(["%s.out0" % item.pcomp_name for item in
self._parent.get_objectives().values()])
if hasattr(self._parent, 'get_constraints'):
outputs.extend(["%s.out0" % item.pcomp_name for item in
self._parent.get_constraints().values()])
if group_nondif is False:
outputs = list(set(tmp_outputs).union(outputs))
if len(outputs) == 0:
msg = "No outputs given for derivatives."
self.scope.raise_exception(msg, RuntimeError)
graph = self.scope._depgraph
# make a copy of the graph because it will be
# modified by mod_for_derivs
dgraph = graph.subgraph(graph.nodes())
mod_for_derivs(dgraph, inputs, outputs, self, fd)
if group_nondif:
self._derivative_graph = dgraph
self._group_nondifferentiables(fd, severed)
else:
# we're being called to determine the deriv graph
# for a subsolver, so get rid of @in and @out nodes
dgraph.remove_nodes_from(['@in%d' % i for i in range(len(inputs))])
dgraph.remove_nodes_from(['@out%d' % i for i in range(len(outputs))])
dgraph.graph['inputs'] = inputs[:]
dgraph.graph['outputs'] = outputs[:]
return dgraph
return self._derivative_graph
