def test_get_inner_edges(self):
self.assertEqual(len(_get_inner_edges(self.dep, ['b'], ['c'])), 6)
self.assertEqual(set(_get_inner_edges(self.dep, ['b'], ['c'])),
set([('b[3]','A.b'),('A.d.z','B.b[4]'),('A.c[2]','B.a.x.y'),
('B.c','C.a'),('B.d','C.b'),('C.c','c')]))
dep, scope = _make_graph(comps=['A','B'],
connections=[('A.c','B.a'),('A.d','B.b')],
inputs=['a','b'],
outputs=['c','d'])
self.assertEqual(len(_get_inner_edges(dep, ['A.a'], ['B.c'])), 2)
self.assertEqual(set(_get_inner_edges(dep, ['A.a'], ['B.c'])),
set([('A.c','B.a'),('A.d','B.b')]))
dep, scope = _make_graph(comps=['A','B', 'C'],
connections=[('A.c','B.a'),('A.d','B.b'),('B.d','C.a')],
inputs=['a','b'],
outputs=['c','d'])
self.assertEqual(set(_get_inner_edges(dep, ['A.a'], ['C.c'])),
set([('A.c','B.a'),('A.d','B.b'),('B.d','C.a')]))
# same output feeding two inputs
dep, scope = _make_graph(comps=['A','B', 'C'],
connections=[('A.d','B.a'),('A.d','B.b'),('B.d','C.a')],
inputs=['a','b'],
outputs=['c','d'])
edges = _get_inner_edges(dep, ['A.a'], ['C.c'])
self.assertEqual(set(edges), set([('A.d','B.a'),('A.d','B.b'),('B.d','C.a')]))
edict = edges_to_dict(edges)
self.assertEqual(len(edict), 2)
self.assertEqual(set(edict['A.d']), set(['B.a','B.b']))
self.assertEqual(edict['B.d'], ['C.a'])
# loop
dep, scope = _make_graph(comps=['A','B', 'C'],
connections=[('A.d','B.a'),('B.d','C.a'),('C.d','A.a')],
inputs=['a','b'],
outputs=['c','d'])
self.assertEqual(set(_get_inner_edges(dep, ['A.a'], ['C.d'])),
set([('A.d','B.a'),('B.d','C.a'),('C.d','A.a')]))
def calc_derivatives(self,
first=False,
second=False,
savebase=True,
required_inputs=None,
required_outputs=None):
"""Calculate the derivatives for this non-differentiable block using
Finite Difference."""
# We don't do this in __init__ because some inputs and outputs
# are added after creation (for nested driver support).
if self.fd is None:
from openmdao.main.derivatives import FiniteDifference
self.fd = FiniteDifference(self)
if hasattr(self.wflow, '_severed_edges'):
self.wflow.sever_edges(self.wflow._severed_edges)
try:
# First, linearize about operating point.
# Note: Only needed for differentiable islands, which are handled
# with Fake Finite Difference.
# Don't do this for full-model finite difference.
if first and self.ffd_order > 0:
for name in self.comps:
# TODO: I think the cache is blown away each time before
# this is called
if name in self.wflow._J_cache:
continue
comp = self.wflow.scope.get(name)
# Assemblies need some required inputs and outputs
# to calculate the Jacobians
if has_interface(comp, IAssembly):
# Need to know which assy bdry variables are
# required, and pass them in. Cache this once.
if name not in self.ffd_cache:
dgraph = self.wflow.scope._depgraph
inputs = [
dgraph.base_var(inp)
for inp in flatten_list_of_iters(self.inputs)
]
outputs = [
dgraph.base_var(outp) for outp in self.outputs
]
from openmdao.main.depgraph import _get_inner_edges
edges = _get_inner_edges(dgraph, inputs, outputs)
req_inputs = []
req_outputs = []
for inp in inputs:
comp_str, _, var_str = inp.partition('.')
if comp_str == name:
req_inputs.append(var_str)
for inp in outputs:
comp_str, _, var_str = inp.partition('.')
if comp_str == name:
req_outputs.append(var_str)
for edge in edges:
src, target = edge
comp_str, _, var_str = src.partition('.')
if comp_str == name:
req_outputs.append(var_str)
comp_str, _, var_str = target.partition('.')
if comp_str == name:
req_inputs.append(var_str)
self.ffd_cache[name] = (req_inputs, req_outputs)
req_inputs, req_outputs = self.ffd_cache[name]
comp.calc_derivatives(first,
second,
savebase=True,
required_inputs=req_inputs,
required_outputs=req_outputs)
# Comp list contains full graph, so don't double up on
# the subdrivers.
elif not has_interface(comp, IDriver):
comp.calc_derivatives(first, second, True)
self.J = self.fd.calculate()
finally:
if hasattr(self.wflow, '_severed_edges'):
self.wflow.unsever_edges()
return self.J
def calc_derivatives(self, first=False, second=False, savebase=True,
required_inputs=None, required_outputs=None):
"""Calculate the derivatives for this non-differentiable block using
Finite Difference."""
# We don't do this in __init__ because some inputs and outputs
# are added after creation (for nested driver support).
if self.fd is None:
from openmdao.main.derivatives import FiniteDifference
self.fd = FiniteDifference(self)
if hasattr(self.wflow, '_severed_edges'):
self.wflow.sever_edges(self.wflow._severed_edges)
try:
# First, linearize about operating point.
# Note: Only needed for differentiable islands, which are handled
# with Fake Finite Difference.
# Don't do this for full-model finite difference.
if first and self.ffd_order > 0:
for name in self.comps:
# TODO: I think the cache is blown away each time before
# this is called
if name in self.wflow._J_cache:
continue
comp = self.wflow.scope.get(name)
# Assemblies need some required inputs and outputs
# to calculate the Jacobians
if has_interface(comp, IAssembly):
# Need to know which assy bdry variables are
# required, and pass them in. Cache this once.
if name not in self.ffd_cache:
dgraph = self.wflow.scope._depgraph
inputs = [dgraph.base_var(inp)
for inp in flatten_list_of_iters(self.inputs)]
outputs = [dgraph.base_var(outp)
for outp in self.outputs]
from openmdao.main.depgraph import _get_inner_edges
edges = _get_inner_edges(dgraph, inputs, outputs)
req_inputs = []
req_outputs = []
for inp in inputs:
comp_str, _, var_str = inp.partition('.')
if comp_str == name:
req_inputs.append(var_str)
for inp in outputs:
comp_str, _, var_str = inp.partition('.')
if comp_str == name:
req_outputs.append(var_str)
for edge in edges:
src, target = edge
comp_str, _, var_str = src.partition('.')
if comp_str == name:
req_outputs.append(var_str)
comp_str, _, var_str = target.partition('.')
if comp_str == name:
req_inputs.append(var_str)
self.ffd_cache[name] = (req_inputs, req_outputs)
req_inputs, req_outputs = self.ffd_cache[name]
comp.calc_derivatives(first, second, savebase=True,
required_inputs=req_inputs,
required_outputs=req_outputs)
# Comp list contains full graph, so don't double up on
# the subdrivers.
elif not has_interface(comp, IDriver):
comp.calc_derivatives(first, second, True)
self.J = self.fd.calculate()
finally:
if hasattr(self.wflow, '_severed_edges'):
self.wflow.unsever_edges()
return self.J
def test_inner_edges(self):
edges = _get_inner_edges(self.dep, ['a'], ['c'])
self.assertEqual(set(edges), set([('C2.s2', 'c'), ('C1.s1', 'C2.a'), ('a', 'C1.s1')]))