def initialize_residual(self):
"""Creates the array that stores the residual. Also returns the
number of edges.
"""
dgraph = self.derivative_graph()
# We need to map any of our edges if they are in a
# pseudo-assy
comps = dgraph.edge_dict_to_comp_list(self.edge_list())
pa_keys = [name for name in comps if '~' in name]
if len(pa_keys) == 0:
self._mapped_severed_edges = self._severed_edges
else:
self._mapped_severed_edges = []
for src, target in self._severed_edges:
compname, _, varname = src.partition('.')
for pa_key in pa_keys:
pseudo = dgraph.node[pa_key]['pa_object']
if src in pseudo.outputs:
src = to_PA_var(src, pseudo.name)
break
compname, _, varname = target.partition('.')
for pa_key in pa_keys:
pseudo = dgraph.node[pa_key]['pa_object']
flat_inputs = set()
for item in pseudo.inputs:
subset = set(item)
flat_inputs = flat_inputs.union(subset)
if target in flat_inputs:
target = to_PA_var(target, pseudo.name)
break
self._mapped_severed_edges.append((src, target))
return super(CyclicWorkflow, self).initialize_residual()
def initialize_residual(self):
"""Creates the array that stores the residual. Also returns the
number of edges.
"""
dgraph = self.derivative_graph()
# We need to map any of our edges if they are in a
# pseudo-assy
pa_keys = set(
[s.split('.', 1)[0] for s in self.edge_list() if '~' in s])
if len(pa_keys) == 0:
self._mapped_severed_edges = self._severed_edges
else:
palist = [dgraph.node[pa_key]['pa_object'] for pa_key in pa_keys]
self._mapped_severed_edges = []
for src, target in self._severed_edges:
compname, _, varname = src.partition('.')
for pseudo in palist:
if src in pseudo.outputs:
src = to_PA_var(src, pseudo.name)
break
compname, _, varname = target.partition('.')
for pseudo in palist:
flat_inputs = set()
for item in pseudo.inputs:
flat_inputs.update(item)
if target in flat_inputs:
target = to_PA_var(target, pseudo.name)
break
self._mapped_severed_edges.append((src, target))
return super(CyclicWorkflow, self).initialize_residual()
def initialize_residual(self):
"""Creates the array that stores the residual. Also returns the
number of edges.
"""
dgraph = self.derivative_graph()
# We need to map any of our edges if they are in a
# pseudo-assy
pa_keys = set([s.split('.', 1)[0] for s in self.edge_list() if '~' in s])
if len(pa_keys) == 0:
self._mapped_severed_edges = self._severed_edges
else:
palist = [dgraph.node[pa_key]['pa_object'] for pa_key in pa_keys]
self._mapped_severed_edges = []
for src, target in self._severed_edges:
compname, _, varname = src.partition('.')
for pseudo in palist:
if src in pseudo.outputs:
src = to_PA_var(src, pseudo.name)
break
compname, _, varname = target.partition('.')
for pseudo in palist:
flat_inputs = set()
for item in pseudo.inputs:
flat_inputs.update(item)
if target in flat_inputs:
target = to_PA_var(target, pseudo.name)
break
self._mapped_severed_edges.append((src, target))
return super(CyclicWorkflow, self).initialize_residual()
def get_implicit_info(self):
""" Return a dict of the form {(residuals) : [states]}
"""
info = {}
dgraph = self.derivative_graph()
comps = dgraph.component_graph().nodes()
# Full model finite difference = no implcit edges
if len(comps) == 1 and '~' in comps[0]:
return info
# Residuals and states for implicit components
for cname in comps:
if cname.startswith('~'):
continue
comp = getattr(self.scope, cname)
if has_interface(comp, IImplicitComponent):
if not comp.eval_only:
key = tuple(
['.'.join([cname, n]) for n in comp.list_residuals()])
info[key] = [
'.'.join([cname, n]) for n in comp.list_states()
]
# Nested solvers act implicitly.
pa_comps = [
dgraph.node[item]['pa_object'] for item in comps if '~' in item
]
for comp in self._parent.iteration_set(solver_only=True):
if has_interface(comp, ISolver):
key = tuple(comp.list_eq_constraint_targets())
# For cyclic workflows in a solver, the edge is already there.
if len(key) == 0:
continue
unmapped_states = comp.list_param_group_targets()
# Need to map the subdriver parameters to any existing
# pseudoassemblies
value = []
for state_tuple in unmapped_states:
value_target = []
for state in state_tuple:
if state not in dgraph:
for pcomp in pa_comps:
if state in pcomp.inputs:
value_target.append(
to_PA_var(state, pcomp.name))
break
else:
value_target.append(state)
value.append(tuple(value_target))
info[key] = value
return info
def get_implicit_info(self):
""" Return a dict of the form {(residuals) : [states]}
"""
info = {}
dgraph = self.derivative_graph()
comps = dgraph.component_graph().nodes()
# Full model finite difference = no implcit edges
if len(comps) == 1 and '~' in comps[0]:
return info
# Residuals and states for implicit components
for cname in comps:
if cname.startswith('~'):
continue
comp = getattr(self.scope, cname)
if has_interface(comp, IImplicitComponent):
if not comp.eval_only:
key = tuple(['.'.join([cname, n])
for n in comp.list_residuals()])
info[key] = ['.'.join([cname, n])
for n in comp.list_states()]
# Nested solvers act implicitly.
pa_comps = [dgraph.node[item]['pa_object']
for item in comps if '~' in item]
for comp in self._parent.iteration_set(solver_only=True):
if has_interface(comp, ISolver):
key = tuple(comp.list_eq_constraint_targets())
# For cyclic workflows in a solver, the edge is already there.
if len(key) == 0:
continue
unmapped_states = comp.list_param_group_targets()
# Need to map the subdriver parameters to any existing
# pseudoassemblies
value = []
for state_tuple in unmapped_states:
value_target = []
for state in state_tuple:
if state not in dgraph:
for pcomp in pa_comps:
if state in pcomp.inputs:
value_target.append(to_PA_var(state,
pcomp.name))
break
else:
value_target.append(state)
value.append(tuple(value_target))
info[key] = value
return info
