def add_to_graph(self, startgraph, dgraph, excludes=()):
"""Add this PseudoAssembly to the given graph, absorbing
nodes that represent components contained in this PA.
"""
from openmdao.main.depgraph import is_subvar_node, \
is_input_base_node, is_output_base_node
self._removed_comps = [c for c in self.comps if c not in excludes]
# Add pseudoassys to graph
dgraph.add_node(self.name,
pa_object=self,
comp=True,
pseudo='assembly',
valid=True)
empty = {}
for oldname, newname in self.renames.items():
attrs = startgraph.node.get(oldname, empty).copy()
if oldname in self.boundary_params:
attrs['iotype'] = 'out'
dgraph.add_node(newname, attr_dict=attrs)
for u, v, data in dgraph.edges(oldname, data=True):
dgraph.add_edge(newname, v, attr_dict=data.copy())
for u, v, data in dgraph.in_edges(oldname, data=True):
dgraph.add_edge(u, newname, attr_dict=data.copy())
if is_subvar_node(dgraph, newname):
dgraph.node[newname]['basevar'] = \
newname.split('[', 1)[0]
if is_input_base_node(dgraph, newname):
dgraph.add_edge(newname, self.name)
elif is_output_base_node(dgraph, newname):
dgraph.add_edge(self.name, newname)
def add_to_graph(self, startgraph, dgraph, excludes=()):
"""Add this PseudoAssembly to the given graph, absorbing
nodes that represent components contained in this PA.
"""
from openmdao.main.depgraph import is_subvar_node, is_input_base_node, is_output_base_node
self._removed_comps = [c for c in self.comps if c not in excludes]
# Add pseudoassys to graph
dgraph.add_node(self.name, pa_object=self, comp=True, pseudo="assembly", valid=True)
empty = {}
for oldname, newname in self.renames.items():
attrs = startgraph.node.get(oldname, empty).copy()
if oldname in self.boundary_params:
attrs["iotype"] = "out"
dgraph.add_node(newname, attr_dict=attrs)
for u, v, data in dgraph.edges(oldname, data=True):
dgraph.add_edge(newname, v, attr_dict=data.copy())
for u, v, data in dgraph.in_edges(oldname, data=True):
dgraph.add_edge(u, newname, attr_dict=data.copy())
if is_subvar_node(dgraph, newname):
dgraph.node[newname]["basevar"] = newname.split("[", 1)[0]
if is_input_base_node(dgraph, newname):
dgraph.add_edge(newname, self.name)
elif is_output_base_node(dgraph, newname):
dgraph.add_edge(self.name, newname)
dgraph.config_changed()
def matvecREV(self, arg):
'''Callback function for performing the matrix vector product of the
workflow's full Jacobian with an incoming vector arg.'''
dgraph = self._derivative_graph
comps = self._comp_edge_list()
result = zeros(len(arg))
# We can call applyJ on each component one-at-a-time, and poke the
# results into the result vector.
for compname, data in comps.iteritems():
if compname == '@fake':
continue
comp_inputs = data['inputs']
comp_outputs = data['outputs']
comp_residuals = data['residuals']
inputs = {}
outputs = {}
out_bounds = []
for varname in comp_outputs:
node = '%s.%s' % (compname, varname)
# Ouputs define unique edges, so don't duplicate anything
if is_subvar_node(dgraph, node):
if dgraph.base_var(node).split('.', 1)[1] in comp_outputs:
continue
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
inputs[varname] = arg[i1].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(len(i1))
out_bounds.append((varname, i1, i2))
else:
inputs[varname] = arg[i1:i2].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(i2 - i1)
out_bounds.append((varname, i1, i2))
for varname in comp_inputs:
node = '%s.%s' % (compname, varname)
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
outputs[varname] = zeros(len(i1))
else:
outputs[varname] = zeros(i2 - i1)
out_bounds.append((varname, i1, i2))
if '~' in compname:
comp = self._derivative_graph.node[compname]['pa_object']
else:
comp = self.scope.get(compname)
# Preconditioning
if hasattr(comp, 'applyMinvT'):
inputs = applyMinvT(comp, inputs, self._shape_cache)
applyJT(comp, inputs, outputs, comp_residuals, self._shape_cache,
self._J_cache.get(compname))
#print inputs, outputs
for varname, i1, i2 in out_bounds:
if isinstance(i1, list):
result[i1] += outputs[varname]
else:
result[i1:i2] += outputs[varname]
# Each parameter adds an equation
for src, target in self._edges.iteritems():
if '@in' in src or '@fake' in src:
if isinstance(target, list):
target = target[0]
i1, i2 = self.get_bounds(target)
result[i1:i2] += arg[i1:i2]
# A fake output needs to make it into the result vector to prevent
# the solution from blowing up. Its derivative will be zero
# regardless.
if '@fake' in target:
i1, i2 = self.get_bounds(src)
result[i1:i2] += arg[i1:i2]
#print arg, result
return result
def matvecREV(self, arg):
'''Callback function for performing the matrix vector product of the
workflow's full Jacobian with an incoming vector arg.'''
dgraph = self._derivative_graph
comps = self._comp_edge_list()
result = zeros(len(arg))
# We can call applyJ on each component one-at-a-time, and poke the
# results into the result vector.
for compname, data in comps.iteritems():
if compname == '@fake':
continue
comp_inputs = data['inputs']
comp_outputs = data['outputs']
comp_residuals = data['residuals']
inputs = {}
outputs = {}
out_bounds = []
for varname in comp_outputs:
node = '%s.%s' % (compname, varname)
# Ouputs define unique edges, so don't duplicate anything
if is_subvar_node(dgraph, node):
if dgraph.base_var(node).split('.', 1)[1] in comp_outputs:
continue
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
inputs[varname] = arg[i1].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(len(i1))
out_bounds.append((varname, i1, i2))
else:
inputs[varname] = arg[i1:i2].copy()
if varname not in comp_residuals:
outputs[varname] = zeros(i2-i1)
out_bounds.append((varname, i1, i2))
for varname in comp_inputs:
node = '%s.%s' % (compname, varname)
i1, i2 = self.get_bounds(node)
if isinstance(i1, list):
outputs[varname] = zeros(len(i1))
else:
outputs[varname] = zeros(i2-i1)
out_bounds.append((varname, i1, i2))
if '~' in compname:
comp = self._derivative_graph.node[compname]['pa_object']
else:
comp = self.scope.get(compname)
# Preconditioning
if hasattr(comp, 'applyMinvT'):
inputs = applyMinvT(comp, inputs, self._shape_cache)
applyJT(comp, inputs, outputs, comp_residuals,
self._shape_cache, self._J_cache.get(compname))
#print inputs, outputs
for varname, i1, i2 in out_bounds:
if isinstance(i1, list):
result[i1] += outputs[varname]
else:
result[i1:i2] += outputs[varname]
# Each parameter adds an equation
for src, target in self._edges.iteritems():
if '@in' in src or '@fake' in src:
if isinstance(target, list):
target = target[0]
i1, i2 = self.get_bounds(target)
result[i1:i2] += arg[i1:i2]
#print arg, result
return result