def annotate_rule(m): m.stoch_rhs = StochasticConstraintBoundsAnnotation() m.stoch_rhs.declare(m.r[3]) m.stoch_rhs.declare(m.r[6]) m.stoch_rhs.declare(m.r[9]) m.stoch_rhs.declare(m.c) m.stoch_rhs.declare(m.b.c)
def pysp_instance_creation_callback(scenario_name, node_names): model = create_instance(scenario_name) # # SMPS Related Annotations # model.stoch_rhs = StochasticConstraintBoundsAnnotation() # declarations can be blocks to imply that all # components (Constraints in this case) should be # considered on that block model.stoch_rhs.declare(model.p_second_stage) model.stoch_rhs.declare(model.r_second_stage) model.stoch_matrix = StochasticConstraintBodyAnnotation() # exercise more of the code by testing this with an # indexed block and a single block model.stoch_matrix.declare(model.c_second_stage, variables=[model.r]) model.stoch_matrix.declare(model.p_second_stage[1]) model.stoch_objective = StochasticObjectiveAnnotation() return model
def pysp_instance_creation_callback(scenario_name, node_names): model = create_instance(scenario_name) # # SMPS Related Annotations # model.stoch_rhs = StochasticConstraintBoundsAnnotation() for con in model.p_second_stage[1].component_data_objects(Constraint, active=True): model.stoch_rhs.declare(con) model.stoch_rhs.declare(model.r_second_stage) model.stoch_matrix = StochasticConstraintBodyAnnotation() # exercise more of the code by testing this with an # indexed block and a single block model.stoch_matrix.declare(model.c_second_stage, variables=[model.r]) model.stoch_matrix.declare(model.p_second_stage[1]) model.stoch_objective = StochasticObjectiveAnnotation() model.stoch_objective.declare(model.o) return model
def __init__(self, reference_model): self.reference_model = None self.objective = None self.time_stages = None self.stage_to_variables_map = {} self.variable_to_stage_map = {} # the set of stochastic data objects # (possibly mapped to some distribution) self.stochastic_data = None # maps between variables and objectives self.variable_to_objectives_map = ComponentMap() self.objective_to_variables_map = ComponentMap() # maps between variables and constraints self.variable_to_constraints_map = ComponentMap() self.constraint_to_variables_map = ComponentMap() # maps between stochastic data and objectives self.stochastic_data_to_objectives_map = ComponentMap() self.objective_to_stochastic_data_map = ComponentMap() # maps between stochastic data and constraints self.stochastic_data_to_constraints_map = ComponentMap() self.constraint_to_stochastic_data_map = ComponentMap() # maps between stochastic data and variable lower and upper bounds self.stochastic_data_to_variables_lb_map = ComponentMap() self.variable_to_stochastic_data_lb_map = ComponentMap() self.stochastic_data_to_variables_ub_map = ComponentMap() self.variable_to_stochastic_data_ub_map = ComponentMap() self.variable_symbols = ComponentMap() if not isinstance(reference_model, Block): raise TypeError("reference model input must be a Pyomo model") self.reference_model = reference_model # # Extract stochastic parameters from the # StochasticDataAnnotation object # self.stochastic_data = \ _extract_stochastic_data(self.reference_model) # # Get the variable stages from the # VariableStageAnnotation object # (self.stage_to_variables_map, self.variable_to_stage_map, self._variable_stage_assignments) = \ _map_variable_stages(self.reference_model) self.time_stages = tuple(sorted(self.stage_to_variables_map)) assert self.time_stages[0] == 1 self.variable_symbols = generate_cuid_names(self.reference_model, ctype=Var) # remove the parent blocks from this map keys_to_delete = [] for var in self.variable_symbols: if var.parent_component().ctype is not Var: keys_to_delete.append(var) for key in keys_to_delete: del self.variable_symbols[key] # # Get the stage cost components from the StageCostAnnotation # and generate a dummy single-scenario scenario tree # stage_cost_annotation = locate_annotations(self.reference_model, StageCostAnnotation, max_allowed=1) if len(stage_cost_annotation) == 0: raise ValueError("Reference model is missing stage cost " "annotation: %s" % (StageCostAnnotation.__name__)) else: assert len(stage_cost_annotation) == 1 stage_cost_annotation = stage_cost_annotation[0][1] stage_cost_assignments = ComponentMap( stage_cost_annotation.expand_entries()) stage1_cost = None stage2_cost = None for cdata, stagenum in stage_cost_assignments.items(): if stagenum == 1: stage1_cost = cdata elif stagenum == 2: stage2_cost = cdata if stage1_cost is None: raise ValueError("Missing stage cost annotation " "for time stage: 1") if stage2_cost is None: raise ValueError("Missing stage cost annotation " "for time stage: 2") assert stage1_cost is not stage2_cost self._stage1_cost = stage1_cost self._stage2_cost = stage2_cost # # Extract the locations of variables and stochastic data # within the model # sto_obj = StochasticObjectiveAnnotation() for objcntr, obj in enumerate( self.reference_model.component_data_objects(Objective, active=True, descend_into=True), 1): if objcntr > 1: raise ValueError( "Reference model can not contain more than one " "active objective") self.objective = obj self.objective_sense = obj.sense obj_params = tuple( self._collect_mutable_parameters(obj.expr).values()) self.objective_to_stochastic_data_map[obj] = [] for paramdata in obj_params: if paramdata in self.stochastic_data: self.stochastic_data_to_objectives_map.\ setdefault(paramdata, []).append(obj) self.objective_to_stochastic_data_map[obj].\ append(paramdata) if len(self.objective_to_stochastic_data_map[obj]) == 0: del self.objective_to_stochastic_data_map[obj] else: # TODO: Can we make this declaration sparse # by identifying which variables have # stochastic coefficients? How to handle # non-linear expressions? sto_obj.declare(obj) obj_variables = tuple(self._collect_variables(obj.expr).values()) self.objective_to_variables_map[obj] = [] for var in obj_variables: self.variable_to_objectives_map.\ setdefault(var, []).append(obj) self.objective_to_variables_map[obj].append(var) if len(self.objective_to_variables_map[obj]) == 0: del self.objective_to_variables_map[obj] sto_conbounds = StochasticConstraintBoundsAnnotation() sto_conbody = StochasticConstraintBodyAnnotation() for con in self.reference_model.component_data_objects( Constraint, active=True, descend_into=True): lower_params = tuple( self._collect_mutable_parameters(con.lower).values()) body_params = tuple( self._collect_mutable_parameters(con.body).values()) upper_params = tuple( self._collect_mutable_parameters(con.upper).values()) # TODO: Can we make this declaration sparse # by idenfifying which variables have # stochastic coefficients? How to handle # non-linear expressions? Currently, this # code also fails to detect that mutable # "constant" expressions might fall out # of the body and into the bounds. if len(body_params): sto_conbody.declare(con) if len(body_params) or \ len(lower_params) or \ len(upper_params): sto_conbounds.declare( con, lb=bool(len(lower_params) or len(body_params)), ub=bool(len(upper_params) or len(body_params))) all_stochastic_params = {} for param in itertools.chain(lower_params, body_params, upper_params): if param in self.stochastic_data: all_stochastic_params[id(param)] = param if len(all_stochastic_params) > 0: self.constraint_to_stochastic_data_map[con] = [] # no params will appear twice in this iteration for param in all_stochastic_params.values(): self.stochastic_data_to_constraints_map.\ setdefault(param, []).append(con) self.constraint_to_stochastic_data_map[con].\ append(param) body_variables = tuple(self._collect_variables(con.body).values()) self.constraint_to_variables_map[con] = [] for var in body_variables: self.variable_to_constraints_map.\ setdefault(var, []).append(con) self.constraint_to_variables_map[con].append(var) # For now, it is okay to have SOSConstraints in the # representation of a problem, but the SOS # constraints can't have custom weights that # represent stochastic data for soscon in self.reference_model.component_data_objects( SOSConstraint, active=True, descend_into=True): for var, weight in soscon.get_items(): weight_params = tuple( self._collect_mutable_parameters(weight).values()) if param in self.stochastic_data: raise ValueError( "SOSConstraints with stochastic data are currently" " not supported in embedded stochastic programs. " "The SOSConstraint component '%s' has a weight " "term for variable '%s' that references stochastic" " parameter '%s'" % (soscon.name, var.name, param.name)) self.variable_to_constraints_map.\ setdefault(var, []).append(soscon) self.constraint_to_variables_map.\ setdefault(soscon, []).append(var) sto_varbounds = StochasticVariableBoundsAnnotation() for var in self.reference_model.component_data_objects( Var, descend_into=True): lower_params = tuple( self._collect_mutable_parameters(var.lb).values()) upper_params = tuple( self._collect_mutable_parameters(var.ub).values()) if (len(lower_params) > 0) or \ (len(upper_params) > 0): sto_varbounds.declare(var, lb=bool(len(lower_params) > 0), ub=bool(len(upper_params) > 0)) self.variable_to_stochastic_data_lb_map[var] = [] for param in lower_params: if param in self.stochastic_data: self.stochastic_data_to_variables_lb_map.\ setdefault(param, []).append(var) self.variable_to_stochastic_data_lb_map[var].\ append(param) if len(self.variable_to_stochastic_data_lb_map[var]) == 0: del self.variable_to_stochastic_data_lb_map[var] self.variable_to_stochastic_data_ub_map[var] = [] for param in upper_params: if param in self.stochastic_data: self.stochastic_data_to_variables_ub_map.\ setdefault(param, []).append(var) self.variable_to_stochastic_data_ub_map[var].\ append(param) if len(self.variable_to_stochastic_data_ub_map[var]) == 0: del self.variable_to_stochastic_data_ub_map[var] # # Generate the explicit annotations # # first make sure these annotations do not already exist if len( locate_annotations(self.reference_model, StochasticConstraintBoundsAnnotation)) > 0: raise ValueError( "Reference model can not contain " "a StochasticConstraintBoundsAnnotation declaration.") if len( locate_annotations(self.reference_model, StochasticConstraintBodyAnnotation)) > 0: raise ValueError( "Reference model can not contain " "a StochasticConstraintBodyAnnotation declaration.") if len( locate_annotations(self.reference_model, StochasticObjectiveAnnotation)) > 0: raise ValueError("Reference model can not contain " "a StochasticObjectiveAnnotation declaration.") # now add any necessary annotations if sto_obj.has_declarations: assert not hasattr( self.reference_model, ".pyspembeddedsp_stochastic_objective_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_objective_annotation", sto_obj) if sto_conbody.has_declarations: assert not hasattr( self.reference_model, ".pyspembeddedsp_stochastic_constraint_body_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_constraint_body_annotation", sto_conbody) if sto_conbounds.has_declarations: assert not hasattr( self.reference_model, ".pyspembeddedsp_stochastic_constraint_bounds_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_constraint_bounds_annotation", sto_conbounds) if sto_varbounds.has_declarations: assert not hasattr( self.reference_model, ".pyspembeddedsp_stochastic_variable_bounds_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_variable_bounds_annotation", sto_varbounds)
d2_rhs_table = [0, 1, 2, 3, 5] d3_rhs_table = [0, 1, 2, 3] model = ConcreteModel() #-------C-------- # model.constraint_stage = PySP_ConstraintStageAnnotation() # model.stoch_matrix = StochasticConstraintBodyAnnotation() # num_scenarios = len(theta1_table)*len(theta2_table) # scenario_data = dict(('Scenario'+str(i), (theta1val, theta2val)) # for i, (theta1val, theta2val) in # enumerate (itertools.product(theta1_table, theta2_table),1)) #---------------- #-------D-------- model.constraint_stage = PySP_ConstraintStageAnnotation() model.stoch_rhs = StochasticConstraintBoundsAnnotation() model.stoch_matrix = StochasticConstraintBodyAnnotation() num_scenarios = len(theta1_table) * len(theta2_table) * len( d1_rhs_table) * len(d2_rhs_table) * len(d3_rhs_table) scenario_data = dict( ('Scenario' + str(i), (theta1val, theta2val, d1_rhs_val, d2_rhs_val, d3_rhs_val)) for i, (theta1val, theta2val, d1_rhs_val, d2_rhs_val, d3_rhs_val) in enumerate( itertools.product(theta1_table, theta2_table, d1_rhs_table, d2_rhs_table, d3_rhs_table), 1)) #---------------- model.d1_rhs = Param(mutable=True, initialize=0.0) model.d2_rhs = Param(mutable=True, initialize=0.0) model.d3_rhs = Param(mutable=True, initialize=0.0)
def test_constraint_bounds(self): m = pyo.ConcreteModel() self._populate_block_with_constraints(m) m.b = pyo.Block() self._populate_block_with_constraints(m.b) m.b_inactive = pyo.Block() self._populate_block_with_constraints(m.b_inactive) m.b_inactive.deactivate() m.B = pyo.Block([1], rule=lambda b: \ self._populate_block_with_constraints(b)) a = StochasticConstraintBoundsAnnotation() self.assertEqual(a.default, True) self.assertEqual(a.has_declarations, False) a.declare(m.c) self.assertEqual(a.has_declarations, True) a.declare(m.C1) a.declare(m.C2[1]) a.declare(m.C3) a.declare(m.b) a.declare(m.b_inactive) a.declare(m.B, lb=False, ub=True) with self.assertRaises(TypeError): a.declare(None, 1) self.assertEqual( set([(v[0].name, v[1]) for v in a.expand_entries()]), set([('c', True), ('C1[1]', True), ('C2[1]', True), ('C3[1]', True), ('b.c', True), ('b.C1[1]', True), ('b.C2[1]', True), ('b.C3[1]', True), ('B[1].c', (False, True)), ('B[1].C1[1]', (False, True)), ('B[1].C2[1]', (False, True)), ('B[1].C3[1]', (False, True))])) self.assertEqual( set([(v[0].name, v[1]) for v in a.expand_entries(expand_containers=False)]), set([('c', True), ('C1', True), ('C2[1]', True), ('C3', True), ('b.c', True), ('b.C1', True), ('b.C2', True), ('b.C3', True), ('B[1].c', (False, True)), ('B[1].C1', (False, True)), ('B[1].C2', (False, True)), ('B[1].C3', (False, True))]))
def __init__(self, reference_model): self.reference_model = None self.objective = None self.time_stages = None self.stage_to_variables_map = {} self.variable_to_stage_map = {} # the set of stochastic data objects # (possibly mapped to some distribution) self.stochastic_data = None # maps between variables and objectives self.variable_to_objectives_map = ComponentMap() self.objective_to_variables_map = ComponentMap() # maps between variables and constraints self.variable_to_constraints_map = ComponentMap() self.constraint_to_variables_map = ComponentMap() # maps between stochastic data and objectives self.stochastic_data_to_objectives_map = ComponentMap() self.objective_to_stochastic_data_map = ComponentMap() # maps between stochastic data and constraints self.stochastic_data_to_constraints_map = ComponentMap() self.constraint_to_stochastic_data_map = ComponentMap() # maps between stochastic data and variable lower and upper bounds self.stochastic_data_to_variables_lb_map = ComponentMap() self.variable_to_stochastic_data_lb_map = ComponentMap() self.stochastic_data_to_variables_ub_map = ComponentMap() self.variable_to_stochastic_data_ub_map = ComponentMap() self.variable_symbols = ComponentMap() if not isinstance(reference_model, Block): raise TypeError("reference model input must be a Pyomo model") self.reference_model = reference_model # # Extract stochastic parameters from the # StochasticDataAnnotation object # self.stochastic_data = \ _extract_stochastic_data(self.reference_model) # # Get the variable stages from the # VariableStageAnnotation object # (self.stage_to_variables_map, self.variable_to_stage_map, self._variable_stage_assignments) = \ _map_variable_stages(self.reference_model) self.time_stages = tuple(sorted(self.stage_to_variables_map)) assert self.time_stages[0] == 1 self.variable_symbols = generate_cuid_names(self.reference_model, ctype=Var) # remove the parent blocks from this map keys_to_delete = [] for var in self.variable_symbols: if var.parent_component().type() is not Var: keys_to_delete.append(var) for key in keys_to_delete: del self.variable_symbols[key] # # Get the stage cost components from the StageCostAnnotation # and generate a dummy single-scenario scenario tree # stage_cost_annotation = locate_annotations( self.reference_model, StageCostAnnotation, max_allowed=1) if len(stage_cost_annotation) == 0: raise ValueError( "Reference model is missing stage cost " "annotation: %s" % (StageCostAnnotation.__name__)) else: assert len(stage_cost_annotation) == 1 stage_cost_annotation = stage_cost_annotation[0][1] stage_cost_assignments = ComponentMap( stage_cost_annotation.expand_entries()) stage1_cost = None stage2_cost = None for cdata, stagenum in stage_cost_assignments.items(): if stagenum == 1: stage1_cost = cdata elif stagenum == 2: stage2_cost = cdata if stage1_cost is None: raise ValueError("Missing stage cost annotation " "for time stage: 1") if stage2_cost is None: raise ValueError("Missing stage cost annotation " "for time stage: 2") assert stage1_cost is not stage2_cost self._stage1_cost = stage1_cost self._stage2_cost = stage2_cost # # Extract the locations of variables and stochastic data # within the model # sto_obj = StochasticObjectiveAnnotation() for objcntr, obj in enumerate( self.reference_model.component_data_objects( Objective, active=True, descend_into=True), 1): if objcntr > 1: raise ValueError( "Reference model can not contain more than one " "active objective") self.objective = obj self.objective_sense = obj.sense obj_params = tuple( self._collect_mutable_parameters(obj.expr).values()) self.objective_to_stochastic_data_map[obj] = [] for paramdata in obj_params: if paramdata in self.stochastic_data: self.stochastic_data_to_objectives_map.\ setdefault(paramdata, []).append(obj) self.objective_to_stochastic_data_map[obj].\ append(paramdata) if len(self.objective_to_stochastic_data_map[obj]) == 0: del self.objective_to_stochastic_data_map[obj] else: # TODO: Can we make this declaration sparse # by identifying which variables have # stochastic coefficients? How to handle # non-linear expressions? sto_obj.declare(obj) obj_variables = tuple( self._collect_variables(obj.expr).values()) self.objective_to_variables_map[obj] = [] for var in obj_variables: self.variable_to_objectives_map.\ setdefault(var, []).append(obj) self.objective_to_variables_map[obj].append(var) if len(self.objective_to_variables_map[obj]) == 0: del self.objective_to_variables_map[obj] sto_conbounds = StochasticConstraintBoundsAnnotation() sto_conbody = StochasticConstraintBodyAnnotation() for con in self.reference_model.component_data_objects( Constraint, active=True, descend_into=True): lower_params = tuple( self._collect_mutable_parameters(con.lower).values()) body_params = tuple( self._collect_mutable_parameters(con.body).values()) upper_params = tuple( self._collect_mutable_parameters(con.upper).values()) # TODO: Can we make this declaration sparse # by idenfifying which variables have # stochastic coefficients? How to handle # non-linear expressions? Currently, this # code also fails to detect that mutable # "constant" expressions might fall out # of the body and into the bounds. if len(body_params): sto_conbody.declare(con) if len(body_params) or \ len(lower_params) or \ len(upper_params): sto_conbounds.declare(con, lb=bool(len(lower_params) or len(body_params)), ub=bool(len(upper_params) or len(body_params))) all_stochastic_params = {} for param in itertools.chain(lower_params, body_params, upper_params): if param in self.stochastic_data: all_stochastic_params[id(param)] = param if len(all_stochastic_params) > 0: self.constraint_to_stochastic_data_map[con] = [] # no params will appear twice in this iteration for param in all_stochastic_params.values(): self.stochastic_data_to_constraints_map.\ setdefault(param, []).append(con) self.constraint_to_stochastic_data_map[con].\ append(param) body_variables = tuple( self._collect_variables(con.body).values()) self.constraint_to_variables_map[con] = [] for var in body_variables: self.variable_to_constraints_map.\ setdefault(var, []).append(con) self.constraint_to_variables_map[con].append(var) # For now, it is okay to have SOSConstraints in the # representation of a problem, but the SOS # constraints can't have custom weights that # represent stochastic data for soscon in self.reference_model.component_data_objects( SOSConstraint, active=True, descend_into=True): for var, weight in soscon.get_items(): weight_params = tuple( self._collect_mutable_parameters(weight).values()) if param in self.stochastic_data: raise ValueError( "SOSConstraints with stochastic data are currently" " not supported in embedded stochastic programs. " "The SOSConstraint component '%s' has a weight " "term for variable '%s' that references stochastic" " parameter '%s'" % (soscon.name, var.name, param.name)) self.variable_to_constraints_map.\ setdefault(var, []).append(soscon) self.constraint_to_variables_map.\ setdefault(soscon, []).append(var) sto_varbounds = StochasticVariableBoundsAnnotation() for var in self.reference_model.component_data_objects( Var, descend_into=True): lower_params = tuple( self._collect_mutable_parameters(var.lb).values()) upper_params = tuple( self._collect_mutable_parameters(var.ub).values()) if (len(lower_params) > 0) or \ (len(upper_params) > 0): sto_varbounds.declare(var, lb=bool(len(lower_params) > 0), ub=bool(len(upper_params) > 0)) self.variable_to_stochastic_data_lb_map[var] = [] for param in lower_params: if param in self.stochastic_data: self.stochastic_data_to_variables_lb_map.\ setdefault(param, []).append(var) self.variable_to_stochastic_data_lb_map[var].\ append(param) if len(self.variable_to_stochastic_data_lb_map[var]) == 0: del self.variable_to_stochastic_data_lb_map[var] self.variable_to_stochastic_data_ub_map[var] = [] for param in upper_params: if param in self.stochastic_data: self.stochastic_data_to_variables_ub_map.\ setdefault(param, []).append(var) self.variable_to_stochastic_data_ub_map[var].\ append(param) if len(self.variable_to_stochastic_data_ub_map[var]) == 0: del self.variable_to_stochastic_data_ub_map[var] # # Generate the explicit annotations # # first make sure these annotations do not already exist if len(locate_annotations(self.reference_model, StochasticConstraintBoundsAnnotation)) > 0: raise ValueError("Reference model can not contain " "a StochasticConstraintBoundsAnnotation declaration.") if len(locate_annotations(self.reference_model, StochasticConstraintBodyAnnotation)) > 0: raise ValueError("Reference model can not contain " "a StochasticConstraintBodyAnnotation declaration.") if len(locate_annotations(self.reference_model, StochasticObjectiveAnnotation)) > 0: raise ValueError("Reference model can not contain " "a StochasticObjectiveAnnotation declaration.") # now add any necessary annotations if sto_obj.has_declarations: assert not hasattr(self.reference_model, ".pyspembeddedsp_stochastic_objective_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_objective_annotation", sto_obj) if sto_conbody.has_declarations: assert not hasattr(self.reference_model, ".pyspembeddedsp_stochastic_constraint_body_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_constraint_body_annotation", sto_conbody) if sto_conbounds.has_declarations: assert not hasattr(self.reference_model, ".pyspembeddedsp_stochastic_constraint_bounds_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_constraint_bounds_annotation", sto_conbounds) if sto_varbounds.has_declarations: assert not hasattr(self.reference_model, ".pyspembeddedsp_stochastic_variable_bounds_annotation") setattr(self.reference_model, ".pyspembeddedsp_stochastic_variable_bounds_annotation", sto_varbounds)
def pysp_annotation_rule(model): # to enable SMPS and or DDSIP model.stoch_rhs = StochasticConstraintBoundsAnnotation() model.stoch_rhs.declare(model.FlowBalanceConstraint) model.stoch_matrix = StochasticConstraintBodyAnnotation() model.stoch_matrix.declare(model.ComputeSecondStageCost)
def pysp_annotation_rule(model): # to enable SMPS and or DDSIP model.stoch_rhs = StochasticConstraintBoundsAnnotation() model.stoch_rhs.declare(model.DemandSatisfiedSecondStage)
def test_constraint_bounds(self): m = aml.ConcreteModel() self._populate_block_with_constraints(m) m.b = aml.Block() self._populate_block_with_constraints(m.b) m.b_inactive = aml.Block() self._populate_block_with_constraints(m.b_inactive) m.b_inactive.deactivate() m.B = aml.Block([1], rule=lambda b: \ self._populate_block_with_constraints(b)) a = StochasticConstraintBoundsAnnotation() self.assertEqual(a.default, True) self.assertEqual(a.has_declarations, False) a.declare(m.c) self.assertEqual(a.has_declarations, True) a.declare(m.C1) a.declare(m.C2[1]) a.declare(m.C3) a.declare(m.b) a.declare(m.b_inactive) a.declare(m.B, lb=False, ub=True) with self.assertRaises(TypeError): a.declare(None, 1) self.assertEqual( set([(v[0].name, v[1]) for v in a.expand_entries()]), set([('c', True), ('C1[1]', True), ('C2[1]', True), ('C3[1]', True), ('b.c', True), ('b.C1[1]', True), ('b.C2[1]', True), ('b.C3[1]', True), ('B[1].c', (False,True)), ('B[1].C1[1]', (False,True)), ('B[1].C2[1]', (False,True)), ('B[1].C3[1]', (False,True))])) self.assertEqual( set([(v[0].name, v[1]) for v in a.expand_entries(expand_containers=False)]), set([('c', True), ('C1', True), ('C2[1]', True), ('C3', True), ('b.c', True), ('b.C1', True), ('b.C2', True), ('b.C3', True), ('B[1].c', (False,True)), ('B[1].C1', (False,True)), ('B[1].C2', (False,True)), ('B[1].C3', (False,True))]))