def test_stats2(self):
model = ConcreteModel()
#
model.x = Var([1, 2])
def obj_rule(model, i):
return sum_product(model.x)
model.y = VarList()
model.y.add()
model.y.add()
#
model.obj = Objective([1, 2], rule=obj_rule)
model.o = ObjectiveList()
model.o.add(model.y[1])
model.o.add(model.y[2])
#
def c_rule(model, i):
expr = 0
for j in [1, 2]:
expr += j * model.x[j]
return expr == 0
model.c = Constraint([1, 2], rule=c_rule)
model.C = ConstraintList()
model.C.add(model.y[1] == 0)
model.C.add(model.y[2] == 0)
#
self.assertEqual(model.nvariables(), 4)
self.assertEqual(model.nobjectives(), 4)
self.assertEqual(model.nconstraints(), 4)
def test_expr5(self):
model = ConcreteModel()
model.A = Set(initialize=[1, 2, 3], doc='set A')
model.B = Param(model.A,
initialize={
1: 100,
2: 200,
3: 300
},
doc='param B',
mutable=True)
model.C = Param(initialize=3, doc='param C', mutable=True)
model.x = Var(model.A, doc='var x')
model.y = Var(doc='var y')
model.o = Objective(expr=model.y, doc='obj o')
model.c1 = Constraint(expr=model.x[1] >= 0, doc='con c1')
def c2_rule(model, a):
return model.B[a] * model.x[a] <= 1
model.c2 = Constraint(model.A, doc='con c2', rule=c2_rule)
model.c3 = ConstraintList(doc='con c3')
model.c3.add(model.y <= 0)
#
OUTPUT = open(join(currdir, "test_expr5.out"), "w")
model.pprint(ostream=OUTPUT)
OUTPUT.close()
_out, _txt = join(currdir,
"test_expr5.out"), join(currdir, "test_expr5.txt")
self.assertTrue(cmp(_out, _txt),
msg="Files %s and %s differ" % (_out, _txt))
def test_add_single_constraint(self):
model = ConcreteModel()
model.X = Var(within=Binary)
opt = SolverFactory("cplex", solver_io="python")
opt._set_instance(model)
self.assertEqual(opt._solver_model.linear_constraints.get_num(), 0)
model.C = Constraint(expr=model.X == 1)
con_interface = opt._solver_model.linear_constraints
with unittest.mock.patch.object(
con_interface, "add",
wraps=con_interface.add) as wrapped_add_call:
opt._add_constraint(model.C)
self.assertEqual(wrapped_add_call.call_count, 1)
self.assertEqual(
wrapped_add_call.call_args,
({
"lin_expr": [[[0], (1, )]],
"names": ["x2"],
"range_values": [0.0],
"rhs": [1.0],
"senses": ["E"],
}, ),
)
self.assertEqual(opt._solver_model.linear_constraints.get_num(), 1)
# ___________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright (c) 2008-2022
# National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
from pyomo.environ import ConcreteModel, Var, Constraint
model = ConcreteModel()
model.X = Var()
def c_rule(m):
try:
return model.X >= 10.0
except Exception:
return model.X >= 20.0
model.C = Constraint(rule=c_rule)
model.Pi_p = Param(model.N, initialize=square(network.maximum_pressure_bounds))
Psi_m = network.minimum_nodal_injections
Psi_p = network.maximum_nodal_injections
for i in model.N:
if model.d[i] != 0:
Psi_m[i] = -model.d[i]
Psi_p[i] = 0
model.Psi_m = Param(model.N, initialize=Psi_m)
model.Psi_p = Param(model.N, initialize=Psi_p)
## Edges
model.P = Set(initialize=network.ref_pipes.keys())
model.C = Set(initialize=network.ref_compressors.keys())
model.E = model.P | model.C
model.c = Param(model.E, initialize=network.friction_coefficients)
model.rho_m = Param(model.E, initialize=square(network.minimum_pressure_ratio))
model.rho_p = Param(model.E, initialize=square(network.maximum_pressure_ratio))
model.kappa = Param(model.E, initialize=network.compression_costs)
## Incidence
temp = {}
for (i, j), value in np.ndenumerate(network.incidence_matrix):
temp[i + 1, j + 1] = value
model.delta = Param(model.N, model.E, initialize=temp, mutable=True)
