def test_sipopt_deprecated(self):
m = param_ex.create_model()
m.perturbed_eta1 = Param(initialize = 4.0)
m.perturbed_eta2 = Param(initialize = 1.0)
output = StringIO()
with LoggingIntercept(output, 'pyomo.contrib.sensitivity_toolbox', logging.WARNING):
sipopt(m,[m.eta1,m.eta1],
[m.perturbed_eta1,m.perturbed_eta2],
cloneModel=False)
self.assertIn("DEPRECATED: The sipopt function has been deprecated. Use the "
"sensitivity_calculation() function with method='sipopt' to access",
output.getvalue().replace('\n', ' '))
def test_bad_arg(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 1))
m.a = Param(initialize=1, mutable=True)
m.b = Param(initialize=2, mutable=True)
m.c = Param(initialize=3, mutable=False)
m.x = Var(m.t)
list_one = [m.a, m.b]
list_two = [m.a, m.b, m.c]
list_three = [m.a, m.x]
list_four = [m.a, m.c]
#verify ValueError thrown when param and perturb list are different
# lengths
try:
Result = sipopt(m, list_one, list_two)
self.fail("Expected ValueError: for different length lists")
except ValueError:
pass
#verify ValueError thrown when param list has a Var in it
try:
Result = sipopt(m, list_three, list_two)
self.fail(
"Expected ValueError: variable sent through paramSubList")
except ValueError:
pass
#verify ValueError thrown when perturb list has Var in it
try:
Result = sipopt(m, list_one, list_three)
self.fail("Expected ValueError: variable sent through perturbList")
except ValueError:
pass
#verify ValueError thrown when param list has an unmutable param
try:
Result = sipopt(m, list_four, list_one)
self.fail("Expected ValueError:"
"unmutable param sent through paramSubList")
except ValueError:
pass
def test_bad_arg(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0,1))
m.a = Param(initialize=1, mutable=True)
m.b = Param(initialize=2, mutable=True)
m.c = Param(initialize=3, mutable=False)
m.x = Var(m.t)
list_one = [m.a,m.b]
list_two = [m.a,m.b,m.c]
list_three = [m.a, m.x]
list_four = [m.a,m.c]
#verify ValueError thrown when param and perturb list are different
# lengths
try:
Result = sipopt(m,list_one,list_two)
self.fail("Expected ValueError: for different length lists")
except ValueError:
pass
#verify ValueError thrown when param list has a Var in it
try:
Result = sipopt(m,list_three,list_two)
self.fail("Expected ValueError: variable sent through paramSubList")
except ValueError:
pass
#verify ValueError thrown when perturb list has Var in it
try:
Result = sipopt(m,list_one,list_three)
self.fail("Expected ValueError: variable sent through perturbList")
except ValueError:
pass
#verify ValueError thrown when param list has an unmutable param
try:
Result = sipopt(m,list_four,list_one)
self.fail("Expected ValueError:"
"unmutable param sent through paramSubList")
except ValueError:
pass
def test_constraintSub(self):
m = ri.create_model()
m.pert_a = Param(initialize=0.01)
m.pert_b = Param(initialize=1.01)
m_sipopt = sipopt(m, [m.a, m.b], [m.pert_a, m.pert_b])
#verify substitutions in equality constraint
self.assertTrue(m_sipopt.C_equal.lower.ctype is Param
and m_sipopt.C_equal.upper.ctype is Param)
self.assertFalse(m_sipopt.C_equal.active)
self.assertTrue(
m_sipopt._sipopt_data.constList[3].lower == 0.0
and m_sipopt._sipopt_data.constList[3].upper == 0.0 and len(
list(
identify_variables(
m_sipopt._sipopt_data.constList[3].body))) == 2)
#verify substitutions in one-sided bounded constraint
self.assertTrue(m_sipopt.C_singleBnd.lower is None
and m_sipopt.C_singleBnd.upper.ctype is Param)
self.assertFalse(m_sipopt.C_singleBnd.active)
self.assertTrue(
m_sipopt._sipopt_data.constList[4].lower is None
and m_sipopt._sipopt_data.constList[4].upper == 0.0 and len(
list(
identify_variables(
m_sipopt._sipopt_data.constList[4].body))) == 2)
#verify substitutions in ranged inequality constraint
self.assertTrue(m_sipopt.C_rangedIn.lower.ctype is Param
and m_sipopt.C_rangedIn.upper.ctype is Param)
self.assertFalse(m_sipopt.C_rangedIn.active)
self.assertTrue(
m_sipopt._sipopt_data.constList[1].lower is None
and m_sipopt._sipopt_data.constList[1].upper == 0.0 and len(
list(
identify_variables(
m_sipopt._sipopt_data.constList[1].body))) == 2)
self.assertTrue(
m_sipopt._sipopt_data.constList[2].lower is None
and m_sipopt._sipopt_data.constList[2].upper == 0.0 and len(
list(
identify_variables(
m_sipopt._sipopt_data.constList[2].body))) == 2)
def test_constraintSub(self):
m = ri.create_model()
m.pert_a = Param(initialize=0.01)
m.pert_b = Param(initialize=1.01)
m_sipopt = sipopt(m,[m.a,m.b], [m.pert_a,m.pert_b])
#verify substitutions in equality constraint
self.assertTrue(m_sipopt.C_equal.lower.type() is Param and
m_sipopt.C_equal.upper.type() is Param)
self.assertFalse(m_sipopt.C_equal.active)
self.assertTrue(m_sipopt._sipopt_data.constList[3].lower == 0.0 and
m_sipopt._sipopt_data.constList[3].upper == 0.0 and
len(list(identify_variables(
m_sipopt._sipopt_data.constList[3].body))) == 2)
#verify substitutions in one-sided bounded constraint
self.assertTrue(m_sipopt.C_singleBnd.lower is None and
m_sipopt.C_singleBnd.upper.type() is Param)
self.assertFalse(m_sipopt.C_singleBnd.active)
self.assertTrue(m_sipopt._sipopt_data.constList[4].lower is None and
m_sipopt._sipopt_data.constList[4].upper == 0.0 and
len(list(identify_variables(
m_sipopt._sipopt_data.constList[4].body))) == 2)
#verify substitutions in ranged inequality constraint
self.assertTrue(m_sipopt.C_rangedIn.lower.type() is Param and
m_sipopt.C_rangedIn.upper.type() is Param)
self.assertFalse(m_sipopt.C_rangedIn.active)
self.assertTrue(m_sipopt._sipopt_data.constList[1].lower is None and
m_sipopt._sipopt_data.constList[1].upper == 0.0 and
len(list(identify_variables(
m_sipopt._sipopt_data.constList[1].body))) == 2)
self.assertTrue(m_sipopt._sipopt_data.constList[2].lower is None and
m_sipopt._sipopt_data.constList[2].upper == 0.0 and
len(list(identify_variables(
m_sipopt._sipopt_data.constList[2].body))) == 2)
def test_indexedParamsMapping(self):
m = hiv.create_model()
hiv.initialize_model(m,10,5,1)
m.epsDelta = Param(initialize = 0.75001)
q_del = {}
q_del[(0,0)] = 1.001
q_del[(0,1)] = 1.002
q_del[(1,0)] = 1.003
q_del[(1,1)] = 1.004
q_del[(2,0)] = 0.83001
q_del[(2,1)] = 0.83002
q_del[(3,0)] = 0.42001
q_del[(4,0)] = 0.17001
m.qqDelta = Param(m.ij, initialize = q_del)
m.aaDelta = Param(initialize =0.0001001)
m_sipopt = sipopt(m, [m.eps,m.qq,m.aa],
[m.epsDelta,m.qqDelta,m.aaDelta])
#param to var data
self.assertEqual(
m_sipopt._sipopt_data.paramConst[1].lower.local_name, 'eps')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[1].body.local_name, 'eps')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[1].upper.local_name, 'eps')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[6].lower.local_name, 'qq[2,0]')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[6].body.local_name, 'qq[2,0]')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[6].upper.local_name, 'qq[2,0]')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[10].lower.local_name, 'aa')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[10].body.local_name, 'aa')
self.assertEqual(
m_sipopt._sipopt_data.paramConst[10].upper.local_name, 'aa')
def test_indexedParamsMapping(self):
m = hiv.create_model()
hiv.initialize_model(m, 10, 5, 1)
m.epsDelta = Param(initialize=0.75001)
q_del = {}
q_del[(0, 0)] = 1.001
q_del[(0, 1)] = 1.002
q_del[(1, 0)] = 1.003
q_del[(1, 1)] = 1.004
q_del[(2, 0)] = 0.83001
q_del[(2, 1)] = 0.83002
q_del[(3, 0)] = 0.42001
q_del[(4, 0)] = 0.17001
m.qqDelta = Param(m.ij, initialize=q_del)
m.aaDelta = Param(initialize=0.0001001)
m_sipopt = sipopt(m, [m.eps, m.qq, m.aa],
[m.epsDelta, m.qqDelta, m.aaDelta])
#param to var data
self.assertEqual(m_sipopt._sipopt_data.paramConst[1].lower.local_name,
'eps')
self.assertEqual(m_sipopt._sipopt_data.paramConst[1].body.local_name,
'eps')
self.assertEqual(m_sipopt._sipopt_data.paramConst[1].upper.local_name,
'eps')
self.assertEqual(m_sipopt._sipopt_data.paramConst[6].lower.local_name,
'qq[2,0]')
self.assertEqual(m_sipopt._sipopt_data.paramConst[6].body.local_name,
'qq[2,0]')
self.assertEqual(m_sipopt._sipopt_data.paramConst[6].upper.local_name,
'qq[2,0]')
self.assertEqual(m_sipopt._sipopt_data.paramConst[10].lower.local_name,
'aa')
self.assertEqual(m_sipopt._sipopt_data.paramConst[10].body.local_name,
'aa')
self.assertEqual(m_sipopt._sipopt_data.paramConst[10].upper.local_name,
'aa')
def test_sipopt_equivalent(self):
m1 = param_ex.create_model()
m1.perturbed_eta1 = Param(initialize = 4.0)
m1.perturbed_eta2 = Param(initialize = 1.0)
m2 = param_ex.create_model()
m2.perturbed_eta1 = Param(initialize = 4.0)
m2.perturbed_eta2 = Param(initialize = 1.0)
m11 = sipopt(m1,[m1.eta1,m1.eta2],
[m1.perturbed_eta1,m1.perturbed_eta2],
cloneModel=True)
m22 = sensitivity_calculation('sipopt',m2,[m2.eta1,m2.eta2],
[m2.perturbed_eta1,m2.perturbed_eta2],
cloneModel=True)
out1 = StringIO()
out2 = StringIO()
m11._SENSITIVITY_TOOLBOX_DATA.constList.pprint(ostream=out1)
m22._SENSITIVITY_TOOLBOX_DATA.constList.pprint(ostream=out2)
self.assertMultiLineEqual(out1.getvalue(), out2.getvalue())
discretizer = TransformationFactory('dae.collocation')
discretizer.apply_to(m,nfe=n_nfe,ncp=n_ncp,scheme='LAGRANGE-RADAU')
sim.initialize_model()
if __name__=='__main__':
m = create_model()
initialize_model(m,10,5,1)
m.epsDelta = Param(initialize = 0.75001)
q_del={}
q_del[(0,0)] = 1.001
q_del[(0,1)] = 1.002
q_del[(1,0)] = 1.003
q_del[(1,1)] = 1.004
q_del[(2,0)] = 0.83001
q_del[(2,1)] = 0.83002
q_del[(3,0)] = 0.42001
q_del[(4,0)] = 0.17001
m.qqDelta = Param(m.ij, initialize=q_del)
m.aaDelta = Param(initialize = .0001001)
m_sipopt = sipopt(m,[m.eps,m.qq,m.aa],
[m.epsDelta,m.qqDelta,m.aaDelta],
streamSoln = True)
plt.subplot(132)
plt.plot(m.t.value,u,'ro',label='u')
plt.title('Control Soln')
plt.xlabel('time')
plt.subplot(133)
plt.plot(m.t.value,F,'ro',label='Cost Integrand')
plt.title('Anti-derivative of \n Cost Integrand')
plt.xlabel('time')
#plt.show()
return plt
######################################
if __name__ == '__main__':
m = create_model()
initialize_model(m,100)
# plt = plot_optimal_solution(m)
# plt.show()
m.perturbed_a = Param(initialize=-0.25)
m.perturbed_H = Param(initialize=0.55)
m_sipopt = sipopt(m,[m.a,m.H],
[m.perturbed_a,m.perturbed_H],
cloneModel=True,
streamSoln=True)
#
from pyomo.environ import ConcreteModel, Param, Var, Constraint, inequality
from pyomo.contrib.sensitivity_toolbox.sens import sipopt
def create_model():
m = ConcreteModel()
m.a = Param(initialize=0, mutable=True)
m.b = Param(initialize=1, mutable=True)
m.x = Var(initialize=1.0)
m.y = Var()
m.C_rangedIn = Constraint(expr=inequality(m.a, m.x, m.b))
m.C_equal = Constraint(expr=m.y == m.b)
m.C_singleBnd = Constraint(expr=m.x <= m.b)
return m
if __name__ == '__main__':
m = create_model()
m.pert_a = Param(initialize=0.01)
m.pert_b = Param(initialize=1.01)
m_sipopt = sipopt(m, [m.a, m.b], [m.pert_a, m.pert_b], streamSoln=True)
from pyomo.contrib.sensitivity_toolbox.sens import sipopt
def create_model():
m = ConcreteModel()
m.x1 = Var(initialize = 0.15, within=NonNegativeReals)
m.x2 = Var(initialize = 0.15, within=NonNegativeReals)
m.x3 = Var(initialize = 0.0, within=NonNegativeReals)
m.eta1 = Param(initialize=4.5,mutable=True)
m.eta2 = Param(initialize=1.0,mutable=True)
m.const1 = Constraint(expr=6*m.x1+3*m.x2+2*m.x3-m.eta1 ==0)
m.const2 = Constraint(expr=m.eta2*m.x1+m.x2-m.x3-1 ==0)
m.cost = Objective(expr=m.x1**2+m.x2**2+m.x3**2)
return m
if __name__=='__main__':
m = create_model()
m.perturbed_eta1 = Param(initialize = 4.0)
m.perturbed_eta2 = Param(initialize = 1.0)
m_sipopt = sipopt(m,[m.eta1,m.eta2],
[m.perturbed_eta1,m.perturbed_eta2],
streamSoln=True)
def test_clonedModel_soln(self):
m_orig = fc.create_model()
fc.initialize_model(m_orig,100)
m_orig.perturbed_a = Param(initialize=-0.25)
m_orig.perturbed_H = Param(initialize=0.55)
m_sipopt = sipopt(m_orig,[m_orig.a,m_orig.H],
[m_orig.perturbed_a,m_orig.perturbed_H],
cloneModel=True)
#verify cloned model has _sipopt_data block
# and original model is untouched
self.assertFalse(m_sipopt == m_orig)
self.assertTrue(hasattr(m_sipopt,'_sipopt_data') and
m_sipopt._sipopt_data.type() is Block)
self.assertFalse(hasattr(m_orig,'_sipopt_data'))
self.assertFalse(hasattr(m_orig,'b'))
#verify variable declaration
self.assertTrue(hasattr(m_sipopt._sipopt_data,'a') and
m_sipopt._sipopt_data.a.type() is Var)
self.assertTrue(hasattr(m_sipopt._sipopt_data,'H') and
m_sipopt._sipopt_data.H.type() is Var)
#verify suffixes
self.assertTrue(hasattr(m_sipopt,'sens_state_0') and
m_sipopt.sens_state_0.type() is Suffix and
m_sipopt.sens_state_0[m_sipopt._sipopt_data.H]==2 and
m_sipopt.sens_state_0[m_sipopt._sipopt_data.a]==1)
self.assertTrue(hasattr(m_sipopt,'sens_state_1') and
m_sipopt.sens_state_1.type() is Suffix and
m_sipopt.sens_state_1[m_sipopt._sipopt_data.H]==2 and
m_sipopt.sens_state_1[m_sipopt._sipopt_data.a]==1)
self.assertTrue(hasattr(m_sipopt,'sens_state_value_1') and
m_sipopt.sens_state_value_1.type() is Suffix and
m_sipopt.sens_state_value_1[
m_sipopt._sipopt_data.H]==0.55 and
m_sipopt.sens_state_value_1[
m_sipopt._sipopt_data.a]==-0.25)
self.assertTrue(hasattr(m_sipopt,'sens_init_constr') and
m_sipopt.sens_init_constr.type() is Suffix and
m_sipopt.sens_init_constr[
m_sipopt._sipopt_data.paramConst[1]]==1 and
m_sipopt.sens_init_constr[
m_sipopt._sipopt_data.paramConst[2]]==2)
self.assertTrue(hasattr(m_sipopt,'sens_sol_state_1') and
m_sipopt.sens_sol_state_1.type() is Suffix)
self.assertAlmostEqual(
m_sipopt.sens_sol_state_1[
m_sipopt.F[15]],-0.00102016765,8)
self.assertTrue(hasattr(m_sipopt,'sens_sol_state_1_z_L') and
m_sipopt.sens_sol_state_1_z_L.type() is Suffix)
self.assertAlmostEqual(
m_sipopt.sens_sol_state_1_z_L[
m_sipopt.u[15]],-2.181712e-09,13)
self.assertTrue(hasattr(m_sipopt,'sens_sol_state_1_z_U') and
m_sipopt.sens_sol_state_1_z_U.type() is Suffix)
self.assertAlmostEqual(
m_sipopt.sens_sol_state_1_z_U[
m_sipopt.u[15]],6.580899e-09,13)
#verify deactivated constraints for cloned model
self.assertFalse(m_sipopt.FDiffCon[0].active and
m_sipopt.FDiffCon[7.5].active and
m_sipopt.FDiffCon[15].active )
self.assertFalse(m_sipopt.x_dot[0].active and
m_sipopt.x_dot[7.5].active and
m_sipopt.x_dot[15].active )
#verify constraints on original model are still active
self.assertTrue(m_orig.FDiffCon[0].active and
m_orig.FDiffCon[7.5].active and
m_orig.FDiffCon[15].active )
self.assertTrue(m_orig.x_dot[0].active and
m_orig.x_dot[7.5].active and
m_orig.x_dot[15].active )
#verify solution
self.assertAlmostEqual(value(m_sipopt.J),0.0048956783,8)
def run_example(print_flag=True):
'''
Execute the example
Arguments:
print_flag: Toggle on/off printing
Returns:
sln_dict: Dictionary containing solution (used for automated testing)
'''
m = create_model()
m.perturbed_eta1 = Param(initialize=4.0)
m.perturbed_eta2 = Param(initialize=1.0)
m_sipopt = sipopt(m, [m.eta1, m.eta2],
[m.perturbed_eta1, m.perturbed_eta2],
streamSoln=True)
if print_flag:
print("\nOriginal parameter values:")
print("\teta1 =", m.eta1())
print("\teta2 =", m.eta2())
print("Initial point:")
print("\tObjective =", value(m.cost))
print("\tx1 =", m.x1())
print("\tx2 =", m.x2())
print("\tx3 =", m.x3())
print("Solution with the original parameter values:")
print("\tObjective =", m_sipopt.cost())
print("\tx1 =", m_sipopt.x1())
print("\tx2 =", m_sipopt.x2())
print("\tx3 =", m_sipopt.x3())
print("\nNew parameter values:")
print("\teta1 =", m_sipopt.perturbed_eta1())
print("\teta2 =", m_sipopt.perturbed_eta2())
# This highlights one limitation of sipopt. It will only return the
# perturbed solution. The user needs to calculate relevant values such as
# the objective or expressions
x1 = m_sipopt.sens_sol_state_1[m_sipopt.x1]
x2 = m_sipopt.sens_sol_state_1[m_sipopt.x2]
x3 = m_sipopt.sens_sol_state_1[m_sipopt.x3]
obj = x1**2 + x2**2 + x3**2
if print_flag:
print("(Approximate) solution with the new parameter values:")
print("\tObjective =", obj)
print("\tx1 =", m_sipopt.sens_sol_state_1[m_sipopt.x1])
print("\tx2 =", m_sipopt.sens_sol_state_1[m_sipopt.x2])
print("\tx3 =", m_sipopt.sens_sol_state_1[m_sipopt.x3])
# Save the results in a dictionary.
# This is optional and makes automated testing convenient.
# This code is not important for a Minimum Working Example (MWE) of sipopt
d = dict()
d['eta1'] = m.eta1()
d['eta2'] = m.eta2()
d['x1_init'] = m.x1()
d['x2_init'] = m.x2()
d['x3_init'] = m.x3()
d['x1_sln'] = m_sipopt.x1()
d['x2_sln'] = m_sipopt.x2()
d['x3_sln'] = m_sipopt.x3()
d['cost_sln'] = m_sipopt.cost()
d['eta1_pert'] = m_sipopt.perturbed_eta1()
d['eta2_pert'] = m_sipopt.perturbed_eta2()
d['x1_pert'] = x1
d['x2_pert'] = x2
d['x3_pert'] = x3
d['cost_pert'] = obj
return d
plt.xlabel('time')
plt.subplot(132)
plt.plot(m.t.value, u, 'ro', label='u')
plt.title('Control Soln')
plt.xlabel('time')
plt.subplot(133)
plt.plot(m.t.value, F, 'ro', label='Cost Integrand')
plt.title('Anti-derivative of \n Cost Integrand')
plt.xlabel('time')
#plt.show()
return plt
######################################
if __name__ == '__main__':
m = create_model()
initialize_model(m, 100)
# plt = plot_optimal_solution(m)
# plt.show()
m.perturbed_a = Param(initialize=-0.25)
m.perturbed_H = Param(initialize=0.55)
m_sipopt = sipopt(m, [m.a, m.H], [m.perturbed_a, m.perturbed_H],
cloneModel=True,
streamSoln=True)
from pyomo.environ import *
from pyomo.contrib.sensitivity_toolbox.sens import sipopt
def create_model():
m = ConcreteModel()
m.x1 = Var(initialize=0.15, within=NonNegativeReals)
m.x2 = Var(initialize=0.15, within=NonNegativeReals)
m.x3 = Var(initialize=0.0, within=NonNegativeReals)
m.eta1 = Param(initialize=4.5, mutable=True)
m.eta2 = Param(initialize=1.0, mutable=True)
m.const1 = Constraint(expr=6 * m.x1 + 3 * m.x2 + 2 * m.x3 - m.eta1 == 0)
m.const2 = Constraint(expr=m.eta2 * m.x1 + m.x2 - m.x3 - 1 == 0)
m.cost = Objective(expr=m.x1**2 + m.x2**2 + m.x3**2)
return m
if __name__ == '__main__':
m = create_model()
m.perturbed_eta1 = Param(initialize=4.0)
m.perturbed_eta2 = Param(initialize=1.0)
m_sipopt = sipopt(m, [m.eta1, m.eta2],
[m.perturbed_eta1, m.perturbed_eta2],
streamSoln=True)
from pyomo.dae import ContinuousSet
from pyomo.contrib.sensitivity_toolbox.sens import sipopt
def create_model():
m = ConcreteModel()
m.a = Param(initialize=0, mutable=True)
m.b = Param(initialize=1, mutable=True)
m.x = Var(initialize = 1.0)
m.y = Var()
m.C_rangedIn = Constraint(expr=inequality(m.a,m.x,m.b))
m.C_equal = Constraint(expr=m.y==m.b)
m.C_singleBnd = Constraint(expr=m.x<=m.b)
return m
if __name__=='__main__':
m = create_model()
m.pert_a = Param(initialize=0.01)
m.pert_b = Param(initialize=1.01)
m_sipopt = sipopt(m,[m.a,m.b],[m.pert_a,m.pert_b],
streamSoln=True)
def test_noClone_soln(self):
m_orig = fc.create_model()
fc.initialize_model(m_orig, 100)
m_orig.perturbed_a = Param(initialize=-0.25)
m_orig.perturbed_H = Param(initialize=0.55)
m_sipopt = sipopt(m_orig, [m_orig.a, m_orig.H],
[m_orig.perturbed_a, m_orig.perturbed_H],
cloneModel=False)
self.assertTrue(m_sipopt == m_orig)
#test _sipopt_data block exists
self.assertTrue(
hasattr(m_orig, '_sipopt_data')
and m_orig._sipopt_data.type() is Block)
#test variable declaration
self.assertTrue(
hasattr(m_sipopt._sipopt_data, 'a')
and m_sipopt._sipopt_data.a.type() is Var)
self.assertTrue(
hasattr(m_sipopt._sipopt_data, 'H')
and m_sipopt._sipopt_data.H.type() is Var)
#test for suffixes
self.assertTrue(
hasattr(m_sipopt, 'sens_state_0')
and m_sipopt.sens_state_0.type() is Suffix
and m_sipopt.sens_state_0[m_sipopt._sipopt_data.H] == 2
and m_sipopt.sens_state_0[m_sipopt._sipopt_data.a] == 1)
self.assertTrue(
hasattr(m_sipopt, 'sens_state_1')
and m_sipopt.sens_state_1.type() is Suffix
and m_sipopt.sens_state_1[m_sipopt._sipopt_data.H] == 2
and m_sipopt.sens_state_1[m_sipopt._sipopt_data.a] == 1)
self.assertTrue(
hasattr(m_sipopt, 'sens_state_value_1')
and m_sipopt.sens_state_value_1.type() is Suffix
and m_sipopt.sens_state_value_1[m_sipopt._sipopt_data.H] == 0.55
and m_sipopt.sens_state_value_1[m_sipopt._sipopt_data.a] == -0.25)
self.assertTrue(
hasattr(m_sipopt, 'sens_init_constr')
and m_sipopt.sens_init_constr.type() is Suffix and
m_sipopt.sens_init_constr[m_sipopt._sipopt_data.paramConst[1]] == 1
and m_sipopt.sens_init_constr[m_sipopt._sipopt_data.paramConst[2]]
== 2)
self.assertTrue(
hasattr(m_sipopt, 'sens_sol_state_1')
and m_sipopt.sens_sol_state_1.type() is Suffix)
self.assertAlmostEqual(m_sipopt.sens_sol_state_1[m_sipopt.F[15]],
-0.00102016765, 8)
self.assertTrue(
hasattr(m_sipopt, 'sens_sol_state_1_z_L')
and m_sipopt.sens_sol_state_1_z_L.type() is Suffix)
self.assertAlmostEqual(m_sipopt.sens_sol_state_1_z_L[m_sipopt.u[15]],
-2.181712e-09, 13)
self.assertTrue(
hasattr(m_sipopt, 'sens_sol_state_1_z_U')
and m_sipopt.sens_sol_state_1_z_U.type() is Suffix)
self.assertAlmostEqual(m_sipopt.sens_sol_state_1_z_U[m_sipopt.u[15]],
6.580899e-09, 13)
#verify deactivated constraints on model
self.assertFalse(m_sipopt.FDiffCon[0].active
and m_sipopt.FDiffCon[7.5].active
and m_sipopt.FDiffCon[15].active)
self.assertFalse(m_sipopt.x_dot[0].active
and m_sipopt.x_dot[7.5].active
and m_sipopt.x_dot[15].active)
#test model solution
self.assertAlmostEqual(value(m_sipopt.J), 0.0048956783, 8)
def test_clonedModel_soln(self):
m_orig = fc.create_model()
fc.initialize_model(m_orig, 100)
m_orig.perturbed_a = Param(initialize=-0.25)
m_orig.perturbed_H = Param(initialize=0.55)
m_sipopt = sipopt(m_orig, [m_orig.a, m_orig.H],
[m_orig.perturbed_a, m_orig.perturbed_H],
cloneModel=True)
#verify cloned model has _sipopt_data block
# and original model is untouched
self.assertFalse(m_sipopt == m_orig)
self.assertTrue(
hasattr(m_sipopt, '_sipopt_data')
and m_sipopt._sipopt_data.ctype is Block)
self.assertFalse(hasattr(m_orig, '_sipopt_data'))
self.assertFalse(hasattr(m_orig, 'b'))
#verify variable declaration
self.assertTrue(
hasattr(m_sipopt._sipopt_data, 'a')
and m_sipopt._sipopt_data.a.ctype is Var)
self.assertTrue(
hasattr(m_sipopt._sipopt_data, 'H')
and m_sipopt._sipopt_data.H.ctype is Var)
#verify suffixes
self.assertTrue(
hasattr(m_sipopt, 'sens_state_0')
and m_sipopt.sens_state_0.ctype is Suffix
and m_sipopt.sens_state_0[m_sipopt._sipopt_data.H] == 2
and m_sipopt.sens_state_0[m_sipopt._sipopt_data.a] == 1)
self.assertTrue(
hasattr(m_sipopt, 'sens_state_1')
and m_sipopt.sens_state_1.ctype is Suffix
and m_sipopt.sens_state_1[m_sipopt._sipopt_data.H] == 2
and m_sipopt.sens_state_1[m_sipopt._sipopt_data.a] == 1)
self.assertTrue(
hasattr(m_sipopt, 'sens_state_value_1')
and m_sipopt.sens_state_value_1.ctype is Suffix
and m_sipopt.sens_state_value_1[m_sipopt._sipopt_data.H] == 0.55
and m_sipopt.sens_state_value_1[m_sipopt._sipopt_data.a] == -0.25)
self.assertTrue(
hasattr(m_sipopt, 'sens_init_constr')
and m_sipopt.sens_init_constr.ctype is Suffix and
m_sipopt.sens_init_constr[m_sipopt._sipopt_data.paramConst[1]] == 1
and m_sipopt.sens_init_constr[m_sipopt._sipopt_data.paramConst[2]]
== 2)
self.assertTrue(
hasattr(m_sipopt, 'sens_sol_state_1')
and m_sipopt.sens_sol_state_1.ctype is Suffix)
self.assertAlmostEqual(m_sipopt.sens_sol_state_1[m_sipopt.F[15]],
-0.00102016765, 8)
self.assertTrue(
hasattr(m_sipopt, 'sens_sol_state_1_z_L')
and m_sipopt.sens_sol_state_1_z_L.ctype is Suffix)
self.assertAlmostEqual(m_sipopt.sens_sol_state_1_z_L[m_sipopt.u[15]],
-2.181712e-09, 13)
self.assertTrue(
hasattr(m_sipopt, 'sens_sol_state_1_z_U')
and m_sipopt.sens_sol_state_1_z_U.ctype is Suffix)
self.assertAlmostEqual(m_sipopt.sens_sol_state_1_z_U[m_sipopt.u[15]],
6.580899e-09, 13)
#verify deactivated constraints for cloned model
self.assertFalse(m_sipopt.FDiffCon[0].active
and m_sipopt.FDiffCon[7.5].active
and m_sipopt.FDiffCon[15].active)
self.assertFalse(m_sipopt.x_dot[0].active
and m_sipopt.x_dot[7.5].active
and m_sipopt.x_dot[15].active)
#verify constraints on original model are still active
self.assertTrue(m_orig.FDiffCon[0].active
and m_orig.FDiffCon[7.5].active
and m_orig.FDiffCon[15].active)
self.assertTrue(m_orig.x_dot[0].active and m_orig.x_dot[7.5].active
and m_orig.x_dot[15].active)
#verify solution
self.assertAlmostEqual(value(m_sipopt.J), 0.0048956783, 8)
m = create_model()
initialize_model(m,10,5,1)
m.epsDelta = Param(initialize = 0.75001)
q_del={}
q_del[(0,0)] = 1.001
q_del[(0,1)] = 1.002
q_del[(1,0)] = 1.003
q_del[(1,1)] = 1.004
q_del[(2,0)] = 0.83001
q_del[(2,1)] = 0.83002
q_del[(3,0)] = 0.42001
q_del[(4,0)] = 0.17001
m.qqDelta = Param(m.ij, initialize=q_del)
m.aaDelta = Param(initialize = .0001001)
m_sipopt = sipopt(m,[m.eps,m.qq,m.aa],
[m.epsDelta,m.qqDelta,m.aaDelta],
streamSoln = True)
