def test_generate_finite_elements(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.t2 = ContinuousSet(bounds=(0, 10))
m.t3 = ContinuousSet(bounds=(0, 1))
oldt = sorted(m.t)
generate_finite_elements(m.t, 1)
self.assertTrue(oldt == sorted(m.t))
self.assertFalse(m.t.get_changed())
generate_finite_elements(m.t, 2)
self.assertFalse(oldt == sorted(m.t))
self.assertTrue(m.t.get_changed())
self.assertTrue([0, 5.0, 10] == sorted(m.t))
generate_finite_elements(m.t, 3)
self.assertTrue([0, 2.5, 5.0, 10] == sorted(m.t))
generate_finite_elements(m.t, 5)
self.assertTrue([0, 1.25, 2.5, 5.0, 7.5, 10] == sorted(m.t))
generate_finite_elements(m.t2, 10)
self.assertTrue(len(m.t2) == 11)
self.assertTrue([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] == sorted(m.t2))
generate_finite_elements(m.t3, 7)
self.assertTrue(len(m.t3) == 8)
t = sorted(m.t3)
print(t[1])
self.assertTrue(t[1] == 0.142857)
def test_generate_collocation_points(self):
m = ConcreteModel()
m.t = ContinuousSet(initialize=[0, 1])
m.t2 = ContinuousSet(initialize=[0, 2, 4, 6])
tau1 = [1]
oldt = sorted(m.t)
generate_colloc_points(m.t, tau1)
self.assertTrue(oldt == sorted(m.t))
self.assertFalse(m.t.get_changed())
tau1 = [0.5]
oldt = sorted(m.t)
generate_colloc_points(m.t, tau1)
self.assertFalse(oldt == sorted(m.t))
self.assertTrue(m.t.get_changed())
self.assertTrue([0, 0.5, 1] == sorted(m.t))
tau2 = [0.2, 0.3, 0.7, 0.8, 1]
generate_colloc_points(m.t, tau2)
self.assertTrue(len(m.t) == 11)
self.assertTrue(
[0, 0.1, 0.15, 0.35, 0.4, 0.5, 0.6, 0.65, 0.85, 0.9, 1] == sorted(
m.t))
generate_colloc_points(m.t2, tau2)
self.assertTrue(len(m.t2) == 16)
self.assertTrue(m.t2.get_changed())
t = sorted(m.t2)
self.assertTrue(t[1] == 0.4)
self.assertTrue(t[13] == 5.4)
def test_bad_kwds(self):
model = ConcreteModel()
try:
model.t = ContinuousSet(bounds=(0, 1), filter=True)
self.fail("Expected TypeError")
except TypeError:
pass
# try:
# model.t = ContinuousSet(bounds=(0,1),within=NonNegativeReals)
# self.fail("Expected TypeError")
# except TypeError:
# pass
try:
model.t = ContinuousSet(bounds=(0, 1), dimen=2)
self.fail("Expected TypeError")
except TypeError:
pass
try:
model.t = ContinuousSet(bounds=(0, 1), virtual=True)
self.fail("Expected TypeError")
except TypeError:
pass
try:
model.t = ContinuousSet(bounds=(0, 1), validate=True)
self.fail("Expected TypeError")
except TypeError:
pass
def test_get_index_information(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.x = ContinuousSet(bounds=(0, 10))
m.s = Set(initialize=['a', 'b', 'c'])
m.v = Var(m.t, m.x, m.s, initialize=1)
m.v2 = Var(m.t, m.s, initialize=1)
disc = TransformationFactory('dae.collocation')
disc.apply_to(m, wrt=m.t, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
disc.apply_to(m, wrt=m.x, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
info = get_index_information(m.v, m.t)
nts = info['non_ds']
index_getter = info['index function']
self.assertEqual(len(nts), 33)
self.assertTrue(m.x in nts.set_tuple)
self.assertTrue(m.s in nts.set_tuple)
self.assertEqual(index_getter((8.0, 'a'), 1, 0), (2.0, 8.0, 'a'))
info = get_index_information(m.v2, m.t)
nts = info['non_ds']
index_getter = info['index function']
self.assertEqual(len(nts), 3)
self.assertTrue(m.s is nts)
self.assertEqual(index_getter('a', 1, 0), (2.0, 'a'))
def test_reclassification(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 1))
m.x = ContinuousSet(bounds=(5, 10))
m.s = Set(initialize=[1, 2, 3])
m.v = Var(m.t)
m.v2 = Var(m.s, m.t)
m.v3 = Var(m.x, m.t)
m.dv = DerivativeVar(m.v)
m.dv2 = DerivativeVar(m.v2, wrt=(m.t, m.t))
m.dv3 = DerivativeVar(m.v3, wrt=m.x)
TransformationFactory('dae.finite_difference').apply_to(m, wrt=m.t)
self.assertTrue(m.dv.ctype is Var)
self.assertTrue(m.dv2.ctype is Var)
self.assertTrue(m.dv.is_fully_discretized())
self.assertTrue(m.dv2.is_fully_discretized())
self.assertTrue(m.dv3.ctype is DerivativeVar)
self.assertFalse(m.dv3.is_fully_discretized())
TransformationFactory('dae.collocation').apply_to(m, wrt=m.x)
self.assertTrue(m.dv3.ctype is Var)
self.assertTrue(m.dv3.is_fully_discretized())
def test_update_contset_indexed_component_expressions_multiple(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.t2 = ContinuousSet(initialize=[1, 2, 3])
m.s1 = Set(initialize=[1, 2, 3])
m.s2 = Set(initialize=[(1, 1), (2, 2)])
def _init(m, i, j):
return j + i
m.p1 = Param(m.s1, m.t, default=_init)
m.v1 = Var(m.s1, m.t, initialize=5)
m.v2 = Var(m.s2, m.t, initialize=2)
m.v3 = Var(m.t2, m.s2, initialize=1)
def _con1(m, si, ti):
return m.v1[si, ti] * m.p1[si, ti]
m.con1 = Expression(m.s1, m.t, rule=_con1)
def _con2(m, i, j, ti):
return m.v2[i, j, ti] + m.p1[1, ti]
m.con2 = Expression(m.s2, m.t, rule=_con2)
def _con3(m, i, ti, ti2, j, k):
return m.v1[i, ti] - m.v3[ti2, j, k] * m.p1[i, ti]
m.con3 = Expression(m.s1, m.t, m.t2, m.s2, rule=_con3)
expansion_map = ComponentMap()
generate_finite_elements(m.t, 5)
update_contset_indexed_component(m.p1, expansion_map)
update_contset_indexed_component(m.v1, expansion_map)
update_contset_indexed_component(m.v2, expansion_map)
update_contset_indexed_component(m.v3, expansion_map)
update_contset_indexed_component(m.con1, expansion_map)
update_contset_indexed_component(m.con2, expansion_map)
update_contset_indexed_component(m.con3, expansion_map)
self.assertTrue(len(m.con1) == 18)
self.assertTrue(len(m.con2) == 12)
self.assertTrue(len(m.con3) == 108)
self.assertEqual(m.con1[1, 4](), 25)
self.assertEqual(m.con1[2, 6](), 40)
self.assertEqual(m.con1[3, 8](), 55)
self.assertEqual(m.con2[1, 1, 2](), 5)
self.assertEqual(m.con2[2, 2, 4](), 7)
self.assertEqual(m.con2[1, 1, 8](), 11)
self.assertEqual(m.con3[1, 2, 1, 1, 1](), 2)
self.assertEqual(m.con3[1, 4, 1, 2, 2](), 0)
self.assertEqual(m.con3[2, 6, 3, 1, 1](), -3)
self.assertEqual(m.con3[3, 8, 2, 2, 2](), -6)
def test_reclassification_collocation(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 1))
m.x = ContinuousSet(bounds=(5, 10))
m.s = Set(initialize=[1, 2, 3])
m.v = Var(m.t)
m.v2 = Var(m.s, m.t)
m.v3 = Var(m.t, m.x)
def _int1(m, t):
return m.v[t]
m.int1 = Integral(m.t, rule=_int1)
def _int2(m, s, t):
return m.v2[s, t]
m.int2 = Integral(m.s, m.t, wrt=m.t, rule=_int2)
def _int3(m, t, x):
return m.v3[t, x]
m.int3 = Integral(m.t, m.x, wrt=m.t, rule=_int3)
def _int4(m, x):
return m.int3[x]
m.int4 = Integral(m.x, wrt=m.x, rule=_int4)
self.assertFalse(m.int1.is_fully_discretized())
self.assertFalse(m.int2.is_fully_discretized())
self.assertFalse(m.int3.is_fully_discretized())
self.assertFalse(m.int4.is_fully_discretized())
TransformationFactory('dae.collocation').apply_to(m, wrt=m.t)
self.assertTrue(m.int1.is_fully_discretized())
self.assertTrue(m.int2.is_fully_discretized())
self.assertFalse(m.int3.is_fully_discretized())
self.assertFalse(m.int4.is_fully_discretized())
self.assertTrue(m.int1.ctype is Integral)
self.assertTrue(m.int2.ctype is Integral)
self.assertTrue(m.int3.ctype is Integral)
self.assertTrue(m.int4.ctype is Integral)
TransformationFactory('dae.collocation').apply_to(m, wrt=m.x)
self.assertTrue(m.int3.is_fully_discretized())
self.assertTrue(m.int4.is_fully_discretized())
self.assertTrue(m.int1.ctype is Expression)
self.assertTrue(m.int2.ctype is Expression)
self.assertTrue(m.int3.ctype is Expression)
self.assertTrue(m.int4.ctype is Expression)
def make_model():
m = ConcreteModel()
m.time = ContinuousSet(bounds=(0, 10))
m.space = ContinuousSet(bounds=(0, 5))
m.set1 = Set(initialize=['a', 'b', 'c'])
m.set2 = Set(initialize=['d', 'e', 'f'])
m.fs = Block()
m.fs.v0 = Var(m.space, initialize=1)
@m.fs.Block()
def b1(b):
b.v = Var(m.time, m.space, initialize=1)
b.dv = DerivativeVar(b.v, wrt=m.time, initialize=0)
b.con = Constraint(m.time,
m.space,
rule=lambda b, t, x: b.dv[t, x] == 7 - b.v[t, x])
# Inconsistent
@b.Block(m.time)
def b2(b, t):
b.v = Var(initialize=2)
@m.fs.Block(m.time, m.space)
def b2(b, t, x):
b.v = Var(m.set1, initialize=2)
@b.Block(m.set1)
def b3(b, c):
b.v = Var(m.set2, initialize=3)
@b.Constraint(m.set2)
def con(b, s):
return (5 * b.v[s] == m.fs.b2[m.time.first(),
m.space.first()].v[c])
# inconsistent
@m.fs.Constraint(m.time)
def con1(fs, t):
return fs.b1.v[t, m.space.last()] == 5
# Will be inconsistent
@m.fs.Constraint(m.space)
def con2(fs, x):
return fs.b1.v[m.time.first(), x] == fs.v0[x]
# will be consistent
disc = TransformationFactory('dae.collocation')
disc.apply_to(m, wrt=m.time, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
disc.apply_to(m, wrt=m.space, nfe=5, ncp=2, scheme='LAGRANGE-RADAU')
return m
def test_disc_second_order_1cp(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0,1))
m.t2 = ContinuousSet(bounds=(0,10))
m.v = Var(m.t, m.t2)
m.dv = DerivativeVar(m.v, wrt=(m.t, m.t2))
TransformationFactory('dae.collocation').apply_to(m, nfe=2, ncp=1)
self.assertTrue(hasattr(m, 'dv_disc_eq'))
self.assertTrue(len(m.dv_disc_eq) == 4)
self.assertTrue(len(m.v) == 9)
def test_invalid(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 1))
m.x = ContinuousSet(bounds=(5, 10))
m.s = Set(initialize=[1, 2, 3])
m.v = Var(m.t)
m.v2 = Var(m.s, m.t)
m.v3 = Var(m.x, m.t)
m.y = Var()
# Not passing a Var as the first positional argument
with self.assertRaises(DAE_Error):
m.ds = DerivativeVar(m.s)
# Specifying both option aliases
with self.assertRaises(TypeError):
m.dv = DerivativeVar(m.v, wrt=m.t, withrespectto=m.t)
# Passing in Var not indexed by a ContinuousSet
with self.assertRaises(DAE_Error):
m.dy = DerivativeVar(m.y)
# Not specifying 'wrt' when Var indexed by multiple ContinuousSets
with self.assertRaises(DAE_Error):
m.dv3 = DerivativeVar(m.v3)
# 'wrt' is not a ContinuousSet
with self.assertRaises(DAE_Error):
m.dv2 = DerivativeVar(m.v2, wrt=m.s)
with self.assertRaises(DAE_Error):
m.dv2 = DerivativeVar(m.v2, wrt=(m.t, m.s))
# Specified ContinuousSet does not index the Var
with self.assertRaises(DAE_Error):
m.dv = DerivativeVar(m.v, wrt=m.x)
with self.assertRaises(DAE_Error):
m.dv2 = DerivativeVar(m.v2, wrt=[m.t, m.x])
# Declaring the same derivative twice
m.dvdt = DerivativeVar(m.v)
with self.assertRaises(DAE_Error):
m.dvdt2 = DerivativeVar(m.v)
m.dv2dt = DerivativeVar(m.v2, wrt=m.t)
with self.assertRaises(DAE_Error):
m.dv2dt2 = DerivativeVar(m.v2, wrt=m.t)
m.dv3 = DerivativeVar(m.v3, wrt=(m.x, m.x))
with self.assertRaises(DAE_Error):
m.dv4 = DerivativeVar(m.v3, wrt=(m.x, m.x))
def test_bad_kwds(self):
model = ConcreteModel()
with self.assertRaises(TypeError):
model.t = ContinuousSet(bounds=(0, 1), filter=True)
with self.assertRaises(TypeError):
model.t = ContinuousSet(bounds=(0, 1), dimen=2)
with self.assertRaises(TypeError):
model.t = ContinuousSet(bounds=(0, 1), virtual=True)
with self.assertRaises(TypeError):
model.t = ContinuousSet(bounds=(0, 1), validate=True)
def test_update_contset_indexed_component_vars_multiple(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.t2 = ContinuousSet(initialize=[1, 2, 3])
m.s = Set(initialize=[1, 2, 3])
m.s2 = Set(initialize=[(1, 1), (2, 2)])
m.v1 = Var(m.s, m.t, initialize=3)
m.v2 = Var(m.s,
m.t,
m.t2,
bounds=(4, 10),
initialize={
(1, 0, 1): 22,
(2, 10, 2): 22
})
def _init(m, i, j, k):
return i
m.v3 = Var(m.t, m.s2, bounds=(-5, 5), initialize=_init)
m.v4 = Var(m.s, m.t2, initialize=7, dense=True)
m.v5 = Var(m.s2)
expansion_map = ComponentMap()
generate_finite_elements(m.t, 5)
update_contset_indexed_component(m.v1, expansion_map)
update_contset_indexed_component(m.v2, expansion_map)
update_contset_indexed_component(m.v3, expansion_map)
update_contset_indexed_component(m.v4, expansion_map)
update_contset_indexed_component(m.v5, expansion_map)
self.assertTrue(len(m.v1) == 18)
self.assertTrue(len(m.v2) == 54)
self.assertTrue(len(m.v3) == 12)
self.assertTrue(len(m.v4) == 9)
self.assertTrue(value(m.v1[1, 4]) == 3)
self.assertTrue(m.v1[2, 2].ub is None)
self.assertTrue(m.v1[3, 8].lb is None)
self.assertTrue(value(m.v2[1, 0, 1]) == 22)
self.assertTrue(m.v2[1, 2, 1].value is None)
self.assertTrue(m.v2[2, 4, 3].lb == 4)
self.assertTrue(m.v2[3, 8, 1].ub == 10)
self.assertTrue(value(m.v3[2, 2, 2]) == 2)
self.assertTrue(m.v3[4, 1, 1].lb == -5)
self.assertTrue(m.v3[8, 2, 2].ub == 5)
self.assertTrue(value(m.v3[6, 1, 1]) == 6)
def test_init(self):
model = ConcreteModel()
model.t = ContinuousSet(bounds=(0, 1))
model = ConcreteModel()
model.t = ContinuousSet(initialize=[1, 2, 3])
model = ConcreteModel()
model.t = ContinuousSet(bounds=(0, 5), initialize=[1, 3, 5])
# Expected ValueError because a ContinuousSet component
# must contain at least two values upon construction
with self.assertRaises(ValueError):
model.t = ContinuousSet()
def test_update_block_derived2(self):
class Foo(Block):
pass
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.s = Set(initialize=[1, 2, 3])
def _block_rule(b, t, s):
m = b.model()
def _init(m, j):
return j * 2
b.p1 = Param(m.t, default=_init)
b.v1 = Var(m.t, initialize=5)
m.foo = Foo(m.t, m.s, rule=_block_rule)
generate_finite_elements(m.t, 5)
expand_components(m)
self.assertEqual(len(m.foo), 18)
self.assertEqual(len(m.foo[0, 1].p1), 6)
self.assertEqual(len(m.foo[2, 2].v1), 6)
self.assertEqual(m.foo[0, 3].p1[6], 12)
def test_update_block_derived_override_construct_nofcn2(self):
class Foo(Block):
def construct(self, data=None):
Block.construct(self, data)
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.s = Set(initialize=[1, 2, 3])
def _block_rule(b, t, s):
m = b.model()
def _init(m, j):
return j * 2
b.p1 = Param(m.t, default=_init)
b.v1 = Var(m.t, initialize=5)
m.foo = Foo(m.t, m.s, rule=_block_rule)
generate_finite_elements(m.t, 5)
OUTPUT = StringIO()
with LoggingIntercept(OUTPUT, 'pyomo.dae'):
expand_components(m)
self.assertIn('transformation to the Block-derived component',
OUTPUT.getvalue())
self.assertEqual(len(m.foo), 18)
self.assertEqual(len(m.foo[0, 1].p1), 6)
self.assertEqual(len(m.foo[2, 2].v1), 6)
self.assertEqual(m.foo[0, 3].p1[6], 12)
def test_update_contset_indexed_component_piecewise_multiple(self):
x = [0.0, 1.5, 3.0, 5.0]
y = [1.1, -1.1, 2.0, 1.1]
model = ConcreteModel()
model.t = ContinuousSet(bounds=(0, 10))
model.s = Set(initialize=['A', 'B', 'C'])
model.x = Var(model.s, model.t, bounds=(min(x), max(x)))
model.y = Var(model.s, model.t)
model.fx = Piecewise(model.s,
model.t,
model.y,
model.x,
pw_pts=x,
pw_constr_type='EQ',
f_rule=y)
self.assertEqual(len(model.fx), 6)
expansion_map = ComponentMap()
generate_finite_elements(model.t, 5)
update_contset_indexed_component(model.fx, expansion_map)
self.assertEqual(len(model.fx), 18)
self.assertEqual(len(model.fx['A', 2].SOS2_constraint), 3)
def test_instanciate(self):
from lms2 import AbsLModel
from lms2 import ScalablePowerSource
import pandas as pd
from pyomo.dae import ContinuousSet
from pyomo.environ import TransformationFactory
m = AbsLModel()
m.time = ContinuousSet()
m.u = ScalablePowerSource(curtailable=False)
df = pd.Series({0: 0, 1: 1, 2: 2, 3: 1, 4: 0})
data_u = {
'time': {None: [0, 4]},
'profile_index': {None: df.index},
'profile_value': df.to_dict()}
data = \
{None:
{
'time': {None: [0, 4]},
'u': data_u
}
}
inst = m.create_instance(data)
TransformationFactory('dae.finite_difference').apply_to(inst, nfe=4)
self.assertFalse(hasattr(inst.u, 'p_curt'))
self.assertTrue(hasattr(inst.u, 'p'))
self.assertTrue(hasattr(inst.u, 'p_scaled'))
self.assertTrue(hasattr(inst.u, 'scale_fact'))
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
msg = ("Length of paramList argument does"
" not equal length of perturbList")
with self.assertRaisesRegex(ValueError, msg):
Result = sensitivity_calculation('sipopt', m, list_one, list_two)
# verify ValueError thrown when param list has an unmutable param
msg = ("Parameters within paramList must be mutable")
with self.assertRaisesRegex(ValueError, msg):
Result = sensitivity_calculation('sipopt', m, list_four, list_one)
# verify ValueError thrown when param list has an unfixed var.
msg = ("Specified \"parameter\" variables must be fixed")
with self.assertRaisesRegex(ValueError, msg) as context:
Result = sensitivity_calculation('sipopt', m, list_three, list_one)
def test_instanciate_prog(self):
from lms2 import ProgrammableLoad, DebugSource
from pyomo.environ import AbstractModel, TransformationFactory, Param, Var
from pyomo.dae import ContinuousSet
import pandas as pd
m = AbstractModel()
m.time = ContinuousSet(bounds=(0, 1))
m.prog = ProgrammableLoad()
UB = 1e6
df = pd.Series({0: 0, 1: 1, 2: 2, 3: 1, 4: 0})
data_prog = {
'time': {None: [0, 10]},
'w1': {None: 3},
'w2': {None: 8},
'window': {None: [2, 8]},
'profile_index': {None: df.index},
'profile_value': df.to_dict()}
data = \
{None:
{
'time': {None: [0, 10]},
'prog': data_prog
}
}
inst = m.create_instance(data)
TransformationFactory('dae.finite_difference').apply_to(inst, nfe=10)
inst.prog.compile()
def test_disc_multi_index2(self):
m = self.m.clone()
m.t2 = ContinuousSet(bounds=(0, 5))
m.v2 = Var(m.t, m.t2)
m.dv2dt = DerivativeVar(m.v2, wrt=m.t)
m.dv2dt2 = DerivativeVar(m.v2, wrt=m.t2)
disc = TransformationFactory('dae.collocation')
disc.apply_to(m, nfe=2, ncp=2)
self.assertTrue(hasattr(m, 'dv2dt_disc_eq'))
self.assertTrue(hasattr(m, 'dv2dt2_disc_eq'))
self.assertTrue(len(m.dv2dt_disc_eq) == 20)
self.assertTrue(len(m.dv2dt2_disc_eq) == 20)
self.assertTrue(len(m.v2) == 25)
expected_t_disc_points = [0, 1.666667, 5.0, 6.666667, 10]
expected_t2_disc_points = [0, 0.833333, 2.5, 3.333333, 5]
for idx, val in enumerate(list(m.t)):
self.assertAlmostEqual(val, expected_t_disc_points[idx])
for idx, val in enumerate(list(m.t2)):
self.assertAlmostEqual(val, expected_t2_disc_points[idx])
self.assertTrue(hasattr(m, '_pyomo_dae_reclassified_derivativevars'))
self.assertTrue(m.dv1 in m._pyomo_dae_reclassified_derivativevars)
self.assertTrue(m.dv2dt in m._pyomo_dae_reclassified_derivativevars)
self.assertTrue(m.dv2dt2 in m._pyomo_dae_reclassified_derivativevars)
def test_flat_model(self):
m = ConcreteModel()
m.T = ContinuousSet(bounds=(0, 1))
m.x = Var()
m.y = Var([1, 2])
m.a = Var(m.T)
m.b = Var(m.T, [1, 2])
m.c = Var([3, 4], m.T)
regular, time = flatten_dae_variables(m, m.T)
regular_id = set(id(_) for _ in regular)
self.assertEqual(len(regular), 3)
self.assertIn(id(m.x), regular_id)
self.assertIn(id(m.y[1]), regular_id)
self.assertIn(id(m.y[2]), regular_id)
# Output for debugging
#for v in time:
# v.pprint()
# for _ in v.values():
# print" -> ", _.name
ref_data = {
self._hashRef(Reference(m.a[:])),
self._hashRef(Reference(m.b[:, 1])),
self._hashRef(Reference(m.b[:, 2])),
self._hashRef(Reference(m.c[3, :])),
self._hashRef(Reference(m.c[4, :])),
}
self.assertEqual(len(time), len(ref_data))
for ref in time:
self.assertIn(self._hashRef(ref), ref_data)
def test_abs(self):
from lms2 import FlowSource
from pyomo.environ import AbstractModel, TransformationFactory
from pyomo.dae import ContinuousSet
from pyomo.network import Port
m = AbstractModel()
m.time = ContinuousSet()
m.u = FlowSource(flow_name='p')
data_unit = dict(time={None: [0, 15]})
data = \
{None:
{
'time': {None: [0, 15]},
'u': data_unit
}
}
inst = m.create_instance(data)
TransformationFactory('dae.finite_difference').apply_to(inst, nfe=1)
self.assertTrue(hasattr(m.u, 'p'))
self.assertTrue(hasattr(m.u, 'outlet'))
self.assertIsInstance(m.u.outlet, Port)
self.assertEqual(inst.u.time.data(), (0, 15))
self.assertEqual(inst.time.data(), (0, 15))
def test_discretized_params_single(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.s1 = Set(initialize=[1, 2, 3])
m.s2 = Set(initialize=[(1, 1), (2, 2)])
m.p1 = Param(m.t, initialize=1)
m.p2 = Param(m.t, default=2)
m.p3 = Param(m.t, initialize=1, default=2)
def _rule1(m, i):
return i**2
def _rule2(m, i):
return 2 * i
m.p4 = Param(m.t, initialize={0: 5, 10: 5}, default=_rule1)
m.p5 = Param(m.t, initialize=_rule1, default=_rule2)
generate_finite_elements(m.t, 5)
# Expected ValueError because no default value was specified
with self.assertRaises(ValueError):
for i in m.t:
m.p1[i]
for i in m.t:
self.assertEqual(m.p2[i], 2)
if i == 0 or i == 10:
self.assertEqual(m.p3[i], 1)
self.assertEqual(m.p4[i], 5)
self.assertEqual(m.p5[i], i**2)
else:
self.assertEqual(m.p3[i], 2)
self.assertEqual(m.p4[i], i**2)
self.assertEqual(m.p5[i], 2 * i)
def test_hierarchical_blocks(self):
m = ConcreteModel()
m.b = Block()
m.b.t = ContinuousSet(bounds=(0, 10))
m.b.c = Block()
def _d_rule(d, t):
m = d.model()
d.x = Var()
return d
m.b.c.d = Block(m.b.t, rule=_d_rule)
m.b.y = Var(m.b.t)
def _con_rule(b, t):
return b.y[t] <= b.c.d[t].x
m.b.con = Constraint(m.b.t, rule=_con_rule)
generate_finite_elements(m.b.t, 5)
expand_components(m)
self.assertEqual(len(m.b.c.d), 6)
self.assertEqual(len(m.b.con), 6)
self.assertEqual(len(m.b.y), 6)
def test_mgv0(self):
from lms2 import MainGridV0
from pyomo.environ import AbstractModel, TransformationFactory, Param, Var
from pyomo.dae import ContinuousSet
from pyomo.network import Port
m = AbstractModel()
m.time = ContinuousSet(bounds=(0, 10))
m.mg = MainGridV0()
data_main_grid_v0 = {'time': {None: [0, 2]}, 'cost': {None: 0.15}}
data = \
{None:
{
'time' : {None: [0, 10]},
'mg' : data_main_grid_v0
}
}
inst = m.create_instance(data)
TransformationFactory('dae.finite_difference').apply_to(inst, nfe=2)
self.assertTrue(hasattr(m.mg, 'outlet'))
self.assertIsInstance(m.mg.outlet, Port)
def create_model():
m = ConcreteModel()
m.a = Param(initialize=-0.2, mutable=True)
m.H = Param(initialize=0.5, mutable=True)
m.T = 15
m.t = ContinuousSet(bounds=(0, m.T))
m.x = Var(m.t)
m.F = Var(m.t)
m.u = Var(m.t, initialize=0, bounds=(-0.2, 0))
m.dx = DerivativeVar(m.x, wrt=m.t)
m.df0 = DerivativeVar(m.F, wrt=m.t)
m.x[0].fix(5)
m.F[0].fix(0)
def _x(m, t):
return m.dx[t] == m.a * m.x[t] + m.u[t]
m.x_dot = Constraint(m.t, rule=_x)
def _f0(m, t):
return m.df0[t] == 0.25 * m.u[t]**2
m.FDiffCon = Constraint(m.t, rule=_f0)
def _Cost(m):
return 0.5 * m.H * m.x[m.T]**2 + m.F[m.T]
m.J = Objective(rule=_Cost)
return m
def test_update_block_derived_override_construct_withfcn2(self):
class Foo(Block):
updated = False
def construct(self, data=None):
Block.construct(self, data)
def update_after_discretization(self):
self.updated = True
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.s = Set(initialize=[1, 2, 3])
def _block_rule(b, t, s):
m = b.model()
def _init(m, j):
return j * 2
b.p1 = Param(m.t, default=_init)
b.v1 = Var(m.t, initialize=5)
m.foo = Foo(m.t, m.s, rule=_block_rule)
generate_finite_elements(m.t, 5)
expand_components(m)
self.assertTrue(m.foo.updated)
self.assertEqual(len(m.foo), 6)
self.assertEqual(len(m.foo[0, 1].v1), 6)
def test_update_contset_indexed_component_expressions_single(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.p = Param(m.t, default=3)
m.v = Var(m.t, initialize=5)
def _con1(m, i):
return m.p[i] * m.v[i]
m.con1 = Expression(m.t, rule=_con1)
# Rules that iterate over a ContinuousSet implicitly are not updated
# after the discretization
def _con2(m):
return sum(m.v[i] for i in m.t)
m.con2 = Expression(rule=_con2)
expansion_map = ComponentMap()
generate_finite_elements(m.t, 5)
update_contset_indexed_component(m.v, expansion_map)
update_contset_indexed_component(m.p, expansion_map)
update_contset_indexed_component(m.con1, expansion_map)
update_contset_indexed_component(m.con2, expansion_map)
self.assertTrue(len(m.con1) == 6)
self.assertEqual(m.con1[2](), 15)
self.assertEqual(m.con1[8](), 15)
self.assertEqual(m.con2(), 10)
def test_2level_model(self):
m = ConcreteModel()
m.T = ContinuousSet(bounds=(0,1))
@m.Block([1,2],m.T)
def B(b, i, t):
@b.Block(list(range(2*i, 2*i+2)))
def bb(bb, j):
bb.y = Var([10,11])
b.x = Var(list(range(2*i, 2*i+2)))
regular, time = flatten_dae_variables(m, m.T)
self.assertEqual(len(regular), 0)
# Output for debugging
#for v in time:
# v.pprint()
# for _ in v.values():
# print" -> ", _.name
ref_data = {
self._hashRef(Reference(m.B[1,:].x[2])),
self._hashRef(Reference(m.B[1,:].x[3])),
self._hashRef(Reference(m.B[2,:].x[4])),
self._hashRef(Reference(m.B[2,:].x[5])),
self._hashRef(Reference(m.B[1,:].bb[2].y[10])),
self._hashRef(Reference(m.B[1,:].bb[2].y[11])),
self._hashRef(Reference(m.B[1,:].bb[3].y[10])),
self._hashRef(Reference(m.B[1,:].bb[3].y[11])),
self._hashRef(Reference(m.B[2,:].bb[4].y[10])),
self._hashRef(Reference(m.B[2,:].bb[4].y[11])),
self._hashRef(Reference(m.B[2,:].bb[5].y[10])),
self._hashRef(Reference(m.B[2,:].bb[5].y[11])),
}
self.assertEqual(len(time), len(ref_data))
for ref in time:
self.assertIn(self._hashRef(ref), ref_data)
def test_invalid_derivative(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.v = Var(m.t)
m.dv = DerivativeVar(m.v, wrt=(m.t, m.t, m.t))
with self.assertRaises(DAE_Error):
TransformationFactory('dae.collocation').apply_to(m)
