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_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 _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_map(ContinuousSet).itervalues():
if currentds is None or currentds is ds.cname(True):
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in ContinuousSet "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." %(ds.cname(True)))
self._nfe[ds.cname(True)] = len(ds) - 1
self._fe[ds.cname(True)] = sorted(ds)
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.cname(True)]
disc_info['scheme'] = self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
for c in block.component_map().itervalues():
update_contset_indexed_component(c)
for d in block.component_map(DerivativeVar).itervalues():
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.cname(True) is currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %(d.cname(True),i.cname(True)))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block, d)
block.reclassify_component_type(d, Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_map(Integral).itervalues():
i.reconstruct()
block.reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in block.component_map(Objective).itervalues():
k.reconstruct()
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_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_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_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 _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_map(ContinuousSet).itervalues():
if currentds is None or currentds == ds.cname(True):
generate_finite_elements(ds,self._nfe[currentds])
if not ds.get_changed():
if len(ds)-1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in ContinuousSet "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." %(ds.cname(True)))
self._nfe[ds.cname(True)]=len(ds)-1
self._fe[ds.cname(True)]=sorted(ds)
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe']=self._nfe[ds.cname(True)]
disc_info['scheme']=self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
for c in block.component_map().itervalues():
update_contset_indexed_component(c)
for d in block.component_map(DerivativeVar).itervalues():
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.cname(True) == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %(d.cname(True),i.cname(True)))
scheme = self._scheme[count-1]
newexpr = create_partial_expression(scheme,oldexpr,i,loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block,d)
block.reclassify_component_type(d,Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_map(Integral).itervalues():
i.reconstruct()
block.reclassify_component_type(i,Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in block.component_map(Objective).itervalues():
k.reconstruct()
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_update_contset_indexed_component_block_single2(self):
model = ConcreteModel()
model.t = ContinuousSet(bounds=(0, 10))
def _block_rule(_b_, t):
m = _b_.model()
b = Block()
b.s1 = Set(initialize=['A1', 'A2', 'A3'])
def _init(m, j):
return j * 2
b.p1 = Param(m.t, default=_init)
b.v1 = Var(m.t, initialize=5)
b.v2 = Var(m.t, initialize=2)
b.v3 = Var(m.t, b.s1, initialize=1)
def _con1(_b, ti):
return _b.v1[ti] * _b.p1[ti] == _b.v1[t]**2
b.con1 = Constraint(m.t, rule=_con1)
def _con2(_b, i, ti):
return _b.v2[ti] - _b.v3[ti, i] + _b.p1[ti]
b.con2 = Expression(b.s1, m.t, rule=_con2)
return b
model.blk = Block(model.t, rule=_block_rule)
expansion_map = ComponentMap()
self.assertTrue(len(model.blk), 2)
generate_finite_elements(model.t, 5)
missing_idx = set(model.blk._index) - set(iterkeys(model.blk._data))
model.blk._dae_missing_idx = missing_idx
update_contset_indexed_component(model.blk, expansion_map)
self.assertEqual(len(model.blk), 6)
self.assertEqual(len(model.blk[10].con1), 2)
self.assertEqual(len(model.blk[2].con1), 6)
self.assertEqual(len(model.blk[10].v2), 2)
self.assertEqual(model.blk[2].p1[2], 4)
self.assertEqual(model.blk[8].p1[6], 12)
self.assertEqual(model.blk[4].con1[4](), 15)
self.assertEqual(model.blk[6].con1[8](), 55)
self.assertEqual(model.blk[0].con2['A1', 10](), 21)
self.assertEqual(model.blk[4].con2['A2', 6](), 13)
def test_update_contset_indexed_component_block_multiple2(self):
model = ConcreteModel()
model.t = ContinuousSet(bounds=(0, 10))
model.s1 = Set(initialize=['A', 'B', 'C'])
model.s2 = Set(initialize=[('x1', 'x1'), ('x2', 'x2')])
def _block_rule(_b_, t, s1):
m = _b_.model()
b = Block()
def _init(m, i, j):
return j * 2
b.p1 = Param(m.s1, m.t, mutable=True, default=_init)
b.v1 = Var(m.s1, m.t, initialize=5)
b.v2 = Var(m.s2, m.t, initialize=2)
b.v3 = Var(m.t, m.s2, initialize=1)
def _con1(_b, si, ti):
return _b.v1[si, ti] * _b.p1[si, ti] == _b.v1[si, t]**2
b.con1 = Constraint(m.s1, m.t, rule=_con1)
def _con2(_b, i, j, ti):
return _b.v2[i, j, ti] - _b.v3[ti, i, j] + _b.p1['A', ti]
b.con2 = Expression(m.s2, m.t, rule=_con2)
return b
model.blk = Block(model.t, model.s1, rule=_block_rule)
expansion_map = ComponentMap()
self.assertTrue(len(model.blk), 6)
generate_finite_elements(model.t, 5)
missing_idx = set(model.blk._index) - set(iterkeys(model.blk._data))
model.blk._dae_missing_idx = missing_idx
update_contset_indexed_component(model.blk, expansion_map)
self.assertEqual(len(model.blk), 18)
self.assertEqual(len(model.blk[10, 'C'].con1), 6)
self.assertEqual(len(model.blk[2, 'B'].con1), 18)
self.assertEqual(len(model.blk[10, 'C'].v2), 4)
self.assertEqual(model.blk[2, 'A'].p1['A', 2], 4)
self.assertEqual(model.blk[8, 'C'].p1['B', 6], 12)
self.assertEqual(model.blk[4, 'B'].con1['B', 4](), 15)
self.assertEqual(model.blk[6, 'A'].con1['C', 8](), 55)
self.assertEqual(model.blk[0, 'A'].con2['x1', 'x1', 10](), 21)
self.assertEqual(model.blk[4, 'C'].con2['x2', 'x2', 6](), 13)
def test_update_contset_indexed_component_unsupported_single(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.s = Set(m.t)
generate_finite_elements(m.t, 5)
expansion_map = ComponentMap()
# Expected TypeError because Set is not a component that supports
# indexing by a ContinuousSet
with self.assertRaises(TypeError):
update_contset_indexed_component(m.s, expansion_map)
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_update_contset_indexed_component_other(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.junk = Suffix()
m.s = Set(initialize=[1, 2, 3])
m.v = Var(m.s)
def _obj(m):
return sum(m.v[i] for i in m.s)
m.obj = Objective(rule=_obj)
expansion_map = ComponentMap
generate_finite_elements(m.t, 5)
update_contset_indexed_component(m.junk, expansion_map)
update_contset_indexed_component(m.s, expansion_map)
update_contset_indexed_component(m.obj, expansion_map)
def test_discretized_params_multiple(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.s1 = Set(initialize=[1, 2, 3])
m.s2 = Set(initialize=[(1, 1), (2, 2)])
def _rule1(m, i):
return i**2
m.p1 = Param(m.s1, m.t, initialize=2, default=_rule1)
m.p2 = Param(m.t, m.s1, default=5)
def _rule2(m, i, j):
return i + j
m.p3 = Param(m.s1, m.t, initialize=2, default=_rule2)
def _rule3(m, i, j, k):
return i + j + k
m.p4 = Param(m.s2, m.t, default=_rule3)
generate_finite_elements(m.t, 5)
# Expected TypeError because a function with the wrong number of
# arguments was specified as the default
with self.assertRaises(TypeError):
for i in m.p1:
m.p1[i]
for i in m.p2:
self.assertEqual(m.p2[i], 5)
for i in m.t:
for j in m.s1:
if i == 0 or i == 10:
self.assertEqual(m.p3[j, i], 2)
else:
self.assertEqual(m.p3[j, i], i + j)
for i in m.t:
for j in m.s2:
self.assertEqual(m.p4[j, i], sum(j, i))
def test_update_contset_indexed_component_vars_single(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
m.t2 = ContinuousSet(initialize=[1, 2, 3])
m.s = Set(initialize=[1, 2, 3])
m.v1 = Var(m.t, initialize=3)
m.v2 = Var(m.t, bounds=(4, 10), initialize={0: 2, 10: 12})
def _init(m, i):
return i
m.v3 = Var(m.t, bounds=(-5, 5), initialize=_init)
m.v4 = Var(m.s, initialize=7, dense=True)
m.v5 = Var(m.t2, dense=True)
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) == 6)
self.assertTrue(len(m.v2) == 6)
self.assertTrue(len(m.v3) == 6)
self.assertTrue(len(m.v4) == 3)
self.assertTrue(len(m.v5) == 3)
self.assertTrue(value(m.v1[2]) == 3)
self.assertTrue(m.v1[4].ub is None)
self.assertTrue(m.v1[6].lb is None)
self.assertTrue(m.v2[2].value is None)
self.assertTrue(m.v2[4].lb == 4)
self.assertTrue(m.v2[8].ub == 10)
self.assertTrue(value(m.v2[0]) == 2)
self.assertTrue(value(m.v3[2]) == 2)
self.assertTrue(m.v3[4].lb == -5)
self.assertTrue(m.v3[6].ub == 5)
self.assertTrue(value(m.v3[8]) == 8)
def test_external_function(self):
m = ConcreteModel()
m.t = ContinuousSet(bounds=(0, 10))
def _fun(x):
return x**2
m.x_func = ExternalFunction(_fun)
m.y = Var(m.t, initialize=3)
m.dy = DerivativeVar(m.y, initialize=3)
def _con(m, t):
return m.dy[t] == m.x_func(m.y[t])
m.con = Constraint(m.t, rule=_con)
generate_finite_elements(m.t, 5)
expand_components(m)
self.assertEqual(len(m.y), 6)
self.assertEqual(len(m.con), 6)
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet):
if currentds is None or currentds == ds.name or currentds is ds:
if 'scheme' in ds.get_discretization_info():
raise DAE_Error(
"Attempting to discretize ContinuousSet "
"'%s' after it has already been discretized. " %
ds.name)
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
logger.warn("More finite elements were found in "
"ContinuousSet '%s' than the number of "
"finite elements specified in apply. The "
"larger number of finite elements will be "
"used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
# Adding discretization information to the ContinuousSet
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['scheme'] = self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in ComponentSet(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to "
"a particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Keep track of any reclassified DerivativeVar components so
# that the Simulator can easily identify them if the model
# is simulated after discretization
# TODO: Update the discretization transformations to use
# a Block to add things to the model and store discretization
# information. Using a list for now because the simulator
# does not yet support models containing active Blocks
reclassified_list = getattr(
block, '_pyomo_dae_reclassified_derivativevars', None)
if reclassified_list is None:
block._pyomo_dae_reclassified_derivativevars = list()
reclassified_list = \
block._pyomo_dae_reclassified_derivativevars
reclassified_list.append(d)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.reconstruct()
i.parent_block().reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to
# appear in the objective function which will need to be
# reconstructed after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet):
if currentds is None or currentds == ds.name or currentds is ds:
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in "
"ContinuousSet '%s' than the number of finite "
"elements specified in apply. The larger number "
"of finite elements will be used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
# Adding discretization information to the differentialset
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['scheme'] = self._scheme_name + ' Difference'
# Maybe check to see if any of the ContinuousSets have been changed,
# if they haven't then the model components need not be updated
# or even iterated through
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to "
"a particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.reconstruct()
i.parent_block().reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to
# appear in the objective function which will need to be
# reconstructed after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet, descend_into=True):
if currentds is None or currentds == ds.name:
if 'scheme' in ds.get_discretization_info():
raise DAE_Error("Attempting to discretize ContinuousSet "
"'%s' after it has already been discretized. "
% ds.name)
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
logger.warn("More finite elements were found in "
"ContinuousSet '%s' than the number of "
"finite elements specified in apply. The "
"larger number of finite elements will be "
"used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
generate_colloc_points(ds, self._tau[currentds])
# Adding discretization information to the continuousset
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['ncp'] = self._ncp[currentds]
disc_info['tau_points'] = self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to a "
"particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(scheme, oldexpr, i,
loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
# Add continuity equations to DerivativeVar's parent
# block
add_continuity_equations(d.parent_block(), d, i, loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Keep track of any reclassified DerivativeVar components so
# that the Simulator can easily identify them if the model
# is simulated after discretization
# TODO: Update the discretization transformations to use
# a Block to add things to the model and store discretization
# information. Using a list for now because the simulator
# does not yet support models containing active Blocks
reclassified_list = getattr(block,
'_pyomo_dae_reclassified_derivativevars',
None)
if reclassified_list is None:
block._pyomo_dae_reclassified_derivativevars = list()
reclassified_list = \
block._pyomo_dae_reclassified_derivativevars
reclassified_list.append(d)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.reconstruct()
i.parent_block().reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to appear
# in the objective function which will need to be reconstructed
# after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
k.reconstruct()
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 _transformBlock(self, block, currentds):
self._fe = {}
for ds in itervalues(block.component_map(ContinuousSet)):
if currentds is None or currentds == ds.name:
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in differentialset "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." % (ds.name,))
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = sorted(ds)
generate_colloc_points(ds, self._tau[currentds])
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['ncp'] = self._ncp[currentds]
disc_info['tau_points'] = self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
for c in itervalues(block.component_map()):
update_contset_indexed_component(c)
for d in itervalues(block.component_map(DerivativeVar)):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %s(d.name,i.name))
scheme = self._scheme[count - 1]
# print("%s %s" % (i.name, scheme.__name__))
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
add_continuity_equations(block, d, i, loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block, d)
block.reclassify_component_type(d, Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in itervalues(block.component_map(Integral)):
i.reconstruct()
block.reclassify_component_type(i, Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in itervalues(block.component_map(Objective)):
k.reconstruct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in block.component_objects(ContinuousSet, descend_into=True):
if currentds is None or currentds == ds.name:
if 'scheme' in ds.get_discretization_info():
raise DAE_Error(
"Attempting to discretize ContinuousSet "
"'%s' after it has already been discretized. " %
ds.name)
generate_finite_elements(ds, self._nfe[currentds])
if not ds.get_changed():
if len(ds) - 1 > self._nfe[currentds]:
logger.warning(
"More finite elements were found in "
"ContinuousSet '%s' than the number of "
"finite elements specified in apply. The "
"larger number of finite elements will be "
"used." % ds.name)
self._nfe[ds.name] = len(ds) - 1
self._fe[ds.name] = list(ds)
generate_colloc_points(ds, self._tau[currentds])
# Adding discretization information to the continuousset
# object itself so that it can be accessed outside of the
# discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe'] = self._nfe[ds.name]
disc_info['ncp'] = self._ncp[currentds]
disc_info['tau_points'] = self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
expand_components(block)
for d in block.component_objects(DerivativeVar, descend_into=True):
dsets = d.get_continuousset_list()
for i in ComponentSet(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. "
"Current implementation only allows for taking the"
" first or second derivative with respect to a "
"particular ContinuousSet" % (d.name, i.name))
scheme = self._scheme[count - 1]
newexpr = create_partial_expression(
scheme, oldexpr, i, loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
# Add continuity equations to DerivativeVar's parent
# block
add_continuity_equations(d.parent_block(), d, i, loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have
# been discretized. Add discretization equations to the
# DerivativeVar's parent block.
if d.is_fully_discretized():
add_discretization_equations(d.parent_block(), d)
d.parent_block().reclassify_component_type(d, Var)
# Keep track of any reclassified DerivativeVar components so
# that the Simulator can easily identify them if the model
# is simulated after discretization
# TODO: Update the discretization transformations to use
# a Block to add things to the model and store discretization
# information. Using a list for now because the simulator
# does not yet support models containing active Blocks
reclassified_list = getattr(
block, '_pyomo_dae_reclassified_derivativevars', None)
if reclassified_list is None:
block._pyomo_dae_reclassified_derivativevars = list()
reclassified_list = \
block._pyomo_dae_reclassified_derivativevars
reclassified_list.append(d)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in block.component_objects(Integral, descend_into=True):
i.parent_block().reclassify_component_type(i, Expression)
# TODO: The following reproduces the old behavior of
# "reconstruct()". We should come up with an
# implementation that does not rely on manipulating
# private attributes
i.clear()
i._constructed = False
i.construct()
# If a model contains integrals they are most likely to appear
# in the objective function which will need to be reconstructed
# after the model is discretized.
for k in block.component_objects(Objective, descend_into=True):
# TODO: check this, reconstruct might not work
# TODO: The following reproduces the old behavior of
# "reconstruct()". We should come up with an
# implementation that does not rely on manipulating
# private attributes
k.clear()
k._constructed = False
k.construct()
def _transformBlock(self, block, currentds):
self._fe = {}
for ds in itervalues(block.component_map(ContinuousSet)):
if currentds is None or currentds == ds.name:
generate_finite_elements(ds,self._nfe[currentds])
if not ds.get_changed():
if len(ds)-1 > self._nfe[currentds]:
print("***WARNING: More finite elements were found in differentialset "\
"'%s' than the number of finite elements specified in apply. "\
"The larger number of finite elements will be used." % (ds.name,))
self._nfe[ds.name]=len(ds)-1
self._fe[ds.name]=sorted(ds)
generate_colloc_points(ds,self._tau[currentds])
# Adding discretization information to the differentialset object itself
# so that it can be accessed outside of the discretization object
disc_info = ds.get_discretization_info()
disc_info['nfe']=self._nfe[ds.name]
disc_info['ncp']=self._ncp[currentds]
disc_info['tau_points']=self._tau[currentds]
disc_info['adot'] = self._adot[currentds]
disc_info['adotdot'] = self._adotdot[currentds]
disc_info['afinal'] = self._afinal[currentds]
disc_info['scheme'] = self._scheme_name
for c in itervalues(block.component_map()):
update_contset_indexed_component(c)
for d in itervalues(block.component_map(DerivativeVar)):
dsets = d.get_continuousset_list()
for i in set(dsets):
if currentds is None or i.name == currentds:
oldexpr = d.get_derivative_expression()
loc = d.get_state_var()._contset[i]
count = dsets.count(i)
if count >= 3:
raise DAE_Error(
"Error discretizing '%s' with respect to '%s'. Current implementation "\
"only allows for taking the first or second derivative with respect to "\
"a particular ContinuousSet" %(d.name,i.name))
scheme = self._scheme[count-1]
# print("%s %s" % (i.name, scheme.__name__))
newexpr = create_partial_expression(scheme,oldexpr,i,loc)
d.set_derivative_expression(newexpr)
if self._scheme_name == 'LAGRANGE-LEGENDRE':
add_continuity_equations(block,d,i,loc)
# Reclassify DerivativeVar if all indexing ContinuousSets have been discretized
if d.is_fully_discretized():
add_discretization_equations(block,d)
block.reclassify_component_type(d,Var)
# Reclassify Integrals if all ContinuousSets have been discretized
if block_fully_discretized(block):
if block.contains_component(Integral):
for i in itervalues(block.component_map(Integral)):
i.reconstruct()
block.reclassify_component_type(i,Expression)
# If a model contains integrals they are most likely to appear in the objective
# function which will need to be reconstructed after the model is discretized.
for k in itervalues(block.component_map(Objective)):
k.reconstruct()
