def test_construct_indexed(self):
controller_set = pyo.Set(initialize=[0, 1, 2])
controller_set.construct()
horizon_map = {0: 1., 1: 3., 2: 5.}
nfe_map = {0: 2, 1: 6, 2: 10}
model_map = {
i: make_model(horizon_map[i], nfe_map[i])
for i in controller_set
}
time_map = {i: model_map[i].time for i in controller_set}
inputs_map = {i: [model_map[i].flow_in[0]] for i in controller_set}
controller = ControllerBlock(
controller_set,
model=model_map,
time=time_map,
inputs=inputs_map,
)
assert type(controller) is IndexedControllerBlock
assert isinstance(controller, IndexedControllerBlock)
controller.construct()
assert all(b.parent_component() is controller
for b in controller.values())
for i in controller_set:
assert i in controller
for i, c in controller.items():
assert c.mod is model_map[i]
assert c.time is time_map[i]
assert all(i1 is i2 for i1, i2 in zip(c._inputs, inputs_map[i]))
def make_block(self, sample_time=0.5, horizon=1, nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[t0, 'A'], model.conc[t0, 'B']]
scalar_vars, dae_vars = flatten_dae_components(
model,
time,
pyo.Var,
)
category_dict = categorize_dae_variables(dae_vars,
time,
inputs,
measurements=measurements)
dyn_block = DynamicBlock(
model=model,
time=time,
category_dict={None: category_dict},
#inputs=inputs,
#measurements=measurements,
)
dyn_block.construct()
dyn_block.set_sample_time(sample_time)
return dyn_block
def test_empty_category(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
inputs = [model.flow_in]
measurements = [
pyo.Reference(model.conc[:, 'A']),
pyo.Reference(model.conc[:, 'B']),
]
category_dict = {
VC.INPUT: inputs,
VC.MEASUREMENT: measurements,
VC.ALGEBRAIC: [],
}
db = DynamicBlock(
model=model,
time=time,
category_dict={None: category_dict},
)
db.construct()
db.set_sample_time(0.5)
# Categories with no variables are removed.
# If they were retained, trying to iterate
# over, e.g., vectors.algebraic[:, :] would
# fail due to inconsistent dimension.
assert VC.ALGEBRAIC not in db.category_dict
def test_extra_category(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
inputs = [model.flow_in]
measurements = [
pyo.Reference(model.conc[:, 'A']),
pyo.Reference(model.conc[:, 'B']),
]
disturbances = [
pyo.Reference(model.conc_in[:, 'A']),
pyo.Reference(model.conc_in[:, 'B']),
]
category_dict = {
VC.INPUT: inputs,
VC.MEASUREMENT: measurements,
VC.DISTURBANCE: disturbances,
}
db = DynamicBlock(
model=model,
time=time,
category_dict={None: category_dict},
)
db.construct()
db.set_sample_time(0.5)
db.vectors.input[:, t0].set_value(1.1)
db.vectors.measurement[:, t0].set_value(2.2)
db.vectors.disturbance[:, t0].set_value(3.3)
assert model.flow_in[t0].value == 1.1
assert model.conc[t0, 'A'].value == 2.2
assert model.conc[t0, 'B'].value == 2.2
assert model.conc_in[t0, 'A'].value == 3.3
assert model.conc_in[t0, 'B'].value == 3.3
def make_controller(self, sample_time=0.5, horizon=1., nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
controller = ControllerBlock(
model=model,
time=time,
inputs=inputs,
)
controller.construct()
controller.set_sample_time(sample_time)
return controller
def make_block(self, sample_time=0.5, horizon=1, nfe=2):
model = make_model(horizon=horizon, nfe=nfe)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[t0,'A'], model.conc[t0,'B']]
dyn_block = DynamicBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
)
dyn_block.construct()
dyn_block.set_sample_time(sample_time)
return dyn_block
def test_validate_sample_time(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[t0,'A'], model.conc[t0,'B']]
blk = DynamicBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
)
blk.construct()
blk.validate_sample_time(0.5)
assert hasattr(blk, 'sample_points')
assert hasattr(blk, 'fe_per_sample')
assert hasattr(blk, 'sample_point_indices')
sample_point_set = set(blk.sample_points)
sample_point_indices = set(blk.sample_point_indices)
for p in [0.0, 0.5, 1.0]:
assert p in sample_point_set
for i in [1, 3, 5]:
assert i in sample_point_indices
assert len(sample_point_set) == 3
assert len(sample_point_indices) == 3
with pytest.raises(ValueError, match=r".*integer divider.*"):
blk.validate_sample_time(0.6)
with pytest.raises(ValueError, match=r"Could not find a time point.*"):
blk.validate_sample_time(1/3.)
with pytest.raises(ValueError,
match=r".*tolerance is larger than.*not.*unique.*"):
blk.validate_sample_time(0.5, tolerance=0.09)
# min spacing in continuous set: 0.166667
blk.validate_sample_time(0.5, tolerance=0.08)
sample_point_set = set(blk.sample_points)
for p in [0.0, 0.5, 1.0]:
assert p in sample_point_set
for i in [1, 3, 5]:
assert i in sample_point_indices
assert len(sample_point_set) == 3
assert len(sample_point_indices) == 3
def test_set_sample_time(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[t0,'A'], model.conc[t0,'B']]
blk = DynamicBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
)
blk.construct()
blk.set_sample_time(1.0)
assert blk.sample_points == [0.0, 1.0]
blk.set_sample_time(0.5)
assert blk.sample_points == [0.0, 0.5, 1.0]
def test_initialize_only_measurement_input(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
inputs = [model.flow_in]
measurements = [
pyo.Reference(model.conc[:, 'A']),
pyo.Reference(model.conc[:, 'B']),
]
category_dict = {
VC.INPUT: inputs,
VC.MEASUREMENT: measurements,
}
db = DynamicBlock(
model=model,
time=time,
category_dict={None: category_dict},
)
db.construct()
db.set_sample_time(0.5)
db.mod.flow_in[:].set_value(3.0)
initialize_t0(db.mod)
copy_values_forward(db.mod)
db.mod.flow_in[:].set_value(2.0)
# Don't need to know any of the special categories to initialize
# by element. This is only because we have an implicit discretization.
db.initialize_by_solving_elements(solver)
t0 = time.first()
tl = time.last()
vectors = db.vectors
assert vectors.input[0, tl].value == 2.0
assert vectors.measurement[0, tl].value == pytest.approx(
3.185595567867036)
assert vectors.measurement[1, tl].value == pytest.approx(
1.1532474073395755)
assert model.dcdt[tl, 'A'].value == pytest.approx(0.44321329639889284)
assert model.dcdt[tl, 'B'].value == pytest.approx(0.8791007531878847)
def test_init_simple(self):
model = make_model(horizon=1, nfe=2)
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[0,'A'], model.conc[0,'B']]
block = DynamicBlock(
model=model,
time=time,
inputs=inputs,
measurements=measurements,
)
# Assert that we have the correct type
assert type(block) is SimpleDynamicBlock
assert isinstance(block, DynamicBlock)
assert isinstance(block, _DynamicBlockData)
block.construct()
# Assert that we behave like a simple block
assert block[None] is block
assert all(b is block for b in block[:])
# Assert that input attributes have been processed correctly
assert block.mod is model
assert block.time is time
assert all(i1 is i2 for i1, i2 in zip(block._inputs, inputs))
assert all(i1 is i2 for i1, i2 in zip(block._measurements, measurements))
# Assert that utility attributes have been added
assert hasattr(block, 'category_dict')
assert hasattr(block, 'vardata_map')
assert hasattr(block, 'measurement_vars')
assert hasattr(block, 'differential_vars')
assert hasattr(block, 'algebraic_vars')
assert hasattr(block, 'derivative_vars')
assert hasattr(block, 'input_vars')
assert hasattr(block, 'fixed_vars')
subblocks = [
block.mod,
block.vectors,
block.DIFFERENTIAL_BLOCK,
block.ALGEBRAIC_BLOCK,
block.INPUT_BLOCK,
block.FIXED_BLOCK,
block.DERIVATIVE_BLOCK,
block.MEASUREMENT_BLOCK,
]
block_objects = ComponentSet(
block.component_objects(pyo.Block, descend_into=False))
# Assert that subblocks have been added
assert len(subblocks) == len(block_objects)
for b in subblocks:
assert b in block_objects
# Assert that we can add variables and constraints to the block
block.v = pyo.Var(initialize=3)
block.c = pyo.Constraint(expr=block.v==5)
assert block.v.value == 3
assert block.v in ComponentSet(identify_variables(block.c.expr))
def test_init_rule(self):
block_set = pyo.Set(initialize=range(3))
block_set.construct()
# Create same maps as before
horizon_map = {0: 1, 1: 3, 2: 5}
nfe_map = {0: 2, 1: 6, 2: 10}
model_map = {
i: make_model(horizon=horizon_map[i], nfe=nfe_map[i])
for i in block_set
}
# Create rule to construct DynamicBlock with
def dynamic_block_rule(b, i):
model = model_map[i]
time = model.time
t0 = time.first()
inputs = [model.flow_in[t0]]
measurements = [model.conc[0,'A'], model.conc[0,'B']]
# Won't be obvious that these attrs need to be set if
# constructing from a rule
b.mod = model
super(_BlockData, b).__setattr__('time', time)
b._inputs = inputs
b._measurements = measurements
# Create DynamicBlock from a rule
block = DynamicBlock(block_set, rule=dynamic_block_rule)
assert type(block) is IndexedDynamicBlock
assert isinstance(block, DynamicBlock)
block.construct()
# Make sure iterating over block.values works as expected
assert all(b.parent_component() is block for b in block.values())
# Make sure __contains__ works
for i in block_set:
assert i in block
# Assert correct attributes and subblocks
for i, b in block.items():
assert b.mod is model_map[i]
assert b.time is model_map[i].time
t0 = b.time.first()
assert all(i1 is i2 for i1, i2 in zip(b._inputs,
[model_map[i].flow_in[t0]]))
assert all(i1 is i2 for i1, i2 in zip(b._measurements,
[model_map[i].conc[t0,'A'], model_map[i].conc[t0,'B']]))
assert hasattr(b, 'category_dict')
assert hasattr(b, 'vardata_map')
assert hasattr(b, 'measurement_vars')
assert hasattr(b, 'differential_vars')
assert hasattr(b, 'algebraic_vars')
assert hasattr(b, 'derivative_vars')
assert hasattr(b, 'input_vars')
assert hasattr(b, 'fixed_vars')
subblocks = [
b.mod,
b.vectors,
b.DIFFERENTIAL_BLOCK,
b.ALGEBRAIC_BLOCK,
b.INPUT_BLOCK,
b.FIXED_BLOCK,
b.DERIVATIVE_BLOCK,
b.MEASUREMENT_BLOCK,
]
block_objects = ComponentSet(
b.component_objects(pyo.Block, descend_into=False))
assert len(subblocks) == len(block_objects)
for sb in subblocks:
assert sb in block_objects
b.v = pyo.Var(initialize=3)
b.c = pyo.Constraint(expr=b.v==5)
assert b.v.value == 3
assert b.v in ComponentSet(identify_variables(b.c.expr))
def test_init_indexed(self):
block_set = pyo.Set(initialize=[0,1,2])
block_set.construct()
horizon_map = {0: 1., 1: 3., 2: 5.}
nfe_map = {0: 2, 1: 6, 2: 10}
model_map = {i: make_model(horizon_map[i], nfe_map[i])
for i in block_set}
time_map = {i: model_map[i].time for i in block_set}
inputs_map = {i: [model_map[i].flow_in[0]] for i in block_set}
measurements_map = {
i: [model_map[i].conc[0,'A'], model_map[i].conc[0,'B']]
for i in block_set
}
# Construct block with a dict for each of its arguments
block = DynamicBlock(
block_set,
model=model_map,
time=time_map,
inputs=inputs_map,
measurements=measurements_map,
)
# Make sure we have the right type
assert type(block) is IndexedDynamicBlock
assert isinstance(block, DynamicBlock)
block.construct()
assert all(b.parent_component() is block for b in block.values())
# Check __contains__
for i in block_set:
assert i in block
# Check attributes and subblocks of each data object
for i, b in block.items():
assert b.mod is model_map[i]
assert b.time is time_map[i]
assert all(i1 is i2 for i1, i2 in zip(b._inputs, inputs_map[i]))
assert all(i1 is i2 for i1, i2 in
zip(b._measurements, measurements_map[i]))
assert hasattr(b, 'category_dict')
assert hasattr(b, 'vardata_map')
assert hasattr(b, 'measurement_vars')
assert hasattr(b, 'differential_vars')
assert hasattr(b, 'algebraic_vars')
assert hasattr(b, 'derivative_vars')
assert hasattr(b, 'input_vars')
assert hasattr(b, 'fixed_vars')
subblocks = [
b.mod,
b.vectors,
b.DIFFERENTIAL_BLOCK,
b.ALGEBRAIC_BLOCK,
b.INPUT_BLOCK,
b.FIXED_BLOCK,
b.DERIVATIVE_BLOCK,
b.MEASUREMENT_BLOCK,
]
block_objects = ComponentSet(
b.component_objects(pyo.Block, descend_into=False))
assert len(subblocks) == len(block_objects)
for sb in subblocks:
assert sb in block_objects
b.v = pyo.Var(initialize=3)
b.c = pyo.Constraint(expr=b.v==5)
assert b.v.value == 3
assert b.v in ComponentSet(identify_variables(b.c.expr))