def test_cluster_add_remove():
n = Node()
n.production[RESOURCE] = lambda: 0
c = Cluster(resource=RESOURCE)
def not_added():
return c.parts() == set() and n.cluster(RESOURCE) is None
def added():
return c.parts() == {n} and n.cluster(RESOURCE) is c
assert not_added()
c.add_part(n)
assert added()
c.add_part(n)
c.remove_part(n)
assert not_added()
c.remove_part(n)
n.set_cluster(c)
assert added()
assert_raises(fs.InsanityError, n.set_cluster, c)
n.unset_cluster(c)
assert not_added()
assert_raises(fs.InsanityError, n.unset_cluster, c)
def test_cluster_balance_constraint():
n = Node()
n.production[RESOURCE] = lambda: 0
assert len(n.constraints()) == 1
c = Cluster(n, resource=RESOURCE)
assert len(n.constraints()) == 0
c.remove_part(n)
assert len(n.constraints()) == 1
def test_cluster_balance_constraint():
n = Node()
n.production[RESOURCE] = lambda idx: 0
index = 0
assert len(n.constraints.make(index)) == 1
c = Cluster(n, resource=RESOURCE)
assert len(n.constraints.make(index)) == 0
c.remove_part(n)
assert len(n.constraints.make(index)) == 1
def test_cluster_add_remove():
n = Node()
n.production[RESOURCE] = lambda: 0
c = Cluster(resource=RESOURCE)
def not_added():
return c.children == set() and n.cluster(RESOURCE) is None
def added():
return c.children == {n} and n.cluster(RESOURCE) is c
assert not_added()
c.add_part(n)
assert added()
c.add_part(n)
c.remove_part(n)
assert not_added()
c.remove_part(n)
n.set_cluster(c)
assert added()
assert_raises(fs.InsanityError, n.set_cluster, c)
n.unset_cluster(c)
assert not_added()
assert_raises(fs.InsanityError, n.unset_cluster, c)
def test_state_vars():
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
m = fs.models.MyopicDispatchModel(t0=0, step=3, horizon=7)
m.add_part(cl)
m.solver = default_solver
m.advance()
m.advance()
def check_variant(variant):
times = list(range(5))
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, variant)
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
prob = fs.Problem()
prob.add_constraints(chain(*(cl.constraints(t) for t in times)))
prob.objective = fs.Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t in times:
c.activity(t).take_value(solution)
p.activity(t).take_value(solution)
for t in times:
assert approx(p.production[RESOURCE](t).value, consumption(t))
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
def check_variant(variant):
times = list(range(5))
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, variant)
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
prob = fs.Problem()
prob.add_constraints(chain(*(cl.constraints(t) for t in times)))
prob.objective = fs.Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t in times:
c.activity(t).take_value(solution)
p.activity(t).take_value(solution)
for t in times:
assert approx(p.production[RESOURCE](t).value, consumption(t))
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
def test_state_vars():
times = list(range(1, 4))
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
prob = fs.Problem()
prob.add_constraints(chain(*(cl.constraints(t) for t in times)))
prob.objective = fs.Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t, part in product(times, cl.parts()):
for v in part.state_variables(t):
v.take_value(solution)
for t in times:
assert approx(p.production[RESOURCE](t).value, consumption(t))
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
def test_state_vars():
times = list(range(1,4))
consumption = lambda t: t * 1.5
p = Producer(name='Producer')
c = Consumer(consumption, name='Consumer')
cl = Cluster(p, c, resource=RESOURCE, name='Cluster')
prob = fs.Problem()
prob.add_constraints(chain(*(cl.constraints(t) for t in times)))
prob.objective = fs.Minimize(sum(p.cost(t) for t in times))
solution = default_solver.solve(prob)
for t, part in product(times, cl.parts()):
for v in part.state_variables(t):
v.take_value(solution)
for t in times:
assert approx(p.production[RESOURCE](t).value, consumption(t))
assert approx(c.consumption[RESOURCE](t).value, consumption(t))
def test_cluster_insanity():
n = Node()
Cluster(n, resource=RESOURCE)
Cluster(n, resource=RESOURCE)
def non_callable_constraint():
cl = Cluster(resource=RESOURCE)
cl.constraints += fs.Variable() == 3
