def test_with_nonstorage_load(self, model):
""" Test load from dict with 'storage_node' key. """
m = model
m.scenarios.setup()
s = Storage(m, 'Storage', max_volume=100.0)
l = Link(m, 'Link')
data = {
"type": "controlcurve",
"control_curve": 0.8,
"values": [10.0, 0.0],
"storage_node": "Storage"
}
l.cost = p = load_parameter(model, data)
assert isinstance(p, ControlCurveParameter)
s.setup(m) # Init memory view on storage (bypasses usual `Model.setup`)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
print(s.volume)
assert_allclose(l.get_cost(m.timestepper.current, si), 0.0)
# When storage volume changes, the cost of the link changes.
s.initial_volume = 90.0
m.reset()
assert_allclose(l.get_cost(m.timestepper.current, si), 10.0)
def test_with_nonstorage_load(self, model):
"""Test load from dict with 'storage_node' key."""
m = model
s = m.nodes["Storage"]
l = Link(m, "Link")
# Connect the link node to the network to create a valid model
o = m.nodes["Output"]
s.connect(l)
l.connect(o)
data = {
"type": "controlcurve",
"control_curve": 0.8,
"values": [10.0, 0.0],
"storage_node": "Storage",
}
l.cost = p = load_parameter(model, data)
assert isinstance(p, ControlCurveParameter)
@assert_rec(m, l.cost)
def expected_func(timestep, scenario_index):
v = s.initial_volume
if v >= 80.0:
expected = 10.0
else:
expected = 0.0
return expected
for initial_volume in (90, 70):
s.initial_volume = initial_volume
m.run()
def test_with_nonstorage(self, model):
"""Test usage on non-`Storage` node."""
# Now test if the parameter is used on a non storage node
m = model
s = m.nodes["Storage"]
l = Link(m, "Link")
# Connect the link node to the network to create a valid model
o = m.nodes["Output"]
s.connect(l)
l.connect(o)
cc = ConstantParameter(model, 0.8)
l.cost = ControlCurveParameter(model, s, cc, [10.0, 0.0])
@assert_rec(m, l.cost)
def expected_func(timestep, scenario_index):
v = s.initial_volume
if v >= 80.0:
expected = 10.0
else:
expected = 0.0
return expected
for initial_volume in (90, 70):
s.initial_volume = initial_volume
m.run()
def create_model():
# create a model
model = Model(start="2016-01-01", end="2019-12-31", timestep=7)
# create three nodes (an input, a link, and an output)
A = Input(model, name="A", max_flow=10.0)
B = Link(model, name="B", cost=1.0)
C = Output(model, name="C", max_flow=5.0, cost=-2.0)
# connect nodes
A.connect(B)
B.connect(C)
return model
def simple_linear_model(request, solver):
"""
Make a simple model with a single Input and Output.
Input -> Link -> Output
"""
model = Model(solver=solver)
inpt = Input(model, name="Input")
lnk = Link(model, name="Link", cost=1.0)
inpt.connect(lnk)
otpt = Output(model, name="Output")
lnk.connect(otpt)
return model
def test_with_nonstorage_load(self, model):
""" Test load from dict with 'storage_node' key. """
m = model
s = m.nodes['Storage']
l = Link(m, 'Link')
data = {
"type": "controlcurve",
"control_curve": 0.8,
"values": [10.0, 0.0],
"storage_node": "Storage"
}
l.cost = p = load_parameter(model, data)
assert isinstance(p, ControlCurveParameter)
@assert_rec(m, l.cost)
def expected_func(timestep, scenario_index):
v = s.initial_volume
if v >= 80.0:
expected = 10.0
else:
expected = 0.0
return expected
for initial_volume in (90, 70):
s.initial_volume = initial_volume
m.run()
def test_daily_profile_control_curve(simple_linear_model):
""" Test `DailyProfileControlCurveParameter` """
model = simple_linear_model
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link2')
data = {
'type': 'dailyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 366, [0.7] * np.arange(366), [0.3] * 366],
'storage_node': 'Storage'
}
l.max_flow = p = load_parameter(model, data)
model.setup()
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
v = s.initial_volume
doy = timestep.dayofyear
if v >= 80.0:
expected = 1.0
elif v >= 60:
expected = 0.7 * (doy - 1)
else:
expected = 0.3
return expected
for initial_volume in (90, 70, 30):
s.initial_volume = initial_volume
model.run()
def test_scaled_profile_nested_load(model):
""" Test `ScaledProfileParameter` loading with `AggregatedParameter` """
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link')
data = {
'type': 'scaledprofile',
'scale': 50.0,
'profile': {
'type':
'aggregated',
'agg_func':
'product',
'parameters': [{
'type': 'monthlyprofile',
'values': [0.5] * 12
}, {
'type':
'monthlyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 12, [0.7] * np.arange(12), [0.3] * 12],
'storage_node':
'Storage'
}]
}
}
l.max_flow = p = load_parameter(model, data)
p.setup(model)
# Test correct aggregation is performed
model.scenarios.setup()
s.setup(
model) # Init memory view on storage (bypasses usual `Model.setup`)
s.initial_volume = 90.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 50.0 * 0.5 * 1.0)
s.initial_volume = 70.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si),
50.0 * 0.5 * 0.7 * (mth - 1))
s.initial_volume = 30.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 50.0 * 0.5 * 0.3)
def test_with_nonstorage(self, model):
""" Test usage on non-`Storage` node. """
# Now test if the parameter is used on a non storage node
m = model
m.scenarios.setup()
s = Storage(m, 'Storage', max_volume=100.0)
l = Link(m, 'Link')
cc = ConstantParameter(0.8)
l.cost = ControlCurveParameter(s, cc, [10.0, 0.0])
s.setup(m) # Init memory view on storage (bypasses usual `Model.setup`)
print(s.volume)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
assert_allclose(l.get_cost(m.timestepper.current, si), 0.0)
# When storage volume changes, the cost of the link changes.
s.initial_volume = 90.0
m.reset()
print(s.volume)
assert_allclose(l.get_cost(m.timestepper.current, si), 10.0)
def test_with_nonstorage(self, model):
""" Test usage on non-`Storage` node. """
# Now test if the parameter is used on a non storage node
m = model
s = m.nodes['Storage']
l = Link(m, 'Link')
cc = ConstantParameter(model, 0.8)
l.cost = ControlCurveParameter(model, s, cc, [10.0, 0.0])
@assert_rec(m, l.cost)
def expected_func(timestep, scenario_index):
v = s.initial_volume
if v >= 80.0:
expected = 10.0
else:
expected = 0.0
return expected
for initial_volume in (90, 70):
s.initial_volume = initial_volume
m.run()
def test_no_scale_no_profile(self, simple_linear_model):
""" No scale or profile specified """
model = simple_linear_model
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link2')
data = {
'type': 'monthlyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 12, [0.7] * np.arange(12), [0.3] * 12],
'storage_node': 'Storage'
}
l.max_flow = p = load_parameter(model, data)
self._assert_results(model, s, p)
def test_scale_no_profile(self, simple_linear_model):
""" Test `MonthlyProfileControlCurveParameter` """
model = simple_linear_model
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link2')
data = {
'type': 'monthlyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 12, [0.7] * np.arange(12), [0.3] * 12],
'storage_node': 'Storage',
'scale': 1.5
}
l.max_flow = p = load_parameter(model, data)
model.setup()
self._assert_results(model, s, p, scale=1.5)
def test_no_scale_profile(self, model):
""" No scale, but profile array specified """
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link')
data = {
'type': 'monthlyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0] * 12, [0.7] * np.arange(12), [0.3] * 12],
'storage_node': 'Storage',
'profile': [1.5]*12
}
l.max_flow = p = load_parameter(model, data)
p.setup(model)
model.scenarios.setup()
self._assert_results(model, s, p, scale=1.5)
def test_daily_profile_control_curve(model):
""" Test `DailyProfileControlCurveParameter` """
s = Storage(model, 'Storage', max_volume=100.0)
l = Link(model, 'Link')
data = {
'type': 'dailyprofilecontrolcurve',
'control_curves': [0.8, 0.6],
'values': [[1.0]*366, [0.7]*np.arange(366), [0.3]*366],
'storage_node': 'Storage'
}
l.max_flow = p = load_parameter(model, data)
p.setup(model)
# Test correct aggregation is performed
model.scenarios.setup()
s.setup(model) # Init memory view on storage (bypasses usual `Model.setup`)
s.initial_volume = 90.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 1.0)
s.initial_volume = 70.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
doy = ts.datetime.dayofyear
np.testing.assert_allclose(p.value(ts, si), 0.7*(doy - 1))
s.initial_volume = 30.0
model.reset() # Set initial volume on storage
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for mth in range(1, 13):
ts = Timestep(datetime.datetime(2016, mth, 1), 366, 1.0)
np.testing.assert_allclose(p.value(ts, si), 0.3)
'id': link_id,
'node_1_id': node_1_id,
'node_2_id': node_2_id,
'from_slot': node_lookup_id[node_1_id]['connect_out'] - 1,
'to_slot': node_lookup_id[node_2_id]['connect_in'] - 1
}
link_lookup_id[link_id] = {
'name': link['name'],
'type': link['types'][0]['name'],
'node_1_id': node_1_id,
'node_2_id': node_2_id,
'from_slot': node_lookup_id[node_1_id]['connect_out'] - 1,
'to_slot': node_lookup_id[node_2_id]['connect_in'] - 1,
'attributes': link['attributes']
}
pywr_links[link_id] = Link(model, name=name)
# remove unconnected (rogue) nodes from analysis
connected_nodes = []
for link_id, trait in link_lookup_id.items():
connected_nodes.append(trait['node_1_id'])
connected_nodes.append(trait['node_2_id'])
rogue_nodes = []
for node in node_lookup_id:
if node not in connected_nodes:
rogue_nodes.append(node)
for node in rogue_nodes:
del node_lookup_id[node]
# create pywr nodes dictionary with format ["name" = pywr type + 'name']
# separate non_storage dicts for greater flexibility with recording model results
def create_model(harmonic=True):
# import flow timeseries for catchments
flow = pd.read_csv(os.path.join('data', 'thames_stochastic_flow.gz'))
flow['Date'] = flow['Date'].apply(pd.to_datetime)
flow.set_index('Date', inplace=True)
# resample input to weekly average
flow = flow.resample('7D', how='mean')
model = InspyredOptimisationModel(
solver='glpk',
start=flow.index[0],
end=flow.index[365*10], # roughly 10 years
timestep=datetime.timedelta(7), # weekly time-step
)
flow_parameter = ArrayIndexedParameter(model, flow['flow'].values)
catchment1 = Input(model, 'catchment1', min_flow=flow_parameter, max_flow=flow_parameter)
catchment2 = Input(model, 'catchment2', min_flow=flow_parameter, max_flow=flow_parameter)
reservoir1 = Storage(model, 'reservoir1', min_volume=3000, max_volume=20000, initial_volume=16000)
reservoir2 = Storage(model, 'reservoir2', min_volume=3000, max_volume=20000, initial_volume=16000)
if harmonic:
control_curve = AnnualHarmonicSeriesParameter(model, 0.5, [0.5], [0.0], mean_upper_bounds=1.0, amplitude_upper_bounds=1.0)
else:
control_curve = MonthlyProfileParameter(model, np.array([0.0]*12), lower_bounds=0.0, upper_bounds=1.0)
control_curve.is_variable = True
controller = ControlCurveParameter(model, reservoir1, control_curve, [0.0, 10.0])
transfer = Link(model, 'transfer', max_flow=controller, cost=-500)
demand1 = Output(model, 'demand1', max_flow=45.0, cost=-101)
demand2 = Output(model, 'demand2', max_flow=20.0, cost=-100)
river1 = Link(model, 'river1')
river2 = Link(model, 'river2')
# compensation flows from reservoirs
compensation1 = Link(model, 'compensation1', max_flow=5.0, cost=-9999)
compensation2 = Link(model, 'compensation2', max_flow=5.0, cost=-9998)
terminator = Output(model, 'terminator', cost=1.0)
catchment1.connect(reservoir1)
catchment2.connect(reservoir2)
reservoir1.connect(demand1)
reservoir2.connect(demand2)
reservoir2.connect(transfer)
transfer.connect(reservoir1)
reservoir1.connect(river1)
reservoir2.connect(river2)
river1.connect(terminator)
river2.connect(terminator)
reservoir1.connect(compensation1)
reservoir2.connect(compensation2)
compensation1.connect(terminator)
compensation2.connect(terminator)
r1 = TotalDeficitNodeRecorder(model, demand1)
r2 = TotalDeficitNodeRecorder(model, demand2)
r3 = AggregatedRecorder(model, [r1, r2], agg_func="mean")
r3.is_objective = 'minimise'
r4 = TotalFlowNodeRecorder(model, transfer)
r4.is_objective = 'minimise'
return model