def test_variable(self, model):
""" Test that variable updating works. """
p1 = AnnualHarmonicSeriesParameter(model,
0.5, [0.25], [np.pi / 4],
is_variable=True)
assert p1.double_size == 3
assert p1.integer_size == 0
new_var = np.array([0.6, 0.1, np.pi / 2])
p1.set_double_variables(new_var)
np.testing.assert_allclose(p1.get_double_variables(), new_var)
with pytest.raises(NotImplementedError):
p1.set_integer_variables(np.arange(3, dtype=np.int32))
with pytest.raises(NotImplementedError):
p1.get_integer_variables()
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for ts in model.timestepper:
doy = (ts.datetime.dayofyear - 1) / 365
np.testing.assert_allclose(
p1.value(ts, si),
0.6 + 0.1 * np.cos(doy * 2 * np.pi + np.pi / 2))
def test_double_harmonic(self, model):
p1 = AnnualHarmonicSeriesParameter(model, 0.5, [0.25, 0.3], [np.pi/4, np.pi/3])
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for ts in model.timestepper:
doy = (ts.datetime.dayofyear - 1) /365
expected = 0.5 + 0.25*np.cos(doy*2*np.pi + np.pi / 4) + 0.3*np.cos(doy*4*np.pi + np.pi/3)
np.testing.assert_allclose(p1.value(ts, si), expected)
def test_single_harmonic(self, model):
p1 = AnnualHarmonicSeriesParameter(0.5, [0.25], [np.pi / 4])
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for ts in model.timestepper:
doy = (ts.datetime.dayofyear - 1) / 365
np.testing.assert_allclose(
p1.value(ts, si),
0.5 + 0.25 * np.cos(doy * 2 * np.pi + np.pi / 4))
def cyclical_storage_model(simple_storage_model):
""" Extends simple_storage_model to have a cyclical boundary condition """
from pywr.parameters import AnnualHarmonicSeriesParameter, ConstantScenarioParameter
m = simple_storage_model
s = Scenario(m, name='Scenario A', size=3)
m.timestepper.end = '2017-12-31'
m.timestepper.delta = 5
inpt = m.nodes['Input']
inpt.max_flow = AnnualHarmonicSeriesParameter(m, 5, [0.1, 0.0, 0.25], [0.0, 0.0, 0.0])
otpt = m.nodes['Output']
otpt.max_flow = ConstantScenarioParameter(m, s, [5, 6, 2])
return m
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