def test_agg_func_get_set(self, model, agg_func):
if agg_func == "custom":
agg_func = custom_test_func
p = AggregatedParameter(model, [], agg_func=agg_func)
assert p.agg_func == agg_func
p.agg_func = "product"
assert p.agg_func == "product"
def test_storage_max_volume_param_raises(solver):
"""Test a that an max_volume with a Parameter that has children is an error
"""
model = Model(solver=solver,
start=pandas.to_datetime('2016-01-01'),
end=pandas.to_datetime('2016-01-01'))
storage = Storage(model, 'storage', num_inputs=1, num_outputs=0)
otpt = Output(model, 'output', max_flow=99999, cost=-99999)
storage.connect(otpt)
p = AggregatedParameter(
model,
[ConstantParameter(model, 20.0),
ConstantParameter(model, 20.0)],
agg_func='sum')
p1 = AggregatedParameter(model, [p, ConstantParameter(model, 1.5)],
agg_func="product")
storage.max_volume = p1
storage.initial_volume = 10.0
with pytest.raises(RuntimeError):
model.run()
def _make_weather_nodes(self, model, weather, cost):
if not isinstance(self.area, Parameter):
raise ValueError(
'Weather nodes can only be created if an area Parameter is given.'
)
rainfall = weather['rainfall'].astype(np.float64)
rainfall_param = MonthlyProfileParameter(model, rainfall)
evaporation = weather['evaporation'].astype(np.float64)
evaporation_param = MonthlyProfileParameter(model, evaporation)
# Assume rainfall/evap is mm/day
# Need to convert:
# Mm2 -> m2
# mm/day -> m/day
# m3/day -> Mm3/day
# TODO allow this to be configured
const = ConstantParameter(model, 1e6 * 1e-3 * 1e-6)
# Create the flow parameters multiplying area by rate of rainfall/evap
rainfall_flow_param = AggregatedParameter(
model, [rainfall_param, const, self.area], agg_func='product')
evaporation_flow_param = AggregatedParameter(
model, [evaporation_param, const, self.area], agg_func='product')
# Create the nodes to provide the flows
rainfall_node = Input(model,
'{}.rainfall'.format(self.name),
parent=self)
rainfall_node.max_flow = rainfall_flow_param
rainfall_node.cost = cost
evporation_node = Output(model,
'{}.evaporation'.format(self.name),
parent=self)
evporation_node.max_flow = evaporation_flow_param
evporation_node.cost = cost
rainfall_node.connect(self)
self.connect(evporation_node)
self.rainfall_node = rainfall_node
self.evaporation_node = evporation_node
# Finally record these flows
self.rainfall_recorder = NumpyArrayNodeRecorder(
model, rainfall_node, name=f'__{rainfall_node.name}__:rainfall')
self.evaporation_recorder = NumpyArrayNodeRecorder(
model,
evporation_node,
name=f'__{evporation_node.name}__:evaporation')
def test_storage_max_volume_param_raises(solver):
"""Test a that an max_volume with a Parameter that has children.
Only some aggregated style parameters should work here.
"""
model = Model(solver=solver,
start=pandas.to_datetime('2016-01-01'),
end=pandas.to_datetime('2016-01-01'))
storage = Storage(model, 'storage', num_inputs=1, num_outputs=0)
otpt = Output(model, 'output', max_flow=99999, cost=-99999)
storage.connect(otpt)
p = AggregatedParameter(
model,
[ConstantParameter(model, 20.0),
ConstantParameter(model, 20.0)],
agg_func='sum')
storage.max_volume = p
storage.initial_volume = 10.0
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.25)
def __init__(self, model, name, *args, **kwargs):
demand = kwargs.pop('demand', ConstantParameter(model, 0))
supply = kwargs.pop('supply', ConstantParameter(model, 0))
headroom = kwargs.pop('headroom', ConstantParameter(model, 0))
super().__init__(model, name, *args, **kwargs)
self.supply_node = Input(model, name=f'{name}-supply', parent=self)
self.demand_node = Output(model, name=f'{name}-demand', parent=self)
self.supply_node.max_flow = MaxParameter(model, supply)
demand_headroom = AggregatedParameter(model,
parameters=[demand, headroom],
agg_func='sum')
self.demand_node.max_flow = MaxParameter(model, demand_headroom)
self.demand_node.cost = -100
self.supply_node.connect(self)
self.connect(self.demand_node)
# Track deficits
deficit_param = DeficitParameter(model,
self.demand_node,
name=f'{name}-deficit_param')
NumpyArrayParameterRecorder(model,
deficit_param,
name=f'__{name}-demand__:deficit',
temporal_agg_func='sum')
def test_max(self, model):
# Add two scenarios
scA = Scenario(model, 'Scenario A', size=2)
scB = Scenario(model, 'Scenario B', size=5)
values = np.arange(366, dtype=np.float64)
p1 = DailyProfileParameter(values)
p2 = ConstantScenarioParameter(scB,
np.arange(scB.size, dtype=np.float64))
p = AggregatedParameter([p1, p2], agg_func='max')
p.setup(model)
for ts in model.timestepper:
month = ts.datetime.month
day = ts.datetime.day
iday = int((datetime.datetime(2016, month, day) -
datetime.datetime(2016, 1, 1)).days)
for i in range(scB.size):
si = ScenarioIndex(i, np.array([0, i], dtype=np.int32))
np.testing.assert_allclose(p.value(ts, si),
max(values[iday], i))
def _make_rainfall_node(self, model, rainfall, cost):
if not isinstance(self.area, Parameter):
log.warning(
'Weather nodes can only be created if an area Parameter is given.'
)
return
if rainfall is None:
try:
rainfall_param = load_parameter(model,
f'__{self.name}__:rainfall')
except KeyError:
log.warning(
f"Please speficy a rainfall or a weather on node {self.name}"
)
return
elif isinstance(rainfall, pd.DataFrame) or isinstance(
rainfall, pd.Series):
#assume it's a dataframe
rainfall = rainfall.astype(np.float64)
rainfall_param = MonthlyProfileParameter(model, rainfall)
else:
rainfall_param = rainfall
# Create the flow parameters multiplying area by rate of rainfall/evap
rainfall_flow_param = AggregatedParameter(
model, [rainfall_param, self.const, self.area], agg_func='product')
# Create the nodes to provide the flows
rainfall_node = Input(model,
'{}.rainfall'.format(self.name),
parent=self)
rainfall_node.max_flow = rainfall_flow_param
rainfall_node.cost = cost
rainfall_node.connect(self)
self.rainfall_node = rainfall_node
# Finally record these flows
self.rainfall_recorder = NumpyArrayNodeRecorder(
model, rainfall_node, name=f'__{rainfall_node.name}__:rainfall')
def test_storage_max_volume_nested_param():
"""Test that a max_volume can be used with a Parameter with children.
"""
model = Model(
start=pandas.to_datetime('2016-01-01'),
end=pandas.to_datetime('2016-01-01')
)
storage = Storage(model, 'storage', num_inputs=1, num_outputs=0)
otpt = Output(model, 'output', max_flow=99999, cost=-99999)
storage.connect(otpt)
p = AggregatedParameter(model, [ConstantParameter(model, 20.0), ConstantParameter(model, 20.0)], agg_func='sum')
storage.max_volume = p
storage.initial_volume = 10.0
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.25)
def _make_evaporation_node(self, model, evaporation, cost):
if not isinstance(self.area, Parameter):
log.warning(
'Evaporation nodes can only be created if an area Parameter is given.'
)
return
if evaporation is None:
try:
evaporation_param = load_parameter(
model, f'__{self.name}__:evaporation')
except KeyError:
log.warning(
f"Please speficy an evaporation or a weather on node {self.name}"
)
return
elif isinstance(evaporation, pd.DataFrame) or isinstance(
evaporation, pd.Series):
evaporation = evaporation.astype(np.float64)
evaporation_param = MonthlyProfileParameter(model, evaporation)
else:
evaporation_param = evaporation
evaporation_flow_param = AggregatedParameter(
model, [evaporation_param, self.const, self.area],
agg_func='product')
evporation_node = Output(model,
'{}.evaporation'.format(self.name),
parent=self)
evporation_node.max_flow = evaporation_flow_param
evporation_node.cost = cost
self.connect(evporation_node)
self.evaporation_node = evporation_node
self.evaporation_recorder = NumpyArrayNodeRecorder(
model,
evporation_node,
name=f'__{evporation_node.name}__:evaporation')
def test_agg(self, simple_linear_model, agg_func):
model = simple_linear_model
model.timestepper.delta = 15
scenarioA = Scenario(model, "Scenario A", size=2)
scenarioB = Scenario(model, "Scenario B", size=5)
values = np.arange(366, dtype=np.float64)
p1 = DailyProfileParameter(model, values)
p2 = ConstantScenarioParameter(model, scenarioB, np.arange(scenarioB.size, dtype=np.float64))
p = AggregatedParameter(model, [p1, p2], agg_func=agg_func)
func = TestAggregatedParameter.funcs[agg_func]
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
x = p1.get_value(scenario_index)
y = p2.get_value(scenario_index)
return func(np.array([x,y]))
model.run()
def test_min(self, model):
# Add two scenarios
scA = Scenario(model, 'Scenario A', size=2)
scB = Scenario(model, 'Scenario B', size=5)
values = np.arange(366, dtype=np.float64)
p1 = DailyProfileParameter(values)
p2 = ConstantScenarioParameter(scB,
np.arange(scB.size, dtype=np.float64))
p = AggregatedParameter([
p1,
], agg_func='min')
p.add(p2)
p.setup(model)
for ts in model.timestepper:
iday = ts.datetime.dayofyear - 1
for i in range(scB.size):
si = ScenarioIndex(i, np.array([0, i], dtype=np.int32))
np.testing.assert_allclose(p.value(ts, si),
min(values[iday], i))
