def _assert_results(self, model, s, p, scale=1.0):
# Test correct aggregation is performed
s.setup(model) # Init memory view on storage (bypasses usual `Model.setup`)
s.initial_volume = 90.0
model.reset() # Set initial volume on storage
ts = next(model.timestepper)
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*scale)
s.initial_volume = 70.0
model.reset() # Set initial volume on storage
ts = next(model.timestepper)
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), 0.7 * (mth - 1)*scale)
s.initial_volume = 30.0
model.reset() # Set initial volume on storage
ts = next(model.timestepper)
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*scale)
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_daily_license():
'''Test daily licence'''
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
lic = TimestepLicense(None, 42.0)
assert(isinstance(lic, License))
assert(lic.value(Timestep(datetime(2015, 1, 1), 0, 0), si) == 42.0)
# daily licences don't have resource state
assert(lic.resource_state(Timestep(datetime(2015, 1, 1), 0, 0)) is None)
def test_daily_license(simple_linear_model):
'''Test daily licence'''
m = simple_linear_model
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
lic = TimestepLicense(m, None, 42.0)
assert (isinstance(lic, License))
assert (lic.value(Timestep(pandas.Period('2015-1-1'), 0, 1), si) == 42.0)
# daily licences don't have resource state
assert (lic.resource_state(Timestep(pandas.Period('2015-1-1'), 0, 1)) is
None)
def test_threshold_parameter(model):
class DummyRecorder(Recorder):
def __init__(self, *args, **kwargs):
super(DummyRecorder, self).__init__(*args, **kwargs)
self.data = np.array([[0.0]], dtype=np.float64)
rec = DummyRecorder(model)
timestep = Timestep(datetime.datetime(2016, 1, 2), 1, 1)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
threshold = 10.0
values = [50.0, 60.0]
expected = [
("LT", (1, 0, 0)),
("GT", (0, 0, 1)),
("EQ", (0, 1, 0)),
("LE", (1, 1, 0)),
("GE", (0, 1, 1)),
]
for predicate, (value_lt, value_eq, value_gt) in expected:
param = RecorderThresholdParameter(rec, threshold, values, predicate)
rec.data[...] = threshold - 5 # data is below threshold
assert_allclose(param.value(timestep, si), values[value_lt])
assert (param.index(timestep, si) == value_lt)
rec.data[...] = threshold # data is at threshold
assert_allclose(param.value(timestep, si), values[value_eq])
assert (param.index(timestep, si) == value_eq)
rec.data[...] = threshold + 5 # data is above threshold
assert_allclose(param.value(timestep, si), values[value_gt])
assert (param.index(timestep, si) == value_gt)
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)
def test_parameter_array_indexed(model):
"""
Test ArrayIndexedParameter
"""
A = np.arange(len(model.timestepper), dtype=np.float64)
p = ArrayIndexedParameter(A)
p.setup(model)
# scenario indices (not used for this test)
si = ScenarioIndex(0, np.array([0], dtype=np.int32))
for v, ts in zip(A, model.timestepper):
np.testing.assert_allclose(p.value(ts, si), v)
# Now check that IndexError is raised if an out of bounds Timestep is given.
ts = Timestep(datetime.datetime(2016, 1, 1), 366, 1.0)
with pytest.raises(IndexError):
p.value(ts, si)
def test_load(self, model):
""" Test load from JSON dict"""
data = {
"type":
"aggregated",
"agg_func":
"product",
"parameters":
[0.8, {
"type": "monthlyprofile",
"values": list(range(12))
}]
}
p = load_parameter(model, data)
# Correct instance is loaded
assert isinstance(p, AggregatedParameter)
# Test correct aggregation is performed
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), (mth - 1) * 0.8)
def build_timestep(model, date):
dt = pandas.to_datetime(date)
timestep = Timestep(dt, (dt - model.timestepper.start).days, 1)
return timestep
