def test_being_a_variable(self, simple_linear_model):
""" Test the basic use of `ConstantParameter` when `is_variable=True` """
model = simple_linear_model
p = ConstantParameter(model, np.pi, name='pi', comment='Mmmmm Pi!', is_variable=True,
lower_bounds=np.pi/2, upper_bounds=2*np.pi)
model.setup()
assert p.is_variable
assert p.double_size == 1
assert p.integer_size == 0
np.testing.assert_allclose(p.get_double_lower_bounds(), np.array([np.pi/2]))
np.testing.assert_allclose(p.get_double_upper_bounds(), np.array([2*np.pi]))
np.testing.assert_allclose(p.get_double_variables(), np.array([np.pi]))
# No test updating the variables
p.set_double_variables(np.array([1.5*np.pi, ]))
np.testing.assert_allclose(p.get_double_variables(), np.array([1.5*np.pi]))
# None of the integer functions should be implemented because this parameter
# has no integer variables
with pytest.raises(NotImplementedError):
p.get_integer_lower_bounds()
with pytest.raises(NotImplementedError):
p.get_integer_upper_bounds()
with pytest.raises(NotImplementedError):
p.get_integer_variables()
def test_storage_max_volume_param():
"""Test a that an max_volume with a Parameter results in the correct current_pc
"""
model = Model(start=pandas.to_datetime('2016-01-01'),
end=pandas.to_datetime('2016-01-01'))
storage = Storage(model, 'storage', inputs=1, outputs=0)
otpt = Output(model, 'output', max_flow=99999, cost=-99999)
storage.connect(otpt)
p = ConstantParameter(model, 20.0)
storage.max_volume = p
storage.initial_volume = 10.0
storage.initial_volume_pc = 0.5
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.5)
model.run()
p.set_double_variables(np.asarray([
40.0,
]))
model.reset()
# This is a demonstration of the issue describe in #470
# https://github.com/pywr/pywr/issues/470
# The initial storage is defined in both absolute and relative terms
# but these are now not consistent with one another and the updated max_volume
np.testing.assert_allclose(storage.volume, 10.0)
np.testing.assert_allclose(storage.current_pc, 0.5)
def test_storage_initial_volume_pc():
"""Test that setting initial volume as a percentage works as expected.
"""
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 = ConstantParameter(model, 20.0)
storage.max_volume = p
storage.initial_volume_pc = 0.5
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.5)
np.testing.assert_allclose(storage.volume, 10.0)
model.run()
p.set_double_variables(np.asarray([40.0, ]))
model.reset()
np.testing.assert_allclose(storage.current_pc, 0.5)
np.testing.assert_allclose(storage.volume, 20.0)
def test_storage_max_volume_param():
"""Test a that an max_volume with a Parameter results in the correct current_pc
"""
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 = ConstantParameter(model, 20.0)
storage.max_volume = p
storage.initial_volume = 10.0
model.setup()
np.testing.assert_allclose(storage.current_pc, 0.5)
model.run()
p.set_double_variables(np.asarray([40.0, ]))
model.reset()
np.testing.assert_allclose(storage.current_pc, 0.25)
def test_control_curve_interpolated(model):
m = model
m.timestepper.delta = 200
s = m.nodes['Storage']
o = m.nodes['Output']
s.connect(o)
cc = ConstantParameter(model, 0.8)
values = [20.0, 5.0, 0.0]
s.cost = p = ControlCurveInterpolatedParameter(model, s, cc, values)
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
v = s.initial_volume
c = cc.value(timestep, scenario_index)
if c == 1.0 and v == 100.0:
expected = values[1]
elif c == 0.0 and v == 0.0:
expected = values[1]
else:
expected = np.interp(v/100.0, [0.0, c, 1.0], values[::-1])
return expected
for control_curve in (0.0, 0.8, 1.0):
cc.set_double_variables(np.array([control_curve,]))
for initial_volume in (0.0, 10.0, 50.0, 80.0, 90.0, 100.0):
s.initial_volume = initial_volume
model.run()
def test_control_curve_interpolated(model, use_parameters):
m = model
m.timestepper.delta = 200
s = m.nodes["Storage"]
o = m.nodes["Output"]
s.connect(o)
cc = ConstantParameter(model, 0.8)
values = [20.0, 5.0, 0.0]
if use_parameters:
# Create the parameter using parameters for the values
parameters = [ConstantParameter(model, v) for v in values]
s.cost = p = ControlCurveInterpolatedParameter(model,
s,
cc,
parameters=parameters)
else:
# Create the parameter using a list of values
s.cost = p = ControlCurveInterpolatedParameter(model, s, cc, values)
@assert_rec(model, p)
def expected_func(timestep, scenario_index):
v = s.initial_volume
c = cc.value(timestep, scenario_index)
if c == 1.0 and v == 100.0:
expected = values[1]
elif c == 0.0 and v == 0.0:
expected = values[1]
else:
expected = np.interp(v / 100.0, [0.0, c, 1.0], values[::-1])
return expected
for control_curve in (0.0, 0.8, 1.0):
cc.set_double_variables(np.array([
control_curve,
]))
for initial_volume in (0.0, 10.0, 50.0, 80.0, 90.0, 100.0):
s.initial_volume = initial_volume
model.run()
