def test_var_not_used(ms4, solver, use_llvm):
m1 = Model(ms4, use_llvm=use_llvm)
s1 = Simulation(m1, t_start=0, t_stop=100, num=10, solver_type=solver)
s1.solve()
df = s1.model.historian_df
assert approx(np.array(df['S1_var.test_item.t1.P2'])) == np.repeat(
11, (11))
def test_chain_item_binding_model_nested(ms3, solver, use_llvm):
ms4 = Subsystem('new_s')
ms4.register_item(ms3)
m1 = Model(ms4, use_llvm=use_llvm)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=10, solver_type=solver)
s1.solve()
assert approx(m1.states_as_vector, rel=0.01) == [2010, 1010, 510, 210]
def test_overloadaction_sum_multiple(system2, solver, use_llvm):
model = Model(system2, use_llvm=use_llvm)
sim = Simulation(model, t_start=0, t_stop=10, num=100, solver_type=solver)
sim.solve()
df = sim.model.historian_df
assert approx(np.array(
df['system2_overload.subsystem1.item1.t1.x'])) == expected_sol(
np.linspace(0, 10, 101))
def test_system_link_1_5(system15, solver, use_llvm):
model = Model(system15, use_llvm=use_llvm)
sim = Simulation(model, t_start=0, t_stop=100, num=200, solver_type=solver)
sim.solve()
df = sim.model.historian_df
assert approx(np.array(df['root_linktest.linkersubsystem_4.item_0.t1.x'])[1:]) == \
expected(len(df.index[:-1]), 5, 0.9)
def test_model_save_only_aliases2(ms3, solver):
of = OutputFilter(only_aliases=True)
m1 = Model(ms3, historian_filter=of)
item = m1.search_items('2')[0]
columns_number = 0
for i, var in enumerate(item.get_variables()):
var[0].alias = str(i)
columns_number += 1
s1 = Simulation(m1, t_start=0, t_stop=1000, num=10, solver_type=solver)
s1.solve()
assert m1.historian_df.columns.size == columns_number
def test_system_link_Success2(solver, use_llvm):
N_inner = 5
N_outer = 2
system = Success2(N_outer=N_outer, N_inner=N_inner)
model = Model(system, use_llvm=use_llvm)
sim = Simulation(model, t_start=0, t_stop=100, num=200, solver_type=solver)
sim.solve()
df = sim.model.historian_df
assert approx(np.array(df['doesnotwork_deeplink.inlet_2.item_5.item_4.item_3.item_2.item_1.boundary_deeplink.t1.x'])[1:], rel=1) == \
expected(len(df.index[:-1]), (N_outer-1)*N_inner, 0.9)
def test_chain_item_binding_model_nested2(ms3, solver, use_llvm):
ms4 = Subsystem('new_s4')
ms4.register_item(ms3)
ms5 = Subsystem('new_s5')
ms5.register_item(ms3)
ms6 = Subsystem('new_s6')
ms6.register_item(ms4)
ms6.register_item(ms5)
ms7 = Subsystem('new_s7')
ms7.register_item(ms6)
m1 = Model(ms7, use_llvm=use_llvm)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=100, solver_type=solver)
s1.solve()
assert len(m1.path_variables) == 50
assert len(m1.variables) == 25
assert approx(m1.states_as_vector, rel=0.01) == [2010, 1010, 510, 210]
def test_external_data(solver, use_llvm):
external_mappings = []
import pandas as pd
import numpy as np
data = {
'time': np.arange(100),
'Dew Point Temperature {C}': np.arange(100) + 1,
'Dry Bulb Temperature {C}': np.arange(100) + 2,
}
df = pd.DataFrame(data,
columns=[
'time', 'Dew Point Temperature {C}',
'Dry Bulb Temperature {C}'
])
index_to_timestep_mapping = 'time'
index_to_timestep_mapping_start = 0
dataframe_aliases = {
'system_external.tm0.test_nm.T1':
("Dew Point Temperature {C}", InterpolationType.PIESEWISE),
'system_external.tm0.test_nm.T2':
('Dry Bulb Temperature {C}', InterpolationType.PIESEWISE)
}
external_mappings.append(
ExternalMappingElement("inmemory", index_to_timestep_mapping,
index_to_timestep_mapping_start, 1,
dataframe_aliases))
data_loader = InMemoryDataLoader(df)
s = Simulation(Model(StaticDataSystem('system_external', n=1),
use_llvm=use_llvm,
external_mappings=external_mappings,
data_loader=data_loader),
t_start=0,
t_stop=100.0,
num=100,
num_inner=100,
max_step=.1,
solver_type=solver)
s.solve()
assert approx(
np.array(s.model.historian_df['system_external.tm0.test_nm.T_i1'])
[1:]) == np.arange(101)[1:]
assert approx(
np.array(s.model.historian_df['system_external.tm0.test_nm.T_i2'])
[1:]) == np.arange(101)[1:] + 1
def test_static_system(solver, use_llvm):
import numpy as np
s = Simulation(Model(StaticDataSystem('system_static', n=1),
use_llvm=use_llvm),
t_start=0,
t_stop=100.0,
num=100,
num_inner=100,
max_step=.1,
solver_type=solver)
s.solve()
assert approx(
np.array(s.model.historian_df['system_static.tm0.test_nm.T_i1'])
[1:]) == np.repeat(0, (100))
assert approx(
np.array(s.model.historian_df['system_static.tm0.test_nm.T_i2'])
[1:]) == np.repeat(0, (100))
def test_callback_step_item_model(ms1, solver):
class SimpleCallback(NumbaCallbackBase):
def finalize(self, var_list):
pass
@NumbaCallback(method_type=CallbackMethodType.INITIALIZE)
def initialize(self, var_count, number_of_timesteps):
pass
@NumbaCallback(method_type=CallbackMethodType.UPDATE,
run_after_init=True)
def update(self, time, variables):
if variables['S1.test_item.t1.T2'] > 1000:
raise ValueError("Overflow of state2")
m1 = Model(ms1)
m1.add_callback(SimpleCallback())
s1 = Simulation(m1, t_start=0, t_stop=1000, num=100, solver_type=solver)
with pytest.raises(ValueError, match=r".*Overflow of state2.*"):
s1.solve()
def test_start_stop_model(filename, solver):
def stop_callback(t, _):
if t > 1:
raise ValueError("Overflow of state2")
hdf = HistoryDataFrame()
m1 = Model(S3('S3_sl'), historian=hdf)
c1 = _SimulationCallback("test")
c1.add_callback_function(stop_callback)
m1.callbacks.append(c1)
m1.save_variables_schedule(0.1, filename)
s1 = Simulation(m1, t_start=0, t_stop=2, num=100, solver_type=solver)
with pytest.raises(ValueError, match=r".*Overflow of state2.*"):
s1.solve()
hdf2 = HistoryDataFrame.load(filename)
m2 = Model(S3('S3_sl2'), historian=hdf2)
m2.restore_state()
assert approx(m2.states_as_vector, rel=0.1) == [89.8, 3.9, 0.7, 3.3]
s2 = Simulation(m2, t_start=0, t_stop=1, num=50, solver_type=solver)
s2.solve()
m3 = Model(S3('S3_sl3'), historian=hdf)
s3 = Simulation(m3, t_start=0, t_stop=2, num=100, solver_type=solver)
s3.solve()
print(m3.info)
assert approx(m3.states_as_vector, rel=0.1) == m2.states_as_vector
def test_race_condition_1(solver, use_llvm):
omega0 = 0.01
dt = 10
s1 = System(item=Link(item1=Item1(omega=omega0)), tag='system_race_1')
s2 = System(item=Item2(omega=omega0), tag='system_race_2')
m1 = Model(s1, use_llvm=use_llvm)
sim1 = Simulation(m1, max_step=dt, num=500, solver_type=solver)
sim1.solve()
df1 = sim1.model.historian_df
m2 = Model(s2, use_llvm=use_llvm)
sim2 = Simulation(m2, max_step=dt, num=500, solver_type=solver)
sim2.solve()
df2 = sim2.model.historian_df
assert np.all(
np.isclose(np.array(df1['system_race_1.link.t1.S'])[2:],
np.array(df2['system_race_2.item2.t1.S'][2:]),
rtol=1e-02,
atol=1e-04))
def test_logger_levels(solver, use_llvm):
num = 100
t_stop = 100
t_start = 0
sys = TestLogSubsystem1()
model = Model(sys, logger_level=ALL, use_llvm=use_llvm)
tvec = np.linspace(t_start, t_stop, num + 1, dtype=np.float64)
sim = Simulation(model, t_start=t_start, t_stop=t_stop, num=num, num_inner=1, solver_type=solver,
rtol=1e-8, atol=1e-8)
sim.solve()
df = sim.model.historian_df
s_analytic = sigmoidlike(tvec)
prefix = 'testlogsubsystem1.testlogitem1.t1'
p = f"{prefix}.p"
v = f"{prefix}.v"
s = f"{prefix}.s"
expected_results = {v: tvec, p: np.ones(num + 1), s: s_analytic}
for k, v in expected_results.items():
assert pytest.approx(v, abs=1e-5) == df.get(k), "expected results do not match actual results"
def test_single_equation_multiple_variables(ms1, solver, use_llvm):
m1 = Model(ms1, use_llvm=use_llvm)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=100, solver_type=solver)
s1.solve()
assert m1.historian_df["S1_inh.test1.t1.P"][100] == 5
assert m1.historian_df["S1_inh.test1.t1.T"][100] == 50
def test_equation_inheritence_2(ms2, solver):
m1 = Model(ms2)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=100, solver_type=solver)
s1.solve()
assert m1.historian_df["S2_inh.test1.t1.P"][100] == 5
assert m1.historian_df["S2_inh.test1.t1.L"][100] == 7
def test_model_var_referencing(ms1, solver, use_llvm):
m1 = Model(ms1, use_llvm=use_llvm)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=10, solver_type=solver)
s1.solve()
assert approx(list(m1.states_as_vector[::-1]),
rel=0.01) == [2010, 1010, 510, 210]
def test_chain_item_binding_model(ms3, solver, use_llvm):
m1 = Model(ms3, use_llvm=use_llvm)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=100, solver_type=solver)
s1.solve()
assert approx(m1.states_as_vector, rel=0.01) == [2010, 1010, 510, 210]
def test_model_save_only_aliases(ms3, solver):
of = OutputFilter(only_aliases=True)
m1 = Model(ms3, historian_filter=of)
s1 = Simulation(m1, t_start=0, t_stop=1000, num=10, solver_type=solver)
s1.solve()
assert m1.historian_df.empty