def test_regression_deepcopy():
initial_state = {'state_a': 0}
state_update_blocks = [
{
'policies': {},
'variables': {
'state_a': update_state_a
}
},
]
params = {}
TIMESTEPS = 10
RUNS = 1
model = Model(initial_state=initial_state,
state_update_blocks=state_update_blocks,
params=params)
simulation = Simulation(model=model)
experiment = Experiment(simulation)
simulation.run()
_ = copy.deepcopy(simulation)
def test_to_HDF5():
states = basic.states
state_update_blocks = basic.state_update_blocks
params = basic.params
TIMESTEPS = basic.TIMESTEPS
RUNS = basic.RUNS
model = Model(initial_state=states, state_update_blocks=state_update_blocks, params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
HDF5_store_file = 'experiment_results.hdf5'
experiment = Experiment(simulations=[simulation])
experiment.after_experiment = lambda experiment: save_to_HDF5(experiment, HDF5_store_file, 'experiment_0')
raw_result = experiment.run()
df = pd.read_hdf(HDF5_store_file, 'experiment_0')
assert df.equals(pd.DataFrame(raw_result))
experiment = Experiment(simulations=[simulation])
experiment.after_experiment = lambda experiment: save_to_HDF5(experiment, HDF5_store_file, 'experiment_1')
raw_result = experiment.run()
df = pd.read_hdf(HDF5_store_file, 'experiment_1')
assert df.equals(pd.DataFrame(raw_result))
assert len(raw_result) > 0
assert raw_result == experiment.results
assert simulation.run() == raw_result
def test_policy_result_type_error():
initial_state = {
'state_a': 0
}
state_update_blocks = [
{
'policies': {
'p1': policy_function_invalid_result
},
'variables': {
'state_a': update_state_a
}
},
]
params = {}
TIMESTEPS = 10
RUNS = 1
model = Model(initial_state=initial_state, state_update_blocks=state_update_blocks, params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
with pytest.raises(Exception) as e:
result = simulation.run()
assert str(e.value) == "Failed to extract policy function result as dictionary"
def test_policy_exception():
initial_state = {
'state_a': 0
}
state_update_blocks = [
{
'policies': {
'p1': policy_function_raises
},
'variables': {
'state_a': update_state_a
}
},
]
params = {}
TIMESTEPS = 10
RUNS = 1
model = Model(initial_state=initial_state, state_update_blocks=state_update_blocks, params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
with pytest.raises(Exception) as e:
result = simulation.run()
assert str(e.value) == "Forced exception from policy function"
def test_regression_policy_signals():
initial_state = {'a': 0, 'b': 0}
state_update_blocks = [
{
'policies': {},
'variables': {
'a': update_a_from_signal,
'b': update_b_from_signal
}
},
{
'policies': {
'policy': policy_two_signals
},
'variables': {
'a': update_a_from_signal,
'b': update_b_from_signal
}
},
]
params = {}
TIMESTEPS = 10
RUNS = 1
model = Model(initial_state=initial_state,
state_update_blocks=state_update_blocks,
params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
result = simulation.run()
df = pd.DataFrame(result)
assert df.query('timestep == 10 and substep == 2')['a'].item() == 10
def test_paralell_state_update():
def update_a(params, substep, state_history, previous_state, policy_input):
if previous_state['timestep'] == 1: assert previous_state['b'] == 0
return 'a', previous_state['a'] + 1
def update_b(params, substep, state_history, previous_state, policy_input):
if previous_state['timestep'] == 1: assert previous_state['a'] == 0
return 'b', previous_state['b'] + 1
initial_state = {'a': 0, 'b': 0}
state_update_blocks = [
{
'policies': {},
'variables': {
'a': update_a,
'b': update_b
}
},
]
params = {}
TIMESTEPS = 1
RUNS = 1
model = Model(initial_state=initial_state,
state_update_blocks=state_update_blocks,
params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
result = simulation.run()
df = pd.DataFrame(result)
def test_state_history():
initial_state = {'a': 1, 'b': 1}
state_update_blocks = [
{
'policies': {},
'variables': {
'a': update_a
}
},
{
'policies': {},
'variables': {
'a': update_a
}
},
]
model = Model(initial_state=initial_state,
state_update_blocks=state_update_blocks,
params={})
simulation = Simulation(model=model, timesteps=10)
result = simulation.run()
assert True
def test_run():
states = basic.states
state_update_blocks = basic.state_update_blocks
params = basic.params
TIMESTEPS = basic.TIMESTEPS
RUNS = basic.RUNS
model = Model(initial_state=states, state_update_blocks=state_update_blocks, params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
experiment = Experiment(simulations=[simulation])
raw_result = experiment.run()
assert len(raw_result) > 0
assert raw_result == experiment.results
assert simulation.run() == raw_result
def test_base_results():
states = basic.states
state_update_blocks = basic.state_update_blocks
params = basic.params
TIMESTEPS = basic.TIMESTEPS
RUNS = basic.RUNS
model = Model(initial_state=states, state_update_blocks=state_update_blocks, params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
experiment = Experiment([simulation])
simulation_results = simulation.run()
experiment_results = experiment.run()
# Check Executable results & exceptions
assert simulation_results == experiment_results
assert simulation.results == experiment.results
assert simulation.exceptions == experiment.exceptions
def test_initial_state_and_previous_state():
model = Model(initial_state=initial_state,
state_update_blocks=state_update_blocks)
simulation = Simulation(model=model, timesteps=10, runs=1)
simulation.engine = Engine(backend=Backend.SINGLE_PROCESS)
result = simulation.run()
df = pd.DataFrame(result)
# Check that the initial state transition is correct
assert timesteps[:4] == [
0, # Timestep 1; substep 1
1, # Timestep 1; substep 2
1, # Timestep 1; substep 3
1 # Timestep 2; substep 1
]
assert substeps[:4] == [
0, # Timestep 1; substep 1
1, # Timestep 1; substep 2
2, # Timestep 1; substep 3
3 # Timestep 2; substep 1
]
# Check that the previous state is correctly passed to next timestep/substep
assert timesteps[len(timesteps) - 4:] == [
9, # Timestep 9; substep 3
9, # Timestep 10; substep 1
10, # Timestep 10; substep 2
10 # Timestep 10; substep 3
]
assert substeps[len(substeps) - 4:] == [
2, # Timestep 9; substep 3
3, # Timestep 10; substep 1
1, # Timestep 10; substep 2
2 # Timestep 10; substep 3
]
assert df.query("timestep == 0")["substep"].iloc[0] == 0
assert df.query("timestep == 1")["substep"].iloc[0] == df.query(
"timestep == 2")["substep"].iloc[0]
assert df.query("timestep == 9")["substep"].iloc[0] == df.query(
"timestep == 10")["substep"].iloc[0]
def test_model_generator():
states = basic.states
state_update_blocks = basic.state_update_blocks
params = basic.params
TIMESTEPS = 10
RUNS = basic.RUNS
model = Model(initial_state=states,
state_update_blocks=state_update_blocks,
params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
experiment = Experiment(simulations=[simulation])
raw_result_experiment = experiment.run()
# The Model generator doesn't handle parameter sweeps, and only executes one run
df_experiment = pd.DataFrame(raw_result_experiment).query(
'run == 1 and subset == 0')
assert len(raw_result_experiment) > 0
assert raw_result_experiment == experiment.results
assert simulation.run() == raw_result_experiment
# Create a generator from the Model instance
model_generator = iter(model)
raw_result_model = []
# Set initial state
raw_result_model.append(model.state)
# Emulate the behaviour of the radCAD Engine
for t in range(TIMESTEPS):
_model = next(model_generator)
raw_result_model.append(_model.substeps)
# Flatten the results
raw_result_model = utils.flatten(raw_result_model)
df_model = pd.DataFrame(raw_result_model)
assert_frame_equal(df_experiment, df_model)
def test_run():
states = basic.states
state_update_blocks = basic.state_update_blocks
params = basic.params
TIMESTEPS = 10
RUNS = 1
model = Model(initial_state=states,
state_update_blocks=state_update_blocks,
params=params)
simulation = Simulation(model=model, timesteps=TIMESTEPS, runs=RUNS)
experiment = Experiment(simulations=[simulation])
experiment.engine = Engine(drop_substeps=True)
drop_substeps_result = pd.DataFrame(experiment.run())
simulation_result = pd.DataFrame(simulation.run())
keep = (simulation_result.substep == simulation_result['substep'].max())
keep |= (simulation_result.substep == 0)
simulation_result = simulation_result.loc[keep]
assert simulation_result.reset_index(drop=True).equals(
drop_substeps_result.reset_index(drop=True))
OO........O...O.OO....O.O
..........O.....O.......O
...........O...O
............OO
'''
cells_to_board(cells)
model.initial_state = {
"full": full,
"board": board,
}
simulation.model = model
if __name__ == "__main__":
result = simulation.run()
df = pd.DataFrame(result)
df
fig = plt.figure(figsize=(8, 8))
fig.set_tight_layout(True)
fig = plt.figure()
state = df.iloc[0]
im = plt.imshow(state["board"], cmap="binary", interpolation="none")
plt.axis("off")
def animate_func(timestep):
state = df.iloc[timestep]
im.set_array(state["board"])
