def test_symmetries(self):
dft = stormpy.dft.load_dft_galileo_file(
get_example_path("dft", "rc.dft"))
symmetries = dft.symmetries()
assert len(symmetries.groups) == 1
for index, group in symmetries.groups.items():
assert len(group) == 3
i = 4
for syms in group:
assert len(syms) == 2
for elem in syms:
assert elem == i or elem == i + 3
i += 1
def test_parametric_model_checking_dd(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "parametric_die.pm"))
prop = "P=? [F s=5]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_symbolic_parametric_model(program, formulas)
assert model.nr_states == 11
assert model.nr_transitions == 17
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
result = stormpy.check_model_dd(model, formulas[0])
assert type(
result) is stormpy.SymbolicParametricQuantitativeCheckResult
def test_build_instantiated_dtmc(self):
jani_model, properties = stormpy.parse_jani_model(get_example_path("dtmc", "brp.jani"))
assert jani_model.has_undefined_constants
assert not jani_model.undefined_constants_are_graph_preserving
description = stormpy.SymbolicModelDescription(jani_model)
constant_definitions = description.parse_constant_definitions("N=16, MAX=2")
instantiated_jani_model = description.instantiate_constants(constant_definitions).as_jani_model()
model = stormpy.build_model(instantiated_jani_model)
assert model.nr_states == 677
assert model.nr_transitions == 867
assert model.model_type == stormpy.ModelType.DTMC
assert not model.supports_parameters
assert type(model) is stormpy.SparseDtmc
def test_derivatives(self):
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P<=0.84 [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
derivatives = stormpy.pars.gather_derivatives(model, list(parameters)[0])
assert len(derivatives) == 0
def test_parametric_dft(self):
import pycarl
pycarl.clear_pools()
dft = stormpy.dft.load_parametric_dft_galileo_file(
get_example_path("dft", "symmetry_param.dft"))
assert dft.nr_elements() == 7
assert dft.nr_be() == 4
assert dft.nr_dynamic() == 0
assert type(dft) is stormpy.dft.DFT_ratfunc
parameters = stormpy.dft.get_parameters(dft)
param_names = [x.name for x in parameters]
assert "x" in param_names
assert "y" in param_names
def test_upload_files(self):
print("Check if uploading files to the server works correctly...",
flush=True)
with open(get_example_path("brp", "brp_16-2.pm"), 'r') as pfile:
prismdata = pfile.read()
with open(get_example_path("brp", "property1.pctl"), 'r') as pfile:
pctldata = pfile.read()
with open(get_example_path("brp", "brp_16-2.rat"), 'r') as pfile:
result_data = pfile.read()
prismfile = ('prism-file', 'brp_16-2.pm', prismdata)
pctlfile = ('pctl-file', 'property1.pctl', pctldata)
result_file = ('result-file', 'brp_16-2.rat', result_data)
ct, data = self.encode_multipart_formdata([], [prismfile])
response = self._sendData('/uploadPrism', data=data, ct=ct)
assert response.code == 200
ct, data = self.encode_multipart_formdata([], [pctlfile])
response = self._sendData('/uploadPctl', data=data, ct=ct)
assert response.code == 200
ct, data = self.encode_multipart_formdata([("result-type", "storm")],
[result_file])
response = self._sendData('/uploadResult', data=data, ct=ct)
assert response.code == 200
print("DONE")
def test_model_checking_dtmc_all_labels(self):
program = stormpy.parse_prism_program(get_example_path("dtmc", "die.pm"))
formulas = stormpy.parse_properties_for_prism_program("P=? [ F \"one\" ]", program)
model = stormpy.build_model(program)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0])
assert math.isclose(result.at(initial_state), 1 / 6)
formulas = stormpy.parse_properties_for_prism_program("P=? [ F \"two\" ]", program)
result = stormpy.model_checking(model, formulas[0])
assert math.isclose(result.at(initial_state), 1 / 6)
def test_model_checking_prism_dd_dtmc(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "die.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F \"one\" ]", program)
model = stormpy.build_symbolic_model(program, formulas)
assert model.nr_states == 13
assert model.nr_transitions == 20
result = stormpy.check_model_dd(model, formulas[0])
assert type(result) is stormpy.SymbolicQuantitativeCheckResult
filter = stormpy.create_filter_initial_states_symbolic(model)
result.filter(filter)
assert result.min == result.max
assert math.isclose(result.min, 1 / 6, rel_tol=1e-6)
def test_model_checking_only_initial(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "die.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"Pmax=? [F{\"coin_flips\"}<=3 \"one\"]", program)
model = stormpy.build_model(program, formulas)
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model,
formulas[0],
only_initial_states=True)
assert not result.result_for_all_states
assert math.isclose(result.at(initial_state), 1 / 8)
def test_build_dtmc_from_prism_program_reward_formulas(self):
program = stormpy.parse_prism_program(get_example_path("dtmc", "die.pm"))
prop = "R=? [F \"done\"]"
properties = stormpy.parse_properties_for_prism_program(prop, program, None)
model = stormpy.build_symbolic_model(program, properties)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert model.model_type == stormpy.ModelType.DTMC
assert len(model.reward_models) == 1
assert not model.reward_models["coin_flips"].has_state_rewards
assert model.reward_models["coin_flips"].has_state_action_rewards
assert not model.reward_models["coin_flips"].has_transition_rewards
assert not model.supports_parameters
assert type(model) is stormpy.SymbolicSylvanDtmc
def test_relevant_events_additional(self):
dft = stormpy.dft.load_dft_json_file(
get_example_path("dft", "and.json"))
properties = stormpy.parse_properties("P=? [ F<=1 \"failed\" ]")
formulas = [p.raw_formula for p in properties]
relevant_events = stormpy.dft.compute_relevant_events(
dft, formulas, ["B", "C"])
assert relevant_events.is_relevant("B")
assert relevant_events.is_relevant("C")
assert not relevant_events.is_relevant("A")
results = stormpy.dft.analyze_dft(dft,
formulas,
relevant_events=relevant_events)
assert math.isclose(results[0], 0.1548181217)
def test_transformation(self):
dft = stormpy.dft.load_dft_galileo_file(
get_example_path("dft", "rc2.dft"))
valid, output = stormpy.dft.is_well_formed(dft)
assert not valid
assert "not binary" in output
dft = stormpy.dft.transform_dft(dft,
unique_constant_be=True,
binary_fdeps=True)
valid, output = stormpy.dft.is_well_formed(dft)
assert valid
formulas = stormpy.parse_properties("Tmin=? [ F \"failed\" ]")
results = stormpy.dft.analyze_dft(dft, [formulas[0].raw_formula])
assert math.isclose(results[0], 6.380930905)
def test_transform_symbolic_parametric_dtmc_to_sparse(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "parametric_die.pm"))
model = stormpy.build_symbolic_parametric_model(program)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert model.model_type == stormpy.ModelType.DTMC
assert model.supports_parameters
assert type(model) is stormpy.SymbolicSylvanParametricDtmc
symbolic_model = stormpy.transform_to_sparse_model(model)
assert symbolic_model.nr_states == 13
assert symbolic_model.nr_transitions == 20
assert symbolic_model.model_type == stormpy.ModelType.DTMC
assert symbolic_model.supports_parameters
assert type(symbolic_model) is stormpy.SparseParametricDtmc
def test_label(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "die.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F \"one\" ]", program)
model = stormpy.build_model(program, formulas)
labeling = model.labeling
labels = labeling.get_labels()
assert len(labels) == 3
assert "init" in labels
assert "one" in labels
assert "init" in model.labels_state(0)
assert "init" in labeling.get_labels_of_state(0)
assert "one" in model.labels_state(7)
assert "one" in labeling.get_labels_of_state(7)
def test_build_ctmc(self):
program = stormpy.parse_prism_program(get_example_path("ctmc", "polling2.sm"), True)
formulas = stormpy.parse_properties_for_prism_program("P=? [ F<=3 \"target\" ]", program)
model = stormpy.build_model(program)
assert model.nr_states == 12
assert model.nr_transitions == 22
assert model.model_type == stormpy.ModelType.CTMC
assert not model.supports_parameters
assert type(model) is stormpy.SparseCtmc
model_for_formula = stormpy.build_model(program, formulas)
assert model_for_formula.nr_states == 12
assert model_for_formula.nr_transitions == 21
assert model_for_formula.model_type == stormpy.ModelType.CTMC
assert not model_for_formula.supports_parameters
assert type(model_for_formula) is stormpy.SparseCtmc
def test_model_checking_jani_dtmc(self):
jani_model, properties = stormpy.parse_jani_model(get_example_path("dtmc", "die.jani"))
formulas = [properties["Probability to throw a six"], properties["Expected number of coin flips"]]
formulas = stormpy.eliminate_reward_accumulations(jani_model, formulas)
assert len(formulas) == 2
model = stormpy.build_model(jani_model, formulas)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0])
assert math.isclose(result.at(initial_state), 1 / 6)
result = stormpy.model_checking(model, formulas[1])
assert math.isclose(result.at(initial_state), 11 / 3)
def test_parametric_model_checking_sparse(self):
program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0])
func = result.at(initial_state)
one = stormpy.FactorizedPolynomial(stormpy.RationalRF(1))
assert func.denominator == one
def test_convert_ma_to_ctmc(self):
program = stormpy.parse_prism_program(get_example_path("ma", "ctmc.ma"), True)
formulas = stormpy.parse_properties_for_prism_program("P=? [ F<=3 s=2 ]", program)
model = stormpy.build_model(program, formulas)
assert model.nr_states == 4
assert model.nr_transitions == 6
assert model.model_type == stormpy.ModelType.MA
assert type(model) is stormpy.SparseMA
assert model.convertible_to_ctmc
ctmc = model.convert_to_ctmc()
assert ctmc.nr_states == 4
assert ctmc.nr_transitions == 6
assert ctmc.model_type == stormpy.ModelType.CTMC
assert type(ctmc) is stormpy.SparseCtmc
def test_filter(self):
program = stormpy.parse_prism_program(get_example_path("dtmc", "die.pm"))
formulas = stormpy.parse_properties_for_prism_program("P=? [ F \"one\" ]", program)
model = stormpy.build_model(program, formulas)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0])
assert math.isclose(result.at(initial_state), 1 / 6)
filter = stormpy.create_filter_initial_states_sparse(model)
result.filter(filter)
assert result.min == result.max
assert math.isclose(result.min, 1 / 6)
def test_model_checking_jani_dtmc(self):
jani_model, formulas = stormpy.parse_jani_model(
get_example_path("dtmc", "die.jani"))
formulas = stormpy.eliminate_reward_accumulations(jani_model, formulas)
assert len(formulas) == 2
model = stormpy.build_model(jani_model, formulas)
assert model.nr_states == 13
assert model.nr_transitions == 20
assert len(model.initial_states) == 1
initial_state = model.initial_states[0]
assert initial_state == 0
result = stormpy.model_checking(model, formulas[0])
assert math.isclose(result.at(initial_state), 1 / 6)
result = stormpy.model_checking(model, formulas[1])
assert math.isclose(result.at(initial_state), 11 / 3)
def test_symbolic_bisimulation(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "crowds5_5.pm"))
prop = "P=? [F \"observe0Greater1\"]"
properties = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_symbolic_model(program, properties)
assert model.nr_states == 7403
assert model.nr_transitions == 13041
assert model.model_type == stormpy.ModelType.DTMC
assert not model.supports_parameters
model_bisim = stormpy.perform_symbolic_bisimulation(model, properties)
assert model_bisim.nr_states == 65
assert model_bisim.nr_transitions == 105
assert model_bisim.model_type == stormpy.ModelType.DTMC
assert not model_bisim.supports_parameters
def test_build_parametric_dtmc_preprocess(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "herman5.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"R=? [ F \"stable\" ]", program)
trans_program, trans_formulas = stormpy.preprocess_symbolic_input(
program, formulas, "")
trans_prism = trans_program.as_prism_program()
model = stormpy.build_parametric_model(trans_prism, trans_formulas)
assert model.nr_states == 33
assert model.nr_transitions == 266
assert model.model_type == stormpy.ModelType.DTMC
assert model.supports_parameters
assert model.has_parameters
assert type(model) is stormpy.SparseParametricDtmc
def test_transform_symbolic_dtmc_to_sparse(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "crowds5_5.pm"))
symbolic_model = stormpy.build_symbolic_model(program)
assert symbolic_model.nr_states == 8607
assert symbolic_model.nr_transitions == 15113
assert symbolic_model.model_type == stormpy.ModelType.DTMC
assert not symbolic_model.supports_parameters
assert type(symbolic_model) is stormpy.SymbolicSylvanDtmc
sparse_model = stormpy.transform_to_sparse_model(symbolic_model)
assert sparse_model.nr_states == 8607
assert sparse_model.nr_transitions == 15113
assert sparse_model.model_type == stormpy.ModelType.DTMC
assert not sparse_model.supports_parameters
assert type(sparse_model) is stormpy.SparseDtmc
def test_label(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "brp-16-2.pm"))
properties = stormpy.parse_properties_for_prism_program(
"P=? [ F s=5 ]", program)
options = stormpy.BuilderOptions([p.raw_formula for p in properties])
options.set_build_choice_labels(True)
model = stormpy.build_sparse_model_with_options(program, options)
clabeling = model.choice_labeling
clabels = clabeling.get_labels()
assert len(clabels) == 7
assert "aB" in clabels
assert "aA" in clabels
assert "NewFile" in clabeling.get_labels_of_choice(0)
assert "aG" in clabeling.get_labels_of_choice(7)
def test_symbolic_parametric_bisimulation(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F \"error\"]"
properties = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_symbolic_parametric_model(program, properties)
assert model.nr_states == 613
assert model.nr_transitions == 803
assert model.model_type == stormpy.ModelType.DTMC
assert model.has_parameters
model_bisim = stormpy.perform_symbolic_bisimulation(model, properties)
assert model_bisim.nr_states == 326
assert model_bisim.nr_transitions == 454
assert model_bisim.model_type == stormpy.ModelType.DTMC
assert model_bisim.has_parameters
def test_simulate_trace(self):
dft = stormpy.dft.load_dft_json_file(
get_example_path("dft", "and.json"))
symmetries = dft.symmetries()
relevant_events = stormpy.dft.compute_relevant_events(dft, [])
dft.set_relevant_events(relevant_events, False)
info = dft.state_generation_info(symmetries)
generator = stormpy.dft.RandomGenerator.create(5)
simulator = stormpy.dft.DFTSimulator_double(dft, info, generator)
res = simulator.simulate_trace(2)
assert res == stormpy.dft.SimulationResult.SUCCESSFUL
res = simulator.simulate_trace(2)
assert res == stormpy.dft.SimulationResult.UNSUCCESSFUL
res = simulator.simulate_trace(2)
assert res == stormpy.dft.SimulationResult.UNSUCCESSFUL
def test_label_parametric(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
formulas = stormpy.parse_properties_for_prism_program(
"P=? [ F s=5 ]", program)
model = stormpy.build_parametric_model(program, formulas)
labels = model.labeling.get_labels()
assert len(labels) == 3
assert "init" in labels
assert "(s = 5)" in labels
assert "init" in model.labels_state(0)
assert "(s = 5)" in model.labels_state(28)
assert "(s = 5)" in model.labels_state(611)
initial_states = model.initial_states
assert len(initial_states) == 1
assert 0 in initial_states
def test_parametric_transitions(self):
program = stormpy.parse_prism_program(
get_example_path("pmdp", "two_dice.nm"))
model = stormpy.build_parametric_model(program)
assert model.states[1].id == 1
one = stormpy.FactorizedPolynomial(stormpy.RationalRF(1))
i = 0
for state in model.states:
assert state.id == i
i += 1
j = 0
for action in state.actions:
assert j == 0 or j == 1
j += 1
for transition in action.transitions:
assert transition.value().denominator == one
def test_set_labeling(self):
program = stormpy.parse_prism_program(
get_example_path("dtmc", "die.pm"))
model = stormpy.build_model(program)
labeling = model.labeling
assert "tmp" not in labeling.get_labels()
assert not labeling.contains_label("tmp")
labeling.add_label("tmp")
assert labeling.contains_label("tmp")
labeling.add_label_to_state("tmp", 0)
assert labeling.has_state_label("tmp", 0)
states = labeling.get_states("tmp")
assert states.get(0)
states.set(3)
labeling.set_states("tmp", states)
assert labeling.has_state_label("tmp", 3)
def test_pla_manual(self):
program = stormpy.parse_prism_program(
get_example_path("pdtmc", "brp16_2.pm"))
prop = "P=? [F s=5 ]"
formulas = stormpy.parse_properties_for_prism_program(prop, program)
model = stormpy.build_parametric_model(program, formulas)
assert model.has_parameters
env = stormpy.Environment()
checker = stormpy.pars.DtmcParameterLiftingModelChecker()
checker.specify(env, model, formulas[0].raw_formula)
parameters = model.collect_probability_parameters()
assert len(parameters) == 2
region = stormpy.pars.ParameterRegion.create_from_string(
"0.7<=pL<=0.9,0.75<=pK<=0.95", parameters)
result = checker.get_bound(env, region, True)
assert math.isclose(float(result.constant_part()),
0.8369631383670559,
rel_tol=1e-6)
