def _get_variables_by_instances(agents):
"""
Return a dictionary of instance->list of variables
"""
st = symb_table()
flatHierarchy = nscompile.cvar.mainFlatHierarchy
# Populate variables with instances
variables = {}
for agent in agents:
variables[agent] = []
varset = nscompile.FlatHierarchy_get_vars(flatHierarchy)
varlist = nsset.Set_Set2List(varset)
ite = nsutils.NodeList_get_first_iter(varlist)
while not nsutils.ListIter_is_end(ite):
var = nsutils.NodeList_get_elem_at(varlist, ite)
varname = nsnode.sprint_node(var)
isVar = nssymb_table.SymbTable_is_symbol_state_var(st._ptr, var)
if isVar:
# Put the var in the variables dictionary, under the right instance
topcontext = varname.partition(".")[0]
if topcontext in variables:
variables[topcontext].append(var)
ite = nsutils.ListIter_get_next(ite)
return variables
def _get_variables_by_instances(agents):
"""
Return a dictionary of instance->list of variables
"""
st = symb_table()
flatHierarchy = nscompile.cvar.mainFlatHierarchy
# Populate variables with instances
variables = {}
for agent in agents:
variables[agent] = []
varset = nscompile.FlatHierarchy_get_vars(flatHierarchy)
varlist = nsset.Set_Set2List(varset)
ite = nsutils.NodeList_get_first_iter(varlist)
while not nsutils.ListIter_is_end(ite):
var = nsutils.NodeList_get_elem_at(varlist, ite)
varname = nsnode.sprint_node(var)
isVar = nssymb_table.SymbTable_is_symbol_state_var(st._ptr, var)
if isVar:
# Put the var in the variables dictionary, under the right instance
topcontext = varname.partition(".")[0]
if topcontext in variables:
variables[topcontext].append(var)
ite = nsutils.ListIter_get_next(ite)
return variables
def test_get_mod_instance_type(self):
glob.load_from_file("tests/pynusmv/models/counters.smv")
glob.compute_model()
sexp = parse_simple_expression("c1")
self.assertIsNotNone(sexp)
st = glob.symb_table()
tp = nssymb_table.SymbTable_get_type_checker(st._ptr)
expr_type = nstype_checking.TypeChecker_get_expression_type(
tp, sexp, None)
self.assertTrue(nssymb_table.SymbType_is_error(expr_type))
def test_get_mod_instance_type(self):
glob.load_from_file("tests/pynusmv/models/counters.smv")
glob.compute_model()
sexp = parse_simple_expression("c1")
self.assertIsNotNone(sexp)
st = glob.symb_table()
tp = nssymb_table.SymbTable_get_type_checker(st._ptr)
expr_type = nstype_checking.TypeChecker_get_expression_type(
tp, sexp, None)
self.assertTrue(nssymb_table.SymbType_is_error(expr_type))
def _flatten_and_filter_variable_args(arguments):
"""
Return a new dictionary instance name -> list of vars where
all vars belong to arguments (under the correct instance name)
all vars are VAR types
all vars are flattened.
arguments -- a dictionary instance name -> list of module arguments
"""
result = {}
st = symb_table()
for instance in arguments:
result[instance] = []
for argument in arguments[instance]:
arg, err = nscompile.FlattenSexp(st._ptr, argument, None)
if not err and nssymb_table.SymbTable_is_symbol_var(st._ptr, arg):
result[instance].append(arg)
return result
def _flatten_and_filter_variable_args(arguments):
"""
Return a new dictionary instance name -> list of vars where
all vars belong to arguments (under the correct instance name)
all vars are VAR types
all vars are flattened.
arguments -- a dictionary instance name -> list of module arguments
"""
result = {}
st = symb_table()
for instance in arguments:
result[instance] = []
for argument in arguments[instance]:
arg, err = nscompile.FlattenSexp(st._ptr, argument, None)
if not err and nssymb_table.SymbTable_is_symbol_var(st._ptr, arg):
result[instance].append(arg)
return result
def test_access_flat_hierarchy(self):
glob.load(*self.counters())
glob.compute_model()
flat = glob.flat_hierarchy()
symb_table = glob.symb_table()
self.assertIsNotNone(flat.init)
self.assertIsNotNone(flat.trans)
self.assertIsNone(flat.invar)
self.assertIsNone(flat.justice)
self.assertIsNone(flat.compassion)
variables = flat.variables
for variable in variables:
var_type = symb_table.get_variable_type(variable)
self.assertEqual(nssymb_table.SymbType_get_tag(var_type),
nssymb_table.SYMB_TYPE_ENUM)
def test_access_flat_hierarchy(self):
glob.load(*self.counters())
glob.compute_model()
flat = glob.flat_hierarchy()
symb_table = glob.symb_table()
self.assertIsNotNone(flat.init)
self.assertIsNotNone(flat.trans)
self.assertIsNone(flat.invar)
self.assertIsNone(flat.justice)
self.assertIsNone(flat.compassion)
variables = flat.variables
for variable in variables:
var_type = symb_table.get_variable_type(variable)
self.assertEqual(nssymb_table.SymbType_get_tag(var_type),
nssymb_table.SYMB_TYPE_ENUM)
def mas(agents=None):
"""
Return (and compute if needed) the multi-agent system represented by
the currently read SMV model.
If agents is not None, the set of agents (and groups) of the MAS is
determined by agents.
Otherwise, every top-level module instantiation is considered an agent
where
- her actions are the inputs variables prefixed by her name;
- her observable variables are composed of
* the state variables of the system prefixed by her name;
* the state variables provided as an argument to the module
instantiation.
Note: if the MAS is already computed, agents argument has no effect.
agents -- a set of agents.
"""
global __mas
if __mas is None:
# Check cmps
if not nscompile.cmp_struct_get_read_model(nscompile.cvar.cmps):
raise NuSMVNoReadModelError("Cannot build MAS; no read file.")
if agents is None:
# Get agents names
tree = nsparser.cvar.parsed_tree
main = None
while tree is not None:
module = nsnode.car(tree)
if (nsnode.sprint_node(nsnode.car(nsnode.car(module))) ==
"main"):
main = module
tree = nsnode.cdr(tree)
if main is None:
print("[ERROR] No main module.")
return # TODO Error, cannot find main module
arguments = _get_instances_args_for_module(main)
# arguments is a dict instancename(str)->listofargs(node)
agents = arguments.keys()
# Compute the model
_compute_model()
st = symb_table()
# Flatten arguments and filter on variables
argvars = _flatten_and_filter_variable_args(arguments)
# Get agents observable variables (locals + module parameters)
localvars = _get_variables_by_instances(agents)
#localvars is a dict instancename(str)->listofvars(node)
inputvars = _get_input_vars_by_instances(agents)
# Merge instance variable arguments and local variables
variables = {key: ((key in argvars and argvars[key] or []) +
(key in localvars and localvars[key] or []))
for key in
list(argvars.keys())+list(localvars.keys())}
# Compute epistemic relation
singletrans = {}
for agent in variables:
transexpr = None
for var in variables[agent]:
var = nsnode.sprint_node(var)
transexpr = nsnode.find_node(nsparser.AND,
_get_epistemic_trans(var),
transexpr)
singletrans[agent] = transexpr
# Process variables to get strings instead of nodes
observedvars = {ag: {nsnode.sprint_node(v) for v in variables[ag]}
for ag in variables.keys()}
inputvars = {ag: {nsnode.sprint_node(v)
for v in inputvars[ag]}
for ag in inputvars.keys()}
groups = None
else:
_compute_model()
# observedvars: a dictionary of agent name -> set of observed vars
observedvars = {str(agent.name): {str(var)
for var in agent.observables}
for agent in agents}
# inputsvars: a dictionary of agent name -> set of inputs vars
inputvars = {str(agent.name): {str(ivar)
for ivar in agent.actions}
for agent in agents}
# groups:
# a dictionary of group name -> names of agents of the group
groups = {str(group.name): {str(agent.name)
for agent in group.agents}
for group in agents if isinstance(group, Group)}
# singletrans: a dictionary of agent name -> epistemic transition
singletrans = {}
for agent in agents:
name = str(agent.name)
transexpr = None
for var in observedvars[name]:
transexpr = nsnode.find_node(nsparser.AND,
_get_epistemic_trans(var),
transexpr)
singletrans[name] = transexpr
# Create the MAS
fsm = _prop_database().master.bddFsm
__mas = MAS(fsm._ptr, observedvars, inputvars, singletrans,
groups=groups, freeit=False)
return __mas
def mas(agents=None, initial_ordering=None):
"""
Return (and compute if needed) the multi-agent system represented by
the currently read SMV model.
If agents is not None, the set of agents (and groups) of the MAS is
determined by agents.
Otherwise, every top-level module instantiation is considered an agent
where
- her actions are the inputs variables prefixed by her name;
- her observable variables are composed of
* the state variables of the system prefixed by her name;
* the state variables provided as an argument to the module
instantiation.
If initial_ordering is not None, it must be the path to a variables
ordering file. It is used as the initial ordering for variables of the
model.
Note: if the MAS is already computed, agents and initial_ordering arguments
have no effect.
agents -- a set of agents.
"""
global __mas
if __mas is None:
# Check cmps
if not nscompile.cmp_struct_get_read_model(nscompile.cvar.cmps):
raise NuSMVNoReadModelError("Cannot build MAS; no read file.")
if agents is None:
# Get agents names
tree = nsparser.cvar.parsed_tree
main = None
while tree is not None:
module = nsnode.car(tree)
if (nsnode.sprint_node(nsnode.car(
nsnode.car(module))) == "main"):
main = module
tree = nsnode.cdr(tree)
if main is None:
print("[ERROR] No main module.")
return # TODO Error, cannot find main module
arguments = _get_instances_args_for_module(main)
# arguments is a dict instancename(str)->listofargs(node)
agents = arguments.keys()
# Compute the model
_compute_model(variables_ordering=initial_ordering)
st = symb_table()
# Flatten arguments and filter on variables
argvars = _flatten_and_filter_variable_args(arguments)
# Get agents observable variables (locals + module parameters)
localvars = _get_variables_by_instances(agents)
#localvars is a dict instancename(str)->listofvars(node)
inputvars = _get_input_vars_by_instances(agents)
# Merge instance variable arguments and local variables
variables = {
key: ((key in argvars and argvars[key] or []) +
(key in localvars and localvars[key] or []))
for key in list(argvars.keys()) + list(localvars.keys())
}
# Compute epistemic relation
singletrans = {}
for agent in variables:
transexpr = None
for var in variables[agent]:
var = nsnode.sprint_node(var)
transexpr = nsnode.find_node(nsparser.AND,
_get_epistemic_trans(var),
transexpr)
singletrans[agent] = transexpr
# Process variables to get strings instead of nodes
observedvars = {
ag: {nsnode.sprint_node(v)
for v in variables[ag]}
for ag in variables.keys()
}
inputvars = {
ag: {nsnode.sprint_node(v)
for v in inputvars[ag]}
for ag in inputvars.keys()
}
groups = None
else:
_compute_model(variables_ordering=initial_ordering)
# observedvars: a dictionary of agent name -> set of observed vars
observedvars = {
str(agent.name): [str(var) for var in agent.observables]
for agent in agents
}
# inputsvars: a dictionary of agent name -> set of inputs vars
inputvars = {
str(agent.name): [str(ivar) for ivar in agent.actions]
for agent in agents
}
# groups:
# a dictionary of group name -> names of agents of the group
groups = {
str(group.name): [str(agent.name) for agent in group.agents]
for group in agents if isinstance(group, Group)
}
# singletrans: a dictionary of agent name -> epistemic transition
singletrans = {}
for agent in agents:
name = str(agent.name)
transexpr = None
for var in observedvars[name]:
transexpr = nsnode.find_node(nsparser.AND,
_get_epistemic_trans(var),
transexpr)
singletrans[name] = transexpr
# Create the MAS
fsm = _prop_database().master.bddFsm
__mas = MAS(fsm._ptr,
observedvars,
inputvars,
singletrans,
groups=groups,
freeit=False)
return __mas
def test_get_symb_table(self):
glob.load_from_file("tests/pynusmv/models/counters.smv")
symb_table = glob.symb_table()
def test_symbol_table(self):
self.assertEqual(self.fsm.symbol_table, glob.symb_table())