def snakes_find_the_head(g):
# We'll want a type that represents a cell in the grid. We use a datatype because we can make it a sum type -
# that is, its 0-arity constructors represent a closed enumeration of distinct objects.
Cell = Datatype("Cell")
for x, y in g.coords():
if g.snake(x, y) is not None:
Cell.declare("cell_{}_{}".format(x, y))
Cell = Cell.create()
# We'll have two functions that we explicitly declare. One is the short-distance "connected" relationship.
# The other is a convenience function for turning the coordinates of a cell into the datatype member that
# represents that position.
Connected = Function("Connected", Cell, Cell, BoolSort())
XYToCell = Function("XYToCell", IntSort(), IntSort(), Cell)
cell = {}
for x, y in g.coords():
if g.snake(x, y) is not None:
cell[x, y] = getattr(Cell, "cell_{}_{}".format(x, y))
g.add(XYToCell(x, y) == cell[x, y])
# Two cells are connected *if and only* if they are adjacent and both hold snakes
# We need to be really clear about the "and only if" part; a naive implementation here will let the
# solver fill in other arbitrary values for `Connected` in order to make the desired outcome true.
# We do this by ensuring there's a value declared for our Connected relationship between every pair
# of potential arguments.
for x1, y1 in g.coords():
c1 = g.snake(x1, y1)
if c1 is None:
continue
# If there's a snake here, the cell is connected to itself
g.add(Connected(cell[x1, y1], cell[x1, y1]) == c1)
for x2, y2 in g.coords():
c2 = g.snake(x2, y2)
if c2 is None or (x1, y1) == (x2, y2):
continue
if manh(x1, y1, x2, y2) == 1:
g.add(Connected(cell[x1, y1], cell[x2, y2]) == And(c1, c2))
else:
# Without this, our function declaration is only partial. The solver can fill in missing values
# in order to scupper our good intentions.
g.add(Not(Connected(cell[x1, y1], cell[x2, y2])))
# The transitive closure of Connectedness is Reaches
Reaches = TransitiveClosure(Connected)
# For every cell in the grid, if it's a snake then we can connect it to the head position
hx, hy = g.head()
for x, y in g.coords():
c = g.snake(x, y)
if c is None:
continue
g.add(Implies(c, Reaches(cell[x, y], XYToCell(hx, hy))))
def sort(x):
if type(x) == bool: return BoolSort()
elif type(x) == int: return IntSort()
elif type(x) == float: return RealSort()
elif hasattr(x, 'sort'):
if x.sort() == BoolSort(): return BoolSort()
if x.sort() == IntSort(): return IntSort()
if x.sort() == RealSort(): return RealSort()
else:
raise Exception('unknown sort for expression')
def main() -> None:
schema = {"a": IntSort(), "b": IntSort()}
constraints = [
SelectProjectQuery(["a"], lambda a, b: a * b == 0),
SelectProjectQuery(["b"], lambda a, b: a == 42),
]
query = SelectProjectQuery(["b"], lambda a, b: Or(And(Or(a == 0, b == 0), a > 0),
And(a == b * (b + 1), b == 6)))
print(check_privacy(schema, constraints, query))
def ISZERO(self):
op0 = self._stack_pop()
if type(op0) is int:
if op0 == 0:
self._stack_push(Word(1))
else:
self._stack_push(Word(0))
elif type(op0) is BoolRef:
self._stack_push(IntSort().cast(Not(op0)))
else:
self._stack_push(IntSort().cast(op0 == 0))
def main():
"""Skyscraper solver example."""
lattice = grilops.get_square_lattice(SIZE)
directions = {d.name: d for d in lattice.edge_sharing_directions()}
sg = grilops.SymbolGrid(lattice, SYM)
# Each row and each column contains each building height exactly once.
for y in range(SIZE):
sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for x in range(SIZE)]))
for x in range(SIZE):
sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for y in range(SIZE)]))
# We'll use the sightlines accumulator to keep track of a tuple storing:
# the tallest building we've seen so far
# the number of visible buildings we've encountered
Acc = Datatype("Acc") # pylint: disable=C0103
Acc.declare("acc", ("tallest", IntSort()), ("num_visible", IntSort()))
Acc = Acc.create() # pylint: disable=C0103
def accumulate(a, height):
return Acc.acc(
If(height > Acc.tallest(a), height, Acc.tallest(a)),
If(height > Acc.tallest(a),
Acc.num_visible(a) + 1, Acc.num_visible(a)))
for x, c in enumerate(GIVEN_TOP):
sg.solver.add(c == Acc.num_visible(
grilops.sightlines.reduce_cells(sg, Point(0, x), directions["S"],
Acc.acc(0, 0), accumulate)))
for y, c in enumerate(GIVEN_LEFT):
sg.solver.add(c == Acc.num_visible(
grilops.sightlines.reduce_cells(sg, Point(y, 0), directions["E"],
Acc.acc(0, 0), accumulate)))
for y, c in enumerate(GIVEN_RIGHT):
sg.solver.add(c == Acc.num_visible(
grilops.sightlines.reduce_cells(sg, Point(
y, SIZE - 1), directions["W"], Acc.acc(0, 0), accumulate)))
for x, c in enumerate(GIVEN_BOTTOM):
sg.solver.add(c == Acc.num_visible(
grilops.sightlines.reduce_cells(sg, Point(
SIZE - 1, x), directions["N"], Acc.acc(0, 0), accumulate)))
if sg.solve():
sg.print()
print()
if sg.is_unique():
print("Unique solution")
else:
print("Alternate solution")
sg.print()
else:
print("No solution")
def no_intersections(belt1: Belt, belt2: Belt):
assert isinstance(belt1, Belt)
assert isinstance(belt2, Belt)
i,j = Consts("i j", IntSort())
return ForAll([i,j], Implies(And(0 <= i, i < belt1.belt_len, 0 <=j, j < belt2.belt_len),
Not(belt1[i] == belt2[j])))
def non_intersecting_seg_belts(belt1: SegmentedBelt , belt2: SegmentedBelt):
assert isinstance(belt1, SegmentedBelt)
assert isinstance(belt2, SegmentedBelt)
i,j = Consts("i j", IntSort())
return ForAll([i,j], Implies(And(0 <= i, i < belt1.num_segs, 0 <=j, j < belt2.num_segs),
non_intersecting_segs(belt1.segment(i), belt2.segment(j))))
def EQ(self):
op0 = self._stack_pop()
op1 = self._stack_pop()
try:
self._stack_push(IntSort().cast(op0 == op1))
except Z3Exception as e:
print(e)
def skyscraper(givens: Dict[Direction, List[int]]) -> str:
"""Solver for Skyscraper minipuzzles."""
sym = grilops.make_number_range_symbol_set(1, SIZE)
sg = grilops.SymbolGrid(LATTICE, sym)
shifter = Shifter(sg.solver)
# Each row and each column contains each building height exactly once.
for y in range(SIZE):
sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for x in range(SIZE)]))
for x in range(SIZE):
sg.solver.add(Distinct(*[sg.grid[Point(y, x)] for y in range(SIZE)]))
# We'll use the sightlines accumulator to keep track of a tuple storing:
# the tallest building we've seen so far
# the number of visible buildings we've encountered
Acc = Datatype("Acc") # pylint: disable=C0103
Acc.declare("acc", ("tallest", IntSort()), ("num_visible", IntSort()))
Acc = Acc.create() # pylint: disable=C0103
def accumulate(a, height):
return Acc.acc(
If(height > Acc.tallest(a), height, Acc.tallest(a)),
If(height > Acc.tallest(a),
Acc.num_visible(a) + 1, Acc.num_visible(a)))
for d, gs in givens.items():
for i, g in enumerate(gs):
if d.vector.dy != 0:
g = g - shifter.col_shifts.get(i, 0)
p = Point(0 if d.vector.dy < 0 else SIZE - 1, i)
elif d.vector.dx != 0:
g = g - shifter.row_shifts.get(i, 0)
p = Point(i, 0 if d.vector.dx < 0 else SIZE - 1)
sg.solver.add(g == Acc.num_visible( # type: ignore[attr-defined]
grilops.sightlines.reduce_cells(
sg,
p,
LATTICE.opposite_direction(d),
Acc.acc(0, 0), # type: ignore[attr-defined]
accumulate)))
assert sg.solve()
sg.print()
print()
shifter.print_shifts()
print()
return shifter.eval_binary()
def main():
dim = int(input("size of the array: "))
i = [Int(f"i_{x}") for x in range(dim + 1)]
j = [Int(f"j_{x}") for x in range(dim + 1)]
A = [Array(f"A_{x}", IntSort(), IntSort()) for x in range(dim + 1)]
tmp = [Int(f"tmp_{x}") for x in range(dim)]
s = Solver()
init_condition = init(i[0], j[0])
s.add(init_condition)
tran_condition = mk_tran_condition(A, i, j, tmp, dim)
s.add(And(*tran_condition))
values = [Int(f"n_{x}") for x in range(dim)]
init_check_condition = check(values, A[-1], dim)
s.add(And(*init_check_condition))
post_condition = mk_post_condition(values)
print("Bubble sort")
print("---------------------")
s.push()
s.add(Not(post_condition))
print("Testing the validity of the algorithm; `valid expected`:")
if s.check() == sat:
print(f"counterexample:\n{s.model()}")
else:
print("valid")
print("---------------------")
s.pop()
s.add(post_condition)
print("Getting a model...")
print("Model:")
if s.check() == sat:
print(s.model())
def test_datatype_payloads(self):
lattice = get_square_lattice(3)
sym = make_number_range_symbol_set(0, 2)
row_grid = SymbolGrid(lattice, sym)
col_grid = SymbolGrid(lattice, sym, solver=row_grid.solver)
RowCol = Datatype("RowCol")
RowCol.declare("row_col", ("row", IntSort()), ("col", IntSort()))
RowCol = RowCol.create()
sc = ShapeConstrainer(lattice, [
Shape([
(Vector(0, 0), RowCol.row_col(0, 0)),
(Vector(0, 1), RowCol.row_col(0, 1)),
(Vector(1, 0), RowCol.row_col(1, 0)),
]),
Shape([
(Vector(0, 1), RowCol.row_col(0, 2)),
(Vector(1, 0), RowCol.row_col(1, 1)),
(Vector(1, 1), RowCol.row_col(1, 2)),
(Vector(2, 1), RowCol.row_col(2, 2)),
]),
Shape([
(Vector(0, 0), RowCol.row_col(2, 0)),
(Vector(0, 1), RowCol.row_col(2, 1)),
]),
],
solver=row_grid.solver,
complete=True)
for p in lattice.points:
row_grid.solver.add(
row_grid.cell_is(p, RowCol.row(sc.shape_payload_grid[p])))
col_grid.solver.add(
col_grid.cell_is(p, RowCol.col(sc.shape_payload_grid[p])))
self.assertTrue(row_grid.solve())
solved_row_grid = row_grid.solved_grid()
solved_col_grid = col_grid.solved_grid()
for p in lattice.points:
self.assertEqual(solved_row_grid[p], p.y)
self.assertEqual(solved_col_grid[p], p.x)
self.assertTrue(row_grid.is_unique())
def GetStringFromType(typ):
srt = GetSortFromType(typ)
if srt == IntSort():
return 'Int'
elif srt == BoolSort():
return 'Bool'
elif is_bv_sort(srt):
sz = srt.size()
return '(BitVec ' + str(sz) + ')'
else:
raise SynthException('Unknonw sort for string conversion.')
def GetValFromType(typ, raw_val):
srt = GetSortFromType(typ)
if srt == IntSort():
return IntVal(raw_val)
elif srt == BoolSort():
return BoolVal(raw_val)
elif is_bv_sort(srt):
sz = srt.size()
return BitVecVal(raw_val, sz)
else:
raise SynthException('Unknown sort')
def add_observation_constraints(self, s, planner, ground_actions, length,
observations):
obsSort = DeclareSort('Obs')
orderObs = Function('orderObs', obsSort, IntSort())
orderExec = Function('orderExec', obsSort, IntSort())
obsConsts = []
for i in range(0, len(observations)):
o = Const(str(observations[i]), obsSort)
obsConsts.append(o)
s.add(orderObs(o) == i)
for t in range(0, length):
# forced_obs = []
for action in ground_actions:
index = observations.index_of(action.signature())
if index > -1:
obsC = obsConsts[index]
# forced_obs.append(planner.action_prop_at(action, t))
s.add(
Implies(planner.action_prop_at(action, t),
orderExec(obsC) == t))
# s.add(Or(*forced_obs))
x = Const('x', obsSort)
y = Const('y', obsSort)
# orderSync = Function('order-sync', BoolSort())
s.add(
ForAll([x, y],
Implies(
orderObs(x) < orderObs(y),
orderExec(x) < orderExec(y))))
s.add(
ForAll([x, y],
Implies(
orderObs(x) == orderObs(y),
orderExec(x) == orderExec(y))))
s.add(
ForAll([x, y],
Implies(
orderObs(x) > orderObs(y),
orderExec(x) > orderExec(y))))
def rational_to_acl2(r):
r = simplify(r)
if (r.sort() == IntSort()):
return (integer_to_acl2(r))
elif ((r.sort() == RealSort()) and (r.decl().name() == 'Real')):
if (r.denominator_as_long() == 1):
return r.numerator().as_string()
else:
return ('(/ ' + r.numerator().as_string() + ' ' +
r.denominator().as_string() + ')')
else:
raise Exception('rational_to_acl2, badarg: ' + str(r))
def non_intersecting_seg_belt_diag_seg(belt: SegmentedBelt, dseg: Segment):
""" all segments of a belt must not intersect a given ordered diagonal segment """
assert isinstance(belt, SegmentedBelt)
assert isinstance(dseg, Segment)
assert dseg.is_diag
i = Const("i", IntSort())
return ForAll([i], Implies(And(0 <= i, i < belt.num_segs),
Or(Max(belt.segment(i).p1.x, belt.segment(i).p2.x) < dseg.p1.x,
Min(belt.segment(i).p1.x, belt.segment(i).p2.x) > dseg.p2.x,
Max(belt.segment(i).p1.y, belt.segment(i).p2.y) < dseg.p1.y,
Min(belt.segment(i).p1.y, belt.segment(i).p2.y) > dseg.p2.y)))
def GetSortFromType(typ):
if typ == 'Int':
return IntSort()
elif typ == 'Bool':
return BoolSort()
elif isinstance(typ, list):
if (len(typ) != 2 or typ[0] != 'BitVec'):
raise SynthException('Unknown Type %r' % (typ))
else:
intName, size = typ[1]
return BitVecSort(size)
else:
raise SynthException('Unknown Type %r' % (typ))
def quantified(self):
"""
Returns a list of Z3 variables, one for each parameter of this type.
"""
if self._qf is not None:
return self._qf
res = []
if isinstance(self.name, tuple):
for i, arg in enumerate(self.name[1:]):
sort = self.type_sort if not arg.endswith(
'defaults_args') else IntSort()
cur = Const("y" + str(i), sort)
res.append(cur)
self._qf = res
return res
def __init__(self, max_segs=None):
self.corners_x = Function(f'seg_belt{self._IDX}_x', IntSort(), IntSort())
self.corners_y = Function(f'seg_belt{self._IDX}_y', IntSort(), IntSort())
self.num_segs = Const(f'seg_belt{self._IDX}_num_segs', IntSort())
SOL.add(self.num_segs > 0)
if max_segs:
SOL.add(self.num_segs <= max_segs)
i,j = Consts("i j", IntSort())
SOL.add(ForAll([i], Implies(And(0 <= i, i < self.num_segs),
Or(self.segment(i).horizontal(), self.segment(i).vertical()))))
self.__class__._IDX += 1
def __init__(self):
self.belt_x = Function(f'belt{self._IDX}_x', IntSort(), IntSort())
self.belt_y = Function(f'belt{self._IDX}_y', IntSort(), IntSort())
self.belt_len = Const(f'belt{self._IDX}_len', IntSort())
SOL.add(self.belt_len > 0)
i,j = Consts("i j", IntSort())
# neighbor condition
SOL.add(ForAll([i], Implies(And(i < self.belt_len - 1, i >= 0),
neighs(self[i], self[i+1]))))
# no intersections
SOL.add(ForAll([i, j], Implies(And(i >= 0, i < j, j < self.belt_len),
Not(self[i] == self[j]))))
self.__class__._IDX += 1
def __create_grids(self):
"""Create the grids used to model shape region constraints."""
self.__shape_type_grid: Dict[Point, ArithRef] = {}
for p in self.__lattice.points:
v = Int(f"scst-{ShapeConstrainer._instance_index}-{p.y}-{p.x}")
if self.__complete:
self.__solver.add(v >= 0)
else:
self.__solver.add(v >= -1)
self.__solver.add(v < len(self.__shapes))
self.__shape_type_grid[p] = v
self.__shape_instance_grid: Dict[Point, ArithRef] = {}
for p in self.__lattice.points:
v = Int(f"scsi-{ShapeConstrainer._instance_index}-{p.y}-{p.x}")
if self.__complete:
self.__solver.add(v >= 0)
else:
self.__solver.add(v >= -1)
self.__solver.add(v < len(self.__lattice.points))
self.__shape_instance_grid[p] = v
sample_payload = self.__shapes[0].offsets_with_payloads[0][1]
if sample_payload is None:
self.__shape_payload_grid: Optional[Dict[Point, Payload]] = None
else:
self.__shape_payload_grid: Optional[Dict[Point, Payload]] = {}
if isinstance(sample_payload, ExprRef):
sort = sample_payload.sort()
elif isinstance(sample_payload, int):
sort = IntSort()
else:
raise Exception(
f"Could not determine z3 sort for {sample_payload}")
for p in self.__lattice.points:
v = Const(
f"scsp-{ShapeConstrainer._instance_index}-{p.y}-{p.x}",
sort)
self.__shape_payload_grid[p] = v
def GT(self):
op0 = self._stack_pop()
op1 = self._stack_pop()
self._stack_push(IntSort().cast(op0 > op1))
def integerp(self, x):
return sort(x) == IntSort()
def __init__(
self,
problem,
debug: Optional[bool] = False,
max_time: Optional[int] = 10,
parallel: Optional[bool] = False,
random_values: Optional[bool] = False,
logics: Optional[str] = None,
verbosity: Optional[int] = 0,
):
"""Scheduling Solver
debug: True or False, False by default
max_time: time in seconds, 60 by default
parallel: True to enable mutlthreading, False by default
"""
self.problem = problem
self.problem_context = problem.context
self.debug = debug
# objectives list
self.objective = None # the list of all objectives defined in this problem
self.current_solution = None # no solution until the problem is solved
# set_option('smt.arith.auto_config_simplex', True)
if debug:
set_option("verbose", 2)
else:
set_option("verbose", verbosity)
if random_values:
set_option("sat.random_seed", random.randint(1, 1e3))
set_option("smt.random_seed", random.randint(1, 1e3))
set_option("smt.arith.random_initial_value", True)
else:
set_option("sat.random_seed", 0)
set_option("smt.random_seed", 0)
set_option("smt.arith.random_initial_value", False)
# set timeout
self.max_time = max_time # in seconds
set_option("timeout", int(self.max_time * 1000)) # in milliseconds
# create the solver
print("Solver type:\n===========")
# check if the problem is an optimization problem
self.is_not_optimization_problem = len(
self.problem_context.objectives) == 0
self.is_optimization_problem = len(self.problem_context.objectives) > 0
self.is_multi_objective_optimization_problem = (len(
self.problem_context.objectives) > 1)
# the Optimize() solver is used only in the case of a mutli-optimization
# problem. This enables to choose the priority method.
# in the case of a single objective optimization, the Optimize() solver
# apperas to be less robust than the basic Solver(). The
# incremental solver is then used.
# see this url for a documentation about logics
# http://smtlib.cs.uiowa.edu/logics.shtml
if logics is None:
self._solver = Solver()
print("\t-> Standard SAT/SMT solver")
else:
self._solver = SolverFor(logics)
print("\t-> SMT solver using logics", logics)
if debug:
set_option(unsat_core=True)
if parallel:
set_option("parallel.enable", True) # enable parallel computation
# add all tasks assertions to the solver
for task in self.problem_context.tasks:
self.add_constraint(task.get_assertions())
self.add_constraint(task.end <= self.problem.horizon)
# then process tasks constraints
for constraint in self.problem_context.constraints:
self.add_constraint(constraint)
# process resources requirements
for ress in self.problem_context.resources:
self.add_constraint(ress.get_assertions())
# process resource intervals
for ress in self.problem_context.resources:
busy_intervals = ress.get_busy_intervals()
nb_intervals = len(busy_intervals)
for i in range(nb_intervals):
start_task_i, end_task_i = busy_intervals[i]
for k in range(i + 1, nb_intervals):
start_task_k, end_task_k = busy_intervals[k]
self.add_constraint(
Or(start_task_k >= end_task_i,
start_task_i >= end_task_k))
# process indicators
for indic in self.problem_context.indicators:
self.add_constraint(indic.get_assertions())
# work amounts
# for each task, compute the total work for all required resources"""
for task in self.problem_context.tasks:
if task.work_amount > 0.0:
work_total_for_all_resources = []
for required_resource in task.required_resources:
# work contribution for the resource
interv_low, interv_up = required_resource.busy_intervals[
task]
work_contribution = required_resource.productivity * (
interv_up - interv_low)
work_total_for_all_resources.append(work_contribution)
self.add_constraint(
Sum(work_total_for_all_resources) >= task.work_amount)
# process buffers
for buffer in self.problem_context.buffers:
#
# create an array that stores the mapping between start times and
# quantities. For example, if a start T1 starts at 2 and consumes
# 8, and T3 ends at 6 and consumes 5 then the mapping array
# will look like : A[2]=8 and A[6]=-5
# SO far, no way to have the same start time at different inst
buffer_mapping = Array("Buffer_%s_mapping" % buffer.name,
IntSort(), IntSort())
for t in buffer.unloading_tasks:
self.add_constraint(buffer_mapping == Store(
buffer_mapping, t.start, -buffer.unloading_tasks[t]))
for t in buffer.loading_tasks:
self.add_constraint(buffer_mapping == Store(
buffer_mapping, t.end, +buffer.loading_tasks[t]))
# sort consume/feed times in asc order
tasks_start_unload = [t.start for t in buffer.unloading_tasks]
tasks_end_load = [t.end for t in buffer.loading_tasks]
sorted_times, sort_assertions = sort_no_duplicates(
tasks_start_unload + tasks_end_load)
self.add_constraint(sort_assertions)
# create as many buffer state changes as sorted_times
buffer.state_changes_time = [
Int("%s_sc_time_%i" % (buffer.name, k))
for k in range(len(sorted_times))
]
# add the constraints that give the buffer state change times
for st, bfst in zip(sorted_times, buffer.state_changes_time):
self.add_constraint(st == bfst)
# compute the different buffer states according to state changes
buffer.buffer_states = [
Int("%s_state_%i" % (buffer.name, k))
for k in range(len(buffer.state_changes_time) + 1)
]
# add constraints for buffer states
# the first buffer state is equal to the buffer initial level
if buffer.initial_state is not None:
self.add_constraint(
buffer.buffer_states[0] == buffer.initial_state)
if buffer.final_state is not None:
self.add_constraint(
buffer.buffer_states[-1] == buffer.final_state)
if buffer.lower_bound is not None:
for st in buffer.buffer_states:
self.add_constraint(st >= buffer.lower_bound)
if buffer.upper_bound is not None:
for st in buffer.buffer_states:
self.add_constraint(st <= buffer.upper_bound)
# and, for the other, the buffer state i+1 is the buffer state i +/- the buffer change
for i in range(len(buffer.buffer_states) - 1):
self.add_constraint(
buffer.buffer_states[i + 1] == buffer.buffer_states[i] +
buffer_mapping[buffer.state_changes_time[i]])
# optimization
if self.is_optimization_problem:
self.create_objective()
for e in range(dim):
yield variables[e] == Select(Ar, e)
def mk_post_condition(values):
condition = []
for v1, v2 in zip(values, values[1:]):
condition.append(v1 <= v2)
return And(*condition)
dim = int(input("size of the array: "))
i = [Int(f"i_{x}") for x in range(dim + 1)]
j = [Int(f"j_{x}") for x in range(dim + 1)]
A = [Array(f"A_{x}", IntSort(), IntSort()) for x in range(dim + 1)]
tmp = [Int(f"tmp_{x}") for x in range(dim)]
s = Solver()
init_condition = init(i[0], j[0])
s.add(init_condition)
tran_condition = mk_tran_condition(A, i, j, tmp, dim)
s.add(And(*tran_condition))
values = [Int(f"n_{x}") for x in range(dim)]
init_check_condition = check(values, A[-1], dim)
s.add(And(*init_check_condition))
post_condition = mk_post_condition(values)
def __init__(self, x=None, y=None):
x = Const(f"p{self._IDX}_x", IntSort()) if x is None else x
y = Const(f"p{self._IDX}_y", IntSort()) if y is None else y
self.__class__._IDX += 1
super().__init__(x, y)
from z3 import Not, And, ForAll, Implies, IntSort, BoolSort, Solver, Function, Int
__counter = 100
__agents = {}
def new_noun(name):
global __counter
global __agents
if name not in __agents:
__agents[name] = __counter
__counter += 1
return Int(__agents[name])
IsKinase = Function("is_kinase", IntSort(), BoolSort())
IsActiveWhenPhosphorylated = Function("is_active_when_phosphorylated",
IntSort(), BoolSort())
Phosphorylates = Function("phosphorylates", IntSort(), IntSort(), BoolSort())
Activates = Function("activates", IntSort(), IntSort(), BoolSort())
ActivityIncreasesActivity = Function("activity_increases_activity", IntSort(),
IntSort(), BoolSort())
solver = Solver()
MEK1 = new_noun("MEK1")
ERK1 = new_noun("ERK1")
RAF = new_noun("RAF")
HRAS = new_noun("HRAS")
"""Z3 implementation of L - the meta-Kappa devised by Adrien Husson and Jean
Krivine - using the Python bindings for Z3.
"""
from z3 import Datatype, ArraySort, IntSort, BoolSort, Function, Const
from z3_helpers import Iff, Equals
# Node is a datatype representing a vertex or node in a Kappa graph.
Node = Datatype('Node')
Node.declare('node', ('unique_identifier', IntSort()))
Node = Node.create()
# A datatype for storing a pair of edges
Edge = Datatype('Edge')
Edge.declare('edge', ('node1', Node), ('node2', Node))
Edge = Edge.create()
Nodeset = ArraySort(Node, BoolSort())
Edgeset = ArraySort(Edge, BoolSort())
Labelset = ArraySort(IntSort(), BoolSort())
Labelmap = ArraySort(Node, Labelset)
# Graph, before a rule or action has applied. Merged Pregraph and Postgraph
# into a single datatype.
Graph = Datatype('Graph')
Graph.declare('graph', ('has', Nodeset), ('links', Edgeset),
('parents', Edgeset), ('labelmap', Labelmap))
Graph = Graph.create()
# Atomic action. An Action is comprised of a set of these.
def not_contains(self, p: Point2D):
i = Const("i", IntSort())
return (ForAll([i], Implies(And(0 <= i, i < self.num_segs), Not(self.segment(i).contains(p)))))
def to_z3_sort(self, ctx: Context) -> SortRef:
if self == Kind.INT:
return IntSort(ctx)
if self == Kind.STR:
return StringSort(ctx)
raise RuntimeError(self)
