Python bindings for OxiZ SMT Solver
Project description
OxiZ Python Bindings
Python bindings for the OxiZ SMT (Satisfiability Modulo Theories) solver.
Installation
From PyPI (when published)
pip install oxiz
From Source
# Install maturin
pip install maturin
# Build and install
cd oxiz-py
maturin develop --release
Quick Start
import oxiz
# Create a term manager and solver
tm = oxiz.TermManager()
solver = oxiz.Solver()
# Create integer variables
x = tm.mk_var("x", "Int")
y = tm.mk_var("y", "Int")
# Create constants
ten = tm.mk_int(10)
five = tm.mk_int(5)
# Create constraints: x + y = 10, x > 5
sum_xy = tm.mk_add([x, y])
constraint1 = tm.mk_eq(sum_xy, ten)
constraint2 = tm.mk_gt(x, five)
# Assert constraints
solver.assert_term(constraint1, tm)
solver.assert_term(constraint2, tm)
# Check satisfiability
result = solver.check_sat(tm)
print(f"Result: {result}") # sat
if result == oxiz.SolverResult.Sat:
model = solver.get_model(tm)
print(f"x = {model['x']}, y = {model['y']}")
API Reference
TermManager
The TermManager creates and manages terms (expressions).
tm = oxiz.TermManager()
# Variables
x = tm.mk_var("x", "Int") # Integer variable
b = tm.mk_var("b", "Bool") # Boolean variable
r = tm.mk_var("r", "Real") # Real variable
# Constants
t = tm.mk_bool(True) # Boolean true
f = tm.mk_bool(False) # Boolean false
n = tm.mk_int(42) # Integer constant
q = tm.mk_real(3, 4) # Rational 3/4
# Boolean operations
not_b = tm.mk_not(b)
and_term = tm.mk_and([b, t])
or_term = tm.mk_or([b, f])
implies = tm.mk_implies(b, t)
# Arithmetic operations
sum_term = tm.mk_add([x, n])
diff = tm.mk_sub(x, n)
prod = tm.mk_mul([x, n])
neg = tm.mk_neg(x)
div = tm.mk_div(x, n)
mod = tm.mk_mod(x, n)
# Comparisons
eq = tm.mk_eq(x, n) # x = 42
lt = tm.mk_lt(x, n) # x < 42
le = tm.mk_le(x, n) # x <= 42
gt = tm.mk_gt(x, n) # x > 42
ge = tm.mk_ge(x, n) # x >= 42
# Other
ite = tm.mk_ite(b, x, n) # if b then x else 42
distinct = tm.mk_distinct([x, n]) # x != 42
Solver
The Solver checks satisfiability of constraints.
solver = oxiz.Solver()
# Set logic (optional)
solver.set_logic("QF_LIA") # Quantifier-free Linear Integer Arithmetic
# Add constraints
solver.assert_term(constraint, tm)
# Check satisfiability
result = solver.check_sat(tm)
# Get model (if sat)
if result == oxiz.SolverResult.Sat:
model = solver.get_model(tm)
# Push/pop for incremental solving
solver.push()
solver.assert_term(additional_constraint, tm)
result = solver.check_sat(tm)
solver.pop() # Removes additional_constraint
# Reset solver
solver.reset()
SolverResult
result = solver.check_sat(tm)
# Compare with enum values
if result == oxiz.SolverResult.Sat:
print("Satisfiable")
elif result == oxiz.SolverResult.Unsat:
print("Unsatisfiable")
elif result == oxiz.SolverResult.Unknown:
print("Unknown")
# Use properties
if result.is_sat:
model = solver.get_model(tm)
Examples
Sudoku Solver
import oxiz
def solve_sudoku(grid):
tm = oxiz.TermManager()
solver = oxiz.Solver()
# Create variables for each cell (1-9)
cells = {}
for i in range(9):
for j in range(9):
cells[(i, j)] = tm.mk_var(f"cell_{i}_{j}", "Int")
one = tm.mk_int(1)
nine = tm.mk_int(9)
# Each cell is between 1 and 9
for (i, j), cell in cells.items():
solver.assert_term(tm.mk_ge(cell, one), tm)
solver.assert_term(tm.mk_le(cell, nine), tm)
# Row constraints: all different
for i in range(9):
row_cells = [cells[(i, j)] for j in range(9)]
solver.assert_term(tm.mk_distinct(row_cells), tm)
# Column constraints: all different
for j in range(9):
col_cells = [cells[(i, j)] for i in range(9)]
solver.assert_term(tm.mk_distinct(col_cells), tm)
# 3x3 box constraints: all different
for box_i in range(3):
for box_j in range(3):
box_cells = []
for i in range(3):
for j in range(3):
box_cells.append(cells[(box_i*3 + i, box_j*3 + j)])
solver.assert_term(tm.mk_distinct(box_cells), tm)
# Given values
for i in range(9):
for j in range(9):
if grid[i][j] != 0:
val = tm.mk_int(grid[i][j])
solver.assert_term(tm.mk_eq(cells[(i, j)], val), tm)
# Solve
if solver.check_sat(tm) == oxiz.SolverResult.Sat:
model = solver.get_model(tm)
result = [[int(model[f"cell_{i}_{j}"]) for j in range(9)] for i in range(9)]
return result
return None
Boolean Satisfiability
import oxiz
tm = oxiz.TermManager()
solver = oxiz.Solver()
# Create boolean variables
p = tm.mk_var("p", "Bool")
q = tm.mk_var("q", "Bool")
r = tm.mk_var("r", "Bool")
# Assert: (p OR q) AND (NOT p OR r) AND (NOT q OR NOT r)
clause1 = tm.mk_or([p, q])
clause2 = tm.mk_or([tm.mk_not(p), r])
clause3 = tm.mk_or([tm.mk_not(q), tm.mk_not(r)])
solver.assert_term(clause1, tm)
solver.assert_term(clause2, tm)
solver.assert_term(clause3, tm)
result = solver.check_sat(tm)
if result == oxiz.SolverResult.Sat:
model = solver.get_model(tm)
print(f"p={model['p']}, q={model['q']}, r={model['r']}")
Supported Sorts
"Bool"- Boolean values"Int"- Arbitrary precision integers"Real"- Rational numbers"BitVec[N]"- Bit vectors of width N (e.g.,"BitVec[32]")
Bitvector Operations
tm = oxiz.TermManager()
# Create bitvector constants
bv8 = tm.mk_bv(42, 8) # 8-bit bitvector with value 42
bv16 = tm.mk_bv(1000, 16) # 16-bit bitvector
# Bitwise operations
not_x = tm.mk_bv_not(x) # Bitwise NOT
and_xy = tm.mk_bv_and(x, y) # Bitwise AND
or_xy = tm.mk_bv_or(x, y) # Bitwise OR
# Arithmetic operations
sum_xy = tm.mk_bv_add(x, y) # Addition (modulo 2^width)
diff_xy = tm.mk_bv_sub(x, y) # Subtraction
prod_xy = tm.mk_bv_mul(x, y) # Multiplication
neg_x = tm.mk_bv_neg(x) # Two's complement negation
# Comparisons (unsigned)
ult = tm.mk_bv_ult(x, y) # x <u y (unsigned less than)
ule = tm.mk_bv_ule(x, y) # x <=u y (unsigned less or equal)
# Comparisons (signed)
slt = tm.mk_bv_slt(x, y) # x <s y (signed less than)
sle = tm.mk_bv_sle(x, y) # x <=s y (signed less or equal)
# Division and remainder
udiv = tm.mk_bv_udiv(x, y) # Unsigned division
sdiv = tm.mk_bv_sdiv(x, y) # Signed division
urem = tm.mk_bv_urem(x, y) # Unsigned remainder
srem = tm.mk_bv_srem(x, y) # Signed remainder
# Bit manipulation
concat = tm.mk_bv_concat(high, low) # Concatenate bitvectors
extract = tm.mk_bv_extract(7, 4, x) # Extract bits[7:4]
Array Operations
tm = oxiz.TermManager()
# Array select and store
value = tm.mk_select(array, index) # array[index]
new_array = tm.mk_store(array, index, value) # array with array[index] = value
Optimization
OxiZ supports optimization (minimize/maximize) using the Optimizer class:
tm = oxiz.TermManager()
opt = oxiz.Optimizer()
opt.set_logic("QF_LIA")
# Variables
x = tm.mk_var("x", "Int")
y = tm.mk_var("y", "Int")
# Constraints
zero = tm.mk_int(0)
ten = tm.mk_int(10)
sum_xy = tm.mk_add([x, y])
opt.assert_term(tm.mk_ge(sum_xy, ten)) # x + y >= 10
opt.assert_term(tm.mk_ge(x, zero)) # x >= 0
opt.assert_term(tm.mk_ge(y, zero)) # y >= 0
# Objective: minimize x + y
opt.minimize(sum_xy)
# Solve
result = opt.optimize(tm)
if result == oxiz.OptimizationResult.Optimal:
model = opt.get_model(tm)
print(f"Optimal: x={model['x']}, y={model['y']}") # Should be x=0, y=10 or x=10, y=0
Optimization Results
OptimizationResult.Optimal- Optimal solution foundOptimizationResult.Unbounded- Objective is unbounded (no finite optimum)OptimizationResult.Unsat- No feasible solution existsOptimizationResult.Unknown- Timeout or incomplete
Examples
See the examples/ directory for complete demos:
basic_sat.py- Boolean satisfiabilitybitvec_example.py- Bitvector operationsoptimization_example.py- Optimization (min/max)sudoku.py- Sudoku solver
Testing
Run the test suite with pytest:
# Install the package in development mode
maturin develop --release
# Run tests
pytest tests/ -v
License
Apache-2.0
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