OxiZ Python Bindings
Version: 0.3.4 | Status: Alpha | Tests: 11 Rust + 117 Python (
pytest, not re-run this release) | Rust LoC: 1,845 (8 source files undersrc/)
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
Metadata
Release files for oxiz 0.3.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| oxiz-0.3.4.tar.gz | 5.4 MB | Details |
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|---|---|
| Size | 1.3 MB |
| Tags | CPython 3.8 Linux glibc 2.17+ ARM64 |
|
SHA-256 checksum How to use checksums |
0b3e8cd317dce389879f2b3b38446243518d3675a9fdf07825cc6cac2243cd9e
|
|
BLAKE2b-256 checksum How to use checksums |
3d75df31b5f211ebdd0d4db6a69c1ee2f04da6784499e9ed5ce2739a122e4359
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
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Signed by GitHub Actions, verified by PyPI on Oct 8, 2026.
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