OxiZ Python Bindings

Version: 0.3.1 | Status: Alpha | Tests: 11 Rust + 117 Python (pytest, not re-run this release) | Rust LoC: 1,842 (8 source files under src/)

Python bindings for the OxiZ SMT (Satisfiability Modulo Theories) solver.

Installation

From PyPI (when published)

pip install oxiz

From Source

Requires Rust and maturin:

# 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 found
  • OptimizationResult.Unbounded - Objective is unbounded (no finite optimum)
  • OptimizationResult.Unsat - No feasible solution exists
  • OptimizationResult.Unknown - Timeout or incomplete

Examples

See the examples/ directory for complete demos:

  • basic_sat.py - Boolean satisfiability
  • bitvec_example.py - Bitvector operations
  • optimization_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

Project links

Data verified by PyPI on Jul 31, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.3.3)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for KitaSan from gravatar.com KitaSan

GitHub Statistics

Data verified by PyPI on Jul 31, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.8

Tags

smt solver sat verification logic theorem-prover

Classifiers

Report project as malware

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0.3.3

33 files

0.3.2

33 files

This release

0.3.1 This release

33 files

0.3.0

33 files

0.2.4

33 files

0.2.3

32 files

0.2.2

32 files

0.2.1

32 files

0.1.3

32 files

0.1.2

32 files

0.1.0

32 files

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