This page is under-construction. For now, it contains a copy of the files within the examples/ folder of the pySMT repository.

If you are interested in helping us create better tutorials, please let us know at info@pysmt.org .

First example

# Checking satisfiability of a formula.
# This example shows:
#  1. How to build a formula
#  2. How to perform substitution
#  3. Printing
#  4. Satisfiability checking
from pysmt.shortcuts import Symbol, And, Not, is_sat

varA = Symbol("A") # Default type is Boolean
varB = Symbol("B")
f = And([varA, Not(varB)])
g = f.substitute({varB:varA})

res = is_sat(f)
assert res # SAT
print("f := %s is SAT? %s" % (f, res))

res = is_sat(g)
print("g := %s is SAT? %s" % (g, res))
assert not res # UNSAT

Hello World word puzzle

# This is the tutorial example of pySMT.
# This example shows how to:
# 1. Deal with Theory atoms
# 2. Specify a solver in the shortcuts (get_model, is_sat etc.)
# 3. Obtain an print a model
# The goal of the puzzle is to assign a value from 1 to 10 to each letter s.t.
#    H+E+L+L+O = W+O+R+L+D = 25
from pysmt.shortcuts import Symbol, And, GE, LT, Plus, Equals, Int, get_model
from pysmt.typing import INT

hello = [Symbol(s, INT) for s in "hello"]
world = [Symbol(s, INT) for s in "world"]
letters = set(hello+world)
domains = And([And(GE(l, Int(1)),
                   LT(l, Int(10))) for l in letters])

sum_hello = Plus(hello) # n-ary operators can take lists
sum_world = Plus(world) # as arguments
problem = And(Equals(sum_hello, sum_world),
              Equals(sum_hello, Int(25)))
formula = And(domains, problem)

print("Serialization of the formula:")

model = get_model(formula)
if model:
    print("No solution found")

Hello World word puzzle using infix-notation

# This is a different take on the puzzle.py example
# This examples shows how to:
# 1. Enable and use infix notation
# 2. Use a solver context
from pysmt.shortcuts import Symbol, And, Plus, Int
from pysmt.shortcuts import Solver
from pysmt.typing import INT

# Infix-Notation is automatically enabled whenever you import pysmt.shortcuts.
# To enable it without using shortcuts, do:
#   from pysmt.environment import get_env
#   get_env().enable_infix_notation = True
# Similarly, you can disable infix_notation to prevent its accidental use.

hello = [Symbol(s, INT) for s in "hello"]
world = [Symbol(s, INT) for s in "world"]
letters = set(hello+world)

# Infix notation for Theory atoms does the overloading of python
# operator. For boolean connectors, we use e.g., x.And(y) This
# increases readability without running into problems of operator
# precedence.
# Note how you can mix prefix and infix notation.
domains = And([(Int(1) <= l).And(Int(10) >= l) for l in letters])

sum_hello = Plus(hello) # n-ary operators can take lists
sum_world = Plus(world) # as arguments
problem = (sum_hello.Equals(sum_world)).And(sum_hello.Equals(Int(25)))
formula = domains.And(problem)

print("Serialization of the formula:")

# A context (with-statment) lets python take care of creating and
# destroying the solver.
with Solver() as solver:
    if solver.solve():
        for l in letters:
            print("%s = %s" %(l, solver.get_value(l)))
        print("No solution found")

Combine multiple solvers

# This example requires Z3 and MathSAT to be installed (but you can
#  replace MathSAT with any other solver for QF_LRA)
# This examples shows how to:
# 1. Define Real valued constants using floats and fractions
# 2. Perform quantifier elimination
# 3. Pass results from one solver to another
from pysmt.shortcuts import Symbol, Or, ForAll, GE, LT, Real, Plus
from pysmt.shortcuts import qelim, is_sat
from pysmt.typing import REAL

x, y, z = [Symbol(s, REAL) for s in "xyz"]

f = ForAll([x], Or(LT(x, Real(5.0)),
                   GE(Plus(x, y, z), Real((17,2))))) # (17,2) ~> 17/2
print("f := %s" % f)
#f := (forall x . ((x < 5.0) | (17/2 <= (x + y + z))))

qf_f = qelim(f, solver_name="z3")
print("Quantifier-Free equivalent: %s" % qf_f)
#Quantifier-Free equivalent: (7/2 <= (z + y))

res = is_sat(qf_f, solver_name="msat")
print("SAT check using MathSAT: %s" % res)
#SAT check using MathSAT: True

Model-Checking an infinite state system (BMC+K-Induction) in ~150 lines

# This example shows a more advance use of pySMT.
# It provides a simple implementation of Bounded Model Checking [1]
# and K-Induction [2] and applies it on a simple infinite-state
# transition system.
# [1] ...
# [2] ...
from six.moves import xrange

from pysmt.shortcuts import Symbol, Not, Equals, And, Times, Int, Plus, LE
from pysmt.shortcuts import is_sat, is_unsat
from pysmt.typing import INT

def next_var(v):
    """Returns the 'next' of the given variable"""
    return Symbol("next(%s)" % v.symbol_name(), v.symbol_type())

def at_time(v, t):
    """Builds an SMT variable representing v at time t"""
    return Symbol("%s@%d" % (v.symbol_name(), t), v.symbol_type())

class TransitionSystem(object):
    """Trivial representation of a Transition System."""

    def __init__(self, variables, init, trans):
        self.variables = variables
        self.init = init
        self.trans = trans

# EOC TransitionSystem

def get_subs(system, i):
    """Builds a map from x to x@i and from x' to x@(i+1), for all x in system."""
    subs_i = {}
    for v in system.variables:
        subs_i[v] = at_time(v, i)
        subs_i[next_var(v)] = at_time(v, i+1)
    return subs_i

def get_unrolling(system, k):
    """Unrolling of the transition relation from 0 to k:

    E.g. T(0,1) & T(1,2) & ... & T(k-1,k)
    res = []
    for i in xrange(k):
        subs_i = get_subs(system, i)
    return And(res)

def get_simple_path(system, k):
    """Simple path constraint for k-induction:
    each time encodes a different state
    res = []
    for i in xrange(k):
        subs_i = get_subs(system, i)
        for j in xrange(i+1, k):
            subs_j = get_subs(system, j)
            for v in system.variables:
                v_i = v.substitute(subs_i)
                v_j = v.substitute(subs_j)
                res.append(Not(Equals(v_i, v_j)))
    return And(res)

def get_k_hypothesis(system, prop, k):
    """Hypothesis for k-induction: each state up to k fulfills the property"""
    res = []
    for i in xrange(k):
        subs_i = get_subs(system, i)
    return And(res)

def get_bmc(system, prop, k):
    """Returns the BMC encoding at step k"""
    init_0 = system.init.substitute(get_subs(system, 0))
    prop_k = prop.substitute(get_subs(system, k))
    return And(get_unrolling(system, k), init_0, Not(prop_k))

def get_k_induction(system, prop, k):
    """Returns the K-Induction encoding at step K"""
    subs_k = get_subs(system, k)
    prop_k = prop.substitute(subs_k)
    return And(get_unrolling(system, k),
               get_k_hypothesis(system, prop, k),
               get_simple_path(system, k),

def check_property(system, prop):
    """Interleaves BMC and K-Ind to verify the property."""
    print("Checking property %s..." % prop)
    for b in xrange(100):
        f = get_bmc(system, prop, b)
        print("   [BMC]    Checking bound %d..." % (b+1))
        if is_sat(f):
            print("--> Bug found at step %d" % (b+1))

        f = get_k_induction(system, prop, b)
        print("   [K-IND]  Checking bound %d..." % (b+1))
        if is_unsat(f):
            print("--> The system is safe!")

def main():
    # Example Transition System (SMV-like syntax)
    # VAR x: integer;
    #     y: integer;
    # INIT: x = 1 & y = 2;
    # TRANS: next(x) = x + 1;
    # TRANS: next(y) = y + 2;

    x, y = [Symbol(s, INT) for s in "xy"]
    nx, ny = [next_var(Symbol(s, INT)) for s in "xy"]

    example = TransitionSystem(variables = [x, y],
               init = And(Equals(x, Int(1)),
                          Equals(y, Int(2))),
               trans = And(Equals(nx, Plus(x, Int(1))),
                           Equals(ny, Plus(y, Int(2)))))

    # A true invariant property: y = x * 2
    true_prop = Equals(y, Times(x, Int(2)))

    # A false invariant property: x <= 10
    false_prop = LE(x, Int(10))

    for prop in [true_prop, false_prop]:
        check_property(example, prop)

if __name__ == "__main__":

How to access functionalities of solvers not currently wrapped by pySMT

# This example requires MathSAT to be installed.
# This example shows how to use a solver converter to access
# functionalities of the solver that are not wrapped by pySMT.
# Our goal is to call the method msat_all_sat from the MathSAT API.
import mathsat
from pysmt.shortcuts import Or, Symbol, Solver, And

def callback(model, converter, result):
    """Callback for msat_all_sat.

    This function is called by the MathSAT API everytime a new model
    is found. If the function returns 1, the search continues,
    otherwise it stops.
    # Elements in model are msat_term .
    # Converter.back() provides the pySMT representation of a solver term.
    py_model = [converter.back(v) for v in model]
    return 1 # go on

x, y = Symbol("x"), Symbol("y")
f = Or(x, y)

msat = Solver(name="msat")
converter = msat.converter # .converter is a property implemented by all solvers
msat.add_assertion(f) # This is still at pySMT level

result = []
# Directly invoke the mathsat API !!!
# The second term is a list of "important variables"
      [converter.convert(x)],      # Convert the pySMT term into a MathSAT term
      lambda model : callback(model, converter, result))

print("'exists y . %s' is equivalent to '%s'" %(f, Or(result)))
#exists y . (x | y) is equivalent to ((! x) | x)

How to use any SMT-LIB complaint SMT solver

# This example shows how to define a generic SMT-LIB solver, and use
# it within pySMT. The example looks for mathsat in /tmp, you can
# create a symlink there.
# Using this process you can experiment with any SMT-LIB 2 compliant
# solver even if it does not have python bindings, or has not been
# integrated with pySMT.
# Note: When using the SMT-LIB wrapper, you can only use logics
# supported by pySMT. If the version of pySMT in use does not support
# Arrays, then you cannot represent arrays.

# To define a Generic Solver you need to provide:
# - A name to associate to the solver
# - The path to the script + Optional arguments
# - The list of logics supported by the solver
# It is usually convenient to wrap the solver in a simple shell script.
# See examples for Z3, Mathsat and Yices in pysmt/test/smtlib/bin/*.template
from pysmt.logics import QF_UFLRA, QF_UFIDL, QF_LRA, QF_IDL, QF_LIA
from pysmt.shortcuts import get_env, GT, Solver, Symbol
from pysmt.typing import REAL, INT
from pysmt.exceptions import NoSolverAvailableError

name = "mathsat" # Note: The API version is called 'msat'
path = ["/tmp/mathsat"] # Path to the solver
logics = [QF_UFLRA, QF_UFIDL] # Some of the supported logics

env = get_env()

# Add the solver to the environment
env.factory.add_generic_solver(name, path, logics)

r, s = Symbol("r", REAL), Symbol("s", REAL)
p, q = Symbol("p", INT), Symbol("q", INT)

f_lra = GT(r, s)
f_idl = GT(p, q)

# PySMT takes care of recognizing that QF_LRA can be solved by a QF_UFLRA solver.
with Solver(name=name, logic=QF_LRA) as s:
    res = s.solve()
    assert res, "Was expecting '%s' to be SAT" % f_lra

with Solver(name=name, logic=QF_IDL) as s:
    res = s.solve()
    assert res, "Was expecting '%s' to be SAT" % f_idl

    with Solver(name=name, logic=QF_LIA) as s:
except NoSolverAvailableError:
    # If we ask for a logic that is not contained by any of the
    # supported logics an exception is thrown
    print("%s does not support QF_LIA" % name)

How to combine two different solvers to solve an Exists Forall problem

# EF-SMT solver implementation
# This example shows:
#  1. How to combine 2 different solvers
#  2. How to extract information from a model
from pysmt.shortcuts import Solver, get_model
from pysmt.shortcuts import Symbol, Bool, Real, Implies, And, Not, Equals
from pysmt.shortcuts import GT, LT, LE, Minus, Times
from pysmt.logics import AUTO, QF_LRA
from pysmt.typing import REAL
from pysmt.exceptions import SolverReturnedUnknownResultError

def efsmt(y, phi, logic=AUTO, maxloops=None,
          esolver_name=None, fsolver_name=None,
    """Solves exists x. forall y. phi(x, y)"""

    y = set(y)
    x = phi.get_free_variables() - y

    with Solver(logic=logic, name=esolver_name) as esolver:

        loops = 0
        while maxloops is None or loops <= maxloops:
            loops += 1

            eres = esolver.solve()
            if not eres:
                return False
                tau = {v: esolver.get_value(v) for v in x}
                sub_phi = phi.substitute(tau).simplify()
                if verbose: print("%d: Tau = %s" % (loops, tau))

                fmodel = get_model(Not(sub_phi),
                                   logic=logic, solver_name=fsolver_name)
                if fmodel is None:
                    return tau
                    sigma = {v: fmodel[v] for v in y}
                    sub_phi = phi.substitute(sigma).simplify()
                    if verbose: print("%d: Sigma = %s" % (loops, sigma))

        raise SolverReturnedUnknownResultError

def run_test(y, f):
    print("Testing " + str(f))
        res = efsmt(y, f, logic=QF_LRA, maxloops=20, verbose=True)
        if res == False:
            print("sat : %s" % str(res))
    except SolverReturnedUnknownResultError:

def main():
    x,y = [Symbol(n, REAL) for n in "xy"]
    f_sat = Implies(And(GT(y, Real(0)), LT(y, Real(10))),
                    LT(Minus(y, Times(x, Real(2))), Real(7)))

    f_incomplete = And(GT(x, Real(0)), LE(x, Real(10)),
                       Implies(And(GT(y, Real(0)), LE(y, Real(10)),
                                   Not(Equals(x, y))),
                               GT(y, x)))

    run_test([y], f_sat)
    run_test([y], f_incomplete)

if __name__ == "__main__":

How to detect the logic of a formula and perform model enumeration

# Perform ALL-SMT on Theory atoms
# This example shows:
#  - How to get the logic of a formula
#  - How to enumerate models
#  - How to extract a partial model
#  - How to use the special operator EqualsOrIff
from pysmt.shortcuts import Solver, Not, And, Symbol, Or
from pysmt.shortcuts import LE, GE, Int, Plus, Equals, EqualsOrIff
from pysmt.typing import INT
from pysmt.oracles import get_logic

def all_smt(formula, keys):
    target_logic = get_logic(formula)
    print("Target Logic: %s" % target_logic)
    with Solver(logic=target_logic) as solver:
        while solver.solve():
            partial_model = [EqualsOrIff(k, solver.get_value(k)) for k in keys]

A0 = Symbol("A0", INT)
A1 = Symbol("A1", INT)
A2 = Symbol("A2", INT)

f = And(GE(A0, Int(0)), LE(A0, Int(5)),
        GE(A1, Int(0)), LE(A1, Int(5)),
        GE(A2, Int(0)), LE(A2, Int(5)),
        Equals(Plus(A0, A1, A2), Int(8)))

all_smt(f, [A0, A1, A2])

# By using the operator EqualsOrIff, we can mix theory and bool variables
x = Symbol("x")
y = Symbol("y")
f = And(f, Or(x,y))

all_smt(f, [A0, A1, A2, x])

Shows how to use multi-processing to perform parallel and asynchronous solving

# This example requires Z3
# This example shows how to parallelize solving using pySMT, by using
# the multiprocessing library.
# All shortcuts (is_sat, is_unsat, get_model, etc.) can be easily
# used. We sho two techniques: map and apply_async.
# Map applies a given function to a list of elements, and returns a
# list of results. We show an example using is_sat.
# Apply_async provides a way to perform operations in an asynchronous
# way. This allows us to start multiple solving processes in parallel,
# and collect the results whenever they become available.
# More information can be found in the Python Standard Library
# documentation for the multiprocessing module.
# NOTE: When running this example, a warning will appear saying:
#   "Contextualizing formula during is_sat"
# Each process will inherit a different formula manager, but the
# formulas that we are solving have been all created in the same
# formula manager. pySMT takes care of moving the formula across
# formula managers. This step is called contextualization, and occurs
# only when using multiprocessing.
# To disable the warning see the python module warnings.
from multiprocessing import Pool, TimeoutError
from time import sleep

from pysmt.test.examples import get_example_formulae
from pysmt.shortcuts import is_sat, is_valid, is_unsat
from pysmt.shortcuts import And

# Ignore this for now
def check_validity_and_test(args):
    """Checks expression and compare the outcome against a known value."""

    expr, expected = args # IMPORTANT: Unpack args !!!
    local_res = is_valid(expr)
    return local_res == expected

# Create the Pool with 4 workers.
pool = Pool(4)

# We use the examples formula from the test suite.
# This generator iterates over all the expressions
f_gen = (f.expr for f in get_example_formulae())

# Call the functino is_sat on each expression
res = pool.map(is_sat, f_gen)

# The result is a list of True/False, in the same order as the input.

sleep(1) # Have some time to look at the result

# Notice that all shortcuts (is_sat, qelim, etc) require only one
# mandatory argument. To pass multiple arguments, we need to pack them
# together.

# Lets create a list of (formula, result), where result is the
# expected result of the is_valid query.
f_gen = (f.expr for f in get_example_formulae())
res_gen = (f.is_valid for f in get_example_formulae())
args_gen = zip(f_gen, res_gen)

# We now define a function that check the formula against the expected
# value of is_valid: See check_validity_and_test(...) above.
# Due to the way multiprocessing works, we need to define the function
# _before_ we create the Pool.

# As before, we call the map on the pool
res = pool.map(check_validity_and_test, args_gen)

# We can also apply a map-reduce strategy, by making sure that all
# results are as expected.
if all(res):
    print("Everything is ok!")
    print("Ooops, something is wrong!")

# A different option is to run solvers in an asynchronous way.  This
# does not provide us with any guarantee on the execution order, but
# it is particular convenient, if we want to perform solving together
# with another long operation (e.g., I/O, network etc.)  or if we want
# to run multiple solvers.

# Create a formula
big_f = And(f.expr for f in get_example_formulae() \
            if not f.logic.theory.bit_vectors and \
               not f.logic.theory.arrays and \

# Create keyword arguments for the function call.
# This is the simplest way to pass multiple arguments to apply_async.
kwargs = {"formula": big_f, "solver_name": "z3"}
future_res_sat = pool.apply_async(is_sat, kwds=kwargs)
future_res_unsat = pool.apply_async(is_unsat, kwds=kwargs)

# In the background, the solving is taking place... We can do other
# stuff in the meanwhile.
print("This is non-blocking...")

# Get the result with a deadline.
# See multiprocessing.pool.AsyncResult for more options
sat_res = future_res_sat.get(10)  # Get result after 10 seconds or kill
    unsat_res = future_res_unsat.get(0) # No wait
except TimeoutError:
    print("UNSAT result was not ready!")
    unsat_res = None
print(sat_res, unsat_res)

Demonstrates how to perform SMT-LIB parsing, dumping and extension

# The last part of the example requires a QF_LIA solver to be installed.
# This example shows how to interact with files in the SMT-LIB
# format. In particular:
# 1. How to read a file in SMT-LIB format
# 2. How to write a file in SMT-LIB format
# 3. Formulas and SMT-LIB script
# 4. How to access annotations from SMT-LIB files
# 5. How to extend the parser with custom commands
from six.moves import cStringIO # Py2-Py3 Compatibility

from pysmt.smtlib.parser import SmtLibParser

# To make the example self contained, we store the example SMT-LIB
# script in a string.
(set-logic QF_LIA)
(declare-fun p () Int)
(declare-fun q () Int)
(declare-fun x () Bool)
(declare-fun y () Bool)
(define-fun .def_1 () Bool (! (and x y) :cost 1))
(assert (=> x (> p q)))
(assert (=> y (> q p)))
(assert .def_1)

# We read the SMT-LIB Script by creating a Parser.
# From here we can get the SMT-LIB script.
parser = SmtLibParser()

# The method SmtLibParser.get_script takes a buffer in input. We use
# cStringIO to simulate an open file.
# See SmtLibParser.get_script_fname() if to pass the path of a file.
script = parser.get_script(cStringIO(DEMO_SMTLIB))

# The SmtLibScript provides an iterable representation of the commands
# that are present in the SMT-LIB file.
# Printing a summary of the issued commands
for cmd in script:

# SmtLibScript provides some utilities to perform common operations: e.g,
# - Checking if a command is present
assert script.contains_command("check-sat")
# - Counting the occurrences of a command
assert script.count_command_occurrences("assert") == 3
# - Obtain all commands of a particular type
decls = script.filter_by_command_name("declare-fun")
for d in decls:

# Most SMT-LIB scripts define a single SAT call. In these cases, the
# result can be obtained by conjoining multiple assertions.  The
# method to do that is SmtLibScript.get_strict_formula() that, raises
# an exception if there are push/pop calls.  To obtain the formula at
# the end of the execution of the Script (accounting for push/pop) we
# use get_last_formula
f = script.get_last_formula()

# Finally, we serialize the script back into SMT-Lib format.  This can
# be dumped into a file (see SmtLibScript.to_file).  The flag daggify,
# specifies whether the printing is done as a DAG or as a tree.

buf_out = cStringIO()
script.serialize(buf_out, daggify=True)

# Expressions can be annotated in order to provide additional
# information. The semantic of annotations is solver/problem
# dependent. For example, VMT uses annotations to identify two
# expressions as 1) the Transition Relation and 2) Initial Condition
# Here we pretend that we make up a ficticious Weighted SMT format
# and label .def1 with cost 1
# The class pysmt.smtlib.annotations.Annotations deals with the
# handling of annotations.
ann = script.annotations


# Annotations are part of the SMT-LIB standard, and are the
# recommended way to perform inter-operable operations. However, in
# many cases, we are interested in prototyping some algorithm/idea and
# need to write the input files by hand. In those cases, using an
# extended version of SMT-LIB usually provides a more readable input.
# We provide now an example on how to define a symbolic transition
# system as an extension of SMT-LIB.
# (A more complete version of this example can be found in :
#    pysmt.tests.smtlib.test_parser_extensibility.py)
(declare-fun A () Bool)
(declare-fun B () Bool)
(init (and A B))
(trans (=> A (next A)))

# We define two new commands (init, trans) and a new
# operator (next). In order to parse this file, we need to create a
# sub-class of the SmtLibParser, and add handlers for he new commands
# and operators.
from pysmt.smtlib.script import SmtLibCommand

class TSSmtLibParser(SmtLibParser):
    def __init__(self, env=None, interactive=False):
        SmtLibParser.__init__(self, env, interactive)

        # Add new commands
        # The mapping function takes care of consuming the command
        # name from the input stream, e.g., '(init' . Therefore,
        # _cmd_init will receive the rest of the stream, in our
        # example, '(and A B)) ...'
        self.commands["init"] = self._cmd_init
        self.commands["trans"] = self._cmd_trans

        # Remove unused commands
        # If some commands are not compatible with the extension, they
        # can be removed from the parser. If found, they will cause
        # the raising of the exception UnknownSmtLibCommandError
        del self.commands["check-sat"]
        del self.commands["get-value"]
        # ...

        # Add 'next' function
        # New operators can be added similarly as done for commands.
        # e.g., 'next'. The helper function _operator_adapter,
        # simplifies the writing of such extensions.  In this example,
        # we will rewrite the content of the next without the need of
        # introducing a new pySMT operator. If you are interested in a
        # simple way of handling new operators in pySMT see
        # pysmt.test.test_dwf.
        self.interpreted["next"] = self._operator_adapter(self._next_var)

    def _cmd_init(self, current, tokens):
        # This cmd performs the parsing of:
        #   <expr> )
        # and returns a new SmtLibCommand
        expr = self.get_expression(tokens)
        self.consume_closing(tokens, current)
        return SmtLibCommand(name="init", args=(expr,))

    def _cmd_trans(self, current, tokens):
        # This performs the same parsing as _cmd_init, but returns a
        # different object. The parser is not restricted to return
        # SmtLibCommand, but using them makes handling them
        # afterwards easier.
        expr = self.get_expression(tokens)
        self.consume_closing(tokens, current)
        return SmtLibCommand(name="trans", args=(expr,))

    def _next_var(self, symbol):
        # The function is called with the arguments obtained from
        # parsing the rest of the SMT-LIB file. In this case, 'next'
        # is a unary function, thus we have only 1 argument. 'symbol'
        # is an FNode. We require that 'symbol' is _really_ a symbol:
        if symbol.is_symbol():
            name = symbol.symbol_name()
            ty = symbol.symbol_type()
            # The return type MUST be an FNode, because this is part
            # of an expression.
            return self.env.formula_manager.Symbol("next_" + name, ty)
            raise ValueError("'next' operator can be applied only to symbols")

    def get_ts(self, script):
        # New Top-Level command that takes a script stream in input.
        # We return a pair (Init, Trans) that defines the symbolic
        # transition system.
        init = self.env.formula_manager.TRUE()
        trans = self.env.formula_manager.TRUE()

        for cmd in self.get_command_generator(script):
            if cmd.name=="init":
                init = cmd.args[0]
            elif cmd.name=="trans":
                trans = cmd.args[0]
                # Ignore other commands

        return (init, trans)

# Time to try out the parser !!!
# First we check that the standard SMT-Lib parser cannot handle the new format.
from pysmt.exceptions import UnknownSmtLibCommandError

except UnknownSmtLibCommandError as ex:
    print("Unsupported command: %s" % ex)

# The new parser can parse our example, and returns the (init, trans) pair
ts_parser = TSSmtLibParser()
init, trans = ts_parser.get_ts(cStringIO(EXT_SMTLIB))
print("INIT: %s" % init.serialize())
print("TRANS: %s" % trans.serialize())

Shows the use of UNSAT Core as debugging tools

# This example requires MathSAT or Z3
# In this example, we will encode a more complex puzzle and see:
# - Use of UNSAT cores as a debugging tool
# - Conjunctive partitioning
# - Symbol handling delegation to auxiliary functions

# This puzzle is known as Einstein Puzzle
# There are five houses in five different colours in a row. In each
# house lives a person with a different nationality. The five owners
# drink a certain type of beverage, smoke a certain brand of cigar and
# keep a certain pet.
# No owners have the same pet, smoke the same brand of cigar, or drink
# the same beverage.
# The Brit lives in the red house.
# The Swede keeps dogs as pets.
# The Dane drinks tea.
# The green house is on the immediate left of the white house.
# The green house owner drinks coffee.
# The owner who smokes Pall Mall rears birds.
# The owner of the yellow house smokes Dunhill.
# The owner living in the center house drinks milk.
# The Norwegian lives in the first house.
# The owner who smokes Blends lives next to the one who keeps cats.
# The owner who keeps the horse lives next to the one who smokes Dunhill.
# The owner who smokes Bluemasters drinks beer.
# The German smokes Prince.
# The Norwegian lives next to the blue house.
# The owner who smokes Blends lives next to the one who drinks water.
# The question is: who owns the fish?

from pysmt.shortcuts import Symbol, ExactlyOne, Or, And, FALSE, Iff
from pysmt.shortcuts import get_model, get_unsat_core, is_sat, is_unsat

# Lets start by expliciting all values for all dimensions

Color = "white", "yellow", "blue", "red", "green"
Nat = "german", "swedish", "british", "norwegian", "danish"
Pet =  "birds", "cats", "horses", "fish", "dogs"
Drink = "beer", "water", "tea", "milk", "coffee"
Smoke = "blends", "pall_mall", "prince", "bluemasters", "dunhill"
Houses = range(0,5)
# We number the houses from 0 to 4, and create the macros to assert
# properties of the i-th house:
#  e.g., color(1, "green")  to indicate that the house 1 is Green
# This is not strictly necessary, but it is a way of making programs
# more readable.
def color(number, name):
    assert name in Color
    if number in Houses:
        return Symbol("%d_color_%s" % (number, name))
    return FALSE()

def nat(number, name):
    assert name in Nat
    if number in Houses:
        return Symbol("%d_nat_%s" % (number, name))
    return FALSE()

def pet(number, name):
    assert name in Pet
    if number in Houses:
        return Symbol("%d_pet_%s" % (number, name))
    return FALSE()

def drink(number, name):
    assert name in Drink
    if number in Houses:
        return Symbol("%d_drink_%s" % (number, name))
    return FALSE()

def smoke(number, name):
    assert name in Smoke
    if number in Houses:
        return Symbol("%d_smoke_%s" % (number, name))
    return FALSE()

# We can encode the facts

facts = And(
    # The Brit lives in the red house.
    And( Iff(nat(i, "british"), color(i, "red")) for i in Houses ),

    # The Swede keeps dogs as pets.
    And( Iff(nat(i, "swedish"), pet(i, "dogs")) for i in Houses ),

    # The Dane drinks tea.
    And( Iff(nat(i, "danish"), drink(i, "tea")) for i in Houses ) ,

    # The green house is on the immediate left of the white house.
    And( Iff(color(i, "green"), color(i+1, "white")) for i in Houses) ,

    # The green house owner drinks coffee.
    And( Iff(color(i, "green"), drink(i, "coffee")) for i in Houses ) ,

    # The owner who smokes Pall Mall rears birds.
    And( Iff(smoke(i, "pall_mall"), pet(i, "birds")) for i in Houses ) ,

    # The owner of the yellow house smokes Dunhill.
    And( Iff(color(i, "yellow"), smoke(i, "dunhill")) for i in Houses ) ,

    # The owner living in the center house drinks milk.
    And( drink(2, "milk") ) ,

    # The Norwegian lives in the first house.
    And( nat(0, "norwegian") ) ,

    # The owner who smokes Blends lives next to the one who keeps cats.
    And( Iff(smoke(i, "blends"), Or(pet(i-1, "cats"), pet(i+1, "cats"))) for i in Houses ) ,

    # The owner who keeps the horse lives next to the one who smokes Dunhill.
    And( Iff(pet(i, "horses"), Or(smoke(i-1, "dunhill"), smoke(i+1, "dunhill"))) for i in Houses ) ,

    # The owner who smokes Bluemasters drinks beer.
    And( Iff(smoke(i, "bluemasters"), drink(i, "beer")) for i in Houses ) ,

    # The German smokes Prince.
    And( Iff(nat(i, "german"), smoke(i, "prince")) for i in Houses ) ,

    # The Norwegian lives next to the blue house.
    # Careful with this!!!
    And( Iff(nat(i, "norwegian"), Or(color(i-1, "blue"), color(i+1, "blue"))) for i in Houses ) ,

    # The owner who smokes Blends lives next to the one who drinks water.
    And( Iff(smoke(i, "blends"), Or(drink(i-1, "water"), drink(i+1, "water"))) for i in Houses )

domain = And(
    And(ExactlyOne(color(i, c) for i in Houses) for c in Color),
    And(ExactlyOne(nat(i, c) for i in Houses) for c in Nat),
    And(ExactlyOne(pet(i, c) for i in Houses) for c in Pet),
    And(ExactlyOne(drink(i, c) for i in Houses) for c in Drink),
    And(ExactlyOne(smoke(i, c) for i in Houses) for c in Smoke),
    And(ExactlyOne(color(i, c) for c in Color) for i in Houses),
    And(ExactlyOne(nat(i, c) for c in Nat) for i in Houses),
    And(ExactlyOne(pet(i, c) for c in Pet) for i in Houses),
    And(ExactlyOne(drink(i, c) for c in Drink) for i in Houses),
    And(ExactlyOne(smoke(i, c) for c in Smoke) for i in Houses),

problem = And(domain, facts)

model = get_model(problem)

if model is None:
    # We first check whether the constraints on the domain and problem
    # are satisfiable in isolation.
    assert is_sat(facts)
    assert is_sat(domain)
    assert is_unsat(problem)

    # In isolation they are both fine, rules from both are probably
    # interacting.
    # The problem is given by a nesting of And().
    # conjunctive_partition can be used to obtain a "flat"
    # structure, i.e., a list of conjuncts.
    from pysmt.rewritings import conjunctive_partition
    conj = conjunctive_partition(problem)
    ucore = get_unsat_core(conj)
    print("UNSAT-Core size '%d'" % len(ucore))
    for f in ucore:

    # The exact version of the UNSAT-Core depends on the solver in
    # use.  Nevertheless, this represents a starting point for your
    # debugging.  A possible way to approach the result is to look for
    # clauses of size 1 (i.e., unit clauses). In the facts list there
    # are only 2 facts:
    #   2_drink_milk
    #   0_nat_norwegian
    # The clause ("1_color_blue" <-> "0_nat_norwegian")
    # Implies that "1_color_blue"
    # But (("3_color_blue" | "1_color_blue") <-> "2_nat_norwegian")
    # Requires "2_nat_norwegian"
    # The ExactlyOne constraint forbids that both 0 and 2 are nowegian
    # thus, we have a better idea of where the problem might be.
    # Please go back to the comment '# Careful with this!!!' in the
    # facts list, and change the Iff with an Implies.
    # Done?
    # Good, you should be getting a model, now!
    for h in Houses:
        # Extract the relevants bits to get some pretty-printing
        c = [x for x in Color if model[color(h, x)].is_true()][0]
        n = [x for x in Nat if model[nat(h, x)].is_true()][0]
        p = [x for x in Pet if model[pet(h, x)].is_true()][0]
        d = [x for x in Drink if model[drink(h, x)].is_true()][0]
        s = [x for x in Smoke if model[smoke(h, x)].is_true()][0]
        print(h, c, n, p, d, s)
        if p == "fish":
            sol = "The '%s' owns the fish!" % n