def test_issue_6194():
x = Symbol('x')
assert unchanged(Contains, x, Interval(0, 1))
assert Interval(0, 1).contains(x) == (S.Zero <= x) & (x <= 1)
assert Contains(x, FiniteSet(0)) != S.false
assert Contains(x, Interval(1, 1)) != S.false
assert Contains(x, S.Integers) != S.false
def test_GammaProcess_symbolic():
t, d, x, y, g, l = symbols('t d x y g l', positive=True)
X = GammaProcess("X", l, g)
raises(NotImplementedError, lambda: X[t])
raises(IndexError, lambda: X(-1))
assert isinstance(X(t), RandomIndexedSymbol)
assert X.state_space == Interval(0, oo)
assert X.distribution(X(t)) == GammaDistribution(g * t, 1 / l)
assert X.joint_distribution(5, X(3)) == JointDistributionHandmade(
Lambda(
(X(5), X(3)),
l**(8 * g) * exp(-l * X(3)) * exp(-l * X(5)) * X(3)**(3 * g - 1) *
X(5)**(5 * g - 1) / (gamma(3 * g) * gamma(5 * g))))
# property of the gamma process at any given timestamp
assert E(X(t)) == g * t / l
assert variance(X(t)).simplify() == g * t / l**2
# Equivalent to E(2*X(1)) + E(X(1)**2) + E(X(1)**3), where E(X(1)) == g/l
assert E(X(t)**2 + X(d)*2 + X(y)**3, Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2)) & Contains(y, Interval.Ropen(3, 4))) == \
2*g/l + (g**2 + g)/l**2 + (g**3 + 3*g**2 + 2*g)/l**3
assert P(X(t) > 3, Contains(t, Interval.Lopen(3, 4))).simplify() == \
1 - lowergamma(g, 3*l)/gamma(g) # equivalent to P(X(1)>3)
def test_issue_6194():
x = Symbol('x')
assert Contains(x, Interval(0, 1)) != (x >= 0) & (x <= 1)
assert Interval(0, 1).contains(x) == (x >= 0) & (x <= 1)
assert Contains(x, FiniteSet(0)) != S.false
assert Contains(x, Interval(1, 1)) != S.false
assert Contains(x, S.Integers) != S.false
def test_contains_basic():
raises(TypeError, lambda: Contains(S.Integers, 1))
assert Contains(2, S.Integers) is S.true
assert Contains(-2, S.Naturals) is S.false
i = Symbol('i', integer=True)
assert Contains(i, S.Naturals) == Contains(i, S.Naturals, evaluate=False)
def eval(cls, i, j):
"""
Evaluates the discrete delta function.
Examples
========
>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy.abc import i, j, k
>>> KroneckerDelta(i, j)
KroneckerDelta(i, j)
>>> KroneckerDelta(i, i)
1
>>> KroneckerDelta(i, i + 1)
0
>>> KroneckerDelta(i, i + 1 + k)
KroneckerDelta(i, i + k + 1)
# indirect doctest
"""
diff = i - j
if diff.is_zero:
return S.One
if fuzzy_not(diff.is_zero):
return S.Zero
if diff.is_nonzero:
return S.Zero
from sympy import Contains
if Contains(i, j.domain).is_BooleanFalse or Contains(j, i.domain).is_BooleanFalse:
return S.Zero
if i.assumptions0.get("below_fermi") and j.assumptions0.get("above_fermi"):
return S.Zero
if j.assumptions0.get("below_fermi") and i.assumptions0.get("above_fermi"):
return S.Zero
if i.is_Add and j.is_Add:
i_args = set(i.args)
j_args = set(j.args)
intersect = i_args & j_args
if intersect:
i_args -= intersect
j_args -= intersect
i = i.func(*i_args)
j = j.func(*j_args)
return cls(i, j)
if j.is_KroneckerDelta:
if i == 1:
return j
if i == 0:
return 1 - j
# to make KroneckerDelta canonical
# following lines will check if inputs are in order
# if not, will return KroneckerDelta with correct order
if i is not min(i, j, key=default_sort_key):
return cls(j, i)
def pmf(self, x):
x = sympify(x)
if not (x.is_number or x.is_Symbol or isinstance(x, RandomSymbol)):
raise ValueError("'x' expected as an argument of type 'number' or 'Symbol' or , "
"'RandomSymbol' not %s" % (type(x)))
cond = Ge(x, 1) & Le(x, self.sides) & Contains(x, S.Integers)
return Piecewise((S.One/self.sides, cond), (S.Zero, True))
def test_as_set():
x = Symbol('x')
y = Symbol('y')
# Contains is a BooleanFunction whose value depends on an arg's
# containment in a Set -- rewriting as a Set is not yet implemented
raises(NotImplementedError, lambda:
Contains(x, FiniteSet(y)).as_set())
def pmf(self, x):
n, p = self.n, self.p
x = sympify(x)
if not (x.is_number or x.is_Symbol or isinstance(x, RandomSymbol)):
raise ValueError("'x' expected as an argument of type 'number' or 'Symbol' or , "
"'RandomSymbol' not %s" % (type(x)))
cond = Ge(x, 0) & Le(x, n) & Contains(x, S.Integers)
return Piecewise((binomial(n, x) * p**x * (1 - p)**(n - x), cond), (S.Zero, True))
def test_GammaProcess_numeric():
t, d, x, y = symbols('t d x y', positive=True)
X = GammaProcess("X", 1, 2)
assert X.state_space == Interval(0, oo)
assert X.index_set == Interval(0, oo)
assert X.lamda == 1
assert X.gamma == 2
raises(ValueError, lambda: GammaProcess("X", -1, 2))
raises(ValueError, lambda: GammaProcess("X", 0, -2))
raises(ValueError, lambda: GammaProcess("X", -1, -2))
# all are independent because of non-overlapping intervals
assert P((X(t) > 4) & (X(d) > 3) & (X(x) > 2) & (X(y) > 1), Contains(t,
Interval.Lopen(0, 1)) & Contains(d, Interval.Lopen(1, 2)) & Contains(x,
Interval.Lopen(2, 3)) & Contains(y, Interval.Lopen(3, 4))).simplify() == \
120*exp(-10)
# Check working with Not and Or
assert P(
Not((X(t) < 5) & (X(d) > 3)),
Contains(t, Interval.Ropen(2, 4)) & Contains(d, Interval.Lopen(
7, 8))).simplify() == -4 * exp(-3) + 472 * exp(-8) / 3 + 1
assert P((X(t) > 2) | (X(t) < 4), Contains(t, Interval.Ropen(1, 4))).simplify() == \
-643*exp(-4)/15 + 109*exp(-2)/15 + 1
assert E(X(t)) == 2 * t # E(X(t)) == gamma*t/l
assert E(X(2) + x * E(X(5))) == 10 * x + 4
def test_binary_symbols():
x = Symbol('x')
y = Symbol('y')
z = Symbol('z')
assert Contains(x, FiniteSet(y, Eq(z, True))).binary_symbols == set([y, z])
def test_issue_10326():
assert Contains(oo, Interval(-oo, oo)) == False
assert Contains(-oo, Interval(-oo, oo)) == False
def test_contains_basic():
assert Contains(2, S.Integers) is S.true
assert Contains(-2, S.Naturals) is S.false
i = Symbol('i', integer=True)
assert Contains(i, S.Naturals) == Contains(i, S.Naturals, evaluate=False)
def test_as_set():
x = Symbol('x')
y = Symbol('y')
assert Contains(x, FiniteSet(y)).as_set() == Contains(x, FiniteSet(y))
def test_WienerProcess():
X = WienerProcess("X")
assert X.state_space == S.Reals
assert X.index_set == Interval(0, oo)
t, d, x, y = symbols('t d x y', positive=True)
assert isinstance(X(t), RandomIndexedSymbol)
assert X.distribution(X(t)) == NormalDistribution(0, sqrt(t))
raises(ValueError, lambda: PoissonProcess("X", -1))
raises(NotImplementedError, lambda: X[t])
raises(IndexError, lambda: X(-2))
assert X.joint_distribution(X(2), X(3)) == JointDistributionHandmade(
Lambda((X(2), X(3)),
sqrt(6) * exp(-X(2)**2 / 4) * exp(-X(3)**2 / 6) / (12 * pi)))
assert X.joint_distribution(4, 6) == JointDistributionHandmade(
Lambda((X(4), X(6)),
sqrt(6) * exp(-X(4)**2 / 8) * exp(-X(6)**2 / 12) / (24 * pi)))
assert P(X(t) < 3).simplify() == erf(3 * sqrt(2) /
(2 * sqrt(t))) / 2 + S(1) / 2
assert P(X(t) > 2, Contains(t, Interval.Lopen(3, 7))).simplify() == S(1)/2 -\
erf(sqrt(2)/2)/2
# Equivalent to P(X(1)>1)**4
assert P((X(t) > 4) & (X(d) > 3) & (X(x) > 2) & (X(y) > 1),
Contains(t, Interval.Lopen(0, 1)) & Contains(d, Interval.Lopen(1, 2))
& Contains(x, Interval.Lopen(2, 3)) & Contains(y, Interval.Lopen(3, 4))).simplify() ==\
(1 - erf(sqrt(2)/2))*(1 - erf(sqrt(2)))*(1 - erf(3*sqrt(2)/2))*(1 - erf(2*sqrt(2)))/16
# Contains an overlapping interval so, return Probability
assert P((X(t) < 2) & (X(d) > 3),
Contains(t, Interval.Lopen(0, 2))
& Contains(d, Interval.Ropen(2, 4))) == Probability(
(X(d) > 3) & (X(t) < 2),
Contains(d, Interval.Ropen(2, 4))
& Contains(t, Interval.Lopen(0, 2)))
assert str(P(Not((X(t) < 5) & (X(d) > 3)), Contains(t, Interval.Ropen(2, 4)) &
Contains(d, Interval.Lopen(7, 8))).simplify()) == \
'-(1 - erf(3*sqrt(2)/2))*(2 - erfc(5/2))/4 + 1'
# Distribution has mean 0 at each timestamp
assert E(X(t)) == 0
assert E(
x * (X(t) + X(d)) * (X(t)**2 + X(d)**2),
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Ropen(1, 2))) == Expectation(
x * (X(d) + X(t)) * (X(d)**2 + X(t)**2),
Contains(d, Interval.Ropen(1, 2))
& Contains(t, Interval.Lopen(0, 1)))
assert E(X(t) + x * E(X(3))) == 0
def test_PoissonProcess():
X = PoissonProcess("X", 3)
assert X.state_space == S.Naturals0
assert X.index_set == Interval(0, oo)
assert X.lamda == 3
t, d, x, y = symbols('t d x y', positive=True)
assert isinstance(X(t), RandomIndexedSymbol)
assert X.distribution(X(t)) == PoissonDistribution(3 * t)
raises(ValueError, lambda: PoissonProcess("X", -1))
raises(NotImplementedError, lambda: X[t])
raises(IndexError, lambda: X(-5))
assert X.joint_distribution(X(2), X(3)) == JointDistributionHandmade(
Lambda((X(2), X(3)), 6**X(2) * 9**X(3) * exp(-15) /
(factorial(X(2)) * factorial(X(3)))))
assert X.joint_distribution(4, 6) == JointDistributionHandmade(
Lambda((X(4), X(6)), 12**X(4) * 18**X(6) * exp(-30) /
(factorial(X(4)) * factorial(X(6)))))
assert P(X(t) < 1) == exp(-3 * t)
assert P(Eq(X(t), 0),
Contains(t, Interval.Lopen(3, 5))) == exp(-6) # exp(-2*lamda)
res = P(Eq(X(t), 1), Contains(t, Interval.Lopen(3, 4)))
assert res == 3 * exp(-3)
# Equivalent to P(Eq(X(t), 1))**4 because of non-overlapping intervals
assert P(
Eq(X(t), 1) & Eq(X(d), 1) & Eq(X(x), 1) & Eq(X(y), 1),
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2)) & Contains(x, Interval.Lopen(2, 3))
& Contains(y, Interval.Lopen(3, 4))) == res**4
# Return Probability because of overlapping intervals
assert P(Eq(X(t), 2) & Eq(X(d), 3), Contains(t, Interval.Lopen(0, 2))
& Contains(d, Interval.Ropen(2, 4))) == \
Probability(Eq(X(d), 3) & Eq(X(t), 2), Contains(t, Interval.Lopen(0, 2))
& Contains(d, Interval.Ropen(2, 4)))
raises(ValueError, lambda: P(
Eq(X(t), 2) & Eq(X(d), 3),
Contains(t, Interval.Lopen(0, 4)) & Contains(d, Interval.Lopen(3, oo)))
) # no bound on d
assert P(Eq(X(3), 2)) == 81 * exp(-9) / 2
assert P(Eq(X(t), 2), Contains(t, Interval.Lopen(0,
5))) == 225 * exp(-15) / 2
# Check that probability works correctly by adding it to 1
res1 = P(X(t) <= 3, Contains(t, Interval.Lopen(0, 5)))
res2 = P(X(t) > 3, Contains(t, Interval.Lopen(0, 5)))
assert res1 == 691 * exp(-15)
assert (res1 + res2).simplify() == 1
# Check Not and Or
assert P(Not(Eq(X(t), 2) & (X(d) > 3)), Contains(t, Interval.Ropen(2, 4)) & \
Contains(d, Interval.Lopen(7, 8))).simplify() == -18*exp(-6) + 234*exp(-9) + 1
assert P(Eq(X(t), 2) | Ne(X(t), 4),
Contains(t, Interval.Ropen(2, 4))) == 1 - 36 * exp(-6)
raises(ValueError, lambda: P(X(t) > 2, X(t) + X(d)))
assert E(
X(t)) == 3 * t # property of the distribution at a given timestamp
assert E(
X(t)**2 + X(d) * 2 + X(y)**3,
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2))
& Contains(y, Interval.Ropen(3, 4))) == 75
assert E(X(t)**2, Contains(t, Interval.Lopen(0, 1))) == 12
assert E(x*(X(t) + X(d))*(X(t)**2+X(d)**2), Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Ropen(1, 2))) == \
Expectation(x*(X(d) + X(t))*(X(d)**2 + X(t)**2), Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Ropen(1, 2)))
# Value Error because of infinite time bound
raises(ValueError, lambda: E(X(t)**3, Contains(t, Interval.Lopen(1, oo))))
# Equivalent to E(X(t)**2) - E(X(d)**2) == E(X(1)**2) - E(X(1)**2) == 0
assert E((X(t) + X(d)) * (X(t) - X(d)),
Contains(t, Interval.Lopen(0, 1))
& Contains(d, Interval.Lopen(1, 2))) == 0
assert E(X(2) + x * E(X(5))) == 15 * x + 6
assert E(x * X(1) + y) == 3 * x + y
assert P(Eq(X(1), 2) & Eq(X(t), 3),
Contains(t, Interval.Lopen(1, 2))) == 81 * exp(-6) / 4
Y = PoissonProcess("Y", 6)
Z = X + Y
assert Z.lamda == X.lamda + Y.lamda == 9
raises(ValueError,
lambda: X + 5) # should be added be only PoissonProcess instance
N, M = Z.split(4, 5)
assert N.lamda == 4
assert M.lamda == 5
raises(ValueError, lambda: Z.split(3, 2)) # 2+3 != 9
raises(
ValueError, lambda: P(Eq(X(t), 0),
Contains(t, Interval.Lopen(1, 3)) & Eq(X(1), 0)))
# check if it handles queries with two random variables in one args
res1 = P(Eq(N(3), N(5)))
assert res1 == P(Eq(N(t), 0), Contains(t, Interval(3, 5)))
res2 = P(N(3) > N(1))
assert res2 == P((N(t) > 0), Contains(t, Interval(1, 3)))
assert P(N(3) < N(1)) == 0 # condition is not possible
res3 = P(N(3) <= N(1)) # holds only for Eq(N(3), N(1))
assert res3 == P(Eq(N(t), 0), Contains(t, Interval(1, 3)))
# tests from https://www.probabilitycourse.com/chapter11/11_1_2_basic_concepts_of_the_poisson_process.php
X = PoissonProcess('X', 10) # 11.1
assert P(Eq(X(S(1) / 3), 3)
& Eq(X(1), 10)) == exp(-10) * Rational(8000000000, 11160261)
assert P(Eq(X(1), 1), Eq(X(S(1) / 3), 3)) == 0
assert P(Eq(X(1), 10), Eq(X(S(1) / 3), 3)) == P(Eq(X(S(2) / 3), 7))
X = PoissonProcess('X', 2) # 11.2
assert P(X(S(1) / 2) < 1) == exp(-1)
assert P(X(3) < 1, Eq(X(1), 0)) == exp(-4)
assert P(Eq(X(4), 3), Eq(X(2), 3)) == exp(-4)
X = PoissonProcess('X', 3)
assert P(Eq(X(2), 5) & Eq(X(1), 2)) == Rational(81, 4) * exp(-6)
# check few properties
assert P(
X(2) <= 3,
X(1) >= 1) == 3 * P(Eq(X(1), 0)) + 2 * P(Eq(X(1), 1)) + P(Eq(X(1), 2))
assert P(X(2) <= 3, X(1) > 1) == 2 * P(Eq(X(1), 0)) + 1 * P(Eq(X(1), 1))
assert P(Eq(X(2), 5) & Eq(X(1), 2)) == P(Eq(X(1), 3)) * P(Eq(X(1), 2))
assert P(Eq(X(3), 4), Eq(X(1), 3)) == P(Eq(X(2), 1))
def test_type_error():
# Pass in a parameter not of type "set"
raises(TypeError, lambda: Contains(2, None))
def test_piecewise1():
# Test canonicalization
assert unchanged(Piecewise, ExprCondPair(x, x < 1), ExprCondPair(0, True))
assert Piecewise((x, x < 1), (0, True)) == Piecewise(ExprCondPair(x, x < 1),
ExprCondPair(0, True))
assert Piecewise((x, x < 1), (0, True), (1, True)) == \
Piecewise((x, x < 1), (0, True))
assert Piecewise((x, x < 1), (0, False), (-1, 1 > 2)) == \
Piecewise((x, x < 1))
assert Piecewise((x, x < 1), (0, x < 1), (0, True)) == \
Piecewise((x, x < 1), (0, True))
assert Piecewise((x, x < 1), (0, x < 2), (0, True)) == \
Piecewise((x, x < 1), (0, True))
assert Piecewise((x, x < 1), (x, x < 2), (0, True)) == \
Piecewise((x, Or(x < 1, x < 2)), (0, True))
assert Piecewise((x, x < 1), (x, x < 2), (x, True)) == x
assert Piecewise((x, True)) == x
# Explicitly constructed empty Piecewise not accepted
raises(TypeError, lambda: Piecewise())
# False condition is never retained
assert Piecewise((2*x, x < 0), (x, False)) == \
Piecewise((2*x, x < 0), (x, False), evaluate=False) == \
Piecewise((2*x, x < 0))
assert Piecewise((x, False)) == Undefined
raises(TypeError, lambda: Piecewise(x))
assert Piecewise((x, 1)) == x # 1 and 0 are accepted as True/False
raises(TypeError, lambda: Piecewise((x, 2)))
raises(TypeError, lambda: Piecewise((x, x**2)))
raises(TypeError, lambda: Piecewise(([1], True)))
assert Piecewise(((1, 2), True)) == Tuple(1, 2)
cond = (Piecewise((1, x < 0), (2, True)) < y)
assert Piecewise((1, cond)
) == Piecewise((1, ITE(x < 0, y > 1, y > 2)))
assert Piecewise((1, x > 0), (2, And(x <= 0, x > -1))
) == Piecewise((1, x > 0), (2, x > -1))
# test for supporting Contains in Piecewise
pwise = Piecewise(
(1, And(x <= 6, x > 1, Contains(x, S.Integers))),
(0, True))
assert pwise.subs(x, pi) == 0
assert pwise.subs(x, 2) == 1
assert pwise.subs(x, 7) == 0
# Test subs
p = Piecewise((-1, x < -1), (x**2, x < 0), (log(x), x >= 0))
p_x2 = Piecewise((-1, x**2 < -1), (x**4, x**2 < 0), (log(x**2), x**2 >= 0))
assert p.subs(x, x**2) == p_x2
assert p.subs(x, -5) == -1
assert p.subs(x, -1) == 1
assert p.subs(x, 1) == log(1)
# More subs tests
p2 = Piecewise((1, x < pi), (-1, x < 2*pi), (0, x > 2*pi))
p3 = Piecewise((1, Eq(x, 0)), (1/x, True))
p4 = Piecewise((1, Eq(x, 0)), (2, 1/x>2))
assert p2.subs(x, 2) == 1
assert p2.subs(x, 4) == -1
assert p2.subs(x, 10) == 0
assert p3.subs(x, 0.0) == 1
assert p4.subs(x, 0.0) == 1
f, g, h = symbols('f,g,h', cls=Function)
pf = Piecewise((f(x), x < -1), (f(x) + h(x) + 2, x <= 1))
pg = Piecewise((g(x), x < -1), (g(x) + h(x) + 2, x <= 1))
assert pg.subs(g, f) == pf
assert Piecewise((1, Eq(x, 0)), (0, True)).subs(x, 0) == 1
assert Piecewise((1, Eq(x, 0)), (0, True)).subs(x, 1) == 0
assert Piecewise((1, Eq(x, y)), (0, True)).subs(x, y) == 1
assert Piecewise((1, Eq(x, z)), (0, True)).subs(x, z) == 1
assert Piecewise((1, Eq(exp(x), cos(z))), (0, True)).subs(x, z) == \
Piecewise((1, Eq(exp(z), cos(z))), (0, True))
p5 = Piecewise( (0, Eq(cos(x) + y, 0)), (1, True))
assert p5.subs(y, 0) == Piecewise( (0, Eq(cos(x), 0)), (1, True))
assert Piecewise((-1, y < 1), (0, x < 0), (1, Eq(x, 0)), (2, True)
).subs(x, 1) == Piecewise((-1, y < 1), (2, True))
assert Piecewise((1, Eq(x**2, -1)), (2, x < 0)).subs(x, I) == 1
p6 = Piecewise((x, x > 0))
n = symbols('n', negative=True)
assert p6.subs(x, n) == Undefined
# Test evalf
assert p.evalf() == p
assert p.evalf(subs={x: -2}) == -1
assert p.evalf(subs={x: -1}) == 1
assert p.evalf(subs={x: 1}) == log(1)
assert p6.evalf(subs={x: -5}) == Undefined
# Test doit
f_int = Piecewise((Integral(x, (x, 0, 1)), x < 1))
assert f_int.doit() == Piecewise( (S(1)/2, x < 1) )
# Test differentiation
f = x
fp = x*p
dp = Piecewise((0, x < -1), (2*x, x < 0), (1/x, x >= 0))
fp_dx = x*dp + p
assert diff(p, x) == dp
assert diff(f*p, x) == fp_dx
# Test simple arithmetic
assert x*p == fp
assert x*p + p == p + x*p
assert p + f == f + p
assert p + dp == dp + p
assert p - dp == -(dp - p)
# Test power
dp2 = Piecewise((0, x < -1), (4*x**2, x < 0), (1/x**2, x >= 0))
assert dp**2 == dp2
# Test _eval_interval
f1 = x*y + 2
f2 = x*y**2 + 3
peval = Piecewise((f1, x < 0), (f2, x > 0))
peval_interval = f1.subs(
x, 0) - f1.subs(x, -1) + f2.subs(x, 1) - f2.subs(x, 0)
assert peval._eval_interval(x, 0, 0) == 0
assert peval._eval_interval(x, -1, 1) == peval_interval
peval2 = Piecewise((f1, x < 0), (f2, True))
assert peval2._eval_interval(x, 0, 0) == 0
assert peval2._eval_interval(x, 1, -1) == -peval_interval
assert peval2._eval_interval(x, -1, -2) == f1.subs(x, -2) - f1.subs(x, -1)
assert peval2._eval_interval(x, -1, 1) == peval_interval
assert peval2._eval_interval(x, None, 0) == peval2.subs(x, 0)
assert peval2._eval_interval(x, -1, None) == -peval2.subs(x, -1)
# Test integration
assert p.integrate() == Piecewise(
(-x, x < -1),
(x**3/3 + S(4)/3, x < 0),
(x*log(x) - x + S(4)/3, True))
p = Piecewise((x, x < 1), (x**2, -1 <= x), (x, 3 < x))
assert integrate(p, (x, -2, 2)) == S(5)/6
assert integrate(p, (x, 2, -2)) == -S(5)/6
p = Piecewise((0, x < 0), (1, x < 1), (0, x < 2), (1, x < 3), (0, True))
assert integrate(p, (x, -oo, oo)) == 2
p = Piecewise((x, x < -10), (x**2, x <= -1), (x, 1 < x))
assert integrate(p, (x, -2, 2)) == Undefined
# Test commutativity
assert isinstance(p, Piecewise) and p.is_commutative is True
def test_binary_symbols():
x = Symbol("x")
y = Symbol("y")
z = Symbol("z")
assert Contains(x, FiniteSet(y, Eq(z, True))).binary_symbols == set([y, z])
