def build_ideal(x, terms):
"""
Build generators for our ideal. Terms is an iterable with elements of
the form (fn, coeff), indicating that we have a generator fn(coeff*x).
If any of the terms is trigonometric, sin(x) and cos(x) are guaranteed
to appear in terms. Similarly for hyperbolic functions. For tan(n*x),
sin(n*x) and cos(n*x) are guaranteed.
"""
gens = []
I = []
y = Dummy('y')
for fn, coeff in terms:
for c, s, t, rel in ([cos, sin, tan,
cos(x)**2 + sin(x)**2 - 1], [
cosh, sinh, tanh,
cosh(x)**2 - sinh(x)**2 - 1
]):
if coeff == 1 and fn in [c, s]:
I.append(rel)
elif fn == t:
I.append(t(coeff * x) * c(coeff * x) - s(coeff * x))
elif fn in [c, s]:
cn = fn(coeff * y).expand(trig=True).subs(y, x)
I.append(fn(coeff * x) - cn)
return list(set(I))
def test_C99CodePrinter__precision():
n = symbols('n', integer=True)
f32_printer = C99CodePrinter(dict(type_aliases={real: float32}))
f64_printer = C99CodePrinter(dict(type_aliases={real: float64}))
f80_printer = C99CodePrinter(dict(type_aliases={real: float80}))
assert f32_printer.doprint(sin(x+2.1)) == 'sinf(x + 2.1F)'
assert f64_printer.doprint(sin(x+2.1)) == 'sin(x + 2.1000000000000001)'
assert f80_printer.doprint(sin(x+Float('2.0'))) == 'sinl(x + 2.0L)'
for printer, suffix in zip([f32_printer, f64_printer, f80_printer], ['f', '', 'l']):
def check(expr, ref):
assert printer.doprint(expr) == ref.format(s=suffix, S=suffix.upper())
check(Abs(n), 'abs(n)')
check(Abs(x + 2.0), 'fabs{s}(x + 2.0{S})')
check(sin(x + 4.0)**cos(x - 2.0), 'pow{s}(sin{s}(x + 4.0{S}), cos{s}(x - 2.0{S}))')
check(exp(x*8.0), 'exp{s}(8.0{S}*x)')
check(exp2(x), 'exp2{s}(x)')
check(expm1(x*4.0), 'expm1{s}(4.0{S}*x)')
check(Mod(n, 2), '((n) % (2))')
check(Mod(2*n + 3, 3*n + 5), '((2*n + 3) % (3*n + 5))')
check(Mod(x + 2.0, 3.0), 'fmod{s}(1.0{S}*x + 2.0{S}, 3.0{S})')
check(Mod(x, 2.0*x + 3.0), 'fmod{s}(1.0{S}*x, 2.0{S}*x + 3.0{S})')
check(log(x/2), 'log{s}((1.0{S}/2.0{S})*x)')
check(log10(3*x/2), 'log10{s}((3.0{S}/2.0{S})*x)')
check(log2(x*8.0), 'log2{s}(8.0{S}*x)')
check(log1p(x), 'log1p{s}(x)')
check(2**x, 'pow{s}(2, x)')
check(2.0**x, 'pow{s}(2.0{S}, x)')
check(x**3, 'pow{s}(x, 3)')
check(x**4.0, 'pow{s}(x, 4.0{S})')
check(sqrt(3+x), 'sqrt{s}(x + 3)')
check(Cbrt(x-2.0), 'cbrt{s}(x - 2.0{S})')
check(hypot(x, y), 'hypot{s}(x, y)')
check(sin(3.*x + 2.), 'sin{s}(3.0{S}*x + 2.0{S})')
check(cos(3.*x - 1.), 'cos{s}(3.0{S}*x - 1.0{S})')
check(tan(4.*y + 2.), 'tan{s}(4.0{S}*y + 2.0{S})')
check(asin(3.*x + 2.), 'asin{s}(3.0{S}*x + 2.0{S})')
check(acos(3.*x + 2.), 'acos{s}(3.0{S}*x + 2.0{S})')
check(atan(3.*x + 2.), 'atan{s}(3.0{S}*x + 2.0{S})')
check(atan2(3.*x, 2.*y), 'atan2{s}(3.0{S}*x, 2.0{S}*y)')
check(sinh(3.*x + 2.), 'sinh{s}(3.0{S}*x + 2.0{S})')
check(cosh(3.*x - 1.), 'cosh{s}(3.0{S}*x - 1.0{S})')
check(tanh(4.0*y + 2.), 'tanh{s}(4.0{S}*y + 2.0{S})')
check(asinh(3.*x + 2.), 'asinh{s}(3.0{S}*x + 2.0{S})')
check(acosh(3.*x + 2.), 'acosh{s}(3.0{S}*x + 2.0{S})')
check(atanh(3.*x + 2.), 'atanh{s}(3.0{S}*x + 2.0{S})')
check(erf(42.*x), 'erf{s}(42.0{S}*x)')
check(erfc(42.*x), 'erfc{s}(42.0{S}*x)')
check(gamma(x), 'tgamma{s}(x)')
check(loggamma(x), 'lgamma{s}(x)')
check(ceiling(x + 2.), "ceil{s}(x + 2.0{S})")
check(floor(x + 2.), "floor{s}(x + 2.0{S})")
check(fma(x, y, -z), 'fma{s}(x, y, -z)')
check(Max(x, 8.0, x**4.0), 'fmax{s}(8.0{S}, fmax{s}(x, pow{s}(x, 4.0{S})))')
check(Min(x, 2.0), 'fmin{s}(2.0{S}, x)')
def test_C99CodePrinter__precision():
n = symbols('n', integer=True)
f32_printer = C99CodePrinter(dict(type_aliases={real: float32}))
f64_printer = C99CodePrinter(dict(type_aliases={real: float64}))
f80_printer = C99CodePrinter(dict(type_aliases={real: float80}))
assert f32_printer.doprint(sin(x+2.1)) == 'sinf(x + 2.1F)'
assert f64_printer.doprint(sin(x+2.1)) == 'sin(x + 2.1000000000000001)'
assert f80_printer.doprint(sin(x+Float('2.0'))) == 'sinl(x + 2.0L)'
for printer, suffix in zip([f32_printer, f64_printer, f80_printer], ['f', '', 'l']):
def check(expr, ref):
assert printer.doprint(expr) == ref.format(s=suffix, S=suffix.upper())
check(Abs(n), 'abs(n)')
check(Abs(x + 2.0), 'fabs{s}(x + 2.0{S})')
check(sin(x + 4.0)**cos(x - 2.0), 'pow{s}(sin{s}(x + 4.0{S}), cos{s}(x - 2.0{S}))')
check(exp(x*8.0), 'exp{s}(8.0{S}*x)')
check(exp2(x), 'exp2{s}(x)')
check(expm1(x*4.0), 'expm1{s}(4.0{S}*x)')
check(Mod(n, 2), '((n) % (2))')
check(Mod(2*n + 3, 3*n + 5), '((2*n + 3) % (3*n + 5))')
check(Mod(x + 2.0, 3.0), 'fmod{s}(1.0{S}*x + 2.0{S}, 3.0{S})')
check(Mod(x, 2.0*x + 3.0), 'fmod{s}(1.0{S}*x, 2.0{S}*x + 3.0{S})')
check(log(x/2), 'log{s}((1.0{S}/2.0{S})*x)')
check(log10(3*x/2), 'log10{s}((3.0{S}/2.0{S})*x)')
check(log2(x*8.0), 'log2{s}(8.0{S}*x)')
check(log1p(x), 'log1p{s}(x)')
check(2**x, 'pow{s}(2, x)')
check(2.0**x, 'pow{s}(2.0{S}, x)')
check(x**3, 'pow{s}(x, 3)')
check(x**4.0, 'pow{s}(x, 4.0{S})')
check(sqrt(3+x), 'sqrt{s}(x + 3)')
check(Cbrt(x-2.0), 'cbrt{s}(x - 2.0{S})')
check(hypot(x, y), 'hypot{s}(x, y)')
check(sin(3.*x + 2.), 'sin{s}(3.0{S}*x + 2.0{S})')
check(cos(3.*x - 1.), 'cos{s}(3.0{S}*x - 1.0{S})')
check(tan(4.*y + 2.), 'tan{s}(4.0{S}*y + 2.0{S})')
check(asin(3.*x + 2.), 'asin{s}(3.0{S}*x + 2.0{S})')
check(acos(3.*x + 2.), 'acos{s}(3.0{S}*x + 2.0{S})')
check(atan(3.*x + 2.), 'atan{s}(3.0{S}*x + 2.0{S})')
check(atan2(3.*x, 2.*y), 'atan2{s}(3.0{S}*x, 2.0{S}*y)')
check(sinh(3.*x + 2.), 'sinh{s}(3.0{S}*x + 2.0{S})')
check(cosh(3.*x - 1.), 'cosh{s}(3.0{S}*x - 1.0{S})')
check(tanh(4.0*y + 2.), 'tanh{s}(4.0{S}*y + 2.0{S})')
check(asinh(3.*x + 2.), 'asinh{s}(3.0{S}*x + 2.0{S})')
check(acosh(3.*x + 2.), 'acosh{s}(3.0{S}*x + 2.0{S})')
check(atanh(3.*x + 2.), 'atanh{s}(3.0{S}*x + 2.0{S})')
check(erf(42.*x), 'erf{s}(42.0{S}*x)')
check(erfc(42.*x), 'erfc{s}(42.0{S}*x)')
check(gamma(x), 'tgamma{s}(x)')
check(loggamma(x), 'lgamma{s}(x)')
check(ceiling(x + 2.), "ceil{s}(x + 2.0{S})")
check(floor(x + 2.), "floor{s}(x + 2.0{S})")
check(fma(x, y, -z), 'fma{s}(x, y, -z)')
check(Max(x, 8.0, x**4.0), 'fmax{s}(8.0{S}, fmax{s}(x, pow{s}(x, 4.0{S})))')
check(Min(x, 2.0), 'fmin{s}(2.0{S}, x)')
def f(rv):
if not rv.is_Mul:
return rv
commutative_part, noncommutative_part = rv.args_cnc()
# Since as_powers_dict loses order information,
# if there is more than one noncommutative factor,
# it should only be used to simplify the commutative part.
if (len(noncommutative_part) > 1):
return f(Mul(*commutative_part)) * Mul(*noncommutative_part)
rvd = rv.as_powers_dict()
newd = rvd.copy()
def signlog(expr, sign=S.One):
if expr is S.Exp1:
return sign, S.One
elif isinstance(expr, exp) or (expr.is_Pow
and expr.base == S.Exp1):
return sign, expr.exp
elif sign is S.One:
return signlog(-expr, sign=-S.One)
else:
return None, None
ee = rvd[S.Exp1]
for k in rvd:
if k.is_Add and len(k.args) == 2:
# k == c*(1 + sign*E**x)
c = k.args[0]
sign, x = signlog(k.args[1] / c)
if not x:
continue
m = rvd[k]
newd[k] -= m
if ee == -x * m / 2:
# sinh and cosh
newd[S.Exp1] -= ee
ee = 0
if sign == 1:
newd[2 * c * cosh(x / 2)] += m
else:
newd[-2 * c * sinh(x / 2)] += m
elif newd[1 - sign * S.Exp1**x] == -m:
# tanh
del newd[1 - sign * S.Exp1**x]
if sign == 1:
newd[-c / tanh(x / 2)] += m
else:
newd[-c * tanh(x / 2)] += m
else:
newd[1 + sign * S.Exp1**x] += m
newd[c] += m
return Mul(*[k**newd[k] for k in newd])
def f(rv):
if not rv.is_Mul:
return rv
commutative_part, noncommutative_part = rv.args_cnc()
# Since as_powers_dict loses order information,
# if there is more than one noncommutative factor,
# it should only be used to simplify the commutative part.
if (len(noncommutative_part) > 1):
return f(Mul(*commutative_part))*Mul(*noncommutative_part)
rvd = rv.as_powers_dict()
newd = rvd.copy()
def signlog(expr, sign=1):
if expr is S.Exp1:
return sign, 1
elif isinstance(expr, exp):
return sign, expr.args[0]
elif sign == 1:
return signlog(-expr, sign=-1)
else:
return None, None
ee = rvd[S.Exp1]
for k in rvd:
if k.is_Add and len(k.args) == 2:
# k == c*(1 + sign*E**x)
c = k.args[0]
sign, x = signlog(k.args[1]/c)
if not x:
continue
m = rvd[k]
newd[k] -= m
if ee == -x*m/2:
# sinh and cosh
newd[S.Exp1] -= ee
ee = 0
if sign == 1:
newd[2*c*cosh(x/2)] += m
else:
newd[-2*c*sinh(x/2)] += m
elif newd[1 - sign*S.Exp1**x] == -m:
# tanh
del newd[1 - sign*S.Exp1**x]
if sign == 1:
newd[-c/tanh(x/2)] += m
else:
newd[-c*tanh(x/2)] += m
else:
newd[1 + sign*S.Exp1**x] += m
newd[c] += m
return Mul(*[k**newd[k] for k in newd])
def f(rv):
if not rv.is_Mul:
return rv
rvd = rv.as_powers_dict()
newd = rvd.copy()
def signlog(expr, sign=1):
if expr is S.Exp1:
return sign, 1
elif isinstance(expr, exp):
return sign, expr.args[0]
elif sign == 1:
return signlog(-expr, sign=-1)
else:
return None, None
ee = rvd[S.Exp1]
for k in rvd:
if k.is_Add and len(k.args) == 2:
# k == c*(1 + sign*E**x)
c = k.args[0]
sign, x = signlog(k.args[1]/c)
if not x:
continue
m = rvd[k]
newd[k] -= m
if ee == -x*m/2:
# sinh and cosh
newd[S.Exp1] -= ee
ee = 0
if sign == 1:
newd[2*c*cosh(x/2)] += m
else:
newd[-2*c*sinh(x/2)] += m
elif newd[1 - sign*S.Exp1**x] == -m:
# tanh
del newd[1 - sign*S.Exp1**x]
if sign == 1:
newd[-c/tanh(x/2)] += m
else:
newd[-c*tanh(x/2)] += m
else:
newd[1 + sign*S.Exp1**x] += m
newd[c] += m
return Mul(*[k**newd[k] for k in newd])
def f(rv):
if not rv.is_Mul:
return rv
rvd = rv.as_powers_dict()
newd = rvd.copy()
def signlog(expr, sign=1):
if expr is S.Exp1:
return sign, 1
elif isinstance(expr, exp):
return sign, expr.args[0]
elif sign == 1:
return signlog(-expr, sign=-1)
else:
return None, None
ee = rvd[S.Exp1]
for k in rvd:
if k.is_Add and len(k.args) == 2:
# k == c*(1 + sign*E**x)
c = k.args[0]
sign, x = signlog(k.args[1]/c)
if not x:
continue
m = rvd[k]
newd[k] -= m
if ee == -x*m/2:
# sinh and cosh
newd[S.Exp1] -= ee
ee = 0
if sign == 1:
newd[2*c*cosh(x/2)] += m
else:
newd[-2*c*sinh(x/2)] += m
elif newd[1 - sign*S.Exp1**x] == -m:
# tanh
del newd[1 - sign*S.Exp1**x]
if sign == 1:
newd[-c/tanh(x/2)] += m
else:
newd[-c*tanh(x/2)] += m
else:
newd[1 + sign*S.Exp1**x] += m
newd[c] += m
return Mul(*[k**newd[k] for k in newd])
def build_ideal(x, terms):
"""
Build generators for our ideal. Terms is an iterable with elements of
the form (fn, coeff), indicating that we have a generator fn(coeff*x).
If any of the terms is trigonometric, sin(x) and cos(x) are guaranteed
to appear in terms. Similarly for hyperbolic functions. For tan(n*x),
sin(n*x) and cos(n*x) are guaranteed.
"""
I = []
y = Dummy('y')
for fn, coeff in terms:
for c, s, t, rel in (
[cos, sin, tan, cos(x)**2 + sin(x)**2 - 1],
[cosh, sinh, tanh, cosh(x)**2 - sinh(x)**2 - 1]):
if coeff == 1 and fn in [c, s]:
I.append(rel)
elif fn == t:
I.append(t(coeff*x)*c(coeff*x) - s(coeff*x))
elif fn in [c, s]:
cn = fn(coeff*y).expand(trig=True).subs(y, x)
I.append(fn(coeff*x) - cn)
return list(set(I))
def _expr_big_minus(cls, a, z, n):
return cosh(2 * a * asinh(sqrt(z)) + 2 * a * pi * I * n)
def test_jscode_functions():
assert jscode(sin(x)**cos(x)) == "Math.pow(Math.sin(x), Math.cos(x))"
assert jscode(sinh(x) * cosh(x)) == "Math.sinh(x)*Math.cosh(x)"
assert jscode(Max(x, y) + Min(x, y)) == "Math.max(x, y) + Math.min(x, y)"
assert jscode(tanh(x) * acosh(y)) == "Math.tanh(x)*Math.acosh(y)"
assert jscode(asin(x) - acos(y)) == "-Math.acos(y) + Math.asin(x)"
def _expr_small_minus(cls, a, z):
return cosh(2 * a * asinh(sqrt(z)))
def _expr_big(cls, a, z, n):
return cosh(2 * a * acosh(sqrt(z)) + a * pi * I * (2 * n - 1))
def _expr_big(cls, a, z, n):
return cosh(2*a*acosh(sqrt(z)) + a*pi*I*(2*n - 1))
def _expr_big_minus(cls, a, z, n):
return cosh(2*a*asinh(sqrt(z)) + 2*a*pi*I*n)
def test_tensorflow_math():
if not tf:
skip("TensorFlow not installed")
expr = Abs(x)
assert tensorflow_code(expr) == "tensorflow.math.abs(x)"
_compare_tensorflow_scalar((x, ), expr)
expr = sign(x)
assert tensorflow_code(expr) == "tensorflow.math.sign(x)"
_compare_tensorflow_scalar((x, ), expr)
expr = ceiling(x)
assert tensorflow_code(expr) == "tensorflow.math.ceil(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = floor(x)
assert tensorflow_code(expr) == "tensorflow.math.floor(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = exp(x)
assert tensorflow_code(expr) == "tensorflow.math.exp(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = sqrt(x)
assert tensorflow_code(expr) == "tensorflow.math.sqrt(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = x**4
assert tensorflow_code(expr) == "tensorflow.math.pow(x, 4)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = cos(x)
assert tensorflow_code(expr) == "tensorflow.math.cos(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = acos(x)
assert tensorflow_code(expr) == "tensorflow.math.acos(x)"
_compare_tensorflow_scalar((x, ),
expr,
rng=lambda: random.uniform(0, 0.95))
expr = sin(x)
assert tensorflow_code(expr) == "tensorflow.math.sin(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = asin(x)
assert tensorflow_code(expr) == "tensorflow.math.asin(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = tan(x)
assert tensorflow_code(expr) == "tensorflow.math.tan(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = atan(x)
assert tensorflow_code(expr) == "tensorflow.math.atan(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = atan2(y, x)
assert tensorflow_code(expr) == "tensorflow.math.atan2(y, x)"
_compare_tensorflow_scalar((y, x), expr, rng=lambda: random.random())
expr = cosh(x)
assert tensorflow_code(expr) == "tensorflow.math.cosh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = acosh(x)
assert tensorflow_code(expr) == "tensorflow.math.acosh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = sinh(x)
assert tensorflow_code(expr) == "tensorflow.math.sinh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = asinh(x)
assert tensorflow_code(expr) == "tensorflow.math.asinh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = tanh(x)
assert tensorflow_code(expr) == "tensorflow.math.tanh(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = atanh(x)
assert tensorflow_code(expr) == "tensorflow.math.atanh(x)"
_compare_tensorflow_scalar((x, ),
expr,
rng=lambda: random.uniform(-.5, .5))
expr = erf(x)
assert tensorflow_code(expr) == "tensorflow.math.erf(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
expr = loggamma(x)
assert tensorflow_code(expr) == "tensorflow.math.lgamma(x)"
_compare_tensorflow_scalar((x, ), expr, rng=lambda: random.random())
def _expr_small_minus(cls, a, z):
return cosh(2*a*asinh(sqrt(z)))
def _trigpats():
global _trigpat
a, b, c = symbols('a b c', cls=Wild)
d = Wild('d', commutative=False)
# for the simplifications like sinh/cosh -> tanh:
# DO NOT REORDER THE FIRST 14 since these are assumed to be in this
# order in _match_div_rewrite.
matchers_division = (
(a * sin(b)**c / cos(b)**c, a * tan(b)**c, sin(b), cos(b)),
(a * tan(b)**c * cos(b)**c, a * sin(b)**c, sin(b), cos(b)),
(a * cot(b)**c * sin(b)**c, a * cos(b)**c, sin(b), cos(b)),
(a * tan(b)**c / sin(b)**c, a / cos(b)**c, sin(b), cos(b)),
(a * cot(b)**c / cos(b)**c, a / sin(b)**c, sin(b), cos(b)),
(a * cot(b)**c * tan(b)**c, a, sin(b), cos(b)),
(a * (cos(b) + 1)**c * (cos(b) - 1)**c, a * (-sin(b)**2)**c,
cos(b) + 1, cos(b) - 1),
(a * (sin(b) + 1)**c * (sin(b) - 1)**c, a * (-cos(b)**2)**c,
sin(b) + 1, sin(b) - 1),
(a * sinh(b)**c / cosh(b)**c, a * tanh(b)**c, S.One, S.One),
(a * tanh(b)**c * cosh(b)**c, a * sinh(b)**c, S.One, S.One),
(a * coth(b)**c * sinh(b)**c, a * cosh(b)**c, S.One, S.One),
(a * tanh(b)**c / sinh(b)**c, a / cosh(b)**c, S.One, S.One),
(a * coth(b)**c / cosh(b)**c, a / sinh(b)**c, S.One, S.One),
(a * coth(b)**c * tanh(b)**c, a, S.One, S.One),
(c * (tanh(a) + tanh(b)) / (1 + tanh(a) * tanh(b)), tanh(a + b) * c,
S.One, S.One),
)
matchers_add = (
(c * sin(a) * cos(b) + c * cos(a) * sin(b) + d, sin(a + b) * c + d),
(c * cos(a) * cos(b) - c * sin(a) * sin(b) + d, cos(a + b) * c + d),
(c * sin(a) * cos(b) - c * cos(a) * sin(b) + d, sin(a - b) * c + d),
(c * cos(a) * cos(b) + c * sin(a) * sin(b) + d, cos(a - b) * c + d),
(c * sinh(a) * cosh(b) + c * sinh(b) * cosh(a) + d,
sinh(a + b) * c + d),
(c * cosh(a) * cosh(b) + c * sinh(a) * sinh(b) + d,
cosh(a + b) * c + d),
)
# for cos(x)**2 + sin(x)**2 -> 1
matchers_identity = (
(a * sin(b)**2, a - a * cos(b)**2),
(a * tan(b)**2, a * (1 / cos(b))**2 - a),
(a * cot(b)**2, a * (1 / sin(b))**2 - a),
(a * sin(b + c), a * (sin(b) * cos(c) + sin(c) * cos(b))),
(a * cos(b + c), a * (cos(b) * cos(c) - sin(b) * sin(c))),
(a * tan(b + c), a * ((tan(b) + tan(c)) / (1 - tan(b) * tan(c)))),
(a * sinh(b)**2, a * cosh(b)**2 - a),
(a * tanh(b)**2, a - a * (1 / cosh(b))**2),
(a * coth(b)**2, a + a * (1 / sinh(b))**2),
(a * sinh(b + c), a * (sinh(b) * cosh(c) + sinh(c) * cosh(b))),
(a * cosh(b + c), a * (cosh(b) * cosh(c) + sinh(b) * sinh(c))),
(a * tanh(b + c), a * ((tanh(b) + tanh(c)) / (1 + tanh(b) * tanh(c)))),
)
# Reduce any lingering artifacts, such as sin(x)**2 changing
# to 1-cos(x)**2 when sin(x)**2 was "simpler"
artifacts = (
(a - a * cos(b)**2 + c, a * sin(b)**2 + c, cos),
(a - a * (1 / cos(b))**2 + c, -a * tan(b)**2 + c, cos),
(a - a * (1 / sin(b))**2 + c, -a * cot(b)**2 + c, sin),
(a - a * cosh(b)**2 + c, -a * sinh(b)**2 + c, cosh),
(a - a * (1 / cosh(b))**2 + c, a * tanh(b)**2 + c, cosh),
(a + a * (1 / sinh(b))**2 + c, a * coth(b)**2 + c, sinh),
# same as above but with noncommutative prefactor
(a * d - a * d * cos(b)**2 + c, a * d * sin(b)**2 + c, cos),
(a * d - a * d * (1 / cos(b))**2 + c, -a * d * tan(b)**2 + c, cos),
(a * d - a * d * (1 / sin(b))**2 + c, -a * d * cot(b)**2 + c, sin),
(a * d - a * d * cosh(b)**2 + c, -a * d * sinh(b)**2 + c, cosh),
(a * d - a * d * (1 / cosh(b))**2 + c, a * d * tanh(b)**2 + c, cosh),
(a * d + a * d * (1 / sinh(b))**2 + c, a * d * coth(b)**2 + c, sinh),
)
_trigpat = (a, b, c, d, matchers_division, matchers_add, matchers_identity,
artifacts)
return _trigpat
def test_jscode_functions():
assert jscode(sin(x) ** cos(x)) == "Math.pow(Math.sin(x), Math.cos(x))"
assert jscode(sinh(x) * cosh(x)) == "Math.sinh(x)*Math.cosh(x)"
assert jscode(Max(x, y) + Min(x, y)) == "Math.max(x, y) + Math.min(x, y)"
assert jscode(tanh(x)*acosh(y)) == "Math.tanh(x)*Math.acosh(y)"
assert jscode(asin(x)-acos(y)) == "-Math.acos(y) + Math.asin(x)"
def exptrigsimp(expr, simplify=True):
"""
Simplifies exponential / trigonometric / hyperbolic functions.
When ``simplify`` is True (default) the expression obtained after the
simplification step will be then be passed through simplify to
precondition it so the final transformations will be applied.
Examples
========
>>> from sympy import exptrigsimp, exp, cosh, sinh
>>> from sympy.abc import z
>>> exptrigsimp(exp(z) + exp(-z))
2*cosh(z)
>>> exptrigsimp(cosh(z) - sinh(z))
exp(-z)
"""
from sympy.simplify.fu import hyper_as_trig, TR2i
from sympy.simplify.simplify import bottom_up
def exp_trig(e):
# select the better of e, and e rewritten in terms of exp or trig
# functions
choices = [e]
if e.has(*_trigs):
choices.append(e.rewrite(exp))
choices.append(e.rewrite(cos))
return min(*choices, key=count_ops)
newexpr = bottom_up(expr, exp_trig)
if simplify:
newexpr = newexpr.simplify()
# conversion from exp to hyperbolic
ex = newexpr.atoms(exp, S.Exp1)
ex = [ei for ei in ex if 1/ei not in ex]
## sinh and cosh
for ei in ex:
e2 = ei**-2
if e2 in ex:
a = e2.args[0]/2 if not e2 is S.Exp1 else S.Half
newexpr = newexpr.subs((e2 + 1)*ei, 2*cosh(a))
newexpr = newexpr.subs((e2 - 1)*ei, 2*sinh(a))
## exp ratios to tan and tanh
for ei in ex:
n, d = ei - 1, ei + 1
et = n/d
etinv = d/n # not 1/et or else recursion errors arise
a = ei.args[0] if ei.func is exp else S.One
if a.is_Mul or a is S.ImaginaryUnit:
c = a.as_coefficient(I)
if c:
t = S.ImaginaryUnit*tan(c/2)
newexpr = newexpr.subs(etinv, 1/t)
newexpr = newexpr.subs(et, t)
continue
t = tanh(a/2)
newexpr = newexpr.subs(etinv, 1/t)
newexpr = newexpr.subs(et, t)
# sin/cos and sinh/cosh ratios to tan and tanh, respectively
if newexpr.has(HyperbolicFunction):
e, f = hyper_as_trig(newexpr)
newexpr = f(TR2i(e))
if newexpr.has(TrigonometricFunction):
newexpr = TR2i(newexpr)
# can we ever generate an I where there was none previously?
if not (newexpr.has(I) and not expr.has(I)):
expr = newexpr
return expr
def exptrigsimp(expr, simplify=True):
"""
Simplifies exponential / trigonometric / hyperbolic functions.
When ``simplify`` is True (default) the expression obtained after the
simplification step will be then be passed through simplify to
precondition it so the final transformations will be applied.
Examples
========
>>> from sympy import exptrigsimp, exp, cosh, sinh
>>> from sympy.abc import z
>>> exptrigsimp(exp(z) + exp(-z))
2*cosh(z)
>>> exptrigsimp(cosh(z) - sinh(z))
exp(-z)
"""
from sympy.simplify.fu import hyper_as_trig, TR2i
from sympy.simplify.simplify import bottom_up
def exp_trig(e):
# select the better of e, and e rewritten in terms of exp or trig
# functions
choices = [e]
if e.has(*_trigs):
choices.append(e.rewrite(exp))
choices.append(e.rewrite(cos))
return min(*choices, key=count_ops)
newexpr = bottom_up(expr, exp_trig)
if simplify:
newexpr = newexpr.simplify()
# conversion from exp to hyperbolic
ex = newexpr.atoms(exp, S.Exp1)
ex = [ei for ei in ex if 1 / ei not in ex]
## sinh and cosh
for ei in ex:
e2 = ei**-2
if e2 in ex:
a = e2.args[0] / 2 if not e2 is S.Exp1 else S.Half
newexpr = newexpr.subs((e2 + 1) * ei, 2 * cosh(a))
newexpr = newexpr.subs((e2 - 1) * ei, 2 * sinh(a))
## exp ratios to tan and tanh
for ei in ex:
n, d = ei - 1, ei + 1
et = n / d
etinv = d / n # not 1/et or else recursion errors arise
a = ei.args[0] if ei.func is exp else S.One
if a.is_Mul or a is S.ImaginaryUnit:
c = a.as_coefficient(I)
if c:
t = S.ImaginaryUnit * tan(c / 2)
newexpr = newexpr.subs(etinv, 1 / t)
newexpr = newexpr.subs(et, t)
continue
t = tanh(a / 2)
newexpr = newexpr.subs(etinv, 1 / t)
newexpr = newexpr.subs(et, t)
# sin/cos and sinh/cosh ratios to tan and tanh, respectively
if newexpr.has(HyperbolicFunction):
e, f = hyper_as_trig(newexpr)
newexpr = f(TR2i(e))
if newexpr.has(TrigonometricFunction):
newexpr = TR2i(newexpr)
# can we ever generate an I where there was none previously?
if not (newexpr.has(I) and not expr.has(I)):
expr = newexpr
return expr
def test_C99CodePrinter__precision():
n = symbols("n", integer=True)
f32_printer = C99CodePrinter(dict(type_aliases={real: float32}))
f64_printer = C99CodePrinter(dict(type_aliases={real: float64}))
f80_printer = C99CodePrinter(dict(type_aliases={real: float80}))
assert f32_printer.doprint(sin(x + 2.1)) == "sinf(x + 2.1F)"
assert f64_printer.doprint(sin(x + 2.1)) == "sin(x + 2.1000000000000001)"
assert f80_printer.doprint(sin(x + Float("2.0"))) == "sinl(x + 2.0L)"
for printer, suffix in zip([f32_printer, f64_printer, f80_printer], ["f", "", "l"]):
def check(expr, ref):
assert printer.doprint(expr) == ref.format(s=suffix, S=suffix.upper())
check(Abs(n), "abs(n)")
check(Abs(x + 2.0), "fabs{s}(x + 2.0{S})")
check(
sin(x + 4.0) ** cos(x - 2.0),
"pow{s}(sin{s}(x + 4.0{S}), cos{s}(x - 2.0{S}))",
)
check(exp(x * 8.0), "exp{s}(8.0{S}*x)")
check(exp2(x), "exp2{s}(x)")
check(expm1(x * 4.0), "expm1{s}(4.0{S}*x)")
check(Mod(n, 2), "((n) % (2))")
check(Mod(2 * n + 3, 3 * n + 5), "((2*n + 3) % (3*n + 5))")
check(Mod(x + 2.0, 3.0), "fmod{s}(1.0{S}*x + 2.0{S}, 3.0{S})")
check(Mod(x, 2.0 * x + 3.0), "fmod{s}(1.0{S}*x, 2.0{S}*x + 3.0{S})")
check(log(x / 2), "log{s}((1.0{S}/2.0{S})*x)")
check(log10(3 * x / 2), "log10{s}((3.0{S}/2.0{S})*x)")
check(log2(x * 8.0), "log2{s}(8.0{S}*x)")
check(log1p(x), "log1p{s}(x)")
check(2 ** x, "pow{s}(2, x)")
check(2.0 ** x, "pow{s}(2.0{S}, x)")
check(x ** 3, "pow{s}(x, 3)")
check(x ** 4.0, "pow{s}(x, 4.0{S})")
check(sqrt(3 + x), "sqrt{s}(x + 3)")
check(Cbrt(x - 2.0), "cbrt{s}(x - 2.0{S})")
check(hypot(x, y), "hypot{s}(x, y)")
check(sin(3.0 * x + 2.0), "sin{s}(3.0{S}*x + 2.0{S})")
check(cos(3.0 * x - 1.0), "cos{s}(3.0{S}*x - 1.0{S})")
check(tan(4.0 * y + 2.0), "tan{s}(4.0{S}*y + 2.0{S})")
check(asin(3.0 * x + 2.0), "asin{s}(3.0{S}*x + 2.0{S})")
check(acos(3.0 * x + 2.0), "acos{s}(3.0{S}*x + 2.0{S})")
check(atan(3.0 * x + 2.0), "atan{s}(3.0{S}*x + 2.0{S})")
check(atan2(3.0 * x, 2.0 * y), "atan2{s}(3.0{S}*x, 2.0{S}*y)")
check(sinh(3.0 * x + 2.0), "sinh{s}(3.0{S}*x + 2.0{S})")
check(cosh(3.0 * x - 1.0), "cosh{s}(3.0{S}*x - 1.0{S})")
check(tanh(4.0 * y + 2.0), "tanh{s}(4.0{S}*y + 2.0{S})")
check(asinh(3.0 * x + 2.0), "asinh{s}(3.0{S}*x + 2.0{S})")
check(acosh(3.0 * x + 2.0), "acosh{s}(3.0{S}*x + 2.0{S})")
check(atanh(3.0 * x + 2.0), "atanh{s}(3.0{S}*x + 2.0{S})")
check(erf(42.0 * x), "erf{s}(42.0{S}*x)")
check(erfc(42.0 * x), "erfc{s}(42.0{S}*x)")
check(gamma(x), "tgamma{s}(x)")
check(loggamma(x), "lgamma{s}(x)")
check(ceiling(x + 2.0), "ceil{s}(x + 2.0{S})")
check(floor(x + 2.0), "floor{s}(x + 2.0{S})")
check(fma(x, y, -z), "fma{s}(x, y, -z)")
check(Max(x, 8.0, x ** 4.0), "fmax{s}(8.0{S}, fmax{s}(x, pow{s}(x, 4.0{S})))")
check(Min(x, 2.0), "fmin{s}(2.0{S}, x)")
def _trigpats():
global _trigpat
a, b, c = symbols('a b c', cls=Wild)
d = Wild('d', commutative=False)
# for the simplifications like sinh/cosh -> tanh:
# DO NOT REORDER THE FIRST 14 since these are assumed to be in this
# order in _match_div_rewrite.
matchers_division = (
(a*sin(b)**c/cos(b)**c, a*tan(b)**c, sin(b), cos(b)),
(a*tan(b)**c*cos(b)**c, a*sin(b)**c, sin(b), cos(b)),
(a*cot(b)**c*sin(b)**c, a*cos(b)**c, sin(b), cos(b)),
(a*tan(b)**c/sin(b)**c, a/cos(b)**c, sin(b), cos(b)),
(a*cot(b)**c/cos(b)**c, a/sin(b)**c, sin(b), cos(b)),
(a*cot(b)**c*tan(b)**c, a, sin(b), cos(b)),
(a*(cos(b) + 1)**c*(cos(b) - 1)**c,
a*(-sin(b)**2)**c, cos(b) + 1, cos(b) - 1),
(a*(sin(b) + 1)**c*(sin(b) - 1)**c,
a*(-cos(b)**2)**c, sin(b) + 1, sin(b) - 1),
(a*sinh(b)**c/cosh(b)**c, a*tanh(b)**c, S.One, S.One),
(a*tanh(b)**c*cosh(b)**c, a*sinh(b)**c, S.One, S.One),
(a*coth(b)**c*sinh(b)**c, a*cosh(b)**c, S.One, S.One),
(a*tanh(b)**c/sinh(b)**c, a/cosh(b)**c, S.One, S.One),
(a*coth(b)**c/cosh(b)**c, a/sinh(b)**c, S.One, S.One),
(a*coth(b)**c*tanh(b)**c, a, S.One, S.One),
(c*(tanh(a) + tanh(b))/(1 + tanh(a)*tanh(b)),
tanh(a + b)*c, S.One, S.One),
)
matchers_add = (
(c*sin(a)*cos(b) + c*cos(a)*sin(b) + d, sin(a + b)*c + d),
(c*cos(a)*cos(b) - c*sin(a)*sin(b) + d, cos(a + b)*c + d),
(c*sin(a)*cos(b) - c*cos(a)*sin(b) + d, sin(a - b)*c + d),
(c*cos(a)*cos(b) + c*sin(a)*sin(b) + d, cos(a - b)*c + d),
(c*sinh(a)*cosh(b) + c*sinh(b)*cosh(a) + d, sinh(a + b)*c + d),
(c*cosh(a)*cosh(b) + c*sinh(a)*sinh(b) + d, cosh(a + b)*c + d),
)
# for cos(x)**2 + sin(x)**2 -> 1
matchers_identity = (
(a*sin(b)**2, a - a*cos(b)**2),
(a*tan(b)**2, a*(1/cos(b))**2 - a),
(a*cot(b)**2, a*(1/sin(b))**2 - a),
(a*sin(b + c), a*(sin(b)*cos(c) + sin(c)*cos(b))),
(a*cos(b + c), a*(cos(b)*cos(c) - sin(b)*sin(c))),
(a*tan(b + c), a*((tan(b) + tan(c))/(1 - tan(b)*tan(c)))),
(a*sinh(b)**2, a*cosh(b)**2 - a),
(a*tanh(b)**2, a - a*(1/cosh(b))**2),
(a*coth(b)**2, a + a*(1/sinh(b))**2),
(a*sinh(b + c), a*(sinh(b)*cosh(c) + sinh(c)*cosh(b))),
(a*cosh(b + c), a*(cosh(b)*cosh(c) + sinh(b)*sinh(c))),
(a*tanh(b + c), a*((tanh(b) + tanh(c))/(1 + tanh(b)*tanh(c)))),
)
# Reduce any lingering artifacts, such as sin(x)**2 changing
# to 1-cos(x)**2 when sin(x)**2 was "simpler"
artifacts = (
(a - a*cos(b)**2 + c, a*sin(b)**2 + c, cos),
(a - a*(1/cos(b))**2 + c, -a*tan(b)**2 + c, cos),
(a - a*(1/sin(b))**2 + c, -a*cot(b)**2 + c, sin),
(a - a*cosh(b)**2 + c, -a*sinh(b)**2 + c, cosh),
(a - a*(1/cosh(b))**2 + c, a*tanh(b)**2 + c, cosh),
(a + a*(1/sinh(b))**2 + c, a*coth(b)**2 + c, sinh),
# same as above but with noncommutative prefactor
(a*d - a*d*cos(b)**2 + c, a*d*sin(b)**2 + c, cos),
(a*d - a*d*(1/cos(b))**2 + c, -a*d*tan(b)**2 + c, cos),
(a*d - a*d*(1/sin(b))**2 + c, -a*d*cot(b)**2 + c, sin),
(a*d - a*d*cosh(b)**2 + c, -a*d*sinh(b)**2 + c, cosh),
(a*d - a*d*(1/cosh(b))**2 + c, a*d*tanh(b)**2 + c, cosh),
(a*d + a*d*(1/sinh(b))**2 + c, a*d*coth(b)**2 + c, sinh),
)
_trigpat = (a, b, c, d, matchers_division, matchers_add,
matchers_identity, artifacts)
return _trigpat
def test_torch_math():
if not torch:
skip("Torch not installed")
ma = torch.tensor([[1, 2, -3, -4]])
expr = Abs(x)
assert torch_code(expr) == "torch.abs(x)"
f = lambdify(x, expr, 'torch')
y = f(ma)
c = torch.abs(ma)
assert (y == c).all()
expr = sign(x)
assert torch_code(expr) == "torch.sign(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.randint(0, 10))
expr = ceiling(x)
assert torch_code(expr) == "torch.ceil(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = floor(x)
assert torch_code(expr) == "torch.floor(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = exp(x)
assert torch_code(expr) == "torch.exp(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
# expr = sqrt(x)
# assert torch_code(expr) == "torch.sqrt(x)"
# _compare_torch_scalar((x,), expr, rng=lambda: random.random())
# expr = x ** 4
# assert torch_code(expr) == "torch.pow(x, 4)"
# _compare_torch_scalar((x,), expr, rng=lambda: random.random())
expr = cos(x)
assert torch_code(expr) == "torch.cos(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = acos(x)
assert torch_code(expr) == "torch.acos(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(0, 0.95))
expr = sin(x)
assert torch_code(expr) == "torch.sin(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = asin(x)
assert torch_code(expr) == "torch.asin(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = tan(x)
assert torch_code(expr) == "torch.tan(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = atan(x)
assert torch_code(expr) == "torch.atan(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
# expr = atan2(y, x)
# assert torch_code(expr) == "torch.atan2(y, x)"
# _compare_torch_scalar((y, x), expr, rng=lambda: random.random())
expr = cosh(x)
assert torch_code(expr) == "torch.cosh(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = acosh(x)
assert torch_code(expr) == "torch.acosh(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = sinh(x)
assert torch_code(expr) == "torch.sinh(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = asinh(x)
assert torch_code(expr) == "torch.asinh(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = tanh(x)
assert torch_code(expr) == "torch.tanh(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(1, 2))
expr = atanh(x)
assert torch_code(expr) == "torch.atanh(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.uniform(-.5, .5))
expr = erf(x)
assert torch_code(expr) == "torch.erf(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())
expr = loggamma(x)
assert torch_code(expr) == "torch.lgamma(x)"
_compare_torch_scalar((x, ), expr, rng=lambda: random.random())