def _eval_expand_func(self, **hints):
from diofant import Sum
n = self.args[0]
m = self.args[1] if len(self.args) == 2 else 1
if m == S.One:
if n.is_Add:
off = n.args[0]
nnew = n - off
if off.is_Integer and off.is_positive:
result = [S.One / (nnew + i)
for i in range(off, 0, -1)] + [harmonic(nnew)]
return Add(*result)
elif off.is_Integer and off.is_negative:
result = [-S.One / (nnew + i)
for i in range(0, off, -1)] + [harmonic(nnew)]
return Add(*result)
if n.is_Rational:
# Expansions for harmonic numbers at general rational arguments (u + p/q)
# Split n as u + p/q with p < q
p, q = n.as_numer_denom()
u = p // q
p = p - u * q
if u.is_nonnegative and p.is_positive and q.is_positive and p < q:
k = Dummy("k")
t1 = q * Sum(1 / (q * k + p), (k, 0, u))
t2 = 2 * Sum(
cos((2 * pi * p * k) / q) * log(sin((pi * k) / q)),
(k, 1, floor((q - 1) / Integer(2))))
t3 = (pi / 2) * cot((pi * p) / q) + log(2 * q)
return t1 + t2 - t3
return self
def _eval_expand_func(self, **hints):
n, z = self.args
if n.is_Integer and n.is_nonnegative:
if z.is_Add:
coeff = z.args[0]
if coeff.is_Integer:
e = -(n + 1)
if coeff > 0:
tail = Add(
*[Pow(z - i, e) for i in range(1,
int(coeff) + 1)])
else:
tail = -Add(
*[Pow(z + i, e) for i in range(0, int(-coeff))])
return polygamma(n,
z - coeff) + (-1)**n * factorial(n) * tail
elif z.is_Mul:
coeff, z = z.as_two_terms()
if coeff.is_Integer and coeff.is_positive:
tail = [
polygamma(n, z + Rational(i, coeff))
for i in range(0, int(coeff))
]
if n == 0:
return Add(*tail) / coeff + log(coeff)
else:
return Add(*tail) / coeff**(n + 1)
z *= coeff
return polygamma(n, z)
def test_sympyissue_7638():
f = pi/log(sqrt(2))
assert ((1 + I)**(I*f/2))**0.3 == (1 + I)**(0.15*I*f)
# if 1/3 -> 1.0/3 this should fail since it cannot be shown that the
# sign will be +/-1; for the previous "small arg" case, it didn't matter
# that this could not be proved
assert (1 + I)**(4*I*f) == ((1 + I)**(12*I*f))**Rational(1, 3)
assert (((1 + I)**(I*(1 + 7*f)))**Rational(1, 3)).exp == Rational(1, 3)
r = symbols('r', extended_real=True)
assert sqrt(r**2) == abs(r)
assert cbrt(r**3) != r
assert sqrt(Pow(2*I, 5*S.Half)) != (2*I)**(5/Integer(4))
p = symbols('p', positive=True)
assert cbrt(p**2) == p**(2/Integer(3))
assert NS(((0.2 + 0.7*I)**(0.7 + 1.0*I))**(0.5 - 0.1*I), 1) == '0.4 + 0.2*I'
assert sqrt(1/(1 + I)) == sqrt((1 - I)/2) # or 1/sqrt(1 + I)
e = 1/(1 - sqrt(2))
assert sqrt(e) == I/sqrt(-1 + sqrt(2))
assert e**-S.Half == -I*sqrt(-1 + sqrt(2))
assert sqrt((cos(1)**2 + sin(1)**2 - 1)**(3 + I)).exp == S.Half
assert sqrt(r**(4/Integer(3))) != r**(2/Integer(3))
assert sqrt((p + I)**(4/Integer(3))) == (p + I)**(2/Integer(3))
assert sqrt((p - p**2*I)**2) == p - p**2*I
assert sqrt((p + r*I)**2) != p + r*I
e = (1 + I/5)
assert sqrt(e**5) == e**(5*S.Half)
assert sqrt(e**6) == e**3
assert sqrt((1 + I*r)**6) != (1 + I*r)**3
def test_sympyissue_7638():
f = pi/log(sqrt(2))
assert ((1 + I)**(I*f/2))**0.3 == (1 + I)**(0.15*I*f)
# if 1/3 -> 1.0/3 this should fail since it cannot be shown that the
# sign will be +/-1; for the previous "small arg" case, it didn't matter
# that this could not be proved
assert (1 + I)**(4*I*f) == cbrt((1 + I)**(12*I*f))
assert cbrt((1 + I)**(I*(1 + 7*f))).exp == Rational(1, 3)
r = symbols('r', extended_real=True)
assert sqrt(r**2) == abs(r)
assert cbrt(r**3) != r
assert sqrt(Pow(2*I, Rational(5, 2))) != (2*I)**Rational(5, 4)
p = symbols('p', positive=True)
assert cbrt(p**2) == p**Rational(2, 3)
assert NS(((0.2 + 0.7*I)**(0.7 + 1.0*I))**(0.5 - 0.1*I), 1) == '0.4 + 0.2*I'
assert sqrt(1/(1 + I)) == sqrt((1 - I)/2) # or 1/sqrt(1 + I)
e = 1/(1 - sqrt(2))
assert sqrt(e) == I/sqrt(-1 + sqrt(2))
assert e**Rational(-1, 2) == -I*sqrt(-1 + sqrt(2))
assert sqrt((cos(1)**2 + sin(1)**2 - 1)**(3 + I)).exp == Rational(1, 2)
assert sqrt(r**Rational(4, 3)) != r**Rational(2, 3)
assert sqrt((p + I)**Rational(4, 3)) == (p + I)**Rational(2, 3)
assert sqrt((p - p**2*I)**2) == p - p**2*I
assert sqrt((p + r*I)**2) != p + r*I
e = (1 + I/5)
assert sqrt(e**5) == e**Rational(5, 2)
assert sqrt(e**6) == e**3
assert sqrt((1 + I*r)**6) != (1 + I*r)**3
def _eval_as_leading_term(self, x):
from diofant import Order
n, z = [a.as_leading_term(x) for a in self.args]
o = Order(z, x)
if n == 0 and o.contains(1 / x):
return o.getn() * log(x)
else:
return self.func(n, z)
def _eval_aseries(self, n, args0, x, logx):
from diofant import Order
if args0[0] != oo:
return super(loggamma, self)._eval_aseries(n, args0, x, logx)
z = self.args[0]
m = min(n, ceiling((n + Integer(1)) / 2))
r = log(z) * (z - Rational(1, 2)) - z + log(2 * pi) / 2
l = [
bernoulli(2 * k) / (2 * k * (2 * k - 1) * z**(2 * k - 1))
for k in range(1, m)
]
o = None
if m == 0:
o = Order(1, x)
else:
o = Order(1 / z**(2 * m - 1), x)
# It is very inefficient to first add the order and then do the nseries
return (r + Add(*l))._eval_nseries(x, n, logx) + o
def eval(cls, z):
z = sympify(z)
if z.is_integer:
if z.is_nonpositive:
return S.Infinity
elif z.is_positive:
return log(gamma(z))
elif z.is_rational:
p, q = z.as_numer_denom()
# Half-integral values:
if p.is_positive and q == 2:
return log(
sqrt(S.Pi) * 2**(1 - p) * gamma(p) / gamma(
(p + 1) * S.Half))
if z is S.Infinity:
return S.Infinity
elif abs(z) is S.Infinity:
return S.ComplexInfinity
def fdiff(self, argindex=2):
from diofant import meijerg, unpolarify
if argindex == 2:
a, z = self.args
return -exp(-unpolarify(z)) * z**(a - 1)
elif argindex == 1:
a, z = self.args
return uppergamma(a, z) * log(z) + meijerg([], [1, 1], [0, 0, a],
[], z)
else:
raise ArgumentIndexError(self, argindex)
def _eval_expand_func(self, **hints):
from diofant import Sum
z = self.args[0]
if z.is_Rational:
p, q = z.as_numer_denom()
# General rational arguments (u + p/q)
# Split z as n + p/q with p < q
n = p // q
p = p - n * q
if p.is_positive and q.is_positive and p < q:
k = Dummy("k")
if n.is_positive:
return loggamma(p / q) - n * log(q) + Sum(
log((k - 1) * q + p), (k, 1, n))
elif n.is_negative:
return loggamma(
p / q) - n * log(q) + S.Pi * S.ImaginaryUnit * n - Sum(
log(k * q - p), (k, 1, -n))
elif n.is_zero:
return loggamma(p / q)
return self
def _eval_aseries(self, n, args0, x, logx):
from diofant import Order
if args0[1] != oo or not \
(self.args[0].is_Integer and self.args[0].is_nonnegative):
return super(polygamma, self)._eval_aseries(n, args0, x, logx)
z = self.args[1]
N = self.args[0]
if N == 0:
# digamma function series
# Abramowitz & Stegun, p. 259, 6.3.18
r = log(z) - 1 / (2 * z)
o = None
if n < 2:
o = Order(1 / z, x)
else:
m = ceiling((n + 1) // 2)
l = [
bernoulli(2 * k) / (2 * k * z**(2 * k))
for k in range(1, m)
]
r -= Add(*l)
o = Order(1 / z**(2 * m), x)
return r._eval_nseries(x, n, logx) + o
else:
# proper polygamma function
# Abramowitz & Stegun, p. 260, 6.4.10
# We return terms to order higher than O(x**n) on purpose
# -- otherwise we would not be able to return any terms for
# quite a long time!
fac = gamma(N)
e0 = fac + N * fac / (2 * z)
m = ceiling((n + 1) // 2)
for k in range(1, m):
fac = fac * (2 * k + N - 1) * (2 * k + N - 2) / ((2 * k) *
(2 * k - 1))
e0 += bernoulli(2 * k) * fac / z**(2 * k)
o = Order(1 / z**(2 * m), x)
if n == 0:
o = Order(1 / z, x)
elif n == 1:
o = Order(1 / z**2, x)
r = e0 + o
return (-1 * (-1 / z)**N * r)._eval_nseries(x, n, logx)
def _eval_rewrite_as_Sum(self, *args):
from diofant.concrete.summations import Sum
return exp(Sum(log(self.function), *self.limits))
def _eval_rewrite_as_intractable(self, z):
return log(gamma(z))
def eval(cls, n, z):
n, z = list(map(sympify, (n, z)))
from diofant import unpolarify
if n.is_integer:
if n.is_nonnegative:
nz = unpolarify(z)
if z != nz:
return polygamma(n, nz)
if n == -1:
return loggamma(z)
else:
if z.is_Number:
if z is S.Infinity:
if n.is_Number:
if n is S.Zero:
return S.Infinity
else:
return S.Zero
elif z.is_Integer:
if z.is_nonpositive:
return S.ComplexInfinity
else:
if n is S.Zero:
return -S.EulerGamma + harmonic(z - 1, 1)
elif n.is_odd:
return (-1)**(n + 1) * factorial(n) * zeta(
n + 1, z)
if n == 0:
if z.is_Rational:
# TODO actually *any* n/m can be done, but that is messy
lookup = {
Rational(1, 2):
-2 * log(2) - S.EulerGamma,
Rational(1, 3):
-S.Pi / 2 / sqrt(3) - 3 * log(3) / 2 - S.EulerGamma,
Rational(1, 4):
-S.Pi / 2 - 3 * log(2) - S.EulerGamma,
Rational(3, 4):
-3 * log(2) - S.EulerGamma + S.Pi / 2,
Rational(2, 3):
-3 * log(3) / 2 + S.Pi / 2 / sqrt(3) - S.EulerGamma
}
if z > 0:
n = floor(z)
z0 = z - n
if z0 in lookup:
return lookup[z0] + Add(
*[1 / (z0 + k) for k in range(n)])
elif z < 0:
n = floor(1 - z)
z0 = z + n
if z0 in lookup:
return lookup[z0] - Add(
*[1 / (z0 - 1 - k) for k in range(n)])
elif z in (S.Infinity, S.NegativeInfinity):
return S.Infinity
else:
t = z.extract_multiplicatively(S.ImaginaryUnit)
if t in (S.Infinity, S.NegativeInfinity):
return S.Infinity
def _eval_integral(self,
f,
x,
meijerg=None,
risch=None,
conds='piecewise'):
"""
Calculate the anti-derivative to the function f(x).
The following algorithms are applied (roughly in this order):
1. Simple heuristics (based on pattern matching and integral table):
- most frequently used functions (e.g. polynomials, products of trig functions)
2. Integration of rational functions:
- A complete algorithm for integrating rational functions is
implemented (the Lazard-Rioboo-Trager algorithm). The algorithm
also uses the partial fraction decomposition algorithm
implemented in apart() as a preprocessor to make this process
faster. Note that the integral of a rational function is always
elementary, but in general, it may include a RootSum.
3. Full Risch algorithm:
- The Risch algorithm is a complete decision
procedure for integrating elementary functions, which means that
given any elementary function, it will either compute an
elementary antiderivative, or else prove that none exists.
Currently, part of transcendental case is implemented, meaning
elementary integrals containing exponentials, logarithms, and
(soon!) trigonometric functions can be computed. The algebraic
case, e.g., functions containing roots, is much more difficult
and is not implemented yet.
- If the routine fails (because the integrand is not elementary, or
because a case is not implemented yet), it continues on to the
next algorithms below. If the routine proves that the integrals
is nonelementary, it still moves on to the algorithms below,
because we might be able to find a closed-form solution in terms
of special functions. If risch=True, however, it will stop here.
4. The Meijer G-Function algorithm:
- This algorithm works by first rewriting the integrand in terms of
very general Meijer G-Function (meijerg in Diofant), integrating
it, and then rewriting the result back, if possible. This
algorithm is particularly powerful for definite integrals (which
is actually part of a different method of Integral), since it can
compute closed-form solutions of definite integrals even when no
closed-form indefinite integral exists. But it also is capable
of computing many indefinite integrals as well.
- Another advantage of this method is that it can use some results
about the Meijer G-Function to give a result in terms of a
Piecewise expression, which allows to express conditionally
convergent integrals.
- Setting meijerg=True will cause integrate() to use only this
method.
5. The Heuristic Risch algorithm:
- This is a heuristic version of the Risch algorithm, meaning that
it is not deterministic. This is tried as a last resort because
it can be very slow. It is still used because not enough of the
full Risch algorithm is implemented, so that there are still some
integrals that can only be computed using this method. The goal
is to implement enough of the Risch and Meijer G-function methods
so that this can be deleted.
"""
from diofant.integrals.deltafunctions import deltaintegrate
from diofant.integrals.heurisch import heurisch, heurisch_wrapper
from diofant.integrals.rationaltools import ratint
from diofant.integrals.risch import risch_integrate
if risch:
try:
return risch_integrate(f, x, conds=conds)
except NotImplementedError:
return
# if it is a poly(x) then let the polynomial integrate itself (fast)
#
# It is important to make this check first, otherwise the other code
# will return a diofant expression instead of a Polynomial.
#
# see Polynomial for details.
if isinstance(f, Poly) and not meijerg:
return f.integrate(x)
# Piecewise antiderivatives need to call special integrate.
if f.func is Piecewise:
return f._eval_integral(x)
# let's cut it short if `f` does not depend on `x`
if not f.has(x):
return f * x
# try to convert to poly(x) and then integrate if successful (fast)
poly = f.as_poly(x)
if poly is not None and not meijerg:
return poly.integrate().as_expr()
if risch is not False:
try:
result, i = risch_integrate(f,
x,
separate_integral=True,
conds=conds)
except NotImplementedError:
pass
else:
if i:
# There was a nonelementary integral. Try integrating it.
return result + i.doit(risch=False)
else:
return result
# since Integral(f=g1+g2+...) == Integral(g1) + Integral(g2) + ...
# we are going to handle Add terms separately,
# if `f` is not Add -- we only have one term
# Note that in general, this is a bad idea, because Integral(g1) +
# Integral(g2) might not be computable, even if Integral(g1 + g2) is.
# For example, Integral(x**x + x**x*log(x)). But many heuristics only
# work term-wise. So we compute this step last, after trying
# risch_integrate. We also try risch_integrate again in this loop,
# because maybe the integral is a sum of an elementary part and a
# nonelementary part (like erf(x) + exp(x)). risch_integrate() is
# quite fast, so this is acceptable.
parts = []
args = Add.make_args(f)
for g in args:
coeff, g = g.as_independent(x)
# g(x) = const
if g is S.One and not meijerg:
parts.append(coeff * x)
continue
# g(x) = expr + O(x**n)
order_term = g.getO()
if order_term is not None:
h = self._eval_integral(g.removeO(), x)
if h is not None:
parts.append(coeff *
(h + self.func(order_term, *self.limits)))
continue
# NOTE: if there is O(x**n) and we fail to integrate then there is
# no point in trying other methods because they will fail anyway.
return
# c
# g(x) = (a*x+b)
if g.is_Pow and not g.exp.has(x) and not meijerg:
a = Wild('a', exclude=[x])
b = Wild('b', exclude=[x])
M = g.base.match(a * x + b)
if M is not None:
if g.exp == -1:
h = log(g.base)
elif conds != 'piecewise':
h = g.base**(g.exp + 1) / (g.exp + 1)
else:
h1 = log(g.base)
h2 = g.base**(g.exp + 1) / (g.exp + 1)
h = Piecewise((h1, Eq(g.exp, -1)), (h2, True))
parts.append(coeff * h / M[a])
continue
# poly(x)
# g(x) = -------
# poly(x)
if g.is_rational_function(x) and not meijerg:
parts.append(coeff * ratint(g, x))
continue
if not meijerg:
# g(x) = Mul(trig)
h = trigintegrate(g, x, conds=conds)
if h is not None:
parts.append(coeff * h)
continue
# g(x) has at least a DiracDelta term
h = deltaintegrate(g, x)
if h is not None:
parts.append(coeff * h)
continue
# Try risch again.
if risch is not False:
try:
h, i = risch_integrate(g,
x,
separate_integral=True,
conds=conds)
except NotImplementedError:
h = None
else:
if i:
h = h + i.doit(risch=False)
parts.append(coeff * h)
continue
# fall back to heurisch
try:
if conds == 'piecewise':
h = heurisch_wrapper(g, x, hints=[])
else:
h = heurisch(g, x, hints=[])
except PolynomialError:
# XXX: this exception means there is a bug in the
# implementation of heuristic Risch integration
# algorithm.
h = None
else:
h = None
if meijerg is not False and h is None:
# rewrite using G functions
try:
h = meijerint_indefinite(g, x)
except NotImplementedError:
from diofant.integrals.meijerint import _debug
_debug('NotImplementedError from meijerint_definite')
res = None
if h is not None:
parts.append(coeff * h)
continue
# if we failed maybe it was because we had
# a product that could have been expanded,
# so let's try an expansion of the whole
# thing before giving up; we don't try this
# at the outset because there are things
# that cannot be solved unless they are
# NOT expanded e.g., x**x*(1+log(x)). There
# should probably be a checker somewhere in this
# routine to look for such cases and try to do
# collection on the expressions if they are already
# in an expanded form
if not h and len(args) == 1:
f = f.expand(mul=True, deep=False)
if f.is_Add:
# Note: risch will be identical on the expanded
# expression, but maybe it will be able to pick out parts,
# like x*(exp(x) + erf(x)).
return self._eval_integral(f,
x,
meijerg=meijerg,
risch=risch,
conds=conds)
if h is not None:
parts.append(coeff * h)
else:
return
return Add(*parts)
def fdiff(self, argindex=1):
from diofant import polygamma, log
n = self.args[0]
return catalan(n) * (polygamma(0, n + Rational(1, 2)) -
polygamma(0, n + 2) + log(4))
