def _eval_rewrite_as_Sum(self, arg):
if arg.is_even:
k = C.Dummy("k", integer=True)
j = C.Dummy("j", integer=True)
n = self.args[0] / 2
Em = (S.ImaginaryUnit * C.Sum( C.Sum( C.binomial(k, j) * ((-1)**j * (k - 2*j)**(2*n + 1)) /
(2**k*S.ImaginaryUnit**k * k), (j, 0, k)), (k, 1, 2*n + 1)))
return Em
def __contains__(self, o):
if isinstance(o, Point):
x = C.Dummy('x', real=True)
y = C.Dummy('y', real=True)
res = self.equation(x, y).subs({x: o[0], y: o[1]})
return trigsimp(simplify(res)) is S.Zero
elif isinstance(o, Ellipse):
return self == o
return False
def fdiff(self, argindex=1):
if argindex == 1:
return 1 / self.args[0]
s = C.Dummy('x')
return Lambda(s**(-1), s)
else:
raise ArgumentIndexError(self, argindex)
def __new__(cls):
from sympy.sets.sets import FiniteSet
x = C.Dummy('x')
#construct "by hand" to avoid infinite loop
obj = Expr.__new__(cls, Tuple(x), x)
obj.nargs = FiniteSet(1)
return obj
def _eval_rewrite_as_Sum(self, ap, bq, z):
from sympy.functions import factorial, RisingFactorial, Piecewise
n = C.Dummy("n", integer=True)
rfap = Tuple(*[RisingFactorial(a, n) for a in ap])
rfbq = Tuple(*[RisingFactorial(b, n) for b in bq])
coeff = Mul(*rfap) / Mul(*rfbq)
return Piecewise((C.Sum(coeff * z**n / factorial(n), (n, 0, oo)),
self.convergence_statement), (self, True))
def _eval_rewrite_as_Sum(self, n, k_sym=None, symbols=None):
if (k_sym is not None) or (symbols is not None):
return self
# Dobinski's formula
if not n.is_nonnegative:
return self
k = C.Dummy('k', integer=True, nonnegative=True)
return 1 / E * C.Sum(k**n / factorial(k), (k, 0, S.Infinity))
def doit(self, **hints):
"""Evaluates limit"""
e, z, z0, dir = self.args
if hints.get('deep', True):
e = e.doit(**hints)
z = z.doit(**hints)
z0 = z0.doit(**hints)
if e == z:
return z0
if not e.has(z):
return e
# gruntz fails on factorials but works with the gamma function
# If no factorial term is present, e should remain unchanged.
# factorial is defined to be zero for negative inputs (which
# differs from gamma) so only rewrite for positive z0.
if z0.is_positive:
e = e.rewrite(factorial, gamma)
if e.is_Mul:
if abs(z0) is S.Infinity:
# XXX todo: this should probably be stated in the
# negative -- i.e. to exclude expressions that should
# not be handled this way but I'm not sure what that
# condition is; when ok is True it means that the leading
# term approach is going to succeed (hopefully)
ok = lambda w: (z in w.free_symbols and
any(a.is_polynomial(z) or
any(z in m.free_symbols and m.is_polynomial(z)
for m in Mul.make_args(a))
for a in Add.make_args(w)))
if all(ok(w) for w in e.as_numer_denom()):
u = C.Dummy(positive=(z0 is S.Infinity))
inve = e.subs(z, 1/u)
r = limit(inve.as_leading_term(u), u,
S.Zero, "+" if z0 is S.Infinity else "-")
if isinstance(r, Limit):
return self
else:
return r
if e.is_Order:
return C.Order(limit(e.expr, z, z0), *e.args[1:])
try:
r = gruntz(e, z, z0, dir)
if r is S.NaN:
raise PoleError()
except (PoleError, ValueError):
r = heuristics(e, z, z0, dir)
if r is None:
return self
return r
def fdiff(self, argindex=1):
"""
Returns the first derivative of the function.
"""
if argindex == 1:
return 1/self.args[0]
s = C.Dummy('x')
return Lambda(s**(-1), s)
else:
raise ArgumentIndexError(self, argindex)
def circumference(self):
"""The circumference of the ellipse.
Examples
--------
>>> from sympy import Point, Ellipse
>>> p1 = Point(0, 0)
>>> e1 = Ellipse(p1, 3, 1)
>>> e1.circumference
12*Integral(sqrt((-8*_x**2/9 + 1)/(-_x**2 + 1)), (_x, 0, 1))
"""
if self.eccentricity == 1:
return 2*pi*self.hradius
else:
x = C.Dummy('x', real=True)
return 4*self.major*\
C.Integral(sqrt((1 - (self.eccentricity*x)**2)/(1 - x**2)),
(x, 0, 1))
def _eval_subs(self, old, new):
if self == old:
return new
arg = self.args[0]
o = old
if old.is_Pow: # handle (exp(3*log(x))).subs(x**2, z) -> z**(3/2)
old = exp(old.exp*log(old.base))
if old.func is exp:
# exp(a*expr) .subs( exp(b*expr), y ) -> y ** (a/b)
a, expr_terms = self.args[0].as_coeff_mul()
b, expr_terms_= old.args[0].as_coeff_mul()
if expr_terms == expr_terms_:
return new**(a/b)
if arg.is_Add: # exp(2*x+a).subs(exp(3*x),y) -> y**(2/3) * exp(a)
# exp(exp(x) + exp(x**2)).subs(exp(exp(x)), w) -> w * exp(exp(x**2))
oarg = old.args[0]
new_l = []
old_al = []
coeff2, terms2 = oarg.as_coeff_mul()
for a in arg.args:
a = a._eval_subs(old, new)
coeff1, terms1 = a.as_coeff_mul()
if terms1 == terms2:
new_l.append(new**(coeff1/coeff2))
else:
old_al.append(a._eval_subs(old, new))
if new_l:
new_l.append(self.func(Add(*old_al)))
r = Mul(*new_l)
return r
if old is S.Exp1:
# treat this however Pow is being treated
u = C.Dummy('u')
return (u**self.args[0]).subs(u, new)
old = o
return Function._eval_subs(self, old, new)
def _eval_subs(self, old, new):
arg = self.args[0]
o = old
if old.is_Pow: # handle (exp(3*log(x))).subs(x**2, z) -> z**(3/2)
o = exp(o.exp*log(o.base))
if o.func is exp:
# exp(a*expr) .subs( exp(b*expr), y ) -> y ** (a/b)
a, expr_terms = self.args[0].as_independent(
C.Symbol, as_Add=False)
b, expr_terms_ = o.args[0].as_independent(
C.Symbol, as_Add=False)
if expr_terms == expr_terms_:
return new**(a/b)
if arg.is_Add: # exp(2*x+a).subs(exp(3*x),y) -> y**(2/3) * exp(a)
# exp(exp(x) + exp(x**2)).subs(exp(exp(x)), w) -> w * exp(exp(x**2))
oarg = o.args[0]
new_l = []
o_al = []
coeff2, terms2 = oarg.as_coeff_mul()
for a in arg.args:
a = a._subs(o, new)
coeff1, terms1 = a.as_coeff_mul()
if terms1 == terms2:
new_l.append(new**(coeff1/coeff2))
else:
o_al.append(a._subs(o, new))
if new_l:
new_l.append(self.func(Add(*o_al)))
r = Mul(*new_l)
return r
if o is S.Exp1:
# treat this however Pow is being treated
u = C.Dummy('u', positive=True)
return (u**self.args[0]).xreplace({u: new})
return Function._eval_subs(self, o, new)
def limit(e, z, z0, dir="+"):
"""
Compute the limit of e(z) at the point z0.
z0 can be any expression, including oo and -oo.
For dir="+" (default) it calculates the limit from the right
(z->z0+) and for dir="-" the limit from the left (z->z0-). For infinite z0
(oo or -oo), the dir argument doesn't matter.
Examples
========
>>> from sympy import limit, sin, Symbol, oo
>>> from sympy.abc import x
>>> limit(sin(x)/x, x, 0)
1
>>> limit(1/x, x, 0, dir="+")
oo
>>> limit(1/x, x, 0, dir="-")
-oo
>>> limit(1/x, x, oo)
0
Notes
=====
First we try some heuristics for easy and frequent cases like "x", "1/x",
"x**2" and similar, so that it's fast. For all other cases, we use the
Gruntz algorithm (see the gruntz() function).
"""
from sympy import Wild, log
e = sympify(e)
z = sympify(z)
z0 = sympify(z0)
if e == z:
return z0
if e.is_Rational:
return e
if not e.has(z):
return e
# gruntz fails on factorials but works with the gamma function
# If no factorial term is present, e should remain unchanged.
# factorial is defined to be zero for negative inputs (which
# differs from gamma) so only rewrite for positive z0.
if z0.is_positive:
e = e.rewrite(factorial, gamma)
if e.func is tan:
# discontinuity at odd multiples of pi/2; 0 at even
disc = S.Pi / 2
sign = 1
if dir == '-':
sign *= -1
i = limit(sign * e.args[0], z, z0) / disc
if i.is_integer:
if i.is_even:
return S.Zero
elif i.is_odd:
if dir == '+':
return S.NegativeInfinity
else:
return S.Infinity
if e.func is cot:
# discontinuity at multiples of pi; 0 at odd pi/2 multiples
disc = S.Pi
sign = 1
if dir == '-':
sign *= -1
i = limit(sign * e.args[0], z, z0) / disc
if i.is_integer:
if dir == '-':
return S.NegativeInfinity
else:
return S.Infinity
elif (2 * i).is_integer:
return S.Zero
if e.is_Pow:
b, ex = e.args
c = None # records sign of b if b is +/-z or has a bounded value
if b.is_Mul:
c, b = b.as_two_terms()
if c is S.NegativeOne and b == z:
c = '-'
elif b == z:
c = '+'
if ex.is_number:
if c is None:
base = b.subs(z, z0)
if base.is_finite and (ex.is_bounded or base is not S.One):
return base**ex
else:
if z0 == 0 and ex < 0:
if dir != c:
# integer
if ex.is_even:
return S.Infinity
elif ex.is_odd:
return S.NegativeInfinity
# rational
elif ex.is_Rational:
return (S.NegativeOne**ex) * S.Infinity
else:
return S.ComplexInfinity
return S.Infinity
return z0**ex
if e.is_Mul or not z0 and e.is_Pow and b.func is log:
if e.is_Mul:
if abs(z0) is S.Infinity:
n, d = e.as_numer_denom()
# XXX todo: this should probably be stated in the
# negative -- i.e. to exclude expressions that should
# not be handled this way but I'm not sure what that
# condition is; when ok is True it means that the leading
# term approach is going to succeed (hopefully)
ok = lambda w: (z in w.free_symbols and any(
a.is_polynomial(z) or any(
z in m.free_symbols and m.is_polynomial(z)
for m in Mul.make_args(a)) for a in Add.make_args(w)))
if all(ok(w) for w in (n, d)):
u = C.Dummy(positive=(z0 is S.Infinity))
inve = (n / d).subs(z, 1 / u)
return limit(inve.as_leading_term(u), u, S.Zero,
"+" if z0 is S.Infinity else "-")
# weed out the z-independent terms
i, d = e.as_independent(z)
if i is not S.One and i.is_bounded:
return i * limit(d, z, z0, dir)
else:
i, d = S.One, e
if not z0:
# look for log(z)**q or z**p*log(z)**q
p, q = Wild("p"), Wild("q")
r = d.match(z**p * log(z)**q)
if r:
p, q = [r.get(w, w) for w in [p, q]]
if q and q.is_number and p.is_number:
if q > 0:
if p > 0:
return S.Zero
else:
return -oo * i
else:
if p >= 0:
return S.Zero
else:
return -oo * i
if e.is_Add:
if e.is_polynomial() and not z0.is_unbounded:
return Add(*[limit(term, z, z0, dir) for term in e.args])
# this is a case like limit(x*y+x*z, z, 2) == x*y+2*x
# but we need to make sure, that the general gruntz() algorithm is
# executed for a case like "limit(sqrt(x+1)-sqrt(x),x,oo)==0"
unbounded = []
unbounded_result = []
unbounded_const = []
unknown = []
unknown_result = []
finite = []
zero = []
def _sift(term):
if z not in term.free_symbols:
if term.is_unbounded:
unbounded_const.append(term)
else:
finite.append(term)
else:
result = term.subs(z, z0)
bounded = result.is_bounded
if bounded is False or result is S.NaN:
unbounded.append(term)
if result != S.NaN:
# take result from direction given
result = limit(term, z, z0, dir)
unbounded_result.append(result)
elif bounded:
if result:
finite.append(result)
else:
zero.append(term)
else:
unknown.append(term)
unknown_result.append(result)
for term in e.args:
_sift(term)
bad = bool(unknown and unbounded)
if bad or len(unknown) > 1 or len(unbounded) > 1 and not zero:
uu = unknown + unbounded
# we won't be able to resolve this with unbounded
# terms, e.g. Sum(1/k, (k, 1, n)) - log(n) as n -> oo:
# since the Sum is unevaluated it's boundedness is
# unknown and the log(n) is oo so you get Sum - oo
# which is unsatisfactory. BUT...if there are both
# unknown and unbounded terms (condition 'bad') or
# there are multiple terms that are unknown, or
# there are multiple symbolic unbounded terms they may
# respond better if they are made into a rational
# function, so give them a chance to do so before
# reporting failure.
u = Add(*uu)
f = u.normal()
if f != u:
unknown = []
unbounded = []
unbounded_result = []
unknown_result = []
_sift(limit(f, z, z0, dir))
# We came in with a) unknown and unbounded terms or b) had multiple
# unknown terms
# At this point we've done one of 3 things.
# (1) We did nothing with f so we now report the error
# showing the troublesome terms which are now in uu. OR
# (2) We did something with f but the result came back as unknown.
# Normally this wouldn't be a problem,
# but we had either multiple terms that were troublesome (unk and
# unbounded or multiple unknown terms) so if we
# weren't able to resolve the boundedness by now, that indicates a
# problem so we report the error showing the troublesome terms which are
# now in uu.
if unknown:
if bad:
msg = 'unknown and unbounded terms present in %s'
elif unknown:
msg = 'multiple terms with unknown boundedness in %s'
raise NotImplementedError(msg % uu)
# OR
# (3) the troublesome terms have been identified as finite or unbounded
# and we proceed with the non-error code since the lists have been updated.
u = Add(*unknown_result)
if unbounded_result or unbounded_const:
unbounded.extend(zero)
inf_limit = Add(*(unbounded_result + unbounded_const))
if inf_limit is not S.NaN:
return inf_limit + u
if finite:
return Add(*finite) + limit(Add(*unbounded), z, z0, dir) + u
else:
return Add(*finite) + u
if e.is_Order:
args = e.args
return C.Order(limit(args[0], z, z0), *args[1:])
try:
r = gruntz(e, z, z0, dir)
if r is S.NaN:
raise PoleError()
except (PoleError, ValueError):
r = heuristics(e, z, z0, dir)
return r
def limit(e, z, z0, dir="+"):
"""
Compute the limit of e(z) at the point z0.
z0 can be any expression, including oo and -oo.
For dir="+" (default) it calculates the limit from the right
(z->z0+) and for dir="-" the limit from the left (z->z0-). For infinite z0
(oo or -oo), the dir argument doesn't matter.
Examples
========
>>> from sympy import limit, sin, Symbol, oo
>>> from sympy.abc import x
>>> limit(sin(x)/x, x, 0)
1
>>> limit(1/x, x, 0, dir="+")
oo
>>> limit(1/x, x, 0, dir="-")
-oo
>>> limit(1/x, x, oo)
0
Notes
=====
First we try some heuristics for easy and frequent cases like "x", "1/x",
"x**2" and similar, so that it's fast. For all other cases, we use the
Gruntz algorithm (see the gruntz() function).
"""
e = sympify(e)
z = sympify(z)
z0 = sympify(z0)
if e == z:
return z0
if not e.has(z):
return e
# gruntz fails on factorials but works with the gamma function
# If no factorial term is present, e should remain unchanged.
# factorial is defined to be zero for negative inputs (which
# differs from gamma) so only rewrite for positive z0.
if z0.is_positive:
e = e.rewrite(factorial, gamma)
if e.is_Mul:
if abs(z0) is S.Infinity:
# XXX todo: this should probably be stated in the
# negative -- i.e. to exclude expressions that should
# not be handled this way but I'm not sure what that
# condition is; when ok is True it means that the leading
# term approach is going to succeed (hopefully)
ok = lambda w: (z in w.free_symbols and any(
a.is_polynomial(z) or any(
z in m.free_symbols and m.is_polynomial(z)
for m in Mul.make_args(a)) for a in Add.make_args(w)))
if all(ok(w) for w in e.as_numer_denom()):
u = C.Dummy(positive=(z0 is S.Infinity))
inve = e.subs(z, 1 / u)
return limit(inve.as_leading_term(u), u, S.Zero,
"+" if z0 is S.Infinity else "-")
if e.is_Order:
return C.Order(limit(e.expr, z, z0), *e.args[1:])
try:
r = gruntz(e, z, z0, dir)
if r is S.NaN:
raise PoleError()
except (PoleError, ValueError):
r = heuristics(e, z, z0, dir)
return r
def __new__(cls):
x = C.Dummy('x')
#construct "by hand" to avoid infinite loop
return Expr.__new__(cls, Tuple(x), x)
def test_print_Dummy():
d = C.Dummy('d')
p = setup_test_printer()
assert p._print_Dummy(d) == "d_%i" % d.dummy_index
def subexp(e, sub):
"""Is "sub" a subexpression of "e"? """
#we substitute some symbol for the "sub", and if the
#expression changes, the substitution was successful, thus the answer
#is yes.
return e.subs(sub, C.Dummy('u')) != e
def limit(e, z, z0, dir="+"):
"""
Compute the limit of e(z) at the point z0.
z0 can be any expression, including oo and -oo.
For dir="+" (default) it calculates the limit from the right
(z->z0+) and for dir="-" the limit from the left (z->z0-). For infinite z0
(oo or -oo), the dir argument doesn't matter.
Examples
========
>>> from sympy import limit, sin, Symbol, oo
>>> from sympy.abc import x
>>> limit(sin(x)/x, x, 0)
1
>>> limit(1/x, x, 0, dir="+")
oo
>>> limit(1/x, x, 0, dir="-")
-oo
>>> limit(1/x, x, oo)
0
Notes
=====
First we try some heuristics for easy and frequent cases like "x", "1/x",
"x**2" and similar, so that it's fast. For all other cases, we use the
Gruntz algorithm (see the gruntz() function).
"""
from sympy import Wild, log
e = sympify(e)
z = sympify(z)
z0 = sympify(z0)
if e == z:
return z0
if e.is_Rational:
return e
if not e.has(z):
return e
# gruntz fails on factorials but works with the gamma function
# If no factorial term is present, e should remain unchanged.
# factorial is defined to be zero for negative inputs (which
# differs from gamma) so only rewrite for positive z0.
if z0.is_positive:
e = e.rewrite(factorial, gamma)
if e.func is tan:
# discontinuity at odd multiples of pi/2; 0 at even
disc = S.Pi/2
sign = 1
if dir == '-':
sign *= -1
i = limit(sign*e.args[0], z, z0)/disc
if i.is_integer:
if i.is_even:
return S.Zero
elif i.is_odd:
if dir == '+':
return S.NegativeInfinity
else:
return S.Infinity
if e.func is cot:
# discontinuity at multiples of pi; 0 at odd pi/2 multiples
disc = S.Pi
sign = 1
if dir == '-':
sign *= -1
i = limit(sign*e.args[0], z, z0)/disc
if i.is_integer:
if dir == '-':
return S.NegativeInfinity
else:
return S.Infinity
elif (2*i).is_integer:
return S.Zero
if e.is_Pow:
b, ex = e.args
c = None # records sign of b if b is +/-z or has a bounded value
if b.is_Mul:
c, b = b.as_two_terms()
if c is S.NegativeOne and b == z:
c = '-'
elif b == z:
c = '+'
if ex.is_number:
if c is None:
base = b.subs(z, z0)
if base != 0 and (ex.is_bounded or base is not S.One):
return base**ex
else:
if z0 == 0 and ex < 0:
if dir != c:
# integer
if ex.is_even:
return S.Infinity
elif ex.is_odd:
return S.NegativeInfinity
# rational
elif ex.is_Rational:
return (S.NegativeOne**ex)*S.Infinity
else:
return S.ComplexInfinity
return S.Infinity
return z0**ex
if e.is_Mul or not z0 and e.is_Pow and b.func is log:
if e.is_Mul:
if abs(z0) is S.Infinity:
n, d = e.as_numer_denom()
# XXX todo: this should probably be stated in the
# negative -- i.e. to exclude expressions that should
# not be handled this way but I'm not sure what that
# condition is; when ok is True it means that the leading
# term approach is going to succeed (hopefully)
ok = lambda w: (z in w.free_symbols and
any(a.is_polynomial(z) or
any(z in m.free_symbols and m.is_polynomial(z)
for m in Mul.make_args(a))
for a in Add.make_args(w)))
if all(ok(w) for w in (n, d)):
u = C.Dummy(positive=(z0 is S.Infinity))
inve = (n/d).subs(z, 1/u)
return limit(inve.as_leading_term(u), u,
S.Zero, "+" if z0 is S.Infinity else "-")
# weed out the z-independent terms
i, d = e.as_independent(z)
if i is not S.One and i.is_bounded:
return i*limit(d, z, z0, dir)
else:
i, d = S.One, e
if not z0:
# look for log(z)**q or z**p*log(z)**q
p, q = Wild("p"), Wild("q")
r = d.match(z**p * log(z)**q)
if r:
p, q = [r.get(w, w) for w in [p, q]]
if q and q.is_number and p.is_number:
if q > 0:
if p > 0:
return S.Zero
else:
return -oo*i
else:
if p >= 0:
return S.Zero
else:
return -oo*i
if e.is_Add:
if e.is_polynomial():
if not z0.is_unbounded:
return Add(*[limit(term, z, z0, dir) for term in e.args])
elif e.is_rational_function(z):
rval = Add(*[limit(term, z, z0, dir) for term in e.args])
if rval != S.NaN:
return rval
if not any([a.is_unbounded for a in e.args]):
e = e.normal() # workaround for issue 3744
if e.is_Order:
args = e.args
return C.Order(limit(args[0], z, z0), *args[1:])
try:
r = gruntz(e, z, z0, dir)
if r is S.NaN:
raise PoleError()
except (PoleError, ValueError):
r = heuristics(e, z, z0, dir)
return r
def _eval_rewrite_as_Sum(self, n, m=None):
k = C.Dummy("k", integer=True)
if m is None:
m = S.One
return C.Sum(k**(-m), (k, 1, n))
"""
This module mainly implements special orthogonal polynomials.
See also functions.combinatorial.numbers which contains some
combinatorial polynomials.
"""
from sympy.core import S, C, Rational, Function
from sympy.utilities.memoization import recurrence_memo, assoc_recurrence_memo
from sympy.functions.combinatorial.factorials import factorial
_x = C.Dummy('x')
class PolynomialSequence(Function):
"""Polynomial sequence with 1 index
n >= 0
"""
nargs = 2
@classmethod
def eval(cls, n, x):
if n.is_integer and n >= 0:
return cls.calc(int(n)).subs(_x, x)
if n.is_negative:
raise ValueError("%s index must be nonnegative integer (got %r)" %
(cls, n))
