def _eval_nseries(self, x, x0, n):
assert len(self.args) == 1
arg = self.args[0]
arg0 = arg.limit(x, 0)
from sympy import oo
if arg0 in [-oo, oo]:
raise PoleError("Cannot expand around %s" % (arg))
if arg0 is not S.Zero:
e = self.func(arg)
e1 = e.expand()
if e == e1:
#for example when e = sin(x+1) or e = sin(cos(x))
#let's try the general algorithm
term = e.subs(x, S.Zero)
series = term
fact = S.One
for i in range(n - 1):
i += 1
fact *= Rational(i)
e = e.diff(x)
term = e.subs(x, S.Zero) * (x**i) / fact
term = term.expand()
series += term
return series + C.Order(x**n, x)
return self.nseries(x, x0, n)
l = []
g = None
for i in xrange(n + 2):
g = self.taylor_term(i, arg, g)
g = g.nseries(x, x0, n)
l.append(g)
return Add(*l) + C.Order(x**n, x)
def _eval_expand_complex(self, *args):
if self.exp.is_Integer:
exp = self.exp
re, im = self.base.as_real_imag()
if exp >= 0:
base = re + S.ImaginaryUnit * im
else:
mag = re ** 2 + im ** 2
base = re / mag - S.ImaginaryUnit * (im / mag)
exp = -exp
return (base ** exp).expand()
elif self.exp.is_Rational:
# NOTE: This is not totally correct since for x**(p/q) with
# x being imaginary there are actually q roots, but
# only a single one is returned from here.
re, im = self.base.as_real_imag()
r = (re ** 2 + im ** 2) ** S.Half
t = C.atan2(im, re)
rp, tp = r ** self.exp, t * self.exp
return rp * C.cos(tp) + rp * C.sin(tp) * S.ImaginaryUnit
else:
return C.re(self) + S.ImaginaryUnit * C.im(self)
def check_convergence(numer, denom, n):
"""
Returns (h, g, p) where
-- h is:
> 0 for convergence of rate 1/factorial(n)**h
< 0 for divergence of rate factorial(n)**(-h)
= 0 for geometric or polynomial convergence or divergence
-- abs(g) is:
> 1 for geometric convergence of rate 1/h**n
< 1 for geometric divergence of rate h**n
= 1 for polynomial convergence or divergence
(g < 0 indicates an alternating series)
-- p is:
> 1 for polynomial convergence of rate 1/n**h
<= 1 for polynomial divergence of rate n**(-h)
"""
npol = C.Poly(numer, n)
dpol = C.Poly(denom, n)
p = npol.degree()
q = dpol.degree()
rate = q - p
if rate:
return rate, None, None
constant = dpol.LC() / npol.LC()
if abs(constant) != 1:
return rate, constant, None
if npol.degree() == dpol.degree() == 0:
return rate, constant, 0
pc = npol.all_coeffs()[1]
qc = dpol.all_coeffs()[1]
return rate, constant, qc - pc
def evalf_log(expr, prec, options):
arg = expr.args[0]
workprec = prec + 10
xre, xim, xacc, _ = evalf(arg, workprec, options)
if xim:
# XXX: use get_abs etc instead
re = evalf_log(C.log(C.abs(arg, evaluate=False), evaluate=False), prec,
options)
im = mpf_atan2(xim, xre or fzero, prec)
return re[0], im, re[2], prec
imaginary_term = (mpf_cmp(xre, fzero) < 0)
re = mpf_log(mpf_abs(xre), prec, round_nearest)
size = fastlog(re)
if prec - size > workprec:
# We actually need to compute 1+x accurately, not x
arg = C.Add(S.NegativeOne, arg, evaluate=False)
xre, xim, xre_acc, xim_acc = evalf_add(arg, prec, options)
prec2 = workprec - fastlog(xre)
re = mpf_log(mpf_add(xre, fone, prec2), prec, round_nearest)
re_acc = prec
if imaginary_term:
return re, mpf_pi(prec), re_acc, prec
else:
return re, None, re_acc, None
def _eval_expand_complex(self, *args):
if self.exp.is_Integer:
exp = self.exp
re, im = self.base.as_real_imag()
if exp >= 0:
base = re + S.ImaginaryUnit*im
else:
mag = re**2 + im**2
base = re/mag - S.ImaginaryUnit*(im/mag)
exp = -exp
return (base**exp).expand()
elif self.exp.is_Rational:
# NOTE: This is not totally correct since for x**(p/q) with
# x being imaginary there are actually q roots, but
# only a single one is returned from here.
re, im = self.base.as_real_imag()
r = (re**2 + im**2)**S.Half
t = C.atan2(im, re)
rp, tp = r**self.exp, t*self.exp
return rp*C.cos(tp) + rp*C.sin(tp)*S.ImaginaryUnit
else:
return C.re(self) + S.ImaginaryUnit*C.im(self)
def evalf_log(expr, prec, options):
arg = expr.args[0]
workprec = prec+10
xre, xim, xacc, _ = evalf(arg, workprec, options)
if xim:
# XXX: use get_abs etc instead
re = evalf_log(C.log(C.abs(arg, evaluate=False), evaluate=False), prec, options)
im = mpf_atan2(xim, xre or fzero, prec)
return re[0], im, re[2], prec
imaginary_term = (mpf_cmp(xre, fzero) < 0)
re = mpf_log(mpf_abs(xre), prec, round_nearest)
size = fastlog(re)
if prec - size > workprec:
# We actually need to compute 1+x accurately, not x
arg = C.Add(S.NegativeOne,arg,evaluate=False)
xre, xim, xre_acc, xim_acc = evalf_add(arg, prec, options)
prec2 = workprec - fastlog(xre)
re = mpf_log(mpf_add(xre, fone, prec2), prec, round_nearest)
re_acc = prec
if imaginary_term:
return re, mpf_pi(prec), re_acc, prec
else:
return re, None, re_acc, None
def as_real_imag(self, deep=True):
other = []
coeff = S(1)
for a in self.args:
if a.is_real:
coeff *= a
else:
other.append(a)
m = Mul(*other)
return (coeff*C.re(m), coeff*C.im(m))
def as_real_imag(self, deep=True):
other = []
coeff = S(1)
for a in self.args:
if a.is_real:
coeff *= a
else:
other.append(a)
m = Mul(*other)
return (coeff * C.re(m), coeff * C.im(m))
def get_integer_part(expr, no, options, return_ints=False):
"""
With no = 1, computes ceiling(expr)
With no = -1, computes floor(expr)
Note: this function either gives the exact result or signals failure.
"""
# The expression is likely less than 2^30 or so
assumed_size = 30
ire, iim, ire_acc, iim_acc = evalf(expr, assumed_size, options)
# We now know the size, so we can calculate how much extra precision
# (if any) is needed to get within the nearest integer
if ire and iim:
gap = max(fastlog(ire) - ire_acc, fastlog(iim) - iim_acc)
elif ire:
gap = fastlog(ire) - ire_acc
elif iim:
gap = fastlog(iim) - iim_acc
else:
# ... or maybe the expression was exactly zero
return None, None, None, None
margin = 10
if gap >= -margin:
ire, iim, ire_acc, iim_acc = evalf(expr, margin + assumed_size + gap,
options)
# We can now easily find the nearest integer, but to find floor/ceil, we
# must also calculate whether the difference to the nearest integer is
# positive or negative (which may fail if very close)
def calc_part(expr, nexpr):
nint = int(to_int(nexpr, round_nearest))
expr = C.Add(expr, -nint, evaluate=False)
x, _, x_acc, _ = evalf(expr, 10, options)
check_target(expr, (x, None, x_acc, None), 3)
nint += int(no * (mpf_cmp(x or fzero, fzero) == no))
nint = from_int(nint)
return nint, fastlog(nint) + 10
re, im, re_acc, im_acc = None, None, None, None
if ire:
re, re_acc = calc_part(C.re(expr, evaluate=False), ire)
if iim:
im, im_acc = calc_part(C.im(expr, evaluate=False), iim)
if return_ints:
return int(to_int(re or fzero)), int(to_int(im or fzero))
return re, im, re_acc, im_acc
def as_real_imag(self, deep=True, **hints):
other = []
coeff = S(1)
for a in self.args:
if a.is_real:
coeff *= a
else:
other.append(a)
m = Mul(*other)
if hints.get('ignore') == m:
return None
else:
return (coeff*C.re(m), coeff*C.im(m))
def evalf_piecewise(expr, prec, options):
if 'subs' in options:
expr = expr.subs(options['subs'])
del options['subs']
if hasattr(expr, 'func'):
return evalf(expr, prec, options)
if type(expr) == float:
return evalf(C.Real(expr), prec, options)
if type(expr) == int:
return evalf(C.Integer(expr), prec, options)
# We still have undefined symbols
raise NotImplementedError
def get_integer_part(expr, no, options, return_ints=False):
"""
With no = 1, computes ceiling(expr)
With no = -1, computes floor(expr)
Note: this function either gives the exact result or signals failure.
"""
# The expression is likely less than 2^30 or so
assumed_size = 30
ire, iim, ire_acc, iim_acc = evalf(expr, assumed_size, options)
# We now know the size, so we can calculate how much extra precision
# (if any) is needed to get within the nearest integer
if ire and iim:
gap = max(fastlog(ire)-ire_acc, fastlog(iim)-iim_acc)
elif ire:
gap = fastlog(ire)-ire_acc
elif iim:
gap = fastlog(iim)-iim_acc
else:
# ... or maybe the expression was exactly zero
return None, None, None, None
margin = 10
if gap >= -margin:
ire, iim, ire_acc, iim_acc = evalf(expr, margin+assumed_size+gap, options)
# We can now easily find the nearest integer, but to find floor/ceil, we
# must also calculate whether the difference to the nearest integer is
# positive or negative (which may fail if very close)
def calc_part(expr, nexpr):
nint = int(to_int(nexpr, round_nearest))
expr = C.Add(expr, -nint, evaluate=False)
x, _, x_acc, _ = evalf(expr, 10, options)
check_target(expr, (x, None, x_acc, None), 3)
nint += int(no*(mpf_cmp(x or fzero, fzero) == no))
nint = from_int(nint)
return nint, fastlog(nint) + 10
re, im, re_acc, im_acc = None, None, None, None
if ire:
re, re_acc = calc_part(C.re(expr, evaluate=False), ire)
if iim:
im, im_acc = calc_part(C.im(expr, evaluate=False), iim)
if return_ints:
return int(to_int(re or fzero)), int(to_int(im or fzero))
return re, im, re_acc, im_acc
def evalf_sum(expr, prec, options):
func = expr.function
limits = expr.limits
if len(limits) != 1 or not isinstance(limits[0], tuple) or \
len(limits[0]) != 3:
raise NotImplementedError
prec2 = prec + 10
try:
n, a, b = limits[0]
if b != S.Infinity or a != int(a):
raise NotImplementedError
# Use fast hypergeometric summation if possible
v = hypsum(func, n, int(a), prec2)
delta = prec - fastlog(v)
if fastlog(v) < -10:
v = hypsum(func, n, int(a), delta)
return v, None, min(prec, delta), None
except NotImplementedError:
# Euler-Maclaurin summation for general series
eps = C.Real(2.0)**(-prec)
for i in range(1, 5):
m = n = 2**i * prec
s, err = expr.euler_maclaurin(m=m, n=n, eps=eps, \
eval_integral=False)
err = err.evalf()
if err <= eps:
break
err = fastlog(evalf(abs(err), 20, options)[0])
re, im, re_acc, im_acc = evalf(s, prec2, options)
re_acc = max(re_acc, -err)
im_acc = max(im_acc, -err)
return re, im, re_acc, im_acc
def extract_leading_order(self, *symbols):
"""
Returns the leading term and it's order.
Examples:
>>> (x+1+1/x**5).extract_leading_order(x)
((1/x**5, O(1/x**5)),)
>>> (1+x).extract_leading_order(x)
((1, O(1, x)),)
>>> (x+x**2).extract_leading_order(x)
((x, O(x)),)
"""
lst = []
seq = [(f, C.Order(f, *symbols)) for f in self.args]
for ef,of in seq:
for e,o in lst:
if o.contains(of) and o != of:
of = None
break
if of is None:
continue
new_lst = [(ef,of)]
for e,o in lst:
if of.contains(o) and o != of:
continue
new_lst.append((e,o))
lst = new_lst
return tuple(lst)
def calc_part(expr, nexpr):
nint = int(to_int(nexpr, round_nearest))
expr = C.Add(expr, -nint, evaluate=False)
x, _, x_acc, _ = evalf(expr, 10, options)
check_target(expr, (x, None, x_acc, None), 3)
nint += int(no * (mpf_cmp(x or fzero, fzero) == no))
nint = from_int(nint)
return nint, fastlog(nint) + 10
def _eval_as_leading_term(self, x):
"""General method for the leading term"""
arg = self.args[0].as_leading_term(x)
if C.Order(1, x).contains(arg):
return arg
else:
return self.func(arg)
def taylor_term(cls, n, x, *previous_terms):
"""General method for the taylor term.
This method is slow, because it differentiates n-times. Subclasses can
redefine it to make it faster by using the "previous_terms".
"""
x = sympify(x)
return cls(x).diff(x, n).subs(x, 0) * x**n / C.Factorial(n)
def as_real_imag(self, deep=True, **hints):
from sympy.core.symbol import symbols
from sympy.polys.polytools import poly
from sympy.core.function import expand_multinomial
if self.exp.is_Integer:
exp = self.exp
re, im = self.base.as_real_imag(deep=deep)
a, b = symbols('a, b', dummy=True)
if exp >= 0:
if re.is_Number and im.is_Number:
# We can be more efficient in this case
expr = expand_multinomial(self.base**exp)
return expr.as_real_imag()
expr = poly((a + b)**exp) # a = re, b = im; expr = (a + b*I)**exp
else:
mag = re**2 + im**2
re, im = re/mag, -im/mag
if re.is_Number and im.is_Number:
# We can be more efficient in this case
expr = expand_multinomial((re + im*S.ImaginaryUnit)**-exp)
return expr.as_real_imag()
expr = poly((a + b)**-exp)
# Terms with even b powers will be real
r = [i for i in expr.terms() if not i[0][1] % 2]
re_part = Add(*[cc*a**aa*b**bb for (aa, bb), cc in r])
# Terms odd b powers will be imaginary
r = [i for i in expr.terms() if i[0][1] % 4 == 1]
im_part1 = Add(*[cc*a**aa*b**bb for (aa, bb), cc in r])
r = [i for i in expr.terms() if i[0][1] % 4 == 3]
im_part3 = Add(*[cc*a**a*b**bb for (aa, bb), cc in r])
return (re_part.subs({a: re, b: S.ImaginaryUnit*im}),
im_part1.subs({a: re, b: im}) + im_part3.subs({a: re, b: -im}))
elif self.exp.is_Rational:
# NOTE: This is not totally correct since for x**(p/q) with
# x being imaginary there are actually q roots, but
# only a single one is returned from here.
re, im = self.base.as_real_imag(deep=deep)
r = (re**2 + im**2)**S.Half
t = C.atan2(im, re)
rp, tp = r**self.exp, t*self.exp
return (rp*C.cos(tp), rp*C.sin(tp))
else:
if deep:
hints['complex'] = False
return (C.re(self.expand(deep, complex=False)),
C.im(self. expand(deep, **hints)))
else:
return (C.re(self), C.im(self))
def _eval_subs(self, old, new):
if self==old: return new
if isinstance(old, self.__class__) and self.base==old.base:
coeff1,terms1 = self.exp.as_coeff_terms()
coeff2,terms2 = old.exp.as_coeff_terms()
if terms1==terms2: return new ** (coeff1/coeff2) # (x**(2*y)).subs(x**(3*y),z) -> z**(2/3*y)
if old.func is C.exp:
coeff1,terms1 = old.args[0].as_coeff_terms()
coeff2,terms2 = (self.exp * C.log(self.base)).as_coeff_terms()
if terms1==terms2: return new ** (coeff1/coeff2) # (x**(2*y)).subs(exp(3*y*log(x)),z) -> z**(2/3*y)
return self.base._eval_subs(old, new) ** self.exp._eval_subs(old, new)
def _eval_subs(self, old, new):
if self==old: return new
if isinstance(old, self.__class__) and self.base==old.base:
coeff1,terms1 = self.exp.as_coeff_terms()
coeff2,terms2 = old.exp.as_coeff_terms()
if terms1==terms2: return new ** (coeff1/coeff2) # (x**(2*y)).subs(x**(3*y),z) -> z**(2/3*y)
if old.func is C.exp:
coeff1,terms1 = old.args[0].as_coeff_terms()
coeff2,terms2 = (self.exp * C.log(self.base)).as_coeff_terms()
if terms1==terms2: return new ** (coeff1/coeff2) # (x**(2*y)).subs(exp(3*y*log(x)),z) -> z**(2/3*y)
return self.base._eval_subs(old, new) ** self.exp._eval_subs(old, new)
def _eval_as_leading_term(self, x):
coeff, factors = self.as_coeff_factors(x)
has_unbounded = bool([f for f in self.args if f.is_unbounded])
if has_unbounded:
if isinstance(factors, Basic):
factors = factors.args
factors = [f for f in factors if not f.is_bounded]
if coeff is not S.Zero:
o = C.Order(x)
else:
o = C.Order(factors[0]*x,x)
n = 1
s = self.nseries(x, 0, n)
while s.is_Order:
n +=1
s = self.nseries(x, 0, n)
if s.is_Add:
s = s.removeO()
if s.is_Add:
lst = s.extract_leading_order(x)
return Add(*[e for (e,f) in lst])
return s.as_leading_term(x)
def eval(cls, *args):
obj = Expr.__new__(cls, *args)
#use dummy variables internally, just to be sure
nargs = len(args) - 1
expression = args[nargs]
funargs = [C.Symbol(arg.name, dummy=True) for arg in args[:nargs]]
#probably could use something like foldl here
for arg, funarg in zip(args[:nargs], funargs):
expression = expression.subs(arg, funarg)
funargs.append(expression)
obj._args = tuple(funargs)
return obj
def as_real_imag(self, deep=True):
"""Performs complex expansion on 'self' and returns a tuple
containing collected both real and imaginary parts. This
method can't be confused with re() and im() functions,
which does not perform complex expansion at evaluation.
However it is possible to expand both re() and im()
functions and get exactly the same results as with
a single call to this function.
>>> from sympy import symbols, I
>>> x, y = symbols('xy', real=True)
>>> (x + y*I).as_real_imag()
(x, y)
>>> from sympy.abc import z, w
>>> (z + w*I).as_real_imag()
(-im(w) + re(z), im(z) + re(w))
"""
return (C.re(self), C.im(self))
def __new__(cls, num, prec=15):
prec = mpmath.settings.dps_to_prec(prec)
if isinstance(num, (int, long)):
return Integer(num)
if isinstance(num, (str, decimal.Decimal)):
_mpf_ = mlib.from_str(str(num), prec, rnd)
elif isinstance(num, tuple) and len(num) == 4:
_mpf_ = num
else:
_mpf_ = mpmath.mpf(num)._mpf_
if not num:
return C.Zero()
obj = Basic.__new__(cls)
obj._mpf_ = _mpf_
obj._prec = prec
return obj
def testIt(self):
from basic import C, call_f
class D(C):
def f(self, x=10):
return x+1
d = D()
c = C()
self.assertEqual(c.f(), 20)
self.assertEqual(c.f(3), 6)
self.assertEqual(d.f(), 11)
self.assertEqual(d.f(3), 4)
self.assertEqual(call_f(c), 20)
self.assertEqual(call_f(c, 4), 8)
self.assertEqual(call_f(d), 11)
self.assertEqual(call_f(d, 3), 4)
def __new__(cls, *args, **assumptions):
if assumptions.get('evaluate') is False:
return Basic.__new__(cls, *map(_sympify, args), **assumptions)
if len(args)==0:
return cls.identity()
if len(args)==1:
return _sympify(args[0])
c_part, nc_part, order_symbols = cls.flatten(map(_sympify, args))
if len(c_part) + len(nc_part) <= 1:
if c_part: obj = c_part[0]
elif nc_part: obj = nc_part[0]
else: obj = cls.identity()
else:
obj = Basic.__new__(cls, *(c_part + nc_part), **assumptions)
obj.is_commutative = not nc_part
if order_symbols is not None:
obj = C.Order(obj, *order_symbols)
return obj
def _create_evalf_table():
global evalf_table
evalf_table = {
C.Symbol : evalf_symbol,
C.Dummy : evalf_symbol,
C.Real : lambda x, prec, options: (x._mpf_, None, prec, None),
C.Rational : lambda x, prec, options: (from_rational(x.p, x.q, prec), None, prec, None),
C.Integer : lambda x, prec, options: (from_int(x.p, prec), None, prec, None),
C.Zero : lambda x, prec, options: (None, None, prec, None),
C.One : lambda x, prec, options: (fone, None, prec, None),
C.Half : lambda x, prec, options: (fhalf, None, prec, None),
C.Pi : lambda x, prec, options: (mpf_pi(prec), None, prec, None),
C.Exp1 : lambda x, prec, options: (mpf_e(prec), None, prec, None),
C.ImaginaryUnit : lambda x, prec, options: (None, fone, None, prec),
C.NegativeOne : lambda x, prec, options: (fnone, None, prec, None),
C.exp : lambda x, prec, options: evalf_pow(C.Pow(S.Exp1, x.args[0],
evaluate=False), prec, options),
C.cos : evalf_trig,
C.sin : evalf_trig,
C.Add : evalf_add,
C.Mul : evalf_mul,
C.Pow : evalf_pow,
C.log : evalf_log,
C.atan : evalf_atan,
C.abs : evalf_abs,
C.re : evalf_re,
C.im : evalf_im,
C.floor : evalf_floor,
C.ceiling : evalf_ceiling,
C.Integral : evalf_integral,
C.Sum : evalf_sum,
C.Piecewise : evalf_piecewise,
C.bernoulli : evalf_bernoulli,
}
def _eval_expand_complex(self, *args):
return C.re(self) + C.im(self)*S.ImaginaryUnit
def do_integral(expr, prec, options):
func = expr.args[0]
x, (xlow, xhigh) = expr.args[1][0]
orig = mp.prec
oldmaxprec = options.get('maxprec', DEFAULT_MAXPREC)
options['maxprec'] = min(oldmaxprec, 2 * prec)
try:
mp.prec = prec + 5
xlow = as_mpmath(xlow, prec + 15, options)
xhigh = as_mpmath(xhigh, prec + 15, options)
# Integration is like summation, and we can phone home from
# the integrand function to update accuracy summation style
# Note that this accuracy is inaccurate, since it fails
# to account for the variable quadrature weights,
# but it is better than nothing
have_part = [False, False]
max_real_term = [MINUS_INF]
max_imag_term = [MINUS_INF]
def f(t):
re, im, re_acc, im_acc = evalf(func, mp.prec, {'subs': {x: t}})
have_part[0] = re or have_part[0]
have_part[1] = im or have_part[1]
max_real_term[0] = max(max_real_term[0], fastlog(re))
max_imag_term[0] = max(max_imag_term[0], fastlog(im))
if im:
return mpc(re or fzero, im)
return mpf(re or fzero)
if options.get('quad') == 'osc':
A = C.Wild('A', exclude=[x])
B = C.Wild('B', exclude=[x])
D = C.Wild('D')
m = func.match(C.cos(A * x + B) * D)
if not m:
m = func.match(C.sin(A * x + B) * D)
if not m:
raise ValueError(
"An integrand of the form sin(A*x+B)*f(x) "
"or cos(A*x+B)*f(x) is required for oscillatory quadrature"
)
period = as_mpmath(2 * S.Pi / m[A], prec + 15, options)
result = quadosc(f, [xlow, xhigh], period=period)
# XXX: quadosc does not do error detection yet
quadrature_error = MINUS_INF
else:
result, quadrature_error = quadts(f, [xlow, xhigh], error=1)
quadrature_error = fastlog(quadrature_error._mpf_)
finally:
options['maxprec'] = oldmaxprec
mp.prec = orig
if have_part[0]:
re = result.real._mpf_
if re == fzero:
re = mpf_shift(fone,
min(-prec, -max_real_term[0], -quadrature_error))
re_acc = -1
else:
re_acc = -max(max_real_term[0] - fastlog(re) - prec,
quadrature_error)
else:
re, re_acc = None, None
if have_part[1]:
im = result.imag._mpf_
if im == fzero:
im = mpf_shift(fone,
min(-prec, -max_imag_term[0], -quadrature_error))
im_acc = -1
else:
im_acc = -max(max_imag_term[0] - fastlog(im) - prec,
quadrature_error)
else:
im, im_acc = None, None
result = re, im, re_acc, im_acc
return result
def as_real_imag(self, deep=True):
return (C.re(self), C.im(self))
def _eval_expand_complex(self, *args):
func = self.func(*[a._eval_expand_complex(*args) for a in self.args])
return C.re(func) + S.ImaginaryUnit * C.im(func)
def do_integral(expr, prec, options):
func = expr.args[0]
x, (xlow, xhigh) = expr.args[1][0]
orig = mp.prec
oldmaxprec = options.get('maxprec', DEFAULT_MAXPREC)
options['maxprec'] = min(oldmaxprec, 2*prec)
try:
mp.prec = prec+5
xlow = as_mpmath(xlow, prec+15, options)
xhigh = as_mpmath(xhigh, prec+15, options)
# Integration is like summation, and we can phone home from
# the integrand function to update accuracy summation style
# Note that this accuracy is inaccurate, since it fails
# to account for the variable quadrature weights,
# but it is better than nothing
have_part = [False, False]
max_real_term = [MINUS_INF]
max_imag_term = [MINUS_INF]
def f(t):
re, im, re_acc, im_acc = evalf(func, mp.prec, {'subs':{x:t}})
have_part[0] = re or have_part[0]
have_part[1] = im or have_part[1]
max_real_term[0] = max(max_real_term[0], fastlog(re))
max_imag_term[0] = max(max_imag_term[0], fastlog(im))
if im:
return mpc(re or fzero, im)
return mpf(re or fzero)
if options.get('quad') == 'osc':
A = C.Wild('A', exclude=[x])
B = C.Wild('B', exclude=[x])
D = C.Wild('D')
m = func.match(C.cos(A*x+B)*D)
if not m:
m = func.match(C.sin(A*x+B)*D)
if not m:
raise ValueError("An integrand of the form sin(A*x+B)*f(x) "
"or cos(A*x+B)*f(x) is required for oscillatory quadrature")
period = as_mpmath(2*S.Pi/m[A], prec+15, options)
result = quadosc(f, [xlow, xhigh], period=period)
# XXX: quadosc does not do error detection yet
quadrature_error = MINUS_INF
else:
result, quadrature_error = quadts(f, [xlow, xhigh], error=1)
quadrature_error = fastlog(quadrature_error._mpf_)
finally:
options['maxprec'] = oldmaxprec
mp.prec = orig
if have_part[0]:
re = result.real._mpf_
if re == fzero:
re = mpf_shift(fone, min(-prec,-max_real_term[0],-quadrature_error))
re_acc = -1
else:
re_acc = -max(max_real_term[0]-fastlog(re)-prec, quadrature_error)
else:
re, re_acc = None, None
if have_part[1]:
im = result.imag._mpf_
if im == fzero:
im = mpf_shift(fone, min(-prec,-max_imag_term[0],-quadrature_error))
im_acc = -1
else:
im_acc = -max(max_imag_term[0]-fastlog(im)-prec, quadrature_error)
else:
im, im_acc = None, None
result = re, im, re_acc, im_acc
return result
def __abs__(self):
return C.abs(self)
def _eval_expand_complex(self, *args):
func = self.func(*[ a._eval_expand_complex(*args) for a in self.args ])
return C.re(func) + S.ImaginaryUnit * C.im(func)
def _eval_as_leading_term(self, x):
if not self.exp.has(x):
return self.base.as_leading_term(x) ** self.exp
return C.exp(self.exp * C.log(self.base)).as_leading_term(x)
def __lt__(self, other):
dif = self - other
if dif.is_negative != dif.is_nonnegative:
return dif.is_negative
return C.StrictInequality(self, other)
def _eval_expand_complex(self, deep=True, **hints):
if deep:
func = self.func(*[ a.expand(deep, **hints) for a in self.args ])
else:
func = self.func(*self.args)
return C.re(func) + S.ImaginaryUnit * C.im(func)
def fdiff(self, *indices):
# FIXME FApply -> ?
return C.FApply(C.FDerivative(*indices), self)
def __gt__(self, other):
dif = self - other
if dif.is_positive != dif.is_nonpositive:
return dif.is_positive
return C.StrictInequality(other, self)
def __le__(self, other):
dif = self - other
if dif.is_nonpositive != dif.is_positive:
return dif.is_nonpositive
return C.Inequality(self, other)
def _eval_expand_complex(self, deep=True, **hints):
if deep:
func = self.func(*[a.expand(deep, **hints) for a in self.args])
else:
func = self.func(*self.args)
return C.re(func) + S.ImaginaryUnit * C.im(func)
def __ge__(self, other):
dif = self - other
if dif.is_nonnegative != dif.is_negative:
return dif.is_nonnegative
return C.Inequality(other, self)
def _eval_power(self, exp):
return C.exp(exp)
def extract_additively(self, c):
"""Return None if it's not possible to make self in the form
something + c in a nice way, i.e. preserving the properties
of arguments of self.
>>> from sympy import symbols
>>> x, y = symbols('xy', real=True)
>>> ((x*y)**3).extract_additively(1)
>>> (x+1).extract_additively(x)
1
>>> (x+1).extract_additively(2*x)
>>> (x+1).extract_additively(-x)
1 + 2*x
>>> (-x+1).extract_additively(2*x)
1 - 3*x
"""
c = sympify(c)
if c is S.Zero:
return self
elif c == self:
return S.Zero
elif self is S.Zero:
return None
elif c.is_Add:
x = self.extract_additively(c.as_two_terms()[0])
if x != None:
return x.extract_additively(c.as_two_terms()[1])
sub = self - c
if self.is_Number:
if self.is_Integer:
if not sub.is_Integer:
return None
elif self.is_positive and sub.is_negative:
return None
else:
return sub
elif self.is_Rational:
if not sub.is_Rational:
return None
elif self.is_positive and sub.is_negative:
return None
else:
return sub
elif self.is_Real:
if not sub.is_Real:
return None
elif self.is_positive and sub.is_negative:
return None
else:
return sub
elif self.is_NumberSymbol or self.is_Symbol or self is S.ImaginaryUnit:
if sub.is_Mul and len(sub.args) == 2:
if sub.args[0].is_Integer and sub.args[
0].is_positive and sub.args[1] == self:
return sub
elif sub.is_Integer:
return sub
elif self.is_Add:
terms = self.as_two_terms()
subs0 = terms[0].extract_additively(c)
if subs0 != None:
return subs0 + terms[1]
else:
subs1 = terms[1].extract_additively(c)
if subs1 != None:
return subs1 + terms[0]
elif self.is_Mul:
self_coeff, self_terms = self.as_coeff_terms()
if c.is_Mul:
c_coeff, c_terms = c.as_coeff_terms()
if c_terms == self_terms:
new_coeff = self_coeff.extract_additively(c_coeff)
if new_coeff != None:
return new_coeff * C.Mul(*self_terms)
elif c == self_terms:
new_coeff = self_coeff.extract_additively(1)
if new_coeff != None:
return new_coeff * C.Mul(*self_terms)
def _eval_derivative(self, s):
dbase = self.base.diff(s)
dexp = self.exp.diff(s)
return self * (dexp * C.log(self.base) + dbase * self.exp / self.base)
def extract_multiplicatively(self, c):
"""Return None if it's not possible to make self in the form
c * something in a nice way, i.e. preserving the properties
of arguments of self.
>>> from sympy import symbols, Rational
>>> x, y = symbols('xy', real=True)
>>> ((x*y)**3).extract_multiplicatively(x**2 * y)
x*y**2
>>> ((x*y)**3).extract_multiplicatively(x**4 * y)
>>> (2*x).extract_multiplicatively(2)
x
>>> (2*x).extract_multiplicatively(3)
>>> (Rational(1,2)*x).extract_multiplicatively(3)
x/6
"""
c = sympify(c)
if c is S.One:
return self
elif c == self:
return S.One
elif c.is_Mul:
x = self.extract_multiplicatively(c.as_two_terms()[0])
if x != None:
return x.extract_multiplicatively(c.as_two_terms()[1])
quotient = self / c
if self.is_Number:
if self is S.Infinity:
if c.is_positive:
return S.Infinity
elif self is S.NegativeInfinity:
if c.is_negative:
return S.Infinity
elif c.is_positive:
return S.NegativeInfinity
elif self is S.ComplexInfinity:
if not c.is_zero:
return S.ComplexInfinity
elif self is S.NaN:
return S.NaN
elif self.is_Integer:
if not quotient.is_Integer:
return None
elif self.is_positive and quotient.is_negative:
return None
else:
return quotient
elif self.is_Rational:
if not quotient.is_Rational:
return None
elif self.is_positive and quotient.is_negative:
return None
else:
return quotient
elif self.is_Real:
if not quotient.is_Real:
return None
elif self.is_positive and quotient.is_negative:
return None
else:
return quotient
elif self.is_NumberSymbol or self.is_Symbol or self is S.ImaginaryUnit:
if quotient.is_Mul and len(quotient.args) == 2:
if quotient.args[0].is_Integer and quotient.args[
0].is_positive and quotient.args[1] == self:
return quotient
elif quotient.is_Integer:
return quotient
elif self.is_Add:
newargs = []
for arg in self.args:
newarg = arg.extract_multiplicatively(c)
if newarg != None:
newargs.append(newarg)
else:
return None
return C.Add(*newargs)
elif self.is_Mul:
for i in xrange(len(self.args)):
newargs = list(self.args)
del (newargs[i])
tmp = C.Mul(*newargs).extract_multiplicatively(c)
if tmp != None:
return tmp * self.args[i]
elif self.is_Pow:
if c.is_Pow and c.base == self.base:
new_exp = self.exp.extract_additively(c.exp)
if new_exp != None:
return self.base**(new_exp)
elif c == self.base:
new_exp = self.exp.extract_additively(1)
if new_exp != None:
return self.base**(new_exp)
def __abs__(self):
return C.abs(self)
def _eval_expand_complex(self, deep=True, **hints):
return C.re(self) + C.im(self)*S.ImaginaryUnit
