本文整理汇总了Python中sympy.core.C.ceiling方法的典型用法代码示例。如果您正苦于以下问题：Python C.ceiling方法的具体用法？Python C.ceiling怎么用？Python C.ceiling使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sympy.core.C的用法示例。
在下文中一共展示了C.ceiling方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _eval_aseries
# 需要导入模块: from sympy.core import C [as 别名]
# 或者: from sympy.core.C import ceiling [as 别名]
def _eval_aseries(self, n, args0, x, logx):
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 = C.Order(1/z, x)
else:
m = C.ceiling((n+1)//2)
l = [bernoulli(2*k) / (2*k*z**(2*k)) for k in range(1, m)]
r -= Add(*l)
o = C.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 = C.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 = C.Order(1/z**(2*m), x)
if n == 0:
o = C.Order(1/z, x)
elif n == 1:
o = C.Order(1/z**2, x)
r = e0._eval_nseries(z, n, logx) + o
return -1 * (-1/z)**N * r示例2: _eval_aseries
# 需要导入模块: from sympy.core import C [as 别名]
# 或者: from sympy.core.C import ceiling [as 别名]
def _eval_aseries(self, n, args0, x, logx):
if args0[0] != oo:
return super(loggamma, self)._eval_aseries(n, args0, x, logx)
z = self.args[0]
m = min(n, C.ceiling((n + S(1)) / 2))
r = log(z) * (z - S(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 = C.Order(1, x)
else:
o = C.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