本文整理汇总了Python中sympy.functions.combinatorial.factorials.rf函数的典型用法代码示例。如果您正苦于以下问题：Python rf函数的具体用法？Python rf怎么用？Python rf使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了rf函数的3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: _compute_formula
def _compute_formula(f, x, P, Q, k, m, k_max):
"""Computes the formula for f."""
from sympy.polys import roots
sol = []
for i in range(k_max + 1, k_max + m + 1):
r = f.diff(x, i).limit(x, 0) / factorial(i)
if r is S.Zero:
continue
kterm = m*k + i
res = r
p = P.subs(k, kterm)
q = Q.subs(k, kterm)
c1 = p.subs(k, 1/k).leadterm(k)[0]
c2 = q.subs(k, 1/k).leadterm(k)[0]
res *= (-c1 / c2)**k
for r, mul in roots(p, k).items():
res *= rf(-r, k)**mul
for r, mul in roots(q, k).items():
res /= rf(-r, k)**mul
sol.append((res, kterm))
return sol示例2: hyper_re
def hyper_re(DE, r, k):
"""Converts a DE into a RE.
Performs the substitution:
.. math::
x^l f^j(x) \\to (k + 1 - l)_j . a_{k + j - l}
Normalises the terms so that lowest order of a term is always r(k).
Examples
========
>>> from sympy import Function, Derivative
>>> from sympy.series.formal import hyper_re
>>> from sympy.abc import x, k
>>> f, r = Function('f'), Function('r')
>>> hyper_re(-f(x) + Derivative(f(x)), r, k)
(k + 1)*r(k + 1) - r(k)
>>> hyper_re(-x*f(x) + Derivative(f(x), x, x), r, k)
(k + 2)*(k + 3)*r(k + 3) - r(k)
See Also
========
sympy.series.formal.exp_re
"""
RE = S.Zero
g = DE.atoms(Function).pop()
x = g.atoms(Symbol).pop()
mini = None
for t in Add.make_args(DE.expand()):
coeff, d = t.as_independent(g)
c, v = coeff.as_independent(x)
l = v.as_coeff_exponent(x)[1]
if isinstance(d, Derivative):
j = len(d.args[1:])
else:
j = 0
RE += c * rf(k + 1 - l, j) * r(k + j - l)
if mini is None or j - l < mini:
mini = j - l
RE = RE.subs(k, k - mini)
m = Wild('m')
return RE.collect(r(k + m))示例3: _eval_nseries
def _eval_nseries(self, x, n, logx):
x0 = self.args[0].limit(x, 0)
if not (x0.is_Integer and x0 <= 0):
return super(gamma, self)._eval_nseries(x, n, logx)
t = self.args[0] - x0
return (gamma(t + 1) / rf(self.args[0], -x0 + 1))._eval_nseries(x, n, logx)