acb_series – power series over complex numbers

class flint.acb_series(val=None, prec=None)

Arb series.

>>> from flint import acb_series, ctx
>>> ctx.cap = 3
>>> x = acb_series([0, 1])
>>> x
1.00000000000000*x + O(x^3)
>>> 1 / (1 + x)
1.00000000000000 + (-1.00000000000000)*x + 1.00000000000000*x^2 + O(x^3)
>>> x.cos()
1.00000000000000 + (-0.500000000000000)*x^2 + O(x^3)
agm(s, t=None)
airy(s)
airy_ai(s)
airy_ai_prime(s)
airy_bi(s)
airy_bi_prime(s)
atan(s)
classmethod beta_lower(cls, a, b, z, int regularized=0)
chi(s)
ci(s)
coeffs(self)
cos(s)
cos_pi(s)
cot_pi(s)
coulomb(self, l, eta)
coulomb_f(self, l, eta)
coulomb_g(self, l, eta)
derivative(s)
dirichlet_l(s, chi, bool deflate=0)
ei(s)
elliptic_k(s)
elliptic_p(s, tau)
erf(s)
erfc(s)
erfi(s)
exp(s)
fresnel(s, bool normalized=True)
fresnel_c(s, bool normalized=True)
fresnel_s(s, bool normalized=True)
gamma(s)
classmethod gamma_lower(cls, s, z, int regularized=0)
classmethod gamma_upper(cls, s, z, int regularized=0)
classmethod hypgeom(cls, a, b, z, long n=-1, bool regularized=False)

Computes the generalized hypergeometric function \({}_pF_q(a;b;z)\) given lists of power series \(a\) and \(b\) and a power series \(z\).

The optional parameter n, if nonnegative, controls the number of terms to add in the hypergeometric series. This is just a tuning parameter: a rigorous error bound is computed regardless of n.

integral(s)
inv(s)
lambertw(s, branch=0)
length(self) long
lgamma(s)
li(s, bool offset=False)
log(s)
modular_theta(self, tau)
classmethod polylog(cls, s, z)
prec

The precision of the finitely approximated power series.

>>> from flint import acb_series, ctx
>>> ctx.cap = 10
>>> a = acb_series([1,2,3])
>>> a
1.00000000000000 + 2.00000000000000*x + 3.00000000000000*x^2 + O(x^10)
>>> a.prec
10
>>> b = acb_series([1,2,3], prec=5)
>>> b
1.00000000000000 + 2.00000000000000*x + 3.00000000000000*x^2 + O(x^5)
>>> b.prec
5
repr(self, **kwargs)
reversion(s)
rgamma(s)
rising(s, ulong n)
rsqrt(s)
shi(s)
si(s)
sin(s)
sin_cos(s)
sin_cos_pi(s)
sin_pi(s)
sqrt(s)
str(self, *args, **kwargs)
tan(s)
valuation(self)
zeta(s, a=1, bool deflate=0)