Traktor/myenv/Lib/site-packages/mpmath/functions/bessel.py
2024-05-23 01:57:24 +02:00

1109 lines
37 KiB
Python

from .functions import defun, defun_wrapped
@defun
def j0(ctx, x):
"""Computes the Bessel function `J_0(x)`. See :func:`~mpmath.besselj`."""
return ctx.besselj(0, x)
@defun
def j1(ctx, x):
"""Computes the Bessel function `J_1(x)`. See :func:`~mpmath.besselj`."""
return ctx.besselj(1, x)
@defun
def besselj(ctx, n, z, derivative=0, **kwargs):
if type(n) is int:
n_isint = True
else:
n = ctx.convert(n)
n_isint = ctx.isint(n)
if n_isint:
n = int(ctx._re(n))
if n_isint and n < 0:
return (-1)**n * ctx.besselj(-n, z, derivative, **kwargs)
z = ctx.convert(z)
M = ctx.mag(z)
if derivative:
d = ctx.convert(derivative)
# TODO: the integer special-casing shouldn't be necessary.
# However, the hypergeometric series gets inaccurate for large d
# because of inaccurate pole cancellation at a pole far from
# zero (needs to be fixed in hypercomb or hypsum)
if ctx.isint(d) and d >= 0:
d = int(d)
orig = ctx.prec
try:
ctx.prec += 15
v = ctx.fsum((-1)**k * ctx.binomial(d,k) * ctx.besselj(2*k+n-d,z)
for k in range(d+1))
finally:
ctx.prec = orig
v *= ctx.mpf(2)**(-d)
else:
def h(n,d):
r = ctx.fmul(ctx.fmul(z, z, prec=ctx.prec+M), -0.25, exact=True)
B = [0.5*(n-d+1), 0.5*(n-d+2)]
T = [([2,ctx.pi,z],[d-2*n,0.5,n-d],[],B,[(n+1)*0.5,(n+2)*0.5],B+[n+1],r)]
return T
v = ctx.hypercomb(h, [n,d], **kwargs)
else:
# Fast case: J_n(x), n int, appropriate magnitude for fixed-point calculation
if (not derivative) and n_isint and abs(M) < 10 and abs(n) < 20:
try:
return ctx._besselj(n, z)
except NotImplementedError:
pass
if not z:
if not n:
v = ctx.one + n+z
elif ctx.re(n) > 0:
v = n*z
else:
v = ctx.inf + z + n
else:
#v = 0
orig = ctx.prec
try:
# XXX: workaround for accuracy in low level hypergeometric series
# when alternating, large arguments
ctx.prec += min(3*abs(M), ctx.prec)
w = ctx.fmul(z, 0.5, exact=True)
def h(n):
r = ctx.fneg(ctx.fmul(w, w, prec=max(0,ctx.prec+M)), exact=True)
return [([w], [n], [], [n+1], [], [n+1], r)]
v = ctx.hypercomb(h, [n], **kwargs)
finally:
ctx.prec = orig
v = +v
return v
@defun
def besseli(ctx, n, z, derivative=0, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
if not z:
if derivative:
raise ValueError
if not n:
# I(0,0) = 1
return 1+n+z
if ctx.isint(n):
return 0*(n+z)
r = ctx.re(n)
if r == 0:
return ctx.nan*(n+z)
elif r > 0:
return 0*(n+z)
else:
return ctx.inf+(n+z)
M = ctx.mag(z)
if derivative:
d = ctx.convert(derivative)
def h(n,d):
r = ctx.fmul(ctx.fmul(z, z, prec=ctx.prec+M), 0.25, exact=True)
B = [0.5*(n-d+1), 0.5*(n-d+2), n+1]
T = [([2,ctx.pi,z],[d-2*n,0.5,n-d],[n+1],B,[(n+1)*0.5,(n+2)*0.5],B,r)]
return T
v = ctx.hypercomb(h, [n,d], **kwargs)
else:
def h(n):
w = ctx.fmul(z, 0.5, exact=True)
r = ctx.fmul(w, w, prec=max(0,ctx.prec+M))
return [([w], [n], [], [n+1], [], [n+1], r)]
v = ctx.hypercomb(h, [n], **kwargs)
return v
@defun_wrapped
def bessely(ctx, n, z, derivative=0, **kwargs):
if not z:
if derivative:
# Not implemented
raise ValueError
if not n:
# ~ log(z/2)
return -ctx.inf + (n+z)
if ctx.im(n):
return ctx.nan * (n+z)
r = ctx.re(n)
q = n+0.5
if ctx.isint(q):
if n > 0:
return -ctx.inf + (n+z)
else:
return 0 * (n+z)
if r < 0 and int(ctx.floor(q)) % 2:
return ctx.inf + (n+z)
else:
return ctx.ninf + (n+z)
# XXX: use hypercomb
ctx.prec += 10
m, d = ctx.nint_distance(n)
if d < -ctx.prec:
h = +ctx.eps
ctx.prec *= 2
n += h
elif d < 0:
ctx.prec -= d
# TODO: avoid cancellation for imaginary arguments
cos, sin = ctx.cospi_sinpi(n)
return (ctx.besselj(n,z,derivative,**kwargs)*cos - \
ctx.besselj(-n,z,derivative,**kwargs))/sin
@defun_wrapped
def besselk(ctx, n, z, **kwargs):
if not z:
return ctx.inf
M = ctx.mag(z)
if M < 1:
# Represent as limit definition
def h(n):
r = (z/2)**2
T1 = [z, 2], [-n, n-1], [n], [], [], [1-n], r
T2 = [z, 2], [n, -n-1], [-n], [], [], [1+n], r
return T1, T2
# We could use the limit definition always, but it leads
# to very bad cancellation (of exponentially large terms)
# for large real z
# Instead represent in terms of 2F0
else:
ctx.prec += M
def h(n):
return [([ctx.pi/2, z, ctx.exp(-z)], [0.5,-0.5,1], [], [], \
[n+0.5, 0.5-n], [], -1/(2*z))]
return ctx.hypercomb(h, [n], **kwargs)
@defun_wrapped
def hankel1(ctx,n,x,**kwargs):
return ctx.besselj(n,x,**kwargs) + ctx.j*ctx.bessely(n,x,**kwargs)
@defun_wrapped
def hankel2(ctx,n,x,**kwargs):
return ctx.besselj(n,x,**kwargs) - ctx.j*ctx.bessely(n,x,**kwargs)
@defun_wrapped
def whitm(ctx,k,m,z,**kwargs):
if z == 0:
# M(k,m,z) = 0^(1/2+m)
if ctx.re(m) > -0.5:
return z
elif ctx.re(m) < -0.5:
return ctx.inf + z
else:
return ctx.nan * z
x = ctx.fmul(-0.5, z, exact=True)
y = 0.5+m
return ctx.exp(x) * z**y * ctx.hyp1f1(y-k, 1+2*m, z, **kwargs)
@defun_wrapped
def whitw(ctx,k,m,z,**kwargs):
if z == 0:
g = abs(ctx.re(m))
if g < 0.5:
return z
elif g > 0.5:
return ctx.inf + z
else:
return ctx.nan * z
x = ctx.fmul(-0.5, z, exact=True)
y = 0.5+m
return ctx.exp(x) * z**y * ctx.hyperu(y-k, 1+2*m, z, **kwargs)
@defun
def hyperu(ctx, a, b, z, **kwargs):
a, atype = ctx._convert_param(a)
b, btype = ctx._convert_param(b)
z = ctx.convert(z)
if not z:
if ctx.re(b) <= 1:
return ctx.gammaprod([1-b],[a-b+1])
else:
return ctx.inf + z
bb = 1+a-b
bb, bbtype = ctx._convert_param(bb)
try:
orig = ctx.prec
try:
ctx.prec += 10
v = ctx.hypsum(2, 0, (atype, bbtype), [a, bb], -1/z, maxterms=ctx.prec)
return v / z**a
finally:
ctx.prec = orig
except ctx.NoConvergence:
pass
def h(a,b):
w = ctx.sinpi(b)
T1 = ([ctx.pi,w],[1,-1],[],[a-b+1,b],[a],[b],z)
T2 = ([-ctx.pi,w,z],[1,-1,1-b],[],[a,2-b],[a-b+1],[2-b],z)
return T1, T2
return ctx.hypercomb(h, [a,b], **kwargs)
@defun
def struveh(ctx,n,z, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
# http://functions.wolfram.com/Bessel-TypeFunctions/StruveH/26/01/02/
def h(n):
return [([z/2, 0.5*ctx.sqrt(ctx.pi)], [n+1, -1], [], [n+1.5], [1], [1.5, n+1.5], -(z/2)**2)]
return ctx.hypercomb(h, [n], **kwargs)
@defun
def struvel(ctx,n,z, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
# http://functions.wolfram.com/Bessel-TypeFunctions/StruveL/26/01/02/
def h(n):
return [([z/2, 0.5*ctx.sqrt(ctx.pi)], [n+1, -1], [], [n+1.5], [1], [1.5, n+1.5], (z/2)**2)]
return ctx.hypercomb(h, [n], **kwargs)
def _anger(ctx,which,v,z,**kwargs):
v = ctx._convert_param(v)[0]
z = ctx.convert(z)
def h(v):
b = ctx.mpq_1_2
u = v*b
m = b*3
a1,a2,b1,b2 = m-u, m+u, 1-u, 1+u
c, s = ctx.cospi_sinpi(u)
if which == 0:
A, B = [b*z, s], [c]
if which == 1:
A, B = [b*z, -c], [s]
w = ctx.square_exp_arg(z, mult=-0.25)
T1 = A, [1, 1], [], [a1,a2], [1], [a1,a2], w
T2 = B, [1], [], [b1,b2], [1], [b1,b2], w
return T1, T2
return ctx.hypercomb(h, [v], **kwargs)
@defun
def angerj(ctx, v, z, **kwargs):
return _anger(ctx, 0, v, z, **kwargs)
@defun
def webere(ctx, v, z, **kwargs):
return _anger(ctx, 1, v, z, **kwargs)
@defun
def lommels1(ctx, u, v, z, **kwargs):
u = ctx._convert_param(u)[0]
v = ctx._convert_param(v)[0]
z = ctx.convert(z)
def h(u,v):
b = ctx.mpq_1_2
w = ctx.square_exp_arg(z, mult=-0.25)
return ([u-v+1, u+v+1, z], [-1, -1, u+1], [], [], [1], \
[b*(u-v+3),b*(u+v+3)], w),
return ctx.hypercomb(h, [u,v], **kwargs)
@defun
def lommels2(ctx, u, v, z, **kwargs):
u = ctx._convert_param(u)[0]
v = ctx._convert_param(v)[0]
z = ctx.convert(z)
# Asymptotic expansion (GR p. 947) -- need to be careful
# not to use for small arguments
# def h(u,v):
# b = ctx.mpq_1_2
# w = -(z/2)**(-2)
# return ([z], [u-1], [], [], [b*(1-u+v)], [b*(1-u-v)], w),
def h(u,v):
b = ctx.mpq_1_2
w = ctx.square_exp_arg(z, mult=-0.25)
T1 = [u-v+1, u+v+1, z], [-1, -1, u+1], [], [], [1], [b*(u-v+3),b*(u+v+3)], w
T2 = [2, z], [u+v-1, -v], [v, b*(u+v+1)], [b*(v-u+1)], [], [1-v], w
T3 = [2, z], [u-v-1, v], [-v, b*(u-v+1)], [b*(1-u-v)], [], [1+v], w
#c1 = ctx.cospi((u-v)*b)
#c2 = ctx.cospi((u+v)*b)
#s = ctx.sinpi(v)
#r1 = (u-v+1)*b
#r2 = (u+v+1)*b
#T2 = [c1, s, z, 2], [1, -1, -v, v], [], [-v+1], [], [-v+1], w
#T3 = [-c2, s, z, 2], [1, -1, v, -v], [], [v+1], [], [v+1], w
#T2 = [c1, s, z, 2], [1, -1, -v, v+u-1], [r1, r2], [-v+1], [], [-v+1], w
#T3 = [-c2, s, z, 2], [1, -1, v, -v+u-1], [r1, r2], [v+1], [], [v+1], w
return T1, T2, T3
return ctx.hypercomb(h, [u,v], **kwargs)
@defun
def ber(ctx, n, z, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
# http://functions.wolfram.com/Bessel-TypeFunctions/KelvinBer2/26/01/02/0001/
def h(n):
r = -(z/4)**4
cos, sin = ctx.cospi_sinpi(-0.75*n)
T1 = [cos, z/2], [1, n], [], [n+1], [], [0.5, 0.5*(n+1), 0.5*n+1], r
T2 = [sin, z/2], [1, n+2], [], [n+2], [], [1.5, 0.5*(n+3), 0.5*n+1], r
return T1, T2
return ctx.hypercomb(h, [n], **kwargs)
@defun
def bei(ctx, n, z, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
# http://functions.wolfram.com/Bessel-TypeFunctions/KelvinBei2/26/01/02/0001/
def h(n):
r = -(z/4)**4
cos, sin = ctx.cospi_sinpi(0.75*n)
T1 = [cos, z/2], [1, n+2], [], [n+2], [], [1.5, 0.5*(n+3), 0.5*n+1], r
T2 = [sin, z/2], [1, n], [], [n+1], [], [0.5, 0.5*(n+1), 0.5*n+1], r
return T1, T2
return ctx.hypercomb(h, [n], **kwargs)
@defun
def ker(ctx, n, z, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
# http://functions.wolfram.com/Bessel-TypeFunctions/KelvinKer2/26/01/02/0001/
def h(n):
r = -(z/4)**4
cos1, sin1 = ctx.cospi_sinpi(0.25*n)
cos2, sin2 = ctx.cospi_sinpi(0.75*n)
T1 = [2, z, 4*cos1], [-n-3, n, 1], [-n], [], [], [0.5, 0.5*(1+n), 0.5*(n+2)], r
T2 = [2, z, -sin1], [-n-3, 2+n, 1], [-n-1], [], [], [1.5, 0.5*(3+n), 0.5*(n+2)], r
T3 = [2, z, 4*cos2], [n-3, -n, 1], [n], [], [], [0.5, 0.5*(1-n), 1-0.5*n], r
T4 = [2, z, -sin2], [n-3, 2-n, 1], [n-1], [], [], [1.5, 0.5*(3-n), 1-0.5*n], r
return T1, T2, T3, T4
return ctx.hypercomb(h, [n], **kwargs)
@defun
def kei(ctx, n, z, **kwargs):
n = ctx.convert(n)
z = ctx.convert(z)
# http://functions.wolfram.com/Bessel-TypeFunctions/KelvinKei2/26/01/02/0001/
def h(n):
r = -(z/4)**4
cos1, sin1 = ctx.cospi_sinpi(0.75*n)
cos2, sin2 = ctx.cospi_sinpi(0.25*n)
T1 = [-cos1, 2, z], [1, n-3, 2-n], [n-1], [], [], [1.5, 0.5*(3-n), 1-0.5*n], r
T2 = [-sin1, 2, z], [1, n-1, -n], [n], [], [], [0.5, 0.5*(1-n), 1-0.5*n], r
T3 = [-sin2, 2, z], [1, -n-1, n], [-n], [], [], [0.5, 0.5*(n+1), 0.5*(n+2)], r
T4 = [-cos2, 2, z], [1, -n-3, n+2], [-n-1], [], [], [1.5, 0.5*(n+3), 0.5*(n+2)], r
return T1, T2, T3, T4
return ctx.hypercomb(h, [n], **kwargs)
# TODO: do this more generically?
def c_memo(f):
name = f.__name__
def f_wrapped(ctx):
cache = ctx._misc_const_cache
prec = ctx.prec
p,v = cache.get(name, (-1,0))
if p >= prec:
return +v
else:
cache[name] = (prec, f(ctx))
return cache[name][1]
return f_wrapped
@c_memo
def _airyai_C1(ctx):
return 1 / (ctx.cbrt(9) * ctx.gamma(ctx.mpf(2)/3))
@c_memo
def _airyai_C2(ctx):
return -1 / (ctx.cbrt(3) * ctx.gamma(ctx.mpf(1)/3))
@c_memo
def _airybi_C1(ctx):
return 1 / (ctx.nthroot(3,6) * ctx.gamma(ctx.mpf(2)/3))
@c_memo
def _airybi_C2(ctx):
return ctx.nthroot(3,6) / ctx.gamma(ctx.mpf(1)/3)
def _airybi_n2_inf(ctx):
prec = ctx.prec
try:
v = ctx.power(3,'2/3')*ctx.gamma('2/3')/(2*ctx.pi)
finally:
ctx.prec = prec
return +v
# Derivatives at z = 0
# TODO: could be expressed more elegantly using triple factorials
def _airyderiv_0(ctx, z, n, ntype, which):
if ntype == 'Z':
if n < 0:
return z
r = ctx.mpq_1_3
prec = ctx.prec
try:
ctx.prec += 10
v = ctx.gamma((n+1)*r) * ctx.power(3,n*r) / ctx.pi
if which == 0:
v *= ctx.sinpi(2*(n+1)*r)
v /= ctx.power(3,'2/3')
else:
v *= abs(ctx.sinpi(2*(n+1)*r))
v /= ctx.power(3,'1/6')
finally:
ctx.prec = prec
return +v + z
else:
# singular (does the limit exist?)
raise NotImplementedError
@defun
def airyai(ctx, z, derivative=0, **kwargs):
z = ctx.convert(z)
if derivative:
n, ntype = ctx._convert_param(derivative)
else:
n = 0
# Values at infinities
if not ctx.isnormal(z) and z:
if n and ntype == 'Z':
if n == -1:
if z == ctx.inf:
return ctx.mpf(1)/3 + 1/z
if z == ctx.ninf:
return ctx.mpf(-2)/3 + 1/z
if n < -1:
if z == ctx.inf:
return z
if z == ctx.ninf:
return (-1)**n * (-z)
if (not n) and z == ctx.inf or z == ctx.ninf:
return 1/z
# TODO: limits
raise ValueError("essential singularity of Ai(z)")
# Account for exponential scaling
if z:
extraprec = max(0, int(1.5*ctx.mag(z)))
else:
extraprec = 0
if n:
if n == 1:
def h():
# http://functions.wolfram.com/03.07.06.0005.01
if ctx._re(z) > 4:
ctx.prec += extraprec
w = z**1.5; r = -0.75/w; u = -2*w/3
ctx.prec -= extraprec
C = -ctx.exp(u)/(2*ctx.sqrt(ctx.pi))*ctx.nthroot(z,4)
return ([C],[1],[],[],[(-1,6),(7,6)],[],r),
# http://functions.wolfram.com/03.07.26.0001.01
else:
ctx.prec += extraprec
w = z**3 / 9
ctx.prec -= extraprec
C1 = _airyai_C1(ctx) * 0.5
C2 = _airyai_C2(ctx)
T1 = [C1,z],[1,2],[],[],[],[ctx.mpq_5_3],w
T2 = [C2],[1],[],[],[],[ctx.mpq_1_3],w
return T1, T2
return ctx.hypercomb(h, [], **kwargs)
else:
if z == 0:
return _airyderiv_0(ctx, z, n, ntype, 0)
# http://functions.wolfram.com/03.05.20.0004.01
def h(n):
ctx.prec += extraprec
w = z**3/9
ctx.prec -= extraprec
q13,q23,q43 = ctx.mpq_1_3, ctx.mpq_2_3, ctx.mpq_4_3
a1=q13; a2=1; b1=(1-n)*q13; b2=(2-n)*q13; b3=1-n*q13
T1 = [3, z], [n-q23, -n], [a1], [b1,b2,b3], \
[a1,a2], [b1,b2,b3], w
a1=q23; b1=(2-n)*q13; b2=1-n*q13; b3=(4-n)*q13
T2 = [3, z, -z], [n-q43, -n, 1], [a1], [b1,b2,b3], \
[a1,a2], [b1,b2,b3], w
return T1, T2
v = ctx.hypercomb(h, [n], **kwargs)
if ctx._is_real_type(z) and ctx.isint(n):
v = ctx._re(v)
return v
else:
def h():
if ctx._re(z) > 4:
# We could use 1F1, but it results in huge cancellation;
# the following expansion is better.
# TODO: asymptotic series for derivatives
ctx.prec += extraprec
w = z**1.5; r = -0.75/w; u = -2*w/3
ctx.prec -= extraprec
C = ctx.exp(u)/(2*ctx.sqrt(ctx.pi)*ctx.nthroot(z,4))
return ([C],[1],[],[],[(1,6),(5,6)],[],r),
else:
ctx.prec += extraprec
w = z**3 / 9
ctx.prec -= extraprec
C1 = _airyai_C1(ctx)
C2 = _airyai_C2(ctx)
T1 = [C1],[1],[],[],[],[ctx.mpq_2_3],w
T2 = [z*C2],[1],[],[],[],[ctx.mpq_4_3],w
return T1, T2
return ctx.hypercomb(h, [], **kwargs)
@defun
def airybi(ctx, z, derivative=0, **kwargs):
z = ctx.convert(z)
if derivative:
n, ntype = ctx._convert_param(derivative)
else:
n = 0
# Values at infinities
if not ctx.isnormal(z) and z:
if n and ntype == 'Z':
if z == ctx.inf:
return z
if z == ctx.ninf:
if n == -1:
return 1/z
if n == -2:
return _airybi_n2_inf(ctx)
if n < -2:
return (-1)**n * (-z)
if not n:
if z == ctx.inf:
return z
if z == ctx.ninf:
return 1/z
# TODO: limits
raise ValueError("essential singularity of Bi(z)")
if z:
extraprec = max(0, int(1.5*ctx.mag(z)))
else:
extraprec = 0
if n:
if n == 1:
# http://functions.wolfram.com/03.08.26.0001.01
def h():
ctx.prec += extraprec
w = z**3 / 9
ctx.prec -= extraprec
C1 = _airybi_C1(ctx)*0.5
C2 = _airybi_C2(ctx)
T1 = [C1,z],[1,2],[],[],[],[ctx.mpq_5_3],w
T2 = [C2],[1],[],[],[],[ctx.mpq_1_3],w
return T1, T2
return ctx.hypercomb(h, [], **kwargs)
else:
if z == 0:
return _airyderiv_0(ctx, z, n, ntype, 1)
def h(n):
ctx.prec += extraprec
w = z**3/9
ctx.prec -= extraprec
q13,q23,q43 = ctx.mpq_1_3, ctx.mpq_2_3, ctx.mpq_4_3
q16 = ctx.mpq_1_6
q56 = ctx.mpq_5_6
a1=q13; a2=1; b1=(1-n)*q13; b2=(2-n)*q13; b3=1-n*q13
T1 = [3, z], [n-q16, -n], [a1], [b1,b2,b3], \
[a1,a2], [b1,b2,b3], w
a1=q23; b1=(2-n)*q13; b2=1-n*q13; b3=(4-n)*q13
T2 = [3, z], [n-q56, 1-n], [a1], [b1,b2,b3], \
[a1,a2], [b1,b2,b3], w
return T1, T2
v = ctx.hypercomb(h, [n], **kwargs)
if ctx._is_real_type(z) and ctx.isint(n):
v = ctx._re(v)
return v
else:
def h():
ctx.prec += extraprec
w = z**3 / 9
ctx.prec -= extraprec
C1 = _airybi_C1(ctx)
C2 = _airybi_C2(ctx)
T1 = [C1],[1],[],[],[],[ctx.mpq_2_3],w
T2 = [z*C2],[1],[],[],[],[ctx.mpq_4_3],w
return T1, T2
return ctx.hypercomb(h, [], **kwargs)
def _airy_zero(ctx, which, k, derivative, complex=False):
# Asymptotic formulas are given in DLMF section 9.9
def U(t): return t**(2/3.)*(1-7/(t**2*48))
def T(t): return t**(2/3.)*(1+5/(t**2*48))
k = int(k)
if k < 1:
raise ValueError("k cannot be less than 1")
if not derivative in (0,1):
raise ValueError("Derivative should lie between 0 and 1")
if which == 0:
if derivative:
return ctx.findroot(lambda z: ctx.airyai(z,1),
-U(3*ctx.pi*(4*k-3)/8))
return ctx.findroot(ctx.airyai, -T(3*ctx.pi*(4*k-1)/8))
if which == 1 and complex == False:
if derivative:
return ctx.findroot(lambda z: ctx.airybi(z,1),
-U(3*ctx.pi*(4*k-1)/8))
return ctx.findroot(ctx.airybi, -T(3*ctx.pi*(4*k-3)/8))
if which == 1 and complex == True:
if derivative:
t = 3*ctx.pi*(4*k-3)/8 + 0.75j*ctx.ln2
s = ctx.expjpi(ctx.mpf(1)/3) * T(t)
return ctx.findroot(lambda z: ctx.airybi(z,1), s)
t = 3*ctx.pi*(4*k-1)/8 + 0.75j*ctx.ln2
s = ctx.expjpi(ctx.mpf(1)/3) * U(t)
return ctx.findroot(ctx.airybi, s)
@defun
def airyaizero(ctx, k, derivative=0):
return _airy_zero(ctx, 0, k, derivative, False)
@defun
def airybizero(ctx, k, derivative=0, complex=False):
return _airy_zero(ctx, 1, k, derivative, complex)
def _scorer(ctx, z, which, kwargs):
z = ctx.convert(z)
if ctx.isinf(z):
if z == ctx.inf:
if which == 0: return 1/z
if which == 1: return z
if z == ctx.ninf:
return 1/z
raise ValueError("essential singularity")
if z:
extraprec = max(0, int(1.5*ctx.mag(z)))
else:
extraprec = 0
if kwargs.get('derivative'):
raise NotImplementedError
# Direct asymptotic expansions, to avoid
# exponentially large cancellation
try:
if ctx.mag(z) > 3:
if which == 0 and abs(ctx.arg(z)) < ctx.pi/3 * 0.999:
def h():
return (([ctx.pi,z],[-1,-1],[],[],[(1,3),(2,3),1],[],9/z**3),)
return ctx.hypercomb(h, [], maxterms=ctx.prec, force_series=True)
if which == 1 and abs(ctx.arg(-z)) < 2*ctx.pi/3 * 0.999:
def h():
return (([-ctx.pi,z],[-1,-1],[],[],[(1,3),(2,3),1],[],9/z**3),)
return ctx.hypercomb(h, [], maxterms=ctx.prec, force_series=True)
except ctx.NoConvergence:
pass
def h():
A = ctx.airybi(z, **kwargs)/3
B = -2*ctx.pi
if which == 1:
A *= 2
B *= -1
ctx.prec += extraprec
w = z**3/9
ctx.prec -= extraprec
T1 = [A], [1], [], [], [], [], 0
T2 = [B,z], [-1,2], [], [], [1], [ctx.mpq_4_3,ctx.mpq_5_3], w
return T1, T2
return ctx.hypercomb(h, [], **kwargs)
@defun
def scorergi(ctx, z, **kwargs):
return _scorer(ctx, z, 0, kwargs)
@defun
def scorerhi(ctx, z, **kwargs):
return _scorer(ctx, z, 1, kwargs)
@defun_wrapped
def coulombc(ctx, l, eta, _cache={}):
if (l, eta) in _cache and _cache[l,eta][0] >= ctx.prec:
return +_cache[l,eta][1]
G3 = ctx.loggamma(2*l+2)
G1 = ctx.loggamma(1+l+ctx.j*eta)
G2 = ctx.loggamma(1+l-ctx.j*eta)
v = 2**l * ctx.exp((-ctx.pi*eta+G1+G2)/2 - G3)
if not (ctx.im(l) or ctx.im(eta)):
v = ctx.re(v)
_cache[l,eta] = (ctx.prec, v)
return v
@defun_wrapped
def coulombf(ctx, l, eta, z, w=1, chop=True, **kwargs):
# Regular Coulomb wave function
# Note: w can be either 1 or -1; the other may be better in some cases
# TODO: check that chop=True chops when and only when it should
#ctx.prec += 10
def h(l, eta):
try:
jw = ctx.j*w
jwz = ctx.fmul(jw, z, exact=True)
jwz2 = ctx.fmul(jwz, -2, exact=True)
C = ctx.coulombc(l, eta)
T1 = [C, z, ctx.exp(jwz)], [1, l+1, 1], [], [], [1+l+jw*eta], \
[2*l+2], jwz2
except ValueError:
T1 = [0], [-1], [], [], [], [], 0
return (T1,)
v = ctx.hypercomb(h, [l,eta], **kwargs)
if chop and (not ctx.im(l)) and (not ctx.im(eta)) and (not ctx.im(z)) and \
(ctx.re(z) >= 0):
v = ctx.re(v)
return v
@defun_wrapped
def _coulomb_chi(ctx, l, eta, _cache={}):
if (l, eta) in _cache and _cache[l,eta][0] >= ctx.prec:
return _cache[l,eta][1]
def terms():
l2 = -l-1
jeta = ctx.j*eta
return [ctx.loggamma(1+l+jeta) * (-0.5j),
ctx.loggamma(1+l-jeta) * (0.5j),
ctx.loggamma(1+l2+jeta) * (0.5j),
ctx.loggamma(1+l2-jeta) * (-0.5j),
-(l+0.5)*ctx.pi]
v = ctx.sum_accurately(terms, 1)
_cache[l,eta] = (ctx.prec, v)
return v
@defun_wrapped
def coulombg(ctx, l, eta, z, w=1, chop=True, **kwargs):
# Irregular Coulomb wave function
# Note: w can be either 1 or -1; the other may be better in some cases
# TODO: check that chop=True chops when and only when it should
if not ctx._im(l):
l = ctx._re(l) # XXX: for isint
def h(l, eta):
# Force perturbation for integers and half-integers
if ctx.isint(l*2):
T1 = [0], [-1], [], [], [], [], 0
return (T1,)
l2 = -l-1
try:
chi = ctx._coulomb_chi(l, eta)
jw = ctx.j*w
s = ctx.sin(chi); c = ctx.cos(chi)
C1 = ctx.coulombc(l,eta)
C2 = ctx.coulombc(l2,eta)
u = ctx.exp(jw*z)
x = -2*jw*z
T1 = [s, C1, z, u, c], [-1, 1, l+1, 1, 1], [], [], \
[1+l+jw*eta], [2*l+2], x
T2 = [-s, C2, z, u], [-1, 1, l2+1, 1], [], [], \
[1+l2+jw*eta], [2*l2+2], x
return T1, T2
except ValueError:
T1 = [0], [-1], [], [], [], [], 0
return (T1,)
v = ctx.hypercomb(h, [l,eta], **kwargs)
if chop and (not ctx._im(l)) and (not ctx._im(eta)) and (not ctx._im(z)) and \
(ctx._re(z) >= 0):
v = ctx._re(v)
return v
def mcmahon(ctx,kind,prime,v,m):
"""
Computes an estimate for the location of the Bessel function zero
j_{v,m}, y_{v,m}, j'_{v,m} or y'_{v,m} using McMahon's asymptotic
expansion (Abramowitz & Stegun 9.5.12-13, DLMF 20.21(vi)).
Returns (r,err) where r is the estimated location of the root
and err is a positive number estimating the error of the
asymptotic expansion.
"""
u = 4*v**2
if kind == 1 and not prime: b = (4*m+2*v-1)*ctx.pi/4
if kind == 2 and not prime: b = (4*m+2*v-3)*ctx.pi/4
if kind == 1 and prime: b = (4*m+2*v-3)*ctx.pi/4
if kind == 2 and prime: b = (4*m+2*v-1)*ctx.pi/4
if not prime:
s1 = b
s2 = -(u-1)/(8*b)
s3 = -4*(u-1)*(7*u-31)/(3*(8*b)**3)
s4 = -32*(u-1)*(83*u**2-982*u+3779)/(15*(8*b)**5)
s5 = -64*(u-1)*(6949*u**3-153855*u**2+1585743*u-6277237)/(105*(8*b)**7)
if prime:
s1 = b
s2 = -(u+3)/(8*b)
s3 = -4*(7*u**2+82*u-9)/(3*(8*b)**3)
s4 = -32*(83*u**3+2075*u**2-3039*u+3537)/(15*(8*b)**5)
s5 = -64*(6949*u**4+296492*u**3-1248002*u**2+7414380*u-5853627)/(105*(8*b)**7)
terms = [s1,s2,s3,s4,s5]
s = s1
err = 0.0
for i in range(1,len(terms)):
if abs(terms[i]) < abs(terms[i-1]):
s += terms[i]
else:
err = abs(terms[i])
if i == len(terms)-1:
err = abs(terms[-1])
return s, err
def generalized_bisection(ctx,f,a,b,n):
"""
Given f known to have exactly n simple roots within [a,b],
return a list of n intervals isolating the roots
and having opposite signs at the endpoints.
TODO: this can be optimized, e.g. by reusing evaluation points.
"""
if n < 1:
raise ValueError("n cannot be less than 1")
N = n+1
points = []
signs = []
while 1:
points = ctx.linspace(a,b,N)
signs = [ctx.sign(f(x)) for x in points]
ok_intervals = [(points[i],points[i+1]) for i in range(N-1) \
if signs[i]*signs[i+1] == -1]
if len(ok_intervals) == n:
return ok_intervals
N = N*2
def find_in_interval(ctx, f, ab):
return ctx.findroot(f, ab, solver='illinois', verify=False)
def bessel_zero(ctx, kind, prime, v, m, isoltol=0.01, _interval_cache={}):
prec = ctx.prec
workprec = max(prec, ctx.mag(v), ctx.mag(m))+10
try:
ctx.prec = workprec
v = ctx.mpf(v)
m = int(m)
prime = int(prime)
if v < 0:
raise ValueError("v cannot be negative")
if m < 1:
raise ValueError("m cannot be less than 1")
if not prime in (0,1):
raise ValueError("prime should lie between 0 and 1")
if kind == 1:
if prime: f = lambda x: ctx.besselj(v,x,derivative=1)
else: f = lambda x: ctx.besselj(v,x)
if kind == 2:
if prime: f = lambda x: ctx.bessely(v,x,derivative=1)
else: f = lambda x: ctx.bessely(v,x)
# The first root of J' is very close to 0 for small
# orders, and this needs to be special-cased
if kind == 1 and prime and m == 1:
if v == 0:
return ctx.zero
if v <= 1:
# TODO: use v <= j'_{v,1} < y_{v,1}?
r = 2*ctx.sqrt(v*(1+v)/(v+2))
return find_in_interval(ctx, f, (r/10, 2*r))
if (kind,prime,v,m) in _interval_cache:
return find_in_interval(ctx, f, _interval_cache[kind,prime,v,m])
r, err = mcmahon(ctx, kind, prime, v, m)
if err < isoltol:
return find_in_interval(ctx, f, (r-isoltol, r+isoltol))
# An x such that 0 < x < r_{v,1}
if kind == 1 and not prime: low = 2.4
if kind == 1 and prime: low = 1.8
if kind == 2 and not prime: low = 0.8
if kind == 2 and prime: low = 2.0
n = m+1
while 1:
r1, err = mcmahon(ctx, kind, prime, v, n)
if err < isoltol:
r2, err2 = mcmahon(ctx, kind, prime, v, n+1)
intervals = generalized_bisection(ctx, f, low, 0.5*(r1+r2), n)
for k, ab in enumerate(intervals):
_interval_cache[kind,prime,v,k+1] = ab
return find_in_interval(ctx, f, intervals[m-1])
else:
n = n*2
finally:
ctx.prec = prec
@defun
def besseljzero(ctx, v, m, derivative=0):
r"""
For a real order `\nu \ge 0` and a positive integer `m`, returns
`j_{\nu,m}`, the `m`-th positive zero of the Bessel function of the
first kind `J_{\nu}(z)` (see :func:`~mpmath.besselj`). Alternatively,
with *derivative=1*, gives the first nonnegative simple zero
`j'_{\nu,m}` of `J'_{\nu}(z)`.
The indexing convention is that used by Abramowitz & Stegun
and the DLMF. Note the special case `j'_{0,1} = 0`, while all other
zeros are positive. In effect, only simple zeros are counted
(all zeros of Bessel functions are simple except possibly `z = 0`)
and `j_{\nu,m}` becomes a monotonic function of both `\nu`
and `m`.
The zeros are interlaced according to the inequalities
.. math ::
j'_{\nu,k} < j_{\nu,k} < j'_{\nu,k+1}
j_{\nu,1} < j_{\nu+1,2} < j_{\nu,2} < j_{\nu+1,2} < j_{\nu,3} < \cdots
**Examples**
Initial zeros of the Bessel functions `J_0(z), J_1(z), J_2(z)`::
>>> from mpmath import *
>>> mp.dps = 25; mp.pretty = True
>>> besseljzero(0,1); besseljzero(0,2); besseljzero(0,3)
2.404825557695772768621632
5.520078110286310649596604
8.653727912911012216954199
>>> besseljzero(1,1); besseljzero(1,2); besseljzero(1,3)
3.831705970207512315614436
7.01558666981561875353705
10.17346813506272207718571
>>> besseljzero(2,1); besseljzero(2,2); besseljzero(2,3)
5.135622301840682556301402
8.417244140399864857783614
11.61984117214905942709415
Initial zeros of `J'_0(z), J'_1(z), J'_2(z)`::
0.0
3.831705970207512315614436
7.01558666981561875353705
>>> besseljzero(1,1,1); besseljzero(1,2,1); besseljzero(1,3,1)
1.84118378134065930264363
5.331442773525032636884016
8.536316366346285834358961
>>> besseljzero(2,1,1); besseljzero(2,2,1); besseljzero(2,3,1)
3.054236928227140322755932
6.706133194158459146634394
9.969467823087595793179143
Zeros with large index::
>>> besseljzero(0,100); besseljzero(0,1000); besseljzero(0,10000)
313.3742660775278447196902
3140.807295225078628895545
31415.14114171350798533666
>>> besseljzero(5,100); besseljzero(5,1000); besseljzero(5,10000)
321.1893195676003157339222
3148.657306813047523500494
31422.9947255486291798943
>>> besseljzero(0,100,1); besseljzero(0,1000,1); besseljzero(0,10000,1)
311.8018681873704508125112
3139.236339643802482833973
31413.57032947022399485808
Zeros of functions with large order::
>>> besseljzero(50,1)
57.11689916011917411936228
>>> besseljzero(50,2)
62.80769876483536093435393
>>> besseljzero(50,100)
388.6936600656058834640981
>>> besseljzero(50,1,1)
52.99764038731665010944037
>>> besseljzero(50,2,1)
60.02631933279942589882363
>>> besseljzero(50,100,1)
387.1083151608726181086283
Zeros of functions with fractional order::
>>> besseljzero(0.5,1); besseljzero(1.5,1); besseljzero(2.25,4)
3.141592653589793238462643
4.493409457909064175307881
15.15657692957458622921634
Both `J_{\nu}(z)` and `J'_{\nu}(z)` can be expressed as infinite
products over their zeros::
>>> v,z = 2, mpf(1)
>>> (z/2)**v/gamma(v+1) * \
... nprod(lambda k: 1-(z/besseljzero(v,k))**2, [1,inf])
...
0.1149034849319004804696469
>>> besselj(v,z)
0.1149034849319004804696469
>>> (z/2)**(v-1)/2/gamma(v) * \
... nprod(lambda k: 1-(z/besseljzero(v,k,1))**2, [1,inf])
...
0.2102436158811325550203884
>>> besselj(v,z,1)
0.2102436158811325550203884
"""
return +bessel_zero(ctx, 1, derivative, v, m)
@defun
def besselyzero(ctx, v, m, derivative=0):
r"""
For a real order `\nu \ge 0` and a positive integer `m`, returns
`y_{\nu,m}`, the `m`-th positive zero of the Bessel function of the
second kind `Y_{\nu}(z)` (see :func:`~mpmath.bessely`). Alternatively,
with *derivative=1*, gives the first positive zero `y'_{\nu,m}` of
`Y'_{\nu}(z)`.
The zeros are interlaced according to the inequalities
.. math ::
y_{\nu,k} < y'_{\nu,k} < y_{\nu,k+1}
y_{\nu,1} < y_{\nu+1,2} < y_{\nu,2} < y_{\nu+1,2} < y_{\nu,3} < \cdots
**Examples**
Initial zeros of the Bessel functions `Y_0(z), Y_1(z), Y_2(z)`::
>>> from mpmath import *
>>> mp.dps = 25; mp.pretty = True
>>> besselyzero(0,1); besselyzero(0,2); besselyzero(0,3)
0.8935769662791675215848871
3.957678419314857868375677
7.086051060301772697623625
>>> besselyzero(1,1); besselyzero(1,2); besselyzero(1,3)
2.197141326031017035149034
5.429681040794135132772005
8.596005868331168926429606
>>> besselyzero(2,1); besselyzero(2,2); besselyzero(2,3)
3.384241767149593472701426
6.793807513268267538291167
10.02347797936003797850539
Initial zeros of `Y'_0(z), Y'_1(z), Y'_2(z)`::
>>> besselyzero(0,1,1); besselyzero(0,2,1); besselyzero(0,3,1)
2.197141326031017035149034
5.429681040794135132772005
8.596005868331168926429606
>>> besselyzero(1,1,1); besselyzero(1,2,1); besselyzero(1,3,1)
3.683022856585177699898967
6.941499953654175655751944
10.12340465543661307978775
>>> besselyzero(2,1,1); besselyzero(2,2,1); besselyzero(2,3,1)
5.002582931446063945200176
8.350724701413079526349714
11.57419546521764654624265
Zeros with large index::
>>> besselyzero(0,100); besselyzero(0,1000); besselyzero(0,10000)
311.8034717601871549333419
3139.236498918198006794026
31413.57034538691205229188
>>> besselyzero(5,100); besselyzero(5,1000); besselyzero(5,10000)
319.6183338562782156235062
3147.086508524556404473186
31421.42392920214673402828
>>> besselyzero(0,100,1); besselyzero(0,1000,1); besselyzero(0,10000,1)
313.3726705426359345050449
3140.807136030340213610065
31415.14112579761578220175
Zeros of functions with large order::
>>> besselyzero(50,1)
53.50285882040036394680237
>>> besselyzero(50,2)
60.11244442774058114686022
>>> besselyzero(50,100)
387.1096509824943957706835
>>> besselyzero(50,1,1)
56.96290427516751320063605
>>> besselyzero(50,2,1)
62.74888166945933944036623
>>> besselyzero(50,100,1)
388.6923300548309258355475
Zeros of functions with fractional order::
>>> besselyzero(0.5,1); besselyzero(1.5,1); besselyzero(2.25,4)
1.570796326794896619231322
2.798386045783887136720249
13.56721208770735123376018
"""
return +bessel_zero(ctx, 2, derivative, v, m)