MLPY/Lib/site-packages/mpmath/functions/factorials.py

from ..libmp.backend import xrange
from .functions import defun, defun_wrapped

@defun
def gammaprod(ctx, a, b, _infsign=False):
    a = [ctx.convert(x) for x in a]
    b = [ctx.convert(x) for x in b]
    poles_num = []
    poles_den = []
    regular_num = []
    regular_den = []
    for x in a: [regular_num, poles_num][ctx.isnpint(x)].append(x)
    for x in b: [regular_den, poles_den][ctx.isnpint(x)].append(x)
    # One more pole in numerator or denominator gives 0 or inf
    if len(poles_num) < len(poles_den): return ctx.zero
    if len(poles_num) > len(poles_den):
        # Get correct sign of infinity for x+h, h -> 0 from above
        # XXX: hack, this should be done properly
        if _infsign:
            a = [x and x*(1+ctx.eps) or x+ctx.eps for x in poles_num]
            b = [x and x*(1+ctx.eps) or x+ctx.eps for x in poles_den]
            return ctx.sign(ctx.gammaprod(a+regular_num,b+regular_den)) * ctx.inf
        else:
            return ctx.inf
    # All poles cancel
    # lim G(i)/G(j) = (-1)**(i+j) * gamma(1-j) / gamma(1-i)
    p = ctx.one
    orig = ctx.prec
    try:
        ctx.prec = orig + 15
        while poles_num:
            i = poles_num.pop()
            j = poles_den.pop()
            p *= (-1)**(i+j) * ctx.gamma(1-j) / ctx.gamma(1-i)
        for x in regular_num: p *= ctx.gamma(x)
        for x in regular_den: p /= ctx.gamma(x)
    finally:
        ctx.prec = orig
    return +p

@defun
def beta(ctx, x, y):
    x = ctx.convert(x)
    y = ctx.convert(y)
    if ctx.isinf(y):
        x, y = y, x
    if ctx.isinf(x):
        if x == ctx.inf and not ctx._im(y):
            if y == ctx.ninf:
                return ctx.nan
            if y > 0:
                return ctx.zero
            if ctx.isint(y):
                return ctx.nan
            if y < 0:
                return ctx.sign(ctx.gamma(y)) * ctx.inf
        return ctx.nan
    xy = ctx.fadd(x, y, prec=2*ctx.prec)
    return ctx.gammaprod([x, y], [xy])

@defun
def binomial(ctx, n, k):
    n1 = ctx.fadd(n, 1, prec=2*ctx.prec)
    k1 = ctx.fadd(k, 1, prec=2*ctx.prec)
    nk1 = ctx.fsub(n1, k, prec=2*ctx.prec)
    return ctx.gammaprod([n1], [k1, nk1])

@defun
def rf(ctx, x, n):
    xn = ctx.fadd(x, n, prec=2*ctx.prec)
    return ctx.gammaprod([xn], [x])

@defun
def ff(ctx, x, n):
    x1 = ctx.fadd(x, 1, prec=2*ctx.prec)
    xn1 = ctx.fadd(ctx.fsub(x, n, prec=2*ctx.prec), 1, prec=2*ctx.prec)
    return ctx.gammaprod([x1], [xn1])

@defun_wrapped
def fac2(ctx, x):
    if ctx.isinf(x):
        if x == ctx.inf:
            return x
        return ctx.nan
    return 2**(x/2)*(ctx.pi/2)**((ctx.cospi(x)-1)/4)*ctx.gamma(x/2+1)

@defun_wrapped
def barnesg(ctx, z):
    if ctx.isinf(z):
        if z == ctx.inf:
            return z
        return ctx.nan
    if ctx.isnan(z):
        return z
    if (not ctx._im(z)) and ctx._re(z) <= 0 and ctx.isint(ctx._re(z)):
        return z*0
    # Account for size (would not be needed if computing log(G))
    if abs(z) > 5:
        ctx.dps += 2*ctx.log(abs(z),2)
    # Reflection formula
    if ctx.re(z) < -ctx.dps:
        w = 1-z
        pi2 = 2*ctx.pi
        u = ctx.expjpi(2*w)
        v = ctx.j*ctx.pi/12 - ctx.j*ctx.pi*w**2/2 + w*ctx.ln(1-u) - \
            ctx.j*ctx.polylog(2, u)/pi2
        v = ctx.barnesg(2-z)*ctx.exp(v)/pi2**w
        if ctx._is_real_type(z):
            v = ctx._re(v)
        return v
    # Estimate terms for asymptotic expansion
    # TODO: fixme, obviously
    N = ctx.dps // 2 + 5
    G = 1
    while abs(z) < N or ctx.re(z) < 1:
        G /= ctx.gamma(z)
        z += 1
    z -= 1
    s = ctx.mpf(1)/12
    s -= ctx.log(ctx.glaisher)
    s += z*ctx.log(2*ctx.pi)/2
    s += (z**2/2-ctx.mpf(1)/12)*ctx.log(z)
    s -= 3*z**2/4
    z2k = z2 = z**2
    for k in xrange(1, N+1):
        t = ctx.bernoulli(2*k+2) / (4*k*(k+1)*z2k)
        if abs(t) < ctx.eps:
            #print k, N      # check how many terms were needed
            break
        z2k *= z2
        s += t
    #if k == N:
    #    print "warning: series for barnesg failed to converge", ctx.dps
    return G*ctx.exp(s)

@defun
def superfac(ctx, z):
    return ctx.barnesg(z+2)

@defun_wrapped
def hyperfac(ctx, z):
    # XXX: estimate needed extra bits accurately
    if z == ctx.inf:
        return z
    if abs(z) > 5:
        extra = 4*int(ctx.log(abs(z),2))
    else:
        extra = 0
    ctx.prec += extra
    if not ctx._im(z) and ctx._re(z) < 0 and ctx.isint(ctx._re(z)):
        n = int(ctx.re(z))
        h = ctx.hyperfac(-n-1)
        if ((n+1)//2) & 1:
            h = -h
        if ctx._is_complex_type(z):
            return h + 0j
        return h
    zp1 = z+1
    # Wrong branch cut
    #v = ctx.gamma(zp1)**z
    #ctx.prec -= extra
    #return v / ctx.barnesg(zp1)
    v = ctx.exp(z*ctx.loggamma(zp1))
    ctx.prec -= extra
    return v / ctx.barnesg(zp1)

'''
@defun
def psi0(ctx, z):
    """Shortcut for psi(0,z) (the digamma function)"""
    return ctx.psi(0, z)

@defun
def psi1(ctx, z):
    """Shortcut for psi(1,z) (the trigamma function)"""
    return ctx.psi(1, z)

@defun
def psi2(ctx, z):
    """Shortcut for psi(2,z) (the tetragamma function)"""
    return ctx.psi(2, z)

@defun
def psi3(ctx, z):
    """Shortcut for psi(3,z) (the pentagamma function)"""
    return ctx.psi(3, z)
'''