5196c2cb84e1a787c43794229370aa2a1975ce16c5a8ae4ded7470fd1bfe6153

lib/python3.11/site-packages/mpmath/functions/functions.py

@@ -0,0 +1,645 @@
+from ..libmp.backend import xrange
+
+class SpecialFunctions(object):
+    """
+    This class implements special functions using high-level code.
+
+    Elementary and some other functions (e.g. gamma function, basecase
+    hypergeometric series) are assumed to be predefined by the context as
+    "builtins" or "low-level" functions.
+    """
+    defined_functions = {}
+
+    # The series for the Jacobi theta functions converge for |q| < 1;
+    # in the current implementation they throw a ValueError for
+    # abs(q) > THETA_Q_LIM
+    THETA_Q_LIM = 1 - 10**-7
+
+    def __init__(self):
+        cls = self.__class__
+        for name in cls.defined_functions:
+            f, wrap = cls.defined_functions[name]
+            cls._wrap_specfun(name, f, wrap)
+
+        self.mpq_1 = self._mpq((1,1))
+        self.mpq_0 = self._mpq((0,1))
+        self.mpq_1_2 = self._mpq((1,2))
+        self.mpq_3_2 = self._mpq((3,2))
+        self.mpq_1_4 = self._mpq((1,4))
+        self.mpq_1_16 = self._mpq((1,16))
+        self.mpq_3_16 = self._mpq((3,16))
+        self.mpq_5_2 = self._mpq((5,2))
+        self.mpq_3_4 = self._mpq((3,4))
+        self.mpq_7_4 = self._mpq((7,4))
+        self.mpq_5_4 = self._mpq((5,4))
+        self.mpq_1_3 = self._mpq((1,3))
+        self.mpq_2_3 = self._mpq((2,3))
+        self.mpq_4_3 = self._mpq((4,3))
+        self.mpq_1_6 = self._mpq((1,6))
+        self.mpq_5_6 = self._mpq((5,6))
+        self.mpq_5_3 = self._mpq((5,3))
+
+        self._misc_const_cache = {}
+
+        self._aliases.update({
+            'phase' : 'arg',
+            'conjugate' : 'conj',
+            'nthroot' : 'root',
+            'polygamma' : 'psi',
+            'hurwitz' : 'zeta',
+            #'digamma' : 'psi0',
+            #'trigamma' : 'psi1',
+            #'tetragamma' : 'psi2',
+            #'pentagamma' : 'psi3',
+            'fibonacci' : 'fib',
+            'factorial' : 'fac',
+        })
+
+        self.zetazero_memoized = self.memoize(self.zetazero)
+
+    # Default -- do nothing
+    @classmethod
+    def _wrap_specfun(cls, name, f, wrap):
+        setattr(cls, name, f)
+
+    # Optional fast versions of common functions in common cases.
+    # If not overridden, default (generic hypergeometric series)
+    # implementations will be used
+    def _besselj(ctx, n, z): raise NotImplementedError
+    def _erf(ctx, z): raise NotImplementedError
+    def _erfc(ctx, z): raise NotImplementedError
+    def _gamma_upper_int(ctx, z, a): raise NotImplementedError
+    def _expint_int(ctx, n, z): raise NotImplementedError
+    def _zeta(ctx, s): raise NotImplementedError
+    def _zetasum_fast(ctx, s, a, n, derivatives, reflect): raise NotImplementedError
+    def _ei(ctx, z): raise NotImplementedError
+    def _e1(ctx, z): raise NotImplementedError
+    def _ci(ctx, z): raise NotImplementedError
+    def _si(ctx, z): raise NotImplementedError
+    def _altzeta(ctx, s): raise NotImplementedError
+
+def defun_wrapped(f):
+    SpecialFunctions.defined_functions[f.__name__] = f, True
+    return f
+
+def defun(f):
+    SpecialFunctions.defined_functions[f.__name__] = f, False
+    return f
+
+def defun_static(f):
+    setattr(SpecialFunctions, f.__name__, f)
+    return f
+
+@defun_wrapped
+def cot(ctx, z): return ctx.one / ctx.tan(z)
+
+@defun_wrapped
+def sec(ctx, z): return ctx.one / ctx.cos(z)
+
+@defun_wrapped
+def csc(ctx, z): return ctx.one / ctx.sin(z)
+
+@defun_wrapped
+def coth(ctx, z): return ctx.one / ctx.tanh(z)
+
+@defun_wrapped
+def sech(ctx, z): return ctx.one / ctx.cosh(z)
+
+@defun_wrapped
+def csch(ctx, z): return ctx.one / ctx.sinh(z)
+
+@defun_wrapped
+def acot(ctx, z):
+    if not z:
+        return ctx.pi * 0.5
+    else:
+        return ctx.atan(ctx.one / z)
+
+@defun_wrapped
+def asec(ctx, z): return ctx.acos(ctx.one / z)
+
+@defun_wrapped
+def acsc(ctx, z): return ctx.asin(ctx.one / z)
+
+@defun_wrapped
+def acoth(ctx, z):
+    if not z:
+        return ctx.pi * 0.5j
+    else:
+        return ctx.atanh(ctx.one / z)
+
+
+@defun_wrapped
+def asech(ctx, z): return ctx.acosh(ctx.one / z)
+
+@defun_wrapped
+def acsch(ctx, z): return ctx.asinh(ctx.one / z)
+
+@defun
+def sign(ctx, x):
+    x = ctx.convert(x)
+    if not x or ctx.isnan(x):
+        return x
+    if ctx._is_real_type(x):
+        if x > 0:
+            return ctx.one
+        else:
+            return -ctx.one
+    return x / abs(x)
+
+@defun
+def agm(ctx, a, b=1):
+    if b == 1:
+        return ctx.agm1(a)
+    a = ctx.convert(a)
+    b = ctx.convert(b)
+    return ctx._agm(a, b)
+
+@defun_wrapped
+def sinc(ctx, x):
+    if ctx.isinf(x):
+        return 1/x
+    if not x:
+        return x+1
+    return ctx.sin(x)/x
+
+@defun_wrapped
+def sincpi(ctx, x):
+    if ctx.isinf(x):
+        return 1/x
+    if not x:
+        return x+1
+    return ctx.sinpi(x)/(ctx.pi*x)
+
+# TODO: tests; improve implementation
+@defun_wrapped
+def expm1(ctx, x):
+    if not x:
+        return ctx.zero
+    # exp(x) - 1 ~ x
+    if ctx.mag(x) < -ctx.prec:
+        return x + 0.5*x**2
+    # TODO: accurately eval the smaller of the real/imag parts
+    return ctx.sum_accurately(lambda: iter([ctx.exp(x),-1]),1)
+
+@defun_wrapped
+def log1p(ctx, x):
+    if not x:
+        return ctx.zero
+    if ctx.mag(x) < -ctx.prec:
+        return x - 0.5*x**2
+    return ctx.log(ctx.fadd(1, x, prec=2*ctx.prec))
+
+@defun_wrapped
+def powm1(ctx, x, y):
+    mag = ctx.mag
+    one = ctx.one
+    w = x**y - one
+    M = mag(w)
+    # Only moderate cancellation
+    if M > -8:
+        return w
+    # Check for the only possible exact cases
+    if not w:
+        if (not y) or (x in (1, -1, 1j, -1j) and ctx.isint(y)):
+            return w
+    x1 = x - one
+    magy = mag(y)
+    lnx = ctx.ln(x)
+    # Small y: x^y - 1 ~ log(x)*y + O(log(x)^2 * y^2)
+    if magy + mag(lnx) < -ctx.prec:
+        return lnx*y + (lnx*y)**2/2
+    # TODO: accurately eval the smaller of the real/imag part
+    return ctx.sum_accurately(lambda: iter([x**y, -1]), 1)
+
+@defun
+def _rootof1(ctx, k, n):
+    k = int(k)
+    n = int(n)
+    k %= n
+    if not k:
+        return ctx.one
+    elif 2*k == n:
+        return -ctx.one
+    elif 4*k == n:
+        return ctx.j
+    elif 4*k == 3*n:
+        return -ctx.j
+    return ctx.expjpi(2*ctx.mpf(k)/n)
+
+@defun
+def root(ctx, x, n, k=0):
+    n = int(n)
+    x = ctx.convert(x)
+    if k:
+        # Special case: there is an exact real root
+        if (n & 1 and 2*k == n-1) and (not ctx.im(x)) and (ctx.re(x) < 0):
+            return -ctx.root(-x, n)
+        # Multiply by root of unity
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            v = ctx.root(x, n, 0) * ctx._rootof1(k, n)
+        finally:
+            ctx.prec = prec
+        return +v
+    return ctx._nthroot(x, n)
+
+@defun
+def unitroots(ctx, n, primitive=False):
+    gcd = ctx._gcd
+    prec = ctx.prec
+    try:
+        ctx.prec += 10
+        if primitive:
+            v = [ctx._rootof1(k,n) for k in range(n) if gcd(k,n) == 1]
+        else:
+            # TODO: this can be done *much* faster
+            v = [ctx._rootof1(k,n) for k in range(n)]
+    finally:
+        ctx.prec = prec
+    return [+x for x in v]
+
+@defun
+def arg(ctx, x):
+    x = ctx.convert(x)
+    re = ctx._re(x)
+    im = ctx._im(x)
+    return ctx.atan2(im, re)
+
+@defun
+def fabs(ctx, x):
+    return abs(ctx.convert(x))
+
+@defun
+def re(ctx, x):
+    x = ctx.convert(x)
+    if hasattr(x, "real"):    # py2.5 doesn't have .real/.imag for all numbers
+        return x.real
+    return x
+
+@defun
+def im(ctx, x):
+    x = ctx.convert(x)
+    if hasattr(x, "imag"):    # py2.5 doesn't have .real/.imag for all numbers
+        return x.imag
+    return ctx.zero
+
+@defun
+def conj(ctx, x):
+    x = ctx.convert(x)
+    try:
+        return x.conjugate()
+    except AttributeError:
+        return x
+
+@defun
+def polar(ctx, z):
+    return (ctx.fabs(z), ctx.arg(z))
+
+@defun_wrapped
+def rect(ctx, r, phi):
+    return r * ctx.mpc(*ctx.cos_sin(phi))
+
+@defun
+def log(ctx, x, b=None):
+    if b is None:
+        return ctx.ln(x)
+    wp = ctx.prec + 20
+    return ctx.ln(x, prec=wp) / ctx.ln(b, prec=wp)
+
+@defun
+def log10(ctx, x):
+    return ctx.log(x, 10)
+
+@defun
+def fmod(ctx, x, y):
+    return ctx.convert(x) % ctx.convert(y)
+
+@defun
+def degrees(ctx, x):
+    return x / ctx.degree
+
+@defun
+def radians(ctx, x):
+    return x * ctx.degree
+
+def _lambertw_special(ctx, z, k):
+    # W(0,0) = 0; all other branches are singular
+    if not z:
+        if not k:
+            return z
+        return ctx.ninf + z
+    if z == ctx.inf:
+        if k == 0:
+            return z
+        else:
+            return z + 2*k*ctx.pi*ctx.j
+    if z == ctx.ninf:
+        return (-z) + (2*k+1)*ctx.pi*ctx.j
+    # Some kind of nan or complex inf/nan?
+    return ctx.ln(z)
+
+import math
+import cmath
+
+def _lambertw_approx_hybrid(z, k):
+    imag_sign = 0
+    if hasattr(z, "imag"):
+        x = float(z.real)
+        y = z.imag
+        if y:
+            imag_sign = (-1) ** (y < 0)
+        y = float(y)
+    else:
+        x = float(z)
+        y = 0.0
+        imag_sign = 0
+    # hack to work regardless of whether Python supports -0.0
+    if not y:
+        y = 0.0
+    z = complex(x,y)
+    if k == 0:
+        if -4.0 < y < 4.0 and -1.0 < x < 2.5:
+            if imag_sign:
+                # Taylor series in upper/lower half-plane
+                if y > 1.00: return (0.876+0.645j) + (0.118-0.174j)*(z-(0.75+2.5j))
+                if y > 0.25: return (0.505+0.204j) + (0.375-0.132j)*(z-(0.75+0.5j))
+                if y < -1.00: return (0.876-0.645j) + (0.118+0.174j)*(z-(0.75-2.5j))
+                if y < -0.25: return (0.505-0.204j) + (0.375+0.132j)*(z-(0.75-0.5j))
+            # Taylor series near -1
+            if x < -0.5:
+                if imag_sign >= 0:
+                    return (-0.318+1.34j) + (-0.697-0.593j)*(z+1)
+                else:
+                    return (-0.318-1.34j) + (-0.697+0.593j)*(z+1)
+            # return real type
+            r = -0.367879441171442
+            if (not imag_sign) and x > r:
+                z = x
+            # Singularity near -1/e
+            if x < -0.2:
+                return -1 + 2.33164398159712*(z-r)**0.5 - 1.81218788563936*(z-r)
+            # Taylor series near 0
+            if x < 0.5: return z
+            # Simple linear approximation
+            return 0.2 + 0.3*z
+        if (not imag_sign) and x > 0.0:
+            L1 = math.log(x); L2 = math.log(L1)
+        else:
+            L1 = cmath.log(z); L2 = cmath.log(L1)
+    elif k == -1:
+        # return real type
+        r = -0.367879441171442
+        if (not imag_sign) and r < x < 0.0:
+            z = x
+        if (imag_sign >= 0) and y < 0.1 and -0.6 < x < -0.2:
+            return -1 - 2.33164398159712*(z-r)**0.5 - 1.81218788563936*(z-r)
+        if (not imag_sign) and -0.2 <= x < 0.0:
+            L1 = math.log(-x)
+            return L1 - math.log(-L1)
+        else:
+            if imag_sign == -1 and (not y) and x < 0.0:
+                L1 = cmath.log(z) - 3.1415926535897932j
+            else:
+                L1 = cmath.log(z) - 6.2831853071795865j
+            L2 = cmath.log(L1)
+    return L1 - L2 + L2/L1 + L2*(L2-2)/(2*L1**2)
+
+def _lambertw_series(ctx, z, k, tol):
+    """
+    Return rough approximation for W_k(z) from an asymptotic series,
+    sufficiently accurate for the Halley iteration to converge to
+    the correct value.
+    """
+    magz = ctx.mag(z)
+    if (-10 < magz < 900) and (-1000 < k < 1000):
+        # Near the branch point at -1/e
+        if magz < 1 and abs(z+0.36787944117144) < 0.05:
+            if k == 0 or (k == -1 and ctx._im(z) >= 0) or \
+                         (k == 1  and ctx._im(z) < 0):
+                delta = ctx.sum_accurately(lambda: [z, ctx.exp(-1)])
+                cancellation = -ctx.mag(delta)
+                ctx.prec += cancellation
+                # Use series given in Corless et al.
+                p = ctx.sqrt(2*(ctx.e*z+1))
+                ctx.prec -= cancellation
+                u = {0:ctx.mpf(-1), 1:ctx.mpf(1)}
+                a = {0:ctx.mpf(2), 1:ctx.mpf(-1)}
+                if k != 0:
+                    p = -p
+                s = ctx.zero
+                # The series converges, so we could use it directly, but unless
+                # *extremely* close, it is better to just use the first few
+                # terms to get a good approximation for the iteration
+                for l in xrange(max(2,cancellation)):
+                    if l not in u:
+                        a[l] = ctx.fsum(u[j]*u[l+1-j] for j in xrange(2,l))
+                        u[l] = (l-1)*(u[l-2]/2+a[l-2]/4)/(l+1)-a[l]/2-u[l-1]/(l+1)
+                    term = u[l] * p**l
+                    s += term
+                    if ctx.mag(term) < -tol:
+                        return s, True
+                    l += 1
+                ctx.prec += cancellation//2
+                return s, False
+        if k == 0 or k == -1:
+            return _lambertw_approx_hybrid(z, k), False
+    if k == 0:
+        if magz < -1:
+            return z*(1-z), False
+        L1 = ctx.ln(z)
+        L2 = ctx.ln(L1)
+    elif k == -1 and (not ctx._im(z)) and (-0.36787944117144 < ctx._re(z) < 0):
+        L1 = ctx.ln(-z)
+        return L1 - ctx.ln(-L1), False
+    else:
+        # This holds both as z -> 0 and z -> inf.
+        # Relative error is O(1/log(z)).
+        L1 = ctx.ln(z) + 2j*ctx.pi*k
+        L2 = ctx.ln(L1)
+    return L1 - L2 + L2/L1 + L2*(L2-2)/(2*L1**2), False
+
+@defun
+def lambertw(ctx, z, k=0):
+    z = ctx.convert(z)
+    k = int(k)
+    if not ctx.isnormal(z):
+        return _lambertw_special(ctx, z, k)
+    prec = ctx.prec
+    ctx.prec += 20 + ctx.mag(k or 1)
+    wp = ctx.prec
+    tol = wp - 5
+    w, done = _lambertw_series(ctx, z, k, tol)
+    if not done:
+        # Use Halley iteration to solve w*exp(w) = z
+        two = ctx.mpf(2)
+        for i in xrange(100):
+            ew = ctx.exp(w)
+            wew = w*ew
+            wewz = wew-z
+            wn = w - wewz/(wew+ew-(w+two)*wewz/(two*w+two))
+            if ctx.mag(wn-w) <= ctx.mag(wn) - tol:
+                w = wn
+                break
+            else:
+                w = wn
+        if i == 100:
+            ctx.warn("Lambert W iteration failed to converge for z = %s" % z)
+    ctx.prec = prec
+    return +w
+
+@defun_wrapped
+def bell(ctx, n, x=1):
+    x = ctx.convert(x)
+    if not n:
+        if ctx.isnan(x):
+            return x
+        return type(x)(1)
+    if ctx.isinf(x) or ctx.isinf(n) or ctx.isnan(x) or ctx.isnan(n):
+        return x**n
+    if n == 1: return x
+    if n == 2: return x*(x+1)
+    if x == 0: return ctx.sincpi(n)
+    return _polyexp(ctx, n, x, True) / ctx.exp(x)
+
+def _polyexp(ctx, n, x, extra=False):
+    def _terms():
+        if extra:
+            yield ctx.sincpi(n)
+        t = x
+        k = 1
+        while 1:
+            yield k**n * t
+            k += 1
+            t = t*x/k
+    return ctx.sum_accurately(_terms, check_step=4)
+
+@defun_wrapped
+def polyexp(ctx, s, z):
+    if ctx.isinf(z) or ctx.isinf(s) or ctx.isnan(z) or ctx.isnan(s):
+        return z**s
+    if z == 0: return z*s
+    if s == 0: return ctx.expm1(z)
+    if s == 1: return ctx.exp(z)*z
+    if s == 2: return ctx.exp(z)*z*(z+1)
+    return _polyexp(ctx, s, z)
+
+@defun_wrapped
+def cyclotomic(ctx, n, z):
+    n = int(n)
+    if n < 0:
+        raise ValueError("n cannot be negative")
+    p = ctx.one
+    if n == 0:
+        return p
+    if n == 1:
+        return z - p
+    if n == 2:
+        return z + p
+    # Use divisor product representation. Unfortunately, this sometimes
+    # includes singularities for roots of unity, which we have to cancel out.
+    # Matching zeros/poles pairwise, we have (1-z^a)/(1-z^b) ~ a/b + O(z-1).
+    a_prod = 1
+    b_prod = 1
+    num_zeros = 0
+    num_poles = 0
+    for d in range(1,n+1):
+        if not n % d:
+            w = ctx.moebius(n//d)
+            # Use powm1 because it is important that we get 0 only
+            # if it really is exactly 0
+            b = -ctx.powm1(z, d)
+            if b:
+                p *= b**w
+            else:
+                if w == 1:
+                    a_prod *= d
+                    num_zeros += 1
+                elif w == -1:
+                    b_prod *= d
+                    num_poles += 1
+    #print n, num_zeros, num_poles
+    if num_zeros:
+        if num_zeros > num_poles:
+            p *= 0
+        else:
+            p *= a_prod
+            p /= b_prod
+    return p
+
+@defun
+def mangoldt(ctx, n):
+    r"""
+    Evaluates the von Mangoldt function `\Lambda(n) = \log p`
+    if `n = p^k` a power of a prime, and `\Lambda(n) = 0` otherwise.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> [mangoldt(n) for n in range(-2,3)]
+        [0.0, 0.0, 0.0, 0.0, 0.6931471805599453094172321]
+        >>> mangoldt(6)
+        0.0
+        >>> mangoldt(7)
+        1.945910149055313305105353
+        >>> mangoldt(8)
+        0.6931471805599453094172321
+        >>> fsum(mangoldt(n) for n in range(101))
+        94.04531122935739224600493
+        >>> fsum(mangoldt(n) for n in range(10001))
+        10013.39669326311478372032
+
+    """
+    n = int(n)
+    if n < 2:
+        return ctx.zero
+    if n % 2 == 0:
+        # Must be a power of two
+        if n & (n-1) == 0:
+            return +ctx.ln2
+        else:
+            return ctx.zero
+    # TODO: the following could be generalized into a perfect
+    # power testing function
+    # ---
+    # Look for a small factor
+    for p in (3,5,7,11,13,17,19,23,29,31):
+        if not n % p:
+            q, r = n // p, 0
+            while q > 1:
+                q, r = divmod(q, p)
+                if r:
+                    return ctx.zero
+            return ctx.ln(p)
+    if ctx.isprime(n):
+        return ctx.ln(n)
+    # Obviously, we could use arbitrary-precision arithmetic for this...
+    if n > 10**30:
+        raise NotImplementedError
+    k = 2
+    while 1:
+        p = int(n**(1./k) + 0.5)
+        if p < 2:
+            return ctx.zero
+        if p ** k == n:
+            if ctx.isprime(p):
+                return ctx.ln(p)
+        k += 1
+
+@defun
+def stirling1(ctx, n, k, exact=False):
+    v = ctx._stirling1(int(n), int(k))
+    if exact:
+        return int(v)
+    else:
+        return ctx.mpf(v)
+
+@defun
+def stirling2(ctx, n, k, exact=False):
+    v = ctx._stirling2(int(n), int(k))
+    if exact:
+        return int(v)
+    else:
+        return ctx.mpf(v)

lib/python3.11/site-packages/mpmath/functions/hypergeometric.py

@@ -0,0 +1,1413 @@
+from ..libmp.backend import xrange
+from .functions import defun, defun_wrapped
+
+def _check_need_perturb(ctx, terms, prec, discard_known_zeros):
+    perturb = recompute = False
+    extraprec = 0
+    discard = []
+    for term_index, term in enumerate(terms):
+        w_s, c_s, alpha_s, beta_s, a_s, b_s, z = term
+        have_singular_nongamma_weight = False
+        # Avoid division by zero in leading factors (TODO:
+        # also check for near division by zero?)
+        for k, w in enumerate(w_s):
+            if not w:
+                if ctx.re(c_s[k]) <= 0 and c_s[k]:
+                    perturb = recompute = True
+                    have_singular_nongamma_weight = True
+        pole_count = [0, 0, 0]
+        # Check for gamma and series poles and near-poles
+        for data_index, data in enumerate([alpha_s, beta_s, b_s]):
+            for i, x in enumerate(data):
+                n, d = ctx.nint_distance(x)
+                # Poles
+                if n > 0:
+                    continue
+                if d == ctx.ninf:
+                    # OK if we have a polynomial
+                    # ------------------------------
+                    ok = False
+                    if data_index == 2:
+                        for u in a_s:
+                            if ctx.isnpint(u) and u >= int(n):
+                                ok = True
+                                break
+                    if ok:
+                        continue
+                    pole_count[data_index] += 1
+                    # ------------------------------
+                    #perturb = recompute = True
+                    #return perturb, recompute, extraprec
+                elif d < -4:
+                    extraprec += -d
+                    recompute = True
+        if discard_known_zeros and pole_count[1] > pole_count[0] + pole_count[2] \
+            and not have_singular_nongamma_weight:
+            discard.append(term_index)
+        elif sum(pole_count):
+            perturb = recompute = True
+    return perturb, recompute, extraprec, discard
+
+_hypercomb_msg = """
+hypercomb() failed to converge to the requested %i bits of accuracy
+using a working precision of %i bits. The function value may be zero or
+infinite; try passing zeroprec=N or infprec=M to bound finite values between
+2^(-N) and 2^M. Otherwise try a higher maxprec or maxterms.
+"""
+
+@defun
+def hypercomb(ctx, function, params=[], discard_known_zeros=True, **kwargs):
+    orig = ctx.prec
+    sumvalue = ctx.zero
+    dist = ctx.nint_distance
+    ninf = ctx.ninf
+    orig_params = params[:]
+    verbose = kwargs.get('verbose', False)
+    maxprec = kwargs.get('maxprec', ctx._default_hyper_maxprec(orig))
+    kwargs['maxprec'] = maxprec   # For calls to hypsum
+    zeroprec = kwargs.get('zeroprec')
+    infprec = kwargs.get('infprec')
+    perturbed_reference_value = None
+    hextra = 0
+    try:
+        while 1:
+            ctx.prec += 10
+            if ctx.prec > maxprec:
+                raise ValueError(_hypercomb_msg % (orig, ctx.prec))
+            orig2 = ctx.prec
+            params = orig_params[:]
+            terms = function(*params)
+            if verbose:
+                print()
+                print("ENTERING hypercomb main loop")
+                print("prec =", ctx.prec)
+                print("hextra", hextra)
+            perturb, recompute, extraprec, discard = \
+                _check_need_perturb(ctx, terms, orig, discard_known_zeros)
+            ctx.prec += extraprec
+            if perturb:
+                if "hmag" in kwargs:
+                    hmag = kwargs["hmag"]
+                elif ctx._fixed_precision:
+                    hmag = int(ctx.prec*0.3)
+                else:
+                    hmag = orig + 10 + hextra
+                h = ctx.ldexp(ctx.one, -hmag)
+                ctx.prec = orig2 + 10 + hmag + 10
+                for k in range(len(params)):
+                    params[k] += h
+                    # Heuristically ensure that the perturbations
+                    # are "independent" so that two perturbations
+                    # don't accidentally cancel each other out
+                    # in a subtraction.
+                    h += h/(k+1)
+            if recompute:
+                terms = function(*params)
+            if discard_known_zeros:
+                terms = [term for (i, term) in enumerate(terms) if i not in discard]
+            if not terms:
+                return ctx.zero
+            evaluated_terms = []
+            for term_index, term_data in enumerate(terms):
+                w_s, c_s, alpha_s, beta_s, a_s, b_s, z = term_data
+                if verbose:
+                    print()
+                    print("  Evaluating term %i/%i : %iF%i" % \
+                        (term_index+1, len(terms), len(a_s), len(b_s)))
+                    print("    powers", ctx.nstr(w_s), ctx.nstr(c_s))
+                    print("    gamma", ctx.nstr(alpha_s), ctx.nstr(beta_s))
+                    print("    hyper", ctx.nstr(a_s), ctx.nstr(b_s))
+                    print("    z", ctx.nstr(z))
+                #v = ctx.hyper(a_s, b_s, z, **kwargs)
+                #for a in alpha_s: v *= ctx.gamma(a)
+                #for b in beta_s: v *= ctx.rgamma(b)
+                #for w, c in zip(w_s, c_s): v *= ctx.power(w, c)
+                v = ctx.fprod([ctx.hyper(a_s, b_s, z, **kwargs)] + \
+                    [ctx.gamma(a) for a in alpha_s] + \
+                    [ctx.rgamma(b) for b in beta_s] + \
+                    [ctx.power(w,c) for (w,c) in zip(w_s,c_s)])
+                if verbose:
+                    print("    Value:", v)
+                evaluated_terms.append(v)
+
+            if len(terms) == 1 and (not perturb):
+                sumvalue = evaluated_terms[0]
+                break
+
+            if ctx._fixed_precision:
+                sumvalue = ctx.fsum(evaluated_terms)
+                break
+
+            sumvalue = ctx.fsum(evaluated_terms)
+            term_magnitudes = [ctx.mag(x) for x in evaluated_terms]
+            max_magnitude = max(term_magnitudes)
+            sum_magnitude = ctx.mag(sumvalue)
+            cancellation = max_magnitude - sum_magnitude
+            if verbose:
+                print()
+                print("  Cancellation:", cancellation, "bits")
+                print("  Increased precision:", ctx.prec - orig, "bits")
+
+            precision_ok = cancellation < ctx.prec - orig
+
+            if zeroprec is None:
+                zero_ok = False
+            else:
+                zero_ok = max_magnitude - ctx.prec < -zeroprec
+            if infprec is None:
+                inf_ok = False
+            else:
+                inf_ok = max_magnitude > infprec
+
+            if precision_ok and (not perturb) or ctx.isnan(cancellation):
+                break
+            elif precision_ok:
+                if perturbed_reference_value is None:
+                    hextra += 20
+                    perturbed_reference_value = sumvalue
+                    continue
+                elif ctx.mag(sumvalue - perturbed_reference_value) <= \
+                        ctx.mag(sumvalue) - orig:
+                    break
+                elif zero_ok:
+                    sumvalue = ctx.zero
+                    break
+                elif inf_ok:
+                    sumvalue = ctx.inf
+                    break
+                elif 'hmag' in kwargs:
+                    break
+                else:
+                    hextra *= 2
+                    perturbed_reference_value = sumvalue
+            # Increase precision
+            else:
+                increment = min(max(cancellation, orig//2), max(extraprec,orig))
+                ctx.prec += increment
+                if verbose:
+                    print("  Must start over with increased precision")
+                continue
+    finally:
+        ctx.prec = orig
+    return +sumvalue
+
+@defun
+def hyper(ctx, a_s, b_s, z, **kwargs):
+    """
+    Hypergeometric function, general case.
+    """
+    z = ctx.convert(z)
+    p = len(a_s)
+    q = len(b_s)
+    a_s = [ctx._convert_param(a) for a in a_s]
+    b_s = [ctx._convert_param(b) for b in b_s]
+    # Reduce degree by eliminating common parameters
+    if kwargs.get('eliminate', True):
+        elim_nonpositive = kwargs.get('eliminate_all', False)
+        i = 0
+        while i < q and a_s:
+            b = b_s[i]
+            if b in a_s and (elim_nonpositive or not ctx.isnpint(b[0])):
+                a_s.remove(b)
+                b_s.remove(b)
+                p -= 1
+                q -= 1
+            else:
+                i += 1
+    # Handle special cases
+    if p == 0:
+        if   q == 1: return ctx._hyp0f1(b_s, z, **kwargs)
+        elif q == 0: return ctx.exp(z)
+    elif p == 1:
+        if   q == 1: return ctx._hyp1f1(a_s, b_s, z, **kwargs)
+        elif q == 2: return ctx._hyp1f2(a_s, b_s, z, **kwargs)
+        elif q == 0: return ctx._hyp1f0(a_s[0][0], z)
+    elif p == 2:
+        if   q == 1: return ctx._hyp2f1(a_s, b_s, z, **kwargs)
+        elif q == 2: return ctx._hyp2f2(a_s, b_s, z, **kwargs)
+        elif q == 3: return ctx._hyp2f3(a_s, b_s, z, **kwargs)
+        elif q == 0: return ctx._hyp2f0(a_s, b_s, z, **kwargs)
+    elif p == q+1:
+        return ctx._hypq1fq(p, q, a_s, b_s, z, **kwargs)
+    elif p > q+1 and not kwargs.get('force_series'):
+        return ctx._hyp_borel(p, q, a_s, b_s, z, **kwargs)
+    coeffs, types = zip(*(a_s+b_s))
+    return ctx.hypsum(p, q, types, coeffs, z, **kwargs)
+
+@defun
+def hyp0f1(ctx,b,z,**kwargs):
+    return ctx.hyper([],[b],z,**kwargs)
+
+@defun
+def hyp1f1(ctx,a,b,z,**kwargs):
+    return ctx.hyper([a],[b],z,**kwargs)
+
+@defun
+def hyp1f2(ctx,a1,b1,b2,z,**kwargs):
+    return ctx.hyper([a1],[b1,b2],z,**kwargs)
+
+@defun
+def hyp2f1(ctx,a,b,c,z,**kwargs):
+    return ctx.hyper([a,b],[c],z,**kwargs)
+
+@defun
+def hyp2f2(ctx,a1,a2,b1,b2,z,**kwargs):
+    return ctx.hyper([a1,a2],[b1,b2],z,**kwargs)
+
+@defun
+def hyp2f3(ctx,a1,a2,b1,b2,b3,z,**kwargs):
+    return ctx.hyper([a1,a2],[b1,b2,b3],z,**kwargs)
+
+@defun
+def hyp2f0(ctx,a,b,z,**kwargs):
+    return ctx.hyper([a,b],[],z,**kwargs)
+
+@defun
+def hyp3f2(ctx,a1,a2,a3,b1,b2,z,**kwargs):
+    return ctx.hyper([a1,a2,a3],[b1,b2],z,**kwargs)
+
+@defun_wrapped
+def _hyp1f0(ctx, a, z):
+    return (1-z) ** (-a)
+
+@defun
+def _hyp0f1(ctx, b_s, z, **kwargs):
+    (b, btype), = b_s
+    if z:
+        magz = ctx.mag(z)
+    else:
+        magz = 0
+    if magz >= 8 and not kwargs.get('force_series'):
+        try:
+            # http://functions.wolfram.com/HypergeometricFunctions/
+            # Hypergeometric0F1/06/02/03/0004/
+            # TODO: handle the all-real case more efficiently!
+            # TODO: figure out how much precision is needed (exponential growth)
+            orig = ctx.prec
+            try:
+                ctx.prec += 12 + magz//2
+                def h():
+                    w = ctx.sqrt(-z)
+                    jw = ctx.j*w
+                    u = 1/(4*jw)
+                    c = ctx.mpq_1_2 - b
+                    E = ctx.exp(2*jw)
+                    T1 = ([-jw,E], [c,-1], [], [], [b-ctx.mpq_1_2, ctx.mpq_3_2-b], [], -u)
+                    T2 = ([jw,E], [c,1], [], [], [b-ctx.mpq_1_2, ctx.mpq_3_2-b], [], u)
+                    return T1, T2
+                v = ctx.hypercomb(h, [], force_series=True)
+                v = ctx.gamma(b)/(2*ctx.sqrt(ctx.pi))*v
+            finally:
+                ctx.prec = orig
+            if ctx._is_real_type(b) and ctx._is_real_type(z):
+                v = ctx._re(v)
+            return +v
+        except ctx.NoConvergence:
+            pass
+    return ctx.hypsum(0, 1, (btype,), [b], z, **kwargs)
+
+@defun
+def _hyp1f1(ctx, a_s, b_s, z, **kwargs):
+    (a, atype), = a_s
+    (b, btype), = b_s
+    if not z:
+        return ctx.one+z
+    magz = ctx.mag(z)
+    if magz >= 7 and not (ctx.isint(a) and ctx.re(a) <= 0):
+        if ctx.isinf(z):
+            if ctx.sign(a) == ctx.sign(b) == ctx.sign(z) == 1:
+                return ctx.inf
+            return ctx.nan * z
+        try:
+            try:
+                ctx.prec += magz
+                sector = ctx._im(z) < 0
+                def h(a,b):
+                    if sector:
+                        E = ctx.expjpi(ctx.fneg(a, exact=True))
+                    else:
+                        E = ctx.expjpi(a)
+                    rz = 1/z
+                    T1 = ([E,z], [1,-a], [b], [b-a], [a, 1+a-b], [], -rz)
+                    T2 = ([ctx.exp(z),z], [1,a-b], [b], [a], [b-a, 1-a], [], rz)
+                    return T1, T2
+                v = ctx.hypercomb(h, [a,b], force_series=True)
+                if ctx._is_real_type(a) and ctx._is_real_type(b) and ctx._is_real_type(z):
+                    v = ctx._re(v)
+                return +v
+            except ctx.NoConvergence:
+                pass
+        finally:
+            ctx.prec -= magz
+    v = ctx.hypsum(1, 1, (atype, btype), [a, b], z, **kwargs)
+    return v
+
+def _hyp2f1_gosper(ctx,a,b,c,z,**kwargs):
+    # Use Gosper's recurrence
+    # See http://www.math.utexas.edu/pipermail/maxima/2006/000126.html
+    _a,_b,_c,_z = a, b, c, z
+    orig = ctx.prec
+    maxprec = kwargs.get('maxprec', 100*orig)
+    extra = 10
+    while 1:
+        ctx.prec = orig + extra
+        #a = ctx.convert(_a)
+        #b = ctx.convert(_b)
+        #c = ctx.convert(_c)
+        z = ctx.convert(_z)
+        d = ctx.mpf(0)
+        e = ctx.mpf(1)
+        f = ctx.mpf(0)
+        k = 0
+        # Common subexpression elimination, unfortunately making
+        # things a bit unreadable. The formula is quite messy to begin
+        # with, though...
+        abz = a*b*z
+        ch = c * ctx.mpq_1_2
+        c1h = (c+1) * ctx.mpq_1_2
+        nz = 1-z
+        g = z/nz
+        abg = a*b*g
+        cba = c-b-a
+        z2 = z-2
+        tol = -ctx.prec - 10
+        nstr = ctx.nstr
+        nprint = ctx.nprint
+        mag = ctx.mag
+        maxmag = ctx.ninf
+        while 1:
+            kch = k+ch
+            kakbz = (k+a)*(k+b)*z / (4*(k+1)*kch*(k+c1h))
+            d1 = kakbz*(e-(k+cba)*d*g)
+            e1 = kakbz*(d*abg+(k+c)*e)
+            ft = d*(k*(cba*z+k*z2-c)-abz)/(2*kch*nz)
+            f1 = f + e - ft
+            maxmag = max(maxmag, mag(f1))
+            if mag(f1-f) < tol:
+                break
+            d, e, f = d1, e1, f1
+            k += 1
+        cancellation = maxmag - mag(f1)
+        if cancellation < extra:
+            break
+        else:
+            extra += cancellation
+            if extra > maxprec:
+                raise ctx.NoConvergence
+    return f1
+
+@defun
+def _hyp2f1(ctx, a_s, b_s, z, **kwargs):
+    (a, atype), (b, btype) = a_s
+    (c, ctype), = b_s
+    if z == 1:
+        # TODO: the following logic can be simplified
+        convergent = ctx.re(c-a-b) > 0
+        finite = (ctx.isint(a) and a <= 0) or (ctx.isint(b) and b <= 0)
+        zerodiv = ctx.isint(c) and c <= 0 and not \
+            ((ctx.isint(a) and c <= a <= 0) or (ctx.isint(b) and c <= b <= 0))
+        #print "bz", a, b, c, z, convergent, finite, zerodiv
+        # Gauss's theorem gives the value if convergent
+        if (convergent or finite) and not zerodiv:
+            return ctx.gammaprod([c, c-a-b], [c-a, c-b], _infsign=True)
+        # Otherwise, there is a pole and we take the
+        # sign to be that when approaching from below
+        # XXX: this evaluation is not necessarily correct in all cases
+        return ctx.hyp2f1(a,b,c,1-ctx.eps*2) * ctx.inf
+
+    # Equal to 1 (first term), unless there is a subsequent
+    # division by zero
+    if not z:
+        # Division by zero but power of z is higher than
+        # first order so cancels
+        if c or a == 0 or b == 0:
+            return 1+z
+        # Indeterminate
+        return ctx.nan
+
+    # Hit zero denominator unless numerator goes to 0 first
+    if ctx.isint(c) and c <= 0:
+        if (ctx.isint(a) and c <= a <= 0) or \
+           (ctx.isint(b) and c <= b <= 0):
+            pass
+        else:
+            # Pole in series
+            return ctx.inf
+
+    absz = abs(z)
+
+    # Fast case: standard series converges rapidly,
+    # possibly in finitely many terms
+    if absz <= 0.8 or (ctx.isint(a) and a <= 0 and a >= -1000) or \
+                      (ctx.isint(b) and b <= 0 and b >= -1000):
+        return ctx.hypsum(2, 1, (atype, btype, ctype), [a, b, c], z, **kwargs)
+
+    orig = ctx.prec
+    try:
+        ctx.prec += 10
+
+        # Use 1/z transformation
+        if absz >= 1.3:
+            def h(a,b):
+                t = ctx.mpq_1-c; ab = a-b; rz = 1/z
+                T1 = ([-z],[-a], [c,-ab],[b,c-a], [a,t+a],[ctx.mpq_1+ab],  rz)
+                T2 = ([-z],[-b], [c,ab],[a,c-b], [b,t+b],[ctx.mpq_1-ab],  rz)
+                return T1, T2
+            v = ctx.hypercomb(h, [a,b], **kwargs)
+
+        # Use 1-z transformation
+        elif abs(1-z) <= 0.75:
+            def h(a,b):
+                t = c-a-b; ca = c-a; cb = c-b; rz = 1-z
+                T1 = [], [], [c,t], [ca,cb], [a,b], [1-t], rz
+                T2 = [rz], [t], [c,a+b-c], [a,b], [ca,cb], [1+t], rz
+                return T1, T2
+            v = ctx.hypercomb(h, [a,b], **kwargs)
+
+        # Use z/(z-1) transformation
+        elif abs(z/(z-1)) <= 0.75:
+            v = ctx.hyp2f1(a, c-b, c, z/(z-1)) / (1-z)**a
+
+        # Remaining part of unit circle
+        else:
+            v = _hyp2f1_gosper(ctx,a,b,c,z,**kwargs)
+
+    finally:
+        ctx.prec = orig
+    return +v
+
+@defun
+def _hypq1fq(ctx, p, q, a_s, b_s, z, **kwargs):
+    r"""
+    Evaluates 3F2, 4F3, 5F4, ...
+    """
+    a_s, a_types = zip(*a_s)
+    b_s, b_types = zip(*b_s)
+    a_s = list(a_s)
+    b_s = list(b_s)
+    absz = abs(z)
+    ispoly = False
+    for a in a_s:
+        if ctx.isint(a) and a <= 0:
+            ispoly = True
+            break
+    # Direct summation
+    if absz < 1 or ispoly:
+        try:
+            return ctx.hypsum(p, q, a_types+b_types, a_s+b_s, z, **kwargs)
+        except ctx.NoConvergence:
+            if absz > 1.1 or ispoly:
+                raise
+    # Use expansion at |z-1| -> 0.
+    # Reference: Wolfgang Buhring, "Generalized Hypergeometric Functions at
+    #   Unit Argument", Proc. Amer. Math. Soc., Vol. 114, No. 1 (Jan. 1992),
+    #   pp.145-153
+    # The current implementation has several problems:
+    # 1. We only implement it for 3F2. The expansion coefficients are
+    #    given by extremely messy nested sums in the higher degree cases
+    #    (see reference). Is efficient sequential generation of the coefficients
+    #    possible in the > 3F2 case?
+    # 2. Although the series converges, it may do so slowly, so we need
+    #    convergence acceleration. The acceleration implemented by
+    #    nsum does not always help, so results returned are sometimes
+    #    inaccurate! Can we do better?
+    # 3. We should check conditions for convergence, and possibly
+    #    do a better job of cancelling out gamma poles if possible.
+    if z == 1:
+        # XXX: should also check for division by zero in the
+        # denominator of the series (cf. hyp2f1)
+        S = ctx.re(sum(b_s)-sum(a_s))
+        if S <= 0:
+            #return ctx.hyper(a_s, b_s, 1-ctx.eps*2, **kwargs) * ctx.inf
+            return ctx.hyper(a_s, b_s, 0.9, **kwargs) * ctx.inf
+    if (p,q) == (3,2) and abs(z-1) < 0.05:   # and kwargs.get('sum1')
+        #print "Using alternate summation (experimental)"
+        a1,a2,a3 = a_s
+        b1,b2 = b_s
+        u = b1+b2-a3
+        initial = ctx.gammaprod([b2-a3,b1-a3,a1,a2],[b2-a3,b1-a3,1,u])
+        def term(k, _cache={0:initial}):
+            u = b1+b2-a3+k
+            if k in _cache:
+                t = _cache[k]
+            else:
+                t = _cache[k-1]
+                t *= (b1+k-a3-1)*(b2+k-a3-1)
+                t /= k*(u-1)
+                _cache[k] = t
+            return t * ctx.hyp2f1(a1,a2,u,z)
+        try:
+            S = ctx.nsum(term, [0,ctx.inf], verbose=kwargs.get('verbose'),
+                strict=kwargs.get('strict', True))
+            return S * ctx.gammaprod([b1,b2],[a1,a2,a3])
+        except ctx.NoConvergence:
+            pass
+    # Try to use convergence acceleration on and close to the unit circle.
+    # Problem: the convergence acceleration degenerates as |z-1| -> 0,
+    # except for special cases. Everywhere else, the Shanks transformation
+    # is very efficient.
+    if absz < 1.1 and ctx._re(z) <= 1:
+
+        def term(kk, _cache={0:ctx.one}):
+            k = int(kk)
+            if k != kk:
+                t = z ** ctx.mpf(kk) / ctx.fac(kk)
+                for a in a_s: t *= ctx.rf(a,kk)
+                for b in b_s: t /= ctx.rf(b,kk)
+                return t
+            if k in _cache:
+                return _cache[k]
+            t = term(k-1)
+            m = k-1
+            for j in xrange(p): t *= (a_s[j]+m)
+            for j in xrange(q): t /= (b_s[j]+m)
+            t *= z
+            t /= k
+            _cache[k] = t
+            return t
+
+        sum_method = kwargs.get('sum_method', 'r+s+e')
+
+        try:
+            return ctx.nsum(term, [0,ctx.inf], verbose=kwargs.get('verbose'),
+                strict=kwargs.get('strict', True),
+                method=sum_method.replace('e',''))
+        except ctx.NoConvergence:
+            if 'e' not in sum_method:
+                raise
+            pass
+
+        if kwargs.get('verbose'):
+            print("Attempting Euler-Maclaurin summation")
+
+
+        """
+        Somewhat slower version (one diffs_exp for each factor).
+        However, this would be faster with fast direct derivatives
+        of the gamma function.
+
+        def power_diffs(k0):
+            r = 0
+            l = ctx.log(z)
+            while 1:
+                yield z**ctx.mpf(k0) * l**r
+                r += 1
+
+        def loggamma_diffs(x, reciprocal=False):
+            sign = (-1) ** reciprocal
+            yield sign * ctx.loggamma(x)
+            i = 0
+            while 1:
+                yield sign * ctx.psi(i,x)
+                i += 1
+
+        def hyper_diffs(k0):
+            b2 = b_s + [1]
+            A = [ctx.diffs_exp(loggamma_diffs(a+k0)) for a in a_s]
+            B = [ctx.diffs_exp(loggamma_diffs(b+k0,True)) for b in b2]
+            Z = [power_diffs(k0)]
+            C = ctx.gammaprod([b for b in b2], [a for a in a_s])
+            for d in ctx.diffs_prod(A + B + Z):
+                v = C * d
+                yield v
+        """
+
+        def log_diffs(k0):
+            b2 = b_s + [1]
+            yield sum(ctx.loggamma(a+k0) for a in a_s) - \
+                sum(ctx.loggamma(b+k0) for b in b2) + k0*ctx.log(z)
+            i = 0
+            while 1:
+                v = sum(ctx.psi(i,a+k0) for a in a_s) - \
+                    sum(ctx.psi(i,b+k0) for b in b2)
+                if i == 0:
+                    v += ctx.log(z)
+                yield v
+                i += 1
+
+        def hyper_diffs(k0):
+            C = ctx.gammaprod([b for b in b_s], [a for a in a_s])
+            for d in ctx.diffs_exp(log_diffs(k0)):
+                v = C * d
+                yield v
+
+        tol = ctx.eps / 1024
+        prec = ctx.prec
+        try:
+            trunc = 50 * ctx.dps
+            ctx.prec += 20
+            for i in xrange(5):
+                head = ctx.fsum(term(k) for k in xrange(trunc))
+                tail, err = ctx.sumem(term, [trunc, ctx.inf], tol=tol,
+                    adiffs=hyper_diffs(trunc),
+                    verbose=kwargs.get('verbose'),
+                    error=True,
+                    _fast_abort=True)
+                if err < tol:
+                    v = head + tail
+                    break
+                trunc *= 2
+                # Need to increase precision because calculation of
+                # derivatives may be inaccurate
+                ctx.prec += ctx.prec//2
+                if i == 4:
+                    raise ctx.NoConvergence(\
+                        "Euler-Maclaurin summation did not converge")
+        finally:
+            ctx.prec = prec
+        return +v
+
+    # Use 1/z transformation
+    # http://functions.wolfram.com/HypergeometricFunctions/
+    #   HypergeometricPFQ/06/01/05/02/0004/
+    def h(*args):
+        a_s = list(args[:p])
+        b_s = list(args[p:])
+        Ts = []
+        recz = ctx.one/z
+        negz = ctx.fneg(z, exact=True)
+        for k in range(q+1):
+            ak = a_s[k]
+            C = [negz]
+            Cp = [-ak]
+            Gn = b_s + [ak] + [a_s[j]-ak for j in range(q+1) if j != k]
+            Gd = a_s + [b_s[j]-ak for j in range(q)]
+            Fn = [ak] + [ak-b_s[j]+1 for j in range(q)]
+            Fd = [1-a_s[j]+ak for j in range(q+1) if j != k]
+            Ts.append((C, Cp, Gn, Gd, Fn, Fd, recz))
+        return Ts
+    return ctx.hypercomb(h, a_s+b_s, **kwargs)
+
+@defun
+def _hyp_borel(ctx, p, q, a_s, b_s, z, **kwargs):
+    if a_s:
+        a_s, a_types = zip(*a_s)
+        a_s = list(a_s)
+    else:
+        a_s, a_types = [], ()
+    if b_s:
+        b_s, b_types = zip(*b_s)
+        b_s = list(b_s)
+    else:
+        b_s, b_types = [], ()
+    kwargs['maxterms'] = kwargs.get('maxterms', ctx.prec)
+    try:
+        return ctx.hypsum(p, q, a_types+b_types, a_s+b_s, z, **kwargs)
+    except ctx.NoConvergence:
+        pass
+    prec = ctx.prec
+    try:
+        tol = kwargs.get('asymp_tol', ctx.eps/4)
+        ctx.prec += 10
+        # hypsum is has a conservative tolerance. So we try again:
+        def term(k, cache={0:ctx.one}):
+            if k in cache:
+                return cache[k]
+            t = term(k-1)
+            for a in a_s: t *= (a+(k-1))
+            for b in b_s: t /= (b+(k-1))
+            t *= z
+            t /= k
+            cache[k] = t
+            return t
+        s = ctx.one
+        for k in xrange(1, ctx.prec):
+            t = term(k)
+            s += t
+            if abs(t) <= tol:
+                return s
+    finally:
+        ctx.prec = prec
+    if p <= q+3:
+        contour = kwargs.get('contour')
+        if not contour:
+            if ctx.arg(z) < 0.25:
+                u = z / max(1, abs(z))
+                if ctx.arg(z) >= 0:
+                    contour = [0, 2j, (2j+2)/u, 2/u, ctx.inf]
+                else:
+                    contour = [0, -2j, (-2j+2)/u, 2/u, ctx.inf]
+                #contour = [0, 2j/z, 2/z, ctx.inf]
+                #contour = [0, 2j, 2/z, ctx.inf]
+                #contour = [0, 2j, ctx.inf]
+            else:
+                contour = [0, ctx.inf]
+        quad_kwargs = kwargs.get('quad_kwargs', {})
+        def g(t):
+            return ctx.exp(-t)*ctx.hyper(a_s, b_s+[1], t*z)
+        I, err = ctx.quad(g, contour, error=True, **quad_kwargs)
+        if err <= abs(I)*ctx.eps*8:
+            return I
+    raise ctx.NoConvergence
+
+
+@defun
+def _hyp2f2(ctx, a_s, b_s, z, **kwargs):
+    (a1, a1type), (a2, a2type) = a_s
+    (b1, b1type), (b2, b2type) = b_s
+
+    absz = abs(z)
+    magz = ctx.mag(z)
+    orig = ctx.prec
+
+    # Asymptotic expansion is ~ exp(z)
+    asymp_extraprec = magz
+
+    # Asymptotic series is in terms of 3F1
+    can_use_asymptotic = (not kwargs.get('force_series')) and \
+        (ctx.mag(absz) > 3)
+
+    # TODO: much of the following could be shared with 2F3 instead of
+    # copypasted
+    if can_use_asymptotic:
+        #print "using asymp"
+        try:
+            try:
+                ctx.prec += asymp_extraprec
+                # http://functions.wolfram.com/HypergeometricFunctions/
+                # Hypergeometric2F2/06/02/02/0002/
+                def h(a1,a2,b1,b2):
+                    X = a1+a2-b1-b2
+                    A2 = a1+a2
+                    B2 = b1+b2
+                    c = {}
+                    c[0] = ctx.one
+                    c[1] = (A2-1)*X+b1*b2-a1*a2
+                    s1 = 0
+                    k = 0
+                    tprev = 0
+                    while 1:
+                        if k not in c:
+                            uu1 = 1-B2+2*a1+a1**2+2*a2+a2**2-A2*B2+a1*a2+b1*b2+(2*B2-3*(A2+1))*k+2*k**2
+                            uu2 = (k-A2+b1-1)*(k-A2+b2-1)*(k-X-2)
+                            c[k] = ctx.one/k * (uu1*c[k-1]-uu2*c[k-2])
+                        t1 = c[k] * z**(-k)
+                        if abs(t1) < 0.1*ctx.eps:
+                            #print "Convergence :)"
+                            break
+                        # Quit if the series doesn't converge quickly enough
+                        if k > 5 and abs(tprev) / abs(t1) < 1.5:
+                            #print "No convergence :("
+                            raise ctx.NoConvergence
+                        s1 += t1
+                        tprev = t1
+                        k += 1
+                    S = ctx.exp(z)*s1
+                    T1 = [z,S], [X,1], [b1,b2],[a1,a2],[],[],0
+                    T2 = [-z],[-a1],[b1,b2,a2-a1],[a2,b1-a1,b2-a1],[a1,a1-b1+1,a1-b2+1],[a1-a2+1],-1/z
+                    T3 = [-z],[-a2],[b1,b2,a1-a2],[a1,b1-a2,b2-a2],[a2,a2-b1+1,a2-b2+1],[-a1+a2+1],-1/z
+                    return T1, T2, T3
+                v = ctx.hypercomb(h, [a1,a2,b1,b2], force_series=True, maxterms=4*ctx.prec)
+                if sum(ctx._is_real_type(u) for u in [a1,a2,b1,b2,z]) == 5:
+                    v = ctx.re(v)
+                return v
+            except ctx.NoConvergence:
+                pass
+        finally:
+            ctx.prec = orig
+
+    return ctx.hypsum(2, 2, (a1type, a2type, b1type, b2type), [a1, a2, b1, b2], z, **kwargs)
+
+
+
+@defun
+def _hyp1f2(ctx, a_s, b_s, z, **kwargs):
+    (a1, a1type), = a_s
+    (b1, b1type), (b2, b2type) = b_s
+
+    absz = abs(z)
+    magz = ctx.mag(z)
+    orig = ctx.prec
+
+    # Asymptotic expansion is ~ exp(sqrt(z))
+    asymp_extraprec = z and magz//2
+
+    # Asymptotic series is in terms of 3F0
+    can_use_asymptotic = (not kwargs.get('force_series')) and \
+        (ctx.mag(absz) > 19) and \
+        (ctx.sqrt(absz) > 1.5*orig)  # and \
+    #   ctx._hyp_check_convergence([a1, a1-b1+1, a1-b2+1], [],
+    #                              1/absz, orig+40+asymp_extraprec)
+
+    # TODO: much of the following could be shared with 2F3 instead of
+    # copypasted
+    if can_use_asymptotic:
+        #print "using asymp"
+        try:
+            try:
+                ctx.prec += asymp_extraprec
+                # http://functions.wolfram.com/HypergeometricFunctions/
+                # Hypergeometric1F2/06/02/03/
+                def h(a1,b1,b2):
+                    X = ctx.mpq_1_2*(a1-b1-b2+ctx.mpq_1_2)
+                    c = {}
+                    c[0] = ctx.one
+                    c[1] = 2*(ctx.mpq_1_4*(3*a1+b1+b2-2)*(a1-b1-b2)+b1*b2-ctx.mpq_3_16)
+                    c[2] = 2*(b1*b2+ctx.mpq_1_4*(a1-b1-b2)*(3*a1+b1+b2-2)-ctx.mpq_3_16)**2+\
+                        ctx.mpq_1_16*(-16*(2*a1-3)*b1*b2 + \
+                        4*(a1-b1-b2)*(-8*a1**2+11*a1+b1+b2-2)-3)
+                    s1 = 0
+                    s2 = 0
+                    k = 0
+                    tprev = 0
+                    while 1:
+                        if k not in c:
+                            uu1 = (3*k**2+(-6*a1+2*b1+2*b2-4)*k + 3*a1**2 - \
+                                (b1-b2)**2 - 2*a1*(b1+b2-2) + ctx.mpq_1_4)
+                            uu2 = (k-a1+b1-b2-ctx.mpq_1_2)*(k-a1-b1+b2-ctx.mpq_1_2)*\
+                                (k-a1+b1+b2-ctx.mpq_5_2)
+                            c[k] = ctx.one/(2*k)*(uu1*c[k-1]-uu2*c[k-2])
+                        w = c[k] * (-z)**(-0.5*k)
+                        t1 = (-ctx.j)**k * ctx.mpf(2)**(-k) * w
+                        t2 = ctx.j**k * ctx.mpf(2)**(-k) * w
+                        if abs(t1) < 0.1*ctx.eps:
+                            #print "Convergence :)"
+                            break
+                        # Quit if the series doesn't converge quickly enough
+                        if k > 5 and abs(tprev) / abs(t1) < 1.5:
+                            #print "No convergence :("
+                            raise ctx.NoConvergence
+                        s1 += t1
+                        s2 += t2
+                        tprev = t1
+                        k += 1
+                    S = ctx.expj(ctx.pi*X+2*ctx.sqrt(-z))*s1 + \
+                        ctx.expj(-(ctx.pi*X+2*ctx.sqrt(-z)))*s2
+                    T1 = [0.5*S, ctx.pi, -z], [1, -0.5, X], [b1, b2], [a1],\
+                        [], [], 0
+                    T2 = [-z], [-a1], [b1,b2],[b1-a1,b2-a1], \
+                        [a1,a1-b1+1,a1-b2+1], [], 1/z
+                    return T1, T2
+                v = ctx.hypercomb(h, [a1,b1,b2], force_series=True, maxterms=4*ctx.prec)
+                if sum(ctx._is_real_type(u) for u in [a1,b1,b2,z]) == 4:
+                    v = ctx.re(v)
+                return v
+            except ctx.NoConvergence:
+                pass
+        finally:
+            ctx.prec = orig
+
+    #print "not using asymp"
+    return ctx.hypsum(1, 2, (a1type, b1type, b2type), [a1, b1, b2], z, **kwargs)
+
+
+
+@defun
+def _hyp2f3(ctx, a_s, b_s, z, **kwargs):
+    (a1, a1type), (a2, a2type) = a_s
+    (b1, b1type), (b2, b2type), (b3, b3type) = b_s
+
+    absz = abs(z)
+    magz = ctx.mag(z)
+
+    # Asymptotic expansion is ~ exp(sqrt(z))
+    asymp_extraprec = z and magz//2
+    orig = ctx.prec
+
+    # Asymptotic series is in terms of 4F1
+    # The square root below empirically provides a plausible criterion
+    # for the leading series to converge
+    can_use_asymptotic = (not kwargs.get('force_series')) and \
+        (ctx.mag(absz) > 19) and (ctx.sqrt(absz) > 1.5*orig)
+
+    if can_use_asymptotic:
+        #print "using asymp"
+        try:
+            try:
+                ctx.prec += asymp_extraprec
+                # http://functions.wolfram.com/HypergeometricFunctions/
+                # Hypergeometric2F3/06/02/03/01/0002/
+                def h(a1,a2,b1,b2,b3):
+                    X = ctx.mpq_1_2*(a1+a2-b1-b2-b3+ctx.mpq_1_2)
+                    A2 = a1+a2
+                    B3 = b1+b2+b3
+                    A = a1*a2
+                    B = b1*b2+b3*b2+b1*b3
+                    R = b1*b2*b3
+                    c = {}
+                    c[0] = ctx.one
+                    c[1] = 2*(B - A + ctx.mpq_1_4*(3*A2+B3-2)*(A2-B3) - ctx.mpq_3_16)
+                    c[2] = ctx.mpq_1_2*c[1]**2 + ctx.mpq_1_16*(-16*(2*A2-3)*(B-A) + 32*R +\
+                        4*(-8*A2**2 + 11*A2 + 8*A + B3 - 2)*(A2-B3)-3)
+                    s1 = 0
+                    s2 = 0
+                    k = 0
+                    tprev = 0
+                    while 1:
+                        if k not in c:
+                            uu1 = (k-2*X-3)*(k-2*X-2*b1-1)*(k-2*X-2*b2-1)*\
+                                (k-2*X-2*b3-1)
+                            uu2 = (4*(k-1)**3 - 6*(4*X+B3)*(k-1)**2 + \
+                                2*(24*X**2+12*B3*X+4*B+B3-1)*(k-1) - 32*X**3 - \
+                                24*B3*X**2 - 4*B - 8*R - 4*(4*B+B3-1)*X + 2*B3-1)
+                            uu3 = (5*(k-1)**2+2*(-10*X+A2-3*B3+3)*(k-1)+2*c[1])
+                            c[k] = ctx.one/(2*k)*(uu1*c[k-3]-uu2*c[k-2]+uu3*c[k-1])
+                        w = c[k] * ctx.power(-z, -0.5*k)
+                        t1 = (-ctx.j)**k * ctx.mpf(2)**(-k) * w
+                        t2 = ctx.j**k * ctx.mpf(2)**(-k) * w
+                        if abs(t1) < 0.1*ctx.eps:
+                            break
+                        # Quit if the series doesn't converge quickly enough
+                        if k > 5 and abs(tprev) / abs(t1) < 1.5:
+                            raise ctx.NoConvergence
+                        s1 += t1
+                        s2 += t2
+                        tprev = t1
+                        k += 1
+                    S = ctx.expj(ctx.pi*X+2*ctx.sqrt(-z))*s1 + \
+                        ctx.expj(-(ctx.pi*X+2*ctx.sqrt(-z)))*s2
+                    T1 = [0.5*S, ctx.pi, -z], [1, -0.5, X], [b1, b2, b3], [a1, a2],\
+                        [], [], 0
+                    T2 = [-z], [-a1], [b1,b2,b3,a2-a1],[a2,b1-a1,b2-a1,b3-a1], \
+                        [a1,a1-b1+1,a1-b2+1,a1-b3+1], [a1-a2+1], 1/z
+                    T3 = [-z], [-a2], [b1,b2,b3,a1-a2],[a1,b1-a2,b2-a2,b3-a2], \
+                        [a2,a2-b1+1,a2-b2+1,a2-b3+1],[-a1+a2+1], 1/z
+                    return T1, T2, T3
+                v = ctx.hypercomb(h, [a1,a2,b1,b2,b3], force_series=True, maxterms=4*ctx.prec)
+                if sum(ctx._is_real_type(u) for u in [a1,a2,b1,b2,b3,z]) == 6:
+                    v = ctx.re(v)
+                return v
+            except ctx.NoConvergence:
+                pass
+        finally:
+            ctx.prec = orig
+
+    return ctx.hypsum(2, 3, (a1type, a2type, b1type, b2type, b3type), [a1, a2, b1, b2, b3], z, **kwargs)
+
+@defun
+def _hyp2f0(ctx, a_s, b_s, z, **kwargs):
+    (a, atype), (b, btype) = a_s
+    # We want to try aggressively to use the asymptotic expansion,
+    # and fall back only when absolutely necessary
+    try:
+        kwargsb = kwargs.copy()
+        kwargsb['maxterms'] = kwargsb.get('maxterms', ctx.prec)
+        return ctx.hypsum(2, 0, (atype,btype), [a,b], z, **kwargsb)
+    except ctx.NoConvergence:
+        if kwargs.get('force_series'):
+            raise
+        pass
+    def h(a, b):
+        w = ctx.sinpi(b)
+        rz = -1/z
+        T1 = ([ctx.pi,w,rz],[1,-1,a],[],[a-b+1,b],[a],[b],rz)
+        T2 = ([-ctx.pi,w,rz],[1,-1,1+a-b],[],[a,2-b],[a-b+1],[2-b],rz)
+        return T1, T2
+    return ctx.hypercomb(h, [a, 1+a-b], **kwargs)
+
+@defun
+def meijerg(ctx, a_s, b_s, z, r=1, series=None, **kwargs):
+    an, ap = a_s
+    bm, bq = b_s
+    n = len(an)
+    p = n + len(ap)
+    m = len(bm)
+    q = m + len(bq)
+    a = an+ap
+    b = bm+bq
+    a = [ctx.convert(_) for _ in a]
+    b = [ctx.convert(_) for _ in b]
+    z = ctx.convert(z)
+    if series is None:
+        if p < q: series = 1
+        if p > q: series = 2
+        if p == q:
+            if m+n == p and abs(z) > 1:
+                series = 2
+            else:
+                series = 1
+    if kwargs.get('verbose'):
+        print("Meijer G m,n,p,q,series =", m,n,p,q,series)
+    if series == 1:
+        def h(*args):
+            a = args[:p]
+            b = args[p:]
+            terms = []
+            for k in range(m):
+                bases = [z]
+                expts = [b[k]/r]
+                gn = [b[j]-b[k] for j in range(m) if j != k]
+                gn += [1-a[j]+b[k] for j in range(n)]
+                gd = [a[j]-b[k] for j in range(n,p)]
+                gd += [1-b[j]+b[k] for j in range(m,q)]
+                hn = [1-a[j]+b[k] for j in range(p)]
+                hd = [1-b[j]+b[k] for j in range(q) if j != k]
+                hz = (-ctx.one)**(p-m-n) * z**(ctx.one/r)
+                terms.append((bases, expts, gn, gd, hn, hd, hz))
+            return terms
+    else:
+        def h(*args):
+            a = args[:p]
+            b = args[p:]
+            terms = []
+            for k in range(n):
+                bases = [z]
+                if r == 1:
+                    expts = [a[k]-1]
+                else:
+                    expts = [(a[k]-1)/ctx.convert(r)]
+                gn = [a[k]-a[j] for j in range(n) if j != k]
+                gn += [1-a[k]+b[j] for j in range(m)]
+                gd = [a[k]-b[j] for j in range(m,q)]
+                gd += [1-a[k]+a[j] for j in range(n,p)]
+                hn = [1-a[k]+b[j] for j in range(q)]
+                hd = [1+a[j]-a[k] for j in range(p) if j != k]
+                hz = (-ctx.one)**(q-m-n) / z**(ctx.one/r)
+                terms.append((bases, expts, gn, gd, hn, hd, hz))
+            return terms
+    return ctx.hypercomb(h, a+b, **kwargs)
+
+@defun_wrapped
+def appellf1(ctx,a,b1,b2,c,x,y,**kwargs):
+    # Assume x smaller
+    # We will use x for the outer loop
+    if abs(x) > abs(y):
+        x, y = y, x
+        b1, b2 = b2, b1
+    def ok(x):
+        return abs(x) < 0.99
+    # Finite cases
+    if ctx.isnpint(a):
+        pass
+    elif ctx.isnpint(b1):
+        pass
+    elif ctx.isnpint(b2):
+        x, y, b1, b2 = y, x, b2, b1
+    else:
+        #print x, y
+        # Note: ok if |y| > 1, because
+        # 2F1 implements analytic continuation
+        if not ok(x):
+            u1 = (x-y)/(x-1)
+            if not ok(u1):
+                raise ValueError("Analytic continuation not implemented")
+            #print "Using analytic continuation"
+            return (1-x)**(-b1)*(1-y)**(c-a-b2)*\
+                ctx.appellf1(c-a,b1,c-b1-b2,c,u1,y,**kwargs)
+    return ctx.hyper2d({'m+n':[a],'m':[b1],'n':[b2]}, {'m+n':[c]}, x,y, **kwargs)
+
+@defun
+def appellf2(ctx,a,b1,b2,c1,c2,x,y,**kwargs):
+    # TODO: continuation
+    return ctx.hyper2d({'m+n':[a],'m':[b1],'n':[b2]},
+        {'m':[c1],'n':[c2]}, x,y, **kwargs)
+
+@defun
+def appellf3(ctx,a1,a2,b1,b2,c,x,y,**kwargs):
+    outer_polynomial = ctx.isnpint(a1) or ctx.isnpint(b1)
+    inner_polynomial = ctx.isnpint(a2) or ctx.isnpint(b2)
+    if not outer_polynomial:
+        if inner_polynomial or abs(x) > abs(y):
+            x, y = y, x
+            a1,a2,b1,b2 = a2,a1,b2,b1
+    return ctx.hyper2d({'m':[a1,b1],'n':[a2,b2]}, {'m+n':[c]},x,y,**kwargs)
+
+@defun
+def appellf4(ctx,a,b,c1,c2,x,y,**kwargs):
+    # TODO: continuation
+    return ctx.hyper2d({'m+n':[a,b]}, {'m':[c1],'n':[c2]},x,y,**kwargs)
+
+@defun
+def hyper2d(ctx, a, b, x, y, **kwargs):
+    r"""
+    Sums the generalized 2D hypergeometric series
+
+    .. math ::
+
+        \sum_{m=0}^{\infty} \sum_{n=0}^{\infty}
+            \frac{P((a),m,n)}{Q((b),m,n)}
+            \frac{x^m y^n} {m! n!}
+
+    where `(a) = (a_1,\ldots,a_r)`, `(b) = (b_1,\ldots,b_s)` and where
+    `P` and `Q` are products of rising factorials such as `(a_j)_n` or
+    `(a_j)_{m+n}`. `P` and `Q` are specified in the form of dicts, with
+    the `m` and `n` dependence as keys and parameter lists as values.
+    The supported rising factorials are given in the following table
+    (note that only a few are supported in `Q`):
+
+    +------------+-------------------+--------+
+    | Key        |  Rising factorial | `Q`    |
+    +============+===================+========+
+    | ``'m'``    |   `(a_j)_m`       | Yes    |
+    +------------+-------------------+--------+
+    | ``'n'``    |   `(a_j)_n`       | Yes    |
+    +------------+-------------------+--------+
+    | ``'m+n'``  |   `(a_j)_{m+n}`   | Yes    |
+    +------------+-------------------+--------+
+    | ``'m-n'``  |   `(a_j)_{m-n}`   | No     |
+    +------------+-------------------+--------+
+    | ``'n-m'``  |   `(a_j)_{n-m}`   | No     |
+    +------------+-------------------+--------+
+    | ``'2m+n'`` |   `(a_j)_{2m+n}`  | No     |
+    +------------+-------------------+--------+
+    | ``'2m-n'`` |   `(a_j)_{2m-n}`  | No     |
+    +------------+-------------------+--------+
+    | ``'2n-m'`` |   `(a_j)_{2n-m}`  | No     |
+    +------------+-------------------+--------+
+
+    For example, the Appell F1 and F4 functions
+
+    .. math ::
+
+        F_1 = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty}
+              \frac{(a)_{m+n} (b)_m (c)_n}{(d)_{m+n}}
+              \frac{x^m y^n}{m! n!}
+
+        F_4 = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty}
+              \frac{(a)_{m+n} (b)_{m+n}}{(c)_m (d)_{n}}
+              \frac{x^m y^n}{m! n!}
+
+    can be represented respectively as
+
+        ``hyper2d({'m+n':[a], 'm':[b], 'n':[c]}, {'m+n':[d]}, x, y)``
+
+        ``hyper2d({'m+n':[a,b]}, {'m':[c], 'n':[d]}, x, y)``
+
+    More generally, :func:`~mpmath.hyper2d` can evaluate any of the 34 distinct
+    convergent second-order (generalized Gaussian) hypergeometric
+    series enumerated by Horn, as well as the Kampe de Feriet
+    function.
+
+    The series is computed by rewriting it so that the inner
+    series (i.e. the series containing `n` and `y`) has the form of an
+    ordinary generalized hypergeometric series and thereby can be
+    evaluated efficiently using :func:`~mpmath.hyper`. If possible,
+    manually swapping `x` and `y` and the corresponding parameters
+    can sometimes give better results.
+
+    **Examples**
+
+    Two separable cases: a product of two geometric series, and a
+    product of two Gaussian hypergeometric functions::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> x, y = mpf(0.25), mpf(0.5)
+        >>> hyper2d({'m':1,'n':1}, {}, x,y)
+        2.666666666666666666666667
+        >>> 1/(1-x)/(1-y)
+        2.666666666666666666666667
+        >>> hyper2d({'m':[1,2],'n':[3,4]}, {'m':[5],'n':[6]}, x,y)
+        4.164358531238938319669856
+        >>> hyp2f1(1,2,5,x)*hyp2f1(3,4,6,y)
+        4.164358531238938319669856
+
+    Some more series that can be done in closed form::
+
+        >>> hyper2d({'m':1,'n':1},{'m+n':1},x,y)
+        2.013417124712514809623881
+        >>> (exp(x)*x-exp(y)*y)/(x-y)
+        2.013417124712514809623881
+
+    Six of the 34 Horn functions, G1-G3 and H1-H3::
+
+        >>> from mpmath import *
+        >>> mp.dps = 10; mp.pretty = True
+        >>> x, y = 0.0625, 0.125
+        >>> a1,a2,b1,b2,c1,c2,d = 1.1,-1.2,-1.3,-1.4,1.5,-1.6,1.7
+        >>> hyper2d({'m+n':a1,'n-m':b1,'m-n':b2},{},x,y)  # G1
+        1.139090746
+        >>> nsum(lambda m,n: rf(a1,m+n)*rf(b1,n-m)*rf(b2,m-n)*\
+        ...     x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf])
+        1.139090746
+        >>> hyper2d({'m':a1,'n':a2,'n-m':b1,'m-n':b2},{},x,y)  # G2
+        0.9503682696
+        >>> nsum(lambda m,n: rf(a1,m)*rf(a2,n)*rf(b1,n-m)*rf(b2,m-n)*\
+        ...     x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf])
+        0.9503682696
+        >>> hyper2d({'2n-m':a1,'2m-n':a2},{},x,y)  # G3
+        1.029372029
+        >>> nsum(lambda m,n: rf(a1,2*n-m)*rf(a2,2*m-n)*\
+        ...     x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf])
+        1.029372029
+        >>> hyper2d({'m-n':a1,'m+n':b1,'n':c1},{'m':d},x,y)  # H1
+        -1.605331256
+        >>> nsum(lambda m,n: rf(a1,m-n)*rf(b1,m+n)*rf(c1,n)/rf(d,m)*\
+        ...     x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf])
+        -1.605331256
+        >>> hyper2d({'m-n':a1,'m':b1,'n':[c1,c2]},{'m':d},x,y)  # H2
+        -2.35405404
+        >>> nsum(lambda m,n: rf(a1,m-n)*rf(b1,m)*rf(c1,n)*rf(c2,n)/rf(d,m)*\
+        ...     x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf])
+        -2.35405404
+        >>> hyper2d({'2m+n':a1,'n':b1},{'m+n':c1},x,y)  # H3
+        0.974479074
+        >>> nsum(lambda m,n: rf(a1,2*m+n)*rf(b1,n)/rf(c1,m+n)*\
+        ...     x**m*y**n/fac(m)/fac(n), [0,inf], [0,inf])
+        0.974479074
+
+    **References**
+
+    1. [SrivastavaKarlsson]_
+    2. [Weisstein]_ http://mathworld.wolfram.com/HornFunction.html
+    3. [Weisstein]_ http://mathworld.wolfram.com/AppellHypergeometricFunction.html
+
+    """
+    x = ctx.convert(x)
+    y = ctx.convert(y)
+    def parse(dct, key):
+        args = dct.pop(key, [])
+        try:
+            args = list(args)
+        except TypeError:
+            args = [args]
+        return [ctx.convert(arg) for arg in args]
+    a_s = dict(a)
+    b_s = dict(b)
+    a_m = parse(a, 'm')
+    a_n = parse(a, 'n')
+    a_m_add_n = parse(a, 'm+n')
+    a_m_sub_n = parse(a, 'm-n')
+    a_n_sub_m = parse(a, 'n-m')
+    a_2m_add_n = parse(a, '2m+n')
+    a_2m_sub_n = parse(a, '2m-n')
+    a_2n_sub_m = parse(a, '2n-m')
+    b_m = parse(b, 'm')
+    b_n = parse(b, 'n')
+    b_m_add_n = parse(b, 'm+n')
+    if a: raise ValueError("unsupported key: %r" % a.keys()[0])
+    if b: raise ValueError("unsupported key: %r" % b.keys()[0])
+    s = 0
+    outer = ctx.one
+    m = ctx.mpf(0)
+    ok_count = 0
+    prec = ctx.prec
+    maxterms = kwargs.get('maxterms', 20*prec)
+    try:
+        ctx.prec += 10
+        tol = +ctx.eps
+        while 1:
+            inner_sign = 1
+            outer_sign = 1
+            inner_a = list(a_n)
+            inner_b = list(b_n)
+            outer_a = [a+m for a in a_m]
+            outer_b = [b+m for b in b_m]
+            # (a)_{m+n} = (a)_m (a+m)_n
+            for a in a_m_add_n:
+                a = a+m
+                inner_a.append(a)
+                outer_a.append(a)
+            # (b)_{m+n} = (b)_m (b+m)_n
+            for b in b_m_add_n:
+                b = b+m
+                inner_b.append(b)
+                outer_b.append(b)
+            # (a)_{n-m} = (a-m)_n / (a-m)_m
+            for a in a_n_sub_m:
+                inner_a.append(a-m)
+                outer_b.append(a-m-1)
+            # (a)_{m-n} = (-1)^(m+n) (1-a-m)_m / (1-a-m)_n
+            for a in a_m_sub_n:
+                inner_sign *= (-1)
+                outer_sign *= (-1)**(m)
+                inner_b.append(1-a-m)
+                outer_a.append(-a-m)
+            # (a)_{2m+n} = (a)_{2m} (a+2m)_n
+            for a in a_2m_add_n:
+                inner_a.append(a+2*m)
+                outer_a.append((a+2*m)*(1+a+2*m))
+            # (a)_{2m-n} = (-1)^(2m+n) (1-a-2m)_{2m} / (1-a-2m)_n
+            for a in a_2m_sub_n:
+                inner_sign *= (-1)
+                inner_b.append(1-a-2*m)
+                outer_a.append((a+2*m)*(1+a+2*m))
+            # (a)_{2n-m} = 4^n ((a-m)/2)_n ((a-m+1)/2)_n / (a-m)_m
+            for a in a_2n_sub_m:
+                inner_sign *= 4
+                inner_a.append(0.5*(a-m))
+                inner_a.append(0.5*(a-m+1))
+                outer_b.append(a-m-1)
+            inner = ctx.hyper(inner_a, inner_b, inner_sign*y,
+                zeroprec=ctx.prec, **kwargs)
+            term = outer * inner * outer_sign
+            if abs(term) < tol:
+                ok_count += 1
+            else:
+                ok_count = 0
+            if ok_count >= 3 or not outer:
+                break
+            s += term
+            for a in outer_a: outer *= a
+            for b in outer_b: outer /= b
+            m += 1
+            outer = outer * x / m
+            if m > maxterms:
+                raise ctx.NoConvergence("maxterms exceeded in hyper2d")
+    finally:
+        ctx.prec = prec
+    return +s
+
+"""
+@defun
+def kampe_de_feriet(ctx,a,b,c,d,e,f,x,y,**kwargs):
+    return ctx.hyper2d({'m+n':a,'m':b,'n':c},
+        {'m+n':d,'m':e,'n':f}, x,y, **kwargs)
+"""
+
+@defun
+def bihyper(ctx, a_s, b_s, z, **kwargs):
+    r"""
+    Evaluates the bilateral hypergeometric series
+
+    .. math ::
+
+        \,_AH_B(a_1, \ldots, a_k; b_1, \ldots, b_B; z) =
+            \sum_{n=-\infty}^{\infty}
+            \frac{(a_1)_n \ldots (a_A)_n}
+                 {(b_1)_n \ldots (b_B)_n} \, z^n
+
+    where, for direct convergence, `A = B` and `|z| = 1`, although a
+    regularized sum exists more generally by considering the
+    bilateral series as a sum of two ordinary hypergeometric
+    functions. In order for the series to make sense, none of the
+    parameters may be integers.
+
+    **Examples**
+
+    The value of `\,_2H_2` at `z = 1` is given by Dougall's formula::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> a,b,c,d = 0.5, 1.5, 2.25, 3.25
+        >>> bihyper([a,b],[c,d],1)
+        -14.49118026212345786148847
+        >>> gammaprod([c,d,1-a,1-b,c+d-a-b-1],[c-a,d-a,c-b,d-b])
+        -14.49118026212345786148847
+
+    The regularized function `\,_1H_0` can be expressed as the
+    sum of one `\,_2F_0` function and one `\,_1F_1` function::
+
+        >>> a = mpf(0.25)
+        >>> z = mpf(0.75)
+        >>> bihyper([a], [], z)
+        (0.2454393389657273841385582 + 0.2454393389657273841385582j)
+        >>> hyper([a,1],[],z) + (hyper([1],[1-a],-1/z)-1)
+        (0.2454393389657273841385582 + 0.2454393389657273841385582j)
+        >>> hyper([a,1],[],z) + hyper([1],[2-a],-1/z)/z/(a-1)
+        (0.2454393389657273841385582 + 0.2454393389657273841385582j)
+
+    **References**
+
+    1. [Slater]_ (chapter 6: "Bilateral Series", pp. 180-189)
+    2. [Wikipedia]_ http://en.wikipedia.org/wiki/Bilateral_hypergeometric_series
+
+    """
+    z = ctx.convert(z)
+    c_s = a_s + b_s
+    p = len(a_s)
+    q = len(b_s)
+    if (p, q) == (0,0) or (p, q) == (1,1):
+        return ctx.zero * z
+    neg = (p-q) % 2
+    def h(*c_s):
+        a_s = list(c_s[:p])
+        b_s = list(c_s[p:])
+        aa_s = [2-b for b in b_s]
+        bb_s = [2-a for a in a_s]
+        rp = [(-1)**neg * z] + [1-b for b in b_s] + [1-a for a in a_s]
+        rc = [-1] + [1]*len(b_s) + [-1]*len(a_s)
+        T1 = [], [], [], [], a_s + [1], b_s, z
+        T2 = rp, rc, [], [], aa_s + [1], bb_s, (-1)**neg / z
+        return T1, T2
+    return ctx.hypercomb(h, c_s, **kwargs)

lib/python3.11/site-packages/mpmath/functions/orthogonal.py

@@ -0,0 +1,493 @@
+from .functions import defun, defun_wrapped
+
+def _hermite_param(ctx, n, z, parabolic_cylinder):
+    """
+    Combined calculation of the Hermite polynomial H_n(z) (and its
+    generalization to complex n) and the parabolic cylinder
+    function D.
+    """
+    n, ntyp = ctx._convert_param(n)
+    z = ctx.convert(z)
+    q = -ctx.mpq_1_2
+    # For re(z) > 0, 2F0 -- http://functions.wolfram.com/
+    #     HypergeometricFunctions/HermiteHGeneral/06/02/0009/
+    # Otherwise, there is a reflection formula
+    # 2F0 + http://functions.wolfram.com/HypergeometricFunctions/
+    #           HermiteHGeneral/16/01/01/0006/
+    #
+    # TODO:
+    # An alternative would be to use
+    # http://functions.wolfram.com/HypergeometricFunctions/
+    #     HermiteHGeneral/06/02/0006/
+    #
+    # Also, the 1F1 expansion
+    # http://functions.wolfram.com/HypergeometricFunctions/
+    #     HermiteHGeneral/26/01/02/0001/
+    # should probably be used for tiny z
+    if not z:
+        T1 = [2, ctx.pi], [n, 0.5], [], [q*(n-1)], [], [], 0
+        if parabolic_cylinder:
+            T1[1][0] += q*n
+        return T1,
+    can_use_2f0 = ctx.isnpint(-n) or ctx.re(z) > 0 or \
+        (ctx.re(z) == 0 and ctx.im(z) > 0)
+    expprec = ctx.prec*4 + 20
+    if parabolic_cylinder:
+        u = ctx.fmul(ctx.fmul(z,z,prec=expprec), -0.25, exact=True)
+        w = ctx.fmul(z, ctx.sqrt(0.5,prec=expprec), prec=expprec)
+    else:
+        w = z
+    w2 = ctx.fmul(w, w, prec=expprec)
+    rw2 = ctx.fdiv(1, w2, prec=expprec)
+    nrw2 = ctx.fneg(rw2, exact=True)
+    nw = ctx.fneg(w, exact=True)
+    if can_use_2f0:
+        T1 = [2, w], [n, n], [], [], [q*n, q*(n-1)], [], nrw2
+        terms = [T1]
+    else:
+        T1 = [2, nw], [n, n], [], [], [q*n, q*(n-1)], [], nrw2
+        T2 = [2, ctx.pi, nw], [n+2, 0.5, 1], [], [q*n], [q*(n-1)], [1-q], w2
+        terms = [T1,T2]
+    # Multiply by prefactor for D_n
+    if parabolic_cylinder:
+        expu = ctx.exp(u)
+        for i in range(len(terms)):
+            terms[i][1][0] += q*n
+            terms[i][0].append(expu)
+            terms[i][1].append(1)
+    return tuple(terms)
+
+@defun
+def hermite(ctx, n, z, **kwargs):
+    return ctx.hypercomb(lambda: _hermite_param(ctx, n, z, 0), [], **kwargs)
+
+@defun
+def pcfd(ctx, n, z, **kwargs):
+    r"""
+    Gives the parabolic cylinder function in Whittaker's notation
+    `D_n(z) = U(-n-1/2, z)` (see :func:`~mpmath.pcfu`).
+    It solves the differential equation
+
+    .. math ::
+
+        y'' + \left(n + \frac{1}{2} - \frac{1}{4} z^2\right) y = 0.
+
+    and can be represented in terms of Hermite polynomials
+    (see :func:`~mpmath.hermite`) as
+
+    .. math ::
+
+        D_n(z) = 2^{-n/2} e^{-z^2/4} H_n\left(\frac{z}{\sqrt{2}}\right).
+
+    **Plots**
+
+    .. literalinclude :: /plots/pcfd.py
+    .. image :: /plots/pcfd.png
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> pcfd(0,0); pcfd(1,0); pcfd(2,0); pcfd(3,0)
+        1.0
+        0.0
+        -1.0
+        0.0
+        >>> pcfd(4,0); pcfd(-3,0)
+        3.0
+        0.6266570686577501256039413
+        >>> pcfd('1/2', 2+3j)
+        (-5.363331161232920734849056 - 3.858877821790010714163487j)
+        >>> pcfd(2, -10)
+        1.374906442631438038871515e-9
+
+    Verifying the differential equation::
+
+        >>> n = mpf(2.5)
+        >>> y = lambda z: pcfd(n,z)
+        >>> z = 1.75
+        >>> chop(diff(y,z,2) + (n+0.5-0.25*z**2)*y(z))
+        0.0
+
+    Rational Taylor series expansion when `n` is an integer::
+
+        >>> taylor(lambda z: pcfd(5,z), 0, 7)
+        [0.0, 15.0, 0.0, -13.75, 0.0, 3.96875, 0.0, -0.6015625]
+
+    """
+    return ctx.hypercomb(lambda: _hermite_param(ctx, n, z, 1), [], **kwargs)
+
+@defun
+def pcfu(ctx, a, z, **kwargs):
+    r"""
+    Gives the parabolic cylinder function `U(a,z)`, which may be
+    defined for `\Re(z) > 0` in terms of the confluent
+    U-function (see :func:`~mpmath.hyperu`) by
+
+    .. math ::
+
+        U(a,z) = 2^{-\frac{1}{4}-\frac{a}{2}} e^{-\frac{1}{4} z^2}
+            U\left(\frac{a}{2}+\frac{1}{4},
+            \frac{1}{2}, \frac{1}{2}z^2\right)
+
+    or, for arbitrary `z`,
+
+    .. math ::
+
+        e^{-\frac{1}{4}z^2} U(a,z) =
+            U(a,0) \,_1F_1\left(-\tfrac{a}{2}+\tfrac{1}{4};
+            \tfrac{1}{2}; -\tfrac{1}{2}z^2\right) +
+            U'(a,0) z \,_1F_1\left(-\tfrac{a}{2}+\tfrac{3}{4};
+            \tfrac{3}{2}; -\tfrac{1}{2}z^2\right).
+
+    **Examples**
+
+    Connection to other functions::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> z = mpf(3)
+        >>> pcfu(0.5,z)
+        0.03210358129311151450551963
+        >>> sqrt(pi/2)*exp(z**2/4)*erfc(z/sqrt(2))
+        0.03210358129311151450551963
+        >>> pcfu(0.5,-z)
+        23.75012332835297233711255
+        >>> sqrt(pi/2)*exp(z**2/4)*erfc(-z/sqrt(2))
+        23.75012332835297233711255
+        >>> pcfu(0.5,-z)
+        23.75012332835297233711255
+        >>> sqrt(pi/2)*exp(z**2/4)*erfc(-z/sqrt(2))
+        23.75012332835297233711255
+
+    """
+    n, _ = ctx._convert_param(a)
+    return ctx.pcfd(-n-ctx.mpq_1_2, z)
+
+@defun
+def pcfv(ctx, a, z, **kwargs):
+    r"""
+    Gives the parabolic cylinder function `V(a,z)`, which can be
+    represented in terms of :func:`~mpmath.pcfu` as
+
+    .. math ::
+
+        V(a,z) = \frac{\Gamma(a+\tfrac{1}{2}) (U(a,-z)-\sin(\pi a) U(a,z)}{\pi}.
+
+    **Examples**
+
+    Wronskian relation between `U` and `V`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> a, z = 2, 3
+        >>> pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z)
+        0.7978845608028653558798921
+        >>> sqrt(2/pi)
+        0.7978845608028653558798921
+        >>> a, z = 2.5, 3
+        >>> pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z)
+        0.7978845608028653558798921
+        >>> a, z = 0.25, -1
+        >>> pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z)
+        0.7978845608028653558798921
+        >>> a, z = 2+1j, 2+3j
+        >>> chop(pcfu(a,z)*diff(pcfv,(a,z),(0,1))-diff(pcfu,(a,z),(0,1))*pcfv(a,z))
+        0.7978845608028653558798921
+
+    """
+    n, ntype = ctx._convert_param(a)
+    z = ctx.convert(z)
+    q = ctx.mpq_1_2
+    r = ctx.mpq_1_4
+    if ntype == 'Q' and ctx.isint(n*2):
+        # Faster for half-integers
+        def h():
+            jz = ctx.fmul(z, -1j, exact=True)
+            T1terms = _hermite_param(ctx, -n-q, z, 1)
+            T2terms = _hermite_param(ctx, n-q, jz, 1)
+            for T in T1terms:
+                T[0].append(1j)
+                T[1].append(1)
+                T[3].append(q-n)
+            u = ctx.expjpi((q*n-r)) * ctx.sqrt(2/ctx.pi)
+            for T in T2terms:
+                T[0].append(u)
+                T[1].append(1)
+            return T1terms + T2terms
+        v = ctx.hypercomb(h, [], **kwargs)
+        if ctx._is_real_type(n) and ctx._is_real_type(z):
+            v = ctx._re(v)
+        return v
+    else:
+        def h(n):
+            w = ctx.square_exp_arg(z, -0.25)
+            u = ctx.square_exp_arg(z, 0.5)
+            e = ctx.exp(w)
+            l = [ctx.pi, q, ctx.exp(w)]
+            Y1 = l, [-q, n*q+r, 1], [r-q*n], [], [q*n+r], [q], u
+            Y2 = l + [z], [-q, n*q-r, 1, 1], [1-r-q*n], [], [q*n+1-r], [1+q], u
+            c, s = ctx.cospi_sinpi(r+q*n)
+            Y1[0].append(s)
+            Y2[0].append(c)
+            for Y in (Y1, Y2):
+                Y[1].append(1)
+                Y[3].append(q-n)
+            return Y1, Y2
+        return ctx.hypercomb(h, [n], **kwargs)
+
+
+@defun
+def pcfw(ctx, a, z, **kwargs):
+    r"""
+    Gives the parabolic cylinder function `W(a,z)` defined in (DLMF 12.14).
+
+    **Examples**
+
+    Value at the origin::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> a = mpf(0.25)
+        >>> pcfw(a,0)
+        0.9722833245718180765617104
+        >>> power(2,-0.75)*sqrt(abs(gamma(0.25+0.5j*a)/gamma(0.75+0.5j*a)))
+        0.9722833245718180765617104
+        >>> diff(pcfw,(a,0),(0,1))
+        -0.5142533944210078966003624
+        >>> -power(2,-0.25)*sqrt(abs(gamma(0.75+0.5j*a)/gamma(0.25+0.5j*a)))
+        -0.5142533944210078966003624
+
+    """
+    n, _ = ctx._convert_param(a)
+    z = ctx.convert(z)
+    def terms():
+        phi2 = ctx.arg(ctx.gamma(0.5 + ctx.j*n))
+        phi2 = (ctx.loggamma(0.5+ctx.j*n) - ctx.loggamma(0.5-ctx.j*n))/2j
+        rho = ctx.pi/8 + 0.5*phi2
+        # XXX: cancellation computing k
+        k = ctx.sqrt(1 + ctx.exp(2*ctx.pi*n)) - ctx.exp(ctx.pi*n)
+        C = ctx.sqrt(k/2) * ctx.exp(0.25*ctx.pi*n)
+        yield C * ctx.expj(rho) * ctx.pcfu(ctx.j*n, z*ctx.expjpi(-0.25))
+        yield C * ctx.expj(-rho) * ctx.pcfu(-ctx.j*n, z*ctx.expjpi(0.25))
+    v = ctx.sum_accurately(terms)
+    if ctx._is_real_type(n) and ctx._is_real_type(z):
+        v = ctx._re(v)
+    return v
+
+"""
+Even/odd PCFs. Useful?
+
+@defun
+def pcfy1(ctx, a, z, **kwargs):
+    a, _ = ctx._convert_param(n)
+    z = ctx.convert(z)
+    def h():
+        w = ctx.square_exp_arg(z)
+        w1 = ctx.fmul(w, -0.25, exact=True)
+        w2 = ctx.fmul(w, 0.5, exact=True)
+        e = ctx.exp(w1)
+        return [e], [1], [], [], [ctx.mpq_1_2*a+ctx.mpq_1_4], [ctx.mpq_1_2], w2
+    return ctx.hypercomb(h, [], **kwargs)
+
+@defun
+def pcfy2(ctx, a, z, **kwargs):
+    a, _ = ctx._convert_param(n)
+    z = ctx.convert(z)
+    def h():
+        w = ctx.square_exp_arg(z)
+        w1 = ctx.fmul(w, -0.25, exact=True)
+        w2 = ctx.fmul(w, 0.5, exact=True)
+        e = ctx.exp(w1)
+        return [e, z], [1, 1], [], [], [ctx.mpq_1_2*a+ctx.mpq_3_4], \
+            [ctx.mpq_3_2], w2
+    return ctx.hypercomb(h, [], **kwargs)
+"""
+
+@defun_wrapped
+def gegenbauer(ctx, n, a, z, **kwargs):
+    # Special cases: a+0.5, a*2 poles
+    if ctx.isnpint(a):
+        return 0*(z+n)
+    if ctx.isnpint(a+0.5):
+        # TODO: something else is required here
+        # E.g.: gegenbauer(-2, -0.5, 3) == -12
+        if ctx.isnpint(n+1):
+            raise NotImplementedError("Gegenbauer function with two limits")
+        def h(a):
+            a2 = 2*a
+            T = [], [], [n+a2], [n+1, a2], [-n, n+a2], [a+0.5], 0.5*(1-z)
+            return [T]
+        return ctx.hypercomb(h, [a], **kwargs)
+    def h(n):
+        a2 = 2*a
+        T = [], [], [n+a2], [n+1, a2], [-n, n+a2], [a+0.5], 0.5*(1-z)
+        return [T]
+    return ctx.hypercomb(h, [n], **kwargs)
+
+@defun_wrapped
+def jacobi(ctx, n, a, b, x, **kwargs):
+    if not ctx.isnpint(a):
+        def h(n):
+            return (([], [], [a+n+1], [n+1, a+1], [-n, a+b+n+1], [a+1], (1-x)*0.5),)
+        return ctx.hypercomb(h, [n], **kwargs)
+    if not ctx.isint(b):
+        def h(n, a):
+            return (([], [], [-b], [n+1, -b-n], [-n, a+b+n+1], [b+1], (x+1)*0.5),)
+        return ctx.hypercomb(h, [n, a], **kwargs)
+    # XXX: determine appropriate limit
+    return ctx.binomial(n+a,n) * ctx.hyp2f1(-n,1+n+a+b,a+1,(1-x)/2, **kwargs)
+
+@defun_wrapped
+def laguerre(ctx, n, a, z, **kwargs):
+    # XXX: limits, poles
+    #if ctx.isnpint(n):
+    #    return 0*(a+z)
+    def h(a):
+        return (([], [], [a+n+1], [a+1, n+1], [-n], [a+1], z),)
+    return ctx.hypercomb(h, [a], **kwargs)
+
+@defun_wrapped
+def legendre(ctx, n, x, **kwargs):
+    if ctx.isint(n):
+        n = int(n)
+        # Accuracy near zeros
+        if (n + (n < 0)) & 1:
+            if not x:
+                return x
+            mag = ctx.mag(x)
+            if mag < -2*ctx.prec-10:
+                return x
+            if mag < -5:
+                ctx.prec += -mag
+    return ctx.hyp2f1(-n,n+1,1,(1-x)/2, **kwargs)
+
+@defun
+def legenp(ctx, n, m, z, type=2, **kwargs):
+    # Legendre function, 1st kind
+    n = ctx.convert(n)
+    m = ctx.convert(m)
+    # Faster
+    if not m:
+        return ctx.legendre(n, z, **kwargs)
+    # TODO: correct evaluation at singularities
+    if type == 2:
+        def h(n,m):
+            g = m*0.5
+            T = [1+z, 1-z], [g, -g], [], [1-m], [-n, n+1], [1-m], 0.5*(1-z)
+            return (T,)
+        return ctx.hypercomb(h, [n,m], **kwargs)
+    if type == 3:
+        def h(n,m):
+            g = m*0.5
+            T = [z+1, z-1], [g, -g], [], [1-m], [-n, n+1], [1-m], 0.5*(1-z)
+            return (T,)
+        return ctx.hypercomb(h, [n,m], **kwargs)
+    raise ValueError("requires type=2 or type=3")
+
+@defun
+def legenq(ctx, n, m, z, type=2, **kwargs):
+    # Legendre function, 2nd kind
+    n = ctx.convert(n)
+    m = ctx.convert(m)
+    z = ctx.convert(z)
+    if z in (1, -1):
+        #if ctx.isint(m):
+        #    return ctx.nan
+        #return ctx.inf  # unsigned
+        return ctx.nan
+    if type == 2:
+        def h(n, m):
+            cos, sin = ctx.cospi_sinpi(m)
+            s = 2 * sin / ctx.pi
+            c = cos
+            a = 1+z
+            b = 1-z
+            u = m/2
+            w = (1-z)/2
+            T1 = [s, c, a, b], [-1, 1, u, -u], [], [1-m], \
+                [-n, n+1], [1-m], w
+            T2 = [-s, a, b], [-1, -u, u], [n+m+1], [n-m+1, m+1], \
+                [-n, n+1], [m+1], w
+            return T1, T2
+        return ctx.hypercomb(h, [n, m], **kwargs)
+    if type == 3:
+        # The following is faster when there only is a single series
+        # Note: not valid for -1 < z < 0 (?)
+        if abs(z) > 1:
+            def h(n, m):
+                T1 = [ctx.expjpi(m), 2, ctx.pi, z, z-1, z+1], \
+                     [1, -n-1, 0.5, -n-m-1, 0.5*m, 0.5*m], \
+                     [n+m+1], [n+1.5], \
+                     [0.5*(2+n+m), 0.5*(1+n+m)], [n+1.5], z**(-2)
+                return [T1]
+            return ctx.hypercomb(h, [n, m], **kwargs)
+        else:
+            # not valid for 1 < z < inf ?
+            def h(n, m):
+                s = 2 * ctx.sinpi(m) / ctx.pi
+                c = ctx.expjpi(m)
+                a = 1+z
+                b = z-1
+                u = m/2
+                w = (1-z)/2
+                T1 = [s, c, a, b], [-1, 1, u, -u], [], [1-m], \
+                    [-n, n+1], [1-m], w
+                T2 = [-s, c, a, b], [-1, 1, -u, u], [n+m+1], [n-m+1, m+1], \
+                    [-n, n+1], [m+1], w
+                return T1, T2
+            return ctx.hypercomb(h, [n, m], **kwargs)
+    raise ValueError("requires type=2 or type=3")
+
+@defun_wrapped
+def chebyt(ctx, n, x, **kwargs):
+    if (not x) and ctx.isint(n) and int(ctx._re(n)) % 2 == 1:
+        return x * 0
+    return ctx.hyp2f1(-n,n,(1,2),(1-x)/2, **kwargs)
+
+@defun_wrapped
+def chebyu(ctx, n, x, **kwargs):
+    if (not x) and ctx.isint(n) and int(ctx._re(n)) % 2 == 1:
+        return x * 0
+    return (n+1) * ctx.hyp2f1(-n, n+2, (3,2), (1-x)/2, **kwargs)
+
+@defun
+def spherharm(ctx, l, m, theta, phi, **kwargs):
+    l = ctx.convert(l)
+    m = ctx.convert(m)
+    theta = ctx.convert(theta)
+    phi = ctx.convert(phi)
+    l_isint = ctx.isint(l)
+    l_natural = l_isint and l >= 0
+    m_isint = ctx.isint(m)
+    if l_isint and l < 0 and m_isint:
+        return ctx.spherharm(-(l+1), m, theta, phi, **kwargs)
+    if theta == 0 and m_isint and m < 0:
+        return ctx.zero * 1j
+    if l_natural and m_isint:
+        if abs(m) > l:
+            return ctx.zero * 1j
+        # http://functions.wolfram.com/Polynomials/
+        #     SphericalHarmonicY/26/01/02/0004/
+        def h(l,m):
+            absm = abs(m)
+            C = [-1, ctx.expj(m*phi),
+                 (2*l+1)*ctx.fac(l+absm)/ctx.pi/ctx.fac(l-absm),
+                 ctx.sin(theta)**2,
+                 ctx.fac(absm), 2]
+            P = [0.5*m*(ctx.sign(m)+1), 1, 0.5, 0.5*absm, -1, -absm-1]
+            return ((C, P, [], [], [absm-l, l+absm+1], [absm+1],
+                ctx.sin(0.5*theta)**2),)
+    else:
+        # http://functions.wolfram.com/HypergeometricFunctions/
+        #     SphericalHarmonicYGeneral/26/01/02/0001/
+        def h(l,m):
+            if ctx.isnpint(l-m+1) or ctx.isnpint(l+m+1) or ctx.isnpint(1-m):
+                return (([0], [-1], [], [], [], [], 0),)
+            cos, sin = ctx.cos_sin(0.5*theta)
+            C = [0.5*ctx.expj(m*phi), (2*l+1)/ctx.pi,
+                 ctx.gamma(l-m+1), ctx.gamma(l+m+1),
+                 cos**2, sin**2]
+            P = [1, 0.5, 0.5, -0.5, 0.5*m, -0.5*m]
+            return ((C, P, [], [1-m], [-l,l+1], [1-m], sin**2),)
+    return ctx.hypercomb(h, [l,m], **kwargs)

lib/python3.11/site-packages/mpmath/functions/qfunctions.py

@@ -0,0 +1,280 @@
+from .functions import defun, defun_wrapped
+
+@defun
+def qp(ctx, a, q=None, n=None, **kwargs):
+    r"""
+    Evaluates the q-Pochhammer symbol (or q-rising factorial)
+
+    .. math ::
+
+        (a; q)_n = \prod_{k=0}^{n-1} (1-a q^k)
+
+    where `n = \infty` is permitted if `|q| < 1`. Called with two arguments,
+    ``qp(a,q)`` computes `(a;q)_{\infty}`; with a single argument, ``qp(q)``
+    computes `(q;q)_{\infty}`. The special case
+
+    .. math ::
+
+        \phi(q) = (q; q)_{\infty} = \prod_{k=1}^{\infty} (1-q^k) =
+            \sum_{k=-\infty}^{\infty} (-1)^k q^{(3k^2-k)/2}
+
+    is also known as the Euler function, or (up to a factor `q^{-1/24}`)
+    the Dedekind eta function.
+
+    **Examples**
+
+    If `n` is a positive integer, the function amounts to a finite product::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> qp(2,3,5)
+        -725305.0
+        >>> fprod(1-2*3**k for k in range(5))
+        -725305.0
+        >>> qp(2,3,0)
+        1.0
+
+    Complex arguments are allowed::
+
+        >>> qp(2-1j, 0.75j)
+        (0.4628842231660149089976379 + 4.481821753552703090628793j)
+
+    The regular Pochhammer symbol `(a)_n` is obtained in the
+    following limit as `q \to 1`::
+
+        >>> a, n = 4, 7
+        >>> limit(lambda q: qp(q**a,q,n) / (1-q)**n, 1)
+        604800.0
+        >>> rf(a,n)
+        604800.0
+
+    The Taylor series of the reciprocal Euler function gives
+    the partition function `P(n)`, i.e. the number of ways of writing
+    `n` as a sum of positive integers::
+
+        >>> taylor(lambda q: 1/qp(q), 0, 10)
+        [1.0, 1.0, 2.0, 3.0, 5.0, 7.0, 11.0, 15.0, 22.0, 30.0, 42.0]
+
+    Special values include::
+
+        >>> qp(0)
+        1.0
+        >>> findroot(diffun(qp), -0.4)   # location of maximum
+        -0.4112484791779547734440257
+        >>> qp(_)
+        1.228348867038575112586878
+
+    The q-Pochhammer symbol is related to the Jacobi theta functions.
+    For example, the following identity holds::
+
+        >>> q = mpf(0.5)    # arbitrary
+        >>> qp(q)
+        0.2887880950866024212788997
+        >>> root(3,-2)*root(q,-24)*jtheta(2,pi/6,root(q,6))
+        0.2887880950866024212788997
+
+    """
+    a = ctx.convert(a)
+    if n is None:
+        n = ctx.inf
+    else:
+        n = ctx.convert(n)
+    if n < 0:
+        raise ValueError("n cannot be negative")
+    if q is None:
+        q = a
+    else:
+        q = ctx.convert(q)
+    if n == 0:
+        return ctx.one + 0*(a+q)
+    infinite = (n == ctx.inf)
+    same = (a == q)
+    if infinite:
+        if abs(q) >= 1:
+            if same and (q == -1 or q == 1):
+                return ctx.zero * q
+            raise ValueError("q-function only defined for |q| < 1")
+        elif q == 0:
+            return ctx.one - a
+    maxterms = kwargs.get('maxterms', 50*ctx.prec)
+    if infinite and same:
+        # Euler's pentagonal theorem
+        def terms():
+            t = 1
+            yield t
+            k = 1
+            x1 = q
+            x2 = q**2
+            while 1:
+                yield (-1)**k * x1
+                yield (-1)**k * x2
+                x1 *= q**(3*k+1)
+                x2 *= q**(3*k+2)
+                k += 1
+                if k > maxterms:
+                    raise ctx.NoConvergence
+        return ctx.sum_accurately(terms)
+    # return ctx.nprod(lambda k: 1-a*q**k, [0,n-1])
+    def factors():
+        k = 0
+        r = ctx.one
+        while 1:
+            yield 1 - a*r
+            r *= q
+            k += 1
+            if k >= n:
+                return
+            if k > maxterms:
+                raise ctx.NoConvergence
+    return ctx.mul_accurately(factors)
+
+@defun_wrapped
+def qgamma(ctx, z, q, **kwargs):
+    r"""
+    Evaluates the q-gamma function
+
+    .. math ::
+
+        \Gamma_q(z) = \frac{(q; q)_{\infty}}{(q^z; q)_{\infty}} (1-q)^{1-z}.
+
+
+    **Examples**
+
+    Evaluation for real and complex arguments::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> qgamma(4,0.75)
+        4.046875
+        >>> qgamma(6,6)
+        121226245.0
+        >>> qgamma(3+4j, 0.5j)
+        (0.1663082382255199834630088 + 0.01952474576025952984418217j)
+
+    The q-gamma function satisfies a functional equation similar
+    to that of the ordinary gamma function::
+
+        >>> q = mpf(0.25)
+        >>> z = mpf(2.5)
+        >>> qgamma(z+1,q)
+        1.428277424823760954685912
+        >>> (1-q**z)/(1-q)*qgamma(z,q)
+        1.428277424823760954685912
+
+    """
+    if abs(q) > 1:
+        return ctx.qgamma(z,1/q)*q**((z-2)*(z-1)*0.5)
+    return ctx.qp(q, q, None, **kwargs) / \
+        ctx.qp(q**z, q, None, **kwargs) * (1-q)**(1-z)
+
+@defun_wrapped
+def qfac(ctx, z, q, **kwargs):
+    r"""
+    Evaluates the q-factorial,
+
+    .. math ::
+
+        [n]_q! = (1+q)(1+q+q^2)\cdots(1+q+\cdots+q^{n-1})
+
+    or more generally
+
+    .. math ::
+
+        [z]_q! = \frac{(q;q)_z}{(1-q)^z}.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> qfac(0,0)
+        1.0
+        >>> qfac(4,3)
+        2080.0
+        >>> qfac(5,6)
+        121226245.0
+        >>> qfac(1+1j, 2+1j)
+        (0.4370556551322672478613695 + 0.2609739839216039203708921j)
+
+    """
+    if ctx.isint(z) and ctx._re(z) > 0:
+        n = int(ctx._re(z))
+        return ctx.qp(q, q, n, **kwargs) / (1-q)**n
+    return ctx.qgamma(z+1, q, **kwargs)
+
+@defun
+def qhyper(ctx, a_s, b_s, q, z, **kwargs):
+    r"""
+    Evaluates the basic hypergeometric series or hypergeometric q-series
+
+    .. math ::
+
+        \,_r\phi_s \left[\begin{matrix}
+            a_1 & a_2 & \ldots & a_r \\
+            b_1 & b_2 & \ldots & b_s
+        \end{matrix} ; q,z \right] =
+        \sum_{n=0}^\infty
+        \frac{(a_1;q)_n, \ldots, (a_r;q)_n}
+             {(b_1;q)_n, \ldots, (b_s;q)_n}
+        \left((-1)^n q^{n\choose 2}\right)^{1+s-r}
+        \frac{z^n}{(q;q)_n}
+
+    where `(a;q)_n` denotes the q-Pochhammer symbol (see :func:`~mpmath.qp`).
+
+    **Examples**
+
+    Evaluation works for real and complex arguments::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> qhyper([0.5], [2.25], 0.25, 4)
+        -0.1975849091263356009534385
+        >>> qhyper([0.5], [2.25], 0.25-0.25j, 4)
+        (2.806330244925716649839237 + 3.568997623337943121769938j)
+        >>> qhyper([1+j], [2,3+0.5j], 0.25, 3+4j)
+        (9.112885171773400017270226 - 1.272756997166375050700388j)
+
+    Comparing with a summation of the defining series, using
+    :func:`~mpmath.nsum`::
+
+        >>> b, q, z = 3, 0.25, 0.5
+        >>> qhyper([], [b], q, z)
+        0.6221136748254495583228324
+        >>> nsum(lambda n: z**n / qp(q,q,n)/qp(b,q,n) * q**(n*(n-1)), [0,inf])
+        0.6221136748254495583228324
+
+    """
+    #a_s = [ctx._convert_param(a)[0] for a in a_s]
+    #b_s = [ctx._convert_param(b)[0] for b in b_s]
+    #q = ctx._convert_param(q)[0]
+    a_s = [ctx.convert(a) for a in a_s]
+    b_s = [ctx.convert(b) for b in b_s]
+    q = ctx.convert(q)
+    z = ctx.convert(z)
+    r = len(a_s)
+    s = len(b_s)
+    d = 1+s-r
+    maxterms = kwargs.get('maxterms', 50*ctx.prec)
+    def terms():
+        t = ctx.one
+        yield t
+        qk = 1
+        k = 0
+        x = 1
+        while 1:
+            for a in a_s:
+                p = 1 - a*qk
+                t *= p
+            for b in b_s:
+                p = 1 - b*qk
+                if not p:
+                    raise ValueError
+                t /= p
+            t *= z
+            x *= (-1)**d * qk ** d
+            qk *= q
+            t /= (1 - qk)
+            k += 1
+            yield t * x
+            if k > maxterms:
+                raise ctx.NoConvergence
+    return ctx.sum_accurately(terms)

lib/python3.11/site-packages/mpmath/functions/rszeta.py

@@ -0,0 +1,1403 @@
+"""
+---------------------------------------------------------------------
+.. sectionauthor:: Juan Arias de Reyna <[email protected]>
+
+This module implements zeta-related functions using the Riemann-Siegel
+expansion: zeta_offline(s,k=0)
+
+* coef(J, eps): Need in the computation of Rzeta(s,k)
+
+* Rzeta_simul(s, der=0) computes Rzeta^(k)(s) and Rzeta^(k)(1-s) simultaneously
+  for  0 <= k <= der. Used by zeta_offline and z_offline
+
+* Rzeta_set(s, derivatives) computes Rzeta^(k)(s) for given derivatives, used by
+  z_half(t,k) and zeta_half
+
+* z_offline(w,k): Z(w) and its derivatives of order k <= 4
+* z_half(t,k): Z(t) (Riemann Siegel function) and its derivatives of order k <= 4
+* zeta_offline(s): zeta(s) and its derivatives of order k<= 4
+* zeta_half(1/2+it,k):  zeta(s)  and its derivatives of order k<= 4
+
+* rs_zeta(s,k=0) Computes zeta^(k)(s)   Unifies zeta_half and zeta_offline
+* rs_z(w,k=0)    Computes Z^(k)(w)      Unifies z_offline and z_half
+----------------------------------------------------------------------
+
+This program uses Riemann-Siegel expansion even to compute
+zeta(s) on points s = sigma + i t  with sigma arbitrary not
+necessarily equal to 1/2.
+
+It is founded on a new deduction of the formula, with rigorous
+and sharp bounds for the  terms and rest of this expansion.
+
+More information on the papers:
+
+ J. Arias de Reyna, High Precision Computation of Riemann's
+ Zeta Function by the Riemann-Siegel Formula I, II
+
+ We refer to them as I, II.
+
+ In them we shall find detailed explanation of all the
+ procedure.
+
+The program uses Riemann-Siegel expansion.
+This  is useful when t is big, ( say  t > 10000 ).
+The precision is limited, roughly it can compute zeta(sigma+it)
+with an error less than exp(-c t) for some constant c depending
+on sigma.  The program gives an error when the Riemann-Siegel
+formula can not compute to the wanted precision.
+
+"""
+
+import math
+
+class RSCache(object):
+    def __init__(ctx):
+        ctx._rs_cache = [0, 10, {}, {}]
+
+from .functions import defun
+
+#-------------------------------------------------------------------------------#
+#                                                                               #
+#                       coef(ctx, J, eps, _cache=[0, 10, {} ] )                 #
+#                                                                               #
+#-------------------------------------------------------------------------------#
+
+#  This function computes the coefficients c[n] defined on (I, equation (47))
+#  but see also  (II, section 3.14).
+#
+#  Since these coefficients are very difficult to compute we save the values
+#  in a cache. So if we compute several values of the functions Rzeta(s) for
+#  near values of s, we do not recompute these coefficients.
+#
+#  c[n] are the Taylor coefficients of the function:
+#
+#  F(z):= (exp(pi*j*(z*z/2+3/8))-j* sqrt(2) cos(pi*z/2))/(2*cos(pi *z))
+#
+#
+
+def _coef(ctx, J, eps):
+    r"""
+    Computes the coefficients  `c_n`  for `0\le n\le 2J` with error less than eps
+
+    **Definition**
+
+    The coefficients c_n are defined by
+
+    .. math ::
+
+        \begin{equation}
+        F(z)=\frac{e^{\pi i
+        \bigl(\frac{z^2}{2}+\frac38\bigr)}-i\sqrt{2}\cos\frac{\pi}{2}z}{2\cos\pi
+        z}=\sum_{n=0}^\infty c_{2n} z^{2n}
+        \end{equation}
+
+    they are computed applying the relation
+
+    .. math ::
+
+        \begin{multline}
+        c_{2n}=-\frac{i}{\sqrt{2}}\Bigl(\frac{\pi}{2}\Bigr)^{2n}
+        \sum_{k=0}^n\frac{(-1)^k}{(2k)!}
+        2^{2n-2k}\frac{(-1)^{n-k}E_{2n-2k}}{(2n-2k)!}+\\
+        +e^{3\pi i/8}\sum_{j=0}^n(-1)^j\frac{
+        E_{2j}}{(2j)!}\frac{i^{n-j}\pi^{n+j}}{(n-j)!2^{n-j+1}}.
+        \end{multline}
+    """
+
+    newJ = J+2        # compute more coefficients that are needed
+    neweps6 = eps/2.  # compute with a slight more precision that are needed
+
+    #  PREPARATION FOR THE COMPUTATION OF V(N) AND W(N)
+    #    See II Section 3.16
+    #
+    #  Computing the exponent wpvw of the error II equation (81)
+    wpvw = max(ctx.mag(10*(newJ+3)), 4*newJ+5-ctx.mag(neweps6))
+
+    #  Preparation of Euler numbers (we need until the 2*RS_NEWJ)
+    E = ctx._eulernum(2*newJ)
+
+    #  Now we have in the cache all the needed Euler numbers.
+    #
+    #  Computing the powers of pi
+    #
+    # We need to compute the powers pi**n for 1<= n <= 2*J
+    # with relative error less than 2**(-wpvw)
+    # it is easy to show that this is obtained
+    # taking wppi as the least d with
+    # 2**d>40*J and 2**d> 4.24 *newJ + 2**wpvw
+    # In II Section 3.9 we need also that
+    #  wppi > wptcoef[0], and that the powers
+    # here computed  0<= k <= 2*newJ are more
+    # than those needed there that are 2*L-2.
+    # so we need  J >= L this will be checked
+    # before computing tcoef[]
+    wppi = max(ctx.mag(40*newJ), ctx.mag(newJ)+3 +wpvw)
+    ctx.prec = wppi
+    pipower = {}
+    pipower[0] = ctx.one
+    pipower[1] = ctx.pi
+    for n in range(2,2*newJ+1):
+        pipower[n] = pipower[n-1]*ctx.pi
+
+    # COMPUTING THE COEFFICIENTS v(n) AND w(n)
+    #  see II equation (61) and equations (81) and (82)
+    ctx.prec = wpvw+2
+    v={}
+    w={}
+    for n in range(0,newJ+1):
+        va = (-1)**n * ctx._eulernum(2*n)
+        va = ctx.mpf(va)/ctx.fac(2*n)
+        v[n]=va*pipower[2*n]
+    for n in range(0,2*newJ+1):
+        wa = ctx.one/ctx.fac(n)
+        wa=wa/(2**n)
+        w[n]=wa*pipower[n]
+
+    # COMPUTATION OF THE CONVOLUTIONS RS_P1 AND RS_P2
+    #  See II Section 3.16
+    ctx.prec = 15
+    wpp1a = 9 - ctx.mag(neweps6)
+    P1 = {}
+    for n in range(0,newJ+1):
+        ctx.prec = 15
+        wpp1 = max(ctx.mag(10*(n+4)),4*n+wpp1a)
+        ctx.prec = wpp1
+        sump = 0
+        for k in range(0,n+1):
+            sump += ((-1)**k) * v[k]*w[2*n-2*k]
+        P1[n]=((-1)**(n+1))*ctx.j*sump
+    P2={}
+    for n in range(0,newJ+1):
+        ctx.prec = 15
+        wpp2 = max(ctx.mag(10*(n+4)),4*n+wpp1a)
+        ctx.prec = wpp2
+        sump = 0
+        for k in range(0,n+1):
+            sump += (ctx.j**(n-k)) * v[k]*w[n-k]
+        P2[n]=sump
+    # COMPUTING THE COEFFICIENTS c[2n]
+    # See II Section 3.14
+    ctx.prec = 15
+    wpc0 = 5 - ctx.mag(neweps6)
+    wpc = max(6,4*newJ+wpc0)
+    ctx.prec = wpc
+    mu = ctx.sqrt(ctx.mpf('2'))/2
+    nu = ctx.expjpi(3./8)/2
+    c={}
+    for n in range(0,newJ):
+        ctx.prec = 15
+        wpc = max(6,4*n+wpc0)
+        ctx.prec = wpc
+        c[2*n] = mu*P1[n]+nu*P2[n]
+    for n in range(1,2*newJ,2):
+        c[n] = 0
+    return [newJ, neweps6, c, pipower]
+
+def coef(ctx, J, eps):
+    _cache = ctx._rs_cache
+    if J <= _cache[0] and eps >= _cache[1]:
+        return _cache[2], _cache[3]
+    orig = ctx._mp.prec
+    try:
+        data = _coef(ctx._mp, J, eps)
+    finally:
+        ctx._mp.prec = orig
+    if ctx is not ctx._mp:
+        data[2] = dict((k,ctx.convert(v)) for (k,v) in data[2].items())
+        data[3] = dict((k,ctx.convert(v)) for (k,v) in data[3].items())
+    ctx._rs_cache[:] = data
+    return ctx._rs_cache[2], ctx._rs_cache[3]
+
+#-------------------------------------------------------------------------------#
+#                                                                               #
+#                          Rzeta_simul(s,k=0)                                   #
+#                                                                               #
+#-------------------------------------------------------------------------------#
+#  This function return a list with the values:
+#  Rzeta(sigma+it), conj(Rzeta(1-sigma+it)),Rzeta'(sigma+it), conj(Rzeta'(1-sigma+it)),
+#  .... , Rzeta^{(k)}(sigma+it), conj(Rzeta^{(k)}(1-sigma+it))
+#
+#  Useful to compute  the function zeta(s) and Z(w)  or its derivatives.
+#
+
+def aux_M_Fp(ctx, xA, xeps4, a, xB1, xL):
+    # COMPUTING M  NUMBER OF DERIVATIVES Fp[m] TO COMPUTE
+    #  See II Section 3.11  equations (47) and (48)
+    aux1 = 126.0657606*xA/xeps4   # 126.06.. = 316/sqrt(2*pi)
+    aux1 = ctx.ln(aux1)
+    aux2 = (2*ctx.ln(ctx.pi)+ctx.ln(xB1)+ctx.ln(a))/3 -ctx.ln(2*ctx.pi)/2
+    m = 3*xL-3
+    aux3= (ctx.loggamma(m+1)-ctx.loggamma(m/3.0+2))/2 -ctx.loggamma((m+1)/2.)
+    while((aux1 < m*aux2+ aux3)and (m>1)):
+        m = m - 1
+        aux3 = (ctx.loggamma(m+1)-ctx.loggamma(m/3.0+2))/2 -ctx.loggamma((m+1)/2.)
+    xM = m
+    return xM
+
+def aux_J_needed(ctx, xA, xeps4, a, xB1, xM):
+    #  DETERMINATION OF  J  THE NUMBER OF TERMS NEEDED
+    #            IN THE TAYLOR SERIES OF F.
+    #  See II Section 3.11 equation (49))
+    #  Only determine one
+    h1 = xeps4/(632*xA)
+    h2 = xB1*a * 126.31337419529260248  # = pi^2*e^2*sqrt(3)
+    h2 = h1 * ctx.power((h2/xM**2),(xM-1)/3) / xM
+    h3 = min(h1,h2)
+    return h3
+
+def Rzeta_simul(ctx, s, der=0):
+    # First we take the value of ctx.prec
+    wpinitial = ctx.prec
+
+    # INITIALIZATION
+    # Take the real and imaginary part of s
+    t = ctx._im(s)
+    xsigma = ctx._re(s)
+    ysigma = 1 - xsigma
+
+    # Now compute several parameter that appear on the program
+    ctx.prec = 15
+    a = ctx.sqrt(t/(2*ctx.pi))
+    xasigma = a ** xsigma
+    yasigma = a ** ysigma
+
+    # We need a simple bound A1 < asigma  (see II Section 3.1 and 3.3)
+    xA1=ctx.power(2, ctx.mag(xasigma)-1)
+    yA1=ctx.power(2, ctx.mag(yasigma)-1)
+
+    # We compute various epsilon's  (see II end of Section 3.1)
+    eps = ctx.power(2, -wpinitial)
+    eps1 = eps/6.
+    xeps2 = eps * xA1/3.
+    yeps2 = eps * yA1/3.
+
+    #  COMPUTING SOME COEFFICIENTS THAT DEPENDS
+    #                ON  sigma
+    #  constant b and c  (see I  Theorem 2 formula (26) )
+    #  coefficients A and B1  (see I Section 6.1 equation (50))
+    #
+    # here we not need high precision
+    ctx.prec = 15
+    if xsigma > 0:
+        xb = 2.
+        xc = math.pow(9,xsigma)/4.44288
+        # 4.44288 =(math.sqrt(2)*math.pi)
+        xA = math.pow(9,xsigma)
+        xB1 = 1
+    else:
+        xb = 2.25158  #  math.sqrt( (3-2* math.log(2))*math.pi )
+        xc = math.pow(2,-xsigma)/4.44288
+        xA = math.pow(2,-xsigma)
+        xB1 = 1.10789   #  = 2*sqrt(1-log(2))
+
+    if(ysigma > 0):
+        yb = 2.
+        yc = math.pow(9,ysigma)/4.44288
+        # 4.44288 =(math.sqrt(2)*math.pi)
+        yA = math.pow(9,ysigma)
+        yB1 = 1
+    else:
+        yb = 2.25158  #  math.sqrt( (3-2* math.log(2))*math.pi )
+        yc = math.pow(2,-ysigma)/4.44288
+        yA = math.pow(2,-ysigma)
+        yB1 = 1.10789   #  = 2*sqrt(1-log(2))
+
+    #  COMPUTING L THE NUMBER OF TERMS NEEDED IN THE RIEMANN-SIEGEL
+    #                         CORRECTION
+    #  See II Section 3.2
+    ctx.prec = 15
+    xL = 1
+    while 3*xc*ctx.gamma(xL*0.5) * ctx.power(xb*a,-xL) >= xeps2:
+        xL = xL+1
+    xL = max(2,xL)
+    yL = 1
+    while 3*yc*ctx.gamma(yL*0.5) * ctx.power(yb*a,-yL) >= yeps2:
+        yL = yL+1
+    yL = max(2,yL)
+
+    #  The number L has to satify some conditions.
+    #  If not RS can not compute Rzeta(s) with the prescribed precision
+    #  (see II, Section 3.2 condition (20)  ) and
+    #  (II, Section 3.3 condition (22) ). Also we have added
+    #  an additional technical  condition in Section 3.17 Proposition 17
+    if ((3*xL >= 2*a*a/25.) or (3*xL+2+xsigma<0) or (abs(xsigma) > a/2.) or \
+        (3*yL >= 2*a*a/25.) or (3*yL+2+ysigma<0) or (abs(ysigma) > a/2.)):
+        ctx.prec = wpinitial
+        raise NotImplementedError("Riemann-Siegel can not compute with such precision")
+
+    #  We take the maximum of the two values
+    L = max(xL, yL)
+
+    #  INITIALIZATION (CONTINUATION)
+    #
+    # eps3 is the constant defined on (II, Section 3.5 equation (27) )
+    # each term of the RS correction must be computed with error <= eps3
+    xeps3 =  xeps2/(4*xL)
+    yeps3 =  yeps2/(4*yL)
+
+    # eps4 is defined on (II Section 3.6  equation (30) )
+    # each component of the formula (II Section 3.6 equation (29) )
+    # must be computed with error <= eps4
+    xeps4 = xeps3/(3*xL)
+    yeps4 = yeps3/(3*yL)
+
+    # COMPUTING M NUMBER OF DERIVATIVES Fp[m] TO COMPUTE
+    xM = aux_M_Fp(ctx, xA, xeps4, a, xB1, xL)
+    yM = aux_M_Fp(ctx, yA, yeps4, a, yB1, yL)
+    M = max(xM, yM)
+
+    # COMPUTING NUMBER OF TERMS J NEEDED
+    h3 = aux_J_needed(ctx, xA, xeps4, a, xB1, xM)
+    h4 = aux_J_needed(ctx, yA, yeps4, a, yB1, yM)
+    h3 = min(h3,h4)
+    J = 12
+    jvalue = (2*ctx.pi)**J / ctx.gamma(J+1)
+    while jvalue > h3:
+        J = J+1
+        jvalue = (2*ctx.pi)*jvalue/J
+
+    # COMPUTING eps5[m] for 1 <= m <= 21
+    #  See II Section 10 equation (43)
+    #  We choose the minimum of the two possibilities
+    eps5={}
+    xforeps5 = math.pi*math.pi*xB1*a
+    yforeps5 = math.pi*math.pi*yB1*a
+    for m in range(0,22):
+        xaux1 = math.pow(xforeps5, m/3)/(316.*xA)
+        yaux1 = math.pow(yforeps5, m/3)/(316.*yA)
+        aux1 = min(xaux1, yaux1)
+        aux2 = ctx.gamma(m+1)/ctx.gamma(m/3.0+0.5)
+        aux2 = math.sqrt(aux2)
+        eps5[m] = (aux1*aux2*min(xeps4,yeps4))
+
+    # COMPUTING wpfp
+    #  See II Section 3.13 equation (59)
+    twenty = min(3*L-3, 21)+1
+    aux = 6812*J
+    wpfp = ctx.mag(44*J)
+    for m in range(0,twenty):
+        wpfp = max(wpfp, ctx.mag(aux*ctx.gamma(m+1)/eps5[m]))
+
+    # COMPUTING N AND p
+    #  See II Section
+    ctx.prec = wpfp + ctx.mag(t)+20
+    a = ctx.sqrt(t/(2*ctx.pi))
+    N = ctx.floor(a)
+    p = 1-2*(a-N)
+
+    # now we get a rounded version of p
+    # to the precision wpfp
+    # this possibly is not necessary
+    num=ctx.floor(p*(ctx.mpf('2')**wpfp))
+    difference = p * (ctx.mpf('2')**wpfp)-num
+    if (difference < 0.5):
+        num = num
+    else:
+        num = num+1
+    p = ctx.convert(num * (ctx.mpf('2')**(-wpfp)))
+
+    # COMPUTING THE COEFFICIENTS c[n] = cc[n]
+    # We shall use the notation cc[n], since there is
+    # a constant that is called c
+    # See II Section 3.14
+    # We compute the coefficients and also save then in a
+    # cache.  The bulk of the computation is passed to
+    # the function  coef()
+    #
+    #  eps6 is defined in II Section 3.13  equation (58)
+    eps6 = ctx.power(ctx.convert(2*ctx.pi), J)/(ctx.gamma(J+1)*3*J)
+
+    #  Now we compute the coefficients
+    cc = {}
+    cont = {}
+    cont, pipowers = coef(ctx, J, eps6)
+    cc=cont.copy()   # we need a copy since we have to change his values.
+    Fp={}            # this is the adequate locus of this
+    for n in range(M, 3*L-2):
+        Fp[n] = 0
+    Fp={}
+    ctx.prec = wpfp
+    for m in range(0,M+1):
+        sumP = 0
+        for k in range(2*J-m-1,-1,-1):
+            sumP = (sumP * p)+ cc[k]
+        Fp[m] = sumP
+        # preparation of the new coefficients
+        for k in range(0,2*J-m-1):
+            cc[k] = (k+1)* cc[k+1]
+
+    # COMPUTING THE NUMBERS  xd[u,n,k], yd[u,n,k]
+    #  See II Section 3.17
+    #
+    #  First we compute the working precisions xwpd[k]
+    #   Se II equation (92)
+    xwpd={}
+    d1 = max(6,ctx.mag(40*L*L))
+    xd2 = 13+ctx.mag((1+abs(xsigma))*xA)-ctx.mag(xeps4)-1
+    xconst = ctx.ln(8/(ctx.pi*ctx.pi*a*a*xB1*xB1)) /2
+    for n in range(0,L):
+        xd3 = ctx.mag(ctx.sqrt(ctx.gamma(n-0.5)))-ctx.floor(n*xconst)+xd2
+        xwpd[n]=max(xd3,d1)
+
+    # procedure of II Section 3.17
+    ctx.prec = xwpd[1]+10
+    xpsigma = 1-(2*xsigma)
+    xd = {}
+    xd[0,0,-2]=0; xd[0,0,-1]=0; xd[0,0,0]=1; xd[0,0,1]=0
+    xd[0,-1,-2]=0; xd[0,-1,-1]=0; xd[0,-1,0]=1; xd[0,-1,1]=0
+    for n in range(1,L):
+        ctx.prec = xwpd[n]+10
+        for k in range(0,3*n//2+1):
+            m = 3*n-2*k
+            if(m!=0):
+                m1 = ctx.one/m
+                c1= m1/4
+                c2=(xpsigma*m1)/2
+                c3=-(m+1)
+                xd[0,n,k]=c3*xd[0,n-1,k-2]+c1*xd[0,n-1,k]+c2*xd[0,n-1,k-1]
+            else:
+                xd[0,n,k]=0
+                for r in range(0,k):
+                    add=xd[0,n,r]*(ctx.mpf('1.0')*ctx.fac(2*k-2*r)/ctx.fac(k-r))
+                    xd[0,n,k] -= ((-1)**(k-r))*add
+        xd[0,n,-2]=0; xd[0,n,-1]=0; xd[0,n,3*n//2+1]=0
+    for mu in range(-2,der+1):
+        for n in range(-2,L):
+            for k in range(-3,max(1,3*n//2+2)):
+                if( (mu<0)or (n<0) or(k<0)or (k>3*n//2)):
+                    xd[mu,n,k] = 0
+    for mu in range(1,der+1):
+        for n in range(0,L):
+            ctx.prec = xwpd[n]+10
+            for k in range(0,3*n//2+1):
+                aux=(2*mu-2)*xd[mu-2,n-2,k-3]+2*(xsigma+n-2)*xd[mu-1,n-2,k-3]
+                xd[mu,n,k] = aux - xd[mu-1,n-1,k-1]
+
+    #  Now we compute the working precisions ywpd[k]
+    #   Se II equation (92)
+    ywpd={}
+    d1 = max(6,ctx.mag(40*L*L))
+    yd2 = 13+ctx.mag((1+abs(ysigma))*yA)-ctx.mag(yeps4)-1
+    yconst = ctx.ln(8/(ctx.pi*ctx.pi*a*a*yB1*yB1)) /2
+    for n in range(0,L):
+        yd3 = ctx.mag(ctx.sqrt(ctx.gamma(n-0.5)))-ctx.floor(n*yconst)+yd2
+        ywpd[n]=max(yd3,d1)
+
+    # procedure of II Section 3.17
+    ctx.prec = ywpd[1]+10
+    ypsigma = 1-(2*ysigma)
+    yd = {}
+    yd[0,0,-2]=0; yd[0,0,-1]=0; yd[0,0,0]=1; yd[0,0,1]=0
+    yd[0,-1,-2]=0; yd[0,-1,-1]=0; yd[0,-1,0]=1; yd[0,-1,1]=0
+    for n in range(1,L):
+        ctx.prec = ywpd[n]+10
+        for k in range(0,3*n//2+1):
+            m = 3*n-2*k
+            if(m!=0):
+                m1 = ctx.one/m
+                c1= m1/4
+                c2=(ypsigma*m1)/2
+                c3=-(m+1)
+                yd[0,n,k]=c3*yd[0,n-1,k-2]+c1*yd[0,n-1,k]+c2*yd[0,n-1,k-1]
+            else:
+                yd[0,n,k]=0
+                for r in range(0,k):
+                    add=yd[0,n,r]*(ctx.mpf('1.0')*ctx.fac(2*k-2*r)/ctx.fac(k-r))
+                    yd[0,n,k] -= ((-1)**(k-r))*add
+        yd[0,n,-2]=0; yd[0,n,-1]=0; yd[0,n,3*n//2+1]=0
+
+    for mu in range(-2,der+1):
+        for n in range(-2,L):
+            for k in range(-3,max(1,3*n//2+2)):
+                if( (mu<0)or (n<0) or(k<0)or (k>3*n//2)):
+                    yd[mu,n,k] = 0
+    for mu in range(1,der+1):
+        for n in range(0,L):
+            ctx.prec = ywpd[n]+10
+            for k in range(0,3*n//2+1):
+                aux=(2*mu-2)*yd[mu-2,n-2,k-3]+2*(ysigma+n-2)*yd[mu-1,n-2,k-3]
+                yd[mu,n,k] = aux - yd[mu-1,n-1,k-1]
+
+    # COMPUTING THE COEFFICIENTS xtcoef[k,l]
+    #  See II Section 3.9
+    #
+    # computing the needed wp
+    xwptcoef={}
+    xwpterm={}
+    ctx.prec = 15
+    c1 = ctx.mag(40*(L+2))
+    xc2 = ctx.mag(68*(L+2)*xA)
+    xc4 = ctx.mag(xB1*a*math.sqrt(ctx.pi))-1
+    for k in range(0,L):
+        xc3 = xc2 - k*xc4+ctx.mag(ctx.fac(k+0.5))/2.
+        xwptcoef[k] = (max(c1,xc3-ctx.mag(xeps4)+1)+1 +20)*1.5
+        xwpterm[k] = (max(c1,ctx.mag(L+2)+xc3-ctx.mag(xeps3)+1)+1 +20)
+    ywptcoef={}
+    ywpterm={}
+    ctx.prec = 15
+    c1 = ctx.mag(40*(L+2))
+    yc2 = ctx.mag(68*(L+2)*yA)
+    yc4 = ctx.mag(yB1*a*math.sqrt(ctx.pi))-1
+    for k in range(0,L):
+        yc3 = yc2 - k*yc4+ctx.mag(ctx.fac(k+0.5))/2.
+        ywptcoef[k] = ((max(c1,yc3-ctx.mag(yeps4)+1))+10)*1.5
+        ywpterm[k] = (max(c1,ctx.mag(L+2)+yc3-ctx.mag(yeps3)+1)+1)+10
+
+    # check of power of pi
+    # computing the fortcoef[mu,k,ell]
+    xfortcoef={}
+    for mu in range(0,der+1):
+        for k in range(0,L):
+            for ell in range(-2,3*k//2+1):
+                xfortcoef[mu,k,ell]=0
+    for mu in range(0,der+1):
+        for k in range(0,L):
+            ctx.prec = xwptcoef[k]
+            for ell in range(0,3*k//2+1):
+                xfortcoef[mu,k,ell]=xd[mu,k,ell]*Fp[3*k-2*ell]/pipowers[2*k-ell]
+                xfortcoef[mu,k,ell]=xfortcoef[mu,k,ell]/((2*ctx.j)**ell)
+
+    def trunc_a(t):
+        wp = ctx.prec
+        ctx.prec = wp + 2
+        aa = ctx.sqrt(t/(2*ctx.pi))
+        ctx.prec = wp
+        return aa
+
+    # computing the tcoef[k,ell]
+    xtcoef={}
+    for mu in range(0,der+1):
+        for k in range(0,L):
+            for ell in range(-2,3*k//2+1):
+                xtcoef[mu,k,ell]=0
+    ctx.prec = max(xwptcoef[0],ywptcoef[0])+3
+    aa= trunc_a(t)
+    la = -ctx.ln(aa)
+
+    for chi in range(0,der+1):
+        for k in range(0,L):
+            ctx.prec = xwptcoef[k]
+            for ell in range(0,3*k//2+1):
+                xtcoef[chi,k,ell] =0
+                for mu in range(0, chi+1):
+                    tcoefter=ctx.binomial(chi,mu)*ctx.power(la,mu)*xfortcoef[chi-mu,k,ell]
+                    xtcoef[chi,k,ell] += tcoefter
+
+    # COMPUTING THE COEFFICIENTS ytcoef[k,l]
+    #  See II Section 3.9
+    #
+    # computing the needed wp
+    # check of power of pi
+    # computing the fortcoef[mu,k,ell]
+    yfortcoef={}
+    for mu in range(0,der+1):
+        for k in range(0,L):
+            for ell in range(-2,3*k//2+1):
+                yfortcoef[mu,k,ell]=0
+    for mu in range(0,der+1):
+        for k in range(0,L):
+            ctx.prec = ywptcoef[k]
+            for ell in range(0,3*k//2+1):
+                yfortcoef[mu,k,ell]=yd[mu,k,ell]*Fp[3*k-2*ell]/pipowers[2*k-ell]
+                yfortcoef[mu,k,ell]=yfortcoef[mu,k,ell]/((2*ctx.j)**ell)
+    # computing the tcoef[k,ell]
+    ytcoef={}
+    for chi in range(0,der+1):
+        for k in range(0,L):
+            for ell in range(-2,3*k//2+1):
+                ytcoef[chi,k,ell]=0
+    for chi in range(0,der+1):
+        for k in range(0,L):
+            ctx.prec = ywptcoef[k]
+            for ell in range(0,3*k//2+1):
+                ytcoef[chi,k,ell] =0
+                for mu in range(0, chi+1):
+                    tcoefter=ctx.binomial(chi,mu)*ctx.power(la,mu)*yfortcoef[chi-mu,k,ell]
+                    ytcoef[chi,k,ell] += tcoefter
+
+    # COMPUTING tv[k,ell]
+    # See II Section 3.8
+    #
+    #  a has a good value
+    ctx.prec = max(xwptcoef[0], ywptcoef[0])+2
+    av = {}
+    av[0] = 1
+    av[1] = av[0]/a
+
+    ctx.prec = max(xwptcoef[0],ywptcoef[0])
+    for k in range(2,L):
+        av[k] = av[k-1] * av[1]
+
+    # Computing the quotients
+    xtv = {}
+    for chi in range(0,der+1):
+        for k in range(0,L):
+            ctx.prec = xwptcoef[k]
+            for ell in range(0,3*k//2+1):
+                xtv[chi,k,ell] = xtcoef[chi,k,ell]* av[k]
+    # Computing the quotients
+    ytv = {}
+    for chi in range(0,der+1):
+        for k in range(0,L):
+            ctx.prec = ywptcoef[k]
+            for ell in range(0,3*k//2+1):
+                ytv[chi,k,ell] = ytcoef[chi,k,ell]* av[k]
+
+    # COMPUTING THE TERMS xterm[k]
+    # See II Section 3.6
+    xterm = {}
+    for chi in range(0,der+1):
+        for n in range(0,L):
+            ctx.prec = xwpterm[n]
+            te = 0
+            for k in range(0, 3*n//2+1):
+                te += xtv[chi,n,k]
+            xterm[chi,n] = te
+
+    # COMPUTING THE TERMS yterm[k]
+    # See II Section 3.6
+    yterm = {}
+    for chi in range(0,der+1):
+        for n in range(0,L):
+            ctx.prec = ywpterm[n]
+            te = 0
+            for k in range(0, 3*n//2+1):
+                te += ytv[chi,n,k]
+            yterm[chi,n] = te
+
+    # COMPUTING  rssum
+    # See II Section 3.5
+    xrssum={}
+    ctx.prec=15
+    xrsbound = math.sqrt(ctx.pi) * xc /(xb*a)
+    ctx.prec=15
+    xwprssum = ctx.mag(4.4*((L+3)**2)*xrsbound / xeps2)
+    xwprssum = max(xwprssum, ctx.mag(10*(L+1)))
+    ctx.prec = xwprssum
+    for chi in range(0,der+1):
+        xrssum[chi] = 0
+        for k in range(1,L+1):
+            xrssum[chi] += xterm[chi,L-k]
+    yrssum={}
+    ctx.prec=15
+    yrsbound = math.sqrt(ctx.pi) * yc /(yb*a)
+    ctx.prec=15
+    ywprssum = ctx.mag(4.4*((L+3)**2)*yrsbound / yeps2)
+    ywprssum = max(ywprssum, ctx.mag(10*(L+1)))
+    ctx.prec = ywprssum
+    for chi in range(0,der+1):
+        yrssum[chi] = 0
+        for k in range(1,L+1):
+            yrssum[chi] += yterm[chi,L-k]
+
+    # COMPUTING S3
+    # See II Section 3.19
+    ctx.prec = 15
+    A2 = 2**(max(ctx.mag(abs(xrssum[0])), ctx.mag(abs(yrssum[0]))))
+    eps8 = eps/(3*A2)
+    T = t *ctx.ln(t/(2*ctx.pi))
+    xwps3 = 5 +  ctx.mag((1+(2/eps8)*ctx.power(a,-xsigma))*T)
+    ywps3 = 5 +  ctx.mag((1+(2/eps8)*ctx.power(a,-ysigma))*T)
+
+    ctx.prec = max(xwps3, ywps3)
+
+    tpi = t/(2*ctx.pi)
+    arg = (t/2)*ctx.ln(tpi)-(t/2)-ctx.pi/8
+    U = ctx.expj(-arg)
+    a = trunc_a(t)
+    xasigma = ctx.power(a, -xsigma)
+    yasigma = ctx.power(a, -ysigma)
+    xS3 = ((-1)**(N-1)) * xasigma * U
+    yS3 = ((-1)**(N-1)) * yasigma * U
+
+    # COMPUTING S1 the zetasum
+    # See II Section 3.18
+    ctx.prec = 15
+    xwpsum =  4+ ctx.mag((N+ctx.power(N,1-xsigma))*ctx.ln(N) /eps1)
+    ywpsum =  4+ ctx.mag((N+ctx.power(N,1-ysigma))*ctx.ln(N) /eps1)
+    wpsum = max(xwpsum, ywpsum)
+
+    ctx.prec = wpsum +10
+    '''
+    # This can be improved
+    xS1={}
+    yS1={}
+    for chi in range(0,der+1):
+        xS1[chi] = 0
+        yS1[chi] = 0
+    for n in range(1,int(N)+1):
+        ln = ctx.ln(n)
+        xexpn = ctx.exp(-ln*(xsigma+ctx.j*t))
+        yexpn = ctx.conj(1/(n*xexpn))
+        for chi in range(0,der+1):
+            pown = ctx.power(-ln, chi)
+            xterm = pown*xexpn
+            yterm = pown*yexpn
+            xS1[chi] += xterm
+            yS1[chi] += yterm
+    '''
+    xS1, yS1 = ctx._zetasum(s, 1, int(N)-1, range(0,der+1), True)
+
+    # END OF COMPUTATION of xrz, yrz
+    #  See II Section 3.1
+    ctx.prec = 15
+    xabsS1 = abs(xS1[der])
+    xabsS2 = abs(xrssum[der] * xS3)
+    xwpend = max(6, wpinitial+ctx.mag(6*(3*xabsS1+7*xabsS2) ) )
+
+    ctx.prec = xwpend
+    xrz={}
+    for chi in range(0,der+1):
+        xrz[chi] = xS1[chi]+xrssum[chi]*xS3
+
+    ctx.prec = 15
+    yabsS1 = abs(yS1[der])
+    yabsS2 = abs(yrssum[der] * yS3)
+    ywpend = max(6, wpinitial+ctx.mag(6*(3*yabsS1+7*yabsS2) ) )
+
+    ctx.prec = ywpend
+    yrz={}
+    for chi in range(0,der+1):
+        yrz[chi] = yS1[chi]+yrssum[chi]*yS3
+        yrz[chi] = ctx.conj(yrz[chi])
+    ctx.prec = wpinitial
+    return xrz, yrz
+
+def Rzeta_set(ctx, s, derivatives=[0]):
+    r"""
+    Computes several derivatives of the auxiliary function of Riemann `R(s)`.
+
+    **Definition**
+
+    The function is defined by
+
+    .. math ::
+
+        \begin{equation}
+        {\mathop{\mathcal R }\nolimits}(s)=
+        \int_{0\swarrow1}\frac{x^{-s} e^{\pi i x^2}}{e^{\pi i x}-
+        e^{-\pi i x}}\,dx
+        \end{equation}
+
+    To this function we apply the Riemann-Siegel expansion.
+    """
+    der = max(derivatives)
+    # First we take the value of ctx.prec
+    # During the computation we will change ctx.prec, and finally we will
+    # restaurate the initial value
+    wpinitial = ctx.prec
+    # Take the real and imaginary part of s
+    t = ctx._im(s)
+    sigma = ctx._re(s)
+    # Now compute several parameter that appear on the program
+    ctx.prec = 15
+    a = ctx.sqrt(t/(2*ctx.pi))     #  Careful
+    asigma = ctx.power(a, sigma)  #  Careful
+    # We need a simple bound A1 < asigma  (see II Section 3.1 and 3.3)
+    A1 = ctx.power(2, ctx.mag(asigma)-1)
+    # We compute various epsilon's  (see II end of Section 3.1)
+    eps = ctx.power(2, -wpinitial)
+    eps1 = eps/6.
+    eps2 = eps * A1/3.
+    # COMPUTING SOME COEFFICIENTS THAT DEPENDS
+    #               ON  sigma
+    # constant b and c  (see I  Theorem 2 formula (26) )
+    # coefficients A and B1  (see I Section 6.1 equation (50))
+    # here we not need high precision
+    ctx.prec = 15
+    if sigma > 0:
+        b = 2.
+        c = math.pow(9,sigma)/4.44288
+        # 4.44288 =(math.sqrt(2)*math.pi)
+        A = math.pow(9,sigma)
+        B1 = 1
+    else:
+        b = 2.25158  #  math.sqrt( (3-2* math.log(2))*math.pi )
+        c = math.pow(2,-sigma)/4.44288
+        A = math.pow(2,-sigma)
+        B1 = 1.10789   #  = 2*sqrt(1-log(2))
+    #  COMPUTING L THE NUMBER OF TERMS NEEDED IN THE RIEMANN-SIEGEL
+    #                         CORRECTION
+    #  See II Section 3.2
+    ctx.prec = 15
+    L = 1
+    while 3*c*ctx.gamma(L*0.5) * ctx.power(b*a,-L) >= eps2:
+        L = L+1
+    L = max(2,L)
+    #  The number L has to satify some conditions.
+    #  If not RS can not compute Rzeta(s) with the prescribed precision
+    #  (see II, Section 3.2 condition (20)  ) and
+    #  (II, Section 3.3 condition (22) ). Also we have added
+    #  an additional technical  condition in Section 3.17 Proposition 17
+    if ((3*L >= 2*a*a/25.) or (3*L+2+sigma<0) or (abs(sigma)> a/2.)):
+        #print 'Error Riemann-Siegel can not compute with such precision'
+        ctx.prec = wpinitial
+        raise NotImplementedError("Riemann-Siegel can not compute with such precision")
+
+    #  INITIALIZATION (CONTINUATION)
+    #
+    # eps3 is the constant defined on (II, Section 3.5 equation (27) )
+    # each term of the RS correction must be computed with error <= eps3
+    eps3 =  eps2/(4*L)
+
+    # eps4 is defined on (II Section 3.6  equation (30) )
+    # each component of the formula (II Section 3.6 equation (29) )
+    # must be computed with error <= eps4
+    eps4 = eps3/(3*L)
+
+    # COMPUTING M.  NUMBER OF DERIVATIVES Fp[m] TO COMPUTE
+    M = aux_M_Fp(ctx, A, eps4, a, B1, L)
+    Fp = {}
+    for n in range(M, 3*L-2):
+        Fp[n] = 0
+
+    #  But I have not seen an instance of  M != 3*L-3
+    #
+    #  DETERMINATION OF  J  THE NUMBER OF TERMS NEEDED
+    #            IN THE TAYLOR SERIES OF F.
+    #  See II Section 3.11 equation (49))
+    h1 = eps4/(632*A)
+    h2 = ctx.pi*ctx.pi*B1*a *ctx.sqrt(3)*math.e*math.e
+    h2 = h1 * ctx.power((h2/M**2),(M-1)/3) / M
+    h3 = min(h1,h2)
+    J=12
+    jvalue = (2*ctx.pi)**J / ctx.gamma(J+1)
+    while jvalue > h3:
+        J = J+1
+        jvalue = (2*ctx.pi)*jvalue/J
+
+    # COMPUTING eps5[m] for 1 <= m <= 21
+    #  See II Section 10 equation (43)
+    eps5={}
+    foreps5 = math.pi*math.pi*B1*a
+    for m in range(0,22):
+        aux1 = math.pow(foreps5, m/3)/(316.*A)
+        aux2 = ctx.gamma(m+1)/ctx.gamma(m/3.0+0.5)
+        aux2 = math.sqrt(aux2)
+        eps5[m] = aux1*aux2*eps4
+
+    # COMPUTING wpfp
+    #  See II Section 3.13 equation (59)
+    twenty = min(3*L-3, 21)+1
+    aux = 6812*J
+    wpfp = ctx.mag(44*J)
+    for m in range(0, twenty):
+        wpfp = max(wpfp, ctx.mag(aux*ctx.gamma(m+1)/eps5[m]))
+    # COMPUTING N AND p
+    #  See II Section
+    ctx.prec = wpfp + ctx.mag(t) + 20
+    a = ctx.sqrt(t/(2*ctx.pi))
+    N = ctx.floor(a)
+    p = 1-2*(a-N)
+
+    # now we get a rounded version of p to the precision wpfp
+    # this possibly is not necessary
+    num = ctx.floor(p*(ctx.mpf(2)**wpfp))
+    difference = p * (ctx.mpf(2)**wpfp)-num
+    if difference < 0.5:
+        num = num
+    else:
+        num = num+1
+    p = ctx.convert(num * (ctx.mpf(2)**(-wpfp)))
+
+    # COMPUTING THE COEFFICIENTS c[n] = cc[n]
+    # We shall use the notation cc[n], since there is
+    # a constant that is called c
+    # See II Section 3.14
+    # We compute the coefficients and also save then in a
+    # cache.  The bulk of the computation is passed to
+    # the function  coef()
+    #
+    #  eps6 is defined in II Section 3.13  equation (58)
+    eps6 = ctx.power(2*ctx.pi, J)/(ctx.gamma(J+1)*3*J)
+
+    #  Now we compute the coefficients
+    cc={}
+    cont={}
+    cont, pipowers = coef(ctx, J, eps6)
+    cc = cont.copy()   # we need a copy since we have
+    Fp={}
+    for n in range(M, 3*L-2):
+        Fp[n] = 0
+    ctx.prec = wpfp
+    for m in range(0,M+1):
+        sumP = 0
+        for k in range(2*J-m-1,-1,-1):
+            sumP = (sumP * p) + cc[k]
+        Fp[m] = sumP
+        # preparation of the new coefficients
+        for k in range(0, 2*J-m-1):
+            cc[k] = (k+1) * cc[k+1]
+
+    # COMPUTING THE NUMBERS  d[n,k]
+    #  See II Section 3.17
+
+    #  First we compute the working precisions wpd[k]
+    #   Se II equation (92)
+    wpd = {}
+    d1 = max(6, ctx.mag(40*L*L))
+    d2 = 13+ctx.mag((1+abs(sigma))*A)-ctx.mag(eps4)-1
+    const = ctx.ln(8/(ctx.pi*ctx.pi*a*a*B1*B1)) /2
+    for n in range(0,L):
+        d3 = ctx.mag(ctx.sqrt(ctx.gamma(n-0.5)))-ctx.floor(n*const)+d2
+        wpd[n] = max(d3,d1)
+
+    # procedure of II Section 3.17
+    ctx.prec = wpd[1]+10
+    psigma = 1-(2*sigma)
+    d = {}
+    d[0,0,-2]=0; d[0,0,-1]=0; d[0,0,0]=1; d[0,0,1]=0
+    d[0,-1,-2]=0; d[0,-1,-1]=0; d[0,-1,0]=1; d[0,-1,1]=0
+    for n in range(1,L):
+        ctx.prec = wpd[n]+10
+        for k in range(0,3*n//2+1):
+            m = 3*n-2*k
+            if (m!=0):
+                m1 = ctx.one/m
+                c1 = m1/4
+                c2 = (psigma*m1)/2
+                c3 = -(m+1)
+                d[0,n,k] = c3*d[0,n-1,k-2]+c1*d[0,n-1,k]+c2*d[0,n-1,k-1]
+            else:
+                d[0,n,k]=0
+                for r in range(0,k):
+                    add = d[0,n,r]*(ctx.one*ctx.fac(2*k-2*r)/ctx.fac(k-r))
+                    d[0,n,k] -= ((-1)**(k-r))*add
+        d[0,n,-2]=0; d[0,n,-1]=0; d[0,n,3*n//2+1]=0
+
+    for mu in range(-2,der+1):
+        for n in range(-2,L):
+            for k in range(-3,max(1,3*n//2+2)):
+                if ((mu<0)or (n<0) or(k<0)or (k>3*n//2)):
+                    d[mu,n,k] = 0
+
+    for mu in range(1,der+1):
+        for n in range(0,L):
+            ctx.prec = wpd[n]+10
+            for k in range(0,3*n//2+1):
+                aux=(2*mu-2)*d[mu-2,n-2,k-3]+2*(sigma+n-2)*d[mu-1,n-2,k-3]
+                d[mu,n,k] = aux - d[mu-1,n-1,k-1]
+
+    # COMPUTING THE COEFFICIENTS t[k,l]
+    #  See II Section 3.9
+    #
+    # computing the needed wp
+    wptcoef = {}
+    wpterm = {}
+    ctx.prec = 15
+    c1 = ctx.mag(40*(L+2))
+    c2 = ctx.mag(68*(L+2)*A)
+    c4 = ctx.mag(B1*a*math.sqrt(ctx.pi))-1
+    for k in range(0,L):
+        c3 = c2 - k*c4+ctx.mag(ctx.fac(k+0.5))/2.
+        wptcoef[k] = max(c1,c3-ctx.mag(eps4)+1)+1 +10
+        wpterm[k] = max(c1,ctx.mag(L+2)+c3-ctx.mag(eps3)+1)+1 +10
+
+    # check of power of pi
+
+    # computing the fortcoef[mu,k,ell]
+    fortcoef={}
+    for mu in derivatives:
+        for k in range(0,L):
+            for ell in range(-2,3*k//2+1):
+                fortcoef[mu,k,ell]=0
+
+    for mu in derivatives:
+        for k in range(0,L):
+            ctx.prec = wptcoef[k]
+            for ell in range(0,3*k//2+1):
+                fortcoef[mu,k,ell]=d[mu,k,ell]*Fp[3*k-2*ell]/pipowers[2*k-ell]
+                fortcoef[mu,k,ell]=fortcoef[mu,k,ell]/((2*ctx.j)**ell)
+
+    def trunc_a(t):
+        wp = ctx.prec
+        ctx.prec = wp + 2
+        aa = ctx.sqrt(t/(2*ctx.pi))
+        ctx.prec = wp
+        return aa
+
+    # computing the tcoef[chi,k,ell]
+    tcoef={}
+    for chi in derivatives:
+        for k in range(0,L):
+            for ell in range(-2,3*k//2+1):
+                tcoef[chi,k,ell]=0
+    ctx.prec = wptcoef[0]+3
+    aa = trunc_a(t)
+    la = -ctx.ln(aa)
+
+    for chi in derivatives:
+        for k in range(0,L):
+            ctx.prec = wptcoef[k]
+            for ell in range(0,3*k//2+1):
+                tcoef[chi,k,ell] = 0
+                for mu in range(0, chi+1):
+                    tcoefter = ctx.binomial(chi,mu) * la**mu * \
+                        fortcoef[chi-mu,k,ell]
+                    tcoef[chi,k,ell] += tcoefter
+
+    # COMPUTING tv[k,ell]
+    # See II Section 3.8
+
+    # Computing the powers av[k] = a**(-k)
+    ctx.prec = wptcoef[0] + 2
+
+    # a has a good value of a.
+    # See II Section 3.6
+    av = {}
+    av[0] = 1
+    av[1] = av[0]/a
+
+    ctx.prec = wptcoef[0]
+    for k in range(2,L):
+        av[k] = av[k-1] * av[1]
+
+    # Computing the quotients
+    tv = {}
+    for chi in derivatives:
+        for k in range(0,L):
+            ctx.prec = wptcoef[k]
+            for ell in range(0,3*k//2+1):
+                tv[chi,k,ell] = tcoef[chi,k,ell]* av[k]
+
+    # COMPUTING THE TERMS term[k]
+    # See II Section 3.6
+    term = {}
+    for chi in derivatives:
+        for n in range(0,L):
+            ctx.prec = wpterm[n]
+            te = 0
+            for k in range(0, 3*n//2+1):
+                te += tv[chi,n,k]
+            term[chi,n] = te
+
+    # COMPUTING  rssum
+    # See II Section 3.5
+    rssum={}
+    ctx.prec=15
+    rsbound = math.sqrt(ctx.pi) * c /(b*a)
+    ctx.prec=15
+    wprssum = ctx.mag(4.4*((L+3)**2)*rsbound / eps2)
+    wprssum = max(wprssum, ctx.mag(10*(L+1)))
+    ctx.prec = wprssum
+    for chi in derivatives:
+        rssum[chi] = 0
+        for k in range(1,L+1):
+            rssum[chi] += term[chi,L-k]
+
+    # COMPUTING S3
+    # See II Section 3.19
+    ctx.prec = 15
+    A2 = 2**(ctx.mag(rssum[0]))
+    eps8 = eps/(3* A2)
+    T = t * ctx.ln(t/(2*ctx.pi))
+    wps3 = 5 + ctx.mag((1+(2/eps8)*ctx.power(a,-sigma))*T)
+
+    ctx.prec = wps3
+    tpi = t/(2*ctx.pi)
+    arg = (t/2)*ctx.ln(tpi)-(t/2)-ctx.pi/8
+    U = ctx.expj(-arg)
+    a = trunc_a(t)
+    asigma = ctx.power(a, -sigma)
+    S3 = ((-1)**(N-1)) * asigma * U
+
+    # COMPUTING S1 the zetasum
+    # See II Section 3.18
+    ctx.prec = 15
+    wpsum = 4 + ctx.mag((N+ctx.power(N,1-sigma))*ctx.ln(N)/eps1)
+
+    ctx.prec = wpsum + 10
+    '''
+    # This can be improved
+    S1 = {}
+    for chi in derivatives:
+        S1[chi] = 0
+    for n in range(1,int(N)+1):
+        ln = ctx.ln(n)
+        expn = ctx.exp(-ln*(sigma+ctx.j*t))
+        for chi in derivatives:
+            term = ctx.power(-ln, chi)*expn
+            S1[chi] += term
+    '''
+    S1 = ctx._zetasum(s, 1, int(N)-1, derivatives)[0]
+
+    # END OF COMPUTATION
+    #  See II Section 3.1
+    ctx.prec = 15
+    absS1 = abs(S1[der])
+    absS2 = abs(rssum[der] * S3)
+    wpend = max(6, wpinitial + ctx.mag(6*(3*absS1+7*absS2)))
+    ctx.prec = wpend
+    rz = {}
+    for chi in derivatives:
+        rz[chi] = S1[chi]+rssum[chi]*S3
+    ctx.prec = wpinitial
+    return rz
+
+
+def z_half(ctx,t,der=0):
+    r"""
+    z_half(t,der=0) Computes Z^(der)(t)
+    """
+    s=ctx.mpf('0.5')+ctx.j*t
+    wpinitial = ctx.prec
+    ctx.prec = 15
+    tt = t/(2*ctx.pi)
+    wptheta = wpinitial +1 + ctx.mag(3*(tt**1.5)*ctx.ln(tt))
+    wpz = wpinitial + 1 + ctx.mag(12*tt*ctx.ln(tt))
+    ctx.prec = wptheta
+    theta = ctx.siegeltheta(t)
+    ctx.prec = wpz
+    rz = Rzeta_set(ctx,s, range(der+1))
+    if der > 0: ps1 = ctx._re(ctx.psi(0,s/2)/2 - ctx.ln(ctx.pi)/2)
+    if der > 1: ps2 = ctx._re(ctx.j*ctx.psi(1,s/2)/4)
+    if der > 2: ps3 = ctx._re(-ctx.psi(2,s/2)/8)
+    if der > 3: ps4 = ctx._re(-ctx.j*ctx.psi(3,s/2)/16)
+    exptheta = ctx.expj(theta)
+    if der == 0:
+        z = 2*exptheta*rz[0]
+    if der == 1:
+        zf = 2j*exptheta
+        z = zf*(ps1*rz[0]+rz[1])
+    if der == 2:
+        zf = 2 * exptheta
+        z = -zf*(2*rz[1]*ps1+rz[0]*ps1**2+rz[2]-ctx.j*rz[0]*ps2)
+    if der == 3:
+        zf = -2j*exptheta
+        z = 3*rz[1]*ps1**2+rz[0]*ps1**3+3*ps1*rz[2]
+        z = zf*(z-3j*rz[1]*ps2-3j*rz[0]*ps1*ps2+rz[3]-rz[0]*ps3)
+    if der == 4:
+        zf = 2*exptheta
+        z = 4*rz[1]*ps1**3+rz[0]*ps1**4+6*ps1**2*rz[2]
+        z = z-12j*rz[1]*ps1*ps2-6j*rz[0]*ps1**2*ps2-6j*rz[2]*ps2-3*rz[0]*ps2*ps2
+        z = z + 4*ps1*rz[3]-4*rz[1]*ps3-4*rz[0]*ps1*ps3+rz[4]+ctx.j*rz[0]*ps4
+        z = zf*z
+    ctx.prec = wpinitial
+    return ctx._re(z)
+
+def zeta_half(ctx, s, k=0):
+    """
+    zeta_half(s,k=0) Computes zeta^(k)(s) when Re s = 0.5
+    """
+    wpinitial = ctx.prec
+    sigma = ctx._re(s)
+    t = ctx._im(s)
+    #--- compute wptheta, wpR, wpbasic ---
+    ctx.prec = 53
+    #  X see II Section 3.21 (109) and (110)
+    if sigma > 0:
+        X = ctx.sqrt(abs(s))
+    else:
+        X = (2*ctx.pi)**(sigma-1) * abs(1-s)**(0.5-sigma)
+    # M1  see II Section 3.21 (111) and (112)
+    if sigma > 0:
+        M1 = 2*ctx.sqrt(t/(2*ctx.pi))
+    else:
+        M1 = 4 * t * X
+    # T  see II Section 3.21 (113)
+    abst = abs(0.5-s)
+    T = 2* abst*math.log(abst)
+    # computing wpbasic, wptheta, wpR  see II Section 3.21
+    wpbasic = max(6,3+ctx.mag(t))
+    wpbasic2 = 2+ctx.mag(2.12*M1+21.2*M1*X+1.3*M1*X*T)+wpinitial+1
+    wpbasic = max(wpbasic, wpbasic2)
+    wptheta = max(4, 3+ctx.mag(2.7*M1*X)+wpinitial+1)
+    wpR = 3+ctx.mag(1.1+2*X)+wpinitial+1
+    ctx.prec = wptheta
+    theta = ctx.siegeltheta(t-ctx.j*(sigma-ctx.mpf('0.5')))
+    if k > 0: ps1 = (ctx._re(ctx.psi(0,s/2)))/2 - ctx.ln(ctx.pi)/2
+    if k > 1: ps2 = -(ctx._im(ctx.psi(1,s/2)))/4
+    if k > 2: ps3 = -(ctx._re(ctx.psi(2,s/2)))/8
+    if k > 3: ps4 = (ctx._im(ctx.psi(3,s/2)))/16
+    ctx.prec = wpR
+    xrz = Rzeta_set(ctx,s,range(k+1))
+    yrz={}
+    for chi in range(0,k+1):
+        yrz[chi] = ctx.conj(xrz[chi])
+    ctx.prec = wpbasic
+    exptheta = ctx.expj(-2*theta)
+    if k==0:
+        zv = xrz[0]+exptheta*yrz[0]
+    if k==1:
+        zv1 = -yrz[1] - 2*yrz[0]*ps1
+        zv = xrz[1] + exptheta*zv1
+    if k==2:
+        zv1 = 4*yrz[1]*ps1+4*yrz[0]*(ps1**2)+yrz[2]+2j*yrz[0]*ps2
+        zv = xrz[2]+exptheta*zv1
+    if k==3:
+        zv1 = -12*yrz[1]*ps1**2-8*yrz[0]*ps1**3-6*yrz[2]*ps1-6j*yrz[1]*ps2
+        zv1 = zv1 - 12j*yrz[0]*ps1*ps2-yrz[3]+2*yrz[0]*ps3
+        zv = xrz[3]+exptheta*zv1
+    if k == 4:
+        zv1 = 32*yrz[1]*ps1**3 +16*yrz[0]*ps1**4+24*yrz[2]*ps1**2
+        zv1 = zv1 +48j*yrz[1]*ps1*ps2+48j*yrz[0]*(ps1**2)*ps2
+        zv1 = zv1+12j*yrz[2]*ps2-12*yrz[0]*ps2**2+8*yrz[3]*ps1-8*yrz[1]*ps3
+        zv1 = zv1-16*yrz[0]*ps1*ps3+yrz[4]-2j*yrz[0]*ps4
+        zv = xrz[4]+exptheta*zv1
+    ctx.prec = wpinitial
+    return zv
+
+def zeta_offline(ctx, s, k=0):
+    """
+    Computes zeta^(k)(s) off the line
+    """
+    wpinitial = ctx.prec
+    sigma = ctx._re(s)
+    t = ctx._im(s)
+    #--- compute wptheta, wpR, wpbasic ---
+    ctx.prec = 53
+    #  X see II Section 3.21 (109) and (110)
+    if sigma > 0:
+        X = ctx.power(abs(s), 0.5)
+    else:
+        X = ctx.power(2*ctx.pi, sigma-1)*ctx.power(abs(1-s),0.5-sigma)
+    # M1  see II Section 3.21 (111) and (112)
+    if (sigma > 0):
+        M1 = 2*ctx.sqrt(t/(2*ctx.pi))
+    else:
+        M1 = 4 * t * X
+    # M2  see II Section 3.21 (111) and (112)
+    if (1-sigma > 0):
+        M2 = 2*ctx.sqrt(t/(2*ctx.pi))
+    else:
+        M2 = 4*t*ctx.power(2*ctx.pi, -sigma)*ctx.power(abs(s),sigma-0.5)
+    # T  see II Section 3.21 (113)
+    abst = abs(0.5-s)
+    T = 2* abst*math.log(abst)
+    # computing wpbasic, wptheta, wpR  see II Section 3.21
+    wpbasic = max(6,3+ctx.mag(t))
+    wpbasic2 = 2+ctx.mag(2.12*M1+21.2*M2*X+1.3*M2*X*T)+wpinitial+1
+    wpbasic = max(wpbasic, wpbasic2)
+    wptheta = max(4, 3+ctx.mag(2.7*M2*X)+wpinitial+1)
+    wpR = 3+ctx.mag(1.1+2*X)+wpinitial+1
+    ctx.prec = wptheta
+    theta = ctx.siegeltheta(t-ctx.j*(sigma-ctx.mpf('0.5')))
+    s1 = s
+    s2 = ctx.conj(1-s1)
+    ctx.prec = wpR
+    xrz, yrz = Rzeta_simul(ctx, s, k)
+    if k > 0: ps1 = (ctx.psi(0,s1/2)+ctx.psi(0,(1-s1)/2))/4 - ctx.ln(ctx.pi)/2
+    if k > 1: ps2 = ctx.j*(ctx.psi(1,s1/2)-ctx.psi(1,(1-s1)/2))/8
+    if k > 2: ps3 = -(ctx.psi(2,s1/2)+ctx.psi(2,(1-s1)/2))/16
+    if k > 3: ps4 = -ctx.j*(ctx.psi(3,s1/2)-ctx.psi(3,(1-s1)/2))/32
+    ctx.prec = wpbasic
+    exptheta = ctx.expj(-2*theta)
+    if k == 0:
+        zv = xrz[0]+exptheta*yrz[0]
+    if k == 1:
+        zv1 = -yrz[1]-2*yrz[0]*ps1
+        zv = xrz[1]+exptheta*zv1
+    if k == 2:
+        zv1 = 4*yrz[1]*ps1+4*yrz[0]*(ps1**2) +yrz[2]+2j*yrz[0]*ps2
+        zv = xrz[2]+exptheta*zv1
+    if k == 3:
+        zv1 = -12*yrz[1]*ps1**2 -8*yrz[0]*ps1**3-6*yrz[2]*ps1-6j*yrz[1]*ps2
+        zv1 = zv1 - 12j*yrz[0]*ps1*ps2-yrz[3]+2*yrz[0]*ps3
+        zv = xrz[3]+exptheta*zv1
+    if k == 4:
+        zv1 = 32*yrz[1]*ps1**3 +16*yrz[0]*ps1**4+24*yrz[2]*ps1**2
+        zv1 = zv1 +48j*yrz[1]*ps1*ps2+48j*yrz[0]*(ps1**2)*ps2
+        zv1 = zv1+12j*yrz[2]*ps2-12*yrz[0]*ps2**2+8*yrz[3]*ps1-8*yrz[1]*ps3
+        zv1 = zv1-16*yrz[0]*ps1*ps3+yrz[4]-2j*yrz[0]*ps4
+        zv = xrz[4]+exptheta*zv1
+    ctx.prec = wpinitial
+    return zv
+
+def z_offline(ctx, w, k=0):
+    r"""
+    Computes Z(w) and its derivatives off the line
+    """
+    s = ctx.mpf('0.5')+ctx.j*w
+    s1 = s
+    s2 = ctx.conj(1-s1)
+    wpinitial = ctx.prec
+    ctx.prec = 35
+    #  X see II Section 3.21 (109) and (110)
+    # M1  see II Section 3.21 (111) and (112)
+    if (ctx._re(s1) >= 0):
+        M1 = 2*ctx.sqrt(ctx._im(s1)/(2 * ctx.pi))
+        X = ctx.sqrt(abs(s1))
+    else:
+        X = (2*ctx.pi)**(ctx._re(s1)-1) * abs(1-s1)**(0.5-ctx._re(s1))
+        M1 = 4 * ctx._im(s1)*X
+    # M2  see II Section 3.21 (111) and (112)
+    if (ctx._re(s2) >= 0):
+        M2 = 2*ctx.sqrt(ctx._im(s2)/(2 * ctx.pi))
+    else:
+        M2 = 4 * ctx._im(s2)*(2*ctx.pi)**(ctx._re(s2)-1)*abs(1-s2)**(0.5-ctx._re(s2))
+    # T  see II Section 3.21  Prop. 27
+    T = 2*abs(ctx.siegeltheta(w))
+    # defining some precisions
+    # see II Section 3.22 (115), (116), (117)
+    aux1 = ctx.sqrt(X)
+    aux2 = aux1*(M1+M2)
+    aux3 = 3 +wpinitial
+    wpbasic = max(6, 3+ctx.mag(T), ctx.mag(aux2*(26+2*T))+aux3)
+    wptheta = max(4,ctx.mag(2.04*aux2)+aux3)
+    wpR = ctx.mag(4*aux1)+aux3
+    # now the computations
+    ctx.prec = wptheta
+    theta = ctx.siegeltheta(w)
+    ctx.prec = wpR
+    xrz, yrz = Rzeta_simul(ctx,s,k)
+    pta = 0.25 + 0.5j*w
+    ptb = 0.25 - 0.5j*w
+    if k > 0: ps1 = 0.25*(ctx.psi(0,pta)+ctx.psi(0,ptb)) - ctx.ln(ctx.pi)/2
+    if k > 1: ps2 = (1j/8)*(ctx.psi(1,pta)-ctx.psi(1,ptb))
+    if k > 2: ps3 = (-1./16)*(ctx.psi(2,pta)+ctx.psi(2,ptb))
+    if k > 3: ps4 = (-1j/32)*(ctx.psi(3,pta)-ctx.psi(3,ptb))
+    ctx.prec = wpbasic
+    exptheta = ctx.expj(theta)
+    if k == 0:
+        zv = exptheta*xrz[0]+yrz[0]/exptheta
+    j = ctx.j
+    if k == 1:
+        zv = j*exptheta*(xrz[1]+xrz[0]*ps1)-j*(yrz[1]+yrz[0]*ps1)/exptheta
+    if k == 2:
+        zv = exptheta*(-2*xrz[1]*ps1-xrz[0]*ps1**2-xrz[2]+j*xrz[0]*ps2)
+        zv =zv + (-2*yrz[1]*ps1-yrz[0]*ps1**2-yrz[2]-j*yrz[0]*ps2)/exptheta
+    if k == 3:
+        zv1 = -3*xrz[1]*ps1**2-xrz[0]*ps1**3-3*xrz[2]*ps1+j*3*xrz[1]*ps2
+        zv1 = (zv1+ 3j*xrz[0]*ps1*ps2-xrz[3]+xrz[0]*ps3)*j*exptheta
+        zv2 = 3*yrz[1]*ps1**2+yrz[0]*ps1**3+3*yrz[2]*ps1+j*3*yrz[1]*ps2
+        zv2 = j*(zv2 + 3j*yrz[0]*ps1*ps2+ yrz[3]-yrz[0]*ps3)/exptheta
+        zv = zv1+zv2
+    if k == 4:
+        zv1 = 4*xrz[1]*ps1**3+xrz[0]*ps1**4 + 6*xrz[2]*ps1**2
+        zv1 = zv1-12j*xrz[1]*ps1*ps2-6j*xrz[0]*ps1**2*ps2-6j*xrz[2]*ps2
+        zv1 = zv1-3*xrz[0]*ps2*ps2+4*xrz[3]*ps1-4*xrz[1]*ps3-4*xrz[0]*ps1*ps3
+        zv1 = zv1+xrz[4]+j*xrz[0]*ps4
+        zv2 = 4*yrz[1]*ps1**3+yrz[0]*ps1**4 + 6*yrz[2]*ps1**2
+        zv2 = zv2+12j*yrz[1]*ps1*ps2+6j*yrz[0]*ps1**2*ps2+6j*yrz[2]*ps2
+        zv2 = zv2-3*yrz[0]*ps2*ps2+4*yrz[3]*ps1-4*yrz[1]*ps3-4*yrz[0]*ps1*ps3
+        zv2 = zv2+yrz[4]-j*yrz[0]*ps4
+        zv = exptheta*zv1+zv2/exptheta
+    ctx.prec = wpinitial
+    return zv
+
+@defun
+def rs_zeta(ctx, s, derivative=0, **kwargs):
+    if derivative > 4:
+        raise NotImplementedError
+    s = ctx.convert(s)
+    re = ctx._re(s); im = ctx._im(s)
+    if im < 0:
+        z = ctx.conj(ctx.rs_zeta(ctx.conj(s), derivative))
+        return z
+    critical_line = (re == 0.5)
+    if critical_line:
+        return zeta_half(ctx, s, derivative)
+    else:
+        return zeta_offline(ctx, s, derivative)
+
+@defun
+def rs_z(ctx, w, derivative=0):
+    w = ctx.convert(w)
+    re = ctx._re(w); im = ctx._im(w)
+    if re < 0:
+        return rs_z(ctx, -w, derivative)
+    critical_line = (im == 0)
+    if critical_line :
+        return z_half(ctx, w, derivative)
+    else:
+        return z_offline(ctx, w, derivative)

lib/python3.11/site-packages/mpmath/functions/signals.py

@@ -0,0 +1,32 @@
+from .functions import defun_wrapped
+
+@defun_wrapped
+def squarew(ctx, t, amplitude=1, period=1):
+    P = period
+    A = amplitude
+    return A*((-1)**ctx.floor(2*t/P))
+
+@defun_wrapped
+def trianglew(ctx, t, amplitude=1, period=1):
+    A = amplitude
+    P = period
+
+    return 2*A*(0.5 - ctx.fabs(1 - 2*ctx.frac(t/P + 0.25)))
+
+@defun_wrapped
+def sawtoothw(ctx, t, amplitude=1, period=1):
+    A = amplitude
+    P = period
+    return A*ctx.frac(t/P)
+
+@defun_wrapped
+def unit_triangle(ctx, t, amplitude=1):
+    A = amplitude
+    if t <= -1 or t >= 1:
+        return ctx.zero
+    return A*(-ctx.fabs(t) + 1)
+
+@defun_wrapped
+def sigmoid(ctx, t, amplitude=1):
+    A = amplitude
+    return A / (1 + ctx.exp(-t))

lib/python3.11/site-packages/mpmath/functions/theta.py

@@ -0,0 +1,1049 @@
+from .functions import defun, defun_wrapped
+
+@defun
+def _jacobi_theta2(ctx, z, q):
+    extra1 = 10
+    extra2 = 20
+    # the loops below break when the fixed precision quantities
+    # a and b go to zero;
+    # right shifting small negative numbers by wp one obtains -1, not zero,
+    # so the condition a**2 + b**2 > MIN is used to break the loops.
+    MIN = 2
+    if z == ctx.zero:
+        if (not ctx._im(q)):
+            wp = ctx.prec + extra1
+            x = ctx.to_fixed(ctx._re(q), wp)
+            x2 = (x*x) >> wp
+            a = b = x2
+            s = x2
+            while abs(a) > MIN:
+                b = (b*x2) >> wp
+                a = (a*b) >> wp
+                s += a
+            s = (1 << (wp+1)) + (s << 1)
+            s = ctx.ldexp(s, -wp)
+        else:
+            wp = ctx.prec + extra1
+            xre = ctx.to_fixed(ctx._re(q), wp)
+            xim = ctx.to_fixed(ctx._im(q), wp)
+            x2re = (xre*xre - xim*xim) >> wp
+            x2im = (xre*xim) >> (wp-1)
+            are = bre = x2re
+            aim = bim = x2im
+            sre = (1<<wp) + are
+            sim = aim
+            while are**2 + aim**2 > MIN:
+                bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                           (bre * x2im + bim * x2re) >> wp
+                are, aim = (are * bre - aim * bim) >> wp,   \
+                           (are * bim + aim * bre) >> wp
+                sre += are
+                sim += aim
+            sre = (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+    else:
+        if (not ctx._im(q)) and (not ctx._im(z)):
+            wp = ctx.prec + extra1
+            x = ctx.to_fixed(ctx._re(q), wp)
+            x2 = (x*x) >> wp
+            a = b = x2
+            c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp)
+            cn = c1 = ctx.to_fixed(c1, wp)
+            sn = s1 = ctx.to_fixed(s1, wp)
+            c2 = (c1*c1 - s1*s1) >> wp
+            s2 = (c1 * s1) >> (wp - 1)
+            cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+            s = c1 + ((a * cn) >> wp)
+            while abs(a) > MIN:
+                b = (b*x2) >> wp
+                a = (a*b) >> wp
+                cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+                s += (a * cn) >> wp
+            s = (s << 1)
+            s = ctx.ldexp(s, -wp)
+            s *= ctx.nthroot(q, 4)
+            return s
+        # case z real, q complex
+        elif not ctx._im(z):
+            wp = ctx.prec + extra2
+            xre = ctx.to_fixed(ctx._re(q), wp)
+            xim = ctx.to_fixed(ctx._im(q), wp)
+            x2re = (xre*xre - xim*xim) >> wp
+            x2im = (xre*xim) >> (wp - 1)
+            are = bre = x2re
+            aim = bim = x2im
+            c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp)
+            cn = c1 = ctx.to_fixed(c1, wp)
+            sn = s1 = ctx.to_fixed(s1, wp)
+            c2 = (c1*c1 - s1*s1) >> wp
+            s2 = (c1 * s1) >> (wp - 1)
+            cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+            sre = c1 + ((are * cn) >> wp)
+            sim = ((aim * cn) >> wp)
+            while are**2 + aim**2 > MIN:
+                bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                           (bre * x2im + bim * x2re) >> wp
+                are, aim = (are * bre - aim * bim) >> wp,   \
+                           (are * bim + aim * bre) >> wp
+                cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+                sre += ((are * cn) >> wp)
+                sim += ((aim * cn) >> wp)
+            sre = (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+        #case z complex, q real
+        elif not ctx._im(q):
+            wp = ctx.prec + extra2
+            x = ctx.to_fixed(ctx._re(q), wp)
+            x2 = (x*x) >> wp
+            a = b = x2
+            prec0 = ctx.prec
+            ctx.prec = wp
+            c1, s1 = ctx.cos_sin(z)
+            ctx.prec = prec0
+            cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+            cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+            snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+            snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+            #c2 = (c1*c1 - s1*s1) >> wp
+            c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp
+            c2im = (c1re*c1im - s1re*s1im) >> (wp - 1)
+            #s2 = (c1 * s1) >> (wp - 1)
+            s2re = (c1re*s1re - c1im*s1im) >> (wp - 1)
+            s2im = (c1re*s1im + c1im*s1re) >> (wp - 1)
+            #cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+            t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+            t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+            t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+            t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+            cnre = t1
+            cnim = t2
+            snre = t3
+            snim = t4
+            sre = c1re + ((a * cnre) >> wp)
+            sim = c1im + ((a * cnim) >> wp)
+            while abs(a) > MIN:
+                b = (b*x2) >> wp
+                a = (a*b) >> wp
+                t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+                t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+                t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+                t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+                cnre = t1
+                cnim = t2
+                snre = t3
+                snim = t4
+                sre += ((a * cnre) >> wp)
+                sim += ((a * cnim) >> wp)
+            sre = (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+        # case z and q complex
+        else:
+            wp = ctx.prec + extra2
+            xre = ctx.to_fixed(ctx._re(q), wp)
+            xim = ctx.to_fixed(ctx._im(q), wp)
+            x2re = (xre*xre - xim*xim) >> wp
+            x2im = (xre*xim) >> (wp - 1)
+            are = bre = x2re
+            aim = bim = x2im
+            prec0 = ctx.prec
+            ctx.prec = wp
+            # cos(z), sin(z) with z complex
+            c1, s1 = ctx.cos_sin(z)
+            ctx.prec = prec0
+            cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+            cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+            snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+            snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+            c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp
+            c2im = (c1re*c1im - s1re*s1im) >> (wp - 1)
+            s2re = (c1re*s1re - c1im*s1im) >> (wp - 1)
+            s2im = (c1re*s1im + c1im*s1re) >> (wp - 1)
+            t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+            t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+            t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+            t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+            cnre = t1
+            cnim = t2
+            snre = t3
+            snim = t4
+            n = 1
+            termre = c1re
+            termim = c1im
+            sre = c1re + ((are * cnre - aim * cnim) >> wp)
+            sim = c1im + ((are * cnim + aim * cnre) >> wp)
+            n = 3
+            termre = ((are * cnre - aim * cnim) >> wp)
+            termim = ((are * cnim + aim * cnre) >> wp)
+            sre = c1re + ((are * cnre - aim * cnim) >> wp)
+            sim = c1im + ((are * cnim + aim * cnre) >> wp)
+            n = 5
+            while are**2 + aim**2 > MIN:
+                bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                           (bre * x2im + bim * x2re) >> wp
+                are, aim = (are * bre - aim * bim) >> wp,   \
+                           (are * bim + aim * bre) >> wp
+                #cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp
+                t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+                t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+                t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+                t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+                cnre = t1
+                cnim = t2
+                snre = t3
+                snim = t4
+                termre = ((are * cnre - aim * cnim) >> wp)
+                termim = ((aim * cnre + are * cnim) >> wp)
+                sre += ((are * cnre - aim * cnim) >> wp)
+                sim += ((aim * cnre + are * cnim) >> wp)
+                n += 2
+            sre = (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+    s *= ctx.nthroot(q, 4)
+    return s
+
+@defun
+def _djacobi_theta2(ctx, z, q, nd):
+    MIN = 2
+    extra1 = 10
+    extra2 = 20
+    if (not ctx._im(q)) and (not ctx._im(z)):
+        wp = ctx.prec + extra1
+        x = ctx.to_fixed(ctx._re(q), wp)
+        x2 = (x*x) >> wp
+        a = b = x2
+        c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp)
+        cn = c1 = ctx.to_fixed(c1, wp)
+        sn = s1 = ctx.to_fixed(s1, wp)
+        c2 = (c1*c1 - s1*s1) >> wp
+        s2 = (c1 * s1) >> (wp - 1)
+        cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+        if (nd&1):
+            s = s1 + ((a * sn * 3**nd) >> wp)
+        else:
+            s = c1 + ((a * cn * 3**nd) >> wp)
+        n = 2
+        while abs(a) > MIN:
+            b = (b*x2) >> wp
+            a = (a*b) >> wp
+            cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+            if nd&1:
+                s += (a * sn * (2*n+1)**nd) >> wp
+            else:
+                s += (a * cn * (2*n+1)**nd) >> wp
+            n += 1
+        s = -(s << 1)
+        s = ctx.ldexp(s, -wp)
+        # case z real, q complex
+    elif not ctx._im(z):
+        wp = ctx.prec + extra2
+        xre = ctx.to_fixed(ctx._re(q), wp)
+        xim = ctx.to_fixed(ctx._im(q), wp)
+        x2re = (xre*xre - xim*xim) >> wp
+        x2im = (xre*xim) >> (wp - 1)
+        are = bre = x2re
+        aim = bim = x2im
+        c1, s1 = ctx.cos_sin(ctx._re(z), prec=wp)
+        cn = c1 = ctx.to_fixed(c1, wp)
+        sn = s1 = ctx.to_fixed(s1, wp)
+        c2 = (c1*c1 - s1*s1) >> wp
+        s2 = (c1 * s1) >> (wp - 1)
+        cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+        if (nd&1):
+            sre = s1 + ((are * sn * 3**nd) >> wp)
+            sim = ((aim * sn * 3**nd) >> wp)
+        else:
+            sre = c1 + ((are * cn * 3**nd) >> wp)
+            sim = ((aim * cn * 3**nd) >> wp)
+        n = 5
+        while are**2 + aim**2 > MIN:
+            bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                       (bre * x2im + bim * x2re) >> wp
+            are, aim = (are * bre - aim * bim) >> wp,   \
+                       (are * bim + aim * bre) >> wp
+            cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+
+            if (nd&1):
+                sre += ((are * sn * n**nd) >> wp)
+                sim += ((aim * sn * n**nd) >> wp)
+            else:
+                sre += ((are * cn * n**nd) >> wp)
+                sim += ((aim * cn * n**nd) >> wp)
+            n += 2
+        sre = -(sre << 1)
+        sim = -(sim << 1)
+        sre = ctx.ldexp(sre, -wp)
+        sim = ctx.ldexp(sim, -wp)
+        s = ctx.mpc(sre, sim)
+    #case z complex, q real
+    elif not ctx._im(q):
+        wp = ctx.prec + extra2
+        x = ctx.to_fixed(ctx._re(q), wp)
+        x2 = (x*x) >> wp
+        a = b = x2
+        prec0 = ctx.prec
+        ctx.prec = wp
+        c1, s1 = ctx.cos_sin(z)
+        ctx.prec = prec0
+        cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+        cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+        snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+        snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+        #c2 = (c1*c1 - s1*s1) >> wp
+        c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp
+        c2im = (c1re*c1im - s1re*s1im) >> (wp - 1)
+        #s2 = (c1 * s1) >> (wp - 1)
+        s2re = (c1re*s1re - c1im*s1im) >> (wp - 1)
+        s2im = (c1re*s1im + c1im*s1re) >> (wp - 1)
+        #cn, sn = (cn*c2 - sn*s2) >> wp, (sn*c2 + cn*s2) >> wp
+        t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+        t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+        t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+        t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+        cnre = t1
+        cnim = t2
+        snre = t3
+        snim = t4
+        if (nd&1):
+            sre = s1re + ((a * snre * 3**nd) >> wp)
+            sim = s1im + ((a * snim * 3**nd) >> wp)
+        else:
+            sre = c1re + ((a * cnre * 3**nd) >> wp)
+            sim = c1im + ((a * cnim * 3**nd) >> wp)
+        n = 5
+        while abs(a) > MIN:
+            b = (b*x2) >> wp
+            a = (a*b) >> wp
+            t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+            t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+            t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+            t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+            cnre = t1
+            cnim = t2
+            snre = t3
+            snim = t4
+            if (nd&1):
+                sre += ((a * snre * n**nd) >> wp)
+                sim += ((a * snim * n**nd) >> wp)
+            else:
+                sre += ((a * cnre * n**nd) >> wp)
+                sim += ((a * cnim * n**nd) >> wp)
+            n += 2
+        sre = -(sre << 1)
+        sim = -(sim << 1)
+        sre = ctx.ldexp(sre, -wp)
+        sim = ctx.ldexp(sim, -wp)
+        s = ctx.mpc(sre, sim)
+    # case z and q complex
+    else:
+        wp = ctx.prec + extra2
+        xre = ctx.to_fixed(ctx._re(q), wp)
+        xim = ctx.to_fixed(ctx._im(q), wp)
+        x2re = (xre*xre - xim*xim) >> wp
+        x2im = (xre*xim) >> (wp - 1)
+        are = bre = x2re
+        aim = bim = x2im
+        prec0 = ctx.prec
+        ctx.prec = wp
+        # cos(2*z), sin(2*z) with z complex
+        c1, s1 = ctx.cos_sin(z)
+        ctx.prec = prec0
+        cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+        cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+        snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+        snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+        c2re = (c1re*c1re - c1im*c1im - s1re*s1re + s1im*s1im) >> wp
+        c2im = (c1re*c1im - s1re*s1im) >> (wp - 1)
+        s2re = (c1re*s1re - c1im*s1im) >> (wp - 1)
+        s2im = (c1re*s1im + c1im*s1re) >> (wp - 1)
+        t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+        t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+        t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+        t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+        cnre = t1
+        cnim = t2
+        snre = t3
+        snim = t4
+        if (nd&1):
+            sre = s1re + (((are * snre - aim * snim) * 3**nd) >> wp)
+            sim = s1im + (((are * snim + aim * snre)* 3**nd) >> wp)
+        else:
+            sre = c1re + (((are * cnre - aim * cnim) * 3**nd) >> wp)
+            sim = c1im + (((are * cnim + aim * cnre)* 3**nd) >> wp)
+        n = 5
+        while are**2 + aim**2 > MIN:
+            bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                       (bre * x2im + bim * x2re) >> wp
+            are, aim = (are * bre - aim * bim) >> wp,   \
+                       (are * bim + aim * bre) >> wp
+            #cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp
+            t1 = (cnre*c2re - cnim*c2im - snre*s2re + snim*s2im) >> wp
+            t2 = (cnre*c2im + cnim*c2re - snre*s2im - snim*s2re) >> wp
+            t3 = (snre*c2re - snim*c2im + cnre*s2re - cnim*s2im) >> wp
+            t4 = (snre*c2im + snim*c2re + cnre*s2im + cnim*s2re) >> wp
+            cnre = t1
+            cnim = t2
+            snre = t3
+            snim = t4
+            if (nd&1):
+                sre += (((are * snre - aim * snim) * n**nd) >> wp)
+                sim += (((aim * snre + are * snim) * n**nd) >> wp)
+            else:
+                sre += (((are * cnre - aim * cnim) * n**nd) >> wp)
+                sim += (((aim * cnre + are * cnim) * n**nd) >> wp)
+            n += 2
+        sre = -(sre << 1)
+        sim = -(sim << 1)
+        sre = ctx.ldexp(sre, -wp)
+        sim = ctx.ldexp(sim, -wp)
+        s = ctx.mpc(sre, sim)
+    s *= ctx.nthroot(q, 4)
+    if (nd&1):
+        return (-1)**(nd//2) * s
+    else:
+        return (-1)**(1 + nd//2) * s
+
+@defun
+def _jacobi_theta3(ctx, z, q):
+    extra1 = 10
+    extra2 = 20
+    MIN = 2
+    if z == ctx.zero:
+        if not ctx._im(q):
+            wp = ctx.prec + extra1
+            x = ctx.to_fixed(ctx._re(q), wp)
+            s = x
+            a = b = x
+            x2 = (x*x) >> wp
+            while abs(a) > MIN:
+                b = (b*x2) >> wp
+                a = (a*b) >> wp
+                s += a
+            s = (1 << wp) + (s << 1)
+            s = ctx.ldexp(s, -wp)
+            return s
+        else:
+            wp = ctx.prec + extra1
+            xre = ctx.to_fixed(ctx._re(q), wp)
+            xim = ctx.to_fixed(ctx._im(q), wp)
+            x2re = (xre*xre - xim*xim) >> wp
+            x2im = (xre*xim) >> (wp - 1)
+            sre = are = bre = xre
+            sim = aim = bim = xim
+            while are**2 + aim**2 > MIN:
+                bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                           (bre * x2im + bim * x2re) >> wp
+                are, aim = (are * bre - aim * bim) >> wp,   \
+                           (are * bim + aim * bre) >> wp
+                sre += are
+                sim += aim
+            sre = (1 << wp) + (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+            return s
+    else:
+        if (not ctx._im(q)) and (not ctx._im(z)):
+            s = 0
+            wp = ctx.prec + extra1
+            x = ctx.to_fixed(ctx._re(q), wp)
+            a = b = x
+            x2 = (x*x) >> wp
+            c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp)
+            c1 = ctx.to_fixed(c1, wp)
+            s1 = ctx.to_fixed(s1, wp)
+            cn = c1
+            sn = s1
+            s += (a * cn) >> wp
+            while abs(a) > MIN:
+                b = (b*x2) >> wp
+                a = (a*b) >> wp
+                cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp
+                s += (a * cn) >> wp
+            s = (1 << wp) + (s << 1)
+            s = ctx.ldexp(s, -wp)
+            return s
+        # case z real, q complex
+        elif not ctx._im(z):
+            wp = ctx.prec + extra2
+            xre = ctx.to_fixed(ctx._re(q), wp)
+            xim = ctx.to_fixed(ctx._im(q), wp)
+            x2re = (xre*xre - xim*xim) >> wp
+            x2im = (xre*xim) >> (wp - 1)
+            are = bre = xre
+            aim = bim = xim
+            c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp)
+            c1 = ctx.to_fixed(c1, wp)
+            s1 = ctx.to_fixed(s1, wp)
+            cn = c1
+            sn = s1
+            sre = (are * cn) >> wp
+            sim = (aim * cn) >> wp
+            while are**2 + aim**2 > MIN:
+                bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                           (bre * x2im + bim * x2re) >> wp
+                are, aim = (are * bre - aim * bim) >> wp,   \
+                           (are * bim + aim * bre) >> wp
+                cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp
+                sre += (are * cn) >> wp
+                sim += (aim * cn) >> wp
+            sre = (1 << wp) + (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+            return s
+        #case z complex, q real
+        elif not ctx._im(q):
+            wp = ctx.prec + extra2
+            x = ctx.to_fixed(ctx._re(q), wp)
+            a = b = x
+            x2 = (x*x) >> wp
+            prec0 = ctx.prec
+            ctx.prec = wp
+            c1, s1 = ctx.cos_sin(2*z)
+            ctx.prec = prec0
+            cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+            cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+            snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+            snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+            sre = (a * cnre) >> wp
+            sim = (a * cnim) >> wp
+            while abs(a) > MIN:
+                b = (b*x2) >> wp
+                a = (a*b) >> wp
+                t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp
+                t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp
+                t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp
+                t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp
+                cnre = t1
+                cnim = t2
+                snre = t3
+                snim = t4
+                sre += (a * cnre) >> wp
+                sim += (a * cnim) >> wp
+            sre = (1 << wp) + (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+            return s
+        # case z and q complex
+        else:
+            wp = ctx.prec + extra2
+            xre = ctx.to_fixed(ctx._re(q), wp)
+            xim = ctx.to_fixed(ctx._im(q), wp)
+            x2re = (xre*xre - xim*xim) >> wp
+            x2im = (xre*xim) >> (wp - 1)
+            are = bre = xre
+            aim = bim = xim
+            prec0 = ctx.prec
+            ctx.prec = wp
+            # cos(2*z), sin(2*z) with z complex
+            c1, s1 = ctx.cos_sin(2*z)
+            ctx.prec = prec0
+            cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+            cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+            snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+            snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+            sre = (are * cnre - aim * cnim) >> wp
+            sim = (aim * cnre + are * cnim) >> wp
+            while are**2 + aim**2 > MIN:
+                bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                           (bre * x2im + bim * x2re) >> wp
+                are, aim = (are * bre - aim * bim) >> wp,   \
+                           (are * bim + aim * bre) >> wp
+                t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp
+                t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp
+                t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp
+                t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp
+                cnre = t1
+                cnim = t2
+                snre = t3
+                snim = t4
+                sre += (are * cnre - aim * cnim) >> wp
+                sim += (aim * cnre + are * cnim) >> wp
+            sre = (1 << wp) + (sre << 1)
+            sim = (sim << 1)
+            sre = ctx.ldexp(sre, -wp)
+            sim = ctx.ldexp(sim, -wp)
+            s = ctx.mpc(sre, sim)
+            return s
+
+@defun
+def _djacobi_theta3(ctx, z, q, nd):
+    """nd=1,2,3 order of the derivative with respect to z"""
+    MIN = 2
+    extra1 = 10
+    extra2 = 20
+    if (not ctx._im(q)) and (not ctx._im(z)):
+        s = 0
+        wp = ctx.prec + extra1
+        x = ctx.to_fixed(ctx._re(q), wp)
+        a = b = x
+        x2 = (x*x) >> wp
+        c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp)
+        c1 = ctx.to_fixed(c1, wp)
+        s1 = ctx.to_fixed(s1, wp)
+        cn = c1
+        sn = s1
+        if (nd&1):
+            s += (a * sn) >> wp
+        else:
+            s += (a * cn) >> wp
+        n = 2
+        while abs(a) > MIN:
+            b = (b*x2) >> wp
+            a = (a*b) >> wp
+            cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp
+            if nd&1:
+                s += (a * sn * n**nd) >> wp
+            else:
+                s += (a * cn * n**nd) >> wp
+            n += 1
+        s = -(s << (nd+1))
+        s = ctx.ldexp(s, -wp)
+    # case z real, q complex
+    elif not ctx._im(z):
+        wp = ctx.prec + extra2
+        xre = ctx.to_fixed(ctx._re(q), wp)
+        xim = ctx.to_fixed(ctx._im(q), wp)
+        x2re = (xre*xre - xim*xim) >> wp
+        x2im = (xre*xim) >> (wp - 1)
+        are = bre = xre
+        aim = bim = xim
+        c1, s1 = ctx.cos_sin(ctx._re(z)*2, prec=wp)
+        c1 = ctx.to_fixed(c1, wp)
+        s1 = ctx.to_fixed(s1, wp)
+        cn = c1
+        sn = s1
+        if (nd&1):
+            sre = (are * sn) >> wp
+            sim = (aim * sn) >> wp
+        else:
+            sre = (are * cn) >> wp
+            sim = (aim * cn) >> wp
+        n = 2
+        while are**2 + aim**2 > MIN:
+            bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                       (bre * x2im + bim * x2re) >> wp
+            are, aim = (are * bre - aim * bim) >> wp,   \
+                       (are * bim + aim * bre) >> wp
+            cn, sn = (cn*c1 - sn*s1) >> wp, (sn*c1 + cn*s1) >> wp
+            if nd&1:
+                sre += (are * sn * n**nd) >> wp
+                sim += (aim * sn * n**nd) >> wp
+            else:
+                sre += (are * cn * n**nd) >> wp
+                sim += (aim * cn * n**nd) >> wp
+            n += 1
+        sre = -(sre << (nd+1))
+        sim = -(sim << (nd+1))
+        sre = ctx.ldexp(sre, -wp)
+        sim = ctx.ldexp(sim, -wp)
+        s = ctx.mpc(sre, sim)
+    #case z complex, q real
+    elif not ctx._im(q):
+        wp = ctx.prec + extra2
+        x = ctx.to_fixed(ctx._re(q), wp)
+        a = b = x
+        x2 = (x*x) >> wp
+        prec0 = ctx.prec
+        ctx.prec = wp
+        c1, s1 = ctx.cos_sin(2*z)
+        ctx.prec = prec0
+        cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+        cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+        snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+        snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+        if (nd&1):
+            sre = (a * snre) >> wp
+            sim = (a * snim) >> wp
+        else:
+            sre = (a * cnre) >> wp
+            sim = (a * cnim) >> wp
+        n = 2
+        while abs(a) > MIN:
+            b = (b*x2) >> wp
+            a = (a*b) >> wp
+            t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp
+            t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp
+            t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp
+            t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp
+            cnre = t1
+            cnim = t2
+            snre = t3
+            snim = t4
+            if (nd&1):
+                sre += (a * snre * n**nd) >> wp
+                sim += (a * snim * n**nd) >> wp
+            else:
+                sre += (a * cnre * n**nd) >> wp
+                sim += (a * cnim * n**nd) >> wp
+            n += 1
+        sre = -(sre << (nd+1))
+        sim = -(sim << (nd+1))
+        sre = ctx.ldexp(sre, -wp)
+        sim = ctx.ldexp(sim, -wp)
+        s = ctx.mpc(sre, sim)
+    # case z and q complex
+    else:
+        wp = ctx.prec + extra2
+        xre = ctx.to_fixed(ctx._re(q), wp)
+        xim = ctx.to_fixed(ctx._im(q), wp)
+        x2re = (xre*xre - xim*xim) >> wp
+        x2im = (xre*xim) >> (wp - 1)
+        are = bre = xre
+        aim = bim = xim
+        prec0 = ctx.prec
+        ctx.prec = wp
+        # cos(2*z), sin(2*z) with z complex
+        c1, s1 = ctx.cos_sin(2*z)
+        ctx.prec = prec0
+        cnre = c1re = ctx.to_fixed(ctx._re(c1), wp)
+        cnim = c1im = ctx.to_fixed(ctx._im(c1), wp)
+        snre = s1re = ctx.to_fixed(ctx._re(s1), wp)
+        snim = s1im = ctx.to_fixed(ctx._im(s1), wp)
+        if (nd&1):
+            sre = (are * snre - aim * snim) >> wp
+            sim = (aim * snre + are * snim) >> wp
+        else:
+            sre = (are * cnre - aim * cnim) >> wp
+            sim = (aim * cnre + are * cnim) >> wp
+        n = 2
+        while are**2 + aim**2 > MIN:
+            bre, bim = (bre * x2re - bim * x2im) >> wp, \
+                       (bre * x2im + bim * x2re) >> wp
+            are, aim = (are * bre - aim * bim) >> wp,   \
+                       (are * bim + aim * bre) >> wp
+            t1 = (cnre*c1re - cnim*c1im - snre*s1re + snim*s1im) >> wp
+            t2 = (cnre*c1im + cnim*c1re - snre*s1im - snim*s1re) >> wp
+            t3 = (snre*c1re - snim*c1im + cnre*s1re - cnim*s1im) >> wp
+            t4 = (snre*c1im + snim*c1re + cnre*s1im + cnim*s1re) >> wp
+            cnre = t1
+            cnim = t2
+            snre = t3
+            snim = t4
+            if(nd&1):
+                sre += ((are * snre - aim * snim) * n**nd) >> wp
+                sim += ((aim * snre + are * snim) * n**nd) >> wp
+            else:
+                sre += ((are * cnre - aim * cnim) * n**nd) >> wp
+                sim += ((aim * cnre + are * cnim) * n**nd) >> wp
+            n += 1
+        sre = -(sre << (nd+1))
+        sim = -(sim << (nd+1))
+        sre = ctx.ldexp(sre, -wp)
+        sim = ctx.ldexp(sim, -wp)
+        s = ctx.mpc(sre, sim)
+    if (nd&1):
+        return (-1)**(nd//2) * s
+    else:
+        return (-1)**(1 + nd//2) * s
+
+@defun
+def _jacobi_theta2a(ctx, z, q):
+    """
+    case ctx._im(z) != 0
+    theta(2, z, q) =
+    q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n=-inf, inf)
+    max term for minimum (2*n+1)*log(q).real - 2* ctx._im(z)
+    n0 = int(ctx._im(z)/log(q).real - 1/2)
+    theta(2, z, q) =
+    q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n=n0, inf) +
+    q**1/4 * Sum(q**(n*n + n) * exp(j*(2*n + 1)*z), n, n0-1, -inf)
+    """
+    n = n0 = int(ctx._im(z)/ctx._re(ctx.log(q)) - 1/2)
+    e2 = ctx.expj(2*z)
+    e = e0 = ctx.expj((2*n+1)*z)
+    a = q**(n*n + n)
+    # leading term
+    term = a * e
+    s = term
+    eps1 = ctx.eps*abs(term)
+    while 1:
+        n += 1
+        e = e * e2
+        term = q**(n*n + n) * e
+        if abs(term) < eps1:
+            break
+        s += term
+    e = e0
+    e2 = ctx.expj(-2*z)
+    n = n0
+    while 1:
+        n -= 1
+        e = e * e2
+        term = q**(n*n + n) * e
+        if abs(term) < eps1:
+            break
+        s += term
+    s = s * ctx.nthroot(q, 4)
+    return s
+
+@defun
+def _jacobi_theta3a(ctx, z, q):
+    """
+    case ctx._im(z) != 0
+    theta3(z, q) = Sum(q**(n*n) * exp(j*2*n*z), n, -inf, inf)
+    max term for n*abs(log(q).real) + ctx._im(z) ~= 0
+    n0 = int(- ctx._im(z)/abs(log(q).real))
+    """
+    n = n0 = int(-ctx._im(z)/abs(ctx._re(ctx.log(q))))
+    e2 = ctx.expj(2*z)
+    e = e0 = ctx.expj(2*n*z)
+    s = term = q**(n*n) * e
+    eps1 = ctx.eps*abs(term)
+    while 1:
+        n += 1
+        e = e * e2
+        term = q**(n*n) * e
+        if abs(term) < eps1:
+            break
+        s += term
+    e = e0
+    e2 = ctx.expj(-2*z)
+    n = n0
+    while 1:
+        n -= 1
+        e = e * e2
+        term = q**(n*n) * e
+        if abs(term) < eps1:
+            break
+        s += term
+    return s
+
+@defun
+def _djacobi_theta2a(ctx, z, q, nd):
+    """
+    case ctx._im(z) != 0
+    dtheta(2, z, q, nd) =
+    j* q**1/4 * Sum(q**(n*n + n) * (2*n+1)*exp(j*(2*n + 1)*z), n=-inf, inf)
+    max term for (2*n0+1)*log(q).real - 2* ctx._im(z) ~= 0
+    n0 = int(ctx._im(z)/log(q).real - 1/2)
+    """
+    n = n0 = int(ctx._im(z)/ctx._re(ctx.log(q)) - 1/2)
+    e2 = ctx.expj(2*z)
+    e = e0 = ctx.expj((2*n + 1)*z)
+    a = q**(n*n + n)
+    # leading term
+    term = (2*n+1)**nd * a * e
+    s = term
+    eps1 = ctx.eps*abs(term)
+    while 1:
+        n += 1
+        e = e * e2
+        term = (2*n+1)**nd * q**(n*n + n) * e
+        if abs(term) < eps1:
+            break
+        s += term
+    e = e0
+    e2 = ctx.expj(-2*z)
+    n = n0
+    while 1:
+        n -= 1
+        e = e * e2
+        term = (2*n+1)**nd * q**(n*n + n) * e
+        if abs(term) < eps1:
+            break
+        s += term
+    return ctx.j**nd * s * ctx.nthroot(q, 4)
+
+@defun
+def _djacobi_theta3a(ctx, z, q, nd):
+    """
+    case ctx._im(z) != 0
+    djtheta3(z, q, nd) = (2*j)**nd *
+      Sum(q**(n*n) * n**nd * exp(j*2*n*z), n, -inf, inf)
+    max term for minimum n*abs(log(q).real) + ctx._im(z)
+    """
+    n = n0 = int(-ctx._im(z)/abs(ctx._re(ctx.log(q))))
+    e2 = ctx.expj(2*z)
+    e = e0 = ctx.expj(2*n*z)
+    a = q**(n*n) * e
+    s = term = n**nd * a
+    if n != 0:
+        eps1 = ctx.eps*abs(term)
+    else:
+        eps1 = ctx.eps*abs(a)
+    while 1:
+        n += 1
+        e = e * e2
+        a = q**(n*n) * e
+        term = n**nd * a
+        if n != 0:
+            aterm = abs(term)
+        else:
+            aterm = abs(a)
+        if aterm < eps1:
+            break
+        s += term
+    e = e0
+    e2 = ctx.expj(-2*z)
+    n = n0
+    while 1:
+        n -= 1
+        e = e * e2
+        a = q**(n*n) * e
+        term = n**nd * a
+        if n != 0:
+            aterm = abs(term)
+        else:
+            aterm = abs(a)
+        if aterm < eps1:
+            break
+        s += term
+    return (2*ctx.j)**nd * s
+
+@defun
+def jtheta(ctx, n, z, q, derivative=0):
+    if derivative:
+        return ctx._djtheta(n, z, q, derivative)
+
+    z = ctx.convert(z)
+    q = ctx.convert(q)
+
+    # Implementation note
+    # If ctx._im(z) is close to zero, _jacobi_theta2 and _jacobi_theta3
+    # are used,
+    # which compute the series starting from n=0 using fixed precision
+    # numbers;
+    # otherwise  _jacobi_theta2a and _jacobi_theta3a are used, which compute
+    # the series starting from n=n0, which is the largest term.
+
+    # TODO: write _jacobi_theta2a and _jacobi_theta3a using fixed-point
+
+    if abs(q) > ctx.THETA_Q_LIM:
+        raise ValueError('abs(q) > THETA_Q_LIM = %f' % ctx.THETA_Q_LIM)
+
+    extra = 10
+    if z:
+        M = ctx.mag(z)
+        if M > 5 or (n == 1 and M < -5):
+            extra += 2*abs(M)
+    cz = 0.5
+    extra2 = 50
+    prec0 = ctx.prec
+    try:
+        ctx.prec += extra
+        if n == 1:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._jacobi_theta2(z - ctx.pi/2, q)
+                else:
+                    ctx.dps += 10
+                    res = ctx._jacobi_theta2a(z - ctx.pi/2, q)
+            else:
+                res = ctx._jacobi_theta2(z - ctx.pi/2, q)
+        elif n == 2:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._jacobi_theta2(z, q)
+                else:
+                    ctx.dps += 10
+                    res = ctx._jacobi_theta2a(z, q)
+            else:
+                res = ctx._jacobi_theta2(z, q)
+        elif n == 3:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._jacobi_theta3(z, q)
+                else:
+                    ctx.dps += 10
+                    res = ctx._jacobi_theta3a(z, q)
+            else:
+                res = ctx._jacobi_theta3(z, q)
+        elif n == 4:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._jacobi_theta3(z, -q)
+                else:
+                    ctx.dps += 10
+                    res = ctx._jacobi_theta3a(z, -q)
+            else:
+                res = ctx._jacobi_theta3(z, -q)
+        else:
+            raise ValueError
+    finally:
+        ctx.prec = prec0
+    return res
+
+@defun
+def _djtheta(ctx, n, z, q, derivative=1):
+    z = ctx.convert(z)
+    q = ctx.convert(q)
+    nd = int(derivative)
+
+    if abs(q) > ctx.THETA_Q_LIM:
+        raise ValueError('abs(q) > THETA_Q_LIM = %f' % ctx.THETA_Q_LIM)
+    extra = 10 + ctx.prec * nd // 10
+    if z:
+        M = ctx.mag(z)
+        if M > 5 or (n != 1 and M < -5):
+            extra += 2*abs(M)
+    cz = 0.5
+    extra2 = 50
+    prec0 = ctx.prec
+    try:
+        ctx.prec += extra
+        if n == 1:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._djacobi_theta2(z - ctx.pi/2, q, nd)
+                else:
+                    ctx.dps += 10
+                    res = ctx._djacobi_theta2a(z - ctx.pi/2, q, nd)
+            else:
+                res = ctx._djacobi_theta2(z - ctx.pi/2, q, nd)
+        elif n == 2:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._djacobi_theta2(z, q, nd)
+                else:
+                    ctx.dps += 10
+                    res = ctx._djacobi_theta2a(z, q, nd)
+            else:
+                res = ctx._djacobi_theta2(z, q, nd)
+        elif n == 3:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._djacobi_theta3(z, q, nd)
+                else:
+                    ctx.dps += 10
+                    res = ctx._djacobi_theta3a(z, q, nd)
+            else:
+                res = ctx._djacobi_theta3(z, q, nd)
+        elif n == 4:
+            if ctx._im(z):
+                if abs(ctx._im(z)) < cz * abs(ctx._re(ctx.log(q))):
+                    ctx.dps += extra2
+                    res = ctx._djacobi_theta3(z, -q, nd)
+                else:
+                    ctx.dps += 10
+                    res = ctx._djacobi_theta3a(z, -q, nd)
+            else:
+                res = ctx._djacobi_theta3(z, -q, nd)
+        else:
+            raise ValueError
+    finally:
+        ctx.prec = prec0
+    return +res

lib/python3.11/site-packages/mpmath/functions/zeta.py

@@ -0,0 +1,1154 @@
+from __future__ import print_function
+
+from ..libmp.backend import xrange
+from .functions import defun, defun_wrapped, defun_static
+
+@defun
+def stieltjes(ctx, n, a=1):
+    n = ctx.convert(n)
+    a = ctx.convert(a)
+    if n < 0:
+        return ctx.bad_domain("Stieltjes constants defined for n >= 0")
+    if hasattr(ctx, "stieltjes_cache"):
+        stieltjes_cache = ctx.stieltjes_cache
+    else:
+        stieltjes_cache = ctx.stieltjes_cache = {}
+    if a == 1:
+        if n == 0:
+            return +ctx.euler
+        if n in stieltjes_cache:
+            prec, s = stieltjes_cache[n]
+            if prec >= ctx.prec:
+                return +s
+    mag = 1
+    def f(x):
+        xa = x/a
+        v = (xa-ctx.j)*ctx.ln(a-ctx.j*x)**n/(1+xa**2)/(ctx.exp(2*ctx.pi*x)-1)
+        return ctx._re(v) / mag
+    orig = ctx.prec
+    try:
+        # Normalize integrand by approx. magnitude to
+        # speed up quadrature (which uses absolute error)
+        if n > 50:
+            ctx.prec = 20
+            mag = ctx.quad(f, [0,ctx.inf], maxdegree=3)
+        ctx.prec = orig + 10 + int(n**0.5)
+        s = ctx.quad(f, [0,ctx.inf], maxdegree=20)
+        v = ctx.ln(a)**n/(2*a) - ctx.ln(a)**(n+1)/(n+1) + 2*s/a*mag
+    finally:
+        ctx.prec = orig
+    if a == 1 and ctx.isint(n):
+        stieltjes_cache[n] = (ctx.prec, v)
+    return +v
+
+@defun_wrapped
+def siegeltheta(ctx, t, derivative=0):
+    d = int(derivative)
+    if  (t == ctx.inf or t == ctx.ninf):
+        if d < 2:
+            if t == ctx.ninf and d == 0:
+                return ctx.ninf
+            return ctx.inf
+        else:
+            return ctx.zero
+    if d == 0:
+        if ctx._im(t):
+            # XXX: cancellation occurs
+            a = ctx.loggamma(0.25+0.5j*t)
+            b = ctx.loggamma(0.25-0.5j*t)
+            return -ctx.ln(ctx.pi)/2*t - 0.5j*(a-b)
+        else:
+            if ctx.isinf(t):
+                return t
+            return ctx._im(ctx.loggamma(0.25+0.5j*t)) - ctx.ln(ctx.pi)/2*t
+    if d > 0:
+        a = (-0.5j)**(d-1)*ctx.polygamma(d-1, 0.25-0.5j*t)
+        b = (0.5j)**(d-1)*ctx.polygamma(d-1, 0.25+0.5j*t)
+        if ctx._im(t):
+            if d == 1:
+                return -0.5*ctx.log(ctx.pi)+0.25*(a+b)
+            else:
+                return 0.25*(a+b)
+        else:
+            if d == 1:
+                return ctx._re(-0.5*ctx.log(ctx.pi)+0.25*(a+b))
+            else:
+                return ctx._re(0.25*(a+b))
+
+@defun_wrapped
+def grampoint(ctx, n):
+    # asymptotic expansion, from
+    # http://mathworld.wolfram.com/GramPoint.html
+    g = 2*ctx.pi*ctx.exp(1+ctx.lambertw((8*n+1)/(8*ctx.e)))
+    return ctx.findroot(lambda t: ctx.siegeltheta(t)-ctx.pi*n, g)
+
+
+@defun_wrapped
+def siegelz(ctx, t, **kwargs):
+    d = int(kwargs.get("derivative", 0))
+    t = ctx.convert(t)
+    t1 = ctx._re(t)
+    t2 = ctx._im(t)
+    prec = ctx.prec
+    try:
+        if abs(t1) > 500*prec and t2**2 < t1:
+            v = ctx.rs_z(t, d)
+            if ctx._is_real_type(t):
+                return ctx._re(v)
+            return v
+    except NotImplementedError:
+        pass
+    ctx.prec += 21
+    e1 = ctx.expj(ctx.siegeltheta(t))
+    z = ctx.zeta(0.5+ctx.j*t)
+    if d == 0:
+        v = e1*z
+        ctx.prec=prec
+        if ctx._is_real_type(t):
+            return ctx._re(v)
+        return +v
+    z1 = ctx.zeta(0.5+ctx.j*t, derivative=1)
+    theta1 = ctx.siegeltheta(t, derivative=1)
+    if d == 1:
+        v =  ctx.j*e1*(z1+z*theta1)
+        ctx.prec=prec
+        if ctx._is_real_type(t):
+            return ctx._re(v)
+        return +v
+    z2 = ctx.zeta(0.5+ctx.j*t, derivative=2)
+    theta2 = ctx.siegeltheta(t, derivative=2)
+    comb1 = theta1**2-ctx.j*theta2
+    if d == 2:
+        def terms():
+            return [2*z1*theta1, z2, z*comb1]
+        v = ctx.sum_accurately(terms, 1)
+        v =  -e1*v
+        ctx.prec = prec
+        if ctx._is_real_type(t):
+            return ctx._re(v)
+        return +v
+    ctx.prec += 10
+    z3 = ctx.zeta(0.5+ctx.j*t, derivative=3)
+    theta3 = ctx.siegeltheta(t, derivative=3)
+    comb2 = theta1**3-3*ctx.j*theta1*theta2-theta3
+    if d == 3:
+        def terms():
+            return  [3*theta1*z2, 3*z1*comb1, z3+z*comb2]
+        v = ctx.sum_accurately(terms, 1)
+        v =  -ctx.j*e1*v
+        ctx.prec = prec
+        if ctx._is_real_type(t):
+            return ctx._re(v)
+        return +v
+    z4 = ctx.zeta(0.5+ctx.j*t, derivative=4)
+    theta4 = ctx.siegeltheta(t, derivative=4)
+    def terms():
+        return [theta1**4, -6*ctx.j*theta1**2*theta2, -3*theta2**2,
+            -4*theta1*theta3, ctx.j*theta4]
+    comb3 = ctx.sum_accurately(terms, 1)
+    if d == 4:
+        def terms():
+            return  [6*theta1**2*z2, -6*ctx.j*z2*theta2, 4*theta1*z3,
+                 4*z1*comb2, z4, z*comb3]
+        v = ctx.sum_accurately(terms, 1)
+        v =  e1*v
+        ctx.prec = prec
+        if ctx._is_real_type(t):
+            return ctx._re(v)
+        return +v
+    if d > 4:
+        h = lambda x: ctx.siegelz(x, derivative=4)
+        return ctx.diff(h, t, n=d-4)
+
+
+_zeta_zeros = [
+14.134725142,21.022039639,25.010857580,30.424876126,32.935061588,
+37.586178159,40.918719012,43.327073281,48.005150881,49.773832478,
+52.970321478,56.446247697,59.347044003,60.831778525,65.112544048,
+67.079810529,69.546401711,72.067157674,75.704690699,77.144840069,
+79.337375020,82.910380854,84.735492981,87.425274613,88.809111208,
+92.491899271,94.651344041,95.870634228,98.831194218,101.317851006,
+103.725538040,105.446623052,107.168611184,111.029535543,111.874659177,
+114.320220915,116.226680321,118.790782866,121.370125002,122.946829294,
+124.256818554,127.516683880,129.578704200,131.087688531,133.497737203,
+134.756509753,138.116042055,139.736208952,141.123707404,143.111845808,
+146.000982487,147.422765343,150.053520421,150.925257612,153.024693811,
+156.112909294,157.597591818,158.849988171,161.188964138,163.030709687,
+165.537069188,167.184439978,169.094515416,169.911976479,173.411536520,
+174.754191523,176.441434298,178.377407776,179.916484020,182.207078484,
+184.874467848,185.598783678,187.228922584,189.416158656,192.026656361,
+193.079726604,195.265396680,196.876481841,198.015309676,201.264751944,
+202.493594514,204.189671803,205.394697202,207.906258888,209.576509717,
+211.690862595,213.347919360,214.547044783,216.169538508,219.067596349,
+220.714918839,221.430705555,224.007000255,224.983324670,227.421444280,
+229.337413306,231.250188700,231.987235253,233.693404179,236.524229666,
+]
+
+def _load_zeta_zeros(url):
+    import urllib
+    d = urllib.urlopen(url)
+    L = [float(x) for x in d.readlines()]
+    # Sanity check
+    assert round(L[0]) == 14
+    _zeta_zeros[:] = L
+
+@defun
+def oldzetazero(ctx, n, url='http://www.dtc.umn.edu/~odlyzko/zeta_tables/zeros1'):
+    n = int(n)
+    if n < 0:
+        return ctx.zetazero(-n).conjugate()
+    if n == 0:
+        raise ValueError("n must be nonzero")
+    if n > len(_zeta_zeros) and n <= 100000:
+        _load_zeta_zeros(url)
+    if n > len(_zeta_zeros):
+        raise NotImplementedError("n too large for zetazeros")
+    return ctx.mpc(0.5, ctx.findroot(ctx.siegelz, _zeta_zeros[n-1]))
+
+@defun_wrapped
+def riemannr(ctx, x):
+    if x == 0:
+        return ctx.zero
+    # Check if a simple asymptotic estimate is accurate enough
+    if abs(x) > 1000:
+        a = ctx.li(x)
+        b = 0.5*ctx.li(ctx.sqrt(x))
+        if abs(b) < abs(a)*ctx.eps:
+            return a
+    if abs(x) < 0.01:
+        # XXX
+        ctx.prec += int(-ctx.log(abs(x),2))
+    # Sum Gram's series
+    s = t = ctx.one
+    u = ctx.ln(x)
+    k = 1
+    while abs(t) > abs(s)*ctx.eps:
+        t = t * u / k
+        s += t / (k * ctx._zeta_int(k+1))
+        k += 1
+    return s
+
+@defun_static
+def primepi(ctx, x):
+    x = int(x)
+    if x < 2:
+        return 0
+    return len(ctx.list_primes(x))
+
+# TODO: fix the interface wrt contexts
+@defun_wrapped
+def primepi2(ctx, x):
+    x = int(x)
+    if x < 2:
+        return ctx._iv.zero
+    if x < 2657:
+        return ctx._iv.mpf(ctx.primepi(x))
+    mid = ctx.li(x)
+    # Schoenfeld's estimate for x >= 2657, assuming RH
+    err = ctx.sqrt(x,rounding='u')*ctx.ln(x,rounding='u')/8/ctx.pi(rounding='d')
+    a = ctx.floor((ctx._iv.mpf(mid)-err).a, rounding='d')
+    b = ctx.ceil((ctx._iv.mpf(mid)+err).b, rounding='u')
+    return ctx._iv.mpf([a,b])
+
+@defun_wrapped
+def primezeta(ctx, s):
+    if ctx.isnan(s):
+        return s
+    if ctx.re(s) <= 0:
+        raise ValueError("prime zeta function defined only for re(s) > 0")
+    if s == 1:
+        return ctx.inf
+    if s == 0.5:
+        return ctx.mpc(ctx.ninf, ctx.pi)
+    r = ctx.re(s)
+    if r > ctx.prec:
+        return 0.5**s
+    else:
+        wp = ctx.prec + int(r)
+        def terms():
+            orig = ctx.prec
+            # zeta ~ 1+eps; need to set precision
+            # to get logarithm accurately
+            k = 0
+            while 1:
+                k += 1
+                u = ctx.moebius(k)
+                if not u:
+                    continue
+                ctx.prec = wp
+                t = u*ctx.ln(ctx.zeta(k*s))/k
+                if not t:
+                    return
+                #print ctx.prec, ctx.nstr(t)
+                ctx.prec = orig
+                yield t
+    return ctx.sum_accurately(terms)
+
+# TODO: for bernpoly and eulerpoly, ensure that all exact zeros are covered
+
+@defun_wrapped
+def bernpoly(ctx, n, z):
+    # Slow implementation:
+    #return sum(ctx.binomial(n,k)*ctx.bernoulli(k)*z**(n-k) for k in xrange(0,n+1))
+    n = int(n)
+    if n < 0:
+        raise ValueError("Bernoulli polynomials only defined for n >= 0")
+    if z == 0 or (z == 1 and n > 1):
+        return ctx.bernoulli(n)
+    if z == 0.5:
+        return (ctx.ldexp(1,1-n)-1)*ctx.bernoulli(n)
+    if n <= 3:
+        if n == 0: return z ** 0
+        if n == 1: return z - 0.5
+        if n == 2: return (6*z*(z-1)+1)/6
+        if n == 3: return z*(z*(z-1.5)+0.5)
+    if ctx.isinf(z):
+        return z ** n
+    if ctx.isnan(z):
+        return z
+    if abs(z) > 2:
+        def terms():
+            t = ctx.one
+            yield t
+            r = ctx.one/z
+            k = 1
+            while k <= n:
+                t = t*(n+1-k)/k*r
+                if not (k > 2 and k & 1):
+                    yield t*ctx.bernoulli(k)
+                k += 1
+        return ctx.sum_accurately(terms) * z**n
+    else:
+        def terms():
+            yield ctx.bernoulli(n)
+            t = ctx.one
+            k = 1
+            while k <= n:
+                t = t*(n+1-k)/k * z
+                m = n-k
+                if not (m > 2 and m & 1):
+                    yield t*ctx.bernoulli(m)
+                k += 1
+        return ctx.sum_accurately(terms)
+
+@defun_wrapped
+def eulerpoly(ctx, n, z):
+    n = int(n)
+    if n < 0:
+        raise ValueError("Euler polynomials only defined for n >= 0")
+    if n <= 2:
+        if n == 0: return z ** 0
+        if n == 1: return z - 0.5
+        if n == 2: return z*(z-1)
+    if ctx.isinf(z):
+        return z**n
+    if ctx.isnan(z):
+        return z
+    m = n+1
+    if z == 0:
+        return -2*(ctx.ldexp(1,m)-1)*ctx.bernoulli(m)/m * z**0
+    if z == 1:
+        return 2*(ctx.ldexp(1,m)-1)*ctx.bernoulli(m)/m * z**0
+    if z == 0.5:
+        if n % 2:
+            return ctx.zero
+        # Use exact code for Euler numbers
+        if n < 100 or n*ctx.mag(0.46839865*n) < ctx.prec*0.25:
+            return ctx.ldexp(ctx._eulernum(n), -n)
+    # http://functions.wolfram.com/Polynomials/EulerE2/06/01/02/01/0002/
+    def terms():
+        t = ctx.one
+        k = 0
+        w = ctx.ldexp(1,n+2)
+        while 1:
+            v = n-k+1
+            if not (v > 2 and v & 1):
+                yield (2-w)*ctx.bernoulli(v)*t
+            k += 1
+            if k > n:
+                break
+            t = t*z*(n-k+2)/k
+            w *= 0.5
+    return ctx.sum_accurately(terms) / m
+
+@defun
+def eulernum(ctx, n, exact=False):
+    n = int(n)
+    if exact:
+        return int(ctx._eulernum(n))
+    if n < 100:
+        return ctx.mpf(ctx._eulernum(n))
+    if n % 2:
+        return ctx.zero
+    return ctx.ldexp(ctx.eulerpoly(n,0.5), n)
+
+# TODO: this should be implemented low-level
+def polylog_series(ctx, s, z):
+    tol = +ctx.eps
+    l = ctx.zero
+    k = 1
+    zk = z
+    while 1:
+        term = zk / k**s
+        l += term
+        if abs(term) < tol:
+            break
+        zk *= z
+        k += 1
+    return l
+
+def polylog_continuation(ctx, n, z):
+    if n < 0:
+        return z*0
+    twopij = 2j * ctx.pi
+    a = -twopij**n/ctx.fac(n) * ctx.bernpoly(n, ctx.ln(z)/twopij)
+    if ctx._is_real_type(z) and z < 0:
+        a = ctx._re(a)
+    if ctx._im(z) < 0 or (ctx._im(z) == 0 and ctx._re(z) >= 1):
+        a -= twopij*ctx.ln(z)**(n-1)/ctx.fac(n-1)
+    return a
+
+def polylog_unitcircle(ctx, n, z):
+    tol = +ctx.eps
+    if n > 1:
+        l = ctx.zero
+        logz = ctx.ln(z)
+        logmz = ctx.one
+        m = 0
+        while 1:
+            if (n-m) != 1:
+                term = ctx.zeta(n-m) * logmz / ctx.fac(m)
+                if term and abs(term) < tol:
+                    break
+                l += term
+            logmz *= logz
+            m += 1
+        l += ctx.ln(z)**(n-1)/ctx.fac(n-1)*(ctx.harmonic(n-1)-ctx.ln(-ctx.ln(z)))
+    elif n < 1:  # else
+        l = ctx.fac(-n)*(-ctx.ln(z))**(n-1)
+        logz = ctx.ln(z)
+        logkz = ctx.one
+        k = 0
+        while 1:
+            b = ctx.bernoulli(k-n+1)
+            if b:
+                term = b*logkz/(ctx.fac(k)*(k-n+1))
+                if abs(term) < tol:
+                    break
+                l -= term
+            logkz *= logz
+            k += 1
+    else:
+        raise ValueError
+    if ctx._is_real_type(z) and z < 0:
+        l = ctx._re(l)
+    return l
+
+def polylog_general(ctx, s, z):
+    v = ctx.zero
+    u = ctx.ln(z)
+    if not abs(u) < 5: # theoretically |u| < 2*pi
+        j = ctx.j
+        v = 1-s
+        y = ctx.ln(-z)/(2*ctx.pi*j)
+        return ctx.gamma(v)*(j**v*ctx.zeta(v,0.5+y) + j**-v*ctx.zeta(v,0.5-y))/(2*ctx.pi)**v
+    t = 1
+    k = 0
+    while 1:
+        term = ctx.zeta(s-k) * t
+        if abs(term) < ctx.eps:
+            break
+        v += term
+        k += 1
+        t *= u
+        t /= k
+    return ctx.gamma(1-s)*(-u)**(s-1) + v
+
+@defun_wrapped
+def polylog(ctx, s, z):
+    s = ctx.convert(s)
+    z = ctx.convert(z)
+    if z == 1:
+        return ctx.zeta(s)
+    if z == -1:
+        return -ctx.altzeta(s)
+    if s == 0:
+        return z/(1-z)
+    if s == 1:
+        return -ctx.ln(1-z)
+    if s == -1:
+        return z/(1-z)**2
+    if abs(z) <= 0.75 or (not ctx.isint(s) and abs(z) < 0.9):
+        return polylog_series(ctx, s, z)
+    if abs(z) >= 1.4 and ctx.isint(s):
+        return (-1)**(s+1)*polylog_series(ctx, s, 1/z) + polylog_continuation(ctx, int(ctx.re(s)), z)
+    if ctx.isint(s):
+        return polylog_unitcircle(ctx, int(ctx.re(s)), z)
+    return polylog_general(ctx, s, z)
+
+@defun_wrapped
+def clsin(ctx, s, z, pi=False):
+    if ctx.isint(s) and s < 0 and int(s) % 2 == 1:
+        return z*0
+    if pi:
+        a = ctx.expjpi(z)
+    else:
+        a = ctx.expj(z)
+    if ctx._is_real_type(z) and ctx._is_real_type(s):
+        return ctx.im(ctx.polylog(s,a))
+    b = 1/a
+    return (-0.5j)*(ctx.polylog(s,a) - ctx.polylog(s,b))
+
+@defun_wrapped
+def clcos(ctx, s, z, pi=False):
+    if ctx.isint(s) and s < 0 and int(s) % 2 == 0:
+        return z*0
+    if pi:
+        a = ctx.expjpi(z)
+    else:
+        a = ctx.expj(z)
+    if ctx._is_real_type(z) and ctx._is_real_type(s):
+        return ctx.re(ctx.polylog(s,a))
+    b = 1/a
+    return 0.5*(ctx.polylog(s,a) + ctx.polylog(s,b))
+
+@defun
+def altzeta(ctx, s, **kwargs):
+    try:
+        return ctx._altzeta(s, **kwargs)
+    except NotImplementedError:
+        return ctx._altzeta_generic(s)
+
+@defun_wrapped
+def _altzeta_generic(ctx, s):
+    if s == 1:
+        return ctx.ln2 + 0*s
+    return -ctx.powm1(2, 1-s) * ctx.zeta(s)
+
+@defun
+def zeta(ctx, s, a=1, derivative=0, method=None, **kwargs):
+    d = int(derivative)
+    if a == 1 and not (d or method):
+        try:
+            return ctx._zeta(s, **kwargs)
+        except NotImplementedError:
+            pass
+    s = ctx.convert(s)
+    prec = ctx.prec
+    method = kwargs.get('method')
+    verbose = kwargs.get('verbose')
+    if (not s) and (not derivative):
+        return ctx.mpf(0.5) - ctx._convert_param(a)[0]
+    if a == 1 and method != 'euler-maclaurin':
+        im = abs(ctx._im(s))
+        re = abs(ctx._re(s))
+        #if (im < prec or method == 'borwein') and not derivative:
+        #    try:
+        #        if verbose:
+        #            print "zeta: Attempting to use the Borwein algorithm"
+        #        return ctx._zeta(s, **kwargs)
+        #    except NotImplementedError:
+        #        if verbose:
+        #            print "zeta: Could not use the Borwein algorithm"
+        #        pass
+        if abs(im) > 500*prec and 10*re < prec and derivative <= 4 or \
+            method == 'riemann-siegel':
+            try:   #  py2.4 compatible try block
+                try:
+                    if verbose:
+                        print("zeta: Attempting to use the Riemann-Siegel algorithm")
+                    return ctx.rs_zeta(s, derivative, **kwargs)
+                except NotImplementedError:
+                    if verbose:
+                        print("zeta: Could not use the Riemann-Siegel algorithm")
+                    pass
+            finally:
+                ctx.prec = prec
+    if s == 1:
+        return ctx.inf
+    abss = abs(s)
+    if abss == ctx.inf:
+        if ctx.re(s) == ctx.inf:
+            if d == 0:
+                return ctx.one
+            return ctx.zero
+        return s*0
+    elif ctx.isnan(abss):
+        return 1/s
+    if ctx.re(s) > 2*ctx.prec and a == 1 and not derivative:
+        return ctx.one + ctx.power(2, -s)
+    return +ctx._hurwitz(s, a, d, **kwargs)
+
+@defun
+def _hurwitz(ctx, s, a=1, d=0, **kwargs):
+    prec = ctx.prec
+    verbose = kwargs.get('verbose')
+    try:
+        extraprec = 10
+        ctx.prec += extraprec
+        # We strongly want to special-case rational a
+        a, atype = ctx._convert_param(a)
+        if ctx.re(s) < 0:
+            if verbose:
+                print("zeta: Attempting reflection formula")
+            try:
+                return _hurwitz_reflection(ctx, s, a, d, atype)
+            except NotImplementedError:
+                pass
+            if verbose:
+                print("zeta: Reflection formula failed")
+        if verbose:
+            print("zeta: Using the Euler-Maclaurin algorithm")
+        while 1:
+            ctx.prec = prec + extraprec
+            T1, T2 = _hurwitz_em(ctx, s, a, d, prec+10, verbose)
+            cancellation = ctx.mag(T1) - ctx.mag(T1+T2)
+            if verbose:
+                print("Term 1:", T1)
+                print("Term 2:", T2)
+                print("Cancellation:", cancellation, "bits")
+            if cancellation < extraprec:
+                return T1 + T2
+            else:
+                extraprec = max(2*extraprec, min(cancellation + 5, 100*prec))
+                if extraprec > kwargs.get('maxprec', 100*prec):
+                    raise ctx.NoConvergence("zeta: too much cancellation")
+    finally:
+        ctx.prec = prec
+
+def _hurwitz_reflection(ctx, s, a, d, atype):
+    # TODO: implement for derivatives
+    if d != 0:
+        raise NotImplementedError
+    res = ctx.re(s)
+    negs = -s
+    # Integer reflection formula
+    if ctx.isnpint(s):
+        n = int(res)
+        if n <= 0:
+            return ctx.bernpoly(1-n, a) / (n-1)
+    if not (atype == 'Q' or atype == 'Z'):
+        raise NotImplementedError
+    t = 1-s
+    # We now require a to be standardized
+    v = 0
+    shift = 0
+    b = a
+    while ctx.re(b) > 1:
+        b -= 1
+        v -= b**negs
+        shift -= 1
+    while ctx.re(b) <= 0:
+        v += b**negs
+        b += 1
+        shift += 1
+    # Rational reflection formula
+    try:
+        p, q = a._mpq_
+    except:
+        assert a == int(a)
+        p = int(a)
+        q = 1
+    p += shift*q
+    assert 1 <= p <= q
+    g = ctx.fsum(ctx.cospi(t/2-2*k*b)*ctx._hurwitz(t,(k,q)) \
+        for k in range(1,q+1))
+    g *= 2*ctx.gamma(t)/(2*ctx.pi*q)**t
+    v += g
+    return v
+
+def _hurwitz_em(ctx, s, a, d, prec, verbose):
+    # May not be converted at this point
+    a = ctx.convert(a)
+    tol = -prec
+    # Estimate number of terms for Euler-Maclaurin summation; could be improved
+    M1 = 0
+    M2 = prec // 3
+    N = M2
+    lsum = 0
+    # This speeds up the recurrence for derivatives
+    if ctx.isint(s):
+        s = int(ctx._re(s))
+    s1 = s-1
+    while 1:
+        # Truncated L-series
+        l = ctx._zetasum(s, M1+a, M2-M1-1, [d])[0][0]
+        #if d:
+        #    l = ctx.fsum((-ctx.ln(n+a))**d * (n+a)**negs for n in range(M1,M2))
+        #else:
+        #    l = ctx.fsum((n+a)**negs for n in range(M1,M2))
+        lsum += l
+        M2a = M2+a
+        logM2a = ctx.ln(M2a)
+        logM2ad = logM2a**d
+        logs = [logM2ad]
+        logr = 1/logM2a
+        rM2a = 1/M2a
+        M2as = M2a**(-s)
+        if d:
+            tailsum = ctx.gammainc(d+1, s1*logM2a) / s1**(d+1)
+        else:
+            tailsum = 1/((s1)*(M2a)**s1)
+        tailsum += 0.5 * logM2ad * M2as
+        U = [1]
+        r = M2as
+        fact = 2
+        for j in range(1, N+1):
+            # TODO: the following could perhaps be tidied a bit
+            j2 = 2*j
+            if j == 1:
+                upds = [1]
+            else:
+                upds = [j2-2, j2-1]
+            for m in upds:
+                D = min(m,d+1)
+                if m <= d:
+                    logs.append(logs[-1] * logr)
+                Un = [0]*(D+1)
+                for i in xrange(D): Un[i] = (1-m-s)*U[i]
+                for i in xrange(1,D+1): Un[i] += (d-(i-1))*U[i-1]
+                U = Un
+                r *= rM2a
+            t = ctx.fdot(U, logs) * r * ctx.bernoulli(j2)/(-fact)
+            tailsum += t
+            if ctx.mag(t) < tol:
+                return lsum, (-1)**d * tailsum
+            fact *= (j2+1)*(j2+2)
+        if verbose:
+            print("Sum range:", M1, M2, "term magnitude", ctx.mag(t), "tolerance", tol)
+        M1, M2 = M2, M2*2
+        if ctx.re(s) < 0:
+            N += N//2
+
+
+
+@defun
+def _zetasum(ctx, s, a, n, derivatives=[0], reflect=False):
+    """
+    Returns [xd0,xd1,...,xdr], [yd0,yd1,...ydr] where
+
+    xdk = D^k     ( 1/a^s     +  1/(a+1)^s      +  ...  +  1/(a+n)^s     )
+    ydk = D^k conj( 1/a^(1-s) +  1/(a+1)^(1-s)  +  ...  +  1/(a+n)^(1-s) )
+
+    D^k = kth derivative with respect to s, k ranges over the given list of
+    derivatives (which should consist of either a single element
+    or a range 0,1,...r). If reflect=False, the ydks are not computed.
+    """
+    #print "zetasum", s, a, n
+    # don't use the fixed-point code if there are large exponentials
+    if abs(ctx.re(s)) < 0.5 * ctx.prec:
+        try:
+            return ctx._zetasum_fast(s, a, n, derivatives, reflect)
+        except NotImplementedError:
+            pass
+    negs = ctx.fneg(s, exact=True)
+    have_derivatives = derivatives != [0]
+    have_one_derivative = len(derivatives) == 1
+    if not reflect:
+        if not have_derivatives:
+            return [ctx.fsum((a+k)**negs for k in xrange(n+1))], []
+        if have_one_derivative:
+            d = derivatives[0]
+            x = ctx.fsum(ctx.ln(a+k)**d * (a+k)**negs for k in xrange(n+1))
+            return [(-1)**d * x], []
+    maxd = max(derivatives)
+    if not have_one_derivative:
+        derivatives = range(maxd+1)
+    xs = [ctx.zero for d in derivatives]
+    if reflect:
+        ys = [ctx.zero for d in derivatives]
+    else:
+        ys = []
+    for k in xrange(n+1):
+        w = a + k
+        xterm = w ** negs
+        if reflect:
+            yterm = ctx.conj(ctx.one / (w * xterm))
+        if have_derivatives:
+            logw = -ctx.ln(w)
+            if have_one_derivative:
+                logw = logw ** maxd
+                xs[0] += xterm * logw
+                if reflect:
+                    ys[0] += yterm * logw
+            else:
+                t = ctx.one
+                for d in derivatives:
+                    xs[d] += xterm * t
+                    if reflect:
+                        ys[d] += yterm * t
+                    t *= logw
+        else:
+            xs[0] += xterm
+            if reflect:
+                ys[0] += yterm
+    return xs, ys
+
+@defun
+def dirichlet(ctx, s, chi=[1], derivative=0):
+    s = ctx.convert(s)
+    q = len(chi)
+    d = int(derivative)
+    if d > 2:
+        raise NotImplementedError("arbitrary order derivatives")
+    prec = ctx.prec
+    try:
+        ctx.prec += 10
+        if s == 1:
+            have_pole = True
+            for x in chi:
+                if x and x != 1:
+                    have_pole = False
+                    h = +ctx.eps
+                    ctx.prec *= 2*(d+1)
+                    s += h
+            if have_pole:
+                return +ctx.inf
+        z = ctx.zero
+        for p in range(1,q+1):
+            if chi[p%q]:
+                if d == 1:
+                    z += chi[p%q] * (ctx.zeta(s, (p,q), 1) - \
+                        ctx.zeta(s, (p,q))*ctx.log(q))
+                else:
+                    z += chi[p%q] * ctx.zeta(s, (p,q))
+        z /= q**s
+    finally:
+        ctx.prec = prec
+    return +z
+
+
+def secondzeta_main_term(ctx, s, a, **kwargs):
+    tol = ctx.eps
+    f = lambda n: ctx.gammainc(0.5*s, a*gamm**2, regularized=True)*gamm**(-s)
+    totsum = term = ctx.zero
+    mg = ctx.inf
+    n = 0
+    while mg > tol:
+        totsum += term
+        n += 1
+        gamm = ctx.im(ctx.zetazero_memoized(n))
+        term = f(n)
+        mg = abs(term)
+    err = 0
+    if kwargs.get("error"):
+        sg = ctx.re(s)
+        err = 0.5*ctx.pi**(-1)*max(1,sg)*a**(sg-0.5)*ctx.log(gamm/(2*ctx.pi))*\
+             ctx.gammainc(-0.5, a*gamm**2)/abs(ctx.gamma(s/2))
+        err = abs(err)
+    return +totsum, err, n
+
+def secondzeta_prime_term(ctx, s, a, **kwargs):
+    tol = ctx.eps
+    f = lambda n: ctx.gammainc(0.5*(1-s),0.25*ctx.log(n)**2 * a**(-1))*\
+        ((0.5*ctx.log(n))**(s-1))*ctx.mangoldt(n)/ctx.sqrt(n)/\
+        (2*ctx.gamma(0.5*s)*ctx.sqrt(ctx.pi))
+    totsum = term = ctx.zero
+    mg = ctx.inf
+    n = 1
+    while mg > tol or n < 9:
+        totsum += term
+        n += 1
+        term = f(n)
+        if term == 0:
+            mg = ctx.inf
+        else:
+            mg = abs(term)
+    if kwargs.get("error"):
+        err = mg
+    return +totsum, err, n
+
+def secondzeta_exp_term(ctx, s, a):
+    if ctx.isint(s) and ctx.re(s) <= 0:
+        m = int(round(ctx.re(s)))
+        if not m & 1:
+            return ctx.mpf('-0.25')**(-m//2)
+    tol = ctx.eps
+    f = lambda n: (0.25*a)**n/((n+0.5*s)*ctx.fac(n))
+    totsum = ctx.zero
+    term = f(0)
+    mg = ctx.inf
+    n = 0
+    while mg > tol:
+        totsum += term
+        n += 1
+        term = f(n)
+        mg = abs(term)
+    v = a**(0.5*s)*totsum/ctx.gamma(0.5*s)
+    return v
+
+def secondzeta_singular_term(ctx, s, a, **kwargs):
+    factor = a**(0.5*(s-1))/(4*ctx.sqrt(ctx.pi)*ctx.gamma(0.5*s))
+    extraprec = ctx.mag(factor)
+    ctx.prec += extraprec
+    factor = a**(0.5*(s-1))/(4*ctx.sqrt(ctx.pi)*ctx.gamma(0.5*s))
+    tol = ctx.eps
+    f = lambda n: ctx.bernpoly(n,0.75)*(4*ctx.sqrt(a))**n*\
+       ctx.gamma(0.5*n)/((s+n-1)*ctx.fac(n))
+    totsum = ctx.zero
+    mg1 = ctx.inf
+    n = 1
+    term = f(n)
+    mg2 = abs(term)
+    while mg2 > tol and mg2 <= mg1:
+        totsum += term
+        n += 1
+        term = f(n)
+        totsum += term
+        n +=1
+        term = f(n)
+        mg1 = mg2
+        mg2 = abs(term)
+    totsum += term
+    pole = -2*(s-1)**(-2)+(ctx.euler+ctx.log(16*ctx.pi**2*a))*(s-1)**(-1)
+    st = factor*(pole+totsum)
+    err = 0
+    if kwargs.get("error"):
+        if not ((mg2 > tol) and (mg2 <= mg1)):
+            if mg2 <= tol:
+                err = ctx.mpf(10)**int(ctx.log(abs(factor*tol),10))
+            if mg2 > mg1:
+                err = ctx.mpf(10)**int(ctx.log(abs(factor*mg1),10))
+        err = max(err, ctx.eps*1.)
+    ctx.prec -= extraprec
+    return +st, err
+
+@defun
+def secondzeta(ctx, s, a = 0.015, **kwargs):
+    r"""
+    Evaluates the secondary zeta function `Z(s)`, defined for
+    `\mathrm{Re}(s)>1` by
+
+    .. math ::
+
+        Z(s) = \sum_{n=1}^{\infty} \frac{1}{\tau_n^s}
+
+    where `\frac12+i\tau_n` runs through the zeros of `\zeta(s)` with
+    imaginary part positive.
+
+    `Z(s)` extends to a meromorphic function on `\mathbb{C}`  with a
+    double pole at `s=1` and  simple poles at the points `-2n` for
+    `n=0`,  1, 2, ...
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.pretty = True; mp.dps = 15
+        >>> secondzeta(2)
+        0.023104993115419
+        >>> xi = lambda s: 0.5*s*(s-1)*pi**(-0.5*s)*gamma(0.5*s)*zeta(s)
+        >>> Xi = lambda t: xi(0.5+t*j)
+        >>> chop(-0.5*diff(Xi,0,n=2)/Xi(0))
+        0.023104993115419
+
+    We may ask for an approximate error value::
+
+        >>> secondzeta(0.5+100j, error=True)
+        ((-0.216272011276718 - 0.844952708937228j), 2.22044604925031e-16)
+
+    The function has poles at the negative odd integers,
+    and dyadic rational values at the negative even integers::
+
+        >>> mp.dps = 30
+        >>> secondzeta(-8)
+        -0.67236328125
+        >>> secondzeta(-7)
+        +inf
+
+    **Implementation notes**
+
+    The function is computed as sum of four terms `Z(s)=A(s)-P(s)+E(s)-S(s)`
+    respectively main, prime, exponential and singular terms.
+    The main term `A(s)` is computed from the zeros of zeta.
+    The prime term depends on the von Mangoldt function.
+    The singular term is responsible for the poles of the function.
+
+    The four terms depends on a small parameter `a`. We may change the
+    value of `a`. Theoretically this has no effect on the sum of the four
+    terms, but in practice may be important.
+
+    A smaller value of the parameter `a` makes `A(s)` depend on
+    a smaller number of zeros of zeta, but `P(s)`  uses more values of
+    von Mangoldt function.
+
+    We may also add a verbose option to obtain data about the
+    values of the four terms.
+
+        >>> mp.dps = 10
+        >>> secondzeta(0.5 + 40j, error=True, verbose=True)
+        main term = (-30190318549.138656312556 - 13964804384.624622876523j)
+            computed using 19 zeros of zeta
+        prime term = (132717176.89212754625045 + 188980555.17563978290601j)
+            computed using 9 values of the von Mangoldt function
+        exponential term = (542447428666.07179812536 + 362434922978.80192435203j)
+        singular term = (512124392939.98154322355 + 348281138038.65531023921j)
+        ((0.059471043 + 0.3463514534j), 1.455191523e-11)
+
+        >>> secondzeta(0.5 + 40j, a=0.04, error=True, verbose=True)
+        main term = (-151962888.19606243907725 - 217930683.90210294051982j)
+            computed using 9 zeros of zeta
+        prime term = (2476659342.3038722372461 + 28711581821.921627163136j)
+            computed using 37 values of the von Mangoldt function
+        exponential term = (178506047114.7838188264 + 819674143244.45677330576j)
+        singular term = (175877424884.22441310708 + 790744630738.28669174871j)
+        ((0.059471043 + 0.3463514534j), 1.455191523e-11)
+
+    Notice the great cancellation between the four terms. Changing `a`, the
+    four terms are very different numbers but the cancellation gives
+    the good value of Z(s).
+
+    **References**
+
+    A. Voros, Zeta functions for the Riemann zeros, Ann. Institute Fourier,
+    53, (2003) 665--699.
+
+    A. Voros, Zeta functions over Zeros of Zeta Functions, Lecture Notes
+    of the Unione Matematica Italiana, Springer, 2009.
+    """
+    s = ctx.convert(s)
+    a = ctx.convert(a)
+    tol = ctx.eps
+    if ctx.isint(s) and ctx.re(s) <= 1:
+        if abs(s-1) < tol*1000:
+            return ctx.inf
+        m = int(round(ctx.re(s)))
+        if m & 1:
+            return ctx.inf
+        else:
+            return ((-1)**(-m//2)*\
+                   ctx.fraction(8-ctx.eulernum(-m,exact=True),2**(-m+3)))
+    prec = ctx.prec
+    try:
+        t3 = secondzeta_exp_term(ctx, s, a)
+        extraprec = max(ctx.mag(t3),0)
+        ctx.prec += extraprec + 3
+        t1, r1, gt = secondzeta_main_term(ctx,s,a,error='True', verbose='True')
+        t2, r2, pt = secondzeta_prime_term(ctx,s,a,error='True', verbose='True')
+        t4, r4 = secondzeta_singular_term(ctx,s,a,error='True')
+        t3 = secondzeta_exp_term(ctx, s, a)
+        err = r1+r2+r4
+        t = t1-t2+t3-t4
+        if kwargs.get("verbose"):
+            print('main term =', t1)
+            print('    computed using', gt, 'zeros of zeta')
+            print('prime term =', t2)
+            print('    computed using', pt, 'values of the von Mangoldt function')
+            print('exponential term =', t3)
+            print('singular term =', t4)
+    finally:
+        ctx.prec = prec
+    if kwargs.get("error"):
+        w = max(ctx.mag(abs(t)),0)
+        err = max(err*2**w, ctx.eps*1.*2**w)
+        return +t, err
+    return +t
+
+
+@defun_wrapped
+def lerchphi(ctx, z, s, a):
+    r"""
+    Gives the Lerch transcendent, defined for `|z| < 1` and
+    `\Re{a} > 0` by
+
+    .. math ::
+
+        \Phi(z,s,a) = \sum_{k=0}^{\infty} \frac{z^k}{(a+k)^s}
+
+    and generally by the recurrence `\Phi(z,s,a) = z \Phi(z,s,a+1) + a^{-s}`
+    along with the integral representation valid for `\Re{a} > 0`
+
+    .. math ::
+
+        \Phi(z,s,a) = \frac{1}{2 a^s} +
+                \int_0^{\infty} \frac{z^t}{(a+t)^s} dt -
+                2 \int_0^{\infty} \frac{\sin(t \log z - s
+                    \operatorname{arctan}(t/a)}{(a^2 + t^2)^{s/2}
+                    (e^{2 \pi t}-1)} dt.
+
+    The Lerch transcendent generalizes the Hurwitz zeta function :func:`zeta`
+    (`z = 1`) and the polylogarithm :func:`polylog` (`a = 1`).
+
+    **Examples**
+
+    Several evaluations in terms of simpler functions::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> lerchphi(-1,2,0.5); 4*catalan
+        3.663862376708876060218414
+        3.663862376708876060218414
+        >>> diff(lerchphi, (-1,-2,1), (0,1,0)); 7*zeta(3)/(4*pi**2)
+        0.2131391994087528954617607
+        0.2131391994087528954617607
+        >>> lerchphi(-4,1,1); log(5)/4
+        0.4023594781085250936501898
+        0.4023594781085250936501898
+        >>> lerchphi(-3+2j,1,0.5); 2*atanh(sqrt(-3+2j))/sqrt(-3+2j)
+        (1.142423447120257137774002 + 0.2118232380980201350495795j)
+        (1.142423447120257137774002 + 0.2118232380980201350495795j)
+
+    Evaluation works for complex arguments and `|z| \ge 1`::
+
+        >>> lerchphi(1+2j, 3-j, 4+2j)
+        (0.002025009957009908600539469 + 0.003327897536813558807438089j)
+        >>> lerchphi(-2,2,-2.5)
+        -12.28676272353094275265944
+        >>> lerchphi(10,10,10)
+        (-4.462130727102185701817349e-11 - 1.575172198981096218823481e-12j)
+        >>> lerchphi(10,10,-10.5)
+        (112658784011940.5605789002 - 498113185.5756221777743631j)
+
+    Some degenerate cases::
+
+        >>> lerchphi(0,1,2)
+        0.5
+        >>> lerchphi(0,1,-2)
+        -0.5
+
+    Reduction to simpler functions::
+
+        >>> lerchphi(1, 4.25+1j, 1)
+        (1.044674457556746668033975 - 0.04674508654012658932271226j)
+        >>> zeta(4.25+1j)
+        (1.044674457556746668033975 - 0.04674508654012658932271226j)
+        >>> lerchphi(1 - 0.5**10, 4.25+1j, 1)
+        (1.044629338021507546737197 - 0.04667768813963388181708101j)
+        >>> lerchphi(3, 4, 1)
+        (1.249503297023366545192592 - 0.2314252413375664776474462j)
+        >>> polylog(4, 3) / 3
+        (1.249503297023366545192592 - 0.2314252413375664776474462j)
+        >>> lerchphi(3, 4, 1 - 0.5**10)
+        (1.253978063946663945672674 - 0.2316736622836535468765376j)
+
+    **References**
+
+    1. [DLMF]_ section 25.14
+
+    """
+    if z == 0:
+        return a ** (-s)
+    # Faster, but these cases are useful for testing right now
+    if z == 1:
+        return ctx.zeta(s, a)
+    if a == 1:
+        return ctx.polylog(s, z) / z
+    if ctx.re(a) < 1:
+        if ctx.isnpint(a):
+            raise ValueError("Lerch transcendent complex infinity")
+        m = int(ctx.ceil(1-ctx.re(a)))
+        v = ctx.zero
+        zpow = ctx.one
+        for n in xrange(m):
+            v += zpow / (a+n)**s
+            zpow *= z
+        return zpow * ctx.lerchphi(z,s, a+m) + v
+    g = ctx.ln(z)
+    v = 1/(2*a**s) + ctx.gammainc(1-s, -a*g) * (-g)**(s-1) / z**a
+    h = s / 2
+    r = 2*ctx.pi
+    f = lambda t: ctx.sin(s*ctx.atan(t/a)-t*g) / \
+        ((a**2+t**2)**h * ctx.expm1(r*t))
+    v += 2*ctx.quad(f, [0, ctx.inf])
+    if not ctx.im(z) and not ctx.im(s) and not ctx.im(a) and ctx.re(z) < 1:
+        v = ctx.chop(v)
+    return v

lib/python3.11/site-packages/mpmath/functions/zetazeros.py

@@ -0,0 +1,1018 @@
+"""
+The function zetazero(n) computes the n-th nontrivial zero of zeta(s).
+
+The general strategy is to locate a block of Gram intervals B where we
+know exactly the number of zeros contained and which of those zeros
+is that which we search.
+
+If n <= 400 000 000  we know exactly the Rosser exceptions, contained
+in a list in this file. Hence for n<=400 000 000 we simply
+look at these list of exceptions. If our zero is implicated in one of
+these exceptions we have our block B.  In other case we simply locate
+the good Rosser block containing our zero.
+
+For n > 400 000 000 we apply the method of Turing, as complemented by
+Lehman, Brent and Trudgian  to find a suitable B.
+"""
+
+from .functions import defun, defun_wrapped
+
+def find_rosser_block_zero(ctx, n):
+    """for n<400 000 000 determines a block were one find our zero"""
+    for k in range(len(_ROSSER_EXCEPTIONS)//2):
+        a=_ROSSER_EXCEPTIONS[2*k][0]
+        b=_ROSSER_EXCEPTIONS[2*k][1]
+        if ((a<= n-2) and (n-1 <= b)):
+            t0 = ctx.grampoint(a)
+            t1 = ctx.grampoint(b)
+            v0 = ctx._fp.siegelz(t0)
+            v1 = ctx._fp.siegelz(t1)
+            my_zero_number = n-a-1
+            zero_number_block = b-a
+            pattern = _ROSSER_EXCEPTIONS[2*k+1]
+            return (my_zero_number, [a,b], [t0,t1], [v0,v1])
+    k = n-2
+    t,v,b = compute_triple_tvb(ctx, k)
+    T = [t]
+    V = [v]
+    while b < 0:
+        k -= 1
+        t,v,b = compute_triple_tvb(ctx, k)
+        T.insert(0,t)
+        V.insert(0,v)
+    my_zero_number = n-k-1
+    m = n-1
+    t,v,b = compute_triple_tvb(ctx, m)
+    T.append(t)
+    V.append(v)
+    while b < 0:
+        m += 1
+        t,v,b = compute_triple_tvb(ctx, m)
+        T.append(t)
+        V.append(v)
+    return (my_zero_number, [k,m], T, V)
+
+def wpzeros(t):
+    """Precision needed to compute higher zeros"""
+    wp = 53
+    if t > 3*10**8:
+        wp = 63
+    if t > 10**11:
+        wp = 70
+    if t > 10**14:
+        wp = 83
+    return wp
+
+def separate_zeros_in_block(ctx, zero_number_block, T, V, limitloop=None,
+    fp_tolerance=None):
+    """Separate the zeros contained in the block T, limitloop
+    determines how long one must search"""
+    if limitloop is None:
+        limitloop = ctx.inf
+    loopnumber = 0
+    variations = count_variations(V)
+    while ((variations < zero_number_block) and (loopnumber <limitloop)):
+        a = T[0]
+        v = V[0]
+        newT = [a]
+        newV = [v]
+        variations = 0
+        for n in range(1,len(T)):
+            b2 = T[n]
+            u = V[n]
+            if (u*v>0):
+                alpha = ctx.sqrt(u/v)
+                b= (alpha*a+b2)/(alpha+1)
+            else:
+                b = (a+b2)/2
+            if fp_tolerance < 10:
+                w = ctx._fp.siegelz(b)
+                if abs(w)<fp_tolerance:
+                    w = ctx.siegelz(b)
+            else:
+                w=ctx.siegelz(b)
+            if v*w<0:
+                variations += 1
+            newT.append(b)
+            newV.append(w)
+            u = V[n]
+            if u*w <0:
+                variations += 1
+            newT.append(b2)
+            newV.append(u)
+            a = b2
+            v = u
+        T = newT
+        V = newV
+        loopnumber +=1
+        if (limitloop>ITERATION_LIMIT)and(loopnumber>2)and(variations+2==zero_number_block):
+            dtMax=0
+            dtSec=0
+            kMax = 0
+            for k1 in range(1,len(T)):
+                dt = T[k1]-T[k1-1]
+                if dt > dtMax:
+                    kMax=k1
+                    dtSec = dtMax
+                    dtMax = dt
+                elif  (dt<dtMax) and(dt >dtSec):
+                    dtSec = dt
+            if dtMax>3*dtSec:
+                f = lambda x: ctx.rs_z(x,derivative=1)
+                t0=T[kMax-1]
+                t1 = T[kMax]
+                t=ctx.findroot(f,  (t0,t1), solver ='illinois',verify=False, verbose=False)
+                v = ctx.siegelz(t)
+                if (t0<t) and (t<t1) and (v*V[kMax]<0):
+                    T.insert(kMax,t)
+                    V.insert(kMax,v)
+        variations = count_variations(V)
+    if variations == zero_number_block:
+        separated = True
+    else:
+        separated = False
+    return (T,V, separated)
+
+def separate_my_zero(ctx, my_zero_number, zero_number_block, T, V, prec):
+    """If we know which zero of this block is mine,
+    the function separates the zero"""
+    variations = 0
+    v0 = V[0]
+    for k in range(1,len(V)):
+        v1 = V[k]
+        if v0*v1 < 0:
+            variations +=1
+            if variations == my_zero_number:
+                k0 = k
+                leftv = v0
+                rightv = v1
+        v0 = v1
+    t1 = T[k0]
+    t0 = T[k0-1]
+    ctx.prec = prec
+    wpz = wpzeros(my_zero_number*ctx.log(my_zero_number))
+
+    guard = 4*ctx.mag(my_zero_number)
+    precs = [ctx.prec+4]
+    index=0
+    while precs[0] > 2*wpz:
+        index +=1
+        precs = [precs[0] // 2 +3+2*index] + precs
+    ctx.prec = precs[0] + guard
+    r = ctx.findroot(lambda x:ctx.siegelz(x), (t0,t1), solver ='illinois', verbose=False)
+    #print "first step at", ctx.dps, "digits"
+    z=ctx.mpc(0.5,r)
+    for prec in precs[1:]:
+        ctx.prec = prec + guard
+        #print "refining to", ctx.dps, "digits"
+        znew = z - ctx.zeta(z) / ctx.zeta(z, derivative=1)
+        #print "difference", ctx.nstr(abs(z-znew))
+        z=ctx.mpc(0.5,ctx.im(znew))
+    return ctx.im(z)
+
+def sure_number_block(ctx, n):
+    """The number of good Rosser blocks needed to apply
+    Turing method
+    References:
+    R. P. Brent, On the Zeros of the Riemann Zeta Function
+    in the Critical Strip, Math. Comp. 33 (1979) 1361--1372
+    T. Trudgian, Improvements to Turing Method, Math. Comp."""
+    if n < 9*10**5:
+        return(2)
+    g = ctx.grampoint(n-100)
+    lg = ctx._fp.ln(g)
+    brent = 0.0061 * lg**2 +0.08*lg
+    trudgian = 0.0031 * lg**2 +0.11*lg
+    N = ctx.ceil(min(brent,trudgian))
+    N = int(N)
+    return N
+
+def compute_triple_tvb(ctx, n):
+    t = ctx.grampoint(n)
+    v = ctx._fp.siegelz(t)
+    if ctx.mag(abs(v))<ctx.mag(t)-45:
+        v = ctx.siegelz(t)
+    b = v*(-1)**n
+    return t,v,b
+
+
+
+ITERATION_LIMIT = 4
+
+def search_supergood_block(ctx, n, fp_tolerance):
+    """To use for n>400 000 000"""
+    sb = sure_number_block(ctx, n)
+    number_goodblocks = 0
+    m2 = n-1
+    t, v, b = compute_triple_tvb(ctx, m2)
+    Tf = [t]
+    Vf = [v]
+    while b < 0:
+        m2 += 1
+        t,v,b = compute_triple_tvb(ctx, m2)
+        Tf.append(t)
+        Vf.append(v)
+    goodpoints = [m2]
+    T = [t]
+    V = [v]
+    while number_goodblocks < 2*sb:
+        m2 += 1
+        t, v, b = compute_triple_tvb(ctx, m2)
+        T.append(t)
+        V.append(v)
+        while b < 0:
+            m2 += 1
+            t,v,b = compute_triple_tvb(ctx, m2)
+            T.append(t)
+            V.append(v)
+        goodpoints.append(m2)
+        zn = len(T)-1
+        A, B, separated =\
+           separate_zeros_in_block(ctx, zn, T, V, limitloop=ITERATION_LIMIT,
+                fp_tolerance=fp_tolerance)
+        Tf.pop()
+        Tf.extend(A)
+        Vf.pop()
+        Vf.extend(B)
+        if separated:
+            number_goodblocks += 1
+        else:
+            number_goodblocks = 0
+        T = [t]
+        V = [v]
+    # Now the same procedure to the left
+    number_goodblocks = 0
+    m2 = n-2
+    t, v, b = compute_triple_tvb(ctx, m2)
+    Tf.insert(0,t)
+    Vf.insert(0,v)
+    while b < 0:
+        m2 -= 1
+        t,v,b = compute_triple_tvb(ctx, m2)
+        Tf.insert(0,t)
+        Vf.insert(0,v)
+    goodpoints.insert(0,m2)
+    T = [t]
+    V = [v]
+    while number_goodblocks < 2*sb:
+        m2 -= 1
+        t, v, b = compute_triple_tvb(ctx, m2)
+        T.insert(0,t)
+        V.insert(0,v)
+        while b < 0:
+            m2 -= 1
+            t,v,b = compute_triple_tvb(ctx, m2)
+            T.insert(0,t)
+            V.insert(0,v)
+        goodpoints.insert(0,m2)
+        zn = len(T)-1
+        A, B, separated =\
+           separate_zeros_in_block(ctx, zn, T, V, limitloop=ITERATION_LIMIT, fp_tolerance=fp_tolerance)
+        A.pop()
+        Tf = A+Tf
+        B.pop()
+        Vf = B+Vf
+        if separated:
+            number_goodblocks += 1
+        else:
+            number_goodblocks = 0
+        T = [t]
+        V = [v]
+    r = goodpoints[2*sb]
+    lg = len(goodpoints)
+    s = goodpoints[lg-2*sb-1]
+    tr, vr, br = compute_triple_tvb(ctx, r)
+    ar = Tf.index(tr)
+    ts, vs, bs = compute_triple_tvb(ctx, s)
+    as1 = Tf.index(ts)
+    T = Tf[ar:as1+1]
+    V = Vf[ar:as1+1]
+    zn = s-r
+    A, B, separated =\
+       separate_zeros_in_block(ctx, zn,T,V,limitloop=ITERATION_LIMIT, fp_tolerance=fp_tolerance)
+    if separated:
+        return (n-r-1,[r,s],A,B)
+    q = goodpoints[sb]
+    lg = len(goodpoints)
+    t = goodpoints[lg-sb-1]
+    tq, vq, bq = compute_triple_tvb(ctx, q)
+    aq = Tf.index(tq)
+    tt, vt, bt = compute_triple_tvb(ctx, t)
+    at = Tf.index(tt)
+    T = Tf[aq:at+1]
+    V = Vf[aq:at+1]
+    return (n-q-1,[q,t],T,V)
+
+def count_variations(V):
+    count = 0
+    vold = V[0]
+    for n in range(1, len(V)):
+        vnew = V[n]
+        if vold*vnew < 0:
+            count +=1
+        vold = vnew
+    return count
+
+def pattern_construct(ctx, block, T, V):
+    pattern = '('
+    a = block[0]
+    b = block[1]
+    t0,v0,b0 = compute_triple_tvb(ctx, a)
+    k = 0
+    k0 = 0
+    for n in range(a+1,b+1):
+        t1,v1,b1 = compute_triple_tvb(ctx, n)
+        lgT =len(T)
+        while (k < lgT) and (T[k] <= t1):
+            k += 1
+        L = V[k0:k]
+        L.append(v1)
+        L.insert(0,v0)
+        count = count_variations(L)
+        pattern = pattern + ("%s" % count)
+        if b1 > 0:
+            pattern = pattern + ')('
+        k0 = k
+        t0,v0,b0 = t1,v1,b1
+    pattern = pattern[:-1]
+    return pattern
+
+@defun
+def zetazero(ctx, n, info=False, round=True):
+    r"""
+    Computes the `n`-th nontrivial zero of `\zeta(s)` on the critical line,
+    i.e. returns an approximation of the `n`-th largest complex number
+    `s = \frac{1}{2} + ti` for which `\zeta(s) = 0`. Equivalently, the
+    imaginary part `t` is a zero of the Z-function (:func:`~mpmath.siegelz`).
+
+    **Examples**
+
+    The first few zeros::
+
+        >>> from mpmath import *
+        >>> mp.dps = 25; mp.pretty = True
+        >>> zetazero(1)
+        (0.5 + 14.13472514173469379045725j)
+        >>> zetazero(2)
+        (0.5 + 21.02203963877155499262848j)
+        >>> zetazero(20)
+        (0.5 + 77.14484006887480537268266j)
+
+    Verifying that the values are zeros::
+
+        >>> for n in range(1,5):
+        ...     s = zetazero(n)
+        ...     chop(zeta(s)), chop(siegelz(s.imag))
+        ...
+        (0.0, 0.0)
+        (0.0, 0.0)
+        (0.0, 0.0)
+        (0.0, 0.0)
+
+    Negative indices give the conjugate zeros (`n = 0` is undefined)::
+
+        >>> zetazero(-1)
+        (0.5 - 14.13472514173469379045725j)
+
+    :func:`~mpmath.zetazero` supports arbitrarily large `n` and arbitrary precision::
+
+        >>> mp.dps = 15
+        >>> zetazero(1234567)
+        (0.5 + 727690.906948208j)
+        >>> mp.dps = 50
+        >>> zetazero(1234567)
+        (0.5 + 727690.9069482075392389420041147142092708393819935j)
+        >>> chop(zeta(_)/_)
+        0.0
+
+    with *info=True*, :func:`~mpmath.zetazero` gives additional information::
+
+        >>> mp.dps = 15
+        >>> zetazero(542964976,info=True)
+        ((0.5 + 209039046.578535j), [542964969, 542964978], 6, '(013111110)')
+
+    This means that the zero is between Gram points 542964969 and 542964978;
+    it is the 6-th zero between them. Finally (01311110) is the pattern
+    of zeros in this interval. The numbers indicate the number of zeros
+    in each Gram interval (Rosser blocks between parenthesis). In this case
+    there is only one Rosser block of length nine.
+    """
+    n = int(n)
+    if n < 0:
+        return ctx.zetazero(-n).conjugate()
+    if n == 0:
+        raise ValueError("n must be nonzero")
+    wpinitial = ctx.prec
+    try:
+        wpz, fp_tolerance = comp_fp_tolerance(ctx, n)
+        ctx.prec = wpz
+        if n < 400000000:
+            my_zero_number, block, T, V =\
+             find_rosser_block_zero(ctx, n)
+        else:
+            my_zero_number, block, T, V =\
+             search_supergood_block(ctx, n, fp_tolerance)
+        zero_number_block = block[1]-block[0]
+        T, V, separated = separate_zeros_in_block(ctx, zero_number_block, T, V,
+            limitloop=ctx.inf, fp_tolerance=fp_tolerance)
+        if info:
+            pattern = pattern_construct(ctx,block,T,V)
+        prec = max(wpinitial, wpz)
+        t = separate_my_zero(ctx, my_zero_number, zero_number_block,T,V,prec)
+        v = ctx.mpc(0.5,t)
+    finally:
+        ctx.prec = wpinitial
+    if round:
+        v =+v
+    if info:
+        return (v,block,my_zero_number,pattern)
+    else:
+        return v
+
+def gram_index(ctx, t):
+    if t > 10**13:
+        wp = 3*ctx.log(t, 10)
+    else:
+        wp = 0
+    prec = ctx.prec
+    try:
+        ctx.prec += wp
+        h = int(ctx.siegeltheta(t)/ctx.pi)
+    finally:
+        ctx.prec = prec
+    return(h)
+
+def count_to(ctx, t, T, V):
+    count = 0
+    vold = V[0]
+    told = T[0]
+    tnew = T[1]
+    k = 1
+    while tnew < t:
+        vnew = V[k]
+        if vold*vnew < 0:
+            count += 1
+        vold = vnew
+        k += 1
+        tnew = T[k]
+    a = ctx.siegelz(t)
+    if a*vold < 0:
+        count += 1
+    return count
+
+def comp_fp_tolerance(ctx, n):
+    wpz = wpzeros(n*ctx.log(n))
+    if n < 15*10**8:
+        fp_tolerance = 0.0005
+    elif n <= 10**14:
+        fp_tolerance = 0.1
+    else:
+        fp_tolerance = 100
+    return wpz, fp_tolerance
+
+@defun
+def nzeros(ctx, t):
+    r"""
+    Computes the number of zeros of the Riemann zeta function in
+    `(0,1) \times (0,t]`, usually denoted by `N(t)`.
+
+    **Examples**
+
+    The first zero has imaginary part between 14 and 15::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> nzeros(14)
+        0
+        >>> nzeros(15)
+        1
+        >>> zetazero(1)
+        (0.5 + 14.1347251417347j)
+
+    Some closely spaced zeros::
+
+        >>> nzeros(10**7)
+        21136125
+        >>> zetazero(21136125)
+        (0.5 + 9999999.32718175j)
+        >>> zetazero(21136126)
+        (0.5 + 10000000.2400236j)
+        >>> nzeros(545439823.215)
+        1500000001
+        >>> zetazero(1500000001)
+        (0.5 + 545439823.201985j)
+        >>> zetazero(1500000002)
+        (0.5 + 545439823.325697j)
+
+    This confirms the data given by J. van de Lune,
+    H. J. J. te Riele and D. T. Winter in 1986.
+    """
+    if t < 14.1347251417347:
+        return 0
+    x = gram_index(ctx, t)
+    k = int(ctx.floor(x))
+    wpinitial = ctx.prec
+    wpz, fp_tolerance = comp_fp_tolerance(ctx, k)
+    ctx.prec = wpz
+    a = ctx.siegelz(t)
+    if k == -1 and a < 0:
+        return 0
+    elif k == -1 and a > 0:
+        return 1
+    if k+2 < 400000000:
+        Rblock = find_rosser_block_zero(ctx, k+2)
+    else:
+        Rblock = search_supergood_block(ctx, k+2, fp_tolerance)
+    n1, n2 = Rblock[1]
+    if n2-n1 == 1:
+        b = Rblock[3][0]
+        if a*b > 0:
+            ctx.prec = wpinitial
+            return k+1
+        else:
+            ctx.prec = wpinitial
+            return k+2
+    my_zero_number,block, T, V = Rblock
+    zero_number_block = n2-n1
+    T, V, separated = separate_zeros_in_block(ctx,\
+                                              zero_number_block, T, V,\
+                                              limitloop=ctx.inf,\
+                                            fp_tolerance=fp_tolerance)
+    n = count_to(ctx, t, T, V)
+    ctx.prec = wpinitial
+    return n+n1+1
+
+@defun_wrapped
+def backlunds(ctx, t):
+    r"""
+    Computes the function
+    `S(t) = \operatorname{arg} \zeta(\frac{1}{2} + it) / \pi`.
+
+    See Titchmarsh Section 9.3 for details of the definition.
+
+    **Examples**
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> backlunds(217.3)
+        0.16302205431184
+
+    Generally, the value is a small number. At Gram points it is an integer,
+    frequently equal to 0::
+
+        >>> chop(backlunds(grampoint(200)))
+        0.0
+        >>> backlunds(extraprec(10)(grampoint)(211))
+        1.0
+        >>> backlunds(extraprec(10)(grampoint)(232))
+        -1.0
+
+    The number of zeros of the Riemann zeta function up to height `t`
+    satisfies `N(t) = \theta(t)/\pi + 1 + S(t)` (see :func:nzeros` and
+    :func:`siegeltheta`)::
+
+        >>> t = 1234.55
+        >>> nzeros(t)
+        842
+        >>> siegeltheta(t)/pi+1+backlunds(t)
+        842.0
+
+    """
+    return ctx.nzeros(t)-1-ctx.siegeltheta(t)/ctx.pi
+
+
+"""
+_ROSSER_EXCEPTIONS is a list of all  exceptions to
+Rosser's rule for n <= 400 000 000.
+
+Alternately the  entry is of type   [n,m], or a string.
+The string is the zero pattern of the Block and the relevant
+adjacent.  For example (010)3 corresponds to a block
+composed of three Gram intervals, the first ant third without
+a zero and the intermediate with a zero. The next Gram interval
+contain three zeros. So that in total we have 4 zeros in 4 Gram
+blocks. n and m are the indices of the Gram points  of this
+interval of four Gram intervals. The Rosser exception is therefore
+formed by the three Gram intervals that are signaled between
+parenthesis.
+
+We have included also some Rosser's exceptions beyond n=400 000 000
+that are noted in the literature by some reason.
+
+The list is composed from the data published in the references:
+
+R. P. Brent, J. van de Lune, H. J. J. te Riele, D. T. Winter,
+'On the Zeros of the Riemann Zeta Function in the Critical Strip. II',
+Math. Comp. 39 (1982) 681--688.
+See also Corrigenda in Math. Comp. 46 (1986) 771.
+
+J. van de Lune, H. J. J. te Riele,
+'On the Zeros of the Riemann Zeta Function in the Critical Strip. III',
+Math. Comp. 41 (1983) 759--767.
+See also  Corrigenda in Math. Comp. 46 (1986) 771.
+
+J. van de Lune,
+'Sums of Equal Powers of Positive Integers',
+Dissertation,
+Vrije Universiteit te Amsterdam, Centrum voor Wiskunde en Informatica,
+Amsterdam, 1984.
+
+Thanks to the authors all this papers and those others that have
+contributed to make this possible.
+"""
+
+
+
+
+
+
+
+_ROSSER_EXCEPTIONS = \
+[[13999525, 13999528], '(00)3',
+[30783329, 30783332], '(00)3',
+[30930926, 30930929], '3(00)',
+[37592215, 37592218], '(00)3',
+[40870156, 40870159], '(00)3',
+[43628107, 43628110], '(00)3',
+[46082042, 46082045], '(00)3',
+[46875667, 46875670], '(00)3',
+[49624540, 49624543], '3(00)',
+[50799238, 50799241], '(00)3',
+[55221453, 55221456], '3(00)',
+[56948779, 56948782], '3(00)',
+[60515663, 60515666], '(00)3',
+[61331766, 61331770], '(00)40',
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+]

lib/python3.11/site-packages/mpmath/identification.py

@@ -0,0 +1,844 @@
+"""
+Implements the PSLQ algorithm for integer relation detection,
+and derivative algorithms for constant recognition.
+"""
+
+from .libmp.backend import xrange
+from .libmp import int_types, sqrt_fixed
+
+# round to nearest integer (can be done more elegantly...)
+def round_fixed(x, prec):
+    return ((x + (1<<(prec-1))) >> prec) << prec
+
+class IdentificationMethods(object):
+    pass
+
+
+def pslq(ctx, x, tol=None, maxcoeff=1000, maxsteps=100, verbose=False):
+    r"""
+    Given a vector of real numbers `x = [x_0, x_1, ..., x_n]`, ``pslq(x)``
+    uses the PSLQ algorithm to find a list of integers
+    `[c_0, c_1, ..., c_n]` such that
+
+    .. math ::
+
+        |c_1 x_1 + c_2 x_2 + ... + c_n x_n| < \mathrm{tol}
+
+    and such that `\max |c_k| < \mathrm{maxcoeff}`. If no such vector
+    exists, :func:`~mpmath.pslq` returns ``None``. The tolerance defaults to
+    3/4 of the working precision.
+
+    **Examples**
+
+    Find rational approximations for `\pi`::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> pslq([-1, pi], tol=0.01)
+        [22, 7]
+        >>> pslq([-1, pi], tol=0.001)
+        [355, 113]
+        >>> mpf(22)/7; mpf(355)/113; +pi
+        3.14285714285714
+        3.14159292035398
+        3.14159265358979
+
+    Pi is not a rational number with denominator less than 1000::
+
+        >>> pslq([-1, pi])
+        >>>
+
+    To within the standard precision, it can however be approximated
+    by at least one rational number with denominator less than `10^{12}`::
+
+        >>> p, q = pslq([-1, pi], maxcoeff=10**12)
+        >>> print(p); print(q)
+        238410049439
+        75888275702
+        >>> mpf(p)/q
+        3.14159265358979
+
+    The PSLQ algorithm can be applied to long vectors. For example,
+    we can investigate the rational (in)dependence of integer square
+    roots::
+
+        >>> mp.dps = 30
+        >>> pslq([sqrt(n) for n in range(2, 5+1)])
+        >>>
+        >>> pslq([sqrt(n) for n in range(2, 6+1)])
+        >>>
+        >>> pslq([sqrt(n) for n in range(2, 8+1)])
+        [2, 0, 0, 0, 0, 0, -1]
+
+    **Machin formulas**
+
+    A famous formula for `\pi` is Machin's,
+
+    .. math ::
+
+        \frac{\pi}{4} = 4 \operatorname{acot} 5 - \operatorname{acot} 239
+
+    There are actually infinitely many formulas of this type. Two
+    others are
+
+    .. math ::
+
+        \frac{\pi}{4} = \operatorname{acot} 1
+
+        \frac{\pi}{4} = 12 \operatorname{acot} 49 + 32 \operatorname{acot} 57
+            + 5 \operatorname{acot} 239 + 12 \operatorname{acot} 110443
+
+    We can easily verify the formulas using the PSLQ algorithm::
+
+        >>> mp.dps = 30
+        >>> pslq([pi/4, acot(1)])
+        [1, -1]
+        >>> pslq([pi/4, acot(5), acot(239)])
+        [1, -4, 1]
+        >>> pslq([pi/4, acot(49), acot(57), acot(239), acot(110443)])
+        [1, -12, -32, 5, -12]
+
+    We could try to generate a custom Machin-like formula by running
+    the PSLQ algorithm with a few inverse cotangent values, for example
+    acot(2), acot(3) ... acot(10). Unfortunately, there is a linear
+    dependence among these values, resulting in only that dependence
+    being detected, with a zero coefficient for `\pi`::
+
+        >>> pslq([pi] + [acot(n) for n in range(2,11)])
+        [0, 1, -1, 0, 0, 0, -1, 0, 0, 0]
+
+    We get better luck by removing linearly dependent terms::
+
+        >>> pslq([pi] + [acot(n) for n in range(2,11) if n not in (3, 5)])
+        [1, -8, 0, 0, 4, 0, 0, 0]
+
+    In other words, we found the following formula::
+
+        >>> 8*acot(2) - 4*acot(7)
+        3.14159265358979323846264338328
+        >>> +pi
+        3.14159265358979323846264338328
+
+    **Algorithm**
+
+    This is a fairly direct translation to Python of the pseudocode given by
+    David Bailey, "The PSLQ Integer Relation Algorithm":
+    http://www.cecm.sfu.ca/organics/papers/bailey/paper/html/node3.html
+
+    The present implementation uses fixed-point instead of floating-point
+    arithmetic, since this is significantly (about 7x) faster.
+    """
+
+    n = len(x)
+    if n < 2:
+        raise ValueError("n cannot be less than 2")
+
+    # At too low precision, the algorithm becomes meaningless
+    prec = ctx.prec
+    if prec < 53:
+        raise ValueError("prec cannot be less than 53")
+
+    if verbose and prec // max(2,n) < 5:
+        print("Warning: precision for PSLQ may be too low")
+
+    target = int(prec * 0.75)
+
+    if tol is None:
+        tol = ctx.mpf(2)**(-target)
+    else:
+        tol = ctx.convert(tol)
+
+    extra = 60
+    prec += extra
+
+    if verbose:
+        print("PSLQ using prec %i and tol %s" % (prec, ctx.nstr(tol)))
+
+    tol = ctx.to_fixed(tol, prec)
+    assert tol
+
+    # Convert to fixed-point numbers. The dummy None is added so we can
+    # use 1-based indexing. (This just allows us to be consistent with
+    # Bailey's indexing. The algorithm is 100 lines long, so debugging
+    # a single wrong index can be painful.)
+    x = [None] + [ctx.to_fixed(ctx.mpf(xk), prec) for xk in x]
+
+    # Sanity check on magnitudes
+    minx = min(abs(xx) for xx in x[1:])
+    if not minx:
+        raise ValueError("PSLQ requires a vector of nonzero numbers")
+    if minx < tol//100:
+        if verbose:
+            print("STOPPING: (one number is too small)")
+        return None
+
+    g = sqrt_fixed((4<<prec)//3, prec)
+    A = {}
+    B = {}
+    H = {}
+    # Initialization
+    # step 1
+    for i in xrange(1, n+1):
+        for j in xrange(1, n+1):
+            A[i,j] = B[i,j] = (i==j) << prec
+            H[i,j] = 0
+    # step 2
+    s = [None] + [0] * n
+    for k in xrange(1, n+1):
+        t = 0
+        for j in xrange(k, n+1):
+            t += (x[j]**2 >> prec)
+        s[k] = sqrt_fixed(t, prec)
+    t = s[1]
+    y = x[:]
+    for k in xrange(1, n+1):
+        y[k] = (x[k] << prec) // t
+        s[k] = (s[k] << prec) // t
+    # step 3
+    for i in xrange(1, n+1):
+        for j in xrange(i+1, n):
+            H[i,j] = 0
+        if i <= n-1:
+            if s[i]:
+                H[i,i] = (s[i+1] << prec) // s[i]
+            else:
+                H[i,i] = 0
+        for j in range(1, i):
+            sjj1 = s[j]*s[j+1]
+            if sjj1:
+                H[i,j] = ((-y[i]*y[j])<<prec)//sjj1
+            else:
+                H[i,j] = 0
+    # step 4
+    for i in xrange(2, n+1):
+        for j in xrange(i-1, 0, -1):
+            #t = floor(H[i,j]/H[j,j] + 0.5)
+            if H[j,j]:
+                t = round_fixed((H[i,j] << prec)//H[j,j], prec)
+            else:
+                #t = 0
+                continue
+            y[j] = y[j] + (t*y[i] >> prec)
+            for k in xrange(1, j+1):
+                H[i,k] = H[i,k] - (t*H[j,k] >> prec)
+            for k in xrange(1, n+1):
+                A[i,k] = A[i,k] - (t*A[j,k] >> prec)
+                B[k,j] = B[k,j] + (t*B[k,i] >> prec)
+    # Main algorithm
+    for REP in range(maxsteps):
+        # Step 1
+        m = -1
+        szmax = -1
+        for i in range(1, n):
+            h = H[i,i]
+            sz = (g**i * abs(h)) >> (prec*(i-1))
+            if sz > szmax:
+                m = i
+                szmax = sz
+        # Step 2
+        y[m], y[m+1] = y[m+1], y[m]
+        for i in xrange(1,n+1): H[m,i], H[m+1,i] = H[m+1,i], H[m,i]
+        for i in xrange(1,n+1): A[m,i], A[m+1,i] = A[m+1,i], A[m,i]
+        for i in xrange(1,n+1): B[i,m], B[i,m+1] = B[i,m+1], B[i,m]
+        # Step 3
+        if m <= n - 2:
+            t0 = sqrt_fixed((H[m,m]**2 + H[m,m+1]**2)>>prec, prec)
+            # A zero element probably indicates that the precision has
+            # been exhausted. XXX: this could be spurious, due to
+            # using fixed-point arithmetic
+            if not t0:
+                break
+            t1 = (H[m,m] << prec) // t0
+            t2 = (H[m,m+1] << prec) // t0
+            for i in xrange(m, n+1):
+                t3 = H[i,m]
+                t4 = H[i,m+1]
+                H[i,m] = (t1*t3+t2*t4) >> prec
+                H[i,m+1] = (-t2*t3+t1*t4) >> prec
+        # Step 4
+        for i in xrange(m+1, n+1):
+            for j in xrange(min(i-1, m+1), 0, -1):
+                try:
+                    t = round_fixed((H[i,j] << prec)//H[j,j], prec)
+                # Precision probably exhausted
+                except ZeroDivisionError:
+                    break
+                y[j] = y[j] + ((t*y[i]) >> prec)
+                for k in xrange(1, j+1):
+                    H[i,k] = H[i,k] - (t*H[j,k] >> prec)
+                for k in xrange(1, n+1):
+                    A[i,k] = A[i,k] - (t*A[j,k] >> prec)
+                    B[k,j] = B[k,j] + (t*B[k,i] >> prec)
+        # Until a relation is found, the error typically decreases
+        # slowly (e.g. a factor 1-10) with each step TODO: we could
+        # compare err from two successive iterations. If there is a
+        # large drop (several orders of magnitude), that indicates a
+        # "high quality" relation was detected. Reporting this to
+        # the user somehow might be useful.
+        best_err = maxcoeff<<prec
+        for i in xrange(1, n+1):
+            err = abs(y[i])
+            # Maybe we are done?
+            if err < tol:
+                # We are done if the coefficients are acceptable
+                vec = [int(round_fixed(B[j,i], prec) >> prec) for j in \
+                range(1,n+1)]
+                if max(abs(v) for v in vec) < maxcoeff:
+                    if verbose:
+                        print("FOUND relation at iter %i/%i, error: %s" % \
+                            (REP, maxsteps, ctx.nstr(err / ctx.mpf(2)**prec, 1)))
+                    return vec
+            best_err = min(err, best_err)
+        # Calculate a lower bound for the norm. We could do this
+        # more exactly (using the Euclidean norm) but there is probably
+        # no practical benefit.
+        recnorm = max(abs(h) for h in H.values())
+        if recnorm:
+            norm = ((1 << (2*prec)) // recnorm) >> prec
+            norm //= 100
+        else:
+            norm = ctx.inf
+        if verbose:
+            print("%i/%i:  Error: %8s   Norm: %s" % \
+                (REP, maxsteps, ctx.nstr(best_err / ctx.mpf(2)**prec, 1), norm))
+        if norm >= maxcoeff:
+            break
+    if verbose:
+        print("CANCELLING after step %i/%i." % (REP, maxsteps))
+        print("Could not find an integer relation. Norm bound: %s" % norm)
+    return None
+
+def findpoly(ctx, x, n=1, **kwargs):
+    r"""
+    ``findpoly(x, n)`` returns the coefficients of an integer
+    polynomial `P` of degree at most `n` such that `P(x) \approx 0`.
+    If no polynomial having `x` as a root can be found,
+    :func:`~mpmath.findpoly` returns ``None``.
+
+    :func:`~mpmath.findpoly` works by successively calling :func:`~mpmath.pslq` with
+    the vectors `[1, x]`, `[1, x, x^2]`, `[1, x, x^2, x^3]`, ...,
+    `[1, x, x^2, .., x^n]` as input. Keyword arguments given to
+    :func:`~mpmath.findpoly` are forwarded verbatim to :func:`~mpmath.pslq`. In
+    particular, you can specify a tolerance for `P(x)` with ``tol``
+    and a maximum permitted coefficient size with ``maxcoeff``.
+
+    For large values of `n`, it is recommended to run :func:`~mpmath.findpoly`
+    at high precision; preferably 50 digits or more.
+
+    **Examples**
+
+    By default (degree `n = 1`), :func:`~mpmath.findpoly` simply finds a linear
+    polynomial with a rational root::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> findpoly(0.7)
+        [-10, 7]
+
+    The generated coefficient list is valid input to ``polyval`` and
+    ``polyroots``::
+
+        >>> nprint(polyval(findpoly(phi, 2), phi), 1)
+        -2.0e-16
+        >>> for r in polyroots(findpoly(phi, 2)):
+        ...     print(r)
+        ...
+        -0.618033988749895
+        1.61803398874989
+
+    Numbers of the form `m + n \sqrt p` for integers `(m, n, p)` are
+    solutions to quadratic equations. As we find here, `1+\sqrt 2`
+    is a root of the polynomial `x^2 - 2x - 1`::
+
+        >>> findpoly(1+sqrt(2), 2)
+        [1, -2, -1]
+        >>> findroot(lambda x: x**2 - 2*x - 1, 1)
+        2.4142135623731
+
+    Despite only containing square roots, the following number results
+    in a polynomial of degree 4::
+
+        >>> findpoly(sqrt(2)+sqrt(3), 4)
+        [1, 0, -10, 0, 1]
+
+    In fact, `x^4 - 10x^2 + 1` is the *minimal polynomial* of
+    `r = \sqrt 2 + \sqrt 3`, meaning that a rational polynomial of
+    lower degree having `r` as a root does not exist. Given sufficient
+    precision, :func:`~mpmath.findpoly` will usually find the correct
+    minimal polynomial of a given algebraic number.
+
+    **Non-algebraic numbers**
+
+    If :func:`~mpmath.findpoly` fails to find a polynomial with given
+    coefficient size and tolerance constraints, that means no such
+    polynomial exists.
+
+    We can verify that `\pi` is not an algebraic number of degree 3 with
+    coefficients less than 1000::
+
+        >>> mp.dps = 15
+        >>> findpoly(pi, 3)
+        >>>
+
+    It is always possible to find an algebraic approximation of a number
+    using one (or several) of the following methods:
+
+        1. Increasing the permitted degree
+        2. Allowing larger coefficients
+        3. Reducing the tolerance
+
+    One example of each method is shown below::
+
+        >>> mp.dps = 15
+        >>> findpoly(pi, 4)
+        [95, -545, 863, -183, -298]
+        >>> findpoly(pi, 3, maxcoeff=10000)
+        [836, -1734, -2658, -457]
+        >>> findpoly(pi, 3, tol=1e-7)
+        [-4, 22, -29, -2]
+
+    It is unknown whether Euler's constant is transcendental (or even
+    irrational). We can use :func:`~mpmath.findpoly` to check that if is
+    an algebraic number, its minimal polynomial must have degree
+    at least 7 and a coefficient of magnitude at least 1000000::
+
+        >>> mp.dps = 200
+        >>> findpoly(euler, 6, maxcoeff=10**6, tol=1e-100, maxsteps=1000)
+        >>>
+
+    Note that the high precision and strict tolerance is necessary
+    for such high-degree runs, since otherwise unwanted low-accuracy
+    approximations will be detected. It may also be necessary to set
+    maxsteps high to prevent a premature exit (before the coefficient
+    bound has been reached). Running with ``verbose=True`` to get an
+    idea what is happening can be useful.
+    """
+    x = ctx.mpf(x)
+    if n < 1:
+        raise ValueError("n cannot be less than 1")
+    if x == 0:
+        return [1, 0]
+    xs = [ctx.mpf(1)]
+    for i in range(1,n+1):
+        xs.append(x**i)
+        a = ctx.pslq(xs, **kwargs)
+        if a is not None:
+            return a[::-1]
+
+def fracgcd(p, q):
+    x, y = p, q
+    while y:
+        x, y = y, x % y
+    if x != 1:
+        p //= x
+        q //= x
+    if q == 1:
+        return p
+    return p, q
+
+def pslqstring(r, constants):
+    q = r[0]
+    r = r[1:]
+    s = []
+    for i in range(len(r)):
+        p = r[i]
+        if p:
+            z = fracgcd(-p,q)
+            cs = constants[i][1]
+            if cs == '1':
+                cs = ''
+            else:
+                cs = '*' + cs
+            if isinstance(z, int_types):
+                if z > 0: term = str(z) + cs
+                else:     term = ("(%s)" % z) + cs
+            else:
+                term = ("(%s/%s)" % z) + cs
+            s.append(term)
+    s = ' + '.join(s)
+    if '+' in s or '*' in s:
+        s = '(' + s + ')'
+    return s or '0'
+
+def prodstring(r, constants):
+    q = r[0]
+    r = r[1:]
+    num = []
+    den = []
+    for i in range(len(r)):
+        p = r[i]
+        if p:
+            z = fracgcd(-p,q)
+            cs = constants[i][1]
+            if isinstance(z, int_types):
+                if abs(z) == 1: t = cs
+                else:           t = '%s**%s' % (cs, abs(z))
+                ([num,den][z<0]).append(t)
+            else:
+                t = '%s**(%s/%s)' % (cs, abs(z[0]), z[1])
+                ([num,den][z[0]<0]).append(t)
+    num = '*'.join(num)
+    den = '*'.join(den)
+    if num and den: return "(%s)/(%s)" % (num, den)
+    if num: return num
+    if den: return "1/(%s)" % den
+
+def quadraticstring(ctx,t,a,b,c):
+    if c < 0:
+        a,b,c = -a,-b,-c
+    u1 = (-b+ctx.sqrt(b**2-4*a*c))/(2*c)
+    u2 = (-b-ctx.sqrt(b**2-4*a*c))/(2*c)
+    if abs(u1-t) < abs(u2-t):
+        if b:  s = '((%s+sqrt(%s))/%s)' % (-b,b**2-4*a*c,2*c)
+        else:  s = '(sqrt(%s)/%s)' % (-4*a*c,2*c)
+    else:
+        if b:  s = '((%s-sqrt(%s))/%s)' % (-b,b**2-4*a*c,2*c)
+        else:  s = '(-sqrt(%s)/%s)' % (-4*a*c,2*c)
+    return s
+
+# Transformation y = f(x,c), with inverse function x = f(y,c)
+# The third entry indicates whether the transformation is
+# redundant when c = 1
+transforms = [
+  (lambda ctx,x,c: x*c, '$y/$c', 0),
+  (lambda ctx,x,c: x/c, '$c*$y', 1),
+  (lambda ctx,x,c: c/x, '$c/$y', 0),
+  (lambda ctx,x,c: (x*c)**2, 'sqrt($y)/$c', 0),
+  (lambda ctx,x,c: (x/c)**2, '$c*sqrt($y)', 1),
+  (lambda ctx,x,c: (c/x)**2, '$c/sqrt($y)', 0),
+  (lambda ctx,x,c: c*x**2, 'sqrt($y)/sqrt($c)', 1),
+  (lambda ctx,x,c: x**2/c, 'sqrt($c)*sqrt($y)', 1),
+  (lambda ctx,x,c: c/x**2, 'sqrt($c)/sqrt($y)', 1),
+  (lambda ctx,x,c: ctx.sqrt(x*c), '$y**2/$c', 0),
+  (lambda ctx,x,c: ctx.sqrt(x/c), '$c*$y**2', 1),
+  (lambda ctx,x,c: ctx.sqrt(c/x), '$c/$y**2', 0),
+  (lambda ctx,x,c: c*ctx.sqrt(x), '$y**2/$c**2', 1),
+  (lambda ctx,x,c: ctx.sqrt(x)/c, '$c**2*$y**2', 1),
+  (lambda ctx,x,c: c/ctx.sqrt(x), '$c**2/$y**2', 1),
+  (lambda ctx,x,c: ctx.exp(x*c), 'log($y)/$c', 0),
+  (lambda ctx,x,c: ctx.exp(x/c), '$c*log($y)', 1),
+  (lambda ctx,x,c: ctx.exp(c/x), '$c/log($y)', 0),
+  (lambda ctx,x,c: c*ctx.exp(x), 'log($y/$c)', 1),
+  (lambda ctx,x,c: ctx.exp(x)/c, 'log($c*$y)', 1),
+  (lambda ctx,x,c: c/ctx.exp(x), 'log($c/$y)', 0),
+  (lambda ctx,x,c: ctx.ln(x*c), 'exp($y)/$c', 0),
+  (lambda ctx,x,c: ctx.ln(x/c), '$c*exp($y)', 1),
+  (lambda ctx,x,c: ctx.ln(c/x), '$c/exp($y)', 0),
+  (lambda ctx,x,c: c*ctx.ln(x), 'exp($y/$c)', 1),
+  (lambda ctx,x,c: ctx.ln(x)/c, 'exp($c*$y)', 1),
+  (lambda ctx,x,c: c/ctx.ln(x), 'exp($c/$y)', 0),
+]
+
+def identify(ctx, x, constants=[], tol=None, maxcoeff=1000, full=False,
+    verbose=False):
+    r"""
+    Given a real number `x`, ``identify(x)`` attempts to find an exact
+    formula for `x`. This formula is returned as a string. If no match
+    is found, ``None`` is returned. With ``full=True``, a list of
+    matching formulas is returned.
+
+    As a simple example, :func:`~mpmath.identify` will find an algebraic
+    formula for the golden ratio::
+
+        >>> from mpmath import *
+        >>> mp.dps = 15; mp.pretty = True
+        >>> identify(phi)
+        '((1+sqrt(5))/2)'
+
+    :func:`~mpmath.identify` can identify simple algebraic numbers and simple
+    combinations of given base constants, as well as certain basic
+    transformations thereof. More specifically, :func:`~mpmath.identify`
+    looks for the following:
+
+        1. Fractions
+        2. Quadratic algebraic numbers
+        3. Rational linear combinations of the base constants
+        4. Any of the above after first transforming `x` into `f(x)` where
+           `f(x)` is `1/x`, `\sqrt x`, `x^2`, `\log x` or `\exp x`, either
+           directly or with `x` or `f(x)` multiplied or divided by one of
+           the base constants
+        5. Products of fractional powers of the base constants and
+           small integers
+
+    Base constants can be given as a list of strings representing mpmath
+    expressions (:func:`~mpmath.identify` will ``eval`` the strings to numerical
+    values and use the original strings for the output), or as a dict of
+    formula:value pairs.
+
+    In order not to produce spurious results, :func:`~mpmath.identify` should
+    be used with high precision; preferably 50 digits or more.
+
+    **Examples**
+
+    Simple identifications can be performed safely at standard
+    precision. Here the default recognition of rational, algebraic,
+    and exp/log of algebraic numbers is demonstrated::
+
+        >>> mp.dps = 15
+        >>> identify(0.22222222222222222)
+        '(2/9)'
+        >>> identify(1.9662210973805663)
+        'sqrt(((24+sqrt(48))/8))'
+        >>> identify(4.1132503787829275)
+        'exp((sqrt(8)/2))'
+        >>> identify(0.881373587019543)
+        'log(((2+sqrt(8))/2))'
+
+    By default, :func:`~mpmath.identify` does not recognize `\pi`. At standard
+    precision it finds a not too useful approximation. At slightly
+    increased precision, this approximation is no longer accurate
+    enough and :func:`~mpmath.identify` more correctly returns ``None``::
+
+        >>> identify(pi)
+        '(2**(176/117)*3**(20/117)*5**(35/39))/(7**(92/117))'
+        >>> mp.dps = 30
+        >>> identify(pi)
+        >>>
+
+    Numbers such as `\pi`, and simple combinations of user-defined
+    constants, can be identified if they are provided explicitly::
+
+        >>> identify(3*pi-2*e, ['pi', 'e'])
+        '(3*pi + (-2)*e)'
+
+    Here is an example using a dict of constants. Note that the
+    constants need not be "atomic"; :func:`~mpmath.identify` can just
+    as well express the given number in terms of expressions
+    given by formulas::
+
+        >>> identify(pi+e, {'a':pi+2, 'b':2*e})
+        '((-2) + 1*a + (1/2)*b)'
+
+    Next, we attempt some identifications with a set of base constants.
+    It is necessary to increase the precision a bit.
+
+        >>> mp.dps = 50
+        >>> base = ['sqrt(2)','pi','log(2)']
+        >>> identify(0.25, base)
+        '(1/4)'
+        >>> identify(3*pi + 2*sqrt(2) + 5*log(2)/7, base)
+        '(2*sqrt(2) + 3*pi + (5/7)*log(2))'
+        >>> identify(exp(pi+2), base)
+        'exp((2 + 1*pi))'
+        >>> identify(1/(3+sqrt(2)), base)
+        '((3/7) + (-1/7)*sqrt(2))'
+        >>> identify(sqrt(2)/(3*pi+4), base)
+        'sqrt(2)/(4 + 3*pi)'
+        >>> identify(5**(mpf(1)/3)*pi*log(2)**2, base)
+        '5**(1/3)*pi*log(2)**2'
+
+    An example of an erroneous solution being found when too low
+    precision is used::
+
+        >>> mp.dps = 15
+        >>> identify(1/(3*pi-4*e+sqrt(8)), ['pi', 'e', 'sqrt(2)'])
+        '((11/25) + (-158/75)*pi + (76/75)*e + (44/15)*sqrt(2))'
+        >>> mp.dps = 50
+        >>> identify(1/(3*pi-4*e+sqrt(8)), ['pi', 'e', 'sqrt(2)'])
+        '1/(3*pi + (-4)*e + 2*sqrt(2))'
+
+    **Finding approximate solutions**
+
+    The tolerance ``tol`` defaults to 3/4 of the working precision.
+    Lowering the tolerance is useful for finding approximate matches.
+    We can for example try to generate approximations for pi::
+
+        >>> mp.dps = 15
+        >>> identify(pi, tol=1e-2)
+        '(22/7)'
+        >>> identify(pi, tol=1e-3)
+        '(355/113)'
+        >>> identify(pi, tol=1e-10)
+        '(5**(339/269))/(2**(64/269)*3**(13/269)*7**(92/269))'
+
+    With ``full=True``, and by supplying a few base constants,
+    ``identify`` can generate almost endless lists of approximations
+    for any number (the output below has been truncated to show only
+    the first few)::
+
+        >>> for p in identify(pi, ['e', 'catalan'], tol=1e-5, full=True):
+        ...     print(p)
+        ...  # doctest: +ELLIPSIS
+        e/log((6 + (-4/3)*e))
+        (3**3*5*e*catalan**2)/(2*7**2)
+        sqrt(((-13) + 1*e + 22*catalan))
+        log(((-6) + 24*e + 4*catalan)/e)
+        exp(catalan*((-1/5) + (8/15)*e))
+        catalan*(6 + (-6)*e + 15*catalan)
+        sqrt((5 + 26*e + (-3)*catalan))/e
+        e*sqrt(((-27) + 2*e + 25*catalan))
+        log(((-1) + (-11)*e + 59*catalan))
+        ((3/20) + (21/20)*e + (3/20)*catalan)
+        ...
+
+    The numerical values are roughly as close to `\pi` as permitted by the
+    specified tolerance:
+
+        >>> e/log(6-4*e/3)
+        3.14157719846001
+        >>> 135*e*catalan**2/98
+        3.14166950419369
+        >>> sqrt(e-13+22*catalan)
+        3.14158000062992
+        >>> log(24*e-6+4*catalan)-1
+        3.14158791577159
+
+    **Symbolic processing**
+
+    The output formula can be evaluated as a Python expression.
+    Note however that if fractions (like '2/3') are present in
+    the formula, Python's :func:`~mpmath.eval()` may erroneously perform
+    integer division. Note also that the output is not necessarily
+    in the algebraically simplest form::
+
+        >>> identify(sqrt(2))
+        '(sqrt(8)/2)'
+
+    As a solution to both problems, consider using SymPy's
+    :func:`~mpmath.sympify` to convert the formula into a symbolic expression.
+    SymPy can be used to pretty-print or further simplify the formula
+    symbolically::
+
+        >>> from sympy import sympify # doctest: +SKIP
+        >>> sympify(identify(sqrt(2))) # doctest: +SKIP
+        2**(1/2)
+
+    Sometimes :func:`~mpmath.identify` can simplify an expression further than
+    a symbolic algorithm::
+
+        >>> from sympy import simplify # doctest: +SKIP
+        >>> x = sympify('-1/(-3/2+(1/2)*5**(1/2))*(3/2-1/2*5**(1/2))**(1/2)') # doctest: +SKIP
+        >>> x # doctest: +SKIP
+        (3/2 - 5**(1/2)/2)**(-1/2)
+        >>> x = simplify(x) # doctest: +SKIP
+        >>> x # doctest: +SKIP
+        2/(6 - 2*5**(1/2))**(1/2)
+        >>> mp.dps = 30 # doctest: +SKIP
+        >>> x = sympify(identify(x.evalf(30))) # doctest: +SKIP
+        >>> x # doctest: +SKIP
+        1/2 + 5**(1/2)/2
+
+    (In fact, this functionality is available directly in SymPy as the
+    function :func:`~mpmath.nsimplify`, which is essentially a wrapper for
+    :func:`~mpmath.identify`.)
+
+    **Miscellaneous issues and limitations**
+
+    The input `x` must be a real number. All base constants must be
+    positive real numbers and must not be rationals or rational linear
+    combinations of each other.
+
+    The worst-case computation time grows quickly with the number of
+    base constants. Already with 3 or 4 base constants,
+    :func:`~mpmath.identify` may require several seconds to finish. To search
+    for relations among a large number of constants, you should
+    consider using :func:`~mpmath.pslq` directly.
+
+    The extended transformations are applied to x, not the constants
+    separately. As a result, ``identify`` will for example be able to
+    recognize ``exp(2*pi+3)`` with ``pi`` given as a base constant, but
+    not ``2*exp(pi)+3``. It will be able to recognize the latter if
+    ``exp(pi)`` is given explicitly as a base constant.
+
+    """
+
+    solutions = []
+
+    def addsolution(s):
+        if verbose: print("Found: ", s)
+        solutions.append(s)
+
+    x = ctx.mpf(x)
+
+    # Further along, x will be assumed positive
+    if x == 0:
+        if full: return ['0']
+        else:    return '0'
+    if x < 0:
+        sol = ctx.identify(-x, constants, tol, maxcoeff, full, verbose)
+        if sol is None:
+            return sol
+        if full:
+            return ["-(%s)"%s for s in sol]
+        else:
+            return "-(%s)" % sol
+
+    if tol:
+        tol = ctx.mpf(tol)
+    else:
+        tol = ctx.eps**0.7
+    M = maxcoeff
+
+    if constants:
+        if isinstance(constants, dict):
+            constants = [(ctx.mpf(v), name) for (name, v) in sorted(constants.items())]
+        else:
+            namespace = dict((name, getattr(ctx,name)) for name in dir(ctx))
+            constants = [(eval(p, namespace), p) for p in constants]
+    else:
+        constants = []
+
+    # We always want to find at least rational terms
+    if 1 not in [value for (name, value) in constants]:
+        constants = [(ctx.mpf(1), '1')] + constants
+
+    # PSLQ with simple algebraic and functional transformations
+    for ft, ftn, red in transforms:
+        for c, cn in constants:
+            if red and cn == '1':
+                continue
+            t = ft(ctx,x,c)
+            # Prevent exponential transforms from wreaking havoc
+            if abs(t) > M**2 or abs(t) < tol:
+                continue
+            # Linear combination of base constants
+            r = ctx.pslq([t] + [a[0] for a in constants], tol, M)
+            s = None
+            if r is not None and max(abs(uw) for uw in r) <= M and r[0]:
+                s = pslqstring(r, constants)
+            # Quadratic algebraic numbers
+            else:
+                q = ctx.pslq([ctx.one, t, t**2], tol, M)
+                if q is not None and len(q) == 3 and q[2]:
+                    aa, bb, cc = q
+                    if max(abs(aa),abs(bb),abs(cc)) <= M:
+                        s = quadraticstring(ctx,t,aa,bb,cc)
+            if s:
+                if cn == '1' and ('/$c' in ftn):
+                    s = ftn.replace('$y', s).replace('/$c', '')
+                else:
+                    s = ftn.replace('$y', s).replace('$c', cn)
+                addsolution(s)
+                if not full: return solutions[0]
+
+            if verbose:
+                print(".")
+
+    # Check for a direct multiplicative formula
+    if x != 1:
+        # Allow fractional powers of fractions
+        ilogs = [2,3,5,7]
+        # Watch out for existing fractional powers of fractions
+        logs = []
+        for a, s in constants:
+            if not sum(bool(ctx.findpoly(ctx.ln(a)/ctx.ln(i),1)) for i in ilogs):
+                logs.append((ctx.ln(a), s))
+        logs = [(ctx.ln(i),str(i)) for i in ilogs] + logs
+        r = ctx.pslq([ctx.ln(x)] + [a[0] for a in logs], tol, M)
+        if r is not None and max(abs(uw) for uw in r) <= M and r[0]:
+            addsolution(prodstring(r, logs))
+            if not full: return solutions[0]
+
+    if full:
+        return sorted(solutions, key=len)
+    else:
+        return None
+
+IdentificationMethods.pslq = pslq
+IdentificationMethods.findpoly = findpoly
+IdentificationMethods.identify = identify
+
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

lib/python3.11/site-packages/mpmath/libmp/__init__.py

@@ -0,0 +1,77 @@
+from .libmpf import (prec_to_dps, dps_to_prec, repr_dps,
+  round_down, round_up, round_floor, round_ceiling, round_nearest,
+  to_pickable, from_pickable, ComplexResult,
+  fzero, fnzero, fone, fnone, ftwo, ften, fhalf, fnan, finf, fninf,
+  math_float_inf, round_int, normalize, normalize1,
+  from_man_exp, from_int, to_man_exp, to_int, mpf_ceil, mpf_floor,
+  mpf_nint, mpf_frac,
+  from_float, from_npfloat, from_Decimal, to_float, from_rational, to_rational, to_fixed,
+  mpf_rand, mpf_eq, mpf_hash, mpf_cmp, mpf_lt, mpf_le, mpf_gt, mpf_ge,
+  mpf_pos, mpf_neg, mpf_abs, mpf_sign, mpf_add, mpf_sub, mpf_sum,
+  mpf_mul, mpf_mul_int, mpf_shift, mpf_frexp,
+  mpf_div, mpf_rdiv_int, mpf_mod, mpf_pow_int,
+  mpf_perturb,
+  to_digits_exp, to_str, str_to_man_exp, from_str, from_bstr, to_bstr,
+  mpf_sqrt, mpf_hypot)
+
+from .libmpc import (mpc_one, mpc_zero, mpc_two, mpc_half,
+  mpc_is_inf, mpc_is_infnan, mpc_to_str, mpc_to_complex, mpc_hash,
+  mpc_conjugate, mpc_is_nonzero, mpc_add, mpc_add_mpf,
+  mpc_sub, mpc_sub_mpf, mpc_pos, mpc_neg, mpc_shift, mpc_abs,
+  mpc_arg, mpc_floor, mpc_ceil,  mpc_nint, mpc_frac, mpc_mul, mpc_square,
+  mpc_mul_mpf, mpc_mul_imag_mpf, mpc_mul_int,
+  mpc_div, mpc_div_mpf, mpc_reciprocal, mpc_mpf_div,
+  complex_int_pow, mpc_pow, mpc_pow_mpf, mpc_pow_int,
+  mpc_sqrt, mpc_nthroot, mpc_cbrt, mpc_exp, mpc_log, mpc_cos, mpc_sin,
+  mpc_tan, mpc_cos_pi, mpc_sin_pi, mpc_cosh, mpc_sinh, mpc_tanh,
+  mpc_atan, mpc_acos, mpc_asin, mpc_asinh, mpc_acosh, mpc_atanh,
+  mpc_fibonacci, mpf_expj, mpf_expjpi, mpc_expj, mpc_expjpi,
+  mpc_cos_sin, mpc_cos_sin_pi)
+
+from .libelefun import (ln2_fixed, mpf_ln2, ln10_fixed, mpf_ln10,
+  pi_fixed, mpf_pi, e_fixed, mpf_e, phi_fixed, mpf_phi,
+  degree_fixed, mpf_degree,
+  mpf_pow, mpf_nthroot, mpf_cbrt, log_int_fixed, agm_fixed,
+  mpf_log, mpf_log_hypot, mpf_exp, mpf_cos_sin, mpf_cos, mpf_sin, mpf_tan,
+  mpf_cos_sin_pi, mpf_cos_pi, mpf_sin_pi, mpf_cosh_sinh,
+  mpf_cosh, mpf_sinh, mpf_tanh, mpf_atan, mpf_atan2, mpf_asin,
+  mpf_acos, mpf_asinh, mpf_acosh, mpf_atanh, mpf_fibonacci)
+
+from .libhyper import (NoConvergence, make_hyp_summator,
+  mpf_erf, mpf_erfc, mpf_ei, mpc_ei, mpf_e1, mpc_e1, mpf_expint,
+  mpf_ci_si, mpf_ci, mpf_si, mpc_ci, mpc_si, mpf_besseljn,
+  mpc_besseljn, mpf_agm, mpf_agm1, mpc_agm, mpc_agm1,
+  mpf_ellipk, mpc_ellipk, mpf_ellipe, mpc_ellipe)
+
+from .gammazeta import (catalan_fixed, mpf_catalan,
+  khinchin_fixed, mpf_khinchin, glaisher_fixed, mpf_glaisher,
+  apery_fixed, mpf_apery, euler_fixed, mpf_euler, mertens_fixed,
+  mpf_mertens, twinprime_fixed, mpf_twinprime,
+  mpf_bernoulli, bernfrac, mpf_gamma_int,
+  mpf_factorial, mpc_factorial, mpf_gamma, mpc_gamma,
+  mpf_loggamma, mpc_loggamma, mpf_rgamma, mpc_rgamma,
+  mpf_harmonic, mpc_harmonic, mpf_psi0, mpc_psi0,
+  mpf_psi, mpc_psi, mpf_zeta_int, mpf_zeta, mpc_zeta,
+  mpf_altzeta, mpc_altzeta, mpf_zetasum, mpc_zetasum)
+
+from .libmpi import (mpi_str,
+  mpi_from_str, mpi_to_str,
+  mpi_eq, mpi_ne,
+  mpi_lt, mpi_le, mpi_gt, mpi_ge,
+  mpi_add, mpi_sub, mpi_delta, mpi_mid,
+  mpi_pos, mpi_neg, mpi_abs, mpi_mul, mpi_div, mpi_exp,
+  mpi_log, mpi_sqrt, mpi_pow_int, mpi_pow, mpi_cos_sin,
+  mpi_cos, mpi_sin, mpi_tan, mpi_cot,
+  mpi_atan, mpi_atan2,
+  mpci_pos, mpci_neg, mpci_add, mpci_sub, mpci_mul, mpci_div, mpci_pow,
+  mpci_abs, mpci_pow, mpci_exp, mpci_log, mpci_cos, mpci_sin,
+  mpi_gamma, mpci_gamma, mpi_loggamma, mpci_loggamma,
+  mpi_rgamma, mpci_rgamma, mpi_factorial, mpci_factorial)
+
+from .libintmath import (trailing, bitcount, numeral, bin_to_radix,
+  isqrt, isqrt_small, isqrt_fast, sqrt_fixed, sqrtrem, ifib, ifac,
+  list_primes, isprime, moebius, gcd, eulernum, stirling1, stirling2)
+
+from .backend import (gmpy, sage, BACKEND, STRICT, MPZ, MPZ_TYPE,
+  MPZ_ZERO, MPZ_ONE, MPZ_TWO, MPZ_THREE, MPZ_FIVE, int_types,
+  HASH_MODULUS, HASH_BITS)

lib/python3.11/site-packages/mpmath/libmp/backend.py

@@ -0,0 +1,115 @@
+import os
+import sys
+
+#----------------------------------------------------------------------------#
+# Support GMPY for high-speed large integer arithmetic.                      #
+#                                                                            #
+# To allow an external module to handle arithmetic, we need to make sure     #
+# that all high-precision variables are declared of the correct type. MPZ    #
+# is the constructor for the high-precision type. It defaults to Python's    #
+# long type but can be assinged another type, typically gmpy.mpz.            #
+#                                                                            #
+# MPZ must be used for the mantissa component of an mpf and must be used     #
+# for internal fixed-point operations.                                       #
+#                                                                            #
+# Side-effects                                                               #
+# 1) "is" cannot be used to test for special values. Must use "==".          #
+# 2) There are bugs in GMPY prior to v1.02 so we must use v1.03 or later.    #
+#----------------------------------------------------------------------------#
+
+# So we can import it from this module
+gmpy = None
+sage = None
+sage_utils = None
+
+if sys.version_info[0] < 3:
+    python3 = False
+else:
+    python3 = True
+
+BACKEND = 'python'
+
+if not python3:
+    MPZ = long
+    xrange = xrange
+    basestring = basestring
+
+    def exec_(_code_, _globs_=None, _locs_=None):
+        """Execute code in a namespace."""
+        if _globs_ is None:
+            frame = sys._getframe(1)
+            _globs_ = frame.f_globals
+            if _locs_ is None:
+                _locs_ = frame.f_locals
+            del frame
+        elif _locs_ is None:
+            _locs_ = _globs_
+        exec("""exec _code_ in _globs_, _locs_""")
+else:
+    MPZ = int
+    xrange = range
+    basestring = str
+
+    import builtins
+    exec_ = getattr(builtins, "exec")
+
+# Define constants for calculating hash on Python 3.2.
+if sys.version_info >= (3, 2):
+    HASH_MODULUS = sys.hash_info.modulus
+    if sys.hash_info.width == 32:
+        HASH_BITS = 31
+    else:
+        HASH_BITS = 61
+else:
+    HASH_MODULUS = None
+    HASH_BITS = None
+
+if 'MPMATH_NOGMPY' not in os.environ:
+    try:
+        try:
+            import gmpy2 as gmpy
+        except ImportError:
+            try:
+                import gmpy
+            except ImportError:
+                raise ImportError
+        if gmpy.version() >= '1.03':
+            BACKEND = 'gmpy'
+            MPZ = gmpy.mpz
+    except:
+        pass
+
+if ('MPMATH_NOSAGE' not in os.environ and 'SAGE_ROOT' in os.environ or
+        'MPMATH_SAGE' in os.environ):
+    try:
+        import sage.all
+        import sage.libs.mpmath.utils as _sage_utils
+        sage = sage.all
+        sage_utils = _sage_utils
+        BACKEND = 'sage'
+        MPZ = sage.Integer
+    except:
+        pass
+
+if 'MPMATH_STRICT' in os.environ:
+    STRICT = True
+else:
+    STRICT = False
+
+MPZ_TYPE = type(MPZ(0))
+MPZ_ZERO = MPZ(0)
+MPZ_ONE = MPZ(1)
+MPZ_TWO = MPZ(2)
+MPZ_THREE = MPZ(3)
+MPZ_FIVE = MPZ(5)
+
+try:
+    if BACKEND == 'python':
+        int_types = (int, long)
+    else:
+        int_types = (int, long, MPZ_TYPE)
+except NameError:
+    if BACKEND == 'python':
+        int_types = (int,)
+    else:
+        int_types = (int, MPZ_TYPE)

lib/python3.11/site-packages/mpmath/libmp/gammazeta.py

@@ -0,0 +1,2167 @@
+"""
+-----------------------------------------------------------------------
+This module implements gamma- and zeta-related functions:
+
+* Bernoulli numbers
+* Factorials
+* The gamma function
+* Polygamma functions
+* Harmonic numbers
+* The Riemann zeta function
+* Constants related to these functions
+
+-----------------------------------------------------------------------
+"""
+
+import math
+import sys
+
+from .backend import xrange
+from .backend import MPZ, MPZ_ZERO, MPZ_ONE, MPZ_THREE, gmpy
+
+from .libintmath import list_primes, ifac, ifac2, moebius
+
+from .libmpf import (\
+    round_floor, round_ceiling, round_down, round_up,
+    round_nearest, round_fast,
+    lshift, sqrt_fixed, isqrt_fast,
+    fzero, fone, fnone, fhalf, ftwo, finf, fninf, fnan,
+    from_int, to_int, to_fixed, from_man_exp, from_rational,
+    mpf_pos, mpf_neg, mpf_abs, mpf_add, mpf_sub,
+    mpf_mul, mpf_mul_int, mpf_div, mpf_sqrt, mpf_pow_int,
+    mpf_rdiv_int,
+    mpf_perturb, mpf_le, mpf_lt, mpf_gt, mpf_shift,
+    negative_rnd, reciprocal_rnd,
+    bitcount, to_float, mpf_floor, mpf_sign, ComplexResult
+)
+
+from .libelefun import (\
+    constant_memo,
+    def_mpf_constant,
+    mpf_pi, pi_fixed, ln2_fixed, log_int_fixed, mpf_ln2,
+    mpf_exp, mpf_log, mpf_pow, mpf_cosh,
+    mpf_cos_sin, mpf_cosh_sinh, mpf_cos_sin_pi, mpf_cos_pi, mpf_sin_pi,
+    ln_sqrt2pi_fixed, mpf_ln_sqrt2pi, sqrtpi_fixed, mpf_sqrtpi,
+    cos_sin_fixed, exp_fixed
+)
+
+from .libmpc import (\
+    mpc_zero, mpc_one, mpc_half, mpc_two,
+    mpc_abs, mpc_shift, mpc_pos, mpc_neg,
+    mpc_add, mpc_sub, mpc_mul, mpc_div,
+    mpc_add_mpf, mpc_mul_mpf, mpc_div_mpf, mpc_mpf_div,
+    mpc_mul_int, mpc_pow_int,
+    mpc_log, mpc_exp, mpc_pow,
+    mpc_cos_pi, mpc_sin_pi,
+    mpc_reciprocal, mpc_square,
+    mpc_sub_mpf
+)
+
+
+
+# Catalan's constant is computed using Lupas's rapidly convergent series
+# (listed on http://mathworld.wolfram.com/CatalansConstant.html)
+#            oo
+#            ___       n-1  8n     2                   3    2
+#        1  \      (-1)    2   (40n  - 24n + 3) [(2n)!] (n!)
+#  K =  ---  )     -----------------------------------------
+#       64  /___               3               2
+#                             n  (2n-1) [(4n)!]
+#           n = 1
+
+@constant_memo
+def catalan_fixed(prec):
+    prec = prec + 20
+    a = one = MPZ_ONE << prec
+    s, t, n = 0, 1, 1
+    while t:
+        a *= 32 * n**3 * (2*n-1)
+        a //= (3-16*n+16*n**2)**2
+        t = a * (-1)**(n-1) * (40*n**2-24*n+3) // (n**3 * (2*n-1))
+        s += t
+        n += 1
+    return s >> (20 + 6)
+
+# Khinchin's constant is relatively difficult to compute. Here
+# we use the rational zeta series
+
+#                    oo                2*n-1
+#                   ___                ___
+#                   \   ` zeta(2*n)-1  \   ` (-1)^(k+1)
+#  log(K)*log(2) =   )    ------------  )    ----------
+#                   /___.      n       /___.      k
+#                   n = 1              k = 1
+
+# which adds half a digit per term. The essential trick for achieving
+# reasonable efficiency is to recycle both the values of the zeta
+# function (essentially Bernoulli numbers) and the partial terms of
+# the inner sum.
+
+# An alternative might be to use K = 2*exp[1/log(2) X] where
+
+#      / 1     1       [ pi*x*(1-x^2) ]
+#  X = |    ------ log [ ------------ ].
+#      / 0  x(1+x)     [  sin(pi*x)   ]
+
+# and integrate numerically. In practice, this seems to be slightly
+# slower than the zeta series at high precision.
+
+@constant_memo
+def khinchin_fixed(prec):
+    wp = int(prec + prec**0.5 + 15)
+    s = MPZ_ZERO
+    fac = from_int(4)
+    t = ONE = MPZ_ONE << wp
+    pi = mpf_pi(wp)
+    pipow = twopi2 = mpf_shift(mpf_mul(pi, pi, wp), 2)
+    n = 1
+    while 1:
+        zeta2n = mpf_abs(mpf_bernoulli(2*n, wp))
+        zeta2n = mpf_mul(zeta2n, pipow, wp)
+        zeta2n = mpf_div(zeta2n, fac, wp)
+        zeta2n = to_fixed(zeta2n, wp)
+        term = (((zeta2n - ONE) * t) // n) >> wp
+        if term < 100:
+            break
+        #if not n % 10:
+        #    print n, math.log(int(abs(term)))
+        s += term
+        t += ONE//(2*n+1) - ONE//(2*n)
+        n += 1
+        fac = mpf_mul_int(fac, (2*n)*(2*n-1), wp)
+        pipow = mpf_mul(pipow, twopi2, wp)
+    s = (s << wp) // ln2_fixed(wp)
+    K = mpf_exp(from_man_exp(s, -wp), wp)
+    K = to_fixed(K, prec)
+    return K
+
+
+# Glaisher's constant is defined as A = exp(1/2 - zeta'(-1)).
+# One way to compute it would be to perform direct numerical
+# differentiation, but computing arbitrary Riemann zeta function
+# values at high precision is expensive. We instead use the formula
+
+#     A = exp((6 (-zeta'(2))/pi^2 + log 2 pi + gamma)/12)
+
+# and compute zeta'(2) from the series representation
+
+#              oo
+#              ___
+#             \     log k
+#  -zeta'(2) = )    -----
+#             /___     2
+#                    k
+#            k = 2
+
+# This series converges exceptionally slowly, but can be accelerated
+# using Euler-Maclaurin formula. The important insight is that the
+# E-M integral can be done in closed form and that the high order
+# are given by
+
+#    n  /       \
+#   d   | log x |   a + b log x
+#   --- | ----- | = -----------
+#     n |   2   |      2 + n
+#   dx  \  x    /     x
+
+# where a and b are integers given by a simple recurrence. Note
+# that just one logarithm is needed. However, lots of integer
+# logarithms are required for the initial summation.
+
+# This algorithm could possibly be turned into a faster algorithm
+# for general evaluation of zeta(s) or zeta'(s); this should be
+# looked into.
+
+@constant_memo
+def glaisher_fixed(prec):
+    wp = prec + 30
+    # Number of direct terms to sum before applying the Euler-Maclaurin
+    # formula to the tail. TODO: choose more intelligently
+    N = int(0.33*prec + 5)
+    ONE = MPZ_ONE << wp
+    # Euler-Maclaurin, step 1: sum log(k)/k**2 for k from 2 to N-1
+    s = MPZ_ZERO
+    for k in range(2, N):
+        #print k, N
+        s += log_int_fixed(k, wp) // k**2
+    logN = log_int_fixed(N, wp)
+    #logN = to_fixed(mpf_log(from_int(N), wp+20), wp)
+    # E-M step 2: integral of log(x)/x**2 from N to inf
+    s += (ONE + logN) // N
+    # E-M step 3: endpoint correction term f(N)/2
+    s += logN // (N**2 * 2)
+    # E-M step 4: the series of derivatives
+    pN = N**3
+    a = 1
+    b = -2
+    j = 3
+    fac = from_int(2)
+    k = 1
+    while 1:
+        # D(2*k-1) * B(2*k) / fac(2*k) [D(n) = nth derivative]
+        D = ((a << wp) + b*logN) // pN
+        D = from_man_exp(D, -wp)
+        B = mpf_bernoulli(2*k, wp)
+        term = mpf_mul(B, D, wp)
+        term = mpf_div(term, fac, wp)
+        term = to_fixed(term, wp)
+        if abs(term) < 100:
+            break
+        #if not k % 10:
+        #    print k, math.log(int(abs(term)), 10)
+        s -= term
+        # Advance derivative twice
+        a, b, pN, j = b-a*j, -j*b, pN*N, j+1
+        a, b, pN, j = b-a*j, -j*b, pN*N, j+1
+        k += 1
+        fac = mpf_mul_int(fac, (2*k)*(2*k-1), wp)
+    # A = exp((6*s/pi**2 + log(2*pi) + euler)/12)
+    pi = pi_fixed(wp)
+    s *= 6
+    s = (s << wp) // (pi**2 >> wp)
+    s += euler_fixed(wp)
+    s += to_fixed(mpf_log(from_man_exp(2*pi, -wp), wp), wp)
+    s //= 12
+    A = mpf_exp(from_man_exp(s, -wp), wp)
+    return to_fixed(A, prec)
+
+# Apery's constant can be computed using the very rapidly convergent
+# series
+#              oo
+#              ___              2                      10
+#             \         n  205 n  + 250 n + 77     (n!)
+#  zeta(3) =   )    (-1)   -------------------  ----------
+#             /___               64                      5
+#             n = 0                             ((2n+1)!)
+
+@constant_memo
+def apery_fixed(prec):
+    prec += 20
+    d = MPZ_ONE << prec
+    term = MPZ(77) << prec
+    n = 1
+    s = MPZ_ZERO
+    while term:
+        s += term
+        d *= (n**10)
+        d //= (((2*n+1)**5) * (2*n)**5)
+        term = (-1)**n * (205*(n**2) + 250*n + 77) * d
+        n += 1
+    return s >> (20 + 6)
+
+"""
+Euler's constant (gamma) is computed using the Brent-McMillan formula,
+gamma ~= I(n)/J(n) - log(n), where
+
+   I(n) = sum_{k=0,1,2,...} (n**k / k!)**2 * H(k)
+   J(n) = sum_{k=0,1,2,...} (n**k / k!)**2
+   H(k) = 1 + 1/2 + 1/3 + ... + 1/k
+
+The error is bounded by O(exp(-4n)). Choosing n to be a power
+of two, 2**p, the logarithm becomes particularly easy to calculate.[1]
+
+We use the formulation of Algorithm 3.9 in [2] to make the summation
+more efficient.
+
+Reference:
+[1] Xavier Gourdon & Pascal Sebah, The Euler constant: gamma
+http://numbers.computation.free.fr/Constants/Gamma/gamma.pdf
+
+[2] [BorweinBailey]_
+"""
+
+@constant_memo
+def euler_fixed(prec):
+    extra = 30
+    prec += extra
+    # choose p such that exp(-4*(2**p)) < 2**-n
+    p = int(math.log((prec/4) * math.log(2), 2)) + 1
+    n = 2**p
+    A = U = -p*ln2_fixed(prec)
+    B = V = MPZ_ONE << prec
+    k = 1
+    while 1:
+        B = B*n**2//k**2
+        A = (A*n**2//k + B)//k
+        U += A
+        V += B
+        if max(abs(A), abs(B)) < 100:
+            break
+        k += 1
+    return (U<<(prec-extra))//V
+
+# Use zeta accelerated formulas for the Mertens and twin
+# prime constants; see
+# http://mathworld.wolfram.com/MertensConstant.html
+# http://mathworld.wolfram.com/TwinPrimesConstant.html
+
+@constant_memo
+def mertens_fixed(prec):
+    wp = prec + 20
+    m = 2
+    s = mpf_euler(wp)
+    while 1:
+        t = mpf_zeta_int(m, wp)
+        if t == fone:
+            break
+        t = mpf_log(t, wp)
+        t = mpf_mul_int(t, moebius(m), wp)
+        t = mpf_div(t, from_int(m), wp)
+        s = mpf_add(s, t)
+        m += 1
+    return to_fixed(s, prec)
+
+@constant_memo
+def twinprime_fixed(prec):
+    def I(n):
+        return sum(moebius(d)<<(n//d) for d in xrange(1,n+1) if not n%d)//n
+    wp = 2*prec + 30
+    res = fone
+    primes = [from_rational(1,p,wp) for p in [2,3,5,7]]
+    ppowers = [mpf_mul(p,p,wp) for p in primes]
+    n = 2
+    while 1:
+        a = mpf_zeta_int(n, wp)
+        for i in range(4):
+            a = mpf_mul(a, mpf_sub(fone, ppowers[i]), wp)
+            ppowers[i] = mpf_mul(ppowers[i], primes[i], wp)
+        a = mpf_pow_int(a, -I(n), wp)
+        if mpf_pos(a, prec+10, 'n') == fone:
+            break
+        #from libmpf import to_str
+        #print n, to_str(mpf_sub(fone, a), 6)
+        res = mpf_mul(res, a, wp)
+        n += 1
+    res = mpf_mul(res, from_int(3*15*35), wp)
+    res = mpf_div(res, from_int(4*16*36), wp)
+    return to_fixed(res, prec)
+
+
+mpf_euler = def_mpf_constant(euler_fixed)
+mpf_apery = def_mpf_constant(apery_fixed)
+mpf_khinchin = def_mpf_constant(khinchin_fixed)
+mpf_glaisher = def_mpf_constant(glaisher_fixed)
+mpf_catalan = def_mpf_constant(catalan_fixed)
+mpf_mertens = def_mpf_constant(mertens_fixed)
+mpf_twinprime = def_mpf_constant(twinprime_fixed)
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                          Bernoulli numbers                            #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+MAX_BERNOULLI_CACHE = 3000
+
+
+r"""
+Small Bernoulli numbers and factorials are used in numerous summations,
+so it is critical for speed that sequential computation is fast and that
+values are cached up to a fairly high threshold.
+
+On the other hand, we also want to support fast computation of isolated
+large numbers. Currently, no such acceleration is provided for integer
+factorials (though it is for large floating-point factorials, which are
+computed via gamma if the precision is low enough).
+
+For sequential computation of Bernoulli numbers, we use Ramanujan's formula
+
+                           / n + 3 \
+  B   =  (A(n) - S(n))  /  |       |
+   n                       \   n   /
+
+where A(n) = (n+3)/3 when n = 0 or 2 (mod 6), A(n) = -(n+3)/6
+when n = 4 (mod 6), and
+
+         [n/6]
+          ___
+         \      /  n + 3  \
+  S(n) =  )     |         | * B
+         /___   \ n - 6*k /    n-6*k
+         k = 1
+
+For isolated large Bernoulli numbers, we use the Riemann zeta function
+to calculate a numerical value for B_n. The von Staudt-Clausen theorem
+can then be used to optionally find the exact value of the
+numerator and denominator.
+"""
+
+bernoulli_cache = {}
+f3 = from_int(3)
+f6 = from_int(6)
+
+def bernoulli_size(n):
+    """Accurately estimate the size of B_n (even n > 2 only)"""
+    lgn = math.log(n,2)
+    return int(2.326 + 0.5*lgn + n*(lgn - 4.094))
+
+BERNOULLI_PREC_CUTOFF = bernoulli_size(MAX_BERNOULLI_CACHE)
+
+def mpf_bernoulli(n, prec, rnd=None):
+    """Computation of Bernoulli numbers (numerically)"""
+    if n < 2:
+        if n < 0:
+            raise ValueError("Bernoulli numbers only defined for n >= 0")
+        if n == 0:
+            return fone
+        if n == 1:
+            return mpf_neg(fhalf)
+    # For odd n > 1, the Bernoulli numbers are zero
+    if n & 1:
+        return fzero
+    # If precision is extremely high, we can save time by computing
+    # the Bernoulli number at a lower precision that is sufficient to
+    # obtain the exact fraction, round to the exact fraction, and
+    # convert the fraction back to an mpf value at the original precision
+    if prec > BERNOULLI_PREC_CUTOFF and prec > bernoulli_size(n)*1.1 + 1000:
+        p, q = bernfrac(n)
+        return from_rational(p, q, prec, rnd or round_floor)
+    if n > MAX_BERNOULLI_CACHE:
+        return mpf_bernoulli_huge(n, prec, rnd)
+    wp = prec + 30
+    # Reuse nearby precisions
+    wp += 32 - (prec & 31)
+    cached = bernoulli_cache.get(wp)
+    if cached:
+        numbers, state = cached
+        if n in numbers:
+            if not rnd:
+                return numbers[n]
+            return mpf_pos(numbers[n], prec, rnd)
+        m, bin, bin1 = state
+        if n - m > 10:
+            return mpf_bernoulli_huge(n, prec, rnd)
+    else:
+        if n > 10:
+            return mpf_bernoulli_huge(n, prec, rnd)
+        numbers = {0:fone}
+        m, bin, bin1 = state = [2, MPZ(10), MPZ_ONE]
+        bernoulli_cache[wp] = (numbers, state)
+    while m <= n:
+        #print m
+        case = m % 6
+        # Accurately estimate size of B_m so we can use
+        # fixed point math without using too much precision
+        szbm = bernoulli_size(m)
+        s = 0
+        sexp = max(0, szbm)  - wp
+        if m < 6:
+            a = MPZ_ZERO
+        else:
+            a = bin1
+        for j in xrange(1, m//6+1):
+            usign, uman, uexp, ubc = u = numbers[m-6*j]
+            if usign:
+                uman = -uman
+            s += lshift(a*uman, uexp-sexp)
+            # Update inner binomial coefficient
+            j6 = 6*j
+            a *= ((m-5-j6)*(m-4-j6)*(m-3-j6)*(m-2-j6)*(m-1-j6)*(m-j6))
+            a //= ((4+j6)*(5+j6)*(6+j6)*(7+j6)*(8+j6)*(9+j6))
+        if case == 0: b = mpf_rdiv_int(m+3, f3, wp)
+        if case == 2: b = mpf_rdiv_int(m+3, f3, wp)
+        if case == 4: b = mpf_rdiv_int(-m-3, f6, wp)
+        s = from_man_exp(s, sexp, wp)
+        b = mpf_div(mpf_sub(b, s, wp), from_int(bin), wp)
+        numbers[m] = b
+        m += 2
+        # Update outer binomial coefficient
+        bin = bin * ((m+2)*(m+3)) // (m*(m-1))
+        if m > 6:
+            bin1 = bin1 * ((2+m)*(3+m)) // ((m-7)*(m-6))
+        state[:] = [m, bin, bin1]
+    return numbers[n]
+
+def mpf_bernoulli_huge(n, prec, rnd=None):
+    wp = prec + 10
+    piprec = wp + int(math.log(n,2))
+    v = mpf_gamma_int(n+1, wp)
+    v = mpf_mul(v, mpf_zeta_int(n, wp), wp)
+    v = mpf_mul(v, mpf_pow_int(mpf_pi(piprec), -n, wp))
+    v = mpf_shift(v, 1-n)
+    if not n & 3:
+        v = mpf_neg(v)
+    return mpf_pos(v, prec, rnd or round_fast)
+
+def bernfrac(n):
+    r"""
+    Returns a tuple of integers `(p, q)` such that `p/q = B_n` exactly,
+    where `B_n` denotes the `n`-th Bernoulli number. The fraction is
+    always reduced to lowest terms. Note that for `n > 1` and `n` odd,
+    `B_n = 0`, and `(0, 1)` is returned.
+
+    **Examples**
+
+    The first few Bernoulli numbers are exactly::
+
+        >>> from mpmath import *
+        >>> for n in range(15):
+        ...     p, q = bernfrac(n)
+        ...     print("%s %s/%s" % (n, p, q))
+        ...
+        0 1/1
+        1 -1/2
+        2 1/6
+        3 0/1
+        4 -1/30
+        5 0/1
+        6 1/42
+        7 0/1
+        8 -1/30
+        9 0/1
+        10 5/66
+        11 0/1
+        12 -691/2730
+        13 0/1
+        14 7/6
+
+    This function works for arbitrarily large `n`::
+
+        >>> p, q = bernfrac(10**4)
+        >>> print(q)
+        2338224387510
+        >>> print(len(str(p)))
+        27692
+        >>> mp.dps = 15
+        >>> print(mpf(p) / q)
+        -9.04942396360948e+27677
+        >>> print(bernoulli(10**4))
+        -9.04942396360948e+27677
+
+    .. note ::
+
+        :func:`~mpmath.bernoulli` computes a floating-point approximation
+        directly, without computing the exact fraction first.
+        This is much faster for large `n`.
+
+    **Algorithm**
+
+    :func:`~mpmath.bernfrac` works by computing the value of `B_n` numerically
+    and then using the von Staudt-Clausen theorem [1] to reconstruct
+    the exact fraction. For large `n`, this is significantly faster than
+    computing `B_1, B_2, \ldots, B_2` recursively with exact arithmetic.
+    The implementation has been tested for `n = 10^m` up to `m = 6`.
+
+    In practice, :func:`~mpmath.bernfrac` appears to be about three times
+    slower than the specialized program calcbn.exe [2]
+
+    **References**
+
+    1. MathWorld, von Staudt-Clausen Theorem:
+       http://mathworld.wolfram.com/vonStaudt-ClausenTheorem.html
+
+    2. The Bernoulli Number Page:
+       http://www.bernoulli.org/
+
+    """
+    n = int(n)
+    if n < 3:
+        return [(1, 1), (-1, 2), (1, 6)][n]
+    if n & 1:
+        return (0, 1)
+    q = 1
+    for k in list_primes(n+1):
+        if not (n % (k-1)):
+            q *= k
+    prec = bernoulli_size(n) + int(math.log(q,2)) + 20
+    b = mpf_bernoulli(n, prec)
+    p = mpf_mul(b, from_int(q))
+    pint = to_int(p, round_nearest)
+    return (pint, q)
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                         Polygamma functions                           #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+r"""
+For all polygamma (psi) functions, we use the Euler-Maclaurin summation
+formula. It looks slightly different in the m = 0 and m > 0 cases.
+
+For m = 0, we have
+                                 oo
+                                ___   B
+       (0)                1    \       2 k    -2 k
+    psi   (z)  ~ log z + --- -  )    ------  z
+                         2 z   /___  (2 k)!
+                               k = 1
+
+Experiment shows that the minimum term of the asymptotic series
+reaches 2^(-p) when Re(z) > 0.11*p. So we simply use the recurrence
+for psi (equivalent, in fact, to summing to the first few terms
+directly before applying E-M) to obtain z large enough.
+
+Since, very crudely, log z ~= 1 for Re(z) > 1, we can use
+fixed-point arithmetic  (if z is extremely large, log(z) itself
+is a sufficient approximation, so we can stop there already).
+
+For Re(z) << 0, we could use recurrence, but this is of course
+inefficient for large negative z, so there we use the
+reflection formula instead.
+
+For m > 0, we have
+
+                  N - 1
+                   ___
+  ~~~(m)       [  \          1    ]         1            1
+  psi   (z)  ~ [   )     -------- ] +  ---------- +  -------- +
+               [  /___        m+1 ]           m+1           m
+                  k = 1  (z+k)    ]    2 (z+N)       m (z+N)
+
+      oo
+     ___    B
+    \        2 k   (m+1) (m+2) ... (m+2k-1)
+  +  )     ------  ------------------------
+    /___   (2 k)!            m + 2 k
+    k = 1               (z+N)
+
+where ~~~ denotes the function rescaled by 1/((-1)^(m+1) m!).
+
+Here again N is chosen to make z+N large enough for the minimum
+term in the last series to become smaller than eps.
+
+TODO: the current estimation of N for m > 0 is *very suboptimal*.
+
+TODO: implement the reflection formula for m > 0, Re(z) << 0.
+It is generally a combination of multiple cotangents. Need to
+figure out a reasonably simple way to generate these formulas
+on the fly.
+
+TODO: maybe use exact algorithms to compute psi for integral
+and certain rational arguments, as this can be much more
+efficient. (On the other hand, the availability of these
+special values provides a convenient way to test the general
+algorithm.)
+"""
+
+# Harmonic numbers are just shifted digamma functions
+# We should calculate these exactly when x is an integer
+# and when doing so is faster.
+
+def mpf_harmonic(x, prec, rnd):
+    if x in (fzero, fnan, finf):
+        return x
+    a = mpf_psi0(mpf_add(fone, x, prec+5), prec)
+    return mpf_add(a, mpf_euler(prec+5, rnd), prec, rnd)
+
+def mpc_harmonic(z, prec, rnd):
+    if z[1] == fzero:
+        return (mpf_harmonic(z[0], prec, rnd), fzero)
+    a = mpc_psi0(mpc_add_mpf(z, fone, prec+5), prec)
+    return mpc_add_mpf(a, mpf_euler(prec+5, rnd), prec, rnd)
+
+def mpf_psi0(x, prec, rnd=round_fast):
+    """
+    Computation of the digamma function (psi function of order 0)
+    of a real argument.
+    """
+    sign, man, exp, bc = x
+    wp = prec + 10
+    if not man:
+        if x == finf: return x
+        if x == fninf or x == fnan: return fnan
+    if x == fzero or (exp >= 0 and sign):
+        raise ValueError("polygamma pole")
+    # Near 0 -- fixed-point arithmetic becomes bad
+    if exp+bc < -5:
+        v = mpf_psi0(mpf_add(x, fone, prec, rnd), prec, rnd)
+        return mpf_sub(v, mpf_div(fone, x, wp, rnd), prec, rnd)
+    # Reflection formula
+    if sign and exp+bc > 3:
+        c, s = mpf_cos_sin_pi(x, wp)
+        q = mpf_mul(mpf_div(c, s, wp), mpf_pi(wp), wp)
+        p = mpf_psi0(mpf_sub(fone, x, wp), wp)
+        return mpf_sub(p, q, prec, rnd)
+    # The logarithmic term is accurate enough
+    if (not sign) and bc + exp > wp:
+        return mpf_log(mpf_sub(x, fone, wp), prec, rnd)
+    # Initial recurrence to obtain a large enough x
+    m = to_int(x)
+    n = int(0.11*wp) + 2
+    s = MPZ_ZERO
+    x = to_fixed(x, wp)
+    one = MPZ_ONE << wp
+    if m < n:
+        for k in xrange(m, n):
+            s -= (one << wp) // x
+            x += one
+    x -= one
+    # Logarithmic term
+    s += to_fixed(mpf_log(from_man_exp(x, -wp, wp), wp), wp)
+    # Endpoint term in Euler-Maclaurin expansion
+    s += (one << wp) // (2*x)
+    # Euler-Maclaurin remainder sum
+    x2 = (x*x) >> wp
+    t = one
+    prev = 0
+    k = 1
+    while 1:
+        t = (t*x2) >> wp
+        bsign, bman, bexp, bbc = mpf_bernoulli(2*k, wp)
+        offset = (bexp + 2*wp)
+        if offset >= 0: term = (bman << offset) // (t*(2*k))
+        else:           term = (bman >> (-offset)) // (t*(2*k))
+        if k & 1: s -= term
+        else:     s += term
+        if k > 2 and term >= prev:
+            break
+        prev = term
+        k += 1
+    return from_man_exp(s, -wp, wp, rnd)
+
+def mpc_psi0(z, prec, rnd=round_fast):
+    """
+    Computation of the digamma function (psi function of order 0)
+    of a complex argument.
+    """
+    re, im = z
+    # Fall back to the real case
+    if im == fzero:
+        return (mpf_psi0(re, prec, rnd), fzero)
+    wp = prec + 20
+    sign, man, exp, bc = re
+    # Reflection formula
+    if sign and exp+bc > 3:
+        c = mpc_cos_pi(z, wp)
+        s = mpc_sin_pi(z, wp)
+        q = mpc_mul_mpf(mpc_div(c, s, wp), mpf_pi(wp), wp)
+        p = mpc_psi0(mpc_sub(mpc_one, z, wp), wp)
+        return mpc_sub(p, q, prec, rnd)
+    # Just the logarithmic term
+    if (not sign) and bc + exp > wp:
+        return mpc_log(mpc_sub(z, mpc_one, wp), prec, rnd)
+    # Initial recurrence to obtain a large enough z
+    w = to_int(re)
+    n = int(0.11*wp) + 2
+    s = mpc_zero
+    if w < n:
+        for k in xrange(w, n):
+            s = mpc_sub(s, mpc_reciprocal(z, wp), wp)
+            z = mpc_add_mpf(z, fone, wp)
+    z = mpc_sub(z, mpc_one, wp)
+    # Logarithmic and endpoint term
+    s = mpc_add(s, mpc_log(z, wp), wp)
+    s = mpc_add(s, mpc_div(mpc_half, z, wp), wp)
+    # Euler-Maclaurin remainder sum
+    z2 = mpc_square(z, wp)
+    t = mpc_one
+    prev = mpc_zero
+    szprev = fzero
+    k = 1
+    eps = mpf_shift(fone, -wp+2)
+    while 1:
+        t = mpc_mul(t, z2, wp)
+        bern = mpf_bernoulli(2*k, wp)
+        term = mpc_mpf_div(bern, mpc_mul_int(t, 2*k, wp), wp)
+        s = mpc_sub(s, term, wp)
+        szterm = mpc_abs(term, 10)
+        if k > 2 and (mpf_le(szterm, eps) or mpf_le(szprev, szterm)):
+            break
+        prev = term
+        szprev = szterm
+        k += 1
+    return s
+
+# Currently unoptimized
+def mpf_psi(m, x, prec, rnd=round_fast):
+    """
+    Computation of the polygamma function of arbitrary integer order
+    m >= 0, for a real argument x.
+    """
+    if m == 0:
+        return mpf_psi0(x, prec, rnd=round_fast)
+    return mpc_psi(m, (x, fzero), prec, rnd)[0]
+
+def mpc_psi(m, z, prec, rnd=round_fast):
+    """
+    Computation of the polygamma function of arbitrary integer order
+    m >= 0, for a complex argument z.
+    """
+    if m == 0:
+        return mpc_psi0(z, prec, rnd)
+    re, im = z
+    wp = prec + 20
+    sign, man, exp, bc = re
+    if not im[1]:
+        if im in (finf, fninf, fnan):
+            return (fnan, fnan)
+    if not man:
+        if re == finf and im == fzero:
+            return (fzero, fzero)
+        if re == fnan:
+            return (fnan, fnan)
+    # Recurrence
+    w = to_int(re)
+    n = int(0.4*wp + 4*m)
+    s = mpc_zero
+    if w < n:
+        for k in xrange(w, n):
+            t = mpc_pow_int(z, -m-1, wp)
+            s = mpc_add(s, t, wp)
+            z = mpc_add_mpf(z, fone, wp)
+    zm = mpc_pow_int(z, -m, wp)
+    z2 = mpc_pow_int(z, -2, wp)
+    # 1/m*(z+N)^m
+    integral_term = mpc_div_mpf(zm, from_int(m), wp)
+    s = mpc_add(s, integral_term, wp)
+    # 1/2*(z+N)^(-(m+1))
+    s = mpc_add(s, mpc_mul_mpf(mpc_div(zm, z, wp), fhalf, wp), wp)
+    a = m + 1
+    b = 2
+    k = 1
+    # Important: we want to sum up to the *relative* error,
+    # not the absolute error, because psi^(m)(z) might be tiny
+    magn = mpc_abs(s, 10)
+    magn = magn[2]+magn[3]
+    eps = mpf_shift(fone, magn-wp+2)
+    while 1:
+        zm = mpc_mul(zm, z2, wp)
+        bern = mpf_bernoulli(2*k, wp)
+        scal = mpf_mul_int(bern, a, wp)
+        scal = mpf_div(scal, from_int(b), wp)
+        term = mpc_mul_mpf(zm, scal, wp)
+        s = mpc_add(s, term, wp)
+        szterm = mpc_abs(term, 10)
+        if k > 2 and mpf_le(szterm, eps):
+            break
+        #print k, to_str(szterm, 10), to_str(eps, 10)
+        a *= (m+2*k)*(m+2*k+1)
+        b *= (2*k+1)*(2*k+2)
+        k += 1
+    # Scale and sign factor
+    v = mpc_mul_mpf(s, mpf_gamma(from_int(m+1), wp), prec, rnd)
+    if not (m & 1):
+        v = mpf_neg(v[0]), mpf_neg(v[1])
+    return v
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                         Riemann zeta function                         #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+r"""
+We use zeta(s) = eta(s) / (1 - 2**(1-s)) and Borwein's approximation
+
+                  n-1
+                  ___       k
+             -1  \      (-1)  (d_k - d_n)
+  eta(s) ~= ----  )     ------------------
+             d_n /___              s
+                 k = 0      (k + 1)
+where
+             k
+             ___                i
+            \     (n + i - 1)! 4
+  d_k  =  n  )    ---------------.
+            /___   (n - i)! (2i)!
+            i = 0
+
+If s = a + b*I, the absolute error for eta(s) is bounded by
+
+    3 (1 + 2|b|)
+    ------------ * exp(|b| pi/2)
+               n
+    (3+sqrt(8))
+
+Disregarding the linear term, we have approximately,
+
+  log(err) ~= log(exp(1.58*|b|)) - log(5.8**n)
+  log(err) ~= 1.58*|b| - log(5.8)*n
+  log(err) ~= 1.58*|b| - 1.76*n
+  log2(err) ~= 2.28*|b| - 2.54*n
+
+So for p bits, we should choose n > (p + 2.28*|b|) / 2.54.
+
+References:
+-----------
+
+Peter Borwein, "An Efficient Algorithm for the Riemann Zeta Function"
+http://www.cecm.sfu.ca/personal/pborwein/PAPERS/P117.ps
+
+http://en.wikipedia.org/wiki/Dirichlet_eta_function
+"""
+
+borwein_cache = {}
+
+def borwein_coefficients(n):
+    if n in borwein_cache:
+        return borwein_cache[n]
+    ds = [MPZ_ZERO] * (n+1)
+    d = MPZ_ONE
+    s = ds[0] = MPZ_ONE
+    for i in range(1, n+1):
+        d = d * 4 * (n+i-1) * (n-i+1)
+        d //= ((2*i) * ((2*i)-1))
+        s += d
+        ds[i] = s
+    borwein_cache[n] = ds
+    return ds
+
+ZETA_INT_CACHE_MAX_PREC = 1000
+zeta_int_cache = {}
+
+def mpf_zeta_int(s, prec, rnd=round_fast):
+    """
+    Optimized computation of zeta(s) for an integer s.
+    """
+    wp = prec + 20
+    s = int(s)
+    if s in zeta_int_cache and zeta_int_cache[s][0] >= wp:
+        return mpf_pos(zeta_int_cache[s][1], prec, rnd)
+    if s < 2:
+        if s == 1:
+            raise ValueError("zeta(1) pole")
+        if not s:
+            return mpf_neg(fhalf)
+        return mpf_div(mpf_bernoulli(-s+1, wp), from_int(s-1), prec, rnd)
+    # 2^-s term vanishes?
+    if s >= wp:
+        return mpf_perturb(fone, 0, prec, rnd)
+    # 5^-s term vanishes?
+    elif s >= wp*0.431:
+        t = one = 1 << wp
+        t += 1 << (wp - s)
+        t += one // (MPZ_THREE ** s)
+        t += 1 << max(0, wp - s*2)
+        return from_man_exp(t, -wp, prec, rnd)
+    else:
+        # Fast enough to sum directly?
+        # Even better, we use the Euler product (idea stolen from pari)
+        m = (float(wp)/(s-1) + 1)
+        if m < 30:
+            needed_terms = int(2.0**m + 1)
+            if needed_terms < int(wp/2.54 + 5) / 10:
+                t = fone
+                for k in list_primes(needed_terms):
+                    #print k, needed_terms
+                    powprec = int(wp - s*math.log(k,2))
+                    if powprec < 2:
+                        break
+                    a = mpf_sub(fone, mpf_pow_int(from_int(k), -s, powprec), wp)
+                    t = mpf_mul(t, a, wp)
+                return mpf_div(fone, t, wp)
+    # Use Borwein's algorithm
+    n = int(wp/2.54 + 5)
+    d = borwein_coefficients(n)
+    t = MPZ_ZERO
+    s = MPZ(s)
+    for k in xrange(n):
+        t += (((-1)**k * (d[k] - d[n])) << wp) // (k+1)**s
+    t = (t << wp) // (-d[n])
+    t = (t << wp) // ((1 << wp) - (1 << (wp+1-s)))
+    if (s in zeta_int_cache and zeta_int_cache[s][0] < wp) or (s not in zeta_int_cache):
+        zeta_int_cache[s] = (wp, from_man_exp(t, -wp-wp))
+    return from_man_exp(t, -wp-wp, prec, rnd)
+
+def mpf_zeta(s, prec, rnd=round_fast, alt=0):
+    sign, man, exp, bc = s
+    if not man:
+        if s == fzero:
+            if alt:
+                return fhalf
+            else:
+                return mpf_neg(fhalf)
+        if s == finf:
+            return fone
+        return fnan
+    wp = prec + 20
+    # First term vanishes?
+    if (not sign) and (exp + bc > (math.log(wp,2) + 2)):
+        return mpf_perturb(fone, alt, prec, rnd)
+    # Optimize for integer arguments
+    elif exp >= 0:
+        if alt:
+            if s == fone:
+                return mpf_ln2(prec, rnd)
+            z = mpf_zeta_int(to_int(s), wp, negative_rnd[rnd])
+            q = mpf_sub(fone, mpf_pow(ftwo, mpf_sub(fone, s, wp), wp), wp)
+            return mpf_mul(z, q, prec, rnd)
+        else:
+            return mpf_zeta_int(to_int(s), prec, rnd)
+    # Negative: use the reflection formula
+    # Borwein only proves the accuracy bound for x >= 1/2. However, based on
+    # tests, the accuracy without reflection is quite good even some distance
+    # to the left of 1/2. XXX: verify this.
+    if sign:
+        # XXX: could use the separate refl. formula for Dirichlet eta
+        if alt:
+            q = mpf_sub(fone, mpf_pow(ftwo, mpf_sub(fone, s, wp), wp), wp)
+            return mpf_mul(mpf_zeta(s, wp), q, prec, rnd)
+        # XXX: -1 should be done exactly
+        y = mpf_sub(fone, s, 10*wp)
+        a = mpf_gamma(y, wp)
+        b = mpf_zeta(y, wp)
+        c = mpf_sin_pi(mpf_shift(s, -1), wp)
+        wp2 = wp + max(0,exp+bc)
+        pi = mpf_pi(wp+wp2)
+        d = mpf_div(mpf_pow(mpf_shift(pi, 1), s, wp2), pi, wp2)
+        return mpf_mul(a,mpf_mul(b,mpf_mul(c,d,wp),wp),prec,rnd)
+
+    # Near pole
+    r = mpf_sub(fone, s, wp)
+    asign, aman, aexp, abc = mpf_abs(r)
+    pole_dist = -2*(aexp+abc)
+    if pole_dist > wp:
+        if alt:
+            return mpf_ln2(prec, rnd)
+        else:
+            q = mpf_neg(mpf_div(fone, r, wp))
+            return mpf_add(q, mpf_euler(wp), prec, rnd)
+    else:
+        wp += max(0, pole_dist)
+
+    t = MPZ_ZERO
+    #wp += 16 - (prec & 15)
+    # Use Borwein's algorithm
+    n = int(wp/2.54 + 5)
+    d = borwein_coefficients(n)
+    t = MPZ_ZERO
+    sf = to_fixed(s, wp)
+    ln2 = ln2_fixed(wp)
+    for k in xrange(n):
+        u = (-sf*log_int_fixed(k+1, wp, ln2)) >> wp
+        #esign, eman, eexp, ebc = mpf_exp(u, wp)
+        #offset = eexp + wp
+        #if offset >= 0:
+        #    w = ((d[k] - d[n]) * eman) << offset
+        #else:
+        #    w = ((d[k] - d[n]) * eman) >> (-offset)
+        eman = exp_fixed(u, wp, ln2)
+        w = (d[k] - d[n]) * eman
+        if k & 1:
+            t -= w
+        else:
+            t += w
+    t = t // (-d[n])
+    t = from_man_exp(t, -wp, wp)
+    if alt:
+        return mpf_pos(t, prec, rnd)
+    else:
+        q = mpf_sub(fone, mpf_pow(ftwo, mpf_sub(fone, s, wp), wp), wp)
+        return mpf_div(t, q, prec, rnd)
+
+def mpc_zeta(s, prec, rnd=round_fast, alt=0, force=False):
+    re, im = s
+    if im == fzero:
+        return mpf_zeta(re, prec, rnd, alt), fzero
+
+    # slow for large s
+    if (not force) and mpf_gt(mpc_abs(s, 10), from_int(prec)):
+        raise NotImplementedError
+
+    wp = prec + 20
+
+    # Near pole
+    r = mpc_sub(mpc_one, s, wp)
+    asign, aman, aexp, abc = mpc_abs(r, 10)
+    pole_dist = -2*(aexp+abc)
+    if pole_dist > wp:
+        if alt:
+            q = mpf_ln2(wp)
+            y = mpf_mul(q, mpf_euler(wp), wp)
+            g = mpf_shift(mpf_mul(q, q, wp), -1)
+            g = mpf_sub(y, g)
+            z = mpc_mul_mpf(r, mpf_neg(g), wp)
+            z = mpc_add_mpf(z, q, wp)
+            return mpc_pos(z, prec, rnd)
+        else:
+            q = mpc_neg(mpc_div(mpc_one, r, wp))
+            q = mpc_add_mpf(q, mpf_euler(wp), wp)
+            return mpc_pos(q, prec, rnd)
+    else:
+        wp += max(0, pole_dist)
+
+    # Reflection formula. To be rigorous, we should reflect to the left of
+    # re = 1/2 (see comments for mpf_zeta), but this leads to unnecessary
+    # slowdown for interesting values of s
+    if mpf_lt(re, fzero):
+        # XXX: could use the separate refl. formula for Dirichlet eta
+        if alt:
+            q = mpc_sub(mpc_one, mpc_pow(mpc_two, mpc_sub(mpc_one, s, wp),
+                wp), wp)
+            return mpc_mul(mpc_zeta(s, wp), q, prec, rnd)
+        # XXX: -1 should be done exactly
+        y = mpc_sub(mpc_one, s, 10*wp)
+        a = mpc_gamma(y, wp)
+        b = mpc_zeta(y, wp)
+        c = mpc_sin_pi(mpc_shift(s, -1), wp)
+        rsign, rman, rexp, rbc = re
+        isign, iman, iexp, ibc = im
+        mag = max(rexp+rbc, iexp+ibc)
+        wp2 = wp + max(0, mag)
+        pi = mpf_pi(wp+wp2)
+        pi2 = (mpf_shift(pi, 1), fzero)
+        d = mpc_div_mpf(mpc_pow(pi2, s, wp2), pi, wp2)
+        return mpc_mul(a,mpc_mul(b,mpc_mul(c,d,wp),wp),prec,rnd)
+    n = int(wp/2.54 + 5)
+    n += int(0.9*abs(to_int(im)))
+    d = borwein_coefficients(n)
+    ref = to_fixed(re, wp)
+    imf = to_fixed(im, wp)
+    tre = MPZ_ZERO
+    tim = MPZ_ZERO
+    one = MPZ_ONE << wp
+    one_2wp = MPZ_ONE << (2*wp)
+    critical_line = re == fhalf
+    ln2 = ln2_fixed(wp)
+    pi2 = pi_fixed(wp-1)
+    wp2 = wp+wp
+    for k in xrange(n):
+        log = log_int_fixed(k+1, wp, ln2)
+        # A square root is much cheaper than an exp
+        if critical_line:
+            w = one_2wp // isqrt_fast((k+1) << wp2)
+        else:
+            w = exp_fixed((-ref*log) >> wp, wp)
+        if k & 1:
+            w *= (d[n] - d[k])
+        else:
+            w *= (d[k] - d[n])
+        wre, wim = cos_sin_fixed((-imf*log)>>wp, wp, pi2)
+        tre += (w * wre) >> wp
+        tim += (w * wim) >> wp
+    tre //= (-d[n])
+    tim //= (-d[n])
+    tre = from_man_exp(tre, -wp, wp)
+    tim = from_man_exp(tim, -wp, wp)
+    if alt:
+        return mpc_pos((tre, tim), prec, rnd)
+    else:
+        q = mpc_sub(mpc_one, mpc_pow(mpc_two, r, wp), wp)
+        return mpc_div((tre, tim), q, prec, rnd)
+
+def mpf_altzeta(s, prec, rnd=round_fast):
+    return mpf_zeta(s, prec, rnd, 1)
+
+def mpc_altzeta(s, prec, rnd=round_fast):
+    return mpc_zeta(s, prec, rnd, 1)
+
+# Not optimized currently
+mpf_zetasum = None
+
+
+def pow_fixed(x, n, wp):
+    if n == 1:
+        return x
+    y = MPZ_ONE << wp
+    while n:
+        if n & 1:
+            y = (y*x) >> wp
+            n -= 1
+        x = (x*x) >> wp
+        n //= 2
+    return y
+
+# TODO: optimize / cleanup interface / unify with list_primes
+sieve_cache = []
+primes_cache = []
+mult_cache = []
+
+def primesieve(n):
+    global sieve_cache, primes_cache, mult_cache
+    if n < len(sieve_cache):
+        sieve = sieve_cache#[:n+1]
+        primes = primes_cache[:primes_cache.index(max(sieve))+1]
+        mult = mult_cache#[:n+1]
+        return sieve, primes, mult
+    sieve = [0] * (n+1)
+    mult = [0] * (n+1)
+    primes = list_primes(n)
+    for p in primes:
+        #sieve[p::p] = p
+        for k in xrange(p,n+1,p):
+            sieve[k] = p
+    for i, p in enumerate(sieve):
+        if i >= 2:
+            m = 1
+            n = i // p
+            while not n % p:
+                n //= p
+                m += 1
+            mult[i] = m
+    sieve_cache = sieve
+    primes_cache = primes
+    mult_cache = mult
+    return sieve, primes, mult
+
+def zetasum_sieved(critical_line, sre, sim, a, n, wp):
+    if a < 1:
+        raise ValueError("a cannot be less than 1")
+    sieve, primes, mult = primesieve(a+n)
+    basic_powers = {}
+    one = MPZ_ONE << wp
+    one_2wp = MPZ_ONE << (2*wp)
+    wp2 = wp+wp
+    ln2 = ln2_fixed(wp)
+    pi2 = pi_fixed(wp-1)
+    for p in primes:
+        if p*2 > a+n:
+            break
+        log = log_int_fixed(p, wp, ln2)
+        cos, sin = cos_sin_fixed((-sim*log)>>wp, wp, pi2)
+        if critical_line:
+            u = one_2wp // isqrt_fast(p<<wp2)
+        else:
+            u = exp_fixed((-sre*log)>>wp, wp)
+        pre = (u*cos) >> wp
+        pim = (u*sin) >> wp
+        basic_powers[p] = [(pre, pim)]
+        tre, tim = pre, pim
+        for m in range(1,int(math.log(a+n,p)+0.01)+1):
+            tre, tim = ((pre*tre-pim*tim)>>wp), ((pim*tre+pre*tim)>>wp)
+            basic_powers[p].append((tre,tim))
+    xre = MPZ_ZERO
+    xim = MPZ_ZERO
+    if a == 1:
+        xre += one
+    aa = max(a,2)
+    for k in xrange(aa, a+n+1):
+        p = sieve[k]
+        if p in basic_powers:
+            m = mult[k]
+            tre, tim = basic_powers[p][m-1]
+            while 1:
+                k //= p**m
+                if k == 1:
+                    break
+                p = sieve[k]
+                m = mult[k]
+                pre, pim = basic_powers[p][m-1]
+                tre, tim = ((pre*tre-pim*tim)>>wp), ((pim*tre+pre*tim)>>wp)
+        else:
+            log = log_int_fixed(k, wp, ln2)
+            cos, sin = cos_sin_fixed((-sim*log)>>wp, wp, pi2)
+            if critical_line:
+                u = one_2wp // isqrt_fast(k<<wp2)
+            else:
+                u = exp_fixed((-sre*log)>>wp, wp)
+            tre = (u*cos) >> wp
+            tim = (u*sin) >> wp
+        xre += tre
+        xim += tim
+    return xre, xim
+
+# Set to something large to disable
+ZETASUM_SIEVE_CUTOFF = 10
+
+def mpc_zetasum(s, a, n, derivatives, reflect, prec):
+    """
+    Fast version of mp._zetasum, assuming s = complex, a = integer.
+    """
+
+    wp = prec + 10
+    derivatives = list(derivatives)
+    have_derivatives = derivatives != [0]
+    have_one_derivative = len(derivatives) == 1
+
+    # parse s
+    sre, sim = s
+    critical_line = (sre == fhalf)
+    sre = to_fixed(sre, wp)
+    sim = to_fixed(sim, wp)
+
+    if a > 0 and n > ZETASUM_SIEVE_CUTOFF and not have_derivatives \
+            and not reflect and (n < 4e7 or sys.maxsize > 2**32):
+        re, im = zetasum_sieved(critical_line, sre, sim, a, n, wp)
+        xs = [(from_man_exp(re, -wp, prec, 'n'), from_man_exp(im, -wp, prec, 'n'))]
+        return xs, []
+
+    maxd = max(derivatives)
+    if not have_one_derivative:
+        derivatives = range(maxd+1)
+
+    # x_d = 0, y_d = 0
+    xre = [MPZ_ZERO for d in derivatives]
+    xim = [MPZ_ZERO for d in derivatives]
+    if reflect:
+        yre = [MPZ_ZERO for d in derivatives]
+        yim = [MPZ_ZERO for d in derivatives]
+    else:
+        yre = yim = []
+
+    one = MPZ_ONE << wp
+    one_2wp = MPZ_ONE << (2*wp)
+
+    ln2 = ln2_fixed(wp)
+    pi2 = pi_fixed(wp-1)
+    wp2 = wp+wp
+
+    for w in xrange(a, a+n+1):
+        log = log_int_fixed(w, wp, ln2)
+        cos, sin = cos_sin_fixed((-sim*log)>>wp, wp, pi2)
+        if critical_line:
+            u = one_2wp // isqrt_fast(w<<wp2)
+        else:
+            u = exp_fixed((-sre*log)>>wp, wp)
+        xterm_re = (u * cos) >> wp
+        xterm_im = (u * sin) >> wp
+        if reflect:
+            reciprocal = (one_2wp // (u*w))
+            yterm_re = (reciprocal * cos) >> wp
+            yterm_im = (reciprocal * sin) >> wp
+
+        if have_derivatives:
+            if have_one_derivative:
+                log = pow_fixed(log, maxd, wp)
+                xre[0] += (xterm_re * log) >> wp
+                xim[0] += (xterm_im * log) >> wp
+                if reflect:
+                    yre[0] += (yterm_re * log) >> wp
+                    yim[0] += (yterm_im * log) >> wp
+            else:
+                t = MPZ_ONE << wp
+                for d in derivatives:
+                    xre[d] += (xterm_re * t) >> wp
+                    xim[d] += (xterm_im * t) >> wp
+                    if reflect:
+                        yre[d] += (yterm_re * t) >> wp
+                        yim[d] += (yterm_im * t) >> wp
+                    t = (t * log) >> wp
+        else:
+            xre[0] += xterm_re
+            xim[0] += xterm_im
+            if reflect:
+                yre[0] += yterm_re
+                yim[0] += yterm_im
+    if have_derivatives:
+        if have_one_derivative:
+            if maxd % 2:
+                xre[0] = -xre[0]
+                xim[0] = -xim[0]
+                if reflect:
+                    yre[0] = -yre[0]
+                    yim[0] = -yim[0]
+        else:
+            xre = [(-1)**d * xre[d] for d in derivatives]
+            xim = [(-1)**d * xim[d] for d in derivatives]
+            if reflect:
+                yre = [(-1)**d * yre[d] for d in derivatives]
+                yim = [(-1)**d * yim[d] for d in derivatives]
+    xs = [(from_man_exp(xa, -wp, prec, 'n'), from_man_exp(xb, -wp, prec, 'n'))
+        for (xa, xb) in zip(xre, xim)]
+    ys = [(from_man_exp(ya, -wp, prec, 'n'), from_man_exp(yb, -wp, prec, 'n'))
+        for (ya, yb) in zip(yre, yim)]
+    return xs, ys
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#              The gamma function  (NEW IMPLEMENTATION)                 #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+# Higher means faster, but more precomputation time
+MAX_GAMMA_TAYLOR_PREC = 5000
+# Need to derive higher bounds for Taylor series to go higher
+assert MAX_GAMMA_TAYLOR_PREC < 15000
+
+# Use Stirling's series if abs(x) > beta*prec
+# Important: must be large enough for convergence!
+GAMMA_STIRLING_BETA = 0.2
+
+SMALL_FACTORIAL_CACHE_SIZE = 150
+
+gamma_taylor_cache = {}
+gamma_stirling_cache = {}
+
+small_factorial_cache = [from_int(ifac(n)) for \
+    n in range(SMALL_FACTORIAL_CACHE_SIZE+1)]
+
+def zeta_array(N, prec):
+    """
+    zeta(n) = A * pi**n / n! + B
+
+    where A is a rational number (A = Bernoulli number
+    for n even) and B is an infinite sum over powers of exp(2*pi).
+    (B = 0 for n even).
+
+    TODO: this is currently only used for gamma, but could
+    be very useful elsewhere.
+    """
+    extra = 30
+    wp = prec+extra
+    zeta_values = [MPZ_ZERO] * (N+2)
+    pi = pi_fixed(wp)
+    # STEP 1:
+    one = MPZ_ONE << wp
+    zeta_values[0] = -one//2
+    f_2pi = mpf_shift(mpf_pi(wp),1)
+    exp_2pi_k = exp_2pi = mpf_exp(f_2pi, wp)
+    # Compute exponential series
+    # Store values of 1/(exp(2*pi*k)-1),
+    # exp(2*pi*k)/(exp(2*pi*k)-1)**2, 1/(exp(2*pi*k)-1)**2
+    # pi*k*exp(2*pi*k)/(exp(2*pi*k)-1)**2
+    exps3 = []
+    k = 1
+    while 1:
+        tp = wp - 9*k
+        if tp < 1:
+            break
+        # 1/(exp(2*pi*k-1)
+        q1 = mpf_div(fone, mpf_sub(exp_2pi_k, fone, tp), tp)
+        # pi*k*exp(2*pi*k)/(exp(2*pi*k)-1)**2
+        q2 = mpf_mul(exp_2pi_k, mpf_mul(q1,q1,tp), tp)
+        q1 = to_fixed(q1, wp)
+        q2 = to_fixed(q2, wp)
+        q2 = (k * q2 * pi) >> wp
+        exps3.append((q1, q2))
+        # Multiply for next round
+        exp_2pi_k = mpf_mul(exp_2pi_k, exp_2pi, wp)
+        k += 1
+    # Exponential sum
+    for n in xrange(3, N+1, 2):
+        s = MPZ_ZERO
+        k = 1
+        for e1, e2 in exps3:
+            if n%4 == 3:
+                t = e1 // k**n
+            else:
+                U = (n-1)//4
+                t = (e1 + e2//U) // k**n
+            if not t:
+                break
+            s += t
+            k += 1
+        zeta_values[n] = -2*s
+    # Even zeta values
+    B = [mpf_abs(mpf_bernoulli(k,wp)) for k in xrange(N+2)]
+    pi_pow = fpi = mpf_pow_int(mpf_shift(mpf_pi(wp), 1), 2, wp)
+    pi_pow = mpf_div(pi_pow, from_int(4), wp)
+    for n in xrange(2,N+2,2):
+        z = mpf_mul(B[n], pi_pow, wp)
+        zeta_values[n] = to_fixed(z, wp)
+        pi_pow = mpf_mul(pi_pow, fpi, wp)
+        pi_pow = mpf_div(pi_pow, from_int((n+1)*(n+2)), wp)
+    # Zeta sum
+    reciprocal_pi = (one << wp) // pi
+    for n in xrange(3, N+1, 4):
+        U = (n-3)//4
+        s = zeta_values[4*U+4]*(4*U+7)//4
+        for k in xrange(1, U+1):
+            s -= (zeta_values[4*k] * zeta_values[4*U+4-4*k]) >> wp
+        zeta_values[n] += (2*s*reciprocal_pi) >> wp
+    for n in xrange(5, N+1, 4):
+        U = (n-1)//4
+        s = zeta_values[4*U+2]*(2*U+1)
+        for k in xrange(1, 2*U+1):
+            s += ((-1)**k*2*k* zeta_values[2*k] * zeta_values[4*U+2-2*k])>>wp
+        zeta_values[n] += ((s*reciprocal_pi)>>wp)//(2*U)
+    return [x>>extra for x in zeta_values]
+
+def gamma_taylor_coefficients(inprec):
+    """
+    Gives the Taylor coefficients of 1/gamma(1+x) as
+    a list of fixed-point numbers. Enough coefficients are returned
+    to ensure that the series converges to the given precision
+    when x is in [0.5, 1.5].
+    """
+    # Reuse nearby cache values (small case)
+    if inprec < 400:
+        prec = inprec + (10-(inprec%10))
+    elif inprec < 1000:
+        prec = inprec + (30-(inprec%30))
+    else:
+        prec = inprec
+    if prec in gamma_taylor_cache:
+        return gamma_taylor_cache[prec], prec
+
+    # Experimentally determined bounds
+    if prec < 1000:
+        N = int(prec**0.76 + 2)
+    else:
+        # Valid to at least 15000 bits
+        N = int(prec**0.787 + 2)
+
+    # Reuse higher precision values
+    for cprec in gamma_taylor_cache:
+        if cprec > prec:
+            coeffs = [x>>(cprec-prec) for x in gamma_taylor_cache[cprec][-N:]]
+            if inprec < 1000:
+                gamma_taylor_cache[prec] = coeffs
+            return coeffs, prec
+
+    # Cache at a higher precision (large case)
+    if prec > 1000:
+        prec = int(prec * 1.2)
+
+    wp = prec + 20
+    A = [0] * N
+    A[0] = MPZ_ZERO
+    A[1] = MPZ_ONE << wp
+    A[2] = euler_fixed(wp)
+    # SLOW, reference implementation
+    #zeta_values = [0,0]+[to_fixed(mpf_zeta_int(k,wp),wp) for k in xrange(2,N)]
+    zeta_values = zeta_array(N, wp)
+    for k in xrange(3, N):
+        a = (-A[2]*A[k-1])>>wp
+        for j in xrange(2,k):
+            a += ((-1)**j * zeta_values[j] * A[k-j]) >> wp
+        a //= (1-k)
+        A[k] = a
+    A = [a>>20 for a in A]
+    A = A[::-1]
+    A = A[:-1]
+    gamma_taylor_cache[prec] = A
+    #return A, prec
+    return gamma_taylor_coefficients(inprec)
+
+def gamma_fixed_taylor(xmpf, x, wp, prec, rnd, type):
+    # Determine nearest multiple of N/2
+    #n = int(x >> (wp-1))
+    #steps = (n-1)>>1
+    nearest_int = ((x >> (wp-1)) + MPZ_ONE) >> 1
+    one = MPZ_ONE << wp
+    coeffs, cwp = gamma_taylor_coefficients(wp)
+    if nearest_int > 0:
+        r = one
+        for i in xrange(nearest_int-1):
+            x -= one
+            r = (r*x) >> wp
+        x -= one
+        p = MPZ_ZERO
+        for c in coeffs:
+            p = c + ((x*p)>>wp)
+        p >>= (cwp-wp)
+        if type == 0:
+            return from_man_exp((r<<wp)//p, -wp, prec, rnd)
+        if type == 2:
+            return mpf_shift(from_rational(p, (r<<wp), prec, rnd), wp)
+        if type == 3:
+            return mpf_log(mpf_abs(from_man_exp((r<<wp)//p, -wp)), prec, rnd)
+    else:
+        r = one
+        for i in xrange(-nearest_int):
+            r = (r*x) >> wp
+            x += one
+        p = MPZ_ZERO
+        for c in coeffs:
+            p = c + ((x*p)>>wp)
+        p >>= (cwp-wp)
+        if wp - bitcount(abs(x)) > 10:
+            # pass very close to 0, so do floating-point multiply
+            g = mpf_add(xmpf, from_int(-nearest_int))  # exact
+            r = from_man_exp(p*r,-wp-wp)
+            r = mpf_mul(r, g, wp)
+            if type == 0:
+                return mpf_div(fone, r, prec, rnd)
+            if type == 2:
+                return mpf_pos(r, prec, rnd)
+            if type == 3:
+                return mpf_log(mpf_abs(mpf_div(fone, r, wp)), prec, rnd)
+        else:
+            r = from_man_exp(x*p*r,-3*wp)
+            if type == 0: return mpf_div(fone, r, prec, rnd)
+            if type == 2: return mpf_pos(r, prec, rnd)
+            if type == 3: return mpf_neg(mpf_log(mpf_abs(r), prec, rnd))
+
+def stirling_coefficient(n):
+    if n in gamma_stirling_cache:
+        return gamma_stirling_cache[n]
+    p, q = bernfrac(n)
+    q *= MPZ(n*(n-1))
+    gamma_stirling_cache[n] = p, q, bitcount(abs(p)), bitcount(q)
+    return gamma_stirling_cache[n]
+
+def real_stirling_series(x, prec):
+    """
+    Sums the rational part of Stirling's expansion,
+
+    log(sqrt(2*pi)) - z + 1/(12*z) - 1/(360*z^3) + ...
+
+    """
+    t = (MPZ_ONE<<(prec+prec)) // x   # t = 1/x
+    u = (t*t)>>prec                  # u = 1/x**2
+    s = ln_sqrt2pi_fixed(prec) - x
+    # Add initial terms of Stirling's series
+    s += t//12;            t = (t*u)>>prec
+    s -= t//360;           t = (t*u)>>prec
+    s += t//1260;          t = (t*u)>>prec
+    s -= t//1680;          t = (t*u)>>prec
+    if not t: return s
+    s += t//1188;          t = (t*u)>>prec
+    s -= 691*t//360360;    t = (t*u)>>prec
+    s += t//156;           t = (t*u)>>prec
+    if not t: return s
+    s -= 3617*t//122400;   t = (t*u)>>prec
+    s += 43867*t//244188;  t = (t*u)>>prec
+    s -= 174611*t//125400;  t = (t*u)>>prec
+    if not t: return s
+    k = 22
+    # From here on, the coefficients are growing, so we
+    # have to keep t at a roughly constant size
+    usize = bitcount(abs(u))
+    tsize = bitcount(abs(t))
+    texp = 0
+    while 1:
+        p, q, pb, qb = stirling_coefficient(k)
+        term_mag = tsize + pb + texp
+        shift = -texp
+        m = pb - term_mag
+        if m > 0 and shift < m:
+            p >>= m
+            shift -= m
+        m = tsize - term_mag
+        if m > 0 and shift < m:
+            w = t >> m
+            shift -= m
+        else:
+            w = t
+        term = (t*p//q) >> shift
+        if not term:
+            break
+        s += term
+        t = (t*u) >> usize
+        texp -= (prec - usize)
+        k += 2
+    return s
+
+def complex_stirling_series(x, y, prec):
+    # t = 1/z
+    _m = (x*x + y*y) >> prec
+    tre = (x << prec) // _m
+    tim = (-y << prec) // _m
+    # u = 1/z**2
+    ure = (tre*tre - tim*tim) >> prec
+    uim = tim*tre >> (prec-1)
+    # s = log(sqrt(2*pi)) - z
+    sre = ln_sqrt2pi_fixed(prec) - x
+    sim = -y
+
+    # Add initial terms of Stirling's series
+    sre += tre//12; sim += tim//12;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= tre//360; sim -= tim//360;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre += tre//1260; sim += tim//1260;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= tre//1680; sim -= tim//1680;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    if abs(tre) + abs(tim) < 5: return sre, sim
+    sre += tre//1188; sim += tim//1188;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= 691*tre//360360; sim -= 691*tim//360360;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre += tre//156; sim += tim//156;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    if abs(tre) + abs(tim) < 5: return sre, sim
+    sre -= 3617*tre//122400; sim -= 3617*tim//122400;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre += 43867*tre//244188; sim += 43867*tim//244188;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    sre -= 174611*tre//125400; sim -= 174611*tim//125400;
+    tre, tim = ((tre*ure-tim*uim)>>prec), ((tre*uim+tim*ure)>>prec)
+    if abs(tre) + abs(tim) < 5: return sre, sim
+
+    k = 22
+    # From here on, the coefficients are growing, so we
+    # have to keep t at a roughly constant size
+    usize = bitcount(max(abs(ure), abs(uim)))
+    tsize = bitcount(max(abs(tre), abs(tim)))
+    texp = 0
+    while 1:
+        p, q, pb, qb = stirling_coefficient(k)
+        term_mag = tsize + pb + texp
+        shift = -texp
+        m = pb - term_mag
+        if m > 0 and shift < m:
+            p >>= m
+            shift -= m
+        m = tsize - term_mag
+        if m > 0 and shift < m:
+            wre = tre >> m
+            wim = tim >> m
+            shift -= m
+        else:
+            wre = tre
+            wim = tim
+        termre = (tre*p//q) >> shift
+        termim = (tim*p//q) >> shift
+        if abs(termre) + abs(termim) < 5:
+            break
+        sre += termre
+        sim += termim
+        tre, tim = ((tre*ure - tim*uim)>>usize), \
+            ((tre*uim + tim*ure)>>usize)
+        texp -= (prec - usize)
+        k += 2
+    return sre, sim
+
+
+def mpf_gamma(x, prec, rnd='d', type=0):
+    """
+    This function implements multipurpose evaluation of the gamma
+    function, G(x), as well as the following versions of the same:
+
+    type = 0 -- G(x)                    [standard gamma function]
+    type = 1 -- G(x+1) = x*G(x+1) = x!  [factorial]
+    type = 2 -- 1/G(x)                  [reciprocal gamma function]
+    type = 3 -- log(|G(x)|)             [log-gamma function, real part]
+    """
+
+    # Specal values
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero:
+            if type == 1: return fone
+            if type == 2: return fzero
+            raise ValueError("gamma function pole")
+        if x == finf:
+            if type == 2: return fzero
+            return finf
+        return fnan
+
+    # First of all, for log gamma, numbers can be well beyond the fixed-point
+    # range, so we must take care of huge numbers before e.g. trying
+    # to convert x to the nearest integer
+    if type == 3:
+        wp = prec+20
+        if exp+bc > wp and not sign:
+            return mpf_sub(mpf_mul(x, mpf_log(x, wp), wp), x, prec, rnd)
+
+    # We strongly want to special-case small integers
+    is_integer = exp >= 0
+    if is_integer:
+        # Poles
+        if sign:
+            if type == 2:
+                return fzero
+            raise ValueError("gamma function pole")
+        # n = x
+        n = man << exp
+        if n < SMALL_FACTORIAL_CACHE_SIZE:
+            if type == 0:
+                return mpf_pos(small_factorial_cache[n-1], prec, rnd)
+            if type == 1:
+                return mpf_pos(small_factorial_cache[n], prec, rnd)
+            if type == 2:
+                return mpf_div(fone, small_factorial_cache[n-1], prec, rnd)
+            if type == 3:
+                return mpf_log(small_factorial_cache[n-1], prec, rnd)
+    else:
+        # floor(abs(x))
+        n = int(man >> (-exp))
+
+    # Estimate size and precision
+    # Estimate log(gamma(|x|),2) as x*log(x,2)
+    mag = exp + bc
+    gamma_size = n*mag
+
+    if type == 3:
+        wp = prec + 20
+    else:
+        wp = prec + bitcount(gamma_size) + 20
+
+    # Very close to 0, pole
+    if mag < -wp:
+        if type == 0:
+            return mpf_sub(mpf_div(fone,x, wp),mpf_shift(fone,-wp),prec,rnd)
+        if type == 1: return mpf_sub(fone, x, prec, rnd)
+        if type == 2: return mpf_add(x, mpf_shift(fone,mag-wp), prec, rnd)
+        if type == 3: return mpf_neg(mpf_log(mpf_abs(x), prec, rnd))
+
+    # From now on, we assume having a gamma function
+    if type == 1:
+        return mpf_gamma(mpf_add(x, fone), prec, rnd, 0)
+
+    # Special case integers (those not small enough to be caught above,
+    # but still small enough for an exact factorial to be faster
+    # than an approximate algorithm), and half-integers
+    if exp >= -1:
+        if is_integer:
+            if gamma_size < 10*wp:
+                if type == 0:
+                    return from_int(ifac(n-1), prec, rnd)
+                if type == 2:
+                    return from_rational(MPZ_ONE, ifac(n-1), prec, rnd)
+                if type == 3:
+                    return mpf_log(from_int(ifac(n-1)), prec, rnd)
+        # half-integer
+        if n < 100 or gamma_size < 10*wp:
+            if sign:
+                w = sqrtpi_fixed(wp)
+                if n % 2: f = ifac2(2*n+1)
+                else:     f = -ifac2(2*n+1)
+                if type == 0:
+                    return mpf_shift(from_rational(w, f, prec, rnd), -wp+n+1)
+                if type == 2:
+                    return mpf_shift(from_rational(f, w, prec, rnd), wp-n-1)
+                if type == 3:
+                    return mpf_log(mpf_shift(from_rational(w, abs(f),
+                        prec, rnd), -wp+n+1), prec, rnd)
+            elif n == 0:
+                if type == 0: return mpf_sqrtpi(prec, rnd)
+                if type == 2: return mpf_div(fone, mpf_sqrtpi(wp), prec, rnd)
+                if type == 3: return mpf_log(mpf_sqrtpi(wp), prec, rnd)
+            else:
+                w = sqrtpi_fixed(wp)
+                w = from_man_exp(w * ifac2(2*n-1), -wp-n)
+                if type == 0: return mpf_pos(w, prec, rnd)
+                if type == 2: return mpf_div(fone, w, prec, rnd)
+                if type == 3: return mpf_log(mpf_abs(w), prec, rnd)
+
+    # Convert to fixed point
+    offset = exp + wp
+    if offset >= 0: absxman = man << offset
+    else:           absxman = man >> (-offset)
+
+    # For log gamma, provide accurate evaluation for x = 1+eps and 2+eps
+    if type == 3 and not sign:
+        one = MPZ_ONE << wp
+        one_dist = abs(absxman-one)
+        two_dist = abs(absxman-2*one)
+        cancellation = (wp - bitcount(min(one_dist, two_dist)))
+        if cancellation > 10:
+            xsub1 = mpf_sub(fone, x)
+            xsub2 = mpf_sub(ftwo, x)
+            xsub1mag = xsub1[2]+xsub1[3]
+            xsub2mag = xsub2[2]+xsub2[3]
+            if xsub1mag < -wp:
+                return mpf_mul(mpf_euler(wp), mpf_sub(fone, x), prec, rnd)
+            if xsub2mag < -wp:
+                return mpf_mul(mpf_sub(fone, mpf_euler(wp)),
+                    mpf_sub(x, ftwo), prec, rnd)
+            # Proceed but increase precision
+            wp += max(-xsub1mag, -xsub2mag)
+            offset = exp + wp
+            if offset >= 0: absxman = man << offset
+            else:           absxman = man >> (-offset)
+
+    # Use Taylor series if appropriate
+    n_for_stirling = int(GAMMA_STIRLING_BETA*wp)
+    if n < max(100, n_for_stirling) and wp < MAX_GAMMA_TAYLOR_PREC:
+        if sign:
+            absxman = -absxman
+        return gamma_fixed_taylor(x, absxman, wp, prec, rnd, type)
+
+    # Use Stirling's series
+    # First ensure that |x| is large enough for rapid convergence
+    xorig = x
+
+    # Argument reduction
+    r = 0
+    if n < n_for_stirling:
+        r = one = MPZ_ONE << wp
+        d = n_for_stirling - n
+        for k in xrange(d):
+            r = (r * absxman) >> wp
+            absxman += one
+        x = xabs = from_man_exp(absxman, -wp)
+        if sign:
+            x = mpf_neg(x)
+    else:
+        xabs = mpf_abs(x)
+
+    # Asymptotic series
+    y = real_stirling_series(absxman, wp)
+    u = to_fixed(mpf_log(xabs, wp), wp)
+    u = ((absxman - (MPZ_ONE<<(wp-1))) * u) >> wp
+    y += u
+    w = from_man_exp(y, -wp)
+
+    # Compute final value
+    if sign:
+        # Reflection formula
+        A = mpf_mul(mpf_sin_pi(xorig, wp), xorig, wp)
+        B = mpf_neg(mpf_pi(wp))
+        if type == 0 or type == 2:
+            A = mpf_mul(A, mpf_exp(w, wp))
+            if r:
+                B = mpf_mul(B, from_man_exp(r, -wp), wp)
+            if type == 0:
+                return mpf_div(B, A, prec, rnd)
+            if type == 2:
+                return mpf_div(A, B, prec, rnd)
+        if type == 3:
+            if r:
+                B = mpf_mul(B, from_man_exp(r, -wp), wp)
+            A = mpf_add(mpf_log(mpf_abs(A), wp), w, wp)
+            return mpf_sub(mpf_log(mpf_abs(B), wp), A, prec, rnd)
+    else:
+        if type == 0:
+            if r:
+                return mpf_div(mpf_exp(w, wp),
+                    from_man_exp(r, -wp), prec, rnd)
+            return mpf_exp(w, prec, rnd)
+        if type == 2:
+            if r:
+                return mpf_div(from_man_exp(r, -wp),
+                    mpf_exp(w, wp), prec, rnd)
+            return mpf_exp(mpf_neg(w), prec, rnd)
+        if type == 3:
+            if r:
+                return mpf_sub(w, mpf_log(from_man_exp(r,-wp), wp), prec, rnd)
+            return mpf_pos(w, prec, rnd)
+
+
+def mpc_gamma(z, prec, rnd='d', type=0):
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+
+    if b == fzero:
+        # Imaginary part on negative half-axis for log-gamma function
+        if type == 3 and asign:
+            re = mpf_gamma(a, prec, rnd, 3)
+            n = (-aman) >> (-aexp)
+            im = mpf_mul_int(mpf_pi(prec+10), n, prec, rnd)
+            return re, im
+        return mpf_gamma(a, prec, rnd, type), fzero
+
+    # Some kind of complex inf/nan
+    if (not aman and aexp) or (not bman and bexp):
+        return (fnan, fnan)
+
+    # Initial working precision
+    wp = prec + 20
+
+    amag = aexp+abc
+    bmag = bexp+bbc
+    if aman:
+        mag = max(amag, bmag)
+    else:
+        mag = bmag
+
+    # Close to 0
+    if mag < -8:
+        if mag < -wp:
+            # 1/gamma(z) = z + euler*z^2 + O(z^3)
+            v = mpc_add(z, mpc_mul_mpf(mpc_mul(z,z,wp),mpf_euler(wp),wp), wp)
+            if type == 0: return mpc_reciprocal(v, prec, rnd)
+            if type == 1: return mpc_div(z, v, prec, rnd)
+            if type == 2: return mpc_pos(v, prec, rnd)
+            if type == 3: return mpc_log(mpc_reciprocal(v, prec), prec, rnd)
+        elif type != 1:
+            wp += (-mag)
+
+    # Handle huge log-gamma values; must do this before converting to
+    # a fixed-point value. TODO: determine a precise cutoff of validity
+    # depending on amag and bmag
+    if type == 3 and mag > wp and ((not asign) or (bmag >= amag)):
+        return mpc_sub(mpc_mul(z, mpc_log(z, wp), wp), z, prec, rnd)
+
+    # From now on, we assume having a gamma function
+    if type == 1:
+        return mpc_gamma((mpf_add(a, fone), b), prec, rnd, 0)
+
+    an = abs(to_int(a))
+    bn = abs(to_int(b))
+    absn = max(an, bn)
+    gamma_size = absn*mag
+    if type == 3:
+        pass
+    else:
+        wp += bitcount(gamma_size)
+
+    # Reflect to the right half-plane. Note that Stirling's expansion
+    # is valid in the left half-plane too, as long as we're not too close
+    # to the real axis, but in order to use this argument reduction
+    # in the negative direction must be implemented.
+    #need_reflection = asign and ((bmag < 0) or (amag-bmag > 4))
+    need_reflection = asign
+    zorig = z
+    if need_reflection:
+        z = mpc_neg(z)
+        asign, aman, aexp, abc = a = z[0]
+        bsign, bman, bexp, bbc = b = z[1]
+
+    # Imaginary part very small compared to real one?
+    yfinal = 0
+    balance_prec = 0
+    if bmag < -10:
+        # Check z ~= 1 and z ~= 2 for loggamma
+        if type == 3:
+            zsub1 = mpc_sub_mpf(z, fone)
+            if zsub1[0] == fzero:
+                cancel1 = -bmag
+            else:
+                cancel1 = -max(zsub1[0][2]+zsub1[0][3], bmag)
+            if cancel1 > wp:
+                pi = mpf_pi(wp)
+                x = mpc_mul_mpf(zsub1, pi, wp)
+                x = mpc_mul(x, x, wp)
+                x = mpc_div_mpf(x, from_int(12), wp)
+                y = mpc_mul_mpf(zsub1, mpf_neg(mpf_euler(wp)), wp)
+                yfinal = mpc_add(x, y, wp)
+                if not need_reflection:
+                    return mpc_pos(yfinal, prec, rnd)
+            elif cancel1 > 0:
+                wp += cancel1
+            zsub2 = mpc_sub_mpf(z, ftwo)
+            if zsub2[0] == fzero:
+                cancel2 = -bmag
+            else:
+                cancel2 = -max(zsub2[0][2]+zsub2[0][3], bmag)
+            if cancel2 > wp:
+                pi = mpf_pi(wp)
+                t = mpf_sub(mpf_mul(pi, pi), from_int(6))
+                x = mpc_mul_mpf(mpc_mul(zsub2, zsub2, wp), t, wp)
+                x = mpc_div_mpf(x, from_int(12), wp)
+                y = mpc_mul_mpf(zsub2, mpf_sub(fone, mpf_euler(wp)), wp)
+                yfinal = mpc_add(x, y, wp)
+                if not need_reflection:
+                    return mpc_pos(yfinal, prec, rnd)
+            elif cancel2 > 0:
+                wp += cancel2
+        if bmag < -wp:
+            # Compute directly from the real gamma function.
+            pp = 2*(wp+10)
+            aabs = mpf_abs(a)
+            eps = mpf_shift(fone, amag-wp)
+            x1 = mpf_gamma(aabs, pp, type=type)
+            x2 = mpf_gamma(mpf_add(aabs, eps), pp, type=type)
+            xprime = mpf_div(mpf_sub(x2, x1, pp), eps, pp)
+            y = mpf_mul(b, xprime, prec, rnd)
+            yfinal = (x1, y)
+            # Note: we still need to use the reflection formula for
+            # near-poles, and the correct branch of the log-gamma function
+            if not need_reflection:
+                return mpc_pos(yfinal, prec, rnd)
+        else:
+            balance_prec += (-bmag)
+
+    wp += balance_prec
+    n_for_stirling = int(GAMMA_STIRLING_BETA*wp)
+    need_reduction = absn < n_for_stirling
+
+    afix = to_fixed(a, wp)
+    bfix = to_fixed(b, wp)
+
+    r = 0
+    if not yfinal:
+        zprered = z
+        # Argument reduction
+        if absn < n_for_stirling:
+            absn = complex(an, bn)
+            d = int((1 + n_for_stirling**2 - bn**2)**0.5 - an)
+            rre = one = MPZ_ONE << wp
+            rim = MPZ_ZERO
+            for k in xrange(d):
+                rre, rim = ((afix*rre-bfix*rim)>>wp), ((afix*rim + bfix*rre)>>wp)
+                afix += one
+            r = from_man_exp(rre, -wp), from_man_exp(rim, -wp)
+            a = from_man_exp(afix, -wp)
+            z = a, b
+
+        yre, yim = complex_stirling_series(afix, bfix, wp)
+        # (z-1/2)*log(z) + S
+        lre, lim = mpc_log(z, wp)
+        lre = to_fixed(lre, wp)
+        lim = to_fixed(lim, wp)
+        yre = ((lre*afix - lim*bfix)>>wp) - (lre>>1) + yre
+        yim = ((lre*bfix + lim*afix)>>wp) - (lim>>1) + yim
+        y = from_man_exp(yre, -wp), from_man_exp(yim, -wp)
+
+        if r and type == 3:
+            # If re(z) > 0 and abs(z) <= 4, the branches of loggamma(z)
+            # and log(gamma(z)) coincide. Otherwise, use the zeroth order
+            # Stirling expansion to compute the correct imaginary part.
+            y = mpc_sub(y, mpc_log(r, wp), wp)
+            zfa = to_float(zprered[0])
+            zfb = to_float(zprered[1])
+            zfabs = math.hypot(zfa,zfb)
+            #if not (zfa > 0.0 and zfabs <= 4):
+            yfb = to_float(y[1])
+            u = math.atan2(zfb, zfa)
+            if zfabs <= 0.5:
+                gi = 0.577216*zfb - u
+            else:
+                gi = -zfb - 0.5*u + zfa*u + zfb*math.log(zfabs)
+            n = int(math.floor((gi-yfb)/(2*math.pi)+0.5))
+            y = (y[0], mpf_add(y[1], mpf_mul_int(mpf_pi(wp), 2*n, wp), wp))
+
+    if need_reflection:
+        if type == 0 or type == 2:
+            A = mpc_mul(mpc_sin_pi(zorig, wp), zorig, wp)
+            B = (mpf_neg(mpf_pi(wp)), fzero)
+            if yfinal:
+                if type == 2:
+                    A = mpc_div(A, yfinal, wp)
+                else:
+                    A = mpc_mul(A, yfinal, wp)
+            else:
+                A = mpc_mul(A, mpc_exp(y, wp), wp)
+            if r:
+                B = mpc_mul(B, r, wp)
+            if type == 0: return mpc_div(B, A, prec, rnd)
+            if type == 2: return mpc_div(A, B, prec, rnd)
+
+        # Reflection formula for the log-gamma function with correct branch
+        # http://functions.wolfram.com/GammaBetaErf/LogGamma/16/01/01/0006/
+        # LogGamma[z] == -LogGamma[-z] - Log[-z] +
+        # Sign[Im[z]] Floor[Re[z]] Pi I + Log[Pi] -
+        #      Log[Sin[Pi (z - Floor[Re[z]])]] -
+        # Pi I (1 - Abs[Sign[Im[z]]]) Abs[Floor[Re[z]]]
+        if type == 3:
+            if yfinal:
+                s1 = mpc_neg(yfinal)
+            else:
+                s1 = mpc_neg(y)
+            # s -= log(-z)
+            s1 = mpc_sub(s1, mpc_log(mpc_neg(zorig), wp), wp)
+            # floor(re(z))
+            rezfloor = mpf_floor(zorig[0])
+            imzsign = mpf_sign(zorig[1])
+            pi = mpf_pi(wp)
+            t = mpf_mul(pi, rezfloor)
+            t = mpf_mul_int(t, imzsign, wp)
+            s1 = (s1[0], mpf_add(s1[1], t, wp))
+            s1 = mpc_add_mpf(s1, mpf_log(pi, wp), wp)
+            t = mpc_sin_pi(mpc_sub_mpf(zorig, rezfloor), wp)
+            t = mpc_log(t, wp)
+            s1 = mpc_sub(s1, t, wp)
+            # Note: may actually be unused, because we fall back
+            # to the mpf_ function for real arguments
+            if not imzsign:
+                t = mpf_mul(pi, mpf_floor(rezfloor), wp)
+                s1 = (s1[0], mpf_sub(s1[1], t, wp))
+            return mpc_pos(s1, prec, rnd)
+    else:
+        if type == 0:
+            if r:
+                return mpc_div(mpc_exp(y, wp), r, prec, rnd)
+            return mpc_exp(y, prec, rnd)
+        if type == 2:
+            if r:
+                return mpc_div(r, mpc_exp(y, wp), prec, rnd)
+            return mpc_exp(mpc_neg(y), prec, rnd)
+        if type == 3:
+            return mpc_pos(y, prec, rnd)
+
+def mpf_factorial(x, prec, rnd='d'):
+    return mpf_gamma(x, prec, rnd, 1)
+
+def mpc_factorial(x, prec, rnd='d'):
+    return mpc_gamma(x, prec, rnd, 1)
+
+def mpf_rgamma(x, prec, rnd='d'):
+    return mpf_gamma(x, prec, rnd, 2)
+
+def mpc_rgamma(x, prec, rnd='d'):
+    return mpc_gamma(x, prec, rnd, 2)
+
+def mpf_loggamma(x, prec, rnd='d'):
+    sign, man, exp, bc = x
+    if sign:
+        raise ComplexResult
+    return mpf_gamma(x, prec, rnd, 3)
+
+def mpc_loggamma(z, prec, rnd='d'):
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if b == fzero and asign:
+        re = mpf_gamma(a, prec, rnd, 3)
+        n = (-aman) >> (-aexp)
+        im = mpf_mul_int(mpf_pi(prec+10), n, prec, rnd)
+        return re, im
+    return mpc_gamma(z, prec, rnd, 3)
+
+def mpf_gamma_int(n, prec, rnd=round_fast):
+    if n < SMALL_FACTORIAL_CACHE_SIZE:
+        return mpf_pos(small_factorial_cache[n-1], prec, rnd)
+    return mpf_gamma(from_int(n), prec, rnd)

lib/python3.11/site-packages/mpmath/libmp/libelefun.py

@@ -0,0 +1,1428 @@
+"""
+This module implements computation of elementary transcendental
+functions (powers, logarithms, trigonometric and hyperbolic
+functions, inverse trigonometric and hyperbolic) for real
+floating-point numbers.
+
+For complex and interval implementations of the same functions,
+see libmpc and libmpi.
+
+"""
+
+import math
+from bisect import bisect
+
+from .backend import xrange
+from .backend import MPZ, MPZ_ZERO, MPZ_ONE, MPZ_TWO, MPZ_FIVE, BACKEND
+
+from .libmpf import (
+    round_floor, round_ceiling, round_down, round_up,
+    round_nearest, round_fast,
+    ComplexResult,
+    bitcount, bctable, lshift, rshift, giant_steps, sqrt_fixed,
+    from_int, to_int, from_man_exp, to_fixed, to_float, from_float,
+    from_rational, normalize,
+    fzero, fone, fnone, fhalf, finf, fninf, fnan,
+    mpf_cmp, mpf_sign, mpf_abs,
+    mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul, mpf_div, mpf_shift,
+    mpf_rdiv_int, mpf_pow_int, mpf_sqrt,
+    reciprocal_rnd, negative_rnd, mpf_perturb,
+    isqrt_fast
+)
+
+from .libintmath import ifib
+
+
+#-------------------------------------------------------------------------------
+# Tuning parameters
+#-------------------------------------------------------------------------------
+
+# Cutoff for computing exp from cosh+sinh. This reduces the
+# number of terms by half, but also requires a square root which
+# is expensive with the pure-Python square root code.
+if BACKEND == 'python':
+    EXP_COSH_CUTOFF = 600
+else:
+    EXP_COSH_CUTOFF = 400
+# Cutoff for using more than 2 series
+EXP_SERIES_U_CUTOFF = 1500
+
+# Also basically determined by sqrt
+if BACKEND == 'python':
+    COS_SIN_CACHE_PREC = 400
+else:
+    COS_SIN_CACHE_PREC = 200
+COS_SIN_CACHE_STEP = 8
+cos_sin_cache = {}
+
+# Number of integer logarithms to cache (for zeta sums)
+MAX_LOG_INT_CACHE = 2000
+log_int_cache = {}
+
+LOG_TAYLOR_PREC = 2500  # Use Taylor series with caching up to this prec
+LOG_TAYLOR_SHIFT = 9    # Cache log values in steps of size 2^-N
+log_taylor_cache = {}
+# prec/size ratio of x for fastest convergence in AGM formula
+LOG_AGM_MAG_PREC_RATIO = 20
+
+ATAN_TAYLOR_PREC = 3000  # Same as for log
+ATAN_TAYLOR_SHIFT = 7   # steps of size 2^-N
+atan_taylor_cache = {}
+
+
+# ~= next power of two + 20
+cache_prec_steps = [22,22]
+for k in xrange(1, bitcount(LOG_TAYLOR_PREC)+1):
+    cache_prec_steps += [min(2**k,LOG_TAYLOR_PREC)+20] * 2**(k-1)
+
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                   Elementary mathematical constants                        #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+def constant_memo(f):
+    """
+    Decorator for caching computed values of mathematical
+    constants. This decorator should be applied to a
+    function taking a single argument prec as input and
+    returning a fixed-point value with the given precision.
+    """
+    f.memo_prec = -1
+    f.memo_val = None
+    def g(prec, **kwargs):
+        memo_prec = f.memo_prec
+        if prec <= memo_prec:
+            return f.memo_val >> (memo_prec-prec)
+        newprec = int(prec*1.05+10)
+        f.memo_val = f(newprec, **kwargs)
+        f.memo_prec = newprec
+        return f.memo_val >> (newprec-prec)
+    g.__name__ = f.__name__
+    g.__doc__ = f.__doc__
+    return g
+
+def def_mpf_constant(fixed):
+    """
+    Create a function that computes the mpf value for a mathematical
+    constant, given a function that computes the fixed-point value.
+
+    Assumptions: the constant is positive and has magnitude ~= 1;
+    the fixed-point function rounds to floor.
+    """
+    def f(prec, rnd=round_fast):
+        wp = prec + 20
+        v = fixed(wp)
+        if rnd in (round_up, round_ceiling):
+            v += 1
+        return normalize(0, v, -wp, bitcount(v), prec, rnd)
+    f.__doc__ = fixed.__doc__
+    return f
+
+def bsp_acot(q, a, b, hyperbolic):
+    if b - a == 1:
+        a1 = MPZ(2*a + 3)
+        if hyperbolic or a&1:
+            return MPZ_ONE, a1 * q**2, a1
+        else:
+            return -MPZ_ONE, a1 * q**2, a1
+    m = (a+b)//2
+    p1, q1, r1 = bsp_acot(q, a, m, hyperbolic)
+    p2, q2, r2 = bsp_acot(q, m, b, hyperbolic)
+    return q2*p1 + r1*p2, q1*q2, r1*r2
+
+# the acoth(x) series converges like the geometric series for x^2
+# N = ceil(p*log(2)/(2*log(x)))
+def acot_fixed(a, prec, hyperbolic):
+    """
+    Compute acot(a) or acoth(a) for an integer a with binary splitting; see
+    http://numbers.computation.free.fr/Constants/Algorithms/splitting.html
+    """
+    N = int(0.35 * prec/math.log(a) + 20)
+    p, q, r = bsp_acot(a, 0,N, hyperbolic)
+    return ((p+q)<<prec)//(q*a)
+
+def machin(coefs, prec, hyperbolic=False):
+    """
+    Evaluate a Machin-like formula, i.e., a linear combination of
+    acot(n) or acoth(n) for specific integer values of n, using fixed-
+    point arithmetic. The input should be a list [(c, n), ...], giving
+    c*acot[h](n) + ...
+    """
+    extraprec = 10
+    s = MPZ_ZERO
+    for a, b in coefs:
+        s += MPZ(a) * acot_fixed(MPZ(b), prec+extraprec, hyperbolic)
+    return (s >> extraprec)
+
+# Logarithms of integers are needed for various computations involving
+# logarithms, powers, radix conversion, etc
+
+@constant_memo
+def ln2_fixed(prec):
+    """
+    Computes ln(2). This is done with a hyperbolic Machin-type formula,
+    with binary splitting at high precision.
+    """
+    return machin([(18, 26), (-2, 4801), (8, 8749)], prec, True)
+
+@constant_memo
+def ln10_fixed(prec):
+    """
+    Computes ln(10). This is done with a hyperbolic Machin-type formula.
+    """
+    return machin([(46, 31), (34, 49), (20, 161)], prec, True)
+
+
+r"""
+For computation of pi, we use the Chudnovsky series:
+
+             oo
+             ___        k
+      1     \       (-1)  (6 k)! (A + B k)
+    ----- =  )     -----------------------
+    12 pi   /___               3  3k+3/2
+                    (3 k)! (k!)  C
+            k = 0
+
+where A, B, and C are certain integer constants. This series adds roughly
+14 digits per term. Note that C^(3/2) can be extracted so that the
+series contains only rational terms. This makes binary splitting very
+efficient.
+
+The recurrence formulas for the binary splitting were taken from
+ftp://ftp.gmplib.org/pub/src/gmp-chudnovsky.c
+
+Previously, Machin's formula was used at low precision and the AGM iteration
+was used at high precision. However, the Chudnovsky series is essentially as
+fast as the Machin formula at low precision and in practice about 3x faster
+than the AGM at high precision (despite theoretically having a worse
+asymptotic complexity), so there is no reason not to use it in all cases.
+
+"""
+
+# Constants in Chudnovsky's series
+CHUD_A = MPZ(13591409)
+CHUD_B = MPZ(545140134)
+CHUD_C = MPZ(640320)
+CHUD_D = MPZ(12)
+
+def bs_chudnovsky(a, b, level, verbose):
+    """
+    Computes the sum from a to b of the series in the Chudnovsky
+    formula. Returns g, p, q where p/q is the sum as an exact
+    fraction and g is a temporary value used to save work
+    for recursive calls.
+    """
+    if b-a == 1:
+        g = MPZ((6*b-5)*(2*b-1)*(6*b-1))
+        p = b**3 * CHUD_C**3 // 24
+        q = (-1)**b * g * (CHUD_A+CHUD_B*b)
+    else:
+        if verbose and level < 4:
+            print("  binary splitting", a, b)
+        mid = (a+b)//2
+        g1, p1, q1 = bs_chudnovsky(a, mid, level+1, verbose)
+        g2, p2, q2 = bs_chudnovsky(mid, b, level+1, verbose)
+        p = p1*p2
+        g = g1*g2
+        q = q1*p2 + q2*g1
+    return g, p, q
+
+@constant_memo
+def pi_fixed(prec, verbose=False, verbose_base=None):
+    """
+    Compute floor(pi * 2**prec) as a big integer.
+
+    This is done using Chudnovsky's series (see comments in
+    libelefun.py for details).
+    """
+    # The Chudnovsky series gives 14.18 digits per term
+    N = int(prec/3.3219280948/14.181647462 + 2)
+    if verbose:
+        print("binary splitting with N =", N)
+    g, p, q = bs_chudnovsky(0, N, 0, verbose)
+    sqrtC = isqrt_fast(CHUD_C<<(2*prec))
+    v = p*CHUD_C*sqrtC//((q+CHUD_A*p)*CHUD_D)
+    return v
+
+def degree_fixed(prec):
+    return pi_fixed(prec)//180
+
+def bspe(a, b):
+    """
+    Sum series for exp(1)-1 between a, b, returning the result
+    as an exact fraction (p, q).
+    """
+    if b-a == 1:
+        return MPZ_ONE, MPZ(b)
+    m = (a+b)//2
+    p1, q1 = bspe(a, m)
+    p2, q2 = bspe(m, b)
+    return p1*q2+p2, q1*q2
+
+@constant_memo
+def e_fixed(prec):
+    """
+    Computes exp(1). This is done using the ordinary Taylor series for
+    exp, with binary splitting. For a description of the algorithm,
+    see:
+
+        http://numbers.computation.free.fr/Constants/
+            Algorithms/splitting.html
+    """
+    # Slight overestimate of N needed for 1/N! < 2**(-prec)
+    # This could be tightened for large N.
+    N = int(1.1*prec/math.log(prec) + 20)
+    p, q = bspe(0,N)
+    return ((p+q)<<prec)//q
+
+@constant_memo
+def phi_fixed(prec):
+    """
+    Computes the golden ratio, (1+sqrt(5))/2
+    """
+    prec += 10
+    a = isqrt_fast(MPZ_FIVE<<(2*prec)) + (MPZ_ONE << prec)
+    return a >> 11
+
+mpf_phi    = def_mpf_constant(phi_fixed)
+mpf_pi     = def_mpf_constant(pi_fixed)
+mpf_e      = def_mpf_constant(e_fixed)
+mpf_degree = def_mpf_constant(degree_fixed)
+mpf_ln2    = def_mpf_constant(ln2_fixed)
+mpf_ln10   = def_mpf_constant(ln10_fixed)
+
+
+@constant_memo
+def ln_sqrt2pi_fixed(prec):
+    wp = prec + 10
+    # ln(sqrt(2*pi)) = ln(2*pi)/2
+    return to_fixed(mpf_log(mpf_shift(mpf_pi(wp), 1), wp), prec-1)
+
+@constant_memo
+def sqrtpi_fixed(prec):
+    return sqrt_fixed(pi_fixed(prec), prec)
+
+mpf_sqrtpi   = def_mpf_constant(sqrtpi_fixed)
+mpf_ln_sqrt2pi   = def_mpf_constant(ln_sqrt2pi_fixed)
+
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                                    Powers                                  #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+def mpf_pow(s, t, prec, rnd=round_fast):
+    """
+    Compute s**t. Raises ComplexResult if s is negative and t is
+    fractional.
+    """
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    if ssign and texp < 0:
+        raise ComplexResult("negative number raised to a fractional power")
+    if texp >= 0:
+        return mpf_pow_int(s, (-1)**tsign * (tman<<texp), prec, rnd)
+    # s**(n/2) = sqrt(s)**n
+    if texp == -1:
+        if tman == 1:
+            if tsign:
+                return mpf_div(fone, mpf_sqrt(s, prec+10,
+                    reciprocal_rnd[rnd]), prec, rnd)
+            return mpf_sqrt(s, prec, rnd)
+        else:
+            if tsign:
+                return mpf_pow_int(mpf_sqrt(s, prec+10,
+                    reciprocal_rnd[rnd]), -tman, prec, rnd)
+            return mpf_pow_int(mpf_sqrt(s, prec+10, rnd), tman, prec, rnd)
+    # General formula: s**t = exp(t*log(s))
+    # TODO: handle rnd direction of the logarithm carefully
+    c = mpf_log(s, prec+10, rnd)
+    return mpf_exp(mpf_mul(t, c), prec, rnd)
+
+def int_pow_fixed(y, n, prec):
+    """n-th power of a fixed point number with precision prec
+
+       Returns the power in the form man, exp,
+       man * 2**exp ~= y**n
+    """
+    if n == 2:
+        return (y*y), 0
+    bc = bitcount(y)
+    exp = 0
+    workprec = 2 * (prec + 4*bitcount(n) + 4)
+    _, pm, pe, pbc = fone
+    while 1:
+        if n & 1:
+            pm = pm*y
+            pe = pe+exp
+            pbc += bc - 2
+            pbc = pbc + bctable[int(pm >> pbc)]
+            if pbc > workprec:
+                pm = pm >> (pbc-workprec)
+                pe += pbc - workprec
+                pbc = workprec
+            n -= 1
+            if not n:
+                break
+        y = y*y
+        exp = exp+exp
+        bc = bc + bc - 2
+        bc = bc + bctable[int(y >> bc)]
+        if bc > workprec:
+            y = y >> (bc-workprec)
+            exp += bc - workprec
+            bc = workprec
+        n = n // 2
+    return pm, pe
+
+# froot(s, n, prec, rnd) computes the real n-th root of a
+# positive mpf tuple s.
+# To compute the root we start from a 50-bit estimate for r
+# generated with ordinary floating-point arithmetic, and then refine
+# the value to full accuracy using the iteration
+
+#            1  /                     y       \
+#   r     = --- | (n-1)  * r   +  ----------  |
+#    n+1     n  \           n     r_n**(n-1)  /
+
+# which is simply Newton's method applied to the equation r**n = y.
+# With giant_steps(start, prec+extra) = [p0,...,pm, prec+extra]
+# and y = man * 2**-shift  one has
+# (man * 2**exp)**(1/n) =
+# y**(1/n) * 2**(start-prec/n) * 2**(p0-start) * ... * 2**(prec+extra-pm) *
+# 2**((exp+shift-(n-1)*prec)/n -extra))
+# The last factor is accounted for in the last line of froot.
+
+def nthroot_fixed(y, n, prec, exp1):
+    start = 50
+    try:
+        y1 = rshift(y, prec - n*start)
+        r = MPZ(int(y1**(1.0/n)))
+    except OverflowError:
+        y1 = from_int(y1, start)
+        fn = from_int(n)
+        fn = mpf_rdiv_int(1, fn, start)
+        r = mpf_pow(y1, fn, start)
+        r = to_int(r)
+    extra = 10
+    extra1 = n
+    prevp = start
+    for p in giant_steps(start, prec+extra):
+        pm, pe = int_pow_fixed(r, n-1, prevp)
+        r2 = rshift(pm, (n-1)*prevp - p - pe - extra1)
+        B = lshift(y, 2*p-prec+extra1)//r2
+        r = (B + (n-1) * lshift(r, p-prevp))//n
+        prevp = p
+    return r
+
+def mpf_nthroot(s, n, prec, rnd=round_fast):
+    """nth-root of a positive number
+
+    Use the Newton method when faster, otherwise use x**(1/n)
+    """
+    sign, man, exp, bc = s
+    if sign:
+        raise ComplexResult("nth root of a negative number")
+    if not man:
+        if s == fnan:
+            return fnan
+        if s == fzero:
+            if n > 0:
+                return fzero
+            if n == 0:
+                return fone
+            return finf
+        # Infinity
+        if not n:
+            return fnan
+        if n < 0:
+            return fzero
+        return finf
+    flag_inverse = False
+    if n < 2:
+        if n == 0:
+            return fone
+        if n == 1:
+            return mpf_pos(s, prec, rnd)
+        if n == -1:
+            return mpf_div(fone, s, prec, rnd)
+        # n < 0
+        rnd = reciprocal_rnd[rnd]
+        flag_inverse = True
+        extra_inverse = 5
+        prec += extra_inverse
+        n = -n
+    if n > 20 and (n >= 20000 or prec < int(233 + 28.3 * n**0.62)):
+        prec2 = prec + 10
+        fn = from_int(n)
+        nth = mpf_rdiv_int(1, fn, prec2)
+        r = mpf_pow(s, nth, prec2, rnd)
+        s = normalize(r[0], r[1], r[2], r[3], prec, rnd)
+        if flag_inverse:
+            return mpf_div(fone, s, prec-extra_inverse, rnd)
+        else:
+            return s
+    # Convert to a fixed-point number with prec2 bits.
+    prec2 = prec + 2*n - (prec%n)
+    # a few tests indicate that
+    # for 10 < n < 10**4 a bit more precision is needed
+    if n > 10:
+        prec2 += prec2//10
+        prec2 = prec2 - prec2%n
+    # Mantissa may have more bits than we need. Trim it down.
+    shift = bc - prec2
+    # Adjust exponents to make prec2 and exp+shift multiples of n.
+    sign1 = 0
+    es = exp+shift
+    if es < 0:
+        sign1 = 1
+        es = -es
+    if sign1:
+        shift += es%n
+    else:
+        shift -= es%n
+    man = rshift(man, shift)
+    extra = 10
+    exp1 = ((exp+shift-(n-1)*prec2)//n) - extra
+    rnd_shift = 0
+    if flag_inverse:
+        if rnd == 'u' or rnd == 'c':
+            rnd_shift = 1
+    else:
+        if rnd == 'd' or rnd == 'f':
+            rnd_shift = 1
+    man = nthroot_fixed(man+rnd_shift, n, prec2, exp1)
+    s = from_man_exp(man, exp1, prec, rnd)
+    if flag_inverse:
+        return mpf_div(fone, s, prec-extra_inverse, rnd)
+    else:
+        return s
+
+def mpf_cbrt(s, prec, rnd=round_fast):
+    """cubic root of a positive number"""
+    return mpf_nthroot(s, 3, prec, rnd)
+
+#----------------------------------------------------------------------------#
+#                                                                            #
+#                                Logarithms                                  #
+#                                                                            #
+#----------------------------------------------------------------------------#
+
+
+def log_int_fixed(n, prec, ln2=None):
+    """
+    Fast computation of log(n), caching the value for small n,
+    intended for zeta sums.
+    """
+    if n in log_int_cache:
+        value, vprec = log_int_cache[n]
+        if vprec >= prec:
+            return value >> (vprec - prec)
+    wp = prec + 10
+    if wp <= LOG_TAYLOR_SHIFT:
+        if ln2 is None:
+            ln2 = ln2_fixed(wp)
+        r = bitcount(n)
+        x = n << (wp-r)
+        v = log_taylor_cached(x, wp) + r*ln2
+    else:
+        v = to_fixed(mpf_log(from_int(n), wp+5), wp)
+    if n < MAX_LOG_INT_CACHE:
+        log_int_cache[n] = (v, wp)
+    return v >> (wp-prec)
+
+def agm_fixed(a, b, prec):
+    """
+    Fixed-point computation of agm(a,b), assuming
+    a, b both close to unit magnitude.
+    """
+    i = 0
+    while 1:
+        anew = (a+b)>>1
+        if i > 4 and abs(a-anew) < 8:
+            return a
+        b = isqrt_fast(a*b)
+        a = anew
+        i += 1
+    return a
+
+def log_agm(x, prec):
+    """
+    Fixed-point computation of -log(x) = log(1/x), suitable
+    for large precision. It is required that 0 < x < 1. The
+    algorithm used is the Sasaki-Kanada formula
+
+        -log(x) = pi/agm(theta2(x)^2,theta3(x)^2). [1]
+
+    For faster convergence in the theta functions, x should
+    be chosen closer to 0.
+
+    Guard bits must be added by the caller.
+
+    HYPOTHESIS: if x = 2^(-n), n bits need to be added to
+    account for the truncation to a fixed-point number,
+    and this is the only significant cancellation error.
+
+    The number of bits lost to roundoff is small and can be
+    considered constant.
+
+    [1] Richard P. Brent, "Fast Algorithms for High-Precision
+        Computation of Elementary Functions (extended abstract)",
+        http://wwwmaths.anu.edu.au/~brent/pd/RNC7-Brent.pdf
+
+    """
+    x2 = (x*x) >> prec
+    # Compute jtheta2(x)**2
+    s = a = b = x2
+    while a:
+        b = (b*x2) >> prec
+        a = (a*b) >> prec
+        s += a
+    s += (MPZ_ONE<<prec)
+    s = (s*s)>>(prec-2)
+    s = (s*isqrt_fast(x<<prec))>>prec
+    # Compute jtheta3(x)**2
+    t = a = b = x
+    while a:
+        b = (b*x2) >> prec
+        a = (a*b) >> prec
+        t += a
+    t = (MPZ_ONE<<prec) + (t<<1)
+    t = (t*t)>>prec
+    # Final formula
+    p = agm_fixed(s, t, prec)
+    return (pi_fixed(prec) << prec) // p
+
+def log_taylor(x, prec, r=0):
+    """
+    Fixed-point calculation of log(x). It is assumed that x is close
+    enough to 1 for the Taylor series to converge quickly. Convergence
+    can be improved by specifying r > 0 to compute
+    log(x^(1/2^r))*2^r, at the cost of performing r square roots.
+
+    The caller must provide sufficient guard bits.
+    """
+    for i in xrange(r):
+        x = isqrt_fast(x<<prec)
+    one = MPZ_ONE << prec
+    v = ((x-one)<<prec)//(x+one)
+    sign = v < 0
+    if sign:
+        v = -v
+    v2 = (v*v) >> prec
+    v4 = (v2*v2) >> prec
+    s0 = v
+    s1 = v//3
+    v = (v*v4) >> prec
+    k = 5
+    while v:
+        s0 += v // k
+        k += 2
+        s1 += v // k
+        v = (v*v4) >> prec
+        k += 2
+    s1 = (s1*v2) >> prec
+    s = (s0+s1) << (1+r)
+    if sign:
+        return -s
+    return s
+
+def log_taylor_cached(x, prec):
+    """
+    Fixed-point computation of log(x), assuming x in (0.5, 2)
+    and prec <= LOG_TAYLOR_PREC.
+    """
+    n = x >> (prec-LOG_TAYLOR_SHIFT)
+    cached_prec = cache_prec_steps[prec]
+    dprec = cached_prec - prec
+    if (n, cached_prec) in log_taylor_cache:
+        a, log_a = log_taylor_cache[n, cached_prec]
+    else:
+        a = n << (cached_prec - LOG_TAYLOR_SHIFT)
+        log_a = log_taylor(a, cached_prec, 8)
+        log_taylor_cache[n, cached_prec] = (a, log_a)
+    a >>= dprec
+    log_a >>= dprec
+    u = ((x - a) << prec) // a
+    v = (u << prec) // ((MPZ_TWO << prec) + u)
+    v2 = (v*v) >> prec
+    v4 = (v2*v2) >> prec
+    s0 = v
+    s1 = v//3
+    v = (v*v4) >> prec
+    k = 5
+    while v:
+        s0 += v//k
+        k += 2
+        s1 += v//k
+        v = (v*v4) >> prec
+        k += 2
+    s1 = (s1*v2) >> prec
+    s = (s0+s1) << 1
+    return log_a + s
+
+def mpf_log(x, prec, rnd=round_fast):
+    """
+    Compute the natural logarithm of the mpf value x. If x is negative,
+    ComplexResult is raised.
+    """
+    sign, man, exp, bc = x
+    #------------------------------------------------------------------
+    # Handle special values
+    if not man:
+        if x == fzero: return fninf
+        if x == finf: return finf
+        if x == fnan: return fnan
+    if sign:
+        raise ComplexResult("logarithm of a negative number")
+    wp = prec + 20
+    #------------------------------------------------------------------
+    # Handle log(2^n) = log(n)*2.
+    # Here we catch the only possible exact value, log(1) = 0
+    if man == 1:
+        if not exp:
+            return fzero
+        return from_man_exp(exp*ln2_fixed(wp), -wp, prec, rnd)
+    mag = exp+bc
+    abs_mag = abs(mag)
+    #------------------------------------------------------------------
+    # Handle x = 1+eps, where log(x) ~ x. We need to check for
+    # cancellation when moving to fixed-point math and compensate
+    # by increasing the precision. Note that abs_mag in (0, 1) <=>
+    # 0.5 < x < 2 and x != 1
+    if abs_mag <= 1:
+        # Calculate t = x-1 to measure distance from 1 in bits
+        tsign = 1-abs_mag
+        if tsign:
+            tman = (MPZ_ONE<<bc) - man
+        else:
+            tman = man - (MPZ_ONE<<(bc-1))
+        tbc = bitcount(tman)
+        cancellation = bc - tbc
+        if cancellation > wp:
+            t = normalize(tsign, tman, abs_mag-bc, tbc, tbc, 'n')
+            return mpf_perturb(t, tsign, prec, rnd)
+        else:
+            wp += cancellation
+        # TODO: if close enough to 1, we could use Taylor series
+        # even in the AGM precision range, since the Taylor series
+        # converges rapidly
+    #------------------------------------------------------------------
+    # Another special case:
+    # n*log(2) is a good enough approximation
+    if abs_mag > 10000:
+        if bitcount(abs_mag) > wp:
+            return from_man_exp(exp*ln2_fixed(wp), -wp, prec, rnd)
+    #------------------------------------------------------------------
+    # General case.
+    # Perform argument reduction using log(x) = log(x*2^n) - n*log(2):
+    # If we are in the Taylor precision range, choose magnitude 0 or 1.
+    # If we are in the AGM precision range, choose magnitude -m for
+    # some large m; benchmarking on one machine showed m = prec/20 to be
+    # optimal between 1000 and 100,000 digits.
+    if wp <= LOG_TAYLOR_PREC:
+        m = log_taylor_cached(lshift(man, wp-bc), wp)
+        if mag:
+            m += mag*ln2_fixed(wp)
+    else:
+        optimal_mag = -wp//LOG_AGM_MAG_PREC_RATIO
+        n = optimal_mag - mag
+        x = mpf_shift(x, n)
+        wp += (-optimal_mag)
+        m = -log_agm(to_fixed(x, wp), wp)
+        m -= n*ln2_fixed(wp)
+    return from_man_exp(m, -wp, prec, rnd)
+
+def mpf_log_hypot(a, b, prec, rnd):
+    """
+    Computes log(sqrt(a^2+b^2)) accurately.
+    """
+    # If either a or b is inf/nan/0, assume it to be a
+    if not b[1]:
+        a, b = b, a
+    # a is inf/nan/0
+    if not a[1]:
+        # both are inf/nan/0
+        if not b[1]:
+            if a == b == fzero:
+                return fninf
+            if fnan in (a, b):
+                return fnan
+            # at least one term is (+/- inf)^2
+            return finf
+        # only a is inf/nan/0
+        if a == fzero:
+            # log(sqrt(0+b^2)) = log(|b|)
+            return mpf_log(mpf_abs(b), prec, rnd)
+        if a == fnan:
+            return fnan
+        return finf
+    # Exact
+    a2 = mpf_mul(a,a)
+    b2 = mpf_mul(b,b)
+    extra = 20
+    # Not exact
+    h2 = mpf_add(a2, b2, prec+extra)
+    cancelled = mpf_add(h2, fnone, 10)
+    mag_cancelled = cancelled[2]+cancelled[3]
+    # Just redo the sum exactly if necessary (could be smarter
+    # and avoid memory allocation when a or b is precisely 1
+    # and the other is tiny...)
+    if cancelled == fzero or mag_cancelled < -extra//2:
+        h2 = mpf_add(a2, b2, prec+extra-min(a2[2],b2[2]))
+    return mpf_shift(mpf_log(h2, prec, rnd), -1)
+
+
+#----------------------------------------------------------------------
+# Inverse tangent
+#
+
+def atan_newton(x, prec):
+    if prec >= 100:
+        r = math.atan(int((x>>(prec-53)))/2.0**53)
+    else:
+        r = math.atan(int(x)/2.0**prec)
+    prevp = 50
+    r = MPZ(int(r * 2.0**53) >> (53-prevp))
+    extra_p = 50
+    for wp in giant_steps(prevp, prec):
+        wp += extra_p
+        r = r << (wp-prevp)
+        cos, sin = cos_sin_fixed(r, wp)
+        tan = (sin << wp) // cos
+        a = ((tan-rshift(x, prec-wp)) << wp) // ((MPZ_ONE<<wp) + ((tan**2)>>wp))
+        r = r - a
+        prevp = wp
+    return rshift(r, prevp-prec)
+
+def atan_taylor_get_cached(n, prec):
+    # Taylor series with caching wins up to huge precisions
+    # To avoid unnecessary precomputation at low precision, we
+    # do it in steps
+    # Round to next power of 2
+    prec2 = (1<<(bitcount(prec-1))) + 20
+    dprec = prec2 - prec
+    if (n, prec2) in atan_taylor_cache:
+        a, atan_a = atan_taylor_cache[n, prec2]
+    else:
+        a = n << (prec2 - ATAN_TAYLOR_SHIFT)
+        atan_a = atan_newton(a, prec2)
+        atan_taylor_cache[n, prec2] = (a, atan_a)
+    return (a >> dprec), (atan_a >> dprec)
+
+def atan_taylor(x, prec):
+    n = (x >> (prec-ATAN_TAYLOR_SHIFT))
+    a, atan_a = atan_taylor_get_cached(n, prec)
+    d = x - a
+    s0 = v = (d << prec) // ((a**2 >> prec) + (a*d >> prec) + (MPZ_ONE << prec))
+    v2 = (v**2 >> prec)
+    v4 = (v2 * v2) >> prec
+    s1 = v//3
+    v = (v * v4) >> prec
+    k = 5
+    while v:
+        s0 += v // k
+        k += 2
+        s1 += v // k
+        v = (v * v4) >> prec
+        k += 2
+    s1 = (s1 * v2) >> prec
+    s = s0 - s1
+    return atan_a + s
+
+def atan_inf(sign, prec, rnd):
+    if not sign:
+        return mpf_shift(mpf_pi(prec, rnd), -1)
+    return mpf_neg(mpf_shift(mpf_pi(prec, negative_rnd[rnd]), -1))
+
+def mpf_atan(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero: return fzero
+        if x == finf: return atan_inf(0, prec, rnd)
+        if x == fninf: return atan_inf(1, prec, rnd)
+        return fnan
+    mag = exp + bc
+    # Essentially infinity
+    if mag > prec+20:
+        return atan_inf(sign, prec, rnd)
+    # Essentially ~ x
+    if -mag > prec+20:
+        return mpf_perturb(x, 1-sign, prec, rnd)
+    wp = prec + 30 + abs(mag)
+    # For large x, use atan(x) = pi/2 - atan(1/x)
+    if mag >= 2:
+        x = mpf_rdiv_int(1, x, wp)
+        reciprocal = True
+    else:
+        reciprocal = False
+    t = to_fixed(x, wp)
+    if sign:
+        t = -t
+    if wp < ATAN_TAYLOR_PREC:
+        a = atan_taylor(t, wp)
+    else:
+        a = atan_newton(t, wp)
+    if reciprocal:
+        a = ((pi_fixed(wp)>>1)+1) - a
+    if sign:
+        a = -a
+    return from_man_exp(a, -wp, prec, rnd)
+
+# TODO: cleanup the special cases
+def mpf_atan2(y, x, prec, rnd=round_fast):
+    xsign, xman, xexp, xbc = x
+    ysign, yman, yexp, ybc = y
+    if not yman:
+        if y == fzero and x != fnan:
+            if mpf_sign(x) >= 0:
+                return fzero
+            return mpf_pi(prec, rnd)
+        if y in (finf, fninf):
+            if x in (finf, fninf):
+                return fnan
+            # pi/2
+            if y == finf:
+                return mpf_shift(mpf_pi(prec, rnd), -1)
+            # -pi/2
+            return mpf_neg(mpf_shift(mpf_pi(prec, negative_rnd[rnd]), -1))
+        return fnan
+    if ysign:
+        return mpf_neg(mpf_atan2(mpf_neg(y), x, prec, negative_rnd[rnd]))
+    if not xman:
+        if x == fnan:
+            return fnan
+        if x == finf:
+            return fzero
+        if x == fninf:
+            return mpf_pi(prec, rnd)
+        if y == fzero:
+            return fzero
+        return mpf_shift(mpf_pi(prec, rnd), -1)
+    tquo = mpf_atan(mpf_div(y, x, prec+4), prec+4)
+    if xsign:
+        return mpf_add(mpf_pi(prec+4), tquo, prec, rnd)
+    else:
+        return mpf_pos(tquo, prec, rnd)
+
+def mpf_asin(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if bc+exp > 0 and x not in (fone, fnone):
+        raise ComplexResult("asin(x) is real only for -1 <= x <= 1")
+    # asin(x) = 2*atan(x/(1+sqrt(1-x**2)))
+    wp = prec + 15
+    a = mpf_mul(x, x)
+    b = mpf_add(fone, mpf_sqrt(mpf_sub(fone, a, wp), wp), wp)
+    c = mpf_div(x, b, wp)
+    return mpf_shift(mpf_atan(c, prec, rnd), 1)
+
+def mpf_acos(x, prec, rnd=round_fast):
+    # acos(x) = 2*atan(sqrt(1-x**2)/(1+x))
+    sign, man, exp, bc = x
+    if bc + exp > 0:
+        if x not in (fone, fnone):
+            raise ComplexResult("acos(x) is real only for -1 <= x <= 1")
+        if x == fnone:
+            return mpf_pi(prec, rnd)
+    wp = prec + 15
+    a = mpf_mul(x, x)
+    b = mpf_sqrt(mpf_sub(fone, a, wp), wp)
+    c = mpf_div(b, mpf_add(fone, x, wp), wp)
+    return mpf_shift(mpf_atan(c, prec, rnd), 1)
+
+def mpf_asinh(x, prec, rnd=round_fast):
+    wp = prec + 20
+    sign, man, exp, bc = x
+    mag = exp+bc
+    if mag < -8:
+        if mag < -wp:
+            return mpf_perturb(x, 1-sign, prec, rnd)
+        wp += (-mag)
+    # asinh(x) = log(x+sqrt(x**2+1))
+    # use reflection symmetry to avoid cancellation
+    q = mpf_sqrt(mpf_add(mpf_mul(x, x), fone, wp), wp)
+    q = mpf_add(mpf_abs(x), q, wp)
+    if sign:
+        return mpf_neg(mpf_log(q, prec, negative_rnd[rnd]))
+    else:
+        return mpf_log(q, prec, rnd)
+
+def mpf_acosh(x, prec, rnd=round_fast):
+    # acosh(x) = log(x+sqrt(x**2-1))
+    wp = prec + 15
+    if mpf_cmp(x, fone) == -1:
+        raise ComplexResult("acosh(x) is real only for x >= 1")
+    q = mpf_sqrt(mpf_add(mpf_mul(x,x), fnone, wp), wp)
+    return mpf_log(mpf_add(x, q, wp), prec, rnd)
+
+def mpf_atanh(x, prec, rnd=round_fast):
+    # atanh(x) = log((1+x)/(1-x))/2
+    sign, man, exp, bc = x
+    if (not man) and exp:
+        if x in (fzero, fnan):
+            return x
+        raise ComplexResult("atanh(x) is real only for -1 <= x <= 1")
+    mag = bc + exp
+    if mag > 0:
+        if mag == 1 and man == 1:
+            return [finf, fninf][sign]
+        raise ComplexResult("atanh(x) is real only for -1 <= x <= 1")
+    wp = prec + 15
+    if mag < -8:
+        if mag < -wp:
+            return mpf_perturb(x, sign, prec, rnd)
+        wp += (-mag)
+    a = mpf_add(x, fone, wp)
+    b = mpf_sub(fone, x, wp)
+    return mpf_shift(mpf_log(mpf_div(a, b, wp), prec, rnd), -1)
+
+def mpf_fibonacci(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fninf:
+            return fnan
+        return x
+    # F(2^n) ~= 2^(2^n)
+    size = abs(exp+bc)
+    if exp >= 0:
+        # Exact
+        if size < 10 or size <= bitcount(prec):
+            return from_int(ifib(to_int(x)), prec, rnd)
+    # Use the modified Binet formula
+    wp = prec + size + 20
+    a = mpf_phi(wp)
+    b = mpf_add(mpf_shift(a, 1), fnone, wp)
+    u = mpf_pow(a, x, wp)
+    v = mpf_cos_pi(x, wp)
+    v = mpf_div(v, u, wp)
+    u = mpf_sub(u, v, wp)
+    u = mpf_div(u, b, prec, rnd)
+    return u
+
+
+#-------------------------------------------------------------------------------
+# Exponential-type functions
+#-------------------------------------------------------------------------------
+
+def exponential_series(x, prec, type=0):
+    """
+    Taylor series for cosh/sinh or cos/sin.
+
+    type = 0 -- returns exp(x)  (slightly faster than cosh+sinh)
+    type = 1 -- returns (cosh(x), sinh(x))
+    type = 2 -- returns (cos(x), sin(x))
+    """
+    if x < 0:
+        x = -x
+        sign = 1
+    else:
+        sign = 0
+    r = int(0.5*prec**0.5)
+    xmag = bitcount(x) - prec
+    r = max(0, xmag + r)
+    extra = 10 + 2*max(r,-xmag)
+    wp = prec + extra
+    x <<= (extra - r)
+    one = MPZ_ONE << wp
+    alt = (type == 2)
+    if prec < EXP_SERIES_U_CUTOFF:
+        x2 = a = (x*x) >> wp
+        x4 = (x2*x2) >> wp
+        s0 = s1 = MPZ_ZERO
+        k = 2
+        while a:
+            a //= (k-1)*k; s0 += a; k += 2
+            a //= (k-1)*k; s1 += a; k += 2
+            a = (a*x4) >> wp
+        s1 = (x2*s1) >> wp
+        if alt:
+            c = s1 - s0 + one
+        else:
+            c = s1 + s0 + one
+    else:
+        u = int(0.3*prec**0.35)
+        x2 = a = (x*x) >> wp
+        xpowers = [one, x2]
+        for i in xrange(1, u):
+            xpowers.append((xpowers[-1]*x2)>>wp)
+        sums = [MPZ_ZERO] * u
+        k = 2
+        while a:
+            for i in xrange(u):
+                a //= (k-1)*k
+                if alt and k & 2: sums[i] -= a
+                else:             sums[i] += a
+                k += 2
+            a = (a*xpowers[-1]) >> wp
+        for i in xrange(1, u):
+            sums[i] = (sums[i]*xpowers[i]) >> wp
+        c = sum(sums) + one
+    if type == 0:
+        s = isqrt_fast(c*c - (one<<wp))
+        if sign:
+            v = c - s
+        else:
+            v = c + s
+        for i in xrange(r):
+            v = (v*v) >> wp
+        return v >> extra
+    else:
+        # Repeatedly apply the double-angle formula
+        # cosh(2*x) = 2*cosh(x)^2 - 1
+        # cos(2*x) = 2*cos(x)^2 - 1
+        pshift = wp-1
+        for i in xrange(r):
+            c = ((c*c) >> pshift) - one
+        # With the abs, this is the same for sinh and sin
+        s = isqrt_fast(abs((one<<wp) - c*c))
+        if sign:
+            s = -s
+        return (c>>extra), (s>>extra)
+
+def exp_basecase(x, prec):
+    """
+    Compute exp(x) as a fixed-point number. Works for any x,
+    but for speed should have |x| < 1. For an arbitrary number,
+    use exp(x) = exp(x-m*log(2)) * 2^m where m = floor(x/log(2)).
+    """
+    if prec > EXP_COSH_CUTOFF:
+        return exponential_series(x, prec, 0)
+    r = int(prec**0.5)
+    prec += r
+    s0 = s1 = (MPZ_ONE << prec)
+    k = 2
+    a = x2 = (x*x) >> prec
+    while a:
+        a //= k; s0 += a; k += 1
+        a //= k; s1 += a; k += 1
+        a = (a*x2) >> prec
+    s1 = (s1*x) >> prec
+    s = s0 + s1
+    u = r
+    while r:
+        s = (s*s) >> prec
+        r -= 1
+    return s >> u
+
+def exp_expneg_basecase(x, prec):
+    """
+    Computation of exp(x), exp(-x)
+    """
+    if prec > EXP_COSH_CUTOFF:
+        cosh, sinh = exponential_series(x, prec, 1)
+        return cosh+sinh, cosh-sinh
+    a = exp_basecase(x, prec)
+    b = (MPZ_ONE << (prec+prec)) // a
+    return a, b
+
+def cos_sin_basecase(x, prec):
+    """
+    Compute cos(x), sin(x) as fixed-point numbers, assuming x
+    in [0, pi/2). For an arbitrary number, use x' = x - m*(pi/2)
+    where m = floor(x/(pi/2)) along with quarter-period symmetries.
+    """
+    if prec > COS_SIN_CACHE_PREC:
+        return exponential_series(x, prec, 2)
+    precs = prec - COS_SIN_CACHE_STEP
+    t = x >> precs
+    n = int(t)
+    if n not in cos_sin_cache:
+        w = t<<(10+COS_SIN_CACHE_PREC-COS_SIN_CACHE_STEP)
+        cos_t, sin_t = exponential_series(w, 10+COS_SIN_CACHE_PREC, 2)
+        cos_sin_cache[n] = (cos_t>>10), (sin_t>>10)
+    cos_t, sin_t = cos_sin_cache[n]
+    offset = COS_SIN_CACHE_PREC - prec
+    cos_t >>= offset
+    sin_t >>= offset
+    x -= t << precs
+    cos = MPZ_ONE << prec
+    sin = x
+    k = 2
+    a = -((x*x) >> prec)
+    while a:
+        a //= k; cos += a; k += 1; a = (a*x) >> prec
+        a //= k; sin += a; k += 1; a = -((a*x) >> prec)
+    return ((cos*cos_t-sin*sin_t) >> prec), ((sin*cos_t+cos*sin_t) >> prec)
+
+def mpf_exp(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if man:
+        mag = bc + exp
+        wp = prec + 14
+        if sign:
+            man = -man
+        # TODO: the best cutoff depends on both x and the precision.
+        if prec > 600 and exp >= 0:
+            # Need about log2(exp(n)) ~= 1.45*mag extra precision
+            e = mpf_e(wp+int(1.45*mag))
+            return mpf_pow_int(e, man<<exp, prec, rnd)
+        if mag < -wp:
+            return mpf_perturb(fone, sign, prec, rnd)
+        # |x| >= 2
+        if mag > 1:
+            # For large arguments: exp(2^mag*(1+eps)) =
+            # exp(2^mag)*exp(2^mag*eps) = exp(2^mag)*(1 + 2^mag*eps + ...)
+            # so about mag extra bits is required.
+            wpmod = wp + mag
+            offset = exp + wpmod
+            if offset >= 0:
+                t = man << offset
+            else:
+                t = man >> (-offset)
+            lg2 = ln2_fixed(wpmod)
+            n, t = divmod(t, lg2)
+            n = int(n)
+            t >>= mag
+        else:
+            offset = exp + wp
+            if offset >= 0:
+                t = man << offset
+            else:
+                t = man >> (-offset)
+            n = 0
+        man = exp_basecase(t, wp)
+        return from_man_exp(man, n-wp, prec, rnd)
+    if not exp:
+        return fone
+    if x == fninf:
+        return fzero
+    return x
+
+
+def mpf_cosh_sinh(x, prec, rnd=round_fast, tanh=0):
+    """Simultaneously compute (cosh(x), sinh(x)) for real x"""
+    sign, man, exp, bc = x
+    if (not man) and exp:
+        if tanh:
+            if x == finf: return fone
+            if x == fninf: return fnone
+            return fnan
+        if x == finf: return (finf, finf)
+        if x == fninf: return (finf, fninf)
+        return fnan, fnan
+    mag = exp+bc
+    wp = prec+14
+    if mag < -4:
+        # Extremely close to 0, sinh(x) ~= x and cosh(x) ~= 1
+        if mag < -wp:
+            if tanh:
+                return mpf_perturb(x, 1-sign, prec, rnd)
+            cosh = mpf_perturb(fone, 0, prec, rnd)
+            sinh = mpf_perturb(x, sign, prec, rnd)
+            return cosh, sinh
+        # Fix for cancellation when computing sinh
+        wp += (-mag)
+    # Does exp(-2*x) vanish?
+    if mag > 10:
+        if 3*(1<<(mag-1)) > wp:
+            # XXX: rounding
+            if tanh:
+                return mpf_perturb([fone,fnone][sign], 1-sign, prec, rnd)
+            c = s = mpf_shift(mpf_exp(mpf_abs(x), prec, rnd), -1)
+            if sign:
+                s = mpf_neg(s)
+            return c, s
+    # |x| > 1
+    if mag > 1:
+        wpmod = wp + mag
+        offset = exp + wpmod
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        lg2 = ln2_fixed(wpmod)
+        n, t = divmod(t, lg2)
+        n = int(n)
+        t >>= mag
+    else:
+        offset = exp + wp
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        n = 0
+    a, b = exp_expneg_basecase(t, wp)
+    # TODO: optimize division precision
+    cosh = a + (b>>(2*n))
+    sinh = a - (b>>(2*n))
+    if sign:
+        sinh = -sinh
+    if tanh:
+        man = (sinh << wp) // cosh
+        return from_man_exp(man, -wp, prec, rnd)
+    else:
+        cosh = from_man_exp(cosh, n-wp-1, prec, rnd)
+        sinh = from_man_exp(sinh, n-wp-1, prec, rnd)
+        return cosh, sinh
+
+
+def mod_pi2(man, exp, mag, wp):
+    # Reduce to standard interval
+    if mag > 0:
+        i = 0
+        while 1:
+            cancellation_prec = 20 << i
+            wpmod = wp + mag + cancellation_prec
+            pi2 = pi_fixed(wpmod-1)
+            pi4 = pi2 >> 1
+            offset = wpmod + exp
+            if offset >= 0:
+                t = man << offset
+            else:
+                t = man >> (-offset)
+            n, y = divmod(t, pi2)
+            if y > pi4:
+                small = pi2 - y
+            else:
+                small = y
+            if small >> (wp+mag-10):
+                n = int(n)
+                t = y >> mag
+                wp = wpmod - mag
+                break
+            i += 1
+    else:
+        wp += (-mag)
+        offset = exp + wp
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        n = 0
+    return t, n, wp
+
+
+def mpf_cos_sin(x, prec, rnd=round_fast, which=0, pi=False):
+    """
+    which:
+    0 -- return cos(x), sin(x)
+    1 -- return cos(x)
+    2 -- return sin(x)
+    3 -- return tan(x)
+
+    if pi=True, compute for pi*x
+    """
+    sign, man, exp, bc = x
+    if not man:
+        if exp:
+            c, s = fnan, fnan
+        else:
+            c, s = fone, fzero
+        if which == 0: return c, s
+        if which == 1: return c
+        if which == 2: return s
+        if which == 3: return s
+
+    mag = bc + exp
+    wp = prec + 10
+
+    # Extremely small?
+    if mag < 0:
+        if mag < -wp:
+            if pi:
+                x = mpf_mul(x, mpf_pi(wp))
+            c = mpf_perturb(fone, 1, prec, rnd)
+            s = mpf_perturb(x, 1-sign, prec, rnd)
+            if which == 0: return c, s
+            if which == 1: return c
+            if which == 2: return s
+            if which == 3: return mpf_perturb(x, sign, prec, rnd)
+    if pi:
+        if exp >= -1:
+            if exp == -1:
+                c = fzero
+                s = (fone, fnone)[bool(man & 2) ^ sign]
+            elif exp == 0:
+                c, s = (fnone, fzero)
+            else:
+                c, s = (fone, fzero)
+            if which == 0: return c, s
+            if which == 1: return c
+            if which == 2: return s
+            if which == 3: return mpf_div(s, c, prec, rnd)
+        # Subtract nearest half-integer (= mod by pi/2)
+        n = ((man >> (-exp-2)) + 1) >> 1
+        man = man - (n << (-exp-1))
+        mag2 = bitcount(man) + exp
+        wp = prec + 10 - mag2
+        offset = exp + wp
+        if offset >= 0:
+            t = man << offset
+        else:
+            t = man >> (-offset)
+        t = (t*pi_fixed(wp)) >> wp
+    else:
+        t, n, wp = mod_pi2(man, exp, mag, wp)
+    c, s = cos_sin_basecase(t, wp)
+    m = n & 3
+    if   m == 1: c, s = -s, c
+    elif m == 2: c, s = -c, -s
+    elif m == 3: c, s = s, -c
+    if sign:
+        s = -s
+    if which == 0:
+        c = from_man_exp(c, -wp, prec, rnd)
+        s = from_man_exp(s, -wp, prec, rnd)
+        return c, s
+    if which == 1:
+        return from_man_exp(c, -wp, prec, rnd)
+    if which == 2:
+        return from_man_exp(s, -wp, prec, rnd)
+    if which == 3:
+        return from_rational(s, c, prec, rnd)
+
+def mpf_cos(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 1)
+def mpf_sin(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 2)
+def mpf_tan(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 3)
+def mpf_cos_sin_pi(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 0, 1)
+def mpf_cos_pi(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 1, 1)
+def mpf_sin_pi(x, prec, rnd=round_fast): return mpf_cos_sin(x, prec, rnd, 2, 1)
+def mpf_cosh(x, prec, rnd=round_fast): return mpf_cosh_sinh(x, prec, rnd)[0]
+def mpf_sinh(x, prec, rnd=round_fast): return mpf_cosh_sinh(x, prec, rnd)[1]
+def mpf_tanh(x, prec, rnd=round_fast): return mpf_cosh_sinh(x, prec, rnd, tanh=1)
+
+
+# Low-overhead fixed-point versions
+
+def cos_sin_fixed(x, prec, pi2=None):
+    if pi2 is None:
+        pi2 = pi_fixed(prec-1)
+    n, t = divmod(x, pi2)
+    n = int(n)
+    c, s = cos_sin_basecase(t, prec)
+    m = n & 3
+    if m == 0: return c, s
+    if m == 1: return -s, c
+    if m == 2: return -c, -s
+    if m == 3: return s, -c
+
+def exp_fixed(x, prec, ln2=None):
+    if ln2 is None:
+        ln2 = ln2_fixed(prec)
+    n, t = divmod(x, ln2)
+    n = int(n)
+    v = exp_basecase(t, prec)
+    if n >= 0:
+        return v << n
+    else:
+        return v >> (-n)
+
+
+if BACKEND == 'sage':
+    try:
+        import sage.libs.mpmath.ext_libmp as _lbmp
+        mpf_sqrt = _lbmp.mpf_sqrt
+        mpf_exp = _lbmp.mpf_exp
+        mpf_log = _lbmp.mpf_log
+        mpf_cos = _lbmp.mpf_cos
+        mpf_sin = _lbmp.mpf_sin
+        mpf_pow = _lbmp.mpf_pow
+        exp_fixed = _lbmp.exp_fixed
+        cos_sin_fixed = _lbmp.cos_sin_fixed
+        log_int_fixed = _lbmp.log_int_fixed
+    except (ImportError, AttributeError):
+        print("Warning: Sage imports in libelefun failed")

lib/python3.11/site-packages/mpmath/libmp/libhyper.py

@@ -0,0 +1,1150 @@
+"""
+This module implements computation of hypergeometric and related
+functions. In particular, it provides code for generic summation
+of hypergeometric series. Optimized versions for various special
+cases are also provided.
+"""
+
+import operator
+import math
+
+from .backend import MPZ_ZERO, MPZ_ONE, BACKEND, xrange, exec_
+
+from .libintmath import gcd
+
+from .libmpf import (\
+    ComplexResult, round_fast, round_nearest,
+    negative_rnd, bitcount, to_fixed, from_man_exp, from_int, to_int,
+    from_rational,
+    fzero, fone, fnone, ftwo, finf, fninf, fnan,
+    mpf_sign, mpf_add, mpf_abs, mpf_pos,
+    mpf_cmp, mpf_lt, mpf_le, mpf_gt, mpf_min_max,
+    mpf_perturb, mpf_neg, mpf_shift, mpf_sub, mpf_mul, mpf_div,
+    sqrt_fixed, mpf_sqrt, mpf_rdiv_int, mpf_pow_int,
+    to_rational,
+)
+
+from .libelefun import (\
+    mpf_pi, mpf_exp, mpf_log, pi_fixed, mpf_cos_sin, mpf_cos, mpf_sin,
+    mpf_sqrt, agm_fixed,
+)
+
+from .libmpc import (\
+    mpc_one, mpc_sub, mpc_mul_mpf, mpc_mul, mpc_neg, complex_int_pow,
+    mpc_div, mpc_add_mpf, mpc_sub_mpf,
+    mpc_log, mpc_add, mpc_pos, mpc_shift,
+    mpc_is_infnan, mpc_zero, mpc_sqrt, mpc_abs,
+    mpc_mpf_div, mpc_square, mpc_exp
+)
+
+from .libintmath import ifac
+from .gammazeta import mpf_gamma_int, mpf_euler, euler_fixed
+
+class NoConvergence(Exception):
+    pass
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                     Generic hypergeometric series                     #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+"""
+TODO:
+
+1. proper mpq parsing
+2. imaginary z special-cased (also: rational, integer?)
+3. more clever handling of series that don't converge because of stupid
+   upwards rounding
+4. checking for cancellation
+
+"""
+
+def make_hyp_summator(key):
+    """
+    Returns a function that sums a generalized hypergeometric series,
+    for given parameter types (integer, rational, real, complex).
+
+    """
+    p, q, param_types, ztype = key
+
+    pstring = "".join(param_types)
+    fname = "hypsum_%i_%i_%s_%s_%s" % (p, q, pstring[:p], pstring[p:], ztype)
+    #print "generating hypsum", fname
+
+    have_complex_param = 'C' in param_types
+    have_complex_arg = ztype == 'C'
+    have_complex = have_complex_param or have_complex_arg
+
+    source = []
+    add = source.append
+
+    aint = []
+    arat = []
+    bint = []
+    brat = []
+    areal = []
+    breal = []
+    acomplex = []
+    bcomplex = []
+
+    #add("wp = prec + 40")
+    add("MAX = kwargs.get('maxterms', wp*100)")
+    add("HIGH = MPZ_ONE<<epsshift")
+    add("LOW = -HIGH")
+
+    # Setup code
+    add("SRE = PRE = one = (MPZ_ONE << wp)")
+    if have_complex:
+        add("SIM = PIM = MPZ_ZERO")
+
+    if have_complex_arg:
+        add("xsign, xm, xe, xbc = z[0]")
+        add("if xsign: xm = -xm")
+        add("ysign, ym, ye, ybc = z[1]")
+        add("if ysign: ym = -ym")
+    else:
+        add("xsign, xm, xe, xbc = z")
+        add("if xsign: xm = -xm")
+
+    add("offset = xe + wp")
+    add("if offset >= 0:")
+    add("    ZRE = xm << offset")
+    add("else:")
+    add("    ZRE = xm >> (-offset)")
+    if have_complex_arg:
+        add("offset = ye + wp")
+        add("if offset >= 0:")
+        add("    ZIM = ym << offset")
+        add("else:")
+        add("    ZIM = ym >> (-offset)")
+
+    for i, flag in enumerate(param_types):
+        W = ["A", "B"][i >= p]
+        if flag == 'Z':
+            ([aint,bint][i >= p]).append(i)
+            add("%sINT_%i = coeffs[%i]" % (W, i, i))
+        elif flag == 'Q':
+            ([arat,brat][i >= p]).append(i)
+            add("%sP_%i, %sQ_%i = coeffs[%i]._mpq_" % (W, i, W, i, i))
+        elif flag == 'R':
+            ([areal,breal][i >= p]).append(i)
+            add("xsign, xm, xe, xbc = coeffs[%i]._mpf_" % i)
+            add("if xsign: xm = -xm")
+            add("offset = xe + wp")
+            add("if offset >= 0:")
+            add("    %sREAL_%i = xm << offset" % (W, i))
+            add("else:")
+            add("    %sREAL_%i = xm >> (-offset)" % (W, i))
+        elif flag == 'C':
+            ([acomplex,bcomplex][i >= p]).append(i)
+            add("__re, __im = coeffs[%i]._mpc_" % i)
+            add("xsign, xm, xe, xbc = __re")
+            add("if xsign: xm = -xm")
+            add("ysign, ym, ye, ybc = __im")
+            add("if ysign: ym = -ym")
+
+            add("offset = xe + wp")
+            add("if offset >= 0:")
+            add("    %sCRE_%i = xm << offset" % (W, i))
+            add("else:")
+            add("    %sCRE_%i = xm >> (-offset)" % (W, i))
+            add("offset = ye + wp")
+            add("if offset >= 0:")
+            add("    %sCIM_%i = ym << offset" % (W, i))
+            add("else:")
+            add("    %sCIM_%i = ym >> (-offset)" % (W, i))
+        else:
+            raise ValueError
+
+    l_areal = len(areal)
+    l_breal = len(breal)
+    cancellable_real = min(l_areal, l_breal)
+    noncancellable_real_num = areal[cancellable_real:]
+    noncancellable_real_den = breal[cancellable_real:]
+
+    # LOOP
+    add("for n in xrange(1,10**8):")
+
+    add("    if n in magnitude_check:")
+    add("        p_mag = bitcount(abs(PRE))")
+    if have_complex:
+        add("        p_mag = max(p_mag, bitcount(abs(PIM)))")
+    add("        magnitude_check[n] = wp-p_mag")
+
+    # Real factors
+    multiplier = " * ".join(["AINT_#".replace("#", str(i)) for i in aint] + \
+                            ["AP_#".replace("#", str(i)) for i in arat] + \
+                            ["BQ_#".replace("#", str(i)) for i in brat])
+
+    divisor    = " * ".join(["BINT_#".replace("#", str(i)) for i in bint] + \
+                            ["BP_#".replace("#", str(i)) for i in brat] + \
+                            ["AQ_#".replace("#", str(i)) for i in arat] + ["n"])
+
+    if multiplier:
+        add("    mul = " + multiplier)
+    add("    div = " + divisor)
+
+    # Check for singular terms
+    add("    if not div:")
+    if multiplier:
+        add("        if not mul:")
+        add("            break")
+    add("        raise ZeroDivisionError")
+
+    # Update product
+    if have_complex:
+
+        # TODO: when there are several real parameters and just a few complex
+        # (maybe just the complex argument), we only need to do about
+        # half as many ops if we accumulate the real factor in a single real variable
+        for k in range(cancellable_real): add("    PRE = PRE * AREAL_%i // BREAL_%i" % (areal[k], breal[k]))
+        for i in noncancellable_real_num: add("    PRE = (PRE * AREAL_#) >> wp".replace("#", str(i)))
+        for i in noncancellable_real_den: add("    PRE = (PRE << wp) // BREAL_#".replace("#", str(i)))
+        for k in range(cancellable_real): add("    PIM = PIM * AREAL_%i // BREAL_%i" % (areal[k], breal[k]))
+        for i in noncancellable_real_num: add("    PIM = (PIM * AREAL_#) >> wp".replace("#", str(i)))
+        for i in noncancellable_real_den: add("    PIM = (PIM << wp) // BREAL_#".replace("#", str(i)))
+
+        if multiplier:
+            if have_complex_arg:
+                add("    PRE, PIM = (mul*(PRE*ZRE-PIM*ZIM))//div, (mul*(PIM*ZRE+PRE*ZIM))//div")
+                add("    PRE >>= wp")
+                add("    PIM >>= wp")
+            else:
+                add("    PRE = ((mul * PRE * ZRE) >> wp) // div")
+                add("    PIM = ((mul * PIM * ZRE) >> wp) // div")
+        else:
+            if have_complex_arg:
+                add("    PRE, PIM = (PRE*ZRE-PIM*ZIM)//div, (PIM*ZRE+PRE*ZIM)//div")
+                add("    PRE >>= wp")
+                add("    PIM >>= wp")
+            else:
+                add("    PRE = ((PRE * ZRE) >> wp) // div")
+                add("    PIM = ((PIM * ZRE) >> wp) // div")
+
+        for i in acomplex:
+            add("    PRE, PIM = PRE*ACRE_#-PIM*ACIM_#, PIM*ACRE_#+PRE*ACIM_#".replace("#", str(i)))
+            add("    PRE >>= wp")
+            add("    PIM >>= wp")
+
+        for i in bcomplex:
+            add("    mag = BCRE_#*BCRE_#+BCIM_#*BCIM_#".replace("#", str(i)))
+            add("    re = PRE*BCRE_# + PIM*BCIM_#".replace("#", str(i)))
+            add("    im = PIM*BCRE_# - PRE*BCIM_#".replace("#", str(i)))
+            add("    PRE = (re << wp) // mag".replace("#", str(i)))
+            add("    PIM = (im << wp) // mag".replace("#", str(i)))
+
+    else:
+        for k in range(cancellable_real): add("    PRE = PRE * AREAL_%i // BREAL_%i" % (areal[k], breal[k]))
+        for i in noncancellable_real_num: add("    PRE = (PRE * AREAL_#) >> wp".replace("#", str(i)))
+        for i in noncancellable_real_den: add("    PRE = (PRE << wp) // BREAL_#".replace("#", str(i)))
+        if multiplier:
+            add("    PRE = ((PRE * mul * ZRE) >> wp) // div")
+        else:
+            add("    PRE = ((PRE * ZRE) >> wp) // div")
+
+    # Add product to sum
+    if have_complex:
+        add("    SRE += PRE")
+        add("    SIM += PIM")
+        add("    if (HIGH > PRE > LOW) and (HIGH > PIM > LOW):")
+        add("        break")
+    else:
+        add("    SRE += PRE")
+        add("    if HIGH > PRE > LOW:")
+        add("        break")
+
+    #add("    from mpmath import nprint, log, ldexp")
+    #add("    nprint([n, log(abs(PRE),2), ldexp(PRE,-wp)])")
+
+    add("    if n > MAX:")
+    add("        raise NoConvergence('Hypergeometric series converges too slowly. Try increasing maxterms.')")
+
+    # +1 all parameters for next loop
+    for i in aint:     add("    AINT_# += 1".replace("#", str(i)))
+    for i in bint:     add("    BINT_# += 1".replace("#", str(i)))
+    for i in arat:     add("    AP_# += AQ_#".replace("#", str(i)))
+    for i in brat:     add("    BP_# += BQ_#".replace("#", str(i)))
+    for i in areal:    add("    AREAL_# += one".replace("#", str(i)))
+    for i in breal:    add("    BREAL_# += one".replace("#", str(i)))
+    for i in acomplex: add("    ACRE_# += one".replace("#", str(i)))
+    for i in bcomplex: add("    BCRE_# += one".replace("#", str(i)))
+
+    if have_complex:
+        add("a = from_man_exp(SRE, -wp, prec, 'n')")
+        add("b = from_man_exp(SIM, -wp, prec, 'n')")
+
+        add("if SRE:")
+        add("    if SIM:")
+        add("        magn = max(a[2]+a[3], b[2]+b[3])")
+        add("    else:")
+        add("        magn = a[2]+a[3]")
+        add("elif SIM:")
+        add("    magn = b[2]+b[3]")
+        add("else:")
+        add("    magn = -wp+1")
+
+        add("return (a, b), True, magn")
+    else:
+        add("a = from_man_exp(SRE, -wp, prec, 'n')")
+
+        add("if SRE:")
+        add("    magn = a[2]+a[3]")
+        add("else:")
+        add("    magn = -wp+1")
+
+        add("return a, False, magn")
+
+    source = "\n".join(("    " + line) for line in source)
+    source = ("def %s(coeffs, z, prec, wp, epsshift, magnitude_check, **kwargs):\n" % fname) + source
+
+    namespace = {}
+
+    exec_(source, globals(), namespace)
+
+    #print source
+    return source, namespace[fname]
+
+
+if BACKEND == 'sage':
+
+    def make_hyp_summator(key):
+        """
+        Returns a function that sums a generalized hypergeometric series,
+        for given parameter types (integer, rational, real, complex).
+        """
+        from sage.libs.mpmath.ext_main import hypsum_internal
+        p, q, param_types, ztype = key
+        def _hypsum(coeffs, z, prec, wp, epsshift, magnitude_check, **kwargs):
+            return hypsum_internal(p, q, param_types, ztype, coeffs, z,
+                prec, wp, epsshift, magnitude_check, kwargs)
+
+        return "(none)", _hypsum
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                              Error functions                          #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+# TODO: mpf_erf should call mpf_erfc when appropriate (currently
+#    only the converse delegation is implemented)
+
+def mpf_erf(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero: return fzero
+        if x == finf: return fone
+        if x== fninf: return fnone
+        return fnan
+    size = exp + bc
+    lg = math.log
+    # The approximation erf(x) = 1 is accurate to > x^2 * log(e,2) bits
+    if size > 3 and 2*(size-1) + 0.528766 > lg(prec,2):
+        if sign:
+            return mpf_perturb(fnone, 0, prec, rnd)
+        else:
+            return mpf_perturb(fone, 1, prec, rnd)
+    # erf(x) ~ 2*x/sqrt(pi) close to 0
+    if size < -prec:
+        # 2*x
+        x = mpf_shift(x,1)
+        c = mpf_sqrt(mpf_pi(prec+20), prec+20)
+        # TODO: interval rounding
+        return mpf_div(x, c, prec, rnd)
+    wp = prec + abs(size) + 25
+    # Taylor series for erf, fixed-point summation
+    t = abs(to_fixed(x, wp))
+    t2 = (t*t) >> wp
+    s, term, k = t, 12345, 1
+    while term:
+        t = ((t * t2) >> wp) // k
+        term = t // (2*k+1)
+        if k & 1:
+            s -= term
+        else:
+            s += term
+        k += 1
+    s = (s << (wp+1)) // sqrt_fixed(pi_fixed(wp), wp)
+    if sign:
+        s = -s
+    return from_man_exp(s, -wp, prec, rnd)
+
+# If possible, we use the asymptotic series for erfc.
+# This is an alternating divergent asymptotic series, so
+# the error is at most equal to the first omitted term.
+# Here we check if the smallest term is small enough
+# for a given x and precision
+def erfc_check_series(x, prec):
+    n = to_int(x)
+    if n**2 * 1.44 > prec:
+        return True
+    return False
+
+def mpf_erfc(x, prec, rnd=round_fast):
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero: return fone
+        if x == finf: return fzero
+        if x == fninf: return ftwo
+        return fnan
+    wp = prec + 20
+    mag = bc+exp
+    # Preserve full accuracy when exponent grows huge
+    wp += max(0, 2*mag)
+    regular_erf = sign or mag < 2
+    if regular_erf or not erfc_check_series(x, wp):
+        if regular_erf:
+            return mpf_sub(fone, mpf_erf(x, prec+10, negative_rnd[rnd]), prec, rnd)
+        # 1-erf(x) ~ exp(-x^2), increase prec to deal with cancellation
+        n = to_int(x)+1
+        return mpf_sub(fone, mpf_erf(x, prec + int(n**2*1.44) + 10), prec, rnd)
+    s = term = MPZ_ONE << wp
+    term_prev = 0
+    t = (2 * to_fixed(x, wp) ** 2) >> wp
+    k = 1
+    while 1:
+        term = ((term * (2*k - 1)) << wp) // t
+        if k > 4 and term > term_prev or not term:
+            break
+        if k & 1:
+            s -= term
+        else:
+            s += term
+        term_prev = term
+        #print k, to_str(from_man_exp(term, -wp, 50), 10)
+        k += 1
+    s = (s << wp) // sqrt_fixed(pi_fixed(wp), wp)
+    s = from_man_exp(s, -wp, wp)
+    z = mpf_exp(mpf_neg(mpf_mul(x,x,wp),wp),wp)
+    y = mpf_div(mpf_mul(z, s, wp), x, prec, rnd)
+    return y
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                         Exponential integrals                         #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+def ei_taylor(x, prec):
+    s = t = x
+    k = 2
+    while t:
+        t = ((t*x) >> prec) // k
+        s += t // k
+        k += 1
+    return s
+
+def complex_ei_taylor(zre, zim, prec):
+    _abs = abs
+    sre = tre = zre
+    sim = tim = zim
+    k = 2
+    while _abs(tre) + _abs(tim) > 5:
+        tre, tim = ((tre*zre-tim*zim)//k)>>prec, ((tre*zim+tim*zre)//k)>>prec
+        sre += tre // k
+        sim += tim // k
+        k += 1
+    return sre, sim
+
+def ei_asymptotic(x, prec):
+    one = MPZ_ONE << prec
+    x = t = ((one << prec) // x)
+    s = one + x
+    k = 2
+    while t:
+        t = (k*t*x) >> prec
+        s += t
+        k += 1
+    return s
+
+def complex_ei_asymptotic(zre, zim, prec):
+    _abs = abs
+    one = MPZ_ONE << prec
+    M = (zim*zim + zre*zre) >> prec
+    # 1 / z
+    xre = tre = (zre << prec) // M
+    xim = tim = ((-zim) << prec) // M
+    sre = one + xre
+    sim = xim
+    k = 2
+    while _abs(tre) + _abs(tim) > 1000:
+        #print tre, tim
+        tre, tim = ((tre*xre-tim*xim)*k)>>prec, ((tre*xim+tim*xre)*k)>>prec
+        sre += tre
+        sim += tim
+        k += 1
+        if k > prec:
+            raise NoConvergence
+    return sre, sim
+
+def mpf_ei(x, prec, rnd=round_fast, e1=False):
+    if e1:
+        x = mpf_neg(x)
+    sign, man, exp, bc = x
+    if e1 and not sign:
+        if x == fzero:
+            return finf
+        raise ComplexResult("E1(x) for x < 0")
+    if man:
+        xabs = 0, man, exp, bc
+        xmag = exp+bc
+        wp = prec + 20
+        can_use_asymp = xmag > wp
+        if not can_use_asymp:
+            if exp >= 0:
+                xabsint = man << exp
+            else:
+                xabsint = man >> (-exp)
+            can_use_asymp = xabsint > int(wp*0.693) + 10
+        if can_use_asymp:
+            if xmag > wp:
+                v = fone
+            else:
+                v = from_man_exp(ei_asymptotic(to_fixed(x, wp), wp), -wp)
+            v = mpf_mul(v, mpf_exp(x, wp), wp)
+            v = mpf_div(v, x, prec, rnd)
+        else:
+            wp += 2*int(to_int(xabs))
+            u = to_fixed(x, wp)
+            v = ei_taylor(u, wp) + euler_fixed(wp)
+            t1 = from_man_exp(v,-wp)
+            t2 = mpf_log(xabs,wp)
+            v = mpf_add(t1, t2, prec, rnd)
+    else:
+        if x == fzero: v = fninf
+        elif x == finf: v = finf
+        elif x == fninf: v = fzero
+        else: v = fnan
+    if e1:
+        v = mpf_neg(v)
+    return v
+
+def mpc_ei(z, prec, rnd=round_fast, e1=False):
+    if e1:
+        z = mpc_neg(z)
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if b == fzero:
+        if e1:
+            x = mpf_neg(mpf_ei(a, prec, rnd))
+            if not asign:
+                y = mpf_neg(mpf_pi(prec, rnd))
+            else:
+                y = fzero
+            return x, y
+        else:
+            return mpf_ei(a, prec, rnd), fzero
+    if a != fzero:
+        if not aman or not bman:
+            return (fnan, fnan)
+    wp = prec + 40
+    amag = aexp+abc
+    bmag = bexp+bbc
+    zmag = max(amag, bmag)
+    can_use_asymp = zmag > wp
+    if not can_use_asymp:
+        zabsint = abs(to_int(a)) + abs(to_int(b))
+        can_use_asymp = zabsint > int(wp*0.693) + 20
+    try:
+        if can_use_asymp:
+            if zmag > wp:
+                v = fone, fzero
+            else:
+                zre = to_fixed(a, wp)
+                zim = to_fixed(b, wp)
+                vre, vim = complex_ei_asymptotic(zre, zim, wp)
+                v = from_man_exp(vre, -wp), from_man_exp(vim, -wp)
+            v = mpc_mul(v, mpc_exp(z, wp), wp)
+            v = mpc_div(v, z, wp)
+            if e1:
+                v = mpc_neg(v, prec, rnd)
+            else:
+                x, y = v
+                if bsign:
+                    v = mpf_pos(x, prec, rnd), mpf_sub(y, mpf_pi(wp), prec, rnd)
+                else:
+                    v = mpf_pos(x, prec, rnd), mpf_add(y, mpf_pi(wp), prec, rnd)
+            return v
+    except NoConvergence:
+        pass
+    #wp += 2*max(0,zmag)
+    wp += 2*int(to_int(mpc_abs(z, 5)))
+    zre = to_fixed(a, wp)
+    zim = to_fixed(b, wp)
+    vre, vim = complex_ei_taylor(zre, zim, wp)
+    vre += euler_fixed(wp)
+    v = from_man_exp(vre,-wp), from_man_exp(vim,-wp)
+    if e1:
+        u = mpc_log(mpc_neg(z),wp)
+    else:
+        u = mpc_log(z,wp)
+    v = mpc_add(v, u, prec, rnd)
+    if e1:
+        v = mpc_neg(v)
+    return v
+
+def mpf_e1(x, prec, rnd=round_fast):
+    return mpf_ei(x, prec, rnd, True)
+
+def mpc_e1(x, prec, rnd=round_fast):
+    return mpc_ei(x, prec, rnd, True)
+
+def mpf_expint(n, x, prec, rnd=round_fast, gamma=False):
+    """
+    E_n(x), n an integer, x real
+
+    With gamma=True, computes Gamma(n,x)   (upper incomplete gamma function)
+
+    Returns (real, None) if real, otherwise (real, imag)
+    The imaginary part is an optional branch cut term
+
+    """
+    sign, man, exp, bc = x
+    if not man:
+        if gamma:
+            if x == fzero:
+                # Actually gamma function pole
+                if n <= 0:
+                    return finf, None
+                return mpf_gamma_int(n, prec, rnd), None
+            if x == finf:
+                return fzero, None
+            # TODO: could return finite imaginary value at -inf
+            return fnan, fnan
+        else:
+            if x == fzero:
+                if n > 1:
+                    return from_rational(1, n-1, prec, rnd), None
+                else:
+                    return finf, None
+            if x == finf:
+                return fzero, None
+            return fnan, fnan
+    n_orig = n
+    if gamma:
+        n = 1-n
+    wp = prec + 20
+    xmag = exp + bc
+    # Beware of near-poles
+    if xmag < -10:
+        raise NotImplementedError
+    nmag = bitcount(abs(n))
+    have_imag = n > 0 and sign
+    negx = mpf_neg(x)
+    # Skip series if direct convergence
+    if n == 0 or 2*nmag - xmag < -wp:
+        if gamma:
+            v = mpf_exp(negx, wp)
+            re = mpf_mul(v, mpf_pow_int(x, n_orig-1, wp), prec, rnd)
+        else:
+            v = mpf_exp(negx, wp)
+            re = mpf_div(v, x, prec, rnd)
+    else:
+        # Finite number of terms, or...
+        can_use_asymptotic_series = -3*wp < n <= 0
+        # ...large enough?
+        if not can_use_asymptotic_series:
+            xi = abs(to_int(x))
+            m = min(max(1, xi-n), 2*wp)
+            siz = -n*nmag + (m+n)*bitcount(abs(m+n)) - m*xmag - (144*m//100)
+            tol = -wp-10
+            can_use_asymptotic_series = siz < tol
+        if can_use_asymptotic_series:
+            r = ((-MPZ_ONE) << (wp+wp)) // to_fixed(x, wp)
+            m = n
+            t = r*m
+            s = MPZ_ONE << wp
+            while m and t:
+                s += t
+                m += 1
+                t = (m*r*t) >> wp
+            v = mpf_exp(negx, wp)
+            if gamma:
+                # ~ exp(-x) * x^(n-1) * (1 + ...)
+                v = mpf_mul(v, mpf_pow_int(x, n_orig-1, wp), wp)
+            else:
+                # ~ exp(-x)/x * (1 + ...)
+                v = mpf_div(v, x, wp)
+            re = mpf_mul(v, from_man_exp(s, -wp), prec, rnd)
+        elif n == 1:
+            re = mpf_neg(mpf_ei(negx, prec, rnd))
+        elif n > 0 and n < 3*wp:
+            T1 = mpf_neg(mpf_ei(negx, wp))
+            if gamma:
+                if n_orig & 1:
+                    T1 = mpf_neg(T1)
+            else:
+                T1 = mpf_mul(T1, mpf_pow_int(negx, n-1, wp), wp)
+            r = t = to_fixed(x, wp)
+            facs = [1] * (n-1)
+            for k in range(1,n-1):
+                facs[k] = facs[k-1] * k
+            facs = facs[::-1]
+            s = facs[0] << wp
+            for k in range(1, n-1):
+                if k & 1:
+                    s -= facs[k] * t
+                else:
+                    s += facs[k] * t
+                t = (t*r) >> wp
+            T2 = from_man_exp(s, -wp, wp)
+            T2 = mpf_mul(T2, mpf_exp(negx, wp))
+            if gamma:
+                T2 = mpf_mul(T2, mpf_pow_int(x, n_orig, wp), wp)
+            R = mpf_add(T1, T2)
+            re = mpf_div(R, from_int(ifac(n-1)), prec, rnd)
+        else:
+            raise NotImplementedError
+    if have_imag:
+        M = from_int(-ifac(n-1))
+        if gamma:
+            im = mpf_div(mpf_pi(wp), M, prec, rnd)
+            if n_orig & 1:
+                im = mpf_neg(im)
+        else:
+            im = mpf_div(mpf_mul(mpf_pi(wp), mpf_pow_int(negx, n_orig-1, wp), wp), M, prec, rnd)
+        return re, im
+    else:
+        return re, None
+
+def mpf_ci_si_taylor(x, wp, which=0):
+    """
+    0 - Ci(x) - (euler+log(x))
+    1 - Si(x)
+    """
+    x = to_fixed(x, wp)
+    x2 = -(x*x) >> wp
+    if which == 0:
+        s, t, k = 0, (MPZ_ONE<<wp), 2
+    else:
+        s, t, k = x, x, 3
+    while t:
+        t = (t*x2//(k*(k-1)))>>wp
+        s += t//k
+        k += 2
+    return from_man_exp(s, -wp)
+
+def mpc_ci_si_taylor(re, im, wp, which=0):
+    # The following code is only designed for small arguments,
+    # and not too small arguments (for relative accuracy)
+    if re[1]:
+        mag = re[2]+re[3]
+    elif im[1]:
+        mag = im[2]+im[3]
+    if im[1]:
+        mag = max(mag, im[2]+im[3])
+    if mag > 2 or mag < -wp:
+        raise NotImplementedError
+    wp += (2-mag)
+    zre = to_fixed(re, wp)
+    zim = to_fixed(im, wp)
+    z2re = (zim*zim-zre*zre)>>wp
+    z2im = (-2*zre*zim)>>wp
+    tre = zre
+    tim = zim
+    one = MPZ_ONE<<wp
+    if which == 0:
+        sre, sim, tre, tim, k = 0, 0, (MPZ_ONE<<wp), 0, 2
+    else:
+        sre, sim, tre, tim, k = zre, zim, zre, zim, 3
+    while max(abs(tre), abs(tim)) > 2:
+        f = k*(k-1)
+        tre, tim = ((tre*z2re-tim*z2im)//f)>>wp, ((tre*z2im+tim*z2re)//f)>>wp
+        sre += tre//k
+        sim += tim//k
+        k += 2
+    return from_man_exp(sre, -wp), from_man_exp(sim, -wp)
+
+def mpf_ci_si(x, prec, rnd=round_fast, which=2):
+    """
+    Calculation of Ci(x), Si(x) for real x.
+
+    which = 0 -- returns (Ci(x), -)
+    which = 1 -- returns (Si(x), -)
+    which = 2 -- returns (Ci(x), Si(x))
+
+    Note: if x < 0, Ci(x) needs an additional imaginary term, pi*i.
+    """
+    wp = prec + 20
+    sign, man, exp, bc = x
+    ci, si = None, None
+    if not man:
+        if x == fzero:
+            return (fninf, fzero)
+        if x == fnan:
+            return (x, x)
+        ci = fzero
+        if which != 0:
+            if x == finf:
+                si = mpf_shift(mpf_pi(prec, rnd), -1)
+            if x == fninf:
+                si = mpf_neg(mpf_shift(mpf_pi(prec, negative_rnd[rnd]), -1))
+        return (ci, si)
+    # For small x: Ci(x) ~ euler + log(x), Si(x) ~ x
+    mag = exp+bc
+    if mag < -wp:
+        if which != 0:
+            si = mpf_perturb(x, 1-sign, prec, rnd)
+        if which != 1:
+            y = mpf_euler(wp)
+            xabs = mpf_abs(x)
+            ci = mpf_add(y, mpf_log(xabs, wp), prec, rnd)
+        return ci, si
+    # For huge x: Ci(x) ~ sin(x)/x, Si(x) ~ pi/2
+    elif mag > wp:
+        if which != 0:
+            if sign:
+                si = mpf_neg(mpf_pi(prec, negative_rnd[rnd]))
+            else:
+                si = mpf_pi(prec, rnd)
+            si = mpf_shift(si, -1)
+        if which != 1:
+            ci = mpf_div(mpf_sin(x, wp), x, prec, rnd)
+        return ci, si
+    else:
+        wp += abs(mag)
+    # Use an asymptotic series? The smallest value of n!/x^n
+    # occurs for n ~ x, where the magnitude is ~ exp(-x).
+    asymptotic = mag-1 > math.log(wp, 2)
+    # Case 1: convergent series near 0
+    if not asymptotic:
+        if which != 0:
+            si = mpf_pos(mpf_ci_si_taylor(x, wp, 1), prec, rnd)
+        if which != 1:
+            ci = mpf_ci_si_taylor(x, wp, 0)
+            ci = mpf_add(ci, mpf_euler(wp), wp)
+            ci = mpf_add(ci, mpf_log(mpf_abs(x), wp), prec, rnd)
+        return ci, si
+    x = mpf_abs(x)
+    # Case 2: asymptotic series for x >> 1
+    xf = to_fixed(x, wp)
+    xr = (MPZ_ONE<<(2*wp)) // xf   # 1/x
+    s1 = (MPZ_ONE << wp)
+    s2 = xr
+    t = xr
+    k = 2
+    while t:
+        t = -t
+        t = (t*xr*k)>>wp
+        k += 1
+        s1 += t
+        t = (t*xr*k)>>wp
+        k += 1
+        s2 += t
+    s1 = from_man_exp(s1, -wp)
+    s2 = from_man_exp(s2, -wp)
+    s1 = mpf_div(s1, x, wp)
+    s2 = mpf_div(s2, x, wp)
+    cos, sin = mpf_cos_sin(x, wp)
+    # Ci(x) = sin(x)*s1-cos(x)*s2
+    # Si(x) = pi/2-cos(x)*s1-sin(x)*s2
+    if which != 0:
+        si = mpf_add(mpf_mul(cos, s1), mpf_mul(sin, s2), wp)
+        si = mpf_sub(mpf_shift(mpf_pi(wp), -1), si, wp)
+        if sign:
+            si = mpf_neg(si)
+        si = mpf_pos(si, prec, rnd)
+    if which != 1:
+        ci = mpf_sub(mpf_mul(sin, s1), mpf_mul(cos, s2), prec, rnd)
+    return ci, si
+
+def mpf_ci(x, prec, rnd=round_fast):
+    if mpf_sign(x) < 0:
+        raise ComplexResult
+    return mpf_ci_si(x, prec, rnd, 0)[0]
+
+def mpf_si(x, prec, rnd=round_fast):
+    return mpf_ci_si(x, prec, rnd, 1)[1]
+
+def mpc_ci(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        ci = mpf_ci_si(re, prec, rnd, 0)[0]
+        if mpf_sign(re) < 0:
+            return (ci, mpf_pi(prec, rnd))
+        return (ci, fzero)
+    wp = prec + 20
+    cre, cim = mpc_ci_si_taylor(re, im, wp, 0)
+    cre = mpf_add(cre, mpf_euler(wp), wp)
+    ci = mpc_add((cre, cim), mpc_log(z, wp), prec, rnd)
+    return ci
+
+def mpc_si(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        return (mpf_ci_si(re, prec, rnd, 1)[1], fzero)
+    wp = prec + 20
+    z = mpc_ci_si_taylor(re, im, wp, 1)
+    return mpc_pos(z, prec, rnd)
+
+
+#-----------------------------------------------------------------------#
+#                                                                       #
+#                             Bessel functions                          #
+#                                                                       #
+#-----------------------------------------------------------------------#
+
+# A Bessel function of the first kind of integer order, J_n(x), is
+# given by the power series
+
+#             oo
+#             ___         k         2 k + n
+#            \        (-1)     / x \
+#    J_n(x) = )    ----------- | - |
+#            /___  k! (k + n)! \ 2 /
+#            k = 0
+
+# Simplifying the quotient between two successive terms gives the
+# ratio x^2 / (-4*k*(k+n)). Hence, we only need one full-precision
+# multiplication and one division by a small integer per term.
+# The complex version is very similar, the only difference being
+# that the multiplication is actually 4 multiplies.
+
+# In the general case, we have
+# J_v(x) = (x/2)**v / v! * 0F1(v+1, (-1/4)*z**2)
+
+# TODO: for extremely large x, we could use an asymptotic
+# trigonometric approximation.
+
+# TODO: recompute at higher precision if the fixed-point mantissa
+# is very small
+
+def mpf_besseljn(n, x, prec, rounding=round_fast):
+    prec += 50
+    negate = n < 0 and n & 1
+    mag = x[2]+x[3]
+    n = abs(n)
+    wp = prec + 20 + n*bitcount(n)
+    if mag < 0:
+        wp -= n * mag
+    x = to_fixed(x, wp)
+    x2 = (x**2) >> wp
+    if not n:
+        s = t = MPZ_ONE << wp
+    else:
+        s = t = (x**n // ifac(n)) >> ((n-1)*wp + n)
+    k = 1
+    while t:
+        t = ((t * x2) // (-4*k*(k+n))) >> wp
+        s += t
+        k += 1
+    if negate:
+        s = -s
+    return from_man_exp(s, -wp, prec, rounding)
+
+def mpc_besseljn(n, z, prec, rounding=round_fast):
+    negate = n < 0 and n & 1
+    n = abs(n)
+    origprec = prec
+    zre, zim = z
+    mag = max(zre[2]+zre[3], zim[2]+zim[3])
+    prec += 20 + n*bitcount(n) + abs(mag)
+    if mag < 0:
+        prec -= n * mag
+    zre = to_fixed(zre, prec)
+    zim = to_fixed(zim, prec)
+    z2re = (zre**2 - zim**2) >> prec
+    z2im = (zre*zim) >> (prec-1)
+    if not n:
+        sre = tre = MPZ_ONE << prec
+        sim = tim = MPZ_ZERO
+    else:
+        re, im = complex_int_pow(zre, zim, n)
+        sre = tre = (re // ifac(n)) >> ((n-1)*prec + n)
+        sim = tim = (im // ifac(n)) >> ((n-1)*prec + n)
+    k = 1
+    while abs(tre) + abs(tim) > 3:
+        p = -4*k*(k+n)
+        tre, tim = tre*z2re - tim*z2im, tim*z2re + tre*z2im
+        tre = (tre // p) >> prec
+        tim = (tim // p) >> prec
+        sre += tre
+        sim += tim
+        k += 1
+    if negate:
+        sre = -sre
+        sim = -sim
+    re = from_man_exp(sre, -prec, origprec, rounding)
+    im = from_man_exp(sim, -prec, origprec, rounding)
+    return (re, im)
+
+def mpf_agm(a, b, prec, rnd=round_fast):
+    """
+    Computes the arithmetic-geometric mean agm(a,b) for
+    nonnegative mpf values a, b.
+    """
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if asign or bsign:
+        raise ComplexResult("agm of a negative number")
+    # Handle inf, nan or zero in either operand
+    if not (aman and bman):
+        if a == fnan or b == fnan:
+            return fnan
+        if a == finf:
+            if b == fzero:
+                return fnan
+            return finf
+        if b == finf:
+            if a == fzero:
+                return fnan
+            return finf
+        # agm(0,x) = agm(x,0) = 0
+        return fzero
+    wp = prec + 20
+    amag = aexp+abc
+    bmag = bexp+bbc
+    mag_delta = amag - bmag
+    # Reduce to roughly the same magnitude using floating-point AGM
+    abs_mag_delta = abs(mag_delta)
+    if abs_mag_delta > 10:
+        while abs_mag_delta > 10:
+            a, b = mpf_shift(mpf_add(a,b,wp),-1), \
+                mpf_sqrt(mpf_mul(a,b,wp),wp)
+            abs_mag_delta //= 2
+        asign, aman, aexp, abc = a
+        bsign, bman, bexp, bbc = b
+        amag = aexp+abc
+        bmag = bexp+bbc
+        mag_delta = amag - bmag
+    #print to_float(a), to_float(b)
+    # Use agm(a,b) = agm(x*a,x*b)/x to obtain a, b ~= 1
+    min_mag = min(amag,bmag)
+    max_mag = max(amag,bmag)
+    n = 0
+    # If too small, we lose precision when going to fixed-point
+    if min_mag < -8:
+        n = -min_mag
+    # If too large, we waste time using fixed-point with large numbers
+    elif max_mag > 20:
+        n = -max_mag
+    if n:
+        a = mpf_shift(a, n)
+        b = mpf_shift(b, n)
+    #print to_float(a), to_float(b)
+    af = to_fixed(a, wp)
+    bf = to_fixed(b, wp)
+    g = agm_fixed(af, bf, wp)
+    return from_man_exp(g, -wp-n, prec, rnd)
+
+def mpf_agm1(a, prec, rnd=round_fast):
+    """
+    Computes the arithmetic-geometric mean agm(1,a) for a nonnegative
+    mpf value a.
+    """
+    return mpf_agm(fone, a, prec, rnd)
+
+def mpc_agm(a, b, prec, rnd=round_fast):
+    """
+    Complex AGM.
+
+    TODO:
+    * check that convergence works as intended
+    * optimize
+    * select a nonarbitrary branch
+    """
+    if mpc_is_infnan(a) or mpc_is_infnan(b):
+        return fnan, fnan
+    if mpc_zero in (a, b):
+        return fzero, fzero
+    if mpc_neg(a) == b:
+        return fzero, fzero
+    wp = prec+20
+    eps = mpf_shift(fone, -wp+10)
+    while 1:
+        a1 = mpc_shift(mpc_add(a, b, wp), -1)
+        b1 = mpc_sqrt(mpc_mul(a, b, wp), wp)
+        a, b = a1, b1
+        size = mpf_min_max([mpc_abs(a,10), mpc_abs(b,10)])[1]
+        err = mpc_abs(mpc_sub(a, b, 10), 10)
+        if size == fzero or mpf_lt(err, mpf_mul(eps, size)):
+            return a
+
+def mpc_agm1(a, prec, rnd=round_fast):
+    return mpc_agm(mpc_one, a, prec, rnd)
+
+def mpf_ellipk(x, prec, rnd=round_fast):
+    if not x[1]:
+        if x == fzero:
+            return mpf_shift(mpf_pi(prec, rnd), -1)
+        if x == fninf:
+            return fzero
+        if x == fnan:
+            return x
+    if x == fone:
+        return finf
+    # TODO: for |x| << 1/2, one could use fall back to
+    # pi/2 * hyp2f1_rat((1,2),(1,2),(1,1), x)
+    wp = prec + 15
+    # Use K(x) = pi/2/agm(1,a) where a = sqrt(1-x)
+    # The sqrt raises ComplexResult if x > 0
+    a = mpf_sqrt(mpf_sub(fone, x, wp), wp)
+    v = mpf_agm1(a, wp)
+    r = mpf_div(mpf_pi(wp), v, prec, rnd)
+    return mpf_shift(r, -1)
+
+def mpc_ellipk(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        if re == finf:
+            return mpc_zero
+        if mpf_le(re, fone):
+            return mpf_ellipk(re, prec, rnd), fzero
+    wp = prec + 15
+    a = mpc_sqrt(mpc_sub(mpc_one, z, wp), wp)
+    v = mpc_agm1(a, wp)
+    r = mpc_mpf_div(mpf_pi(wp), v, prec, rnd)
+    return mpc_shift(r, -1)
+
+def mpf_ellipe(x, prec, rnd=round_fast):
+    # http://functions.wolfram.com/EllipticIntegrals/
+    # EllipticK/20/01/0001/
+    # E = (1-m)*(K'(m)*2*m + K(m))
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero:
+            return mpf_shift(mpf_pi(prec, rnd), -1)
+        if x == fninf:
+            return finf
+        if x == fnan:
+            return x
+        if x == finf:
+            raise ComplexResult
+    if x == fone:
+        return fone
+    wp = prec+20
+    mag = exp+bc
+    if mag < -wp:
+        return mpf_shift(mpf_pi(prec, rnd), -1)
+    # Compute a finite difference for K'
+    p = max(mag, 0) - wp
+    h = mpf_shift(fone, p)
+    K = mpf_ellipk(x, 2*wp)
+    Kh = mpf_ellipk(mpf_sub(x, h), 2*wp)
+    Kdiff = mpf_shift(mpf_sub(K, Kh), -p)
+    t = mpf_sub(fone, x)
+    b = mpf_mul(Kdiff, mpf_shift(x,1), wp)
+    return mpf_mul(t, mpf_add(K, b), prec, rnd)
+
+def mpc_ellipe(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        if re == finf:
+            return (fzero, finf)
+        if mpf_le(re, fone):
+            return mpf_ellipe(re, prec, rnd), fzero
+    wp = prec + 15
+    mag = mpc_abs(z, 1)
+    p = max(mag[2]+mag[3], 0) - wp
+    h = mpf_shift(fone, p)
+    K = mpc_ellipk(z, 2*wp)
+    Kh = mpc_ellipk(mpc_add_mpf(z, h, 2*wp), 2*wp)
+    Kdiff = mpc_shift(mpc_sub(Kh, K, wp), -p)
+    t = mpc_sub(mpc_one, z, wp)
+    b = mpc_mul(Kdiff, mpc_shift(z,1), wp)
+    return mpc_mul(t, mpc_add(K, b, wp), prec, rnd)

lib/python3.11/site-packages/mpmath/libmp/libintmath.py

@@ -0,0 +1,584 @@
+"""
+Utility functions for integer math.
+
+TODO: rename, cleanup, perhaps move the gmpy wrapper code
+here from settings.py
+
+"""
+
+import math
+from bisect import bisect
+
+from .backend import xrange
+from .backend import BACKEND, gmpy, sage, sage_utils, MPZ, MPZ_ONE, MPZ_ZERO
+
+small_trailing = [0] * 256
+for j in range(1,8):
+    small_trailing[1<<j::1<<(j+1)] = [j] * (1<<(7-j))
+
+def giant_steps(start, target, n=2):
+    """
+    Return a list of integers ~=
+
+    [start, n*start, ..., target/n^2, target/n, target]
+
+    but conservatively rounded so that the quotient between two
+    successive elements is actually slightly less than n.
+
+    With n = 2, this describes suitable precision steps for a
+    quadratically convergent algorithm such as Newton's method;
+    with n = 3 steps for cubic convergence (Halley's method), etc.
+
+        >>> giant_steps(50,1000)
+        [66, 128, 253, 502, 1000]
+        >>> giant_steps(50,1000,4)
+        [65, 252, 1000]
+
+    """
+    L = [target]
+    while L[-1] > start*n:
+        L = L + [L[-1]//n + 2]
+    return L[::-1]
+
+def rshift(x, n):
+    """For an integer x, calculate x >> n with the fastest (floor)
+    rounding. Unlike the plain Python expression (x >> n), n is
+    allowed to be negative, in which case a left shift is performed."""
+    if n >= 0: return x >> n
+    else:      return x << (-n)
+
+def lshift(x, n):
+    """For an integer x, calculate x << n. Unlike the plain Python
+    expression (x << n), n is allowed to be negative, in which case a
+    right shift with default (floor) rounding is performed."""
+    if n >= 0: return x << n
+    else:      return x >> (-n)
+
+if BACKEND == 'sage':
+    import operator
+    rshift = operator.rshift
+    lshift = operator.lshift
+
+def python_trailing(n):
+    """Count the number of trailing zero bits in abs(n)."""
+    if not n:
+        return 0
+    low_byte = n & 0xff
+    if low_byte:
+        return small_trailing[low_byte]
+    t = 8
+    n >>= 8
+    while not n & 0xff:
+        n >>= 8
+        t += 8
+    return t + small_trailing[n & 0xff]
+
+if BACKEND == 'gmpy':
+    if gmpy.version() >= '2':
+        def gmpy_trailing(n):
+            """Count the number of trailing zero bits in abs(n) using gmpy."""
+            if n: return MPZ(n).bit_scan1()
+            else: return 0
+    else:
+        def gmpy_trailing(n):
+            """Count the number of trailing zero bits in abs(n) using gmpy."""
+            if n: return MPZ(n).scan1()
+            else: return 0
+
+# Small powers of 2
+powers = [1<<_ for _ in range(300)]
+
+def python_bitcount(n):
+    """Calculate bit size of the nonnegative integer n."""
+    bc = bisect(powers, n)
+    if bc != 300:
+        return bc
+    bc = int(math.log(n, 2)) - 4
+    return bc + bctable[n>>bc]
+
+def gmpy_bitcount(n):
+    """Calculate bit size of the nonnegative integer n."""
+    if n: return MPZ(n).numdigits(2)
+    else: return 0
+
+#def sage_bitcount(n):
+#    if n: return MPZ(n).nbits()
+#    else: return 0
+
+def sage_trailing(n):
+    return MPZ(n).trailing_zero_bits()
+
+if BACKEND == 'gmpy':
+    bitcount = gmpy_bitcount
+    trailing = gmpy_trailing
+elif BACKEND == 'sage':
+    sage_bitcount = sage_utils.bitcount
+    bitcount = sage_bitcount
+    trailing = sage_trailing
+else:
+    bitcount = python_bitcount
+    trailing = python_trailing
+
+if BACKEND == 'gmpy' and 'bit_length' in dir(gmpy):
+    bitcount = gmpy.bit_length
+
+# Used to avoid slow function calls as far as possible
+trailtable = [trailing(n) for n in range(256)]
+bctable = [bitcount(n) for n in range(1024)]
+
+# TODO: speed up for bases 2, 4, 8, 16, ...
+
+def bin_to_radix(x, xbits, base, bdigits):
+    """Changes radix of a fixed-point number; i.e., converts
+    x * 2**xbits to floor(x * 10**bdigits)."""
+    return x * (MPZ(base)**bdigits) >> xbits
+
+stddigits = '0123456789abcdefghijklmnopqrstuvwxyz'
+
+def small_numeral(n, base=10, digits=stddigits):
+    """Return the string numeral of a positive integer in an arbitrary
+    base. Most efficient for small input."""
+    if base == 10:
+        return str(n)
+    digs = []
+    while n:
+        n, digit = divmod(n, base)
+        digs.append(digits[digit])
+    return "".join(digs[::-1])
+
+def numeral_python(n, base=10, size=0, digits=stddigits):
+    """Represent the integer n as a string of digits in the given base.
+    Recursive division is used to make this function about 3x faster
+    than Python's str() for converting integers to decimal strings.
+
+    The 'size' parameters specifies the number of digits in n; this
+    number is only used to determine splitting points and need not be
+    exact."""
+    if n <= 0:
+        if not n:
+            return "0"
+        return "-" + numeral(-n, base, size, digits)
+    # Fast enough to do directly
+    if size < 250:
+        return small_numeral(n, base, digits)
+    # Divide in half
+    half = (size // 2) + (size & 1)
+    A, B = divmod(n, base**half)
+    ad = numeral(A, base, half, digits)
+    bd = numeral(B, base, half, digits).rjust(half, "0")
+    return ad + bd
+
+def numeral_gmpy(n, base=10, size=0, digits=stddigits):
+    """Represent the integer n as a string of digits in the given base.
+    Recursive division is used to make this function about 3x faster
+    than Python's str() for converting integers to decimal strings.
+
+    The 'size' parameters specifies the number of digits in n; this
+    number is only used to determine splitting points and need not be
+    exact."""
+    if n < 0:
+        return "-" + numeral(-n, base, size, digits)
+    # gmpy.digits() may cause a segmentation fault when trying to convert
+    # extremely large values to a string. The size limit may need to be
+    # adjusted on some platforms, but 1500000 works on Windows and Linux.
+    if size < 1500000:
+        return gmpy.digits(n, base)
+    # Divide in half
+    half = (size // 2) + (size & 1)
+    A, B = divmod(n, MPZ(base)**half)
+    ad = numeral(A, base, half, digits)
+    bd = numeral(B, base, half, digits).rjust(half, "0")
+    return ad + bd
+
+if BACKEND == "gmpy":
+    numeral = numeral_gmpy
+else:
+    numeral = numeral_python
+
+_1_800 = 1<<800
+_1_600 = 1<<600
+_1_400 = 1<<400
+_1_200 = 1<<200
+_1_100 = 1<<100
+_1_50 = 1<<50
+
+def isqrt_small_python(x):
+    """
+    Correctly (floor) rounded integer square root, using
+    division. Fast up to ~200 digits.
+    """
+    if not x:
+        return x
+    if x < _1_800:
+        # Exact with IEEE double precision arithmetic
+        if x < _1_50:
+            return int(x**0.5)
+        # Initial estimate can be any integer >= the true root; round up
+        r = int(x**0.5 * 1.00000000000001) + 1
+    else:
+        bc = bitcount(x)
+        n = bc//2
+        r = int((x>>(2*n-100))**0.5+2)<<(n-50)  # +2 is to round up
+    # The following iteration now precisely computes floor(sqrt(x))
+    # See e.g. Crandall & Pomerance, "Prime Numbers: A Computational
+    # Perspective"
+    while 1:
+        y = (r+x//r)>>1
+        if y >= r:
+            return r
+        r = y
+
+def isqrt_fast_python(x):
+    """
+    Fast approximate integer square root, computed using division-free
+    Newton iteration for large x. For random integers the result is almost
+    always correct (floor(sqrt(x))), but is 1 ulp too small with a roughly
+    0.1% probability. If x is very close to an exact square, the answer is
+    1 ulp wrong with high probability.
+
+    With 0 guard bits, the largest error over a set of 10^5 random
+    inputs of size 1-10^5 bits was 3 ulp. The use of 10 guard bits
+    almost certainly guarantees a max 1 ulp error.
+    """
+    # Use direct division-based iteration if sqrt(x) < 2^400
+    # Assume floating-point square root accurate to within 1 ulp, then:
+    # 0 Newton iterations good to 52 bits
+    # 1 Newton iterations good to 104 bits
+    # 2 Newton iterations good to 208 bits
+    # 3 Newton iterations good to 416 bits
+    if x < _1_800:
+        y = int(x**0.5)
+        if x >= _1_100:
+            y = (y + x//y) >> 1
+            if x >= _1_200:
+                y = (y + x//y) >> 1
+                if x >= _1_400:
+                    y = (y + x//y) >> 1
+        return y
+    bc = bitcount(x)
+    guard_bits = 10
+    x <<= 2*guard_bits
+    bc += 2*guard_bits
+    bc += (bc&1)
+    hbc = bc//2
+    startprec = min(50, hbc)
+    # Newton iteration for 1/sqrt(x), with floating-point starting value
+    r = int(2.0**(2*startprec) * (x >> (bc-2*startprec)) ** -0.5)
+    pp = startprec
+    for p in giant_steps(startprec, hbc):
+        # r**2, scaled from real size 2**(-bc) to 2**p
+        r2 = (r*r) >> (2*pp - p)
+        # x*r**2, scaled from real size ~1.0 to 2**p
+        xr2 = ((x >> (bc-p)) * r2) >> p
+        # New value of r, scaled from real size 2**(-bc/2) to 2**p
+        r = (r * ((3<<p) - xr2)) >> (pp+1)
+        pp = p
+    # (1/sqrt(x))*x = sqrt(x)
+    return (r*(x>>hbc)) >> (p+guard_bits)
+
+def sqrtrem_python(x):
+    """Correctly rounded integer (floor) square root with remainder."""
+    # to check cutoff:
+    # plot(lambda x: timing(isqrt, 2**int(x)), [0,2000])
+    if x < _1_600:
+        y = isqrt_small_python(x)
+        return y, x - y*y
+    y = isqrt_fast_python(x) + 1
+    rem = x - y*y
+    # Correct remainder
+    while rem < 0:
+        y -= 1
+        rem += (1+2*y)
+    else:
+        if rem:
+            while rem > 2*(1+y):
+                y += 1
+                rem -= (1+2*y)
+    return y, rem
+
+def isqrt_python(x):
+    """Integer square root with correct (floor) rounding."""
+    return sqrtrem_python(x)[0]
+
+def sqrt_fixed(x, prec):
+    return isqrt_fast(x<<prec)
+
+sqrt_fixed2 = sqrt_fixed
+
+if BACKEND == 'gmpy':
+    if gmpy.version() >= '2':
+        isqrt_small = isqrt_fast = isqrt = gmpy.isqrt
+        sqrtrem = gmpy.isqrt_rem
+    else:
+        isqrt_small = isqrt_fast = isqrt = gmpy.sqrt
+        sqrtrem = gmpy.sqrtrem
+elif BACKEND == 'sage':
+    isqrt_small = isqrt_fast = isqrt = \
+        getattr(sage_utils, "isqrt", lambda n: MPZ(n).isqrt())
+    sqrtrem = lambda n: MPZ(n).sqrtrem()
+else:
+    isqrt_small = isqrt_small_python
+    isqrt_fast = isqrt_fast_python
+    isqrt = isqrt_python
+    sqrtrem = sqrtrem_python
+
+
+def ifib(n, _cache={}):
+    """Computes the nth Fibonacci number as an integer, for
+    integer n."""
+    if n < 0:
+        return (-1)**(-n+1) * ifib(-n)
+    if n in _cache:
+        return _cache[n]
+    m = n
+    # Use Dijkstra's logarithmic algorithm
+    # The following implementation is basically equivalent to
+    # http://en.literateprograms.org/Fibonacci_numbers_(Scheme)
+    a, b, p, q = MPZ_ONE, MPZ_ZERO, MPZ_ZERO, MPZ_ONE
+    while n:
+        if n & 1:
+            aq = a*q
+            a, b = b*q+aq+a*p, b*p+aq
+            n -= 1
+        else:
+            qq = q*q
+            p, q = p*p+qq, qq+2*p*q
+            n >>= 1
+    if m < 250:
+        _cache[m] = b
+    return b
+
+MAX_FACTORIAL_CACHE = 1000
+
+def ifac(n, memo={0:1, 1:1}):
+    """Return n factorial (for integers n >= 0 only)."""
+    f = memo.get(n)
+    if f:
+        return f
+    k = len(memo)
+    p = memo[k-1]
+    MAX = MAX_FACTORIAL_CACHE
+    while k <= n:
+        p *= k
+        if k <= MAX:
+            memo[k] = p
+        k += 1
+    return p
+
+def ifac2(n, memo_pair=[{0:1}, {1:1}]):
+    """Return n!! (double factorial), integers n >= 0 only."""
+    memo = memo_pair[n&1]
+    f = memo.get(n)
+    if f:
+        return f
+    k = max(memo)
+    p = memo[k]
+    MAX = MAX_FACTORIAL_CACHE
+    while k < n:
+        k += 2
+        p *= k
+        if k <= MAX:
+            memo[k] = p
+    return p
+
+if BACKEND == 'gmpy':
+    ifac = gmpy.fac
+elif BACKEND == 'sage':
+    ifac = lambda n: int(sage.factorial(n))
+    ifib = sage.fibonacci
+
+def list_primes(n):
+    n = n + 1
+    sieve = list(xrange(n))
+    sieve[:2] = [0, 0]
+    for i in xrange(2, int(n**0.5)+1):
+        if sieve[i]:
+            for j in xrange(i**2, n, i):
+                sieve[j] = 0
+    return [p for p in sieve if p]
+
+if BACKEND == 'sage':
+    # Note: it is *VERY* important for performance that we convert
+    # the list to Python ints.
+    def list_primes(n):
+        return [int(_) for _ in sage.primes(n+1)]
+
+small_odd_primes = (3,5,7,11,13,17,19,23,29,31,37,41,43,47)
+small_odd_primes_set = set(small_odd_primes)
+
+def isprime(n):
+    """
+    Determines whether n is a prime number. A probabilistic test is
+    performed if n is very large. No special trick is used for detecting
+    perfect powers.
+
+        >>> sum(list_primes(100000))
+        454396537
+        >>> sum(n*isprime(n) for n in range(100000))
+        454396537
+
+    """
+    n = int(n)
+    if not n & 1:
+        return n == 2
+    if n < 50:
+        return n in small_odd_primes_set
+    for p in small_odd_primes:
+        if not n % p:
+            return False
+    m = n-1
+    s = trailing(m)
+    d = m >> s
+    def test(a):
+        x = pow(a,d,n)
+        if x == 1 or x == m:
+            return True
+        for r in xrange(1,s):
+            x = x**2 % n
+            if x == m:
+                return True
+        return False
+    # See http://primes.utm.edu/prove/prove2_3.html
+    if n < 1373653:
+        witnesses = [2,3]
+    elif n < 341550071728321:
+        witnesses = [2,3,5,7,11,13,17]
+    else:
+        witnesses = small_odd_primes
+    for a in witnesses:
+        if not test(a):
+            return False
+    return True
+
+def moebius(n):
+    """
+    Evaluates the Moebius function which is `mu(n) = (-1)^k` if `n`
+    is a product of `k` distinct primes and `mu(n) = 0` otherwise.
+
+    TODO: speed up using factorization
+    """
+    n = abs(int(n))
+    if n < 2:
+        return n
+    factors = []
+    for p in xrange(2, n+1):
+        if not (n % p):
+            if not (n % p**2):
+                return 0
+            if not sum(p % f for f in factors):
+                factors.append(p)
+    return (-1)**len(factors)
+
+def gcd(*args):
+    a = 0
+    for b in args:
+        if a:
+            while b:
+                a, b = b, a % b
+        else:
+            a = b
+    return a
+
+
+#  Comment by Juan Arias de Reyna:
+#
+#  I learn this method to compute EulerE[2n] from van de Lune.
+#
+#  We apply the formula   EulerE[2n] = (-1)^n 2**(-2n) sum_{j=0}^n a(2n,2j+1)
+#
+#  where the numbers a(n,j) vanish for  j > n+1 or j <= -1  and satisfies
+#
+#  a(0,-1) = a(0,0) = 0;  a(0,1)= 1; a(0,2) = a(0,3) = 0
+#
+#  a(n,j) = a(n-1,j)                              when n+j is even
+#  a(n,j) = (j-1) a(n-1,j-1) + (j+1) a(n-1,j+1)   when n+j is odd
+#
+#
+#  But we can use only one array unidimensional a(j) since to compute
+#  a(n,j) we only need to know a(n-1,k) where k and j are of different parity
+#  and we have not to conserve the used values.
+#
+#  We cached up the values of Euler numbers to sufficiently high order.
+#
+#  Important Observation: If we pretend to use the numbers
+#     EulerE[1], EulerE[2], ... , EulerE[n]
+#     it is convenient to compute first EulerE[n], since the algorithm
+#     computes first all
+#     the previous ones, and keeps them in the CACHE
+
+MAX_EULER_CACHE = 500
+
+def eulernum(m, _cache={0:MPZ_ONE}):
+    r"""
+    Computes the Euler numbers `E(n)`, which can be defined as
+    coefficients of the Taylor expansion of `1/cosh x`:
+
+    .. math ::
+
+        \frac{1}{\cosh x} = \sum_{n=0}^\infty \frac{E_n}{n!} x^n
+
+    Example::
+
+        >>> [int(eulernum(n)) for n in range(11)]
+        [1, 0, -1, 0, 5, 0, -61, 0, 1385, 0, -50521]
+        >>> [int(eulernum(n)) for n in range(11)]   # test cache
+        [1, 0, -1, 0, 5, 0, -61, 0, 1385, 0, -50521]
+
+    """
+    # for odd m > 1, the Euler numbers are zero
+    if m & 1:
+        return MPZ_ZERO
+    f = _cache.get(m)
+    if f:
+        return f
+    MAX = MAX_EULER_CACHE
+    n = m
+    a = [MPZ(_) for _ in [0,0,1,0,0,0]]
+    for  n in range(1, m+1):
+        for j in range(n+1, -1, -2):
+            a[j+1] = (j-1)*a[j] + (j+1)*a[j+2]
+        a.append(0)
+        suma = 0
+        for k in range(n+1, -1, -2):
+            suma += a[k+1]
+            if n <= MAX:
+                _cache[n] = ((-1)**(n//2))*(suma // 2**n)
+        if n == m:
+            return ((-1)**(n//2))*suma // 2**n
+
+def stirling1(n, k):
+    """
+    Stirling number of the first kind.
+    """
+    if n < 0 or k < 0:
+        raise ValueError
+    if k >= n:
+        return MPZ(n == k)
+    if k < 1:
+        return MPZ_ZERO
+    L = [MPZ_ZERO] * (k+1)
+    L[1] = MPZ_ONE
+    for m in xrange(2, n+1):
+        for j in xrange(min(k, m), 0, -1):
+            L[j] = (m-1) * L[j] + L[j-1]
+    return (-1)**(n+k) * L[k]
+
+def stirling2(n, k):
+    """
+    Stirling number of the second kind.
+    """
+    if n < 0 or k < 0:
+        raise ValueError
+    if k >= n:
+        return MPZ(n == k)
+    if k <= 1:
+        return MPZ(k == 1)
+    s = MPZ_ZERO
+    t = MPZ_ONE
+    for j in xrange(k+1):
+        if (k + j) & 1:
+            s -= t * MPZ(j)**n
+        else:
+            s += t * MPZ(j)**n
+        t = t * (k - j) // (j + 1)
+    return s // ifac(k)

lib/python3.11/site-packages/mpmath/libmp/libmpc.py

@@ -0,0 +1,835 @@
+"""
+Low-level functions for complex arithmetic.
+"""
+
+import sys
+
+from .backend import MPZ, MPZ_ZERO, MPZ_ONE, MPZ_TWO, BACKEND
+
+from .libmpf import (\
+    round_floor, round_ceiling, round_down, round_up,
+    round_nearest, round_fast, bitcount,
+    bctable, normalize, normalize1, reciprocal_rnd, rshift, lshift, giant_steps,
+    negative_rnd,
+    to_str, to_fixed, from_man_exp, from_float, to_float, from_int, to_int,
+    fzero, fone, ftwo, fhalf, finf, fninf, fnan, fnone,
+    mpf_abs, mpf_pos, mpf_neg, mpf_add, mpf_sub, mpf_mul,
+    mpf_div, mpf_mul_int, mpf_shift, mpf_sqrt, mpf_hypot,
+    mpf_rdiv_int, mpf_floor, mpf_ceil, mpf_nint, mpf_frac,
+    mpf_sign, mpf_hash,
+    ComplexResult
+)
+
+from .libelefun import (\
+    mpf_pi, mpf_exp, mpf_log, mpf_cos_sin, mpf_cosh_sinh, mpf_tan, mpf_pow_int,
+    mpf_log_hypot,
+    mpf_cos_sin_pi, mpf_phi,
+    mpf_cos, mpf_sin, mpf_cos_pi, mpf_sin_pi,
+    mpf_atan, mpf_atan2, mpf_cosh, mpf_sinh, mpf_tanh,
+    mpf_asin, mpf_acos, mpf_acosh, mpf_nthroot, mpf_fibonacci
+)
+
+# An mpc value is a (real, imag) tuple
+mpc_one = fone, fzero
+mpc_zero = fzero, fzero
+mpc_two = ftwo, fzero
+mpc_half = (fhalf, fzero)
+
+_infs = (finf, fninf)
+_infs_nan = (finf, fninf, fnan)
+
+def mpc_is_inf(z):
+    """Check if either real or imaginary part is infinite"""
+    re, im = z
+    if re in _infs: return True
+    if im in _infs: return True
+    return False
+
+def mpc_is_infnan(z):
+    """Check if either real or imaginary part is infinite or nan"""
+    re, im = z
+    if re in _infs_nan: return True
+    if im in _infs_nan: return True
+    return False
+
+def mpc_to_str(z, dps, **kwargs):
+    re, im = z
+    rs = to_str(re, dps)
+    if im[0]:
+        return rs + " - " + to_str(mpf_neg(im), dps, **kwargs) + "j"
+    else:
+        return rs + " + " + to_str(im, dps, **kwargs) + "j"
+
+def mpc_to_complex(z, strict=False, rnd=round_fast):
+    re, im = z
+    return complex(to_float(re, strict, rnd), to_float(im, strict, rnd))
+
+def mpc_hash(z):
+    if sys.version_info >= (3, 2):
+        re, im = z
+        h = mpf_hash(re) + sys.hash_info.imag * mpf_hash(im)
+        # Need to reduce either module 2^32 or 2^64
+        h = h % (2**sys.hash_info.width)
+        return int(h)
+    else:
+        try:
+            return hash(mpc_to_complex(z, strict=True))
+        except OverflowError:
+            return hash(z)
+
+def mpc_conjugate(z, prec, rnd=round_fast):
+    re, im = z
+    return re, mpf_neg(im, prec, rnd)
+
+def mpc_is_nonzero(z):
+    return z != mpc_zero
+
+def mpc_add(z, w, prec, rnd=round_fast):
+    a, b = z
+    c, d = w
+    return mpf_add(a, c, prec, rnd), mpf_add(b, d, prec, rnd)
+
+def mpc_add_mpf(z, x, prec, rnd=round_fast):
+    a, b = z
+    return mpf_add(a, x, prec, rnd), b
+
+def mpc_sub(z, w, prec=0, rnd=round_fast):
+    a, b = z
+    c, d = w
+    return mpf_sub(a, c, prec, rnd), mpf_sub(b, d, prec, rnd)
+
+def mpc_sub_mpf(z, p, prec=0, rnd=round_fast):
+    a, b = z
+    return mpf_sub(a, p, prec, rnd), b
+
+def mpc_pos(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_pos(a, prec, rnd), mpf_pos(b, prec, rnd)
+
+def mpc_neg(z, prec=None, rnd=round_fast):
+    a, b = z
+    return mpf_neg(a, prec, rnd), mpf_neg(b, prec, rnd)
+
+def mpc_shift(z, n):
+    a, b = z
+    return mpf_shift(a, n), mpf_shift(b, n)
+
+def mpc_abs(z, prec, rnd=round_fast):
+    """Absolute value of a complex number, |a+bi|.
+    Returns an mpf value."""
+    a, b = z
+    return mpf_hypot(a, b, prec, rnd)
+
+def mpc_arg(z, prec, rnd=round_fast):
+    """Argument of a complex number. Returns an mpf value."""
+    a, b = z
+    return mpf_atan2(b, a, prec, rnd)
+
+def mpc_floor(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_floor(a, prec, rnd), mpf_floor(b, prec, rnd)
+
+def mpc_ceil(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_ceil(a, prec, rnd), mpf_ceil(b, prec, rnd)
+
+def mpc_nint(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_nint(a, prec, rnd), mpf_nint(b, prec, rnd)
+
+def mpc_frac(z, prec, rnd=round_fast):
+    a, b = z
+    return mpf_frac(a, prec, rnd), mpf_frac(b, prec, rnd)
+
+
+def mpc_mul(z, w, prec, rnd=round_fast):
+    """
+    Complex multiplication.
+
+    Returns the real and imaginary part of (a+bi)*(c+di), rounded to
+    the specified precision. The rounding mode applies to the real and
+    imaginary parts separately.
+    """
+    a, b = z
+    c, d = w
+    p = mpf_mul(a, c)
+    q = mpf_mul(b, d)
+    r = mpf_mul(a, d)
+    s = mpf_mul(b, c)
+    re = mpf_sub(p, q, prec, rnd)
+    im = mpf_add(r, s, prec, rnd)
+    return re, im
+
+def mpc_square(z, prec, rnd=round_fast):
+    # (a+b*I)**2 == a**2 - b**2 + 2*I*a*b
+    a, b = z
+    p = mpf_mul(a,a)
+    q = mpf_mul(b,b)
+    r = mpf_mul(a,b, prec, rnd)
+    re = mpf_sub(p, q, prec, rnd)
+    im = mpf_shift(r, 1)
+    return re, im
+
+def mpc_mul_mpf(z, p, prec, rnd=round_fast):
+    a, b = z
+    re = mpf_mul(a, p, prec, rnd)
+    im = mpf_mul(b, p, prec, rnd)
+    return re, im
+
+def mpc_mul_imag_mpf(z, x, prec, rnd=round_fast):
+    """
+    Multiply the mpc value z by I*x where x is an mpf value.
+    """
+    a, b = z
+    re = mpf_neg(mpf_mul(b, x, prec, rnd))
+    im = mpf_mul(a, x, prec, rnd)
+    return re, im
+
+def mpc_mul_int(z, n, prec, rnd=round_fast):
+    a, b = z
+    re = mpf_mul_int(a, n, prec, rnd)
+    im = mpf_mul_int(b, n, prec, rnd)
+    return re, im
+
+def mpc_div(z, w, prec, rnd=round_fast):
+    a, b = z
+    c, d = w
+    wp = prec + 10
+    # mag = c*c + d*d
+    mag = mpf_add(mpf_mul(c, c), mpf_mul(d, d), wp)
+    # (a*c+b*d)/mag, (b*c-a*d)/mag
+    t = mpf_add(mpf_mul(a,c), mpf_mul(b,d), wp)
+    u = mpf_sub(mpf_mul(b,c), mpf_mul(a,d), wp)
+    return mpf_div(t,mag,prec,rnd), mpf_div(u,mag,prec,rnd)
+
+def mpc_div_mpf(z, p, prec, rnd=round_fast):
+    """Calculate z/p where p is real"""
+    a, b = z
+    re = mpf_div(a, p, prec, rnd)
+    im = mpf_div(b, p, prec, rnd)
+    return re, im
+
+def mpc_reciprocal(z, prec, rnd=round_fast):
+    """Calculate 1/z efficiently"""
+    a, b = z
+    m = mpf_add(mpf_mul(a,a),mpf_mul(b,b),prec+10)
+    re = mpf_div(a, m, prec, rnd)
+    im = mpf_neg(mpf_div(b, m, prec, rnd))
+    return re, im
+
+def mpc_mpf_div(p, z, prec, rnd=round_fast):
+    """Calculate p/z where p is real efficiently"""
+    a, b = z
+    m = mpf_add(mpf_mul(a,a),mpf_mul(b,b), prec+10)
+    re = mpf_div(mpf_mul(a,p), m, prec, rnd)
+    im = mpf_div(mpf_neg(mpf_mul(b,p)), m, prec, rnd)
+    return re, im
+
+def complex_int_pow(a, b, n):
+    """Complex integer power: computes (a+b*I)**n exactly for
+    nonnegative n (a and b must be Python ints)."""
+    wre = 1
+    wim = 0
+    while n:
+        if n & 1:
+            wre, wim = wre*a - wim*b, wim*a + wre*b
+            n -= 1
+        a, b = a*a - b*b, 2*a*b
+        n //= 2
+    return wre, wim
+
+def mpc_pow(z, w, prec, rnd=round_fast):
+    if w[1] == fzero:
+        return mpc_pow_mpf(z, w[0], prec, rnd)
+    return mpc_exp(mpc_mul(mpc_log(z, prec+10), w, prec+10), prec, rnd)
+
+def mpc_pow_mpf(z, p, prec, rnd=round_fast):
+    psign, pman, pexp, pbc = p
+    if pexp >= 0:
+        return mpc_pow_int(z, (-1)**psign * (pman<<pexp), prec, rnd)
+    if pexp == -1:
+        sqrtz = mpc_sqrt(z, prec+10)
+        return mpc_pow_int(sqrtz, (-1)**psign * pman, prec, rnd)
+    return mpc_exp(mpc_mul_mpf(mpc_log(z, prec+10), p, prec+10), prec, rnd)
+
+def mpc_pow_int(z, n, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        return mpf_pow_int(a, n, prec, rnd), fzero
+    if a == fzero:
+        v = mpf_pow_int(b, n, prec, rnd)
+        n %= 4
+        if n == 0:
+            return v, fzero
+        elif n == 1:
+            return fzero, v
+        elif n == 2:
+            return mpf_neg(v), fzero
+        elif n == 3:
+            return fzero, mpf_neg(v)
+    if n == 0: return mpc_one
+    if n == 1: return mpc_pos(z, prec, rnd)
+    if n == 2: return mpc_square(z, prec, rnd)
+    if n == -1: return mpc_reciprocal(z, prec, rnd)
+    if n < 0: return mpc_reciprocal(mpc_pow_int(z, -n, prec+4), prec, rnd)
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if asign: aman = -aman
+    if bsign: bman = -bman
+    de = aexp - bexp
+    abs_de = abs(de)
+    exact_size = n*(abs_de + max(abc, bbc))
+    if exact_size < 10000:
+        if de > 0:
+            aman <<= de
+            aexp = bexp
+        else:
+            bman <<= (-de)
+            bexp = aexp
+        re, im = complex_int_pow(aman, bman, n)
+        re = from_man_exp(re, int(n*aexp), prec, rnd)
+        im = from_man_exp(im, int(n*bexp), prec, rnd)
+        return re, im
+    return mpc_exp(mpc_mul_int(mpc_log(z, prec+10), n, prec+10), prec, rnd)
+
+def mpc_sqrt(z, prec, rnd=round_fast):
+    """Complex square root (principal branch).
+
+    We have sqrt(a+bi) = sqrt((r+a)/2) + b/sqrt(2*(r+a))*i where
+    r = abs(a+bi), when a+bi is not a negative real number."""
+    a, b = z
+    if b == fzero:
+        if a == fzero:
+            return (a, b)
+        # When a+bi is a negative real number, we get a real sqrt times i
+        if a[0]:
+            im = mpf_sqrt(mpf_neg(a), prec, rnd)
+            return (fzero, im)
+        else:
+            re = mpf_sqrt(a, prec, rnd)
+            return (re, fzero)
+    wp = prec+20
+    if not a[0]:                               # case a positive
+        t  = mpf_add(mpc_abs((a, b), wp), a, wp)  # t = abs(a+bi) + a
+        u = mpf_shift(t, -1)                      # u = t/2
+        re = mpf_sqrt(u, prec, rnd)               # re = sqrt(u)
+        v = mpf_shift(t, 1)                       # v = 2*t
+        w  = mpf_sqrt(v, wp)                      # w = sqrt(v)
+        im = mpf_div(b, w, prec, rnd)             # im = b / w
+    else:                                      # case a negative
+        t = mpf_sub(mpc_abs((a, b), wp), a, wp)   # t = abs(a+bi) - a
+        u = mpf_shift(t, -1)                      # u = t/2
+        im = mpf_sqrt(u, prec, rnd)               # im = sqrt(u)
+        v = mpf_shift(t, 1)                       # v = 2*t
+        w  = mpf_sqrt(v, wp)                      # w = sqrt(v)
+        re = mpf_div(b, w, prec, rnd)             # re = b/w
+        if b[0]:
+            re = mpf_neg(re)
+            im = mpf_neg(im)
+    return re, im
+
+def mpc_nthroot_fixed(a, b, n, prec):
+    # a, b signed integers at fixed precision prec
+    start = 50
+    a1 = int(rshift(a, prec - n*start))
+    b1 = int(rshift(b, prec - n*start))
+    try:
+        r = (a1 + 1j * b1)**(1.0/n)
+        re = r.real
+        im = r.imag
+        re = MPZ(int(re))
+        im = MPZ(int(im))
+    except OverflowError:
+        a1 = from_int(a1, start)
+        b1 = from_int(b1, start)
+        fn = from_int(n)
+        nth = mpf_rdiv_int(1, fn, start)
+        re, im = mpc_pow((a1, b1), (nth, fzero), start)
+        re = to_int(re)
+        im = to_int(im)
+    extra = 10
+    prevp = start
+    extra1 = n
+    for p in giant_steps(start, prec+extra):
+        # this is slow for large n, unlike int_pow_fixed
+        re2, im2 = complex_int_pow(re, im, n-1)
+        re2 = rshift(re2, (n-1)*prevp - p - extra1)
+        im2 = rshift(im2, (n-1)*prevp - p - extra1)
+        r4 = (re2*re2 + im2*im2) >> (p + extra1)
+        ap = rshift(a, prec - p)
+        bp = rshift(b, prec - p)
+        rec = (ap * re2 + bp * im2) >> p
+        imc = (-ap * im2 + bp * re2) >> p
+        reb = (rec << p) // r4
+        imb = (imc << p) // r4
+        re = (reb + (n-1)*lshift(re, p-prevp))//n
+        im = (imb + (n-1)*lshift(im, p-prevp))//n
+        prevp = p
+    return re, im
+
+def mpc_nthroot(z, n, prec, rnd=round_fast):
+    """
+    Complex n-th root.
+
+    Use Newton method as in the real case when it is faster,
+    otherwise use z**(1/n)
+    """
+    a, b = z
+    if a[0] == 0 and b == fzero:
+        re = mpf_nthroot(a, n, prec, rnd)
+        return (re, fzero)
+    if n < 2:
+        if n == 0:
+            return mpc_one
+        if n == 1:
+            return mpc_pos((a, b), prec, rnd)
+        if n == -1:
+            return mpc_div(mpc_one, (a, b), prec, rnd)
+        inverse = mpc_nthroot((a, b), -n, prec+5, reciprocal_rnd[rnd])
+        return mpc_div(mpc_one, inverse, prec, rnd)
+    if n <= 20:
+        prec2 = int(1.2 * (prec + 10))
+        asign, aman, aexp, abc = a
+        bsign, bman, bexp, bbc = b
+        pf = mpc_abs((a,b), prec)
+        if pf[-2] + pf[-1] > -10  and pf[-2] + pf[-1] < prec:
+            af = to_fixed(a, prec2)
+            bf = to_fixed(b, prec2)
+            re, im = mpc_nthroot_fixed(af, bf, n, prec2)
+            extra = 10
+            re = from_man_exp(re, -prec2-extra, prec2, rnd)
+            im = from_man_exp(im, -prec2-extra, prec2, rnd)
+            return re, im
+    fn = from_int(n)
+    prec2 = prec+10 + 10
+    nth = mpf_rdiv_int(1, fn, prec2)
+    re, im = mpc_pow((a, b), (nth, fzero), prec2, rnd)
+    re = normalize(re[0], re[1], re[2], re[3], prec, rnd)
+    im = normalize(im[0], im[1], im[2], im[3], prec, rnd)
+    return re, im
+
+def mpc_cbrt(z, prec, rnd=round_fast):
+    """
+    Complex cubic root.
+    """
+    return mpc_nthroot(z, 3, prec, rnd)
+
+def mpc_exp(z, prec, rnd=round_fast):
+    """
+    Complex exponential function.
+
+    We use the direct formula exp(a+bi) = exp(a) * (cos(b) + sin(b)*i)
+    for the computation. This formula is very nice because it is
+    pefectly stable; since we just do real multiplications, the only
+    numerical errors that can creep in are single-ulp rounding errors.
+
+    The formula is efficient since mpmath's real exp is quite fast and
+    since we can compute cos and sin simultaneously.
+
+    It is no problem if a and b are large; if the implementations of
+    exp/cos/sin are accurate and efficient for all real numbers, then
+    so is this function for all complex numbers.
+    """
+    a, b = z
+    if a == fzero:
+        return mpf_cos_sin(b, prec, rnd)
+    if b == fzero:
+        return mpf_exp(a, prec, rnd), fzero
+    mag = mpf_exp(a, prec+4, rnd)
+    c, s = mpf_cos_sin(b, prec+4, rnd)
+    re = mpf_mul(mag, c, prec, rnd)
+    im = mpf_mul(mag, s, prec, rnd)
+    return re, im
+
+def mpc_log(z, prec, rnd=round_fast):
+    re = mpf_log_hypot(z[0], z[1], prec, rnd)
+    im = mpc_arg(z, prec, rnd)
+    return re, im
+
+def mpc_cos(z, prec, rnd=round_fast):
+    """Complex cosine. The formula used is cos(a+bi) = cos(a)*cosh(b) -
+    sin(a)*sinh(b)*i.
+
+    The same comments apply as for the complex exp: only real
+    multiplications are pewrormed, so no cancellation errors are
+    possible. The formula is also efficient since we can compute both
+    pairs (cos, sin) and (cosh, sinh) in single stwps."""
+    a, b = z
+    if b == fzero:
+        return mpf_cos(a, prec, rnd), fzero
+    if a == fzero:
+        return mpf_cosh(b, prec, rnd), fzero
+    wp = prec + 6
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(c, ch, prec, rnd)
+    im = mpf_mul(s, sh, prec, rnd)
+    return re, mpf_neg(im)
+
+def mpc_sin(z, prec, rnd=round_fast):
+    """Complex sine. We have sin(a+bi) = sin(a)*cosh(b) +
+    cos(a)*sinh(b)*i. See the docstring for mpc_cos for additional
+    comments."""
+    a, b = z
+    if b == fzero:
+        return mpf_sin(a, prec, rnd), fzero
+    if a == fzero:
+        return fzero, mpf_sinh(b, prec, rnd)
+    wp = prec + 6
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(s, ch, prec, rnd)
+    im = mpf_mul(c, sh, prec, rnd)
+    return re, im
+
+def mpc_tan(z, prec, rnd=round_fast):
+    """Complex tangent. Computed as tan(a+bi) = sin(2a)/M + sinh(2b)/M*i
+    where M = cos(2a) + cosh(2b)."""
+    a, b = z
+    asign, aman, aexp, abc = a
+    bsign, bman, bexp, bbc = b
+    if b == fzero: return mpf_tan(a, prec, rnd), fzero
+    if a == fzero: return fzero, mpf_tanh(b, prec, rnd)
+    wp = prec + 15
+    a = mpf_shift(a, 1)
+    b = mpf_shift(b, 1)
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    # TODO: handle cancellation when c ~=  -1 and ch ~= 1
+    mag = mpf_add(c, ch, wp)
+    re = mpf_div(s, mag, prec, rnd)
+    im = mpf_div(sh, mag, prec, rnd)
+    return re, im
+
+def mpc_cos_pi(z, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        return mpf_cos_pi(a, prec, rnd), fzero
+    b = mpf_mul(b, mpf_pi(prec+5), prec+5)
+    if a == fzero:
+        return mpf_cosh(b, prec, rnd), fzero
+    wp = prec + 6
+    c, s = mpf_cos_sin_pi(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(c, ch, prec, rnd)
+    im = mpf_mul(s, sh, prec, rnd)
+    return re, mpf_neg(im)
+
+def mpc_sin_pi(z, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        return mpf_sin_pi(a, prec, rnd), fzero
+    b = mpf_mul(b, mpf_pi(prec+5), prec+5)
+    if a == fzero:
+        return fzero, mpf_sinh(b, prec, rnd)
+    wp = prec + 6
+    c, s = mpf_cos_sin_pi(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    re = mpf_mul(s, ch, prec, rnd)
+    im = mpf_mul(c, sh, prec, rnd)
+    return re, im
+
+def mpc_cos_sin(z, prec, rnd=round_fast):
+    a, b = z
+    if a == fzero:
+        ch, sh = mpf_cosh_sinh(b, prec, rnd)
+        return (ch, fzero), (fzero, sh)
+    if b == fzero:
+        c, s = mpf_cos_sin(a, prec, rnd)
+        return (c, fzero), (s, fzero)
+    wp = prec + 6
+    c, s = mpf_cos_sin(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    cre = mpf_mul(c, ch, prec, rnd)
+    cim = mpf_mul(s, sh, prec, rnd)
+    sre = mpf_mul(s, ch, prec, rnd)
+    sim = mpf_mul(c, sh, prec, rnd)
+    return (cre, mpf_neg(cim)), (sre, sim)
+
+def mpc_cos_sin_pi(z, prec, rnd=round_fast):
+    a, b = z
+    if b == fzero:
+        c, s = mpf_cos_sin_pi(a, prec, rnd)
+        return (c, fzero), (s, fzero)
+    b = mpf_mul(b, mpf_pi(prec+5), prec+5)
+    if a == fzero:
+        ch, sh = mpf_cosh_sinh(b, prec, rnd)
+        return (ch, fzero), (fzero, sh)
+    wp = prec + 6
+    c, s = mpf_cos_sin_pi(a, wp)
+    ch, sh = mpf_cosh_sinh(b, wp)
+    cre = mpf_mul(c, ch, prec, rnd)
+    cim = mpf_mul(s, sh, prec, rnd)
+    sre = mpf_mul(s, ch, prec, rnd)
+    sim = mpf_mul(c, sh, prec, rnd)
+    return (cre, mpf_neg(cim)), (sre, sim)
+
+def mpc_cosh(z, prec, rnd=round_fast):
+    """Complex hyperbolic cosine. Computed as cosh(z) = cos(z*i)."""
+    a, b = z
+    return mpc_cos((b, mpf_neg(a)), prec, rnd)
+
+def mpc_sinh(z, prec, rnd=round_fast):
+    """Complex hyperbolic sine. Computed as sinh(z) = -i*sin(z*i)."""
+    a, b = z
+    b, a = mpc_sin((b, a), prec, rnd)
+    return a, b
+
+def mpc_tanh(z, prec, rnd=round_fast):
+    """Complex hyperbolic tangent. Computed as tanh(z) = -i*tan(z*i)."""
+    a, b = z
+    b, a = mpc_tan((b, a), prec, rnd)
+    return a, b
+
+# TODO: avoid loss of accuracy
+def mpc_atan(z, prec, rnd=round_fast):
+    a, b = z
+    # atan(z) = (I/2)*(log(1-I*z) - log(1+I*z))
+    # x = 1-I*z = 1 + b - I*a
+    # y = 1+I*z = 1 - b + I*a
+    wp = prec + 15
+    x = mpf_add(fone, b, wp), mpf_neg(a)
+    y = mpf_sub(fone, b, wp), a
+    l1 = mpc_log(x, wp)
+    l2 = mpc_log(y, wp)
+    a, b = mpc_sub(l1, l2, prec, rnd)
+    # (I/2) * (a+b*I) = (-b/2 + a/2*I)
+    v = mpf_neg(mpf_shift(b,-1)), mpf_shift(a,-1)
+    # Subtraction at infinity gives correct real part but
+    # wrong imaginary part (should be zero)
+    if v[1] == fnan and mpc_is_inf(z):
+        v = (v[0], fzero)
+    return v
+
+beta_crossover = from_float(0.6417)
+alpha_crossover = from_float(1.5)
+
+def acos_asin(z, prec, rnd, n):
+    """ complex acos for n = 0, asin for n = 1
+    The algorithm is described in
+    T.E. Hull, T.F. Fairgrieve and P.T.P. Tang
+    'Implementing the Complex Arcsine and Arcosine Functions
+    using Exception Handling',
+    ACM Trans. on Math. Software Vol. 23 (1997), p299
+    The complex acos and asin can be defined as
+    acos(z) = acos(beta) - I*sign(a)* log(alpha + sqrt(alpha**2 -1))
+    asin(z) = asin(beta) + I*sign(a)* log(alpha + sqrt(alpha**2 -1))
+    where z = a + I*b
+    alpha = (1/2)*(r + s); beta = (1/2)*(r - s) = a/alpha
+    r = sqrt((a+1)**2 + y**2); s = sqrt((a-1)**2 + y**2)
+    These expressions are rewritten in different ways in different
+    regions, delimited by two crossovers alpha_crossover and beta_crossover,
+    and by abs(a) <= 1, in order to improve the numerical accuracy.
+    """
+    a, b = z
+    wp = prec + 10
+    # special cases with real argument
+    if b == fzero:
+        am = mpf_sub(fone, mpf_abs(a), wp)
+        # case abs(a) <= 1
+        if not am[0]:
+            if n == 0:
+                return mpf_acos(a, prec, rnd), fzero
+            else:
+                return mpf_asin(a, prec, rnd), fzero
+        # cases abs(a) > 1
+        else:
+            # case a < -1
+            if a[0]:
+                pi = mpf_pi(prec, rnd)
+                c = mpf_acosh(mpf_neg(a), prec, rnd)
+                if n == 0:
+                    return pi, mpf_neg(c)
+                else:
+                    return mpf_neg(mpf_shift(pi, -1)), c
+            # case a > 1
+            else:
+                c = mpf_acosh(a, prec, rnd)
+                if n == 0:
+                    return fzero, c
+                else:
+                    pi = mpf_pi(prec, rnd)
+                    return mpf_shift(pi, -1), mpf_neg(c)
+    asign = bsign = 0
+    if a[0]:
+        a = mpf_neg(a)
+        asign = 1
+    if b[0]:
+        b = mpf_neg(b)
+        bsign = 1
+    am = mpf_sub(fone, a, wp)
+    ap = mpf_add(fone, a, wp)
+    r = mpf_hypot(ap, b, wp)
+    s = mpf_hypot(am, b, wp)
+    alpha = mpf_shift(mpf_add(r, s, wp), -1)
+    beta = mpf_div(a, alpha, wp)
+    b2 = mpf_mul(b,b, wp)
+    # case beta <= beta_crossover
+    if not mpf_sub(beta_crossover, beta, wp)[0]:
+        if n == 0:
+            re = mpf_acos(beta, wp)
+        else:
+            re = mpf_asin(beta, wp)
+    else:
+        # to compute the real part in this region use the identity
+        # asin(beta) = atan(beta/sqrt(1-beta**2))
+        # beta/sqrt(1-beta**2) = (alpha + a) * (alpha - a)
+        # alpha + a is numerically accurate; alpha - a can have
+        # cancellations leading to numerical inaccuracies, so rewrite
+        # it in differente ways according to the region
+        Ax = mpf_add(alpha, a, wp)
+        # case a <= 1
+        if not am[0]:
+            # c = b*b/(r + (a+1)); d = (s + (1-a))
+            # alpha - a = (1/2)*(c + d)
+            # case n=0: re = atan(sqrt((1/2) * Ax * (c + d))/a)
+            # case n=1: re = atan(a/sqrt((1/2) * Ax * (c + d)))
+            c = mpf_div(b2, mpf_add(r, ap, wp), wp)
+            d = mpf_add(s, am, wp)
+            re = mpf_shift(mpf_mul(Ax, mpf_add(c, d, wp), wp), -1)
+            if n == 0:
+                re = mpf_atan(mpf_div(mpf_sqrt(re, wp), a, wp), wp)
+            else:
+                re = mpf_atan(mpf_div(a, mpf_sqrt(re, wp), wp), wp)
+        else:
+            # c = Ax/(r + (a+1)); d = Ax/(s - (1-a))
+            # alpha - a = (1/2)*(c + d)
+            # case n = 0: re = atan(b*sqrt(c + d)/2/a)
+            # case n = 1: re = atan(a/(b*sqrt(c + d)/2)
+            c = mpf_div(Ax, mpf_add(r, ap, wp), wp)
+            d = mpf_div(Ax, mpf_sub(s, am, wp), wp)
+            re = mpf_shift(mpf_add(c, d, wp), -1)
+            re = mpf_mul(b, mpf_sqrt(re, wp), wp)
+            if n == 0:
+                re = mpf_atan(mpf_div(re, a, wp), wp)
+            else:
+                re = mpf_atan(mpf_div(a, re, wp), wp)
+    # to compute alpha + sqrt(alpha**2 - 1), if alpha <= alpha_crossover
+    # replace it with 1 + Am1 + sqrt(Am1*(alpha+1)))
+    # where Am1 = alpha -1
+    # if alpha <= alpha_crossover:
+    if not mpf_sub(alpha_crossover, alpha, wp)[0]:
+        c1 = mpf_div(b2, mpf_add(r, ap, wp), wp)
+        # case a < 1
+        if mpf_neg(am)[0]:
+            # Am1 = (1/2) * (b*b/(r + (a+1)) + b*b/(s + (1-a))
+            c2 = mpf_add(s, am, wp)
+            c2 = mpf_div(b2, c2, wp)
+            Am1 = mpf_shift(mpf_add(c1, c2, wp), -1)
+        else:
+            # Am1 = (1/2) * (b*b/(r + (a+1)) + (s - (1-a)))
+            c2 = mpf_sub(s, am, wp)
+            Am1 = mpf_shift(mpf_add(c1, c2, wp), -1)
+        # im = log(1 + Am1 + sqrt(Am1*(alpha+1)))
+        im = mpf_mul(Am1, mpf_add(alpha, fone, wp), wp)
+        im = mpf_log(mpf_add(fone, mpf_add(Am1, mpf_sqrt(im, wp), wp), wp), wp)
+    else:
+        # im = log(alpha + sqrt(alpha*alpha - 1))
+        im = mpf_sqrt(mpf_sub(mpf_mul(alpha, alpha, wp), fone, wp), wp)
+        im = mpf_log(mpf_add(alpha, im, wp), wp)
+    if asign:
+        if n == 0:
+            re = mpf_sub(mpf_pi(wp), re, wp)
+        else:
+            re = mpf_neg(re)
+    if not bsign and n == 0:
+        im = mpf_neg(im)
+    if bsign and n == 1:
+        im = mpf_neg(im)
+    re = normalize(re[0], re[1], re[2], re[3], prec, rnd)
+    im = normalize(im[0], im[1], im[2], im[3], prec, rnd)
+    return re, im
+
+def mpc_acos(z, prec, rnd=round_fast):
+    return acos_asin(z, prec, rnd, 0)
+
+def mpc_asin(z, prec, rnd=round_fast):
+    return acos_asin(z, prec, rnd, 1)
+
+def mpc_asinh(z, prec, rnd=round_fast):
+    # asinh(z) = I * asin(-I z)
+    a, b = z
+    a, b =  mpc_asin((b, mpf_neg(a)), prec, rnd)
+    return mpf_neg(b), a
+
+def mpc_acosh(z, prec, rnd=round_fast):
+    # acosh(z) = -I * acos(z)   for Im(acos(z)) <= 0
+    #            +I * acos(z)   otherwise
+    a, b = mpc_acos(z, prec, rnd)
+    if b[0] or b == fzero:
+        return mpf_neg(b), a
+    else:
+        return b, mpf_neg(a)
+
+def mpc_atanh(z, prec, rnd=round_fast):
+    # atanh(z) = (log(1+z)-log(1-z))/2
+    wp = prec + 15
+    a = mpc_add(z, mpc_one, wp)
+    b = mpc_sub(mpc_one, z, wp)
+    a = mpc_log(a, wp)
+    b = mpc_log(b, wp)
+    v = mpc_shift(mpc_sub(a, b, wp), -1)
+    # Subtraction at infinity gives correct imaginary part but
+    # wrong real part (should be zero)
+    if v[0] == fnan and mpc_is_inf(z):
+        v = (fzero, v[1])
+    return v
+
+def mpc_fibonacci(z, prec, rnd=round_fast):
+    re, im = z
+    if im == fzero:
+        return (mpf_fibonacci(re, prec, rnd), fzero)
+    size = max(abs(re[2]+re[3]), abs(re[2]+re[3]))
+    wp = prec + size + 20
+    a = mpf_phi(wp)
+    b = mpf_add(mpf_shift(a, 1), fnone, wp)
+    u = mpc_pow((a, fzero), z, wp)
+    v = mpc_cos_pi(z, wp)
+    v = mpc_div(v, u, wp)
+    u = mpc_sub(u, v, wp)
+    u = mpc_div_mpf(u, b, prec, rnd)
+    return u
+
+def mpf_expj(x, prec, rnd='f'):
+    raise ComplexResult
+
+def mpc_expj(z, prec, rnd='f'):
+    re, im = z
+    if im == fzero:
+        return mpf_cos_sin(re, prec, rnd)
+    if re == fzero:
+        return mpf_exp(mpf_neg(im), prec, rnd), fzero
+    ey = mpf_exp(mpf_neg(im), prec+10)
+    c, s = mpf_cos_sin(re, prec+10)
+    re = mpf_mul(ey, c, prec, rnd)
+    im = mpf_mul(ey, s, prec, rnd)
+    return re, im
+
+def mpf_expjpi(x, prec, rnd='f'):
+    raise ComplexResult
+
+def mpc_expjpi(z, prec, rnd='f'):
+    re, im = z
+    if im == fzero:
+        return mpf_cos_sin_pi(re, prec, rnd)
+    sign, man, exp, bc = im
+    wp = prec+10
+    if man:
+        wp += max(0, exp+bc)
+    im = mpf_neg(mpf_mul(mpf_pi(wp), im, wp))
+    if re == fzero:
+        return mpf_exp(im, prec, rnd), fzero
+    ey = mpf_exp(im, prec+10)
+    c, s = mpf_cos_sin_pi(re, prec+10)
+    re = mpf_mul(ey, c, prec, rnd)
+    im = mpf_mul(ey, s, prec, rnd)
+    return re, im
+
+
+if BACKEND == 'sage':
+    try:
+        import sage.libs.mpmath.ext_libmp as _lbmp
+        mpc_exp = _lbmp.mpc_exp
+        mpc_sqrt = _lbmp.mpc_sqrt
+    except (ImportError, AttributeError):
+        print("Warning: Sage imports in libmpc failed")

lib/python3.11/site-packages/mpmath/libmp/libmpf.py

@@ -0,0 +1,1414 @@
+"""
+Low-level functions for arbitrary-precision floating-point arithmetic.
+"""
+
+__docformat__ = 'plaintext'
+
+import math
+
+from bisect import bisect
+
+import sys
+
+# Importing random is slow
+#from random import getrandbits
+getrandbits = None
+
+from .backend import (MPZ, MPZ_TYPE, MPZ_ZERO, MPZ_ONE, MPZ_TWO, MPZ_FIVE,
+    BACKEND, STRICT, HASH_MODULUS, HASH_BITS, gmpy, sage, sage_utils)
+
+from .libintmath import (giant_steps,
+    trailtable, bctable, lshift, rshift, bitcount, trailing,
+    sqrt_fixed, numeral, isqrt, isqrt_fast, sqrtrem,
+    bin_to_radix)
+
+# We don't pickle tuples directly for the following reasons:
+#   1: pickle uses str() for ints, which is inefficient when they are large
+#   2: pickle doesn't work for gmpy mpzs
+# Both problems are solved by using hex()
+
+if BACKEND == 'sage':
+    def to_pickable(x):
+        sign, man, exp, bc = x
+        return sign, hex(man), exp, bc
+else:
+    def to_pickable(x):
+        sign, man, exp, bc = x
+        return sign, hex(man)[2:], exp, bc
+
+def from_pickable(x):
+    sign, man, exp, bc = x
+    return (sign, MPZ(man, 16), exp, bc)
+
+class ComplexResult(ValueError):
+    pass
+
+try:
+    intern
+except NameError:
+    intern = lambda x: x
+
+# All supported rounding modes
+round_nearest = intern('n')
+round_floor = intern('f')
+round_ceiling = intern('c')
+round_up = intern('u')
+round_down = intern('d')
+round_fast = round_down
+
+def prec_to_dps(n):
+    """Return number of accurate decimals that can be represented
+    with a precision of n bits."""
+    return max(1, int(round(int(n)/3.3219280948873626)-1))
+
+def dps_to_prec(n):
+    """Return the number of bits required to represent n decimals
+    accurately."""
+    return max(1, int(round((int(n)+1)*3.3219280948873626)))
+
+def repr_dps(n):
+    """Return the number of decimal digits required to represent
+    a number with n-bit precision so that it can be uniquely
+    reconstructed from the representation."""
+    dps = prec_to_dps(n)
+    if dps == 15:
+        return 17
+    return dps + 3
+
+#----------------------------------------------------------------------------#
+#                    Some commonly needed float values                       #
+#----------------------------------------------------------------------------#
+
+# Regular number format:
+# (-1)**sign * mantissa * 2**exponent, plus bitcount of mantissa
+fzero = (0, MPZ_ZERO, 0, 0)
+fnzero = (1, MPZ_ZERO, 0, 0)
+fone = (0, MPZ_ONE, 0, 1)
+fnone = (1, MPZ_ONE, 0, 1)
+ftwo = (0, MPZ_ONE, 1, 1)
+ften = (0, MPZ_FIVE, 1, 3)
+fhalf = (0, MPZ_ONE, -1, 1)
+
+# Arbitrary encoding for special numbers: zero mantissa, nonzero exponent
+fnan = (0, MPZ_ZERO, -123, -1)
+finf = (0, MPZ_ZERO, -456, -2)
+fninf = (1, MPZ_ZERO, -789, -3)
+
+# Was 1e1000; this is broken in Python 2.4
+math_float_inf = 1e300 * 1e300
+
+
+#----------------------------------------------------------------------------#
+#                                  Rounding                                  #
+#----------------------------------------------------------------------------#
+
+# This function can be used to round a mantissa generally. However,
+# we will try to do most rounding inline for efficiency.
+def round_int(x, n, rnd):
+    if rnd == round_nearest:
+        if x >= 0:
+            t = x >> (n-1)
+            if t & 1 and ((t & 2) or (x & h_mask[n<300][n])):
+                return (t>>1)+1
+            else:
+                return t>>1
+        else:
+            return -round_int(-x, n, rnd)
+    if rnd == round_floor:
+        return x >> n
+    if rnd == round_ceiling:
+        return -((-x) >> n)
+    if rnd == round_down:
+        if x >= 0:
+            return x >> n
+        return -((-x) >> n)
+    if rnd == round_up:
+        if x >= 0:
+            return -((-x) >> n)
+        return x >> n
+
+# These masks are used to pick out segments of numbers to determine
+# which direction to round when rounding to nearest.
+class h_mask_big:
+    def __getitem__(self, n):
+        return (MPZ_ONE<<(n-1))-1
+
+h_mask_small = [0]+[((MPZ_ONE<<(_-1))-1) for _ in range(1, 300)]
+h_mask = [h_mask_big(), h_mask_small]
+
+# The >> operator rounds to floor. shifts_down[rnd][sign]
+# tells whether this is the right direction to use, or if the
+# number should be negated before shifting
+shifts_down = {round_floor:(1,0), round_ceiling:(0,1),
+    round_down:(1,1), round_up:(0,0)}
+
+
+#----------------------------------------------------------------------------#
+#                          Normalization of raw mpfs                         #
+#----------------------------------------------------------------------------#
+
+# This function is called almost every time an mpf is created.
+# It has been optimized accordingly.
+
+def _normalize(sign, man, exp, bc, prec, rnd):
+    """
+    Create a raw mpf tuple with value (-1)**sign * man * 2**exp and
+    normalized mantissa. The mantissa is rounded in the specified
+    direction if its size exceeds the precision. Trailing zero bits
+    are also stripped from the mantissa to ensure that the
+    representation is canonical.
+
+    Conditions on the input:
+    * The input must represent a regular (finite) number
+    * The sign bit must be 0 or 1
+    * The mantissa must be positive
+    * The exponent must be an integer
+    * The bitcount must be exact
+
+    If these conditions are not met, use from_man_exp, mpf_pos, or any
+    of the conversion functions to create normalized raw mpf tuples.
+    """
+    if not man:
+        return fzero
+    # Cut mantissa down to size if larger than target precision
+    n = bc - prec
+    if n > 0:
+        if rnd == round_nearest:
+            t = man >> (n-1)
+            if t & 1 and ((t & 2) or (man & h_mask[n<300][n])):
+                man = (t>>1)+1
+            else:
+                man = t>>1
+        elif shifts_down[rnd][sign]:
+            man >>= n
+        else:
+            man = -((-man)>>n)
+        exp += n
+        bc = prec
+    # Strip trailing bits
+    if not man & 1:
+        t = trailtable[int(man & 255)]
+        if not t:
+            while not man & 255:
+                man >>= 8
+                exp += 8
+                bc -= 8
+            t = trailtable[int(man & 255)]
+        man >>= t
+        exp += t
+        bc -= t
+    # Bit count can be wrong if the input mantissa was 1 less than
+    # a power of 2 and got rounded up, thereby adding an extra bit.
+    # With trailing bits removed, all powers of two have mantissa 1,
+    # so this is easy to check for.
+    if man == 1:
+        bc = 1
+    return sign, man, exp, bc
+
+def _normalize1(sign, man, exp, bc, prec, rnd):
+    """same as normalize, but with the added condition that
+       man is odd or zero
+    """
+    if not man:
+        return fzero
+    if bc <= prec:
+        return sign, man, exp, bc
+    n = bc - prec
+    if rnd == round_nearest:
+        t = man >> (n-1)
+        if t & 1 and ((t & 2) or (man & h_mask[n<300][n])):
+            man = (t>>1)+1
+        else:
+            man = t>>1
+    elif shifts_down[rnd][sign]:
+        man >>= n
+    else:
+        man = -((-man)>>n)
+    exp += n
+    bc = prec
+    # Strip trailing bits
+    if not man & 1:
+        t = trailtable[int(man & 255)]
+        if not t:
+            while not man & 255:
+                man >>= 8
+                exp += 8
+                bc -= 8
+            t = trailtable[int(man & 255)]
+        man >>= t
+        exp += t
+        bc -= t
+    # Bit count can be wrong if the input mantissa was 1 less than
+    # a power of 2 and got rounded up, thereby adding an extra bit.
+    # With trailing bits removed, all powers of two have mantissa 1,
+    # so this is easy to check for.
+    if man == 1:
+        bc = 1
+    return sign, man, exp, bc
+
+try:
+    _exp_types = (int, long)
+except NameError:
+    _exp_types = (int,)
+
+def strict_normalize(sign, man, exp, bc, prec, rnd):
+    """Additional checks on the components of an mpf. Enable tests by setting
+       the environment variable MPMATH_STRICT to Y."""
+    assert type(man) == MPZ_TYPE
+    assert type(bc) in _exp_types
+    assert type(exp) in _exp_types
+    assert bc == bitcount(man)
+    return _normalize(sign, man, exp, bc, prec, rnd)
+
+def strict_normalize1(sign, man, exp, bc, prec, rnd):
+    """Additional checks on the components of an mpf. Enable tests by setting
+       the environment variable MPMATH_STRICT to Y."""
+    assert type(man) == MPZ_TYPE
+    assert type(bc) in _exp_types
+    assert type(exp) in _exp_types
+    assert bc == bitcount(man)
+    assert (not man) or (man & 1)
+    return _normalize1(sign, man, exp, bc, prec, rnd)
+
+if BACKEND == 'gmpy' and '_mpmath_normalize' in dir(gmpy):
+    _normalize = gmpy._mpmath_normalize
+    _normalize1 = gmpy._mpmath_normalize
+
+if BACKEND == 'sage':
+    _normalize = _normalize1 = sage_utils.normalize
+
+if STRICT:
+    normalize = strict_normalize
+    normalize1 = strict_normalize1
+else:
+    normalize = _normalize
+    normalize1 = _normalize1
+
+#----------------------------------------------------------------------------#
+#                            Conversion functions                            #
+#----------------------------------------------------------------------------#
+
+def from_man_exp(man, exp, prec=None, rnd=round_fast):
+    """Create raw mpf from (man, exp) pair. The mantissa may be signed.
+    If no precision is specified, the mantissa is stored exactly."""
+    man = MPZ(man)
+    sign = 0
+    if man < 0:
+        sign = 1
+        man = -man
+    if man < 1024:
+        bc = bctable[int(man)]
+    else:
+        bc = bitcount(man)
+    if not prec:
+        if not man:
+            return fzero
+        if not man & 1:
+            if man & 2:
+                return (sign, man >> 1, exp + 1, bc - 1)
+            t = trailtable[int(man & 255)]
+            if not t:
+                while not man & 255:
+                    man >>= 8
+                    exp += 8
+                    bc -= 8
+                t = trailtable[int(man & 255)]
+            man >>= t
+            exp += t
+            bc -= t
+        return (sign, man, exp, bc)
+    return normalize(sign, man, exp, bc, prec, rnd)
+
+int_cache = dict((n, from_man_exp(n, 0)) for n in range(-10, 257))
+
+if BACKEND == 'gmpy' and '_mpmath_create' in dir(gmpy):
+    from_man_exp = gmpy._mpmath_create
+
+if BACKEND == 'sage':
+    from_man_exp = sage_utils.from_man_exp
+
+def from_int(n, prec=0, rnd=round_fast):
+    """Create a raw mpf from an integer. If no precision is specified,
+    the mantissa is stored exactly."""
+    if not prec:
+        if n in int_cache:
+            return int_cache[n]
+    return from_man_exp(n, 0, prec, rnd)
+
+def to_man_exp(s):
+    """Return (man, exp) of a raw mpf. Raise an error if inf/nan."""
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        raise ValueError("mantissa and exponent are undefined for %s" % man)
+    return man, exp
+
+def to_int(s, rnd=None):
+    """Convert a raw mpf to the nearest int. Rounding is done down by
+    default (same as int(float) in Python), but can be changed. If the
+    input is inf/nan, an exception is raised."""
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        raise ValueError("cannot convert inf or nan to int")
+    if exp >= 0:
+        if sign:
+            return (-man) << exp
+        return man << exp
+    # Make default rounding fast
+    if not rnd:
+        if sign:
+            return -(man >> (-exp))
+        else:
+            return man >> (-exp)
+    if sign:
+        return round_int(-man, -exp, rnd)
+    else:
+        return round_int(man, -exp, rnd)
+
+def mpf_round_int(s, rnd):
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        return s
+    if exp >= 0:
+        return s
+    mag = exp+bc
+    if mag < 1:
+        if rnd == round_ceiling:
+            if sign: return fzero
+            else:    return fone
+        elif rnd == round_floor:
+            if sign: return fnone
+            else:    return fzero
+        elif rnd == round_nearest:
+            if mag < 0 or man == MPZ_ONE: return fzero
+            elif sign: return fnone
+            else:      return fone
+        else:
+            raise NotImplementedError
+    return mpf_pos(s, min(bc, mag), rnd)
+
+def mpf_floor(s, prec=0, rnd=round_fast):
+    v = mpf_round_int(s, round_floor)
+    if prec:
+        v = mpf_pos(v, prec, rnd)
+    return v
+
+def mpf_ceil(s, prec=0, rnd=round_fast):
+    v = mpf_round_int(s, round_ceiling)
+    if prec:
+        v = mpf_pos(v, prec, rnd)
+    return v
+
+def mpf_nint(s, prec=0, rnd=round_fast):
+    v = mpf_round_int(s, round_nearest)
+    if prec:
+        v = mpf_pos(v, prec, rnd)
+    return v
+
+def mpf_frac(s, prec=0, rnd=round_fast):
+    return mpf_sub(s, mpf_floor(s), prec, rnd)
+
+def from_float(x, prec=53, rnd=round_fast):
+    """Create a raw mpf from a Python float, rounding if necessary.
+    If prec >= 53, the result is guaranteed to represent exactly the
+    same number as the input. If prec is not specified, use prec=53."""
+    # frexp only raises an exception for nan on some platforms
+    if x != x:
+        return fnan
+    # in Python2.5 math.frexp gives an exception for float infinity
+    # in Python2.6 it returns (float infinity, 0)
+    try:
+        m, e = math.frexp(x)
+    except:
+        if x == math_float_inf: return finf
+        if x == -math_float_inf: return fninf
+        return fnan
+    if x == math_float_inf: return finf
+    if x == -math_float_inf: return fninf
+    return from_man_exp(int(m*(1<<53)), e-53, prec, rnd)
+
+def from_npfloat(x, prec=113, rnd=round_fast):
+    """Create a raw mpf from a numpy float, rounding if necessary.
+    If prec >= 113, the result is guaranteed to represent exactly the
+    same number as the input. If prec is not specified, use prec=113."""
+    y = float(x)
+    if x == y: # ldexp overflows for float16
+        return from_float(y, prec, rnd)
+    import numpy as np
+    if np.isfinite(x):
+        m, e = np.frexp(x)
+        return from_man_exp(int(np.ldexp(m, 113)), int(e-113), prec, rnd)
+    if np.isposinf(x): return finf
+    if np.isneginf(x): return fninf
+    return fnan
+
+def from_Decimal(x, prec=None, rnd=round_fast):
+    """Create a raw mpf from a Decimal, rounding if necessary.
+    If prec is not specified, use the equivalent bit precision
+    of the number of significant digits in x."""
+    if x.is_nan(): return fnan
+    if x.is_infinite(): return fninf if x.is_signed() else finf
+    if prec is None:
+        prec = int(len(x.as_tuple()[1])*3.3219280948873626)
+    return from_str(str(x), prec, rnd)
+
+def to_float(s, strict=False, rnd=round_fast):
+    """
+    Convert a raw mpf to a Python float. The result is exact if the
+    bitcount of s is <= 53 and no underflow/overflow occurs.
+
+    If the number is too large or too small to represent as a regular
+    float, it will be converted to inf or 0.0. Setting strict=True
+    forces an OverflowError to be raised instead.
+
+    Warning: with a directed rounding mode, the correct nearest representable
+    floating-point number in the specified direction might not be computed
+    in case of overflow or (gradual) underflow.
+    """
+    sign, man, exp, bc = s
+    if not man:
+        if s == fzero: return 0.0
+        if s == finf: return math_float_inf
+        if s == fninf: return -math_float_inf
+        return math_float_inf/math_float_inf
+    if bc > 53:
+        sign, man, exp, bc = normalize1(sign, man, exp, bc, 53, rnd)
+    if sign:
+        man = -man
+    try:
+        return math.ldexp(man, exp)
+    except OverflowError:
+        if strict:
+            raise
+        # Overflow to infinity
+        if exp + bc > 0:
+            if sign:
+                return -math_float_inf
+            else:
+                return math_float_inf
+        # Underflow to zero
+        return 0.0
+
+def from_rational(p, q, prec, rnd=round_fast):
+    """Create a raw mpf from a rational number p/q, round if
+    necessary."""
+    return mpf_div(from_int(p), from_int(q), prec, rnd)
+
+def to_rational(s):
+    """Convert a raw mpf to a rational number. Return integers (p, q)
+    such that s = p/q exactly."""
+    sign, man, exp, bc = s
+    if sign:
+        man = -man
+    if bc == -1:
+        raise ValueError("cannot convert %s to a rational number" % man)
+    if exp >= 0:
+        return man * (1<<exp), 1
+    else:
+        return man, 1<<(-exp)
+
+def to_fixed(s, prec):
+    """Convert a raw mpf to a fixed-point big integer"""
+    sign, man, exp, bc = s
+    offset = exp + prec
+    if sign:
+        if offset >= 0: return (-man) << offset
+        else:           return (-man) >> (-offset)
+    else:
+        if offset >= 0: return man << offset
+        else:           return man >> (-offset)
+
+
+##############################################################################
+##############################################################################
+
+#----------------------------------------------------------------------------#
+#                       Arithmetic operations, etc.                          #
+#----------------------------------------------------------------------------#
+
+def mpf_rand(prec):
+    """Return a raw mpf chosen randomly from [0, 1), with prec bits
+    in the mantissa."""
+    global getrandbits
+    if not getrandbits:
+        import random
+        getrandbits = random.getrandbits
+    return from_man_exp(getrandbits(prec), -prec, prec, round_floor)
+
+def mpf_eq(s, t):
+    """Test equality of two raw mpfs. This is simply tuple comparison
+    unless either number is nan, in which case the result is False."""
+    if not s[1] or not t[1]:
+        if s == fnan or t == fnan:
+            return False
+    return s == t
+
+def mpf_hash(s):
+    # Duplicate the new hash algorithm introduces in Python 3.2.
+    if sys.version_info >= (3, 2):
+        ssign, sman, sexp, sbc = s
+
+        # Handle special numbers
+        if not sman:
+            if s == fnan: return sys.hash_info.nan
+            if s == finf: return sys.hash_info.inf
+            if s == fninf: return -sys.hash_info.inf
+        h = sman % HASH_MODULUS
+        if sexp >= 0:
+            sexp = sexp % HASH_BITS
+        else:
+            sexp = HASH_BITS - 1 - ((-1 - sexp) % HASH_BITS)
+        h = (h << sexp) % HASH_MODULUS
+        if ssign: h = -h
+        if h == -1: h = -2
+        return int(h)
+    else:
+        try:
+            # Try to be compatible with hash values for floats and ints
+            return hash(to_float(s, strict=1))
+        except OverflowError:
+            # We must unfortunately sacrifice compatibility with ints here.
+            # We could do hash(man << exp) when the exponent is positive, but
+            # this would cause unreasonable inefficiency for large numbers.
+            return hash(s)
+
+def mpf_cmp(s, t):
+    """Compare the raw mpfs s and t. Return -1 if s < t, 0 if s == t,
+    and 1 if s > t. (Same convention as Python's cmp() function.)"""
+
+    # In principle, a comparison amounts to determining the sign of s-t.
+    # A full subtraction is relatively slow, however, so we first try to
+    # look at the components.
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+
+    # Handle zeros and special numbers
+    if not sman or not tman:
+        if s == fzero: return -mpf_sign(t)
+        if t == fzero: return mpf_sign(s)
+        if s == t: return 0
+        # Follow same convention as Python's cmp for float nan
+        if t == fnan: return 1
+        if s == finf: return 1
+        if t == fninf: return 1
+        return -1
+    # Different sides of zero
+    if ssign != tsign:
+        if not ssign: return 1
+        return -1
+    # This reduces to direct integer comparison
+    if sexp == texp:
+        if sman == tman:
+            return 0
+        if sman > tman:
+            if ssign: return -1
+            else:     return 1
+        else:
+            if ssign: return 1
+            else:     return -1
+    # Check position of the highest set bit in each number. If
+    # different, there is certainly an inequality.
+    a = sbc + sexp
+    b = tbc + texp
+    if ssign:
+        if a < b: return 1
+        if a > b: return -1
+    else:
+        if a < b: return -1
+        if a > b: return 1
+
+    # Both numbers have the same highest bit. Subtract to find
+    # how the lower bits compare.
+    delta = mpf_sub(s, t, 5, round_floor)
+    if delta[0]:
+        return -1
+    return 1
+
+def mpf_lt(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) < 0
+
+def mpf_le(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) <= 0
+
+def mpf_gt(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) > 0
+
+def mpf_ge(s, t):
+    if s == fnan or t == fnan:
+        return False
+    return mpf_cmp(s, t) >= 0
+
+def mpf_min_max(seq):
+    min = max = seq[0]
+    for x in seq[1:]:
+        if mpf_lt(x, min): min = x
+        if mpf_gt(x, max): max = x
+    return min, max
+
+def mpf_pos(s, prec=0, rnd=round_fast):
+    """Calculate 0+s for a raw mpf (i.e., just round s to the specified
+    precision)."""
+    if prec:
+        sign, man, exp, bc = s
+        if (not man) and exp:
+            return s
+        return normalize1(sign, man, exp, bc, prec, rnd)
+    return s
+
+def mpf_neg(s, prec=None, rnd=round_fast):
+    """Negate a raw mpf (return -s), rounding the result to the
+    specified precision. The prec argument can be omitted to do the
+    operation exactly."""
+    sign, man, exp, bc = s
+    if not man:
+        if exp:
+            if s == finf: return fninf
+            if s == fninf: return finf
+        return s
+    if not prec:
+        return (1-sign, man, exp, bc)
+    return normalize1(1-sign, man, exp, bc, prec, rnd)
+
+def mpf_abs(s, prec=None, rnd=round_fast):
+    """Return abs(s) of the raw mpf s, rounded to the specified
+    precision. The prec argument can be omitted to generate an
+    exact result."""
+    sign, man, exp, bc = s
+    if (not man) and exp:
+        if s == fninf:
+            return finf
+        return s
+    if not prec:
+        if sign:
+            return (0, man, exp, bc)
+        return s
+    return normalize1(0, man, exp, bc, prec, rnd)
+
+def mpf_sign(s):
+    """Return -1, 0, or 1 (as a Python int, not a raw mpf) depending on
+    whether s is negative, zero, or positive. (Nan is taken to give 0.)"""
+    sign, man, exp, bc = s
+    if not man:
+        if s == finf: return 1
+        if s == fninf: return -1
+        return 0
+    return (-1) ** sign
+
+def mpf_add(s, t, prec=0, rnd=round_fast, _sub=0):
+    """
+    Add the two raw mpf values s and t.
+
+    With prec=0, no rounding is performed. Note that this can
+    produce a very large mantissa (potentially too large to fit
+    in memory) if exponents are far apart.
+    """
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    tsign ^= _sub
+    # Standard case: two nonzero, regular numbers
+    if sman and tman:
+        offset = sexp - texp
+        if offset:
+            if offset > 0:
+                # Outside precision range; only need to perturb
+                if offset > 100 and prec:
+                    delta = sbc + sexp - tbc - texp
+                    if delta > prec + 4:
+                        offset = prec + 4
+                        sman <<= offset
+                        if tsign == ssign: sman += 1
+                        else:              sman -= 1
+                        return normalize1(ssign, sman, sexp-offset,
+                            bitcount(sman), prec, rnd)
+                # Add
+                if ssign == tsign:
+                    man = tman + (sman << offset)
+                # Subtract
+                else:
+                    if ssign: man = tman - (sman << offset)
+                    else:     man = (sman << offset) - tman
+                    if man >= 0:
+                        ssign = 0
+                    else:
+                        man = -man
+                        ssign = 1
+                bc = bitcount(man)
+                return normalize1(ssign, man, texp, bc, prec or bc, rnd)
+            elif offset < 0:
+                # Outside precision range; only need to perturb
+                if offset < -100 and prec:
+                    delta = tbc + texp - sbc - sexp
+                    if delta > prec + 4:
+                        offset = prec + 4
+                        tman <<= offset
+                        if ssign == tsign: tman += 1
+                        else:              tman -= 1
+                        return normalize1(tsign, tman, texp-offset,
+                            bitcount(tman), prec, rnd)
+                # Add
+                if ssign == tsign:
+                    man = sman + (tman << -offset)
+                # Subtract
+                else:
+                    if tsign: man = sman - (tman << -offset)
+                    else:     man = (tman << -offset) - sman
+                    if man >= 0:
+                        ssign = 0
+                    else:
+                        man = -man
+                        ssign = 1
+                bc = bitcount(man)
+                return normalize1(ssign, man, sexp, bc, prec or bc, rnd)
+        # Equal exponents; no shifting necessary
+        if ssign == tsign:
+            man = tman + sman
+        else:
+            if ssign: man = tman - sman
+            else:     man = sman - tman
+            if man >= 0:
+                ssign = 0
+            else:
+                man = -man
+                ssign = 1
+        bc = bitcount(man)
+        return normalize(ssign, man, texp, bc, prec or bc, rnd)
+    # Handle zeros and special numbers
+    if _sub:
+        t = mpf_neg(t)
+    if not sman:
+        if sexp:
+            if s == t or tman or not texp:
+                return s
+            return fnan
+        if tman:
+            return normalize1(tsign, tman, texp, tbc, prec or tbc, rnd)
+        return t
+    if texp:
+        return t
+    if sman:
+        return normalize1(ssign, sman, sexp, sbc, prec or sbc, rnd)
+    return s
+
+def mpf_sub(s, t, prec=0, rnd=round_fast):
+    """Return the difference of two raw mpfs, s-t. This function is
+    simply a wrapper of mpf_add that changes the sign of t."""
+    return mpf_add(s, t, prec, rnd, 1)
+
+def mpf_sum(xs, prec=0, rnd=round_fast, absolute=False):
+    """
+    Sum a list of mpf values efficiently and accurately
+    (typically no temporary roundoff occurs). If prec=0,
+    the final result will not be rounded either.
+
+    There may be roundoff error or cancellation if extremely
+    large exponent differences occur.
+
+    With absolute=True, sums the absolute values.
+    """
+    man = 0
+    exp = 0
+    max_extra_prec = prec*2 or 1000000  # XXX
+    special = None
+    for x in xs:
+        xsign, xman, xexp, xbc = x
+        if xman:
+            if xsign and not absolute:
+                xman = -xman
+            delta = xexp - exp
+            if xexp >= exp:
+                # x much larger than existing sum?
+                # first: quick test
+                if (delta > max_extra_prec) and \
+                    ((not man) or delta-bitcount(abs(man)) > max_extra_prec):
+                    man = xman
+                    exp = xexp
+                else:
+                    man += (xman << delta)
+            else:
+                delta = -delta
+                # x much smaller than existing sum?
+                if delta-xbc > max_extra_prec:
+                    if not man:
+                        man, exp = xman, xexp
+                else:
+                    man = (man << delta) + xman
+                    exp = xexp
+        elif xexp:
+            if absolute:
+                x = mpf_abs(x)
+            special = mpf_add(special or fzero, x, 1)
+    # Will be inf or nan
+    if special:
+        return special
+    return from_man_exp(man, exp, prec, rnd)
+
+def gmpy_mpf_mul(s, t, prec=0, rnd=round_fast):
+    """Multiply two raw mpfs"""
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    sign = ssign ^ tsign
+    man = sman*tman
+    if man:
+        bc = bitcount(man)
+        if prec:
+            return normalize1(sign, man, sexp+texp, bc, prec, rnd)
+        else:
+            return (sign, man, sexp+texp, bc)
+    s_special = (not sman) and sexp
+    t_special = (not tman) and texp
+    if not s_special and not t_special:
+        return fzero
+    if fnan in (s, t): return fnan
+    if (not tman) and texp: s, t = t, s
+    if t == fzero: return fnan
+    return {1:finf, -1:fninf}[mpf_sign(s) * mpf_sign(t)]
+
+def gmpy_mpf_mul_int(s, n, prec, rnd=round_fast):
+    """Multiply by a Python integer."""
+    sign, man, exp, bc = s
+    if not man:
+        return mpf_mul(s, from_int(n), prec, rnd)
+    if not n:
+        return fzero
+    if n < 0:
+        sign ^= 1
+        n = -n
+    man *= n
+    return normalize(sign, man, exp, bitcount(man), prec, rnd)
+
+def python_mpf_mul(s, t, prec=0, rnd=round_fast):
+    """Multiply two raw mpfs"""
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    sign = ssign ^ tsign
+    man = sman*tman
+    if man:
+        bc = sbc + tbc - 1
+        bc += int(man>>bc)
+        if prec:
+            return normalize1(sign, man, sexp+texp, bc, prec, rnd)
+        else:
+            return (sign, man, sexp+texp, bc)
+    s_special = (not sman) and sexp
+    t_special = (not tman) and texp
+    if not s_special and not t_special:
+        return fzero
+    if fnan in (s, t): return fnan
+    if (not tman) and texp: s, t = t, s
+    if t == fzero: return fnan
+    return {1:finf, -1:fninf}[mpf_sign(s) * mpf_sign(t)]
+
+def python_mpf_mul_int(s, n, prec, rnd=round_fast):
+    """Multiply by a Python integer."""
+    sign, man, exp, bc = s
+    if not man:
+        return mpf_mul(s, from_int(n), prec, rnd)
+    if not n:
+        return fzero
+    if n < 0:
+        sign ^= 1
+        n = -n
+    man *= n
+    # Generally n will be small
+    if n < 1024:
+        bc += bctable[int(n)] - 1
+    else:
+        bc += bitcount(n) - 1
+    bc += int(man>>bc)
+    return normalize(sign, man, exp, bc, prec, rnd)
+
+
+if BACKEND == 'gmpy':
+    mpf_mul = gmpy_mpf_mul
+    mpf_mul_int = gmpy_mpf_mul_int
+else:
+    mpf_mul = python_mpf_mul
+    mpf_mul_int = python_mpf_mul_int
+
+def mpf_shift(s, n):
+    """Quickly multiply the raw mpf s by 2**n without rounding."""
+    sign, man, exp, bc = s
+    if not man:
+        return s
+    return sign, man, exp+n, bc
+
+def mpf_frexp(x):
+    """Convert x = y*2**n to (y, n) with abs(y) in [0.5, 1) if nonzero"""
+    sign, man, exp, bc = x
+    if not man:
+        if x == fzero:
+            return (fzero, 0)
+        else:
+            raise ValueError
+    return mpf_shift(x, -bc-exp), bc+exp
+
+def mpf_div(s, t, prec, rnd=round_fast):
+    """Floating-point division"""
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    if not sman or not tman:
+        if s == fzero:
+            if t == fzero: raise ZeroDivisionError
+            if t == fnan: return fnan
+            return fzero
+        if t == fzero:
+            raise ZeroDivisionError
+        s_special = (not sman) and sexp
+        t_special = (not tman) and texp
+        if s_special and t_special:
+            return fnan
+        if s == fnan or t == fnan:
+            return fnan
+        if not t_special:
+            if t == fzero:
+                return fnan
+            return {1:finf, -1:fninf}[mpf_sign(s) * mpf_sign(t)]
+        return fzero
+    sign = ssign ^ tsign
+    if tman == 1:
+        return normalize1(sign, sman, sexp-texp, sbc, prec, rnd)
+    # Same strategy as for addition: if there is a remainder, perturb
+    # the result a few bits outside the precision range before rounding
+    extra = prec - sbc + tbc + 5
+    if extra < 5:
+        extra = 5
+    quot, rem = divmod(sman<<extra, tman)
+    if rem:
+        quot = (quot<<1) + 1
+        extra += 1
+        return normalize1(sign, quot, sexp-texp-extra, bitcount(quot), prec, rnd)
+    return normalize(sign, quot, sexp-texp-extra, bitcount(quot), prec, rnd)
+
+def mpf_rdiv_int(n, t, prec, rnd=round_fast):
+    """Floating-point division n/t with a Python integer as numerator"""
+    sign, man, exp, bc = t
+    if not n or not man:
+        return mpf_div(from_int(n), t, prec, rnd)
+    if n < 0:
+        sign ^= 1
+        n = -n
+    extra = prec + bc + 5
+    quot, rem = divmod(n<<extra, man)
+    if rem:
+        quot = (quot<<1) + 1
+        extra += 1
+        return normalize1(sign, quot, -exp-extra, bitcount(quot), prec, rnd)
+    return normalize(sign, quot, -exp-extra, bitcount(quot), prec, rnd)
+
+def mpf_mod(s, t, prec, rnd=round_fast):
+    ssign, sman, sexp, sbc = s
+    tsign, tman, texp, tbc = t
+    if ((not sman) and sexp) or ((not tman) and texp):
+        return fnan
+    # Important special case: do nothing if t is larger
+    if ssign == tsign and texp > sexp+sbc:
+        return s
+    # Another important special case: this allows us to do e.g. x % 1.0
+    # to find the fractional part of x, and it will work when x is huge.
+    if tman == 1 and sexp > texp+tbc:
+        return fzero
+    base = min(sexp, texp)
+    sman = (-1)**ssign * sman
+    tman = (-1)**tsign * tman
+    man = (sman << (sexp-base)) % (tman << (texp-base))
+    if man >= 0:
+        sign = 0
+    else:
+        man = -man
+        sign = 1
+    return normalize(sign, man, base, bitcount(man), prec, rnd)
+
+reciprocal_rnd = {
+  round_down : round_up,
+  round_up : round_down,
+  round_floor : round_ceiling,
+  round_ceiling : round_floor,
+  round_nearest : round_nearest
+}
+
+negative_rnd = {
+  round_down : round_down,
+  round_up : round_up,
+  round_floor : round_ceiling,
+  round_ceiling : round_floor,
+  round_nearest : round_nearest
+}
+
+def mpf_pow_int(s, n, prec, rnd=round_fast):
+    """Compute s**n, where s is a raw mpf and n is a Python integer."""
+    sign, man, exp, bc = s
+
+    if (not man) and exp:
+        if s == finf:
+            if n > 0: return s
+            if n == 0: return fnan
+            return fzero
+        if s == fninf:
+            if n > 0: return [finf, fninf][n & 1]
+            if n == 0: return fnan
+            return fzero
+        return fnan
+
+    n = int(n)
+    if n == 0: return fone
+    if n == 1: return mpf_pos(s, prec, rnd)
+    if n == 2:
+        _, man, exp, bc = s
+        if not man:
+            return fzero
+        man = man*man
+        if man == 1:
+            return (0, MPZ_ONE, exp+exp, 1)
+        bc = bc + bc - 2
+        bc += bctable[int(man>>bc)]
+        return normalize1(0, man, exp+exp, bc, prec, rnd)
+    if n == -1: return mpf_div(fone, s, prec, rnd)
+    if n < 0:
+        inverse = mpf_pow_int(s, -n, prec+5, reciprocal_rnd[rnd])
+        return mpf_div(fone, inverse, prec, rnd)
+
+    result_sign = sign & n
+
+    # Use exact integer power when the exact mantissa is small
+    if man == 1:
+        return (result_sign, MPZ_ONE, exp*n, 1)
+    if bc*n < 1000:
+        man **= n
+        return normalize1(result_sign, man, exp*n, bitcount(man), prec, rnd)
+
+    # Use directed rounding all the way through to maintain rigorous
+    # bounds for interval arithmetic
+    rounds_down = (rnd == round_nearest) or \
+        shifts_down[rnd][result_sign]
+
+    # Now we perform binary exponentiation. Need to estimate precision
+    # to avoid rounding errors from temporary operations. Roughly log_2(n)
+    # operations are performed.
+    workprec = prec + 4*bitcount(n) + 4
+    _, pm, pe, pbc = fone
+    while 1:
+        if n & 1:
+            pm = pm*man
+            pe = pe+exp
+            pbc += bc - 2
+            pbc = pbc + bctable[int(pm >> pbc)]
+            if pbc > workprec:
+                if rounds_down:
+                    pm = pm >> (pbc-workprec)
+                else:
+                    pm = -((-pm) >> (pbc-workprec))
+                pe += pbc - workprec
+                pbc = workprec
+            n -= 1
+            if not n:
+                break
+        man = man*man
+        exp = exp+exp
+        bc = bc + bc - 2
+        bc = bc + bctable[int(man >> bc)]
+        if bc > workprec:
+            if rounds_down:
+                man = man >> (bc-workprec)
+            else:
+                man = -((-man) >> (bc-workprec))
+            exp += bc - workprec
+            bc = workprec
+        n = n // 2
+
+    return normalize(result_sign, pm, pe, pbc, prec, rnd)
+
+
+def mpf_perturb(x, eps_sign, prec, rnd):
+    """
+    For nonzero x, calculate x + eps with directed rounding, where
+    eps < prec relatively and eps has the given sign (0 for
+    positive, 1 for negative).
+
+    With rounding to nearest, this is taken to simply normalize
+    x to the given precision.
+    """
+    if rnd == round_nearest:
+        return mpf_pos(x, prec, rnd)
+    sign, man, exp, bc = x
+    eps = (eps_sign, MPZ_ONE, exp+bc-prec-1, 1)
+    if sign:
+        away = (rnd in (round_down, round_ceiling)) ^ eps_sign
+    else:
+        away = (rnd in (round_up, round_ceiling)) ^ eps_sign
+    if away:
+        return mpf_add(x, eps, prec, rnd)
+    else:
+        return mpf_pos(x, prec, rnd)
+
+
+#----------------------------------------------------------------------------#
+#                              Radix conversion                              #
+#----------------------------------------------------------------------------#
+
+def to_digits_exp(s, dps):
+    """Helper function for representing the floating-point number s as
+    a decimal with dps digits. Returns (sign, string, exponent) where
+    sign is '' or '-', string is the digit string, and exponent is
+    the decimal exponent as an int.
+
+    If inexact, the decimal representation is rounded toward zero."""
+
+    # Extract sign first so it doesn't mess up the string digit count
+    if s[0]:
+        sign = '-'
+        s = mpf_neg(s)
+    else:
+        sign = ''
+    _sign, man, exp, bc = s
+
+    if not man:
+        return '', '0', 0
+
+    bitprec = int(dps * math.log(10,2)) + 10
+
+    # Cut down to size
+    # TODO: account for precision when doing this
+    exp_from_1 = exp + bc
+    if abs(exp_from_1) > 3500:
+        from .libelefun import mpf_ln2, mpf_ln10
+        # Set b = int(exp * log(2)/log(10))
+        # If exp is huge, we must use high-precision arithmetic to
+        # find the nearest power of ten
+        expprec = bitcount(abs(exp)) + 5
+        tmp = from_int(exp)
+        tmp = mpf_mul(tmp, mpf_ln2(expprec))
+        tmp = mpf_div(tmp, mpf_ln10(expprec), expprec)
+        b = to_int(tmp)
+        s = mpf_div(s, mpf_pow_int(ften, b, bitprec), bitprec)
+        _sign, man, exp, bc = s
+        exponent = b
+    else:
+        exponent = 0
+
+    # First, calculate mantissa digits by converting to a binary
+    # fixed-point number and then converting that number to
+    # a decimal fixed-point number.
+    fixprec = max(bitprec - exp - bc, 0)
+    fixdps = int(fixprec / math.log(10,2) + 0.5)
+    sf = to_fixed(s, fixprec)
+    sd = bin_to_radix(sf, fixprec, 10, fixdps)
+    digits = numeral(sd, base=10, size=dps)
+
+    exponent += len(digits) - fixdps - 1
+    return sign, digits, exponent
+
+def to_str(s, dps, strip_zeros=True, min_fixed=None, max_fixed=None,
+    show_zero_exponent=False):
+    """
+    Convert a raw mpf to a decimal floating-point literal with at
+    most `dps` decimal digits in the mantissa (not counting extra zeros
+    that may be inserted for visual purposes).
+
+    The number will be printed in fixed-point format if the position
+    of the leading digit is strictly between min_fixed
+    (default = min(-dps/3,-5)) and max_fixed (default = dps).
+
+    To force fixed-point format always, set min_fixed = -inf,
+    max_fixed = +inf. To force floating-point format, set
+    min_fixed >= max_fixed.
+
+    The literal is formatted so that it can be parsed back to a number
+    by to_str, float() or Decimal().
+    """
+
+    # Special numbers
+    if not s[1]:
+        if s == fzero:
+            if dps: t = '0.0'
+            else:   t = '.0'
+            if show_zero_exponent:
+                t += 'e+0'
+            return t
+        if s == finf: return '+inf'
+        if s == fninf: return '-inf'
+        if s == fnan: return 'nan'
+        raise ValueError
+
+    if min_fixed is None: min_fixed = min(-(dps//3), -5)
+    if max_fixed is None: max_fixed = dps
+
+    # to_digits_exp rounds to floor.
+    # This sometimes kills some instances of "...00001"
+    sign, digits, exponent = to_digits_exp(s, dps+3)
+
+    # No digits: show only .0; round exponent to nearest
+    if not dps:
+        if digits[0] in '56789':
+            exponent += 1
+        digits = ".0"
+
+    else:
+        # Rounding up kills some instances of "...99999"
+        if len(digits) > dps and digits[dps] in '56789':
+            digits = digits[:dps]
+            i = dps - 1
+            while i >= 0 and digits[i] == '9':
+                i -= 1
+            if i >= 0:
+                digits = digits[:i] + str(int(digits[i]) + 1) + '0' * (dps - i - 1)
+            else:
+                digits = '1' + '0' * (dps - 1)
+                exponent += 1
+        else:
+            digits = digits[:dps]
+
+        # Prettify numbers close to unit magnitude
+        if min_fixed < exponent < max_fixed:
+            if exponent < 0:
+                digits = ("0"*int(-exponent)) + digits
+                split = 1
+            else:
+                split = exponent + 1
+                if split > dps:
+                    digits += "0"*(split-dps)
+            exponent = 0
+        else:
+            split = 1
+
+        digits = (digits[:split] + "." + digits[split:])
+
+        if strip_zeros:
+            # Clean up trailing zeros
+            digits = digits.rstrip('0')
+            if digits[-1] == ".":
+                digits += "0"
+
+    if exponent == 0 and dps and not show_zero_exponent: return sign + digits
+    if exponent >= 0: return sign + digits + "e+" + str(exponent)
+    if exponent < 0: return sign + digits + "e" + str(exponent)
+
+def str_to_man_exp(x, base=10):
+    """Helper function for from_str."""
+    x = x.lower().rstrip('l')
+    # Verify that the input is a valid float literal
+    float(x)
+    # Split into mantissa, exponent
+    parts = x.split('e')
+    if len(parts) == 1:
+        exp = 0
+    else: # == 2
+        x = parts[0]
+        exp = int(parts[1])
+    # Look for radix point in mantissa
+    parts = x.split('.')
+    if len(parts) == 2:
+        a, b = parts[0], parts[1].rstrip('0')
+        exp -= len(b)
+        x = a + b
+    x = MPZ(int(x, base))
+    return x, exp
+
+special_str = {'inf':finf, '+inf':finf, '-inf':fninf, 'nan':fnan}
+
+def from_str(x, prec, rnd=round_fast):
+    """Create a raw mpf from a decimal literal, rounding in the
+    specified direction if the input number cannot be represented
+    exactly as a binary floating-point number with the given number of
+    bits. The literal syntax accepted is the same as for Python
+    floats.
+
+    TODO: the rounding does not work properly for large exponents.
+    """
+    x = x.lower().strip()
+    if x in special_str:
+        return special_str[x]
+
+    if '/' in x:
+        p, q = x.split('/')
+        p, q = p.rstrip('l'), q.rstrip('l')
+        return from_rational(int(p), int(q), prec, rnd)
+
+    man, exp = str_to_man_exp(x, base=10)
+
+    # XXX: appropriate cutoffs & track direction
+    # note no factors of 5
+    if abs(exp) > 400:
+        s = from_int(man, prec+10)
+        s = mpf_mul(s, mpf_pow_int(ften, exp, prec+10), prec, rnd)
+    else:
+        if exp >= 0:
+            s = from_int(man * 10**exp, prec, rnd)
+        else:
+            s = from_rational(man, 10**-exp, prec, rnd)
+    return s
+
+# Binary string conversion. These are currently mainly used for debugging
+# and could use some improvement in the future
+
+def from_bstr(x):
+    man, exp = str_to_man_exp(x, base=2)
+    man = MPZ(man)
+    sign = 0
+    if man < 0:
+        man = -man
+        sign = 1
+    bc = bitcount(man)
+    return normalize(sign, man, exp, bc, bc, round_floor)
+
+def to_bstr(x):
+    sign, man, exp, bc = x
+    return ['','-'][sign] + numeral(man, size=bitcount(man), base=2) + ("e%i" % exp)
+
+
+#----------------------------------------------------------------------------#
+#                                Square roots                                #
+#----------------------------------------------------------------------------#
+
+
+def mpf_sqrt(s, prec, rnd=round_fast):
+    """
+    Compute the square root of a nonnegative mpf value. The
+    result is correctly rounded.
+    """
+    sign, man, exp, bc = s
+    if sign:
+        raise ComplexResult("square root of a negative number")
+    if not man:
+        return s
+    if exp & 1:
+        exp -= 1
+        man <<= 1
+        bc += 1
+    elif man == 1:
+        return normalize1(sign, man, exp//2, bc, prec, rnd)
+    shift = max(4, 2*prec-bc+4)
+    shift += shift & 1
+    if rnd in 'fd':
+        man = isqrt(man<<shift)
+    else:
+        man, rem = sqrtrem(man<<shift)
+        # Perturb up
+        if rem:
+            man = (man<<1)+1
+            shift += 2
+    return from_man_exp(man, (exp-shift)//2, prec, rnd)
+
+def mpf_hypot(x, y, prec, rnd=round_fast):
+    """Compute the Euclidean norm sqrt(x**2 + y**2) of two raw mpfs
+    x and y."""
+    if y == fzero: return mpf_abs(x, prec, rnd)
+    if x == fzero: return mpf_abs(y, prec, rnd)
+    hypot2 = mpf_add(mpf_mul(x,x), mpf_mul(y,y), prec+4)
+    return mpf_sqrt(hypot2, prec, rnd)
+
+
+if BACKEND == 'sage':
+    try:
+        import sage.libs.mpmath.ext_libmp as ext_lib
+        mpf_add = ext_lib.mpf_add
+        mpf_sub = ext_lib.mpf_sub
+        mpf_mul = ext_lib.mpf_mul
+        mpf_div = ext_lib.mpf_div
+        mpf_sqrt = ext_lib.mpf_sqrt
+    except ImportError:
+        pass

lib/python3.11/site-packages/mpmath/libmp/libmpi.py

@@ -0,0 +1,935 @@
+"""
+Computational functions for interval arithmetic.
+
+"""
+
+from .backend import xrange
+
+from .libmpf import (
+    ComplexResult,
+    round_down, round_up, round_floor, round_ceiling, round_nearest,
+    prec_to_dps, repr_dps, dps_to_prec,
+    bitcount,
+    from_float,
+    fnan, finf, fninf, fzero, fhalf, fone, fnone,
+    mpf_sign, mpf_lt, mpf_le, mpf_gt, mpf_ge, mpf_eq, mpf_cmp,
+    mpf_min_max,
+    mpf_floor, from_int, to_int, to_str, from_str,
+    mpf_abs, mpf_neg, mpf_pos, mpf_add, mpf_sub, mpf_mul, mpf_mul_int,
+    mpf_div, mpf_shift, mpf_pow_int,
+    from_man_exp, MPZ_ONE)
+
+from .libelefun import (
+    mpf_log, mpf_exp, mpf_sqrt, mpf_atan, mpf_atan2,
+    mpf_pi, mod_pi2, mpf_cos_sin
+)
+
+from .gammazeta import mpf_gamma, mpf_rgamma, mpf_loggamma, mpc_loggamma
+
+def mpi_str(s, prec):
+    sa, sb = s
+    dps = prec_to_dps(prec) + 5
+    return "[%s, %s]" % (to_str(sa, dps), to_str(sb, dps))
+    #dps = prec_to_dps(prec)
+    #m = mpi_mid(s, prec)
+    #d = mpf_shift(mpi_delta(s, 20), -1)
+    #return "%s +/- %s" % (to_str(m, dps), to_str(d, 3))
+
+mpi_zero = (fzero, fzero)
+mpi_one = (fone, fone)
+
+def mpi_eq(s, t):
+    return s == t
+
+def mpi_ne(s, t):
+    return s != t
+
+def mpi_lt(s, t):
+    sa, sb = s
+    ta, tb = t
+    if mpf_lt(sb, ta): return True
+    if mpf_ge(sa, tb): return False
+    return None
+
+def mpi_le(s, t):
+    sa, sb = s
+    ta, tb = t
+    if mpf_le(sb, ta): return True
+    if mpf_gt(sa, tb): return False
+    return None
+
+def mpi_gt(s, t): return mpi_lt(t, s)
+def mpi_ge(s, t): return mpi_le(t, s)
+
+def mpi_add(s, t, prec=0):
+    sa, sb = s
+    ta, tb = t
+    a = mpf_add(sa, ta, prec, round_floor)
+    b = mpf_add(sb, tb, prec, round_ceiling)
+    if a == fnan: a = fninf
+    if b == fnan: b = finf
+    return a, b
+
+def mpi_sub(s, t, prec=0):
+    sa, sb = s
+    ta, tb = t
+    a = mpf_sub(sa, tb, prec, round_floor)
+    b = mpf_sub(sb, ta, prec, round_ceiling)
+    if a == fnan: a = fninf
+    if b == fnan: b = finf
+    return a, b
+
+def mpi_delta(s, prec):
+    sa, sb = s
+    return mpf_sub(sb, sa, prec, round_up)
+
+def mpi_mid(s, prec):
+    sa, sb = s
+    return mpf_shift(mpf_add(sa, sb, prec, round_nearest), -1)
+
+def mpi_pos(s, prec):
+    sa, sb = s
+    a = mpf_pos(sa, prec, round_floor)
+    b = mpf_pos(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_neg(s, prec=0):
+    sa, sb = s
+    a = mpf_neg(sb, prec, round_floor)
+    b = mpf_neg(sa, prec, round_ceiling)
+    return a, b
+
+def mpi_abs(s, prec=0):
+    sa, sb = s
+    sas = mpf_sign(sa)
+    sbs = mpf_sign(sb)
+    # Both points nonnegative?
+    if sas >= 0:
+        a = mpf_pos(sa, prec, round_floor)
+        b = mpf_pos(sb, prec, round_ceiling)
+    # Upper point nonnegative?
+    elif sbs >= 0:
+        a = fzero
+        negsa = mpf_neg(sa)
+        if mpf_lt(negsa, sb):
+            b = mpf_pos(sb, prec, round_ceiling)
+        else:
+            b = mpf_pos(negsa, prec, round_ceiling)
+    # Both negative?
+    else:
+        a = mpf_neg(sb, prec, round_floor)
+        b = mpf_neg(sa, prec, round_ceiling)
+    return a, b
+
+# TODO: optimize
+def mpi_mul_mpf(s, t, prec):
+    return mpi_mul(s, (t, t), prec)
+
+def mpi_div_mpf(s, t, prec):
+    return mpi_div(s, (t, t), prec)
+
+def mpi_mul(s, t, prec=0):
+    sa, sb = s
+    ta, tb = t
+    sas = mpf_sign(sa)
+    sbs = mpf_sign(sb)
+    tas = mpf_sign(ta)
+    tbs = mpf_sign(tb)
+    if sas == sbs == 0:
+        # Should maybe be undefined
+        if ta == fninf or tb == finf:
+            return fninf, finf
+        return fzero, fzero
+    if tas == tbs == 0:
+        # Should maybe be undefined
+        if sa == fninf or sb == finf:
+            return fninf, finf
+        return fzero, fzero
+    if sas >= 0:
+        # positive * positive
+        if tas >= 0:
+            a = mpf_mul(sa, ta, prec, round_floor)
+            b = mpf_mul(sb, tb, prec, round_ceiling)
+            if a == fnan: a = fzero
+            if b == fnan: b = finf
+        # positive * negative
+        elif tbs <= 0:
+            a = mpf_mul(sb, ta, prec, round_floor)
+            b = mpf_mul(sa, tb, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = fzero
+        # positive * both signs
+        else:
+            a = mpf_mul(sb, ta, prec, round_floor)
+            b = mpf_mul(sb, tb, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = finf
+    elif sbs <= 0:
+        # negative * positive
+        if tas >= 0:
+            a = mpf_mul(sa, tb, prec, round_floor)
+            b = mpf_mul(sb, ta, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = fzero
+        # negative * negative
+        elif tbs <= 0:
+            a = mpf_mul(sb, tb, prec, round_floor)
+            b = mpf_mul(sa, ta, prec, round_ceiling)
+            if a == fnan: a = fzero
+            if b == fnan: b = finf
+        # negative * both signs
+        else:
+            a = mpf_mul(sa, tb, prec, round_floor)
+            b = mpf_mul(sa, ta, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = finf
+    else:
+        # General case: perform all cross-multiplications and compare
+        # Since the multiplications can be done exactly, we need only
+        # do 4 (instead of 8: two for each rounding mode)
+        cases = [mpf_mul(sa, ta), mpf_mul(sa, tb), mpf_mul(sb, ta), mpf_mul(sb, tb)]
+        if fnan in cases:
+            a, b = (fninf, finf)
+        else:
+            a, b = mpf_min_max(cases)
+            a = mpf_pos(a, prec, round_floor)
+            b = mpf_pos(b, prec, round_ceiling)
+    return a, b
+
+def mpi_square(s, prec=0):
+    sa, sb = s
+    if mpf_ge(sa, fzero):
+        a = mpf_mul(sa, sa, prec, round_floor)
+        b = mpf_mul(sb, sb, prec, round_ceiling)
+    elif mpf_le(sb, fzero):
+        a = mpf_mul(sb, sb, prec, round_floor)
+        b = mpf_mul(sa, sa, prec, round_ceiling)
+    else:
+        sa = mpf_neg(sa)
+        sa, sb = mpf_min_max([sa, sb])
+        a = fzero
+        b = mpf_mul(sb, sb, prec, round_ceiling)
+    return a, b
+
+def mpi_div(s, t, prec):
+    sa, sb = s
+    ta, tb = t
+    sas = mpf_sign(sa)
+    sbs = mpf_sign(sb)
+    tas = mpf_sign(ta)
+    tbs = mpf_sign(tb)
+    # 0 / X
+    if sas == sbs == 0:
+        # 0 / <interval containing 0>
+        if (tas < 0 and tbs > 0) or (tas == 0 or tbs == 0):
+            return fninf, finf
+        return fzero, fzero
+    # Denominator contains both negative and positive numbers;
+    # this should properly be a multi-interval, but the closest
+    # match is the entire (extended) real line
+    if tas < 0 and tbs > 0:
+        return fninf, finf
+    # Assume denominator to be nonnegative
+    if tas < 0:
+        return mpi_div(mpi_neg(s), mpi_neg(t), prec)
+    # Division by zero
+    # XXX: make sure all results make sense
+    if tas == 0:
+        # Numerator contains both signs?
+        if sas < 0 and sbs > 0:
+            return fninf, finf
+        if tas == tbs:
+            return fninf, finf
+        # Numerator positive?
+        if sas >= 0:
+            a = mpf_div(sa, tb, prec, round_floor)
+            b = finf
+        if sbs <= 0:
+            a = fninf
+            b = mpf_div(sb, tb, prec, round_ceiling)
+    # Division with positive denominator
+    # We still have to handle nans resulting from inf/0 or inf/inf
+    else:
+        # Nonnegative numerator
+        if sas >= 0:
+            a = mpf_div(sa, tb, prec, round_floor)
+            b = mpf_div(sb, ta, prec, round_ceiling)
+            if a == fnan: a = fzero
+            if b == fnan: b = finf
+        # Nonpositive numerator
+        elif sbs <= 0:
+            a = mpf_div(sa, ta, prec, round_floor)
+            b = mpf_div(sb, tb, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = fzero
+        # Numerator contains both signs?
+        else:
+            a = mpf_div(sa, ta, prec, round_floor)
+            b = mpf_div(sb, ta, prec, round_ceiling)
+            if a == fnan: a = fninf
+            if b == fnan: b = finf
+    return a, b
+
+def mpi_pi(prec):
+    a = mpf_pi(prec, round_floor)
+    b = mpf_pi(prec, round_ceiling)
+    return a, b
+
+def mpi_exp(s, prec):
+    sa, sb = s
+    # exp is monotonic
+    a = mpf_exp(sa, prec, round_floor)
+    b = mpf_exp(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_log(s, prec):
+    sa, sb = s
+    # log is monotonic
+    a = mpf_log(sa, prec, round_floor)
+    b = mpf_log(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_sqrt(s, prec):
+    sa, sb = s
+    # sqrt is monotonic
+    a = mpf_sqrt(sa, prec, round_floor)
+    b = mpf_sqrt(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_atan(s, prec):
+    sa, sb = s
+    a = mpf_atan(sa, prec, round_floor)
+    b = mpf_atan(sb, prec, round_ceiling)
+    return a, b
+
+def mpi_pow_int(s, n, prec):
+    sa, sb = s
+    if n < 0:
+        return mpi_div((fone, fone), mpi_pow_int(s, -n, prec+20), prec)
+    if n == 0:
+        return (fone, fone)
+    if n == 1:
+        return s
+    if n == 2:
+        return mpi_square(s, prec)
+    # Odd -- signs are preserved
+    if n & 1:
+        a = mpf_pow_int(sa, n, prec, round_floor)
+        b = mpf_pow_int(sb, n, prec, round_ceiling)
+    # Even -- important to ensure positivity
+    else:
+        sas = mpf_sign(sa)
+        sbs = mpf_sign(sb)
+        # Nonnegative?
+        if sas >= 0:
+            a = mpf_pow_int(sa, n, prec, round_floor)
+            b = mpf_pow_int(sb, n, prec, round_ceiling)
+        # Nonpositive?
+        elif sbs <= 0:
+            a = mpf_pow_int(sb, n, prec, round_floor)
+            b = mpf_pow_int(sa, n, prec, round_ceiling)
+        # Mixed signs?
+        else:
+            a = fzero
+            # max(-a,b)**n
+            sa = mpf_neg(sa)
+            if mpf_ge(sa, sb):
+                b = mpf_pow_int(sa, n, prec, round_ceiling)
+            else:
+                b = mpf_pow_int(sb, n, prec, round_ceiling)
+    return a, b
+
+def mpi_pow(s, t, prec):
+    ta, tb = t
+    if ta == tb and ta not in (finf, fninf):
+        if ta == from_int(to_int(ta)):
+            return mpi_pow_int(s, to_int(ta), prec)
+        if ta == fhalf:
+            return mpi_sqrt(s, prec)
+    u = mpi_log(s, prec + 20)
+    v = mpi_mul(u, t, prec + 20)
+    return mpi_exp(v, prec)
+
+def MIN(x, y):
+    if mpf_le(x, y):
+        return x
+    return y
+
+def MAX(x, y):
+    if mpf_ge(x, y):
+        return x
+    return y
+
+def cos_sin_quadrant(x, wp):
+    sign, man, exp, bc = x
+    if x == fzero:
+        return fone, fzero, 0
+    # TODO: combine evaluation code to avoid duplicate modulo
+    c, s = mpf_cos_sin(x, wp)
+    t, n, wp_ = mod_pi2(man, exp, exp+bc, 15)
+    if sign:
+        n = -1-n
+    return c, s, n
+
+def mpi_cos_sin(x, prec):
+    a, b = x
+    if a == b == fzero:
+        return (fone, fone), (fzero, fzero)
+    # Guaranteed to contain both -1 and 1
+    if (finf in x) or (fninf in x):
+        return (fnone, fone), (fnone, fone)
+    wp = prec + 20
+    ca, sa, na = cos_sin_quadrant(a, wp)
+    cb, sb, nb = cos_sin_quadrant(b, wp)
+    ca, cb = mpf_min_max([ca, cb])
+    sa, sb = mpf_min_max([sa, sb])
+    # Both functions are monotonic within one quadrant
+    if na == nb:
+        pass
+    # Guaranteed to contain both -1 and 1
+    elif nb - na >= 4:
+        return (fnone, fone), (fnone, fone)
+    else:
+        # cos has maximum between a and b
+        if na//4 != nb//4:
+            cb = fone
+        # cos has minimum
+        if (na-2)//4 != (nb-2)//4:
+            ca = fnone
+        # sin has maximum
+        if (na-1)//4 != (nb-1)//4:
+            sb = fone
+        # sin has minimum
+        if (na-3)//4 != (nb-3)//4:
+            sa = fnone
+    # Perturb to force interval rounding
+    more = from_man_exp((MPZ_ONE<<wp) + (MPZ_ONE<<10), -wp)
+    less = from_man_exp((MPZ_ONE<<wp) - (MPZ_ONE<<10), -wp)
+    def finalize(v, rounding):
+        if bool(v[0]) == (rounding == round_floor):
+            p = more
+        else:
+            p = less
+        v = mpf_mul(v, p, prec, rounding)
+        sign, man, exp, bc = v
+        if exp+bc >= 1:
+            if sign:
+                return fnone
+            return fone
+        return v
+    ca = finalize(ca, round_floor)
+    cb = finalize(cb, round_ceiling)
+    sa = finalize(sa, round_floor)
+    sb = finalize(sb, round_ceiling)
+    return (ca,cb), (sa,sb)
+
+def mpi_cos(x, prec):
+    return mpi_cos_sin(x, prec)[0]
+
+def mpi_sin(x, prec):
+    return mpi_cos_sin(x, prec)[1]
+
+def mpi_tan(x, prec):
+    cos, sin = mpi_cos_sin(x, prec+20)
+    return mpi_div(sin, cos, prec)
+
+def mpi_cot(x, prec):
+    cos, sin = mpi_cos_sin(x, prec+20)
+    return mpi_div(cos, sin, prec)
+
+def mpi_from_str_a_b(x, y, percent, prec):
+    wp = prec + 20
+    xa = from_str(x, wp, round_floor)
+    xb = from_str(x, wp, round_ceiling)
+    #ya = from_str(y, wp, round_floor)
+    y = from_str(y, wp, round_ceiling)
+    assert mpf_ge(y, fzero)
+    if percent:
+        y = mpf_mul(MAX(mpf_abs(xa), mpf_abs(xb)), y, wp, round_ceiling)
+        y = mpf_div(y, from_int(100), wp, round_ceiling)
+    a = mpf_sub(xa, y, prec, round_floor)
+    b = mpf_add(xb, y, prec, round_ceiling)
+    return a, b
+
+def mpi_from_str(s, prec):
+    """
+    Parse an interval number given as a string.
+
+    Allowed forms are
+
+    "-1.23e-27"
+        Any single decimal floating-point literal.
+    "a +- b"  or  "a (b)"
+        a is the midpoint of the interval and b is the half-width
+    "a +- b%"  or  "a (b%)"
+        a is the midpoint of the interval and the half-width
+        is b percent of a (`a \times b / 100`).
+    "[a, b]"
+        The interval indicated directly.
+    "x[y,z]e"
+        x are shared digits, y and z are unequal digits, e is the exponent.
+
+    """
+    e = ValueError("Improperly formed interval number '%s'" % s)
+    s = s.replace(" ", "")
+    wp = prec + 20
+    if "+-" in s:
+        x, y = s.split("+-")
+        return mpi_from_str_a_b(x, y, False, prec)
+    # case 2
+    elif "(" in s:
+        # Don't confuse with a complex number (x,y)
+        if s[0] == "(" or ")" not in s:
+            raise e
+        s = s.replace(")", "")
+        percent = False
+        if "%" in s:
+            if s[-1] != "%":
+                raise e
+            percent = True
+            s = s.replace("%", "")
+        x, y = s.split("(")
+        return mpi_from_str_a_b(x, y, percent, prec)
+    elif "," in s:
+        if ('[' not in s) or (']' not in s):
+            raise e
+        if s[0] == '[':
+            # case 3
+            s = s.replace("[", "")
+            s = s.replace("]", "")
+            a, b = s.split(",")
+            a = from_str(a, prec, round_floor)
+            b = from_str(b, prec, round_ceiling)
+            return a, b
+        else:
+            # case 4
+            x, y = s.split('[')
+            y, z = y.split(',')
+            if 'e' in s:
+                z, e = z.split(']')
+            else:
+                z, e = z.rstrip(']'), ''
+            a = from_str(x+y+e, prec, round_floor)
+            b = from_str(x+z+e, prec, round_ceiling)
+            return a, b
+    else:
+        a = from_str(s, prec, round_floor)
+        b = from_str(s, prec, round_ceiling)
+        return a, b
+
+def mpi_to_str(x, dps, use_spaces=True, brackets='[]', mode='brackets', error_dps=4, **kwargs):
+    """
+    Convert a mpi interval to a string.
+
+    **Arguments**
+
+    *dps*
+        decimal places to use for printing
+    *use_spaces*
+        use spaces for more readable output, defaults to true
+    *brackets*
+        pair of strings (or two-character string) giving left and right brackets
+    *mode*
+        mode of display: 'plusminus', 'percent', 'brackets' (default) or 'diff'
+    *error_dps*
+        limit the error to *error_dps* digits (mode 'plusminus and 'percent')
+
+    Additional keyword arguments are forwarded to the mpf-to-string conversion
+    for the components of the output.
+
+    **Examples**
+
+        >>> from mpmath import mpi, mp
+        >>> mp.dps = 30
+        >>> x = mpi(1, 2)._mpi_
+        >>> mpi_to_str(x, 2, mode='plusminus')
+        '1.5 +- 0.5'
+        >>> mpi_to_str(x, 2, mode='percent')
+        '1.5 (33.33%)'
+        >>> mpi_to_str(x, 2, mode='brackets')
+        '[1.0, 2.0]'
+        >>> mpi_to_str(x, 2, mode='brackets' , brackets=('<', '>'))
+        '<1.0, 2.0>'
+        >>> x = mpi('5.2582327113062393041', '5.2582327113062749951')._mpi_
+        >>> mpi_to_str(x, 15, mode='diff')
+        '5.2582327113062[4, 7]'
+        >>> mpi_to_str(mpi(0)._mpi_, 2, mode='percent')
+        '0.0 (0.0%)'
+
+    """
+    prec = dps_to_prec(dps)
+    wp = prec + 20
+    a, b = x
+    mid = mpi_mid(x, prec)
+    delta = mpi_delta(x, prec)
+    a_str = to_str(a, dps, **kwargs)
+    b_str = to_str(b, dps, **kwargs)
+    mid_str = to_str(mid, dps, **kwargs)
+    sp = ""
+    if use_spaces:
+        sp = " "
+    br1, br2 = brackets
+    if mode == 'plusminus':
+        delta_str = to_str(mpf_shift(delta,-1), dps, **kwargs)
+        s = mid_str + sp + "+-" + sp + delta_str
+    elif mode == 'percent':
+        if mid == fzero:
+            p = fzero
+        else:
+            # p = 100 * delta(x) / (2*mid(x))
+            p = mpf_mul(delta, from_int(100))
+            p = mpf_div(p, mpf_mul(mid, from_int(2)), wp)
+        s = mid_str + sp + "(" + to_str(p, error_dps) + "%)"
+    elif mode == 'brackets':
+        s = br1 + a_str + "," + sp + b_str + br2
+    elif mode == 'diff':
+        # use more digits if str(x.a) and str(x.b) are equal
+        if a_str == b_str:
+            a_str = to_str(a, dps+3, **kwargs)
+            b_str = to_str(b, dps+3, **kwargs)
+        # separate mantissa and exponent
+        a = a_str.split('e')
+        if len(a) == 1:
+            a.append('')
+        b = b_str.split('e')
+        if len(b) == 1:
+            b.append('')
+        if a[1] == b[1]:
+            if a[0] != b[0]:
+                for i in xrange(len(a[0]) + 1):
+                    if a[0][i] != b[0][i]:
+                        break
+                s = (a[0][:i] + br1 + a[0][i:] + ',' + sp + b[0][i:] + br2
+                     + 'e'*min(len(a[1]), 1) + a[1])
+            else: # no difference
+                s = a[0] + br1 + br2 + 'e'*min(len(a[1]), 1) + a[1]
+        else:
+            s = br1 + 'e'.join(a) + ',' + sp + 'e'.join(b) + br2
+    else:
+        raise ValueError("'%s' is unknown mode for printing mpi" % mode)
+    return s
+
+def mpci_add(x, y, prec):
+    a, b = x
+    c, d = y
+    return mpi_add(a, c, prec), mpi_add(b, d, prec)
+
+def mpci_sub(x, y, prec):
+    a, b = x
+    c, d = y
+    return mpi_sub(a, c, prec), mpi_sub(b, d, prec)
+
+def mpci_neg(x, prec=0):
+    a, b = x
+    return mpi_neg(a, prec), mpi_neg(b, prec)
+
+def mpci_pos(x, prec):
+    a, b = x
+    return mpi_pos(a, prec), mpi_pos(b, prec)
+
+def mpci_mul(x, y, prec):
+    # TODO: optimize for real/imag cases
+    a, b = x
+    c, d = y
+    r1 = mpi_mul(a,c)
+    r2 = mpi_mul(b,d)
+    re = mpi_sub(r1,r2,prec)
+    i1 = mpi_mul(a,d)
+    i2 = mpi_mul(b,c)
+    im = mpi_add(i1,i2,prec)
+    return re, im
+
+def mpci_div(x, y, prec):
+    # TODO: optimize for real/imag cases
+    a, b = x
+    c, d = y
+    wp = prec+20
+    m1 = mpi_square(c)
+    m2 = mpi_square(d)
+    m = mpi_add(m1,m2,wp)
+    re = mpi_add(mpi_mul(a,c), mpi_mul(b,d), wp)
+    im = mpi_sub(mpi_mul(b,c), mpi_mul(a,d), wp)
+    re = mpi_div(re, m, prec)
+    im = mpi_div(im, m, prec)
+    return re, im
+
+def mpci_exp(x, prec):
+    a, b = x
+    wp = prec+20
+    r = mpi_exp(a, wp)
+    c, s = mpi_cos_sin(b, wp)
+    a = mpi_mul(r, c, prec)
+    b = mpi_mul(r, s, prec)
+    return a, b
+
+def mpi_shift(x, n):
+    a, b = x
+    return mpf_shift(a,n), mpf_shift(b,n)
+
+def mpi_cosh_sinh(x, prec):
+    # TODO: accuracy for small x
+    wp = prec+20
+    e1 = mpi_exp(x, wp)
+    e2 = mpi_div(mpi_one, e1, wp)
+    c = mpi_add(e1, e2, prec)
+    s = mpi_sub(e1, e2, prec)
+    c = mpi_shift(c, -1)
+    s = mpi_shift(s, -1)
+    return c, s
+
+def mpci_cos(x, prec):
+    a, b = x
+    wp = prec+10
+    c, s = mpi_cos_sin(a, wp)
+    ch, sh = mpi_cosh_sinh(b, wp)
+    re = mpi_mul(c, ch, prec)
+    im = mpi_mul(s, sh, prec)
+    return re, mpi_neg(im)
+
+def mpci_sin(x, prec):
+    a, b = x
+    wp = prec+10
+    c, s = mpi_cos_sin(a, wp)
+    ch, sh = mpi_cosh_sinh(b, wp)
+    re = mpi_mul(s, ch, prec)
+    im = mpi_mul(c, sh, prec)
+    return re, im
+
+def mpci_abs(x, prec):
+    a, b = x
+    if a == mpi_zero:
+        return mpi_abs(b)
+    if b == mpi_zero:
+        return mpi_abs(a)
+    # Important: nonnegative
+    a = mpi_square(a)
+    b = mpi_square(b)
+    t = mpi_add(a, b, prec+20)
+    return mpi_sqrt(t, prec)
+
+def mpi_atan2(y, x, prec):
+    ya, yb = y
+    xa, xb = x
+    # Constrained to the real line
+    if ya == yb == fzero:
+        if mpf_ge(xa, fzero):
+            return mpi_zero
+        return mpi_pi(prec)
+    # Right half-plane
+    if mpf_ge(xa, fzero):
+        if mpf_ge(ya, fzero):
+            a = mpf_atan2(ya, xb, prec, round_floor)
+        else:
+            a = mpf_atan2(ya, xa, prec, round_floor)
+        if mpf_ge(yb, fzero):
+            b = mpf_atan2(yb, xa, prec, round_ceiling)
+        else:
+            b = mpf_atan2(yb, xb, prec, round_ceiling)
+    # Upper half-plane
+    elif mpf_ge(ya, fzero):
+        b = mpf_atan2(ya, xa, prec, round_ceiling)
+        if mpf_le(xb, fzero):
+            a = mpf_atan2(yb, xb, prec, round_floor)
+        else:
+            a = mpf_atan2(ya, xb, prec, round_floor)
+    # Lower half-plane
+    elif mpf_le(yb, fzero):
+        a = mpf_atan2(yb, xa, prec, round_floor)
+        if mpf_le(xb, fzero):
+            b = mpf_atan2(ya, xb, prec, round_ceiling)
+        else:
+            b = mpf_atan2(yb, xb, prec, round_ceiling)
+    # Covering the origin
+    else:
+        b = mpf_pi(prec, round_ceiling)
+        a = mpf_neg(b)
+    return a, b
+
+def mpci_arg(z, prec):
+    x, y = z
+    return mpi_atan2(y, x, prec)
+
+def mpci_log(z, prec):
+    x, y = z
+    re = mpi_log(mpci_abs(z, prec+20), prec)
+    im = mpci_arg(z, prec)
+    return re, im
+
+def mpci_pow(x, y, prec):
+    # TODO: recognize/speed up real cases, integer y
+    yre, yim = y
+    if yim == mpi_zero:
+        ya, yb = yre
+        if ya == yb:
+            sign, man, exp, bc = yb
+            if man and exp >= 0:
+                return mpci_pow_int(x, (-1)**sign * int(man<<exp), prec)
+            # x^0
+            if yb == fzero:
+                return mpci_pow_int(x, 0, prec)
+    wp = prec+20
+    return mpci_exp(mpci_mul(y, mpci_log(x, wp), wp), prec)
+
+def mpci_square(x, prec):
+    a, b = x
+    # (a+bi)^2 = (a^2-b^2) + 2abi
+    re = mpi_sub(mpi_square(a), mpi_square(b), prec)
+    im = mpi_mul(a, b, prec)
+    im = mpi_shift(im, 1)
+    return re, im
+
+def mpci_pow_int(x, n, prec):
+    if n < 0:
+        return mpci_div((mpi_one,mpi_zero), mpci_pow_int(x, -n, prec+20), prec)
+    if n == 0:
+        return mpi_one, mpi_zero
+    if n == 1:
+        return mpci_pos(x, prec)
+    if n == 2:
+        return mpci_square(x, prec)
+    wp = prec + 20
+    result = (mpi_one, mpi_zero)
+    while n:
+        if n & 1:
+            result = mpci_mul(result, x, wp)
+            n -= 1
+        x = mpci_square(x, wp)
+        n >>= 1
+    return mpci_pos(result, prec)
+
+gamma_min_a = from_float(1.46163214496)
+gamma_min_b = from_float(1.46163214497)
+gamma_min = (gamma_min_a, gamma_min_b)
+gamma_mono_imag_a = from_float(-1.1)
+gamma_mono_imag_b = from_float(1.1)
+
+def mpi_overlap(x, y):
+    a, b = x
+    c, d = y
+    if mpf_lt(d, a): return False
+    if mpf_gt(c, b): return False
+    return True
+
+# type = 0 -- gamma
+# type = 1 -- factorial
+# type = 2 -- 1/gamma
+# type = 3 -- log-gamma
+
+def mpi_gamma(z, prec, type=0):
+    a, b = z
+    wp = prec+20
+
+    if type == 1:
+        return mpi_gamma(mpi_add(z, mpi_one, wp), prec, 0)
+
+    # increasing
+    if mpf_gt(a, gamma_min_b):
+        if type == 0:
+            c = mpf_gamma(a, prec, round_floor)
+            d = mpf_gamma(b, prec, round_ceiling)
+        elif type == 2:
+            c = mpf_rgamma(b, prec, round_floor)
+            d = mpf_rgamma(a, prec, round_ceiling)
+        elif type == 3:
+            c = mpf_loggamma(a, prec, round_floor)
+            d = mpf_loggamma(b, prec, round_ceiling)
+    # decreasing
+    elif mpf_gt(a, fzero) and mpf_lt(b, gamma_min_a):
+        if type == 0:
+            c = mpf_gamma(b, prec, round_floor)
+            d = mpf_gamma(a, prec, round_ceiling)
+        elif type == 2:
+            c = mpf_rgamma(a, prec, round_floor)
+            d = mpf_rgamma(b, prec, round_ceiling)
+        elif type == 3:
+            c = mpf_loggamma(b, prec, round_floor)
+            d = mpf_loggamma(a, prec, round_ceiling)
+    else:
+        # TODO: reflection formula
+        znew = mpi_add(z, mpi_one, wp)
+        if type == 0: return mpi_div(mpi_gamma(znew, prec+2, 0), z, prec)
+        if type == 2: return mpi_mul(mpi_gamma(znew, prec+2, 2), z, prec)
+        if type == 3: return mpi_sub(mpi_gamma(znew, prec+2, 3), mpi_log(z, prec+2), prec)
+    return c, d
+
+def mpci_gamma(z, prec, type=0):
+    (a1,a2), (b1,b2) = z
+
+    # Real case
+    if b1 == b2 == fzero and (type != 3 or mpf_gt(a1,fzero)):
+        return mpi_gamma(z, prec, type), mpi_zero
+
+    # Estimate precision
+    wp = prec+20
+    if type != 3:
+        amag = a2[2]+a2[3]
+        bmag = b2[2]+b2[3]
+        if a2 != fzero:
+            mag = max(amag, bmag)
+        else:
+            mag = bmag
+        an = abs(to_int(a2))
+        bn = abs(to_int(b2))
+        absn = max(an, bn)
+        gamma_size = max(0,absn*mag)
+        wp += bitcount(gamma_size)
+
+    # Assume type != 1
+    if type == 1:
+        (a1,a2) = mpi_add((a1,a2), mpi_one, wp); z = (a1,a2), (b1,b2)
+        type = 0
+
+    # Avoid non-monotonic region near the negative real axis
+    if mpf_lt(a1, gamma_min_b):
+        if mpi_overlap((b1,b2), (gamma_mono_imag_a, gamma_mono_imag_b)):
+            # TODO: reflection formula
+            #if mpf_lt(a2, mpf_shift(fone,-1)):
+            #    znew = mpci_sub((mpi_one,mpi_zero),z,wp)
+            #    ...
+            # Recurrence:
+            # gamma(z) = gamma(z+1)/z
+            znew = mpi_add((a1,a2), mpi_one, wp), (b1,b2)
+            if type == 0: return mpci_div(mpci_gamma(znew, prec+2, 0), z, prec)
+            if type == 2: return mpci_mul(mpci_gamma(znew, prec+2, 2), z, prec)
+            if type == 3: return mpci_sub(mpci_gamma(znew, prec+2, 3), mpci_log(z,prec+2), prec)
+
+    # Use monotonicity (except for a small region close to the
+    # origin and near poles)
+    # upper half-plane
+    if mpf_ge(b1, fzero):
+        minre = mpc_loggamma((a1,b2), wp, round_floor)
+        maxre = mpc_loggamma((a2,b1), wp, round_ceiling)
+        minim = mpc_loggamma((a1,b1), wp, round_floor)
+        maxim = mpc_loggamma((a2,b2), wp, round_ceiling)
+    # lower half-plane
+    elif mpf_le(b2, fzero):
+        minre = mpc_loggamma((a1,b1), wp, round_floor)
+        maxre = mpc_loggamma((a2,b2), wp, round_ceiling)
+        minim = mpc_loggamma((a2,b1), wp, round_floor)
+        maxim = mpc_loggamma((a1,b2), wp, round_ceiling)
+    # crosses real axis
+    else:
+        maxre = mpc_loggamma((a2,fzero), wp, round_ceiling)
+        # stretches more into the lower half-plane
+        if mpf_gt(mpf_neg(b1), b2):
+            minre = mpc_loggamma((a1,b1), wp, round_ceiling)
+        else:
+            minre = mpc_loggamma((a1,b2), wp, round_ceiling)
+        minim = mpc_loggamma((a2,b1), wp, round_floor)
+        maxim = mpc_loggamma((a2,b2), wp, round_floor)
+
+    w = (minre[0], maxre[0]), (minim[1], maxim[1])
+    if type == 3:
+        return mpi_pos(w[0], prec), mpi_pos(w[1], prec)
+    if type == 2:
+        w = mpci_neg(w)
+    return mpci_exp(w, prec)
+
+def mpi_loggamma(z, prec): return mpi_gamma(z, prec, type=3)
+def mpci_loggamma(z, prec): return mpci_gamma(z, prec, type=3)
+
+def mpi_rgamma(z, prec): return mpi_gamma(z, prec, type=2)
+def mpci_rgamma(z, prec): return mpci_gamma(z, prec, type=2)
+
+def mpi_factorial(z, prec): return mpi_gamma(z, prec, type=1)
+def mpci_factorial(z, prec): return mpci_gamma(z, prec, type=1)

lib/python3.11/site-packages/mpmath/math2.py

@@ -0,0 +1,672 @@
+"""
+This module complements the math and cmath builtin modules by providing
+fast machine precision versions of some additional functions (gamma, ...)
+and wrapping math/cmath functions so that they can be called with either
+real or complex arguments.
+"""
+
+import operator
+import math
+import cmath
+
+# Irrational (?) constants
+pi = 3.1415926535897932385
+e = 2.7182818284590452354
+sqrt2 = 1.4142135623730950488
+sqrt5 = 2.2360679774997896964
+phi = 1.6180339887498948482
+ln2 = 0.69314718055994530942
+ln10 = 2.302585092994045684
+euler = 0.57721566490153286061
+catalan = 0.91596559417721901505
+khinchin = 2.6854520010653064453
+apery = 1.2020569031595942854
+
+logpi = 1.1447298858494001741
+
+def _mathfun_real(f_real, f_complex):
+    def f(x, **kwargs):
+        if type(x) is float:
+            return f_real(x)
+        if type(x) is complex:
+            return f_complex(x)
+        try:
+            x = float(x)
+            return f_real(x)
+        except (TypeError, ValueError):
+            x = complex(x)
+            return f_complex(x)
+    f.__name__ = f_real.__name__
+    return f
+
+def _mathfun(f_real, f_complex):
+    def f(x, **kwargs):
+        if type(x) is complex:
+            return f_complex(x)
+        try:
+            return f_real(float(x))
+        except (TypeError, ValueError):
+            return f_complex(complex(x))
+    f.__name__ = f_real.__name__
+    return f
+
+def _mathfun_n(f_real, f_complex):
+    def f(*args, **kwargs):
+        try:
+            return f_real(*(float(x) for x in args))
+        except (TypeError, ValueError):
+            return f_complex(*(complex(x) for x in args))
+    f.__name__ = f_real.__name__
+    return f
+
+# Workaround for non-raising log and sqrt in Python 2.5 and 2.4
+# on Unix system
+try:
+    math.log(-2.0)
+    def math_log(x):
+        if x <= 0.0:
+            raise ValueError("math domain error")
+        return math.log(x)
+    def math_sqrt(x):
+        if x < 0.0:
+            raise ValueError("math domain error")
+        return math.sqrt(x)
+except (ValueError, TypeError):
+    math_log = math.log
+    math_sqrt = math.sqrt
+
+pow = _mathfun_n(operator.pow, lambda x, y: complex(x)**y)
+log = _mathfun_n(math_log, cmath.log)
+sqrt = _mathfun(math_sqrt, cmath.sqrt)
+exp = _mathfun_real(math.exp, cmath.exp)
+
+cos = _mathfun_real(math.cos, cmath.cos)
+sin = _mathfun_real(math.sin, cmath.sin)
+tan = _mathfun_real(math.tan, cmath.tan)
+
+acos = _mathfun(math.acos, cmath.acos)
+asin = _mathfun(math.asin, cmath.asin)
+atan = _mathfun_real(math.atan, cmath.atan)
+
+cosh = _mathfun_real(math.cosh, cmath.cosh)
+sinh = _mathfun_real(math.sinh, cmath.sinh)
+tanh = _mathfun_real(math.tanh, cmath.tanh)
+
+floor = _mathfun_real(math.floor,
+    lambda z: complex(math.floor(z.real), math.floor(z.imag)))
+ceil = _mathfun_real(math.ceil,
+    lambda z: complex(math.ceil(z.real), math.ceil(z.imag)))
+
+
+cos_sin = _mathfun_real(lambda x: (math.cos(x), math.sin(x)),
+                        lambda z: (cmath.cos(z), cmath.sin(z)))
+
+cbrt = _mathfun(lambda x: x**(1./3), lambda z: z**(1./3))
+
+def nthroot(x, n):
+    r = 1./n
+    try:
+        return float(x) ** r
+    except (ValueError, TypeError):
+        return complex(x) ** r
+
+def _sinpi_real(x):
+    if x < 0:
+        return -_sinpi_real(-x)
+    n, r = divmod(x, 0.5)
+    r *= pi
+    n %= 4
+    if n == 0: return math.sin(r)
+    if n == 1: return math.cos(r)
+    if n == 2: return -math.sin(r)
+    if n == 3: return -math.cos(r)
+
+def _cospi_real(x):
+    if x < 0:
+        x = -x
+    n, r = divmod(x, 0.5)
+    r *= pi
+    n %= 4
+    if n == 0: return math.cos(r)
+    if n == 1: return -math.sin(r)
+    if n == 2: return -math.cos(r)
+    if n == 3: return math.sin(r)
+
+def _sinpi_complex(z):
+    if z.real < 0:
+        return -_sinpi_complex(-z)
+    n, r = divmod(z.real, 0.5)
+    z = pi*complex(r, z.imag)
+    n %= 4
+    if n == 0: return cmath.sin(z)
+    if n == 1: return cmath.cos(z)
+    if n == 2: return -cmath.sin(z)
+    if n == 3: return -cmath.cos(z)
+
+def _cospi_complex(z):
+    if z.real < 0:
+        z = -z
+    n, r = divmod(z.real, 0.5)
+    z = pi*complex(r, z.imag)
+    n %= 4
+    if n == 0: return cmath.cos(z)
+    if n == 1: return -cmath.sin(z)
+    if n == 2: return -cmath.cos(z)
+    if n == 3: return cmath.sin(z)
+
+cospi = _mathfun_real(_cospi_real, _cospi_complex)
+sinpi = _mathfun_real(_sinpi_real, _sinpi_complex)
+
+def tanpi(x):
+    try:
+        return sinpi(x) / cospi(x)
+    except OverflowError:
+        if complex(x).imag > 10:
+            return 1j
+        if complex(x).imag < 10:
+            return -1j
+        raise
+
+def cotpi(x):
+    try:
+        return cospi(x) / sinpi(x)
+    except OverflowError:
+        if complex(x).imag > 10:
+            return -1j
+        if complex(x).imag < 10:
+            return 1j
+        raise
+
+INF = 1e300*1e300
+NINF = -INF
+NAN = INF-INF
+EPS = 2.2204460492503131e-16
+
+_exact_gamma = (INF, 1.0, 1.0, 2.0, 6.0, 24.0, 120.0, 720.0, 5040.0, 40320.0,
+  362880.0, 3628800.0, 39916800.0, 479001600.0, 6227020800.0, 87178291200.0,
+  1307674368000.0, 20922789888000.0, 355687428096000.0, 6402373705728000.0,
+  121645100408832000.0, 2432902008176640000.0)
+
+_max_exact_gamma = len(_exact_gamma)-1
+
+# Lanczos coefficients used by the GNU Scientific Library
+_lanczos_g = 7
+_lanczos_p = (0.99999999999980993, 676.5203681218851, -1259.1392167224028,
+     771.32342877765313, -176.61502916214059, 12.507343278686905,
+     -0.13857109526572012, 9.9843695780195716e-6, 1.5056327351493116e-7)
+
+def _gamma_real(x):
+    _intx = int(x)
+    if _intx == x:
+        if _intx <= 0:
+            #return (-1)**_intx * INF
+            raise ZeroDivisionError("gamma function pole")
+        if _intx <= _max_exact_gamma:
+            return _exact_gamma[_intx]
+    if x < 0.5:
+        # TODO: sinpi
+        return pi / (_sinpi_real(x)*_gamma_real(1-x))
+    else:
+        x -= 1.0
+        r = _lanczos_p[0]
+        for i in range(1, _lanczos_g+2):
+            r += _lanczos_p[i]/(x+i)
+        t = x + _lanczos_g + 0.5
+        return 2.506628274631000502417 * t**(x+0.5) * math.exp(-t) * r
+
+def _gamma_complex(x):
+    if not x.imag:
+        return complex(_gamma_real(x.real))
+    if x.real < 0.5:
+        # TODO: sinpi
+        return pi / (_sinpi_complex(x)*_gamma_complex(1-x))
+    else:
+        x -= 1.0
+        r = _lanczos_p[0]
+        for i in range(1, _lanczos_g+2):
+            r += _lanczos_p[i]/(x+i)
+        t = x + _lanczos_g + 0.5
+        return 2.506628274631000502417 * t**(x+0.5) * cmath.exp(-t) * r
+
+gamma = _mathfun_real(_gamma_real, _gamma_complex)
+
+def rgamma(x):
+    try:
+        return 1./gamma(x)
+    except ZeroDivisionError:
+        return x*0.0
+
+def factorial(x):
+    return gamma(x+1.0)
+
+def arg(x):
+    if type(x) is float:
+        return math.atan2(0.0,x)
+    return math.atan2(x.imag,x.real)
+
+# XXX: broken for negatives
+def loggamma(x):
+    if type(x) not in (float, complex):
+        try:
+            x = float(x)
+        except (ValueError, TypeError):
+            x = complex(x)
+    try:
+        xreal = x.real
+        ximag = x.imag
+    except AttributeError:   # py2.5
+        xreal = x
+        ximag = 0.0
+    # Reflection formula
+    # http://functions.wolfram.com/GammaBetaErf/LogGamma/16/01/01/0003/
+    if xreal < 0.0:
+        if abs(x) < 0.5:
+            v = log(gamma(x))
+            if ximag == 0:
+                v = v.conjugate()
+            return v
+        z = 1-x
+        try:
+            re = z.real
+            im = z.imag
+        except AttributeError:   # py2.5
+            re = z
+            im = 0.0
+        refloor = floor(re)
+        if im == 0.0:
+            imsign = 0
+        elif im < 0.0:
+            imsign = -1
+        else:
+            imsign = 1
+        return (-pi*1j)*abs(refloor)*(1-abs(imsign)) + logpi - \
+            log(sinpi(z-refloor)) - loggamma(z) + 1j*pi*refloor*imsign
+    if x == 1.0 or x == 2.0:
+        return x*0
+    p = 0.
+    while abs(x) < 11:
+        p -= log(x)
+        x += 1.0
+    s = 0.918938533204672742 + (x-0.5)*log(x) - x
+    r = 1./x
+    r2 = r*r
+    s += 0.083333333333333333333*r; r *= r2
+    s += -0.0027777777777777777778*r; r *= r2
+    s += 0.00079365079365079365079*r; r *= r2
+    s += -0.0005952380952380952381*r; r *= r2
+    s += 0.00084175084175084175084*r; r *= r2
+    s += -0.0019175269175269175269*r; r *= r2
+    s += 0.0064102564102564102564*r; r *= r2
+    s += -0.02955065359477124183*r
+    return s + p
+
+_psi_coeff = [
+0.083333333333333333333,
+-0.0083333333333333333333,
+0.003968253968253968254,
+-0.0041666666666666666667,
+0.0075757575757575757576,
+-0.021092796092796092796,
+0.083333333333333333333,
+-0.44325980392156862745,
+3.0539543302701197438,
+-26.456212121212121212]
+
+def _digamma_real(x):
+    _intx = int(x)
+    if _intx == x:
+        if _intx <= 0:
+            raise ZeroDivisionError("polygamma pole")
+    if x < 0.5:
+        x = 1.0-x
+        s = pi*cotpi(x)
+    else:
+        s = 0.0
+    while x < 10.0:
+        s -= 1.0/x
+        x += 1.0
+    x2 = x**-2
+    t = x2
+    for c in _psi_coeff:
+        s -= c*t
+        if t < 1e-20:
+            break
+        t *= x2
+    return s + math_log(x) - 0.5/x
+
+def _digamma_complex(x):
+    if not x.imag:
+        return complex(_digamma_real(x.real))
+    if x.real < 0.5:
+        x = 1.0-x
+        s = pi*cotpi(x)
+    else:
+        s = 0.0
+    while abs(x) < 10.0:
+        s -= 1.0/x
+        x += 1.0
+    x2 = x**-2
+    t = x2
+    for c in _psi_coeff:
+        s -= c*t
+        if abs(t) < 1e-20:
+            break
+        t *= x2
+    return s + cmath.log(x) - 0.5/x
+
+digamma = _mathfun_real(_digamma_real, _digamma_complex)
+
+# TODO: could implement complex erf and erfc here. Need
+# to find an accurate method (avoiding cancellation)
+# for approx. 1 < abs(x) < 9.
+
+_erfc_coeff_P = [
+    1.0000000161203922312,
+    2.1275306946297962644,
+    2.2280433377390253297,
+    1.4695509105618423961,
+    0.66275911699770787537,
+    0.20924776504163751585,
+    0.045459713768411264339,
+    0.0063065951710717791934,
+    0.00044560259661560421715][::-1]
+
+_erfc_coeff_Q = [
+    1.0000000000000000000,
+    3.2559100272784894318,
+    4.9019435608903239131,
+    4.4971472894498014205,
+    2.7845640601891186528,
+    1.2146026030046904138,
+    0.37647108453729465912,
+    0.080970149639040548613,
+    0.011178148899483545902,
+    0.00078981003831980423513][::-1]
+
+def _polyval(coeffs, x):
+    p = coeffs[0]
+    for c in coeffs[1:]:
+        p = c + x*p
+    return p
+
+def _erf_taylor(x):
+    # Taylor series assuming 0 <= x <= 1
+    x2 = x*x
+    s = t = x
+    n = 1
+    while abs(t) > 1e-17:
+        t *= x2/n
+        s -= t/(n+n+1)
+        n += 1
+        t *= x2/n
+        s += t/(n+n+1)
+        n += 1
+    return 1.1283791670955125739*s
+
+def _erfc_mid(x):
+    # Rational approximation assuming 0 <= x <= 9
+    return exp(-x*x)*_polyval(_erfc_coeff_P,x)/_polyval(_erfc_coeff_Q,x)
+
+def _erfc_asymp(x):
+    # Asymptotic expansion assuming x >= 9
+    x2 = x*x
+    v = exp(-x2)/x*0.56418958354775628695
+    r = t = 0.5 / x2
+    s = 1.0
+    for n in range(1,22,4):
+        s -= t
+        t *= r * (n+2)
+        s += t
+        t *= r * (n+4)
+        if abs(t) < 1e-17:
+            break
+    return s * v
+
+def erf(x):
+    """
+    erf of a real number.
+    """
+    x = float(x)
+    if x != x:
+        return x
+    if x < 0.0:
+        return -erf(-x)
+    if x >= 1.0:
+        if x >= 6.0:
+            return 1.0
+        return 1.0 - _erfc_mid(x)
+    return _erf_taylor(x)
+
+def erfc(x):
+    """
+    erfc of a real number.
+    """
+    x = float(x)
+    if x != x:
+        return x
+    if x < 0.0:
+        if x < -6.0:
+            return 2.0
+        return 2.0-erfc(-x)
+    if x > 9.0:
+        return _erfc_asymp(x)
+    if x >= 1.0:
+        return _erfc_mid(x)
+    return 1.0 - _erf_taylor(x)
+
+gauss42 = [\
+(0.99839961899006235, 0.0041059986046490839),
+(-0.99839961899006235, 0.0041059986046490839),
+(0.9915772883408609, 0.009536220301748501),
+(-0.9915772883408609,0.009536220301748501),
+(0.97934250806374812, 0.014922443697357493),
+(-0.97934250806374812, 0.014922443697357493),
+(0.96175936533820439,0.020227869569052644),
+(-0.96175936533820439, 0.020227869569052644),
+(0.93892355735498811, 0.025422959526113047),
+(-0.93892355735498811,0.025422959526113047),
+(0.91095972490412735, 0.030479240699603467),
+(-0.91095972490412735, 0.030479240699603467),
+(0.87802056981217269,0.03536907109759211),
+(-0.87802056981217269, 0.03536907109759211),
+(0.8402859832618168, 0.040065735180692258),
+(-0.8402859832618168,0.040065735180692258),
+(0.7979620532554873, 0.044543577771965874),
+(-0.7979620532554873, 0.044543577771965874),
+(0.75127993568948048,0.048778140792803244),
+(-0.75127993568948048, 0.048778140792803244),
+(0.70049459055617114, 0.052746295699174064),
+(-0.70049459055617114,0.052746295699174064),
+(0.64588338886924779, 0.056426369358018376),
+(-0.64588338886924779, 0.056426369358018376),
+(0.58774459748510932, 0.059798262227586649),
+(-0.58774459748510932, 0.059798262227586649),
+(0.5263957499311922, 0.062843558045002565),
+(-0.5263957499311922, 0.062843558045002565),
+(0.46217191207042191, 0.065545624364908975),
+(-0.46217191207042191, 0.065545624364908975),
+(0.39542385204297503, 0.067889703376521934),
+(-0.39542385204297503, 0.067889703376521934),
+(0.32651612446541151, 0.069862992492594159),
+(-0.32651612446541151, 0.069862992492594159),
+(0.25582507934287907, 0.071454714265170971),
+(-0.25582507934287907, 0.071454714265170971),
+(0.18373680656485453, 0.072656175243804091),
+(-0.18373680656485453, 0.072656175243804091),
+(0.11064502720851986, 0.073460813453467527),
+(-0.11064502720851986, 0.073460813453467527),
+(0.036948943165351772, 0.073864234232172879),
+(-0.036948943165351772, 0.073864234232172879)]
+
+EI_ASYMP_CONVERGENCE_RADIUS = 40.0
+
+def ei_asymp(z, _e1=False):
+    r = 1./z
+    s = t = 1.0
+    k = 1
+    while 1:
+        t *= k*r
+        s += t
+        if abs(t) < 1e-16:
+            break
+        k += 1
+    v = s*exp(z)/z
+    if _e1:
+        if type(z) is complex:
+            zreal = z.real
+            zimag = z.imag
+        else:
+            zreal = z
+            zimag = 0.0
+        if zimag == 0.0 and zreal > 0.0:
+            v += pi*1j
+    else:
+        if type(z) is complex:
+            if z.imag > 0:
+                v += pi*1j
+            if z.imag < 0:
+                v -= pi*1j
+    return v
+
+def ei_taylor(z, _e1=False):
+    s = t = z
+    k = 2
+    while 1:
+        t = t*z/k
+        term = t/k
+        if abs(term) < 1e-17:
+            break
+        s += term
+        k += 1
+    s += euler
+    if _e1:
+        s += log(-z)
+    else:
+        if type(z) is float or z.imag == 0.0:
+            s += math_log(abs(z))
+        else:
+            s += cmath.log(z)
+    return s
+
+def ei(z, _e1=False):
+    typez = type(z)
+    if typez not in (float, complex):
+        try:
+            z = float(z)
+            typez = float
+        except (TypeError, ValueError):
+            z = complex(z)
+            typez = complex
+    if not z:
+        return -INF
+    absz = abs(z)
+    if absz > EI_ASYMP_CONVERGENCE_RADIUS:
+        return ei_asymp(z, _e1)
+    elif absz <= 2.0 or (typez is float and z > 0.0):
+        return ei_taylor(z, _e1)
+    # Integrate, starting from whichever is smaller of a Taylor
+    # series value or an asymptotic series value
+    if typez is complex and z.real > 0.0:
+        zref = z / absz
+        ref = ei_taylor(zref, _e1)
+    else:
+        zref = EI_ASYMP_CONVERGENCE_RADIUS * z / absz
+        ref = ei_asymp(zref, _e1)
+    C = (zref-z)*0.5
+    D = (zref+z)*0.5
+    s = 0.0
+    if type(z) is complex:
+        _exp = cmath.exp
+    else:
+        _exp = math.exp
+    for x,w in gauss42:
+        t = C*x+D
+        s += w*_exp(t)/t
+    ref -= C*s
+    return ref
+
+def e1(z):
+    # hack to get consistent signs if the imaginary part if 0
+    # and signed
+    typez = type(z)
+    if type(z) not in (float, complex):
+        try:
+            z = float(z)
+            typez = float
+        except (TypeError, ValueError):
+            z = complex(z)
+            typez = complex
+    if typez is complex and not z.imag:
+        z = complex(z.real, 0.0)
+    # end hack
+    return -ei(-z, _e1=True)
+
+_zeta_int = [\
+-0.5,
+0.0,
+1.6449340668482264365,1.2020569031595942854,1.0823232337111381915,
+1.0369277551433699263,1.0173430619844491397,1.0083492773819228268,
+1.0040773561979443394,1.0020083928260822144,1.0009945751278180853,
+1.0004941886041194646,1.0002460865533080483,1.0001227133475784891,
+1.0000612481350587048,1.0000305882363070205,1.0000152822594086519,
+1.0000076371976378998,1.0000038172932649998,1.0000019082127165539,
+1.0000009539620338728,1.0000004769329867878,1.0000002384505027277,
+1.0000001192199259653,1.0000000596081890513,1.0000000298035035147,
+1.0000000149015548284]
+
+_zeta_P = [-3.50000000087575873, -0.701274355654678147,
+-0.0672313458590012612, -0.00398731457954257841,
+-0.000160948723019303141, -4.67633010038383371e-6,
+-1.02078104417700585e-7, -1.68030037095896287e-9,
+-1.85231868742346722e-11][::-1]
+
+_zeta_Q = [1.00000000000000000, -0.936552848762465319,
+-0.0588835413263763741, -0.00441498861482948666,
+-0.000143416758067432622, -5.10691659585090782e-6,
+-9.58813053268913799e-8, -1.72963791443181972e-9,
+-1.83527919681474132e-11][::-1]
+
+_zeta_1 = [3.03768838606128127e-10, -1.21924525236601262e-8,
+2.01201845887608893e-7, -1.53917240683468381e-6,
+-5.09890411005967954e-7, 0.000122464707271619326,
+-0.000905721539353130232, -0.00239315326074843037,
+0.084239750013159168, 0.418938517907442414, 0.500000001921884009]
+
+_zeta_0 = [-3.46092485016748794e-10, -6.42610089468292485e-9,
+1.76409071536679773e-7, -1.47141263991560698e-6, -6.38880222546167613e-7,
+0.000122641099800668209, -0.000905894913516772796, -0.00239303348507992713,
+0.0842396947501199816, 0.418938533204660256, 0.500000000000000052]
+
+def zeta(s):
+    """
+    Riemann zeta function, real argument
+    """
+    if not isinstance(s, (float, int)):
+        try:
+            s = float(s)
+        except (ValueError, TypeError):
+            try:
+                s = complex(s)
+                if not s.imag:
+                    return complex(zeta(s.real))
+            except (ValueError, TypeError):
+                pass
+            raise NotImplementedError
+    if s == 1:
+        raise ValueError("zeta(1) pole")
+    if s >= 27:
+        return 1.0 + 2.0**(-s) + 3.0**(-s)
+    n = int(s)
+    if n == s:
+        if n >= 0:
+            return _zeta_int[n]
+        if not (n % 2):
+            return 0.0
+    if s <= 0.0:
+        return 2.**s*pi**(s-1)*_sinpi_real(0.5*s)*_gamma_real(1-s)*zeta(1-s)
+    if s <= 2.0:
+        if s <= 1.0:
+            return _polyval(_zeta_0,s)/(s-1)
+        return _polyval(_zeta_1,s)/(s-1)
+    z = _polyval(_zeta_P,s) / _polyval(_zeta_Q,s)
+    return 1.0 + 2.0**(-s) + 3.0**(-s) + 4.0**(-s)*z

lib/python3.11/site-packages/mpmath/matrices/__init__.py

@@ -0,0 +1,2 @@
+from . import eigen           # to set methods
+from . import eigen_symmetric # to set methods

lib/python3.11/site-packages/mpmath/matrices/calculus.py

@@ -0,0 +1,531 @@
+from ..libmp.backend import xrange
+
+# TODO: should use diagonalization-based algorithms
+
+class MatrixCalculusMethods(object):
+
+    def _exp_pade(ctx, a):
+        """
+        Exponential of a matrix using Pade approximants.
+
+        See G. H. Golub, C. F. van Loan 'Matrix Computations',
+        third Ed., page 572
+
+        TODO:
+         - find a good estimate for q
+         - reduce the number of matrix multiplications to improve
+           performance
+        """
+        def eps_pade(p):
+            return ctx.mpf(2)**(3-2*p) * \
+                ctx.factorial(p)**2/(ctx.factorial(2*p)**2 * (2*p + 1))
+        q = 4
+        extraq = 8
+        while 1:
+            if eps_pade(q) < ctx.eps:
+                break
+            q += 1
+        q += extraq
+        j = int(max(1, ctx.mag(ctx.mnorm(a,'inf'))))
+        extra = q
+        prec = ctx.prec
+        ctx.dps += extra + 3
+        try:
+            a = a/2**j
+            na = a.rows
+            den = ctx.eye(na)
+            num = ctx.eye(na)
+            x = ctx.eye(na)
+            c = ctx.mpf(1)
+            for k in range(1, q+1):
+                c *= ctx.mpf(q - k + 1)/((2*q - k + 1) * k)
+                x = a*x
+                cx = c*x
+                num += cx
+                den += (-1)**k * cx
+            f = ctx.lu_solve_mat(den, num)
+            for k in range(j):
+                f = f*f
+        finally:
+            ctx.prec = prec
+        return f*1
+
+    def expm(ctx, A, method='taylor'):
+        r"""
+        Computes the matrix exponential of a square matrix `A`, which is defined
+        by the power series
+
+        .. math ::
+
+            \exp(A) = I + A + \frac{A^2}{2!} + \frac{A^3}{3!} + \ldots
+
+        With method='taylor', the matrix exponential is computed
+        using the Taylor series. With method='pade', Pade approximants
+        are used instead.
+
+        **Examples**
+
+        Basic examples::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> expm(zeros(3))
+            [1.0  0.0  0.0]
+            [0.0  1.0  0.0]
+            [0.0  0.0  1.0]
+            >>> expm(eye(3))
+            [2.71828182845905               0.0               0.0]
+            [             0.0  2.71828182845905               0.0]
+            [             0.0               0.0  2.71828182845905]
+            >>> expm([[1,1,0],[1,0,1],[0,1,0]])
+            [ 3.86814500615414  2.26812870852145  0.841130841230196]
+            [ 2.26812870852145  2.44114713886289   1.42699786729125]
+            [0.841130841230196  1.42699786729125    1.6000162976327]
+            >>> expm([[1,1,0],[1,0,1],[0,1,0]], method='pade')
+            [ 3.86814500615414  2.26812870852145  0.841130841230196]
+            [ 2.26812870852145  2.44114713886289   1.42699786729125]
+            [0.841130841230196  1.42699786729125    1.6000162976327]
+            >>> expm([[1+j, 0], [1+j,1]])
+            [(1.46869393991589 + 2.28735528717884j)                        0.0]
+            [  (1.03776739863568 + 3.536943175722j)  (2.71828182845905 + 0.0j)]
+
+        Matrices with large entries are allowed::
+
+            >>> expm(matrix([[1,2],[2,3]])**25)
+            [5.65024064048415e+2050488462815550  9.14228140091932e+2050488462815550]
+            [9.14228140091932e+2050488462815550  1.47925220414035e+2050488462815551]
+
+        The identity `\exp(A+B) = \exp(A) \exp(B)` does not hold for
+        noncommuting matrices::
+
+            >>> A = hilbert(3)
+            >>> B = A + eye(3)
+            >>> chop(mnorm(A*B - B*A))
+            0.0
+            >>> chop(mnorm(expm(A+B) - expm(A)*expm(B)))
+            0.0
+            >>> B = A + ones(3)
+            >>> mnorm(A*B - B*A)
+            1.8
+            >>> mnorm(expm(A+B) - expm(A)*expm(B))
+            42.0927851137247
+
+        """
+        if method == 'pade':
+            prec = ctx.prec
+            try:
+                A = ctx.matrix(A)
+                ctx.prec += 2*A.rows
+                res = ctx._exp_pade(A)
+            finally:
+                ctx.prec = prec
+            return res
+        A = ctx.matrix(A)
+        prec = ctx.prec
+        j = int(max(1, ctx.mag(ctx.mnorm(A,'inf'))))
+        j += int(0.5*prec**0.5)
+        try:
+            ctx.prec += 10 + 2*j
+            tol = +ctx.eps
+            A = A/2**j
+            T = A
+            Y = A**0 + A
+            k = 2
+            while 1:
+                T *= A * (1/ctx.mpf(k))
+                if ctx.mnorm(T, 'inf') < tol:
+                    break
+                Y += T
+                k += 1
+            for k in xrange(j):
+                Y = Y*Y
+        finally:
+            ctx.prec = prec
+        Y *= 1
+        return Y
+
+    def cosm(ctx, A):
+        r"""
+        Gives the cosine of a square matrix `A`, defined in analogy
+        with the matrix exponential.
+
+        Examples::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> X = eye(3)
+            >>> cosm(X)
+            [0.54030230586814               0.0               0.0]
+            [             0.0  0.54030230586814               0.0]
+            [             0.0               0.0  0.54030230586814]
+            >>> X = hilbert(3)
+            >>> cosm(X)
+            [ 0.424403834569555  -0.316643413047167  -0.221474945949293]
+            [-0.316643413047167   0.820646708837824  -0.127183694770039]
+            [-0.221474945949293  -0.127183694770039   0.909236687217541]
+            >>> X = matrix([[1+j,-2],[0,-j]])
+            >>> cosm(X)
+            [(0.833730025131149 - 0.988897705762865j)  (1.07485840848393 - 0.17192140544213j)]
+            [                                     0.0               (1.54308063481524 + 0.0j)]
+        """
+        B = 0.5 * (ctx.expm(A*ctx.j) + ctx.expm(A*(-ctx.j)))
+        if not sum(A.apply(ctx.im).apply(abs)):
+            B = B.apply(ctx.re)
+        return B
+
+    def sinm(ctx, A):
+        r"""
+        Gives the sine of a square matrix `A`, defined in analogy
+        with the matrix exponential.
+
+        Examples::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> X = eye(3)
+            >>> sinm(X)
+            [0.841470984807897                0.0                0.0]
+            [              0.0  0.841470984807897                0.0]
+            [              0.0                0.0  0.841470984807897]
+            >>> X = hilbert(3)
+            >>> sinm(X)
+            [0.711608512150994  0.339783913247439  0.220742837314741]
+            [0.339783913247439  0.244113865695532  0.187231271174372]
+            [0.220742837314741  0.187231271174372  0.155816730769635]
+            >>> X = matrix([[1+j,-2],[0,-j]])
+            >>> sinm(X)
+            [(1.29845758141598 + 0.634963914784736j)  (-1.96751511930922 + 0.314700021761367j)]
+            [                                    0.0                  (0.0 - 1.1752011936438j)]
+        """
+        B = (-0.5j) * (ctx.expm(A*ctx.j) - ctx.expm(A*(-ctx.j)))
+        if not sum(A.apply(ctx.im).apply(abs)):
+            B = B.apply(ctx.re)
+        return B
+
+    def _sqrtm_rot(ctx, A, _may_rotate):
+        # If the iteration fails to converge, cheat by performing
+        # a rotation by a complex number
+        u = ctx.j**0.3
+        return ctx.sqrtm(u*A, _may_rotate) / ctx.sqrt(u)
+
+    def sqrtm(ctx, A, _may_rotate=2):
+        r"""
+        Computes a square root of the square matrix `A`, i.e. returns
+        a matrix `B = A^{1/2}` such that `B^2 = A`. The square root
+        of a matrix, if it exists, is not unique.
+
+        **Examples**
+
+        Square roots of some simple matrices::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> sqrtm([[1,0], [0,1]])
+            [1.0  0.0]
+            [0.0  1.0]
+            >>> sqrtm([[0,0], [0,0]])
+            [0.0  0.0]
+            [0.0  0.0]
+            >>> sqrtm([[2,0],[0,1]])
+            [1.4142135623731  0.0]
+            [            0.0  1.0]
+            >>> sqrtm([[1,1],[1,0]])
+            [ (0.920442065259926 - 0.21728689675164j)  (0.568864481005783 + 0.351577584254143j)]
+            [(0.568864481005783 + 0.351577584254143j)  (0.351577584254143 - 0.568864481005783j)]
+            >>> sqrtm([[1,0],[0,1]])
+            [1.0  0.0]
+            [0.0  1.0]
+            >>> sqrtm([[-1,0],[0,1]])
+            [(0.0 - 1.0j)           0.0]
+            [         0.0  (1.0 + 0.0j)]
+            >>> sqrtm([[j,0],[0,j]])
+            [(0.707106781186547 + 0.707106781186547j)                                       0.0]
+            [                                     0.0  (0.707106781186547 + 0.707106781186547j)]
+
+        A square root of a rotation matrix, giving the corresponding
+        half-angle rotation matrix::
+
+            >>> t1 = 0.75
+            >>> t2 = t1 * 0.5
+            >>> A1 = matrix([[cos(t1), -sin(t1)], [sin(t1), cos(t1)]])
+            >>> A2 = matrix([[cos(t2), -sin(t2)], [sin(t2), cos(t2)]])
+            >>> sqrtm(A1)
+            [0.930507621912314  -0.366272529086048]
+            [0.366272529086048   0.930507621912314]
+            >>> A2
+            [0.930507621912314  -0.366272529086048]
+            [0.366272529086048   0.930507621912314]
+
+        The identity `(A^2)^{1/2} = A` does not necessarily hold::
+
+            >>> A = matrix([[4,1,4],[7,8,9],[10,2,11]])
+            >>> sqrtm(A**2)
+            [ 4.0  1.0   4.0]
+            [ 7.0  8.0   9.0]
+            [10.0  2.0  11.0]
+            >>> sqrtm(A)**2
+            [ 4.0  1.0   4.0]
+            [ 7.0  8.0   9.0]
+            [10.0  2.0  11.0]
+            >>> A = matrix([[-4,1,4],[7,-8,9],[10,2,11]])
+            >>> sqrtm(A**2)
+            [  7.43715112194995  -0.324127569985474   1.8481718827526]
+            [-0.251549715716942    9.32699765900402  2.48221180985147]
+            [  4.11609388833616   0.775751877098258   13.017955697342]
+            >>> chop(sqrtm(A)**2)
+            [-4.0   1.0   4.0]
+            [ 7.0  -8.0   9.0]
+            [10.0   2.0  11.0]
+
+        For some matrices, a square root does not exist::
+
+            >>> sqrtm([[0,1], [0,0]])
+            Traceback (most recent call last):
+              ...
+            ZeroDivisionError: matrix is numerically singular
+
+        Two examples from the documentation for Matlab's ``sqrtm``::
+
+            >>> mp.dps = 15; mp.pretty = True
+            >>> sqrtm([[7,10],[15,22]])
+            [1.56669890360128  1.74077655955698]
+            [2.61116483933547  4.17786374293675]
+            >>>
+            >>> X = matrix(\
+            ...   [[5,-4,1,0,0],
+            ...   [-4,6,-4,1,0],
+            ...   [1,-4,6,-4,1],
+            ...   [0,1,-4,6,-4],
+            ...   [0,0,1,-4,5]])
+            >>> Y = matrix(\
+            ...   [[2,-1,-0,-0,-0],
+            ...   [-1,2,-1,0,-0],
+            ...   [0,-1,2,-1,0],
+            ...   [-0,0,-1,2,-1],
+            ...   [-0,-0,-0,-1,2]])
+            >>> mnorm(sqrtm(X) - Y)
+            4.53155328326114e-19
+
+        """
+        A = ctx.matrix(A)
+        # Trivial
+        if A*0 == A:
+            return A
+        prec = ctx.prec
+        if _may_rotate:
+            d = ctx.det(A)
+            if abs(ctx.im(d)) < 16*ctx.eps and ctx.re(d) < 0:
+                return ctx._sqrtm_rot(A, _may_rotate-1)
+        try:
+            ctx.prec += 10
+            tol = ctx.eps * 128
+            Y = A
+            Z = I = A**0
+            k = 0
+            # Denman-Beavers iteration
+            while 1:
+                Yprev = Y
+                try:
+                    Y, Z = 0.5*(Y+ctx.inverse(Z)), 0.5*(Z+ctx.inverse(Y))
+                except ZeroDivisionError:
+                    if _may_rotate:
+                        Y = ctx._sqrtm_rot(A, _may_rotate-1)
+                        break
+                    else:
+                        raise
+                mag1 = ctx.mnorm(Y-Yprev, 'inf')
+                mag2 = ctx.mnorm(Y, 'inf')
+                if mag1 <= mag2*tol:
+                    break
+                if _may_rotate and k > 6 and not mag1 < mag2 * 0.001:
+                    return ctx._sqrtm_rot(A, _may_rotate-1)
+                k += 1
+                if k > ctx.prec:
+                    raise ctx.NoConvergence
+        finally:
+            ctx.prec = prec
+        Y *= 1
+        return Y
+
+    def logm(ctx, A):
+        r"""
+        Computes a logarithm of the square matrix `A`, i.e. returns
+        a matrix `B = \log(A)` such that `\exp(B) = A`. The logarithm
+        of a matrix, if it exists, is not unique.
+
+        **Examples**
+
+        Logarithms of some simple matrices::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> X = eye(3)
+            >>> logm(X)
+            [0.0  0.0  0.0]
+            [0.0  0.0  0.0]
+            [0.0  0.0  0.0]
+            >>> logm(2*X)
+            [0.693147180559945                0.0                0.0]
+            [              0.0  0.693147180559945                0.0]
+            [              0.0                0.0  0.693147180559945]
+            >>> logm(expm(X))
+            [1.0  0.0  0.0]
+            [0.0  1.0  0.0]
+            [0.0  0.0  1.0]
+
+        A logarithm of a complex matrix::
+
+            >>> X = matrix([[2+j, 1, 3], [1-j, 1-2*j, 1], [-4, -5, j]])
+            >>> B = logm(X)
+            >>> nprint(B)
+            [ (0.808757 + 0.107759j)    (2.20752 + 0.202762j)   (1.07376 - 0.773874j)]
+            [ (0.905709 - 0.107795j)  (0.0287395 - 0.824993j)  (0.111619 + 0.514272j)]
+            [(-0.930151 + 0.399512j)   (-2.06266 - 0.674397j)  (0.791552 + 0.519839j)]
+            >>> chop(expm(B))
+            [(2.0 + 1.0j)           1.0           3.0]
+            [(1.0 - 1.0j)  (1.0 - 2.0j)           1.0]
+            [        -4.0          -5.0  (0.0 + 1.0j)]
+
+        A matrix `X` close to the identity matrix, for which
+        `\log(\exp(X)) = \exp(\log(X)) = X` holds::
+
+            >>> X = eye(3) + hilbert(3)/4
+            >>> X
+            [              1.25             0.125  0.0833333333333333]
+            [             0.125  1.08333333333333              0.0625]
+            [0.0833333333333333            0.0625                1.05]
+            >>> logm(expm(X))
+            [              1.25             0.125  0.0833333333333333]
+            [             0.125  1.08333333333333              0.0625]
+            [0.0833333333333333            0.0625                1.05]
+            >>> expm(logm(X))
+            [              1.25             0.125  0.0833333333333333]
+            [             0.125  1.08333333333333              0.0625]
+            [0.0833333333333333            0.0625                1.05]
+
+        A logarithm of a rotation matrix, giving back the angle of
+        the rotation::
+
+            >>> t = 3.7
+            >>> A = matrix([[cos(t),sin(t)],[-sin(t),cos(t)]])
+            >>> chop(logm(A))
+            [             0.0  -2.58318530717959]
+            [2.58318530717959                0.0]
+            >>> (2*pi-t)
+            2.58318530717959
+
+        For some matrices, a logarithm does not exist::
+
+            >>> logm([[1,0], [0,0]])
+            Traceback (most recent call last):
+              ...
+            ZeroDivisionError: matrix is numerically singular
+
+        Logarithm of a matrix with large entries::
+
+            >>> logm(hilbert(3) * 10**20).apply(re)
+            [ 45.5597513593433  1.27721006042799  0.317662687717978]
+            [ 1.27721006042799  42.5222778973542   2.24003708791604]
+            [0.317662687717978  2.24003708791604    42.395212822267]
+
+        """
+        A = ctx.matrix(A)
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            tol = ctx.eps * 128
+            I = A**0
+            B = A
+            n = 0
+            while 1:
+                B = ctx.sqrtm(B)
+                n += 1
+                if ctx.mnorm(B-I, 'inf') < 0.125:
+                    break
+            T = X = B-I
+            L = X*0
+            k = 1
+            while 1:
+                if k & 1:
+                    L += T / k
+                else:
+                    L -= T / k
+                T *= X
+                if ctx.mnorm(T, 'inf') < tol:
+                    break
+                k += 1
+                if k > ctx.prec:
+                    raise ctx.NoConvergence
+        finally:
+            ctx.prec = prec
+        L *= 2**n
+        return L
+
+    def powm(ctx, A, r):
+        r"""
+        Computes `A^r = \exp(A \log r)` for a matrix `A` and complex
+        number `r`.
+
+        **Examples**
+
+        Powers and inverse powers of a matrix::
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = True
+            >>> A = matrix([[4,1,4],[7,8,9],[10,2,11]])
+            >>> powm(A, 2)
+            [ 63.0  20.0   69.0]
+            [174.0  89.0  199.0]
+            [164.0  48.0  179.0]
+            >>> chop(powm(powm(A, 4), 1/4.))
+            [ 4.0  1.0   4.0]
+            [ 7.0  8.0   9.0]
+            [10.0  2.0  11.0]
+            >>> powm(extraprec(20)(powm)(A, -4), -1/4.)
+            [ 4.0  1.0   4.0]
+            [ 7.0  8.0   9.0]
+            [10.0  2.0  11.0]
+            >>> chop(powm(powm(A, 1+0.5j), 1/(1+0.5j)))
+            [ 4.0  1.0   4.0]
+            [ 7.0  8.0   9.0]
+            [10.0  2.0  11.0]
+            >>> powm(extraprec(5)(powm)(A, -1.5), -1/(1.5))
+            [ 4.0  1.0   4.0]
+            [ 7.0  8.0   9.0]
+            [10.0  2.0  11.0]
+
+        A Fibonacci-generating matrix::
+
+            >>> powm([[1,1],[1,0]], 10)
+            [89.0  55.0]
+            [55.0  34.0]
+            >>> fib(10)
+            55.0
+            >>> powm([[1,1],[1,0]], 6.5)
+            [(16.5166626964253 - 0.0121089837381789j)  (10.2078589271083 + 0.0195927472575932j)]
+            [(10.2078589271083 + 0.0195927472575932j)  (6.30880376931698 - 0.0317017309957721j)]
+            >>> (phi**6.5 - (1-phi)**6.5)/sqrt(5)
+            (10.2078589271083 - 0.0195927472575932j)
+            >>> powm([[1,1],[1,0]], 6.2)
+            [ (14.3076953002666 - 0.008222855781077j)  (8.81733464837593 + 0.0133048601383712j)]
+            [(8.81733464837593 + 0.0133048601383712j)  (5.49036065189071 - 0.0215277159194482j)]
+            >>> (phi**6.2 - (1-phi)**6.2)/sqrt(5)
+            (8.81733464837593 - 0.0133048601383712j)
+
+        """
+        A = ctx.matrix(A)
+        r = ctx.convert(r)
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            if ctx.isint(r):
+                v = A ** int(r)
+            elif ctx.isint(r*2):
+                y = int(r*2)
+                v = ctx.sqrtm(A) ** y
+            else:
+                v = ctx.expm(r*ctx.logm(A))
+        finally:
+            ctx.prec = prec
+        v *= 1
+        return v

lib/python3.11/site-packages/mpmath/matrices/eigen.py

@@ -0,0 +1,877 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+##################################################################################################
+#     module for the eigenvalue problem
+#       Copyright 2013 Timo Hartmann (thartmann15 at gmail.com)
+#
+# todo:
+#  - implement balancing
+#  - agressive early deflation
+#
+##################################################################################################
+
+"""
+The eigenvalue problem
+----------------------
+
+This file contains routines for the eigenvalue problem.
+
+high level routines:
+
+  hessenberg : reduction of a real or complex square matrix to upper Hessenberg form
+  schur : reduction of a real or complex square matrix to upper Schur form
+  eig : eigenvalues and eigenvectors of a real or complex square matrix
+
+low level routines:
+
+  hessenberg_reduce_0 : reduction of a real or complex square matrix to upper Hessenberg form
+  hessenberg_reduce_1 : auxiliary routine to hessenberg_reduce_0
+  qr_step : a single implicitly shifted QR step for an upper Hessenberg matrix
+  hessenberg_qr : Schur decomposition of an upper Hessenberg matrix
+  eig_tr_r : right eigenvectors of an upper triangular matrix
+  eig_tr_l : left  eigenvectors of an upper triangular matrix
+"""
+
+from ..libmp.backend import xrange
+
+class Eigen(object):
+    pass
+
+def defun(f):
+    setattr(Eigen, f.__name__, f)
+    return f
+
+def hessenberg_reduce_0(ctx, A, T):
+    """
+    This routine computes the (upper) Hessenberg decomposition of a square matrix A.
+    Given A, an unitary matrix Q is calculated such that
+
+               Q' A Q = H              and             Q' Q = Q Q' = 1
+
+    where H is an upper Hessenberg matrix, meaning that it only contains zeros
+    below the first subdiagonal. Here ' denotes the hermitian transpose (i.e.
+    transposition and conjugation).
+
+    parameters:
+      A         (input/output) On input, A contains the square matrix A of
+                dimension (n,n). On output, A contains a compressed representation
+                of Q and H.
+      T         (output) An array of length n containing the first elements of
+                the Householder reflectors.
+    """
+
+    # internally we work with householder reflections from the right.
+    # let u be a row vector (i.e. u[i]=A[i,:i]). then
+    # Q is build up by reflectors of the type (1-v'v) where v is a suitable
+    # modification of u. these reflectors are applyed to A from the right.
+    # because we work with reflectors from the right we have to start with
+    # the bottom row of A and work then upwards (this corresponds to
+    # some kind of RQ decomposition).
+    # the first part of the vectors v (i.e. A[i,:(i-1)]) are stored as row vectors
+    # in the lower left part of A (excluding the diagonal and subdiagonal).
+    # the last entry of v is stored in T.
+    # the upper right part of A (including diagonal and subdiagonal) becomes H.
+
+
+    n = A.rows
+    if n <= 2: return
+
+    for i in xrange(n-1, 1, -1):
+
+        # scale the vector
+
+        scale = 0
+        for k in xrange(0, i):
+            scale += abs(ctx.re(A[i,k])) + abs(ctx.im(A[i,k]))
+
+        scale_inv = 0
+        if scale != 0:
+            scale_inv = 1 / scale
+
+        if scale == 0 or ctx.isinf(scale_inv):
+            # sadly there are floating point numbers not equal to zero whose reciprocal is infinity
+            T[i] = 0
+            A[i,i-1] = 0
+            continue
+
+        # calculate parameters for housholder transformation
+
+        H = 0
+        for k in xrange(0, i):
+            A[i,k] *= scale_inv
+            rr = ctx.re(A[i,k])
+            ii = ctx.im(A[i,k])
+            H += rr * rr + ii * ii
+
+        F = A[i,i-1]
+        f = abs(F)
+        G = ctx.sqrt(H)
+        A[i,i-1] = - G * scale
+
+        if f == 0:
+            T[i] = G
+        else:
+            ff = F / f
+            T[i] = F + G * ff
+            A[i,i-1] *= ff
+
+        H += G * f
+        H = 1 / ctx.sqrt(H)
+
+        T[i] *= H
+        for k in xrange(0, i - 1):
+            A[i,k] *= H
+
+        for j in xrange(0, i):
+            # apply housholder transformation (from right)
+
+            G = ctx.conj(T[i]) * A[j,i-1]
+            for k in xrange(0, i-1):
+                G += ctx.conj(A[i,k]) * A[j,k]
+
+            A[j,i-1] -= G * T[i]
+            for k in xrange(0, i-1):
+                A[j,k] -= G * A[i,k]
+
+        for j in xrange(0, n):
+            # apply housholder transformation (from left)
+
+            G = T[i] * A[i-1,j]
+            for k in xrange(0, i-1):
+                G += A[i,k] * A[k,j]
+
+            A[i-1,j] -= G * ctx.conj(T[i])
+            for k in xrange(0, i-1):
+                A[k,j] -= G * ctx.conj(A[i,k])
+
+
+
+def hessenberg_reduce_1(ctx, A, T):
+    """
+    This routine forms the unitary matrix Q described in hessenberg_reduce_0.
+
+    parameters:
+      A    (input/output) On input, A is the same matrix as delivered by
+           hessenberg_reduce_0. On output, A is set to Q.
+
+      T    (input) On input, T is the same array as delivered by hessenberg_reduce_0.
+    """
+
+    n = A.rows
+
+    if n == 1:
+        A[0,0] = 1
+        return
+
+    A[0,0] = A[1,1] = 1
+    A[0,1] = A[1,0] = 0
+
+    for i in xrange(2, n):
+        if T[i] != 0:
+
+            for j in xrange(0, i):
+                G = T[i] * A[i-1,j]
+                for k in xrange(0, i-1):
+                    G += A[i,k] * A[k,j]
+
+                A[i-1,j] -= G * ctx.conj(T[i])
+                for k in xrange(0, i-1):
+                    A[k,j] -= G * ctx.conj(A[i,k])
+
+        A[i,i] = 1
+        for j in xrange(0, i):
+            A[j,i] = A[i,j] = 0
+
+
+
+@defun
+def hessenberg(ctx, A, overwrite_a = False):
+    """
+    This routine computes the Hessenberg decomposition of a square matrix A.
+    Given A, an unitary matrix Q is determined such that
+
+          Q' A Q = H                and               Q' Q = Q Q' = 1
+
+    where H is an upper right Hessenberg matrix. Here ' denotes the hermitian
+    transpose (i.e. transposition and conjugation).
+
+    input:
+      A            : a real or complex square matrix
+      overwrite_a  : if true, allows modification of A which may improve
+                     performance. if false, A is not modified.
+
+    output:
+      Q : an unitary matrix
+      H : an upper right Hessenberg matrix
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]])
+      >>> Q, H = mp.hessenberg(A)
+      >>> mp.nprint(H, 3) # doctest:+SKIP
+      [  3.15  2.23  4.44]
+      [-0.769  4.85  3.05]
+      [   0.0  3.61   7.0]
+      >>> print(mp.chop(A - Q * H * Q.transpose_conj()))
+      [0.0  0.0  0.0]
+      [0.0  0.0  0.0]
+      [0.0  0.0  0.0]
+
+    return value:   (Q, H)
+    """
+
+    n = A.rows
+
+    if n == 1:
+        return (ctx.matrix([[1]]), A)
+
+    if not overwrite_a:
+        A = A.copy()
+
+    T = ctx.matrix(n, 1)
+
+    hessenberg_reduce_0(ctx, A, T)
+    Q = A.copy()
+    hessenberg_reduce_1(ctx, Q, T)
+
+    for x in xrange(n):
+        for y in xrange(x+2, n):
+            A[y,x] = 0
+
+    return Q, A
+
+
+###########################################################################
+
+
+def qr_step(ctx, n0, n1, A, Q, shift):
+    """
+    This subroutine executes a single implicitly shifted QR step applied to an
+    upper Hessenberg matrix A. Given A and shift as input, first an QR
+    decomposition is calculated:
+
+      Q R = A - shift * 1 .
+
+    The output is then following matrix:
+
+      R Q + shift * 1
+
+    parameters:
+      n0, n1    (input) Two integers which specify the submatrix A[n0:n1,n0:n1]
+                on which this subroutine operators. The subdiagonal elements
+                to the left and below this submatrix must be deflated (i.e. zero).
+                following restriction is imposed: n1>=n0+2
+      A         (input/output) On input, A is an upper Hessenberg matrix.
+                On output, A is replaced by "R Q + shift * 1"
+      Q         (input/output) The parameter Q is multiplied by the unitary matrix
+                Q arising from the QR decomposition. Q can also be false, in which
+                case the unitary matrix Q is not computated.
+      shift     (input) a complex number specifying the shift. idealy close to an
+                eigenvalue of the bottemmost part of the submatrix A[n0:n1,n0:n1].
+
+    references:
+      Stoer, Bulirsch - Introduction to Numerical Analysis.
+      Kresser : Numerical Methods for General and Structured Eigenvalue Problems
+    """
+
+    # implicitly shifted and bulge chasing is explained at p.398/399 in "Stoer, Bulirsch - Introduction to Numerical Analysis"
+    # for bulge chasing see also "Watkins - The Matrix Eigenvalue Problem" sec.4.5,p.173
+
+    # the Givens rotation we used is determined as follows: let c,s be two complex
+    # numbers. then we have following relation:
+    #
+    #     v = sqrt(|c|^2 + |s|^2)
+    #
+    #     1/v [ c~  s~]  [c] = [v]
+    #         [-s   c ]  [s]   [0]
+    #
+    # the matrix on the left is our Givens rotation.
+
+    n = A.rows
+
+    # first step
+
+    # calculate givens rotation
+    c = A[n0  ,n0] - shift
+    s = A[n0+1,n0]
+
+    v = ctx.hypot(ctx.hypot(ctx.re(c), ctx.im(c)), ctx.hypot(ctx.re(s), ctx.im(s)))
+
+    if v == 0:
+        v = 1
+        c = 1
+        s = 0
+    else:
+        c /= v
+        s /= v
+
+    cc = ctx.conj(c)
+    cs = ctx.conj(s)
+
+    for k in xrange(n0, n):
+        # apply givens rotation from the left
+        x = A[n0  ,k]
+        y = A[n0+1,k]
+        A[n0  ,k] = cc * x + cs * y
+        A[n0+1,k] = c * y - s * x
+
+    for k in xrange(min(n1, n0+3)):
+        # apply givens rotation from the right
+        x = A[k,n0  ]
+        y = A[k,n0+1]
+        A[k,n0  ] = c * x + s * y
+        A[k,n0+1] = cc * y - cs * x
+
+    if not isinstance(Q, bool):
+        for k in xrange(n):
+            # eigenvectors
+            x = Q[k,n0  ]
+            y = Q[k,n0+1]
+            Q[k,n0  ] = c * x + s * y
+            Q[k,n0+1] = cc * y - cs * x
+
+    # chase the bulge
+
+    for j in xrange(n0, n1 - 2):
+        # calculate givens rotation
+
+        c = A[j+1,j]
+        s = A[j+2,j]
+
+        v = ctx.hypot(ctx.hypot(ctx.re(c), ctx.im(c)), ctx.hypot(ctx.re(s), ctx.im(s)))
+
+        if v == 0:
+            A[j+1,j] = 0
+            v = 1
+            c = 1
+            s = 0
+        else:
+            A[j+1,j] = v
+            c /= v
+            s /= v
+
+        A[j+2,j] = 0
+
+        cc = ctx.conj(c)
+        cs = ctx.conj(s)
+
+        for k in xrange(j+1, n):
+            # apply givens rotation from the left
+            x = A[j+1,k]
+            y = A[j+2,k]
+            A[j+1,k] = cc * x + cs * y
+            A[j+2,k] = c * y - s * x
+
+        for k in xrange(0, min(n1, j+4)):
+            # apply givens rotation from the right
+            x = A[k,j+1]
+            y = A[k,j+2]
+            A[k,j+1] = c * x + s * y
+            A[k,j+2] = cc * y - cs * x
+
+        if not isinstance(Q, bool):
+            for k in xrange(0, n):
+                # eigenvectors
+                x = Q[k,j+1]
+                y = Q[k,j+2]
+                Q[k,j+1] = c * x + s * y
+                Q[k,j+2] = cc * y - cs * x
+
+
+
+def hessenberg_qr(ctx, A, Q):
+    """
+    This routine computes the Schur decomposition of an upper Hessenberg matrix A.
+    Given A, an unitary matrix Q is determined such that
+
+          Q' A Q = R                   and                  Q' Q = Q Q' = 1
+
+    where R is an upper right triangular matrix. Here ' denotes the hermitian
+    transpose (i.e. transposition and conjugation).
+
+    parameters:
+      A         (input/output) On input, A contains an upper Hessenberg matrix.
+                On output, A is replace by the upper right triangluar matrix R.
+
+      Q         (input/output) The parameter Q is multiplied by the unitary
+                matrix Q arising from the Schur decomposition. Q can also be
+                false, in which case the unitary matrix Q is not computated.
+    """
+
+    n = A.rows
+
+    norm = 0
+    for x in xrange(n):
+        for y in xrange(min(x+2, n)):
+            norm += ctx.re(A[y,x]) ** 2 + ctx.im(A[y,x]) ** 2
+    norm = ctx.sqrt(norm) / n
+
+    if norm == 0:
+        return
+
+    n0 = 0
+    n1 = n
+
+    eps = ctx.eps / (100 * n)
+    maxits = ctx.dps * 4
+
+    its = totalits = 0
+
+    while 1:
+        # kressner p.32 algo 3
+        # the active submatrix is A[n0:n1,n0:n1]
+
+        k = n0
+
+        while k + 1 < n1:
+            s = abs(ctx.re(A[k,k])) + abs(ctx.im(A[k,k])) + abs(ctx.re(A[k+1,k+1])) + abs(ctx.im(A[k+1,k+1]))
+            if s < eps * norm:
+                s = norm
+            if abs(A[k+1,k]) < eps * s:
+                break
+            k += 1
+
+        if k + 1 < n1:
+            # deflation found at position (k+1, k)
+
+            A[k+1,k] = 0
+            n0 = k + 1
+
+            its = 0
+
+            if n0 + 1 >= n1:
+                # block of size at most two has converged
+                n0 = 0
+                n1 = k + 1
+                if n1 < 2:
+                    # QR algorithm has converged
+                    return
+        else:
+            if (its % 30) == 10:
+                # exceptional shift
+                shift = A[n1-1,n1-2]
+            elif (its % 30) == 20:
+                # exceptional shift
+                shift = abs(A[n1-1,n1-2])
+            elif (its % 30) == 29:
+                # exceptional shift
+                shift = norm
+            else:
+                #    A = [ a b ]       det(x-A)=x*x-x*tr(A)+det(A)
+                #        [ c d ]
+                #
+                # eigenvalues bad:   (tr(A)+sqrt((tr(A))**2-4*det(A)))/2
+                #     bad because of cancellation if |c| is small and |a-d| is small, too.
+                #
+                # eigenvalues good:     (a+d+sqrt((a-d)**2+4*b*c))/2
+
+                t =  A[n1-2,n1-2] + A[n1-1,n1-1]
+                s = (A[n1-1,n1-1] - A[n1-2,n1-2]) ** 2 + 4 * A[n1-1,n1-2] * A[n1-2,n1-1]
+                if ctx.re(s) > 0:
+                    s = ctx.sqrt(s)
+                else:
+                    s = ctx.sqrt(-s) * 1j
+                a = (t + s) / 2
+                b = (t - s) / 2
+                if abs(A[n1-1,n1-1] - a) > abs(A[n1-1,n1-1] - b):
+                    shift = b
+                else:
+                    shift = a
+
+            its += 1
+            totalits += 1
+
+            qr_step(ctx, n0, n1, A, Q, shift)
+
+            if its > maxits:
+                raise RuntimeError("qr: failed to converge after %d steps" % its)
+
+
+@defun
+def schur(ctx, A, overwrite_a = False):
+    """
+    This routine computes the Schur decomposition of a square matrix A.
+    Given A, an unitary matrix Q is determined such that
+
+          Q' A Q = R                and               Q' Q = Q Q' = 1
+
+    where R is an upper right triangular matrix. Here ' denotes the
+    hermitian transpose (i.e. transposition and conjugation).
+
+    input:
+      A            : a real or complex square matrix
+      overwrite_a  : if true, allows modification of A which may improve
+                     performance. if false, A is not modified.
+
+    output:
+      Q : an unitary matrix
+      R : an upper right triangular matrix
+
+    return value:   (Q, R)
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]])
+      >>> Q, R = mp.schur(A)
+      >>> mp.nprint(R, 3) # doctest:+SKIP
+      [2.0  0.417  -2.53]
+      [0.0    4.0  -4.74]
+      [0.0    0.0    9.0]
+      >>> print(mp.chop(A - Q * R * Q.transpose_conj()))
+      [0.0  0.0  0.0]
+      [0.0  0.0  0.0]
+      [0.0  0.0  0.0]
+
+    warning: The Schur decomposition is not unique.
+    """
+
+    n = A.rows
+
+    if n == 1:
+        return (ctx.matrix([[1]]), A)
+
+    if not overwrite_a:
+        A = A.copy()
+
+    T = ctx.matrix(n, 1)
+
+    hessenberg_reduce_0(ctx, A, T)
+    Q = A.copy()
+    hessenberg_reduce_1(ctx, Q, T)
+
+    for x in xrange(n):
+        for y in xrange(x + 2, n):
+            A[y,x] = 0
+
+    hessenberg_qr(ctx, A, Q)
+
+    return Q, A
+
+
+def eig_tr_r(ctx, A):
+    """
+    This routine calculates the right eigenvectors of an upper right triangular matrix.
+
+    input:
+      A      an upper right triangular matrix
+
+    output:
+      ER     a matrix whose columns form the right eigenvectors of A
+
+    return value: ER
+    """
+
+    # this subroutine is inspired by the lapack routines ctrevc.f,clatrs.f
+
+    n = A.rows
+
+    ER = ctx.eye(n)
+
+    eps = ctx.eps
+
+    unfl = ctx.ldexp(ctx.one, -ctx.prec * 30)
+    # since mpmath effectively has no limits on the exponent, we simply scale doubles up
+    # original double has prec*20
+
+    smlnum = unfl * (n / eps)
+    simin = 1 / ctx.sqrt(eps)
+
+    rmax = 1
+
+    for i in xrange(1, n):
+        s = A[i,i]
+
+        smin = max(eps * abs(s), smlnum)
+
+        for j in xrange(i - 1, -1, -1):
+
+            r = 0
+            for k in xrange(j + 1, i + 1):
+                r += A[j,k] * ER[k,i]
+
+            t = A[j,j] - s
+            if abs(t) < smin:
+                t = smin
+
+            r = -r / t
+            ER[j,i] = r
+
+            rmax = max(rmax, abs(r))
+            if rmax > simin:
+                for k in xrange(j, i+1):
+                    ER[k,i] /= rmax
+                rmax = 1
+
+        if rmax != 1:
+            for k in xrange(0, i + 1):
+                ER[k,i] /= rmax
+
+    return ER
+
+def eig_tr_l(ctx, A):
+    """
+    This routine calculates the left eigenvectors of an upper right triangular matrix.
+
+    input:
+      A      an upper right triangular matrix
+
+    output:
+      EL     a matrix whose rows form the left eigenvectors of A
+
+    return value:  EL
+    """
+
+    n = A.rows
+
+    EL = ctx.eye(n)
+
+    eps = ctx.eps
+
+    unfl = ctx.ldexp(ctx.one, -ctx.prec * 30)
+    # since mpmath effectively has no limits on the exponent, we simply scale doubles up
+    # original double has prec*20
+
+    smlnum = unfl * (n / eps)
+    simin = 1 / ctx.sqrt(eps)
+
+    rmax = 1
+
+    for i in xrange(0, n - 1):
+        s = A[i,i]
+
+        smin = max(eps * abs(s), smlnum)
+
+        for j in xrange(i + 1, n):
+
+            r = 0
+            for k in xrange(i, j):
+                r += EL[i,k] * A[k,j]
+
+            t = A[j,j] - s
+            if abs(t) < smin:
+                t = smin
+
+            r = -r / t
+            EL[i,j] = r
+
+            rmax = max(rmax, abs(r))
+            if rmax > simin:
+                for k in xrange(i, j + 1):
+                    EL[i,k] /= rmax
+                rmax = 1
+
+        if rmax != 1:
+            for k in xrange(i, n):
+                EL[i,k] /= rmax
+
+    return EL
+
+@defun
+def eig(ctx, A, left = False, right = True, overwrite_a = False):
+    """
+    This routine computes the eigenvalues and optionally the left and right
+    eigenvectors of a square matrix A. Given A, a vector E and matrices ER
+    and EL are calculated such that
+
+                        A ER[:,i] =         E[i] ER[:,i]
+                EL[i,:] A         = EL[i,:] E[i]
+
+    E contains the eigenvalues of A. The columns of ER contain the right eigenvectors
+    of A whereas the rows of EL contain the left eigenvectors.
+
+
+    input:
+      A           : a real or complex square matrix of shape (n, n)
+      left        : if true, the left eigenvectors are calculated.
+      right       : if true, the right eigenvectors are calculated.
+      overwrite_a : if true, allows modification of A which may improve
+                    performance. if false, A is not modified.
+
+    output:
+      E    : a list of length n containing the eigenvalues of A.
+      ER   : a matrix whose columns contain the right eigenvectors of A.
+      EL   : a matrix whose rows contain the left eigenvectors of A.
+
+    return values:
+       E            if left and right are both false.
+      (E, ER)       if right is true and left is false.
+      (E, EL)       if left is true and right is false.
+      (E, EL, ER)   if left and right are true.
+
+
+    examples:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]])
+      >>> E, ER = mp.eig(A)
+      >>> print(mp.chop(A * ER[:,0] - E[0] * ER[:,0]))
+      [0.0]
+      [0.0]
+      [0.0]
+
+      >>> E, EL, ER = mp.eig(A,left = True, right = True)
+      >>> E, EL, ER = mp.eig_sort(E, EL, ER)
+      >>> mp.nprint(E)
+      [2.0, 4.0, 9.0]
+      >>> print(mp.chop(A * ER[:,0] - E[0] * ER[:,0]))
+      [0.0]
+      [0.0]
+      [0.0]
+      >>> print(mp.chop( EL[0,:] * A - EL[0,:] * E[0]))
+      [0.0  0.0  0.0]
+
+    warning:
+     - If there are multiple eigenvalues, the eigenvectors do not necessarily
+       span the whole vectorspace, i.e. ER and EL may have not full rank.
+       Furthermore in that case the eigenvectors are numerical ill-conditioned.
+     - In the general case the eigenvalues have no natural order.
+
+    see also:
+      - eigh (or eigsy, eighe) for the symmetric eigenvalue problem.
+      - eig_sort for sorting of eigenvalues and eigenvectors
+    """
+
+    n = A.rows
+
+    if n == 1:
+        if left and (not right):
+            return ([A[0]], ctx.matrix([[1]]))
+
+        if right and (not left):
+            return ([A[0]], ctx.matrix([[1]]))
+
+        return ([A[0]], ctx.matrix([[1]]), ctx.matrix([[1]]))
+
+    if not overwrite_a:
+        A = A.copy()
+
+    T = ctx.zeros(n, 1)
+
+    hessenberg_reduce_0(ctx, A, T)
+
+    if left or right:
+        Q = A.copy()
+        hessenberg_reduce_1(ctx, Q, T)
+    else:
+        Q = False
+
+    for x in xrange(n):
+        for y in xrange(x + 2, n):
+            A[y,x] = 0
+
+    hessenberg_qr(ctx, A, Q)
+
+    E = [0 for i in xrange(n)]
+    for i in xrange(n):
+        E[i] = A[i,i]
+
+    if not (left or right):
+        return E
+
+    if left:
+        EL = eig_tr_l(ctx, A)
+        EL = EL * Q.transpose_conj()
+
+    if right:
+        ER = eig_tr_r(ctx, A)
+        ER = Q * ER
+
+    if left and (not right):
+        return (E, EL)
+
+    if right and (not left):
+        return (E, ER)
+
+    return (E, EL, ER)
+
+@defun
+def eig_sort(ctx, E, EL = False, ER = False, f = "real"):
+    """
+    This routine sorts the eigenvalues and eigenvectors delivered by ``eig``.
+
+    parameters:
+      E  : the eigenvalues as delivered by eig
+      EL : the left  eigenvectors as delivered by eig, or false
+      ER : the right eigenvectors as delivered by eig, or false
+      f  : either a string ("real" sort by increasing real part, "imag" sort by
+           increasing imag part, "abs" sort by absolute value) or a function
+           mapping complexs to the reals, i.e. ``f = lambda x: -mp.re(x) ``
+           would sort the eigenvalues by decreasing real part.
+
+    return values:
+       E            if EL and ER are both false.
+      (E, ER)       if ER is not false and left is false.
+      (E, EL)       if EL is not false and right is false.
+      (E, EL, ER)   if EL and ER are not false.
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[3, -1, 2], [2, 5, -5], [-2, -3, 7]])
+      >>> E, EL, ER = mp.eig(A,left = True, right = True)
+      >>> E, EL, ER = mp.eig_sort(E, EL, ER)
+      >>> mp.nprint(E)
+      [2.0, 4.0, 9.0]
+      >>> E, EL, ER = mp.eig_sort(E, EL, ER,f = lambda x: -mp.re(x))
+      >>> mp.nprint(E)
+      [9.0, 4.0, 2.0]
+      >>> print(mp.chop(A * ER[:,0] - E[0] * ER[:,0]))
+      [0.0]
+      [0.0]
+      [0.0]
+      >>> print(mp.chop( EL[0,:] * A - EL[0,:] * E[0]))
+      [0.0  0.0  0.0]
+    """
+
+    if isinstance(f, str):
+        if f == "real":
+            f = ctx.re
+        elif f == "imag":
+            f = ctx.im
+        elif f == "abs":
+            f = abs
+        else:
+            raise RuntimeError("unknown function %s" % f)
+
+    n = len(E)
+
+    # Sort eigenvalues (bubble-sort)
+
+    for i in xrange(n):
+        imax = i
+        s = f(E[i])         # s is the current maximal element
+
+        for j in xrange(i + 1, n):
+            c = f(E[j])
+            if c < s:
+                s = c
+                imax = j
+
+        if imax != i:
+            # swap eigenvalues
+
+            z = E[i]
+            E[i] = E[imax]
+            E[imax] = z
+
+            if not isinstance(EL, bool):
+                for j in xrange(n):
+                    z = EL[i,j]
+                    EL[i,j] = EL[imax,j]
+                    EL[imax,j] = z
+
+            if not isinstance(ER, bool):
+                for j in xrange(n):
+                    z = ER[j,i]
+                    ER[j,i] = ER[j,imax]
+                    ER[j,imax] = z
+
+    if isinstance(EL, bool) and isinstance(ER, bool):
+        return E
+
+    if isinstance(EL, bool) and not(isinstance(ER, bool)):
+        return (E, ER)
+
+    if isinstance(ER, bool) and not(isinstance(EL, bool)):
+        return (E, EL)
+
+    return (E, EL, ER)

lib/python3.11/site-packages/mpmath/matrices/eigen_symmetric.py

@@ -0,0 +1,1807 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+
+##################################################################################################
+#     module for the symmetric eigenvalue problem
+#       Copyright 2013 Timo Hartmann (thartmann15 at gmail.com)
+#
+# todo:
+#  - implement balancing
+#
+##################################################################################################
+
+"""
+The symmetric eigenvalue problem.
+---------------------------------
+
+This file contains routines for the symmetric eigenvalue problem.
+
+high level routines:
+
+  eigsy : real symmetric (ordinary) eigenvalue problem
+  eighe : complex hermitian (ordinary) eigenvalue problem
+  eigh  : unified interface for eigsy and eighe
+  svd_r : singular value decomposition for real matrices
+  svd_c : singular value decomposition for complex matrices
+  svd   : unified interface for svd_r and svd_c
+
+
+low level routines:
+
+  r_sy_tridiag : reduction of real symmetric matrix to real symmetric tridiagonal matrix
+  c_he_tridiag_0 : reduction of complex hermitian matrix to real symmetric tridiagonal matrix
+  c_he_tridiag_1 : auxiliary routine to c_he_tridiag_0
+  c_he_tridiag_2 : auxiliary routine to c_he_tridiag_0
+  tridiag_eigen : solves the real symmetric tridiagonal matrix eigenvalue problem
+  svd_r_raw : raw singular value decomposition for real matrices
+  svd_c_raw : raw singular value decomposition for complex matrices
+"""
+
+from ..libmp.backend import xrange
+from .eigen import defun
+
+
+def r_sy_tridiag(ctx, A, D, E, calc_ev = True):
+    """
+    This routine transforms a real symmetric matrix A to a real symmetric
+    tridiagonal matrix T using an orthogonal similarity transformation:
+          Q' * A * Q = T     (here ' denotes the matrix transpose).
+    The orthogonal matrix Q is build up from Householder reflectors.
+
+    parameters:
+      A         (input/output) On input, A contains the real symmetric matrix of
+                dimension (n,n). On output, if calc_ev is true, A contains the
+                orthogonal matrix Q, otherwise A is destroyed.
+
+      D         (output) real array of length n, contains the diagonal elements
+                of the tridiagonal matrix
+
+      E         (output) real array of length n, contains the offdiagonal elements
+                of the tridiagonal matrix in E[0:(n-1)] where is the dimension of
+                the matrix A. E[n-1] is undefined.
+
+      calc_ev   (input) If calc_ev is true, this routine explicitly calculates the
+                orthogonal matrix Q which is then returned in A. If calc_ev is
+                false, Q is not explicitly calculated resulting in a shorter run time.
+
+    This routine is a python translation of the fortran routine tred2.f in the
+    software library EISPACK (see netlib.org) which itself is based on the algol
+    procedure tred2 described in:
+      - Num. Math. 11, p.181-195 (1968) by Martin, Reinsch and Wilkonson
+      - Handbook for auto. comp., Vol II, Linear Algebra, p.212-226 (1971)
+
+    For a good introduction to Householder reflections, see also
+      Stoer, Bulirsch - Introduction to Numerical Analysis.
+    """
+
+    # note : the vector v of the i-th houshoulder reflector is stored in a[(i+1):,i]
+    #        whereas v/<v,v> is stored in a[i,(i+1):]
+
+    n = A.rows
+    for i in xrange(n - 1, 0, -1):
+        # scale the vector
+
+        scale = 0
+        for k in xrange(0, i):
+            scale += abs(A[k,i])
+
+        scale_inv = 0
+        if scale != 0:
+            scale_inv = 1/scale
+
+        # sadly there are floating point numbers not equal to zero whose reciprocal is infinity
+
+        if i == 1 or scale == 0 or ctx.isinf(scale_inv):
+            E[i] = A[i-1,i]        # nothing to do
+            D[i] = 0
+            continue
+
+        # calculate parameters for housholder transformation
+
+        H = 0
+        for k in xrange(0, i):
+            A[k,i] *= scale_inv
+            H += A[k,i] * A[k,i]
+
+        F = A[i-1,i]
+        G = ctx.sqrt(H)
+        if F > 0:
+            G = -G
+        E[i] = scale * G
+        H -= F * G
+        A[i-1,i] = F - G
+        F = 0
+
+        # apply housholder transformation
+
+        for j in xrange(0, i):
+            if calc_ev:
+                A[i,j] = A[j,i] / H
+
+            G = 0                  # calculate A*U
+            for k in xrange(0, j + 1):
+                G += A[k,j] * A[k,i]
+            for k in xrange(j + 1, i):
+                G += A[j,k] * A[k,i]
+
+            E[j] = G / H           # calculate P
+            F += E[j] * A[j,i]
+
+        HH = F / (2 * H)
+
+        for j in xrange(0, i):     # calculate reduced A
+            F = A[j,i]
+            G = E[j] - HH * F      # calculate Q
+            E[j] = G
+
+            for k in xrange(0, j + 1):
+                A[k,j] -= F * E[k] + G * A[k,i]
+
+        D[i] = H
+
+    for i in xrange(1, n):         # better for compatibility
+        E[i-1] = E[i]
+    E[n-1] = 0
+
+    if calc_ev:
+        D[0] = 0
+        for i in xrange(0, n):
+            if D[i] != 0:
+                for j in xrange(0, i):     # accumulate transformation matrices
+                    G = 0
+                    for k in xrange(0, i):
+                        G += A[i,k] * A[k,j]
+                    for k in xrange(0, i):
+                        A[k,j] -= G * A[k,i]
+
+            D[i] = A[i,i]
+            A[i,i] = 1
+
+            for j in xrange(0, i):
+                A[j,i] = A[i,j] = 0
+    else:
+        for i in xrange(0, n):
+            D[i] = A[i,i]
+
+
+
+
+
+def c_he_tridiag_0(ctx, A, D, E, T):
+    """
+    This routine transforms a complex hermitian matrix A to a real symmetric
+    tridiagonal matrix T using an unitary similarity transformation:
+          Q' * A * Q = T     (here ' denotes the hermitian matrix transpose,
+                              i.e. transposition und conjugation).
+    The unitary matrix Q is build up from Householder reflectors and
+    an unitary diagonal matrix.
+
+    parameters:
+      A         (input/output) On input, A contains the complex hermitian matrix
+                of dimension (n,n). On output, A contains the unitary matrix Q
+                in compressed form.
+
+      D         (output) real array of length n, contains the diagonal elements
+                of the tridiagonal matrix.
+
+      E         (output) real array of length n, contains the offdiagonal elements
+                of the tridiagonal matrix in E[0:(n-1)] where is the dimension of
+                the matrix A. E[n-1] is undefined.
+
+      T         (output) complex array of length n, contains a unitary diagonal
+                matrix.
+
+    This routine is a python translation (in slightly modified form) of the fortran
+    routine htridi.f in the software library EISPACK (see netlib.org) which itself
+    is a complex version of the algol procedure tred1 described in:
+      - Num. Math. 11, p.181-195 (1968) by Martin, Reinsch and Wilkonson
+      - Handbook for auto. comp., Vol II, Linear Algebra, p.212-226 (1971)
+
+    For a good introduction to Householder reflections, see also
+      Stoer, Bulirsch - Introduction to Numerical Analysis.
+    """
+
+    n = A.rows
+    T[n-1] = 1
+    for i in xrange(n - 1, 0, -1):
+
+        # scale the vector
+
+        scale = 0
+        for k in xrange(0, i):
+            scale += abs(ctx.re(A[k,i])) + abs(ctx.im(A[k,i]))
+
+        scale_inv = 0
+        if scale != 0:
+            scale_inv = 1 / scale
+
+        # sadly there are floating point numbers not equal to zero whose reciprocal is infinity
+
+        if scale == 0 or ctx.isinf(scale_inv):
+            E[i] = 0
+            D[i] = 0
+            T[i-1] = 1
+            continue
+
+        if i == 1:
+            F = A[i-1,i]
+            f = abs(F)
+            E[i] = f
+            D[i] = 0
+            if f != 0:
+                T[i-1] = T[i] * F / f
+            else:
+                T[i-1] = T[i]
+            continue
+
+        # calculate parameters for housholder transformation
+
+        H = 0
+        for k in xrange(0, i):
+            A[k,i] *= scale_inv
+            rr = ctx.re(A[k,i])
+            ii = ctx.im(A[k,i])
+            H += rr * rr + ii * ii
+
+        F = A[i-1,i]
+        f = abs(F)
+        G = ctx.sqrt(H)
+        H += G * f
+        E[i] = scale * G
+        if f != 0:
+            F = F / f
+            TZ = - T[i] * F              # T[i-1]=-T[i]*F, but we need T[i-1] as temporary storage
+            G *= F
+        else:
+            TZ = -T[i]                   # T[i-1]=-T[i]
+        A[i-1,i] += G
+        F = 0
+
+        # apply housholder transformation
+
+        for j in xrange(0, i):
+            A[i,j] = A[j,i] / H
+
+            G = 0                        # calculate A*U
+            for k in xrange(0, j + 1):
+                G += ctx.conj(A[k,j]) * A[k,i]
+            for k in xrange(j + 1, i):
+                G += A[j,k] * A[k,i]
+
+            T[j] = G / H                 # calculate P
+            F += ctx.conj(T[j]) * A[j,i]
+
+        HH = F / (2 * H)
+
+        for j in xrange(0, i):           # calculate reduced A
+            F = A[j,i]
+            G = T[j] - HH * F            # calculate Q
+            T[j] = G
+
+            for k in xrange(0, j + 1):
+                A[k,j] -= ctx.conj(F) * T[k] + ctx.conj(G) * A[k,i]
+                # as we use the lower left part for storage
+                # we have to use the transpose of the normal formula
+
+        T[i-1] = TZ
+        D[i] = H
+
+    for i in xrange(1, n):                # better for compatibility
+        E[i-1] = E[i]
+    E[n-1] = 0
+
+    D[0] = 0
+    for i in xrange(0, n):
+        zw = D[i]
+        D[i] = ctx.re(A[i,i])
+        A[i,i] = zw
+
+
+
+
+
+
+
+def c_he_tridiag_1(ctx, A, T):
+    """
+    This routine forms the unitary matrix Q described in c_he_tridiag_0.
+
+    parameters:
+      A    (input/output) On input, A is the same matrix as delivered by
+           c_he_tridiag_0. On output, A is set to Q.
+
+      T    (input) On input, T is the same array as delivered by c_he_tridiag_0.
+
+    """
+
+    n = A.rows
+
+    for i in xrange(0, n):
+        if A[i,i] != 0:
+            for j in xrange(0, i):
+                G = 0
+                for k in xrange(0, i):
+                    G += ctx.conj(A[i,k]) * A[k,j]
+                for k in xrange(0, i):
+                    A[k,j] -= G * A[k,i]
+
+        A[i,i] = 1
+
+        for j in xrange(0, i):
+            A[j,i] = A[i,j] = 0
+
+    for i in xrange(0, n):
+        for k in xrange(0, n):
+            A[i,k] *= T[k]
+
+
+
+
+def c_he_tridiag_2(ctx, A, T, B):
+    """
+    This routine applied the unitary matrix Q described in c_he_tridiag_0
+    onto the the matrix B, i.e. it forms Q*B.
+
+    parameters:
+      A    (input) On input, A is the same matrix as delivered by c_he_tridiag_0.
+
+      T    (input) On input, T is the same array as delivered by c_he_tridiag_0.
+
+      B    (input/output) On input, B is a complex matrix. On output B is replaced
+           by Q*B.
+
+    This routine is a python translation of the fortran routine htribk.f in the
+    software library EISPACK (see netlib.org). See c_he_tridiag_0 for more
+    references.
+    """
+
+    n = A.rows
+
+    for i in xrange(0, n):
+        for k in xrange(0, n):
+            B[k,i] *= T[k]
+
+    for i in xrange(0, n):
+        if A[i,i] != 0:
+            for j in xrange(0, n):
+                G = 0
+                for k in xrange(0, i):
+                    G += ctx.conj(A[i,k]) * B[k,j]
+                for k in xrange(0, i):
+                    B[k,j] -= G * A[k,i]
+
+
+
+
+
+def tridiag_eigen(ctx, d, e, z = False):
+    """
+    This subroutine find the eigenvalues and the first components of the
+    eigenvectors of a real symmetric tridiagonal matrix using the implicit
+    QL method.
+
+    parameters:
+
+      d (input/output) real array of length n. on input, d contains the diagonal
+        elements of the input matrix. on output, d contains the eigenvalues in
+        ascending order.
+
+      e (input) real array of length n. on input, e contains the offdiagonal
+        elements of the input matrix in e[0:(n-1)]. On output, e has been
+        destroyed.
+
+      z (input/output) If z is equal to False, no eigenvectors will be computed.
+        Otherwise on input z should have the format z[0:m,0:n] (i.e. a real or
+        complex matrix of dimension (m,n) ). On output this matrix will be
+        multiplied by the matrix of the eigenvectors (i.e. the columns of this
+        matrix are the eigenvectors): z --> z*EV
+        That means if z[i,j]={1 if j==j; 0 otherwise} on input, then on output
+        z will contain the first m components of the eigenvectors. That means
+        if m is equal to n, the i-th eigenvector will be z[:,i].
+
+    This routine is a python translation (in slightly modified form) of the
+    fortran routine imtql2.f in the software library EISPACK (see netlib.org)
+    which itself is based on the algol procudure imtql2 desribed in:
+     - num. math. 12, p. 377-383(1968) by matrin and wilkinson
+     - modified in num. math. 15, p. 450(1970) by dubrulle
+     - handbook for auto. comp., vol. II-linear algebra, p. 241-248 (1971)
+    See also the routine gaussq.f in netlog.org or acm algorithm 726.
+    """
+
+    n = len(d)
+    e[n-1] = 0
+    iterlim = 2 * ctx.dps
+
+    for l in xrange(n):
+        j = 0
+        while 1:
+            m = l
+            while 1:
+                # look for a small subdiagonal element
+                if m + 1 == n:
+                    break
+                if abs(e[m]) <= ctx.eps * (abs(d[m]) + abs(d[m + 1])):
+                    break
+                m = m + 1
+            if m == l:
+                break
+
+            if j >= iterlim:
+                raise RuntimeError("tridiag_eigen: no convergence to an eigenvalue after %d iterations" % iterlim)
+
+            j += 1
+
+            # form shift
+
+            p = d[l]
+            g = (d[l + 1] - p) / (2 * e[l])
+            r = ctx.hypot(g, 1)
+
+            if g < 0:
+                s = g - r
+            else:
+                s = g + r
+
+            g = d[m] - p + e[l] / s
+
+            s, c, p = 1, 1, 0
+
+            for i in xrange(m - 1, l - 1, -1):
+                f = s * e[i]
+                b = c * e[i]
+                if abs(f) > abs(g):             # this here is a slight improvement also used in gaussq.f or acm algorithm 726.
+                    c = g / f
+                    r = ctx.hypot(c, 1)
+                    e[i + 1] = f * r
+                    s = 1 / r
+                    c = c * s
+                else:
+                    s = f / g
+                    r = ctx.hypot(s, 1)
+                    e[i + 1] = g * r
+                    c = 1 / r
+                    s = s * c
+                g = d[i + 1] - p
+                r = (d[i] - g) * s + 2 * c * b
+                p = s * r
+                d[i + 1] = g + p
+                g = c * r - b
+
+                if not isinstance(z, bool):
+                    # calculate eigenvectors
+                    for w in xrange(z.rows):
+                        f = z[w,i+1]
+                        z[w,i+1] = s * z[w,i] + c * f
+                        z[w,i  ] = c * z[w,i] - s * f
+
+            d[l] = d[l] - p
+            e[l] = g
+            e[m] = 0
+
+    for ii in xrange(1, n):
+        # sort eigenvalues and eigenvectors (bubble-sort)
+        i = ii - 1
+        k = i
+        p = d[i]
+        for j in xrange(ii, n):
+            if d[j] >= p:
+                continue
+            k = j
+            p = d[k]
+        if k == i:
+            continue
+        d[k] = d[i]
+        d[i] = p
+
+        if not isinstance(z, bool):
+            for w in xrange(z.rows):
+                p = z[w,i]
+                z[w,i] = z[w,k]
+                z[w,k] = p
+
+########################################################################################
+
+@defun
+def eigsy(ctx, A, eigvals_only = False, overwrite_a = False):
+    """
+    This routine solves the (ordinary) eigenvalue problem for a real symmetric
+    square matrix A. Given A, an orthogonal matrix Q is calculated which
+    diagonalizes A:
+
+          Q' A Q = diag(E)               and                Q Q' = Q' Q = 1
+
+    Here diag(E) is a diagonal matrix whose diagonal is E.
+    ' denotes the transpose.
+
+    The columns of Q are the eigenvectors of A and E contains the eigenvalues:
+
+          A Q[:,i] = E[i] Q[:,i]
+
+
+    input:
+
+      A: real matrix of format (n,n) which is symmetric
+         (i.e. A=A' or A[i,j]=A[j,i])
+
+      eigvals_only: if true, calculates only the eigenvalues E.
+                    if false, calculates both eigenvectors and eigenvalues.
+
+      overwrite_a: if true, allows modification of A which may improve
+                   performance. if false, A is not modified.
+
+    output:
+
+      E: vector of format (n). contains the eigenvalues of A in ascending order.
+
+      Q: orthogonal matrix of format (n,n). contains the eigenvectors
+         of A as columns.
+
+    return value:
+
+          E          if eigvals_only is true
+         (E, Q)      if eigvals_only is false
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[3, 2], [2, 0]])
+      >>> E = mp.eigsy(A, eigvals_only = True)
+      >>> print(E)
+      [-1.0]
+      [ 4.0]
+
+      >>> A = mp.matrix([[1, 2], [2, 3]])
+      >>> E, Q = mp.eigsy(A)
+      >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0]))
+      [0.0]
+      [0.0]
+
+    see also: eighe, eigh, eig
+    """
+
+    if not overwrite_a:
+        A = A.copy()
+
+    d = ctx.zeros(A.rows, 1)
+    e = ctx.zeros(A.rows, 1)
+
+    if eigvals_only:
+        r_sy_tridiag(ctx, A, d, e, calc_ev = False)
+        tridiag_eigen(ctx, d, e, False)
+        return d
+    else:
+        r_sy_tridiag(ctx, A, d, e, calc_ev = True)
+        tridiag_eigen(ctx, d, e, A)
+        return (d, A)
+
+
+@defun
+def eighe(ctx, A, eigvals_only = False, overwrite_a = False):
+    """
+    This routine solves the (ordinary) eigenvalue problem for a complex
+    hermitian square matrix A. Given A, an unitary matrix Q is calculated which
+    diagonalizes A:
+
+        Q' A Q = diag(E)               and                Q Q' = Q' Q = 1
+
+    Here diag(E) a is diagonal matrix whose diagonal is E.
+    ' denotes the hermitian transpose (i.e. ordinary transposition and
+    complex conjugation).
+
+    The columns of Q are the eigenvectors of A and E contains the eigenvalues:
+
+        A Q[:,i] = E[i] Q[:,i]
+
+
+    input:
+
+      A: complex matrix of format (n,n) which is hermitian
+         (i.e. A=A' or A[i,j]=conj(A[j,i]))
+
+      eigvals_only: if true, calculates only the eigenvalues E.
+                    if false, calculates both eigenvectors and eigenvalues.
+
+      overwrite_a: if true, allows modification of A which may improve
+                   performance. if false, A is not modified.
+
+    output:
+
+      E: vector of format (n). contains the eigenvalues of A in ascending order.
+
+      Q: unitary matrix of format (n,n). contains the eigenvectors
+         of A as columns.
+
+    return value:
+
+           E         if eigvals_only is true
+          (E, Q)     if eigvals_only is false
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[1, -3 - 1j], [-3 + 1j, -2]])
+      >>> E = mp.eighe(A, eigvals_only = True)
+      >>> print(E)
+      [-4.0]
+      [ 3.0]
+
+      >>> A = mp.matrix([[1, 2 + 5j], [2 - 5j, 3]])
+      >>> E, Q = mp.eighe(A)
+      >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0]))
+      [0.0]
+      [0.0]
+
+    see also: eigsy, eigh, eig
+    """
+
+    if not overwrite_a:
+        A = A.copy()
+
+    d = ctx.zeros(A.rows, 1)
+    e = ctx.zeros(A.rows, 1)
+    t = ctx.zeros(A.rows, 1)
+
+    if eigvals_only:
+        c_he_tridiag_0(ctx, A, d, e, t)
+        tridiag_eigen(ctx, d, e, False)
+        return d
+    else:
+        c_he_tridiag_0(ctx, A, d, e, t)
+        B = ctx.eye(A.rows)
+        tridiag_eigen(ctx, d, e, B)
+        c_he_tridiag_2(ctx, A, t, B)
+        return (d, B)
+
+@defun
+def eigh(ctx, A, eigvals_only = False, overwrite_a = False):
+    """
+    "eigh" is a unified interface for "eigsy" and "eighe". Depending on
+    whether A is real or complex the appropriate function is called.
+
+    This routine solves the (ordinary) eigenvalue problem for a real symmetric
+    or complex hermitian square matrix A. Given A, an orthogonal (A real) or
+    unitary (A complex) matrix Q is calculated which diagonalizes A:
+
+        Q' A Q = diag(E)               and                Q Q' = Q' Q = 1
+
+    Here diag(E) a is diagonal matrix whose diagonal is E.
+    ' denotes the hermitian transpose (i.e. ordinary transposition and
+    complex conjugation).
+
+    The columns of Q are the eigenvectors of A and E contains the eigenvalues:
+
+        A Q[:,i] = E[i] Q[:,i]
+
+    input:
+
+      A: a real or complex square matrix of format (n,n) which is symmetric
+         (i.e. A[i,j]=A[j,i]) or hermitian (i.e. A[i,j]=conj(A[j,i])).
+
+      eigvals_only: if true, calculates only the eigenvalues E.
+                    if false, calculates both eigenvectors and eigenvalues.
+
+      overwrite_a: if true, allows modification of A which may improve
+                   performance. if false, A is not modified.
+
+    output:
+
+      E: vector of format (n). contains the eigenvalues of A in ascending order.
+
+      Q: an orthogonal or unitary matrix of format (n,n). contains the
+         eigenvectors of A as columns.
+
+    return value:
+
+          E         if eigvals_only is true
+         (E, Q)     if eigvals_only is false
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[3, 2], [2, 0]])
+      >>> E = mp.eigh(A, eigvals_only = True)
+      >>> print(E)
+      [-1.0]
+      [ 4.0]
+
+      >>> A = mp.matrix([[1, 2], [2, 3]])
+      >>> E, Q = mp.eigh(A)
+      >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0]))
+      [0.0]
+      [0.0]
+
+      >>> A = mp.matrix([[1, 2 + 5j], [2 - 5j, 3]])
+      >>> E, Q = mp.eigh(A)
+      >>> print(mp.chop(A * Q[:,0] - E[0] * Q[:,0]))
+      [0.0]
+      [0.0]
+
+    see also: eigsy, eighe, eig
+    """
+
+    iscomplex = any(type(x) is ctx.mpc for x in A)
+
+    if iscomplex:
+        return ctx.eighe(A, eigvals_only = eigvals_only, overwrite_a = overwrite_a)
+    else:
+        return ctx.eigsy(A, eigvals_only = eigvals_only, overwrite_a = overwrite_a)
+
+
+@defun
+def gauss_quadrature(ctx, n, qtype = "legendre", alpha = 0, beta = 0):
+    """
+    This routine calulates gaussian quadrature rules for different
+    families of orthogonal polynomials. Let (a, b) be an interval,
+    W(x) a positive weight function and n a positive integer.
+    Then the purpose of this routine is to calculate pairs (x_k, w_k)
+    for k=0, 1, 2, ... (n-1) which give
+
+      int(W(x) * F(x), x = a..b) = sum(w_k * F(x_k),k = 0..(n-1))
+
+    exact for all polynomials F(x) of degree (strictly) less than 2*n. For all
+    integrable functions F(x) the sum is a (more or less) good approximation to
+    the integral. The x_k are called nodes (which are the zeros of the
+    related orthogonal polynomials) and the w_k are called the weights.
+
+    parameters
+       n        (input) The degree of the quadrature rule, i.e. its number of
+                nodes.
+
+       qtype    (input) The family of orthogonal polynmomials for which to
+                compute the quadrature rule. See the list below.
+
+       alpha    (input) real number, used as parameter for some orthogonal
+                polynomials
+
+       beta     (input) real number, used as parameter for some orthogonal
+                polynomials.
+
+    return value
+
+      (X, W)    a pair of two real arrays where x_k = X[k] and w_k = W[k].
+
+
+    orthogonal polynomials:
+
+      qtype           polynomial
+      -----           ----------
+
+      "legendre"      Legendre polynomials, W(x)=1 on the interval (-1, +1)
+      "legendre01"    shifted Legendre polynomials, W(x)=1 on the interval (0, +1)
+      "hermite"       Hermite polynomials, W(x)=exp(-x*x) on (-infinity,+infinity)
+      "laguerre"      Laguerre polynomials, W(x)=exp(-x) on (0,+infinity)
+      "glaguerre"     generalized Laguerre polynomials, W(x)=exp(-x)*x**alpha
+                      on (0, +infinity)
+      "chebyshev1"    Chebyshev polynomials of the first kind, W(x)=1/sqrt(1-x*x)
+                      on (-1, +1)
+      "chebyshev2"    Chebyshev polynomials of the second kind, W(x)=sqrt(1-x*x)
+                      on (-1, +1)
+      "jacobi"        Jacobi polynomials, W(x)=(1-x)**alpha * (1+x)**beta on (-1, +1)
+                      with alpha>-1 and beta>-1
+
+    examples:
+      >>> from mpmath import mp
+      >>> f = lambda x: x**8 + 2 * x**6 - 3 * x**4 + 5 * x**2 - 7
+      >>> X, W = mp.gauss_quadrature(5, "hermite")
+      >>> A = mp.fdot([(f(x), w) for x, w in zip(X, W)])
+      >>> B = mp.sqrt(mp.pi) * 57 / 16
+      >>> C = mp.quad(lambda x: mp.exp(- x * x) * f(x), [-mp.inf, +mp.inf])
+      >>> mp.nprint((mp.chop(A-B, tol = 1e-10), mp.chop(A-C, tol = 1e-10)))
+      (0.0, 0.0)
+
+      >>> f = lambda x: x**5 - 2 * x**4 + 3 * x**3 - 5 * x**2 + 7 * x - 11
+      >>> X, W = mp.gauss_quadrature(3, "laguerre")
+      >>> A = mp.fdot([(f(x), w) for x, w in zip(X, W)])
+      >>> B = 76
+      >>> C = mp.quad(lambda x: mp.exp(-x) * f(x), [0, +mp.inf])
+      >>> mp.nprint(mp.chop(A-B, tol = 1e-10), mp.chop(A-C, tol = 1e-10))
+      .0
+
+      # orthogonality of the chebyshev polynomials:
+      >>> f = lambda x: mp.chebyt(3, x) * mp.chebyt(2, x)
+      >>> X, W = mp.gauss_quadrature(3, "chebyshev1")
+      >>> A = mp.fdot([(f(x), w) for x, w in zip(X, W)])
+      >>> print(mp.chop(A, tol = 1e-10))
+      0.0
+
+    references:
+      - golub and welsch, "calculations of gaussian quadrature rules", mathematics of
+        computation 23, p. 221-230 (1969)
+      - golub, "some modified matrix eigenvalue problems", siam review 15, p. 318-334 (1973)
+      - stroud and secrest, "gaussian quadrature formulas", prentice-hall (1966)
+
+    See also the routine gaussq.f in netlog.org or ACM Transactions on
+    Mathematical Software algorithm 726.
+    """
+
+    d = ctx.zeros(n, 1)
+    e = ctx.zeros(n, 1)
+    z = ctx.zeros(1, n)
+
+    z[0,0] = 1
+
+    if qtype == "legendre":
+        # legendre on the range -1 +1 , abramowitz, table 25.4, p.916
+        w = 2
+        for i in xrange(n):
+            j = i + 1
+            e[i] = ctx.sqrt(j * j / (4 * j * j - ctx.mpf(1)))
+    elif qtype == "legendre01":
+        # legendre shifted to 0 1        , abramowitz, table 25.8, p.921
+        w = 1
+        for i in xrange(n):
+            d[i] = 1 / ctx.mpf(2)
+            j = i + 1
+            e[i] = ctx.sqrt(j * j / (16 * j * j - ctx.mpf(4)))
+    elif qtype == "hermite":
+        # hermite on the range -inf +inf , abramowitz, table 25.10,p.924
+        w = ctx.sqrt(ctx.pi)
+        for i in xrange(n):
+            j = i + 1
+            e[i] = ctx.sqrt(j / ctx.mpf(2))
+    elif qtype == "laguerre":
+        # laguerre on the range 0 +inf , abramowitz, table 25.9, p. 923
+        w = 1
+        for i in xrange(n):
+            j = i + 1
+            d[i] = 2 * j - 1
+            e[i] = j
+    elif qtype=="chebyshev1":
+        # chebyshev polynimials of the first kind
+        w = ctx.pi
+        for i in xrange(n):
+            e[i] = 1 / ctx.mpf(2)
+        e[0] = ctx.sqrt(1 / ctx.mpf(2))
+    elif qtype == "chebyshev2":
+        # chebyshev polynimials of the second kind
+        w = ctx.pi / 2
+        for i in xrange(n):
+            e[i] = 1 / ctx.mpf(2)
+    elif qtype == "glaguerre":
+        # generalized laguerre on the range 0 +inf
+        w = ctx.gamma(1 + alpha)
+        for i in xrange(n):
+            j = i + 1
+            d[i] = 2 * j - 1 + alpha
+            e[i] = ctx.sqrt(j * (j + alpha))
+    elif qtype == "jacobi":
+        # jacobi polynomials
+        alpha = ctx.mpf(alpha)
+        beta = ctx.mpf(beta)
+        ab = alpha + beta
+        abi = ab + 2
+        w = (2**(ab+1)) * ctx.gamma(alpha + 1) * ctx.gamma(beta + 1) / ctx.gamma(abi)
+        d[0] = (beta - alpha) / abi
+        e[0] = ctx.sqrt(4 * (1 + alpha) * (1 + beta) / ((abi + 1) * (abi * abi)))
+        a2b2 = beta * beta - alpha * alpha
+        for i in xrange(1, n):
+            j = i + 1
+            abi = 2 * j + ab
+            d[i] = a2b2 / ((abi - 2) * abi)
+            e[i] = ctx.sqrt(4 * j * (j + alpha) * (j + beta) * (j + ab) / ((abi * abi - 1) * abi * abi))
+    elif isinstance(qtype, str):
+        raise ValueError("unknown quadrature rule \"%s\"" % qtype)
+    elif not isinstance(qtype, str):
+        w = qtype(d, e)
+    else:
+        assert 0
+
+    tridiag_eigen(ctx, d, e, z)
+
+    for i in xrange(len(z)):
+        z[i] *= z[i]
+
+    z = z.transpose()
+    return (d, w * z)
+
+##################################################################################################
+##################################################################################################
+##################################################################################################
+
+def svd_r_raw(ctx, A, V = False, calc_u = False):
+    """
+    This routine computes the singular value decomposition of a matrix A.
+    Given A, two orthogonal matrices U and V are calculated such that
+
+                    A = U S V
+
+    where S is a suitable shaped matrix whose off-diagonal elements are zero.
+    The diagonal elements of S are the singular values of A, i.e. the
+    squareroots of the eigenvalues of A' A or A A'. Here ' denotes the transpose.
+    Householder bidiagonalization and a variant of the QR algorithm is used.
+
+    overview of the matrices :
+
+      A : m*n       A gets replaced by U
+      U : m*n       U replaces A. If n>m then only the first m*m block of U is
+                    non-zero. column-orthogonal: U' U = B
+                    here B is a n*n matrix whose first min(m,n) diagonal
+                    elements are 1 and all other elements are zero.
+      S : n*n       diagonal matrix, only the diagonal elements are stored in
+                    the array S. only the first min(m,n) diagonal elements are non-zero.
+      V : n*n       orthogonal: V V' = V' V = 1
+
+    parameters:
+      A        (input/output) On input, A contains a real matrix of shape m*n.
+               On output, if calc_u is true A contains the column-orthogonal
+               matrix U; otherwise A is simply used as workspace and thus destroyed.
+
+      V        (input/output) if false, the matrix V is not calculated. otherwise
+               V must be a matrix of shape n*n.
+
+      calc_u   (input) If true, the matrix U is calculated and replaces A.
+               if false, U is not calculated and A is simply destroyed
+
+    return value:
+      S        an array of length n containing the singular values of A sorted by
+               decreasing magnitude. only the first min(m,n) elements are non-zero.
+
+    This routine is a python translation of the fortran routine svd.f in the
+    software library EISPACK (see netlib.org) which itself is based on the
+    algol procedure svd described in:
+      - num. math. 14, 403-420(1970) by golub and reinsch.
+      - wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971).
+
+    """
+
+    m, n = A.rows, A.cols
+
+    S = ctx.zeros(n, 1)
+
+    # work is a temporary array of size n
+    work = ctx.zeros(n, 1)
+
+    g = scale = anorm = 0
+    maxits = 3 * ctx.dps
+
+    for i in xrange(n):     # householder reduction to bidiagonal form
+        work[i] = scale*g
+        g = s = scale = 0
+        if i < m:
+            for k in xrange(i, m):
+                scale += ctx.fabs(A[k,i])
+            if scale != 0:
+                for k in xrange(i, m):
+                    A[k,i] /= scale
+                    s += A[k,i] * A[k,i]
+                f = A[i,i]
+                g = -ctx.sqrt(s)
+                if f < 0:
+                    g = -g
+                h = f * g - s
+                A[i,i] = f - g
+                for j in xrange(i+1, n):
+                    s = 0
+                    for k in xrange(i, m):
+                        s += A[k,i] * A[k,j]
+                    f = s / h
+                    for k in xrange(i, m):
+                        A[k,j] += f * A[k,i]
+                for k in xrange(i,m):
+                    A[k,i] *= scale
+
+        S[i] = scale * g
+        g = s = scale = 0
+
+        if i < m and i != n - 1:
+            for k in xrange(i+1, n):
+                scale += ctx.fabs(A[i,k])
+            if scale:
+                for k in xrange(i+1, n):
+                    A[i,k] /= scale
+                    s += A[i,k] * A[i,k]
+                f = A[i,i+1]
+                g = -ctx.sqrt(s)
+                if f < 0:
+                    g = -g
+                h = f * g - s
+                A[i,i+1] = f - g
+
+                for k in xrange(i+1, n):
+                    work[k] = A[i,k] / h
+
+                for j in xrange(i+1, m):
+                    s = 0
+                    for k in xrange(i+1, n):
+                        s += A[j,k] * A[i,k]
+                    for k in xrange(i+1, n):
+                        A[j,k] += s * work[k]
+
+                for k in xrange(i+1, n):
+                    A[i,k] *= scale
+
+        anorm = max(anorm, ctx.fabs(S[i]) + ctx.fabs(work[i]))
+
+    if not isinstance(V, bool):
+        for i in xrange(n-2, -1, -1):     # accumulation of right hand transformations
+            V[i+1,i+1] = 1
+
+            if work[i+1] != 0:
+                for j in xrange(i+1, n):
+                    V[i,j] = (A[i,j] / A[i,i+1]) / work[i+1]
+                for j in xrange(i+1, n):
+                    s = 0
+                    for k in xrange(i+1, n):
+                        s += A[i,k] * V[j,k]
+                    for k in xrange(i+1, n):
+                        V[j,k] += s * V[i,k]
+
+            for j in xrange(i+1, n):
+                V[j,i] = V[i,j] = 0
+
+        V[0,0] = 1
+
+    if m<n : minnm = m
+    else   : minnm = n
+
+    if calc_u:
+        for i in xrange(minnm-1, -1, -1): # accumulation of left hand transformations
+            g = S[i]
+            for j in xrange(i+1, n):
+                A[i,j] = 0
+            if g != 0:
+                g = 1 / g
+                for j in xrange(i+1, n):
+                    s = 0
+                    for k in xrange(i+1, m):
+                        s += A[k,i] * A[k,j]
+                    f = (s / A[i,i]) * g
+                    for k in xrange(i, m):
+                        A[k,j] += f * A[k,i]
+                for j in xrange(i, m):
+                    A[j,i] *= g
+            else:
+                for j in xrange(i, m):
+                    A[j,i] = 0
+            A[i,i] += 1
+
+    for k in xrange(n - 1, -1, -1):
+        # diagonalization of the bidiagonal form:
+        #   loop over singular values, and over allowed itations
+
+        its = 0
+        while 1:
+            its += 1
+            flag = True
+
+            for l in xrange(k, -1, -1):
+                nm = l-1
+
+                if ctx.fabs(work[l]) + anorm == anorm:
+                    flag = False
+                    break
+
+                if ctx.fabs(S[nm]) + anorm == anorm:
+                    break
+
+            if flag:
+                c = 0
+                s = 1
+                for i in xrange(l, k + 1):
+                    f = s * work[i]
+                    work[i] *= c
+                    if ctx.fabs(f) + anorm == anorm:
+                        break
+                    g = S[i]
+                    h = ctx.hypot(f, g)
+                    S[i] = h
+                    h = 1 / h
+                    c = g * h
+                    s = - f * h
+
+                    if calc_u:
+                        for j in xrange(m):
+                            y = A[j,nm]
+                            z = A[j,i]
+                            A[j,nm] = y * c + z * s
+                            A[j,i]  = z * c - y * s
+
+            z = S[k]
+
+            if l == k:               # convergence
+                if z < 0:            # singular value is made nonnegative
+                    S[k] = -z
+                    if not isinstance(V, bool):
+                        for j in xrange(n):
+                            V[k,j] = -V[k,j]
+                break
+
+            if its >= maxits:
+                raise RuntimeError("svd: no convergence to an eigenvalue after %d iterations" % its)
+
+            x = S[l]         # shift from bottom 2 by 2 minor
+            nm = k-1
+            y = S[nm]
+            g = work[nm]
+            h = work[k]
+            f = ((y - z) * (y + z) + (g - h) * (g + h))/(2 * h * y)
+            g = ctx.hypot(f, 1)
+            if f >= 0: f = ((x - z) * (x + z) + h * ((y / (f + g)) - h)) / x
+            else:      f = ((x - z) * (x + z) + h * ((y / (f - g)) - h)) / x
+
+            c = s = 1         # next qt transformation
+
+            for j in xrange(l, nm + 1):
+                g = work[j+1]
+                y = S[j+1]
+                h = s * g
+                g = c * g
+                z = ctx.hypot(f, h)
+                work[j] = z
+                c = f / z
+                s = h / z
+                f = x * c + g * s
+                g = g * c - x * s
+                h = y * s
+                y *= c
+                if not isinstance(V, bool):
+                    for jj in xrange(n):
+                        x = V[j  ,jj]
+                        z = V[j+1,jj]
+                        V[j    ,jj]= x * c + z * s
+                        V[j+1  ,jj]= z * c - x * s
+                z = ctx.hypot(f, h)
+                S[j] = z
+                if z != 0:            # rotation can be arbitray if z=0
+                    z = 1 / z
+                    c = f * z
+                    s = h * z
+                f = c * g + s * y
+                x = c * y - s * g
+
+                if calc_u:
+                    for jj in xrange(m):
+                        y = A[jj,j  ]
+                        z = A[jj,j+1]
+                        A[jj,j    ] = y * c + z * s
+                        A[jj,j+1  ] = z * c - y * s
+
+            work[l] = 0
+            work[k] = f
+            S[k] = x
+
+    ##########################
+
+    # Sort singular values into decreasing order (bubble-sort)
+
+    for i in xrange(n):
+        imax = i
+        s = ctx.fabs(S[i])         # s is the current maximal element
+
+        for j in xrange(i + 1, n):
+            c = ctx.fabs(S[j])
+            if c > s:
+                s = c
+                imax = j
+
+        if imax != i:
+            # swap singular values
+
+            z = S[i]
+            S[i] = S[imax]
+            S[imax] = z
+
+            if calc_u:
+                for j in xrange(m):
+                    z = A[j,i]
+                    A[j,i] = A[j,imax]
+                    A[j,imax] = z
+
+            if not isinstance(V, bool):
+                for j in xrange(n):
+                    z = V[i,j]
+                    V[i,j] = V[imax,j]
+                    V[imax,j] = z
+
+    return S
+
+#######################
+
+def svd_c_raw(ctx, A, V = False, calc_u = False):
+    """
+    This routine computes the singular value decomposition of a matrix A.
+    Given A, two unitary matrices U and V are calculated such that
+
+                    A = U S V
+
+    where S is a suitable shaped matrix whose off-diagonal elements are zero.
+    The diagonal elements of S are the singular values of A, i.e. the
+    squareroots of the eigenvalues of A' A or A A'. Here ' denotes the hermitian
+    transpose (i.e. transposition and conjugation). Householder bidiagonalization
+    and a variant of the QR algorithm is used.
+
+    overview of the matrices :
+
+      A : m*n       A gets replaced by U
+      U : m*n       U replaces A. If n>m then only the first m*m block of U is
+                    non-zero. column-unitary: U' U = B
+                    here B is a n*n matrix whose first min(m,n) diagonal
+                    elements are 1 and all other elements are zero.
+      S : n*n       diagonal matrix, only the diagonal elements are stored in
+                    the array S. only the first min(m,n) diagonal elements are non-zero.
+      V : n*n       unitary: V V' = V' V = 1
+
+    parameters:
+      A        (input/output) On input, A contains a complex matrix of shape m*n.
+               On output, if calc_u is true A contains the column-unitary
+               matrix U; otherwise A is simply used as workspace and thus destroyed.
+
+      V        (input/output) if false, the matrix V is not calculated. otherwise
+               V must be a matrix of shape n*n.
+
+      calc_u   (input) If true, the matrix U is calculated and replaces A.
+               if false, U is not calculated and A is simply destroyed
+
+    return value:
+      S        an array of length n containing the singular values of A sorted by
+               decreasing magnitude. only the first min(m,n) elements are non-zero.
+
+    This routine is a python translation of the fortran routine svd.f in the
+    software library EISPACK (see netlib.org) which itself is based on the
+    algol procedure svd described in:
+      - num. math. 14, 403-420(1970) by golub and reinsch.
+      - wilkinson/reinsch: handbook for auto. comp., vol ii-linear algebra, 134-151(1971).
+
+    """
+
+    m, n = A.rows, A.cols
+
+    S = ctx.zeros(n, 1)
+
+    # work is a temporary array of size n
+    work  = ctx.zeros(n, 1)
+    lbeta = ctx.zeros(n, 1)
+    rbeta = ctx.zeros(n, 1)
+    dwork = ctx.zeros(n, 1)
+
+    g = scale = anorm = 0
+    maxits = 3 * ctx.dps
+
+    for i in xrange(n):         # householder reduction to bidiagonal form
+        dwork[i] = scale * g    # dwork are the side-diagonal elements
+        g = s = scale = 0
+        if i < m:
+            for k in xrange(i, m):
+                scale += ctx.fabs(ctx.re(A[k,i])) + ctx.fabs(ctx.im(A[k,i]))
+            if scale != 0:
+                for k in xrange(i, m):
+                    A[k,i] /= scale
+                    ar = ctx.re(A[k,i])
+                    ai = ctx.im(A[k,i])
+                    s += ar * ar + ai * ai
+                f = A[i,i]
+                g = -ctx.sqrt(s)
+                if ctx.re(f) < 0:
+                    beta = -g - ctx.conj(f)
+                    g = -g
+                else:
+                    beta = -g + ctx.conj(f)
+                beta /= ctx.conj(beta)
+                beta += 1
+                h = 2 * (ctx.re(f) * g - s)
+                A[i,i] = f - g
+                beta /= h
+                lbeta[i] = (beta / scale) / scale
+                for j in xrange(i+1, n):
+                    s = 0
+                    for k in xrange(i, m):
+                        s += ctx.conj(A[k,i]) * A[k,j]
+                    f = beta * s
+                    for k in xrange(i, m):
+                        A[k,j] += f * A[k,i]
+                for k in xrange(i, m):
+                    A[k,i] *= scale
+
+        S[i] = scale * g     # S are the diagonal elements
+        g = s = scale = 0
+
+        if i < m and i != n - 1:
+            for k in xrange(i+1, n):
+                scale += ctx.fabs(ctx.re(A[i,k])) + ctx.fabs(ctx.im(A[i,k]))
+            if scale:
+                for k in xrange(i+1, n):
+                    A[i,k] /= scale
+                    ar = ctx.re(A[i,k])
+                    ai = ctx.im(A[i,k])
+                    s += ar * ar + ai * ai
+                f = A[i,i+1]
+                g = -ctx.sqrt(s)
+                if ctx.re(f) < 0:
+                    beta = -g - ctx.conj(f)
+                    g = -g
+                else:
+                    beta = -g + ctx.conj(f)
+
+                beta /= ctx.conj(beta)
+                beta += 1
+
+                h = 2 * (ctx.re(f) * g - s)
+                A[i,i+1] = f - g
+
+                beta /= h
+                rbeta[i] = (beta / scale) / scale
+
+                for k in xrange(i+1, n):
+                    work[k] = A[i, k]
+
+                for j in xrange(i+1, m):
+                    s = 0
+                    for k in xrange(i+1, n):
+                        s += ctx.conj(A[i,k]) * A[j,k]
+                    f = s * beta
+                    for k in xrange(i+1,n):
+                        A[j,k] += f * work[k]
+
+                for k in xrange(i+1, n):
+                    A[i,k] *= scale
+
+        anorm = max(anorm,ctx.fabs(S[i]) + ctx.fabs(dwork[i]))
+
+    if not isinstance(V, bool):
+        for i in xrange(n-2, -1, -1):     # accumulation of right hand transformations
+            V[i+1,i+1] = 1
+
+            if dwork[i+1] != 0:
+                f = ctx.conj(rbeta[i])
+                for j in xrange(i+1, n):
+                    V[i,j] = A[i,j] * f
+                for j in xrange(i+1, n):
+                    s = 0
+                    for k in xrange(i+1, n):
+                        s += ctx.conj(A[i,k]) * V[j,k]
+                    for k in xrange(i+1, n):
+                        V[j,k] += s * V[i,k]
+
+            for j in xrange(i+1,n):
+                V[j,i] = V[i,j] = 0
+
+        V[0,0] = 1
+
+    if m < n : minnm = m
+    else     : minnm = n
+
+    if calc_u:
+        for i in xrange(minnm-1, -1, -1): # accumulation of left hand transformations
+            g = S[i]
+            for j in xrange(i+1, n):
+                A[i,j] = 0
+            if g != 0:
+                g = 1 / g
+                for j in xrange(i+1, n):
+                    s = 0
+                    for k in xrange(i+1, m):
+                        s += ctx.conj(A[k,i]) * A[k,j]
+                    f = s * ctx.conj(lbeta[i])
+                    for k in xrange(i, m):
+                        A[k,j] += f * A[k,i]
+                for j in xrange(i, m):
+                    A[j,i] *= g
+            else:
+                for j in xrange(i, m):
+                    A[j,i] = 0
+            A[i,i] += 1
+
+    for k in xrange(n-1, -1, -1):
+        # diagonalization of the bidiagonal form:
+        #   loop over singular values, and over allowed itations
+
+        its = 0
+        while 1:
+            its += 1
+            flag = True
+
+            for l in xrange(k, -1, -1):
+                nm = l - 1
+
+                if ctx.fabs(dwork[l]) + anorm == anorm:
+                    flag = False
+                    break
+
+                if ctx.fabs(S[nm]) + anorm == anorm:
+                    break
+
+            if flag:
+                c = 0
+                s = 1
+                for i in xrange(l, k+1):
+                    f = s * dwork[i]
+                    dwork[i] *= c
+                    if ctx.fabs(f) + anorm == anorm:
+                        break
+                    g = S[i]
+                    h = ctx.hypot(f, g)
+                    S[i] = h
+                    h = 1 / h
+                    c = g * h
+                    s = -f * h
+
+                    if calc_u:
+                        for j in xrange(m):
+                            y = A[j,nm]
+                            z = A[j,i]
+                            A[j,nm]= y * c + z * s
+                            A[j,i] = z * c - y * s
+
+            z = S[k]
+
+            if l == k:         # convergence
+                if z < 0:    # singular value is made nonnegative
+                    S[k] = -z
+                    if not isinstance(V, bool):
+                        for j in xrange(n):
+                            V[k,j] = -V[k,j]
+                break
+
+            if its >= maxits:
+                raise RuntimeError("svd: no convergence to an eigenvalue after %d iterations" % its)
+
+            x = S[l]         # shift from bottom 2 by 2 minor
+            nm = k-1
+            y = S[nm]
+            g = dwork[nm]
+            h = dwork[k]
+            f = ((y - z) * (y + z) + (g - h) * (g + h)) / (2 * h * y)
+            g = ctx.hypot(f, 1)
+            if f >=0: f = (( x - z) *( x + z) + h *((y / (f + g)) - h)) / x
+            else:     f = (( x - z) *( x + z) + h *((y / (f - g)) - h)) / x
+
+            c = s = 1         # next qt transformation
+
+            for j in xrange(l, nm + 1):
+                g = dwork[j+1]
+                y = S[j+1]
+                h = s * g
+                g = c * g
+                z = ctx.hypot(f, h)
+                dwork[j] = z
+                c = f / z
+                s = h / z
+                f = x * c + g * s
+                g = g * c - x * s
+                h = y * s
+                y *= c
+                if not isinstance(V, bool):
+                    for jj in xrange(n):
+                        x = V[j  ,jj]
+                        z = V[j+1,jj]
+                        V[j    ,jj]= x * c + z * s
+                        V[j+1,jj  ]= z * c - x * s
+                z = ctx.hypot(f, h)
+                S[j] = z
+                if z != 0:            # rotation can be arbitray if z=0
+                    z = 1 / z
+                    c = f * z
+                    s = h * z
+                f = c * g + s * y
+                x = c * y - s * g
+                if calc_u:
+                    for jj in xrange(m):
+                        y = A[jj,j  ]
+                        z = A[jj,j+1]
+                        A[jj,j    ]= y * c + z * s
+                        A[jj,j+1  ]= z * c - y * s
+
+            dwork[l] = 0
+            dwork[k] = f
+            S[k] = x
+
+    ##########################
+
+    # Sort singular values into decreasing order (bubble-sort)
+
+    for i in xrange(n):
+        imax = i
+        s = ctx.fabs(S[i])         # s is the current maximal element
+
+        for j in xrange(i + 1, n):
+            c = ctx.fabs(S[j])
+            if c > s:
+                s = c
+                imax = j
+
+        if imax != i:
+            # swap singular values
+
+            z = S[i]
+            S[i] = S[imax]
+            S[imax] = z
+
+            if calc_u:
+                for j in xrange(m):
+                    z = A[j,i]
+                    A[j,i] = A[j,imax]
+                    A[j,imax] = z
+
+            if not isinstance(V, bool):
+                for j in xrange(n):
+                    z = V[i,j]
+                    V[i,j] = V[imax,j]
+                    V[imax,j] = z
+
+    return S
+
+##################################################################################################
+
+@defun
+def svd_r(ctx, A, full_matrices = False, compute_uv = True, overwrite_a = False):
+    """
+    This routine computes the singular value decomposition of a matrix A.
+    Given A, two orthogonal matrices U and V are calculated such that
+
+           A = U S V        and        U' U = 1         and         V V' = 1
+
+    where S is a suitable shaped matrix whose off-diagonal elements are zero.
+    Here ' denotes the transpose. The diagonal elements of S are the singular
+    values of A, i.e. the squareroots of the eigenvalues of A' A or A A'.
+
+    input:
+      A             : a real matrix of shape (m, n)
+      full_matrices : if true, U and V are of shape (m, m) and (n, n).
+                      if false, U and V are of shape (m, min(m, n)) and (min(m, n), n).
+      compute_uv    : if true, U and V are calculated. if false, only S is calculated.
+      overwrite_a   : if true, allows modification of A which may improve
+                      performance. if false, A is not modified.
+
+    output:
+      U : an orthogonal matrix: U' U = 1. if full_matrices is true, U is of
+          shape (m, m). ortherwise it is of shape (m, min(m, n)).
+
+      S : an array of length min(m, n) containing the singular values of A sorted by
+          decreasing magnitude.
+
+      V : an orthogonal matrix: V V' = 1. if full_matrices is true, V is of
+          shape (n, n). ortherwise it is of shape (min(m, n), n).
+
+    return value:
+
+           S          if compute_uv is false
+       (U, S, V)      if compute_uv is true
+
+    overview of the matrices:
+
+      full_matrices true:
+        A           : m*n
+        U           : m*m     U' U  = 1
+        S as matrix : m*n
+        V           : n*n     V  V' = 1
+
+     full_matrices false:
+        A           : m*n
+        U           : m*min(n,m)             U' U  = 1
+        S as matrix : min(m,n)*min(m,n)
+        V           : min(m,n)*n             V  V' = 1
+
+    examples:
+
+       >>> from mpmath import mp
+       >>> A = mp.matrix([[2, -2, -1], [3, 4, -2], [-2, -2, 0]])
+       >>> S = mp.svd_r(A, compute_uv = False)
+       >>> print(S)
+       [6.0]
+       [3.0]
+       [1.0]
+
+       >>> U, S, V = mp.svd_r(A)
+       >>> print(mp.chop(A - U * mp.diag(S) * V))
+       [0.0  0.0  0.0]
+       [0.0  0.0  0.0]
+       [0.0  0.0  0.0]
+
+
+    see also: svd, svd_c
+    """
+
+    m, n = A.rows, A.cols
+
+    if not compute_uv:
+        if not overwrite_a:
+            A = A.copy()
+        S = svd_r_raw(ctx, A, V = False, calc_u = False)
+        S = S[:min(m,n)]
+        return S
+
+    if full_matrices and n < m:
+        V = ctx.zeros(m, m)
+        A0 = ctx.zeros(m, m)
+        A0[:,:n] = A
+        S = svd_r_raw(ctx, A0, V, calc_u = True)
+
+        S = S[:n]
+        V = V[:n,:n]
+
+        return (A0, S, V)
+    else:
+        if not overwrite_a:
+            A = A.copy()
+        V = ctx.zeros(n, n)
+        S = svd_r_raw(ctx, A, V, calc_u = True)
+
+        if n > m:
+            if full_matrices == False:
+                V = V[:m,:]
+
+            S = S[:m]
+            A = A[:,:m]
+
+        return (A, S, V)
+
+##############################
+
+@defun
+def svd_c(ctx, A, full_matrices = False, compute_uv = True, overwrite_a = False):
+    """
+    This routine computes the singular value decomposition of a matrix A.
+    Given A, two unitary matrices U and V are calculated such that
+
+           A = U S V        and        U' U = 1         and         V V' = 1
+
+    where S is a suitable shaped matrix whose off-diagonal elements are zero.
+    Here ' denotes the hermitian transpose (i.e. transposition and complex
+    conjugation). The diagonal elements of S are the singular values of A,
+    i.e. the squareroots of the eigenvalues of A' A or A A'.
+
+    input:
+      A             : a complex matrix of shape (m, n)
+      full_matrices : if true, U and V are of shape (m, m) and (n, n).
+                      if false, U and V are of shape (m, min(m, n)) and (min(m, n), n).
+      compute_uv    : if true, U and V are calculated. if false, only S is calculated.
+      overwrite_a   : if true, allows modification of A which may improve
+                      performance. if false, A is not modified.
+
+    output:
+      U : an unitary matrix: U' U = 1. if full_matrices is true, U is of
+          shape (m, m). ortherwise it is of shape (m, min(m, n)).
+
+      S : an array of length min(m, n) containing the singular values of A sorted by
+          decreasing magnitude.
+
+      V : an unitary matrix: V V' = 1. if full_matrices is true, V is of
+          shape (n, n). ortherwise it is of shape (min(m, n), n).
+
+    return value:
+
+           S          if compute_uv is false
+       (U, S, V)      if compute_uv is true
+
+    overview of the matrices:
+
+      full_matrices true:
+        A           : m*n
+        U           : m*m     U' U  = 1
+        S as matrix : m*n
+        V           : n*n     V  V' = 1
+
+     full_matrices false:
+        A           : m*n
+        U           : m*min(n,m)             U' U  = 1
+        S as matrix : min(m,n)*min(m,n)
+        V           : min(m,n)*n             V  V' = 1
+
+    example:
+      >>> from mpmath import mp
+      >>> A = mp.matrix([[-2j, -1-3j, -2+2j], [2-2j, -1-3j, 1], [-3+1j,-2j,0]])
+      >>> S = mp.svd_c(A, compute_uv = False)
+      >>> print(mp.chop(S - mp.matrix([mp.sqrt(34), mp.sqrt(15), mp.sqrt(6)])))
+      [0.0]
+      [0.0]
+      [0.0]
+
+      >>> U, S, V = mp.svd_c(A)
+      >>> print(mp.chop(A - U * mp.diag(S) * V))
+      [0.0  0.0  0.0]
+      [0.0  0.0  0.0]
+      [0.0  0.0  0.0]
+
+    see also: svd, svd_r
+    """
+
+    m, n = A.rows, A.cols
+
+    if not compute_uv:
+        if not overwrite_a:
+            A = A.copy()
+        S = svd_c_raw(ctx, A, V = False, calc_u = False)
+        S = S[:min(m,n)]
+        return S
+
+    if full_matrices and n < m:
+        V = ctx.zeros(m, m)
+        A0 = ctx.zeros(m, m)
+        A0[:,:n] = A
+        S = svd_c_raw(ctx, A0, V, calc_u = True)
+
+        S = S[:n]
+        V = V[:n,:n]
+
+        return (A0, S, V)
+    else:
+        if not overwrite_a:
+            A = A.copy()
+        V = ctx.zeros(n, n)
+        S = svd_c_raw(ctx, A, V, calc_u = True)
+
+        if n > m:
+            if full_matrices == False:
+                V = V[:m,:]
+
+            S = S[:m]
+            A = A[:,:m]
+
+        return (A, S, V)
+
+@defun
+def svd(ctx, A, full_matrices = False, compute_uv = True, overwrite_a = False):
+    """
+    "svd" is a unified interface for "svd_r" and "svd_c". Depending on
+    whether A is real or complex the appropriate function is called.
+
+    This routine computes the singular value decomposition of a matrix A.
+    Given A, two orthogonal (A real) or unitary (A complex) matrices U and V
+    are calculated such that
+
+           A = U S V        and        U' U = 1         and         V V' = 1
+
+    where S is a suitable shaped matrix whose off-diagonal elements are zero.
+    Here ' denotes the hermitian transpose (i.e. transposition and complex
+    conjugation). The diagonal elements of S are the singular values of A,
+    i.e. the squareroots of the eigenvalues of A' A or A A'.
+
+    input:
+      A             : a real or complex matrix of shape (m, n)
+      full_matrices : if true, U and V are of shape (m, m) and (n, n).
+                      if false, U and V are of shape (m, min(m, n)) and (min(m, n), n).
+      compute_uv    : if true, U and V are calculated. if false, only S is calculated.
+      overwrite_a   : if true, allows modification of A which may improve
+                      performance. if false, A is not modified.
+
+    output:
+      U : an orthogonal or unitary matrix: U' U = 1. if full_matrices is true, U is of
+          shape (m, m). ortherwise it is of shape (m, min(m, n)).
+
+      S : an array of length min(m, n) containing the singular values of A sorted by
+          decreasing magnitude.
+
+      V : an orthogonal or unitary matrix: V V' = 1. if full_matrices is true, V is of
+          shape (n, n). ortherwise it is of shape (min(m, n), n).
+
+    return value:
+
+           S          if compute_uv is false
+       (U, S, V)      if compute_uv is true
+
+    overview of the matrices:
+
+      full_matrices true:
+        A           : m*n
+        U           : m*m     U' U  = 1
+        S as matrix : m*n
+        V           : n*n     V  V' = 1
+
+     full_matrices false:
+        A           : m*n
+        U           : m*min(n,m)             U' U  = 1
+        S as matrix : min(m,n)*min(m,n)
+        V           : min(m,n)*n             V  V' = 1
+
+    examples:
+
+       >>> from mpmath import mp
+       >>> A = mp.matrix([[2, -2, -1], [3, 4, -2], [-2, -2, 0]])
+       >>> S = mp.svd(A, compute_uv = False)
+       >>> print(S)
+       [6.0]
+       [3.0]
+       [1.0]
+
+       >>> U, S, V = mp.svd(A)
+       >>> print(mp.chop(A - U * mp.diag(S) * V))
+       [0.0  0.0  0.0]
+       [0.0  0.0  0.0]
+       [0.0  0.0  0.0]
+
+    see also: svd_r, svd_c
+    """
+
+    iscomplex = any(type(x) is ctx.mpc for x in A)
+
+    if iscomplex:
+        return ctx.svd_c(A, full_matrices = full_matrices, compute_uv = compute_uv, overwrite_a = overwrite_a)
+    else:
+        return ctx.svd_r(A, full_matrices = full_matrices, compute_uv = compute_uv, overwrite_a = overwrite_a)

lib/python3.11/site-packages/mpmath/matrices/linalg.py

@@ -0,0 +1,790 @@
+"""
+Linear algebra
+--------------
+
+Linear equations
+................
+
+Basic linear algebra is implemented; you can for example solve the linear
+equation system::
+
+      x + 2*y = -10
+    3*x + 4*y =  10
+
+using ``lu_solve``::
+
+    >>> from mpmath import *
+    >>> mp.pretty = False
+    >>> A = matrix([[1, 2], [3, 4]])
+    >>> b = matrix([-10, 10])
+    >>> x = lu_solve(A, b)
+    >>> x
+    matrix(
+    [['30.0'],
+     ['-20.0']])
+
+If you don't trust the result, use ``residual`` to calculate the residual ||A*x-b||::
+
+    >>> residual(A, x, b)
+    matrix(
+    [['3.46944695195361e-18'],
+     ['3.46944695195361e-18']])
+    >>> str(eps)
+    '2.22044604925031e-16'
+
+As you can see, the solution is quite accurate. The error is caused by the
+inaccuracy of the internal floating point arithmetic. Though, it's even smaller
+than the current machine epsilon, which basically means you can trust the
+result.
+
+If you need more speed, use NumPy, or ``fp.lu_solve`` for a floating-point computation.
+
+    >>> fp.lu_solve(A, b)   # doctest: +ELLIPSIS
+    matrix(...)
+
+``lu_solve`` accepts overdetermined systems. It is usually not possible to solve
+such systems, so the residual is minimized instead. Internally this is done
+using Cholesky decomposition to compute a least squares approximation. This means
+that that ``lu_solve`` will square the errors. If you can't afford this, use
+``qr_solve`` instead. It is twice as slow but more accurate, and it calculates
+the residual automatically.
+
+
+Matrix factorization
+....................
+
+The function ``lu`` computes an explicit LU factorization of a matrix::
+
+    >>> P, L, U = lu(matrix([[0,2,3],[4,5,6],[7,8,9]]))
+    >>> print(P)
+    [0.0  0.0  1.0]
+    [1.0  0.0  0.0]
+    [0.0  1.0  0.0]
+    >>> print(L)
+    [              1.0                0.0  0.0]
+    [              0.0                1.0  0.0]
+    [0.571428571428571  0.214285714285714  1.0]
+    >>> print(U)
+    [7.0  8.0                9.0]
+    [0.0  2.0                3.0]
+    [0.0  0.0  0.214285714285714]
+    >>> print(P.T*L*U)
+    [0.0  2.0  3.0]
+    [4.0  5.0  6.0]
+    [7.0  8.0  9.0]
+
+Interval matrices
+-----------------
+
+Matrices may contain interval elements. This allows one to perform
+basic linear algebra operations such as matrix multiplication
+and equation solving with rigorous error bounds::
+
+    >>> a = iv.matrix([['0.1','0.3','1.0'],
+    ...             ['7.1','5.5','4.8'],
+    ...             ['3.2','4.4','5.6']])
+    >>>
+    >>> b = iv.matrix(['4','0.6','0.5'])
+    >>> c = iv.lu_solve(a, b)
+    >>> print(c)
+    [   [5.2582327113062568605927528666, 5.25823271130625686059275702219]]
+    [[-13.1550493962678375411635581388, -13.1550493962678375411635540152]]
+    [  [7.42069154774972557628979076189, 7.42069154774972557628979190734]]
+    >>> print(a*c)
+    [  [3.99999999999999999999999844904, 4.00000000000000000000000155096]]
+    [[0.599999999999999999999968898009, 0.600000000000000000000031763736]]
+    [[0.499999999999999999999979320485, 0.500000000000000000000020679515]]
+"""
+
+# TODO:
+# *implement high-level qr()
+# *test unitvector
+# *iterative solving
+
+from copy import copy
+
+from ..libmp.backend import xrange
+
+class LinearAlgebraMethods(object):
+
+    def LU_decomp(ctx, A, overwrite=False, use_cache=True):
+        """
+        LU-factorization of a n*n matrix using the Gauss algorithm.
+        Returns L and U in one matrix and the pivot indices.
+
+        Use overwrite to specify whether A will be overwritten with L and U.
+        """
+        if not A.rows == A.cols:
+            raise ValueError('need n*n matrix')
+        # get from cache if possible
+        if use_cache and isinstance(A, ctx.matrix) and A._LU:
+            return A._LU
+        if not overwrite:
+            orig = A
+            A = A.copy()
+        tol = ctx.absmin(ctx.mnorm(A,1) * ctx.eps) # each pivot element has to be bigger
+        n = A.rows
+        p = [None]*(n - 1)
+        for j in xrange(n - 1):
+            # pivoting, choose max(abs(reciprocal row sum)*abs(pivot element))
+            biggest = 0
+            for k in xrange(j, n):
+                s = ctx.fsum([ctx.absmin(A[k,l]) for l in xrange(j, n)])
+                if ctx.absmin(s) <= tol:
+                    raise ZeroDivisionError('matrix is numerically singular')
+                current = 1/s * ctx.absmin(A[k,j])
+                if current > biggest: # TODO: what if equal?
+                    biggest = current
+                    p[j] = k
+            # swap rows according to p
+            ctx.swap_row(A, j, p[j])
+            if ctx.absmin(A[j,j]) <= tol:
+                raise ZeroDivisionError('matrix is numerically singular')
+            # calculate elimination factors and add rows
+            for i in xrange(j + 1, n):
+                A[i,j] /= A[j,j]
+                for k in xrange(j + 1, n):
+                    A[i,k] -= A[i,j]*A[j,k]
+        if ctx.absmin(A[n - 1,n - 1]) <= tol:
+            raise ZeroDivisionError('matrix is numerically singular')
+        # cache decomposition
+        if not overwrite and isinstance(orig, ctx.matrix):
+            orig._LU = (A, p)
+        return A, p
+
+    def L_solve(ctx, L, b, p=None):
+        """
+        Solve the lower part of a LU factorized matrix for y.
+        """
+        if L.rows != L.cols:
+            raise RuntimeError("need n*n matrix")
+        n = L.rows
+        if len(b) != n:
+            raise ValueError("Value should be equal to n")
+        b = copy(b)
+        if p: # swap b according to p
+            for k in xrange(0, len(p)):
+                ctx.swap_row(b, k, p[k])
+        # solve
+        for i in xrange(1, n):
+            for j in xrange(i):
+                b[i] -= L[i,j] * b[j]
+        return b
+
+    def U_solve(ctx, U, y):
+        """
+        Solve the upper part of a LU factorized matrix for x.
+        """
+        if U.rows != U.cols:
+            raise RuntimeError("need n*n matrix")
+        n = U.rows
+        if len(y) != n:
+            raise ValueError("Value should be equal to n")
+        x = copy(y)
+        for i in xrange(n - 1, -1, -1):
+            for j in xrange(i + 1, n):
+                x[i] -= U[i,j] * x[j]
+            x[i] /= U[i,i]
+        return x
+
+    def lu_solve(ctx, A, b, **kwargs):
+        """
+        Ax = b => x
+
+        Solve a determined or overdetermined linear equations system.
+        Fast LU decomposition is used, which is less accurate than QR decomposition
+        (especially for overdetermined systems), but it's twice as efficient.
+        Use qr_solve if you want more precision or have to solve a very ill-
+        conditioned system.
+
+        If you specify real=True, it does not check for overdeterminded complex
+        systems.
+        """
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            # do not overwrite A nor b
+            A, b = ctx.matrix(A, **kwargs).copy(), ctx.matrix(b, **kwargs).copy()
+            if A.rows < A.cols:
+                raise ValueError('cannot solve underdetermined system')
+            if A.rows > A.cols:
+                # use least-squares method if overdetermined
+                # (this increases errors)
+                AH = A.H
+                A = AH * A
+                b = AH * b
+                if (kwargs.get('real', False) or
+                    not sum(type(i) is ctx.mpc for i in A)):
+                    # TODO: necessary to check also b?
+                    x = ctx.cholesky_solve(A, b)
+                else:
+                    x = ctx.lu_solve(A, b)
+            else:
+                # LU factorization
+                A, p = ctx.LU_decomp(A)
+                b = ctx.L_solve(A, b, p)
+                x = ctx.U_solve(A, b)
+        finally:
+            ctx.prec = prec
+        return x
+
+    def improve_solution(ctx, A, x, b, maxsteps=1):
+        """
+        Improve a solution to a linear equation system iteratively.
+
+        This re-uses the LU decomposition and is thus cheap.
+        Usually 3 up to 4 iterations are giving the maximal improvement.
+        """
+        if A.rows != A.cols:
+            raise RuntimeError("need n*n matrix") # TODO: really?
+        for _ in xrange(maxsteps):
+            r = ctx.residual(A, x, b)
+            if ctx.norm(r, 2) < 10*ctx.eps:
+                break
+            # this uses cached LU decomposition and is thus cheap
+            dx = ctx.lu_solve(A, -r)
+            x += dx
+        return x
+
+    def lu(ctx, A):
+        """
+        A -> P, L, U
+
+        LU factorisation of a square matrix A. L is the lower, U the upper part.
+        P is the permutation matrix indicating the row swaps.
+
+        P*A = L*U
+
+        If you need efficiency, use the low-level method LU_decomp instead, it's
+        much more memory efficient.
+        """
+        # get factorization
+        A, p = ctx.LU_decomp(A)
+        n = A.rows
+        L = ctx.matrix(n)
+        U = ctx.matrix(n)
+        for i in xrange(n):
+            for j in xrange(n):
+                if i > j:
+                    L[i,j] = A[i,j]
+                elif i == j:
+                    L[i,j] = 1
+                    U[i,j] = A[i,j]
+                else:
+                    U[i,j] = A[i,j]
+        # calculate permutation matrix
+        P = ctx.eye(n)
+        for k in xrange(len(p)):
+            ctx.swap_row(P, k, p[k])
+        return P, L, U
+
+    def unitvector(ctx, n, i):
+        """
+        Return the i-th n-dimensional unit vector.
+        """
+        assert 0 < i <= n, 'this unit vector does not exist'
+        return [ctx.zero]*(i-1) + [ctx.one] + [ctx.zero]*(n-i)
+
+    def inverse(ctx, A, **kwargs):
+        """
+        Calculate the inverse of a matrix.
+
+        If you want to solve an equation system Ax = b, it's recommended to use
+        solve(A, b) instead, it's about 3 times more efficient.
+        """
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            # do not overwrite A
+            A = ctx.matrix(A, **kwargs).copy()
+            n = A.rows
+            # get LU factorisation
+            A, p = ctx.LU_decomp(A)
+            cols = []
+            # calculate unit vectors and solve corresponding system to get columns
+            for i in xrange(1, n + 1):
+                e = ctx.unitvector(n, i)
+                y = ctx.L_solve(A, e, p)
+                cols.append(ctx.U_solve(A, y))
+            # convert columns to matrix
+            inv = []
+            for i in xrange(n):
+                row = []
+                for j in xrange(n):
+                    row.append(cols[j][i])
+                inv.append(row)
+            result = ctx.matrix(inv, **kwargs)
+        finally:
+            ctx.prec = prec
+        return result
+
+    def householder(ctx, A):
+        """
+        (A|b) -> H, p, x, res
+
+        (A|b) is the coefficient matrix with left hand side of an optionally
+        overdetermined linear equation system.
+        H and p contain all information about the transformation matrices.
+        x is the solution, res the residual.
+        """
+        if not isinstance(A, ctx.matrix):
+            raise TypeError("A should be a type of ctx.matrix")
+        m = A.rows
+        n = A.cols
+        if m < n - 1:
+            raise RuntimeError("Columns should not be less than rows")
+        # calculate Householder matrix
+        p = []
+        for j in xrange(0, n - 1):
+            s = ctx.fsum(abs(A[i,j])**2 for i in xrange(j, m))
+            if not abs(s) > ctx.eps:
+                raise ValueError('matrix is numerically singular')
+            p.append(-ctx.sign(ctx.re(A[j,j])) * ctx.sqrt(s))
+            kappa = ctx.one / (s - p[j] * A[j,j])
+            A[j,j] -= p[j]
+            for k in xrange(j+1, n):
+                y = ctx.fsum(ctx.conj(A[i,j]) * A[i,k] for i in xrange(j, m)) * kappa
+                for i in xrange(j, m):
+                    A[i,k] -= A[i,j] * y
+        # solve Rx = c1
+        x = [A[i,n - 1] for i in xrange(n - 1)]
+        for i in xrange(n - 2, -1, -1):
+            x[i] -= ctx.fsum(A[i,j] * x[j] for j in xrange(i + 1, n - 1))
+            x[i] /= p[i]
+        # calculate residual
+        if not m == n - 1:
+            r = [A[m-1-i, n-1] for i in xrange(m - n + 1)]
+        else:
+            # determined system, residual should be 0
+            r = [0]*m # maybe a bad idea, changing r[i] will change all elements
+        return A, p, x, r
+
+    #def qr(ctx, A):
+    #    """
+    #    A -> Q, R
+    #
+    #    QR factorisation of a square matrix A using Householder decomposition.
+    #    Q is orthogonal, this leads to very few numerical errors.
+    #
+    #    A = Q*R
+    #    """
+    #    H, p, x, res = householder(A)
+    # TODO: implement this
+
+    def residual(ctx, A, x, b, **kwargs):
+        """
+        Calculate the residual of a solution to a linear equation system.
+
+        r = A*x - b for A*x = b
+        """
+        oldprec = ctx.prec
+        try:
+            ctx.prec *= 2
+            A, x, b = ctx.matrix(A, **kwargs), ctx.matrix(x, **kwargs), ctx.matrix(b, **kwargs)
+            return A*x - b
+        finally:
+            ctx.prec = oldprec
+
+    def qr_solve(ctx, A, b, norm=None, **kwargs):
+        """
+        Ax = b => x, ||Ax - b||
+
+        Solve a determined or overdetermined linear equations system and
+        calculate the norm of the residual (error).
+        QR decomposition using Householder factorization is applied, which gives very
+        accurate results even for ill-conditioned matrices. qr_solve is twice as
+        efficient.
+        """
+        if norm is None:
+            norm = ctx.norm
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            # do not overwrite A nor b
+            A, b = ctx.matrix(A, **kwargs).copy(), ctx.matrix(b, **kwargs).copy()
+            if A.rows < A.cols:
+                raise ValueError('cannot solve underdetermined system')
+            H, p, x, r = ctx.householder(ctx.extend(A, b))
+            res = ctx.norm(r)
+            # calculate residual "manually" for determined systems
+            if res == 0:
+                res = ctx.norm(ctx.residual(A, x, b))
+            return ctx.matrix(x, **kwargs), res
+        finally:
+            ctx.prec = prec
+
+    def cholesky(ctx, A, tol=None):
+        r"""
+        Cholesky decomposition of a symmetric positive-definite matrix `A`.
+        Returns a lower triangular matrix `L` such that `A = L \times L^T`.
+        More generally, for a complex Hermitian positive-definite matrix,
+        a Cholesky decomposition satisfying `A = L \times L^H` is returned.
+
+        The Cholesky decomposition can be used to solve linear equation
+        systems twice as efficiently as LU decomposition, or to
+        test whether `A` is positive-definite.
+
+        The optional parameter ``tol`` determines the tolerance for
+        verifying positive-definiteness.
+
+        **Examples**
+
+        Cholesky decomposition of a positive-definite symmetric matrix::
+
+            >>> from mpmath import *
+            >>> mp.dps = 25; mp.pretty = True
+            >>> A = eye(3) + hilbert(3)
+            >>> nprint(A)
+            [     2.0      0.5  0.333333]
+            [     0.5  1.33333      0.25]
+            [0.333333     0.25       1.2]
+            >>> L = cholesky(A)
+            >>> nprint(L)
+            [ 1.41421      0.0      0.0]
+            [0.353553  1.09924      0.0]
+            [0.235702  0.15162  1.05899]
+            >>> chop(A - L*L.T)
+            [0.0  0.0  0.0]
+            [0.0  0.0  0.0]
+            [0.0  0.0  0.0]
+
+        Cholesky decomposition of a Hermitian matrix::
+
+            >>> A = eye(3) + matrix([[0,0.25j,-0.5j],[-0.25j,0,0],[0.5j,0,0]])
+            >>> L = cholesky(A)
+            >>> nprint(L)
+            [          1.0                0.0                0.0]
+            [(0.0 - 0.25j)  (0.968246 + 0.0j)                0.0]
+            [ (0.0 + 0.5j)  (0.129099 + 0.0j)  (0.856349 + 0.0j)]
+            >>> chop(A - L*L.H)
+            [0.0  0.0  0.0]
+            [0.0  0.0  0.0]
+            [0.0  0.0  0.0]
+
+        Attempted Cholesky decomposition of a matrix that is not positive
+        definite::
+
+            >>> A = -eye(3) + hilbert(3)
+            >>> L = cholesky(A)
+            Traceback (most recent call last):
+              ...
+            ValueError: matrix is not positive-definite
+
+        **References**
+
+        1. [Wikipedia]_ http://en.wikipedia.org/wiki/Cholesky_decomposition
+
+        """
+        if not isinstance(A, ctx.matrix):
+            raise RuntimeError("A should be a type of ctx.matrix")
+        if not A.rows == A.cols:
+            raise ValueError('need n*n matrix')
+        if tol is None:
+            tol = +ctx.eps
+        n = A.rows
+        L = ctx.matrix(n)
+        for j in xrange(n):
+            c = ctx.re(A[j,j])
+            if abs(c-A[j,j]) > tol:
+                raise ValueError('matrix is not Hermitian')
+            s = c - ctx.fsum((L[j,k] for k in xrange(j)),
+                absolute=True, squared=True)
+            if s < tol:
+                raise ValueError('matrix is not positive-definite')
+            L[j,j] = ctx.sqrt(s)
+            for i in xrange(j, n):
+                it1 = (L[i,k] for k in xrange(j))
+                it2 = (L[j,k] for k in xrange(j))
+                t = ctx.fdot(it1, it2, conjugate=True)
+                L[i,j] = (A[i,j] - t) / L[j,j]
+        return L
+
+    def cholesky_solve(ctx, A, b, **kwargs):
+        """
+        Ax = b => x
+
+        Solve a symmetric positive-definite linear equation system.
+        This is twice as efficient as lu_solve.
+
+        Typical use cases:
+        * A.T*A
+        * Hessian matrix
+        * differential equations
+        """
+        prec = ctx.prec
+        try:
+            ctx.prec += 10
+            # do not overwrite A nor b
+            A, b = ctx.matrix(A, **kwargs).copy(), ctx.matrix(b, **kwargs).copy()
+            if A.rows !=  A.cols:
+                raise ValueError('can only solve determined system')
+            # Cholesky factorization
+            L = ctx.cholesky(A)
+            # solve
+            n = L.rows
+            if len(b) != n:
+                raise ValueError("Value should be equal to n")
+            for i in xrange(n):
+                b[i] -= ctx.fsum(L[i,j] * b[j] for j in xrange(i))
+                b[i] /= L[i,i]
+            x = ctx.U_solve(L.T, b)
+            return x
+        finally:
+            ctx.prec = prec
+
+    def det(ctx, A):
+        """
+        Calculate the determinant of a matrix.
+        """
+        prec = ctx.prec
+        try:
+            # do not overwrite A
+            A = ctx.matrix(A).copy()
+            # use LU factorization to calculate determinant
+            try:
+                R, p = ctx.LU_decomp(A)
+            except ZeroDivisionError:
+                return 0
+            z = 1
+            for i, e in enumerate(p):
+                if i != e:
+                    z *= -1
+            for i in xrange(A.rows):
+                z *= R[i,i]
+            return z
+        finally:
+            ctx.prec = prec
+
+    def cond(ctx, A, norm=None):
+        """
+        Calculate the condition number of a matrix using a specified matrix norm.
+
+        The condition number estimates the sensitivity of a matrix to errors.
+        Example: small input errors for ill-conditioned coefficient matrices
+        alter the solution of the system dramatically.
+
+        For ill-conditioned matrices it's recommended to use qr_solve() instead
+        of lu_solve(). This does not help with input errors however, it just avoids
+        to add additional errors.
+
+        Definition:    cond(A) = ||A|| * ||A**-1||
+        """
+        if norm is None:
+            norm = lambda x: ctx.mnorm(x,1)
+        return norm(A) * norm(ctx.inverse(A))
+
+    def lu_solve_mat(ctx, a, b):
+        """Solve a * x = b  where a and b are matrices."""
+        r = ctx.matrix(a.rows, b.cols)
+        for i in range(b.cols):
+            c = ctx.lu_solve(a, b.column(i))
+            for j in range(len(c)):
+                r[j, i] = c[j]
+        return r
+
+    def qr(ctx, A, mode = 'full', edps = 10):
+        """
+        Compute a QR factorization $A = QR$ where
+        A is an m x n matrix of real or complex numbers where m >= n
+
+        mode has following meanings:
+        (1) mode = 'raw' returns two matrixes (A, tau) in the
+            internal format used by LAPACK
+        (2) mode = 'skinny' returns the leading n columns of Q
+            and n rows of R
+        (3) Any other value returns the leading m columns of Q
+            and m rows of R
+
+        edps is the increase in mp precision used for calculations
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.dps = 15
+            >>> mp.pretty = True
+            >>> A = matrix([[1, 2], [3, 4], [1, 1]])
+            >>> Q, R = qr(A)
+            >>> Q
+            [-0.301511344577764   0.861640436855329   0.408248290463863]
+            [-0.904534033733291  -0.123091490979333  -0.408248290463863]
+            [-0.301511344577764  -0.492365963917331   0.816496580927726]
+            >>> R
+            [-3.3166247903554  -4.52267016866645]
+            [             0.0  0.738548945875996]
+            [             0.0                0.0]
+            >>> Q * R
+            [1.0  2.0]
+            [3.0  4.0]
+            [1.0  1.0]
+            >>> chop(Q.T * Q)
+            [1.0  0.0  0.0]
+            [0.0  1.0  0.0]
+            [0.0  0.0  1.0]
+            >>> B = matrix([[1+0j, 2-3j], [3+j, 4+5j]])
+            >>> Q, R = qr(B)
+            >>> nprint(Q)
+            [     (-0.301511 + 0.0j)   (0.0695795 - 0.95092j)]
+            [(-0.904534 - 0.301511j)  (-0.115966 + 0.278318j)]
+            >>> nprint(R)
+            [(-3.31662 + 0.0j)  (-5.72872 - 2.41209j)]
+            [              0.0       (3.91965 + 0.0j)]
+            >>> Q * R
+            [(1.0 + 0.0j)  (2.0 - 3.0j)]
+            [(3.0 + 1.0j)  (4.0 + 5.0j)]
+            >>> chop(Q.T * Q.conjugate())
+            [1.0  0.0]
+            [0.0  1.0]
+
+        """
+
+        # check values before continuing
+        assert isinstance(A, ctx.matrix)
+        m = A.rows
+        n = A.cols
+        assert n >= 0
+        assert m >= n
+        assert edps >= 0
+
+        # check for complex data type
+        cmplx = any(type(x) is ctx.mpc for x in A)
+
+        # temporarily increase the precision and initialize
+        with ctx.extradps(edps):
+            tau = ctx.matrix(n,1)
+            A = A.copy()
+
+            # ---------------
+            # FACTOR MATRIX A
+            # ---------------
+            if cmplx:
+                one = ctx.mpc('1.0', '0.0')
+                zero = ctx.mpc('0.0', '0.0')
+                rzero = ctx.mpf('0.0')
+
+                # main loop to factor A (complex)
+                for j in xrange(0, n):
+                    alpha = A[j,j]
+                    alphr = ctx.re(alpha)
+                    alphi = ctx.im(alpha)
+
+                    if (m-j) >= 2:
+                        xnorm = ctx.fsum( A[i,j]*ctx.conj(A[i,j]) for i in xrange(j+1, m) )
+                        xnorm = ctx.re( ctx.sqrt(xnorm) )
+                    else:
+                        xnorm = rzero
+
+                    if (xnorm == rzero) and (alphi == rzero):
+                        tau[j] = zero
+                        continue
+
+                    if alphr < rzero:
+                        beta = ctx.sqrt(alphr**2 + alphi**2 + xnorm**2)
+                    else:
+                        beta = -ctx.sqrt(alphr**2 + alphi**2 + xnorm**2)
+
+                    tau[j] = ctx.mpc( (beta - alphr) / beta, -alphi / beta )
+                    t = -ctx.conj(tau[j])
+                    za = one / (alpha - beta)
+
+                    for i in xrange(j+1, m):
+                        A[i,j] *= za
+
+                    A[j,j] = one
+                    for k in xrange(j+1, n):
+                        y = ctx.fsum(A[i,j] * ctx.conj(A[i,k]) for i in xrange(j, m))
+                        temp = t * ctx.conj(y)
+                        for i in xrange(j, m):
+                            A[i,k] += A[i,j] * temp
+
+                    A[j,j] = ctx.mpc(beta, '0.0')
+            else:
+                one = ctx.mpf('1.0')
+                zero = ctx.mpf('0.0')
+
+                # main loop to factor A (real)
+                for j in xrange(0, n):
+                    alpha = A[j,j]
+
+                    if (m-j) > 2:
+                        xnorm = ctx.fsum( (A[i,j])**2 for i in xrange(j+1, m) )
+                        xnorm = ctx.sqrt(xnorm)
+                    elif (m-j) == 2:
+                        xnorm = abs( A[m-1,j] )
+                    else:
+                        xnorm = zero
+
+                    if xnorm == zero:
+                        tau[j] = zero
+                        continue
+
+                    if alpha < zero:
+                        beta = ctx.sqrt(alpha**2 + xnorm**2)
+                    else:
+                        beta = -ctx.sqrt(alpha**2 + xnorm**2)
+
+                    tau[j] = (beta - alpha) / beta
+                    t = -tau[j]
+                    da = one / (alpha - beta)
+
+                    for i in xrange(j+1, m):
+                        A[i,j] *= da
+
+                    A[j,j] = one
+                    for k in xrange(j+1, n):
+                        y = ctx.fsum( A[i,j] * A[i,k] for i in xrange(j, m) )
+                        temp = t * y
+                        for i in xrange(j,m):
+                            A[i,k] += A[i,j] * temp
+
+                    A[j,j] = beta
+
+            # return factorization in same internal format as LAPACK
+            if (mode == 'raw') or (mode == 'RAW'):
+                return A, tau
+
+            # ----------------------------------
+            # FORM Q USING BACKWARD ACCUMULATION
+            # ----------------------------------
+
+            # form R before the values are overwritten
+            R = A.copy()
+            for j in xrange(0, n):
+                for i in xrange(j+1, m):
+                    R[i,j] = zero
+
+            # set the value of p (number of columns of Q to return)
+            p = m
+            if (mode == 'skinny') or (mode == 'SKINNY'):
+                p = n
+
+            # add columns to A if needed and initialize
+            A.cols += (p-n)
+            for j in xrange(0, p):
+                A[j,j] = one
+                for i in xrange(0, j):
+                    A[i,j] = zero
+
+            # main loop to form Q
+            for j in xrange(n-1, -1, -1):
+                t = -tau[j]
+                A[j,j] += t
+
+                for k in xrange(j+1, p):
+                    if cmplx:
+                        y = ctx.fsum(A[i,j] * ctx.conj(A[i,k]) for i in xrange(j+1, m))
+                        temp = t * ctx.conj(y)
+                    else:
+                        y = ctx.fsum(A[i,j] * A[i,k] for i in xrange(j+1, m))
+                        temp = t * y
+                    A[j,k] = temp
+                    for i in xrange(j+1, m):
+                        A[i,k] += A[i,j] * temp
+
+                for i in xrange(j+1, m):
+                    A[i, j] *= t
+
+            return A, R[0:p,0:n]
+
+        # ------------------
+        # END OF FUNCTION QR
+        # ------------------

lib/python3.11/site-packages/mpmath/matrices/matrices.py

@@ -0,0 +1,1005 @@
+from ..libmp.backend import xrange
+import warnings
+
+# TODO: interpret list as vectors (for multiplication)
+
+rowsep = '\n'
+colsep = '  '
+
+class _matrix(object):
+    """
+    Numerical matrix.
+
+    Specify the dimensions or the data as a nested list.
+    Elements default to zero.
+    Use a flat list to create a column vector easily.
+
+    The datatype of the context (mpf for mp, mpi for iv, and float for fp) is used to store the data.
+
+    Creating matrices
+    -----------------
+
+    Matrices in mpmath are implemented using dictionaries. Only non-zero values
+    are stored, so it is cheap to represent sparse matrices.
+
+    The most basic way to create one is to use the ``matrix`` class directly.
+    You can create an empty matrix specifying the dimensions:
+
+        >>> from mpmath import *
+        >>> mp.dps = 15
+        >>> matrix(2)
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', '0.0']])
+        >>> matrix(2, 3)
+        matrix(
+        [['0.0', '0.0', '0.0'],
+         ['0.0', '0.0', '0.0']])
+
+    Calling ``matrix`` with one dimension will create a square matrix.
+
+    To access the dimensions of a matrix, use the ``rows`` or ``cols`` keyword:
+
+        >>> A = matrix(3, 2)
+        >>> A
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', '0.0'],
+         ['0.0', '0.0']])
+        >>> A.rows
+        3
+        >>> A.cols
+        2
+
+    You can also change the dimension of an existing matrix. This will set the
+    new elements to 0. If the new dimension is smaller than before, the
+    concerning elements are discarded:
+
+        >>> A.rows = 2
+        >>> A
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', '0.0']])
+
+    Internally ``mpmathify`` is used every time an element is set. This
+    is done using the syntax A[row,column], counting from 0:
+
+        >>> A = matrix(2)
+        >>> A[1,1] = 1 + 1j
+        >>> A
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', mpc(real='1.0', imag='1.0')]])
+
+    A more comfortable way to create a matrix lets you use nested lists:
+
+        >>> matrix([[1, 2], [3, 4]])
+        matrix(
+        [['1.0', '2.0'],
+         ['3.0', '4.0']])
+
+    Convenient advanced functions are available for creating various standard
+    matrices, see ``zeros``, ``ones``, ``diag``, ``eye``, ``randmatrix`` and
+    ``hilbert``.
+
+    Vectors
+    .......
+
+    Vectors may also be represented by the ``matrix`` class (with rows = 1 or cols = 1).
+    For vectors there are some things which make life easier. A column vector can
+    be created using a flat list, a row vectors using an almost flat nested list::
+
+        >>> matrix([1, 2, 3])
+        matrix(
+        [['1.0'],
+         ['2.0'],
+         ['3.0']])
+        >>> matrix([[1, 2, 3]])
+        matrix(
+        [['1.0', '2.0', '3.0']])
+
+    Optionally vectors can be accessed like lists, using only a single index::
+
+        >>> x = matrix([1, 2, 3])
+        >>> x[1]
+        mpf('2.0')
+        >>> x[1,0]
+        mpf('2.0')
+
+    Other
+    .....
+
+    Like you probably expected, matrices can be printed::
+
+        >>> print randmatrix(3) # doctest:+SKIP
+        [ 0.782963853573023  0.802057689719883  0.427895717335467]
+        [0.0541876859348597  0.708243266653103  0.615134039977379]
+        [ 0.856151514955773  0.544759264818486  0.686210904770947]
+
+    Use ``nstr`` or ``nprint`` to specify the number of digits to print::
+
+        >>> nprint(randmatrix(5), 3) # doctest:+SKIP
+        [2.07e-1  1.66e-1  5.06e-1  1.89e-1  8.29e-1]
+        [6.62e-1  6.55e-1  4.47e-1  4.82e-1  2.06e-2]
+        [4.33e-1  7.75e-1  6.93e-2  2.86e-1  5.71e-1]
+        [1.01e-1  2.53e-1  6.13e-1  3.32e-1  2.59e-1]
+        [1.56e-1  7.27e-2  6.05e-1  6.67e-2  2.79e-1]
+
+    As matrices are mutable, you will need to copy them sometimes::
+
+        >>> A = matrix(2)
+        >>> A
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', '0.0']])
+        >>> B = A.copy()
+        >>> B[0,0] = 1
+        >>> B
+        matrix(
+        [['1.0', '0.0'],
+         ['0.0', '0.0']])
+        >>> A
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', '0.0']])
+
+    Finally, it is possible to convert a matrix to a nested list. This is very useful,
+    as most Python libraries involving matrices or arrays (namely NumPy or SymPy)
+    support this format::
+
+        >>> B.tolist()
+        [[mpf('1.0'), mpf('0.0')], [mpf('0.0'), mpf('0.0')]]
+
+
+    Matrix operations
+    -----------------
+
+    You can add and subtract matrices of compatible dimensions::
+
+        >>> A = matrix([[1, 2], [3, 4]])
+        >>> B = matrix([[-2, 4], [5, 9]])
+        >>> A + B
+        matrix(
+        [['-1.0', '6.0'],
+         ['8.0', '13.0']])
+        >>> A - B
+        matrix(
+        [['3.0', '-2.0'],
+         ['-2.0', '-5.0']])
+        >>> A + ones(3) # doctest:+ELLIPSIS
+        Traceback (most recent call last):
+          ...
+        ValueError: incompatible dimensions for addition
+
+    It is possible to multiply or add matrices and scalars. In the latter case the
+    operation will be done element-wise::
+
+        >>> A * 2
+        matrix(
+        [['2.0', '4.0'],
+         ['6.0', '8.0']])
+        >>> A / 4
+        matrix(
+        [['0.25', '0.5'],
+         ['0.75', '1.0']])
+        >>> A - 1
+        matrix(
+        [['0.0', '1.0'],
+         ['2.0', '3.0']])
+
+    Of course you can perform matrix multiplication, if the dimensions are
+    compatible, using ``@`` (for Python >= 3.5) or ``*``. For clarity, ``@`` is
+    recommended (`PEP 465 <https://www.python.org/dev/peps/pep-0465/>`), because
+    the meaning of ``*`` is different in many other Python libraries such as NumPy.
+
+        >>> A @ B # doctest:+SKIP
+        matrix(
+        [['8.0', '22.0'],
+         ['14.0', '48.0']])
+        >>> A * B # same as A @ B
+        matrix(
+        [['8.0', '22.0'],
+         ['14.0', '48.0']])
+        >>> matrix([[1, 2, 3]]) * matrix([[-6], [7], [-2]])
+        matrix(
+        [['2.0']])
+
+    ..
+        COMMENT: TODO: the above "doctest:+SKIP" may be removed as soon as we
+        have dropped support for Python 3.5 and below.
+
+    You can raise powers of square matrices::
+
+        >>> A**2
+        matrix(
+        [['7.0', '10.0'],
+         ['15.0', '22.0']])
+
+    Negative powers will calculate the inverse::
+
+        >>> A**-1
+        matrix(
+        [['-2.0', '1.0'],
+         ['1.5', '-0.5']])
+        >>> A * A**-1
+        matrix(
+        [['1.0', '1.0842021724855e-19'],
+         ['-2.16840434497101e-19', '1.0']])
+
+
+
+    Matrix transposition is straightforward::
+
+        >>> A = ones(2, 3)
+        >>> A
+        matrix(
+        [['1.0', '1.0', '1.0'],
+         ['1.0', '1.0', '1.0']])
+        >>> A.T
+        matrix(
+        [['1.0', '1.0'],
+         ['1.0', '1.0'],
+         ['1.0', '1.0']])
+
+    Norms
+    .....
+
+    Sometimes you need to know how "large" a matrix or vector is. Due to their
+    multidimensional nature it's not possible to compare them, but there are
+    several functions to map a matrix or a vector to a positive real number, the
+    so called norms.
+
+    For vectors the p-norm is intended, usually the 1-, the 2- and the oo-norm are
+    used.
+
+        >>> x = matrix([-10, 2, 100])
+        >>> norm(x, 1)
+        mpf('112.0')
+        >>> norm(x, 2)
+        mpf('100.5186549850325')
+        >>> norm(x, inf)
+        mpf('100.0')
+
+    Please note that the 2-norm is the most used one, though it is more expensive
+    to calculate than the 1- or oo-norm.
+
+    It is possible to generalize some vector norms to matrix norm::
+
+        >>> A = matrix([[1, -1000], [100, 50]])
+        >>> mnorm(A, 1)
+        mpf('1050.0')
+        >>> mnorm(A, inf)
+        mpf('1001.0')
+        >>> mnorm(A, 'F')
+        mpf('1006.2310867787777')
+
+    The last norm (the "Frobenius-norm") is an approximation for the 2-norm, which
+    is hard to calculate and not available. The Frobenius-norm lacks some
+    mathematical properties you might expect from a norm.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.__data = {}
+        # LU decompostion cache, this is useful when solving the same system
+        # multiple times, when calculating the inverse and when calculating the
+        # determinant
+        self._LU = None
+        if "force_type" in kwargs:
+            warnings.warn("The force_type argument was removed, it did not work"
+                " properly anyway. If you want to force floating-point or"
+                " interval computations, use the respective methods from `fp`"
+                " or `mp` instead, e.g., `fp.matrix()` or `iv.matrix()`."
+                " If you want to truncate values to integer, use .apply(int) instead.")
+        if isinstance(args[0], (list, tuple)):
+            if isinstance(args[0][0], (list, tuple)):
+                # interpret nested list as matrix
+                A = args[0]
+                self.__rows = len(A)
+                self.__cols = len(A[0])
+                for i, row in enumerate(A):
+                    for j, a in enumerate(row):
+                        # note: this will call __setitem__ which will call self.ctx.convert() to convert the datatype.
+                        self[i, j] = a
+            else:
+                # interpret list as row vector
+                v = args[0]
+                self.__rows = len(v)
+                self.__cols = 1
+                for i, e in enumerate(v):
+                    self[i, 0] = e
+        elif isinstance(args[0], int):
+            # create empty matrix of given dimensions
+            if len(args) == 1:
+                self.__rows = self.__cols = args[0]
+            else:
+                if not isinstance(args[1], int):
+                    raise TypeError("expected int")
+                self.__rows = args[0]
+                self.__cols = args[1]
+        elif isinstance(args[0], _matrix):
+            A = args[0]
+            self.__rows = A._matrix__rows
+            self.__cols = A._matrix__cols
+            for i in xrange(A.__rows):
+                for j in xrange(A.__cols):
+                    self[i, j] = A[i, j]
+        elif hasattr(args[0], 'tolist'):
+            A = self.ctx.matrix(args[0].tolist())
+            self.__data = A._matrix__data
+            self.__rows = A._matrix__rows
+            self.__cols = A._matrix__cols
+        else:
+            raise TypeError('could not interpret given arguments')
+
+    def apply(self, f):
+        """
+        Return a copy of self with the function `f` applied elementwise.
+        """
+        new = self.ctx.matrix(self.__rows, self.__cols)
+        for i in xrange(self.__rows):
+            for j in xrange(self.__cols):
+                new[i,j] = f(self[i,j])
+        return new
+
+    def __nstr__(self, n=None, **kwargs):
+        # Build table of string representations of the elements
+        res = []
+        # Track per-column max lengths for pretty alignment
+        maxlen = [0] * self.cols
+        for i in range(self.rows):
+            res.append([])
+            for j in range(self.cols):
+                if n:
+                    string = self.ctx.nstr(self[i,j], n, **kwargs)
+                else:
+                    string = str(self[i,j])
+                res[-1].append(string)
+                maxlen[j] = max(len(string), maxlen[j])
+        # Patch strings together
+        for i, row in enumerate(res):
+            for j, elem in enumerate(row):
+                # Pad each element up to maxlen so the columns line up
+                row[j] = elem.rjust(maxlen[j])
+            res[i] = "[" + colsep.join(row) + "]"
+        return rowsep.join(res)
+
+    def __str__(self):
+        return self.__nstr__()
+
+    def _toliststr(self, avoid_type=False):
+        """
+        Create a list string from a matrix.
+
+        If avoid_type: avoid multiple 'mpf's.
+        """
+        # XXX: should be something like self.ctx._types
+        typ = self.ctx.mpf
+        s = '['
+        for i in xrange(self.__rows):
+            s += '['
+            for j in xrange(self.__cols):
+                if not avoid_type or not isinstance(self[i,j], typ):
+                    a = repr(self[i,j])
+                else:
+                    a = "'" + str(self[i,j]) + "'"
+                s += a + ', '
+            s = s[:-2]
+            s += '],\n '
+        s = s[:-3]
+        s += ']'
+        return s
+
+    def tolist(self):
+        """
+        Convert the matrix to a nested list.
+        """
+        return [[self[i,j] for j in range(self.__cols)] for i in range(self.__rows)]
+
+    def __repr__(self):
+        if self.ctx.pretty:
+            return self.__str__()
+        s = 'matrix(\n'
+        s += self._toliststr(avoid_type=True) + ')'
+        return s
+
+    def __get_element(self, key):
+        '''
+        Fast extraction of the i,j element from the matrix
+            This function is for private use only because is unsafe:
+                1. Does not check on the value of key it expects key to be a integer tuple (i,j)
+                2. Does not check bounds
+        '''
+        if key in self.__data:
+            return self.__data[key]
+        else:
+            return self.ctx.zero
+
+    def __set_element(self, key, value):
+        '''
+        Fast assignment of the i,j element in the matrix
+            This function is unsafe:
+                1. Does not check on the value of key it expects key to be a integer tuple (i,j)
+                2. Does not check bounds
+                3. Does not check the value type
+                4. Does not reset the LU cache
+        '''
+        if value: # only store non-zeros
+            self.__data[key] = value
+        elif key in self.__data:
+            del self.__data[key]
+
+
+    def __getitem__(self, key):
+        '''
+            Getitem function for mp matrix class with slice index enabled
+            it allows the following assingments
+            scalar to a slice of the matrix
+         B = A[:,2:6]
+        '''
+        # Convert vector to matrix indexing
+        if isinstance(key, int) or isinstance(key,slice):
+            # only sufficent for vectors
+            if self.__rows == 1:
+                key = (0, key)
+            elif self.__cols == 1:
+                key = (key, 0)
+            else:
+                raise IndexError('insufficient indices for matrix')
+
+        if isinstance(key[0],slice) or isinstance(key[1],slice):
+
+            #Rows
+            if isinstance(key[0],slice):
+                #Check bounds
+                if (key[0].start is None or key[0].start >= 0) and \
+                    (key[0].stop is None or key[0].stop <= self.__rows+1):
+                    # Generate indices
+                    rows = xrange(*key[0].indices(self.__rows))
+                else:
+                    raise IndexError('Row index out of bounds')
+            else:
+                # Single row
+                rows = [key[0]]
+
+            # Columns
+            if isinstance(key[1],slice):
+                # Check bounds
+                if (key[1].start is None or key[1].start >= 0) and \
+                    (key[1].stop is None or key[1].stop <= self.__cols+1):
+                    # Generate indices
+                    columns = xrange(*key[1].indices(self.__cols))
+                else:
+                    raise IndexError('Column index out of bounds')
+
+            else:
+                # Single column
+                columns = [key[1]]
+
+            # Create matrix slice
+            m = self.ctx.matrix(len(rows),len(columns))
+
+            # Assign elements to the output matrix
+            for i,x in enumerate(rows):
+                for j,y in enumerate(columns):
+                    m.__set_element((i,j),self.__get_element((x,y)))
+
+            return m
+
+        else:
+            # single element extraction
+            if key[0] >= self.__rows or key[1] >= self.__cols:
+                raise IndexError('matrix index out of range')
+            if key in self.__data:
+                return self.__data[key]
+            else:
+                return self.ctx.zero
+
+    def __setitem__(self, key, value):
+        # setitem function for mp matrix class with slice index enabled
+        # it allows the following assingments
+        #  scalar to a slice of the matrix
+        # A[:,2:6] = 2.5
+        #  submatrix to matrix (the value matrix should be the same size as the slice size)
+        # A[3,:] = B   where A is n x m  and B is n x 1
+        # Convert vector to matrix indexing
+        if isinstance(key, int) or isinstance(key,slice):
+            # only sufficent for vectors
+            if self.__rows == 1:
+                key = (0, key)
+            elif self.__cols == 1:
+                key = (key, 0)
+            else:
+                raise IndexError('insufficient indices for matrix')
+        # Slice indexing
+        if isinstance(key[0],slice) or isinstance(key[1],slice):
+            # Rows
+            if isinstance(key[0],slice):
+                # Check bounds
+                if (key[0].start is None or key[0].start >= 0) and \
+                    (key[0].stop is None or key[0].stop <= self.__rows+1):
+                    # generate row indices
+                    rows = xrange(*key[0].indices(self.__rows))
+                else:
+                    raise IndexError('Row index out of bounds')
+            else:
+                # Single row
+                rows = [key[0]]
+            # Columns
+            if isinstance(key[1],slice):
+                # Check bounds
+                if (key[1].start is None or key[1].start >= 0) and \
+                    (key[1].stop is None or key[1].stop <= self.__cols+1):
+                    # Generate column indices
+                    columns = xrange(*key[1].indices(self.__cols))
+                else:
+                    raise IndexError('Column index out of bounds')
+            else:
+                # Single column
+                columns = [key[1]]
+            # Assign slice with a scalar
+            if isinstance(value,self.ctx.matrix):
+                # Assign elements to matrix if input and output dimensions match
+                if len(rows) == value.rows and len(columns) == value.cols:
+                    for i,x in enumerate(rows):
+                        for j,y in enumerate(columns):
+                            self.__set_element((x,y), value.__get_element((i,j)))
+                else:
+                    raise ValueError('Dimensions do not match')
+            else:
+                # Assign slice with scalars
+                value = self.ctx.convert(value)
+                for i in rows:
+                    for j in columns:
+                        self.__set_element((i,j), value)
+        else:
+            # Single element assingment
+            # Check bounds
+            if key[0] >= self.__rows or key[1] >= self.__cols:
+                raise IndexError('matrix index out of range')
+            # Convert and store value
+            value = self.ctx.convert(value)
+            if value: # only store non-zeros
+                self.__data[key] = value
+            elif key in self.__data:
+                del self.__data[key]
+
+        if self._LU:
+            self._LU = None
+        return
+
+    def __iter__(self):
+        for i in xrange(self.__rows):
+            for j in xrange(self.__cols):
+                yield self[i,j]
+
+    def __mul__(self, other):
+        if isinstance(other, self.ctx.matrix):
+            # dot multiplication
+            if self.__cols != other.__rows:
+                raise ValueError('dimensions not compatible for multiplication')
+            new = self.ctx.matrix(self.__rows, other.__cols)
+            self_zero = self.ctx.zero
+            self_get = self.__data.get
+            other_zero = other.ctx.zero
+            other_get = other.__data.get
+            for i in xrange(self.__rows):
+                for j in xrange(other.__cols):
+                    new[i, j] = self.ctx.fdot((self_get((i,k), self_zero), other_get((k,j), other_zero))
+                                     for k in xrange(other.__rows))
+            return new
+        else:
+            # try scalar multiplication
+            new = self.ctx.matrix(self.__rows, self.__cols)
+            for i in xrange(self.__rows):
+                for j in xrange(self.__cols):
+                    new[i, j] = other * self[i, j]
+            return new
+
+    def __matmul__(self, other):
+        return self.__mul__(other)
+
+    def __rmul__(self, other):
+        # assume other is scalar and thus commutative
+        if isinstance(other, self.ctx.matrix):
+            raise TypeError("other should not be type of ctx.matrix")
+        return self.__mul__(other)
+
+    def __pow__(self, other):
+        # avoid cyclic import problems
+        #from linalg import inverse
+        if not isinstance(other, int):
+            raise ValueError('only integer exponents are supported')
+        if not self.__rows == self.__cols:
+            raise ValueError('only powers of square matrices are defined')
+        n = other
+        if n == 0:
+            return self.ctx.eye(self.__rows)
+        if n < 0:
+            n = -n
+            neg = True
+        else:
+            neg = False
+        i = n
+        y = 1
+        z = self.copy()
+        while i != 0:
+            if i % 2 == 1:
+                y = y * z
+            z = z*z
+            i = i // 2
+        if neg:
+            y = self.ctx.inverse(y)
+        return y
+
+    def __div__(self, other):
+        # assume other is scalar and do element-wise divison
+        assert not isinstance(other, self.ctx.matrix)
+        new = self.ctx.matrix(self.__rows, self.__cols)
+        for i in xrange(self.__rows):
+            for j in xrange(self.__cols):
+                new[i,j] = self[i,j] / other
+        return new
+
+    __truediv__ = __div__
+
+    def __add__(self, other):
+        if isinstance(other, self.ctx.matrix):
+            if not (self.__rows == other.__rows and self.__cols == other.__cols):
+                raise ValueError('incompatible dimensions for addition')
+            new = self.ctx.matrix(self.__rows, self.__cols)
+            for i in xrange(self.__rows):
+                for j in xrange(self.__cols):
+                    new[i,j] = self[i,j] + other[i,j]
+            return new
+        else:
+            # assume other is scalar and add element-wise
+            new = self.ctx.matrix(self.__rows, self.__cols)
+            for i in xrange(self.__rows):
+                for j in xrange(self.__cols):
+                    new[i,j] += self[i,j] + other
+            return new
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __sub__(self, other):
+        if isinstance(other, self.ctx.matrix) and not (self.__rows == other.__rows
+                                              and self.__cols == other.__cols):
+            raise ValueError('incompatible dimensions for subtraction')
+        return self.__add__(other * (-1))
+
+    def __pos__(self):
+        """
+        +M returns a copy of M, rounded to current working precision.
+        """
+        return (+1) * self
+
+    def __neg__(self):
+        return (-1) * self
+
+    def __rsub__(self, other):
+        return -self + other
+
+    def __eq__(self, other):
+        return self.__rows == other.__rows and self.__cols == other.__cols \
+               and self.__data == other.__data
+
+    def __len__(self):
+        if self.rows == 1:
+            return self.cols
+        elif self.cols == 1:
+            return self.rows
+        else:
+            return self.rows # do it like numpy
+
+    def __getrows(self):
+        return self.__rows
+
+    def __setrows(self, value):
+        for key in self.__data.copy():
+            if key[0] >= value:
+                del self.__data[key]
+        self.__rows = value
+
+    rows = property(__getrows, __setrows, doc='number of rows')
+
+    def __getcols(self):
+        return self.__cols
+
+    def __setcols(self, value):
+        for key in self.__data.copy():
+            if key[1] >= value:
+                del self.__data[key]
+        self.__cols = value
+
+    cols = property(__getcols, __setcols, doc='number of columns')
+
+    def transpose(self):
+        new = self.ctx.matrix(self.__cols, self.__rows)
+        for i in xrange(self.__rows):
+            for j in xrange(self.__cols):
+                new[j,i] = self[i,j]
+        return new
+
+    T = property(transpose)
+
+    def conjugate(self):
+        return self.apply(self.ctx.conj)
+
+    def transpose_conj(self):
+        return self.conjugate().transpose()
+
+    H = property(transpose_conj)
+
+    def copy(self):
+        new = self.ctx.matrix(self.__rows, self.__cols)
+        new.__data = self.__data.copy()
+        return new
+
+    __copy__ = copy
+
+    def column(self, n):
+        m = self.ctx.matrix(self.rows, 1)
+        for i in range(self.rows):
+            m[i] = self[i,n]
+        return m
+
+class MatrixMethods(object):
+
+    def __init__(ctx):
+        # XXX: subclass
+        ctx.matrix = type('matrix', (_matrix,), {})
+        ctx.matrix.ctx = ctx
+        ctx.matrix.convert = ctx.convert
+
+    def eye(ctx, n, **kwargs):
+        """
+        Create square identity matrix n x n.
+        """
+        A = ctx.matrix(n, **kwargs)
+        for i in xrange(n):
+            A[i,i] = 1
+        return A
+
+    def diag(ctx, diagonal, **kwargs):
+        """
+        Create square diagonal matrix using given list.
+
+        Example:
+        >>> from mpmath import diag, mp
+        >>> mp.pretty = False
+        >>> diag([1, 2, 3])
+        matrix(
+        [['1.0', '0.0', '0.0'],
+         ['0.0', '2.0', '0.0'],
+         ['0.0', '0.0', '3.0']])
+        """
+        A = ctx.matrix(len(diagonal), **kwargs)
+        for i in xrange(len(diagonal)):
+            A[i,i] = diagonal[i]
+        return A
+
+    def zeros(ctx, *args, **kwargs):
+        """
+        Create matrix m x n filled with zeros.
+        One given dimension will create square matrix n x n.
+
+        Example:
+        >>> from mpmath import zeros, mp
+        >>> mp.pretty = False
+        >>> zeros(2)
+        matrix(
+        [['0.0', '0.0'],
+         ['0.0', '0.0']])
+        """
+        if len(args) == 1:
+            m = n = args[0]
+        elif len(args) == 2:
+            m = args[0]
+            n = args[1]
+        else:
+            raise TypeError('zeros expected at most 2 arguments, got %i' % len(args))
+        A = ctx.matrix(m, n, **kwargs)
+        for i in xrange(m):
+            for j in xrange(n):
+                A[i,j] = 0
+        return A
+
+    def ones(ctx, *args, **kwargs):
+        """
+        Create matrix m x n filled with ones.
+        One given dimension will create square matrix n x n.
+
+        Example:
+        >>> from mpmath import ones, mp
+        >>> mp.pretty = False
+        >>> ones(2)
+        matrix(
+        [['1.0', '1.0'],
+         ['1.0', '1.0']])
+        """
+        if len(args) == 1:
+            m = n = args[0]
+        elif len(args) == 2:
+            m = args[0]
+            n = args[1]
+        else:
+            raise TypeError('ones expected at most 2 arguments, got %i' % len(args))
+        A = ctx.matrix(m, n, **kwargs)
+        for i in xrange(m):
+            for j in xrange(n):
+                A[i,j] = 1
+        return A
+
+    def hilbert(ctx, m, n=None):
+        """
+        Create (pseudo) hilbert matrix m x n.
+        One given dimension will create hilbert matrix n x n.
+
+        The matrix is very ill-conditioned and symmetric, positive definite if
+        square.
+        """
+        if n is None:
+            n = m
+        A = ctx.matrix(m, n)
+        for i in xrange(m):
+            for j in xrange(n):
+                A[i,j] = ctx.one / (i + j + 1)
+        return A
+
+    def randmatrix(ctx, m, n=None, min=0, max=1, **kwargs):
+        """
+        Create a random m x n matrix.
+
+        All values are >= min and <max.
+        n defaults to m.
+
+        Example:
+        >>> from mpmath import randmatrix
+        >>> randmatrix(2) # doctest:+SKIP
+        matrix(
+        [['0.53491598236191806', '0.57195669543302752'],
+         ['0.85589992269513615', '0.82444367501382143']])
+        """
+        if not n:
+            n = m
+        A = ctx.matrix(m, n, **kwargs)
+        for i in xrange(m):
+            for j in xrange(n):
+                A[i,j] = ctx.rand() * (max - min) + min
+        return A
+
+    def swap_row(ctx, A, i, j):
+        """
+        Swap row i with row j.
+        """
+        if i == j:
+            return
+        if isinstance(A, ctx.matrix):
+            for k in xrange(A.cols):
+                A[i,k], A[j,k] = A[j,k], A[i,k]
+        elif isinstance(A, list):
+            A[i], A[j] = A[j], A[i]
+        else:
+            raise TypeError('could not interpret type')
+
+    def extend(ctx, A, b):
+        """
+        Extend matrix A with column b and return result.
+        """
+        if not isinstance(A, ctx.matrix):
+            raise TypeError("A should be a type of ctx.matrix")
+        if A.rows != len(b):
+            raise ValueError("Value should be equal to len(b)")
+        A = A.copy()
+        A.cols += 1
+        for i in xrange(A.rows):
+            A[i, A.cols-1] = b[i]
+        return A
+
+    def norm(ctx, x, p=2):
+        r"""
+        Gives the entrywise `p`-norm of an iterable *x*, i.e. the vector norm
+        `\left(\sum_k |x_k|^p\right)^{1/p}`, for any given `1 \le p \le \infty`.
+
+        Special cases:
+
+        If *x* is not iterable, this just returns ``absmax(x)``.
+
+        ``p=1`` gives the sum of absolute values.
+
+        ``p=2`` is the standard Euclidean vector norm.
+
+        ``p=inf`` gives the magnitude of the largest element.
+
+        For *x* a matrix, ``p=2`` is the Frobenius norm.
+        For operator matrix norms, use :func:`~mpmath.mnorm` instead.
+
+        You can use the string 'inf' as well as float('inf') or mpf('inf')
+        to specify the infinity norm.
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> x = matrix([-10, 2, 100])
+            >>> norm(x, 1)
+            mpf('112.0')
+            >>> norm(x, 2)
+            mpf('100.5186549850325')
+            >>> norm(x, inf)
+            mpf('100.0')
+
+        """
+        try:
+            iter(x)
+        except TypeError:
+            return ctx.absmax(x)
+        if type(p) is not int:
+            p = ctx.convert(p)
+        if p == ctx.inf:
+            return max(ctx.absmax(i) for i in x)
+        elif p == 1:
+            return ctx.fsum(x, absolute=1)
+        elif p == 2:
+            return ctx.sqrt(ctx.fsum(x, absolute=1, squared=1))
+        elif p > 1:
+            return ctx.nthroot(ctx.fsum(abs(i)**p for i in x), p)
+        else:
+            raise ValueError('p has to be >= 1')
+
+    def mnorm(ctx, A, p=1):
+        r"""
+        Gives the matrix (operator) `p`-norm of A. Currently ``p=1`` and ``p=inf``
+        are supported:
+
+        ``p=1`` gives the 1-norm (maximal column sum)
+
+        ``p=inf`` gives the `\infty`-norm (maximal row sum).
+        You can use the string 'inf' as well as float('inf') or mpf('inf')
+
+        ``p=2`` (not implemented) for a square matrix is the usual spectral
+        matrix norm, i.e. the largest singular value.
+
+        ``p='f'`` (or 'F', 'fro', 'Frobenius, 'frobenius') gives the
+        Frobenius norm, which is the elementwise 2-norm. The Frobenius norm is an
+        approximation of the spectral norm and satisfies
+
+        .. math ::
+
+            \frac{1}{\sqrt{\mathrm{rank}(A)}} \|A\|_F \le \|A\|_2 \le \|A\|_F
+
+        The Frobenius norm lacks some mathematical properties that might
+        be expected of a norm.
+
+        For general elementwise `p`-norms, use :func:`~mpmath.norm` instead.
+
+        **Examples**
+
+            >>> from mpmath import *
+            >>> mp.dps = 15; mp.pretty = False
+            >>> A = matrix([[1, -1000], [100, 50]])
+            >>> mnorm(A, 1)
+            mpf('1050.0')
+            >>> mnorm(A, inf)
+            mpf('1001.0')
+            >>> mnorm(A, 'F')
+            mpf('1006.2310867787777')
+
+        """
+        A = ctx.matrix(A)
+        if type(p) is not int:
+            if type(p) is str and 'frobenius'.startswith(p.lower()):
+                return ctx.norm(A, 2)
+            p = ctx.convert(p)
+        m, n = A.rows, A.cols
+        if p == 1:
+            return max(ctx.fsum((A[i,j] for i in xrange(m)), absolute=1) for j in xrange(n))
+        elif p == ctx.inf:
+            return max(ctx.fsum((A[i,j] for j in xrange(n)), absolute=1) for i in xrange(m))
+        else:
+            raise NotImplementedError("matrix p-norm for arbitrary p")
+
+if __name__ == '__main__':
+    import doctest
+    doctest.testmod()

lib/python3.11/site-packages/mpmath/rational.py

@@ -0,0 +1,240 @@
+import operator
+import sys
+from .libmp import int_types, mpf_hash, bitcount, from_man_exp, HASH_MODULUS
+
+new = object.__new__
+
+def create_reduced(p, q, _cache={}):
+    key = p, q
+    if key in _cache:
+        return _cache[key]
+    x, y = p, q
+    while y:
+        x, y = y, x % y
+    if x != 1:
+        p //= x
+        q //= x
+    v = new(mpq)
+    v._mpq_ = p, q
+    # Speedup integers, half-integers and other small fractions
+    if q <= 4 and abs(key[0]) < 100:
+        _cache[key] = v
+    return v
+
+class mpq(object):
+    """
+    Exact rational type, currently only intended for internal use.
+    """
+
+    __slots__ = ["_mpq_"]
+
+    def __new__(cls, p, q=1):
+        if type(p) is tuple:
+            p, q = p
+        elif hasattr(p, '_mpq_'):
+            p, q = p._mpq_
+        return create_reduced(p, q)
+
+    def __repr__(s):
+        return "mpq(%s,%s)" % s._mpq_
+
+    def __str__(s):
+        return "(%s/%s)" % s._mpq_
+
+    def __int__(s):
+        a, b = s._mpq_
+        return a // b
+
+    def __nonzero__(s):
+        return bool(s._mpq_[0])
+
+    __bool__ = __nonzero__
+
+    def __hash__(s):
+        a, b = s._mpq_
+        if sys.version_info >= (3, 2):
+            inverse = pow(b, HASH_MODULUS-2, HASH_MODULUS)
+            if not inverse:
+                h = sys.hash_info.inf
+            else:
+                h = (abs(a) * inverse) % HASH_MODULUS
+            if a < 0: h = -h
+            if h == -1: h = -2
+            return h
+        else:
+            if b == 1:
+                return hash(a)
+            # Power of two: mpf compatible hash
+            if not (b & (b-1)):
+                return mpf_hash(from_man_exp(a, 1-bitcount(b)))
+            return hash((a,b))
+
+    def __eq__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            return s._mpq_ == t._mpq_
+        if ttype in int_types:
+            a, b = s._mpq_
+            if b != 1:
+                return False
+            return a == t
+        return NotImplemented
+
+    def __ne__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            return s._mpq_ != t._mpq_
+        if ttype in int_types:
+            a, b = s._mpq_
+            if b != 1:
+                return True
+            return a != t
+        return NotImplemented
+
+    def _cmp(s, t, op):
+        ttype = type(t)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return op(a, t*b)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return op(a*d, b*c)
+        return NotImplementedError
+
+    def __lt__(s, t): return s._cmp(t, operator.lt)
+    def __le__(s, t): return s._cmp(t, operator.le)
+    def __gt__(s, t): return s._cmp(t, operator.gt)
+    def __ge__(s, t): return s._cmp(t, operator.ge)
+
+    def __abs__(s):
+        a, b = s._mpq_
+        if a >= 0:
+            return s
+        v = new(mpq)
+        v._mpq_ = -a, b
+        return v
+
+    def __neg__(s):
+        a, b = s._mpq_
+        v = new(mpq)
+        v._mpq_ = -a, b
+        return v
+
+    def __pos__(s):
+        return s
+
+    def __add__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*d+b*c, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            v = new(mpq)
+            v._mpq_ = a+b*t, b
+            return v
+        return NotImplemented
+
+    __radd__ = __add__
+
+    def __sub__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*d-b*c, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            v = new(mpq)
+            v._mpq_ = a-b*t, b
+            return v
+        return NotImplemented
+
+    def __rsub__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(b*c-a*d, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            v = new(mpq)
+            v._mpq_ = b*t-a, b
+            return v
+        return NotImplemented
+
+    def __mul__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*c, b*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return create_reduced(a*t, b)
+        return NotImplemented
+
+    __rmul__ = __mul__
+
+    def __div__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(a*d, b*c)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return create_reduced(a, b*t)
+        return NotImplemented
+
+    def __rdiv__(s, t):
+        ttype = type(t)
+        if ttype is mpq:
+            a, b = s._mpq_
+            c, d = t._mpq_
+            return create_reduced(b*c, a*d)
+        if ttype in int_types:
+            a, b = s._mpq_
+            return create_reduced(b*t, a)
+        return NotImplemented
+
+    def __pow__(s, t):
+        ttype = type(t)
+        if ttype in int_types:
+            a, b = s._mpq_
+            if t:
+                if t < 0:
+                    a, b, t = b, a, -t
+                v = new(mpq)
+                v._mpq_ = a**t, b**t
+                return v
+            raise ZeroDivisionError
+        return NotImplemented
+
+
+mpq_1 = mpq((1,1))
+mpq_0 = mpq((0,1))
+mpq_1_2 = mpq((1,2))
+mpq_3_2 = mpq((3,2))
+mpq_1_4 = mpq((1,4))
+mpq_1_16 = mpq((1,16))
+mpq_3_16 = mpq((3,16))
+mpq_5_2 = mpq((5,2))
+mpq_3_4 = mpq((3,4))
+mpq_7_4 = mpq((7,4))
+mpq_5_4 = mpq((5,4))
+
+
+# Register with "numbers" ABC
+#     We do not subclass, hence we do not use the @abstractmethod checks. While
+#     this is less invasive it may turn out that we do not actually support
+#     parts of the expected interfaces.  See
+#     http://docs.python.org/2/library/numbers.html for list of abstract
+#     methods.
+try:
+    import numbers
+    numbers.Rational.register(mpq)
+except ImportError:
+    pass