# Source code for sympy.functions.special.hyper

"""Hypergeometric and Meijer G-functions"""

from sympy.core import S, I, pi, oo, ilcm, Mod
from sympy.core.function import Function, Derivative, ArgumentIndexError
from sympy.core.containers import Tuple
from sympy.core.mul import Mul

from sympy.functions import (sqrt, exp, log, sin, cos, asin, atan,
sinh, cosh, asinh, acosh, atanh, acoth)
from functools import reduce

# TODO should __new__ accept **options?
# TODO should constructors should check if parameters are sensible?

def _prep_tuple(v):
"""
Turn an iterable argument V into a Tuple and unpolarify, since both
hypergeometric and meijer g-functions are unbranched in their parameters.

Examples:
>>> from sympy.functions.special.hyper import _prep_tuple
>>> _prep_tuple([1, 2, 3])
(1, 2, 3)
>>> _prep_tuple((4, 5))
(4, 5)
>>> _prep_tuple((7, 8, 9))
(7, 8, 9)
"""
from sympy.simplify.simplify import unpolarify
return Tuple(*[unpolarify(x) for x in v])

class TupleParametersBase(Function):
""" Base class that takes care of differentiation, when some of
the arguments are actually tuples. """
# This is not deduced automatically since there are Tuples as arguments.
is_commutative = True

def _eval_derivative(self, s):
try:
res = 0
if self.args[0].has(s) or self.args[1].has(s):
for i, p in enumerate(self._diffargs):
m = self._diffargs[i].diff(s)
if m != 0:
res += self.fdiff((1, i))*m
return res + self.fdiff(3)*self.args[2].diff(s)
except (ArgumentIndexError, NotImplementedError):
return Derivative(self, s)

[docs]class hyper(TupleParametersBase):
r"""
The (generalized) hypergeometric function is defined by a series where
the ratios of successive terms are a rational function of the summation
index. When convergent, it is continued analytically to the largest
possible domain.

The hypergeometric function depends on two vectors of parameters, called
the numerator parameters :math:a_p, and the denominator parameters
:math:b_q. It also has an argument :math:z. The series definition is

.. math ::
{}_pF_q\left(\begin{matrix} a_1, \dots, a_p \\ b_1, \dots, b_q \end{matrix}
\middle| z \right)
= \sum_{n=0}^\infty \frac{(a_1)_n \dots (a_p)_n}{(b_1)_n \dots (b_q)_n}
\frac{z^n}{n!},

where :math:(a)_n = (a)(a+1)\dots(a+n-1) denotes the rising factorial.

If one of the :math:b_q is a non-positive integer then the series is
undefined unless one of the a_p is a larger (i.e. smaller in
magnitude) non-positive integer. If none of the :math:b_q is a
non-positive integer and one of the :math:a_p is a non-positive
integer, then the series reduces to a polynomial. To simplify the
following discussion, we assume that none of the :math:a_p or
:math:b_q is a non-positive integer. For more details, see the
references.

The series converges for all :math:z if :math:p \le q, and thus
defines an entire single-valued function in this case. If :math:p =
q+1 the series converges for :math:|z| < 1, and can be continued
analytically into a half-plane. If :math:p > q+1 the series is
divergent for all :math:z.

Note: The hypergeometric function constructor currently does *not* check
if the parameters actually yield a well-defined function.

Examples
========

The parameters :math:a_p and :math:b_q can be passed as arbitrary
iterables, for example:

>>> from sympy.functions import hyper
>>> from sympy.abc import x, n, a
>>> hyper((1, 2, 3), [3, 4], x)
hyper((1, 2, 3), (3, 4), x)

There is also pretty printing (it looks better using unicode):

>>> from sympy import pprint
>>> pprint(hyper((1, 2, 3), [3, 4], x), use_unicode=False)
_
|_  /1, 2, 3 |  \
|   |        | x|
3  2 \  3, 4  |  /

The parameters must always be iterables, even if they are vectors of
length one or zero:

>>> hyper((1, ), [], x)
hyper((1,), (), x)

But of course they may be variables (but if they depend on x then you
should not expect much implemented functionality):

>>> hyper((n, a), (n**2,), x)
hyper((n, a), (n**2,), x)

The hypergeometric function generalizes many named special functions.
The function hyperexpand() tries to express a hypergeometric function
using named special functions.
For example:

>>> from sympy import hyperexpand
>>> hyperexpand(hyper([], [], x))
exp(x)

You can also use expand_func:

>>> from sympy import expand_func
>>> expand_func(x*hyper([1, 1], [2], -x))
log(x + 1)

More examples:

>>> from sympy import S
>>> hyperexpand(hyper([], [S(1)/2], -x**2/4))
cos(x)
>>> hyperexpand(x*hyper([S(1)/2, S(1)/2], [S(3)/2], x**2))
asin(x)

We can also sometimes hyperexpand parametric functions:

>>> from sympy.abc import a
>>> hyperexpand(hyper([-a], [], x))
(-x + 1)**a

========

sympy.simplify.hyperexpand
sympy.functions.special.gamma_functions.gamma
meijerg

References
==========

- Luke, Y. L. (1969), The Special Functions and Their Approximations,
Volume 1
- http://en.wikipedia.org/wiki/Generalized_hypergeometric_function
"""

nargs = 3

def __new__(cls, ap, bq, z):
# TODO should we check convergence conditions?
return Function.__new__(cls, _prep_tuple(ap), _prep_tuple(bq), z)

@classmethod
def eval(cls, ap, bq, z):
from sympy import unpolarify
if len(ap) <= len(bq):
nz = unpolarify(z)
if z != nz:
return hyper(ap, bq, nz)

def fdiff(self, argindex=3):
if argindex != 3:
raise ArgumentIndexError(self, argindex)
nap = Tuple(*[a + 1 for a in self.ap])
nbq = Tuple(*[b + 1 for b in self.bq])
fac = Mul(*self.ap)/Mul(*self.bq)
return fac*hyper(nap, nbq, self.argument)

def _eval_expand_func(self, **hints):
from sympy import gamma, hyperexpand
if len(self.ap) == 2 and len(self.bq) == 1 and self.argument == 1:
a, b = self.ap
c = self.bq[0]
return gamma(c)*gamma(c - a - b)/gamma(c - a)/gamma(c - b)
return hyperexpand(self)

@property
[docs]    def argument(self):
""" Argument of the hypergeometric function. """
return self.args[2]

@property
[docs]    def ap(self):
""" Numerator parameters of the hypergeometric function. """
return self.args[0]

@property
[docs]    def bq(self):
""" Denominator parameters of the hypergeometric function. """
return self.args[1]

@property
def _diffargs(self):
return self.ap + self.bq

@property
[docs]    def eta(self):
""" A quantity related to the convergence of the series. """
return sum(self.ap) - sum(self.bq)

@property
"""
Compute the radius of convergence of the defining series.

Note that even if this is not oo, the function may still be evaluated
outside of the radius of convergence by analytic continuation. But if
this is zero, then the function is not actually defined anywhere else.

>>> from sympy.functions import hyper
>>> from sympy.abc import z
1
>>> hyper((1, 2, 3), [4], z).radius_of_convergence
0
>>> hyper((1, 2), (3, 4), z).radius_of_convergence
oo
"""
if any(a.is_integer and a <= 0 for a in self.ap + self.bq):
aints = [a for a in self.ap if a.is_Integer and a <= 0]
bints = [a for a in self.bq if a.is_Integer and a <= 0]
if len(aints) < len(bints):
return S(0)
popped = False
for b in bints:
cancelled = False
while aints:
a = aints.pop()
if a >= b:
cancelled = True
break
popped = True
if not cancelled:
return S(0)
if aints or popped:
# There are still non-positive numerator parameters.
# This is a polynomial.
return oo
if len(self.ap) == len(self.bq) + 1:
return S(1)
elif len(self.ap) <= len(self.bq):
return oo
else:
return S(0)

@property
[docs]    def convergence_statement(self):
""" Return a condition on z under which the series converges. """
from sympy import And, Or, re, Ne, oo
if R == 0:
return False
if R == oo:
return True
# The special functions and their approximations, page 44
e = self.eta
z = self.argument
c1 = And(re(e) < 0, abs(z) <= 1)
c2 = And(0 <= re(e), re(e) < 1, abs(z) <= 1, Ne(z, 1))
c3 = And(re(e) >= 1, abs(z) < 1)
return Or(c1, c2, c3)

[docs]class meijerg(TupleParametersBase):
r"""
The Meijer G-function is defined by a Mellin-Barnes type integral that
resembles an inverse Mellin transform. It generalizes the hypergeometric
functions.

The Meijer G-function depends on four sets of parameters. There are
"*numerator parameters*"
:math:a_1, \dots, a_n and :math:a_{n+1}, \dots, a_p, and there are
"*denominator parameters*"
:math:b_1, \dots, b_m and :math:b_{m+1}, \dots, b_q.
Confusingly, it is traditionally denoted as follows (note the position
of m, n, p, q, and how they relate to the lengths of the four
parameter vectors):

.. math ::
G_{p,q}^{m,n} \left(\begin{matrix}a_1, \dots, a_n & a_{n+1}, \dots, a_p \\
b_1, \dots, b_m & b_{m+1}, \dots, b_q
\end{matrix} \middle| z \right).

However, in sympy the four parameter vectors are always available
separately (see examples), so that there is no need to keep track of the
decorating sub- and super-scripts on the G symbol.

The G function is defined as the following integral:

.. math ::
\frac{1}{2 \pi i} \int_L \frac{\prod_{j=1}^m \Gamma(b_j - s)
\prod_{j=1}^n \Gamma(1 - a_j + s)}{\prod_{j=m+1}^q \Gamma(1- b_j +s)
\prod_{j=n+1}^p \Gamma(a_j - s)} z^s \mathrm{d}s,

where :math:\Gamma(z) is the gamma function. There are three possible
contours which we will not describe in detail here (see the references).
If the integral converges along more than one of them the definitions
agree. The contours all separate the poles of :math:\Gamma(1-a_j+s)
from the poles of :math:\Gamma(b_k-s), so in particular the G function
is undefined if :math:a_j - b_k \in \mathbb{Z}_{>0} for some
:math:j \le n and :math:k \le m.

The conditions under which one of the contours yields a convergent integral
are complicated and we do not state them here, see the references.

Note: Currently the Meijer G-function constructor does *not* check any
convergence conditions.

Examples
========

You can pass the parameters either as four separate vectors:

>>> from sympy.functions import meijerg
>>> from sympy.abc import x, a
>>> from sympy.core.containers import Tuple
>>> from sympy import pprint
>>> pprint(meijerg((1, 2), (a, 4), (5,), [], x), use_unicode=False)
__1, 2 /1, 2  a, 4 |  \
/__     |           | x|
\_|4, 1 \ 5         |  /

or as two nested vectors:

>>> pprint(meijerg([(1, 2), (3, 4)], ([5], Tuple()), x), use_unicode=False)
__1, 2 /1, 2  3, 4 |  \
/__     |           | x|
\_|4, 1 \ 5         |  /

As with the hypergeometric function, the parameters may be passed as
arbitrary iterables. Vectors of length zero and one also have to be
passed as iterables. The parameters need not be constants, but if they
depend on the argument then not much implemented functionality should be
expected.

All the subvectors of parameters are available:

>>> from sympy import pprint
>>> g = meijerg([1], [2], [3], [4], x)
>>> pprint(g, use_unicode=False)
__1, 1 /1  2 |  \
/__     |     | x|
\_|2, 2 \3  4 |  /
>>> g.an
(1,)
>>> g.ap
(1, 2)
>>> g.aother
(2,)
>>> g.bm
(3,)
>>> g.bq
(3, 4)
>>> g.bother
(4,)

The Meijer G-function generalizes the hypergeometric functions.
In some cases it can be expressed in terms of hypergeometric functions,
using Slater's theorem. For example:

>>> from sympy import hyperexpand
>>> from sympy.abc import a, b, c
>>> hyperexpand(meijerg([a], [], [c], [b], x), allow_hyper=True)
x**c*gamma(-a + c + 1)*hyper((-a + c + 1,), (-b + c + 1,), -x)/gamma(-b + c + 1)

Thus the Meijer G-function also subsumes many named functions as special
cases. You can use expand_func or hyperexpand to (try to) rewrite a
Meijer G-function in terms of named special functions. For example:

>>> from sympy import expand_func, S
>>> expand_func(meijerg([[],[]], [[0],[]], -x))
exp(x)
>>> hyperexpand(meijerg([[],[]], [[S(1)/2],[0]], (x/2)**2))
sin(x)/sqrt(pi)

========

hyper
sympy.simplify.hyperexpand

References
==========

- Luke, Y. L. (1969), The Special Functions and Their Approximations,
Volume 1
- http://en.wikipedia.org/wiki/Meijer_G-function

"""

nargs = 3

def __new__(cls, *args):
if len(args) == 5:
args = [(args[0], args[1]), (args[2], args[3]), args[4]]
if len(args) != 3:
raise TypeError("args must eiter be as, as', bs, bs', z or " \
"as, bs, z")
def tr(p):
if len(p) != 2:
raise TypeError("wrong argument")
return Tuple(_prep_tuple(p[0]), _prep_tuple(p[1]))

# TODO should we check convergence conditions?
return Function.__new__(cls, tr(args[0]), tr(args[1]), args[2])

def fdiff(self, argindex=3):
if argindex != 3:
return self._diff_wrt_parameter(argindex[1])
if len(self.an) >= 1:
a = list(self.an)
a[0] -= 1
G = meijerg(a, self.aother, self.bm, self.bother, self.argument)
return 1/self.argument * ((self.an[0]-1)*self + G)
elif len(self.bm) >= 1:
b = list(self.bm)
b[0] += 1
G = meijerg(self.an, self.aother, b, self.bother, self.argument)
return 1/self.argument * (self.bm[0]*self - G)
else:
return S.Zero

def _diff_wrt_parameter(self, idx):
# Differentiation wrt a parameter can only be done in very special
# cases. In particular, if we want to differentiate with respect to
# a, all other gamma factors have to reduce to rational functions.
#
# Let MT denote mellin transform. Suppose T(-s) is the gamma factor
# appearing in the definition of G. Then
#
#   MT(log(z)G(z)) = d/ds T(s) = d/da T(s) + ...
#
# Thus d/da G(z) = log(z)G(z) - ...
# The ... can be evaluated as a G function under the above conditions,
# the formula being most easily derived by using
#
# d  Gamma(s + n)    Gamma(s + n) / 1    1                1     \
# -- ------------ =  ------------ | - + ----  + ... + --------- |
# ds Gamma(s)        Gamma(s)     \ s   s + 1         s + n - 1 /
#
# which follows from the difference equation of the digamma function.
# (There is a similar equation for -n instead of +n).

# We first figure out how to pair the parameters.
an = list(self.an)
ap = list(self.aother)
bm = list(self.bm)
bq = list(self.bother)
if idx < len(an):
an.pop(idx)
else:
idx -= len(an)
if idx < len(ap):
ap.pop(idx)
else:
idx -= len(ap)
if idx < len(bm):
bm.pop(idx)
else:
bq.pop(idx - len(bm))
pairs1 = []
pairs2 = []
for l1, l2, pairs in [(an, bq, pairs1), (ap, bm, pairs2)]:
while l1:
x = l1.pop()
found = None
for i, y in enumerate(l2):
if not Mod((x - y).simplify(), 1):
found = i
break
if found is None:
raise NotImplementedError('Derivative not expressible ' \
'as G-function?')
y = l2[i]
l2.pop(i)
pairs.append((x, y))

# Now build the result.
res = log(self.argument)*self

for a, b in pairs1:
sign = 1
n = a - b
base = b
if n < 0:
sign = -1
n = b - a
base = a
for k in range(n):
res -= sign*meijerg(self.an + (base + k + 1,), self.aother,
self.bm, self.bother + (base + k + 0,),
self.argument)

for a, b in pairs2:
sign = 1
n = b - a
base = a
if n < 0:
sign = -1
n = a - b
base = b
for k in range(n):
res -= sign*meijerg(self.an, self.aother + (base + k + 1,),
self.bm + (base + k + 0,), self.bother,
self.argument)

return res

[docs]    def get_period(self):
"""
Return a number P such that G(x*exp(I*P)) == G(x).

>>> from sympy.functions.special.hyper import meijerg
>>> from sympy.abc import z
>>> from sympy import pi, S

>>> meijerg([1], [], [], [], z).get_period()
2*pi
>>> meijerg([pi], [], [], [], z).get_period()
oo
>>> meijerg([1, 2], [], [], [], z).get_period()
oo
>>> meijerg([1,1], [2], [1, S(1)/2, S(1)/3], [1], z).get_period()
12*pi
"""
# This follows from slater's theorem.
def compute(l):
# first check that no two differ by an integer
for i, b in enumerate(l):
if not b.is_Rational:
return oo
for j in range(i + 1, len(l)):
if not Mod((b - l[j]).simplify(), 1):
return oo
return reduce(ilcm, (x.q for x in l), 1)
beta = compute(self.bm)
alpha = compute(self.an)
p, q = len(self.ap), len(self.bq)
if p == q:
if beta == oo or alpha == oo:
return oo
return 2*pi*ilcm(alpha, beta)
elif p < q:
return 2*pi*beta
else:
return 2*pi*alpha

def _eval_expand_func(self, **hints):
from sympy import hyperexpand
return hyperexpand(self)

def _eval_evalf(self, prec):
# The default code is insufficient for polar arguments.
# mpmath provides an optional argument "r", which evaluates
# G(z**(1/r)). I am not sure what its intended use is, but we hijack it
# here in the following way: to evaluate at a number z of |argument|
# less than (say) n*pi, we put r=1/n, compute z' = root(z, n)
# (carefully so as not to loose the branch information), and evaluate
# G(z'**(1/r)) = G(z'**n) = G(z).
from sympy.functions import exp_polar, ceiling
from sympy import mpmath, Expr
z = self.argument
znum = self.argument._eval_evalf(prec)
if znum.has(exp_polar):
znum, branch = znum.as_coeff_mul(exp_polar)
if len(branch) != 1:
return
branch = branch[0].args[0]/I
else:
branch = S(0)
n = ceiling(abs(branch/S.Pi)) + 1
znum = znum**(S(1)/n)*exp(I*branch / n)
#print znum, branch, n

# Convert all args to mpf or mpc
try:
[z, r, ap, bq] = [arg._to_mpmath(prec)
for arg in [znum, 1/n, self.args[0], self.args[1]]]
except ValueError:
return

# Set mpmath precision and apply. Make sure precision is restored
# afterwards
orig = mpmath.mp.prec
try:
mpmath.mp.prec = prec
v = mpmath.meijerg(ap, bq, z, r)
#print ap, bq, z, r, v
finally:
mpmath.mp.prec = orig

return Expr._from_mpmath(v, prec)

[docs]    def integrand(self, s):
""" Get the defining integrand D(s). """
from sympy import gamma
return self.argument**s \
* Mul(*(gamma(b - s) for b in self.bm)) \
* Mul(*(gamma(1 - a + s) for a in self.an)) \
/ Mul(*(gamma(1 - b + s) for b in self.bother)) \
/ Mul(*(gamma(a - s) for a in self.aother))

@property
[docs]    def argument(self):
""" Argument of the Meijer G-function. """
return self.args[2]

@property
[docs]    def an(self):
""" First set of numerator parameters. """
return self.args[0][0]

@property
[docs]    def ap(self):
""" Combined numerator parameters. """
return self.args[0][0] + self.args[0][1]

@property
[docs]    def aother(self):
""" Second set of numerator parameters. """
return self.args[0][1]

@property
[docs]    def bm(self):
""" First set of denominator parameters. """
return self.args[1][0]

@property
[docs]    def bq(self):
""" Combined denominator parameters. """
return self.args[1][0] + self.args[1][1]

@property
[docs]    def bother(self):
""" Second set of denominator parameters. """
return self.args[1][1]

@property
def _diffargs(self):
return self.ap + self.bq

@property
[docs]    def nu(self):
""" A quantity related to the convergence region of the integral,
c.f. references. """
return sum(self.bq) - sum(self.ap)

@property
[docs]    def delta(self):
""" A quantity related to the convergence region of the integral,
c.f. references. """
return len(self.bm) + len(self.an) - S(len(self.ap) + len(self.bq))/2

class HyperRep(Function):
"""
A base class for "hyper representation functions".

This is used exclusively in hyperexpand(), but fits more logically here.

pFq is branched at 1 if p == q+1. For use with slater-expansion, we want
define an "analytic continuation" to all polar numbers, which is
continuous on circles and on the ray t*exp_polar(I*pi). Moreover, we want
a "nice" expression for the various cases.

This base class contains the core logic, concrete derived classes only
supply the actual functions.
"""

nargs = 1

@classmethod
def eval(cls, *args):
from sympy import unpolarify
nargs = tuple(map(unpolarify, args[:-1])) + args[-1:]
if args != nargs:
return cls(*nargs)

@classmethod
def _expr_small(cls, x):
""" An expression for F(x) which holds for |x| < 1. """
raise NotImplementedError

@classmethod
def _expr_small_minus(cls, x):
""" An expression for F(-x) which holds for |x| < 1. """
raise NotImplementedError

@classmethod
def _expr_big(cls, x, n):
""" An expression for F(exp_polar(2*I*pi*n)*x), |x| > 1. """
raise NotImplementedError

@classmethod
def _expr_big_minus(cls, x, n):
""" An expression for F(exp_polar(2*I*pi*n + pi*I)*x), |x| > 1. """
raise NotImplementedError

def _eval_rewrite_as_nonrep(self, *args):
from sympy import Piecewise
x, n = self.args[-1].extract_branch_factor(allow_half=True)
minus = False
nargs = self.args[:-1] + (x,)
if not n.is_Integer:
minus = True
n -= S(1)/2
nnargs = nargs + (n,)
if minus:
small = self._expr_small_minus(*nargs)
big = self._expr_big_minus(*nnargs)
else:
small = self._expr_small(*nargs)
big = self._expr_big(*nnargs)

if big == small:
return small
return Piecewise((big, abs(x) > 1), (small, True))

def _eval_rewrite_as_nonrepsmall(self, *args):
x, n = self.args[-1].extract_branch_factor(allow_half=True)
args = self.args[:-1] + (x,)
if not n.is_Integer:
return self._expr_small_minus(*args)
return self._expr_small(*args)

class HyperRep_power1(HyperRep):
""" Return a representative for hyper([-a], [], z) == (1 - z)**a. """
nargs = 2

@classmethod
def _expr_small(cls, a, x):
return (1 - x)**a

@classmethod
def _expr_small_minus(cls, a, x):
return (1 + x)**a

@classmethod
def _expr_big(cls, a, x, n):
if a.is_integer:
return cls._expr_small(a, x)
return (x - 1)**a*exp((2*n - 1)*pi*I*a)

@classmethod
def _expr_big_minus(cls, a, x, n):
if a.is_integer:
return cls._expr_small_minus(a, x)
return (1 + x)**a*exp(2*n*pi*I*a)

class HyperRep_power2(HyperRep):
""" Return a representative for hyper([a, a - 1/2], [2*a], z). """
nargs = 2

@classmethod
def _expr_small(cls, a, x):
return 2**(2*a - 1)*(1 + sqrt(1 - x))**(1 - 2*a)

@classmethod
def _expr_small_minus(cls, a, x):
return 2**(2*a - 1)*(1 + sqrt(1 + x))**(1 - 2*a)

@classmethod
def _expr_big(cls, a, x, n):
sgn = -1
if n.is_odd:
sgn = 1
n -= 1
return 2**(2*a - 1)*(1 + sgn*I*sqrt(x - 1))**(1 - 2*a) \
*exp(-2*n*pi*I*a)

@classmethod
def _expr_big_minus(cls, a, x, n):
sgn = 1
if n.is_odd:
sgn = -1
return sgn*2**(2*a - 1)*(sqrt(1 + x) + sgn)**(1 - 2*a)*exp(-2*pi*I*a*n)

class HyperRep_log1(HyperRep):
""" Represent -z*hyper([1, 1], [2], z) == log(1 - z). """
@classmethod
def _expr_small(cls, x):
return log(1 - x)

@classmethod
def _expr_small_minus(cls, x):
return log(1 + x)

@classmethod
def _expr_big(cls, x, n):
return log(x - 1) + (2*n-1)*pi*I

@classmethod
def _expr_big_minus(cls, x, n):
return log(1 + x) + 2*n*pi*I

class HyperRep_atanh(HyperRep):
""" Represent hyper([1/2, 1], [3/2], z) == atanh(sqrt(z))/sqrt(z). """
@classmethod
def _expr_small(cls, x):
return atanh(sqrt(x))/sqrt(x)

def _expr_small_minus(cls, x):
return atan(sqrt(x))/sqrt(x)

def _expr_big(cls, x, n):
if n.is_even:
return (acoth(sqrt(x)) + I*pi/2)/sqrt(x)
else:
return (acoth(sqrt(x)) - I*pi/2)/sqrt(x)

def _expr_big_minus(cls, x, n):
if n.is_even:
return atan(sqrt(x))/sqrt(x)
else:
return (atan(sqrt(x)) - pi)/sqrt(x)

class HyperRep_asin1(HyperRep):
""" Represent hyper([1/2, 1/2], [3/2], z) == asin(sqrt(z))/sqrt(z). """
@classmethod
def _expr_small(cls, z):
return asin(sqrt(z))/sqrt(z)

@classmethod
def _expr_small_minus(cls, z):
return asinh(sqrt(z))/sqrt(z)

@classmethod
def _expr_big(cls, z, n):
return S(-1)**n*((S(1)/2 - n)*pi/sqrt(z) + I*acosh(sqrt(z))/sqrt(z))

@classmethod
def _expr_big_minus(cls, z, n):
return S(-1)**n*(asinh(sqrt(z))/sqrt(z)+n*pi*I/sqrt(z))

class HyperRep_asin2(HyperRep):
""" Represent hyper([1, 1], [3/2], z) == asin(sqrt(z))/sqrt(z)/sqrt(1-z). """
# TODO this can be nicer
@classmethod
def _expr_small(cls, z):
return HyperRep_asin1._expr_small(z) \
/HyperRep_power1._expr_small(S(1)/2, z)

@classmethod
def _expr_small_minus(cls, z):
return HyperRep_asin1._expr_small_minus(z) \
/HyperRep_power1._expr_small_minus(S(1)/2, z)

@classmethod
def _expr_big(cls, z, n):
return HyperRep_asin1._expr_big(z, n) \
/HyperRep_power1._expr_big(S(1)/2, z, n)

@classmethod
def _expr_big_minus(cls, z, n):
return HyperRep_asin1._expr_big_minus(z, n) \
/HyperRep_power1._expr_big_minus(S(1)/2, z, n)

class HyperRep_sqrts1(HyperRep):
""" Return a representative for hyper([-a, 1/2 - a], [1/2], z). """
nargs = 2

@classmethod
def _expr_small(cls, a, z):
return ((1 - sqrt(z))**(2*a) + (1 + sqrt(z))**(2*a))/2

@classmethod
def _expr_small_minus(cls, a, z):
return (1 + z)**a*cos(2*a*atan(sqrt(z)))

@classmethod
def _expr_big(cls, a, z, n):
if n.is_even:
return ((sqrt(z) + 1)**(2*a)*exp(2*pi*I*n*a) +
(sqrt(z) - 1)**(2*a)*exp(2*pi*I*(n - 1)*a))/2
else:
n -= 1
return ((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n + 1)) +
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))/2

@classmethod
def _expr_big_minus(cls, a, z, n):
if n.is_even:
return (1 + z)**a*exp(2*pi*I*n*a)*cos(2*a*atan(sqrt(z)))
else:
return (1 + z)**a*exp(2*pi*I*n*a)*cos(2*a*atan(sqrt(z)) - 2*pi*a)

class HyperRep_sqrts2(HyperRep):
""" Return a representative for
sqrt(z)/2*[(1-sqrt(z))**2a - (1 + sqrt(z))**2a]
== -2*z/(2*a+1) d/dz hyper([-a - 1/2, -a], [1/2], z)"""
nargs = 2

@classmethod
def _expr_small(cls, a, z):
return sqrt(z)*((1 - sqrt(z))**(2*a) - (1 + sqrt(z))**(2*a))/2

@classmethod
def _expr_small_minus(cls, a, z):
return sqrt(z)*(1 + z)**a*sin(2*a*atan(sqrt(z)))

@classmethod
def _expr_big(cls, a, z, n):
if n.is_even:
return sqrt(z)/2*((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n - 1)) -
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))
else:
n -= 1
return sqrt(z)/2*((sqrt(z) - 1)**(2*a)*exp(2*pi*I*a*(n + 1)) -
(sqrt(z) + 1)**(2*a)*exp(2*pi*I*a*n))

def _expr_big_minus(cls, a, z, n):
if n.is_even:
return (1 + z)**a*exp(2*pi*I*n*a)*sqrt(z)*sin(2*a*atan(sqrt(z)))
else:
return (1 + z)**a*exp(2*pi*I*n*a)*sqrt(z) \
*sin(2*a*atan(sqrt(z)) - 2*pi*a)

class HyperRep_log2(HyperRep):
""" Represent log(1/2 + sqrt(1 - z)/2) == -z/4*hyper([3/2, 1, 1], [2, 2], z) """

@classmethod
def _expr_small(cls, z):
return log(S(1)/2 + sqrt(1 - z)/2)

@classmethod
def _expr_small_minus(cls, z):
return log(S(1)/2 + sqrt(1 + z)/2)

@classmethod
def _expr_big(cls, z, n):
if n.is_even:
return (n - S(1)/2)*pi*I + log(sqrt(z)/2) + I*asin(1/sqrt(z))
else:
return (n - S(1)/2)*pi*I + log(sqrt(z)/2) - I*asin(1/sqrt(z))

def _expr_big_minus(cls, z, n):
if n.is_even:
return pi*I*n + log(S(1)/2 + sqrt(1 + z)/2)
else:
return pi*I*n + log(sqrt(1 + z)/2 - S(1)/2)

class HyperRep_cosasin(HyperRep):
""" Represent hyper([a, -a], [1/2], z) == cos(2*a*asin(sqrt(z))). """
# Note there are many alternative expressions, e.g. as powers of a sum of
# square roots.
nargs = 2

@classmethod
def _expr_small(cls, a, z):
return cos(2*a*asin(sqrt(z)))

@classmethod
def _expr_small_minus(cls, a, z):
return cosh(2*a*asinh(sqrt(z)))

@classmethod
def _expr_big(cls, a, z, n):
return cosh(2*a*acosh(sqrt(z)) + a*pi*I*(2*n - 1))

@classmethod
def _expr_big_minus(cls, a, z, n):
return cosh(2*a*asinh(sqrt(z)) + 2*a*pi*I*n)

class HyperRep_sinasin(HyperRep):
""" Represent 2*a*z*hyper([1 - a, 1 + a], [3/2], z)
== sqrt(z)/sqrt(1-z)*sin(2*a*asin(sqrt(z))) """
nargs = 2

@classmethod
def _expr_small(cls, a, z):
return sqrt(z)/sqrt(1 - z)*sin(2*a*asin(sqrt(z)))

@classmethod
def _expr_small_minus(cls, a, z):
return -sqrt(z)/sqrt(1 + z)*sinh(2*a*asinh(sqrt(z)))

@classmethod
def _expr_big(cls, a, z, n):
return -1/sqrt(1 - 1/z)*sinh(2*a*acosh(sqrt(z)) + a*pi*I*(2*n - 1))

@classmethod
def _expr_big_minus(cls, a, z, n):
return -1/sqrt(1 + 1/z)*sinh(2*a*asinh(sqrt(z)) + 2*a*pi*I*n)