/

# Source code for sympy.simplify.hyperexpand

"""
Expand Hypergeometric (and Meijer G) functions into named
special functions.

The algorithm for doing this uses a collection of lookup tables of
hypergeometric functions, and various of their properties, to expand
many hypergeometric functions in terms of special functions.

It is based on the following paper:
Kelly B. Roach.  Meijer G Function Representations.
In: Proceedings of the 1997 International Symposium on Symbolic and
Algebraic Computation, pages 205-211, New York, 1997. ACM.

It is described in great(er) detail in the Sphinx documentation.
"""
from sympy.core import S, Dummy, symbols, sympify, Tuple, expand, I, Mul
from sympy import SYMPY_DEBUG

""" Create our knowledge base.
Leave this at the top for easy reference. """
z = Dummy('z')
a, b, c = symbols('a b c', cls=Dummy)
formulae.append(Formula(ap, bq, z, res, (a, b, c)))
def addb(ap, bq, B, C, M):
formulae.append(Formula(ap, bq, z, None, (a, b, c), B, C, M))

from sympy.matrices import diag, Matrix

# Luke, Y. L. (1969), The Special Functions and Their Approximations,
# Volume 1, section 6.2

from sympy import (exp, sqrt, cosh, log, asin, atan, I, lowergamma, cos,
atanh, besseli, gamma, erf, pi, sin, besselj)

# 0F0

# 1F0

# 2F1
Matrix([2**(2*a-1)*(1 + sqrt(1-z))**(1-2*a),
2**(2*a-1)*(1 + sqrt(1-z))**(-2*a)]),
Matrix([[1, 0]]),
Matrix([[(a-S.Half)*z/(1-z), (S.Half-a)*z/(1-z)],
[a/(1-z), a*(z-2)/(1-z)]]))
Matrix([log(1 - z), 1]), Matrix([[-1/z, 0]]),
Matrix([[0, z/(z - 1)], [0, 0]]))
Matrix([log((1 + sqrt(z))/(1 - sqrt(z)))/sqrt(z), 1]),
Matrix([[S(1)/2, 0]]),
Matrix([[-S(1)/2, 1/(1 - z)], [0, 0]]))
Matrix([asin(sqrt(z))/sqrt(z), 1/sqrt(1 - z)]),
Matrix([[1, 0]]),
Matrix([[-S(1)/2, S(1)/2], [0, z/(1 - z)/2]]))
Matrix([(1 + sqrt(z))**(2*a) + (1 - sqrt(z))**(2*a),
sqrt(z)*(1 + sqrt(z))**(2*a-1)
- sqrt(z)*(1 - sqrt(z))**(2*a-1)]),
Matrix([[S.Half, 0]]),
Matrix([[0, a], [z*(2*a-1)/2/(1-z), S.Half - z*(2*a-1)/(1-z)]]))

# A. P. Prudnikov, Yu. A. Brychkov and O. I. Marichev (1990).
# Integrals and Series: More Special Functions, Vol. 3,.
# Gordon and Breach Science Publisher
Matrix([asin(sqrt(z))/sqrt(z*(1-z)), 1]), Matrix([[1, 0]]),
Matrix([[(z - S.Half)/(1 - z), 1/(1 - z)/2], [0, 0]]))

# 3F2
Matrix([sqrt(z)*atanh(sqrt(z)), log(1 - z), 1]),
Matrix([[-S(2)/3, -S(1)/(3*z), S(2)/3]]),
Matrix([[S(1)/2, 0, z/(1 - z)/2],
[0, 0, z/(z - 1)],
[0, 0, 0]]))
# actually the formula for 3/2 is much nicer ...
Matrix([sqrt(1 - z), log(sqrt(1 - z)/2 + S.Half), 1]),
Matrix([[S(4)/9 - 16/(9*z), 4/(3*z), 16/(9*z)]]),
Matrix([[z/2/(z - 1), 0, 0], [1/(2*(z - 1)), 0, S.Half], [0, 0, 0]]))

# 1F1
addb([1], [b], Matrix([z**(1 - b) * exp(z) * lowergamma(b - 1, z), 1]),
Matrix([[b - 1, 0]]),Matrix([[1 - b + z, 1], [0, 0]]))
Matrix([z**(S.Half - a)*exp(z/2)*besseli(a - S.Half, z/2)
* gamma(a + S.Half)/4**(S.Half - a),
z**(S.Half - a)*exp(z/2)*besseli(a + S.Half, z/2)
* gamma(a + S.Half)/4**(S.Half - a)]),
Matrix([[1, 0]]),
Matrix([[z/2, z/2], [z/2, (z/2 - 2*a)]]))

# 2F2
addb([S.Half, a], [S(3)/2, a + 1],
Matrix([a/(2*a - 1)*(-I)*sqrt(pi/z)*erf(I*sqrt(z)),
a/(2*a - 1)*(-z)**(-a)*lowergamma(a, -z), a/(2*a - 1)*exp(z)]),
Matrix([[1, -1, 0]]),
Matrix([[-S.Half, 0, 1], [0, -a, 1], [0, 0, z]]))

# 0F1
Matrix([gamma(b)*z**((1-b)/2)*besseli(b-1, 2*sqrt(z)),
gamma(b)*z**(1 - b/2)*besseli(b  , 2*sqrt(z))]),
Matrix([[1, 0]]), Matrix([[0, 1], [z, (1-b)]]))

# 0F3
x = 4*z**(S(1)/4)
def fp(a,z): return besseli(a, x) + besselj(a, x)
def fm(a,z): return besseli(a, x) - besselj(a, x)
Matrix([fp(2*a - 1, z), fm(2*a, z)*z**(S(1)/4),
fm(2*a - 1, z)*z**(S(1)/2), fp(2*a, z)*z**(S(3)/4)])
* 2**(-2*a)*gamma(2*a)*z**((1-2*a)/4),
Matrix([[1, 0, 0, 0]]),
Matrix([[0, 1, 0, 0],
[0, S(1)/2 - a, 1, 0],
[0, 0, S(1)/2, 1],
[z, 0, 0, 1 - a]]))
x = 2*(-4*z)**(S(1)/4)
addb([], [a, a + S.Half, 2*a],
(2*sqrt(-z))**(1-2*a)*gamma(2*a)**2 *
Matrix([besselj(2*a-1, x)*besseli(2*a-1, x),
x*(besseli(2*a, x)*besselj(2*a-1, x)
- besseli(2*a-1, x)*besselj(2*a, x)),
x**2*besseli(2*a, x)*besselj(2*a, x),
x**3*(besseli(2*a,x)*besselj(2*a-1,x)
+ besseli(2*a-1, x)*besselj(2*a, x))]),
Matrix([[1, 0, 0, 0]]),
Matrix([[0, S(1)/4, 0, 0],
[0, (1-2*a)/2, -S(1)/2, 0],
[0, 0, 1-2*a, S(1)/4],
[-32*z, 0, 0, 1-a]]))

# 1F2
Matrix([z**(S.Half - a)*besseli(a-S.Half, sqrt(z))**2,
z**(1-a)*besseli(a-S.Half, sqrt(z))
*besseli(a-S(3)/2, sqrt(z)),
z**(S(3)/2-a)*besseli(a-S(3)/2, sqrt(z))**2]),
Matrix([[-gamma(a + S.Half)**2/4**(S.Half - a),
2*gamma(a - S.Half)*gamma(a + S.Half)/4**(1 - a),
0]]),
Matrix([[1 - 2*a, 1, 0], [z/2, S.Half - a, S.Half], [0, z, 0]]))
pi*(1-b)/sin(pi*b) *
Matrix([besseli(1-b, sqrt(z))*besseli(b-1, sqrt(z)),
sqrt(z)*(besseli(-b, sqrt(z))*besseli(b-1, sqrt(z))
+ besseli(1-b, sqrt(z))*besseli(b, sqrt(z))),
besseli(-b, sqrt(z))*besseli(b, sqrt(z))]),
Matrix([[1, 0, 0]]),
Matrix([[b-1, S(1)/2, 0],
[z, 0, z],
[0, S(1)/2, -b]]))

# 2F3
# XXX with this five-parameter formula is pretty slow with the current
#     Formula.find_instantiations (creates 2!*3!*3**(2+3) ~ 3000
#     instantiations ... But it's not too bad.
addb([a, a + S.Half], [2*a, b, 2*a - b + 1],
gamma(b)*gamma(2*a - b + 1) * (sqrt(z)/2)**(1-2*a) *
Matrix([besseli(b-1, sqrt(z))*besseli(2*a-b, sqrt(z)),
sqrt(z)*besseli(b, sqrt(z))*besseli(2*a-b, sqrt(z)),
sqrt(z)*besseli(b-1, sqrt(z))*besseli(2*a-b+1, sqrt(z)),
besseli(b, sqrt(z))*besseli(2*a-b+1, sqrt(z))]),
Matrix([[1, 0, 0, 0]]),
Matrix([[0, S(1)/2, S(1)/2, 0],
[z/2, 1-b, 0, z/2],
[z/2, 0, b-2*a, z/2],
[0, S(1)/2, S(1)/2, -2*a]]))

def make_simp(z):
""" Create a function that simplifies rational functions in z. """
def simp(expr):
""" Efficiently simplify the rational function expr. """
from sympy import poly
numer, denom = expr.as_numer_denom()
c, numer, denom = poly(numer, z).cancel(poly(denom, z))
return c * numer.as_expr() / denom.as_expr()
return simp

def debug(*args):
if SYMPY_DEBUG:
for a in args:
print a,
print

class Mod1(object):
"""
Represent an expression 'mod 1'.

Beware: __eq__ and the hash are NOT compatible. (by design)
This means that m1 == m2 does not imply hash(m1) == hash(m2).
Code that creates Mod1 objects (like compute_buckets below) should be
careful only to produce one instance of Mod1 for each class.
"""
# TODO this should be backported to any implementation of a Mod object
#      (c/f issue 2490)

def __new__(cls, r):
if r.is_Rational and not r.free_symbols:
return r - r.p//r.q
res = object.__new__(cls)
res.expr = r
return res

def __repr__(self):
return str(self.expr) + ' % 1'

def __eq__(self, other):
from sympy import simplify
if not isinstance(other, Mod1):
return False
if simplify(self.expr - other.expr).is_integer is True:
return True
return False

class IndexPair(object):
""" Holds a pair of indices, and methods to compute their invariants. """

def __init__(self, ap, bq):
from sympy import expand, Tuple
self.ap = Tuple(*[expand(x) for x in sympify(ap)])
self.bq = Tuple(*[expand(x) for x in sympify(bq)])

@property
def sizes(self):
return (len(self.ap), len(self.bq))

def __str__(self):
return 'IndexPair(%s, %s)' % (self.ap, self.bq)

def compute_buckets(self, oabuckets=None, obbuckets=None):
"""
Partition parameters ap, bq into buckets, that is return two dicts
abuckets, bbuckets such that every key in [ab]buckets is a rational in
range [0, 1) and the corresponding items are items of ap/bq congruent to
the key mod 1.

If oabuckets, obbuckets is specified, try to use the same Mod1 objects
for parameters where possible.

>>> from sympy.simplify.hyperexpand import IndexPair
>>> from sympy import S
>>> ap = (S(1)/2, S(1)/3, S(-1)/2, -2)
>>> bq = (1, 2)
>>> IndexPair(ap, bq).compute_buckets()
({0: (-2,), 1/3: (1/3,), 1/2: (1/2, -1/2)}, {0: (1, 2)})
"""
# TODO this should probably be cached somewhere
abuckets = {}
bbuckets = {}

oaparametric = []
obparametric = []
if oabuckets is not None:
for parametric, buckets in [(oaparametric, oabuckets),
(obparametric, obbuckets)]:
parametric += filter(lambda x: isinstance(x, Mod1),
buckets.keys())

for params, bucket, oparametric in [(self.ap, abuckets, oaparametric),
(self.bq, bbuckets, obparametric)]:
parametric = []
for p in params:
res = Mod1(p)
if isinstance(res, Mod1):
parametric.append(p)
continue
if res in bucket:
bucket[res] += (p,)
else:
bucket[res] = (p,)
while parametric:
p0 = parametric[0]
p0mod1 = Mod1(p0)
if oparametric.count(p0mod1):
i = oparametric.index(p0mod1)
p0mod1 = oparametric.pop(i)
bucket[p0mod1] = (p0,)
pos = []
for po in parametric[1:]:
if Mod1(po) == p0mod1:
bucket[p0mod1] += (po,)
else:
pos.append(po)
parametric = pos

return abuckets, bbuckets

def build_invariants(self):
"""
Compute the invariant vector of (ap, bq), that is:
(gamma, ((s1, n1), ..., (sk, nk)), ((t1, m1), ..., (tr, mr)))
where gamma is the number of integer a < 0,
s1 < ... < sk
nl is the number of parameters a_i congruent to sl mod 1
t1 < ... < tr
ml is the number of parameters b_i congruent to tl mod 1

If the index pair contains parameters, then this is not truly an
invariant, since the parameters cannot be sorted uniquely mod1.

>>> from sympy.simplify.hyperexpand import IndexPair
>>> from sympy import S
>>> ap = (S(1)/2, S(1)/3, S(-1)/2, -2)
>>> bq = (1, 2)

Here gamma = 1,
k = 3, s1 = 0, s2 = 1/3, s3 = 1/2
n1 = 1, n2 = 1,   n2 = 2
r = 1, t1 = 0
m1 = 2:
>>> IndexPair(ap, bq).build_invariants()
(1, ((0, 1), (1/3, 1), (1/2, 2)), ((0, 2),))
"""
abuckets, bbuckets = self.compute_buckets()

gamma = 0
if S(0) in abuckets:
gamma = len(filter(lambda x: x < 0, abuckets[S(0)]))

def tr(bucket):
bucket = bucket.items()
if not any(isinstance(x[0], Mod1) for x in bucket):
bucket.sort(key=lambda x: x[0])
bucket = tuple(map(lambda x: (x[0], len(x[1])), bucket))
return bucket

return (gamma, tr(abuckets), tr(bbuckets))

def difficulty(self, ip):
""" Estimate how many steps it takes to reach ip from self.
Return -1 if impossible. """
oabuckets, obbuckets = self.compute_buckets()
abuckets, bbuckets = ip.compute_buckets(oabuckets, obbuckets)

gt0 = lambda x: (x > 0) is True
if S(0) in abuckets and (not S(0) in oabuckets or
len(filter(gt0, abuckets[S(0)])) != len(filter(gt0, oabuckets[S(0)]))):
return -1

diff = 0
for bucket, obucket in [(abuckets, oabuckets), (bbuckets, obbuckets)]:
for mod in set(bucket.keys() + obucket.keys()):
if (not mod in bucket) or (not mod in obucket) \
or len(bucket[mod]) != len(obucket[mod]):
return -1
l1 = list(bucket[mod])
l2 = list(obucket[mod])
l1.sort()
l2.sort()
for i, j in zip(l1, l2):
diff += abs(i - j)

return diff

""" Holds a quadruple of indices. """
def __init__(self, an, ap, bm, bq):
from sympy import expand, Tuple
def tr(l): return Tuple(*[expand(x) for x in sympify(l)])
self.an = tr(an)
self.ap = tr(ap)
self.bm = tr(bm)
self.bq = tr(bq)

def compute_buckets(self):
"""
Compute buckets for the fours sets of parameters.
We guarantee that any two equal Mod1 objects returned are actually the
same, and that the buckets are sorted by real part (an and bq
descendending, bm and ap ascending).

>>> from sympy.abc import y
>>> from sympy import S
>>> IndexQuadruple([1, 3, 2, S(3)/2], [1 + y, y, 2, y + 3], [2], [y]).compute_buckets()
({0: [3, 2, 1], 1/2: [3/2]}, {y + 1 % 1: [y, y + 1, y + 3], 0: [2]}, {0: [2]}, {y + 1 % 1: [y]})
"""
mod1s = []
pan, pap, pbm, pbq = {}, {}, {}, {}
for dic, lis in [(pan, self.an), (pap, self.ap), (pbm, self.bm),
(pbq, self.bq)]:
for x in lis:
m = Mod1(x)
if mod1s.count(m):
i = mod1s.index(m)
m = mod1s[i]
else:
mod1s.append(m)
dic.setdefault(m, []).append(x)

for dic, flip in [(pan, True), (pap, False), (pbm, False), (pbq, True)]:
l = dic.items()
dic.clear()
for m, items in l:
x0 = items[0]
items.sort(key=lambda x: x-x0)
if flip:
items.reverse()
dic[m] = items

return pan, pap, pbm, pbq

def __str__(self):
return 'IndexQuadruple(%s, %s, %s, %s)' % (self.an, self.ap,
self.bm, self.bq)

# Dummy generator
x = Dummy('x')

class Formula(object):
"""
This class represents hypergeometric formulae.

Its data members are:
- z, the argument
- closed_form, the closed form expression
- symbols, the free symbols (parameters) in the formula
- indices, the parameters
- B, C, M (see _compute_basis)
- lcms, a dictionary which maps symbol -> lcm of denominators
- isolation, a dictonary which maps symbol -> (num, coeff) pairs

>>> from sympy.abc import a, b, z
>>> from sympy.simplify.hyperexpand import Formula
>>> f = Formula((a/2, a/3 + b, (1+a)/2), (a, b, (a+b)/7), z, None, [a, b])

The lcm of all denominators of coefficients of a is 2*3*7
>>> f.lcms[a]
42

for b it is just 7:
>>> f.lcms[b]
7

We can isolate a in the (1+a)/2 term, with denominator 2:
>>> f.isolation[a]
(2, 2, 1)

b is isolated in the b term, with coefficient one:
>>> f.isolation[b]
(4, 1, 1)
"""

def _compute_basis(self, closed_form):
"""
Compute a set of functions B=(f1, ..., fn), a nxn matrix M
and a 1xn matrix C such that:
closed_form = C B
z d/dz B = M B.
"""
from sympy.matrices import Matrix, eye, zeros

afactors = map(lambda a: x + a, self.indices.ap)
bfactors = map(lambda b: x + b - 1, self.indices.bq)
expr = x*Mul(*bfactors) - self.z*Mul(*afactors)
poly = Poly(expr, x)

n = poly.degree() - 1
b = [closed_form]
for _ in xrange(n):
b.append(self.z*b[-1].diff(self.z))

self.B = Matrix(b)
self.C = Matrix([[1] + [0]*n])

m = eye(n)
m = m.col_insert(0, zeros((n, 1)))
l = poly.all_coeffs()[1:]
l.reverse()
self.M = m.row_insert(n, -Matrix([l])/poly.all_coeffs()[0])

def __init__(self, ap, bq, z, res, symbols, B=None, C=None, M=None):
ap = Tuple(*map(expand, sympify(ap)))
bq = Tuple(*map(expand, sympify(bq)))
z  = sympify(z)
res = sympify(res)
symbols = filter(lambda x: ap.has(x) or bq.has(x), sympify(symbols))

self.z  = z
self.symbols = symbols
self.B = B
self.C = C
self.M = M

params = list(ap) + list(bq)
lcms = {}
isolation = {}
for a in symbols:
from sympy import ilcm
l = 1
isolating = []
others = list(symbols[:])
others.remove(a)
i = 0
for p in params:
if p.has(a):
c, m = None, None
c, m = p.as_independent(a)[1].as_coeff_mul(a)
else:
c, m = p.as_coeff_mul(a)
if m != (a,) or not c.is_Rational:
raise NotImplementedError('?')
l = ilcm(l, c.q)

if not p.has(*others):
isolating.append((i, c.q, c.p))
lcms[a] = l
i += 1
if len(isolating) == 0:
raise NotImplementedError('parameter is not isolated')
isolating.sort(key=lambda x:x[1])
isolating.sort(key=lambda x:-x[2])
isolation[a] = isolating[-1]

self.lcms = lcms
self.isolation = isolation

self.indices = IndexPair(ap, bq)

# TODO with symbolic parameters, it could be advantageous
#      (for prettier answers) to compute a basis only *after*
#      instantiation
if res is not None:
self._compute_basis(res)

@property
def closed_form(self):
return (self.C*self.B)[0]

def find_instantiations(self, ip):
"""
Try to find instantiations of the free symbols that match
ip.ap, ip.bq. Return the instantiated formulae as a list.
Note that the returned instantiations need not actually match,
or be valid!
"""
ap = ip.ap
bq = ip.bq
if len(ap) != len(self.indices.ap) or len(bq) != len(self.indices.bq):
raise TypeError('Cannot instantiate other number of parameters')

from sympy import solve
from sympy.core.compatibility import permutations, product
res = []
our_params = list(self.indices.ap) + list(self.indices.bq)
for na in permutations(ap):
for nb in permutations(bq):
all_params = list(na) + list(nb)
repl = {}
for a in self.symbols:
i, d, _ = self.isolation[a]
repl[a] = (solve(our_params[i] - all_params[i], a)[0], d)
for change in product(*[(-1, 0, 1)]*len(self.symbols)):
rep = {}
for i, a in zip(change, repl.keys()):
rep[a] = repl[a][0] + i*repl[a][1]
res.append(Formula(self.indices.ap.subs(rep),
self.indices.bq.subs(rep),
self.z, None, [], self.B.subs(rep),
self.C.subs(rep), self.M.subs(rep)))
# if say a = -1/2, and there is 2*a in the formula, then
# there will be a negative integer. But this origin is also
# reachable from a = 1/2 ...
# So throw this in as well.
# The code is not as general as it could be, but good enough.
if len(self.symbols) == 1:
a = self.symbols[0]
aval, d = repl[a]
if aval < 0 and d == 1:
from sympy import ceiling
aval -= ceiling(aval) - 1
res.append(Formula(self.indices.ap.subs(a, aval),
self.indices.bq.subs(a, aval),
self.z, None, [], self.B.subs(a, aval),
self.C.subs(rep), self.M.subs(a, aval)))
return res

def is_suitable(self):
"""
Decide if self is a suitable origin.

>>> from sympy.simplify.hyperexpand import Formula
>>> from sympy import S

If ai - bq in Z and bq >= ai this is fine:
>>> Formula((S(1)/2,), (S(3)/2,), None, None, []).is_suitable()
True

but ai = bq is not:
>>> Formula((S(1)/2,), (S(1)/2,), None, None, []).is_suitable()
False

and ai > bq is not either:
>>> Formula((S(1)/2,), (-S(1)/2,), None, None, []).is_suitable()
False

None of the bj can be a non-positive integer:
>>> Formula((S(1)/2,), (0,), None, None, []).is_suitable()
False
>>> Formula((S(1)/2,), (-1, 1,), None, None, []).is_suitable()
False

None of the ai can be zero:
>>> Formula((S(1)/2, 0), (1,), None, None, []).is_suitable()
False

More complicated examples:
>>> Formula((S(1)/2, 1), (2, -S(2)/3), None, None, []).is_suitable()
True
>>> Formula((S(1)/2, 1), (2, -S(2)/3, S(3)/2), None, None, []).is_suitable()
True
"""
from sympy import oo, zoo
if len(self.symbols) > 0:
return None
for a in self.indices.ap:
for b in self.indices.bq:
if (a-b).is_integer and not a < b:
return False
for a in self.indices.ap:
if a == 0:
return False
for b in self.indices.bq:
if b <= 0 and b.is_integer:
return False
for e in [self.B, self.M, self.C]:
if e is None:
continue
if e.has(S.NaN) or e.has(oo) or e.has(-oo) or e.has(zoo):
return False
return True

class FormulaCollection(object):
""" A collection of formulae to use as origins. """

def __init__(self):
""" Doing this globally at module init time is a pain ... """
self.symbolic_formulae = {}
self.concrete_formulae = {}
self.formulae = []

# Now process the formulae into a helpful form.
# These dicts are indexed by (p, q).

for f in self.formulae:
sizes = f.indices.sizes
if len(f.symbols) > 0:
self.symbolic_formulae.setdefault(sizes, []).append(f)
else:
inv = f.indices.build_invariants()
self.concrete_formulae.setdefault(sizes, {})[inv] = f

def lookup_origin(self, ip):
"""
Given the suitable parameters ip.ap, ip.bq, try to find an origin
in our knowledge base.

>>> from sympy.simplify.hyperexpand import FormulaCollection, IndexPair
>>> f = FormulaCollection()
>>> f.lookup_origin(IndexPair((), ())).closed_form
exp(_z)
>>> f.lookup_origin(IndexPair([1], ())).closed_form
1/(-_z + 1)

>>> from sympy import S
>>> f.lookup_origin(IndexPair([S('1/4'), S('3/4 + 4')], [S.Half])).closed_form
1/(2*(_z**(1/2) + 1)**(17/2)) + 1/(2*(-_z**(1/2) + 1)**(17/2))
"""
inv = ip.build_invariants()
sizes = ip.sizes
if sizes in self.concrete_formulae and \
inv in self.concrete_formulae[sizes]:
return self.concrete_formulae[sizes][inv]

# We don't have a concrete formula. Try to instantiate.
if not sizes in self.symbolic_formulae:

possible = []
for f in self.symbolic_formulae[sizes]:
l = f.find_instantiations(ip)
for f2 in l:
if not f2.is_suitable():
continue
diff = f2.indices.difficulty(ip)
if diff != -1:
possible.append((diff, f2))

if not possible:
# Give up.
return None

# find the nearest origin
possible.sort(key=lambda x:x[0])
return possible[0][1]

class Operator(object):
"""
Base class for operators to be applied to our functions.

These operators are differential operators. They are by convention
expressed in the variable D = z*d/dz (although this base class does
not actually care).
Note that when the operator is applied to an object, we typically do
*not* blindly differentiate but instead use a different representation
of the z*d/dz operator (see make_derivative_operator).

To subclass from this, define a __init__ method that initalises a
self._poly variable. This variable stores a polynomial. By convention
the generator is z*d/dz, and acts to the right of all coefficients.

Thus this poly
x**2 + 2*z*x + 1
represents the differential operator
(z*d/dz)**2 + 2*z**2*d/dz.

This class is used only in the implementation of the hypergeometric
function expansion algorithm.
"""

def apply(self, obj, op):
"""
Apply self to the object obj, where the generator is given by op.

>>> from sympy.simplify.hyperexpand import Operator
>>> from sympy.polys.polytools import Poly
>>> from sympy.abc import x, y, z
>>> op = Operator()
>>> op._poly = Poly(x**2 + z*x + y, x)
>>> op.apply(z**7, lambda f: f.diff(z))
y*z**7 + 7*z**7 + 42*z**5
"""
coeffs = self._poly.all_coeffs()
coeffs.reverse()
diffs  = [obj]
for c in coeffs[1:]:
diffs.append(op(diffs[-1]))
r = coeffs[0]*diffs[0]
for c, d in zip(coeffs[1:], diffs[1:]):
r += c*d
return r

class MultOperator(Operator):
""" Simply multiply by a "constant" """

def __init__(self, p):
self._poly = Poly(p, x)

class ShiftA(Operator):
""" Increment an upper index. """

def __init__(self, ai):
ai = sympify(ai)
if ai == 0:
raise ValueError('Cannot increment zero upper index.')
self._poly = Poly(x/ai + 1, x)

def __str__(self):
return '<Increment upper %s.>' % (1/self._poly.all_coeffs()[0])

class ShiftB(Operator):
""" Decrement a lower index. """

def __init__(self, bi):
bi = sympify(bi)
if bi == 1:
raise ValueError('Cannot decrement unit lower index.')
self._poly = Poly(x/(bi - 1) + 1, x)

def __str__(self):
return '<Decrement lower %s.>' % (1/self._poly.all_coeffs()[0] + 1)

class UnShiftA(Operator):
""" Decrement an upper index. """

def __init__(self, ap, bq, i, z):
""" Note: i counts from zero! """
ap, bq, i = map(sympify, [ap, bq, i])

self._ap = ap
self._bq = bq
self._i  = i

ap = list(ap)
bq = list(bq)
ai = ap.pop(i) - 1

if ai == 0:
raise ValueError('Cannot decrement unit upper index.')

m = Poly(z*ai, x)
for a in ap:
m *= Poly(x + a, x)
#print m

A = Dummy('A')
D = Poly(ai*A - ai, A)
n = 1*D
for b in bq:
n *= (D + b - 1)
#print n

b0 = -n.all_coeffs()[-1]
if b0 == 0:
raise ValueError('Cannot decrement upper index: ' \
'cancels with lower')
#print b0

n = Poly(Poly(n.all_coeffs()[:-1], A).as_expr().subs(A, x/ai + 1), x)

self._poly = Poly((n-m)/b0, x)

def __str__(self):
return '<Decrement upper index #%s of %s, %s.>' % (self._i,
self._ap, self._bq)

class UnShiftB(Operator):
""" Increment a lower index. """

def __init__(self, ap, bq, i, z):
""" Note: i counts from zero! """
ap, bq, i = map(sympify, [ap, bq, i])

self._ap = ap
self._bq = bq
self._i  = i

ap = list(ap)
bq = list(bq)
bi = bq.pop(i) + 1

if bi == 0:
raise ValueError('Cannot increment -1 lower index.')

m = Poly(x*(bi-1), x)
for b in bq:
m *= Poly(x + b - 1, x)
#print m

B = Dummy('B')
D = Poly((bi-1)*B - bi + 1, B)
n = Poly(z, B)
for a in ap:
n *= (D + a)
#print n

b0 = n.all_coeffs()[-1]
#print b0
if b0 == 0:
raise ValueError('Cannot increment index: ' \
'cancels with upper')
#print b0

n = Poly(Poly(n.all_coeffs()[:-1], B).as_expr().subs(B, x/(bi-1) + 1), x)
#print n

self._poly = Poly((m-n)/b0, x)

def __str__(self):
return '<Increment lower index #%s of %s, %s.>' % (self._i,
self._ap, self._bq)

class ReduceOrder(Operator):
""" Reduce Order by cancelling an upper and a lower index. """

def __new__(cls, ai, bj):
""" For convenience if reduction is not possible, return None. """
ai = sympify(ai)
bj = sympify(bj)
n = ai - bj
if n < 0 or not n.is_Integer:
return None
if bj.is_integer and bj <= 0 and bj + n - 1 >= 0:
return None

self = Operator.__new__(cls)

p = S(1)
for k in xrange(n):
p *= (x + bj + k)/(bj + k)

self._poly = Poly(p, x)
self._a = ai
self._b = bj

return self

@classmethod
def _meijer(cls, b, a, sign):
""" Cancel b + sign*s and a + sign*s
This is for meijer G functions. """
b = sympify(b)
a = sympify(a)
n = b - a
if n < 0 or not n.is_Integer:
return None

self = Operator.__new__(cls)

p = S(1)
for k in xrange(n):
p *= (sign*x + a + k)

self._poly = Poly(p, x)
if sign == -1:
self._a = b
self._b = a
else:
self._b = Add(1, a - 1, evaluate=False)
self._a = Add(1, b - 1, evaluate=False)

return self

@classmethod
def meijer_minus(cls, b, a):
return cls._meijer(b, a, -1)
@classmethod
def meijer_plus(cls, a, b):
return cls._meijer(1 - a, 1 - b, 1)

def __str__(self):
return '<Reduce order by cancelling upper %s with lower %s.>' % \
(self._a, self._b)

def _reduce_order(ap, bq, gen, key):
""" Order reduction algorithm common to both Hypergeometric and Meijer G """
ap = list(ap)
bq = list(bq)

ap.sort(key=key)
bq.sort(key=key)

nap = []
# we will edit bq in place
operators = []
for a in ap:
op = None
for i in xrange(len(bq)):
op = gen(a, bq[i])
if op is not None:
bq.pop(i)
break
if op is None:
nap.append(a)
else:
operators.append(op)

return nap, bq, operators

def reduce_order(ip):
"""
Given the hypergeometric parameters ip.ap, ip.bq, find a sequence of operators
to reduces order as much as possible.

Return (nip, [operators]), where applying the operators to the
hypergeometric function specified by nip.ap, nip.bq yields ap, bq.

Examples:

>>> from sympy.simplify.hyperexpand import reduce_order, IndexPair
>>> reduce_order(IndexPair((1, 2), (3, 4)))
(IndexPair((1, 2), (3, 4)), [])
>>> reduce_order(IndexPair((1,), (1,)))
(IndexPair((), ()), [<Reduce order by cancelling upper 1 with lower 1.>])
>>> reduce_order(IndexPair((2, 4), (3, 3)))
(IndexPair((2,), (3,)), [<Reduce order by cancelling upper 4 with lower 3.>])
"""
nap, nbq, operators = _reduce_order(ip.ap, ip.bq, ReduceOrder, lambda x: x)

return IndexPair(Tuple(*nap), Tuple(*nbq)), operators

def reduce_order_meijer(iq):
"""
Given the Meijer G function parameters, iq.am, iq.ap, iq.bm,
iq.bq, find a sequence of operators that reduces order as much as possible.

Return niq, [operators].

Examples:

>>> from sympy.simplify.hyperexpand import reduce_order_meijer, IndexQuadruple
>>> reduce_order_meijer(IndexQuadruple([3, 4], [5, 6], [3, 4], [1, 2]))[0]
IndexQuadruple((4, 3), (5, 6), (3, 4), (2, 1))
>>> reduce_order_meijer(IndexQuadruple([3, 4], [5, 6], [3, 4], [1, 8]))[0]
IndexQuadruple((3,), (5, 6), (3, 4), (1,))
>>> reduce_order_meijer(IndexQuadruple([3, 4], [5, 6], [7, 5], [1, 5]))[0]
>>> reduce_order_meijer(IndexQuadruple([3, 4], [5, 6], [7, 5], [5, 3]))[0]
"""

nan, nbq, ops1 = _reduce_order(iq.an, iq.bq, ReduceOrder.meijer_plus, lambda x: -x)
nbm, nap, ops2 = _reduce_order(iq.bm, iq.ap, ReduceOrder.meijer_minus, lambda x: x)

return IndexQuadruple(Tuple(*nan), Tuple(*nap), Tuple(*nbm), Tuple(*nbq)), \
ops1 + ops2

def make_derivative_operator(M, z):
""" Create a derivative operator, to be passed to Operator.apply. """
from sympy import poly
def doit(C):
r = z*C.diff(z) + C*M
r = r.applyfunc(make_simp(z))
return r
return doit

def apply_operators(obj, ops, op):
"""
Apply the list of operators ops to object obj, substituting op for the
generator.
"""
res = obj
for o in reversed(ops):
res = o.apply(res, op)
return res

def devise_plan(ip, nip, z):
"""
Devise a plan (consisting of shift and un-shift operators) to be applied
to the hypergeometric function (nip.ap, nip.bq) to yield
(ip.ap, ip.bq).
Returns a list of operators.

>>> from sympy.simplify.hyperexpand import devise_plan, IndexPair
>>> from sympy.abc import z

Nothing to do:

>>> devise_plan(IndexPair((1, 2), ()), IndexPair((1, 2), ()), z)
[]
>>> devise_plan(IndexPair((), (1, 2)), IndexPair((), (1, 2)), z)
[]

Very simple plans:

>>> devise_plan(IndexPair((2,), ()), IndexPair((1,), ()), z)
[<Increment upper 1.>]
>>> devise_plan(IndexPair((), (2,)), IndexPair((), (1,)), z)
[<Increment lower index #0 of [], [1].>]

Several buckets:

>>> from sympy import S
>>> devise_plan(IndexPair((1, S.Half), ()), IndexPair((2, S('3/2')), ()), z)
[<Decrement upper index #0 of [2, 1/2], [].>, <Decrement upper index #0 of [3/2, 2], [].>]

A slightly more complicated plan:

>>> devise_plan(IndexPair((1, 3), ()), IndexPair((2, 2), ()), z)
[<Increment upper 2.>, <Decrement upper index #0 of [2, 2], [].>]

Another more complicated plan: (note that the ap have to be shifted first!)

>>> devise_plan(IndexPair((1, -1), (2,)), IndexPair((3, -2), (4,)), z)
[<Decrement lower 3.>, <Decrement lower 4.>, <Decrement upper index #1 of [-1, 2], [4].>, <Decrement upper index #1 of [-1, 3], [4].>, <Increment upper -2.>]
"""
abuckets, bbuckets = ip.compute_buckets()
nabuckets, nbbuckets = nip.compute_buckets(abuckets, bbuckets)

if len(abuckets.keys()) != len(nabuckets.keys()) or \
len(bbuckets.keys()) != len(nbbuckets.keys()):
raise ValueError('%s not reachable from %s' % (ip, nip))

ops = []

def do_shifts(fro, to, inc, dec):
ops = []
for i in xrange(len(fro)):
if to[i] - fro[i] > 0:
sh = inc
ch = 1
else:
sh = dec
ch = -1

while to[i] != fro[i]:
ops += [sh(fro, i)]
fro[i] += ch

return ops

def do_shifts_a(nal, nbk, al, aother, bother):
""" Shift us from (nal, nbk) to (al, nbk). """
return do_shifts(nal, al, lambda p, i: ShiftA(p[i]),
lambda p, i: UnShiftA(p + aother, nbk + bother, i, z))

def do_shifts_b(nal, nbk, bk, aother, bother):
""" Shift us from (nal, nbk) to (nal, bk). """
return do_shifts(nbk, bk,
lambda p, i: UnShiftB(nal + aother, p + bother, i, z),
lambda p, i: ShiftB(p[i]))

for r in set(abuckets.keys() + bbuckets.keys()):
al = ()
nal = ()
bk = ()
nbk = ()
if r in abuckets:
al = abuckets[r]
nal = nabuckets[r]
if r in bbuckets:
bk = bbuckets[r]
nbk = nbbuckets[r]
if len(al) != len(nal) or len(bk) != len(nbk):
raise ValueError('%s not reachable from %s' % ((ap, bq), (nap, nbq)))

al = sorted(list(al))
nal = sorted(list(nal))
bk = sorted(list(bk))
nbk = sorted(list(nbk))

def others(dic, key):
l = []
for k, value in dic.iteritems():
if k != key:
l += list(dic[k])
return l
aother = others(nabuckets, r)
bother = others(nbbuckets, r)

if len(al) == 0:
# there can be no complications, just shift the bs as we please
ops += do_shifts_b([], nbk, bk, aother, bother)
elif len(bk) == 0:
# there can be no complications, just shift the as as we please
ops += do_shifts_a(nal, [], al, aother, bother)
else:
namax = nal[-1]
amax  = al[-1]

if nbk[0] <= namax or bk[0] <= amax:
raise ValueError('Non-suitable parameters.')

if namax > amax:
# we are going to shift down - first do the as, then the bs
ops += do_shifts_a(nal, nbk, al, aother, bother)
ops += do_shifts_b(al, nbk, bk, aother, bother)
else:
# we are going to shift up - first do the bs, then the as
ops += do_shifts_b(nal, nbk, bk, aother, bother)
ops += do_shifts_a(nal, bk, al, aother, bother)

nabuckets[r] = al
nbbuckets[r] = bk

ops.reverse()
return ops

def try_shifted_sum(ip, z):
""" Try to recognise a hypergeometric sum that starts from k > 0. """
from sympy.functions import rf, factorial
abuckets, bbuckets = ip.compute_buckets()
if not S(0) in abuckets or len(abuckets[S(0)]) != 1:
return None
r = abuckets[S(0)][0]
if r <= 0:
return None
if not S(0) in bbuckets:
return None
l = list(bbuckets[S(0)])
l.sort()
k = l[0]
if k <= 0:
return None

nap = list(ip.ap)
nap.remove(r)
nbq = list(ip.bq)
nbq.remove(k)
k -= 1
nap = map(lambda x: x - k, nap)
nbq = map(lambda x: x - k, nbq)

ops = []
for n in xrange(r - 1):
ops.append(ShiftA(n + 1))
ops.reverse()

fac = factorial(k)/z**k
for a in nap:
fac /= rf(a, k)
for b in nbq:
fac *= rf(b, k)

ops += [MultOperator(fac)]

p = 0
for n in xrange(k):
m = z**n/factorial(n)
for a in nap:
m *= rf(a, n)
for b in nbq:
m /= rf(b, n)
p += m

return IndexPair(nap, nbq), ops, -p

def try_polynomial(ip, z):
""" Recognise polynomial cases. Returns None if not such a case.
Requires order to be fully reduced. """
from sympy import oo, factorial, rf
abuckets, bbuckets = ip.compute_buckets()
a0 = list(abuckets.get(S(0), []))
b0 = list(bbuckets.get(S(0), []))
a0.sort()
b0.sort()
al0 = filter(lambda x: x <= 0, a0)
bl0 = filter(lambda x: x <= 0, b0)

if bl0:
return oo
if not al0:
return None

a = al0[-1]
fac = 1
res = S(1)
for n in xrange(-a):
fac *= z
fac /= n + 1
for a in ip.ap: fac *= a + n
for b in ip.bq: fac /= b + n
res += fac
return res

collection = None
def _hyperexpand(ip, z, ops0=[], z0=Dummy('z0'), premult=1, chainmult=1):
"""
Try to find an expression for the hypergeometric function
ip.ap, ip.bq.

The result is expressed in terms of a dummy variable z0. Then it
is multiplied by premult. Then ops0 is applied, using chainmult*t*d/dt
for the operator.

These latter parameters are all trickery to make _meijergexpand short.
"""
from sympy.simplify import powdenest, simplify

# TODO
# The following would be possible:
# 1) Partial simplification (i.e. return a simpler hypergeometric function,
#    even if we cannot express it in terms of named special functions).
# 2) PFD Duplication (see Kelly Roach's paper)
# 3) If the coefficients are a rational function of n (numerator parameters
#    k, a1, ..., an, denominator parameters a1+k1, a2+k2, ..., an+kn, where
#    k, k1, ..., kn are integers) then result can be expressed using Lerch
#    transcendent. Under certain conditions, this simplifies to polylogs
#    or even zeta functions. C/f Kelly Roach's paper.

global collection
if collection is None:
collection = FormulaCollection()

debug('Trying to expand hypergeometric function corresponding to', ip)

# First reduce order as much as possible.
nip, ops = reduce_order(ip)
if ops:
debug('  Reduced order to', nip)
else:
debug('  Could not reduce order.')

# Now try polynomial cases
res = try_polynomial(nip, z0)
if res is not None:
debug('  Recognised polynomial.')
p = apply_operators(res, ops, lambda f: z0*f.diff(z0))
p = apply_operators(p*premult, ops0, lambda f: chainmult*z0*f.diff(z0))
return simplify(p).subs(z0, z)

# Try to recognise a shifted sum.
p = S(0)
res = try_shifted_sum(nip, z0)
if res != None:
nip, nops, p = res
debug('  Recognised shifted sum, reducerd order to', nip)
ops += nops

# apply the plan for poly
p = apply_operators(p, ops, lambda f: z0*f.diff(z0))
p = apply_operators(p*premult, ops0, lambda f: chainmult*z0*f.diff(z0))
p = simplify(p).subs(z0, z)

# Now try to find a formula
f = collection.lookup_origin(nip)

if f is None:
debug('  Could not find an origin.')
# There is nothing we can do.
return None

# We need to find the operators that convert f into (nap, nbq).
ops += devise_plan(nip, f.indices, z0)

# Now carry out the plan.
C = apply_operators(f.C.subs(f.z, z0), ops,
make_derivative_operator(f.M.subs(f.z, z0), z0))
C = apply_operators(C*premult, ops0,
make_derivative_operator(f.M.subs(f.z, z0)*chainmult, z0))

if premult == 1:
C = C.applyfunc(make_simp(z0))
r = C*f.B.subs(f.z, z0)
r = r[0].subs(z0, z) + p

# This will simpliy things like sqrt(-z**2) to i*z.
# It would be wrong under certain choices of branch, but all results we
# return are under an "implicit suitable choice of branch" anyway.
return powdenest(r, force=True)

def _meijergexpand(iq, z0, allow_hyper=False):
"""
Try to find an expression for the Meijer G function specified
by the IndexQuadruple iq. If allow_hyper is True, then returning
an expression in terms of hypergeometric functions is allowed.

Currently this just does slater's theorem.
"""
from sympy import hyper, Piecewise, meijerg, powdenest
iq_ = iq
debug('Try to expand meijer G function corresponding to', iq)

# We will play games with analytic continuation - rather use a fresh symbol
z = Dummy('z')

iq, ops = reduce_order_meijer(iq)
if ops:
debug('  Reduced order to', iq)
else:
debug('  Could not reduce order.')

# TODO the following would be possible:
# 1) Set up a collection of meijer g formulae.
#    This handles some cases that cannot be done using Slater's theorem,
#    and also yields nicer looking results.
# 2) Paired Index Theorems
# 3) PFD Duplication
#    (See Kelly Roach's paper for (2) and (3).)
#
# TODO Also, we tend to create combinations of gamma functions that can be
#      simplified.

def can_do(pbm, pap):
""" Test if slater applies. """
for i in pbm:
if len(pbm[i]) > 1:
l = 0
if i in pap:
l = len(pap[i])
if l + 1 < len(pbm[i]):
return False
return True

def do_slater(an, bm, ap, bq, z, t, chainmult, realz):
from sympy import gamma, residue, factorial, rf, expand_func
iq = IndexQuadruple(an, bm, ap, bq)
_, pbm, pap, _ = iq.compute_buckets()
if not can_do(pbm, pap):
return S(0), False

res = S(0)
for m in pbm:
if len(pbm[m]) == 1:
bh = pbm[m][0]
fac = 1
bo = list(bm)
bo.remove(bh)
for bj in bo: fac *= gamma(bj - bh)
for aj in an: fac *= gamma(1 + bh - aj)
for bj in bq: fac /= gamma(1 + bh - bj)
for aj in ap: fac /= gamma(aj - bh)
nap = [1 + bh - a for a in list(an) + list(ap)]
nbq = [1 + bh - b for b in list(bo) + list(bq)]

k = S(-1)**(len(ap) - len(bm))
harg = k*z
premult = (k*t)**bh
hyp = _hyperexpand(IndexPair(nap, nbq), harg, ops,
t, premult, chainmult)
if hyp is None:
hyp = apply_operators(premult*hyper(nap, nbq, t), ops,
lambda f: chainmult*t*f.diff(t)).subs(t, harg)
res += fac * hyp
else:
b_ = pbm[m][0]
ki = [bi - b_ for bi in pbm[m][1:]]
u = len(ki)
li = [ai - b_ for ai in pap[m][0:u+1]]
bo = list(bm)
for b in pbm[m]:
bo.remove(b)
ao = list(ap)
for a in pap[m][:u]:
ao.remove(a)
lu = li[-1]
di = [l - k for (l, k) in zip(li, ki)]

# We first work out the integrand:
s = Dummy('s')
integrand = z**s
for b in bm:
integrand *= gamma(b - s)
for a in an:
integrand *= gamma(1 - a + s)
for b in bq:
integrand /= gamma(1 - b + s)
for a in ap:
integrand /= gamma(a - s)

# Now sum the finitely many residues:
# XXX This speeds up some cases - is it a good idea?
integrand = expand_func(integrand)
for r in range(lu):
resid = residue(integrand, s, b_ + r)
resid = apply_operators(resid, ops, lambda f: realz*f.diff(realz))
res -= resid

# Now the hypergeometric term.
au = b_ + lu
k = S(-1)**(len(ao) + len(bo) + 1)
harg = k*z
premult = (k*t)**au
nap = [1 + au - a for a in list(an) + list(ap)] + [1]
nbq = [1 + au - b for b in list(bm) + list(bq)]

hyp = _hyperexpand(IndexPair(nap, nbq), harg, ops,
t, premult, chainmult)
if hyp is None:
hyp = apply_operators(premult*hyper(nap, nbq, t), ops,
lambda f: chainmult*t*f.diff(t)).subs(t, harg)

C = S(-1)**(lu)/factorial(lu)
for i in range(u):
C *= S(-1)**di[i]/rf(lu - li[i] + 1, di[i])
for a in an:
C *= gamma(1 - a + au)
for b in bo:
C *= gamma(b - au)
for a in ao:
C /= gamma(a - au)
for b in bq:
C /= gamma(1 - b + au)

res += C*hyp

cond = len(an) + len(ap) < len(bm) + len(bq)
if len(an) + len(ap) == len(bm) + len(bq):
cond = abs(z) < 1
return res, cond

t = Dummy('t')
slater1, cond1 = do_slater(iq.an, iq.bm, iq.ap, iq.bq, z, t, 1, z)

def tr(l): return [1 - x for x in l]
for op in ops:
op._poly = Poly(op._poly.subs(z, S(-1)**(len(iq.an) - len(iq.bq))/t), x)
slater2, cond2 = do_slater(tr(iq.bm), tr(iq.an), tr(iq.bq), tr(iq.ap),
1/z, t, -1, z)

slater1 = powdenest(slater1.subs(z, z0), force=True)
slater2 = powdenest(slater2.subs(z, z0), force=True)

if meijerg(iq.an, iq.ap, iq.bm, iq.bq, z).delta > 0:
# The above condition means that the convergence region is connected.
# Any expression we find can be continued analytically to the entire
# convergence region.
if cond1 is not False:
cond1 = True
if cond2 is not False:
cond2 = True

if not isinstance(cond1, bool): cond1 = cond1.subs(z, z0)
if not isinstance(cond2, bool): cond2 = cond2.subs(z, z0)

if cond1 is True and not slater1.has(hyper):
return slater1
if cond2 is True and not slater2.has(hyper):
return slater2

# We couldn't find an expression without hypergeometric functions.
# TODO it would be helpful to give conditions under which the integral
#      is known to diverge.
r =  Piecewise((slater1, cond1), (slater2, cond2),
(meijerg(iq_.an, iq_.ap, iq_.bm, iq_.bq, z0), True))
if r.has(hyper) and not allow_hyper:
debug('  Could express using hypergeometric functions, but not allowed.')
if not r.has(hyper) or allow_hyper:
return r

return meijerg(iq_.an, iq_.ap, iq_.bm, iq_.bq, z0)

[docs]def hyperexpand(f, allow_hyper=False):
"""
Expand hypergeometric functions. If allow_hyper is True, allow partial
simplification (that is a result different from input,
but still containing hypergeometric functions).

Examples:

>>> from sympy.simplify.hyperexpand import hyperexpand
>>> from sympy.functions import hyper
>>> from sympy.abc import z
>>> hyperexpand(hyper([], [], z))
exp(z)

Non-hyperegeometric parts of the expression and hypergeometric expressions
that are not recognised are left unchanged:

>>> hyperexpand(1 + hyper([1, 1, 1], [], z))
1 + hyper((1, 1, 1), (), z)
"""
from sympy.functions import hyper, meijerg
from sympy import nan, zoo, oo
f = sympify(f)
def do_replace(ap, bq, z):
r = _hyperexpand(IndexPair(ap, bq), z)
if r is None:
return hyper(ap, bq, z)
else:
return r
def do_meijer(ap, bq, z):
r = _meijergexpand(IndexQuadruple(ap[0], ap[1], bq[0], bq[1]), z,
allow_hyper)
if not r.has(nan, zoo, oo, -oo):
return r
return f.replace(hyper, do_replace).replace(meijerg, do_meijer)

from sympy.polys.polytools import Poly