# Source code for sympy.concrete.summations

from __future__ import print_function, division

from sympy.concrete.expr_with_limits import AddWithLimits
from sympy.concrete.expr_with_intlimits import ExprWithIntLimits
from sympy.core.basic import C
from sympy.core.containers import Tuple
from sympy.core.expr import Expr
from sympy.core.function import Derivative
from sympy.core.relational import Eq
from sympy.core.singleton import S
from sympy.core.symbol import (Dummy, Wild)
from sympy.core.sympify import sympify
from sympy.concrete.gosper import gosper_sum
from sympy.functions.elementary.piecewise import piecewise_fold, Piecewise
from sympy.polys import apart, PolynomialError
from sympy.solvers import solve
from sympy.core.compatibility import xrange

"""Represents unevaluated summation.

Sum represents a finite or infinite series, with the first argument
being the general form of terms in the series, and the second argument
being (dummy_variable, start, end), with dummy_variable taking
all integer values from start through end. In accordance with
long-standing mathematical convention, the end term is included in the
summation.

Finite sums
===========

For finite sums (and sums with symbolic limits assumed to be finite) we
follow the summation convention described by Karr [1], especially
definition 3 of section 1.4. The sum:

.. math::

\sum_{m \leq i < n} f(i)

has *the obvious meaning* for m < n, namely:

.. math::

\sum_{m \leq i < n} f(i) = f(m) + f(m+1) + \ldots + f(n-2) + f(n-1)

with the upper limit value f(n) excluded. The sum over an empty set is
zero if and only if m = n:

.. math::

\sum_{m \leq i < n} f(i) = 0  \quad \mathrm{for} \quad  m = n

Finally, for all other sums over empty sets we assume the following
definition:

.. math::

\sum_{m \leq i < n} f(i) = - \sum_{n \leq i < m} f(i)  \quad \mathrm{for} \quad  m > n

It is important to note that Karr defines all sums with the upper
limit being exclusive. This is in contrast to the usual mathematical notation,
but does not affect the summation convention. Indeed we have:

.. math::

\sum_{m \leq i < n} f(i) = \sum_{i = m}^{n - 1} f(i)

where the difference in notation is intentional to emphasize the meaning,
with limits typeset on the top being inclusive.

Examples
========

>>> from sympy.abc import i, k, m, n, x
>>> from sympy import Sum, factorial, oo
>>> Sum(k,(k,1,m))
Sum(k, (k, 1, m))
>>> Sum(k,(k,1,m)).doit()
m**2/2 + m/2
>>> Sum(k**2,(k,1,m))
Sum(k**2, (k, 1, m))
>>> Sum(k**2,(k,1,m)).doit()
m**3/3 + m**2/2 + m/6
>>> Sum(x**k,(k,0,oo))
Sum(x**k, (k, 0, oo))
>>> Sum(x**k,(k,0,oo)).doit()
Piecewise((1/(-x + 1), Abs(x) < 1), (Sum(x**k, (k, 0, oo)), True))
>>> Sum(x**k/factorial(k),(k,0,oo)).doit()
exp(x)

An example showing that the symbolic result of a summation is still
valid for seemingly nonsensical values of the limits. Then the Karr
convention allows us to give a perfectly valid interpretation to
those sums by interchanging the limits according to the above rules:

>>> S = Sum(i, (i,1,n)).doit()
>>> S
n**2/2 + n/2
>>> S.subs(n, -4)
6
>>> Sum(i, (i, 1, -4)).doit()
6
>>> Sum(-i, (i, -3, 0)).doit()
6

An explicit example of the Karr summation convention:

>>> S1 = Sum(i**2, (i, m, m+n-1)).doit()
>>> S1
m**2*n + m*n**2 - m*n + n**3/3 - n**2/2 + n/6
>>> S2 = Sum(i**2, (i, m+n, m-1)).doit()
>>> S2
-m**2*n - m*n**2 + m*n - n**3/3 + n**2/2 - n/6
>>> S1 + S2
0
>>> S3 = Sum(i, (i, m, m-1)).doit()
>>> S3
0

========

summation
Product, product

References
==========

.. [1] Michael Karr, "Summation in Finite Terms", Journal of the ACM,
Volume 28 Issue 2, April 1981, Pages 305-350
http://dl.acm.org/citation.cfm?doid=322248.322255
.. [2] http://en.wikipedia.org/wiki/Summation#Capital-sigma_notation
.. [3] http://en.wikipedia.org/wiki/Empty_sum
"""

__slots__ = ['is_commutative']

def __new__(cls, function, *symbols, **assumptions):
obj = AddWithLimits.__new__(cls, function, *symbols, **assumptions)
if not hasattr(obj, 'limits'):
return obj
if any(len(l) != 3 or None in l for l in obj.limits):
raise ValueError('Sum requires values for lower and upper bounds.')

return obj

@property
[docs]    def is_zero(self):
"""A Sum is only zero if its function is zero or if all terms
cancel out. This only answers whether the summand zero."""

return self.function.is_zero

@property
[docs]    def is_number(self):
"""
Return True if the Sum will result in a number, else False.

Sums are a special case since they contain symbols that can
be replaced with numbers. Whether the sum can be done or not in
closed form is another issue. But answering whether the final
result is a number is not difficult.

Examples
========

>>> from sympy import Sum
>>> from sympy.abc import x, y
>>> Sum(x, (y, 1, x)).is_number
False
>>> Sum(1, (y, 1, x)).is_number
False
>>> Sum(0, (y, 1, x)).is_number
True
>>> Sum(x, (y, 1, 2)).is_number
False
>>> Sum(x, (y, 1, 1)).is_number
False
>>> Sum(x, (x, 1, 2)).is_number
True
>>> Sum(x*y, (x, 1, 2), (y, 1, 3)).is_number
True
"""

return self.function.is_zero or not self.free_symbols

def doit(self, **hints):
if hints.get('deep', True):
f = self.function.doit(**hints)
else:
f = self.function

for n, limit in enumerate(self.limits):
i, a, b = limit
dif = b - a
if dif.is_integer and (dif < 0) is True:
a, b = b + 1, a - 1
f = -f

newf = eval_sum(f, (i, a, b))
if newf is None:
if f == self.function:
return self
else:
return self.func(f, *self.limits[n:])
f = newf

if hints.get('deep', True):
# eval_sum could return partially unevaluated
# result with Piecewise.  In this case we won't
# doit() recursively.
if not isinstance(f, Piecewise):
return f.doit(**hints)

return f

def _eval_derivative(self, x):
"""
Differentiate wrt x as long as x is not in the free symbols of any of
the upper or lower limits.

Sum(a*b*x, (x, 1, a)) can be differentiated wrt x or b but not a
since the value of the sum is discontinuous in a. In a case
involving a limit variable, the unevaluated derivative is returned.
"""

# diff already confirmed that x is in the free symbols of self, but we
# don't want to differentiate wrt any free symbol in the upper or lower
# limits
# XXX remove this test for free_symbols when the default _eval_derivative is in
if x not in self.free_symbols:
return S.Zero

# get limits and the function
f, limits = self.function, list(self.limits)

limit = limits.pop(-1)

if limits:  # f is the argument to a Sum
f = self.func(f, *limits)

if len(limit) == 3:
_, a, b = limit
if x in a.free_symbols or x in b.free_symbols:
return None
df = Derivative(f, x, evaluate=True)
rv = self.func(df, limit)
if limit[0] not in df.free_symbols:
rv = rv.doit()
return rv
else:
return NotImplementedError('Lower and upper bound expected.')

def _eval_simplify(self, ratio, measure):
from sympy.simplify.simplify import sum_simplify
return sum_simplify(self)

def _eval_summation(self, f, x):
return None

[docs]    def euler_maclaurin(self, m=0, n=0, eps=0, eval_integral=True):
"""
Return an Euler-Maclaurin approximation of self, where m is the
number of leading terms to sum directly and n is the number of
terms in the tail.

With m = n = 0, this is simply the corresponding integral
plus a first-order endpoint correction.

Returns (s, e) where s is the Euler-Maclaurin approximation
and e is the estimated error (taken to be the magnitude of
the first omitted term in the tail):

>>> from sympy.abc import k, a, b
>>> from sympy import Sum
>>> Sum(1/k, (k, 2, 5)).doit().evalf()
1.28333333333333
>>> s, e = Sum(1/k, (k, 2, 5)).euler_maclaurin()
>>> s
-log(2) + 7/20 + log(5)
>>> from sympy import sstr
>>> print(sstr((s.evalf(), e.evalf()), full_prec=True))
(1.26629073187415, 0.0175000000000000)

The endpoints may be symbolic:

>>> s, e = Sum(1/k, (k, a, b)).euler_maclaurin()
>>> s
-log(a) + log(b) + 1/(2*b) + 1/(2*a)
>>> e
Abs(-1/(12*b**2) + 1/(12*a**2))

If the function is a polynomial of degree at most 2n+1, the
Euler-Maclaurin formula becomes exact (and e = 0 is returned):

>>> Sum(k, (k, 2, b)).euler_maclaurin()
(b**2/2 + b/2 - 1, 0)
>>> Sum(k, (k, 2, b)).doit()
b**2/2 + b/2 - 1

With a nonzero eps specified, the summation is ended
as soon as the remainder term is less than the epsilon.
"""
m = int(m)
n = int(n)
f = self.function
assert len(self.limits) == 1
i, a, b = self.limits[0]
if (a > b) is True:
if a - b == 1:
return S.Zero,S.Zero
a, b = b + 1, a - 1
f = -f
s = S.Zero
if m:
if b.is_Integer and a.is_Integer:
m = min(m, b - a + 1)
if not eps:
for k in range(m):
s += f.subs(i, a + k)
else:
term = f.subs(i, a)
if term:
test = abs(term.evalf(3)) < eps
if isinstance(test, bool):
if test is True:
return s, abs(term)
else:
# a symbolic Relational class, can't go further
return term, S.Zero
s += term
for k in range(1, m):
term = f.subs(i, a + k)
if abs(term.evalf(3)) < eps:
return s, abs(term)
s += term
if b - a + 1 == m:
return s, S.Zero
a += m
x = Dummy('x')
I = C.Integral(f.subs(i, x), (x, a, b))
if eval_integral:
I = I.doit()
s += I

def fpoint(expr):
if b is S.Infinity:
return expr.subs(i, a), 0
return expr.subs(i, a), expr.subs(i, b)
fa, fb = fpoint(f)
iterm = (fa + fb)/2
g = f.diff(i)
for k in xrange(1, n + 2):
ga, gb = fpoint(g)
term = C.bernoulli(2*k)/C.factorial(2*k)*(gb - ga)
if (eps and term and abs(term.evalf(3)) < eps) or (k > n):
break
s += term
g = g.diff(i, 2, simplify=False)
return s + iterm, abs(term)

[docs]    def reverse_order(self, *indices):
"""
Reverse the order of a limit in a Sum.

Usage
=====

reverse_order(self, *indices) reverses some limits in the expression
self which can be either a Sum or a Product. The selectors in
the argument indices specify some indices whose limits get reversed.
These selectors are either variable names or numerical indices counted
starting from the inner-most limit tuple.

Examples
========

>>> from sympy import Sum
>>> from sympy.abc import x, y, a, b, c, d

>>> Sum(x, (x, 0, 3)).reverse_order(x)
Sum(-x, (x, 4, -1))
>>> Sum(x*y, (x, 1, 5), (y, 0, 6)).reverse_order(x, y)
Sum(x*y, (x, 6, 0), (y, 7, -1))
>>> Sum(x, (x, a, b)).reverse_order(x)
Sum(-x, (x, b + 1, a - 1))
>>> Sum(x, (x, a, b)).reverse_order(0)
Sum(-x, (x, b + 1, a - 1))

While one should prefer variable names when specifying which limits
to reverse, the index counting notation comes in handy in case there
are several symbols with the same name.

>>> S = Sum(x**2, (x, a, b), (x, c, d))
>>> S
Sum(x**2, (x, a, b), (x, c, d))
>>> S0 = S.reverse_order( 0)
>>> S0
Sum(-x**2, (x, b + 1, a - 1), (x, c, d))
>>> S1 = S0.reverse_order( 1)
>>> S1
Sum(x**2, (x, b + 1, a - 1), (x, d + 1, c - 1))

Of course we can mix both notations:

>>> Sum(x*y, (x, a, b), (y, 2, 5)).reverse_order( x, 1)
Sum(x*y, (x, b + 1, a - 1), (y, 6, 1))
>>> Sum(x*y, (x, a, b), (y, 2, 5)).reverse_order( y, x)
Sum(x*y, (x, b + 1, a - 1), (y, 6, 1))

========

index, reorder_limit, reorder

References
==========

.. [1] Michael Karr, "Summation in Finite Terms", Journal of the ACM,
Volume 28 Issue 2, April 1981, Pages 305-350
http://dl.acm.org/citation.cfm?doid=322248.322255
"""
l_indices = list(indices)

for i, indx in enumerate(l_indices):
if not isinstance(indx, int):
l_indices[i] = self.index(indx)

e = 1
limits = []
for i, limit in enumerate(self.limits):
l = limit
if i in l_indices:
e = -e
l = (limit[0], limit[2] + 1 , limit[1] - 1)
limits.append(l)

return Sum(e * self.function, *limits)

[docs]def summation(f, *symbols, **kwargs):
r"""
Compute the summation of f with respect to symbols.

The notation for symbols is similar to the notation used in Integral.
summation(f, (i, a, b)) computes the sum of f with respect to i from a to b,
i.e.,

::

b
____
\
summation(f, (i, a, b)) =  )    f
/___,
i = a

If it cannot compute the sum, it returns an unevaluated Sum object.
Repeated sums can be computed by introducing additional symbols tuples::

>>> from sympy import summation, oo, symbols, log
>>> i, n, m = symbols('i n m', integer=True)

>>> summation(2*i - 1, (i, 1, n))
n**2
>>> summation(1/2**i, (i, 0, oo))
2
>>> summation(1/log(n)**n, (n, 2, oo))
Sum(log(n)**(-n), (n, 2, oo))
>>> summation(i, (i, 0, n), (n, 0, m))
m**3/6 + m**2/2 + m/3

>>> from sympy.abc import x
>>> from sympy import factorial
>>> summation(x**n/factorial(n), (n, 0, oo))
exp(x)

========

Sum
Product, product

"""
return Sum(f, *symbols, **kwargs).doit(deep=False)

def telescopic_direct(L, R, n, limits):
"""Returns the direct summation of the terms of a telescopic sum

L is the term with lower index
R is the term with higher index
n difference between the indexes of L and R

For example:

>>> from sympy.concrete.summations import telescopic_direct
>>> from sympy.abc import k, a, b
>>> telescopic_direct(1/k, -1/(k+2), 2, (k, a, b))
-1/(b + 2) - 1/(b + 1) + 1/(a + 1) + 1/a

"""
(i, a, b) = limits
s = 0
for m in xrange(n):
s += L.subs(i, a + m) + R.subs(i, b - m)
return s

def telescopic(L, R, limits):
'''Tries to perform the summation using the telescopic property

return None if not possible
'''
(i, a, b) = limits
return None

# We want to solve(L.subs(i, i + m) + R, m)
# First we try a simple match since this does things that
# solve doesn't do, e.g. solve(f(k+m)-f(k), m) fails

k = Wild("k")
sol = (-R).match(L.subs(i, i + k))
s = None
if sol and k in sol:
s = sol[k]
if not (s.is_Integer and L.subs(i, i + s) == -R):
#sometimes match fail(f(x+2).match(-f(x+k))->{k: -2 - 2x}))
s = None

# But there are things that match doesn't do that solve
# can do, e.g. determine that 1/(x + m) = 1/(1 - x) when m = 1

if s is None:
m = Dummy('m')
try:
sol = solve(L.subs(i, i + m) + R, m) or []
except NotImplementedError:
return None
sol = [si for si in sol if si.is_Integer and
(L.subs(i, i + si) + R).expand().is_zero]
if len(sol) != 1:
return None
s = sol[0]

if s < 0:
return telescopic_direct(R, L, abs(s), (i, a, b))
elif s > 0:
return telescopic_direct(L, R, s, (i, a, b))

def eval_sum(f, limits):
from sympy.concrete.delta import deltasummation, _has_simple_delta
from sympy.functions import KroneckerDelta

(i, a, b) = limits
if f is S.Zero:
return S.Zero
if i not in f.free_symbols:
return f*(b - a + 1)
if a == b:
return f.subs(i, a)
if isinstance(f, Piecewise):
from sympy.utilities.iterables import flatten
if i not in flatten([arg.args[1].free_symbols for arg in f.args]):
# Piecewise conditions do not depend on the dummy summation variable,
# therefore we can fold:     Sum(Piecewise((e, c), ...), limits)
#                        --> Piecewise((Sum(e, limits), c), ...)
newargs = []
for arg in f.args:
newexpr = eval_sum(arg.expr, limits)
if newexpr is None:
return None
newargs.append((newexpr, arg.cond))
return f.func(*newargs)

if f.has(KroneckerDelta) and _has_simple_delta(f, limits[0]):
return deltasummation(f, limits)

dif = b - a
definite = dif.is_Integer
# Doing it directly may be faster if there are very few terms.
if definite and (dif < 100):
return eval_sum_direct(f, (i, a, b))
# Try to do it symbolically. Even when the number of terms is known,
# this can save time when b-a is big.
# We should try to transform to partial fractions
value = eval_sum_symbolic(f.expand(), (i, a, b))
if value is not None:
return value
# Do it directly
if definite:
return eval_sum_direct(f, (i, a, b))

def eval_sum_direct(expr, limits):
(i, a, b) = limits

dif = b - a
return C.Add(*[expr.subs(i, a + j) for j in xrange(dif + 1)])

def eval_sum_symbolic(f, limits):
(i, a, b) = limits
if not f.has(i):
return f*(b - a + 1)

# Linearity
if f.is_Mul:
L, R = f.as_two_terms()

if not L.has(i):
sR = eval_sum_symbolic(R, (i, a, b))
if sR:
return L*sR

if not R.has(i):
sL = eval_sum_symbolic(L, (i, a, b))
if sL:
return R*sL

try:
f = apart(f, i)  # see if it becomes an Add
except PolynomialError:
pass

L, R = f.as_two_terms()
lrsum = telescopic(L, R, (i, a, b))

if lrsum:
return lrsum

lsum = eval_sum_symbolic(L, (i, a, b))
rsum = eval_sum_symbolic(R, (i, a, b))

if None not in (lsum, rsum):
return lsum + rsum

# Polynomial terms with Faulhaber's formula
n = Wild('n')
result = f.match(i**n)

if result is not None:
n = result[n]

if n.is_Integer:
if n >= 0:
if (b is S.Infinity and not a is S.NegativeInfinity) or \
(a is S.NegativeInfinity and not b is S.Infinity):
return S.Infinity
return ((C.bernoulli(n + 1, b + 1) - C.bernoulli(n + 1, a))/(n + 1)).expand()
elif a.is_Integer and a >= 1:
if n == -1:
return C.harmonic(b) - C.harmonic(a - 1)
else:
return C.harmonic(b, abs(n)) - C.harmonic(a - 1, abs(n))

if not (a.has(S.Infinity, S.NegativeInfinity) or
b.has(S.Infinity, S.NegativeInfinity)):
# Geometric terms
c1 = C.Wild('c1', exclude=[i])
c2 = C.Wild('c2', exclude=[i])
c3 = C.Wild('c3', exclude=[i])

e = f.match(c1**(c2*i + c3))

if e is not None:
p = (c1**c3).subs(e)
q = (c1**c2).subs(e)

r = p*(q**a - q**(b + 1))/(1 - q)
l = p*(b - a + 1)

return Piecewise((l, Eq(q, S.One)), (r, True))

r = gosper_sum(f, (i, a, b))

if not r in (None, S.NaN):
return r

return eval_sum_hyper(f, (i, a, b))

def _eval_sum_hyper(f, i, a):
""" Returns (res, cond). Sums from a to oo. """
from sympy.functions import hyper
from sympy.simplify import hyperexpand, hypersimp, fraction, simplify
from sympy.polys.polytools import Poly, factor

if a != 0:
return _eval_sum_hyper(f.subs(i, i + a), i, 0)

if f.subs(i, 0) == 0:
if simplify(f.subs(i, Dummy('i', integer=True, positive=True))) == 0:
return S(0), True
return _eval_sum_hyper(f.subs(i, i + 1), i, 0)

hs = hypersimp(f, i)
if hs is None:
return None

numer, denom = fraction(factor(hs))
top, topl = numer.as_coeff_mul(i)
bot, botl = denom.as_coeff_mul(i)
ab = [top, bot]
factors = [topl, botl]
params = [[], []]
for k in range(2):
for fac in factors[k]:
mul = 1
if fac.is_Pow:
mul = fac.exp
fac = fac.base
if not mul.is_Integer:
return None
p = Poly(fac, i)
if p.degree() != 1:
return None
m, n = p.all_coeffs()
ab[k] *= m**mul
params[k] += [n/m]*mul

# Add "1" to numerator parameters, to account for implicit n! in
# hypergeometric series.
ap = params[0] + [1]
bq = params[1]
x = ab[0]/ab[1]
h = hyper(ap, bq, x)

return f.subs(i, 0)*hyperexpand(h), h.convergence_statement

def eval_sum_hyper(f, i_a_b):
from sympy.logic.boolalg import And

i, a, b = i_a_b

if (b - a).is_Integer:
# We are never going to do better than doing the sum in the obvious way
return None

old_sum = Sum(f, (i, a, b))

if b != S.Infinity:
if a == S.NegativeInfinity:
res = _eval_sum_hyper(f.subs(i, -i), i, -b)
if res is not None:
return Piecewise(res, (old_sum, True))
else:
res1 = _eval_sum_hyper(f, i, a)
res2 = _eval_sum_hyper(f, i, b + 1)
if res1 is None or res2 is None:
return None
(res1, cond1), (res2, cond2) = res1, res2
cond = And(cond1, cond2)
if cond is False:
return None
return Piecewise((res1 - res2, cond), (old_sum, True))

if a == S.NegativeInfinity:
res1 = _eval_sum_hyper(f.subs(i, -i), i, 1)
res2 = _eval_sum_hyper(f, i, 0)
if res1 is None or res2 is None:
return None
res1, cond1 = res1
res2, cond2 = res2
cond = And(cond1, cond2)
if cond is False:
return None
return Piecewise((res1 + res2, cond), (old_sum, True))

# Now b == oo, a != -oo
res = _eval_sum_hyper(f, i, a)
if res is not None:
r, c = res
if c == False:
if r.is_number:
f = f.subs(i, Dummy('i', integer=True, positive=True) + a)
if f.is_positive or f.is_zero:
return S.Infinity
elif f.is_negative:
return S.NegativeInfinity
return None
return Piecewise(res, (old_sum, True))
`