from sympy.core import Add, C, Derivative, Dummy, Expr, S, sympify, Wild
from sympy.concrete.gosper import gosper_sum
from sympy.functions.elementary.piecewise import piecewise_fold
from sympy.polys import apart, PolynomialError
from sympy.solvers import solve

def _free_symbols(function, limits):
    """Helper function to return the symbols that appear in a sum-like object
    once it is evaluated.
    """
    isyms = function.free_symbols
    for xab in limits:
        # take out the target symbol
        if xab[0] in isyms:
            isyms.remove(xab[0])
        # add in the new symbols
        for i in xab[1:]:
            isyms.update(i.free_symbols)
    return isyms

class Sum(Expr):
    """Represents unevaluated summation."""

    def __new__(cls, function, *symbols, **assumptions):
        from sympy.integrals.integrals import _process_limits

        # Any embedded piecewise functions need to be brought out to the
        # top level so that integration can go into piecewise mode at the
        # earliest possible moment.
        function = piecewise_fold(sympify(function))

        if function is S.NaN:
            return S.NaN

        if not symbols:
            raise ValueError("Summation variables must be given")

        limits, sign = _process_limits(*symbols)

        # Only limits with lower and upper bounds are supported; the indefinite Sum
        # is not supported
        if any(len(l) != 3 or None in l for l in limits):
            raise ValueError('Sum requires values for lower and upper bounds.')

        obj = Expr.__new__(cls, **assumptions)
        arglist = [sign*function]
        arglist.extend(limits)
        obj._args = tuple(arglist)

        return obj

    @property
    def function(self):
        return self._args[0]

    @property
    def limits(self):
        return self._args[1:]

    @property
    def variables(self):
        """Return a list of the summation variables

        >>> from sympy import Sum
        >>> from sympy.abc import x, i
        >>> Sum(x**i, (i, 1, 3)).variables
        [i]
        """
        return [l[0] for l in self.limits]

    @property
    def free_symbols(self):
        """
        This method returns the symbols that will exist when the
        summation is evaluated. This is useful if one is trying to
        determine whether a sum depends on a certain symbol or not.

        >>> from sympy import Sum
        >>> from sympy.abc import x, y
        >>> Sum(x, (x, y, 1)).free_symbols
        set([y])
        """
        if self.function.is_zero:
            return set()
        return _free_symbols(self.function, self.limits)

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

        sympy considers anything that will result in a number to have
        is_number == True.

        >>> from sympy import log
        >>> log(2).is_number
        True

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

        >>> 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 not hints.get('sums', True):
        #    return self
        f = self.function
        for limit in self.limits:
            i, a, b = limit
            dif = b - a
            if dif.is_Integer and dif < 0:
                a, b = b, a

            f = eval_sum(f, (i, a, b))
            if f is None:
                return self

        if hints.get('deep', True):
            return f.doit(**hints)
        else:
            return f

    def _eval_summation(self, f, x):
        return

    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 = Sum(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 = Sum(df, limit)
            if limit[0] not in df.free_symbols:
                rv = rv.doit()
            return rv
        else:
            return NotImplementedError('Lower and upper bound expected.')

    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]
        s = S.Zero
        if m:
            for k in range(m):
                term = f.subs(i, a+k)
                if (eps and term and abs(term.evalf(3)) < eps):
                    return s, abs(term)
                s += term
            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)
        return s + iterm, abs(term)

    def _eval_subs(self, old, new): # XXX this should be the same as Integral's
        if any(old == v for v in self.variables):
            return self

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)

    """
    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
    if L.is_Add or R.is_Add:
        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):
    (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)

    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 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

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

        if lrsum: