"""
There are three types of functions implemented in SymPy:

    1) defined functions (in the sense that they can be evaluated) like
       exp or sin; they have a name and a body:
           f = exp
    2) undefined function which have a name but no body. Undefined
       functions can be defined using a Function class as follows:
           f = Function('f')
       (the result will be a Function instance)
    3) anonymous function (or lambda function) which have a body (defined
       with dummy variables) but have no name:
           f = Lambda(x, exp(x)*x)
           f = Lambda((x, y), exp(x)*y)
    The fourth type of functions are composites, like (sin + cos)(x); these work in
    SymPy core, but are not yet part of SymPy.

    Examples
    ========

    >>> import sympy
    >>> f = sympy.Function("f")
    >>> from sympy.abc import x
    >>> f(x)
    f(x)
    >>> print(sympy.srepr(f(x).func))
    Function('f')
    >>> f(x).args
    (x,)

"""
from __future__ import print_function, division

from .add import Add
from .assumptions import ManagedProperties
from .basic import Basic
from .cache import cacheit
from .compatibility import iterable, is_sequence, as_int, ordered
from .core import BasicMeta, C
from .decorators import _sympifyit
from .expr import Expr, AtomicExpr
from .numbers import Rational, Float
from .rules import Transform
from .singleton import S
from .sympify import sympify

from sympy.core.containers import Tuple, Dict
from sympy.core.logic import fuzzy_and
from sympy.core.compatibility import string_types, with_metaclass, xrange
from sympy.utilities import default_sort_key
from sympy.utilities.iterables import uniq

from sympy import mpmath
import sympy.mpmath.libmp as mlib

import inspect


def _coeff_isneg(a):
    """Return True if the leading Number is negative.

    Examples
    ========

    >>> from sympy.core.function import _coeff_isneg
    >>> from sympy import S, Symbol, oo, pi
    >>> _coeff_isneg(-3*pi)
    True
    >>> _coeff_isneg(S(3))
    False
    >>> _coeff_isneg(-oo)
    True
    >>> _coeff_isneg(Symbol('n', negative=True)) # coeff is 1
    False

    """

    if a.is_Mul:
        a = a.args[0]
    return a.is_Number and a.is_negative


class PoleError(Exception):
    pass


class ArgumentIndexError(ValueError):
    def __str__(self):
        return ("Invalid operation with argument number %s for Function %s" %
               (self.args[1], self.args[0]))


class FunctionClass(with_metaclass(BasicMeta, ManagedProperties)):
    """
    Base class for function classes. FunctionClass is a subclass of type.

    Use Function('<function name>' [ , signature ]) to create
    undefined function classes.
    """
    _new = type.__new__

    def __init__(cls, *args, **kwargs):
        if hasattr(cls, 'eval'):
            evalargspec = inspect.getargspec(cls.eval)
            if evalargspec.varargs:
                evalargs = None
            else:
                evalargs = len(evalargspec.args) - 1  # subtract 1 for cls
                if evalargspec.defaults:
                    # if there are default args then they are optional; the
                    # fewest args will occur when all defaults are used and
                    # the most when none are used (i.e. all args are given)
                    evalargs = tuple(range(
                        evalargs - len(evalargspec.defaults), evalargs + 1))
        else:
            evalargs = None
        # honor kwarg value or class-defined value before using
        # the number of arguments in the eval function (if present)
        nargs = kwargs.pop('nargs', cls.__dict__.get('nargs', evalargs))
        super(FunctionClass, cls).__init__(args, kwargs)

        # Canonicalize nargs here; change to set in nargs.
        if is_sequence(nargs):
            nargs = tuple(ordered(set(nargs)))
        elif nargs is not None:
            nargs = (as_int(nargs),)
        cls._nargs = nargs

    @property
    def nargs(self):
        """Return a set of the allowed number of arguments for the function.

        Examples
        ========

        >>> from sympy.core.function import Function
        >>> from sympy.abc import x, y
        >>> f = Function('f')

        If the function can take any number of arguments, the set of whole
        numbers is returned:

        >>> Function('f').nargs
        Naturals0()

        If the function was initialized to accept one or more arguments, a
        corresponding set will be returned:

        >>> Function('f', nargs=1).nargs
        {1}
        >>> Function('f', nargs=(2, 1)).nargs
        {1, 2}

        The undefined function, after application, also has the nargs
        attribute; the actual number of arguments is always available by
        checking the ``args`` attribute:

        >>> f = Function('f')
        >>> f(1).nargs
        Naturals0()
        >>> len(f(1).args)
        1
        """
        from sympy.core.sets import FiniteSet
        # XXX it would be nice to handle this in __init__ but there are import
        # problems with trying to import FiniteSet there
        return FiniteSet(self._nargs) if self._nargs else S.Naturals0

    def __repr__(cls):
        return cls.__name__


class Application(with_metaclass(FunctionClass, Basic)):
    """
    Base class for applied functions.

    Instances of Application represent the result of applying an application of
    any type to any object.
    """

    is_Function = True

    @cacheit
    def __new__(cls, *args, **options):
        from sympy.sets.fancysets import Naturals0
        from sympy.core.sets import FiniteSet

        args = list(map(sympify, args))
        evaluate = options.pop('evaluate', True)
        # WildFunction (and anything else like it) may have nargs defined
        # and we throw that value away here
        options.pop('nargs', None)

        if options:
            raise ValueError("Unknown options: %s" % options)

        if evaluate:
            evaluated = cls.eval(*args)
            if evaluated is not None:
                return evaluated

        obj = super(Application, cls).__new__(cls, *args, **options)

        # make nargs uniform here
        try:
            # things passing through here:
            #  - functions subclassed from Function (e.g. myfunc(1).nargs)
            #  - functions like cos(1).nargs
            #  - AppliedUndef with given nargs like Function('f', nargs=1)(1).nargs
            obj.nargs = FiniteSet(obj.nargs) if obj.nargs is not None \
                else Naturals0()
        except AttributeError:
            # things passing through here:
            #  - WildFunction('f').nargs
            #  - AppliedUndef with no nargs like Function('f')(1).nargs
            obj.nargs = FiniteSet(obj._nargs) if obj._nargs is not None \
                else Naturals0()
        return obj

    @classmethod
    def eval(cls, *args):
        """
        Returns a canonical form of cls applied to arguments args.

        The eval() method is called when the class cls is about to be
        instantiated and it should return either some simplified instance
        (possible of some other class), or if the class cls should be
        unmodified, return None.

        Examples of eval() for the function "sign"
        ---------------------------------------------

        @classmethod
        def eval(cls, arg):
            if arg is S.NaN:
                return S.NaN
            if arg is S.Zero: return S.Zero
            if arg.is_positive: return S.One
            if arg.is_negative: return S.NegativeOne
            if isinstance(arg, C.Mul):
                coeff, terms = arg.as_coeff_Mul(rational=True)
                if coeff is not S.One:
                    return cls(coeff) * cls(terms)

        """
        return

    @property
    def func(self):
        return self.__class__

    def _eval_subs(self, old, new):
        if (old.is_Function and new.is_Function and old == self.func and
            len(self.args) in new.nargs):
            return new(*self.args)


class Function(Application, Expr):
    """Base class for applied mathematical functions.

    It also serves as a constructor for undefined function classes.

    Examples
    ========

    First example shows how to use Function as a constructor for undefined
    function classes:

    >>> from sympy import Function, Symbol
    >>> x = Symbol('x')
    >>> f = Function('f')
    >>> g = Function('g')(x)
    >>> f
    f
    >>> f(x)
    f(x)
    >>> g
    g(x)
    >>> f(x).diff(x)
    Derivative(f(x), x)
    >>> g.diff(x)
    Derivative(g(x), x)

    In the following example Function is used as a base class for
    ``my_func`` that represents a mathematical function *my_func*. Suppose
    that it is well known, that *my_func(0)* is *1* and *my_func* at infinity
    goes to *0*, so we want those two simplifications to occur automatically.
    Suppose also that *my_func(x)* is real exactly when *x* is real. Here is
    an implementation that honours those requirements:

    >>> from sympy import Function, S, oo, I, sin
    >>> class my_func(Function):
    ...
    ...     @classmethod
    ...     def eval(cls, x):
    ...         if x.is_Number:
    ...             if x is S.Zero:
    ...                 return S.One
    ...             elif x is S.Infinity:
    ...                 return S.Zero
    ...
    ...     def _eval_is_real(self):
    ...         return self.args[0].is_real
    ...
    >>> x = S('x')
    >>> my_func(0) + sin(0)
    1
    >>> my_func(oo)
    0
    >>> my_func(3.54).n() # Not yet implemented for my_func.
    my_func(3.54)
    >>> my_func(I).is_real
    False

    In order for ``my_func`` to become useful, several other methods would
    need to be implemented. See source code of some of the already
    implemented functions for more complete examples.

    Also, if the function can take more than one argument, then ``nargs``
    must be defined, e.g. if ``my_func`` can take one or two arguments
    then,

    >>> class my_func(Function):
    ...     nargs = (1, 2)
    ...
    >>>
    """

    @property
    def _diff_wrt(self):
        """Allow derivatives wrt functions.

        Examples
        ========

        >>> from sympy import Function, Symbol
        >>> f = Function('f')
        >>> x = Symbol('x')
        >>> f(x)._diff_wrt
        True

        """
        return True

    @cacheit
    def __new__(cls, *args, **options):
        # Handle calls like Function('f')
        if cls is Function:
            return UndefinedFunction(*args, **options)

        n = len(args)
        if n not in cls.nargs:
            # XXX: exception message must be in exactly this format to
            # make it work with NumPy's functions like vectorize(). See,
            # for example, https://github.com/numpy/numpy/issues/1697.
            # The ideal solution would be just to attach metadata to
            # the exception and change NumPy to take advantage of this.
            temp = ('%(name)s takes %(qual)s %(args)s '
                   'argument%(plural)s (%(given)s given)')
            raise TypeError(temp % {
                'name': cls,
                'qual': 'exactly' if len(cls.nargs) == 1 else 'at least',
                'args': min(cls.nargs),
                'plural': 's'*(min(cls.nargs) != 1),
                'given': n})

        evaluate = options.get('evaluate', True)
        result = super(Function, cls).__new__(cls, *args, **options)
        if not evaluate or not isinstance(result, cls):
            return result

        pr = max(cls._should_evalf(a) for a in result.args)
        pr2 = min(cls._should_evalf(a) for a in result.args)
        if pr2 > 0:
            return result.evalf(mlib.libmpf.prec_to_dps(pr))
        return result

    @classmethod
    def _should_evalf(cls, arg):
        """
        Decide if the function should automatically evalf().

        By default (in this implementation), this happens if (and only if) the
        ARG is a floating point number.
        This function is used by __new__.
        """
        if arg.is_Float:
            return arg._prec
        if not arg.is_Add:
            return -1
        re, im = arg.as_real_imag()
        l = [a._prec for a in [re, im] if a.is_Float]
        l.append(-1)
        return max(l)

    @classmethod
    def class_key(cls):
        from sympy.sets.fancysets import Naturals0
        funcs = {
            'exp': 10,
            'log': 11,
            'sin': 20,
            'cos': 21,
            'tan': 22,
            'cot': 23,
            'sinh': 30,
            'cosh': 31,
            'tanh': 32,
            'coth': 33,
            'conjugate': 40,
            're': 41,
            'im': 42,
            'arg': 43,
        }
        name = cls.__name__

        try:
            i = funcs[name]
        except KeyError:
            i = 0 if isinstance(cls.nargs, Naturals0) else 10000

        return 4, i, name

    @property
    def is_commutative(self):
        """
        Returns whether the functon is commutative.
        """
        if all(getattr(t, 'is_commutative') for t in self.args):
            return True
        else:
            return False

    def _eval_evalf(self, prec):
        # Lookup mpmath function based on name
        fname = self.func.__name__
        try:
            if not hasattr(mpmath, fname):
                from sympy.utilities.lambdify import MPMATH_TRANSLATIONS
                fname = MPMATH_TRANSLATIONS[fname]
            func = getattr(mpmath, fname)
        except (AttributeError, KeyError):
            try:
                return C.Float(self._imp_(*self.args), prec)
            except (AttributeError, TypeError):
                return

        # Convert all args to mpf or mpc
        # Convert the arguments to *higher* precision than requested for the
        # final result.
        # XXX + 5 is a guess, it is similar to what is used in evalf.py. Should
        #     we be more intelligent about it?
        try:
            args = [arg._to_mpmath(prec + 5) for arg in self.args]
            def bad(m):
                from sympy.mpmath import mpf, mpc
                # the precision of an mpf value is the last element
                # if that is 1 (and m[1] is not 1 which would indicate a
                # power of 2), then the eval failed; so check that none of
                # the arguments failed to compute to a finite precision.
                # Note: An mpc value has two parts, the re and imag tuple;
                # check each of those parts, too. Anything else is allowed to
                # pass
                if isinstance(m, mpf):
                    m = m._mpf_
                    return m[1] !=1 and m[-1] == 1
                elif isinstance(m, mpc):
                    m, n = m._mpc_
                    return m[1] !=1 and m[-1] == 1 and \
                        n[1] !=1 and n[-1] == 1
                else:
                    return False
            if any(bad(a) for a in args):
                raise ValueError  # one or more args failed to compute with significance
        except ValueError:
            return

        # Set mpmath precision and apply. Make sure precision is restored
        # afterwards
        orig = mpmath.mp.prec
        try:
            mpmath.mp.prec = prec
            v = func(*args)
        finally:
            mpmath.mp.prec = orig

        return Expr._from_mpmath(v, prec)

    def _eval_derivative(self, s):
        # f(x).diff(s) -> x.diff(s) * f.fdiff(1)(s)
        i = 0
        l = []
        for a in self.args:
            i += 1
            da = a.diff(s)
            if da is S.Zero:
                continue
            try:
                df = self.fdiff(i)
            except ArgumentIndexError:
                df = Function.fdiff(self, i)
            l.append(df * da)
        return Add(*l)

    def _eval_is_commutative(self):
        return fuzzy_and(a.is_commutative for a in self.args)

    def _eval_is_complex(self):
        return fuzzy_and(a.is_complex for a in self.args)

    def as_base_exp(self):
        """
        Returns the method as the 2-tuple (base, exponent).
        """
        return self, S.One

    def _eval_aseries(self, n, args0, x, logx):
        """
        Compute an asymptotic expansion around args0, in terms of self.args.
        This function is only used internally by _eval_nseries and should not
        be called directly; derived classes can overwrite this to implement
        asymptotic expansions.
        """
        from sympy.utilities.misc import filldedent
        raise PoleError(filldedent('''
            Asymptotic expansion of %s around %s is
            not implemented.''' % (type(self), args0)))

    def _eval_nseries(self, x, n, logx):
        """
        This function does compute series for multivariate functions,
        but the expansion is always in terms of *one* variable.
        Examples
        ========

        >>> from sympy import atan2
        >>> from sympy.abc import x, y
        >>> atan2(x, y).series(x, n=2)
        atan2(0, y) + x/y + O(x**2)
        >>> atan2(x, y).series(y, n=2)
        -y/x + atan2(x, 0) + O(y**2)

        This function also computes asymptotic expansions, if necessary
        and possible:

        >>> from sympy import loggamma
        >>> loggamma(1/x)._eval_nseries(x,0,None)
        -1/x - log(x)/x + log(x)/2 + O(1)

        """
        from sympy.core.sets import FiniteSet
        args = self.args
        args0 = [t.limit(x, 0) for t in args]
        if any(t.is_bounded is False for t in args0):
            from sympy import oo, zoo, nan
            # XXX could use t.as_leading_term(x) here but it's a little
            # slower
            a = [t.compute_leading_term(x, logx=logx) for t in args]
            a0 = [t.limit(x, 0) for t in a]
            if any([t.has(oo, -oo, zoo, nan) for t in a0]):
                return self._eval_aseries(n, args0, x, logx
                                          )._eval_nseries(x, n, logx)
            # Careful: the argument goes to oo, but only logarithmically so. We
            # are supposed to do a power series expansion "around the
            # logarithmic term". e.g.
            #      f(1+x+log(x))
            #     -> f(1+logx) + x*f'(1+logx) + O(x**2)
            # where 'logx' is given in the argument
            a = [t._eval_nseries(x, n, logx) for t in args]
            z = [r - r0 for (r, r0) in zip(a, a0)]
            p = [Dummy() for t in z]
            q = []
            v = None
            for ai, zi, pi in zip(a0, z, p):
                if zi.has(x):
                    if v is not None:
                        raise NotImplementedError
                    q.append(ai + pi)
                    v = pi
                else:
                    q.append(ai)
            e1 = self.func(*q)
            if v is None:
                return e1
            s = e1._eval_nseries(v, n, logx)
            o = s.getO()
            s = s.removeO()
            s = s.subs(v, zi).expand() + C.Order(o.expr.subs(v, zi), x)
            return s
        if (self.func.nargs is S.Naturals0
                or (self.func.nargs == FiniteSet(1) and args0[0])
                or any(c > 1 for c in self.func.nargs)):
            e = self
            e1 = e.expand()
            if e == e1:
                #for example when e = sin(x+1) or e = sin(cos(x))
                #let's try the general algorithm
                term = e.subs(x, S.Zero)
                if term.is_bounded is False or term is S.NaN:
                    raise PoleError("Cannot expand %s around 0" % (self))
                series = term
                fact = S.One
                _x = Dummy('x')
                e = e.subs(x, _x)
                for i in range(n - 1):
                    i += 1
                    fact *= Rational(i)
                    e = e.diff(_x)
                    subs = e.subs(_x, S.Zero)
                    if subs is S.NaN:
                        # try to evaluate a limit if we have to
                        subs = e.limit(_x, S.Zero)
                    if subs.is_bounded is False:
                        raise PoleError("Cannot expand %s around 0" % (self))
                    term = subs*(x**i)/fact
                    term = term.expand()
                    series += term
                return series + C.Order(x**n, x)
            return e1.nseries(x, n=n, logx=logx)
        arg = self.args[0]
        l = []
        g = None
        # try to predict a number of terms needed
        nterms = n + 2
        cf = C.Order(arg.as_leading_term(x), x).getn()
        if cf != 0:
            nterms = int(nterms / cf)
        for i in xrange(nterms):
            g = self.taylor_term(i, arg, g)
            g = g.nseries(x, n=n, logx=logx)
            l.append(g)
        return Add(*l) + C.Order(x**n, x)

    def _eval_rewrite(self, pattern, rule, **hints):
        if hints.get('deep', False):
            args = [a._eval_rewrite(pattern, rule, **hints) for a in self.args]
        else:
            args = self.args

        if pattern is None or isinstance(self.func, pattern):
            if hasattr(self, rule):
                rewritten = getattr(self, rule)(*args)

                if rewritten is not None:
                    return rewritten

        return self.func(*args)

    def fdiff(self, argindex=1):
        """
        Returns the first derivative of the function.
        """
        if not (1 <= argindex <= len(self.args)):
            raise ArgumentIndexError(self, argindex)
        if not self.args[argindex - 1].is_Symbol:
            # See issue 1525 and issue 1620 and issue 2501
            arg_dummy = C.Dummy('xi_%i' % argindex)
            arg_dummy.dummy_index = hash(self.args[argindex - 1])
            return Subs(Derivative(
                self.subs(self.args[argindex - 1], arg_dummy),
                arg_dummy), arg_dummy, self.args[argindex - 1])
        return Derivative(self, self.args[argindex - 1], evaluate=False)

    def _eval_as_leading_term(self, x):
        """Stub that should be overridden by new Functions to return
        the first non-zero term in a series if ever an x-dependent
        argument whose leading term vanishes as x -> 0 might be encountered.
        See, for example, cos._eval_as_leading_term.
        """
        args = [a.as_leading_term(x) for a in self.args]
        o = C.Order(1, x)
        if any(x in a.free_symbols and o.contains(a) for a in args):
            # Whereas x and any finite number are contained in O(1, x),
            # expressions like 1/x are not. If any arg simplified to a
            # vanishing expression as x -> 0 (like x or x**2, but not
            # 3, 1/x, etc...) then the _eval_as_leading_term is needed
            # to supply the first non-zero term of the series,
            #
            # e.g. expression    leading term
            #      ----------    ------------
            #      cos(1/x)      cos(1/x)
            #      cos(cos(x))   cos(1)
            #      cos(x)        1        <- _eval_as_leading_term needed
            #      sin(x)        x        <- _eval_as_leading_term needed
            #
            raise NotImplementedError(
                '%s has no _eval_as_leading_term routine' % self.func)
        else:
            return self.func(*args)


class AppliedUndef(Function):
    """
    Base class for expressions resulting from the application of an undefined
    function.
    """

    def __new__(cls, *args, **options):
        args = list(map(sympify, args))
        obj = super(AppliedUndef, cls).__new__(cls, *args, **options)
        return obj

    def _eval_as_leading_term(self, x):
        return self


class UndefinedFunction(FunctionClass):
    """
    The (meta)class of undefined functions.
    """
    def __new__(mcl, name, **kwargs):
        ret = BasicMeta.__new__(mcl, name, (AppliedUndef,), kwargs)
        ret.__module__ = None
        return ret

UndefinedFunction.__eq__ = lambda s, o: (isinstance(o, s.__class__) and
                                         (s.class_key() == o.class_key()))

class WildFunction(Function, AtomicExpr):
    """
    A WildFunction function matches any function (with its arguments).

    Examples
    ========

    >>> from sympy import WildFunction, Function, cos
    >>> from sympy.abc import x, y
    >>> F = WildFunction('F')
    >>> f = Function('f')
    >>> F.nargs
    Naturals0()
    >>> x.match(F)
    >>> F.match(F)
    {F_: F_}
    >>> f(x).match(F)
    {F_: f(x)}
    >>> cos(x).match(F)
    {F_: cos(x)}
    >>> f(x, y).match(F)
    {F_: f(x, y)}

    To match functions with a given number of arguments, set ``nargs`` to the
    desired value at instantiation:

    >>> F = WildFunction('F', nargs=2)
    >>> F.nargs
    {2}
    >>> f(x).match(F)
    >>> f(x, y).match(F)
    {F_: f(x, y)}

    To match functions with a range of arguments, set ``nargs`` to a tuple
    containing the desired number of arguments, e.g. if ``nargs = (1, 2)``
    then functions with 1 or 2 arguments will be matched.

    >>> F = WildFunction('F', nargs=(1, 2))
    >>> F.nargs
    {1, 2}
    >>> f(x).match(F)
    {F_: f(x)}
    >>> f(x, y).match(F)
    {F_: f(x, y)}
    >>> f(x, y, 1).match(F)

    """

    include = set()

    def __init__(cls, name, **assumptions):
        from sympy.core.sets import Set, FiniteSet
        cls.name = name
        nargs = assumptions.pop('nargs', S.Naturals0)
        if not isinstance(nargs, Set):
            nargs = FiniteSet(nargs)
        cls.nargs = nargs

    def matches(self, expr, repl_dict={}, old=False):
        if not isinstance(expr, (AppliedUndef, Function)):
            return None
        if len(expr.args) not in self.nargs:
            return None

        repl_dict = repl_dict.copy()
        repl_dict[self] = expr
        return repl_dict

    @property
    def is_number(self):
        return False


class Derivative(Expr):
    """
    Carries out differentiation of the given expression with respect to symbols.

    expr must define ._eval_derivative(symbol) method that returns
    the differentiation result. This function only needs to consider the
    non-trivial case where expr contains symbol and it should call the diff()
    method internally (not _eval_derivative); Derivative should be the only
    one to call _eval_derivative.

    Simplification of high-order derivatives:

    Because there can be a significant amount of simplification that can be
    done when multiple differentiations are performed, results will be
    automatically simplified in a fairly conservative fashion unless the
    keyword ``simplify`` is set to False.

        >>> from sympy import sqrt, diff
        >>> from sympy.abc import x
        >>> e = sqrt((x + 1)**2 + x)
        >>> diff(e, x, 5, simplify=False).count_ops()
        136
        >>> diff(e, x, 5).count_ops()
        30

    Ordering of variables:

    If evaluate is set to True and the expression can not be evaluated, the
    list of differentiation symbols will be sorted, that is, the expression is
    assumed to have continuous derivatives up to the order asked. This sorting
    assumes that derivatives wrt Symbols commute, derivatives wrt non-Symbols
    commute, but Symbol and non-Symbol derivatives don't commute with each
    other.

    Derivative wrt non-Symbols:

    This class also allows derivatives wrt non-Symbols that have _diff_wrt
    set to True, such as Function and Derivative. When a derivative wrt a non-
    Symbol is attempted, the non-Symbol is temporarily converted to a Symbol
    while the differentiation is performed.

    Note that this may seem strange, that Derivative allows things like
    f(g(x)).diff(g(x)), or even f(cos(x)).diff(cos(x)).  The motivation for
    allowing this syntax is to make it easier to work with variational calculus
    (i.e., the Euler-Lagrange method).  The best way to understand this is that
    the action of derivative with respect to a non-Symbol is defined by the
    above description:  the object is substituted for a Symbol and the
    derivative is taken with respect to that.  This action is only allowed for
    objects for which this can be done unambiguously, for example Function and
    Derivative objects.  Note that this leads to what may appear to be
    mathematically inconsistent results.  For example::

        >>> from sympy import cos, sin, sqrt
        >>> from sympy.abc import x
        >>> (2*cos(x)).diff(cos(x))
        2
        >>> (2*sqrt(1 - sin(x)**2)).diff(cos(x))
        0

    This appears wrong because in fact 2*cos(x) and 2*sqrt(1 - sin(x)**2) are
    identically equal.  However this is the wrong way to think of this.  Think
    of it instead as if we have something like this::

        >>> from sympy.abc import c, s
        >>> def F(u):
        ...     return 2*u
        ...
        >>> def G(u):
        ...     return 2*sqrt(1 - u**2)
        ...
        >>> F(cos(x))
        2*cos(x)
        >>> G(sin(x))
        2*sqrt(-sin(x)**2 + 1)
        >>> F(c).diff(c)
        2
        >>> F(c).diff(c)
        2
        >>> G(s).diff(c)
        0
        >>> G(sin(x)).diff(cos(x))
        0

    Here, the Symbols c and s act just like the functions cos(x) and sin(x),
    respectively. Think of 2*cos(x) as f(c).subs(c, cos(x)) (or f(c) *at*
    c = cos(x)) and 2*sqrt(1 - sin(x)**2) as g(s).subs(s, sin(x)) (or g(s) *at*
    s = sin(x)), where f(u) == 2*u and g(u) == 2*sqrt(1 - u**2).  Here, we
    define the function first and evaluate it at the function, but we can
    actually unambiguously do this in reverse in SymPy, because
    expr.subs(Function, Symbol) is well-defined:  just structurally replace the
    function everywhere it appears in the expression.

    This is actually the same notational convenience used in the Euler-Lagrange
    method when one says F(t, f(t), f'(t)).diff(f(t)).  What is actually meant
    is that the expression in question is represented by some F(t, u, v) at
    u = f(t) and v = f'(t), and F(t, f(t), f'(t)).diff(f(t)) simply means
    F(t, u, v).diff(u) at u = f(t).

    We do not allow derivatives to be taken with respect to expressions where this
    is not so well defined.  For example, we do not allow expr.diff(x*y)
    because there are multiple ways of structurally defining where x*y appears
    in an expression, some of which may surprise the reader (for example, a
    very strict definition would have that (x*y*z).diff(x*y) == 0).

        >>> from sympy.abc import x, y, z
        >>> (x*y*z).diff(x*y)
        Traceback (most recent call last):
        ...
        ValueError: Can't differentiate wrt the variable: x*y, 1

    Note that this definition also fits in nicely with the definition of the
    chain rule.  Note how the chain rule in SymPy is defined using unevaluated
    Subs objects::

        >>> from sympy import symbols, Function
        >>> f, g = symbols('f g', cls=Function)
        >>> f(2*g(x)).diff(x)
        2*Derivative(g(x), x)*Subs(Derivative(f(_xi_1), _xi_1),
                                              (_xi_1,), (2*g(x),))
        >>> f(g(x)).diff(x)
        Derivative(g(x), x)*Subs(Derivative(f(_xi_1), _xi_1),
                                            (_xi_1,), (g(x),))

    Finally, note that, to be consistent with variational calculus, and to
    ensure that the definition of substituting a Function for a Symbol in an
    expression is well-defined, derivatives of functions are assumed to not be
    related to the function.  In other words, we have::

        >>> from sympy import diff
        >>> diff(f(x), x).diff(f(x))
        0

    The same is actually true for derivatives of different orders::

        >>> diff(f(x), x, 2).diff(diff(f(x), x, 1))
        0
        >>> diff(f(x), x, 1).diff(diff(f(x), x, 2))
        0

    Note, any class can allow derivatives to be taken with respect to itself.
    See the docstring of Expr._diff_wrt.

    Examples
    ========

    Some basic examples:

        >>> from sympy import Derivative, Symbol, Function
        >>> f = Function('f')
        >>> g = Function('g')
        >>> x = Symbol('x')
        >>> y = Symbol('y')

        >>> Derivative(x**2, x, evaluate=True)
        2*x
        >>> Derivative(Derivative(f(x,y), x), y)
        Derivative(f(x, y), x, y)
        >>> Derivative(f(x), x, 3)
        Derivative(f(x), x, x, x)
        >>> Derivative(f(x, y), y, x, evaluate=True)
        Derivative(f(x, y), x, y)

    Now some derivatives wrt functions:

        >>> Derivative(f(x)**2, f(x), evaluate=True)
        2*f(x)
        >>> Derivative(f(g(x)), x, evaluate=True)
        Derivative(g(x), x)*Subs(Derivative(f(_xi_1), _xi_1),
                                            (_xi_1,), (g(x),))
    """

    is_Derivative = True

    @property
    def _diff_wrt(self):
        """Allow derivatives wrt Derivatives if it contains a function.

        Examples
        ========

            >>> from sympy import Function, Symbol, Derivative
            >>> f = Function('f')
            >>> x = Symbol('x')
            >>> Derivative(f(x),x)._diff_wrt
            True
            >>> Derivative(x**2,x)._diff_wrt
            False
        """
        if self.expr.is_Function:
            return True
        else:
            return False

    def __new__(cls, expr, *variables, **assumptions):

        expr = sympify(expr)

        # There are no variables, we differentiate wrt all of the free symbols
        # in expr.
        if not variables:
            variables = expr.free_symbols
            if len(variables) != 1:
                from sympy.utilities.misc import filldedent
                raise ValueError(filldedent('''
                    Since there is more than one variable in the
                    expression, the variable(s) of differentiation
                    must be supplied to differentiate %s''' % expr))

        # Standardize the variables by sympifying them and making appending a
        # count of 1 if there is only one variable: diff(e,x)->diff(e,x,1).
        variables = list(sympify(variables))
        if not variables[-1].is_Integer or len(variables) == 1:
            variables.append(S.One)

        # Split the list of variables into a list of the variables we are diff
        # wrt, where each element of the list has the form (s, count) where
        # s is the entity to diff wrt and count is the order of the
        # derivative.
        variable_count = []
        all_zero = True
        i = 0
        while i < len(variables) - 1:  # process up to final Integer
            v, count = variables[i: i + 2]
            iwas = i
            if v._diff_wrt:
                # We need to test the more specific case of count being an
                # Integer first.
                if count.is_Integer:
                    count = int(count)
                    i += 2
                elif count._diff_wrt:
                    count = 1
                    i += 1

            if i == iwas:  # didn't get an update because of bad input
                from sympy.utilities.misc import filldedent
                raise ValueError(filldedent('''
                Can\'t differentiate wrt the variable: %s, %s''' % (v, count)))

            if all_zero and not count == 0:
                all_zero = False

            if count:
                variable_count.append((v, count))

        # We make a special case for 0th derivative, because there is no
        # good way to unambiguously print this.
        if all_zero:
            return expr

        # Pop evaluate because it is not really an assumption and we will need
        # to track it carefully below.
        evaluate = assumptions.pop('evaluate', False)

        # Look for a quick exit if there are symbols that don't appear in
        # expression at all. Note, this cannnot check non-symbols like
        # functions and Derivatives as those can be created by intermediate
        # derivatives.
        if evaluate:
            symbol_set = set(sc[0] for sc in variable_count if sc[0].is_Symbol)
            if symbol_set.difference(expr.free_symbols):
                return S.Zero

        # We make a generator so as to only generate a variable when necessary.
        # If a high order of derivative is requested and the expr becomes 0
        # after a few differentiations, then we won't need the other variables.
        variablegen = (v for v, count in variable_count for i in xrange(count))

        # If we can't compute the derivative of expr (but we wanted to) and
        # expr is itself not a Derivative, finish building an unevaluated
        # derivative class by calling Expr.__new__.
        if (not (hasattr(expr, '_eval_derivative') and evaluate) and
           (not isinstance(expr, Derivative))):
            variables = list(variablegen)
            # If we wanted to evaluate, we sort the variables into standard
            # order for later comparisons. This is too aggressive if evaluate
            # is False, so we don't do it in that case.
            if evaluate:
                #TODO: check if assumption of discontinuous derivatives exist
                variables = cls._sort_variables(variables)
            # Here we *don't* need to reinject evaluate into assumptions
            # because we are done with it and it is not an assumption that
            # Expr knows about.
            obj = Expr.__new__(cls, expr, *variables, **assumptions)
            return obj

        # Compute the derivative now by repeatedly calling the
        # _eval_derivative method of expr for each variable. When this method
        # returns None, the derivative couldn't be computed wrt that variable
        # and we save the variable for later.
        unhandled_variables = []

        # Once we encouter a non_symbol that is unhandled, we stop taking
        # derivatives entirely. This is because derivatives wrt functions
        # don't commute with derivatives wrt symbols and we can't safely
        # continue.
        unhandled_non_symbol = False
        nderivs = 0  # how many derivatives were performed
        for v in variablegen:
            is_symbol = v.is_Symbol

            if unhandled_non_symbol:
                obj = None
            else:
                if not is_symbol:
                    new_v = C.Dummy('xi_%i' % i)
                    new_v.dummy_index = hash(v)
                    expr = expr.subs(v, new_v)
                    old_v = v
                    v = new_v
                obj = expr._eval_derivative(v)
                nderivs += 1
                if not is_symbol:
                    if obj is not None:
                        obj = obj.subs(v, old_v)
                    v = old_v

            if obj is None:
                unhandled_variables.append(v)
                if not is_symbol:
                    unhandled_non_symbol = True
            elif obj is S.Zero:
                return S.Zero
            else:
                expr = obj

        if unhandled_variables:
            unhandled_variables = cls._sort_variables(unhandled_variables)
            expr = Expr.__new__(cls, expr, *unhandled_variables, **assumptions)
        else:
            # We got a Derivative at the end of it all, and we rebuild it by
            # sorting its variables.
            if isinstance(expr, Derivative):
                expr = cls(
                    expr.args[0], *cls._sort_variables(expr.args[1:])
                )

        if nderivs > 1 and assumptions.get('simplify', True):
            from sympy.core.exprtools import factor_terms
            from sympy.simplify.simplify import signsimp
            expr = factor_terms(signsimp(expr))
        return expr

    @classmethod
    def _sort_variables(cls, vars):
        """Sort variables, but disallow sorting of non-symbols.

        When taking derivatives, the following rules usually hold:

        * Derivative wrt different symbols commute.
        * Derivative wrt different non-symbols commute.
        * Derivatives wrt symbols and non-symbols don't commute.

        Examples
        --------

        >>> from sympy import Derivative, Function, symbols
        >>> vsort = Derivative._sort_variables
        >>> x, y, z = symbols('x y z')
        >>> f, g, h = symbols('f g h', cls=Function)

        >>> vsort((x,y,z))
        [x, y, z]

        >>> vsort((h(x),g(x),f(x)))
        [f(x), g(x), h(x)]

        >>> vsort((z,y,x,h(x),g(x),f(x)))
        [x, y, z, f(x), g(x), h(x)]

        >>> vsort((x,f(x),y,f(y)))
        [x, f(x), y, f(y)]

        >>> vsort((y,x,g(x),f(x),z,h(x),y,x))
        [x, y, f(x), g(x), z, h(x), x, y]

        >>> vsort((z,y,f(x),x,f(x),g(x)))
        [y, z, f(x), x, f(x), g(x)]

        >>> vsort((z,y,f(x),x,f(x),g(x),z,z,y,x))
        [y, z, f(x), x, f(x), g(x), x, y, z, z]
        """

        sorted_vars = []
        symbol_part = []
        non_symbol_part = []
        for v in vars:
            if not v.is_Symbol:
                if len(symbol_part) > 0:
                    sorted_vars.extend(sorted(symbol_part,
                                              key=default_sort_key))
                    symbol_part = []
                non_symbol_part.append(v)
            else:
                if len(non_symbol_part) > 0:
                    sorted_vars.extend(sorted(non_symbol_part,
                                              key=default_sort_key))
                    non_symbol_part = []
                symbol_part.append(v)
        if len(non_symbol_part) > 0:
            sorted_vars.extend(sorted(non_symbol_part,
                                      key=default_sort_key))
        if len(symbol_part) > 0:
            sorted_vars.extend(sorted(symbol_part,
                                      key=default_sort_key))
        return sorted_vars

    def _eval_is_commutative(self):
        return self.expr.is_commutative

    def _eval_derivative(self, v):
        # If the variable s we are diff wrt is not in self.variables, we
        # assume that we might be able to take the derivative.
        if v not in self.variables:
            obj = self.expr.diff(v)
            if obj is S.Zero:
                return S.Zero
            if isinstance(obj, Derivative):
                return obj.func(obj.expr, *(self.variables + obj.variables))
            # The derivative wrt s could have simplified things such that the
            # derivative wrt things in self.variables can now be done. Thus,
            # we set evaluate=True to see if there are any other derivatives
            # that can be done. The most common case is when obj is a simple
            # number so that the derivative wrt anything else will vanish.
            return self.func(obj, *self.variables, evaluate=True)
        # In this case s was in self.variables so the derivatve wrt s has
        # already been attempted and was not computed, either because it
        # couldn't be or evaluate=False originally.
        return self.func(self.expr, *(self.variables + (v, )), evaluate=False)

    def doit(self, **hints):
        expr = self.expr
        if hints.get('deep', True):
            expr = expr.doit(**hints)
        hints['evaluate'] = True
        return self.func(expr, *self.variables, **hints)

    @_sympifyit('z0', NotImplementedError)
    def doit_numerically(self, z0):
        """
        Evaluate the derivative at z numerically.

        When we can represent derivatives at a point, this should be folded
        into the normal evalf. For now, we need a special method.
        """
        from sympy import mpmath
        from sympy.core.expr import Expr
        if len(self.free_symbols) != 1 or len(self.variables) != 1:
            raise NotImplementedError('partials and higher order derivatives')
        z = list(self.free_symbols)[0]

        def eval(x):
            f0 = self.expr.subs(z, Expr._from_mpmath(x, prec=mpmath.mp.prec))
            f0 = f0.evalf(mlib.libmpf.prec_to_dps(mpmath.mp.prec))
            return f0._to_mpmath(mpmath.mp.prec)
        return Expr._from_mpmath(mpmath.diff(eval,
                                             z0._to_mpmath(mpmath.mp.prec)),
                                 mpmath.mp.prec)

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

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

    @property
    def free_symbols(self):
        return self.expr.free_symbols

    def _eval_subs(self, old, new):
        if old in self.variables and not new.is_Symbol:
            # Issue 1620
            return Subs(self, old, new)
        # If both are Derivatives with the same expr, check if old is
        # equivalent to self or if old is a subderivative of self.
        if old.is_Derivative and old.expr == self.args[0]:
            # Check if canonnical order of variables is equal.
            old_vars = Derivative._sort_variables(old.variables)
            self_vars = Derivative._sort_variables(self.args[1:])
            if old_vars == self_vars:
                return new

            # Check if olf is a subderivative of self.
            if len(old_vars) < len(self_vars):
                self_vars_front = []
                match = True
                while old_vars and self_vars and match:
                    if old_vars[0] == self_vars[0]:
                        old_vars.pop(0)
                        self_vars.pop(0)
                    else:
                        # If self_v does not match old_v, we need to check if
                        # the types are the same (symbol vs non-symbol). If
                        # they are, we can continue checking self_vars for a
                        # match.
                        if old_vars[0].is_Symbol != self_vars[0].is_Symbol:
                            match = False
                        else:
                            self_vars_front.append(self_vars.pop(0))
                if match:
                    variables = self_vars_front + self_vars
                    return Derivative(new, *variables)
        return Derivative(*map(lambda x: x._subs(old, new), self.args))

    def _eval_lseries(self, x, logx):
        dx = self.args[1:]
        for term in self.args[0].lseries(x, logx=logx):
            yield self.func(term, *dx)

    def _eval_nseries(self, x, n, logx):
        arg = self.args[0].nseries(x, n=n, logx=logx)
        o = arg.getO()
        dx = self.args[1:]
        rv = [self.func(a, *dx) for a in Add.make_args(arg.removeO())]
        if o:
            rv.append(o/x)
        return Add(*rv)

    def _eval_as_leading_term(self, x):
        return self.args[0].as_leading_term(x)


class Lambda(Expr):
    """
    Lambda(x, expr) represents a lambda function similar to Python's
    'lambda x: expr'. A function of several variables is written as
    Lambda((x, y, ...), expr).

    A simple example:

    >>> from sympy import Lambda
    >>> from sympy.abc import x
    >>> f = Lambda(x, x**2)
    >>> f(4)
    16

    For multivariate functions, use:

    >>> from sympy.abc import y, z, t
    >>> f2 = Lambda((x, y, z, t), x + y**z + t**z)
    >>> f2(1, 2, 3, 4)
    73

    A handy shortcut for lots of arguments:

    >>> p = x, y, z
    >>> f = Lambda(p, x + y*z)
    >>> f(*p)
    x + y*z

    """
    is_Function = True

    def __new__(cls, variables, expr):
        from sympy.core.sets import FiniteSet
        try:
            for v in variables if iterable(variables) else [variables]:
                if not v.is_Symbol:
                    raise TypeError("v is not a symbol")
        except (AssertionError, AttributeError):
            raise ValueError('variable is not a Symbol: %s' % v)
        try:
            variables = Tuple(*variables)
        except TypeError:
            variables = Tuple(variables)
        if len(variables) == 1 and variables[0] == expr:
            return S.IdentityFunction

        obj = Expr.__new__(cls, Tuple(*variables), S(expr))
        obj.nargs = FiniteSet(len(variables))
        return obj

    @property
    def variables(self):
        """The variables used in the internal representation of the function"""
        return self._args[0]

    @property
    def expr(self):
        """The return value of the function"""
        return self._args[1]

    @property
    def free_symbols(self):
        return self.expr.free_symbols - set(self.variables)

    def __call__(self, *args):
        from sympy.core.sets import FiniteSet
        n = len(args)
        if n not in self.nargs:  # Lambda only ever has 1 value in nargs
            # XXX: exception message must be in exactly this format to
            # make it work with NumPy's functions like vectorize(). See,
            # for example, https://github.com/numpy/numpy/issues/1697.
            # The ideal solution would be just to attach metadata to
            # the exception and change NumPy to take advantage of this.
            ## XXX does this apply to Lambda? If not, remove this comment.
            temp = ('%(name)s takes exactly %(args)s '
                   'argument%(plural)s (%(given)s given)')
            raise TypeError(temp % {
                'name': self,
                'args': list(self.nargs)[0],
                'plural': 's'*(list(self.nargs)[0] != 1),
                'given': n})
        return self.expr.xreplace(dict(list(zip(self.variables, args))))

    def __eq__(self, other):
        if not isinstance(other, Lambda):
            return False
        if self.nargs != other.nargs:
            return False

        selfexpr = self.args[1]
        otherexpr = other.args[1]
        otherexpr = otherexpr.xreplace(dict(list(zip(other.args[0], self.args[0]))))
        return selfexpr == otherexpr

    def __ne__(self, other):
        return not(self == other)

    def __hash__(self):
        return super(Lambda, self).__hash__()

    def _hashable_content(self):
        return (self.expr.xreplace(self.canonical_variables),)

    @property
    def is_identity(self):
        """Return ``True`` if this ``Lambda`` is an identity function. """
        if len(self.args) == 2:
            return self.args[0] == self.args[1]
        else:
            return None


class Subs(Expr):
    """
    Represents unevaluated substitutions of an expression.

    ``Subs(expr, x, x0)`` receives 3 arguments: an expression, a variable or
    list of distinct variables and a point or list of evaluation points
    corresponding to those variables.

    ``Subs`` objects are generally useful to represent unevaluated derivatives
    calculated at a point.

    The variables may be expressions, but they are subjected to the limitations
    of subs(), so it is usually a good practice to use only symbols for
    variables, since in that case there can be no ambiguity.

    There's no automatic expansion - use the method .doit() to effect all
    possible substitutions of the object and also of objects inside the
    expression.

    When evaluating derivatives at a point that is not a symbol, a Subs object
    is returned. One is also able to calculate derivatives of Subs objects - in
    this case the expression is always expanded (for the unevaluated form, use
    Derivative()).

    A simple example:

    >>> from sympy import Subs, Function, sin
    >>> from sympy.abc import x, y, z
    >>> f = Function('f')
    >>> e = Subs(f(x).diff(x), x, y)
    >>> e.subs(y, 0)
    Subs(Derivative(f(x), x), (x,), (0,))
    >>> e.subs(f, sin).doit()
    cos(y)

    An example with several variables:

    >>> Subs(f(x)*sin(y) + z, (x, y), (0, 1))
    Subs(z + f(x)*sin(y), (x, y), (0, 1))
    >>> _.doit()
    z + f(0)*sin(1)

    """
    def __new__(cls, expr, variables, point, **assumptions):
        from sympy import Symbol
        if not is_sequence(variables, Tuple):
            variables = [variables]
        variables = list(sympify(variables))

        if list(uniq(variables)) != variables:
            repeated = [ v for v in set(variables) if variables.count(v) > 1 ]
            raise ValueError('cannot substitute expressions %s more than '
                             'once.' % repeated)

        point = Tuple(*(point if is_sequence(point, Tuple) else [point]))

        if len(point) != len(variables):
            raise ValueError('Number of point values must be the same as '
                             'the number of variables.')

        expr = sympify(expr)

        # use symbols with names equal to the point value (with preppended _)
        # to give a variable-independent expression
        pre = "_"
        pts = sorted(set(point), key=default_sort_key)
        while 1:
            s_pts = dict([(p, Symbol(pre + str(p))) for p in pts])
            reps = [(v, s_pts[p])
                for v, p in zip(variables, point)]
            # if any underscore-preppended symbol is already a free symbol
            # and is a variable with a different point value, then there
            # is a clash, e.g. _0 clashes in Subs(_0 + _1, (_0, _1), (1, 0))
            # because the new symbol that would be created is _1 but _1
            # is already mapped to 0 so __0 and __1 are used for the new
            # symbols
            if any(r in expr.free_symbols and
                   r in variables and
                   Symbol(pre + str(point[variables.index(r)])) != r
                   for _, r in reps):
                pre += "_"
                continue
            break

        obj = Expr.__new__(cls, expr, Tuple(*variables), point)
        obj._expr = expr.subs(reps)
        return obj

    def _eval_is_commutative(self):
        return self.expr.is_commutative

    def doit(self):
        return self.expr.doit().subs(list(zip(self.variables, self.point)))

    def evalf(self, prec=None, **options):
        if prec is None:
            return self.doit().evalf(**options)
        else:
            return self.doit().evalf(prec, **options)

    n = evalf

    @property
    def variables(self):
        """The variables to be evaluated"""
        return self._args[1]

    @property
    def expr(self):
        """The expression on which the substitution operates"""
        return self._args[0]

    @property
    def point(self):
        """The values for which the variables are to be substituted"""
        return self._args[2]

    @property
    def free_symbols(self):
        return (self.expr.free_symbols - set(self.variables) |
            set(self.point.free_symbols))

    def __eq__(self, other):
        if not isinstance(other, Subs):
            return False
        return self._expr == other._expr

    def __ne__(self, other):
        return not(self == other)

    def __hash__(self):
        return super(Subs, self).__hash__()

    def _hashable_content(self):
        return (self._expr.xreplace(self.canonical_variables),)

    def _eval_subs(self, old, new):
        if old in self.variables:
            pts = list(self.point.args)
            pts[self.variables.index(old)] = new
            return self.func(self.expr, self.variables, pts)

    def _eval_derivative(self, s):
        if s not in self.free_symbols:
            return S.Zero
        return self.func(self.expr.diff(s), self.variables, self.point).doit() \
            + Add(*[ Subs(point.diff(s) * self.expr.diff(arg),
                    self.variables, self.point).doit() for arg,
                    point in zip(self.variables, self.point) ])


def diff(f, *symbols, **kwargs):
    """
    Differentiate f with respect to symbols.

    This is just a wrapper to unify .diff() and the Derivative class; its
    interface is similar to that of integrate().  You can use the same
    shortcuts for multiple variables as with Derivative.  For example,
    diff(f(x), x, x, x) and diff(f(x), x, 3) both return the third derivative
    of f(x).

    You can pass evaluate=False to get an unevaluated Derivative class.  Note
    that if there are 0 symbols (such as diff(f(x), x, 0), then the result will
    be the function (the zeroth derivative), even if evaluate=False.

    Examples
    ========

    >>> from sympy import sin, cos, Function, diff
    >>> from sympy.abc import x, y
    >>> f = Function('f')

    >>> diff(sin(x), x)
    cos(x)
    >>> diff(f(x), x, x, x)
    Derivative(f(x), x, x, x)
    >>> diff(f(x), x, 3)
    Derivative(f(x), x, x, x)
    >>> diff(sin(x)*cos(y), x, 2, y, 2)
    sin(x)*cos(y)

    >>> type(diff(sin(x), x))
    cos
    >>> type(diff(sin(x), x, evaluate=False))
    <class 'sympy.core.function.Derivative'>
    >>> type(diff(sin(x), x, 0))
    sin
    >>> type(diff(sin(x), x, 0, evaluate=False))
    sin

    >>> diff(sin(x))
    cos(x)
    >>> diff(sin(x*y))
    Traceback (most recent call last):
    ...
    ValueError: specify differentiation variables to differentiate sin(x*y)

    Note that ``diff(sin(x))`` syntax is meant only for convenience
    in interactive sessions and should be avoided in library code.

    References
    ==========

    http://reference.wolfram.com/legacy/v5_2/Built-inFunctions/AlgebraicComputation/Calculus/D.html

    See Also
    ========

    Derivative
    sympy.geometry.util.idiff: computes the derivative implicitly

    """
    kwargs.setdefault('evaluate', True)
    return Derivative(f, *symbols, **kwargs)


def expand(e, deep=True, modulus=None, power_base=True, power_exp=True,
        mul=True, log=True, multinomial=True, basic=True, **hints):
    """
    Expand an expression using methods given as hints.

    Hints evaluated unless explicitly set to False are:  ``basic``, ``log``,
    ``multinomial``, ``mul``, ``power_base``, and ``power_exp`` The following
    hints are supported but not applied unless set to True:  ``complex``,
    ``func``, and ``trig``.  In addition, the following meta-hints are
    supported by some or all of the other hints:  ``frac``, ``numer``,
    ``denom``, ``modulus``, and ``force``.  ``deep`` is supported by all
    hints.  Additionally, subclasses of Expr may define their own hints or
    meta-hints.

    The ``basic`` hint is used for any special rewriting of an object that
    should be done automatically (along with the other hints like ``mul``)
    when expand is called. This is a catch-all hint to handle any sort of
    expansion that may not be described by the existing hint names. To use
    this hint an object should override the ``_eval_expand_basic`` method.
    Objects may also define their own expand methods, which are not run by
    default.  See the API section below.

    If ``deep`` is set to ``True`` (the default), things like arguments of
    functions are recursively expanded.  Use ``deep=False`` to only expand on
    the top level.

    If the ``force`` hint is used, assumptions about variables will be ignored
    in making the expansion.

    Hints
    =====

    These hints are run by default

    mul
    ---

    Distributes multiplication over addition:

    >>> from sympy import cos, exp, sin
    >>> from sympy.abc import x, y, z
    >>> (y*(x + z)).expand(mul=True)
    x*y + y*z

    multinomial
    -----------

    Expand (x + y + ...)**n where n is a positive integer.

    >>> ((x + y + z)**2).expand(multinomial=True)
    x**2 + 2*x*y + 2*x*z + y**2 + 2*y*z + z**2

    power_exp
    ---------

    Expand addition in exponents into multiplied bases.

    >>> exp(x + y).expand(power_exp=True)
    exp(x)*exp(y)
    >>> (2**(x + y)).expand(power_exp=True)
    2**x*2**y

    power_base
    ----------

    Split powers of multiplied bases.

    This only happens by default if assumptions allow, or if the
    ``force`` meta-hint is used:

    >>> ((x*y)**z).expand(power_base=True)
    (x*y)**z
    >>> ((x*y)**z).expand(power_base=True, force=True)
    x**z*y**z
    >>> ((2*y)**z).expand(power_base=True)
    2**z*y**z

    Note that in some cases where this expansion always holds, SymPy performs
    it automatically:

    >>> (x*y)**2
    x**2*y**2

    log
    ---

    Pull out power of an argument as a coefficient and split logs products
    into sums of logs.

    Note that these only work if the arguments of the log function have the
    proper assumptions--the arguments must be positive and the exponents must
    be real--or else the ``force`` hint must be True:

    >>> from sympy import log, symbols
    >>> log(x**2*y).expand(log=True)
    log(x**2*y)
    >>> log(x**2*y).expand(log=True, force=True)
    2*log(x) + log(y)
    >>> x, y = symbols('x,y', positive=True)
    >>> log(x**2*y).expand(log=True)
    2*log(x) + log(y)

    basic
    -----

    This hint is intended primarily as a way for custom subclasses to enable
    expansion by default.

    These hints are not run by default:

    complex
    -------

    Split an expression into real and imaginary parts.

    >>> x, y = symbols('x,y')
    >>> (x + y).expand(complex=True)
    re(x) + re(y) + I*im(x) + I*im(y)
    >>> cos(x).expand(complex=True)
    -I*sin(re(x))*sinh(im(x)) + cos(re(x))*cosh(im(x))

    Note that this is just a wrapper around ``as_real_imag()``.  Most objects
    that wish to redefine ``_eval_expand_complex()`` should consider
    redefining ``as_real_imag()`` instead.

    func
    ----

    Expand other functions.

    >>> from sympy import gamma
    >>> gamma(x + 1).expand(func=True)
    x*gamma(x)

    trig
    ----

    Do trigonometric expansions.

    >>> cos(x + y).expand(trig=True)
    -sin(x)*sin(y) + cos(x)*cos(y)
    >>> sin(2*x).expand(trig=True)
    2*sin(x)*cos(x)

    Note that the forms of ``sin(n*x)`` and ``cos(n*x)`` in terms of ``sin(x)``
    and ``cos(x)`` are not unique, due to the identity `\sin^2(x) + \cos^2(x)
    = 1`.  The current implementation uses the form obtained from Chebyshev
    polynomials, but this may change.  See `this MathWorld article
    <http://mathworld.wolfram.com/Multiple-AngleFormulas.html>`_ for more
    information.

    Notes
    =====

    - You can shut off unwanted methods::

        >>> (exp(x + y)*(x + y)).expand()
        x*exp(x)*exp(y) + y*exp(x)*exp(y)
        >>> (exp(x + y)*(x + y)).expand(power_exp=False)
        x*exp(x + y) + y*exp(x + y)
        >>> (exp(x + y)*(x + y)).expand(mul=False)
        (x + y)*exp(x)*exp(y)

    - Use deep=False to only expand on the top level::

        >>> exp(x + exp(x + y)).expand()
        exp(x)*exp(exp(x)*exp(y))
        >>> exp(x + exp(x + y)).expand(deep=False)
        exp(x)*exp(exp(x + y))

    - Hints are applied in an arbitrary, but consistent order (in the current
      implementation, they are applied in alphabetical order, except
      multinomial comes before mul, but this may change).  Because of this,
      some hints may prevent expansion by other hints if they are applied
      first. For example, ``mul`` may distribute multiplications and prevent
      ``log`` and ``power_base`` from expanding them. Also, if ``mul`` is
      applied before ``multinomial`, the expression might not be fully
      distributed. The solution is to use the various ``expand_hint`` helper
      functions or to use ``hint=False`` to this function to finely control
      which hints are applied. Here are some examples::

        >>> from sympy import expand, expand_mul, expand_power_base
        >>> x, y, z = symbols('x,y,z', positive=True)

        >>> expand(log(x*(y + z)))
        log(x) + log(y + z)

      Here, we see that ``log`` was applied before ``mul``.  To get the mul
      expanded form, either of the following will work::

        >>> expand_mul(log(x*(y + z)))
        log(x*y + x*z)
        >>> expand(log(x*(y + z)), log=False)
        log(x*y + x*z)

      A similar thing can happen with the ``power_base`` hint::

        >>> expand((x*(y + z))**x)
        (x*y + x*z)**x

      To get the ``power_base`` expanded form, either of the following will
      work::

        >>> expand((x*(y + z))**x, mul=False)
        x**x*(y + z)**x
        >>> expand_power_base((x*(y + z))**x)
        x**x*(y + z)**x

        >>> expand((x + y)*y/x)
        y + y**2/x

      The parts of a rational expression can be targeted::

        >>> expand((x + y)*y/x/(x + 1), frac=True)
        (x*y + y**2)/(x**2 + x)
        >>> expand((x + y)*y/x/(x + 1), numer=True)
        (x*y + y**2)/(x*(x + 1))
        >>> expand((x + y)*y/x/(x + 1), denom=True)
        y*(x + y)/(x**2 + x)

    - The ``modulus`` meta-hint can be used to reduce the coefficients of an
      expression post-expansion::

        >>> expand((3*x + 1)**2)
        9*x**2 + 6*x + 1
        >>> expand((3*x + 1)**2, modulus=5)
        4*x**2 + x + 1

    - Either ``expand()`` the function or ``.expand()`` the method can be
      used.  Both are equivalent::

        >>> expand((x + 1)**2)
        x**2 + 2*x + 1
        >>> ((x + 1)**2).expand()
        x**2 + 2*x + 1

    API
    ===

    Objects can define their own expand hints by defining
    ``_eval_expand_hint()``.  The function should take the form::

        def _eval_expand_hint(self, **hints):
            # Only apply the method to the top-level expression
            ...

    See also the example below.  Objects should define ``_eval_expand_hint()``
    methods only if ``hint`` applies to that specific object.  The generic
    ``_eval_expand_hint()`` method defined in Expr will handle the no-op case.

    Each hint should be responsible for expanding that hint only.
    Furthermore, the expansion should be applied to the top-level expression
    only.  ``expand()`` takes care of the recursion that happens when
    ``deep=True``.

    You should only call ``_eval_expand_hint()`` methods directly if you are
    100% sure that the object has the method, as otherwise you are liable to
    get unexpected ``AttributeError``s.  Note, again, that you do not need to
    recursively apply the hint to args of your object: this is handled
    automatically by ``expand()``.  ``_eval_expand_hint()`` should
    generally not be used at all outside of an ``_eval_expand_hint()`` method.
    If you want to apply a specific expansion from within another method, use
    the public ``expand()`` function, method, or ``expand_hint()`` functions.

    In order for expand to work, objects must be rebuildable by their args,
    i.e., ``obj.func(*obj.args) == obj`` must hold.

    Expand methods are passed ``**hints`` so that expand hints may use
    'metahints'--hints that control how different expand methods are applied.
    For example, the ``force=True`` hint described above that causes
    ``expand(log=True)`` to ignore assumptions is such a metahint.  The
    ``deep`` meta-hint is handled exclusively by ``expand()`` and is not
    passed to ``_eval_expand_hint()`` methods.

    Note that expansion hints should generally be methods that perform some
    kind of 'expansion'.  For hints that simply rewrite an expression, use the
    .rewrite() API.

    Example
    -------

    >>> from sympy import Expr, sympify
    >>> class MyClass(Expr):
    ...     def __new__(cls, *args):
    ...         args = sympify(args)
    ...         return Expr.__new__(cls, *args)
    ...
    ...     def _eval_expand_double(self, **hints):
    ...         '''
    ...         Doubles the args of MyClass.
    ...
    ...         If there more than four args, doubling is not performed,
    ...         unless force=True is also used (False by default).
    ...         '''
    ...         force = hints.pop('force', False)
    ...         if not force and len(self.args) > 4:
    ...             return self
    ...         return self.func(*(self.args + self.args))
    ...
    >>> a = MyClass(1, 2, MyClass(3, 4))
    >>> a
    MyClass(1, 2, MyClass(3, 4))
    >>> a.expand(double=True)
    MyClass(1, 2, MyClass(3, 4, 3, 4), 1, 2, MyClass(3, 4, 3, 4))
    >>> a.expand(double=True, deep=False)
    MyClass(1, 2, MyClass(3, 4), 1, 2, MyClass(3, 4))

    >>> b = MyClass(1, 2, 3, 4, 5)
    >>> b.expand(double=True)
    MyClass(1, 2, 3, 4, 5)
    >>> b.expand(double=True, force=True)
    MyClass(1, 2, 3, 4, 5, 1, 2, 3, 4, 5)

    See Also
    ========

    expand_log, expand_mul, expand_multinomial, expand_complex, expand_trig,
    expand_power_base, expand_power_exp, expand_func, hyperexpand

    """
    # don't modify this; modify the Expr.expand method
    hints['power_base'] = power_base
    hints['power_exp'] = power_exp
    hints['mul'] = mul
    hints['log'] = log
    hints['multinomial'] = multinomial
    hints['basic'] = basic
    return sympify(e).expand(deep=deep, modulus=modulus, **hints)

# These are simple wrappers around single hints.


def expand_mul(expr, deep=True):
    """
    Wrapper around expand that only uses the mul hint.  See the expand
    docstring for more information.

    Examples
    ========

    >>> from sympy import symbols, expand_mul, exp, log
    >>> x, y = symbols('x,y', positive=True)
    >>> expand_mul(exp(x+y)*(x+y)*log(x*y**2))
    x*exp(x + y)*log(x*y**2) + y*exp(x + y)*log(x*y**2)

    """
    return sympify(expr).expand(deep=deep, mul=True, power_exp=False,
    power_base=False, basic=False, multinomial=False, log=False)


def expand_multinomial(expr, deep=True):
    """
    Wrapper around expand that only uses the multinomial hint.  See the expand
    docstring for more information.

    Examples
    ========

    >>> from sympy import symbols, expand_multinomial, exp
    >>> x, y = symbols('x y', positive=True)
    >>> expand_multinomial((x + exp(x + 1))**2)
    x**2 + 2*x*exp(x + 1) + exp(2*x + 2)

    """
    return sympify(expr).expand(deep=deep, mul=False, power_exp=False,
    power_base=False, basic=False, multinomial=True, log=False)


def expand_log(expr, deep=True, force=False):
    """
    Wrapper around expand that only uses the log hint.  See the expand
    docstring for more information.

    Examples
    ========

    >>> from sympy import symbols, expand_log, exp, log
    >>> x, y = symbols('x,y', positive=True)
    >>> expand_log(exp(x+y)*(x+y)*log(x*y**2))
    (x + y)*(log(x) + 2*log(y))*exp(x + y)

    """
    return sympify(expr).expand(deep=deep, log=True, mul=False,
        power_exp=False, power_base=False, multinomial=False,
        basic=False, force=force)


def expand_func(expr, deep=True):
    """
    Wrapper around expand that only uses the func hint.  See the expand
    docstring for more information.

    Examples
    ========

    >>> from sympy import expand_func, gamma
    >>> from sympy.abc import x
    >>> expand_func(gamma(x + 2))
    x*(x + 1)*gamma(x)

    """
    return sympify(expr).expand(deep=deep, func=True, basic=False,
    log=False, mul=False, power_exp=False, power_base=False, multinomial=False)


def expand_trig(expr, deep=True):
    """
    Wrapper around expand that only uses the trig hint.  See the expand
    docstring for more information.

    Examples
    ========

    >>> from sympy import expand_trig, sin
    >>> from sympy.abc import x, y
    >>> expand_trig(sin(x+y)*(x+y))
    (x + y)*(sin(x)*cos(y) + sin(y)*cos(x))

    """
    return sympify(expr).expand(deep=deep, trig=True, basic=False,
    log=False, mul=False, power_exp=False, power_base=False, multinomial=False)


def expand_complex(expr, deep=True):
    """
    Wrapper around expand that only uses the complex hint.  See the expand
    docstring for more information.

    Examples
    ========

    >>> from sympy import expand_complex, exp, sqrt, I
    >>> from sympy.abc import z
    >>> expand_complex(exp(z))
    I*exp(re(z))*sin(im(z)) + exp(re(z))*cos(im(z))
    >>> expand_complex(sqrt(I))
    sqrt(2)/2 + sqrt(2)*I/2

    See Also
    ========
    Expr.as_real_imag
    """
    return sympify(expr).expand(deep=deep, complex=True, basic=False,
    log=False, mul=False, power_exp=False, power_base=False, multinomial=False)


def expand_power_base(expr, deep=True, force=False):
    """
    Wrapper around expand that only uses the power_base hint.

    See the expand docstring for more information.

    A wrapper to expand(power_base=True) which separates a power with a base
    that is a Mul into a product of powers, without performing any other
    expansions, provided that assumptions about the power's base and exponent
    allow.

    deep=False (default is True) will only apply to the top-level expression.

    force=True (default is False) will cause the expansion to ignore
    assumptions about the base and exponent. When False, the expansion will
    only happen if the base is non-negative or the exponent is an integer.

    >>> from sympy.abc import x, y, z
    >>> from sympy import expand_power_base, sin, cos, exp

    >>> (x*y)**2
    x**2*y**2

    >>> (2*x)**y
    (2*x)**y
    >>> expand_power_base(_)
    2**y*x**y

    >>> expand_power_base((x*y)**z)
    (x*y)**z
    >>> expand_power_base((x*y)**z, force=True)
    x**z*y**z
    >>> expand_power_base(sin((x*y)**z), deep=False)
    sin((x*y)**z)
    >>> expand_power_base(sin((x*y)**z), force=True)
    sin(x**z*y**z)

    >>> expand_power_base((2*sin(x))**y + (2*cos(x))**y)
    2**y*sin(x)**y + 2**y*cos(x)**y

    >>> expand_power_base((2*exp(y))**x)
    2**x*exp(y)**x

    >>> expand_power_base((2*cos(x))**y)
    2**y*cos(x)**y

    Notice that sums are left untouched. If this is not the desired behavior,
    apply full ``expand()`` to the expression:

    >>> expand_power_base(((x+y)*z)**2)
    z**2*(x + y)**2
    >>> (((x+y)*z)**2).expand()
    x**2*z**2 + 2*x*y*z**2 + y**2*z**2

    >>> expand_power_base((2*y)**(1+z))
    2**(z + 1)*y**(z + 1)
    >>> ((2*y)**(1+z)).expand()
    2*2**z*y*y**z

    """
    return sympify(expr).expand(deep=deep, log=False, mul=False,
        power_exp=False, power_base=True, multinomial=False,
        basic=False, force=force)


def expand_power_exp(expr, deep=True):
    """
    Wrapper around expand that only uses the power_exp hint.

    See the expand docstring for more information.

    Examples
    ========

    >>> from sympy import expand_power_exp
    >>> from sympy.abc import x, y
    >>> expand_power_exp(x**(y + 2))
    x**2*x**y
    """
    return sympify(expr).expand(deep=deep, complex=False, basic=False,
    log=False, mul=False, power_exp=True, power_base=False, multinomial=False)


def count_ops(expr, visual=False):
    """
    Return a representation (integer or expression) of the operations in expr.

    If ``visual`` is ``False`` (default) then the sum of the coefficients of the
    visual expression will be returned.

    If ``visual`` is ``True`` then the number of each type of operation is shown
    with the core class types (or their virtual equivalent) multiplied by the
    number of times they occur.

    If expr is an iterable, the sum of the op counts of the
    items will be returned.

    Examples
    ========

    >>> from sympy.abc import a, b, x, y
    >>> from sympy import sin, count_ops

    Although there isn't a SUB object, minus signs are interpreted as
    either negations or subtractions:

    >>> (x - y).count_ops(visual=True)
    SUB
    >>> (-x).count_ops(visual=True)
    NEG

    Here, there are two Adds and a Pow:

    >>> (1 + a + b**2).count_ops(visual=True)
    2*ADD + POW

    In the following, an Add, Mul, Pow and two functions:

    >>> (sin(x)*x + sin(x)**2).count_ops(visual=True)
    ADD + MUL + POW + 2*SIN

    for a total of 5:

    >>> (sin(x)*x + sin(x)**2).count_ops(visual=False)
    5

    Note that "what you type" is not always what you get. The expression
    1/x/y is translated by sympy into 1/(x*y) so it gives a DIV and MUL rather
    than two DIVs:

    >>> (1/x/y).count_ops(visual=True)
    DIV + MUL

    The visual option can be used to demonstrate the difference in
    operations for expressions in different forms. Here, the Horner
    representation is compared with the expanded form of a polynomial:

    >>> eq=x*(1 + x*(2 + x*(3 + x)))
    >>> count_ops(eq.expand(), visual=True) - count_ops(eq, visual=True)
    -MUL + 3*POW

    The count_ops function also handles iterables:

    >>> count_ops([x, sin(x), None, True, x + 2], visual=False)
    2
    >>> count_ops([x, sin(x), None, True, x + 2], visual=True)
    ADD + SIN
    >>> count_ops({x: sin(x), x + 2: y + 1}, visual=True)
    2*ADD + SIN

    """
    from sympy.simplify.simplify import fraction

    expr = sympify(expr)
    if isinstance(expr, Expr):

        ops = []
        args = [expr]
        NEG = C.Symbol('NEG')
        DIV = C.Symbol('DIV')
        SUB = C.Symbol('SUB')
        ADD = C.Symbol('ADD')
        while args:
            a = args.pop()
            if a.is_Rational:
                #-1/3 = NEG + DIV
                if a is not S.One:
                    if a.p < 0:
                        ops.append(NEG)
                    if a.q != 1:
                        ops.append(DIV)
                    continue
            elif a.is_Mul:
                if _coeff_isneg(a):
                    ops.append(NEG)
                    if a.args[0] is S.NegativeOne:
                        a = a.as_two_terms()[1]
                    else:
                        a = -a
                n, d = fraction(a)
                if n.is_Integer:
                    ops.append(DIV)
                    if n < 0:
                        ops.append(NEG)
                    args.append(d)
                    continue  # won't be -Mul but could be Add
                elif d is not S.One:
                    if not d.is_Integer:
                        args.append(d)
                    ops.append(DIV)
                    args.append(n)
                    continue  # could be -Mul
            elif a.is_Add:
                aargs = list(a.args)
                negs = 0
                for i, ai in enumerate(aargs):
                    if _coeff_isneg(ai):
                        negs += 1
                        args.append(-ai)
                        if i > 0:
                            ops.append(SUB)
                    else:
                        args.append(ai)
                        if i > 0:
                            ops.append(ADD)
                if negs == len(aargs):  # -x - y = NEG + SUB
                    ops.append(NEG)
                elif _coeff_isneg(aargs[0]):  # -x + y = SUB, but already recorded ADD
                    ops.append(SUB - ADD)
                continue
            if a.is_Pow and a.exp is S.NegativeOne:
                ops.append(DIV)
                args.append(a.base)  # won't be -Mul but could be Add
                continue
            if (a.is_Mul or
                a.is_Pow or
                a.is_Function or
                isinstance(a, Derivative) or
                    isinstance(a, C.Integral)):

                o = C.Symbol(a.func.__name__.upper())
                # count the args
                if (a.is_Mul or isinstance(a, C.LatticeOp)):
                    ops.append(o*(len(a.args) - 1))
                else:
                    ops.append(o)
            if not a.is_Symbol:
                args.extend(a.args)

    elif type(expr) is dict:
        ops = [count_ops(k, visual=visual) +
               count_ops(v, visual=visual) for k, v in expr.items()]
    elif iterable(expr):
        ops = [count_ops(i, visual=visual) for i in expr]
    elif not isinstance(expr, Basic):
        ops = []
    else:  # it's Basic not isinstance(expr, Expr):
        if not isinstance(expr, Basic):
            raise TypeError("Invalid type of expr")
        ops = [count_ops(a, visual=visual) for a in expr.args]

    if not ops:
        if visual:
            return S.Zero
        return 0

    ops = Add(*ops)

    if visual:
        return ops

    if ops.is_Number:
        return int(ops)

    return sum(int((a.args or [1])[0]) for a in Add.make_args(ops))


def nfloat(expr, n=15, exponent=False):
    """Make all Rationals in expr Floats except those in exponents
    (unless the exponents flag is set to True).

    Examples
    ========

    >>> from sympy.core.function import nfloat
    >>> from sympy.abc import x, y
    >>> from sympy import cos, pi, sqrt
    >>> nfloat(x**4 + x/2 + cos(pi/3) + 1 + sqrt(y))
    x**4 + 0.5*x + sqrt(y) + 1.5
    >>> nfloat(x**4 + sqrt(y), exponent=True)
    x**4.0 + y**0.5

    """
    from sympy.core.power import Pow
    from sympy.polys.rootoftools import RootOf

    if iterable(expr, exclude=string_types):
        if isinstance(expr, (dict, Dict)):
            return type(expr)([(k, nfloat(v, n, exponent)) for k, v in
                               list(expr.items())])
        return type(expr)([nfloat(a, n, exponent) for a in expr])
    rv = sympify(expr)

    if rv.is_Number:
        return Float(rv, n)
    elif rv.is_number:
        # evalf doesn't always set the precision
        rv = rv.n(n)
        if rv.is_Number:
            rv = Float(rv.n(n), n)
        else:
            pass  # pure_complex(rv) is likely True
        return rv

    # watch out for RootOf instances that don't like to have
    # their exponents replaced with Dummies and also sometimes have
    # problems with evaluating at low precision (issue 3294)
    rv = rv.xreplace(dict([(ro, ro.n(n)) for ro in rv.atoms(RootOf)]))

    if not exponent:
        reps = [(p, Pow(p.base, Dummy())) for p in rv.atoms(Pow)]
        rv = rv.xreplace(dict(reps))
    rv = rv.n(n)
    if not exponent:
        rv = rv.xreplace(dict([(d.exp, p.exp) for p, d in reps]))
    else:
        # Pow._eval_evalf special cases Integer exponents so if
        # exponent is suppose to be handled we have to do so here
        rv = rv.xreplace(Transform(
            lambda x: Pow(x.base, Float(x.exp, n)),
            lambda x: x.is_Pow and x.exp.is_Integer))

    return rv.xreplace(Transform(
        lambda x: x.func(*nfloat(x.args, n, exponent)),
        lambda x: isinstance(x, Function)))


from sympy.core.symbol import Dummy
