Python basic.preorder_traversal函数代码示例

def test_preorder_traversal():
    expr = Basic(b21, b3)
    assert list(preorder_traversal(expr)) == [expr, b21, b2, b1, b1, b3, b2, b1]
    assert list(preorder_traversal(("abc", ("d", "ef")))) == [("abc", ("d", "ef")), "abc", ("d", "ef"), "d", "ef"]

    result = []
    pt = preorder_traversal(expr)
    for i in pt:
        result.append(i)
        if i == b2:
            pt.skip()
    assert result == [expr, b21, b2, b1, b3, b2]

    w, x, y, z = symbols("w:z")
    expr = z + w * (x + y)
    assert list(preorder_traversal([expr], key=default_sort_key)) == [
        [w * (x + y) + z],
        w * (x + y) + z,
        z,
        w * (x + y),
        w,
        x + y,
        x,
        y,
    ]

def test_preorder_traversal():
    expr = Basic(b21, b3)
    assert list(preorder_traversal(expr)) == [expr, b21, b2, b1, b1, b3, b2, b1]
    assert list(preorder_traversal(('abc', ('d', 'ef')))) == [
        ('abc', ('d', 'ef')), 'abc', ('d', 'ef'), 'd', 'ef']

    result = []
    pt = preorder_traversal(expr)
    for i in pt:
        result.append(i)
        if i == b2:
            pt.skip()
    assert result == [expr, b21, b2, b1, b3, b2]

def test_preorder_traversal():
    expr = Basic(b21, b3)
    assert list(preorder_traversal(expr)) == [expr, b21, b2, b1, b1, b3, b2, b1]
    assert list(preorder_traversal(('abc', ('d', 'ef')))) == [
        ('abc', ('d', 'ef')), 'abc', ('d', 'ef'), 'd', 'ef']

    result = []
    pt = preorder_traversal(expr)
    for i in pt:
        result.append(i)
        if i == b2:
            pt.skip()
    assert result == [expr, b21, b2, b1, b3, b2]

    w, x, y, z = symbols('w:z')
    expr = z + w*(x+y)
    assert list(preorder_traversal([expr], key=default_sort_key)) == \
        [[w*(x + y) + z], w*(x + y) + z, z, w*(x + y), w, x + y, x, y]

def sub_post(e):
    """ Replace Neg(x) with -x.
    """
    replacements = []
    for node in preorder_traversal(e):
        if isinstance(node, Neg):
            replacements.append((node, -node.args[0]))
    for node, replacement in replacements:
        e = e.xreplace({node: replacement})

    return e

def sub_post(e):
    """ Replace Neg(x) with -x.
    """
    replacements = []
    for node in preorder_traversal(e):
        if isinstance(node, Mul) and node.args[:2] is (S.One, S.NegativeOne):
            replacements.append((node, -node))
    for node, replacement in replacements:
        e = e.xreplace({node: replacement})

    return e

def sub_post(e):
    """ Replace 1*-1*x with -x.
    """
    replacements = []
    for node in preorder_traversal(e):
        if isinstance(node, Mul) and \
            node.args[0] is S.One and node.args[1] is S.NegativeOne:
            replacements.append((node, -Mul._from_args(node.args[2:])))
    for node, replacement in replacements:
        e = e.xreplace({node: replacement})

    return e

def combsimp(expr):
    r"""
    Simplify combinatorial expressions.

    This function takes as input an expression containing factorials,
    binomials, Pochhammer symbol and other "combinatorial" functions,
    and tries to minimize the number of those functions and reduce
    the size of their arguments.

    The algorithm works by rewriting all combinatorial functions as
    gamma functions and applying gammasimp() except simplification
    steps that may make an integer argument non-integer. See docstring
    of gammasimp for more information.

    Then it rewrites expression in terms of factorials and binomials by
    rewriting gammas as factorials and converting (a+b)!/a!b! into
    binomials.

    If expression has gamma functions or combinatorial functions
    with non-integer argument, it is automatically passed to gammasimp.

    Examples
    ========

    >>> from sympy.simplify import combsimp
    >>> from sympy import factorial, binomial, symbols
    >>> n, k = symbols('n k', integer = True)

    >>> combsimp(factorial(n)/factorial(n - 3))
    n*(n - 2)*(n - 1)
    >>> combsimp(binomial(n+1, k+1)/binomial(n, k))
    (n + 1)/(k + 1)

    """

    expr = expr.rewrite(gamma)
    if any(isinstance(node, gamma) and not node.args[0].is_integer
        for node in preorder_traversal(expr)):
        return gammasimp(expr);

    expr = _gammasimp(expr, as_comb = True)
    expr = _gamma_as_comb(expr)
    return expr

def apart(f, x=None, full=False, **options):
    """
    Compute partial fraction decomposition of a rational function.

    Given a rational function ``f`` compute partial fraction decomposition
    of ``f``. Two algorithms are available: one is based on undetermined
    coefficients method and the other is Bronstein's full partial fraction
    decomposition algorithm.

    Examples
    ========

    >>> from sympy.polys.partfrac import apart
    >>> from sympy.abc import x, y

    By default, using the undetermined coefficients method:

    >>> apart(y/(x + 2)/(x + 1), x)
    -y/(x + 2) + y/(x + 1)

    You can choose Bronstein's algorithm by setting ``full=True``:

    >>> apart(y/(x**2 + x + 1), x)
    y/(x**2 + x + 1)
    >>> apart(y/(x**2 + x + 1), x, full=True)
    RootSum(_w**2 + _w + 1, Lambda(_a, (-2*_a*y/3 - y/3)/(-_a + x)))

    See Also
    ========

    apart_list, assemble_partfrac_list
    """
    allowed_flags(options, [])

    f = sympify(f)

    if f.is_Atom:
        return f
    else:
        P, Q = f.as_numer_denom()

    _options = options.copy()
    options = set_defaults(options, extension=True)
    try:
        (P, Q), opt = parallel_poly_from_expr((P, Q), x, **options)
    except PolynomialError as msg:
        if f.is_commutative:
            raise PolynomialError(msg)
        # non-commutative
        if f.is_Mul:
            c, nc = f.args_cnc(split_1=False)
            nc = Mul(*[apart(i, x=x, full=full, **_options) for i in nc])
            if c:
                c = apart(Mul._from_args(c), x=x, full=full, **_options)
                return c*nc
            else:
                return nc
        elif f.is_Add:
            c = []
            nc = []
            for i in f.args:
                if i.is_commutative:
                    c.append(i)
                else:
                    try:
                        nc.append(apart(i, x=x, full=full, **_options))
                    except NotImplementedError:
                        nc.append(i)
            return apart(Add(*c), x=x, full=full, **_options) + Add(*nc)
        else:
            reps = []
            pot = preorder_traversal(f)
            pot.next()
            for e in pot:
                try:
                    reps.append((e, apart(e, x=x, full=full, **_options)))
                    pot.skip()  # this was handled successfully
                except NotImplementedError:
                    pass
            return f.xreplace(dict(reps))

    if P.is_multivariate:
        fc = f.cancel()
        if fc != f:
            return apart(fc, x=x, full=full, **_options)

        raise NotImplementedError(
            "multivariate partial fraction decomposition")

    common, P, Q = P.cancel(Q)

    poly, P = P.div(Q, auto=True)
    P, Q = P.rat_clear_denoms(Q)

    if Q.degree() <= 1:
        partial = P/Q
    else:
        if not full:
            partial = apart_undetermined_coeffs(P, Q)
        else:
#.........这里部分代码省略.........

def apart(f, x=None, full=False, **options):
    """
    Compute partial fraction decomposition of a rational function.

    Given a rational function ``f``, computes the partial fraction
    decomposition of ``f``. Two algorithms are available: One is based on the
    undertermined coefficients method, the other is Bronstein's full partial
    fraction decomposition algorithm.

    The undetermined coefficients method (selected by ``full=False``) uses
    polynomial factorization (and therefore accepts the same options as
    factor) for the denominator. Per default it works over the rational
    numbers, therefore decomposition of denominators with non-rational roots
    (e.g. irrational, complex roots) is not supported by default (see options
    of factor).

    Bronstein's algorithm can be selected by using ``full=True`` and allows a
    decomposition of denominators with non-rational roots. A human-readable
    result can be obtained via ``doit()`` (see examples below).

    Examples
    ========

    >>> from sympy.polys.partfrac import apart
    >>> from sympy.abc import x, y

    By default, using the undetermined coefficients method:

    >>> apart(y/(x + 2)/(x + 1), x)
    -y/(x + 2) + y/(x + 1)

    The undetermined coefficients method does not provide a result when the
    denominators roots are not rational:

    >>> apart(y/(x**2 + x + 1), x)
    y/(x**2 + x + 1)

    You can choose Bronstein's algorithm by setting ``full=True``:

    >>> apart(y/(x**2 + x + 1), x, full=True)
    RootSum(_w**2 + _w + 1, Lambda(_a, (-2*_a*y/3 - y/3)/(-_a + x)))

    Calling ``doit()`` yields a human-readable result:

    >>> apart(y/(x**2 + x + 1), x, full=True).doit()
    (-y/3 - 2*y*(-1/2 - sqrt(3)*I/2)/3)/(x + 1/2 + sqrt(3)*I/2) + (-y/3 -
        2*y*(-1/2 + sqrt(3)*I/2)/3)/(x + 1/2 - sqrt(3)*I/2)


    See Also
    ========

    apart_list, assemble_partfrac_list
    """
    allowed_flags(options, [])

    f = sympify(f)

    if f.is_Atom:
        return f
    else:
        P, Q = f.as_numer_denom()

    _options = options.copy()
    options = set_defaults(options, extension=True)
    try:
        (P, Q), opt = parallel_poly_from_expr((P, Q), x, **options)
    except PolynomialError as msg:
        if f.is_commutative:
            raise PolynomialError(msg)
        # non-commutative
        if f.is_Mul:
            c, nc = f.args_cnc(split_1=False)
            nc = f.func(*nc)
            if c:
                c = apart(f.func._from_args(c), x=x, full=full, **_options)
                return c*nc
            else:
                return nc
        elif f.is_Add:
            c = []
            nc = []
            for i in f.args:
                if i.is_commutative:
                    c.append(i)
                else:
                    try:
                        nc.append(apart(i, x=x, full=full, **_options))
                    except NotImplementedError:
                        nc.append(i)
            return apart(f.func(*c), x=x, full=full, **_options) + f.func(*nc)
        else:
            reps = []
            pot = preorder_traversal(f)
            next(pot)
            for e in pot:
                try:
                    reps.append((e, apart(e, x=x, full=full, **_options)))
                    pot.skip()  # this was handled successfully
                except NotImplementedError:
#.........这里部分代码省略.........

def _mask_nc(eq):
    """Return ``eq`` with non-commutative objects replaced with dummy
    symbols. A dictionary that can be used to restore the original
    values is returned: if it is None, the expression is
    noncommutative and cannot be made commutative. The third value
    returned is a list of any non-commutative symbols that appear
    in the returned equation.

    Notes
    =====
    All non-commutative objects other than Symbols are replaced with
    a non-commutative Symbol. Identical objects will be identified
    by identical symbols.

    If there is only 1 non-commutative object in an expression it will
    be replaced with a commutative symbol. Otherwise, the non-commutative
    entities are retained and the calling routine should handle
    replacements in this case since some care must be taken to keep
    track of the ordering of symbols when they occur within Muls.

    Examples
    ========
    >>> from sympy.physics.secondquant import Commutator, NO, F, Fd
    >>> from sympy import Dummy, symbols, Sum, Mul, Basic
    >>> from sympy.abc import x, y
    >>> from sympy.core.exprtools import _mask_nc
    >>> A, B, C = symbols('A,B,C', commutative=False)
    >>> Dummy._count = 0 # reset for doctest purposes

    One nc-symbol:

    >>> _mask_nc(A**2 - x**2)
    (_0**2 - x**2, {_0: A}, [])

    Multiple nc-symbols:

    >>> _mask_nc(A**2 - B**2)
    (A**2 - B**2, None, [A, B])

    An nc-object with nc-symbols but no others outside of it:

    >>> _mask_nc(1 + x*Commutator(A, B))
    (_1*x + 1, {_1: Commutator(A, B)}, [])
    >>> _mask_nc(NO(Fd(x)*F(y)))
    (_2, {_2: NO(CreateFermion(x)*AnnihilateFermion(y))}, [])

    Multiple nc-objects:

    >>> eq = x*Commutator(A, B) + x*Commutator(A, C)*Commutator(A, B)
    >>> _mask_nc(eq)
    (x*_3 + x*_4*_3, {_3: Commutator(A, B), _4: Commutator(A, C)}, [_3, _4])

    Multiple nc-objects and nc-symbols:

    >>> eq = A*Commutator(A, B) + B*Commutator(A, C)
    >>> _mask_nc(eq)
    (A*_5 + B*_6, {_5: Commutator(A, B), _6: Commutator(A, C)}, [_5, _6, A, B])

    If there is an object that:

        - doesn't contain nc-symbols
        - but has arguments which derive from Basic, not Expr
        - and doesn't define an _eval_is_commutative routine

    then it will give False (or None?) for the is_commutative test. Such
    objects are also removed by this routine:

    >>> from sympy import Basic, Mul
    >>> eq = (1 + Mul(Basic(), Basic(), evaluate=False))
    >>> eq.is_commutative
    False
    >>> _mask_nc(eq)
    (_7**2 + 1, {_7: Basic()}, [])

    """
    expr = eq
    if expr.is_commutative:
        return eq, {}, []

    # identify nc-objects; symbols and other
    rep = []
    nc_obj = set()
    nc_syms = set()
    pot = preorder_traversal(expr, keys=default_sort_key)
    for i, a in enumerate(pot):
        if any(a == r[0] for r in rep):
            pot.skip()
        elif not a.is_commutative:
            if a.is_Symbol:
                nc_syms.add(a)
            elif not (a.is_Add or a.is_Mul or a.is_Pow):
                if all(s.is_commutative for s in a.free_symbols):
                    rep.append((a, Dummy()))
                else:
                    nc_obj.add(a)
                pot.skip()

    # If there is only one nc symbol or object, it can be factored regularly
    # but polys is going to complain, so replace it with a Dummy.
    if len(nc_obj) == 1 and not nc_syms:
#.........这里部分代码省略.........

def minimal_polynomial(ex, x=None, **args):
    """
    Computes the minimal polynomial of an algebraic element.

    Parameters
    ==========

    ex : algebraic element expression
    x : independent variable of the minimal polynomial

    Options
    =======

    compose : if ``True`` ``_minpoly_compose`` is used, if ``False`` the ``groebner`` algorithm
    polys : if ``True`` returns a ``Poly`` object
    domain : ground domain

    Notes
    =====

    By default ``compose=True``, the minimal polynomial of the subexpressions of ``ex``
    are computed, then the arithmetic operations on them are performed using the resultant
    and factorization.
    If ``compose=False``, a bottom-up algorithm is used with ``groebner``.
    The default algorithm stalls less frequently.

    If no ground domain is given, it will be generated automatically from the expression.

    Examples
    ========

    >>> from sympy import minimal_polynomial, sqrt, solve, QQ
    >>> from sympy.abc import x, y

    >>> minimal_polynomial(sqrt(2), x)
    x**2 - 2
    >>> minimal_polynomial(sqrt(2), x, domain=QQ.algebraic_field(sqrt(2)))
    x - sqrt(2)
    >>> minimal_polynomial(sqrt(2) + sqrt(3), x)
    x**4 - 10*x**2 + 1
    >>> minimal_polynomial(solve(x**3 + x + 3)[0], x)
    x**3 + x + 3
    >>> minimal_polynomial(sqrt(y), x)
    x**2 - y

    """
    from sympy.polys.polytools import degree
    from sympy.polys.domains import FractionField
    from sympy.core.basic import preorder_traversal

    compose = args.get('compose', True)
    polys = args.get('polys', False)
    dom = args.get('domain', None)

    ex = sympify(ex)
    for expr in preorder_traversal(ex):
        if expr.is_AlgebraicNumber:
            compose = False
            break

    if x is not None:
        x, cls = sympify(x), Poly
    else:
        x, cls = Dummy('x'), PurePoly

    if not dom:
        dom = FractionField(QQ, list(ex.free_symbols)) if ex.free_symbols else QQ
    if hasattr(dom, 'symbols') and x in dom.symbols:
        raise GeneratorsError("the variable %s is an element of the ground domain %s" % (x, dom))

    if compose:
        result = _minpoly_compose(ex, x, dom)
        result = result.primitive()[1]
        c = result.coeff(x**degree(result, x))
        if c.is_negative:
            result = expand_mul(-result)
        return cls(result, x, field=True) if polys else result.collect(x)

    if not dom.is_QQ:
        raise NotImplementedError("groebner method only works for QQ")

    result = _minpoly_groebner(ex, x, cls)
    return cls(result, x, field=True) if polys else result.collect(x)

def cse(exprs, symbols=None, optimizations=None, postprocess=None):
    """ Perform common subexpression elimination on an expression.

    Parameters
    ==========

    exprs : list of sympy expressions, or a single sympy expression
        The expressions to reduce.
    symbols : infinite iterator yielding unique Symbols
        The symbols used to label the common subexpressions which are pulled
        out. The ``numbered_symbols`` generator is useful. The default is a
        stream of symbols of the form "x0", "x1", etc. This must be an infinite
        iterator.
    optimizations : list of (callable, callable) pairs, optional
        The (preprocessor, postprocessor) pairs. If not provided,
        ``sympy.simplify.cse.cse_optimizations`` is used.
    postprocess : a function which accepts the two return values of cse and
        returns the desired form of output from cse, e.g. if you want the
        replacements reversed the function might be the following lambda:
        lambda r, e: return reversed(r), e

    Returns
    =======

    replacements : list of (Symbol, expression) pairs
        All of the common subexpressions that were replaced. Subexpressions
        earlier in this list might show up in subexpressions later in this list.
    reduced_exprs : list of sympy expressions
        The reduced expressions with all of the replacements above.
    """
    from sympy.matrices import Matrix
    from sympy.simplify.simplify import fraction

    if symbols is None:
        symbols = numbered_symbols()
    else:
        # In case we get passed an iterable with an __iter__ method instead of
        # an actual iterator.
        symbols = iter(symbols)
    seen_subexp = set()
    muls = set()
    adds = set()
    to_eliminate = []
    to_eliminate_ops_count = []

    if optimizations is None:
        # Pull out the default here just in case there are some weird
        # manipulations of the module-level list in some other thread.
        optimizations = list(cse_optimizations)

    # Handle the case if just one expression was passed.
    if isinstance(exprs, Basic):
        exprs = [exprs]

    # Preprocess the expressions to give us better optimization opportunities.
    reduced_exprs = [preprocess_for_cse(e, optimizations) for e in exprs]
    # Find all of the repeated subexpressions.
    def insert(subtree):
        '''This helper will insert the subtree into to_eliminate while
        maintaining the ordering by op count and will skip the insertion
        if subtree is already present.'''
        ops_count = (subtree.count_ops(), subtree.is_Mul) # prefer non-Mul to Mul
        index_to_insert = bisect.bisect(to_eliminate_ops_count, ops_count)
        # all i up to this index have op count <= the current op count
        # so check that subtree is not yet present from this index down
        # (if necessary) to zero.
        for i in xrange(index_to_insert - 1, -1, -1):
            if to_eliminate_ops_count[i] == ops_count and \
               subtree == to_eliminate[i]:
                return # already have it
        to_eliminate_ops_count.insert(index_to_insert, ops_count)
        to_eliminate.insert(index_to_insert, subtree)

    for expr in reduced_exprs:
        if not isinstance(expr, Basic):
            continue
        pt = preorder_traversal(expr, key=default_sort_key)
        for subtree in pt:

            inv = 1/subtree if subtree.is_Pow else None

            if subtree.is_Atom or iterable(subtree) or inv and inv.is_Atom:
                # Exclude atoms, since there is no point in renaming them.
                continue

            if subtree in seen_subexp:
                if inv and _coeff_isneg(subtree.exp):
                    # save the form with positive exponent
                    subtree = inv
                insert(subtree)
                pt.skip()
                continue

            if inv and inv in seen_subexp:
                if _coeff_isneg(subtree.exp):
                    # save the form with positive exponent
                    subtree = inv
                insert(subtree)
                pt.skip()
                continue
#.........这里部分代码省略.........

#.........这里部分代码省略.........
    >>> _mask_nc(A**2 - x**2, 'd')
    (_d0**2 - x**2, {_d0: A}, [])

    Multiple nc-symbols:

    >>> _mask_nc(A**2 - B**2, 'd')
    (A**2 - B**2, None, [A, B])

    An nc-object with nc-symbols but no others outside of it:

    >>> _mask_nc(1 + x*Commutator(A, B), 'd')
    (_d0*x + 1, {_d0: Commutator(A, B)}, [])
    >>> _mask_nc(NO(Fd(x)*F(y)), 'd')
    (_d0, {_d0: NO(CreateFermion(x)*AnnihilateFermion(y))}, [])

    Multiple nc-objects:

    >>> eq = x*Commutator(A, B) + x*Commutator(A, C)*Commutator(A, B)
    >>> _mask_nc(eq, 'd')
    (x*_d0 + x*_d1*_d0, {_d0: Commutator(A, B), _d1: Commutator(A, C)}, [_d0, _d1])

    Multiple nc-objects and nc-symbols:

    >>> eq = A*Commutator(A, B) + B*Commutator(A, C)
    >>> _mask_nc(eq, 'd')
    (A*_d0 + B*_d1, {_d0: Commutator(A, B), _d1: Commutator(A, C)}, [_d0, _d1, A, B])

    If there is an object that:

        - doesn't contain nc-symbols
        - but has arguments which derive from Basic, not Expr
        - and doesn't define an _eval_is_commutative routine

    then it will give False (or None?) for the is_commutative test. Such
    objects are also removed by this routine:

    >>> from sympy import Basic
    >>> eq = (1 + Mul(Basic(), Basic(), evaluate=False))
    >>> eq.is_commutative
    False
    >>> _mask_nc(eq, 'd')
    (_d0**2 + 1, {_d0: Basic()}, [])

    """
    name = name or 'mask'
    # Make Dummy() append sequential numbers to the name

    def numbered_names():
        i = 0
        while True:
            yield name + str(i)
            i += 1

    names = numbered_names()

    def Dummy(*args, **kwargs):
        from sympy import Dummy
        return Dummy(next(names), *args, **kwargs)

    expr = eq
    if expr.is_commutative:
        return eq, {}, []

    # identify nc-objects; symbols and other
    rep = []
    nc_obj = set()
    nc_syms = set()
    pot = preorder_traversal(expr, keys=default_sort_key)
    for i, a in enumerate(pot):
        if any(a == r[0] for r in rep):
            pot.skip()
        elif not a.is_commutative:
            if a.is_Symbol:
                nc_syms.add(a)
            elif not (a.is_Add or a.is_Mul or a.is_Pow):
                if all(s.is_commutative for s in a.free_symbols):
                    rep.append((a, Dummy()))
                else:
                    nc_obj.add(a)
                pot.skip()

    # If there is only one nc symbol or object, it can be factored regularly
    # but polys is going to complain, so replace it with a Dummy.
    if len(nc_obj) == 1 and not nc_syms:
        rep.append((nc_obj.pop(), Dummy()))
    elif len(nc_syms) == 1 and not nc_obj:
        rep.append((nc_syms.pop(), Dummy()))

    # Any remaining nc-objects will be replaced with an nc-Dummy and
    # identified as an nc-Symbol to watch out for
    nc_obj = sorted(nc_obj, key=default_sort_key)
    for n in nc_obj:
        nc = Dummy(commutative=False)
        rep.append((n, nc))
        nc_syms.add(nc)
    expr = expr.subs(rep)

    nc_syms = list(nc_syms)
    nc_syms.sort(key=default_sort_key)
    return expr, {v: k for k, v in rep} or None, nc_syms

def minimal_polynomial(ex, x=None, compose=True, polys=False, domain=None):
    """
    Computes the minimal polynomial of an algebraic element.

    Parameters
    ==========

    ex : Expr
        Element or expression whose minimal polynomial is to be calculated.

    x : Symbol, optional
        Independent variable of the minimal polynomial

    compose : boolean, optional (default=True)
        Method to use for computing minimal polynomial. If ``compose=True``
        (default) then ``_minpoly_compose`` is used, if ``compose=False`` then
        groebner bases are used.

    polys : boolean, optional (default=False)
        If ``True`` returns a ``Poly`` object else an ``Expr`` object.

    domain : Domain, optional
        Ground domain

    Notes
    =====

    By default ``compose=True``, the minimal polynomial of the subexpressions of ``ex``
    are computed, then the arithmetic operations on them are performed using the resultant
    and factorization.
    If ``compose=False``, a bottom-up algorithm is used with ``groebner``.
    The default algorithm stalls less frequently.

    If no ground domain is given, it will be generated automatically from the expression.

    Examples
    ========

    >>> from sympy import minimal_polynomial, sqrt, solve, QQ
    >>> from sympy.abc import x, y

    >>> minimal_polynomial(sqrt(2), x)
    x**2 - 2
    >>> minimal_polynomial(sqrt(2), x, domain=QQ.algebraic_field(sqrt(2)))
    x - sqrt(2)
    >>> minimal_polynomial(sqrt(2) + sqrt(3), x)
    x**4 - 10*x**2 + 1
    >>> minimal_polynomial(solve(x**3 + x + 3)[0], x)
    x**3 + x + 3
    >>> minimal_polynomial(sqrt(y), x)
    x**2 - y

    """
    from sympy.polys.polytools import degree
    from sympy.polys.domains import FractionField
    from sympy.core.basic import preorder_traversal

    ex = sympify(ex)
    if ex.is_number:
        # not sure if it's always needed but try it for numbers (issue 8354)
        ex = _mexpand(ex, recursive=True)
    for expr in preorder_traversal(ex):
        if expr.is_AlgebraicNumber:
            compose = False
            break

    if x is not None:
        x, cls = sympify(x), Poly
    else:
        x, cls = Dummy('x'), PurePoly

    if not domain:
        if ex.free_symbols:
            domain = FractionField(QQ, list(ex.free_symbols))
        else:
            domain = QQ
    if hasattr(domain, 'symbols') and x in domain.symbols:
        raise GeneratorsError("the variable %s is an element of the ground "
                              "domain %s" % (x, domain))

    if compose:
        result = _minpoly_compose(ex, x, domain)
        result = result.primitive()[1]
        c = result.coeff(x**degree(result, x))
        if c.is_negative:
            result = expand_mul(-result)
        return cls(result, x, field=True) if polys else result.collect(x)

    if not domain.is_QQ:
        raise NotImplementedError("groebner method only works for QQ")

    result = _minpoly_groebner(ex, x, cls)
    return cls(result, x, field=True) if polys else result.collect(x)

def minimal_polynomial(ex, x=None, **args):
    """
    Computes the minimal polynomial of an algebraic number.

    Parameters
    ==========

    ex : algebraic number expression

    x : indipendent variable of the minimal polynomial

    Options
    =======

    compose : if ``True`` _minpoly1`` is used, else the ``groebner`` algorithm

    polys : if ``True`` returns a ``Poly`` object

    Notes
    =====

    By default ``compose=True``, the minimal polynomial of the subexpressions of ``ex``
    are computed, then the arithmetic operations on them are performed using the resultant
    and factorization.
    If ``compose=False``, a bottom-up algorithm is used with ``groebner``.
    The default algorithm stalls less frequently.

    Examples
    ========

    >>> from sympy import minimal_polynomial, sqrt, solve
    >>> from sympy.abc import x

    >>> minimal_polynomial(sqrt(2), x)
    x**2 - 2
    >>> minimal_polynomial(sqrt(2) + sqrt(3), x)
    x**4 - 10*x**2 + 1
    >>> minimal_polynomial(solve(x**3 + x + 3)[0], x)
    x**3 + x + 3

    """
    from sympy.polys.polytools import degree
    from sympy.core.function import expand_multinomial
    from sympy.core.basic import preorder_traversal

    compose = args.get('compose', True)
    polys = args.get('polys', False)
    ex = sympify(ex)
    for expr in preorder_traversal(ex):
        if expr.is_AlgebraicNumber:
            compose = False
            break

    if ex.is_AlgebraicNumber:
        compose = False

    if x is not None:
        x, cls = sympify(x), Poly
    else:
        x, cls = Dummy('x'), PurePoly

    if compose:
        result = _minpoly1(ex, x)
        result = result.primitive()[1]
        c = result.coeff(x**degree(result, x))
        if c < 0:
            result = expand_mul(-result)
            c = -c
        return cls(result, x, field=True) if polys else result

    generator = numbered_symbols('a', cls=Dummy)
    mapping, symbols, replace = {}, {}, []

    def update_mapping(ex, exp, base=None):
        a = generator.next()
        symbols[ex] = a

        if base is not None:
            mapping[ex] = a**exp + base
        else:
            mapping[ex] = exp.as_expr(a)

        return a

    def bottom_up_scan(ex):
        if ex.is_Atom:
            if ex is S.ImaginaryUnit:
                if ex not in mapping:
                    return update_mapping(ex, 2, 1)
                else:
                    return symbols[ex]
            elif ex.is_Rational:
                return ex
        elif ex.is_Add:
            return Add(*[ bottom_up_scan(g) for g in ex.args ])
        elif ex.is_Mul:
            return Mul(*[ bottom_up_scan(g) for g in ex.args ])
        elif ex.is_Pow:
            if ex.exp.is_Rational:
                if ex.exp < 0 and ex.base.is_Add:
#.........这里部分代码省略.........