Python utilities.sift函数代码示例

 def _refine_imaginary(cls, complexes):
     sifted = sift(complexes, lambda c: c[1])
     complexes = []
     for f in ordered(sifted):
         nimag = _imag_count_of_factor(f)
         if nimag == 0:
             # refine until xbounds are neg or pos
             for u, f, k in sifted[f]:
                 while u.ax*u.bx <= 0:
                     u = u._inner_refine()
                 complexes.append((u, f, k))
         else:
             # refine until all but nimag xbounds are neg or pos
             potential_imag = list(range(len(sifted[f])))
             while True:
                 assert len(potential_imag) > 1
                 for i in list(potential_imag):
                     u, f, k = sifted[f][i]
                     if u.ax*u.bx > 0:
                         potential_imag.remove(i)
                     elif u.ax != u.bx:
                         u = u._inner_refine()
                         sifted[f][i] = u, f, k
                 if len(potential_imag) == nimag:
                     break
             complexes.extend(sifted[f])
     return complexes

def merge_explicit(matadd):
    """ Merge explicit MatrixBase arguments

    Examples
    ========

    >>> from sympy import MatrixSymbol, eye, Matrix, MatAdd, pprint
    >>> from sympy.matrices.expressions.matadd import merge_explicit
    >>> A = MatrixSymbol('A', 2, 2)
    >>> B = eye(2)
    >>> C = Matrix([[1, 2], [3, 4]])
    >>> X = MatAdd(A, B, C)
    >>> pprint(X)
        [1  0]   [1  2]
    A + [    ] + [    ]
        [0  1]   [3  4]
    >>> pprint(merge_explicit(X))
        [2  2]
    A + [    ]
        [3  5]
    """
    groups = sift(matadd.args, lambda arg: isinstance(arg, MatrixBase))
    if len(groups[True]) > 1:
        return MatAdd(*(groups[False] + [reduce(add, groups[True])]))
    else:
        return matadd

def bc_matadd(expr):
    args = sift(expr.args, lambda M: isinstance(M, BlockMatrix))
    blocks = args[True]
    if not blocks:
        return expr

    nonblocks = args[False]
    block = blocks[0]
    for b in blocks[1:]:
        block = block._blockadd(b)
    if nonblocks:
        return MatAdd(*nonblocks) + block
    else:
        return block

def kronecker_mat_add(expr):
    from functools import reduce
    args = sift(expr.args, _kronecker_dims_key)
    nonkrons = args.pop((0,), None)
    if not args:
        return expr

    krons = [reduce(lambda x, y: x._kronecker_add(y), group)
             for group in args.values()]

    if not nonkrons:
        return MatAdd(*krons)
    else:
        return MatAdd(*krons) + nonkrons

def _unique_and_repeated(inds):
    """
    Returns the unique and repeated indices. Also note, from the examples given below
    that the order of indices is maintained as given in the input.

    Examples
    ========

    >>> from sympy.tensor.index_methods import _unique_and_repeated
    >>> _unique_and_repeated([2, 3, 1, 3, 0, 4, 0])
    ([2, 1, 4], [3, 0])
    """
    uniq = OrderedDict()
    for i in inds:
        if i in uniq:
            uniq[i] = 0
        else:
            uniq[i] = 1
    return sift(uniq, lambda x: uniq[x], binary=True)

def _separate_sq(p):
    """
    helper function for ``_minimal_polynomial_sq``

    It selects a rational ``g`` such that the polynomial ``p``
    consists of a sum of terms whose surds squared have gcd equal to ``g``
    and a sum of terms with surds squared prime with ``g``;
    then it takes the field norm to eliminate ``sqrt(g)``

    See simplify.simplify.split_surds and polytools.sqf_norm.

    Examples
    ========

    >>> from sympy import sqrt
    >>> from sympy.abc import x
    >>> from sympy.polys.numberfields import _separate_sq
    >>> p= -x + sqrt(2) + sqrt(3) + sqrt(7)
    >>> p = _separate_sq(p); p
    -x**2 + 2*sqrt(3)*x + 2*sqrt(7)*x - 2*sqrt(21) - 8
    >>> p = _separate_sq(p); p
    -x**4 + 4*sqrt(7)*x**3 - 32*x**2 + 8*sqrt(7)*x + 20
    >>> p = _separate_sq(p); p
    -x**8 + 48*x**6 - 536*x**4 + 1728*x**2 - 400

    """
    from sympy.simplify.simplify import _split_gcd, _mexpand
    from sympy.utilities.iterables import sift
    def is_sqrt(expr):
        return expr.is_Pow and expr.exp is S.Half
    # p = c1*sqrt(q1) + ... + cn*sqrt(qn) -> a = [(c1, q1), .., (cn, qn)]
    a = []
    for y in p.args:
        if not y.is_Mul:
            if is_sqrt(y):
                a.append((S.One, y**2))
            elif y.is_Atom:
                a.append((y, S.One))
            elif y.is_Pow and y.exp.is_integer:
                a.append((y, S.One))
            else:
                raise NotImplementedError
            continue
        sifted = sift(y.args, is_sqrt)
        a.append((Mul(*sifted[False]), Mul(*sifted[True])**2))
    a.sort(key=lambda z: z[1])
    if a[-1][1] is S.One:
        # there are no surds
        return p
    surds = [z for y, z in a]
    for i in range(len(surds)):
        if surds[i] != 1:
            break
    g, b1, b2 = _split_gcd(*surds[i:])
    a1 = []
    a2 = []
    for y, z in a:
        if z in b1:
            a1.append(y*z**S.Half)
        else:
            a2.append(y*z**S.Half)
    p1 = Add(*a1)
    p2 = Add(*a2)
    p = _mexpand(p1**2) - _mexpand(p2**2)
    return p

def _minpoly_compose(ex, x, dom):
    """
    Computes the minimal polynomial of an algebraic element
    using operations on minimal polynomials

    Examples
    ========

    >>> from sympy import minimal_polynomial, sqrt, Rational
    >>> from sympy.abc import x, y
    >>> minimal_polynomial(sqrt(2) + 3*Rational(1, 3), x, compose=True)
    x**2 - 2*x - 1
    >>> minimal_polynomial(sqrt(y) + 1/y, x, compose=True)
    x**2*y**2 - 2*x*y - y**3 + 1

    """
    if ex.is_Rational:
        return ex.q*x - ex.p
    if ex is I:
        return x**2 + 1
    if hasattr(dom, 'symbols') and ex in dom.symbols:
        return x - ex

    if dom.is_QQ and _is_sum_surds(ex):
        # eliminate the square roots
        ex -= x
        while 1:
            ex1 = _separate_sq(ex)
            if ex1 is ex:
                return ex
            else:
                ex = ex1

    if ex.is_Add:
        res = _minpoly_add(x, dom, *ex.args)
    elif ex.is_Mul:
        f = Factors(ex).factors
        r = sift(f.items(), lambda itx: itx[0].is_rational and itx[1].is_rational)
        if r[True] and dom == QQ:
            ex1 = Mul(*[bx**ex for bx, ex in r[False] + r[None]])
            r1 = r[True]
            dens = [y.q for _, y in r1]
            lcmdens = reduce(lcm, dens, 1)
            nums = [base**(y.p*lcmdens // y.q) for base, y in r1]
            ex2 = Mul(*nums)
            mp1 = minimal_polynomial(ex1, x)
            # use the fact that in SymPy canonicalization products of integers
            # raised to rational powers are organized in relatively prime
            # bases, and that in ``base**(n/d)`` a perfect power is
            # simplified with the root
            mp2 = ex2.q*x**lcmdens - ex2.p
            ex2 = ex2**Rational(1, lcmdens)
            res = _minpoly_op_algebraic_element(Mul, ex1, ex2, x, dom, mp1=mp1, mp2=mp2)
        else:
            res = _minpoly_mul(x, dom, *ex.args)
    elif ex.is_Pow:
        res = _minpoly_pow(ex.base, ex.exp, x, dom)
    elif ex.__class__ is C.sin:
        res = _minpoly_sin(ex, x)
    elif ex.__class__ is C.cos:
        res = _minpoly_cos(ex, x)
    elif ex.__class__ is C.exp:
        res = _minpoly_exp(ex, x)
    elif ex.__class__ is RootOf:
        res = _minpoly_rootof(ex, x)
    else:
        raise NotAlgebraic("%s doesn't seem to be an algebraic element" % ex)
    return res