Python sympy.Basic类代码示例

def my_compare(a, b):
   main_var = s
   p1, p2, p3 = Wild("p1"), Wild("p2"), Wild("p3")
   r_a = a.match(p1 * s**p3)
   r_b = b.match(p1 * s**p3)
   if r_a is not None and r_b is not None:
       c = Basic.compare(r_a[p3], r_b[p3])
       if c!=0:
           return c

   return Basic._compare_pretty(a,b)

 def __new__(cls, i, j):
     i, j = map(sympify, (i, j))
     r = cls.canonize(i, j)
     if isinstance(r, Basic):
         return r
     obj = Basic.__new__(cls, i, j, commutative=True)
     return obj

 def __new__(cls, dimension):
     dimension = sympify(dimension)
     r = cls.eval(dimension)
     if isinstance(r, Basic):
         return r
     obj = Basic.__new__(cls, dimension, **{'commutative': False})
     return obj

    def __new__ (cls, arg, **options):
#        print "Inverse(", arg, ")"
#        print INVERTIBLE
        if isinstance(arg, Inverse):
            return arg.args[0]
        if arg.is_Number:
            return 1 / arg

        arg_rank = expr_rank(arg)
        if arg_rank == 1:
            raise NotInvertibleError

        if is_one(arg):
            return arg
        if is_zero(arg):
            raise NotInvertibleError

        # FIXME: Funky case trying to catch lower triangular or diagonal
        #        muls like T(P_0)*A*P_0
        if arg in INVERTIBLE:
            pass
        elif isinstance(arg, TensorExpr) and not arg.has_inverse:
            raise NotInvertibleError
        elif isinstance(arg, Mul):
            if arg.args[0] == S(-1):
                return - Inverse(reduce(operator.mul, arg.args[1:]))
        if not expr_invertible(arg):
            raise NotInvertibleError
        options['commutative'] = arg.is_commutative
        return Basic.__new__(cls, arg, **options)

 def __new__(cls, sym, dist):
     sym = sympify(sym)
     if isinstance(dist, SingleContinuousDistribution):
         return SingleContinuousPSpace(sym, dist)
     if isinstance(dist, SingleDiscreteDistribution):
         return SingleDiscretePSpace(sym, dist)
     return Basic.__new__(cls, sym, dist)

 def _new(cls, *args, **kwargs):
     if len(args)==1 and isinstance(args[0], ImmutableMatrix):
         return args[0]
     rows, cols, mat = MatrixBase._handle_creation_inputs(*args, **kwargs)
     shape = Tuple(rows, cols)
     mat = Tuple(*mat)
     return Basic.__new__(cls, shape, mat)

    def __new__(cls, iterable=None, shape=None, **kwargs):
        from sympy.utilities.iterables import flatten

        shape, flat_list = cls._handle_ndarray_creation_inputs(iterable, shape, **kwargs)
        shape = Tuple(*map(_sympify, shape))
        loop_size = functools.reduce(lambda x,y: x*y, shape) if shape else 0

        # Sparse array:
        if isinstance(flat_list, (dict, Dict)):
            sparse_array = Dict(flat_list)
        else:
            sparse_array = {}
            for i, el in enumerate(flatten(flat_list)):
                if el != 0:
                    sparse_array[i] = _sympify(el)

        sparse_array = Dict(sparse_array)

        self = Basic.__new__(cls, sparse_array, shape, **kwargs)
        self._shape = shape
        self._rank = len(shape)
        self._loop_size = loop_size
        self._sparse_array = sparse_array

        return self

def sample2d(f, x_args):
    """
    Samples a 2d function f over specified intervals and returns two
    arrays (X, Y) suitable for plotting with matlab (matplotlib)
    syntax. See examples\mplot2d.py.

    f is a function of one variable, such as x**2.
    x_args is an interval given in the form (var, min, max, n)
    """
    try:
        f = Basic.sympify(f)
    except:
        raise ValueError("f could not be interpretted as a SymPy function")
    try:
        x, x_min, x_max, x_n = x_args
    except:
        raise ValueError("x_args must be a tuple of the form (var, min, max, n)")

    x_l = float(x_max - x_min)
    x_d = x_l/float(x_n)
    X = arange(float(x_min), float(x_max)+x_d, x_d)

    Y = empty(len(X))
    for i in range(len(X)):
        try:
            Y[i] = float(f.subs(x, X[i]))
        except:
            Y[i] = None
    return X, Y

 def __new__(cls, dimension):
     dimension = sympify(dimension)
     r = cls.eval(dimension)
     if isinstance(r, Basic):
         return r
     obj = Basic.__new__(cls, dimension)
     return obj

 def __new__(cls, M):
     if not isinstance(M, Matrix):
         M = Matrix(M)
     data = Tuple(*M._mat)
     shape = Tuple(*sympify(M.shape))
     obj = Basic.__new__(cls, data, shape)
     obj._mat = M
     return obj

    def __new__(cls, mat):
        if not mat.is_Matrix:
            raise TypeError("input to Trace, %s, is not a matrix" % str(mat))

        if not mat.is_square:
            raise ShapeError("Trace of a non-square matrix")

        return Basic.__new__(cls, mat)

 def _new(cls, *args, **kwargs):
     if len(args) == 1 and isinstance(args[0], ImmutableMatrix):
         return args[0]
     rows, cols, flat_list = MatrixBase._handle_creation_inputs(*args, **kwargs)
     rows = Integer(rows)
     cols = Integer(cols)
     mat = Tuple(*flat_list)
     return Basic.__new__(cls, rows, cols, mat)

    def __new__(cls, *args):
        # args should be a tuple - a variable length argument list
        obj = Basic.__new__(cls, *args)
        obj._circuit = Mul(*args)
        obj._rules = generate_gate_rules(args)
        obj._eq_ids = generate_equivalent_ids(args)

        return obj

 def __new__(cls, mat):
     if not isinstance(mat, Matrix):
         mat = Matrix(mat)
     data = Tuple(*mat.mat)
     shape = Tuple(*sympify(mat.shape))
     obj = Basic.__new__(cls, data, shape)
     obj.mat = mat
     return obj

 def set_v_max(self, v_max):
     if v_max is None:
         self._v_max = None
         return
     try:
         self._v_max = Basic.sympify(v_max)
         float(self._v_max.evalf())
     except:
         raise ValueError("v_max could not be interpreted as a number.")