Python numpy.ldexp方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def clear_fuss(ar, fuss_binary_bits=7):
    """Clears trailing `fuss_binary_bits` of mantissa of a floating number"""
    x = np.asanyarray(ar)
    if np.iscomplexobj(x):
        return clear_fuss(x.real) + 1j * clear_fuss(x.imag)

    significant_binary_bits = np.finfo(x.dtype).nmant
    x_mant, x_exp = np.frexp(x)
    f = 2.0**(significant_binary_bits - fuss_binary_bits)
    x_mant *= f
    np.rint(x_mant, out=x_mant)
    x_mant /= f

    return np.ldexp(x_mant, x_exp)


# XXX: This function should be available through numpy.testing 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def _roots(p):
    """Modified version of NumPy's roots function.

    NumPy's roots uses the companion matrix method, which divides by
    p[0]. This can causes overflows/underflows. Instead form a
    modified companion matrix that is scaled by 2^c * p[0], where the
    exponent c is chosen to balance the magnitudes of the
    coefficients. Since scaling the matrix just scales the
    eigenvalues, we can remove the scaling at the end.

    Scaling by a power of 2 is chosen to avoid rounding errors.

    """
    _, e = np.frexp(p)
    # Balance the most extreme exponents e_max and e_min by solving
    # the equation
    #
    # |c + e_max| = |c + e_min|.
    #
    # Round the exponent to an integer to avoid rounding errors.
    c = int(-0.5 * (np.max(e) + np.min(e)))
    p = np.ldexp(p, c)

    A = np.diag(np.full(p.size - 2, p[0]), k=-1)
    A[0,:] = -p[1:]
    eigenvalues = np.linalg.eigvals(A)
    return eigenvalues / p[0] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def helper_ldexp(f, unit1, unit2):
    if unit2 is not None:
        raise TypeError("Cannot use ldexp with a quantity "
                        "as second argument.")
    else:
        return [None, None], _d(unit1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def test_ldexp_scalar(self):
        assert np.ldexp(4. * u.m, 2) == 16. * u.m 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def test_ldexp_array(self):
        assert np.all(np.ldexp(np.array([1., 2., 3.]) * u.m, [3, 2, 1])
                      == np.array([8., 8., 6.]) * u.m) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def test_ldexp_invalid(self):
        with pytest.raises(TypeError):
            np.ldexp(3. * u.m, 4.)

        with pytest.raises(TypeError):
            np.ldexp(3., u.Quantity(4, u.m, dtype=int)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def _hdr_read(filename, use_imageio=False):
    """Read hdr file.

.. TODO:

    * Support axis other than -Y +X
"""
    if use_imageio:
        return imageio.imread(filename, **kwargs)

    with open(filename, "rb") as f:
        MAGIC = f.readline().strip()
        assert MAGIC == b'#?RADIANCE', "Wrong header found in {}".format(filename)
        comments = b""
        while comments[:6] != b"FORMAT":
            comments = f.readline().strip()
            assert comments[:3] != b"-Y ", "Could not find data format"
        assert comments == b'FORMAT=32-bit_rle_rgbe', "Format not supported"
        while comments[:3] != b"-Y ":
            comments = f.readline().strip()
        _, height, _, width = comments.decode("ascii").split(" ")
        height, width = int(height), int(width)
        rgbe = np.fromfile(f, dtype=np.uint8).reshape((height, width, 4))
        rgb = np.empty((height, width, 3), dtype=np.float)
        rgb[...,0] = np.ldexp(rgbe[...,0], rgbe[...,3].astype('int') - 128)
        rgb[...,1] = np.ldexp(rgbe[...,1], rgbe[...,3].astype('int') - 128)
        rgb[...,2] = np.ldexp(rgbe[...,2], rgbe[...,3].astype('int') - 128)
        # TODO: This will rescale all the values to be in [0, 1]. Find a way to retrieve the original values.
        rgb /= rgb.max()
    return rgb 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def test_ldexp():
    m = te.var("m",)
    A = te.placeholder((m,), name='A')
    B = te.placeholder((m,), name='B', dtype="int32")
    C = te.compute((m,), lambda *i: tvm.tir.ldexp(A(*i), B(*i)), name='C')
    s = te.create_schedule(C.op)
    f = tvm.build(s, [A, B, C], "llvm")
    ctx = tvm.cpu(0)
    n = 10
    a = tvm.nd.array(np.random.uniform(0, 1, size=n).astype(A.dtype), ctx)
    b = tvm.nd.array(np.random.randint(0, 5, size=n).astype(B.dtype), ctx)
    c = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
    f(a, b, c)
    tvm.testing.assert_allclose(
        c.asnumpy(), np.ldexp(a.asnumpy(), b.asnumpy()), atol=1e-5, rtol=1e-5) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def ldexp(x1, x2, out=None, where=None, **kwargs):
    """
    Returns x1 * 2**x2, element-wise.

    The mantissas `x1` and twos exponents `x2` are used to construct
    floating point numbers ``x1 * 2**x2``.

    Parameters
    ----------
    x1 : array_like
        Tensor of multipliers.
    x2 : array_like, int
        Tensor of twos exponents.
    out : Tensor, None, or tuple of Tensor and None, optional
        A location into which the result is stored. If provided, it must have
        a shape that the inputs broadcast to. If not provided or `None`,
        a freshly-allocated tensor is returned. A tuple (possible only as a
        keyword argument) must have length equal to the number of outputs.
    where : array_like, optional
        Values of True indicate to calculate the ufunc at that position, values
        of False indicate to leave the value in the output alone.
    **kwargs

    Returns
    -------
    y : Tensor or scalar
        The result of ``x1 * 2**x2``.

    See Also
    --------
    frexp : Return (y1, y2) from ``x = y1 * 2**y2``, inverse to `ldexp`.

    Notes
    -----
    Complex dtypes are not supported, they will raise a TypeError.

    `ldexp` is useful as the inverse of `frexp`, if used by itself it is
    more clear to simply use the expression ``x1 * 2**x2``.

    Examples
    --------
    >>> import mars.tensor as mt

    >>> mt.ldexp(5, mt.arange(4)).execute()
    array([  5.,  10.,  20.,  40.], dtype=float32)

    >>> x = mt.arange(6)
    >>> mt.ldexp(*mt.frexp(x)).execute()
    array([ 0.,  1.,  2.,  3.,  4.,  5.])
    """
    x2_dtype = astensor(x2).dtype
    casting = kwargs.get('casting', 'safe')
    if not np.can_cast(x2_dtype, np.int64, casting=casting):
        raise TypeError("ufunc 'ldexp' not supported for the input types, "
                        "and the inputs could not be safely coerced to any supported types "
                        "according to the casting rule ''{0}''".format(casting))

    op = TensorLdexp(**kwargs)
    return op(x1, x2, out=out, where=where) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def test_half_ufuncs(self):
        """Test the various ufuncs"""

        a = np.array([0, 1, 2, 4, 2], dtype=float16)
        b = np.array([-2, 5, 1, 4, 3], dtype=float16)
        c = np.array([0, -1, -np.inf, np.nan, 6], dtype=float16)

        assert_equal(np.add(a, b), [-2, 6, 3, 8, 5])
        assert_equal(np.subtract(a, b), [2, -4, 1, 0, -1])
        assert_equal(np.multiply(a, b), [0, 5, 2, 16, 6])
        assert_equal(np.divide(a, b), [0, 0.199951171875, 2, 1, 0.66650390625])

        assert_equal(np.equal(a, b), [False, False, False, True, False])
        assert_equal(np.not_equal(a, b), [True, True, True, False, True])
        assert_equal(np.less(a, b), [False, True, False, False, True])
        assert_equal(np.less_equal(a, b), [False, True, False, True, True])
        assert_equal(np.greater(a, b), [True, False, True, False, False])
        assert_equal(np.greater_equal(a, b), [True, False, True, True, False])
        assert_equal(np.logical_and(a, b), [False, True, True, True, True])
        assert_equal(np.logical_or(a, b), [True, True, True, True, True])
        assert_equal(np.logical_xor(a, b), [True, False, False, False, False])
        assert_equal(np.logical_not(a), [True, False, False, False, False])

        assert_equal(np.isnan(c), [False, False, False, True, False])
        assert_equal(np.isinf(c), [False, False, True, False, False])
        assert_equal(np.isfinite(c), [True, True, False, False, True])
        assert_equal(np.signbit(b), [True, False, False, False, False])

        assert_equal(np.copysign(b, a), [2, 5, 1, 4, 3])

        assert_equal(np.maximum(a, b), [0, 5, 2, 4, 3])
        x = np.maximum(b, c)
        assert_(np.isnan(x[3]))
        x[3] = 0
        assert_equal(x, [0, 5, 1, 0, 6])
        assert_equal(np.minimum(a, b), [-2, 1, 1, 4, 2])
        x = np.minimum(b, c)
        assert_(np.isnan(x[3]))
        x[3] = 0
        assert_equal(x, [-2, -1, -np.inf, 0, 3])
        assert_equal(np.fmax(a, b), [0, 5, 2, 4, 3])
        assert_equal(np.fmax(b, c), [0, 5, 1, 4, 6])
        assert_equal(np.fmin(a, b), [-2, 1, 1, 4, 2])
        assert_equal(np.fmin(b, c), [-2, -1, -np.inf, 4, 3])

        assert_equal(np.floor_divide(a, b), [0, 0, 2, 1, 0])
        assert_equal(np.remainder(a, b), [0, 1, 0, 0, 2])
        assert_equal(np.square(b), [4, 25, 1, 16, 9])
        assert_equal(np.reciprocal(b), [-0.5, 0.199951171875, 1, 0.25, 0.333251953125])
        assert_equal(np.ones_like(b), [1, 1, 1, 1, 1])
        assert_equal(np.conjugate(b), b)
        assert_equal(np.absolute(b), [2, 5, 1, 4, 3])
        assert_equal(np.negative(b), [2, -5, -1, -4, -3])
        assert_equal(np.sign(b), [-1, 1, 1, 1, 1])
        assert_equal(np.modf(b), ([0, 0, 0, 0, 0], b))
        assert_equal(np.frexp(b), ([-0.5, 0.625, 0.5, 0.5, 0.75], [2, 3, 1, 3, 2]))
        assert_equal(np.ldexp(b, [0, 1, 2, 4, 2]), [-2, 10, 4, 64, 12]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import ldexp [as 別名]
def test_half_ufuncs(self):
        """Test the various ufuncs"""

        a = np.array([0, 1, 2, 4, 2], dtype=float16)
        b = np.array([-2, 5, 1, 4, 3], dtype=float16)
        c = np.array([0, -1, -np.inf, np.nan, 6], dtype=float16)

        assert_equal(np.add(a, b), [-2, 6, 3, 8, 5])
        assert_equal(np.subtract(a, b), [2, -4, 1, 0, -1])
        assert_equal(np.multiply(a, b), [0, 5, 2, 16, 6])
        assert_equal(np.divide(a, b), [0, 0.199951171875, 2, 1, 0.66650390625])

        assert_equal(np.equal(a, b), [False, False, False, True, False])
        assert_equal(np.not_equal(a, b), [True, True, True, False, True])
        assert_equal(np.less(a, b), [False, True, False, False, True])
        assert_equal(np.less_equal(a, b), [False, True, False, True, True])
        assert_equal(np.greater(a, b), [True, False, True, False, False])
        assert_equal(np.greater_equal(a, b), [True, False, True, True, False])
        assert_equal(np.logical_and(a, b), [False, True, True, True, True])
        assert_equal(np.logical_or(a, b), [True, True, True, True, True])
        assert_equal(np.logical_xor(a, b), [True, False, False, False, False])
        assert_equal(np.logical_not(a), [True, False, False, False, False])

        assert_equal(np.isnan(c), [False, False, False, True, False])
        assert_equal(np.isinf(c), [False, False, True, False, False])
        assert_equal(np.isfinite(c), [True, True, False, False, True])
        assert_equal(np.signbit(b), [True, False, False, False, False])

        assert_equal(np.copysign(b, a), [2, 5, 1, 4, 3])

        assert_equal(np.maximum(a, b), [0, 5, 2, 4, 3])
        x = np.maximum(b, c)
        assert_(np.isnan(x[3]))
        x[3] = 0
        assert_equal(x, [0, 5, 1, 0, 6])
        assert_equal(np.minimum(a, b), [-2, 1, 1, 4, 2])
        x = np.minimum(b, c)
        assert_(np.isnan(x[3]))
        x[3] = 0
        assert_equal(x, [-2, -1, -np.inf, 0, 3])
        assert_equal(np.fmax(a, b), [0, 5, 2, 4, 3])
        assert_equal(np.fmax(b, c), [0, 5, 1, 4, 6])
        assert_equal(np.fmin(a, b), [-2, 1, 1, 4, 2])
        assert_equal(np.fmin(b, c), [-2, -1, -np.inf, 4, 3])

        assert_equal(np.floor_divide(a, b), [0, 0, 2, 1, 0])
        assert_equal(np.remainder(a, b), [0, 1, 0, 0, 2])
        assert_equal(np.divmod(a, b), ([0, 0, 2, 1, 0], [0, 1, 0, 0, 2]))
        assert_equal(np.square(b), [4, 25, 1, 16, 9])
        assert_equal(np.reciprocal(b), [-0.5, 0.199951171875, 1, 0.25, 0.333251953125])
        assert_equal(np.ones_like(b), [1, 1, 1, 1, 1])
        assert_equal(np.conjugate(b), b)
        assert_equal(np.absolute(b), [2, 5, 1, 4, 3])
        assert_equal(np.negative(b), [2, -5, -1, -4, -3])
        assert_equal(np.positive(b), b)
        assert_equal(np.sign(b), [-1, 1, 1, 1, 1])
        assert_equal(np.modf(b), ([0, 0, 0, 0, 0], b))
        assert_equal(np.frexp(b), ([-0.5, 0.625, 0.5, 0.5, 0.75], [2, 3, 1, 3, 2]))
        assert_equal(np.ldexp(b, [0, 1, 2, 4, 2]), [-2, 10, 4, 64, 12])