Python lib.iscomplexobj方法代码示例

# 需要导入模块: from numpy import lib [as 别名]
# 或者: from numpy.lib import iscomplexobj [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(maximum(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    __tracebackhide__ = True  # Hide traceback for py.test
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg) 

# 需要导入模块: from numpy import lib [as 别名]
# 或者: from numpy.lib import iscomplexobj [as 别名]
def nulp_diff(x, y, dtype=None):
    """For each item in x and y, return the number of representable floating
    points between them.

    Parameters
    ----------
    x : array_like
        first input array
    y : array_like
        second input array
    dtype : dtype, optional
        Data-type to convert `x` and `y` to if given. Default is None.

    Returns
    -------
    nulp : array_like
        number of representable floating point numbers between each item in x
        and y.

    Examples
    --------
    # By definition, epsilon is the smallest number such as 1 + eps != 1, so
    # there should be exactly one ULP between 1 and 1 + eps
    >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps)
    1.0
    """
    import numpy as np
    if dtype:
        x = np.array(x, dtype=dtype)
        y = np.array(y, dtype=dtype)
    else:
        x = np.array(x)
        y = np.array(y)

    t = np.common_type(x, y)
    if np.iscomplexobj(x) or np.iscomplexobj(y):
        raise NotImplementedError("_nulp not implemented for complex array")

    x = np.array(x, dtype=t)
    y = np.array(y, dtype=t)

    if not x.shape == y.shape:
        raise ValueError("x and y do not have the same shape: %s - %s" %
                         (x.shape, y.shape))

    def _diff(rx, ry, vdt):
        diff = np.array(rx-ry, dtype=vdt)
        return np.abs(diff)

    rx = integer_repr(x)
    ry = integer_repr(y)
    return _diff(rx, ry, t) 

# 需要导入模块: from numpy import lib [as 别名]
# 或者: from numpy.lib import iscomplexobj [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(max(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    ------------------------------------------------------------
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg) 

# 需要导入模块: from numpy import lib [as 别名]
# 或者: from numpy.lib import iscomplexobj [as 别名]
def nulp_diff(x, y, dtype=None):
    """For each item in x and y, return the number of representable floating
    points between them.

    Parameters
    ----------
    x : array_like
        first input array
    y : array_like
        second input array

    Returns
    -------
    nulp : array_like
        number of representable floating point numbers between each item in x
        and y.

    Examples
    --------
    # By definition, epsilon is the smallest number such as 1 + eps != 1, so
    # there should be exactly one ULP between 1 and 1 + eps
    >>> nulp_diff(1, 1 + np.finfo(x.dtype).eps)
    1.0
    """
    import numpy as np
    if dtype:
        x = np.array(x, dtype=dtype)
        y = np.array(y, dtype=dtype)
    else:
        x = np.array(x)
        y = np.array(y)

    t = np.common_type(x, y)
    if np.iscomplexobj(x) or np.iscomplexobj(y):
        raise NotImplementedError("_nulp not implemented for complex array")

    x = np.array(x, dtype=t)
    y = np.array(y, dtype=t)

    if not x.shape == y.shape:
        raise ValueError("x and y do not have the same shape: %s - %s" % \
                         (x.shape, y.shape))

    def _diff(rx, ry, vdt):
        diff = np.array(rx-ry, dtype=vdt)
        return np.abs(diff)

    rx = integer_repr(x)
    ry = integer_repr(y)
    return _diff(rx, ry, t) 

# 需要导入模块: from numpy import lib [as 别名]
# 或者: from numpy.lib import iscomplexobj [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(max(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg) 

# 需要导入模块: from numpy import lib [as 别名]
# 或者: from numpy.lib import iscomplexobj [as 别名]
def assert_array_almost_equal_nulp(x, y, nulp=1):
    """
    Compare two arrays relatively to their spacing.

    This is a relatively robust method to compare two arrays whose amplitude
    is variable.

    Parameters
    ----------
    x, y : array_like
        Input arrays.
    nulp : int, optional
        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

    Raises
    ------
    AssertionError
        If the spacing between `x` and `y` for one or more elements is larger
        than `nulp`.

    See Also
    --------
    assert_array_max_ulp : Check that all items of arrays differ in at most
        N Units in the Last Place.
    spacing : Return the distance between x and the nearest adjacent number.

    Notes
    -----
    An assertion is raised if the following condition is not met::

        abs(x - y) <= nulps * spacing(maximum(abs(x), abs(y)))

    Examples
    --------
    >>> x = np.array([1., 1e-10, 1e-20])
    >>> eps = np.finfo(x.dtype).eps
    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)

    >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
    Traceback (most recent call last):
      ...
    AssertionError: X and Y are not equal to 1 ULP (max is 2)

    """
    import numpy as np
    ax = np.abs(x)
    ay = np.abs(y)
    ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
    if not np.all(np.abs(x-y) <= ref):
        if np.iscomplexobj(x) or np.iscomplexobj(y):
            msg = "X and Y are not equal to %d ULP" % nulp
        else:
            max_nulp = np.max(nulp_diff(x, y))
            msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
        raise AssertionError(msg)