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    err_msg : str, optional
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    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired are not equal.

    Examples
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    >>> np.testing.assert_equal([4,5], [4,6])
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        The object to test for equality against `desired`.
    desired : object
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    Examples
    --------
    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
    >>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
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              instead of this function for more consistent floating point
              comparisons.

    The test verifies that the elements of ``actual`` and ``desired`` satisfy.

        ``abs(desired-actual) < 1.5 * 10**(-decimal)``

    That is a looser test than originally documented, but agrees with what the
    actual implementation in `assert_array_almost_equal` did up to rounding
    vagaries. An exception is raised at conflicting values. For ndarrays this
    delegates to assert_array_almost_equal

    Parameters
    ----------
    actual : array_like
        The object to check.
    desired : array_like
        The expected object.
    decimal : int, optional
        Desired precision, default is 7.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
      If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    >>> import numpy.testing as npt
    >>> npt.assert_almost_equal(2.3333333333333, 2.33333334)
    >>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
    ...
    <type 'exceptions.AssertionError'>:
    Items are not equal:
     ACTUAL: 2.3333333333333002
     DESIRED: 2.3333333399999998

    >>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
    ...                         np.array([1.0,2.33333334]), decimal=9)
    ...
    <type 'exceptions.AssertionError'>:
    Arrays are not almost equal
    <BLANKLINE>
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     x: array([ 1.        ,  2.33333333])
     y: array([ 1.        ,  2.33333334])

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    Raises an AssertionError if two items are not equal up to significant
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              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
              instead of this function for more consistent floating point
              comparisons.

    Given two numbers, check that they are approximately equal.
    Approximately equal is defined as the number of significant digits
    that agree.

    Parameters
    ----------
    actual : scalar
        The object to check.
    desired : scalar
        The expected object.
    significant : int, optional
        Desired precision, default is 7.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
      If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    >>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)
    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,
                                       significant=8)
    >>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,
                                       significant=8)
    ...
    <type 'exceptions.AssertionError'>:
    Items are not equal to 8 significant digits:
     ACTUAL: 1.234567e-021
     DESIRED: 1.2345672000000001e-021

    the evaluated condition that raises the exception is

    >>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
    True

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    Raises an AssertionError if two array_like objects are not equal.

    Given two array_like objects, check that the shape is equal and all
    elements of these objects are equal. An exception is raised at
    shape mismatch or conflicting values. In contrast to the standard usage
    in numpy, NaNs are compared like numbers, no assertion is raised if
    both objects have NaNs in the same positions.

    The usual caution for verifying equality with floating point numbers is
    advised.

    Parameters
    ----------
    x : array_like
        The actual object to check.
    y : array_like
        The desired, expected object.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired objects are not equal.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    The first assert does not raise an exception:

    >>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
    ...                               [np.exp(0),2.33333, np.nan])

    Assert fails with numerical inprecision with floats:

    >>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
    ...                               [1, np.sqrt(np.pi)**2, np.nan])
    ...
    <type 'exceptions.ValueError'>:
    AssertionError:
    Arrays are not equal
    <BLANKLINE>
    (mismatch 50.0%)
     x: array([ 1.        ,  3.14159265,         NaN])
     y: array([ 1.        ,  3.14159265,         NaN])

    Use `assert_allclose` or one of the nulp (number of floating point values)
    functions for these cases instead:

    >>> np.testing.assert_allclose([1.0,np.pi,np.nan],
    ...                            [1, np.sqrt(np.pi)**2, np.nan],
    ...                            rtol=1e-10, atol=0)

    Tr�r%r�zArrays are not equalN)r��operator�__eq__)rMr�r�r%rGr:r:r;rs?cs�d}ddlm}m�m�m�m�ddlm�ddlm	��������fdd�}t
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    Raises an AssertionError if two objects are not equal up to desired
    precision.

    .. note:: It is recommended to use one of `assert_allclose`,
              `assert_array_almost_equal_nulp` or `assert_array_max_ulp`
              instead of this function for more consistent floating point
              comparisons.

    The test verifies identical shapes and that the elements of ``actual`` and
    ``desired`` satisfy.

        ``abs(desired-actual) < 1.5 * 10**(-decimal)``

    That is a looser test than originally documented, but agrees with what the
    actual implementation did up to rounding vagaries. An exception is raised
    at shape mismatch or conflicting values. In contrast to the standard usage
    in numpy, NaNs are compared like numbers, no assertion is raised if both
    objects have NaNs in the same positions.

    Parameters
    ----------
    x : array_like
        The actual object to check.
    y : array_like
        The desired, expected object.
    decimal : int, optional
        Desired precision, default is 6.
    err_msg : str, optional
      The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_allclose: Compare two array_like objects for equality with desired
                     relative and/or absolute precision.
    assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal

    Examples
    --------
    the first assert does not raise an exception

    >>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
                                             [1.0,2.333,np.nan])

    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
    ...                                      [1.0,2.33339,np.nan], decimal=5)
    ...
    <type 'exceptions.AssertionError'>:
    AssertionError:
    Arrays are not almost equal
    <BLANKLINE>
    (mismatch 50.0%)
     x: array([ 1.     ,  2.33333,      NaN])
     y: array([ 1.     ,  2.33339,      NaN])

    >>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
    ...                                      [1.0,2.33333, 5], decimal=5)
    <type 'exceptions.ValueError'>:
    ValueError:
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     x: array([ 1.     ,  2.33333,      NaN])
     y: array([ 1.     ,  2.33333,  5.     ])

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    elements of the first object are strictly smaller than those of the
    second object. An exception is raised at shape mismatch or incorrectly
    ordered values. Shape mismatch does not raise if an object has zero
    dimension. In contrast to the standard usage in numpy, NaNs are
    compared, no assertion is raised if both objects have NaNs in the same
    positions.



    Parameters
    ----------
    x : array_like
      The smaller object to check.
    y : array_like
      The larger object to compare.
    err_msg : string
      The error message to be printed in case of failure.
    verbose : bool
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
      If actual and desired objects are not equal.

    See Also
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    assert_array_equal: tests objects for equality
    assert_array_almost_equal: test objects for equality up to precision



    Examples
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    >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
    >>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
    ...
    <type 'exceptions.ValueError'>:
    Arrays are not less-ordered
    (mismatch 50.0%)
     x: array([  1.,   1.,  NaN])
     y: array([  1.,   2.,  NaN])

    >>> np.testing.assert_array_less([1.0, 4.0], 3)
    ...
    <type 'exceptions.ValueError'>:
    Arrays are not less-ordered
    (mismatch 50.0%)
     x: array([ 1.,  4.])
     y: array(3)

    >>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
    ...
    <type 'exceptions.ValueError'>:
    Arrays are not less-ordered
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     y: array([4])

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    actual : str
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    desired : str
        The expected string.

    Examples
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    raise_on_error : bool
        Whether to raise an AssertionError when a doctest fails. Default is
        True.

    Notes
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    The doctests can be run by the user/developer by adding the ``doctests``
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    deemed to have suffered an error, exactly as for an
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    with an underscore, are ignored.

    Parameters
    ----------
    cls : class
        Class whose methods to decorate.
    decorator : function
        Decorator to apply to methods
    testmatch : compiled regexp or str, optional
        The regular expression. Default value is None, in which case the
        nose default (``re.compile(r'(?:^|[\b_\.%s-])[Tt]est' % os.sep)``)
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    timing accuracy.

    Parameters
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    code_str : str
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    times : int, optional
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        only compiled once.
    label : str, optional
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        as the second argument (for run-time error messages).

    Returns
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    elapsed : float
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    Examples
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    >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)',
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    ``atol + rtol * abs(desired)``.

    .. versionadded:: 1.5.0

    Parameters
    ----------
    actual : array_like
        Array obtained.
    desired : array_like
        Array desired.
    rtol : float, optional
        Relative tolerance.
    atol : float, optional
        Absolute tolerance.
    equal_nan : bool, optional.
        If True, NaNs will compare equal.
    err_msg : str, optional
        The error message to be printed in case of failure.
    verbose : bool, optional
        If True, the conflicting values are appended to the error message.

    Raises
    ------
    AssertionError
        If actual and desired are not equal up to specified precision.

    See Also
    --------
    assert_array_almost_equal_nulp, assert_array_max_ulp

    Examples
    --------
    >>> x = [1e-5, 1e-3, 1e-1]
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        The maximum number of unit in the last place for tolerance (see Notes).
        Default is 1.

    Returns
    -------
    None

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

    See Also
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    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
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    An assertion is raised if the following condition is not met::

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    Examples
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    >>> 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)

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    dtype : dtype, optional
        Data-type to convert `a` and `b` to if given. Default is None.

    Returns
    -------
    ret : ndarray
        Array containing number of representable floating point numbers between
        items in `a` and `b`.

    Raises
    ------
    AssertionError
        If one or more elements differ by more than `maxulp`.

    See Also
    --------
    assert_array_almost_equal_nulp : Compare two arrays relatively to their
        spacing.

    Examples
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    Returns
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    nulp : array_like
        number of representable floating point numbers between each item in x
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    Examples
    --------
    # By definition, epsilon is the smallest number such as 1 + eps != 1, so
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