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File: //opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyo
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Utility function to facilitate testing.

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    Assert that works in release mode.
    Accepts callable msg to allow deferring evaluation until failure.

    The Python built-in ``assert`` does not work when executing code in
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    For documentation on usage, refer to the Python documentation.

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cC`sDddlm}||�}t|tt��r@td��n|S(s�like isnan, but always raise an error if type not supported instead of
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    This should be removed once this problem is solved at the Ufunc level.i(tisfiniteterrstatetinvalidtignores$isfinite not supported for this typeN(R8R?R@R9R:R;R2(R<R?R@R=((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyt	gisfinite`scC`s`ddlm}m}|dd��5||�}t|tt��rVtd��nWdQX|S(s�like isinf, but always raise an error if type not supported instead of
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    exception is always raised.

    This should be removed once this problem is solved at the Ufunc level.i(tisinfR@RARBs!isinf not supported for this typeN(R8RDR@R9R:R;R2(R<RDR@R=((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pytgisinfrstmessagesNnumpy.testing.rand is deprecated in numpy 1.11. Use numpy.random.rand instead.cG`skddl}ddlm}m}|||�}|j}x*tt|��D]}|j�||<qMW|S(s�Returns an array of random numbers with the given shape.

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        Return virtual memory size in bytes of the running python.

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        Return number of jiffies elapsed.

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        iNRjRki
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        Return number of jiffies elapsed.

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        iNid(RtRuRp(RvRt((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR�ssItems are not equal:tACTUALtDESIREDicC`s?d|g}|rl|jd�dkr\t|�dt|�kr\|dd|g}ql|j|�n|r2x�t|�D]�\}}t|t�r�ttd|�}	nt}	y|	|�}
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"cC`s�t}t|t�r�t|t�s?ttt|����ntt|�t|�||�x`|j�D]R\}}||kr�tt|���nt||||d||f|�qkWdSt|t	t
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    Raises an AssertionError if two objects are not equal.

    Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
    check that all elements of these objects are equal. An exception is raised
    at the first conflicting values.

    Parameters
    ----------
    actual : array_like
        The object to check.
    desired : array_like
        The 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 are not equal.

    Examples
    --------
    >>> np.testing.assert_equal([4,5], [4,6])
    ...
    <type 'exceptions.AssertionError'>:
    Items are not equal:
    item=1
     ACTUAL: 5
     DESIRED: 6

    s	key=%r
%sNs
item=%r
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cC`s�t}ddl}||ks�t�}|j|�|jd�|j||�|jd�|j||�t|j���ndS(s�
    Test if two objects are equal, and print an error message if test fails.

    The test is performed with ``actual == desired``.

    Parameters
    ----------
    test_string : str
        The message supplied to AssertionError.
    actual : object
        The object to test for equality against `desired`.
    desired : object
        The expected result.

    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])
    Traceback (most recent call last):
    ...
    AssertionError: Test XYZ of func xyz failed
    ACTUAL:
    [0, 1]
    DESIRED:
    [0, 2]

    iNs failed
ACTUAL: 
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DESIRED: 
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ic`sTt}ddlm}ddlm}m}m}	y|��pJ|��}
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    Raises an AssertionError if two items 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 is equivalent to ``abs(desired-actual) < 0.5 * 10**(-decimal)``.

    Given two objects (numbers or ndarrays), check that all elements of these
    objects are almost equal. 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>
    (mismatch 50.0%)
     x: array([ 1.        ,  2.33333333])
     y: array([ 1.        ,  2.33333334])

    i(R(R�R�R�c`s)d�}t��g�d�d|�S(Ns*Arrays are not almost equal to %d decimalsRR�(R(R�(R�tdecimalR�R�R(sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyt_build_err_msg�s
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c	C`s�t}ddl}tt||f�\}}||kr=dS|jdd��Id|j|�|j|�}|jd|j|j|���}WdQXy||}Wnt	k
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    Raises an AssertionError if two items are not equal up to significant
    digits.

    .. 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.

    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

    iNRARBg�?i
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	*ic`s�t}ddlm}m}	m}
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(mismatch %s%%)serror during assertion:

%s

%s(((R<R�(R<R�(R<R�(R�R8R�R7RDR�R�R�R�tshapeRR3R9tbooltravelttolistR}RLR�t	tracebackt
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!
c
C`s)ttj||d|d|dd�dS(s,
    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)

    R�RR�sArrays are not equalN(R�toperatort__eq__(R<R�R�R((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR�s?c`s�t}ddlm�m�m�m�m�ddlm�ddl	m
���������fd�}t|||d|d|dd	�d
��dS(s�	
    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 verifies values with
    ``abs(desired-actual) < 0.5 * 10**(-decimal)``.

    Given two array_like objects, check that the shape is equal and all
    elements of these objects are almost 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.

    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:
    Arrays are not almost equal
     x: array([ 1.     ,  2.33333,      NaN])
     y: array([ 1.     ,  2.33333,  5.     ])

    i(taroundtnumbertfloat_tresult_typeR�(t
issubdtype(R�c`s(y��t|��s'�t|��r�t|�}t|�}||ksOtS|j|jkoldknr{||kS||}||}nWnttfk
r�nX�|d�}�|d|dtdt�}t||�}�|j��s|j��}n�|��d�kS(Nig�?R�R�R�g$@(	RER�tsizeR2RsR�R�R�tastype(R<R�txinfidtyinfidR�tz(R�R�R�R�R�tnpanyR�R�(sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pytcomparezs$$"
R�RR�s*Arrays are not almost equal to %d decimalsRyN(R�R8R�R�R�R�R�tnumpy.core.numerictypesR�tnumpy.core.fromnumericR�R�(R<R�R�R�RR�R�((R�R�R�R�R�R�R�R�sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR/sF($
c
C`s/t}ttj||d|d|dd�dS(sF
    Raises an AssertionError if two array_like objects are not ordered by less
    than.

    Given two array_like objects, check that the shape is equal and all
    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
    --------
    assert_array_equal: tests objects for equality
    assert_array_almost_equal: test objects for equality up to precision



    Examples
    --------
    >>> 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
    (shapes (3,), (1,) mismatch)
     x: array([ 1.,  2.,  3.])
     y: array([4])

    R�RR�sArrays are not less-orderedN(R�R�R�t__lt__(R<R�R�RR�((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR�sBcB`s||UdS(N((tastrR�((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR�scC`sMt}ddl}t|t�s<ttt|����nt|t�sfttt|����ntjd|d|tj	�r�dSt
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    Test if two strings are equal.

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    between the strings is shown.

    Parameters
    ----------
    actual : str
        The string to test for equality against the expected string.
    desired : str
        The expected string.

    Examples
    --------
    >>> np.testing.assert_string_equal('abc', 'abc')
    >>> np.testing.assert_string_equal('abc', 'abcd')
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    ...
    AssertionError: Differences in strings:
    - abc+ abcd?    +

    iNs\As\Zis  s- s? s+ isDifferences in strings:
%sR1(R�tdifflibR9tstrR3R|R:treR�tMR�tDifferR�Rtpopt
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c`sddlm}ddl}|dkrGtjd�}|jd}ntjj	tjj
|��d}|||�}|j�j|�}|j
dt�}g�|r��fd�}	nd}	x!|D]}
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    Run doctests found in the given file.

    By default `rundocs` raises an AssertionError on failure.

    Parameters
    ----------
    filename : str
        The path to the file for which the doctests are run.
    raise_on_error : bool
        Whether to raise an AssertionError when a doctest fails. Default is
        True.

    Notes
    -----
    The doctests can be run by the user/developer by adding the ``doctests``
    argument to the ``test()`` call. For example, to run all tests (including
    doctests) for `numpy.lib`:

    >>> np.lib.test(doctests=True) #doctest: +SKIP
    i(tnpy_load_moduleNit__file__Rc`s
�j|�S(N(Ru(ts(R5(sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyt<lambda>LstoutsSome doctests failed:
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cO`st�}|jj||�S(N(RttoolsR(RMR�tnose((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyRWs	cO`s"t}t�}|jj||�S(s�
    assert_raises(exception_class, callable, *args, **kwargs)

    Fail unless an exception of class exception_class is thrown
    by callable when invoked with arguments args and keyword
    arguments kwargs. If a different type of exception is
    thrown, it will not be caught, and the test case will be
    deemed to have suffered an error, exactly as for an
    unexpected exception.

    Alternatively, `assert_raises` can be used as a context manager:

    >>> from numpy.testing import assert_raises
    >>> with assert_raises(ZeroDivisionError):
    ...   1 / 0

    is equivalent to

    >>> def div(x, y):
    ...    return x / y
    >>> assert_raises(ZeroDivisionError, div, 1, 0)

    (R�RRR(RMR�R�R((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR\s	c`s�t}t�}tdkr�y|jjaWq�tk
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r�dtfd��Y��fd�}|aq�Xq�Xnt|||||�S(s5
    Fail unless an exception of class exception_class and with message that
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    all versions down to 2.6.

    t_AssertRaisesContextcB`s5eZdZdd�Zd�Zd�Zd�ZRS(sCA context manager used to implement TestCase.assertRaises* methods.cS`s||_||_dS(N(texpectedtexpected_regexp(tselfRR((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyt__init__�s	cS`s
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dkr�tS|j
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�r�tj|�}n|jt|��s�|jd|jt|�f��ntS(Ns{0} not raiseds"%s" does not match "%s"(RSRR.tAttributeErrorR�RR`t
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%	c	C`s|dkr%tjdtj�}ntj|�}|j}ddlm}g|j�D]}||�rZ|^qZ}x�|D]}}y(t	|d�r�|j
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    Apply a decorator to all methods in a class matching a regular expression.

    The given decorator is applied to all public methods of `cls` that are
    matched by the regular expression `testmatch`
    (``testmatch.search(methodname)``). Methods that are private, i.e. start
    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)``)
        is used.
        If `testmatch` is a string, it is compiled to a regular expression
        first.

    s(?:^|[\\b_\\.%s-])[Tt]esti(t
isfunctiontcompat_func_namet_N(RSR�RRtsept__dict__tinspectR/tvaluesthasattrR0R.RRR�tsetattr(	R&t	decoratort	testmatchtcls_attrR/t_mtmethodstfunctiontfuncname((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR�s 	+


c	B`s�ejd�}|j|j}}e|d|d�}d}e�}x$||krm|d7}|||UqJWe�|}d|S(sE
    Return elapsed time for executing code in the namespace of the caller.

    The supplied code string is compiled with the Python builtin ``compile``.
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    fast on this timescale, it can be executed many times to get reasonable
    timing accuracy.

    Parameters
    ----------
    code_str : str
        The code to be timed.
    times : int, optional
        The number of times the code is executed. Default is 1. The code is
        only compiled once.
    label : str, optional
        A label to identify `code_str` with. This is passed into ``compile``
        as the second argument (for run-time error messages).

    Returns
    -------
    elapsed : float
        Total elapsed time in seconds for executing `code_str` `times` times.

    Examples
    --------
    >>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)',
    ...                            times=times)
    >>> print("Time for a single execution : ", etime / times, "s")
    Time for a single execution :  0.005 s

    isTest name: %s texecig{�G�z�?(RRtf_localsRRR(	tcode_strttimestlabeltframetlocstglobstcodeRPtelapsed((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR �s!		

cC`s�ddl}|jd�jdd�}|}d}tj|�}x#td�D]}|||�}qOWttj|�|k�~dS(sg
    Check that ufuncs don't mishandle refcount of object `1`.
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    iNidiii'(R�R	treshapeRtgetrefcountRKR!(topR�tbtcRPtrctjtd((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyt_assert_valid_refcount'sgH�����z>c

`s�t}ddl�����fd�}�j|��j|�}}d��f}	t|||dt|�d|d|	�dS(sq
    Raises an AssertionError if two objects are not equal up to desired
    tolerance.

    The test is equivalent to ``allclose(actual, desired, rtol, atol)``.
    It compares the difference between `actual` and `desired` to
    ``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]
    >>> y = np.arccos(np.cos(x))
    >>> assert_allclose(x, y, rtol=1e-5, atol=0)

    iNc	`s(�jjj||d�d�d��S(Ntrtoltatolt	equal_nan(tcoretnumerictisclose(R<R�(RSRTR�RR(sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR�is!s'Not equal to tolerance rtol=%g, atol=%gR�RR�(R�R�t
asanyarrayR�R�(
R�R�RRRSRTR�RR�R�R�((RSRTR�RRsF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR'9s-c
C`s�t}ddl}|j|�}|j|�}||j|j||k||��}|j|j||�|k�s�|j|�s�|j|�r�d|}n(|jt||��}	d||	f}t	|��ndS(s�
    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)

    iNsX and Y are not equal to %d ULPs+X and Y are not equal to %d ULP (max is %g)(
R�R�R�tspacingtwhereR�R�tmaxt	nulp_diffR3(
R<R�tnulpR�R�taxtaytrefR5tmax_nulp((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR"rs1("
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    Check that all items of arrays differ in at most N Units in the Last Place.

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    a, b : array_like
        Input arrays to be compared.
    maxulp : int, optional
        The maximum number of units in the last place that elements of `a` and
        `b` can differ. Default is 1.
    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
    --------
    >>> a = np.linspace(0., 1., 100)
    >>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))

    iNs(Arrays are not almost equal up to %g ULP(R�R�R\R�R3(R�RLtmaxulpR�R�R�tret((sF/opt/alt/python27/lib64/python2.7/site-packages/numpy/testing/utils.pyR$�s$
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    y : array_like
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    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
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    Fail if the given callable produces any warnings.

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    Parameters
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	cB`s5eZdZdZedd�Zd�Zd�ZRS(s] Context manager that resets warning registry for catching warnings

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    * stores and removes any ``__warningregistry__`` entries in given `modules`
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    This makes it possible to trigger any warning afresh inside the context
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    For compatibility with Python 3.0, please consider all arguments to be
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        returned by the context manager. Otherwise None is returned by the
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        attributes mirror the arguments to ``showwarning()``.
    modules : sequence, optional
        Sequence of modules for which to reset warnings registry on entry and
        restore on exit

    Examples
    --------
    >>> import warnings
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