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# numpy.testing.assert_array_equal

`numpy.testing.``assert_array_equal`(x, y, err_msg='', verbose=True)

  Raise an assertion 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

      (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)

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