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# numpy.testing.assert_almost_equal

`numpy.testing.``assert_almost_equal`(actual, desired, decimal=7, err_msg='', verbose=True)

  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

      (mismatch 50.0%)
       x: array([ 1.        ,  2.33333333])
       y: array([ 1.        ,  2.33333334])