Time for action – asserting arrays almost equal

Let's form arrays with the values from the previous Time for action tutorial by adding a 0 to each array:

1. Calling the function with lower precision:

   print "Decimal 8", np.testing.assert_array_almost_equal([0, 0.123456789], [0, 0.123456780], decimal=8)

   The result is:

   Decimal 8 None

2. Calling the function with higher precision:

   print "Decimal 9", np.testing.assert_array_almost_equal([0, 0.123456789], [0, 0.123456780], decimal=9)

   An exception is thrown:

   Decimal 9
   Traceback (most recent call last):
     …
   assert_array_compare
   raiseAssertionError(msg)
   AssertionError:
   Arrays are not almost equal
   
   (mismatch 50.0%)
   x: array([ 0.        ,  0.12345679])
    y: array([ 0.        ,  0.12345678])

What just happened?

We compared two arrays with the NumPy array_almost_equal function

Have a go hero – comparing array with different shapes

Use the NumPy array_almost_equal function to compare two arrays with different shapes.