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.

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