assert_array_equal(x, y, err_msg='', verbose=True)¶
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 (but see the Notes for the special handling of a scalar). 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.
The actual object to check.
The desired, expected object.
The error message to be printed in case of failure.
If True, the conflicting values are appended to the error message.
If actual and desired objects are not equal.
Compare two array_like objects for equality with desired relative and/or absolute precision.
When one of x and y is a scalar and the other is array_like, the function checks that each element of the array_like object is equal to the scalar.
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 imprecision with floats:
>>> np.testing.assert_array_equal([1.0,np.pi,np.nan], ... [1, np.sqrt(np.pi)**2, np.nan]) Traceback (most recent call last): ... AssertionError: Arrays are not equal Mismatch: 33.3% Max absolute difference: 4.4408921e-16 Max relative difference: 1.41357986e-16 x: array([1. , 3.141593, nan]) y: array([1. , 3.141593, nan])
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)
As mentioned in the Notes section,
assert_array_equal has special
handling for scalars. Here the test checks that each value in x is 3:
>>> x = np.full((2, 5), fill_value=3) >>> np.testing.assert_array_equal(x, 3)