diff options
Diffstat (limited to 'numpy/testing/_private/utils.py')
| -rw-r--r-- | numpy/testing/_private/utils.py | 27 |
1 files changed, 22 insertions, 5 deletions
diff --git a/numpy/testing/_private/utils.py b/numpy/testing/_private/utils.py index 8a31fcf15..4eb168d18 100644 --- a/numpy/testing/_private/utils.py +++ b/numpy/testing/_private/utils.py @@ -284,6 +284,10 @@ def assert_equal(actual, desired, err_msg='', verbose=True): check that all elements of these objects are equal. An exception is raised at the first conflicting values. + When one of `actual` and `desired` 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. + This function handles NaN comparisons as if NaN was a "normal" number. That is, no assertion is raised if both objects have NaNs in the same positions. This is in contrast to the IEEE standard on NaNs, which says @@ -841,10 +845,11 @@ def 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. 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. + 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. @@ -871,6 +876,12 @@ def assert_array_equal(x, y, err_msg='', verbose=True): relative and/or absolute precision. assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal + Notes + ----- + 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. + Examples -------- The first assert does not raise an exception: @@ -878,7 +889,7 @@ def assert_array_equal(x, y, err_msg='', verbose=True): >>> 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: + 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]) @@ -899,6 +910,12 @@ def assert_array_equal(x, y, err_msg='', verbose=True): ... [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) + """ __tracebackhide__ = True # Hide traceback for py.test assert_array_compare(operator.__eq__, x, y, err_msg=err_msg, |