(arr, expected)
| 1231 | [False, True]])), |
| 1232 | ]) |
| 1233 | def test__nan_mask(arr, expected): |
| 1234 | for out in [None, np.empty(arr.shape, dtype=np.bool_)]: |
| 1235 | actual = _nan_mask(arr, out=out) |
| 1236 | assert_equal(actual, expected) |
| 1237 | # the above won't distinguish between True proper |
| 1238 | # and an array of True values; we want True proper |
| 1239 | # for types that can't possibly contain NaN |
| 1240 | if type(expected) is not np.ndarray: |
| 1241 | assert actual is True |
| 1242 | |
| 1243 | |
| 1244 | def test__replace_nan(): |
nothing calls this directly
no test coverage detected