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| author | Huon Wilson <Huon.Wilson@data61.csiro.au> | 2020-03-12 20:07:33 -0400 |
|---|---|---|
| committer | Warren Weckesser <warren.weckesser@gmail.com> | 2020-03-15 11:35:39 -0400 |
| commit | 353bf737fb84fe285b430e6496aa7537f56a674f (patch) | |
| tree | 4bbe653d4b7695cd6c1255a43b85c175a5eb2788 /numpy/lib/tests | |
| parent | 26527695f9ee7b7bc3fdd3af67d7e8b068cb1e0b (diff) | |
| download | numpy-353bf737fb84fe285b430e6496aa7537f56a674f.tar.gz | |
TST: lib: Add a unit test for np.unique applied to arrays with a length 0 axis.
This code is from github user huonw, from this PR:
https://github.com/numpy/numpy/pull/15565
Diffstat (limited to 'numpy/lib/tests')
| -rw-r--r-- | numpy/lib/tests/test_arraysetops.py | 35 |
1 files changed, 35 insertions, 0 deletions
diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py index 851fd31ea..f7d91a26a 100644 --- a/numpy/lib/tests/test_arraysetops.py +++ b/numpy/lib/tests/test_arraysetops.py @@ -563,6 +563,41 @@ class TestUnique: result = np.array([[-0.0, 0.0]]) assert_array_equal(unique(data, axis=0), result, msg) + def test_unique_axis_zeros(self): + # issue 15559 + single_zero = np.empty(shape=(2, 0), dtype=np.int8) + uniq, idx, inv, cnt = unique(single_zero, axis=0, return_index=True, + return_inverse=True, return_counts=True) + + # there's 1 element of shape (0,) along axis 0 + assert_equal(uniq.dtype, single_zero.dtype) + assert_array_equal(uniq, np.empty(shape=(1, 0))) + assert_array_equal(idx, np.array([0])) + assert_array_equal(inv, np.array([0, 0])) + assert_array_equal(cnt, np.array([2])) + + # there's 0 elements of shape (2,) along axis 1 + uniq, idx, inv, cnt = unique(single_zero, axis=1, return_index=True, + return_inverse=True, return_counts=True) + + assert_equal(uniq.dtype, single_zero.dtype) + assert_array_equal(uniq, np.empty(shape=(2, 0))) + assert_array_equal(idx, np.array([])) + assert_array_equal(inv, np.array([])) + assert_array_equal(cnt, np.array([])) + + # test a "complicated" shape + shape = (0, 2, 0, 3, 0, 4, 0) + multiple_zeros = np.empty(shape=shape) + for axis in range(len(shape)): + expected_shape = list(shape) + if shape[axis] == 0: + expected_shape[axis] = 0 + else: + expected_shape[axis] = 1 + + assert_array_equal(unique(multiple_zeros, axis=axis), np.empty(shape=expected_shape)) + def test_unique_masked(self): # issue 8664 x = np.array([64, 0, 1, 2, 3, 63, 63, 0, 0, 0, 1, 2, 0, 63, 0], dtype='uint8') |