BUG: Fix apply_along_axis() for when func1d() returns a non-ndarray #8426
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Fixes issue in apply_along_axis() where func1d() returns a non ndarray
charris
reviewed
Dec 31, 2016
| @@ -58,6 +58,10 @@ class MinimalSubclass(np.ndarray): | |||
| assert isinstance(res, MinimalSubclass) | |||
| assert_array_equal(res, np.array([6, 6, 6]).view(MinimalSubclass)) | |||
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| def test_tuple_func1d(self): | |||
| res = np.apply_along_axis((lambda x: (x[1], x[0])), 1, np.array([[1, 2], [3, 4]])) | |||
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Line is too long, please keep under 80 characters.
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Fix the long line, otherwise LGTM. The commit message summary line is pretty uninformative, note the edited title, and |
Closes issue numpy#8419. Fixes issue in apply_along_axis() where func1d() returns a non ndarray by calling asanyarray() on result. This commit fixes a too long line in the test case.
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Thanks @bennyrowland. I did a squash merge here. You can do the squashing yourself next time by using |
charris
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Dec 31, 2016
…umpy#8426) * BUG: Closes issue numpy#8419 Fixes issue in apply_along_axis() where func1d() returns a non ndarray * BUG: Fix apply_along_axis() when func1d() returns a non-ndarray Closes issue numpy#8419. Fixes issue in apply_along_axis() where func1d() returns a non ndarray by calling asanyarray() on result. This commit fixes a too long line in the test case.
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Fixes issue in apply_along_axis() where func1d() returns a non ndarray