8000 DEP: Remove support for non-tuple nd-indices. · hawkinsp/numpy@38d60ed · GitHub
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DEP: Remove support for non-tuple nd-indices.
This behavior has been deprecated since NumPy 1.15 (numpy#9686).
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-112
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‎doc/release/upcoming_changes/pr_tbd.expired.rst

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@@ -0,0 +1,9 @@
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Multidimensional indexing with non-tuple values is no longer permitted
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----------------------------------------------------------------------
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Multidimensional indexing with anything but a tuple was
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deprecated in NumPy 1.15. Code such as ``ind = [slice(None), 0]``,
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``arr[[slice(None), 0]]`` should be changed to ``arr[tuple(ind)]``. This is
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necessary to avoid ambiguity in expressions such as ``arr[[[0, 1], [0, 1]]]``
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which was previously interpreted as ``arr[array([0, 1]), array([0, 1])]``,
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but is now interpreted as ``arr[array([[0, 1], [0, 1]])]``.

‎doc/source/user/basics.indexing.rst

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@@ -104,14 +104,6 @@ integer, or a tuple of slice objects and integers. :py:data:`Ellipsis`
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and :const:`newaxis` objects can be interspersed with these as
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well.
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.. deprecated:: 1.15.0
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In order to remain backward compatible with a common usage in
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Numeric, basic slicing is also initiated if the selection object is
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any non-ndarray and non-tuple sequence (such as a :class:`list`) containing
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:class:`slice` objects, the :py:data:`Ellipsis` object, or the :const:`newaxis`
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object, but not for integer arrays or other embedded sequences.
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.. index::
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triple: ndarray; special methods; getitem
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triple: ndarray; special methods; setitem

‎numpy/core/src/multiarray/mapping.c

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@@ -228,8 +228,7 @@ unpack_scalar(PyObject *index, PyObject **result, npy_intp NPY_UNUSED(result_n))
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NPY_NO_EXPORT npy_intp
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unpack_indices(PyObject *index, PyObject **result, npy_intp result_n)
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{
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npy_intp n, i;
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npy_bool commit_to_unpack;
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npy_intp n;
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/* Fast route for passing a tuple */
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if (PyTuple_CheckExact(index)) {
@@ -264,93 +263,9 @@ unpack_indices(PyObject *index, PyObject **result, npy_intp result_n)
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265264
/*
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* At this point, we're left with a non-tuple, non-array, sequence:
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* typically, a list. We use some somewhat-arbitrary heuristics from here
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* onwards to decided whether to treat that list as a single index, or a
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* list of indices.
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*/
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/* if len fails, treat like a scalar */
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n = PySequence_Size(index);
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if (n < 0) {
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PyErr_Clear();
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return unpack_scalar(index, result, result_n);
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}
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/*
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* Backwards compatibility only takes effect for short sequences - otherwise
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* we treat it like any other scalar.
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*
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* Sequences < NPY_MAXDIMS with any slice objects
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* or newaxis, Ellipsis or other arrays or sequences
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* embedded, are considered equivalent to an indexing
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* tuple. (`a[[[1,2], [3,4]]] == a[[1,2], [3,4]]`)
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*/
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if (n >= NPY_MAXDIMS) {
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return unpack_scalar(index, result, result_n);
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}
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/* In case we change result_n elsewhere */
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assert(n <= result_n);
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/*
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* Some other type of short sequence - assume we should unpack it like a
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* tuple, and then decide whether that was actually necessary.
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* typically, a list. We always treat these as single indices.
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*/
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commit_to_unpack = 0;
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for (i = 0; i < n; i++) {
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PyObject *tmp_obj = result[i] = PySequence_GetItem(index, i);
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if (commit_to_unpack) {
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/* propagate errors */
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if (tmp_obj == NULL) {
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goto fail;
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}
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}
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else {
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/*
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* if getitem fails (unusual) before we've committed, then stop
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* unpacking
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*/
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if (tmp_obj == NULL) {
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PyErr_Clear();
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break;
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}
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/* decide if we should treat this sequence like a tuple */
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if (PyArray_Check(tmp_obj)
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|| PySequence_Check(tmp_obj)
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|| PySlice_Check(tmp_obj)
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|| tmp_obj == Py_Ellipsis
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|| tmp_obj == Py_None) {
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if (DEPRECATE_FUTUREWARNING(
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"Using a non-tuple sequence for multidimensional "
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"indexing is deprecated; use `arr[tuple(seq)]` "
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"instead of `arr[seq]`. I 9E81 n the future this will be "
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"interpreted as an array index, `arr[np.array(seq)]`, "
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"which will result either in an error or a different "
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"result.") < 0) {
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i++; /* since loop update doesn't run */
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goto fail;
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}
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commit_to_unpack = 1;
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}
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}
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}
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/* unpacking was the right thing to do, and we already did it */
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if (commit_to_unpack) {
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return n;
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}
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/* got to the end, never found an indication that we should have unpacked */
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else {
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/* we partially filled result, so empty it first */
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multi_DECREF(result, i);
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return unpack_scalar(index, result, result_n);
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}
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fail:
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multi_DECREF(result, i);
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return -1;
268+
return unpack_scalar(index, result, result_n);
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}
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/**

‎numpy/core/tests/test_deprecations.py

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@@ -138,22 +138,6 @@ class _VisibleDeprecationTestCase(_DeprecationTestCase):
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warning_cls = np.VisibleDeprecationWarning
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class TestNonTupleNDIndexDeprecation:
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def test_basic(self):
143-
a = np.zeros((5, 5))
144-
with warnings.catch_warnings():
145-
warnings.filterwarnings('always')
146-
assert_warns(FutureWarning, a.__getitem__, [[0, 1], [0, 1]])
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assert_warns(FutureWarning, a.__getitem__, [slice(None)])
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warnings.filterwarnings('error')
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assert_raises(FutureWarning, a.__getitem__, [[0, 1], [0, 1]])
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assert_raises(FutureWarning, a.__getitem__, [slice(None)])
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# a a[[0, 1]] always was advanced indexing, so no error/warning
154-
a[[0, 1]]
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156-
157141
class TestComparisonDeprecations(_DeprecationTestCase):
158142
"""This tests the deprecation, for non-element-wise comparison logic.
159143
This used to mean that when an error occurred during element-wise comparison

‎numpy/core/tests/test_indexing.py

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@@ -587,6 +587,12 @@ def func(arr):
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588588
assert arr.dtype is dt
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590+
def test_nontuple_ndindex(self):
591+
a = np.arange(25).reshape((5, 5))
592+
assert_equal(a[[0, 1]], np.array([a[0], a[1]]))
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assert_equal(a[[0, 1], [0, 1]], np.array([0, 6]))
594+
assert_raises(IndexError, a.__getitem__, [slice(None)])
595+
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591597
class TestFieldIndexing:
592598
def test_scalar_return_type(self):

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