Remove use of deprecated NumPy list indexing

aesara/tensor/sort.py

@@ -291,10 +291,10 @@ def _topk_py_impl(op, x, k, axis, idx_dtype):
     idx[axis] = slice(-k, None) if k > 0 else slice(-k)
 
     if not op.return_indices:
-        zv = np.partition(x, -k, axis=axis)[idx]
+        zv = np.partition(x, -k, axis=axis)[tuple(idx)]
         return zv
     elif op.return_values:
-        zi = np.argpartition(x, -k, axis=axis)[idx]
+        zi = np.argpartition(x, -k, axis=axis)[tuple(idx)]
         idx2 = tuple(
             np.arange(s).reshape((s,) + (1,) * (ndim - i - 1)) if i != axis else zi
             for i, s in enumerate(x.shape)
@@ -302,7 +302,7 @@ def _topk_py_impl(op, x, k, axis, idx_dtype):
         zv = x[idx2]
         return zv, zi.astype(idx_dtype)
     else:
-        zi = np.argpartition(x, -k, axis=axis)[idx]
+        zi = np.argpartition(x, -k, axis=axis)[tuple(idx)]
         return zi.astype(idx_dtype)
 
 

tests/tensor/signal/test_pool.py

@@ -216,7 +216,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                         r_stride = builtins.max(r_stride, pad[i])
                         r_end = builtins.min(r_end, input.shape[-nd + i] + pad[i])
                     region.append(slice(r_stride, r_end))
-                patch = padded_input[l][region]
+                patch = padded_input[l][tuple(region)]
                 output_val[l][r] = func(patch)
         return output_val
 
@@ -325,7 +325,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                     r_stride = r[i] * stride[i]
                     r_end = builtins.min(r_stride + ws[i], input.shape[-nd + i])
                     region.append(slice(r_stride, r_end))
-                patch = input[l][region]
+                patch = input[l][tuple(region)]
                 output_val[l][r] = func(patch)
         return output_val
 

tests/tensor/test_subtensor.py

@@ -527,12 +527,6 @@ class TestSubtensor(utt.OptimizationTestMixin):
         with pytest.raises(Exception):
             n[: (2**63)]
 
-    def test_list_slice(self):
-        x = at.arange(100).reshape((5, 5, 4))
-        res = x[[slice(1, -1)] * x.ndim].eval()
-        x = np.arange(100).reshape((5, 5, 4))
-        np.allclose(res, x[[slice(1, -1)] * x.ndim])
-
     def test_slice_symbol(self):
         x = self.shared(np.random.random((5, 4)).astype(self.dtype))
         y = self.shared(np.random.random((1, 2, 3)).astype(self.dtype))
@@ -623,15 +617,15 @@ class TestSubtensor(utt.OptimizationTestMixin):
             test_array_np[np.newaxis, 1:, mask], test_array[np.newaxis, 1:, mask].eval()
         )
         assert_array_equal(
-            numpy_inc_subtensor(test_array_np, [0, mask], 1),
+            numpy_inc_subtensor(test_array_np, (0, mask), 1),
             inc_subtensor(test_array[(0,) + mask.nonzero()], 1).eval(),
         )
         assert_array_equal(
-            numpy_inc_subtensor(test_array_np, [0, mask], 1),
+            numpy_inc_subtensor(test_array_np, (0, mask), 1),
             inc_subtensor(test_array[0, mask], 1).eval(),
         )
         assert_array_equal(
-            numpy_inc_subtensor(test_array_np, [slice(None), mask], 1),
+            numpy_inc_subtensor(test_array_np, (slice(None), mask), 1),
             inc_subtensor(test_array[:, mask], 1).eval(),
         )
 
@@ -657,15 +651,15 @@ class TestSubtensor(utt.OptimizationTestMixin):
             numpy_n4[test_array_np > 2, ..., 0, 1], n4[test_array > 2, ..., 0, 1].eval()
         )
         assert_array_equal(
-            numpy_inc_subtensor(numpy_n4, [test_array_np > 2, Ellipsis], 1),
+            numpy_inc_subtensor(numpy_n4, (test_array_np > 2, Ellipsis), 1),
             inc_subtensor(n4[test_array > 2, ...], 1).eval(),
         )
         assert_array_equal(
-            numpy_inc_subtensor(numpy_n4, [test_array_np > 2, Ellipsis, 1], 1),
+            numpy_inc_subtensor(numpy_n4, (test_array_np > 2, Ellipsis, 1), 1),
             inc_subtensor(n4[test_array > 2, ..., 1], 1).eval(),
         )
         assert_array_equal(
-            numpy_inc_subtensor(numpy_n4, [test_array_np > 2, Ellipsis, 0, 1], 1),
+            numpy_inc_subtensor(numpy_n4, (test_array_np > 2, Ellipsis, 0, 1), 1),
             inc_subtensor(n4[test_array > 2, ..., 0, 1], 1).eval(),
         )
 
@@ -730,8 +724,6 @@ class TestSubtensor(utt.OptimizationTestMixin):
                 test_array.__getitem__((False,))
             with pytest.raises(TypeError):
                 test_array.__getitem__((True, False))
-            with pytest.raises(TypeError):
-                test_array.__getitem__([True, False])
             with pytest.raises(TypeError):
                 test_array.__getitem__(([0, 1], [0, False]))
             with pytest.raises(TypeError):