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authorMatti Picus <matti.picus@gmail.com>2018-10-09 13:31:20 +0300
committerGitHub <noreply@github.com>2018-10-09 13:31:20 +0300
commitdb5f9d3a61d639b4fdb95c161012678ea8847424 (patch)
treef57717d29b797fd9a8d44ecc9a0647bf2e4d5325 /numpy
parent86a7acc8582923604fac849bb19f0b08efc0a91a (diff)
parent2beafe73822cd6b47b4138ab83359585e0caa6b8 (diff)
downloadnumpy-db5f9d3a61d639b4fdb95c161012678ea8847424.tar.gz
Merge pull request #11979 from hmaarrfk/block_single_array_copy_test
MAINT: Ensure that a copy of the array is returned when calling `block`.
Diffstat (limited to 'numpy')
-rw-r--r--numpy/core/shape_base.py9
-rw-r--r--numpy/core/tests/test_shape_base.py12
2 files changed, 20 insertions, 1 deletions
diff --git a/numpy/core/shape_base.py b/numpy/core/shape_base.py
index 52717abda..feb1605bc 100644
--- a/numpy/core/shape_base.py
+++ b/numpy/core/shape_base.py
@@ -617,4 +617,11 @@ def block(arrays):
_block_format_index(bottom_index)
)
)
- return _block(arrays, list_ndim, max(arr_ndim, list_ndim))
+ result = _block(arrays, list_ndim, max(arr_ndim, list_ndim))
+ if list_ndim == 0:
+ # Catch an edge case where _block returns a view because
+ # `arrays` is a single numpy array and not a list of numpy arrays.
+ # This might copy scalars or lists twice, but this isn't a likely
+ # usecase for those interested in performance
+ result = result.copy()
+ return result
diff --git a/numpy/core/tests/test_shape_base.py b/numpy/core/tests/test_shape_base.py
index 72b3451a4..df819b73f 100644
--- a/numpy/core/tests/test_shape_base.py
+++ b/numpy/core/tests/test_shape_base.py
@@ -191,6 +191,12 @@ class TestVstack(object):
class TestConcatenate(object):
+ def test_returns_copy(self):
+ a = np.eye(3)
+ b = np.concatenate([a])
+ b[0, 0] = 2
+ assert b[0, 0] != a[0, 0]
+
def test_exceptions(self):
# test axis must be in bounds
for ndim in [1, 2, 3]:
@@ -367,6 +373,12 @@ def test_stack():
class TestBlock(object):
+ def test_returns_copy(self):
+ a = np.eye(3)
+ b = np.block(a)
+ b[0, 0] = 2
+ assert b[0, 0] != a[0, 0]
+
def test_block_simple_row_wise(self):
a_2d = np.ones((2, 2))
b_2d = 2 * a_2d