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authorTravis E. Oliphant <teoliphant@gmail.com>2012-08-02 20:52:39 -0700
committerTravis E. Oliphant <teoliphant@gmail.com>2012-08-02 20:52:39 -0700
commitfd15162fbff5dd68c548284947d39bb2a2481183 (patch)
treeec083e03053183ccd970580ce10ac55194b43044 /numpy
parent26fed25e7f040564e7be4f82d7cd8a8fcc8fa287 (diff)
parentd1839283a2b0b393096cf6bda9b386ea5809d4b0 (diff)
downloadnumpy-fd15162fbff5dd68c548284947d39bb2a2481183.tar.gz
Merge pull request #371 from rlamy/bytes-pickle
Copy bytes object for small arrays when unpickling an array
Diffstat (limited to 'numpy')
-rw-r--r--numpy/core/src/multiarray/methods.c5
-rw-r--r--numpy/core/tests/test_regression.py8
2 files changed, 11 insertions, 2 deletions
diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c
index c4147eff2..5ad4f2fd6 100644
--- a/numpy/core/src/multiarray/methods.c
+++ b/numpy/core/src/multiarray/methods.c
@@ -1587,8 +1587,9 @@ array_setstate(PyArrayObject *self, PyObject *args)
/* Check that the string is not interned */
if (!_IsAligned(self) || swap || PyString_CHECK_INTERNED(rawdata)) {
#else
- /* Bytes are never interned */
- if (!_IsAligned(self) || swap) {
+ /* Bytes should always be considered immutable, but we just grab the
+ * pointer if they are large, to save memory. */
+ if (!_IsAligned(self) || swap || (len <= 1000)) {
#endif
npy_intp num = PyArray_NBYTES(self);
fa->data = PyDataMem_NEW(num);
diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py
index ac36aa479..492a4f64b 100644
--- a/numpy/core/tests/test_regression.py
+++ b/numpy/core/tests/test_regression.py
@@ -1601,6 +1601,14 @@ class TestRegression(TestCase):
s = re.sub("a(.)", "\x01\\1", "a_")
assert_equal(s[0], "\x01")
+ def test_pickle_bytes_overwrite(self):
+ if sys.version_info[0] >= 3:
+ data = np.array([1], dtype='b')
+ data = pickle.loads(pickle.dumps(data))
+ data[0] = 0xdd
+ bytestring = "\x01 ".encode('ascii')
+ assert_equal(bytestring[0:1], '\x01'.encode('ascii'))
+
def test_structured_type_to_object(self):
a_rec = np.array([(0,1), (3,2)], dtype='i4,i8')
a_obj = np.empty((2,), dtype=object)