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-rw-r--r--numpy/core/_internal.py55
-rw-r--r--numpy/core/tests/test_multiarray.py24
-rw-r--r--numpy/lib/function_base.py10
3 files changed, 41 insertions, 48 deletions
diff --git a/numpy/core/_internal.py b/numpy/core/_internal.py
index b0ea603e1..5fd643505 100644
--- a/numpy/core/_internal.py
+++ b/numpy/core/_internal.py
@@ -247,55 +247,13 @@ class _missing_ctypes(object):
self.value = ptr
-class _unsafe_first_element_pointer(object):
- """
- Helper to allow viewing an array as a ctypes pointer to the first element
-
- This avoids:
- * dealing with strides
- * `.view` rejecting object-containing arrays
- * `memoryview` not supporting overlapping fields
- """
- def __init__(self, arr):
- self.base = arr
-
- @property
- def __array_interface__(self):
- i = dict(
- shape=(),
- typestr='|V0',
- data=(self.base.__array_interface__['data'][0], False),
- strides=(),
- version=3,
- )
- return i
-
-
-def _get_void_ptr(arr):
- """
- Get a `ctypes.c_void_p` to arr.data, that keeps a reference to the array
- """
- import numpy as np
- # convert to a 0d array that has a data pointer referrign to the start
- # of arr. This holds a reference to arr.
- simple_arr = np.asarray(_unsafe_first_element_pointer(arr))
-
- # create a `char[0]` using the same memory.
- c_arr = (ctypes.c_char * 0).from_buffer(simple_arr)
-
- # finally cast to void*
- return ctypes.cast(ctypes.pointer(c_arr), ctypes.c_void_p)
-
-
class _ctypes(object):
def __init__(self, array, ptr=None):
self._arr = array
if ctypes:
self._ctypes = ctypes
- # get a void pointer to the buffer, which keeps the array alive
- self._data = _get_void_ptr(array)
- assert self._data.value == ptr
+ self._data = self._ctypes.c_void_p(ptr)
else:
# fake a pointer-like object that holds onto the reference
self._ctypes = _missing_ctypes()
@@ -317,7 +275,14 @@ class _ctypes(object):
The returned pointer will keep a reference to the array.
"""
- return self._ctypes.cast(self._data, obj)
+ # _ctypes.cast function causes a circular reference of self._data in
+ # self._data._objects. Attributes of self._data cannot be released
+ # until gc.collect is called. Make a copy of the pointer first then let
+ # it hold the array reference. This is a workaround to circumvent the
+ # CPython bug https://bugs.python.org/issue12836
+ ptr = self._ctypes.cast(self._data, obj)
+ ptr._arr = self._arr
+ return ptr
def shape_as(self, obj):
"""
@@ -385,7 +350,7 @@ class _ctypes(object):
Enables `c_func(some_array.ctypes)`
"""
- return self._data
+ return self.data_as(ctypes.c_void_p)
# kept for compatibility
get_data = data.fget
diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py
index 58312f997..66e3e3c60 100644
--- a/numpy/core/tests/test_multiarray.py
+++ b/numpy/core/tests/test_multiarray.py
@@ -7975,6 +7975,8 @@ class TestFormat(object):
dst = object.__format__(a, '30')
assert_equal(res, dst)
+from numpy.testing import IS_PYPY
+
class TestCTypes(object):
def test_ctypes_is_available(self):
@@ -8041,7 +8043,29 @@ class TestCTypes(object):
# but when the `ctypes_ptr` object dies, so should `arr`
del ctypes_ptr
+ if IS_PYPY:
+ # Pypy does not recycle arr objects immediately. Trigger gc to
+ # release arr. Cpython uses refcounts. An explicit call to gc
+ # should not be needed here.
+ break_cycles()
+ assert_(arr_ref() is None, "unknowable whether ctypes pointer holds a reference")
+
+ def test_ctypes_as_parameter_holds_reference(self):
+ arr = np.array([None]).copy()
+
+ arr_ref = weakref.ref(arr)
+
+ ctypes_ptr = arr.ctypes._as_parameter_
+
+ # `ctypes_ptr` should hold onto `arr`
+ del arr
break_cycles()
+ assert_(arr_ref() is not None, "ctypes pointer did not hold onto a reference")
+
+ # but when the `ctypes_ptr` object dies, so should `arr`
+ del ctypes_ptr
+ if IS_PYPY:
+ break_cycles()
assert_(arr_ref() is None, "unknowable whether ctypes pointer holds a reference")
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 46950bc95..c39c2eea1 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -1314,9 +1314,13 @@ def interp(x, xp, fp, left=None, right=None, period=None):
Notes
-----
- Does not check that the x-coordinate sequence `xp` is increasing.
- If `xp` is not increasing, the results are nonsense.
- A simple check for increasing is::
+ The x-coordinate sequence is expected to be increasing, but this is not
+ explicitly enforced. However, if the sequence `xp` is non-increasing,
+ interpolation results are meaningless.
+
+ Note that, since NaN is unsortable, `xp` also cannot contain NaNs.
+
+ A simple check for `xp` being strictly increasing is::
np.all(np.diff(xp) > 0)