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-rw-r--r--numpy/random/generator.pyx4
-rw-r--r--numpy/random/mtrand.pyx4
2 files changed, 4 insertions, 4 deletions
diff --git a/numpy/random/generator.pyx b/numpy/random/generator.pyx
index a233810da..37ac57c06 100644
--- a/numpy/random/generator.pyx
+++ b/numpy/random/generator.pyx
@@ -3837,7 +3837,7 @@ cdef class Generator:
# Fast, statically typed path: shuffle the underlying buffer.
# Only for non-empty, 1d objects of class ndarray (subclasses such
# as MaskedArrays may not support this approach).
- x_ptr = <char*><size_t>x.ctypes.data
+ x_ptr = <char*><size_t>np.PyArray_DATA(x)
stride = x.strides[0]
itemsize = x.dtype.itemsize
# As the array x could contain python objects we use a buffer
@@ -3845,7 +3845,7 @@ cdef class Generator:
# within the buffer and erroneously decrementing it's refcount
# when the function exits.
buf = np.empty(itemsize, dtype=np.int8) # GC'd at function exit
- buf_ptr = <char*><size_t>buf.ctypes.data
+ buf_ptr = <char*><size_t>np.PyArray_DATA(buf)
with self.lock:
# We trick gcc into providing a specialized implementation for
# the most common case, yielding a ~33% performance improvement.
diff --git a/numpy/random/mtrand.pyx b/numpy/random/mtrand.pyx
index 468703e38..267a056e8 100644
--- a/numpy/random/mtrand.pyx
+++ b/numpy/random/mtrand.pyx
@@ -4070,7 +4070,7 @@ cdef class RandomState:
# Fast, statically typed path: shuffle the underlying buffer.
# Only for non-empty, 1d objects of class ndarray (subclasses such
# as MaskedArrays may not support this approach).
- x_ptr = <char*><size_t>x.ctypes.data
+ x_ptr = <char*><size_t>np.PyArray_DATA(x)
stride = x.strides[0]
itemsize = x.dtype.itemsize
# As the array x could contain python objects we use a buffer
@@ -4078,7 +4078,7 @@ cdef class RandomState:
# within the buffer and erroneously decrementing it's refcount
# when the function exits.
buf = np.empty(itemsize, dtype=np.int8) # GC'd at function exit
- buf_ptr = <char*><size_t>buf.ctypes.data
+ buf_ptr = <char*><size_t>np.PyArray_DATA(buf)
with self.lock:
# We trick gcc into providing a specialized implementation for
# the most common case, yielding a ~33% performance improvement.