Commit 46d075e9 authored by Stefan Behnel's avatar Stefan Behnel

merge 0.19.x branch into master

parents f94ad597 39d9e0fc
...@@ -76,21 +76,25 @@ e.g.:: ...@@ -76,21 +76,25 @@ e.g.::
cdef double* data cdef double* data
def __init__(self, number): def __cinit__(self, number):
# allocate some memory (filled with random data) # allocate some memory (filled with random data)
self.data = <double*> malloc(number * sizeof(double)) self.data = <double*> PyMem_Malloc(number * sizeof(double))
if self.data == NULL: if not self.data:
raise MemoryError() raise MemoryError()
def resize(self, new_number): def resize(self, new_number):
# Allocates new_number * sizeof(double) bytes, # Allocates new_number * sizeof(double) bytes,
# preserving the contents and making a best-effort to # preserving the contents and making a best-effort to
# re-use the original data location. # re-use the original data location.
self.data = <double*> realloc(self.data, new_number * sizeof(double)) mem = <double*> PyMem_Realloc(self.data, new_number * sizeof(double))
if not mem:
raise MemoryError()
# Only overwrite the pointer if the memory was really reallocated.
# On error (mem is NULL), the originally memory has not been freed.
self.data = mem
def __dealloc__(self, number): def __dealloc__(self, number):
if self.data != NULL: PyMem_Free(self.data) # no-op if self.data is NULL
free(self.data)
It should be noted that Cython has special support for (multi-dimensional) It should be noted that Cython has special support for (multi-dimensional)
arrays of simple types via NumPy and memory views which are more full featured arrays of simple types via NumPy and memory views which are more full featured
......
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