Gist showing a potential memory leak when using the 'out' parameter of PyArray_CumSum of the numpy's C-api.

cumsum_leak.py

import resource
import numpy as np
from scipy import weave

x = np.ones(1e6, dtype=np.float32)

# There is no memory leak, when we let PyArray_CumSum creates the output array, i.e. when out=NULL.
code_good = r"""
npy_intp shape[1] = { PyArray_SIZE(x_array) };
int dtype = PyArray_DTYPE(x_array)->type_num;
PyArrayObject* res = (PyArrayObject*)PyArray_CumSum(x_array, NPY_MAXDIMS, dtype, NULL);

return_val = (PyObject*)res;
Py_XDECREF(res);
"""

print "RAM: {0:.2}Gb".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.**2)
for i in xrange(100):
    weave.inline(code_good, ['x'])
    print "RAM: {0:.2}Gb".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.**2)

res = weave.inline(code_good, ['x'])
print np.all(res == np.cumsum(x))

# There is memory leak, when we first create an array let with PyArray_SimpleNew and let 
#  PyArray_CumSum fills it, i.e. when out=res.
code_leak = r"""
npy_intp shape[1] = { PyArray_SIZE(x_array) };
int dtype = PyArray_DTYPE(x_array)->type_num;
PyArrayObject* res = (PyArrayObject*) PyArray_SimpleNew(1, shape, dtype);
PyArray_CumSum(x_array, NPY_MAXDIMS, dtype, res);

return_val = (PyObject*)res;
Py_XDECREF(res);
"""

print "RAM: {0:.2}Gb".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.**2)
for i in xrange(100):
    weave.inline(code_leak, ['x'])
    print "RAM: {0:.2}Gb".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.**2)


res = weave.inline(code_leak, ['x'])
print np.all(res == np.cumsum(x))