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copied!<p>Python's <code>PyIntObject</code> uses a C <code>long</code> internally, which is 64-bit on most 64-bit platforms (excluding 64-bit Windows). However, ctypes assigns the converted result to <code>pa->value.i</code>, where <code>value</code> is a union and the <code>i</code> field is a 32-bit <code>int</code>. For the details, see <code>ConvParam</code> in <a href="http://hg.python.org/cpython/file/ab05e7dd2788/Modules/_ctypes/callproc.c#l559" rel="noreferrer">Modules/_ctypes/callproc.c</a>, lines 588-607 and 645-664. ctypes was developed on Windows, where a <code>long</code> is always 32-bit, but I don't know why this hasn't been changed to use the <code>long</code> field instead, i.e. <code>pa->value.l</code>. Probably, it's just more convenient most of the time to default to creating a C <code>int</code> instead of using the full range of the <code>long</code>.</p> <p>Anyway, this means you can't simply pass a Python <code>int</code> to create a 64-bit pointer. You have to explicitly create a ctypes pointer. You have a number of options for this. If you're not concerned about type safety, the simplest option for a NumPy array is to use its <code>ctypes</code> attribute. This defines the hook <code>_as_parameter_</code> that lets Python objects set how they're converted in ctypes function calls (see lines 707-719 in the previous link). In this case it creates a <code>void *</code>. For example, you'd call <code>plate</code> like this:</p> <pre><code>plate.plate(x.ctypes, y.ctypes, N) </code></pre> <p>However, this doesn't offer any type safety to prevent the function from being called with an array of the wrong type, which will result in either nonsense, bugs, or a segmentation fault. <a href="http://docs.scipy.org/doc/numpy/reference/routines.ctypeslib.html#numpy.ctypeslib.ndpointer" rel="noreferrer"><code>np.ctypeslib.ndpointer</code></a> solves this problem. This creates a custom type that you can use in setting the <code>argtypes</code> and <code>restype</code> of a ctypes function pointer. This type can verify the array's data type, number of dimensions, shape, and flags. For example:</p> <pre><code>import numpy as np import ctypes c_npfloat32_1 = np.ctypeslib.ndpointer( dtype=np.float32, ndim=1, flags=['C', 'W']) plate = ctypes.CDLL('libplate.so') plate.plate.argtypes = [ c_npfloat32_1, c_npfloat32_1, ctypes.c_int, ] N = 3 x = np.ones(N, dtype=np.float32) y = np.ones(N, dtype=np.float32) plate.plate(x, y, N) # the parameter is the array itself </code></pre>

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