Clarke, Trevor wrote:

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I'm calling some python code from a C++ app via an intermediary library (i.e. I can't directly create Python C objects like Buffers). I'm passing a void* (cast to a long) to the python method and I'd like to use numpy to access that memory as an array. I'll know what the C data type is and the minimum length of the data. It's a continuous array but is multiple dimensions (either 2 dimensional or 3 dimensional BIP). I was able to find a may to create an array accessor over a python buffer but I'm not sure how to back an array with a void* (as a long) or create a buffer object for that memory inside python. Could someone point me in the right direction?

A couple of options that I see: 1) Use ctypes to wrap your void* into an object that can be passed into numpy.frombuffer. 2) Use the "intentionally not well publicisized function" numpy.core.multiarray.int_asbuffer The latter function is not well publicisized because I think solution #1 if you can figure it out may be better long term. But, #2 has some interesting features like being able to classify the memory as read-only and being able to check to see if de-referencing the beginning and end of the region causes a segfault (which is caught on some platforms) and handled gracefully with a Python error. The calling syntax is int_asbuffer(mem=, size=, readonly=, check=) where mem: long integer representing a void * size: The size of the memory region being converted to a buffer object. readonly: Should this memory object be readonly or not (default FALSE) check: Should the routine try to de-reference the first and last byte of the memory to catch any segfault? (default TRUE). -Travis O.