Would SWIG example `normalize_numpy` leak memory?

[adamryczkowski]

Here's a quote from the wrapper in bindings_swig/bingins.i:

    void normalize_numpy(double* in_1, unsigned size_in_1,
                         double** out_1, int* size_out_1)
    {
        const std::vector<double> vector(in_1, in_1 + size_in_1);

        auto result = mymath::normalize(vector);

        *out_1 = static_cast<double*>(malloc(result.size() * sizeof(double)));
        std::copy(result.begin(), result.end(), *out_1);

        *size_out_1 = result.size();
    }

I can see malloc. The question is when the vector is going to be deallocated on the Python side? Or would numpy do it implicitly (perhaps using Python's GC)?

[ferdnyc]

@adamryczkowski There won't be any leak.

normalize_numpy() is using the numpy typemap on line 45, which defines the output variable as an ARGOUTVIEWM_ARRAY1 — the M after ARGOUTVIEW stands for "Managed", and tells SWIG to not only create the returned Python object from the allocated C++ object's memory directly, without copying it (ARGOUTVIEW), but also to take ownership of that underlying object, and deallocate it once the Python object is deleted and it's no longer required (ARGOUTVIEWM).

A list of the available typemaps with very minimal descriptions is in the numpy documentation for numpy.i, and the more-explanatory blog post dead-linked there can be read using the wayback machine.

[ferdnyc]

(Actually, perhaps more informative is the SciPy Cookbook entry that was created, along with the typemaps in question, based on the automatic deallocation code originally published in that blog post.)