I'm using cython to wrap a c++ library. A data structure to be wrapped in c++ side is:
struct StructInts {
int a;
int b;
int c;
};
typedef std::vector<StructInts> VectorTris;
I'm hope for using numpy.ndarray
as an interface in cython side(because the data source is numpy.ndarray
), with imagined functions like:
cdef numpy.ndarray _from_vector_tris(VectorTris vector_tris):
...
return _a_ndarray
cdef VectorTris _to_vector_tris(numpy.ndarray data_source):
...
return _a_vector_tris
I tried field by field copy from/to VectorTris
(construct new numpy.ndarray
or VectorTris
and fill it) like this:
cdef numpy.ndarray _from_vector_tris(VectorTris vector_tris):
cdef int dim0 = vector_tris.size() * 3
cdef numpy.ndarray _a_ndarray = numpy.empty(dim0, dtype=int)
for i in range(vector_tris.size()):
_a_ndarray[i*3] = vector_tris[i].a
_a_ndarray[i*3 + 1] = vector_tris[i].b
_a_ndarray[i*3 + 2] = vector_tris[i].c
return _a_ndarray
cdef VectorTris _to_vector_tris(numpy.ndarray data_source):
# construct each StructInts and init .a, .b, .c with corresponding
# items in data_source, and then push_back into _a_vector_tris
# the procedure seems like _from_vector_tris() but reversed
return _a_vector_tris
It works but with large data source, it's too slow
How to convert between these two data structures efficiently? Thanks !