/**
* Here, we'll take the alternate approach of using the strides to access the array's memory directly.
* This can be much faster for large arrays.
*/
static void copy_ndarray_to_mv2(bn::ndarray const & array, matrix2 & mat) {
// Unfortunately, get_strides() can't be inlined, so it's best to call it once up-front.
Py_intptr_t const * strides = array.get_strides();
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 2; ++j) {
mat(i, j) = *reinterpret_cast<double const *>(array.get_data() + i * strides[0] + j * strides[1]);
}
}
}
NumPyArrayData<T>(const np::ndarray &arr){
np::dtype dtype = arr.get_dtype();
np::dtype dtype_expected = np::dtype::get_builtin<T>();
if(dtype != dtype_expected) {
std::stringstream ss;
ss << "NumPyArrayData: Unexpected data type (" << bp::extract<const char*>(dtype.attr("__str__")()) << ") received";
ss << "Expected " << bp::extract<const char*>(dtype_expected.attr("__str__")());
throw std::runtime_error(ss.str().c_str());
}
data_ = arr.get_data();
strides_ = arr.get_strides();
}
