OK <get_ready_for_this.mp3> here's what I did to solve this.
First, I created a type representing the buffer I wanted, along with some helpers functions to cast a buffer of data to the target format. You could easily amend this to be more flexible, but I just want an array of complex floating point values.
// vector of complex values
typedef vector<cfloat> cbuffer;
// helper to copy data
template<typename T>
void cbuffer_copy_from(cbuffer& cbuf, void *ptr, ssize_t len, ssize_t stride) {
cbuf.reserve(len);
// convert elements into buffer
char* cptr = (char*)ptr;
for (ssize_t ii=0; ii < len; ii++) {
cbuf.emplace_back(*reinterpret_cast<T*>(cptr));
cptr += stride;
}
};
// populate vector from source
template<typename T>
void cbuffer_from(cbuffer& cbuf, void *ptr, ssize_t len, ssize_t stride) {
cbuffer_copy_from<T>(cbuf, ptr, len, stride);
}
// fast path for data that's already cfloat
template <>
void cbuffer_from<cfloat>(cbuffer& cbuf, void *ptr, ssize_t len, ssize_t stride) {
// if stride is right, we can just copy the data
if (stride == sizeof(cfloat)) {
cbuf.resize(len);
memcpy(&cbuf[0], ptr, len*sizeof(cfloat));
} else {
cbuffer_copy_from<cfloat>(cbuf, ptr, len, stride);
}
}
Then, I built a custom converter from python to my cbuffer type:
// python -> cbuffer conversion
struct python_to_cbuffer {
// register converter
python_to_cbuffer() {
converter::registry::push_back(
&convertible,
&construct,
type_id<cbuffer>()
);
}
// does python object implement buffer protocol?
static void* convertible(PyObject* object) {
return PyObject_CheckBuffer(object) ? object : nullptr;
}
// convert object into a complex number
static void construct(
PyObject* object,
converter::rvalue_from_python_stage1_data* data
) {
// grab pointer to memory into which to construct the new value
void* storage = ((converter::rvalue_from_python_storage<cbuffer>*)data)->storage.bytes;
// create buffer object from export source, require format
Py_buffer view;
if (PyObject_GetBuffer(object, &view, PyBUF_FORMAT | PyBUF_STRIDES) < 0) {
return;
}
// make sure it's a one dimensional array
if (view.ndim != 1) {
PyBuffer_Release(&view);
throw std::runtime_error("Array object is not one dimensional");
}
// build new cbuffer to store data
new (storage) cbuffer;
cbuffer* buffer = static_cast<cbuffer*>(storage);
// try to convert view data into cfloat format
string type(view.format);
if (type == "f") cbuffer_from<float> (*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "d") cbuffer_from<double> (*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "Zf") cbuffer_from<cfloat> (*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "Zd") cbuffer_from<cdouble>(*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "b") cbuffer_from<int8_t> (*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "h") cbuffer_from<int16_t>(*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "i") cbuffer_from<int32_t>(*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "l") cbuffer_from<int32_t>(*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "q") cbuffer_from<int32_t>(*buffer, view.buf, view.shape[0], view.strides[0]);
else if (type == "n") cbuffer_from<ssize_t>(*buffer, view.buf, view.shape[0], view.strides[0]);
else {
buffer->~cbuffer();
throw std::runtime_error("Unable to marshall '" + string(view.format) + "' data format");
}
// Stash the memory chunk pointer for later use by boost.python
data->convertible = storage;
}
};
The convertible() function checks that the Python object implements the buffer protocol. Then the construct() function actually extracts a buffer from the object, and converts it to the desired format via the above helper functions. If we fail at any step, cleanup and throw a runtime exception.
Lastly we instantiate the converter in the module:
// define python module
BOOST_PYTHON_MODULE(module) {
// register python -> c++ converters
python_to_cbuffer();
def("test", test);
}
And, if we create a test function:
void test(cbuffer buf) {
for (cfloat val : buf) {
printf("(%f, %f)\n", val.re, val.im);
}
}
Then in python:
>>> module.test(numpy.array([1+2j,3+4j],dtype=numpy.complex64))
(1.000000, 2.000000)
(3.000000, 4.000000)
>>> module.test(numpy.array([1,2],'b'))
(1.000000, 0.000000)
(2.000000, 0.000000)
>>> module.test(numpy.array([1,2],'i'))
(1.000000, 0.000000)
(2.000000, 0.000000)
>>> module.test(numpy.array([1,2],'l'))
(1.000000, 0.000000)
(2.000000, 0.000000)
Enjoy!