On 09/05/2012 07:10 PM, bearophile wrote:
NumPy arrays <==> D arrays
I've been thinking about this one a bit more, and I am not sure it
belongs in pyd.
First, the conversion is not symmetric. One can convert a numpy.ndarray
to a d array like so:
PyObject* ndarray;
double[][] matrix = d_type!(double[][])(ndarray);
however, going back
PyObject* res = _py(matrix);
It is not at all clear that the user wants res to be a numpy.ndarray.
The problem is partially that d arrays would be overloaded to a few too
many things (list, str, array, any iterable, any buffer). That last one
is a doozy. d_type never actually touches ndarray's type, so _py can
hardly know what to use to convert matrix. (what if ndarray is actually
a foo.BizBar matrix?)
I could just specialize _py for numpy.ndarrays, defaulting to lists of
lists (which is what we do already), but I kinda want a specialized type
for numpy.ndarrays.
Also, all these conversions imply data copying; is this reasonable for
numpy arrays?
It is easy enough to get a void* and shape information out of the
ndarray, but building a decent matrix type out of them is not trivial.
Is there a good matrix library for D that would be suitable for this?
Oh yeah, also: rectangular matrices. For static arrays, the conversion
is 1 memcpy. For dynamic arrays: lots of memcpys. I suppose I could
abuse slicing much.
