Oliver Kranz wrote: > Hi, > > I am working on a Python extension module using of the NumPy C-API. The > extension module is an interface to an image processing and analysis > library written in C++. The C++ functions are exported with > boos::python. Currently I am implementing the support of > three-dimensional data sets which can consume a huge amount of memory. > The 3D data is stored in a numpy.ndarray. This array is passed to C++ > functions which do the calculations. > > In general, multi-dimensional arrays can be organized in memory in four > different ways: > 1. C order contiguous > 2. Fortran order contiguous > 3. C order non-contiguous > 4. Fortran order non-contiguous > > Am I right that the NumPy C-API can only distinguish between three ways > the array is organized in memory? These are: > 1. C order contiguous e.g. with PyArray_ISCONTIGUOUS(arr) > 2. Fortran order contiguous e.g. with PyArray_ISFORTRAN(arr) > 3. non-contiguous e.g. with !PyArray_ISCONTIGUOUS(arr) && > !PyArray_ISFORTRAN(arr) > > So there is no way to find out if a non-contiguous array has C order or > Fortran order. The same holds for Python code e.g. by use of the flags. > > a.flags.contiguous > a.flags.fortran > > This is very important for me because I just want to avoid to copy every > non-contiguous array into a contiguous array. This would be very > inefficient. But I can't find an other solution than copying the array. It is inefficient depending on what you mean by inefficient. Memory-wise, copying is obviously inefficient. But speed-wise, copying the array into a contiguous array in C order is faster in most if not all cases, because of memory access times.
You may want to read the following article from Ulrich Drepper on memory and cache: http://lwn.net/Articles/252125/ cheers, David _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
