Dag Sverre Seljebotn wrote: > Here's an HPC perspective...: > At least I feel that the transparency of NumPy is a huge part of its > current success. Many more than me spend half their time in C/Fortran > and half their time in Python.
Absolutely -- and this point has been raised a couple times in the discussion, so I hope it is not forgotten. > I tend to look at NumPy this way: Assuming you have some data in memory > (possibly loaded by a C or Fortran library). (Almost) no matter how it > is allocated, ordered, packed, aligned -- there's a way to find strides > and dtypes to put a nice NumPy wrapper around it and use the memory from > Python. and vice-versa -- Assuming you have some data in numpy arrays, there's a way to process it with a C or Fortran library without copying the data. And this is where I am skeptical of the bit-pattern idea -- while one can expect C and fortran and GPU, and ??? to understand NaNs for floating point data, is there any support in compilers or hardware for special bit patterns for NA values to integers? I've never seen in my (very limited experience). Maybe having the mask option, too, will make that irrelevant, but I want to be clear about that kind of use case. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
