If you know that they are always numpy arrays, then it doesn't really help.
However, if they could be numpy arrays, lists, strings, ... then using
buffer() on them gets you the memory buffer of the object, basically an
array of bytes you could copy. Basically it helps generalize the problem to
mor
Jeff,
Thank you for quick reply. Can you give any example of how would buffer()
help converting numpy/python arrays to managed?
For now I developed as decorator (on my personal time) that handles all I/O
conversion on python side (assumes one input and one output arbitrary
array):
def decornet
I would highly recommend PTVS:
1. supports mixed-mode debugging between Python, C#, C/C++, Fortran, etc.
2. integrates ipython nicely into workflow
3. intellisense is quite powerful
The only issue I found (reported as bug) is that remote debugging does not
work with pythonnet.
please note that m
