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
To copy from a list or tuple you need to use the Python buffer() function.
You can use that on numpy arrays as well. In buffer form, the len() is the
byte length so then you don't need to know the data type or size. However,
some additional work would be required if you wanted to make sure the C#
a
How to copy unmanaged array (python list/tuple or numpy array) into managed
C# array? I guess using Marshal.Copy, but can anyone point to example?
Thanks,
Denis
On Thu, Oct 30, 2014 at 12:19 PM, Nikhil Garg
wrote:
> Thanks Brad and Jeff for the detailed info. For now, fromiter is serving
> me w
And more important question - is it possible to generalize the copying of
python array object to managed C# array object without knowing the data
type/size/length?
On Wed, Nov 5, 2014 at 8:58 AM, Denis Akhiyarov
wrote:
> How to copy unmanaged array (python list/tuple or numpy array) into
> manag
Finally decided to generate the managed object on Python side and return it
to C# with no conversion necessary in C#. This way I can even wrap regular
function with Python with @decorator to handle the conversion. I suppose
the dynamic version of pythonnet may have auto conversion for Python 3, bu
Thanks Brad and Jeff for the detailed info. For now, fromiter is serving me
well and has reduced my processing time considerably, so I am just going to
stick with it.
On 29 October 2014 11:04, Jeffrey Bush wrote:
> I finally have a chance to chime in, and Bradley is exactly right.
> Marshall.Co
I finally have a chance to chime in, and Bradley is exactly right.
Marshall.Copy copies the raw data, and apparently your file library does
not store that data in a nice, contiguous, manner. While it is highly
likely that copying all the data to an array in C# will be faster than the
fromiter in Py
Well it makes sense to me that doing it via an iterator, and element at a time,
would be slow. There’s a lot of call overhead associated with each iteration
step. Whether it’s done in .net, or in python, or a call from one to the
other, it will be slow. It’s still a call where you’d be better
Hello,
Yeah, I read data from a file say at each node and each time step, but when i
try to use Marshal approach i get gibberish but when i use simple iter i get
correct values. i have been trying the approach used in example in the previous
post and that example makes sense but it doesnt make s
You indicate that you are reading from a file. The thread you reference was
about copying data in memory. I’d think matters of buffering and read-ahead
caches would be far more relevant than anything else. Am I missing something?
> On Oct 26, 2014, at 5:18 AM, Nikhil Garg wrote:
>
>
> Hi,
The problem with your current code is that as soon as you call
src_hndl.Free() the pointer is not necessarily valid any more! .NET is
allowed to move the memory contents of objects at will unless they are
pinned (which is what the first line does). By freeing the GCHandle it is
no longer pinned and
I was tempted to code it up, and it turns out you can do it in pure python.
I thought of 4 ways to copy the data: using a for loop (like you did),
using numpy.fromiter, using numpy.fromstring, and using Marshal.Copy.
Obviously the for loop is the slowest. numpy.fromiter is still slow, but
~2.5x f
You could write a .NET function to do this with fixed pointers and "memcpy"
from the .NET array to the numpy data (the raw data). This would be the
absolute fastest way, but does involve a number of assumptions (for
example that the data in the two arrays are laid out in the same way). If
you want
An aside that may be useful:
.net will skip array bounds checking within simple for-loops, as an
optimization. But only if the binaries have all their optimizations turned on.
A binary built for debug has them turned off. There is a huge speed up for
iterating over an array when these optimizat
15 matches
Mail list logo
