[Numpy-discussion] Re: [ctypes-users] copying/slicing ctypes arrays, (c_ulong *n)() - to Numeric

Ray S rays at blue-cove.com
Thu Feb 3 12:25:47 CST 2005

At 07:32 PM 2/3/2005 +0100, you wrote:

>Don't be confused that the buffer() object says <read-only buffer ...>!
>The buffer call only asks for readable memory..., but ctypes doesn't
>care about the readonly attribute - it will happily write into this

Hi Thomas,

Yes, I was thinking of what the shell error said upon assignment...

Upon adding to some working code all is well:

 >>> import Numeric, ctypes, string
 >>> N = Numeric.zeros((10,), Numeric.Float)
 >>> buf = buffer(N)
 >>> buf
<read-only buffer for 0x008F9C28, ptr 0x008D7780, size 80 at 0x008FE220>
 >>> int(string.split(repr(buf))[5][:-1], 16)

## numarray version
# nAddress = int(string.split(repr(N._data))[2], 16)

## Numeric version
NAddress = int(string.split(repr(buffer(N)))[5][:-1], 16)

## Load DLL here...
## do this to get data from the USB A/D's DLL
usb.GetData(usb.Sn, (bufferInsertPos * N.itemsize()) + NAddress,
                                     ctypes.byref( (types.c_long * 
buffersize)() ) )

Which is faster than getting data into a ctypes array (c_ulong *n)() and 
then doing memmove() to Numeric - one less step.

Maybe this snip would be of help to some others, although more so to numpy 

Of course, the Python array works the same:
 >>> a = array.array('l',[1,2,3])
 >>> int(string.split(repr(buffer(a)))[5][:-1], 16)

>If this is too confusing, and this may well be, ctypes could expose a
>memory() function which would insist on read-write memory, but apart
>from that do the same that buffer does:

No, not confusing, just not clear to a non-expert C person that ctypes 
ignores where Python is read-only. A simple note in the tutorial would be 
Some over at numpy were also unaware of memmove()s' existence in the new 
releases, and seemed interested.

Thanks again,

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