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

[RayS <ra...@bl...>]

Thanks Florian,

My preliminary assessment from trials yesterday (based only on a rough benchmark of idle time) is that
        for i in range(2000): numArray[pntr+i] = buf[i]
is >10x slower than
        numArray[pntr:pntr+2000] = map(None, buf)
which is 25% slower than using array.array() & memmove
        memmove(array.buffer_info()[0]+pntr, buf)

I'll test the direct assignment today, as below, as subsequent operations on numarray should be faster than array. I don't recall why I didn't.

The numarray buffer interface is interesting, but I mainly need to do a (deep) copy out of the ctypes memory.
Since the main issue is that src (the ctypes obj) is being updated with 2KB A/D data @125x per second (250K samp./s), I need to get it out of the cytpes obj into storage as efficiently as possible. That storage must then work with slicing and FFT.

If the ctypes construct buf = (c_ulong * 2000)() supported slicing, I would just leave it there...

The small buffer_read thread I made has a time.sleep(.001) for when the A/D buffer is not ready, but even at .001s, it only gets hit once per cycle, if at all. It also seemed that sleep(.0001)==sleep(0); too bad.

Thanks,
Ray

At 10:14 AM 12/16/2004 +0100, Florian Schulze wrote:
>On Wed, 15 Dec 2004 20:16:07 -0800, RJ <ra...@sa...> wrote:
>
>>I'm posting here to see if numarray has a method that could work like array.buffer_info().  numarray.info() returns a text output that can't be used like the array method is to memmove() between ctypes and Python arrays without parsing, apparently.
>>
>>ctypes thread with Thomas Heller below. His main question: "Maybe you should ask to make sure that there's no way to copy between
>>objects implementing the buffer protocol with some Python function that
>>I do not know about?"
>
>I just tried some things:
>>>>import ctypes
>>>>a = (ctypes.c_int * 5)()
>>>>a[0] = 1; a[1] = 2; a[2] = 3; a[3] = 4; a[4] = 5
>>>>list(a)
>[1, 2, 3, 4, 5]
>>>>import numarray
>
>>>>buf = numarray.zeros(shape=20, type='i4')
>>>>buf
>array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>>>buf[2:7] = a
>>>>buf
>array([0, 0, 1, 2, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>
>>>>temp = numarray.array(sequence=buffer(a), shape=5, type='i4')
>>>>temp
>array([1, 2, 3, 4, 5])
>>>>temp._data
><read-only buffer for 0x00FA9230, ptr 0x00BFA668, size 20 at 0x010D6DA0>
>>>>buffer(a)
><read-only buffer for 0x00FA9230, ptr 0x00BFA668, size 20 at 0x00FAF600>
>
>>>>a[2] = 10
>>>>temp
>array([ 1,  2, 10,  4,  5])
>
>The first block just creates the ctypes data.
>
>The second block uses slice assignment to copy the data from the ctypes array into the numarray.array.
>
>The third block uses the buffer interface to create a numarray.array which points to the same memory location as the ctypes array.
>
>The forth block illustrates that it's really the same memory.
>
>You have to benchmark which one of the two solutions is the better one for you.
>
>Regards,
>Florian Schulze
>
>
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