[Numpy-discussion] converting a C bytes array to two dimensional numpy array

[Chris Barker - NOAA Federal]

You can also directly build a numpy array from a pointer with the numpy
API.

And I recommend Cython as an interface to make these things easy.

This does mean you’d need to have the numpy lib at build time, .which may
be a downside.

-CHB


Christopher Barker, Ph.D.
Oceanographer

Emergency Response Division
NOAA/NOS/OR&R            (206) 526-6959   voice
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Seattle, WA  98115 <x-apple-data-detectors://6/1>       (206) 526-6317
main reception

On Jul 16, 2019, at 5:48 AM, Derek Homeier <
derek at astro.physik.uni-goettingen.de> wrote:

On 16 Jul 2019, at 9:30 am, Omry Levy <omrylevy at gmail.com> wrote:


I have a question, regarding conversion of C (unsigned char *) buffer to a
two dimensional numpy array


this is what i am doing:

1) I get a C network buffer of unsigned char *  let's call it the source
buffer

the size of the source buffer is:

W * H * 2  bytes


2)  I am using PyByteArray_FromStringAndSize() to convert the source buffer
(a C unsigned char *) to python bytes array.

a = PyByteArray_FromStringAndSize(source buffer, W * H * 2)


3) i am using numpy.frombuffer   to convert the python bytes array to a 1
dimensional numpy array of size W *H *2 bytes

b = numpy.frombuffer(a, dtype = np.uint8)


4) i am creating a 2 dimensional numpy array from (3) when each element in
that array is made of 2 bytes from the python bytes array

c = b.view(np.uint16).reshape((H, W))


Is there a way to optimize this some how ?

Can you suggest a faster and better solution ?


The PyByteArray conversion seems unnecessary - if you can access your input
as a buffer,
calling np.frombuffer on it directly with the correct dtype should work
just as well, and you
can reshape it on the fly:

c = np.frombuffer(source_buffer, dtype=np.uint16, [count=W*H]).reshape((H,
W))

The optional ‘count’ argument would only be required if you cannot simply
read the buffer
to its end.

HTH,
                   Derek

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