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From: Chris Barker <chris.bar...@noaa.gov>
Date: Mon, 6 Aug 2012 10:28:10 -0700
Message-ID: <CALGmxEKjBzOAWwk_ORjvC6h0dbBoVd8CKRrWmtTcF39k7do...@mail.gmail.com>
Subject: Re: [cython-users] Re: Best Practices for passing numpy data pointer
 to C ?
To: cython-users@googlegroups.com
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On Mon, Jul 30, 2012 at 1:37 PM, Sturla Molden <sturlamol...@yahoo.no> wrote:

> So I suggest one of these idioms for calling C from Cython:

> cdef double[:,::1] array = numpy_array
> cdef Py_ssize_t m, n
> m = array.shape[0]
> n = array.shape[1]
> foobar(m, n, &array[0,0])
>
> cdef view.array array = <double[:,::1]> numpy_array
> cdef Py_ssize_t m, n
> m = array.shape[0]
> n = array.shape[1]
> foobar(m, n, <double*> array.data)

The only difference is that first line: will they generate the same C
code?  -- why would one choose on or the other?

(there should be one obvious way to do it...)

I'll probably go update the wiki page, but to make sure I'm clear:

Using a view.array will build a Cython "memoryview", using the
extended buffer interface -- so you can pass in any Python object that
supports PEP 3118 buffers (does  anything other than numpy suport that
now??)

This is a lightweight way to both get a pointer to the data, and to
have a way, in Cython, to slice and dice the data.

Will this check for C-contiguous and raise an error if not?

-Chris

















>
>
> Sturla



-- 

Christopher Barker, Ph.D.
Oceanographer

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Chris.Bar...@noaa.gov