# [SciPy-user] numpy array in ctype struct

Travis E. Oliphant oliphant@enthought....
Tue Jan 15 08:59:06 CST 2008

```Paul Kienzle wrote:
> Hi,
>
> We are trying to create the following struct in ctypes:
>
>   struct {
>     int n,k;
>     double A[4][4], B[4][4];
>   } ;
>
> Easy enough:
>
>   class embedded_array(Structure):
>     _fields_ = [("n",c_int),("k",c_int),("A",c_double*16),("B",c_double*16)]
>   instance = embedded_array()
>
> Question:
>
>   Is there a way to map the data in A/B into a numpy array, so that we
>   can use it directly?
>
>
Yes.   You can create an array from a pointer to memory and a
description of the data that is much like the ctypes structure.   At
some point, there should be a direct conversion from ctypes objects to
NumPy data-types, but nobody has written that yet.  At the moment you
have to create a dtype object that is parallel to the ctypes one:

import numpy as np
dt = np.dtype([('n',np.intc),('k',np.intc),("A", float, 16), ("B",
float, 16)])

Then,

arr = np.frombuffer(instance, dtype=dt)

will create a 1-d array of these structures mapping onto the data
pointed to by instance.   Of course, in this case, the 1-d array only
has one element.

Access to the fields of the c-structure is obtained by "dictionary" access:

arr['n'] = 10
arr["A"] = 1

You will notice that this accesses the memory directly:

print instance.n
print [x for x in instance.A]

Ask if you need more help.

Best regards,

-Travis O.

```