Ondrej Certik wrote:
> Hi,
>
> what is now the canonical way to convert between (python) numpy array and
>
> int *a
>
> or
>
> double *a
>
> ?
>
Yes, this should probably be fixed/be made more available.
The simplest thing is something like the (untested) code below. It is
certainly possible to do some C calls instead of Python calls here (like
petsc4py does); however I tink this definitely belong to the 80% of the
code that only takes 20% of the time; Cython seems to be more about
optimizing the other part that tends to get repeated :-)
import numpy as np
arr = some generic numpy array
assert arr.dtype == np.float64 # or whatever
cdef ndarray contarr
if not arr.flags['C_CONTIGUOUS']:
# Array is not contiguous, need to make contiguous copy
contarr = arr.copy(order='C')
else:
contarr = arr
# Get data pointer. Important that contarr is cdef-ed ndarray
cdef npy.float64_t* ptr = <npy.float64_t*>contarr.data
call_c_function(ptr)
# If the C function modifies the data, and the array was not contiguous,
# then the data of contarr must be copied into arr, using standard NumPy
calls:
arr[:] = contarr[:]
Comment: The only "shady" part here is "contarr.data", which accesses
implementation details of NumPy arrays. I'm guessing that this will
never change, but I once planned to make a generic cython function
"cython.buffer.bufptr" which would return the same pointer but it could
be acquired through the buffer API.
Note that the code above does not make use of the buffer API, as it is
written to work regardless of number of dimensions.
Dag Sverre
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