Commit 5f97a022 authored by gabrieldemarmiesse's avatar gabrieldemarmiesse

Merge branch 'master' of github.com:macdentalr12/cython into memview_to_c

parents c2a3457a ec49ce2f
...@@ -296,3 +296,42 @@ There is some speed penalty to this though (as one makes more assumptions ...@@ -296,3 +296,42 @@ There is some speed penalty to this though (as one makes more assumptions
compile-time if the type is set to :obj:`np.ndarray`, specifically it is compile-time if the type is set to :obj:`np.ndarray`, specifically it is
assumed that the data is stored in pure strided mode and not in indirect assumed that the data is stored in pure strided mode and not in indirect
mode). mode).
Pass data from a C function via pointer
==================
Since use of pointers in C is ubiquitous, here we give a quick example of how
to call C functions whose arguments contain pointers. Suppose you want to
manage an array (allocate and deallocate) with NumPy, but its data are
computed by an external C function declared in :file:`C_func_file.h`::
void C_func(double * CPointer, unsigned int N);
where CPointer points to the array and N is its size.
You can call the function in a Cython file in the following way::
cdef extern from "C_func_file.h":
void C_func(double *, unsigned int)
import cython
import numpy as np
cimport numpy as np
def f(arr): # 'arr' is a one-dimensional array of size N
# Before calling the external function, we need to check whether the
# memory for 'arr' is contiguous or not; if not, we store the computed
# data in an contiguous array and then copy the data from that array.
np.ndarray[np.double_t, ndim=1, mode="c"] contig_arr
if arr.flags.c_contiguous:
contig_arr = arr
else:
contig_arr = arr.copy('C')
C_func(<cython.double *> contig_arr.data, contig_arr.size)
if contig_arr is not arr:
arr[...] = contig_arr
return
This way, you can have access the function more or less as a regular
Python function while its data and associated memory gracefully managed
by NumPy.
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