Long post, but I hoped to be thorough.
I'm having some trouble generalizing a simple Cython implementation that
made feasible a previously infeasible Python implementation. The problem
can be cast as follows:
Let f(x,p) be a function, for example f(x,p) = sin(p*x).
Pick a range of values for p, call this: pvalues
Then, in pseudocode:
for p in pvalues:
xn = x0
for i in range(n):
# this could benefit from ticket #474
xn = f(xn, p)
# increment count for bin at (xn, p)
# ...
return bincounts
I'm following the technique in the Cython tutorial that was written for
Scipy 2009, and I have the following extension class defined:
ctypedef np.float_t FTYPE_t
cdef class MyFunction:
cpdef FTYPE_t evaluate(self, FTYPE_t x)
This gives me the flexibility to define the function in Python space as well
(which is desirable). So I've coded this function as above, making sure to
provide types for everything, with the following call signature:
@cython.boundscheck(False)
@cython.wraparound(False)
def histogram(MyFunction func not None,
np.ndarray[FTYPE_t, ndim=1] pvalues,
FTYPE_t x0,
int n,
int bins):
Then the fundamental line which exists in the inner loop is:
xn = func.evaluate(xn, p)
Everything works and is quick. Now, I'm having trouble generalising this so
that both x and p can be vectors of arbitrary (but fixed) length. My
understanding says that I must go from cpdef to just def (due to ticket
#177):
ctypedef np.float_t FTYPE_t
cdef class MyFunction:
def evaluate(self, np.ndarray[FTYPE_t, ndim=1, mode="c"] x,
np.ndarray[FTYPE_t, ndim=1, mode="c"] p):
Of course, the problem now is that I have making a Python call inside a
doubly nested forloop and things get much slower. I can see that p would be
fixed in the inner loop, but I'd still have to deal with the fact that xn is
changing each time.
So how can I implement this quickly and generally? Thanks!
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