On Mon, Nov 22, 2010 at 1:39 PM, Keith Goodman <[email protected]> wrote: > On Mon, Nov 22, 2010 at 10:32 AM, <[email protected]> wrote: >> On Mon, Nov 22, 2010 at 1:26 PM, Keith Goodman <[email protected]> wrote: >>> On Mon, Nov 22, 2010 at 9:03 AM, Keith Goodman <[email protected]> wrote: >>> >>>> @cython.boundscheck(False) >>>> @cython.wraparound(False) >>>> def nanstd_twopass(np.ndarray[np.float64_t, ndim=1] a, int ddof): >>>> "nanstd of 1d numpy array with dtype=np.float64 along axis=0." >>>> cdef Py_ssize_t i >>>> cdef int a0 = a.shape[0], count = 0 >>>> cdef np.float64_t asum = 0, a2sum=0, amean, ai, da >>>> for i in range(a0): >>>> ai = a[i] >>>> if ai == ai: >>>> asum += ai >>>> count += 1 >>>> if count > 0: >>>> amean = asum / count >>>> asum = 0 >>>> for i in range(a0): >>>> ai = a[i] >>>> if ai == ai: >>>> da = ai - amean >>>> asum += da >>>> a2sum += (da * da) >>>> asum = asum * asum >>>> return sqrt((a2sum - asum / count) / (count - ddof)) >>>> else: >>>> return np.float64(NAN) >>> >>> This is 5% faster: >>> >>> @cython.boundscheck(False) >>> @cython.wraparound(False) >>> def nanstd_1d_float64_axis0_2(np.ndarray[np.float64_t, ndim=1] a, int ddof): >>> "nanstd of 1d numpy array with dtype=np.float64 along axis=0." >>> cdef Py_ssize_t i >>> cdef int a0 = a.shape[0], count = 0 >>> cdef np.float64_t asum = 0, amean, ai >>> for i in range(a0): >>> ai = a[i] >>> if ai == ai: >>> asum += ai >>> count += 1 >>> if count > 0: >>> amean = asum / count >>> asum = 0 >>> for i in range(a0): >>> ai = a[i] >>> if ai == ai: >>> ai -= amean >>> asum += (ai * ai) >>> return sqrt(asum / (count - ddof)) >>> else: >>> return np.float64(NAN) >> >> I think it would be better to write nanvar instead of nanstd and take >> the square root only in a delegating nanstd, instead of the other way >> around. (Also a change that should be made in scipy.stats) > > Yeah, I noticed that numpy does that. I was planning to have separate > var and std functions. Here's why (from the readme file, but maybe I > should template it, the sqrt automatically converts large ddof to > NaN):
I'm not sure what you are saying, dropping the squareroot in the function doesn't require nan handling in the inner loop. If you want to return nan when count-ddof<=0, then you could just replace if count > 0: ... by if count -ddof > 0: ... Or am I missing the point? Josef > > Under the hood Nanny uses a separate Cython function for each > combination of ndim, dtype, and axis. A lot of the overhead in > ny.nanmax, for example, is in checking that your axis is within range, > converting non-array data to an array, and selecting the function to > use to calculate nanmax. > > You can get rid of the overhead by doing all this before you, say, > enter an inner loop: > >>>> arr = np.random.rand(10,10) >>>> axis = 0 >>>> func, a = ny.func.nanmax_selector(arr, axis) >>>> func.__name__ > 'nanmax_2d_float64_axis0' > > Let's see how much faster than runs: > >>> timeit np.nanmax(arr, axis=0) > 10000 loops, best of 3: 25.7 us per loop >>> timeit ny.nanmax(arr, axis=0) > 100000 loops, best of 3: 5.25 us per loop >>> timeit func(a) > 100000 loops, best of 3: 2.5 us per loop > > Note that func is faster than the Numpy's non-nan version of max: > >>> timeit arr.max(axis=0) > 100000 loops, best of 3: 3.28 us per loop > > So adding NaN protection to your inner loops has a negative cost! > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
