On Tue, Jul 7, 2009 at 6:16 AM, John [H2O]<washa...@gmail.com> wrote: > > Here's a function I wrote to calculate hourly averages: > > It seems a bit slow, however... any thoughts on how to improve it? > > def calc_hravg(X): > """Calculates hourly average from input data""" > > X_hr = [] > minX = X[:,0].min() > hr = dt.datetime(*minX.timetuple()[0:4]) > > while hr <= dt.datetime(*X[-1,0].timetuple()[0:4]): > nhr = hr + dt.timedelta(hours=1) > ind = np.where( (X[:,0] > hr) & (X[:,0] < nhr) ) > vals = X[ind,1][0].T > try: > #hr_avg = np.sum(vals) / len(vals) > hr_avg = np.average(vals) > > except: > hr_avg = np.nan > X_hr.append([hr,hr_avg]) > hr = hr + dt.timedelta(hours=1) > > return np.array(X_hr) > > > --
One quick thought, as I haven't read your code very carefully, but using reduce is faster than sum or average (though you sacrifice readability) if the ndarray is big enough to matter ie., instead of np.average(vals) you could have np.add.reduce(vals)/len(vals). You might have some better luck on the numpy mailing list <http://www.scipy.org/Mailing_Lists>. It's very active and there are people that are much more knowledgeable than me. You might want to include an example of your X in the example to help them help you to optimize. Cheers, Skipper _______________________________________________ Tutor maillist - Tutor@python.org http://mail.python.org/mailman/listinfo/tutor
