erikcw wrote: > Hi all, > > I have a collection of ordered numerical data in a list. The numbers > when plotted on a line chart make a low-high-low-high-high-low (random) > pattern. I need an algorithm to extract the "significant" high and low > points from this data. > > Here is some sample data: > data = [0.10, 0.50, 0.60, 0.40, 0.39, 0.50, 1.00, 0.80, 0.60, 1.20, > 1.10, 1.30, 1.40, 1.50, 1.05, 1.20, 0.90, 0.70, 0.80, 0.40, 0.45, 0.35, > 0.10] > > In this data, some of the significant points include: > data[0] > data[2] > data[4] > data[6] > data[8] > data[9] > data[13] > data[14] > .... > > How do I sort through this data and pull out these points of > significance?
Its obviously a kind of time series and you are search for a "moving_max(data,t,window)>data(t)" / "moving_min(data,t,window)<data(t)": an extremum within a certain (time) window. And obviously your time window is as low as 2 or 3 or so. Unfortunately a moving_max func is not yet in numpy and probably not achievable from other existing array functions. You have to create slow looping code. Robert -- http://mail.python.org/mailman/listinfo/python-list
