If the order doesn't matter, you can sort the data and remove x * 0.5 * n where x is the proportion of numbers you want. If you have too many similar values though, this falls down. I suggest you check out quantiles in a good statistics book.
Alan. Peter Otten wrote: > 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? > > I think you are looking for "extrema": > > def w3(items): > items = iter(items) > view = None, items.next(), items.next() > for item in items: > view = view[1:] + (item,) > yield view > > for i, (a, b, c) in enumerate(w3(data)): > if a > b < c: > print i+1, "min", b > elif a < b > c: > print i+1, "max", b > else: > print i+1, "---", b > > Peter -- http://mail.python.org/mailman/listinfo/python-list
