Robert: It's always easier to manipulate series withoutmissing data. The trick I gave you earlier about computing a moving average after having removed the missing dates was that, just a trick. However, I'm confident it should work. Unfortunately, there's no easy way to define new frequencies, and it's not on or todo list either. Frequencies are defined in the C part of the code... On Nov 28, 2008, at 12:09 AM, Robert Ferrell wrote:
On Nov 27, 2008, at 11:23 AM, Robert Ferrell wrote:
That has a hole on Sep 1. This matters for things like moving average calculation. Sep 1 should be treated like a Saturday or Sunday, but instead causes a 5-day mov_average calculation to not compute anything from Sep 2 through Sep 7:
timeseries([-- -- -- -- 22.998 -- -- -- -- -- 21.06], dates = [25-Aug-2008 ... 08-Sep-2008], freq = B)
My question: What is a good way to handle (get rid of?) the holes in the series?
Mmh. On the top of my head, I'd do something like that: * create a new series by using .compressed on your initial series. You'll get rid of the masked data and will have incomplete dates, but it shouldn't matter. * use your moving average function on the new series. * if needed, reset the missing dates by using fill_missing_dates on the filtered series.
Let me know how it goes. P.
Since the date arrays has holes, I can't use timeseries date range calculations. So, for instance, to get the previous 5 days of data I can't just use series[d-5:d]. Instead I need to (I think) convert to an index, series.date_to_index(d), and then use that index. I'm going to try that, along with using .compressed(), and see how I do.
Is there any possibility of allowing user defined frequencies?
thanks, -robert _______________________________________________ SciPy-user mailing list SciPy-user@scipy.org http://projects.scipy.org/mailman/listinfo/scipy-user