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Hello Dharhas, welcome as new user of timeseries user! Learning this scikit will soon pay off. I have seen a huge boost in the simplicity and usability of my analysis through the code I wrote using the timeseries. A special praise shall be given to the developers Pierre & Matt. By patiently answering my "advanced python newbie" questions they really help me to get the maximum of the numpy.ma and scikits.timeseries tool box. This did really bring my numerical python coding forward.
The documentation is a bit scarce indeed, and some functions are being rewritten (eg, loadtxt). For now, here's what you can do: I tried to add some of my questions to the cookbook. Here is one that may help you in the current situation:
More extensive answer - http://www.scipy.org/Cookbook/TimeSeries/FAQ#head-9f5c8c4d4aa0de90c9851b972d... All other Q&A in form of mails sent in by other users and myself are at gmane: * creating timeseries for non convertional custom frequencies - http://article.gmane.org/gmane.comp.python.scientific.user/15688 Search for "time series" or timeseries http://search.gmane.org/?query=%22time+series%22+timeseries&author=&group=gmane.comp.python.scientific.user&sort=relevance&DEFAULTOP=or&xP=time%09series&xFILTERS=Gcomp.python.scientific.user---A Some answers to feature requests may also help: http://scipy.org/scipy/scikits/query?status=new&status=assigned&status=reopened&status=closed&component=timeseries&order=priority @Pierre, Are there plans to include timeseries into the scipy online doc editor? What for do you suggest if I would like to contribute examples here and there?
* Now, construct your time series
series = ts.time_series([_[-1] for _ in loaded, dates=dates) after this step you'd probably want to fill the missing dates: series_filled = series.fill_missing_dates
=> now you can save the data to csv using reportlib from the scikit and to other neat things. Hope that helps. Regards, Timmie