[SciPy-user] timeseries: logging of defective time series

Pierre GM pgmdevlist@gmail....
Mon Feb 2 13:02:57 CST 2009


Timmie,

Remember that the mask is an array of boolean and can be used for  
indexing.
I will also assume that your data is 1D

* To find the dates corresponding to the missing values in your series:
 >>> series.dates[series.mask]


* To find the missing dates, use fill_missing_dates first (to make  
sure the dates are continuous) and get the missing dates by
 >>> series.dates[series.mask]

With your example:
 >>> mser_1_filled = ts.fill_missing_dates(mser_1)
 >>> missing_dates = mser_1_filled.dates[mser_1.mask]

Note that if your initial `series` has already some missing dates,  
you'll pick those ones up as well. you shuld then check whether you  
have missing values in the first place, find the corresponding dates,  
fill the dates, recheck the missing ones, and take the difference  
between the two sets.



* To find duplicated dates:
Things get a tad more complicated:
1. make sure that your `series` is sorted chronologically first
2. construct the following array:
 >>> d = series.dates
 >>> dupcheck = np.r_[False, (d[1:]==d[:-1])]

dupcheck is a ndarray of booleans with True values where the  
corresponding date is the same as the previous ones. Note that the  
first date of a duplicated series is flagged as False
Gimme a few days to whip up a more useable function that would  
reproduce that (I think I already have something along those lines  
somewhere on my HD).

>
> Such series may return may have one or more of the following  
> properties:
>
> * duplicate dates (ts.time_series.has_duplicated_dates() )
> * missing dates (ts.time_series.has_missing_dates() )
> * masked values (ts.time_series.mask)

has_duplicated_dates and has_missing_dates were not really meant to be  
used directly, but more internally to keep track of some info on the  
distribution of dates



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