On Tue, Jun 7, 2011 at 7:28 PM, Pierre GM <pgmdevl...@gmail.com> wrote:
> > > > It supports .astype(), with a truncation policy. This is motivated > partially because that's how Pythons integer division works, and partially > because if you consider a full datetime '2011-03-14T13:22:16', it's natural > to think of the year as '2011', the date as '2011-03-14', etc, which is > truncation. With regards to converting in the other direction, you can think > of a datetime as representing a single moment in time, regardless of its > unit of precision, and equate '2011' with '2011-01', etc. > > OK from high to low, less from low to high. That's where our keyword ('END' > or "START") comes into play in scikits.timeseries, so that you can decide > whether '2011' should be '2011-01' or '2011-06'... > Because datetime64 is a NumPy data type, it needs a well-defined rule for these kinds of conversions. Treating datetimes as moments in time instead of time intervals makes a very nice rule which appears to be very amenable to a variety of computations, which is why I like the approach. > > We needed the concept to convert time series, for example from monthly to > quarterly (what is the first month of the year (as in succession of 12 > months) you want to start with ?) > > > > Does that need to be in the underlying datetime for layering a good > timeseries implementation on top? > > Mmh. How would you define a quarter unit ? [3M] ? But then, what if you > want your year to start in December, say (we often use DJF/MAM/JJA/SON as a > way to decompose a year in four 'hydrological' seasons, for example) > With origin metadata I suppose defining these things as NumPy data types will work, but it still feels like it belongs at a higher level. Of course I'm not going to stop anyone from doing it... -Mark > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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