These are chat archives for thunder-project/thunder
Seriesthat allow for using single- or multi-level indices (ala multi-indices in Pandas) for aggregating/sub-selecting time series data. The base functions that I have written to work with such index structures seem to be working well, but when I go to expose the functionality, I find that I'm recreating a lot of the functionality is already implemented in code like
Series.seriesStat(for aggregation...plus related helper functions) and
Series.select(for selecting). How do we feel about simply adding keyword arguments onto these functions that indicate that they should consider the index structure? Or is this too complicated (keyword arguments would also be needed to indicate which levels/values to group/select over) and a completely different set of functions should be implemented?
Series.seriesStatis basically two lines, and a lookup table. You could move that lookup table out so both functions can use it, but otherwise doesn't seem like too much overlap?
will there be a performance difference between these?
imDat = imDat.toSeries().toTimeSeries().detrend(method='nonlin',order=2).normalize()
imDat = imDat.toSeries() imDat.saveAsBinarySeries(path) imDat = tsc.loadseries(path).toTimeSeries().detrend(method='nonlin',order=2).normalize()