On Mon, May 25, 2009 at 8:30 PM, Pierre GM <pgmdevl...@gmail.com> wrote: > > On May 25, 2009, at 8:06 PM, josef.p...@gmail.com wrote: >>>> >>>> The problem is, if the functions are enhanced in the current numpy, >>>> then scikits.timeseries is not (yet) available. >>> >>> Mmh, I'm not following you here... >> >> The original question was how we can enhance numpy.financial, eg. >> np.irr >> So we are restricted to use only what is available in numpy and in >> standard python. > > Ah OK. But it seems that you're now running into a pb w/ dates > handling, which might be a bit too specialized for numpy. Anyway, the > call isn't mine. > >>> >> I looked at your moving functions, autocorrelation function and so on >> a while ago. That's were I learned how to use np.correlate or the >> scipy versions of it, and the filter functions. I've written the >> standard array versions for the moving functions and acf, ccf, in one >> of my experiments. > > The moving functions were written in C and they work even w/ > timeseries (they work quite OK w/ pure MaskedArraysP. We put them in > scikits.timeseries because it was easier to have them there than in > scipy, for example. > > >> If Skipper has enough time in his google summer of code, we would like >> to include some basic timeseries econometrics (ARMA, VAR, ...?) >> however most likely only for regularly spaced data. > > Well, we can easily restrict the functions to the case were there's no > missing data nor missing dates. Checking the mask is easy, and we have > a method to chek the dates (is_valid) > > >>> Anyhow, if the pb you have are just to specify dates, I really think >>> you should give the scikits a try. And send feedback, of course... >> >> Skipper intends to write some examples to show how to work with the >> extensions to scipy.stats, which, I think, will include examples using >> time series, besides recarrays, and other array types. > > > Dealing with TimeSeries is pretty much the same thing as dealing with > MaskedArray, with the extra convenience of converting from one > frequency to another and so forth.... Quite often, an analysis can be > performed by dropping the .dates part, working on the .series part > (the underlying MaskedArray), and repatching the dates at the end... > > >> >> Is there a time line for including the timeseries scikits in numpy/ >> scipy? >> With code that is intended for incorporation in numpy/scipy, we are >> restricted in our external dependencies. > > I can't tell, because the decision is not mine. For what I understood, > there could be an inclusion in scipy if there's a need for it. For > that, we need more users end more feedback.... If you catch my drift...
Thanks for the info, we will keep this in mind. Personally, I still think of data just as an array or matrix of numbers, when they still have dates and units attached to them, they are usually a pain. And I'm only slowly getting used to the possibility that it doesn't necessarily need to be so painful. (I didn't know you moved the moving functions to C, I thought I saw them in python.) Josef _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
