> I was glad to see formula coming up over your side of the coding
> divide; we've also been working on it a little in nipy.
> I think we're beginning to get the idea, and it seems like it's a very
> good idea, so it would be very good to share some work in increasing
> First - Jonathan Taylor - who wrote this code - has commented on the
> design a bit on the NIPY mailing list - we've put his comments into
> our version of the code in:
> The original comments are here:
>> here is my *current* opinion and information on it, one example is below
>> - it looks useful when working with categorical data
>> (I don't think it's very useful for non-categorical data, as we
>> where working on in the last year.)
>> - it's very difficult to understand because of all the indirections
>> a class produces a class which returns another class, and how do I
>> get the data in and out? ...
> I think here you mean the aliased functions. That stuff is - as you
> know - Sympy code, and after looking at it a while (with Ondrej Certik
> last week for example), I think we concluded that the indirection
> stuff could not be avoided because of the way Sympy makes new
> functions. However, with a nice set of docstrings and examples, I
> honestly think that won't matter because the concept of the
> AliasedFunction is actually very simple, and goes like this:
> An AliasedFunction is a symbolic function that carries with it its own
> implementation as a numerical function. When we call 'lambidify' for
> expressions containing this function, the function's own
> implementation is used.
>> - the only usage examples, I have seen are in the test file
> We could certainly do with some more examples, but there are some you
> may not have found in our tree:
>> - in order to use it, we need to create examples and documentation for it
>> - I'm not sure everything is correct, in spite of passing the tests,
>> or maybe I don't get expected results because I don't know how to use it
>> - because of all the indirection, usage inside statistical models makes them
>> also difficult to understand
>> - it doesn't do the data handling, which has been more the focus of Skipper and
>> Vincent, the "namespace" in formulas need to be filled with actual data
>> For now, I don't have the time to struggle with it for several days, but maybe
>> during summer, if I don't think distributions, time series analysis and lot's
>> of other things are more fun.
> Maybe more fun with some help? We're very keen to work on this (at
> least, I know I am).
> Thanks for thinking and working on this. I do think it could be very useful,
One potential issue is the use of sympy since it would add dependency.
Of course, it may be possible to work around that so that it can be used