[SciPy-Dev] scikits datasmooth (was: scikits contribution?)
Fri Jul 16 13:09:56 CDT 2010
On 7/16/10 11:55 , firstname.lastname@example.org wrote:
>>> It would be better to upload also the sdist,
>>> >> Since scikits.datasmooth is pure python, an egg doesn't really have an
>>> >> advantage and is specific to a python version, py2.6 only.
>>> >> (I'm on py25)
>>> >> Why do you have the code duplication in two modules and the
>>> >> conditional import in __init__.py ?
>>> >> try:
>>> >> from regularsmooth import *
>>> >> except ImportError as error:
>>> >> What is the import error that might occur with regularsmooth ?
>>> >> Also an example to quickly try out the scikit would be very useful.
>>> >> (And tests and docs are not included yet.)
>> > Thanks for the feedback. I was confused what was the "sdist", but I think I
>> > figured it out and uploaded it.
>> > I will get to examples, tests, and docs later when I have time (this will
>> > require more learning for me). The functions are well documented and the
>> > primary use function "smooth_data" shows an example.
>> > There are two primary implementations: "smooth_data" that includes
>> > cross-validation, and "smooth_data_constr" which will take constraints but
>> > does not include cross-validation. The later requires the module "cvxopt".
>> > If this is not available, I wanted to allow a user to still use the
>> > unconstrained smoother. Maybe there is a better way to do this than my
>> > "try/except" hack. Suggestions are welcome.
> Try/except are fine for this, but it wasn't very informative. You
> could also try/except import cvxopt, or add a note that the full
> version requires cvxopt. Maybe it's noted somewhere in your code, I
> haven't looked that carefully.
Kind of noted in the README. I am thinking of putting the try/except in
the script itself rather than in __init__.py, but I will get to it later.
> Is it possible to replace cvxopt with scipy optimizers? But this is
> not necessary for a scikit.
I needed a quadratic programming (qp) implementation, and I couldn't
find any qp in scipy. I found the one in cvxopt to be quite good.
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