I like that. Maybe the filter coefs can be a method on that object that
takes a dt?


On Thu, Jun 13, 2013 at 11:11 AM, Trevor Bekolay
<notifications@github.com>wrote:

> In nengo/model_objects.py:
>
> > +
> > +    def remove_from_model(self, model):
> > +        raise NotImplementedError
> > +
> > +
> > +# class Probe(object):
> > +#     def __init__(self, target, sample_every=None, filter=None):
> > +#         if pstc is not None and pstc > self.dt:
> > +#             fcoef, tcoef = _filter_coefs(pstc=pstc, dt=self.dt)
> > +#             probe_sig = self.signal(obj.sig.n)
> > +#             self.filter(fcoef, probe_sig, probe_sig)
> > +#             self.transform(tcoef, obj.sig, probe_sig)
> > +#             self.probe = SimModel.probe(self, probe_sig, sample_every)
> > +
> > +
> > +#     @staticmethod
>
> Right now I don't have filtered connections; I thought we had decided not
> to have pstc keyword args? They were going to be done like we're doing
> neuron classes, something like PostsynapticCurrent(tau=0.01)?
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/ctn-waterloo/nengo/pull/39/files#r4680931>
> .
>