That's absolutely fine with me.
Would you agree with:
- scaling = None -> lambda x: np.abs(np.max(x))
- scaling = dict -> directly set vmim/max for channel type
- sacling = 'peak-peak' -> olde behaviour
- scaling = callable -> custom function compliant with default call
signature (function returing score from object acceptet by np.array)




On Wed, Jun 12, 2013 at 10:58 PM, Alexandre Gramfort <
notifications@github.com> wrote:

> In mne/viz.py:
>
> > @@ -250,6 +276,16 @@ def plot_topo(evoked, layout, layout_scale=0.945, color=None, title=None):
> >          Everything matplotlib accepts to specify colors. If not list-like,
> >          the color specified will be repeated. If None, colors are
> >          automatically drawn.
> > +    border : str
> > +        Borders style to be used for each sensor plot.
> > +    scaling : None | dict | callable
> > +        The scaling method to be applied to channel types. If None, peak-to-peak
> > +        scaling will be used for each sensor and each channel type. If dict
> > +        a scalar value can be assigned to each sensor type, e.g.,
> > +        dict(mag=mag=1e-4). If callable a functoon is expected that returns
> > +        a float value and takes an array-like objet as argument, e.g.
> > +        lambda x: max(abs(x)). The funciton will then be applied separately
> > +        to each sensor type.
>
> The peak to peak is not really a good idea don't you think? That means we
> change the default behavior but it's for the better no?
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/mne-tools/mne-python/pull/634/files#r4666865>
> .
>