Re: Suggestion for dgrid3d

[Philipp K. J. <ja...@ie...>]

(snip)
>
> We do consider backward compatibility a bit of a holy grail in this
> project.  It's always bad if a change to the program modifies the result
> of a previously established usage.  So new features (like a modified
> weight function) should always be introduced such that they're only
> triggered by commands that would have done nothing useful in earlier
> versions.

That's fine - this is the reason why I suggested adding
an additional (optional) argument to dgrid3d, which 
allows the user to select the smoothing kernel. It would
default to the current kernel (thus not breaking any
existing apps), but allows users to make a different
choice for new development.

(snip)
> >
> > Yes, that's true. But I think that's exactly what
> > would be nice to have - to put the ability to
> > control the range into the hands of users, who
> > DO know their data!
>
> I'm afraid you're still thing "filter radius" when you now say "range".
> When I say range, I mean the xrange and friends.  That's sufficiently
> controllable by the user.

It seems as if we still have terminology problems.
Yes, I mean "filter radius" - I am obviously not 
talking about the plot range here!

>
> > For example, if I know that
> > my data is very smooth, but not on a grid, I might
> > want to use dgrid3d with a small averaging range,
> > just to get a surface drawn. But when I know my data
> > to be noisy, I might want to have a wide averaging
> > range, to get some of the noise out.
>
> This kind of "range" control is what the power parameter is about.
> The higher the power, the more local the filter.  But it's not a range:
> its effect changes with the distribution of input points.

But the effect is just not very good. Did you ever
check out the samples I show at
	www.philipp-janert.com/gnuplot

There, I show a very "smooth" data set. Using 
dgrid3d on it introduces all kinds of spurious
"wiggles" and NO choice of the power parameter
gets rid of them.

The gaussian kernel on the other hand leads to
quite faithful representation of the data set.

Please: let's get past the discussion of the 
status-quo and instead start to look forward.
I have tried to give several reasons why having
a gaussian kernel can be desirable. If we add
an optional argument, so that users decide
which kernel to use, we don't break any existing
apps. And we provide more value for the users. 
What's wrong with that?

Best,

		Ph.