On Fri, 2008-08-08 at 16:05 +0200, Grégory Lielens wrote:
> Hello everybody,
>
> I have sent this message to the user group, but thinking of it, it may be more
> relevant to the development mailing list...so here it is again.
>
>
>
> We are looking for the best way to plot a waterfall diagram in
> Matplotlib. The 2 functions which could be used
> to do that are (as far as I have found) imshow and pcolormesh. Here is a
> small script that use both to compare the output:
>
> -----------------
>
> from pylab import *
>
>
> delta = 0.2
> x = arange(-3.0, 3.0, delta)
> y = arange(-2.0, 2.0, delta)
> X, Y = meshgrid(x, y)
> Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
> Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
> # difference of Gaussians
> Z = 10.0 * (Z2 - Z1)
> figure(1)
> im = imshow(Z,extent=(-3,3,-2,2))
> CS = contour(X, -Y, Z, 6,
> colors='k', # negative contours will be dashed by default
> )
> clabel(CS, fontsize=9, inline=1)
> title('Using imshow')
> figure(2)
> im = pcolormesh(X,-Y,Z)
> CS = contour(X, -Y, Z, 6,
> colors='k', # negative contours will be dashed by default
> )
> clabel(CS, fontsize=9, inline=1)
> title('Using pcolormesh')
> show()
>
> ---------------------
>
>
> The problem is that we need some of the flexibility of pcolormesh (which
> is able to map the matrix of value on any deformed mesh), while
> we would like to use the interpolations available in imshow (which
> explain why the imshow version is much "smoother" than the pcolormesh
> one).
>
> In fact, what would be needed is not the full flexibility of pcolormesh
> (which can map the grid to any kind of shape), we "only" have to deal
> with rectangular grids where x- and y- graduations are irregularly spaced.
>
> Is there a drawing function in Matplotlib which would be able to work
> with such a rectangular non-uniform grid?
> And if not (and a quick look at the example and the code make me think
> that indeed the capability is currently not present),
> what about an extension of imshow which would work as this:
>
> im = imshow(Z,x_gridpos=x, y_gridpos=y) #specify the
> position of the grid's nodes, instead of giving the extend and assuming
> uniform spacing.
>
> Longer term, would a pcolormesh accepting interpolation be possible? The
> current behavior, averaging the color of the grids node to get a uniform
> cell color,
> is quite rough except for a large number of cells...And even then, it
> soon shows when you zoom in...
>
> The best would be to allow the same interpolations as in imshow (or a
> subset of it), and also allows to use interpolation before colormap
> lookup (or after),
> like in Matlab. Indeed, Matlab allows to finely tune interpolation by
> specifying Gouraud (interpolation after color
> lookup)/Phong(interpolation before color lookup, i.e. for each pixel).
> Phong is usually much better but also more CPU intensive. Phong is
> especially when using discrete colormap, producing banded colors
> equivalent to countour lines, while Gouraud does not work in those
> cases.
>
> Of course, the performance will be impacted by some of those
> interpolation options, which would degrade performance in animations for
> example.... but I think that having the different options available
> would be very useful, it allows to have the highest map quality, or have
> a "quick and dirty" map depending on situation (grid spacing, type of
> map, animation or not, ...).
>
> Best regards,
>
> Greg.
I have found a method which implement the proposed extension to imshow:
NonUniformImage...
However, this image instance support only nearest neighbor
interpolation. Trying to set the interpolation (using the
set_interpolation method)
to something akin imshow throw a "NotImplementedError: Only nearest
neighbor supported" exception....
So basically I am still stuck, it seems that currently there is no way
in matplotlib to plot interpolated
colormap on irregular rectangular grid, and even less on arbitrarily
mapped grid...
Is there any plans to add support for more interpolation in
NonUniformImage in the future? Or maybe there is another
drawing function that I did not find yet, with this ability?
Best regards,
Greg.
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