#17618: Update matplotlib so that plot_directive is less broken
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Reporter: tmonteil | Owner:
Type: enhancement | Status: new
Priority: major | Milestone: sage-6.5
Component: packages: | Resolution:
standard | Merged in:
Keywords: | Reviewers:
Authors: Thierry Monteil | Work issues:
Report Upstream: N/A | Commit:
Branch: | 065dc779e98d32ec5b081680db5356df98f15ff2
u/tmonteil/MPL-1.4 | Stopgaps:
Dependencies: |
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Comment (by tmonteil):
Replying to [comment:39 fbissey]:
> I tried to post a comment on Tuesday but trac wouldn't let me :( so in
scipy we can produce a Delaunay object and use it for linear interpolation
but the nearest neighbor method cannot use a Delaunay object just a list
of points. The linear interpolation can use the same kind of list of
points. So I think I will give the delaunay object the hard shoulder and
go directly to the list of points for both.
I definitely agree with that: using Delaunay triangulations eases finding
nearest neighbors, but nearest neighbors can be found without that, see
also [http://matplotlib.1069221.n5.nabble.com/Replacing-matplotlib-
delaunay-natural-neighbor-interpolation-td42786.html#a42796 this comment].
The only loss we could have not using an underlying Delaunay triangulation
should be with respect to speed (though computing Delaunay triangulation
also costs, even with `qhull`). Do you plan to use
`scipy.interpolate.NearestNDInterpolator` (or
`scipy.interpolate.griddata`) for `nn` interpolation ? If yes, it seems to
rely on `scipy.spatial.cKDTree` which seems to be a method unrelated to
relying on a Delaunay triangulation, perhaps is it faster ?
Also, it could be nice to keep both matplotlib's and scipy's `linear` and
`cubic` interpolations in order to compare them (both visually and in
terms of speed), at least for now. Depending on that, we could give the
whole job to `scipy`.
--
Ticket URL: <http://trac.sagemath.org/ticket/17618#comment:40>
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