[SciPy-Dev] SciPy Goal

[Pauli Virtanen]

05.01.2012 04:16, Zachary Pincus kirjoitti:
> Just one point here: one of the current shortcomings in scipy
> from my perspective is interpolation, which is spread between
> interpolate, signal, and ndimage, each package with strengths
> and inexplicable (to a new user) weaknesses.

Interpolation and splines are indeed a weak point currently.

What's missing is:

- interface for interpolating gridded data (unifying ndimage,
  RectBivariateSpline, and scipy.spline routines)

- the interface for `griddata` could be simplified a bit
  (-> allow variable number of arguments). Also, no natural neighbor
  interpolation so far.

- FITPACK is a quirky beast, especially its 2D-routines (apart from
  RectBivariateSpline) which very often don't work for real data.
  I'm also not fully sure how far it and its smoothing can be trusted
  in 1D (see stackoverflow)

- There are two sets of incompatible spline routines in
  scipy.interpolate, which should be cleaned up.

  The *Spline class interfaces are also not very pretty, as there is
  __class__ changing magic going on.

  The interp2d interface is somewhat confusing, and IMO would be best
  deprecated.

- There is also a problem with large 1D data sets: FITPACK is slow, and
  the other set of spline routines try to invert a dense matrix,
  rather than e.g. using the band matrix routines.

- RBF sort of works, but uses dense matrices and is not suitable for
  large data sets. IDW interpolation could be an useful addition here.

And probably more: making a laundry list of what to fix could be helpful.