wradlib Documentation - Bitbucket

wradlib Documentation, Release 0.1.1
Idw
Linear
interpolate
interpolate_polar
Table 3.6 – continued from previous page
Inverse distance weighting interpolation in N dimensions.
Interface to the scipy.interpolate.LinearNDInterpolator class.
Convenience function to use the interpolation classes in an efficient way
Convenience function to interpolate polar data
3.6.1 wradlib.ipol.Nearest
class wradlib.ipol.Nearest(src, trg)
Nearest-neighbour interpolation in N dimensions.
Parameters src : ndarray of floats, shape (npoints, ndims)
Data point coordinates of the source points.
trg : ndarray of floats, shape (npoints, ndims)
Data point coordinates of the target points.
Notes
Uses scipy.spatial.cKDTree
__call__(vals[, maxdist])
Evaluate interpolator for values given at the source points.
wradlib.ipol.Nearest.__call__
Nearest.__call__(vals, maxdist=None)
Evaluate interpolator for values given at the source points.
Parameters vals : ndarray of float, shape (numsourcepoints, ...)
Values at the source points which to interpolate
maxdist : the maximum distance up to which an interpolated values is
assigned - if maxdist is exceeded, np.nan will be assigned If maxdist==None, values
will be assigned everywhere
Returns output : ndarray of float with shape (numtargetpoints,...)
3.6.2 wradlib.ipol.Idw
class wradlib.ipol.Idw(src, trg, nnearest=4, p=2.)
Inverse distance weighting interpolation in N dimensions.
Parameters src : ndarray of floats, shape (npoints, ndims)
Data point coordinates of the source points.
trg : ndarray of floats, shape (npoints, ndims)
Data point coordinates of the target points.
nnearest : integer - max. number of neighbours to be considered
p : float - inverse distance power used in 1/dist**p
48 Chapter 3. Library Reference