wradlib Documentation - Bitbucket

wradlib Documentation, Release 0.1.1
3.6.4 wradlib.ipol.interpolate
wradlib.ipol.interpolate(src, trg, vals, Interpolator, *args, **kwargs)
Convenience function to use the interpolation classes in an efficient way
ATTENTION: Works only for one- and two-dimensional vals arrays, yet.
The interpolation classes in wradlib.ipol are computationally very efficient if they are applied on large multidimensional
arrays of which the first dimension must be the locations’ dimension (1d or 2d coordinates) and the
following dimensions can be anything (e.g. time or ensembles). This way, the weights need to be computed only
once. However, this can only be done with success if all source values for the interpolation are valid numbers.
If the source values contain let’s say np.nan types, the result of the interpolation will be np.nan in the vicinity
of the corresponding points, too. Imagine that you have a time series of observations at points and in each time
step one observation is missing. You would still like to efficiently apply the interpolation classes, but you will
need to account for the resulting np.nan values in the interpolation output.
In order to still allow for the efficient application, you have to take care of the remaining np.nan in your interpolation
result. This is done by this convenience function.
Alternatively, you have to make sure that your vals argument does not contain any np.nan values OR you have
to post-process missing values in your interpolation result in another way.
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.
vals : ndarray of float, shape (numsourcepoints, ...)
Values at the source points which to interpolate
Interpolator : a class which inherits from IpolBase
*args : arguments of Interpolator (see class documentation)
**kwargs : keyword arguments of Interpolator (see class documentation)
Examples
>>> # test for 1 dimension in space and two value dimensions
>>> src = np.arange(10)[:,None]
>>> trg = np.linspace(0,20,40)[:,None]
>>> vals = np.hstack((np.sin(src), 10.+np.sin(src)))
>>> # here we introduce missing values only in the second dimension
>>> vals[3:5,1] = np.nan
>>> ipol_result = interpolate(src, trg, vals, Idw, nnearest=2)
>>> # plot if you like
>>> import pylab as pl
>>> pl.plot(trg, ipol_result, ’b+’)
>>> pl.plot(src, vals, ’ro’)
>>> pl.show()
3.6.5 wradlib.ipol.interpolate_polar
wradlib.ipol.interpolate_polar(data, mask=None, Interpolator=)
Convenience function to interpolate polar data
50 Chapter 3. Library Reference