Interpolation/Kriging

ParaView Support
2

An external Kriging python library can be applied using the Programmable Filter.

For example, if you want to use PyKrige (PyKrige — PyKrige 1.6.1 documentation), first, prepare the same version of the Python environment that ParaView uses, and install Pykrige on it. Then, copy:

  • Anaconda3/envs/pyKrige/Lib/site-packages/pykrige

  • Anaconda3/envs/pyKrige/Lib/site-packages/scipy
    (This is an Anaconda example)

under (ParaView install folder)/bin/Lib/site-packages. After this preparation, start ParaView and apply a programmable filter to your geometry and observation points. Below is a concrete sample and its State file.

kriging_sample
kriging_sample992×700 205 KB

pyKrige.pvsm (567.3 KB)

The operation procedure is as follows.

  1. Select the geometry data (UnstructuredCellTypes1) you want to interpolate or extrapolate in the Pipeline Browser.
  2. Continue to select the observation point data (RandomAttributes1) while holding down the Ctrl key.
  3. Then, start the Programmable Filter
  4. Type in the following in Script:
from pykrige.ok3d import OrdinaryKriging3D

grid = self.GetInputDataObject(0, 0)
pnts = inputs[1]
output.CopyStructure(grid)

k_psill = 10.0
k_range = 5.0
k_nugget = 0.002

ok3d = OrdinaryKriging3D(
    pnts.Points[:,0], pnts.Points[:,1], pnts.Points[:,2], pnts.PointData['RandomPointScalars'],
    variogram_model="gaussian", variogram_parameters ={'psill': k_psill, 'range': k_range, 'nugget': k_nugget}
    #anisotropy_scaling_y=None, anisotropy_angle_z=None
)

k3d1, ss3d = ok3d.execute('points', output.Points[:,0], output.Points[:,1], output.Points[:,2])

output.PointData.append(k3d1, 'kriging')
2 Likes