ParaView - Mixing NumPy and VTK APIs with high efficient using Python Programmable filter

ParaView的User Guides中关于Python Programmable filter中的一个例子。

The previous examples demonstrate how the Programmable Filter can be used as an advanced Python Calculator. However, the full power of the Programmable Filter can only be harnessed by using the VTK API. The following is a simple example. Create a Sphere source and apply the Programmable Filter with the following script in ParaView software.

input = inputs[0]
newPoints = vtk.vtkPoints()
numPoints = input.GetNumberOfPoints()
for i in range(numPoints):
    x, y, z = input.GetPoint(i)
    newPoints.InsertPoint(i, x, y, 1 + z*0.3)
output.SetPoints(newPoints)

Note: Python is an interpreted language and Python scripts do not execute as efficiently as compiled C++ code.
Therefore, using a for loop that iterates over all points or cells may be a significant bottleneck when processing large
datasets.
The NumPy and VTK APIs can be mixed to achieve good performance. Even though this may seem a bit
complicated at first, it can be used with great effect. For instance, the example above can be rewritten as follows.

from paraview.vtk.dataset_adapter import numpyTovtkDataArray
input = inputs[0]
newPoints = vtk.vtkPoints()
zs = 1 + input.Points[:,2]*0.3
coords = hstack([input.Points[:,0:2],zs])
newPoints.SetData(numpyTovtkDataArray(coords))
output.SetPoints(newPoints)

Even though this produces exactly the same result, it is much more efficient because the for loop was moved it from
Python to C. Under the hood, NumPy uses C and Fortran for tight loops.