Editing Software Plugins and Codes (section)

====Method 1: Using a Programmable Filter====

This example will demonstrate several things:
* Using a filter with multiple inputs.
* Using a programmable filter.
* Using numpy arrays within a ParaView Programmable Filter.  (See [http://paraview.org/Wiki/Python_Programmable_Filter#Center_Data_using_numpy].)

Here are the steps:

# Open both data files.  In this example, one will be a FAIM output file, while the other will be in the form of a VTK Unstructured Grid file.
# Select both files at once in the Pipeline Browser.  This is done in OS-X by clicking on one, then holding down the Command key and clicking on the other.
# Now select a Programmable Filter from the menu.
# Enter this script:
<source lang=python>
from paraview.vtk import dataset_adapter as DA

# Get inputs/outputs as VTK objects
input0 = self.GetInputDataObject(0,0)
input1 = self.GetInputDataObject(0,1)
output = self.GetOutputDataObject(0)

# We can get the array data from the "inputs" object, which is an alternative interface (more Pythonesque) to the same data.
# This gives us numpy arrays.
x0 = inputs[0].PointData.GetArray("NODAL_DISPLACEMENTS")
# Change the following to GetArray("NODAL_DISPLACEMENTS") if your second input is also a FAIM data file.
x1 = inputs[1].PointData.GetArray("displacement")

# Numpy arrays can be operated on element-by-element with simple expressions.  (Both x0 and x1 are Nx3 arrays.)
# You could also do a loop over the elements, but it would be slower and more verbose.
xdiff = x1 - x0

# Convert from a numpy array to a VTK array.
# Call it "dispdiff", but you could name it anything.
xdiff_vtk = DA.numpyTovtkDataArray(xdiff, 'dispdiff')

# The output is a copy of the first input, except we attach our new vector data to the points.
output.ShallowCopy(input0)
output.GetPointData().AddArray(xdiff_vtk)
</source>
# Click OK
# Change the Display of the programmable filter to "dispdiff", and click "Toggle Color Legend Visibility" button.

[[Image:ParaviewDifferenceInDisp.png]]