PrincipleStrainPython

<source lang="python"> ParaView Programmable Filter script. ParaView 5.0.1. This code will read in an eigenvector, calculate an eigenvalue, and place it into three variables. Be sure to correct the input variables below. Also, note that the code uses ZX, not XZ. This code only works with any multiblock or vtk datasets (including ones with only one block - i.e., Exodus datasets as input). Usage: Run the Programmable Filter. Cut and paste this file in the section named "Script". Leave "Output Data Set Type" as "Same as Input". Click Apply button Written by Jeff Mauldin and Alan Scott import numpy as np def process_composite_dataset(input0): # Pick up input arrays xxar = input0.CellData["EPSXX"] xyar = input0.CellData["EPSXY"] zxar = input0.CellData["EPSZX"] yyar = input0.CellData["EPSYY"] yzar = input0.CellData["EPSYZ"] zzar = input0.CellData["EPSZZ"] #print `xxar` #print len(xxar.Arrays) # Set output arrays to same type as input array. # Do a multiply to make sure we don't just have a # pointer to the original. outarray0 = xxar*0.5 outarray1 = xxar*0.5 outarray2 = xxar*0.5 # Run a for loop over all blocks numsubarrays = len(xxar.Arrays) for ii in range(0, numsubarrays): # pick up input arrays for each block. xxarsub = xxar.Arrays[ii] xyarsub = xyar.Arrays[ii] zxarsub = zxar.Arrays[ii] yyarsub = yyar.Arrays[ii] yzarsub = yzar.Arrays[ii] zzarsub = zzar.Arrays[ii] #print `xxarsub` # Transpose and calculate the principle strain. strain = np.transpose( np.array( [ [xxarsub, xyarsub, zxarsub], [xyarsub, yyarsub, yzarsub], [zxarsub, yzarsub, zzarsub] ] ), (2,0,1)) principal_strain = np.linalg.eigvalsh(strain) # Move principle strain to temp output arrays for this block outarray0.Arrays[ii] = principal_strain[:,0] outarray1.Arrays[ii] = principal_strain[:,1] outarray2.Arrays[ii] = principal_strain[:,2] #ps0 = principal_strain[:,0] #print "ps0 len: " + str(len(ps0)) # Finally, move the temp arrays to output arrays output.CellData.append(outarray0, "principal_strain_0") output.CellData.append(outarray1, "principal_strain_1") output.CellData.append(outarray2, "principal_strain_2") def process_unstructured_dataset(input0): # Pick up input arrays xxar = input0.CellData["EPSXX"] xyar = input0.CellData["EPSXY"] zxar = input0.CellData["EPSZX"] yyar = input0.CellData["EPSYY"] yzar = input0.CellData["EPSYZ"] zzar = input0.CellData["EPSZZ"] #print `xxar` #print len(xxar.Arrays) # Transpose and calculate the principle strain. strain = np.transpose( np.array( [ [xxar, xyar, zxar], [xyar, yyar, yzar], [zxar, yzar, zzar] ] ), (2,0,1)) principal_strain = np.linalg.eigvalsh(strain) #ps0 = principal_strain[:,0] #print "ps0 len: " + str(len(ps0)) # Finally, move the temp arrays to output arrays output.CellData.append(principal_strain[:,0], "principal_strain_0") output.CellData.append(principal_strain[:,1], "principal_strain_1") output.CellData.append(principal_strain[:,2], "principal_strain_2") input0 = inputs[0] if input0.IsA("vtkCompositeDataSet"): process_composite_dataset(input0) elif input0.IsA("vtkUnstructuredGrid"): process_unstructured_dataset(input0) else: print "Bad dataset type for this script" print "\n" </source>