Merge branch 'development' of magit1.mpie.de:damask/DAMASK into miscImprovements
This commit is contained in:
Martin Diehl
commit
469ec4b00e
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@ -427,7 +427,7 @@ program DAMASK_spectral
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! prepare MPI parallel out (including opening of file)
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allocate(outputSize(worldsize), source = 0_MPI_OFFSET_KIND)
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outputSize(worldrank+1) = size(materialpoint_results,kind=MPI_OFFSET_KIND)*int(pReal,MPI_OFFSET_KIND)
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call MPI_allreduce(MPI_IN_PLACE,outputSize,worldsize,MPI_LONG,MPI_SUM,PETSC_COMM_WORLD,ierr)! get total output size over each process
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call MPI_allreduce(MPI_IN_PLACE,outputSize,worldsize,MPI_LONG,MPI_SUM,PETSC_COMM_WORLD,ierr) ! get total output size over each process
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if(ierr /=0_pInt) call IO_error(894_pInt, ext_msg='MPI_allreduce')
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call MPI_file_open(PETSC_COMM_WORLD, &
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trim(getSolverWorkingDirectoryName())//trim(getSolverJobName())//'.spectralOut', &
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@ -72,18 +72,18 @@ Deals with both vector- and tensor-valued fields.
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parser.add_option('-c','--coordinates',
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dest = 'coords',
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type = 'string', metavar='string',
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help = 'column heading for coordinates [%default]')
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type = 'string', metavar = 'string',
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help = 'column label of coordinates [%default]')
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parser.add_option('-v','--vector',
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dest = 'vector',
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action = 'extend', metavar = '<string LIST>',
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help = 'heading of columns containing vector field values')
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help = 'column label(s) of vector field values')
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parser.add_option('-t','--tensor',
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dest = 'tensor',
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action = 'extend', metavar = '<string LIST>',
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help = 'heading of columns containing tensor field values')
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help = 'column label(s) of tensor field values')
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parser.set_defaults(coords = 'ipinitialcoord',
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parser.set_defaults(coords = 'pos',
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)
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(options,filenames) = parser.parse_args()
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@ -1,164 +0,0 @@
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#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,math
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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#--------------------------------------------------------------------------------------------------
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def deformedCoordsFFT(F,undeformed=False):
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wgt = 1.0/grid.prod()
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integrator = np.array([0.+1.j,0.+1.j,0.+1.j],'c16') * size/ 2.0 / math.pi
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step = size/grid
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F_fourier = np.fft.rfftn(F,axes=(0,1,2))
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coords_fourier = np.zeros(F_fourier.shape[0:4],'c16')
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if undeformed:
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Favg=np.eye(3)
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else:
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Favg=np.real(F_fourier[0,0,0,:,:])*wgt
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#--------------------------------------------------------------------------------------------------
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# integration in Fourier space
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k_s = np.zeros([3],'i')
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for i in xrange(grid[2]):
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k_s[2] = i
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if(i > grid[2]//2 ): k_s[2] = k_s[2] - grid[2]
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for j in xrange(grid[1]):
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k_s[1] = j
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if(j > grid[1]//2 ): k_s[1] = k_s[1] - grid[1]
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for k in xrange(grid[0]//2+1):
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k_s[0] = k
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for m in xrange(3):
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coords_fourier[i,j,k,m] = sum(F_fourier[i,j,k,m,0:3]*k_s*integrator)
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if (any(k_s != 0)):
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coords_fourier[i,j,k,0:3] /= -sum(k_s*k_s)
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#--------------------------------------------------------------------------------------------------
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# add average to scaled fluctuation and put (0,0,0) on (0,0,0)
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coords = np.fft.irfftn(coords_fourier,F.shape[0:3],axes=(0,1,2))
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offset_coords = np.dot(F[0,0,0,:,:],step/2.0) - scaling*coords[0,0,0,0:3]
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for z in xrange(grid[2]):
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for y in xrange(grid[1]):
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for x in xrange(grid[0]):
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coords[z,y,x,0:3] = scaling*coords[z,y,x,0:3] \
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+ offset_coords \
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+ np.dot(Favg,step*np.array([x,y,z]))
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return coords
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options file[s]', description = """
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Add deformed configuration of given initial coordinates.
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Operates on periodic three-dimensional x,y,z-ordered data sets.
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""", version = scriptID)
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parser.add_option('-f', '--defgrad',dest='defgrad', metavar = 'string',
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help='heading of deformation gradient columns [%default]')
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parser.add_option('--reference', dest='undeformed', action='store_true',
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help='map results to reference (undeformed) average configuration [%default]')
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parser.add_option('--scaling', dest='scaling', action='extend', metavar = '<float LIST>',
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help='scaling of fluctuation')
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parser.add_option('-u', '--unitlength', dest='unitlength', type='float', metavar = 'float',
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help='set unit length for 2D model [%default]')
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parser.add_option('--coordinates', dest='coords', metavar='string',
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help='column heading for coordinates [%default]')
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parser.set_defaults(defgrad = 'f')
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parser.set_defaults(coords = 'ipinitialcoord')
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parser.set_defaults(scaling = [])
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parser.set_defaults(undeformed = False)
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parser.set_defaults(unitlength = 0.0)
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(options,filenames) = parser.parse_args()
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options.scaling += [1.0 for i in xrange(max(0,3-len(options.scaling)))]
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scaling = map(float, options.scaling)
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try:
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table = damask.ASCIItable(name = name,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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remarks = []
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if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
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else: colCoord = table.label_index(options.coords)
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if table.label_dimension(options.defgrad) != 9: errors.append('deformation gradient {} is not a tensor.'.format(options.defgrad))
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else: colF = table.label_index(options.defgrad)
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# --------------- figure out size and grid ---------------------------------------------------------
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table.data_readArray()
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coords = [np.unique(table.data[:,colCoord+i]) for i in xrange(3)]
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mincorner = np.array(map(min,coords))
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maxcorner = np.array(map(max,coords))
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grid = np.array(map(len,coords),'i')
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size = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
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size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 set to smallest among other spacings
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N = grid.prod()
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if N != len(table.data): errors.append('data count {} does not match grid {}x{}x{}.'.format(N,*grid))
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header ---------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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for coord in xrange(3):
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label = '{}_{}_{}'.format(coord+1,options.defgrad,options.coords)
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if np.any(scaling) != 1.0: label+='_{}_{}_{}'.format(scaling)
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if options.undeformed: label+='_undeformed'
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table.labels_append([label]) # extend ASCII header with new labels
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table.head_write()
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# ------------------------------------------ read deformation gradient field -----------------------
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centroids = deformedCoordsFFT(table.data[:,colF:colF+9].reshape(grid[2],grid[1],grid[0],3,3),
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options.undeformed)
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# ------------------------------------------ process data ------------------------------------------
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table.data_rewind()
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for z in xrange(grid[2]):
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for y in xrange(grid[1]):
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for x in xrange(grid[0]):
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table.data_read()
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table.data_append(list(centroids[z,y,x,:]))
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table.data_write()
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close ASCII tables
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@ -0,0 +1,227 @@
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#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,math
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import numpy as np
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import scipy.ndimage
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from optparse import OptionParser
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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#--------------------------------------------------------------------------------------------------
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def cell2node(cellData,grid):
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nodeData = 0.0
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datalen = np.array(cellData.shape[3:]).prod()
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for i in xrange(datalen):
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node = scipy.ndimage.convolve(cellData.reshape(tuple(grid)+(datalen,))[...,i],
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np.ones((2,2,2))/8., # 2x2x2 neighborhood of cells
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mode = 'wrap',
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origin = -1, # offset to have cell origin as center
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) # now averaged at cell origins
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node = np.append(node,node[np.newaxis,0,:,:,...],axis=0) # wrap along z
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node = np.append(node,node[:,0,np.newaxis,:,...],axis=1) # wrap along y
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node = np.append(node,node[:,:,0,np.newaxis,...],axis=2) # wrap along x
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nodeData = node[...,np.newaxis] if i==0 else np.concatenate((nodeData,node[...,np.newaxis]),axis=-1)
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return nodeData
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#--------------------------------------------------------------------------------------------------
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def displacementAvgFFT(F,grid,size,nodal=False,transformed=False):
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"""calculate average cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
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if nodal:
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x, y, z = np.meshgrid(np.linspace(0,size[0],1+grid[0]),
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np.linspace(0,size[1],1+grid[1]),
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np.linspace(0,size[2],1+grid[2]),
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indexing = 'ij')
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else:
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x, y, z = np.meshgrid(np.linspace(0,size[0],grid[0],endpoint=False),
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np.linspace(0,size[1],grid[1],endpoint=False),
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np.linspace(0,size[2],grid[2],endpoint=False),
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indexing = 'ij')
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origCoords = np.concatenate((z[:,:,:,None],y[:,:,:,None],x[:,:,:,None]),axis = 3)
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F_fourier = F if transformed else np.fft.rfftn(F,axes=(0,1,2)) # transform or use provided data
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Favg = np.real(F_fourier[0,0,0,:,:])/grid.prod() # take zero freq for average
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avgDisplacement = np.einsum('ml,ijkl->ijkm',Favg-np.eye(3),origCoords) # dX = Favg.X
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return avgDisplacement
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#--------------------------------------------------------------------------------------------------
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def displacementFluctFFT(F,grid,size,nodal=False,transformed=False):
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"""calculate cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
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integrator = 0.5j * size / math.pi
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kk, kj, ki = np.meshgrid(np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2])),
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np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1])),
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np.arange(grid[0]//2+1),
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indexing = 'ij')
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k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
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k_sSquared = np.einsum('...l,...l',k_s,k_s)
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k_sSquared[0,0,0] = 1.0 # ignore global average frequency
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#--------------------------------------------------------------------------------------------------
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# integration in Fourier space
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displacement_fourier = -np.einsum('ijkml,ijkl,l->ijkm',
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F if transformed else np.fft.rfftn(F,axes=(0,1,2)),
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k_s,
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integrator,
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) / k_sSquared[...,np.newaxis]
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#--------------------------------------------------------------------------------------------------
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# backtransformation to real space
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displacement = np.fft.irfftn(displacement_fourier,grid,axes=(0,1,2))
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return cell2node(displacement,grid) if nodal else displacement
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options file[s]', description = """
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Add displacments resulting from deformation gradient field.
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Operates on periodic three-dimensional x,y,z-ordered data sets.
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Outputs at cell centers or cell nodes (into separate file).
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""", version = scriptID)
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parser.add_option('-f', '--defgrad',
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dest = 'defgrad',
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metavar = 'string',
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help = 'column label of deformation gradient [%default]')
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parser.add_option('-c', '--coordinates',
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dest = 'coords',
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metavar = 'string',
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help = 'column label of coordinates [%default]')
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parser.add_option('--nodal',
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dest = 'nodal',
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action = 'store_true',
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help = 'output nodal (not cell-centered) displacements')
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parser.set_defaults(defgrad = 'f',
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coords = 'pos',
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nodal = False,
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)
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(options,filenames) = parser.parse_args()
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try: table = damask.ASCIItable(name = name,
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outname = (os.path.splitext(name)[0]+
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'_nodal'+
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os.path.splitext(name)[1]) if (options.nodal and name) else None,
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buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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errors = []
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remarks = []
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if table.label_dimension(options.defgrad) != 9:
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errors.append('deformation gradient "{}" is not a 3x3 tensor.'.format(options.defgrad))
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coordDim = table.label_dimension(options.coords)
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if not 3 >= coordDim >= 1:
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errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.coords))
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elif coordDim < 3:
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remarks.append('appending {} dimension{} to coordinates "{}"...'.format(3-coordDim,
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's' if coordDim < 2 else '',
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options.coords))
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss=True)
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continue
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# --------------- figure out size and grid ---------------------------------------------------------
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table.data_readArray([options.defgrad,options.coords])
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table.data_rewind()
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if len(table.data.shape) < 2: table.data.shape += (1,) # expand to 2D shape
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if table.data[:,9:].shape[1] < 3:
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table.data = np.hstack((table.data,
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np.zeros((table.data.shape[0],
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3-table.data[:,9:].shape[1]),dtype='f'))) # fill coords up to 3D with zeros
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# --------------- figure out size and grid ---------------------------------------------------------
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coords = [np.unique(table.data[:,9+i]) for i in xrange(3)]
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mincorner = np.array(map(min,coords))
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maxcorner = np.array(map(max,coords))
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grid = np.array(map(len,coords),'i')
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size = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
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size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 set to smallest among other spacings
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N = grid.prod()
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if N != len(table.data): errors.append('data count {} does not match grid {}x{}x{}.'.format(N,*grid))
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ process data ------------------------------------------
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F_fourier = np.fft.rfftn(table.data[:,:9].reshape(grid[2],grid[1],grid[0],3,3),axes=(0,1,2)) # perform transform only once...
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displacement = displacementFluctFFT(F_fourier,grid,size,options.nodal,transformed=True)
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avgDisplacement = displacementAvgFFT (F_fourier,grid,size,options.nodal,transformed=True)
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# ------------------------------------------ assemble header ---------------------------------------
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if options.nodal:
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table.info_clear()
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table.labels_clear()
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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table.labels_append((['{}_pos' .format(i+1) for i in xrange(3)] if options.nodal else []) +
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['{}_avg({}).{}' .format(i+1,options.defgrad,options.coords) for i in xrange(3)] +
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['{}_fluct({}).{}'.format(i+1,options.defgrad,options.coords) for i in xrange(3)] )
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table.head_write()
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# ------------------------------------------ output data -------------------------------------------
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||||
|
||||
zrange = np.linspace(0,size[2],1+grid[2]) if options.nodal else xrange(grid[2])
|
||||
yrange = np.linspace(0,size[1],1+grid[1]) if options.nodal else xrange(grid[1])
|
||||
xrange = np.linspace(0,size[0],1+grid[0]) if options.nodal else xrange(grid[0])
|
||||
|
||||
for i,z in enumerate(zrange):
|
||||
for j,y in enumerate(yrange):
|
||||
for k,x in enumerate(xrange):
|
||||
if options.nodal: table.data_clear()
|
||||
else: table.data_read()
|
||||
table.data_append([x,y,z] if options.nodal else [])
|
||||
table.data_append(list(avgDisplacement[i,j,k,:]))
|
||||
table.data_append(list( displacement[i,j,k,:]))
|
||||
table.data_write()
|
||||
|
||||
# ------------------------------------------ output finalization -----------------------------------
|
||||
|
||||
table.close() # close ASCII tables
|
||||
|
|
@ -59,17 +59,17 @@ Deals with both vector- and tensor-valued fields.
|
|||
parser.add_option('-c','--coordinates',
|
||||
dest = 'coords',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'column heading for coordinates [%default]')
|
||||
help = 'column label of coordinates [%default]')
|
||||
parser.add_option('-v','--vector',
|
||||
dest = 'vector',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'heading of columns containing vector field values')
|
||||
help = 'column label(s) of vector field values')
|
||||
parser.add_option('-t','--tensor',
|
||||
dest = 'tensor',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'heading of columns containing tensor field values')
|
||||
help = 'column label(s) of tensor field values')
|
||||
|
||||
parser.set_defaults(coords = 'ipinitialcoord',
|
||||
parser.set_defaults(coords = 'pos',
|
||||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
|
|
|||
|
|
@ -89,19 +89,20 @@ Add column(s) containing Euclidean distance to grain structural features: bounda
|
|||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-c','--coordinates', dest='coords', metavar='string',
|
||||
help='column heading for coordinates [%default]')
|
||||
help='column label of coordinates [%default]')
|
||||
parser.add_option('-i','--identifier', dest='id', metavar = 'string',
|
||||
help='heading of column containing grain identifier [%default]')
|
||||
help='column label of grain identifier [%default]')
|
||||
parser.add_option('-t','--type', dest = 'type', action = 'extend', metavar = '<string LIST>',
|
||||
help = 'feature type {%s} '%(', '.join(map(lambda x:'/'.join(x['names']),features))) )
|
||||
parser.add_option('-n','--neighborhood',dest='neighborhood', choices = neighborhoods.keys(), metavar = 'string',
|
||||
help = 'type of neighborhood [neumann] {%s}'%(', '.join(neighborhoods.keys())))
|
||||
parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar='float',
|
||||
parser.add_option('-s', '--scale', dest = 'scale', type = 'float', metavar = 'float',
|
||||
help = 'voxel size [%default]')
|
||||
parser.set_defaults(coords = 'ipinitialcoord')
|
||||
parser.set_defaults(id = 'texture')
|
||||
parser.set_defaults(neighborhood = 'neumann')
|
||||
parser.set_defaults(scale = 1.0)
|
||||
parser.set_defaults(coords = 'pos',
|
||||
id = 'texture',
|
||||
neighborhood = 'neumann',
|
||||
scale = 1.0,
|
||||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
|
|
@ -125,10 +126,8 @@ for i,feature in enumerate(features):
|
|||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name, buffered = False)
|
||||
except:
|
||||
continue
|
||||
try: table = damask.ASCIItable(name = name, buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
|
|
@ -141,9 +140,11 @@ for name in filenames:
|
|||
remarks = []
|
||||
column = {}
|
||||
|
||||
if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
|
||||
coordDim = table.label_dimension(options.coords)
|
||||
if not 3 >= coordDim >= 1:
|
||||
errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.coords))
|
||||
else: coordCol = table.label_index(options.coords)
|
||||
|
||||
|
||||
if table.label_dimension(options.id) != 1: errors.append('grain identifier {} not found.'.format(options.id))
|
||||
else: idCol = table.label_index(options.id)
|
||||
|
||||
|
|
@ -164,18 +165,20 @@ for name in filenames:
|
|||
|
||||
table.data_readArray()
|
||||
|
||||
coords = [{},{},{}]
|
||||
for i in xrange(len(table.data)):
|
||||
for j in xrange(3):
|
||||
coords[j][str(table.data[i,coordCol+j])] = True
|
||||
grid = np.array(map(len,coords),'i')
|
||||
size = grid/np.maximum(np.ones(3,'d'),grid-1.0)* \
|
||||
np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
|
||||
max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
|
||||
max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
|
||||
],'d') # size from bounding box, corrected for cell-centeredness
|
||||
coords = [np.unique(table.data[:,coordCol+i]) for i in xrange(coordDim)]
|
||||
mincorner = np.array(map(min,coords))
|
||||
maxcorner = np.array(map(max,coords))
|
||||
grid = np.array(map(len,coords)+[1]*(3-len(coords)),'i')
|
||||
|
||||
size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 set to smallest among other spacings
|
||||
N = grid.prod()
|
||||
|
||||
if N != len(table.data): errors.append('data count {} does not match grid '.format(N) +
|
||||
'x'.join(map(str,grid)) +
|
||||
'.')
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# ------------------------------------------ process value field -----------------------------------
|
||||
|
||||
|
|
|
|||
|
|
@ -14,11 +14,10 @@ def gradFFT(geomdim,field):
|
|||
grid = np.array(np.shape(field)[2::-1])
|
||||
N = grid.prod() # field size
|
||||
n = np.array(np.shape(field)[3:]).prod() # data size
|
||||
|
||||
if n == 3: dataType = 'vector'
|
||||
elif n == 1: dataType = 'scalar'
|
||||
|
||||
field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2),s=shapeFFT)
|
||||
field_fourier = nps=shapeFFT).fft.fftpack.rfftn(field,axes=(0,1,2),
|
||||
grad_fourier = np.empty(field_fourier.shape+(3,),'c16')
|
||||
|
||||
# differentiation in Fourier space
|
||||
|
|
@ -98,7 +97,7 @@ for name in filenames:
|
|||
|
||||
items = {
|
||||
'scalar': {'dim': 1, 'shape': [1], 'labels':options.scalar, 'active':[], 'column': []},
|
||||
'vector': {'dim': 3, 'shape': [3], 'labels':options.vector, 'active':[], 'column': []},
|
||||
'vector': {'dim': 3, 'shape': [3], 'labels':options.vector, 'active':[], 'column': []},
|
||||
}
|
||||
errors = []
|
||||
remarks = []
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ import numpy as np
|
|||
import damask
|
||||
from optparse import OptionParser
|
||||
from scipy import spatial
|
||||
from collections import defaultdict
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
|
@ -23,7 +22,7 @@ parser.add_option('-r', '--radius',
|
|||
parser.add_option('-d', '--disorientation',
|
||||
dest = 'disorientation',
|
||||
type = 'float', metavar = 'float',
|
||||
help = 'disorientation threshold per grain [%default] (degrees)')
|
||||
help = 'disorientation threshold in degrees [%default]')
|
||||
parser.add_option('-s', '--symmetry',
|
||||
dest = 'symmetry',
|
||||
type = 'string', metavar = 'string',
|
||||
|
|
@ -61,7 +60,8 @@ parser.add_option('-p', '--position',
|
|||
type = 'string', metavar = 'string',
|
||||
help = 'spatial position of voxel [%default]')
|
||||
|
||||
parser.set_defaults(symmetry = 'cubic',
|
||||
parser.set_defaults(disorientation = 5,
|
||||
symmetry = 'cubic',
|
||||
coords = 'pos',
|
||||
degrees = False,
|
||||
)
|
||||
|
|
@ -86,17 +86,16 @@ if np.sum(input) != 1: parser.error('needs exactly one input format.')
|
|||
(options.matrix,9,'matrix'),
|
||||
(options.quaternion,4,'quaternion'),
|
||||
][np.where(input)[0][0]] # select input label that was requested
|
||||
toRadians = np.pi/180.0 if options.degrees else 1.0 # rescale degrees to radians
|
||||
cos_disorientation = np.cos(options.disorientation/2.*toRadians)
|
||||
toRadians = np.pi/180.0 if options.degrees else 1.0 # rescale degrees to radians
|
||||
cos_disorientation = np.cos(np.radians(options.disorientation/2.)) # cos of half the disorientation angle
|
||||
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False)
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
|
|
@ -109,8 +108,10 @@ for name in filenames:
|
|||
errors = []
|
||||
remarks = []
|
||||
|
||||
if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
|
||||
if not np.all(table.label_dimension(label) == dim): errors.append('input {} does not have dimension {}.'.format(label,dim))
|
||||
if not 3 >= table.label_dimension(options.coords) >= 1:
|
||||
errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.coords))
|
||||
if not np.all(table.label_dimension(label) == dim):
|
||||
errors.append('input {} does not have dimension {}.'.format(label,dim))
|
||||
else: column = table.label_index(label)
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
|
|
@ -122,8 +123,10 @@ for name in filenames:
|
|||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.labels_append('grainID_{}@{}'.format(label,
|
||||
options.disorientation if options.degrees else np.degrees(options.disorientation))) # report orientation source and disorientation in degrees
|
||||
table.labels_append('grainID_{}@{:g}'.format('+'.join(label)
|
||||
if isinstance(label, (list,tuple))
|
||||
else label,
|
||||
options.disorientation)) # report orientation source and disorientation
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data ------------------------------------------
|
||||
|
|
@ -162,7 +165,7 @@ for name in filenames:
|
|||
|
||||
time_delta = (time.clock()-tick) * (len(grainID) - p) / p
|
||||
bg.set_message('(%02i:%02i:%02i) processing point %i of %i (grain count %i)...'\
|
||||
%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),len(orientations)))
|
||||
%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),np.count_nonzero(memberCounts)))
|
||||
|
||||
if inputtype == 'eulers':
|
||||
o = damask.Orientation(Eulers = np.array(map(float,table.data[column:column+3]))*toRadians,
|
||||
|
|
@ -179,84 +182,51 @@ for name in filenames:
|
|||
o = damask.Orientation(quaternion = np.array(map(float,table.data[column:column+4])),
|
||||
symmetry = options.symmetry).reduced()
|
||||
|
||||
matched = False
|
||||
matched = False
|
||||
alreadyChecked = {}
|
||||
candidates = []
|
||||
bestDisorientation = damask.Quaternion([0,0,0,1]) # initialize to 180 deg rotation as worst case
|
||||
|
||||
# check against last matched needs to be really picky. best would be to exclude jumps across the poke (checking distance between last and me?)
|
||||
# when walking through neighborhood first check whether grainID of that point has already been tested, if yes, skip!
|
||||
|
||||
if matchedID != -1: # has matched before?
|
||||
matched = (o.quaternion.conjugated() * orientations[matchedID].quaternion).w > cos_disorientation
|
||||
|
||||
if not matched:
|
||||
alreadyChecked = {}
|
||||
bestDisorientation = damask.Quaternion([0,0,0,1]) # initialize to 180 deg rotation as worst case
|
||||
for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring points
|
||||
gID = grainID[i]
|
||||
if gID != -1 and gID not in alreadyChecked: # indexed point belonging to a grain not yet tested?
|
||||
alreadyChecked[gID] = True # remember not to check again
|
||||
disorientation = o.disorientation(orientations[gID],SST = False)[0] # compare against other orientation
|
||||
if disorientation.quaternion.w > cos_disorientation and \
|
||||
disorientation.quaternion.w >= bestDisorientation.w: # within threshold and betterthan current best?
|
||||
for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring points
|
||||
gID = grainID[i]
|
||||
if gID != -1 and gID not in alreadyChecked: # indexed point belonging to a grain not yet tested?
|
||||
alreadyChecked[gID] = True # remember not to check again
|
||||
disorientation = o.disorientation(orientations[gID],SST = False)[0] # compare against other orientation
|
||||
if disorientation.quaternion.w > cos_disorientation: # within threshold ...
|
||||
candidates.append(gID) # remember as potential candidate
|
||||
if disorientation.quaternion.w >= bestDisorientation.w: # ... and better than current best?
|
||||
matched = True
|
||||
matchedID = gID # remember that grain
|
||||
bestDisorientation = disorientation.quaternion
|
||||
|
||||
if not matched: # no match -> new grain found
|
||||
memberCounts += [1] # start new membership counter
|
||||
if matched: # did match existing grain
|
||||
memberCounts[matchedID] += 1
|
||||
if len(candidates) > 1: # ambiguity in grain identification?
|
||||
largestGrain = sorted(candidates,key=lambda x:memberCounts[x])[-1] # find largest among potential candidate grains
|
||||
matchedID = largestGrain
|
||||
for c in [c for c in candidates if c != largestGrain]: # loop over smaller candidates
|
||||
memberCounts[largestGrain] += memberCounts[c] # reassign member count of smaller to largest
|
||||
memberCounts[c] = 0
|
||||
grainID = np.where(np.in1d(grainID,candidates), largestGrain, grainID) # relabel grid points of smaller candidates as largest one
|
||||
|
||||
else: # no match -> new grain found
|
||||
orientations += [o] # initialize with current orientation
|
||||
memberCounts += [1] # start new membership counter
|
||||
matchedID = g
|
||||
g += 1 # increment grain counter
|
||||
|
||||
else: # did match existing grain
|
||||
memberCounts[matchedID] += 1
|
||||
|
||||
grainID[p] = matchedID # remember grain index assigned to point
|
||||
p += 1 # increment point
|
||||
|
||||
bg.set_message('identifying similar orientations among {} grains...'.format(len(orientations)))
|
||||
|
||||
memberCounts = np.array(memberCounts)
|
||||
similarOrientations = [[] for i in xrange(len(orientations))]
|
||||
|
||||
for i,orientation in enumerate(orientations[:-1]): # compare each identified orientation...
|
||||
for j in xrange(i+1,len(orientations)): # ...against all others that were defined afterwards
|
||||
if orientation.disorientation(orientations[j],SST = False)[0].quaternion.w > cos_disorientation: # similar orientations in both grainIDs?
|
||||
similarOrientations[i].append(j) # remember in upper triangle...
|
||||
similarOrientations[j].append(i) # ...and lower triangle of matrix
|
||||
|
||||
if similarOrientations[i] != []:
|
||||
bg.set_message('grainID {} is as: {}'.format(i,' '.join(map(str,similarOrientations[i]))))
|
||||
|
||||
stillShifting = True
|
||||
while stillShifting:
|
||||
stillShifting = False
|
||||
tick = time.clock()
|
||||
|
||||
for p,gID in enumerate(grainID): # walk through all points
|
||||
if p > 0 and p % 1000 == 0:
|
||||
|
||||
time_delta = (time.clock()-tick) * (len(grainID) - p) / p
|
||||
bg.set_message('(%02i:%02i:%02i) shifting ID of point %i out of %i (grain count %i)...'
|
||||
%(time_delta//3600,time_delta%3600//60,time_delta%60,p,len(grainID),len(orientations)))
|
||||
if similarOrientations[gID] != []: # orientation of my grainID is similar to someone else?
|
||||
similarNeighbors = defaultdict(int) # frequency of neighboring grainIDs sharing my orientation
|
||||
for i in kdtree.query_ball_point(kdtree.data[p],options.radius): # check all neighboring point
|
||||
if grainID[i] in similarOrientations[gID]: # neighboring point shares my orientation?
|
||||
similarNeighbors[grainID[i]] += 1 # remember its grainID
|
||||
if similarNeighbors != {}: # found similar orientation(s) in neighborhood
|
||||
candidates = np.array([gID]+similarNeighbors.keys()) # possible replacement grainIDs for me
|
||||
grainID[p] = candidates[np.argsort(memberCounts[candidates])[-1]] # adopt ID that is most frequent in overall dataset
|
||||
memberCounts[gID] -= 1 # my former ID loses one fellow
|
||||
memberCounts[grainID[p]] += 1 # my new ID gains one fellow
|
||||
bg.set_message('{}:{} --> {}'.format(p,gID,grainID[p])) # report switch of grainID
|
||||
stillShifting = True
|
||||
|
||||
grainIDs = np.where(np.array(memberCounts) > 0)[0] # identify "live" grain identifiers
|
||||
packingMap = dict(zip(list(grainIDs),range(len(grainIDs)))) # map to condense into consecutive IDs
|
||||
|
||||
table.data_rewind()
|
||||
|
||||
outputAlive = True
|
||||
p = 0
|
||||
while outputAlive and table.data_read(): # read next data line of ASCII table
|
||||
table.data_append(1+grainID[p]) # add grain ID
|
||||
table.data_append(1+packingMap[grainID[p]]) # add (condensed) grain ID
|
||||
outputAlive = table.data_write() # output processed line
|
||||
p += 1
|
||||
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ Average each data block of size 'packing' into single values thus reducing the f
|
|||
parser.add_option('-c','--coordinates',
|
||||
dest = 'coords',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'column heading for coordinates [%default]')
|
||||
help = 'column label of coordinates [%default]')
|
||||
parser.add_option('-p','--packing',
|
||||
dest = 'packing',
|
||||
type = 'int', nargs = 3, metavar = 'int int int',
|
||||
|
|
@ -39,7 +39,7 @@ parser.add_option('-s', '--size',
|
|||
dest = 'size',
|
||||
type = 'float', nargs = 3, metavar = 'float float float',
|
||||
help = 'size in x,y,z [autodetect]')
|
||||
parser.set_defaults(coords = 'ipinitialcoord',
|
||||
parser.set_defaults(coords = 'pos',
|
||||
packing = (2,2,2),
|
||||
shift = (0,0,0),
|
||||
grid = (0,0,0),
|
||||
|
|
@ -59,11 +59,10 @@ if any(shift != 0): prefix += 'shift{:+}{:+}{:+}_'.format(*shift)
|
|||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
outname = os.path.join(os.path.dirname(name),
|
||||
prefix+os.path.basename(name)) if name else name,
|
||||
buffered = False)
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
outname = os.path.join(os.path.dirname(name),
|
||||
prefix+os.path.basename(name)) if name else name,
|
||||
buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
|
|
@ -75,7 +74,6 @@ for name in filenames:
|
|||
|
||||
errors = []
|
||||
remarks = []
|
||||
colCoord = None
|
||||
|
||||
if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
|
||||
else: colCoord = table.label_index(options.coords)
|
||||
|
|
@ -86,7 +84,6 @@ for name in filenames:
|
|||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
|
|
|
|||
|
|
@ -19,83 +19,82 @@ to resolution*packing.
|
|||
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-c','--coordinates', dest='coords', metavar='string',
|
||||
help='column heading for coordinates [%default]')
|
||||
parser.add_option('-p','--packing', dest='packing', type='int', nargs=3, metavar='int int int',
|
||||
help='dimension of packed group [%default]')
|
||||
parser.add_option('-g','--grid', dest='resolution', type='int', nargs=3, metavar='int int int',
|
||||
help='resolution in x,y,z [autodetect]')
|
||||
parser.add_option('-s','--size', dest='dimension', type='float', nargs=3, metavar='int int int',
|
||||
help='dimension in x,y,z [autodetect]')
|
||||
parser.set_defaults(coords = 'ipinitialcoord')
|
||||
parser.set_defaults(packing = (2,2,2))
|
||||
parser.set_defaults(grid = (0,0,0))
|
||||
parser.set_defaults(size = (0.0,0.0,0.0))
|
||||
parser.add_option('-c','--coordinates',
|
||||
dest = 'coords', metavar = 'string',
|
||||
help = 'column label of coordinates [%default]')
|
||||
parser.add_option('-p','--packing',
|
||||
dest = 'packing', type = 'int', nargs = 3, metavar = 'int int int',
|
||||
help = 'dimension of packed group [%default]')
|
||||
parser.add_option('-g','--grid',
|
||||
dest = 'resolution', type = 'int', nargs = 3, metavar = 'int int int',
|
||||
help = 'resolution in x,y,z [autodetect]')
|
||||
parser.add_option('-s','--size',
|
||||
dest = 'dimension', type = 'float', nargs = 3, metavar = 'int int int',
|
||||
help = 'dimension in x,y,z [autodetect]')
|
||||
parser.set_defaults(coords = 'pos',
|
||||
packing = (2,2,2),
|
||||
grid = (0,0,0),
|
||||
size = (0.0,0.0,0.0),
|
||||
)
|
||||
|
||||
(options,filenames) = parser.parse_args()
|
||||
|
||||
|
||||
options.packing = np.array(options.packing)
|
||||
prefix = 'blowUp%ix%ix%i_'%(options.packing[0],options.packing[1],options.packing[2])
|
||||
prefix = 'blowUp{}x{}x{}_'.format(*options.packing)
|
||||
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
outname = os.path.join(os.path.dirname(name),
|
||||
prefix+ \
|
||||
os.path.basename(name)) if name else name,
|
||||
buffered = False)
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
outname = os.path.join(os.path.dirname(name),
|
||||
prefix+os.path.basename(name)) if name else name,
|
||||
buffered = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
# ------------------------------------------ read header ------------------------------------------
|
||||
|
||||
table.head_read()
|
||||
errors = []
|
||||
|
||||
# ------------------------------------------ sanity checks ----------------------------------------
|
||||
|
||||
if table.label_dimension(options.coords) != 3:
|
||||
damask.util.croak('coordinates {} are not a vector.'.format(options.coords))
|
||||
errors = []
|
||||
remarks = []
|
||||
|
||||
if table.label_dimension(options.coords) != 3: errors.append('coordinates {} are not a vector.'.format(options.coords))
|
||||
else: colCoord = table.label_index(options.coords)
|
||||
|
||||
colElem = table.label_index('elem')
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
else:
|
||||
coordCol = table.label_index(options.coords)
|
||||
|
||||
|
||||
# ------------------------------------------ assemble header --------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
|
||||
# --------------- figure out size and grid ---------------------------------------------------------
|
||||
|
||||
table.data_readArray()
|
||||
|
||||
coords = [{},{},{}]
|
||||
for i in xrange(len(table.data)):
|
||||
for j in xrange(3):
|
||||
coords[j][str(table.data[i,coordCol+j])] = True
|
||||
grid = np.array(map(len,coords),'i')
|
||||
size = grid/np.maximum(np.ones(3,'d'),grid-1.0)* \
|
||||
np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
|
||||
max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
|
||||
max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
|
||||
],'d') # size from bounding box, corrected for cell-centeredness
|
||||
|
||||
size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 set to smallest among other spacings
|
||||
table.data_readArray(options.coords)
|
||||
|
||||
coords = [np.unique(table.data[:,i]) for i in xrange(3)]
|
||||
mincorner = np.array(map(min,coords))
|
||||
maxcorner = np.array(map(max,coords))
|
||||
grid = np.array(map(len,coords),'i')
|
||||
size = grid/np.maximum(np.ones(3,'d'), grid-1.0) * (maxcorner-mincorner) # size from edge to edge = dim * n/(n-1)
|
||||
size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 set to smallest among other spacings
|
||||
|
||||
packing = np.array(options.packing,'i')
|
||||
outSize = grid*packing
|
||||
|
||||
# ------------------------------------------ assemble header ---------------------------------------
|
||||
# ------------------------------------------ assemble header --------------------------------------
|
||||
|
||||
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
|
||||
table.head_write()
|
||||
|
||||
# ------------------------------------------ process data -------------------------------------------
|
||||
|
||||
table.data_rewind()
|
||||
data = np.zeros(outSize.tolist()+[len(table.labels)])
|
||||
p = np.zeros(3,'i')
|
||||
|
|
@ -107,15 +106,15 @@ for name in filenames:
|
|||
table.data_read()
|
||||
data[d[0]:d[0]+packing[0],
|
||||
d[1]:d[1]+packing[1],
|
||||
d[2]:d[2]+packing[2],
|
||||
d[2]:d[2]+packing[2],
|
||||
: ] = np.tile(np.array(table.data_asFloat(),'d'),packing.tolist()+[1]) # tile to match blowUp voxel size
|
||||
elementSize = size/grid/packing
|
||||
elem = 1
|
||||
for c in xrange(outSize[2]):
|
||||
for b in xrange(outSize[1]):
|
||||
for a in xrange(outSize[0]):
|
||||
data[a,b,c,coordCol:coordCol+3] = [a+0.5,b+0.5,c+0.5]*elementSize
|
||||
data[a,b,c,table.label_index('elem')] = elem
|
||||
data[a,b,c,colCoord:colCoord+3] = [a+0.5,b+0.5,c+0.5]*elementSize
|
||||
if colElem != -1: data[a,b,c,colElem] = elem
|
||||
table.data = data[a,b,c,:].tolist()
|
||||
outputAlive = table.data_write() # output processed line
|
||||
elem += 1
|
||||
|
|
|
|||
|
|
@ -1003,11 +1003,8 @@ fileOpen = False
|
|||
assembleHeader = True
|
||||
header = []
|
||||
standard = ['inc'] + \
|
||||
{True: ['time'],
|
||||
False:[]}[options.time] + \
|
||||
['elem','node','ip','grain'] + \
|
||||
{True: ['1_nodeinitialcoord','2_nodeinitialcoord','3_nodeinitialcoord'],
|
||||
False:['1_ipinitialcoord','2_ipinitialcoord','3_ipinitialcoord']}[options.nodalScalar != []]
|
||||
['time'] if options.time else [] + \
|
||||
['elem','node','ip','grain','1_pos','2_pos','3_pos']
|
||||
|
||||
# --------------------------- loop over positions --------------------------------
|
||||
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
#!/usr/bin/env python
|
||||
# -*- coding: UTF-8 no BOM -*-
|
||||
|
||||
import os,sys,vtk
|
||||
import os,vtk
|
||||
import damask
|
||||
from collections import defaultdict
|
||||
from optparse import OptionParser
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
|
|
@ -17,125 +18,166 @@ Add scalar and RGB tuples from ASCIItable to existing VTK point cloud (.vtp).
|
|||
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-v', '--vtk', dest='vtk', \
|
||||
parser.add_option( '--vtk',
|
||||
dest = 'vtk',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'VTK file name')
|
||||
parser.add_option( '--inplace',
|
||||
dest = 'inplace',
|
||||
action = 'store_true',
|
||||
help = 'modify VTK file in-place')
|
||||
parser.add_option('-r', '--render',
|
||||
dest = 'render',
|
||||
action = 'store_true',
|
||||
help = 'open output in VTK render window')
|
||||
parser.add_option('-s', '--scalar', dest='scalar', action='extend', \
|
||||
help = 'scalar values')
|
||||
parser.add_option('-v', '--vector',
|
||||
dest = 'vector',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
help = 'vector value label(s)')
|
||||
parser.add_option('-c', '--color', dest='color', action='extend', \
|
||||
help = 'RGB color tuples')
|
||||
|
||||
parser.set_defaults(scalar = [])
|
||||
parser.set_defaults(color = [])
|
||||
parser.set_defaults(scalar = [],
|
||||
vector = [],
|
||||
color = [],
|
||||
inplace = False,
|
||||
render = False,
|
||||
)
|
||||
|
||||
(options, filenames) = parser.parse_args()
|
||||
|
||||
datainfo = { # list of requested labels per datatype
|
||||
'scalar': {'len':1,
|
||||
'label':[]},
|
||||
'color': {'len':3,
|
||||
'label':[]},
|
||||
}
|
||||
if not options.vtk: parser.error('No VTK file specified.')
|
||||
if not os.path.exists(options.vtk): parser.error('VTK file does not exist.')
|
||||
|
||||
if not os.path.exists(options.vtk):
|
||||
parser.error('VTK file does not exist'); sys.exit()
|
||||
if os.path.splitext(options.vtk)[1] == '.vtp':
|
||||
reader = vtk.vtkXMLPolyDataReader()
|
||||
reader.SetFileName(options.vtk)
|
||||
reader.Update()
|
||||
Polydata = reader.GetOutput()
|
||||
elif os.path.splitext(options.vtk)[1] == '.vtk':
|
||||
reader = vtk.vtkGenericDataObjectReader()
|
||||
reader.SetFileName(options.vtk)
|
||||
reader.Update()
|
||||
Polydata = reader.GetPolyDataOutput()
|
||||
else:
|
||||
parser.error('Unsupported VTK file type extension.')
|
||||
|
||||
reader = vtk.vtkXMLPolyDataReader()
|
||||
reader.SetFileName(options.vtk)
|
||||
reader.Update()
|
||||
Npoints = reader.GetNumberOfPoints()
|
||||
Ncells = reader.GetNumberOfCells()
|
||||
Nvertices = reader.GetNumberOfVerts()
|
||||
Polydata = reader.GetOutput()
|
||||
Npoints = Polydata.GetNumberOfPoints()
|
||||
Ncells = Polydata.GetNumberOfCells()
|
||||
Nvertices = Polydata.GetNumberOfVerts()
|
||||
|
||||
if Npoints != Ncells or Npoints != Nvertices:
|
||||
parser.error('Number of points, cells, and vertices in VTK differ from each other'); sys.exit()
|
||||
if options.scalar is not None: datainfo['scalar']['label'] += options.scalar
|
||||
if options.color is not None: datainfo['color']['label'] += options.color
|
||||
parser.error('Number of points, cells, and vertices in VTK differ from each other.')
|
||||
|
||||
# ------------------------------------------ setup file handles ---------------------------------------
|
||||
damask.util.croak('{}: {} points, {} vertices, and {} cells...'.format(options.vtk,Npoints,Nvertices,Ncells))
|
||||
|
||||
files = []
|
||||
if filenames == []:
|
||||
files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
|
||||
else:
|
||||
for name in filenames:
|
||||
if os.path.exists(name):
|
||||
files.append({'name':name, 'input':open(name), 'output':sys.stderr, 'croak':sys.stderr})
|
||||
# --- loop over input files -------------------------------------------------------------------------
|
||||
|
||||
#--- loop over input files ------------------------------------------------------------------------
|
||||
for file in files:
|
||||
if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
|
||||
else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
|
||||
if filenames == []: filenames = [None]
|
||||
|
||||
table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
|
||||
table.head_read() # read ASCII header info
|
||||
for name in filenames:
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
buffered = False,
|
||||
readonly = True)
|
||||
except: continue
|
||||
damask.util.report(scriptName, name)
|
||||
|
||||
# --------------- figure out columns to process
|
||||
active = {}
|
||||
column = {}
|
||||
# --- interpret header ----------------------------------------------------------------------------
|
||||
|
||||
array = {}
|
||||
table.head_read()
|
||||
|
||||
remarks = []
|
||||
errors = []
|
||||
VTKarray = {}
|
||||
active = defaultdict(list)
|
||||
|
||||
for datatype,info in datainfo.items():
|
||||
for label in info['label']:
|
||||
foundIt = False
|
||||
for key in ['1_'+label,label]:
|
||||
if key in table.labels:
|
||||
foundIt = True
|
||||
if datatype not in active: active[datatype] = []
|
||||
if datatype not in column: column[datatype] = {}
|
||||
if datatype not in array: array[datatype] = {}
|
||||
active[datatype].append(label)
|
||||
column[datatype][label] = table.labels.index(key) # remember columns of requested data
|
||||
if datatype == 'scalar':
|
||||
array[datatype][label] = vtk.vtkDoubleArray()
|
||||
array[datatype][label].SetNumberOfComponents(1)
|
||||
array[datatype][label].SetName(label)
|
||||
elif datatype == 'color':
|
||||
array[datatype][label] = vtk.vtkUnsignedCharArray()
|
||||
array[datatype][label].SetNumberOfComponents(3)
|
||||
array[datatype][label].SetName(label)
|
||||
if not foundIt:
|
||||
file['croak'].write('column %s not found...\n'%label)
|
||||
|
||||
for datatype,dimension,label in [['scalar',1,options.scalar],
|
||||
['vector',3,options.vector],
|
||||
['color',3,options.color],
|
||||
]:
|
||||
for i,dim in enumerate(table.label_dimension(label)):
|
||||
me = label[i]
|
||||
if dim == -1: remarks.append('{} "{}" not found...'.format(datatype,me))
|
||||
elif dim > dimension: remarks.append('"{}" not of dimension {}...'.format(me,dimension))
|
||||
else:
|
||||
remarks.append('adding {} "{}"...'.format(datatype,me))
|
||||
active[datatype].append(me)
|
||||
|
||||
if datatype in ['scalar','vector']: VTKarray[me] = vtk.vtkDoubleArray()
|
||||
elif datatype == 'color': VTKarray[me] = vtk.vtkUnsignedCharArray()
|
||||
|
||||
VTKarray[me].SetNumberOfComponents(dimension)
|
||||
VTKarray[me].SetName(label[i])
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
continue
|
||||
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
|
||||
for datatype,labels in active.items(): # loop over scalar,color
|
||||
for label in labels: # loop over all requested items
|
||||
theData = table.data[column[datatype][label]:\
|
||||
column[datatype][label]+datainfo[datatype]['len']] # read strings
|
||||
if datatype == 'color':
|
||||
theData = map(lambda x: int(255.*float(x)),theData)
|
||||
array[datatype][label].InsertNextTuple3(theData[0],theData[1],theData[2],)
|
||||
elif datatype == 'scalar':
|
||||
array[datatype][label].InsertNextValue(float(theData[0]))
|
||||
for datatype,labels in active.items(): # loop over scalar,color
|
||||
for me in labels: # loop over all requested items
|
||||
theData = [table.data[i] for i in table.label_indexrange(me)] # read strings
|
||||
if datatype == 'color': VTKarray[me].InsertNextTuple3(*map(lambda x: int(255.*float(x)),theData))
|
||||
elif datatype == 'vector': VTKarray[me].InsertNextTuple3(*map(float,theData))
|
||||
elif datatype == 'scalar': VTKarray[me].InsertNextValue(float(theData[0]))
|
||||
|
||||
table.input_close() # close input ASCII table
|
||||
|
||||
# ------------------------------------------ add data ---------------------------------------
|
||||
|
||||
for datatype,labels in active.items(): # loop over scalar,color
|
||||
for datatype,labels in active.items(): # loop over scalar,color
|
||||
if datatype == 'color':
|
||||
Polydata.GetPointData().SetScalars(array[datatype][labels[0]])
|
||||
Polydata.GetCellData().SetScalars(array[datatype][labels[0]])
|
||||
for label in labels: # loop over all requested items
|
||||
Polydata.GetPointData().AddArray(array[datatype][label])
|
||||
Polydata.GetCellData().AddArray(array[datatype][label])
|
||||
Polydata.GetPointData().SetScalars(VTKarray[active['color'][0]])
|
||||
Polydata.GetCellData().SetScalars(VTKarray[active['color'][0]])
|
||||
for me in labels: # loop over all requested items
|
||||
Polydata.GetPointData().AddArray(VTKarray[me])
|
||||
Polydata.GetCellData().AddArray(VTKarray[me])
|
||||
|
||||
Polydata.Modified()
|
||||
if vtk.VTK_MAJOR_VERSION <= 5:
|
||||
Polydata.Update()
|
||||
if vtk.VTK_MAJOR_VERSION <= 5: Polydata.Update()
|
||||
|
||||
# ------------------------------------------ output result ---------------------------------------
|
||||
|
||||
writer = vtk.vtkXMLPolyDataWriter()
|
||||
writer.SetDataModeToBinary()
|
||||
writer.SetCompressorTypeToZLib()
|
||||
writer.SetFileName(os.path.splitext(options.vtk)[0]+'_added.vtp')
|
||||
if vtk.VTK_MAJOR_VERSION <= 5:
|
||||
writer.SetInput(Polydata)
|
||||
else:
|
||||
writer.SetInputData(Polydata)
|
||||
writer.Write()
|
||||
writer = vtk.vtkXMLPolyDataWriter()
|
||||
writer.SetDataModeToBinary()
|
||||
writer.SetCompressorTypeToZLib()
|
||||
writer.SetFileName(os.path.splitext(options.vtk)[0]+('.vtp' if options.inplace else '_added.vtp'))
|
||||
if vtk.VTK_MAJOR_VERSION <= 5: writer.SetInput(Polydata)
|
||||
else: writer.SetInputData(Polydata)
|
||||
writer.Write()
|
||||
|
||||
# ------------------------------------------ render result ---------------------------------------
|
||||
|
||||
if options.render:
|
||||
mapper = vtk.vtkDataSetMapper()
|
||||
mapper.SetInputData(Polydata)
|
||||
actor = vtk.vtkActor()
|
||||
actor.SetMapper(mapper)
|
||||
|
||||
# Create the graphics structure. The renderer renders into the
|
||||
# render window. The render window interactor captures mouse events
|
||||
# and will perform appropriate camera or actor manipulation
|
||||
# depending on the nature of the events.
|
||||
|
||||
ren = vtk.vtkRenderer()
|
||||
|
||||
renWin = vtk.vtkRenderWindow()
|
||||
renWin.AddRenderer(ren)
|
||||
|
||||
ren.AddActor(actor)
|
||||
ren.SetBackground(1, 1, 1)
|
||||
renWin.SetSize(200, 200)
|
||||
|
||||
iren = vtk.vtkRenderWindowInteractor()
|
||||
iren.SetRenderWindow(renWin)
|
||||
|
||||
iren.Initialize()
|
||||
renWin.Render()
|
||||
iren.Start()
|
||||
|
|
|
|||
|
|
@ -30,10 +30,6 @@ parser.add_option('-r', '--render',
|
|||
dest = 'render',
|
||||
action = 'store_true',
|
||||
help = 'open output in VTK render window')
|
||||
parser.add_option('-m', '--mode',
|
||||
dest = 'mode',
|
||||
type = 'choice', metavar = 'string', choices = ['cell', 'point'],
|
||||
help = 'cell-centered or point-centered data')
|
||||
parser.add_option('-s', '--scalar',
|
||||
dest = 'scalar',
|
||||
action = 'extend', metavar = '<string LIST>',
|
||||
|
|
@ -56,7 +52,6 @@ parser.set_defaults(scalar = [],
|
|||
|
||||
(options, filenames) = parser.parse_args()
|
||||
|
||||
if not options.mode: parser.error('No data mode specified.')
|
||||
if not options.vtk: parser.error('No VTK file specified.')
|
||||
if not os.path.exists(options.vtk): parser.error('VTK file does not exist.')
|
||||
|
||||
|
|
@ -83,9 +78,9 @@ damask.util.croak('{}: {} points and {} cells...'.format(options.vtk,Npoints,Nce
|
|||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False, readonly = True)
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
buffered = False,
|
||||
readonly = True)
|
||||
except: continue
|
||||
damask.util.report(scriptName, name)
|
||||
|
||||
|
|
@ -124,8 +119,11 @@ for name in filenames:
|
|||
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
|
||||
datacount = 0
|
||||
|
||||
while table.data_read(): # read next data line of ASCII table
|
||||
|
||||
|
||||
datacount += 1 # count data lines
|
||||
for datatype,labels in active.items(): # loop over scalar,color
|
||||
for me in labels: # loop over all requested items
|
||||
theData = [table.data[i] for i in table.label_indexrange(me)] # read strings
|
||||
|
|
@ -133,15 +131,25 @@ for name in filenames:
|
|||
elif datatype == 'vector': VTKarray[me].InsertNextTuple3(*map(float,theData))
|
||||
elif datatype == 'scalar': VTKarray[me].InsertNextValue(float(theData[0]))
|
||||
|
||||
table.close() # close input ASCII table
|
||||
|
||||
# ------------------------------------------ add data ---------------------------------------
|
||||
|
||||
if datacount == Npoints: mode = 'point'
|
||||
elif datacount == Ncells: mode = 'cell'
|
||||
else:
|
||||
damask.util.croak('Data count is incompatible with grid...')
|
||||
continue
|
||||
|
||||
damask.util.croak('{} mode...'.format(mode))
|
||||
|
||||
for datatype,labels in active.items(): # loop over scalar,color
|
||||
if datatype == 'color':
|
||||
if options.mode == 'cell': rGrid.GetCellData().SetScalars(VTKarray[active['color'][0]])
|
||||
elif options.mode == 'point': rGrid.GetPointData().SetScalars(VTKarray[active['color'][0]])
|
||||
if mode == 'cell': rGrid.GetCellData().SetScalars(VTKarray[active['color'][0]])
|
||||
elif mode == 'point': rGrid.GetPointData().SetScalars(VTKarray[active['color'][0]])
|
||||
for me in labels: # loop over all requested items
|
||||
if options.mode == 'cell': rGrid.GetCellData().AddArray(VTKarray[me])
|
||||
elif options.mode == 'point': rGrid.GetPointData().AddArray(VTKarray[me])
|
||||
if mode == 'cell': rGrid.GetCellData().AddArray(VTKarray[me])
|
||||
elif mode == 'point': rGrid.GetPointData().AddArray(VTKarray[me])
|
||||
|
||||
rGrid.Modified()
|
||||
if vtk.VTK_MAJOR_VERSION <= 5: rGrid.Update()
|
||||
|
|
|
|||
|
|
@ -18,12 +18,12 @@ Produce a VTK point cloud dataset based on coordinates given in an ASCIItable.
|
|||
|
||||
""", version = scriptID)
|
||||
|
||||
parser.add_option('-d', '--deformed',
|
||||
dest = 'deformed',
|
||||
parser.add_option('-c', '--coordinates',
|
||||
dest = 'pos',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'deformed coordinate label [%default]')
|
||||
help = 'coordinate label [%default]')
|
||||
|
||||
parser.set_defaults(deformed = 'ipdeformedcoord'
|
||||
parser.set_defaults(pos = 'pos'
|
||||
)
|
||||
|
||||
(options, filenames) = parser.parse_args()
|
||||
|
|
@ -46,9 +46,9 @@ for name in filenames:
|
|||
|
||||
errors = []
|
||||
remarks = []
|
||||
coordDim = table.label_dimension(options.deformed)
|
||||
if not 3 >= coordDim >= 1: errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.deformed))
|
||||
elif coordDim < 3: remarks.append('appending {} dimensions to coordinates "{}"...'.format(3-coordDim,options.deformed))
|
||||
coordDim = table.label_dimension(options.pos)
|
||||
if not 3 >= coordDim >= 1: errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.pos))
|
||||
elif coordDim < 3: remarks.append('appending {} dimensions to coordinates "{}"...'.format(3-coordDim,options.pos))
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
|
|
@ -58,7 +58,7 @@ for name in filenames:
|
|||
|
||||
# ------------------------------------------ process data ---------------------------------------
|
||||
|
||||
table.data_readArray(options.deformed)
|
||||
table.data_readArray(options.pos)
|
||||
if len(table.data.shape) < 2: table.data.shape += (1,) # expand to 2D shape
|
||||
if table.data.shape[1] < 3:
|
||||
table.data = np.hstack((table.data,
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ scriptID = ' '.join([scriptName,damask.version])
|
|||
# --------------------------------------------------------------------
|
||||
|
||||
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
|
||||
Create regular voxel grid from points in an ASCIItable.
|
||||
Create regular voxel grid from points in an ASCIItable (or geom file).
|
||||
|
||||
""", version = scriptID)
|
||||
|
||||
|
|
@ -24,11 +24,11 @@ parser.add_option('-m', '--mode',
|
|||
type = 'choice', choices = ['cell','point'],
|
||||
help = 'cell-centered or point-centered coordinates ')
|
||||
parser.add_option('-c', '--coordinates',
|
||||
dest = 'position',
|
||||
dest = 'coords',
|
||||
type = 'string', metavar = 'string',
|
||||
help = 'coordinate label [%default]')
|
||||
parser.set_defaults(position ='ipinitialcoord',
|
||||
mode ='cell'
|
||||
parser.set_defaults(coords = 'pos',
|
||||
mode = 'cell'
|
||||
)
|
||||
|
||||
(options, filenames) = parser.parse_args()
|
||||
|
|
@ -38,9 +38,12 @@ parser.set_defaults(position ='ipinitialcoord',
|
|||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False, readonly = True)
|
||||
isGeom = name.endswith('.geom')
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
buffered = False,
|
||||
labeled = not isGeom,
|
||||
readonly = True,
|
||||
)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
|
|
@ -48,10 +51,13 @@ for name in filenames:
|
|||
|
||||
table.head_read()
|
||||
|
||||
errors = []
|
||||
if table.label_dimension(options.position) != 3:
|
||||
errors.append('coordinates {} are not a vector.'.format(options.position))
|
||||
remarks = []
|
||||
errors = []
|
||||
coordDim = 3 if isGeom else table.label_dimension(options.coords)
|
||||
if not 3 >= coordDim >= 1: errors.append('coordinates "{}" need to have one, two, or three dimensions.'.format(options.coords))
|
||||
elif coordDim < 3: remarks.append('appending {} dimensions to coordinates "{}"...'.format(3-coordDim,options.coords))
|
||||
|
||||
if remarks != []: damask.util.croak(remarks)
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss=True)
|
||||
|
|
@ -59,17 +65,33 @@ for name in filenames:
|
|||
|
||||
# --------------- figure out size and grid ---------------------------------------------------------
|
||||
|
||||
table.data_readArray(options.position)
|
||||
if isGeom:
|
||||
info,extra_header = table.head_getGeom()
|
||||
coords = [np.linspace(info['origin'][i],
|
||||
info['origin'][i]+info['size'][i],
|
||||
num = info['grid'][i]+1,
|
||||
endpoint = True,
|
||||
) for i in xrange(3)]
|
||||
else:
|
||||
table.data_readArray(options.coords)
|
||||
if len(table.data.shape) < 2: table.data.shape += (1,) # expand to 2D shape
|
||||
if table.data.shape[1] < 3:
|
||||
table.data = np.hstack((table.data,
|
||||
np.zeros((table.data.shape[0],
|
||||
3-table.data.shape[1]),dtype='f'))) # fill coords up to 3D with zeros
|
||||
|
||||
coords = [np.unique(table.data[:,i]) for i in xrange(3)]
|
||||
if options.mode == 'cell':
|
||||
coords = [0.5 * np.array([3.0 * coords[i][0] - coords[i][0 + len(coords[i]) > 1]] + \
|
||||
[coords[i][j-1] + coords[i][j] for j in xrange(1,len(coords[i]))] + \
|
||||
[3.0 * coords[i][-1] - coords[i][-1 - (len(coords[i]) > 1)]]) for i in xrange(3)]
|
||||
grid = np.array(map(len,coords),'i')
|
||||
N = grid.prod() if options.mode == 'point' else (grid-1).prod()
|
||||
coords = [np.unique(table.data[:,i]) for i in xrange(3)]
|
||||
|
||||
if N != len(table.data): errors.append('data count {} does not match grid {}x{}x{}.'.format(N,*(grid - options.mode == 'cell') ))
|
||||
if options.mode == 'cell':
|
||||
coords = [0.5 * np.array([3.0 * coords[i][0] - coords[i][0 + len(coords[i]) > 1]] + \
|
||||
[coords[i][j-1] + coords[i][j] for j in xrange(1,len(coords[i]))] + \
|
||||
[3.0 * coords[i][-1] - coords[i][-1 - (len(coords[i]) > 1)]]) for i in xrange(3)]
|
||||
|
||||
grid = np.array(map(len,coords),'i')
|
||||
N = grid.prod() if options.mode == 'point' or isGeom else (grid-1).prod()
|
||||
|
||||
if not isGeom and N != len(table.data):
|
||||
errors.append('data count {} does not match grid {}x{}x{}.'.format(N,*(grid - (options.mode == 'cell')) ))
|
||||
if errors != []:
|
||||
damask.util.croak(errors)
|
||||
table.close(dismiss = True)
|
||||
|
|
@ -100,9 +122,9 @@ for name in filenames:
|
|||
(directory,filename) = os.path.split(name)
|
||||
writer.SetDataModeToBinary()
|
||||
writer.SetCompressorTypeToZLib()
|
||||
writer.SetFileName(os.path.join(directory,os.path.splitext(filename)[0] \
|
||||
+'_{}({})'.format(options.position, options.mode) \
|
||||
+'.'+writer.GetDefaultFileExtension()))
|
||||
writer.SetFileName(os.path.join(directory,os.path.splitext(filename)[0] +
|
||||
('' if isGeom else '_{}({})'.format(options.coords, options.mode)) +
|
||||
'.' + writer.GetDefaultFileExtension()))
|
||||
else:
|
||||
writer = vtk.vtkDataSetWriter()
|
||||
writer.WriteToOutputStringOn()
|
||||
|
|
|
|||
|
|
@ -6,15 +6,12 @@ import numpy as np
|
|||
import damask
|
||||
from scipy import ndimage
|
||||
from optparse import OptionParser
|
||||
from collections import defaultdict
|
||||
|
||||
scriptName = os.path.splitext(os.path.basename(__file__))[0]
|
||||
scriptID = ' '.join([scriptName,damask.version])
|
||||
|
||||
def mostFrequent(arr):
|
||||
d = defaultdict(int)
|
||||
for i in arr: d[i] += 1
|
||||
return sorted(d.iteritems(), key=lambda x: x[1], reverse=True)[0][0] # return value of most frequent microstructure
|
||||
return np.argmax(np.bincount(arr))
|
||||
|
||||
|
||||
#--------------------------------------------------------------------------------------------------
|
||||
|
|
@ -43,10 +40,9 @@ parser.set_defaults(stencil = 3,
|
|||
if filenames == []: filenames = [None]
|
||||
|
||||
for name in filenames:
|
||||
try:
|
||||
table = damask.ASCIItable(name = name,
|
||||
buffered = False,
|
||||
labeled = False)
|
||||
try: table = damask.ASCIItable(name = name,
|
||||
buffered = False,
|
||||
labeled = False)
|
||||
except: continue
|
||||
damask.util.report(scriptName,name)
|
||||
|
||||
|
|
@ -72,7 +68,7 @@ for name in filenames:
|
|||
|
||||
# --- read data ------------------------------------------------------------------------------------
|
||||
|
||||
microstructure = table.microstructure_read(info['grid']).reshape(info['grid'],order='F') # read microstructure
|
||||
microstructure = table.microstructure_read(info['grid']).reshape(info['grid'],order='F') # read microstructure
|
||||
|
||||
# --- do work ------------------------------------------------------------------------------------
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue