DAMASK_EICMD/processing/post/addDisplacement.py

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#!/usr/bin/env python2
# -*- coding: UTF-8 no BOM -*-
import os,sys,math
import numpy as np
import scipy.ndimage
from optparse import OptionParser
import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
#--------------------------------------------------------------------------------------------------
def cell2node(cellData,grid):
nodeData = 0.0
datalen = np.array(cellData.shape[3:]).prod()
for i in xrange(datalen):
node = scipy.ndimage.convolve(cellData.reshape(tuple(grid)+(datalen,))[...,i],
np.ones((2,2,2))/8., # 2x2x2 neighborhood of cells
mode = 'wrap',
origin = -1, # offset to have cell origin as center
) # now averaged at cell origins
node = np.append(node,node[np.newaxis,0,:,:,...],axis=0) # wrap along z
node = np.append(node,node[:,0,np.newaxis,:,...],axis=1) # wrap along y
node = np.append(node,node[:,:,0,np.newaxis,...],axis=2) # wrap along x
nodeData = node[...,np.newaxis] if i==0 else np.concatenate((nodeData,node[...,np.newaxis]),axis=-1)
return nodeData
#--------------------------------------------------------------------------------------------------
def displacementAvgFFT(F,grid,size,nodal=False,transformed=False):
"""calculate average cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
if nodal:
x, y, z = np.meshgrid(np.linspace(0,size[0],1+grid[0]),
np.linspace(0,size[1],1+grid[1]),
np.linspace(0,size[2],1+grid[2]),
indexing = 'ij')
else:
x, y, z = np.meshgrid(np.linspace(0,size[0],grid[0],endpoint=False),
np.linspace(0,size[1],grid[1],endpoint=False),
np.linspace(0,size[2],grid[2],endpoint=False),
indexing = 'ij')
origCoords = np.concatenate((z[:,:,:,None],y[:,:,:,None],x[:,:,:,None]),axis = 3)
F_fourier = F if transformed else np.fft.rfftn(F,axes=(0,1,2)) # transform or use provided data
Favg = np.real(F_fourier[0,0,0,:,:])/grid.prod() # take zero freq for average
avgDisplacement = np.einsum('ml,ijkl->ijkm',Favg-np.eye(3),origCoords) # dX = Favg.X
return avgDisplacement
#--------------------------------------------------------------------------------------------------
def displacementFluctFFT(F,grid,size,nodal=False,transformed=False):
"""calculate cell center (or nodal) displacement for deformation gradient field specified in each grid cell"""
integrator = 0.5j * size / math.pi
kk, kj, ki = np.meshgrid(np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2])),
np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1])),
np.arange(grid[0]//2+1),
indexing = 'ij')
k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3)
k_sSquared = np.einsum('...l,...l',k_s,k_s)
k_sSquared[0,0,0] = 1.0 # ignore global average frequency
#--------------------------------------------------------------------------------------------------
# integration in Fourier space
displacement_fourier = -np.einsum('ijkml,ijkl,l->ijkm',
F if transformed else np.fft.rfftn(F,axes=(0,1,2)),
k_s,
integrator,
) / k_sSquared[...,np.newaxis]
#--------------------------------------------------------------------------------------------------
# backtransformation to real space
displacement = np.fft.irfftn(displacement_fourier,grid,axes=(0,1,2))
return cell2node(displacement,grid) if nodal else displacement
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog [options] [ASCIItable(s)]', description = """
Add displacments resulting from deformation gradient field.
Operates on periodic three-dimensional x,y,z-ordered data sets.
Outputs at cell centers or cell nodes (into separate file).
""", version = scriptID)
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parser.add_option('-f',
'--defgrad',
dest = 'defgrad',
metavar = 'string',
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help = 'label of deformation gradient [%default]')
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parser.add_option('-p',
'--pos', '--position',
dest = 'pos',
metavar = 'string',
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help = 'label of coordinates [%default]')
parser.add_option('--nodal',
dest = 'nodal',
action = 'store_true',
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help = 'output nodal (instad of cell-centered) displacements')
parser.set_defaults(defgrad = 'f',
pos = 'pos',
nodal = False,
)
(options,filenames) = parser.parse_args()
# --- loop over input files -------------------------------------------------------------------------
if filenames == []: filenames = [None]
for name in filenames:
outname = (os.path.splitext(name)[0] +
'_nodal' +
os.path.splitext(name)[1]) if (options.nodal and name) else None
try: table = damask.ASCIItable(name = name,
outname = outname,
buffered = False)
except: continue
damask.util.report(scriptName,'{}{}'.format(name,' --> {}'.format(outname) if outname else ''))
# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
errors = []
remarks = []
if table.label_dimension(options.defgrad) != 9:
errors.append('deformation gradient "{}" is not a 3x3 tensor.'.format(options.defgrad))
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 {} dimension{} to coordinates "{}"...'.format(3-coordDim,
's' if coordDim < 2 else '',
options.pos))
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss=True)
continue
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray([options.defgrad,options.pos])
table.data_rewind()
if len(table.data.shape) < 2: table.data.shape += (1,) # expand to 2D shape
if table.data[:,9:].shape[1] < 3:
table.data = np.hstack((table.data,
np.zeros((table.data.shape[0],
3-table.data[:,9:].shape[1]),dtype='f'))) # fill coords up to 3D with zeros
coords = [np.unique(table.data[:,9+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
N = grid.prod()
if N != len(table.data): errors.append('data count {} does not match grid {}x{}x{}.'.format(N,*grid))
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ process data ------------------------------------------
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...
displacement = displacementFluctFFT(F_fourier,grid,size,options.nodal,transformed=True)
avgDisplacement = displacementAvgFFT (F_fourier,grid,size,options.nodal,transformed=True)
# ------------------------------------------ assemble header ---------------------------------------
if options.nodal:
table.info_clear()
table.labels_clear()
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 []) +
['{}_avg({}).{}' .format(i+1,options.defgrad,options.pos) for i in xrange(3)] +
['{}_fluct({}).{}'.format(i+1,options.defgrad,options.pos) for i in xrange(3)] )
table.head_write()
# ------------------------------------------ output data -------------------------------------------
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