DAMASK_EICMD/processing/post/addDivergence.py

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#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,math
import numpy as np
from optparse import OptionParser
import damask
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
def divFFT(geomdim,field):
N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2))
div_fourier = np.zeros(field_fourier.shape[0:len(np.shape(field))-1],'c16') # size depents on whether tensor or vector
# differentiation in Fourier space
k_s=np.zeros([3],'i')
TWOPIIMG = (0.0+2.0j*math.pi)
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for i in xrange(grid[2]):
k_s[0] = i
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if(grid[2]%2==0 and i == grid[2]//2): # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_s[0]=0
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elif (i > grid[2]//2):
k_s[0] = k_s[0] - grid[2]
for j in xrange(grid[1]):
k_s[1] = j
if(grid[1]%2==0 and j == grid[1]//2): # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_s[1]=0
elif (j > grid[1]//2):
k_s[1] = k_s[1] - grid[1]
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for k in xrange(grid[0]//2+1):
k_s[2] = k
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if(grid[0]%2==0 and k == grid[0]//2): # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_s[2]=0
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xi=np.array([k_s[2]/geomdim[2]+0.0j,k_s[1]/geomdim[1]+0.j,k_s[0]/geomdim[0]+0.j],'c16')
if n == 9: # tensor, 3x3 -> 3
for l in xrange(3):
div_fourier[i,j,k,l] = sum(field_fourier[i,j,k,l,0:3]*xi) *TWOPIIMG
elif n == 3: # vector, 3 -> 1
div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
return np.fft.fftpack.irfftn(div_fourier,axes=(0,1,2)).reshape([N,n/3])
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing divergence of requested column(s).
Operates on periodic ordered three-dimensional data sets.
Deals with both vector- and tensor-valued fields.
""", version = scriptID)
parser.add_option('-c','--coordinates',
dest = 'coords',
type = 'string', metavar = 'string',
help = 'column heading for coordinates [%default]')
parser.add_option('-v','--vector',
dest = 'vector',
action = 'extend', metavar = '<string LIST>',
help = 'heading of columns containing vector field values')
parser.add_option('-t','--tensor',
dest = 'tensor',
action = 'extend', metavar = '<string LIST>',
help = 'heading of columns containing tensor field values')
parser.set_defaults(coords = 'ipinitialcoord',
)
(options,filenames) = parser.parse_args()
if options.vector is None and options.tensor is None:
parser.error('no data column specified.')
# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = [None]
for name in filenames:
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try:
table = damask.ASCIItable(name = name,buffered = False)
except:
continue
damask.util.report(scriptName,name)
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# ------------------------------------------ read header ------------------------------------------
table.head_read()
# ------------------------------------------ sanity checks ----------------------------------------
items = {
'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.tensor, 'active':[], 'column': []},
'vector': {'dim': 3, 'shape': [3], 'labels':options.vector, 'active':[], 'column': []},
}
errors = []
remarks = []
column = {}
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)
for type, data in items.iteritems():
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for what in (data['labels'] if data['labels'] is not None else []):
dim = table.label_dimension(what)
if dim != data['dim']: remarks.append('column {} is not a {}.'.format(what,type))
else:
items[type]['active'].append(what)
items[type]['column'].append(table.label_index(what))
if remarks != []: damask.util.croak(remarks)
if errors != []:
damask.util.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for type, data in items.iteritems():
for label in data['active']:
table.labels_append(['divFFT({})'.format(label) if type == 'vector' else
'{}_divFFT({})'.format(i+1,label) for i in xrange(data['dim']//3)]) # extend ASCII header with new labels
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
table.data_readArray()
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coords = [np.unique(table.data[:,colCoord+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
# ------------------------------------------ process value field -----------------------------------
stack = [table.data]
for type, data in items.iteritems():
for i,label in enumerate(data['active']):
stack.append(divFFT(size[::-1], # we need to reverse order here, because x
table.data[:,data['column'][i]:data['column'][i]+data['dim']]. # is fastest,ie rightmost, but leftmost in
reshape([grid[2],grid[1],grid[0]]+data['shape']))) # our x,y,z notation
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# ------------------------------------------ output result -----------------------------------------
if len(stack) > 1: table.data = np.hstack(tuple(stack))
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table.data_writeArray('%.12g')
# ------------------------------------------ output finalization -----------------------------------
table.close() # close input ASCII table (works for stdin)