DAMASK_EICMD/processing/post/addDivergence.py

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#!/usr/bin/env python
# -*- coding: UTF-8 no BOM -*-
import os,sys,string,math,operator
import numpy as np
from collections import defaultdict
from optparse import OptionParser
import damask
scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
def divFFT(geomdim,field):
grid = np.array(np.shape(field)[0:3])
wgt = 1.0/np.array(grid).prod()
field_fourier=np.fft.fftpack.rfftn(field,axes=(0,1,2))
if len(np.shape(field)) == 4:
dataType = 'vector'
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div_fourier=np.zeros(field_fourier.shape[0:3],'c16') # div is a scalar
elif len(np.shape(field)) == 5:
dataType = 'tensor'
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div_fourier=np.zeros(field_fourier.shape[0:4],'c16') # div is a vector
# differentiation in Fourier space
k_s=np.zeros([3],'i')
TWOPIIMG = (0.0+2.0j*math.pi)
for i in xrange(grid[0]):
k_s[0] = i
if(i > grid[0]/2 ): k_s[0] = k_s[0] - grid[0]
for j in xrange(grid[1]):
k_s[1] = j
if(j > grid[1]/2 ): k_s[1] = k_s[1] - grid[1]
for k in xrange(grid[2]/2+1):
k_s[2] = k
if(k > grid[2]/2 ): k_s[2] = k_s[2] - grid[2]
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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 dataType == 'tensor':
for l in xrange(3):
div_fourier[i,j,k,l] = sum(field_fourier[i,j,k,l,0:3]*xi) *TWOPIIMG
elif dataType == 'vector':
div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
div=np.fft.fftpack.irfftn(div_fourier,axes=(0,1,2))
print div.shape
if dataType == 'tensor':
return div.reshape([grid.prod(),3])
if dataType == 'vector':
return div.reshape([grid.prod(),1])
# --------------------------------------------------------------------
# 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', 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')
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parser.set_defaults(coords = 'ipinitialcoord')
(options,filenames) = parser.parse_args()
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if options.vector == None and options.tensor == None:
parser.error('no data column specified...')
datainfo = { # list of requested labels per datatype
'vector': {'shape':[3],
'len':3,
'label':[]},
'tensor': {'shape':[3,3],
'len':9,
'label':[]},
}
if options.vector != None: datainfo['vector']['label'] = options.vector
if options.tensor != None: datainfo['tensor']['label'] = options.tensor
# ------------------------------------------ setup file handles ------------------------------------
files = []
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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':open(name+'_tmp','w'), 'croak':sys.stderr})
#--- loop over input files -------------------------------------------------------------------------
for file in files:
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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')
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table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
table.head_read() # read ASCII header info
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table.data_readArray()
# --------------- figure out name of coordinate data (support for legacy .x notation) -------------
coordLabels=['%i_%s'%(i+1,options.coords) for i in xrange(3)] # store labels for column keys
if not set(coordLabels).issubset(table.labels):
directions = ['x','y','z']
coordLabels=['%s.%s'%(options.coords,directions[i]) for i in xrange(3)] # store labels for column keys
if not set(coordLabels).issubset(table.labels):
file['croak'].write('no coordinate data (1_%s) found...\n'%options.coords)
continue
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coordColumns = [table.labels.index(label) for label in coordLabels]
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# --------------- figure out active columns -------------------------------------------------------
active = defaultdict(list)
for datatype,info in datainfo.items():
for label in info['label']:
key = '1_%s'%label
if key not in table.labels:
file['croak'].write('column %s not found...\n'%key)
else:
active[datatype].append(label)
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# --------------- assemble new header (metadata and columns containing curl) ----------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels:
table.labels_append(['divFFT(%s)'%(label) if datatype == 'vector' else
'%i_divFFT(%s)'%(i+1,label) for i in xrange(datainfo[datatype]['len']//3)])# extend ASCII header with new labels
table.head_write()
# --------------- figure out size and grid ---------------------------------------------------------
coords = [{},{},{}]
for i in xrange(table.data.shape[0]):
for j in xrange(3):
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coords[j][str(table.data[i,coordColumns[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
for i, points in enumerate(grid):
if points == 1:
mask = np.ones(3,dtype=bool)
mask[i]=0
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size[i] = min(size[mask]/grid[mask]) # third spacing equal to smaller of other spacing
# ------------------------------------------ process value field -----------------------------------
div = defaultdict(dict)
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested curls
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startColumn=table.labels.index('1_'+label)
div[datatype][label] = divFFT(size[::-1], # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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table.data[:,startColumn:startColumn+datainfo[datatype]['len']].\
reshape([grid[2],grid[1],grid[0]]+datainfo[datatype]['shape']))
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# ------------------------------------------ add data ------------------------------------------
for datatype,labels in active.items(): # loop over vector,tensor
for label in labels: # loop over all requested curls
for c in xrange(div[datatype][label][0,:].shape[0]): # append column by column
lastRow = table.data.shape[1]
table.data=np.insert(table.data,lastRow,div[datatype][label][:,c],1)
# ------------------------------------------ output result -----------------------------------------
table.data_writeArray('%.12g')
table.input_close() # close input ASCII table (works for stdin)
table.output_close() # close output ASCII table (works for stdout)
if file['name'] != 'STDIN':
os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new