DAMASK_EICMD/processing/post/addCurl.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 curlFFT(geomdim,field):
grid = np.array(np.shape(field)[0:3])
N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
wgt = 1.0/N
if n == 3:
dataType = 'vector'
elif n == 9:
dataType = 'tensor'
field_fourier = np.fft.fftpack.rfftn(field,axes=(0,1,2))
curl_fourier = np.zeros(field_fourier.shape,'c16')
# 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(grid[0]%2==0 and i == grid[0]//2): # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_s[0]=0
elif (i > grid[0]//2):
k_s[0] = k_s[0] - grid[0]
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]
for k in xrange(grid[2]//2+1):
k_s[2] = k
if(grid[2]%2==0 and k == grid[2]//2): # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
k_s[2]=0
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):
curl_fourier[i,j,k,0,l] = ( field_fourier[i,j,k,l,2]*xi[1]\
-field_fourier[i,j,k,l,1]*xi[2]) *TWOPIIMG
curl_fourier[i,j,k,1,l] = (-field_fourier[i,j,k,l,2]*xi[0]\
+field_fourier[i,j,k,l,0]*xi[2]) *TWOPIIMG
curl_fourier[i,j,k,2,l] = ( field_fourier[i,j,k,l,1]*xi[0]\
-field_fourier[i,j,k,l,0]*xi[1]) *TWOPIIMG
elif dataType == 'vector':
curl_fourier[i,j,k,0] = ( field_fourier[i,j,k,2]*xi[1]\
-field_fourier[i,j,k,1]*xi[2]) *TWOPIIMG
curl_fourier[i,j,k,1] = (-field_fourier[i,j,k,2]*xi[0]\
+field_fourier[i,j,k,0]*xi[2]) *TWOPIIMG
curl_fourier[i,j,k,2] = ( field_fourier[i,j,k,1]*xi[0]\
-field_fourier[i,j,k,0]*xi[1]) *TWOPIIMG
return np.fft.fftpack.irfftn(curl_fourier,axes=(0,1,2)).reshape([N,n])
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
Add column(s) containing curl 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 == None and options.tensor == 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
table.croak(damask.util.emph(scriptName)+(': '+name if name else ''))
# ------------------------------------------ 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))
else: coordCol = 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 != []: table.croak(remarks)
if errors != []:
table.croak(errors)
table.close(dismiss = True)
continue
# ------------------------------------------ assemble header --------------------------------------
table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
for type, data in items.iteritems():
for label in data['active']:
table.labels_append(['{}_curlFFT({})'.format(i+1,label) for i in xrange(data['dim'])]) # extend ASCII header with new labels
table.head_write()
# --------------- 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 equal 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(curlFFT(size[::-1], # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
table.data[:,data['column'][i]:data['column'][i]+data['dim']].\
reshape([grid[2],grid[1],grid[0]]+data['shape'])))
# ------------------------------------------ output result -----------------------------------------
if len(stack) > 1: table.data = np.hstack(tuple(stack))
table.data_writeArray('%.12g')
# ------------------------------------------ output finalization -----------------------------------
table.close() # close input ASCII table (works for stdin)