162 lines
6.9 KiB
Python
Executable File
162 lines
6.9 KiB
Python
Executable File
#!/usr/bin/env python
|
|
# -*- coding: UTF-8 no BOM -*-
|
|
|
|
import os,sys,string,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)
|
|
for i in xrange(grid[2]):
|
|
k_s[0] = i
|
|
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
|
|
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]
|
|
|
|
for k in xrange(grid[0]//2+1):
|
|
k_s[2] = k
|
|
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
|
|
|
|
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 == None and options.tensor == None:
|
|
parser.error('no data column specified.')
|
|
|
|
# --- loop over input files -------------------------------------------------------------------------
|
|
|
|
if filenames == []: filenames = [None]
|
|
|
|
for name in filenames:
|
|
try:
|
|
table = damask.ASCIItable(name = name,buffered = False)
|
|
except:
|
|
continue
|
|
damask.util.report(scriptName,name)
|
|
|
|
# ------------------------------------------ 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: colCoord = table.label_index(options.coords)
|
|
|
|
for type, data in items.iteritems():
|
|
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 --------------------------------------
|
|
|
|
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()
|
|
|
|
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 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(divFFT(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)
|