DAMASK_EICMD/processing/post/addDivergence.py

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#!/usr/bin/env python2.7
# -*- 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):
shapeFFT = np.array(np.shape(field))[0:3]
grid = np.array(np.shape(field)[2::-1])
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N = grid.prod() # field size
n = np.array(np.shape(field)[3:]).prod() # data size
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field_fourier = np.fft.rfftn(field,axes=(0,1,2),s=shapeFFT)
div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16') # size depents on whether tensor or vector
# differentiation in Fourier space
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k_s = np.zeros([3],'i')
TWOPIIMG = 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: k_s[0] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
elif i > grid[2]//2: k_s[0] -= grid[2]
for j in xrange(grid[1]):
k_s[1] = j
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if grid[1]%2 == 0 and j == grid[1]//2: k_s[1] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
elif j > grid[1]//2: 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: k_s[2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011)
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xi = (k_s/geomdim)[2::-1].astype('c16') # reversing the field input order
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
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elif n == 3: # vector, 3 -> 1
div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
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return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
# --------------------------------------------------------------------
# MAIN
# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [ASCIItable(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)
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parser.add_option('-p','--pos','--periodiccellcenter',
dest = 'pos',
type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
parser.add_option('-v','--vector',
dest = 'vector',
action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of vector field values')
parser.add_option('-t','--tensor',
dest = 'tensor',
action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of tensor field values')
parser.set_defaults(pos = 'pos',
)
(options,filenames) = parser.parse_args()
if options.vector is None and options.tensor is None:
parser.error('no data column specified.')
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# --- loop over input files ------------------------------------------------------------------------
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if filenames == []: filenames = [None]
for name in filenames:
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.pos) != 3: errors.append('coordinates {} are not a vector.'.format(options.pos))
else: colCoord = table.label_index(options.pos)
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)
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size = np.where(grid > 1, size, min(size[grid > 1]/grid[grid > 1])) # spacing for grid==1 equal to smallest among other ones
# ------------------------------------------ process value field -----------------------------------
stack = [table.data]
for type, data in items.iteritems():
for i,label in enumerate(data['active']):
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# we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
stack.append(divFFT(size[::-1],
table.data[:,data['column'][i]:data['column'][i]+data['dim']].
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reshape(grid[::-1].tolist()+data['shape'])))
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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)