#!/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 curlFFT(geomdim,field): N = grid.prod() # field size n = np.array(np.shape(field)[3:]).prod() # data size 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[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 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 = '', help = 'heading of columns containing vector field values') parser.add_option('-t','--tensor', dest = 'tensor', action = 'extend', metavar = '', 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(['{}_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 = [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])) # ------------------------------------------ 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)