#!/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 curlFFT(geomdim,field): shapeFFT = np.array(np.shape(field))[0:3] grid = np.array(np.shape(field)[2::-1]) 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.rfftn(field,axes=(0,1,2),s=shapeFFT) curl_fourier = np.empty(field_fourier.shape,'c16') # differentiation in Fourier space TWOPIIMG = 2.0j*math.pi k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0] if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1] if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # for even grid, set Nyquist freq to 0 (Johnson, MIT, 2011) k_si = np.arange(grid[0]//2+1)/geomdim[2] kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij') k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16') e = np.zeros((3, 3, 3)) e[0, 1, 2] = e[1, 2, 0] = e[2, 0, 1] = 1.0 # Levi-Civita symbols e[0, 2, 1] = e[2, 1, 0] = e[1, 0, 2] = -1.0 if dataType == 'tensor': # tensor, 3x3 -> 3x3 curl_fourier = np.einsum('slm,ijkl,ijknm->ijksn',e,k_s,field_fourier)*TWOPIIMG elif dataType == 'vector': # vector, 3 -> 3 curl_fourier = np.einsum('slm,ijkl,ijkm->ijks',e,k_s,field_fourier)*TWOPIIMG return np.fft.irfftn(curl_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n]) # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [ASCIItable(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 fields. """, version = scriptID) parser.add_option('-p','--pos','--periodiccellcenter', dest = 'pos', type = 'string', metavar = 'string', help = 'label of coordinates [%default]') parser.add_option('-v','--vector', dest = 'vector', action = 'extend', metavar = '', help = 'label(s) of vector field values') parser.add_option('-t','--tensor', dest = 'tensor', action = 'extend', metavar = '', 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.') # --- 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.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(): 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 range(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 range(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 ones # ------------------------------------------ process value field ----------------------------------- stack = [table.data] for type, data in items.iteritems(): for i,label in enumerate(data['active']): # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation stack.append(curlFFT(size[::-1], table.data[:,data['column'][i]:data['column'][i]+data['dim']]. reshape(grid[::-1].tolist()+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)