158 lines
6.3 KiB
Python
Executable File
158 lines
6.3 KiB
Python
Executable File
#!/usr/bin/env python2.7
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,math
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import numpy as np
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from optparse import OptionParser
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import damask
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scriptName = os.path.splitext(os.path.basename(__file__))[0]
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scriptID = ' '.join([scriptName,damask.version])
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def merge_dicts(*dict_args):
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"""Given any number of dicts, shallow copy and merge into a new dict, with precedence going to key value pairs in latter dicts."""
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result = {}
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for dictionary in dict_args:
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result.update(dictionary)
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return result
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def divFFT(geomdim,field):
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"""Calculate divergence of a vector or tensor field by transforming into Fourier space."""
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shapeFFT = np.array(np.shape(field))[0:3]
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grid = np.array(np.shape(field)[2::-1])
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N = grid.prod() # field size
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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)
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div_fourier = np.empty(field_fourier.shape[0:len(np.shape(field))-1],'c16')
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# differentiation in Fourier space
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TWOPIIMG = 2.0j*math.pi
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einsums = {
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3:'ijkl,ijkl->ijk', # vector, 3 -> 1
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9:'ijkm,ijklm->ijkl', # tensor, 3x3 -> 3
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}
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k_sk = np.where(np.arange(grid[2])>grid[2]//2,np.arange(grid[2])-grid[2],np.arange(grid[2]))/geomdim[0]
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if grid[2]%2 == 0: k_sk[grid[2]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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k_sj = np.where(np.arange(grid[1])>grid[1]//2,np.arange(grid[1])-grid[1],np.arange(grid[1]))/geomdim[1]
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if grid[1]%2 == 0: k_sj[grid[1]//2] = 0 # Nyquist freq=0 for even grid (Johnson, MIT, 2011)
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k_si = np.arange(grid[0]//2+1)/geomdim[2]
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kk, kj, ki = np.meshgrid(k_sk,k_sj,k_si,indexing = 'ij')
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k_s = np.concatenate((ki[:,:,:,None],kj[:,:,:,None],kk[:,:,:,None]),axis = 3).astype('c16')
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div_fourier = np.einsum(einsums[n],k_s,field_fourier)*TWOPIIMG
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return np.fft.irfftn(div_fourier,axes=(0,1,2),s=shapeFFT).reshape([N,n/3])
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog option(s) [ASCIItable(s)]', description = """
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Add column(s) containing curl of requested column(s).
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Operates on periodic ordered three-dimensional data sets
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of vector and tensor fields.
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""", version = scriptID)
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parser.add_option('-p','--pos','--periodiccellcenter',
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dest = 'pos',
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type = 'string', metavar = 'string',
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help = 'label of coordinates [%default]')
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parser.add_option('-l','--label',
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dest = 'data',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of field values')
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parser.set_defaults(pos = 'pos',
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)
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(options,filenames) = parser.parse_args()
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if options.data is None: parser.error('no data column specified.')
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# --- define possible data types -------------------------------------------------------------------
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datatypes = {
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3: {'name': 'vector',
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'shape': [3],
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},
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9: {'name': 'tensor',
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'shape': [3,3],
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},
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}
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# --- loop over input files ------------------------------------------------------------------------
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if filenames == []: filenames = [None]
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for name in filenames:
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try: table = damask.ASCIItable(name = name,buffered = False)
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except: continue
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damask.util.report(scriptName,name)
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# --- interpret header ----------------------------------------------------------------------------
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table.head_read()
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remarks = []
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errors = []
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active = []
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coordDim = table.label_dimension(options.pos)
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if coordDim != 3:
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errors.append('coordinates "{}" must be three-dimensional.'.format(options.pos))
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else: coordCol = table.label_index(options.pos)
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for me in options.data:
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dim = table.label_dimension(me)
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if dim in datatypes:
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active.append(merge_dicts({'label':me},datatypes[dim]))
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remarks.append('differentiating {} "{}"...'.format(datatypes[dim]['name'],me))
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else:
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remarks.append('skipping "{}" of dimension {}...'.format(me,dim) if dim != -1 else \
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'"{}" not found...'.format(me) )
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if remarks != []: damask.util.croak(remarks)
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if errors != []:
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damask.util.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ assemble header --------------------------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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for data in active:
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table.labels_append(['divFFT({})'.format(data['label']) if data['shape'] == [3] \
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else '{}_divFFT({})'.format(i+1,data['label'])
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for i in range(np.prod(np.array(data['shape']))//3)]) # extend ASCII header with new labels
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table.head_write()
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# --------------- figure out size and grid ---------------------------------------------------------
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table.data_readArray()
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grid,size = damask.util.coordGridAndSize(table.data[:,table.label_indexrange(options.pos)])
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# ------------------------------------------ process value field -----------------------------------
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stack = [table.data]
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for data in 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
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stack.append(divFFT(size[::-1],
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table.data[:,table.label_indexrange(data['label'])].
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reshape(grid[::-1].tolist()+data['shape'])))
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# ------------------------------------------ output result -----------------------------------------
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if len(stack) > 1: table.data = np.hstack(tuple(stack))
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table.data_writeArray('%.12g')
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# ------------------------------------------ output finalization -----------------------------------
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table.close() # close input ASCII table (works for stdin)
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