154 lines
6.7 KiB
Python
Executable File
154 lines
6.7 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 divFFT(geomdim,field):
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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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if n == 3: dataType = 'vector'
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elif n == 9: dataType = 'tensor'
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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') # size depents on whether tensor or vector
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# differentiation in Fourier space
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TWOPIIMG = 2.0j*math.pi
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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 # for even grid, set Nyquist freq to 0 (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 # for even grid, set Nyquist freq to 0 (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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if dataType == 'tensor': # tensor, 3x3 -> 3
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div_fourier = np.einsum('ijklm,ijkm->ijkl',field_fourier,k_s)*TWOPIIMG
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elif dataType == 'vector': # vector, 3 -> 1
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div_fourier = np.einsum('ijkl,ijkl->ijk',field_fourier,k_s)*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 divergence of requested column(s).
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Operates on periodic ordered three-dimensional data sets.
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Deals with both vector- and tensor-valued 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('-v','--vector',
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dest = 'vector',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of vector field values')
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parser.add_option('-t','--tensor',
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dest = 'tensor',
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action = 'extend', metavar = '<string LIST>',
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help = 'label(s) of tensor 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.vector is None and options.tensor is None:
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parser.error('no data column specified.')
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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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# ------------------------------------------ read header ------------------------------------------
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table.head_read()
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# ------------------------------------------ sanity checks ----------------------------------------
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items = {
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'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.tensor, 'active':[], 'column': []},
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'vector': {'dim': 3, 'shape': [3], 'labels':options.vector, 'active':[], 'column': []},
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}
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errors = []
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remarks = []
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column = {}
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if table.label_dimension(options.pos) != 3: errors.append('coordinates {} are not a vector.'.format(options.pos))
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else: colCoord = table.label_index(options.pos)
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for type, data in items.iteritems():
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for what in (data['labels'] if data['labels'] is not None else []):
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dim = table.label_dimension(what)
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if dim != data['dim']: remarks.append('column {} is not a {}.'.format(what,type))
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else:
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items[type]['active'].append(what)
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items[type]['column'].append(table.label_index(what))
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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 type, data in items.iteritems():
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for label in data['active']:
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table.labels_append(['divFFT({})'.format(label) if type == 'vector' else
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'{}_divFFT({})'.format(i+1,label) for i in range(data['dim']//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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coords = [np.unique(table.data[:,colCoord+i]) for i in range(3)]
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mincorner = np.array(map(min,coords))
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maxcorner = np.array(map(max,coords))
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grid = np.array(map(len,coords),'i')
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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
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# ------------------------------------------ process value field -----------------------------------
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stack = [table.data]
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for type, data in items.iteritems():
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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
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stack.append(divFFT(size[::-1],
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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 -----------------------------------------
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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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