174 lines
8.7 KiB
Python
Executable File
174 lines
8.7 KiB
Python
Executable File
#!/usr/bin/env python
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# -*- coding: UTF-8 no BOM -*-
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import os,sys,string,math,operator
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import numpy as np
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from collections import defaultdict
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from optparse import OptionParser
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import damask
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = os.path.splitext(scriptID.split()[1])[0]
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def divFFT(geomdim,field):
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grid = np.array(np.shape(field)[0:3])
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wgt = 1.0/np.array(grid).prod()
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field_fourier=np.fft.fftpack.rfftn(field,axes=(0,1,2))
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if len(np.shape(field)) == 4:
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dataType = 'vector'
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div_fourier=np.zeros(field_fourier.shape[0:3],'c8') # div is a scalar
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elif len(np.shape(field)) == 5:
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dataType = 'tensor'
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div_fourier=np.zeros(field_fourier.shape[0:4],'c8') # div is a vector
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# differentiation in Fourier space
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k_s=np.zeros([3],'i')
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TWOPIIMG = (0.0+2.0j*math.pi)
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for i in xrange(grid[0]):
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k_s[0] = i
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if(i > grid[0]/2 ): k_s[0] = k_s[0] - grid[0]
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for j in xrange(grid[1]):
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k_s[1] = j
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if(j > grid[1]/2 ): k_s[1] = k_s[1] - grid[1]
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for k in xrange(grid[2]/2+1):
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k_s[2] = k
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if(k > grid[2]/2 ): k_s[2] = k_s[2] - grid[2]
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xi=np.array([k_s[2]/geomdim[2]+0.0j,k_s[1]/geomdim[1]+0.j,k_s[0]/geomdim[0]+0.j],'c8')
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if dataType == 'tensor':
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for l in xrange(3):
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div_fourier[i,j,k,l] = sum(field_fourier[i,j,k,l,0:3]*xi) *TWOPIIMG
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elif dataType == 'vector':
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div_fourier[i,j,k] = sum(field_fourier[i,j,k,0:3]*xi) *TWOPIIMG
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div=np.fft.fftpack.irfftn(div_fourier,axes=(0,1,2))
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print div.shape
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if dataType == 'tensor':
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return div.reshape([grid.prod(),3])
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if dataType == 'vector':
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return div.reshape([grid.prod(),1])
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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 options [file[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('-c','--coordinates', dest='coords', metavar='string',
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help='column heading for coordinates [%default]')
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parser.add_option('-v','--vector', dest='vector', action='extend', metavar='<string LIST>',
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help='heading of columns containing vector field values')
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parser.add_option('-t','--tensor', dest='tensor', action='extend', metavar='<string LIST>',
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help='heading of columns containing tensor field values')
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parser.set_defaults(coords = 'ipinitialcoord')
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(options,filenames) = parser.parse_args()
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if options.vector == None and options.tensor == None:
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parser.error('no data column specified...')
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datainfo = { # list of requested labels per datatype
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'vector': {'shape':[3],
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'len':3,
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'label':[]},
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'tensor': {'shape':[3,3],
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'len':9,
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'label':[]},
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}
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if options.vector != None: datainfo['vector']['label'] = options.vector
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if options.tensor != None: datainfo['tensor']['label'] = options.tensor
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# ------------------------------------------ setup file handles ------------------------------------
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files = []
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if filenames == []:
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files.append({'name':'STDIN', 'input':sys.stdin, 'output':sys.stdout, 'croak':sys.stderr})
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else:
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for name in filenames:
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if os.path.exists(name):
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files.append({'name':name, 'input':open(name), 'output':open(name+'_tmp','w'), 'croak':sys.stderr})
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#--- loop over input files -------------------------------------------------------------------------
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for file in files:
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if file['name'] != 'STDIN': file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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else: file['croak'].write('\033[1m'+scriptName+'\033[0m\n')
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table = damask.ASCIItable(file['input'],file['output'],False) # make unbuffered ASCII_table
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table.head_read() # read ASCII header info
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table.data_readArray()
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# --------------- figure out name of coordinate data (support for legacy .x notation) -------------
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coordLabels=['%i_%s'%(i+1,options.coords) for i in xrange(3)] # store labels for column keys
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if not set(coordLabels).issubset(table.labels):
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directions = ['x','y','z']
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coordLabels=['%s.%s'%(options.coords,directions[i]) for i in xrange(3)] # store labels for column keys
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if not set(coordLabels).issubset(table.labels):
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file['croak'].write('no coordinate data (1_%s) found...\n'%options.coords)
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continue
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coordColumns = [table.labels.index(label) for label in coordLabels]
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# --------------- figure out active columns -------------------------------------------------------
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active = defaultdict(list)
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for datatype,info in datainfo.items():
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for label in info['label']:
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key = '1_%s'%label
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if key not in table.labels:
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file['croak'].write('column %s not found...\n'%key)
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else:
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active[datatype].append(label)
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# --------------- assemble new header (metadata and columns containing curl) ----------------------
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels:
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table.labels_append(['divFFT(%s)'%(label) if datatype == 'vector' else
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'%i_divFFT(%s)'%(i+1,label) for i in xrange(datainfo[datatype]['len']//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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coords = [{},{},{}]
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for i in xrange(table.data.shape[0]):
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for j in xrange(3):
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coords[j][str(table.data[i,coordColumns[j]])] = True
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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)* \
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np.array([max(map(float,coords[0].keys()))-min(map(float,coords[0].keys())),\
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max(map(float,coords[1].keys()))-min(map(float,coords[1].keys())),\
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max(map(float,coords[2].keys()))-min(map(float,coords[2].keys())),\
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],'d') # size from bounding box, corrected for cell-centeredness
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for i, points in enumerate(grid):
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if points == 1:
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mask = np.ones(3,dtype=bool)
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mask[i]=0
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size[i] = min(size[mask]/grid[mask]) # third spacing equal to smaller of other spacing
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# ------------------------------------------ process value field -----------------------------------
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div = defaultdict(dict)
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested curls
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startColumn=table.labels.index('1_'+label)
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div[datatype][label] = divFFT(size[::-1], # we need to reverse order here, because x is fastest,ie rightmost, but leftmost in our x,y,z notation
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table.data[:,startColumn:startColumn+datainfo[datatype]['len']].\
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reshape([grid[2],grid[1],grid[0]]+datainfo[datatype]['shape']))
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# ------------------------------------------ add data ------------------------------------------
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for datatype,labels in active.items(): # loop over vector,tensor
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for label in labels: # loop over all requested curls
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for c in xrange(div[datatype][label][0,:].shape[0]): # append column by column
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lastRow = table.data.shape[1]
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table.data=np.insert(table.data,lastRow,div[datatype][label][:,c],1)
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# ------------------------------------------ output result -----------------------------------------
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table.data_writeArray('%.12g')
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table.input_close() # close input ASCII table (works for stdin)
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table.output_close() # close output ASCII table (works for stdout)
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if file['name'] != 'STDIN':
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os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
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