176 lines
9.9 KiB
Python
Executable File
176 lines
9.9 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
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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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# --------------------------------------------------------------------
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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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accuracyChoices = ['2','4','6','8']
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parser.add_option('--fdm', dest='accuracy', action='extend', metavar='<int LIST>',
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help='degree of central difference accuracy (%s)'%(','.join(accuracyChoices)))
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parser.add_option('--fft', dest='fft', action='store_true',
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help='calculate divergence in Fourier space')
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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 = 'ip')
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parser.set_defaults(accuracy = [])
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parser.set_defaults(fft = False)
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parser.set_defaults(vector = [])
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parser.set_defaults(tensor = [])
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(options,filenames) = parser.parse_args()
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if len(options.vector) + len(options.tensor) == 0:
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parser.error('no data column specified...')
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if not set(options.accuracy).issubset(set(accuracyChoices)):
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parser.error('accuracy must be chosen from %s...'%(', '.join(accuracyChoices)))
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if options.fft: options.accuracy.append('FFT')
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if not options.accuracy:
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parser.error('no accuracy selected')
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datainfo = { # list of requested labels per datatype
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'vector': {'len':3,
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'label':[]},
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'tensor': {'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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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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file['croak'].write('\033[1m'+scriptName+'\033[0m: '+file['name']+'\n')
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table = damask.ASCIItable(file['input'],file['output'],True) # make unbuffered ASCII_table
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table.head_read() # read ASCII header info
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table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:]))
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# --------------- figure out size and grid ---------------------------------------------------------
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try:
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locationCol = table.labels.index('%s.x'%options.coords) # columns containing location data
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except ValueError:
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file['croak'].write('no coordinate data (%s.x) found...\n'%options.coords)
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continue
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coords = [{},{},{}]
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while table.data_read(): # read next data line of ASCII table
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for j in xrange(3):
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coords[j][str(table.data[locationCol+j])] = True # remember coordinate along x,y,z
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grid = np.array([len(coords[0]),\
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len(coords[1]),\
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len(coords[2]),],'i') # grid is number of distinct coordinates found
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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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N = grid.prod()
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# --------------- figure out columns to process ---------------------------------------------------
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active = defaultdict(list)
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column = defaultdict(dict)
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values = defaultdict(dict)
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divergence = defaultdict(dict)
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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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column[datatype][label] = table.labels.index(key) # remember columns of requested data
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values[datatype][label] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len'])]).\
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reshape(list(grid)+[datainfo[datatype]['len']//3,3])
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if label not in divergence[datatype]: divergence[datatype][label] = {}
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for accuracy in options.accuracy:
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divergence[datatype][label][accuracy] = np.array([0.0 for i in xrange(N*datainfo[datatype]['len']//3)]).\
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reshape(list(grid)+[datainfo[datatype]['len']//3])
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# ------------------------------------------ assemble header ---------------------------------------
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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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for accuracy in options.accuracy:
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table.labels_append({True: ['%i_div%s(%s)'%(i+1,accuracy,label) for i in xrange(3)], # extend ASCII header with new labels
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False:['div%s(%s)'%(accuracy,label)]} [datatype == 'tensor'])
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table.head_write()
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# ------------------------------------------ read value field --------------------------------------
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table.data_rewind()
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idx = 0
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while table.data_read(): # read next data line of ASCII table
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(x,y,z) = damask.util.gridLocation(idx,grid) # figure out (x,y,z) position from line count
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idx += 1
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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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values[datatype][label][x,y,z] = np.array(
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map(float,table.data[column[datatype][label]:
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column[datatype][label]+datainfo[datatype]['len']]),'d') \
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.reshape(datainfo[datatype]['len']//3,3)
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# ------------------------------------------ process value field -----------------------------------
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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 divergencies
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for accuracy in options.accuracy:
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if accuracy == 'FFT':
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divergence[datatype][label][accuracy] =\
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damask.core.math.divergenceFFT(size,values[datatype][label])
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else:
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divergence[datatype][label][accuracy] =\
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damask.core.math.divergenceFDM(size,eval(accuracy)//2-1,values[datatype][label])
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# ------------------------------------------ process data ------------------------------------------
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table.data_rewind()
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idx = 0
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outputAlive = True
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while outputAlive and table.data_read(): # read next data line of ASCII table
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(x,y,z) = damask.util.gridLocation(idx,grid) # figure out (x,y,z) position from line count
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idx += 1
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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
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for accuracy in options.accuracy:
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table.data_append(list(divergence[datatype][label][accuracy][x,y,z].reshape(datainfo[datatype]['len']//3)))
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outputAlive = table.data_write() # output processed line
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# ------------------------------------------ output result -----------------------------------------
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outputAlive and table.output_flush() # just in case of buffered ASCII table
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table.input_close() # close input ASCII table
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table.output_close() # close output ASCII table
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os.rename(file['name']+'_tmp',file['name']) # overwrite old one with tmp new
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