#!/usr/bin/env python releases = ['2010b3','2008r1','2007r1','2005r3'] import os, sys, math, re, threading, time from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP for release in releases: libPath = '/msc/mentat%s/shlib/'%release if os.path.exists(libPath): sys.path.append(libPath) break from py_post import * # ----------------------------- class MyOption(Option): # ----------------------------- # used for definition of new option parser action 'extend', which enables to take multiple option arguments # taken from online tutorial http://docs.python.org/library/optparse.html ACTIONS = Option.ACTIONS + ("extend",) STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",) TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",) ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",) def take_action(self, action, dest, opt, value, values, parser): if action == "extend": lvalue = value.split(",") values.ensure_value(dest, []).extend(lvalue) else: Option.take_action(self, action, dest, opt, value, values, parser) # ----------------------------- class backgroundMessage(threading.Thread): # ----------------------------- def __init__(self): threading.Thread.__init__(self) self.message = '' self.new_message = '' self.counter = 0 self.symbols = ['- ', '\ ', '| ', '/ '] self.waittime = 0.5 def __quit__(self): length = len(self.message) + len(self.symbols[self.counter]) sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length) sys.stderr.write('') def run(self): while not threading.enumerate()[0]._Thread__stopped: time.sleep(self.waittime) self.update_message() self.__quit__() def set_message(self, new_message): self.new_message = new_message self.print_message() def print_message(self): length = len(self.message) + len(self.symbols[self.counter]) sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length) # delete former message sys.stderr.write(self.symbols[self.counter] + self.new_message) # print new message self.message = self.new_message def update_message(self): self.counter = (self.counter + 1)%len(self.symbols) self.print_message() # ----------------------------- def ipCoords(elemType, nodalCoordinates): # # returns IP coordinates for a given element # ----------------------------- nodeWeightsPerNode = { 7: [ [27.0, 9.0, 3.0, 9.0, 9.0, 3.0, 1.0, 3.0], [ 9.0, 27.0, 9.0, 3.0, 3.0, 9.0, 3.0, 1.0], [ 3.0, 9.0, 27.0, 9.0, 1.0, 3.0, 9.0, 3.0], [ 9.0, 3.0, 9.0, 27.0, 3.0, 1.0, 3.0, 9.0], [ 9.0, 3.0, 1.0, 3.0, 27.0, 9.0, 3.0, 9.0], [ 3.0, 9.0, 3.0, 1.0, 9.0, 27.0, 9.0, 3.0], [ 1.0, 3.0, 9.0, 3.0, 3.0, 9.0, 27.0, 9.0], [ 3.0, 1.0, 3.0, 9.0, 9.0, 3.0, 9.0, 27.0] ], 117: [ [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ], 136: [ [42.0, 15.0, 15.0, 14.0, 5.0, 5.0], [15.0, 42.0, 15.0, 5.0, 14.0, 5.0], [15.0, 15.0, 42.0, 5.0, 5.0, 14.0], [14.0, 5.0, 5.0, 42.0, 15.0, 15.0], [ 5.0, 14.0, 5.0, 15.0, 42.0, 15.0], [ 5.0, 5.0, 14.0, 15.0, 15.0, 42.0] ], } ipCoordinates = [[0.0,0.0,0.0] for i in range(len(nodalCoordinates))] for ip in range(len(nodeWeightsPerNode[elemType])): for node in range(len(nodeWeightsPerNode[elemType][ip])): for i in range(3): ipCoordinates[ip][i] += nodeWeightsPerNode[elemType][ip][node] * nodalCoordinates[node][i] for i in range(3): ipCoordinates[ip][i] /= sum(nodeWeightsPerNode[elemType][ip]) return ipCoordinates # ----------------------------- def sortBySeparation(dataArray, criteria, offset): # # sorting of groupValue array according to list of criteria # ----------------------------- where = { 'elem': 1, 'node': 2, 'grain': 3, 'x': 4, 'y': 5, 'z': 6, } theKeys = [] for criterium in criteria: if criterium in where: theKeys.append('x[%i]'%(offset+where[criterium])) exec('sortedArray = sorted(dataArray,key=lambda x:(%s))'%(','.join(theKeys))) return sortedArray # ----------------------------- def substituteLocation(string, mesh, coords): # # do variable interpolation in group and filter strings # ----------------------------- substitute = string substitute = substitute.replace('elem', str(mesh[0])) substitute = substitute.replace('node', str(mesh[1])) substitute = substitute.replace('grain', str(mesh[2])) substitute = substitute.replace('x', '%.6g'%coords[0]) substitute = substitute.replace('y', '%.6g'%coords[1]) substitute = substitute.replace('z', '%.6g'%coords[2]) return substitute # ----------------------------- def average(theList): # # calcs the average of a list of numbers # ----------------------------- return sum(map(float,theList))/len(theList) # ----------------------------- def mapFunc(label, chunks, func): # # applies the function defined by "func" # (can be either 'min','max','avg', 'sum', or user specified) # to a list of lists of data # ----------------------------- illegal = { 'eulerangles': ['min','max','avg','sum'], 'defgrad': ['min','max','avg','sum'], 'orientation': ['min','max','sum'], } if label.lower() in illegal and func in illegal[label.lower()]: # for illegal mappings:... return ['n/a' for i in range(len(chunks[0]))] # ...return 'n/a' else: if func in ['min','max','avg']: mapped = [{ 'min': lambda x: min(x), 'max': lambda x: max(x), 'avg': lambda x: average(x), 'sum': lambda x: sum(x), }[func](column) for column in zip(*chunks)] # map one of the standard functions to colums in chunks if label.lower() == 'orientation': # orientation is special case:... orientationNorm = math.sqrt(sum([q*q for q in mapped])) # ...calc norm of average quaternion mapped = map(lambda x: x/orientationNorm, mapped) # ...renormalize quaternion else: try: mapped = eval('map(%s,zip(*chunks))'%func) # map user defined function to colums in chunks except: mapped = ['n/a' for i in range(len(chunks[0]))] return mapped # ----------------------------- def OpenPostfile(name): # # open postfile with extrapolation mode "translate" # ----------------------------- p = post_open(name) p.extrapolation('translate') p.moveto(1) return p # ----------------------------- def ParseOutputFormat(filename,what,me): # # parse .output* files in order to get a list of outputs # ----------------------------- format = {'outputs':{},'specials':{'brothers':[]}} for prefix in ['']+map(str,range(1,17)): if os.path.exists(prefix+filename+'.output'+what): break try: file = open(prefix+filename+'.output'+what) content = file.readlines() file.close() except: return format tag = '' tagID = 0 for line in content: if re.match("\s*$",line) or re.match("#",line): # skip blank lines and comments continue m = re.match("\[(.+)\]",line) # look for block indicator if m: # next section tag = m.group(1) tagID += 1 format['specials']['brothers'].append(tag) if tag == me or (me.isdigit() and tagID == int(me)): format['specials']['_id'] = tagID format['outputs'] = [] tag = me else: # data from section if tag == me: (output,length) = line.split() output.lower() if length.isdigit(): length = int(length) if re.match("\((.+)\)",output): # special data, e.g. (Ngrains) format['specials'][output] = length elif length > 0: format['outputs'].append([output,length]) return format # ----------------------------- def ParsePostfile(p,filename, outputFormat): # # parse postfile in order to get position and labels of outputs # needs "outputFormat" for mapping of output names to postfile output indices # ----------------------------- # --- build statistics stat = { \ 'IndexOfLabel': {}, \ 'Title': p.title(), \ 'Extrapolation': p.extrapolate, \ 'NumberOfIncrements': p.increments(), \ 'NumberOfNodes': p.nodes(), \ 'NumberOfNodalScalars': p.node_scalars(), \ 'LabelOfNodalScalar': [None]*p.node_scalars() , \ 'NumberOfElements': p.elements(), \ 'NumberOfElementalScalars': p.element_scalars(), \ 'LabelOfElementalScalar': [None]*p.element_scalars() , \ 'NumberOfElementalTensors': p.element_tensors(), \ 'LabelOfElementalTensor': [None]*p.element_tensors(), \ } # --- find labels for labelIndex in range(stat['NumberOfNodalScalars']): label = p.node_scalar_label(labelIndex) stat['IndexOfLabel'][label] = labelIndex stat['LabelOfNodalScalar'][labelIndex] = label for labelIndex in range(stat['NumberOfElementalScalars']): label = p.element_scalar_label(labelIndex) stat['IndexOfLabel'][label] = labelIndex stat['LabelOfElementalScalar'][labelIndex] = label for labelIndex in range(stat['NumberOfElementalTensors']): label = p.element_tensor_label(labelIndex) stat['IndexOfLabel'][label] = labelIndex stat['LabelOfElementalTensor'][labelIndex] = label if 'User Defined Variable 1' in stat['IndexOfLabel']: stat['IndexOfLabel']['GrainCount'] = stat['IndexOfLabel']['User Defined Variable 1'] if 'GrainCount' in stat['IndexOfLabel']: # does the result file contain relevant user defined output at all? startIndex = stat['IndexOfLabel']['GrainCount'] - 1 # We now have to find a mapping for each output label as defined in the .output* files to the output position in the post file # Since we know where the user defined outputs start ("startIndex"), we can simply assign increasing indices to the labels # given in the .output* file offset = 0 stat['LabelOfElementalScalar'][startIndex + 2 + offset] = 'HomogenizationCount' for var in outputFormat['Homogenization']['outputs']: if var[1] > 1: for i in range(var[1]): stat['IndexOfLabel']['%i_%s'%(i+1,var[0])] = startIndex + 2 + offset + (i+1) else: stat['IndexOfLabel']['%s'%(var[0])] = startIndex + 2 + offset + 1 offset += var[1] for grain in range(outputFormat['Homogenization']['specials']['(ngrains)']): stat['IndexOfLabel']['%i_CrystalliteCount'%(grain+1)] = startIndex + 3 + offset for var in outputFormat['Crystallite']['outputs']: if var[1] > 1: for i in range(var[1]): stat['IndexOfLabel']['%i_%i_%s'%(grain+1,i+1,var[0])] = startIndex + 3 + offset + (i+1) else: stat['IndexOfLabel']['%i_%s'%(grain+1,var[0])] = startIndex + 3 + offset + 1 offset += var[1] stat['IndexOfLabel']['%i_ConstitutiveCount'%(grain+1)] = startIndex + 4 + offset for var in outputFormat['Constitutive']['outputs']: if var[1] > 1: for i in range(var[1]): stat['IndexOfLabel']['%i_%i_%s'%(grain+1,i+1,var[0])] = startIndex + 4 + offset + (i+1) else: stat['IndexOfLabel']['%i_%s'%(grain+1,var[0])] = startIndex + 4 + offset + 1 offset += var[1] return stat # ----------------------------- def SummarizePostfile(stat,where=sys.stdout): # ----------------------------- where.write('title:\t%s'%stat['Title'] + '\n\n') where.write('extraplation:\t%s'%stat['Extrapolation'] + '\n\n') where.write('increments:\t%i+1'%(stat['NumberOfIncrements']-1) + '\n\n') where.write('nodes:\t%i'%stat['NumberOfNodes'] + '\n\n') where.write('elements:\t%i'%stat['NumberOfElements'] + '\n\n') where.write('nodal scalars:\t%i'%stat['NumberOfNodalScalars'] + '\n\n ' + '\n '.join(stat['LabelOfNodalScalar']) + '\n\n') where.write('elemental scalars:\t%i'%stat['NumberOfElementalScalars'] + '\n\n ' + '\n '.join(stat['LabelOfElementalScalar']) + '\n\n') where.write('elemental tensors:\t%i'%stat['NumberOfElementalTensors'] + '\n\n ' + '\n '.join(stat['LabelOfElementalTensor']) + '\n\n') return True # ----------------------------- # MAIN FUNCTION STARTS HERE # ----------------------------- # --- input parsing parser = OptionParser(option_class=MyOption, usage='%prog [options] resultfile', description = """ Extract data from a .t16 MSC.Marc results file. List of output variables is given by options '--ns','--es','--et','--ho','--cr','--co'. Filter and separations use 'elem','node','grain', and 'x','y','z' as key words. Example: 1) get averaged results in slices perpendicular to x for all positive y coordinates --filter 'y >= 0.0' --separation x --map 'avg' 2) global sum of squared data falling into first quadrant arc between R1 and R2 --filter 'x*x + y*y >= R1*R1 and x*x + y*y <= R2*R2' --map 'lambda list: sum([item*item for item in list])' $Id: postResults 205 2010-06-08 15:23:31Z MPIE\p.eisenlohr $ """) parser.add_option('-i','--info', action='store_true', dest='info', \ help='list contents of resultfile [%default]') parser.add_option('-d','--dir', dest='directory', \ help='name of subdirectory to hold output [%default]') parser.add_option('-r','--range', dest='range', type='int', nargs=3, \ help='range of increments to output (start, end, step) [all]') parser.add_option('-m','--map', dest='func', type='string', \ help='data reduction mapping ["%default"] out of min, max, avg, sum or user-lambda') group_material = OptionGroup(parser,'Material identifier') group_special = OptionGroup(parser,'Special outputs') group_general = OptionGroup(parser,'General outputs') group_material.add_option('--homogenization', dest='homog', type='string', \ help='homogenization identifier (as string or integer [%default])') group_material.add_option('--crystallite', dest='cryst', type='string', \ help='crystallite identifier (as string or integer [%default])') group_material.add_option('--phase', dest='phase', type='string', \ help='phase identifier (as string or integer [%default])') group_special.add_option('-t','--time', action='store_true', dest='time', \ help='output time of increment [%default]') group_special.add_option('-f','--filter', dest='filter', type='string', \ help='condition(s) to filter results [%default]') group_special.add_option('--separation', action='extend', dest='separation', type='string', \ help='properties to separate results [%default]') parser.add_option('-s','--split', action='store_true', dest='separateFiles', \ help='split output per increment [%default]') group_general.add_option('--ns', action='extend', dest='nodalScalar', type='string', \ help='list of nodal scalars to extract') group_general.add_option('--es', action='extend', dest='elementalScalar', type='string', \ help='list of elemental scalars to extract') group_general.add_option('--et', action='extend', dest='elementalTensor', type='string', \ help='list of elemental tensors to extract') group_general.add_option('--ho', action='extend', dest='homogenizationResult', type='string', \ help='list of homogenization results to extract') group_general.add_option('--cr', action='extend', dest='crystalliteResult', type='string', \ help='list of crystallite results to extract') group_general.add_option('--co', action='extend', dest='constitutiveResult', type='string', \ help='list of constitutive results to extract') parser.add_option_group(group_material) parser.add_option_group(group_general) parser.add_option_group(group_special) parser.set_defaults(info = False) parser.set_defaults(directory = 'postProc') parser.set_defaults(func = 'avg') parser.set_defaults(homog = '1') parser.set_defaults(cryst = '1') parser.set_defaults(phase = '1') parser.set_defaults(filter = '') parser.set_defaults(separation = []) parser.set_defaults(inc = False) parser.set_defaults(time = False) parser.set_defaults(separateFiles = False) (options, file) = parser.parse_args() bg = backgroundMessage() bg.start() # --- sanity checks if not file: parser.print_help() parser.error('no file specified...') if options.constitutiveResult and not options.phase: parser.print_help() parser.error('constitutive results require phase...') if options.nodalScalar and ( options.elementalScalar or options.elementalTensor or options.homogenizationResult or options.crystalliteResult or options.constitutiveResult ): parser.print_help() parser.error('not allowed to mix nodal with elemental results...') # --- parse .output and .t16 files bg.set_message('parsing .output and .t16 files...') filename = os.path.splitext(file[0])[0] dirname = os.path.abspath(os.path.dirname(filename))+os.sep+options.directory if not os.path.isdir(dirname): os.mkdir(dirname,0755) outputFormat = {} me = { 'Homogenization': options.homog, 'Crystallite': options.cryst, 'Constitutive': options.phase, } for what in me: outputFormat[what] = ParseOutputFormat(filename, what, me[what]) if not '_id' in outputFormat[what]['specials']: print "'%s' not found in <%s>"%(me[what], what) print '\n'.join(map(lambda x:' '+x, outputFormat[what]['specials']['brothers'])) sys.exit(1) p = OpenPostfile(filename+'.t16') stat = ParsePostfile(p, filename, outputFormat) # --- sanity check for output variables # for mentat variables (nodalScalar,elementalScalar,elementalTensor) we simply have to check whether the label is found in the stat[indexOfLabel] dictionary # for user defined variables (homogenizationResult,crystalliteResult,constitutiveResult) we have to check the corresponding outputFormat, since the namescheme in stat['IndexOfLabel'] is different for opt in ['nodalScalar','elementalScalar','elementalTensor','homogenizationResult','crystalliteResult','constitutiveResult']: if eval('options.%s'%opt): for label in eval('options.%s'%opt): if (opt in ['nodalScalar','elementalScalar','elementalTensor'] and not label in stat['IndexOfLabel']) \ or (opt in ['homogenizationResult','crystalliteResult','constitutiveResult'] \ and (not outputFormat[opt[:-6].capitalize()]['outputs'] or not label in zip(*outputFormat[opt[:-6].capitalize()]['outputs'])[0])): parser.error('%s "%s" unknown...'%(opt,label)) # --- output info if options.info: print '\nMentat release %s\n'%release SummarizePostfile(stat,sys.stderr) print '\nUser Defined Outputs' for what in me: print '\n ',what,':' for output in outputFormat[what]['outputs']: print ' ',output sys.exit(0) # --- get output data from .t16 file if options.range: increments = range( max(0,options.range[0]), min(stat['NumberOfIncrements'],options.range[1]+1), options.range[2]) else: increments = range(stat['NumberOfIncrements']-1) fileOpen = False assembleHeader = True header = [] for increment in increments: p.moveto(increment+1) bg.set_message('read data from increment %i...'%increment) data = {} if options.nodalScalar: for n in range(stat['NumberOfNodes']): nodeID = p.node_id(n) nodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z] elemID = 0 grainID = 0 # --- filter valid locations filter = substituteLocation(options.filter, [elemID,nodeID,grainID], nodeCoordinates) # generates an expression that is only true for the locations specified by options.filter if filter != '' and not eval(filter): # for all filter expressions that are not true:... continue # ... ignore this data point and continue with next # --- group data locations group = substituteLocation('#'.join(options.separation), [elemID,nodeID,grainID], nodeCoordinates) # generates a unique key for a group of separated data based on the separation criterium for the location if group not in data: # create a new group if not yet present data[group] = [] data[group].append([]) # append a new list for each group member; each list will contain dictionaries with keys 'label, and 'content' for the associated data data[group][-1].append({ 'label': 'location', 'content': [elemID,nodeID,grainID] + nodeCoordinates, }) # first entry in this list always contains the location data # --- get data from t16 file for label in options.nodalScalar: if assembleHeader: header.append(label.replace(' ','')) data[group][-1].append({ 'label': label, 'content': [ p.node_scalar(n,stat['IndexOfLabel'][label]) ], }) assembleHeader = False else: for e in range(stat['NumberOfElements']): nodeCoordinates = map(lambda node: [node.x, node.y, node.z], map(p.node, map(p.node_sequence,p.element(e).items))) ipCoordinates = ipCoords(p.element(e).type, nodeCoordinates) elemID = p.element_id(e) for n in range(p.element(e).len): nodeID = p.element(e).items[n] for g in range(('GrainCount' in stat['IndexOfLabel'] and int(p.element_scalar(e, stat['IndexOfLabel']['GrainCount'])[0].value)) or 1): grainID = g + 1 # --- filter valid locations filter = substituteLocation(options.filter, [elemID,nodeID,grainID], ipCoordinates[n]) # generates an expression that is only true for the locations specified by options.filter if filter != '' and not eval(filter): # for all filter expressions that are not true:... continue # ... ignore this data point and continue with next # --- group data locations group = substituteLocation('#'.join(options.separation), [elemID,nodeID,grainID], ipCoordinates[n]) # generates a unique key for a group of separated data based on the separation criterium for the location if group not in data: # create a new group if not yet present data[group] = [] data[group].append([]) # append a new list for each group member; each list will contain dictionaries with keys 'label, and 'content' for the associated data data[group][-1].append({ 'label': 'location', 'content': [elemID,nodeID,grainID] + ipCoordinates[n], }) # first entry in this list always contains the location data # --- get data from t16 file if options.elementalScalar: for label in options.elementalScalar: if assembleHeader: header.append(label.replace(' ','')) data[group][-1].append({ 'label': label, 'content': [ p.element_scalar(e,stat['IndexOfLabel'][label])[n].value ], }) if options.elementalTensor: for label in options.elementalTensor: if assembleHeader: header += ['%s.%s'%(label.replace(' ',''),component) for component in ['intensity','t11','t22','t33','t12','t23','t13']] data[group][-1].append({ 'label': label, 'content': [ eval("p.element_tensor(e,stat['IndexOfLabel'][label])[n].%s"%component) for component in ['intensity','t11','t22','t33','t12','t23','t13'] ], }) if options.homogenizationResult: for label in options.homogenizationResult: outputIndex = list(zip(*outputFormat['Homogenization']['outputs'])[0]).index(label) # find the position of this output in the outputFormat length = int(outputFormat['Homogenization']['outputs'][outputIndex][1]) if length > 1: if assembleHeader: header += ['%i_%s'%(component+1,label) for component in range(length)] data[group][-1].append({ 'label': label, 'content': [ p.element_scalar(e,stat['IndexOfLabel']['%i_%s'%(component+1,label)])[n].value for component in range(length) ], }) else: if assembleHeader: header.append(label) data[group][-1].append({ 'label': label, 'content': [ p.element_scalar(e,stat['IndexOfLabel']['%s'%label])[n].value ], }) if options.crystalliteResult: for label in options.crystalliteResult: outputIndex = list(zip(*outputFormat['Crystallite']['outputs'])[0]).index(label) # find the position of this output in the outputFormat length = int(outputFormat['Crystallite']['outputs'][outputIndex][1]) if length > 1: if assembleHeader: header += ['%i_%i_%s'%(g+1,component+1,label) for component in range(length)] data[group][-1].append({ 'label': label, 'content': [ p.element_scalar(e,stat['IndexOfLabel']['%i_%i_%s'%(g+1,component+1,label)])[n].value for component in range(length) ], }) else: if assembleHeader: header.append('%i_%s'%(g+1,label)) data[group][-1].append({ 'label':label, 'content': [ p.element_scalar(e,stat['IndexOfLabel']['%i_%s'%(g+1,label)])[n].value ], }) if options.constitutiveResult: for label in options.constitutiveResult: outputIndex = list(zip(*outputFormat['Constitutive']['outputs'])[0]).index(label) # find the position of this output in the outputFormat length = int(outputFormat['Constitutive']['outputs'][outputIndex][1]) if length > 1: if assembleHeader: header += ['%i_%i_%s'%(g+1,component+1,label) for component in range(length)] data[group][-1].append({ 'label':label, 'content': [ p.element_scalar(e,stat['IndexOfLabel']['%i_%i_%s'%(g+1,component+1,label)])[n].value for component in range(length) ], }) else: if assembleHeader: header.append('%i_%s'%(g+1,label)) data[group][-1].append({ 'label':label, 'content': [ p.element_scalar(e,stat['IndexOfLabel']['%i_%s'%(g+1,label)])[n].value ], }) assembleHeader = False if options.separateFiles: if fileOpen: file.close() fileOpen = False outFilename = eval('"'+eval("'%%s_inc%%0%ii.txt'%(math.log10(max(increments))+1)")+'"%(dirname + os.sep + os.path.split(filename)[1],increment)') else: outFilename = '%s.txt'%(dirname + os.sep + os.path.split(filename)[1]) # --- write header to file if not fileOpen: file = open(outFilename,'w') fileOpen = True file.write('2\theader\n') file.write('$Id: postResults 205 2010-06-08 15:23:31Z MPIE\p.eisenlohr $\n') if options.time: basic = ['inc','time'] else: basic = ['inc'] if options.nodalScalar: file.write('\t'.join(basic + ['elem','node','grain','node.x','node.y','node.z'] + header) + '\n') else: file.write('\t'.join(basic + ['elem','node','grain','ip.x','ip.y','ip.z'] + header) + '\n') # --- write data to file output = [] for group in data: if options.time: output.append([increment, p.time]) else: output.append([increment]) for chunk in range(len(data[group][0])): label = data[group][0][chunk]['label'] # name of chunk (e.g. 'orientation', or 'flow stress') groupContent = [data[group][member][chunk]['content'] for member in range(len(data[group]))] # list of each member's chunk if label == 'location': condensedGroupContent = mapFunc(label, groupContent, 'avg') # always average location if len(groupContent) > 1: # e,n,g nonsense if averaged over more than one entry... condensedGroupContent[:3] = ['n/a']*3 # ...so return 'n/a' else: condensedGroupContent = mapFunc(label, groupContent, options.func) # map function to groupContent to get condensed data of this group's chunk output[-1] += condensedGroupContent for groupvalues in sortBySeparation(output, options.separation, int(options.time)): # sort output according to separation criteria file.write('\t'.join(map(str,groupvalues)) + '\n') if fileOpen: file.close() # --------------------------- DONE --------------------------------