#!/usr/bin/env python # -*- coding: UTF-8 no BOM -*- import pdb, os, sys, gc, math, re, threading, time, struct, string import damask from optparse import OptionParser, OptionGroup scriptID = string.replace('$Id$','\n','\\n') scriptName = os.path.splitext(scriptID.split()[1])[0] fileExtensions = { \ 'marc': ['.t16',], 'spectral': ['.spectralOut',], } # ----------------------------- class vector: # mimic py_post node object # ----------------------------- x,y,z = [None,None,None] def __init__(self,coords): self.x = coords[0] self.y = coords[1] self.z = coords[2] # ----------------------------- class element: # mimic py_post element object # ----------------------------- items = [] type = None def __init__(self,nodes,type): self.items = nodes self.type = type # ----------------------------- class elemental_scalar: # mimic py_post element_scalar object # ----------------------------- id = None value = None def __init__(self,node,value): self.id = node self.value = value # ----------------------------- class MPIEspectral_result: # mimic py_post result object # ----------------------------- file = None dataOffset = 0 N_elemental_scalars = 0 grid = [0,0,0] size = [0.0,0.0,0.0] theTitle = '' wd = '' geometry = '' extrapolate = '' N_loadcases = 0 N_increments = 0 N_positions = 0 _frequencies = [] _increments = [] _times = [] increment = 0 startingIncrement = 0 position = 0 time = 0.0 # this is a dummy at the moment, we need to parse the load file and figure out what time a particular increment corresponds to N_nodes = 0 N_node_scalars = 0 N_elements = 0 N_element_scalars = 0 N_element_tensors = 0 def __init__(self,filename): self.file = open(filename, 'rb') self.filesize = os.path.getsize(filename) self.dataOffset = 0 while self.dataOffset < self.filesize: self.file.seek(self.dataOffset) if self.file.read(3) == 'eoh': break self.dataOffset += 1 self.dataOffset += 7 #search for the old keywords without ':' in case not found the new ones. Old ones are critical, if e.g. a load file is called 'load' self.theTitle = self._keyedString('load:') if self.theTitle == None: self.theTitle = self._keyedString('load') self.wd = self._keyedString('workingdir:') if self.wd == None: self.wd = self._keyedString('workingdir') self.geometry = self._keyedString('geometry:') if self.geometry == None: self.geometry = self._keyedString('geometry') self.N_loadcases = self._keyedPackedArray('loadcases:',count=1,type='i',default=1)[0] if self.N_loadcases == None: self.N_loadcases = self._keyedPackedArray('loadcases',count=1,type='i',default=1)[0] self._frequencies = self._keyedPackedArray('frequencies:',count=self.N_loadcases,type='i',default=1) if all ( i == None for i in self._frequencies) == None: self._frequencies = self._keyedPackedArray('frequencies',count=self.N_loadcases,type='i',default=1) self._increments = self._keyedPackedArray('increments:',count=self.N_loadcases,type='i') if all (i == None for i in self._increments) == None: self._increments = self._keyedPackedArray('increments',count=self.N_loadcases,type='i') self.startingIncrement = self._keyedPackedArray('startingIncrement:',count=1,type='i',default=0)[0] if self.startingIncrement == None: self.startingIncrement = self._keyedPackedArray('startingIncrement',count=1,type='i',default=0)[0] self._times = self._keyedPackedArray('times:',count=self.N_loadcases,type='d',default=0.0) if all (i == None for i in self._times) == None: self._times = self._keyedPackedArray('times',count=self.N_loadcases,type='d',default=0.0) self._logscales = self._keyedPackedArray('logscales:',count=self.N_loadcases,type='i',default=0) if all (i == None for i in self._logscales) == None: self._logscales = self._keyedPackedArray('logscales',count=self.N_loadcases,type='i',default=0) self.size = self._keyedPackedArray('size:',count=3,type='d') if self.size == [None,None,None]: # no size found, try legacy alias 'dimension' self.size = self._keyedPackedArray('dimension',count=3,type='d') self.grid = self._keyedPackedArray('grid:',count=3,type='i') if self.grid == [None,None,None]: self.grid = self._keyedPackedArray('resolution',count=3,type='i') self.N_nodes = (self.grid[0]+1)*(self.grid[1]+1)*(self.grid[2]+1) self.N_elements = self.grid[0] * self.grid[1] * self.grid[2] self.N_element_scalars = self._keyedPackedArray('materialpoint_sizeResults:',count=1,type='i',default=0)[0] if self.element_scalars == None: self.N_element_scalars = self._keyedPackedArray('materialpoint_sizeResults',count=1,type='i',default=0)[0] self.N_positions = (self.filesize-self.dataOffset)/(8+self.N_elements*self.N_element_scalars*8) self.N_increments = 1 # add zero'th entry for i in range(self.N_loadcases): self.N_increments += self._increments[i]//self._frequencies[i] def __str__(self): return '\n'.join([ 'workdir: %s'%self.wd, 'geometry: %s'%self.geometry, 'loadcases: %i'%self.N_loadcases, 'grid: %s'%(','.join(map(str,self.grid))), 'size: %s'%(','.join(map(str,self.size))), 'header size: %i'%self.dataOffset, 'actual file size: %i'%self.filesize, 'expected file size: %i'%(self.dataOffset+self.N_increments*(8+self.N_elements*self.N_element_scalars*8)), 'positions in file : %i'%self.N_positions, 'starting increment: %i'%self.startingIncrement, ] ) def locateKeyValue(self,identifier): key = {'name':'','pos':0} filepos = 0 tag = self.file.read(4) # read the starting tag while tag+key['name']+tag != tag+identifier+tag and filepos < self.dataOffset: self.file.seek(filepos) key['name'] = self.file.read(len(identifier)) # anticipate identifier key['pos'] = self.file.tell() # remember position right after identifier filepos += 1 # try next position return key def _keyedPackedArray(self,identifier,count = 3,type = 'd',default = None): bytecount = {'d': 8,'i': 4} values = [default]*count key = self.locateKeyValue(identifier) if key['name'] == identifier: self.file.seek(key['pos']) for i in range(count): values[i] = struct.unpack(type,self.file.read(bytecount[type]))[0] return values def _keyedString(self,identifier,default=None): value = default self.file.seek(0) m = re.search(r'(.{4})%s(.*?)\1'%identifier,self.file.read(self.dataOffset),re.DOTALL) if m: value = m.group(2) return value def title(self): return self.theTitle def moveto(self,pos): self.position = pos self.increment = 0 self.time = 0.0 p = pos for l in range(self.N_loadcases): if p <= self._increments[l]//self._frequencies[l]: break else: self.increment += self._increments[l] self.time += self._times[l] p -= self._increments[l]//self._frequencies[l] self.increment += self._frequencies[l] * p if self._logscales[l] > 0: # logarithmic time scale if l == 0: self.time = 2**(self._increments[l] - (1+self._frequencies[l]*p)) * self._times[l] # first loadcase else: self.time *= ((self.time + self._times[l])/self.time)**((1+self._frequencies[l]*p)/self._increments[l]) # any subsequent loadcase else: # linear time scale self.time += self._times[l]/self._increments[l] * self._frequencies[l] * p def extrapolation(self,value): self.extrapolate = value def node_sequence(self,n): return n-1 def node_id(self,n): return n+1 def node(self,n): a = self.grid[0]+1 b = self.grid[1]+1 c = self.grid[2]+1 return vector([self.size[0] * (n%a) / self.grid[0], self.size[1] * ((n/a)%b) / self.grid[1], self.size[2] * ((n/a/b)%c) / self.grid[2], ]) def element_sequence(self,e): return e-1 def element_id(self,e): return e+1 def element(self,e): a = self.grid[0]+1 b = self.grid[1]+1 c = self.grid[2]+1 basenode = 1 + e+e/self.grid[0] + e/self.grid[0]/self.grid[1]*a basenode2 = basenode+a*b return (element([basenode ,basenode +1,basenode +a+1,basenode +a, basenode2 ,basenode2+1,basenode2+a+1,basenode2+a, ],117)) def increments(self): return self.N_positions def nodes(self): return self.N_nodes def node_scalars(self): return self.N_node_scalars def elements(self): return self.N_elements def element_scalars(self): return self.N_element_scalars def element_scalar(self,e,idx): incStart = self.dataOffset \ + self.position*8*(self.N_elements*self.N_element_scalars) # header & footer + extra header and footer for 4 byte int range (Fortran) # values where = (e*self.N_element_scalars + idx)*8 try: self.file.seek(incStart+where) value = struct.unpack('d',self.file.read(8))[0] except: print 'seeking',incStart+where print 'e',e,'idx',idx sys.exit(1) return [elemental_scalar(node,value) for node in self.element(e).items] def element_scalar_label(elem,idx): return 'User Defined Variable %i'%(idx+1) def element_tensors(self): return self.N_element_tensors # ----------------------------- 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] ], 57: [ [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] ], 125: [ [ 3.0, 0.0, 0.0, 4.0, 1.0, 4.0], [ 0.0, 3.0, 0.0, 4.0, 4.0, 1.0], [ 0.0, 0.0, 3.0, 1.0, 4.0, 4.0],], 127: [ [ 45.0, 17.0, 17.0, 17.0], [ 17.0, 45.0, 17.0, 17.0], [ 17.0, 17.0, 45.0, 17.0], [ 17.0, 17.0, 17.0, 45.0],], 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] ], } Nips = len(nodeWeightsPerNode[elemType]) ipCoordinates = [[0.0,0.0,0.0] for i in range(Nips)] for ip in range(Nips): 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 ipIDs(elemType): # # returns IP numbers for given element type # ----------------------------- ipPerNode = { 7: [ 1, 2, 4, 3, 5, 6, 8, 7 ], 57: [ 1, 2, 4, 3, 5, 6, 8, 7 ], 117: [ 1 ], 125: [ 1, 2, 3 ], 127: [ 1, 2, 3, 4 ], 136: [ 1, 2, 3, 4, 5, 6 ], } return ipPerNode[elemType] # ----------------------------- 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('ip', str(mesh[2])) substitute = substitute.replace('grain', str(mesh[3])) substitute = substitute.replace('x', '%.6g'%coords[0]) substitute = substitute.replace('y', '%.6g'%coords[1]) substitute = substitute.replace('z', '%.6g'%coords[2]) return substitute # ----------------------------- def heading(glue,parts): # # joins pieces from parts by glue. second to last entry in pieces tells multiplicity # ----------------------------- header = [] for pieces in parts: if pieces[-2] == 0: del pieces[-2] header.append(glue.join(map(str,pieces))) return header # ----------------------------- def mapIncremental(label, mapping, N, base, new): # # applies the function defined by "mapping" # (can be either 'min','max','avg', 'sum', or user specified) # to a list of data # ----------------------------- theMap = { 'min': lambda n,b,a: min(b,a), 'max': lambda n,b,a: max(b,a), 'avg': lambda n,b,a: (n*b+a)/(n+1), 'avgabs': lambda n,b,a: (n*b+abs(a))/(n+1), 'sum': lambda n,b,a: b+a, 'sumabs': lambda n,b,a: b+abs(a), 'unique': lambda n,b,a: {True:a,False:'n/a'}[n==0 or b==a] } if mapping in theMap: mapped = map(theMap[mapping],[N]*len(base),base,new) # map one of the standard functions to data 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,[N]*len(base),base,new)'%mapping) # map user defined function to colums in chunks except: mapped = ['n/a']*len(base) return mapped # ----------------------------- def OpenPostfile(name,type,nodal = False): # # open postfile with extrapolation mode "translate" # ----------------------------- p = {\ 'spectral': MPIEspectral_result,\ 'marc': post_open,\ }[type](name) p.extrapolation({True:'linear',False:'translate'}[nodal]) p.moveto(1) return p # ----------------------------- def ParseOutputFormat(filename,what,me): # # parse .output* files in order to get a list of outputs # ----------------------------- content = [] format = {'outputs':{},'specials':{'brothers':[]}} for prefix in ['']+map(str,range(1,17)): if os.path.exists(prefix+filename+'.output'+what): try: file = open(prefix+filename+'.output'+what) content = file.readlines() file.close() break except: pass if content == []: return format # nothing found... 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, legacyFormat): # # parse postfile in order to get position and labels of outputs # needs "outputFormat" for mapping of output names to postfile output indices # ----------------------------- startVar = {True: 'GrainCount', False:'HomogenizationCount'} # --- 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']: # output format without dedicated names? stat['IndexOfLabel'][startVar[legacyFormat]] = stat['IndexOfLabel']['User Defined Variable 1'] # adjust first named entry if startVar[legacyFormat] in stat['IndexOfLabel']: # does the result file contain relevant user defined output at all? startIndex = stat['IndexOfLabel'][startVar[legacyFormat]] stat['LabelOfElementalScalar'][startIndex] = startVar[legacyFormat] # 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 = 1 if legacyFormat: stat['LabelOfElementalScalar'][startIndex + offset] = startVar[not legacyFormat] # add HomogenizationCount as second offset += 1 for (name,N) in outputFormat['Homogenization']['outputs']: for i in range(N): label = {False: '%s'%( name), True:'%i_%s'%(i+1,name)}[N > 1] stat['IndexOfLabel'][label] = startIndex + offset stat['LabelOfElementalScalar'][startIndex + offset] = label offset += 1 if not legacyFormat: stat['IndexOfLabel'][startVar[not legacyFormat]] = startIndex + offset stat['LabelOfElementalScalar'][startIndex + offset] = startVar[not legacyFormat] # add GrainCount offset += 1 if '(ngrains)' in outputFormat['Homogenization']['specials']: for grain in range(outputFormat['Homogenization']['specials']['(ngrains)']): stat['IndexOfLabel']['%i_CrystalliteCount'%(grain+1)] = startIndex + offset # report crystallite count stat['LabelOfElementalScalar'][startIndex + offset] = '%i_CrystalliteCount'%(grain+1) # add GrainCount offset += 1 for (name,N) in outputFormat['Crystallite']['outputs']: # add crystallite outputs for i in range(N): label = {False: '%i_%s'%(grain+1, name), True:'%i_%i_%s'%(grain+1,i+1,name)}[N > 1] stat['IndexOfLabel'][label] = startIndex + offset stat['LabelOfElementalScalar'][startIndex + offset] = label offset += 1 stat['IndexOfLabel']['%i_ConstitutiveCount'%(grain+1)] = startIndex + offset # report constitutive count stat['LabelOfElementalScalar'][startIndex + offset] = '%i_ConstitutiveCount'%(grain+1) # add GrainCount offset += 1 for (name,N) in outputFormat['Constitutive']['outputs']: # add constitutive outputs for i in range(N): label = {False: '%i_%s'%(grain+1, name), True:'%i_%i_%s'%(grain+1,i+1,name)}[N > 1] stat['IndexOfLabel'][label] = startIndex + offset try: stat['LabelOfElementalScalar'][startIndex + offset] = label except IndexError: print 'trying to assign %s at position %i+%i'%(label,startIndex,offset) sys.exit(1) offset += 1 return stat # ----------------------------- def SummarizePostfile(stat,where=sys.stdout,format='marc'): # ----------------------------- where.write('\n\n') where.write('title:\t%s'%stat['Title'] + '\n\n') where.write('extraplation:\t%s'%stat['Extrapolation'] + '\n\n') where.write('increments:\t%i'%(stat['NumberOfIncrements']) + '\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=damask.extendableOption, usage='%prog options [file[s]]', description = """ Extract data from a .t16 (MSC.Marc) or .spectralOut results file. List of output variables is given by options '--ns','--es','--et','--ho','--cr','--co'. Filters and separations use 'elem','node','ip','grain', and 'x','y','z' as key words. Example: 1) get averaged results in slices perpendicular to x for all negative 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 >= 0.0 and y >= 0.0 and x*x + y*y >= R1*R1 and x*x + y*y <= R2*R2' --map 'lambda n,b,a: n*b+a*a' User mappings need to be formulated in an incremental fashion for each new data point, a(dd), and may use the current (incremental) result, b(ase), as well as the number, n(umber), of already processed data points for evaluation. """, version = scriptID) parser.add_option('-i','--info', action='store_true', dest='info', \ help='list contents of resultfile [%default]') parser.add_option('-l','--legacy', action='store_true', dest='legacy', \ help='legacy user result block (starts with GrainCount) [%default]') parser.add_option('-n','--nodal', action='store_true', dest='nodal', \ help='data is extrapolated to nodal value [%default]') parser.add_option( '--prefix', dest='prefix', \ help='prefix to result file name [%default]') parser.add_option( '--suffix', dest='suffix', \ help='suffix to result file name [%default]') parser.add_option('-d','--dir', dest='dir', \ help='name of subdirectory to hold output [%default]') parser.add_option('-s','--split', action='store_true', dest='separateFiles', \ help='split output per increment [%default]') parser.add_option('-r','--range', dest='range', type='int', nargs=3, \ help='range of positions (or increments) to output (start, end, step) [all]') parser.add_option('--increments', action='store_true', dest='getIncrements', \ help='switch to increment range [%default]') parser.add_option('-m','--map', dest='func', \ help='data reduction mapping ["%default"] out of min, max, avg, avgabs, sum, sumabs or user-lambda') parser.add_option('-p','--type', dest='filetype', \ help = 'type of result file [auto]') group_material = OptionGroup(parser,'Material identifier') group_material.add_option('--homogenization', dest='homog', \ help='homogenization identifier (as string or integer [%default])', metavar='') group_material.add_option('--crystallite', dest='cryst', \ help='crystallite identifier (as string or integer [%default])', metavar='') group_material.add_option('--phase', dest='phase', \ help='phase identifier (as string or integer [%default])', metavar='') group_special = OptionGroup(parser,'Special outputs') 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', \ help='condition(s) to filter results [%default]', metavar='') group_special.add_option('--separation', action='extend', dest='sep', \ help='properties to separate results [%default]', metavar='') group_special.add_option('--sort', action='extend', dest='sort', \ help='properties to sort results [%default]', metavar='') group_general = OptionGroup(parser,'General outputs') group_general.add_option('--ns', action='extend', dest='nodalScalar', \ help='nodal scalars to extract', metavar='') group_general.add_option('--es', action='extend', dest='elemScalar', \ help='elemental scalars to extract', metavar='') group_general.add_option('--et', action='extend', dest='elemTensor', \ help='elemental tensors to extract', metavar='') group_general.add_option('--ho', action='extend', dest='homogenizationResult', \ help='homogenization results to extract', metavar='') group_general.add_option('--cr', action='extend', dest='crystalliteResult', \ help='crystallite results to extract', metavar='') group_general.add_option('--co', action='extend', dest='constitutiveResult', \ help='constitutive results to extract', metavar='') 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(legacy = False) parser.set_defaults(nodal = False) parser.set_defaults(prefix = '') parser.set_defaults(suffix = '') parser.set_defaults(dir = 'postProc') parser.set_defaults(filetype = None) 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(sep = []) parser.set_defaults(sort = []) parser.set_defaults(inc = False) parser.set_defaults(time = False) parser.set_defaults(separateFiles = False) parser.set_defaults(getIncrements= False) (options, files) = parser.parse_args() # --- basic sanity checks if files == []: parser.print_help() parser.error('no file specified...') if not os.path.exists(files[0]): parser.print_help() parser.error('invalid file "%s" specified...'%files[0]) # --- figure out filetype if options.filetype == None: ext = os.path.splitext(files[0])[1] for theType in fileExtensions.keys(): if ext in fileExtensions[theType]: options.filetype = theType break if options.filetype != None: options.filetype = options.filetype.lower() if options.filetype == 'marc': offset_pos = 1 else: offset_pos = 0 # --- more sanity checks if options.filetype not in ['marc','spectral']: parser.print_help() parser.error('file type "%s" not supported...'%options.filetype) if options.filetype == 'marc': sys.path.append(damask.solver.Marc().libraryPath('../../')) try: from py_post import * except: print('error: no valid Mentat release found') sys.exit(-1) else: def post_open(): return if options.constitutiveResult and not options.phase: parser.print_help() parser.error('constitutive results require phase...') if options.nodalScalar and ( options.elemScalar or options.elemTensor or options.homogenizationResult or options.crystalliteResult or options.constitutiveResult ): parser.print_help() parser.error('not allowed to mix nodal with elemental results...') if not options.nodalScalar: options.nodalScalar = [] if not options.elemScalar: options.elemScalar = [] if not options.elemTensor: options.elemTensor = [] if not options.homogenizationResult: options.homogenizationResult = [] if not options.crystalliteResult: options.crystalliteResult = [] if not options.constitutiveResult: options.constitutiveResult = [] options.sort.reverse() options.sep.reverse() # --- start background messaging bg = backgroundMessage() bg.start() # --- parse .output and .t16 files if os.path.splitext(files[0])[1] == '': filename = files[0] extension = fileExtensions[options.filetype] else: filename = os.path.splitext(files[0])[0] extension = os.path.splitext(files[0])[1] outputFormat = {} me = { 'Homogenization': options.homog, 'Crystallite': options.cryst, 'Constitutive': options.phase, } bg.set_message('parsing .output files...') for what in me: outputFormat[what] = ParseOutputFormat(filename, what, me[what]) if not '_id' in outputFormat[what]['specials']: print "\nsection '%s' not found in <%s>"%(me[what], what) print '\n'.join(map(lambda x:' [%s]'%x, outputFormat[what]['specials']['brothers'])) bg.set_message('opening result file...') p = OpenPostfile(filename+extension,options.filetype,options.nodal) bg.set_message('parsing result file...') stat = ParsePostfile(p, filename, outputFormat,options.legacy) if options.filetype == 'marc': stat['NumberOfIncrements'] -= 1 # t16 contains one "virtual" increment (at 0) # --- sanity check for output variables # for mentat variables (nodalScalar,elemScalar,elemTensor) 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','elemScalar','elemTensor','homogenizationResult','crystalliteResult','constitutiveResult']: if eval('options.%s'%opt): for label in eval('options.%s'%opt): if (opt in ['nodalScalar','elemScalar','elemTensor'] and label not in stat['IndexOfLabel'] and label not in ['elements',]) \ 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: if options.filetype == 'marc': print '\n\nMentat release %s'%damask.solver.Marc().version('../../') if options.filetype == 'spectral': print '\n\n',p SummarizePostfile(stat) print '\nUser Defined Outputs' for what in me: print '\n ',what,':' for output in outputFormat[what]['outputs']: print ' ',output sys.exit(0) # --- build connectivity maps elementsOfNode = {} for e in xrange(stat['NumberOfElements']): if e%1000 == 0: bg.set_message('connect elem %i...'%e) for n in map(p.node_sequence,p.element(e).items): if n not in elementsOfNode: elementsOfNode[n] = [p.element_id(e)] else: elementsOfNode[n] += [p.element_id(e)] maxCountElementsOfNode = 0 for l in elementsOfNode.values(): maxCountElementsOfNode = max(maxCountElementsOfNode,len(l)) # --------------------------- build group membership -------------------------------- p.moveto(offset_pos) index = {} groups = [] groupCount = 0 memberCount = 0 if options.nodalScalar: for n in xrange(stat['NumberOfNodes']): if n%1000 == 0: bg.set_message('scan node %i...'%n) myNodeID = p.node_id(n) myNodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z] myElemID = 0 myIpID = 0 myGrainID = 0 # --- filter valid locations filter = substituteLocation(options.filter, [myElemID,myNodeID,myIpID,myGrainID], myNodeCoordinates) # 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 grp = substituteLocation('#'.join(options.sep), [myElemID,myNodeID,myIpID,myGrainID], myNodeCoordinates) # generates a unique key for a group of separated data based on the separation criterium for the location if grp not in index: # create a new group if not yet present index[grp] = groupCount groups.append([[0,0,0,0,0.0,0.0,0.0]]) # initialize with avg location groupCount += 1 groups[index[grp]][0][:4] = mapIncremental('','unique', len(groups[index[grp]])-1, groups[index[grp]][0][:4], [myElemID,myNodeID,myIpID,myGrainID]) # keep only if unique average location groups[index[grp]][0][4:] = mapIncremental('','avg', len(groups[index[grp]])-1, groups[index[grp]][0][4:], myNodeCoordinates) # incrementally update average location groups[index[grp]].append([myElemID,myNodeID,myIpID,myGrainID,0]) # append a new list defining each group member memberCount += 1 else: for e in xrange(stat['NumberOfElements']): if e%1000 == 0: bg.set_message('scan elem %i...'%e) myElemID = p.element_id(e) myIpCoordinates = ipCoords(p.element(e).type, map(lambda node: [node.x, node.y, node.z], map(p.node, map(p.node_sequence, p.element(e).items)))) myIpIDs = ipIDs(p.element(e).type) Nips = len(myIpIDs) myNodeIDs = p.element(e).items[:Nips] for n in range(Nips): myIpID = myIpIDs[n] myNodeID = myNodeIDs[n] for g in range(('GrainCount' in stat['IndexOfLabel'] and int(p.element_scalar(e, stat['IndexOfLabel']['GrainCount'])[0].value)) or 1): myGrainID = g + 1 # --- filter valid locations filter = substituteLocation(options.filter, [myElemID,myNodeID,myIpID,myGrainID], myIpCoordinates[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 grp = substituteLocation('#'.join(options.sep), [myElemID,myNodeID,myIpID,myGrainID], myIpCoordinates[n]) # generates a unique key for a group of separated data based on the separation criterium for the location if grp not in index: # create a new group if not yet present index[grp] = groupCount groups.append([[0,0,0,0,0.0,0.0,0.0]]) # initialize with avg location groupCount += 1 groups[index[grp]][0][:4] = mapIncremental('','unique', len(groups[index[grp]])-1, groups[index[grp]][0][:4], [myElemID,myNodeID,myIpID,myGrainID]) # keep only if unique average location groups[index[grp]][0][4:] = mapIncremental('','avg', len(groups[index[grp]])-1, groups[index[grp]][0][4:], myIpCoordinates[n]) # incrementally update average location groups[index[grp]].append([myElemID,myNodeID,myIpID,myGrainID,n]) # append a new list defining each group member memberCount += 1 # --------------------------- sort groups -------------------------------- where = { 'elem': 0, 'node': 1, 'ip': 2, 'grain': 3, 'x': 4, 'y': 5, 'z': 6, } sortProperties = [] for item in options.sep: if item not in options.sort: sortProperties.append(item) theKeys = [] if 'none' not in map(str.lower, options.sort): for criterium in options.sort + sortProperties: if criterium in where: theKeys.append('x[0][%i]'%where[criterium]) sortKeys = eval('lambda x:(%s)'%(','.join(theKeys))) bg.set_message('sorting groups...') groups.sort(key = sortKeys) # in-place sorting to save mem # --------------------------- create output dir -------------------------------- dirname = os.path.abspath(os.path.join(os.path.dirname(filename),options.dir)) if not os.path.isdir(dirname): os.mkdir(dirname,0755) fileOpen = False assembleHeader = True header = [] standard = ['inc'] + \ {True: ['time'], False:[]}[options.time] + \ ['elem','node','ip','grain'] + \ {True: ['1_nodeinitialcoord','2_nodeinitialcoord','3_nodeinitialcoord'], False:['1_ipinitialcoord','2_ipinitialcoord','3_ipinitialcoord']}[options.nodalScalar != []] # --------------------------- loop over positions -------------------------------- bg.set_message('getting map between positions and increments...') incAtPosition = {} positionOfInc = {} for position in range(stat['NumberOfIncrements']): p.moveto(position+offset_pos) incAtPosition[position] = p.increment # remember "real" increment at this position positionOfInc[p.increment] = position # remember position of "real" increment if not options.range: options.getIncrements = False locations = range(stat['NumberOfIncrements']) # process all positions else: options.range = list(options.range) # convert to list if options.getIncrements: locations = [positionOfInc[x] for x in range(options.range[0],options.range[1]+1,options.range[2]) if x in positionOfInc] else: locations = range( max(0,options.range[0]), min(stat['NumberOfIncrements'],options.range[1]+1), options.range[2] ) increments = [incAtPosition[x] for x in locations] # build list of increments to process time_start = time.time() for incCount,position in enumerate(locations): # walk through locations p.moveto(position+offset_pos) # wind to correct position # --------------------------- file management -------------------------------- if options.separateFiles: if fileOpen: file.close() fileOpen = False outFilename = eval('"'+eval("'%%s_inc%%0%ii%%s.txt'%(math.log10(max(increments+[1]))+1)")+'"%(dirname + os.sep + options.prefix + os.path.split(filename)[1],increments[incCount],options.suffix)') else: outFilename = '%s.txt'%(dirname + os.sep + options.prefix + os.path.split(filename)[1] + options.suffix) if not fileOpen: file = open(outFilename,'w') fileOpen = True file.write('2\theader\n') file.write(string.replace('$Id$','\n','\\n')+ '\t' + ' '.join(sys.argv[1:]) + '\n') headerWritten = False file.flush() # --------------------------- read and map data per group -------------------------------- member = 0 for group in groups: N = 0 # group member counter for (e,n,i,g,n_local) in group[1:]: # loop over group members member += 1 if member%1000 == 0: time_delta = ((len(locations)*memberCount)/float(member+incCount*memberCount)-1.0)*(time.time()-time_start) bg.set_message('(%02i:%02i:%02i) processing point %i of %i from increment %i (position %i)...'%(time_delta//3600,time_delta%3600//60,time_delta%60,member,memberCount,increments[incCount],position)) newby = [] # current member's data if options.nodalScalar: for label in options.nodalScalar: if label == 'elements': length = maxCountElementsOfNode content = elementsOfNode[p.node_sequence(n)]+[0]*(length-len(elementsOfNode[p.node_sequence(n)])) else: length = 1 content = [ p.node_scalar(p.node_sequence(n),stat['IndexOfLabel'][label]) ] if assembleHeader: header += heading('_',[[component,''.join( label.split() )] for component in range(int(length>1),length+int(length>1))]) newby.append({'label':label, 'len':length, 'content':content }) if options.elemScalar: for label in options.elemScalar: if assembleHeader: header += [''.join( label.split() )] newby.append({'label':label, 'len':1, 'content':[ p.element_scalar(p.element_sequence(e),stat['IndexOfLabel'][label])[n_local].value ]}) if options.elemTensor: for label in options.elemTensor: if assembleHeader: header += heading('.',[[''.join( label.split() ),component] for component in ['intensity','t11','t22','t33','t12','t23','t13']]) myTensor = p.element_tensor(p.element_sequence(e),stat['IndexOfLabel'][label])[n_local] newby.append({'label':label, 'len':7, 'content':[ myTensor.intensity, myTensor.t11, myTensor.t22, myTensor.t33, myTensor.t12, myTensor.t23, myTensor.t13, ]}) if options.homogenizationResult or \ options.crystalliteResult or \ options.constitutiveResult: for (label,resultType) in zip(options.homogenizationResult + options.crystalliteResult + options.constitutiveResult, ['Homogenization']*len(options.homogenizationResult) + ['Crystallite']*len(options.crystalliteResult) + ['Constitutive']*len(options.constitutiveResult) ): outputIndex = list(zip(*outputFormat[resultType]['outputs'])[0]).index(label) # find the position of this output in the outputFormat length = int(outputFormat[resultType]['outputs'][outputIndex][1]) thisHead = heading('_',[[component,''.join( label.split() )] for component in range(int(length>1),length+int(length>1))]) if assembleHeader: header += thisHead if resultType != 'Homogenization': thisHead = heading('_',[[g,component,label] for component in range(int(length>1),length+int(length>1))]) newby.append({'label':label, 'len':length, 'content':[ p.element_scalar(p.element_sequence(e),stat['IndexOfLabel'][head])[n_local].value for head in thisHead ]}) assembleHeader = False if N == 0: mappedResult = [float(x) for x in xrange(len(header))] # initialize with debug data (should get deleted by *N at N=0) pos = 0 for chunk in newby: mappedResult[pos:pos+chunk['len']] = mapIncremental(chunk['label'],options.func, N,mappedResult[pos:pos+chunk['len']],chunk['content']) pos += chunk['len'] N += 1 # --- write data row to file --- if not headerWritten: file.write('\t'.join(standard + header) + '\n') headerWritten = True file.write('\t'.join(map(str,[p.increment] + \ {True:[p.time],False:[]}[options.time] + \ group[0] + \ mappedResult) ) + '\n') if fileOpen: file.close() # --------------------------- DONE --------------------------------