#!/usr/bin/env python
import os, sys, math, re, threading, time
from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP
releases = {'2010':['linux64',''],
'2008r1':[''],
'2007r1':[''],
'2005r3':[''],
}
file = open('%s/../MSCpath'%os.path.dirname(sys.argv[0]))
MSCpath = os.path.normpath(file.readline().strip())
file.close()
for release,subdirs in sorted(releases.items(),reverse=True):
for subdir in subdirs:
libPath = '%s/mentat%s/shlib/%s'%(MSCpath,release,subdir)
if os.path.exists(libPath):
sys.path.append(libPath)
break
else:
continue
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])'
$Id$
""")
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('$Id$\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'
elif len(groupContent) == 1:
condensedGroupContent = map(str,groupContent[0])
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 --------------------------------