DAMASK_EICMD/processing/post/postResults

#!/usr/bin/env python 

import pdb, os, sys, gc, math, re, threading, time, struct, string
from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP

releases = {'2010':['linux64',''],
            '2008r1':[''],
            '2007r1':[''],
            '2005r3':[''],
           }


# -----------------------------
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
  resolution = [0,0,0]
  dimension = [0.0,0.0,0.0]
  theTitle = ''
  wd = ''
  geometry = ''
  extrapolate = ''
  N_increments = 0
  increment = 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.theTitle = self._keyedString('load')
    self.wd = self._keyedString('workingdir')
    self.geometry = self._keyedString('geometry')
    self.N_increments =  self._keyedInt('increments')
    self.N_element_scalars = self._keyedInt('materialpoint_sizeResults')
    self.resolution = self._keyedPackedArray('resolution',3,'i')
    self.N_nodes = (self.resolution[0]+1)*(self.resolution[1]+1)*(self.resolution[2]+1)
    self.N_elements = self.resolution[0]*self.resolution[1]*self.resolution[2]
    self.dimension = self._keyedPackedArray('dimension',3,'d')
  
    self.file.seek(0)
    self.dataOffset = self.file.read(2048).find('eoh')+7

  def __str__(self):
    return '\n'.join([
      'title: %s'%self.theTitle,
      'workdir: %s'%self.wd,
      'geometry: %s'%self.geometry,
      'extrapolation: %s'%self.extrapolate,
      'increments: %i'%self.N_increments,
      'increment: %i'%self.increment,
      'nodes: %i'%self.N_nodes,
      'resolution: %s'%(','.join(map(str,self.resolution))),
      'dimension: %s'%(','.join(map(str,self.dimension))),
      'elements: %i'%self.N_elements,
      'nodal_scalars: %i'%self.N_node_scalars,
      'elemental scalars: %i'%self.N_element_scalars,
      'elemental tensors: %i'%self.N_element_tensors,
      ]
    )

  def _keyedPackedArray(self,identifier,length = 3,type = 'd'):
    match = {'d': 8,'i': 4}
    self.file.seek(0)
    m = re.search('%s%s'%(identifier,'(.{%i})'%(match[type])*length),self.file.read(2048),re.DOTALL)
    values = []
    if m:
      for i in m.groups():
        values.append(struct.unpack(type,i)[0])
    return values

  def _keyedInt(self,identifier):
    value = None
    self.file.seek(0)
    m = re.search('%s%s'%(identifier,'(.{4})'),self.file.read(2048),re.DOTALL)
    if m:
      value = struct.unpack('i',m.group(1))[0]
    return value

  def _keyedString(self,identifier):
    value = None
    self.file.seek(0)
    m = re.search(r'(.{4})%s(.*?)\1'%identifier,self.file.read(2048),re.DOTALL)
    if m:
      value = m.group(2)
    return value

  def title(self):
    return self.theTitle

  def moveto(self,inc):
    self.increment = inc

  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.resolution[0]+1
    b = self.resolution[1]+1
    c = self.resolution[2]+1
    return vector([self.dimension[0] *       (n%a) / self.resolution[0],
                   self.dimension[1] *   ((n/a)%b) / self.resolution[1],
                   self.dimension[2] * ((n/a/b)%c) / self.resolution[2],
            ])

  def element_sequence(self,e):
    return e-1
 
  def element_id(self,e):
    return e+1
 
  def element(self,e):
     a = self.resolution[0]+1
     b = self.resolution[1]+1
     c = self.resolution[2]+1
     basenode = 1 + e+e/self.resolution[0] + e/self.resolution[0]/self.resolution[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_increments

  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):
    self.file.seek(self.dataOffset+(self.increment*(4+self.N_elements*self.N_element_scalars*8+4) + 4+(e*self.N_element_scalars + idx)*8))
    value = struct.unpack('d',self.file.read(8))[0]
    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 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] ], 
              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],], 
              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 ], 
              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),
              'sum': lambda n,b,a: b+a,
              'abssum': 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):
# 
# open postfile with extrapolation mode "translate"
# -----------------------------

  p = {\
         'spectral': MPIEspectral_result,\
         'marc':     post_open,\
      }[type]\
            (name+
                {\
                 'marc':     '.t16',\
                 'spectral': '.spectralOut',\
                }[type]
            )
  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 = 2
    stat['LabelOfElementalScalar'][startIndex + 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 + offset + (i+1)
      else:
        stat['IndexOfLabel']['%s'%(var[0])] = startIndex + offset + 1 
      offset += var[1]
      
    for grain in range(outputFormat['Homogenization']['specials']['(ngrains)']):
      stat['IndexOfLabel']['%i_CrystalliteCount'%(grain+1)] = startIndex + offset + 1
      offset += 1
      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 + offset + (i+1)
        else:
          stat['IndexOfLabel']['%i_%s'%(grain+1,var[0])] = startIndex + offset + 1
        offset += var[1]

      stat['IndexOfLabel']['%i_ConstitutiveCount'%(grain+1)] = startIndex + offset + 1
      offset += 1
      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 + offset + (i+1)
        else:
          stat['IndexOfLabel']['%i_%s'%(grain+1,var[0])] = startIndex + offset + 1
        offset += var[1]
  
  return stat


# -----------------------------
def SummarizePostfile(stat,where=sys.stdout):
# -----------------------------

  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=MyOption, usage='%prog [options] resultfile', 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.

$Id$
""")

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('-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 increments to output (start, end, step) [all]')
parser.add_option('--sloppy', action='store_true', dest='sloppy', \
          help='do not pre-check validity of increment range')
parser.add_option('-m','--map', dest='func', type='string', \
          help='data reduction mapping ["%default"] out of min, max, avg, sum, abssum or user-lambda')
parser.add_option('-p','--type', dest='filetype', type='string', \
          help = 'type of result file [%default]')
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]')
group_special.add_option('--sort', action='extend', dest='sort', type='string', \
          help='properties to sort results [%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(sloppy = False)
parser.set_defaults(directory = 'postProc')
parser.set_defaults(filetype = 'marc')
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(sort = [])
parser.set_defaults(inc = False)
parser.set_defaults(time = False)
parser.set_defaults(separateFiles = False)

(options, files) = parser.parse_args()

options.filetype = options.filetype.lower()

if options.filetype == 'marc':
  try:
    file = open('%s/../MSCpath'%os.path.dirname(os.path.realpath(sys.argv[0])))
    MSCpath = os.path.normpath(file.readline().strip())
    file.close()
  except:
    MSCpath = '/msc'
  
  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
  
  try:
    from py_post import *
  except:
    print('error: no valid Mentat release found in %s'%MSCpath)
    sys.exit(-1)
else:
  def post_open():
    return



# --- 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])

if options.filetype not in ['marc','spectral']:
  parser.print_help()
  parser.error('file type "%s" not supported...'%options.filetype)

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...')

if not options.nodalScalar:          options.nodalScalar          = []
if not options.elementalScalar:      options.elementalScalar      = []
if not options.elementalTensor:      options.elementalTensor      = []
if not options.homogenizationResult: options.homogenizationResult = []
if not options.crystalliteResult:    options.crystalliteResult    = []
if not options.constitutiveResult:   options.constitutiveResult   = []

options.sort.reverse()
options.separation.reverse()

# --- start background messaging

bg = backgroundMessage()
bg.start()

# --- parse .output and .t16 files

filename = os.path.splitext(files[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,
     }

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 "'%s' not found in <%s>"%(me[what], what)
    print '\n'.join(map(lambda x:'  '+x, outputFormat[what]['specials']['brothers']))
    sys.exit(1)
    
bg.set_message('opening result file...')
p = OpenPostfile(filename,options.filetype)
bg.set_message('parsing result file...')
stat = ParsePostfile(p, filename, outputFormat)
if options.filetype == 'marc':
  stat['NumberOfIncrements'] -= 1             # t16 contains one "virtual" increment (at 0)

# --- 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:
  if options.filetype == 'marc':
    print '\n\nMentat release %s'%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

  
increments = range(stat['NumberOfIncrements'])
if options.filetype == 'marc':
  offset_inc = 1
else:
  offset_inc = 0
if options.range:
  options.range = list(options.range)
  if options.sloppy:
    increments = range(options.range[0],options.range[1]+1,options.range[2])
  else:
    increments = range( max(0,options.range[0]),
                        min(stat['NumberOfIncrements'],options.range[1]+1),
                        options.range[2])


# ---------------------------   build group membership   --------------------------------

p.moveto(increments[0]+offset_inc)
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.separation), [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[groupCount] = [[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.separation), [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.separation:
  if item not in options.sort:
    sortProperties.append(item)

theKeys = []
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


fileOpen = False
assembleHeader = True
header = []
standard = ['inc'] + \
           {True: ['time'],
            False:[]}[options.time] + \
           ['elem','node','ip','grain'] + \
           {True: ['node.x','node.y','node.z'],
            False:['ip.x','ip.y','ip.z']}[options.nodalScalar != []]

# ---------------------------   loop over increments   --------------------------------

time_start = time.time()

for incCount,increment in enumerate(increments):
  p.moveto(increment+offset_inc)
  
# ---------------------------   file management   --------------------------------

  if options.separateFiles:
    if fileOpen:
      file.close()
      fileOpen = False
    outFilename = eval('"'+eval("'%%s_inc%%0%ii.txt'%(math.log10(max(increments+[1]))+1)")+'"%(dirname + os.sep + os.path.split(filename)[1],increment)')
  else:
    outFilename = '%s.txt'%(dirname + os.sep + os.path.split(filename)[1])
  
  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(increments)*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...'%(time_delta//3600,time_delta%3600//60,time_delta%60,member,memberCount,increment))

      newby = []                                                                   # current member's data

      if options.elementalScalar:
        for label in options.elementalScalar:
          if assembleHeader: 
            header += [label.replace(' ','')]
          newby.append({'label':label,
                        'len':1,
                        'content':[ p.element_scalar(p.element_sequence(e),stat['IndexOfLabel'][label])[n_local].value ]})

      if options.elementalTensor:
        for label in options.elementalTensor:
          if assembleHeader: 
            header += heading('.',[[label.replace(' ',''),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,label] 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,[increment] + \
                                 {True:[p.time],False:[]}[options.time] + \
                                 group[0] + \
                                 mappedResult)
                        ) + '\n')
    
if fileOpen:
  file.close()


# ---------------------------       DONE     --------------------------------