987 lines
42 KiB
Python
Executable File
987 lines
42 KiB
Python
Executable File
#!/usr/bin/env python
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import os, sys, math, re, threading, time, struct
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from optparse import OptionParser, OptionGroup, Option, SUPPRESS_HELP
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releases = {'2010':['linux64',''],
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'2008r1':[''],
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'2007r1':[''],
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'2005r3':[''],
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}
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try:
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file = open('%s/../MSCpath'%os.path.dirname(os.path.realpath(sys.argv[0])))
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MSCpath = os.path.normpath(file.readline().strip())
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file.close()
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except:
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MSCpath = '/msc'
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for release,subdirs in sorted(releases.items(),reverse=True):
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for subdir in subdirs:
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libPath = '%s/mentat%s/shlib/%s'%(MSCpath,release,subdir)
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if os.path.exists(libPath):
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sys.path.append(libPath)
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break
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else:
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continue
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break
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try:
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from py_post import *
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except:
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print('error: no valid Mentat release found in %s'%MSCpath)
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sys.exit(-1)
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# -----------------------------
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class vector: # mimic py_post node object
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# -----------------------------
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x,y,z = [None,None,None]
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def __init__(self,coords):
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self.x = coords[0]
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self.y = coords[1]
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self.z = coords[2]
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# -----------------------------
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class element: # mimic py_post element object
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# -----------------------------
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items = []
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type = None
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def __init__(self,nodes,type):
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self.items = nodes
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self.type = type
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# -----------------------------
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class elemental_scalar: # mimic py_post element_scalar object
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# -----------------------------
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id = None
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value = None
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def __init__(self,node,value):
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self.id = node
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self.value = value
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# -----------------------------
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class MPIEspectral_result: # mimic py_post result object
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# -----------------------------
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file = None
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dataOffset = 0
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N_elemental_scalars = 0
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resolution = [0,0,0]
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dimension = [0.0,0.0,0.0]
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theTitle = ''
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wd = ''
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extrapolate = ''
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N_increments = 0
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increment = 0
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N_nodes = 0
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N_node_scalars = 0
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N_elements = 0
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N_element_scalars = 0
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N_element_tensors = 0
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theNodes = []
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theElements = []
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def __init__(self,filename):
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self.file = open(filename, 'rb')
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self.theTitle = self._keyedString('load')
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self.wd = self._keyedString('workingdir')
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self.geometry = self._keyedString('geometry')
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self.N_increments = self._keyedInt('increments')
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self.N_element_scalars = self._keyedInt('materialpoint_sizeResults')
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self.resolution = self._keyedPackedArray('resolution',3,'i')
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self.N_nodes = (self.resolution[0]+1)*(self.resolution[1]+1)*(self.resolution[2]+1)
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self.N_elements = self.resolution[0]*self.resolution[1]*self.resolution[2]
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self.dimension = self._keyedPackedArray('dimension',3,'d')
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a = self.resolution[0]+1
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b = self.resolution[1]+1
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c = self.resolution[2]+1
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for n in range(self.N_nodes):
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self.theNodes.append(vector([self.dimension[0] * (n%a) / self.resolution[0],
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self.dimension[1] * ((n/a)%b) / self.resolution[1],
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self.dimension[2] * ((n/a/b)%c) / self.resolution[2],
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]))
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for e in range(self.N_elements):
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basenode = e+e/self.resolution[0] + e/self.resolution[0]/self.resolution[1]*a
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basenode2 = basenode+a*b
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self.theElements.append(element([basenode ,basenode+1 ,basenode+a+1 ,basenode+a,
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basenode2,basenode2+1,basenode2+a+1,basenode2+a,
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],117))
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self.file.seek(0)
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self.dataOffset = self.file.read(2048).find('eoh')+7
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def __str__(self):
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return '\n'.join([
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'title: %s'%self.theTitle,
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'workdir: %s'%self.wd,
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'extrapolation: %s'%self.extrapolate,
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'increments: %i'%self.N_increments,
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'increment: %i'%self.increment,
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'nodes: %i'%self.N_nodes,
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'resolution: %s'%(','.join(map(str,self.resolution))),
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'dimension: %s'%(','.join(map(str,self.dimension))),
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'elements: %i'%self.N_elements,
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'nodal_scalars: %i'%self.N_node_scalars,
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'elemental scalars: %i'%self.N_element_scalars,
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'elemental tensors: %i'%self.N_element_tensors,
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]
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)
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def _keyedPackedArray(self,identifier,length = 3,type = 'd'):
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match = {'d': 8,'i': 4}
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self.file.seek(0)
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m = re.search('%s%s'%(identifier,'(.{%i})'%(match[type])*length),self.file.read(2048))
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values = []
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if m:
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for i in m.groups():
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values.append(struct.unpack(type,i)[0])
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return values
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def _keyedInt(self,identifier):
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value = None
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self.file.seek(0)
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m = re.search('%s%s'%(identifier,'(.{4})'),self.file.read(2048))
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if m:
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value = struct.unpack('i',m.group(1))[0]
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return value
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def _keyedString(self,identifier):
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value = None
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self.file.seek(0)
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m = re.search(r'(.{4})%s(.*?)\1'%identifier,self.file.read(2048))
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if m:
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value = m.group(2)
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return value
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def title(self):
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return self.theTitle
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def moveto(self,inc):
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self.increment = inc
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def extrapolation(self,value):
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self.extrapolate = value
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def node_sequence(self,node):
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return node
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def node_id(self,node):
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return node+1
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def node(self,node):
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return self.theNodes[node]
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def element_id(self,elem):
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return elem+1
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def element(self,elem):
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return self.theElements[elem]
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def increments(self):
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return self.N_increments
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def nodes(self):
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return self.N_nodes
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def node_scalars(self):
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return self.N_node_scalars
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def elements(self):
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return self.N_elements
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def element_scalars(self):
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return self.N_element_scalars
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def element_scalar(self,elem,idx):
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self.file.seek(self.dataOffset+(self.increment*(4+self.N_elements*self.N_element_scalars*8+4) + 4+(elem*self.N_element_scalars + idx)*8))
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value = struct.unpack('d',self.file.read(8))[0]
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return [elemental_scalar(node,value) for node in self.theElements[elem].items]
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def element_scalar_label(elem,idx):
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return 'User Defined Variable %i'%(idx+1)
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def element_tensors(self):
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return self.N_element_tensors
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# -----------------------------
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class MyOption(Option):
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# -----------------------------
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# used for definition of new option parser action 'extend', which enables to take multiple option arguments
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# taken from online tutorial http://docs.python.org/library/optparse.html
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ACTIONS = Option.ACTIONS + ("extend",)
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STORE_ACTIONS = Option.STORE_ACTIONS + ("extend",)
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TYPED_ACTIONS = Option.TYPED_ACTIONS + ("extend",)
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ALWAYS_TYPED_ACTIONS = Option.ALWAYS_TYPED_ACTIONS + ("extend",)
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def take_action(self, action, dest, opt, value, values, parser):
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if action == "extend":
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lvalue = value.split(",")
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values.ensure_value(dest, []).extend(lvalue)
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else:
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Option.take_action(self, action, dest, opt, value, values, parser)
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# -----------------------------
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class backgroundMessage(threading.Thread):
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# -----------------------------
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def __init__(self):
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threading.Thread.__init__(self)
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self.message = ''
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self.new_message = ''
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self.counter = 0
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self.symbols = ['- ', '\ ', '| ', '/ ']
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self.waittime = 0.5
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def __quit__(self):
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length = len(self.message) + len(self.symbols[self.counter])
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sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length)
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sys.stderr.write('')
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def run(self):
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while not threading.enumerate()[0]._Thread__stopped:
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time.sleep(self.waittime)
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self.update_message()
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self.__quit__()
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def set_message(self, new_message):
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self.new_message = new_message
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self.print_message()
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def print_message(self):
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length = len(self.message) + len(self.symbols[self.counter])
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sys.stderr.write(chr(8)*length + ' '*length + chr(8)*length) # delete former message
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sys.stderr.write(self.symbols[self.counter] + self.new_message) # print new message
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self.message = self.new_message
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def update_message(self):
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self.counter = (self.counter + 1)%len(self.symbols)
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self.print_message()
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# -----------------------------
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def ipCoords(elemType, nodalCoordinates):
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#
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# returns IP coordinates for a given element
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# -----------------------------
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nodeWeightsPerNode = {
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7: [ [27.0, 9.0, 3.0, 9.0, 9.0, 3.0, 1.0, 3.0],
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[ 9.0, 27.0, 9.0, 3.0, 3.0, 9.0, 3.0, 1.0],
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[ 3.0, 9.0, 27.0, 9.0, 1.0, 3.0, 9.0, 3.0],
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[ 9.0, 3.0, 9.0, 27.0, 3.0, 1.0, 3.0, 9.0],
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[ 9.0, 3.0, 1.0, 3.0, 27.0, 9.0, 3.0, 9.0],
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[ 3.0, 9.0, 3.0, 1.0, 9.0, 27.0, 9.0, 3.0],
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[ 1.0, 3.0, 9.0, 3.0, 3.0, 9.0, 27.0, 9.0],
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[ 3.0, 1.0, 3.0, 9.0, 9.0, 3.0, 9.0, 27.0] ],
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117: [ [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
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[ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ],
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136: [ [42.0, 15.0, 15.0, 14.0, 5.0, 5.0],
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[15.0, 42.0, 15.0, 5.0, 14.0, 5.0],
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[15.0, 15.0, 42.0, 5.0, 5.0, 14.0],
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[14.0, 5.0, 5.0, 42.0, 15.0, 15.0],
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[ 5.0, 14.0, 5.0, 15.0, 42.0, 15.0],
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[ 5.0, 5.0, 14.0, 15.0, 15.0, 42.0] ],
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}
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ipCoordinates = [[0.0,0.0,0.0] for i in range(len(nodalCoordinates))]
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for ip in range(len(nodeWeightsPerNode[elemType])):
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for node in range(len(nodeWeightsPerNode[elemType][ip])):
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for i in range(3):
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ipCoordinates[ip][i] += nodeWeightsPerNode[elemType][ip][node] * nodalCoordinates[node][i]
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for i in range(3):
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ipCoordinates[ip][i] /= sum(nodeWeightsPerNode[elemType][ip])
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return ipCoordinates
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# -----------------------------
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def sortBySeparation(dataArray, criteria, offset):
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#
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# sorting of groupValue array according to list of criteria
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# -----------------------------
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where = {
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'elem': 1,
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'node': 2,
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'grain': 3,
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'x': 4,
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'y': 5,
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'z': 6,
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}
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theKeys = []
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for criterium in criteria:
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if criterium in where:
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theKeys.append('x[%i]'%(offset+where[criterium]))
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exec('sortedArray = sorted(dataArray,key=lambda x:(%s))'%(','.join(theKeys)))
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return sortedArray
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# -----------------------------
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def substituteLocation(string, mesh, coords):
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#
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# do variable interpolation in group and filter strings
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# -----------------------------
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substitute = string
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substitute = substitute.replace('elem', str(mesh[0]))
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substitute = substitute.replace('node', str(mesh[1]))
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substitute = substitute.replace('grain', str(mesh[2]))
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substitute = substitute.replace('x', '%.6g'%coords[0])
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substitute = substitute.replace('y', '%.6g'%coords[1])
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substitute = substitute.replace('z', '%.6g'%coords[2])
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return substitute
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# -----------------------------
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def average(theList):
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#
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# calcs the average of a list of numbers
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# -----------------------------
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return sum(map(float,theList))/len(theList)
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# -----------------------------
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def mapFunc(label, chunks, func):
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#
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# applies the function defined by "func"
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# (can be either 'min','max','avg', 'sum', or user specified)
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# to a list of lists of data
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# -----------------------------
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illegal = {
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'eulerangles': ['min','max','avg','sum'],
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'defgrad': ['min','max','avg','sum'],
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'orientation': ['min','max','sum'],
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}
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if label.lower() in illegal and func in illegal[label.lower()]: # for illegal mappings:...
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return ['n/a' for i in range(len(chunks[0]))] # ...return 'n/a'
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else:
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if func in ['min','max','avg']:
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mapped = [{ 'min': lambda x: min(x),
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'max': lambda x: max(x),
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'avg': lambda x: average(x),
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'sum': lambda x: sum(x),
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}[func](column) for column in zip(*chunks)] # map one of the standard functions to colums in chunks
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if label.lower() == 'orientation': # orientation is special case:...
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orientationNorm = math.sqrt(sum([q*q for q in mapped])) # ...calc norm of average quaternion
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mapped = map(lambda x: x/orientationNorm, mapped) # ...renormalize quaternion
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else:
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try:
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mapped = eval('map(%s,zip(*chunks))'%func) # map user defined function to colums in chunks
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except:
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mapped = ['n/a' for i in range(len(chunks[0]))]
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return mapped
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# -----------------------------
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def OpenPostfile(name,type):
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#
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# open postfile with extrapolation mode "translate"
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# -----------------------------
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p = {\
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'marc': post_open,\
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'spectral': MPIEspectral_result,\
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}[type.lower()]\
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(name+
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{\
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'marc': '.t16',\
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'spectral': '.spectralOut',\
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}[type.lower()]
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)
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p.extrapolation('translate')
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p.moveto(1)
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return p
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# -----------------------------
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def ParseOutputFormat(filename,what,me):
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#
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# parse .output* files in order to get a list of outputs
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# -----------------------------
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format = {'outputs':{},'specials':{'brothers':[]}}
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for prefix in ['']+map(str,range(1,17)):
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if os.path.exists(prefix+filename+'.output'+what):
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break
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try:
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file = open(prefix+filename+'.output'+what)
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content = file.readlines()
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file.close()
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except:
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return format
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tag = ''
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tagID = 0
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for line in content:
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if re.match("\s*$",line) or re.match("#",line): # skip blank lines and comments
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continue
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m = re.match("\[(.+)\]",line) # look for block indicator
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if m: # next section
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tag = m.group(1)
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tagID += 1
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format['specials']['brothers'].append(tag)
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if tag == me or (me.isdigit() and tagID == int(me)):
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format['specials']['_id'] = tagID
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format['outputs'] = []
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tag = me
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else: # data from section
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if tag == me:
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(output,length) = line.split()
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output.lower()
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if length.isdigit():
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length = int(length)
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if re.match("\((.+)\)",output): # special data, e.g. (Ngrains)
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format['specials'][output] = length
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elif length > 0:
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format['outputs'].append([output,length])
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return format
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# -----------------------------
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def ParsePostfile(p,filename, outputFormat):
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#
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# parse postfile in order to get position and labels of outputs
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# needs "outputFormat" for mapping of output names to postfile output indices
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# -----------------------------
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# --- build statistics
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stat = { \
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'IndexOfLabel': {}, \
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'Title': p.title(), \
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'Extrapolation': p.extrapolate, \
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'NumberOfIncrements': p.increments(), \
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'NumberOfNodes': p.nodes(), \
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'NumberOfNodalScalars': p.node_scalars(), \
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'LabelOfNodalScalar': [None]*p.node_scalars() , \
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'NumberOfElements': p.elements(), \
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'NumberOfElementalScalars': p.element_scalars(), \
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'LabelOfElementalScalar': [None]*p.element_scalars() , \
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'NumberOfElementalTensors': p.element_tensors(), \
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'LabelOfElementalTensor': [None]*p.element_tensors(), \
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}
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# --- find labels
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for labelIndex in range(stat['NumberOfNodalScalars']):
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|
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('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) or .spectralOut 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('-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('-m','--map', dest='func', type='string', \
|
|
help='data reduction mapping ["%default"] out of min, max, avg, sum 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(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, file) = parser.parse_args()
|
|
|
|
bg = backgroundMessage()
|
|
bg.start()
|
|
|
|
|
|
# --- sanity checks
|
|
|
|
if not file[0]:
|
|
parser.print_help()
|
|
parser.error('no file specified...')
|
|
|
|
if not os.path.exists(file[0]):
|
|
parser.print_help()
|
|
parser.error('invalid file "%s" specified...'%file[0])
|
|
|
|
if options.filetype.lower() 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...')
|
|
|
|
|
|
# --- parse .output and .t16 files
|
|
|
|
bg.set_message('parsing .output and result 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,options.filetype)
|
|
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 = []
|
|
|
|
element_scalar = {}
|
|
element_tensor = {}
|
|
|
|
# --- store geometry information
|
|
|
|
p.moveto(0)
|
|
|
|
nodeID = [ 0 for n in range(stat['NumberOfNodes'])]
|
|
nodeCoordinates = [[] for n in range(stat['NumberOfNodes'])]
|
|
|
|
elemID = [ 0 for e in range(stat['NumberOfElements'])]
|
|
elemNodeID = [[] for e in range(stat['NumberOfElements'])]
|
|
ipCoordinates = [[] for e in range(stat['NumberOfElements'])]
|
|
|
|
for n in range(stat['NumberOfNodes']):
|
|
nodeID[n] = p.node_id(n)
|
|
nodeCoordinates[n] = [p.node(n).x, p.node(n).y, p.node(n).z]
|
|
|
|
for e in range(stat['NumberOfElements']):
|
|
elemID[e] = p.element_id(e)
|
|
elemNodeID[e] = p.element(e).items
|
|
ipCoordinates[e] = 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))))
|
|
|
|
# --- loop over increments
|
|
|
|
time_start = time.time()
|
|
|
|
for incCount,increment in enumerate(increments):
|
|
p.moveto(increment+1)
|
|
time_delta = (len(increments)-incCount)*(time.time()-time_start)/max(1.0,incCount)
|
|
bg.set_message('(%02i:%02i:%02i) read data from increment %i...'%(time_delta//3600,time_delta%3600//60,time_delta%60,increment))
|
|
data = {}
|
|
|
|
if options.nodalScalar:
|
|
for n in range(stat['NumberOfNodes']):
|
|
myNodeID = nodeID[n]
|
|
myNodeCoordinates = nodeCoordinates[n]
|
|
myElemID = 0
|
|
myGrainID = 0
|
|
|
|
# --- filter valid locations
|
|
|
|
filter = substituteLocation(options.filter, [myElemID,myNodeID,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
|
|
|
|
group = substituteLocation('#'.join(options.separation), [myElemID,myNodeID,myGrainID], myNodeCoordinates) # 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': [myElemID,myNodeID,myGrainID] + myNodeCoordinates,
|
|
}) # 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']):
|
|
myElemID = elemID[e]
|
|
myIpCoordinates = ipCoordinates[e]
|
|
for n,myNodeID in enumerate(elemNodeID[e]):
|
|
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,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
|
|
|
|
group = substituteLocation('#'.join(options.separation), [myElemID,myNodeID,myGrainID], myIpCoordinates[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': [myElemID,myNodeID,myGrainID] + myIpCoordinates[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']]
|
|
myTensor = p.element_tensor(e,stat['IndexOfLabel'][label])[n]
|
|
data[group][-1].append({
|
|
'label': label,
|
|
'content': [ myTensor.intensity,
|
|
myTensor.t11, myTensor.t22, myTensor.t33,
|
|
myTensor.t12, myTensor.t23, myTensor.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]))+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
|
|
|
|
sortProperties = options.separation
|
|
for item in options.sort:
|
|
if item not in sortProperties:
|
|
sortProperties.append(item)
|
|
|
|
for groupvalues in sortBySeparation(output, sortProperties, int(options.time)): # sort output according to separation criteria
|
|
file.write('\t'.join(map(str,groupvalues)) + '\n')
|
|
|
|
if fileOpen:
|
|
file.close()
|
|
|
|
|
|
# --------------------------- DONE --------------------------------
|
|
|