output now contains both node and ip number
relation between node and ip numbering is given in new function ipIDs
This commit is contained in:
Christoph Kords
parent
56b2b3e572
commit
1928fa816c
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@ -272,14 +272,7 @@ def ipCoords(elemType, nodalCoordinates):
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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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117: [ [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ],
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125: [ [ 3.0, 0.0, 0.0, 4.0, 1.0, 4.0],
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[ 0.0, 3.0, 0.0, 4.0, 4.0, 1.0],
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[ 0.0, 0.0, 3.0, 1.0, 4.0, 4.0],],
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@ -304,6 +297,24 @@ def ipCoords(elemType, nodalCoordinates):
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# -----------------------------
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def ipIDs(elemType):
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#
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# returns IP numbers for given element type
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# -----------------------------
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ipPerNode = {
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7: [ 1, 2, 4, 3, 5, 6, 8, 7 ],
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57: [ 1, 2, 4, 3, 5, 6, 8, 7 ],
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117: [ 1 ],
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125: [ 1, 2, 3 ],
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136: [ 1, 2, 3, 4, 5, 6 ],
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}
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return ipPerNode[elemType]
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# -----------------------------
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def substituteLocation(string, mesh, coords):
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#
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@ -312,7 +323,8 @@ def substituteLocation(string, mesh, coords):
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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('ip', str(mesh[2]))
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substitute = substitute.replace('grain', str(mesh[3]))
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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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@ -543,7 +555,7 @@ Extract data from a .t16 (MSC.Marc) or .spectralOut results file.
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List of output variables is given by options '--ns','--es','--et','--ho','--cr','--co'.
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Filter and separations use 'elem','node','grain', and 'x','y','z' as key words.
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Filter and separations use 'elem','node','ip','grain', and 'x','y','z' as key words.
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Example:
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1) get averaged results in slices perpendicular to x for all positive y coordinates
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--filter 'y >= 0.0' --separation x --map 'avg'
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@ -783,84 +795,88 @@ if options.nodalScalar:
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myNodeID = p.node_id(n)
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myNodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z]
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myElemID = 0
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myIpID = 0
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myGrainID = 0
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# --- filter valid locations
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filter = substituteLocation(options.filter, [myElemID,myNodeID,myGrainID], myNodeCoordinates) # generates an expression that is only true for the locations specified by options.filter
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filter = substituteLocation(options.filter, [myElemID,myNodeID,myIpID,myGrainID], myNodeCoordinates) # generates an expression that is only true for the locations specified by options.filter
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if filter != '' and not eval(filter): # for all filter expressions that are not true:...
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continue # ... ignore this data point and continue with next
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# --- group data locations
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grp = 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
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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
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if grp not in index: # create a new group if not yet present
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index[grp] = groupCount
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groups[groupCount] = [[0,0,0,0.0,0.0,0.0]] # initialize with avg location
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groups[groupCount] = [[0,0,0,0,0.0,0.0,0.0]] # initialize with avg location
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groupCount += 1
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groups[index[grp]][0][:3] = mapIncremental('','unique',
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groups[index[grp]][0][:4] = mapIncremental('','unique',
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len(groups[index[grp]])-1,
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groups[index[grp]][0][:3],
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[myElemID,myNodeID,myGrainID]) # keep only if unique average location
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groups[index[grp]][0][3:] = mapIncremental('','avg',
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groups[index[grp]][0][:4],
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[myElemID,myNodeID,myIpID,myGrainID]) # keep only if unique average location
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groups[index[grp]][0][4:] = mapIncremental('','avg',
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len(groups[index[grp]])-1,
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groups[index[grp]][0][3:],
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myNodeCoordinates) # incrementally update average location
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groups[index[grp]].append([myElemID,myNodeID,myGrainID,0]) # append a new list defining each group member
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groups[index[grp]][0][4:],
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myNodeCoordinates) # incrementally update average location
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groups[index[grp]].append([myElemID,myNodeID,myIpID,myGrainID,0]) # append a new list defining each group member
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memberCount += 1
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else:
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for e in xrange(stat['NumberOfElements']):
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if p.element(e).type == 57:
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myNodeIDs = p.element(e).items[:8]
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else:
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myNodeIDs = p.element(e).items
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if e%1000 == 0:
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bg.set_message('scan elem %i...'%e)
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myElemID = p.element_id(e)
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myIpCoordinates = ipCoords(p.element(e).type, map(lambda node: [node.x, node.y, node.z], map(p.node, map(p.node_sequence,myNodeIDs))))
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for n,myNodeID in enumerate(myNodeIDs):
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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))))
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myIpIDs = ipIDs(p.element(e).type)
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Nips = len(myIpIDs)
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myNodeIDs = p.element(e).items[:Nips]
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for n in range(Nips):
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myIpID = myIpIDs[n]
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myNodeID = myNodeIDs[n]
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for g in range(('GrainCount' in stat['IndexOfLabel'] and int(p.element_scalar(e, stat['IndexOfLabel']['GrainCount'])[0].value))
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or 1):
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myGrainID = g + 1
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# --- filter valid locations
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filter = substituteLocation(options.filter, [myElemID,myNodeID,myGrainID], myIpCoordinates[n]) # generates an expression that is only true for the locations specified by options.filter
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filter = substituteLocation(options.filter, [myElemID,myNodeID,myIpID,myGrainID], myIpCoordinates[n]) # generates an expression that is only true for the locations specified by options.filter
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if filter != '' and not eval(filter): # for all filter expressions that are not true:...
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continue # ... ignore this data point and continue with next
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# --- group data locations
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grp = 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
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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
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if grp not in index: # create a new group if not yet present
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index[grp] = groupCount
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groups.append([[0,0,0,0.0,0.0,0.0]]) # initialize with avg location
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groups.append([[0,0,0,0,0.0,0.0,0.0]]) # initialize with avg location
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groupCount += 1
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groups[index[grp]][0][:3] = mapIncremental('','unique',
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groups[index[grp]][0][:4] = mapIncremental('','unique',
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len(groups[index[grp]])-1,
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groups[index[grp]][0][:3],
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[myElemID,myNodeID,myGrainID]) # keep only if unique average location
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groups[index[grp]][0][3:] = mapIncremental('','avg',
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groups[index[grp]][0][:4],
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[myElemID,myNodeID,myIpID,myGrainID]) # keep only if unique average location
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groups[index[grp]][0][4:] = mapIncremental('','avg',
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len(groups[index[grp]])-1,
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groups[index[grp]][0][3:],
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myIpCoordinates[n]) # incrementally update average location
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groups[index[grp]].append([myElemID,myNodeID,myGrainID,n]) # append a new list defining each group member
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groups[index[grp]][0][4:],
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myIpCoordinates[n]) # incrementally update average location
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groups[index[grp]].append([myElemID,myNodeID,myIpID,myGrainID,n]) # append a new list defining each group member
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memberCount += 1
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# --------------------------- sort groups --------------------------------
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where = {
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'elem': 0,
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'node': 1,
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'grain': 2,
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'x': 3,
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'y': 4,
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'z': 5,
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'elem': 0,
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'node': 1,
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'ip': 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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sortProperties = []
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@ -884,7 +900,7 @@ header = []
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standard = ['inc'] + \
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{True: ['time'],
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False:[]}[options.time] + \
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['elem','node','grain'] + \
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['elem','node','ip','grain'] + \
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{True: ['node.x','node.y','node.z'],
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False:['ip.x','ip.y','ip.z']}[options.nodalScalar != []]
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@ -917,10 +933,10 @@ for incCount,increment in enumerate(increments):
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# --------------------------- read and map data per group --------------------------------
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member = 0
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for i,group in enumerate(groups):
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for group in groups:
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N = 0 # group member counter
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for (e,n,g,n_local) in group[1:]: # loop over group members
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for (e,n,i,g,n_local) in group[1:]: # loop over group members
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member += 1
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if member%1000 == 0:
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time_delta = ((len(increments)*memberCount)/float(member+incCount*memberCount)-1.0)*(time.time()-time_start)
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