diff --git a/processing/post/postResults b/processing/post/postResults index 5e1b2ad95..ec2a34bd1 100755 --- a/processing/post/postResults +++ b/processing/post/postResults @@ -272,14 +272,7 @@ def ipCoords(elemType, nodalCoordinates): [ 3.0, 9.0, 3.0, 1.0, 9.0, 27.0, 9.0, 3.0], [ 1.0, 3.0, 9.0, 3.0, 3.0, 9.0, 27.0, 9.0], [ 3.0, 1.0, 3.0, 9.0, 9.0, 3.0, 9.0, 27.0] ], - 117: [ [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], - [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] ], + 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],], @@ -304,6 +297,24 @@ def ipCoords(elemType, nodalCoordinates): +# ----------------------------- +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): # @@ -312,7 +323,8 @@ def substituteLocation(string, mesh, coords): substitute = string substitute = substitute.replace('elem', str(mesh[0])) substitute = substitute.replace('node', str(mesh[1])) - substitute = substitute.replace('grain', str(mesh[2])) + substitute = substitute.replace('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]) @@ -543,7 +555,7 @@ 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. +Filter 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 positive y coordinates --filter 'y >= 0.0' --separation x --map 'avg' @@ -783,84 +795,88 @@ if options.nodalScalar: 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,myGrainID], myNodeCoordinates) # generates an expression that is only true for the locations specified by options.filter + 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,myGrainID], myNodeCoordinates) # generates a unique key for a group of separated data based on the separation criterium for the location + 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]] # initialize with avg location + groups[groupCount] = [[0,0,0,0,0.0,0.0,0.0]] # initialize with avg location groupCount += 1 - groups[index[grp]][0][:3] = mapIncremental('','unique', + groups[index[grp]][0][:4] = mapIncremental('','unique', len(groups[index[grp]])-1, - groups[index[grp]][0][:3], - [myElemID,myNodeID,myGrainID]) # keep only if unique average location - groups[index[grp]][0][3:] = mapIncremental('','avg', + 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][3:], - myNodeCoordinates) # incrementally update average location - groups[index[grp]].append([myElemID,myNodeID,myGrainID,0]) # append a new list defining each group member + 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 p.element(e).type == 57: - myNodeIDs = p.element(e).items[:8] - else: - myNodeIDs = p.element(e).items 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,myNodeIDs)))) - for n,myNodeID in enumerate(myNodeIDs): + 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,myGrainID], myIpCoordinates[n]) # generates an expression that is only true for the locations specified by options.filter + 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,myGrainID], myIpCoordinates[n]) # generates a unique key for a group of separated data based on the separation criterium for the location + 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]]) # initialize with avg location + groups.append([[0,0,0,0,0.0,0.0,0.0]]) # initialize with avg location groupCount += 1 - groups[index[grp]][0][:3] = mapIncremental('','unique', + groups[index[grp]][0][:4] = mapIncremental('','unique', len(groups[index[grp]])-1, - groups[index[grp]][0][:3], - [myElemID,myNodeID,myGrainID]) # keep only if unique average location - groups[index[grp]][0][3:] = mapIncremental('','avg', + 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][3:], - myIpCoordinates[n]) # incrementally update average location - groups[index[grp]].append([myElemID,myNodeID,myGrainID,n]) # append a new list defining each group member + 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, - 'grain': 2, - 'x': 3, - 'y': 4, - 'z': 5, + 'elem': 0, + 'node': 1, + 'ip': 2, + 'grain': 3, + 'x': 4, + 'y': 5, + 'z': 6, } sortProperties = [] @@ -884,7 +900,7 @@ header = [] standard = ['inc'] + \ {True: ['time'], False:[]}[options.time] + \ - ['elem','node','grain'] + \ + ['elem','node','ip','grain'] + \ {True: ['node.x','node.y','node.z'], False:['ip.x','ip.y','ip.z']}[options.nodalScalar != []] @@ -917,10 +933,10 @@ for incCount,increment in enumerate(increments): # --------------------------- read and map data per group -------------------------------- member = 0 - for i,group in enumerate(groups): + for group in groups: N = 0 # group member counter - for (e,n,g,n_local) in group[1:]: # loop over group members + 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)