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DAMASK_EICMD
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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 2011-05-06 10:00:27 +00:00
parent 56b2b3e572
commit 1928fa816c
1 changed files with 61 additions and 45 deletions
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106
processing/post/postResults
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@ -272,14 +272,7 @@ def ipCoords(elemType, nodalCoordinates):
[ 3.0, 9.0, 3.0, 1.0, 9.0, 27.0, 9.0, 3.0], [ 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], [ 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] ], [ 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], 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] ],
125: [ [ 3.0, 0.0, 0.0, 4.0, 1.0, 4.0], 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, 3.0, 0.0, 4.0, 4.0, 1.0],
[ 0.0, 0.0, 3.0, 1.0, 4.0, 4.0],], [ 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): def substituteLocation(string, mesh, coords):
# #
@ -312,7 +323,8 @@ def substituteLocation(string, mesh, coords):
substitute = string substitute = string
substitute = substitute.replace('elem', str(mesh[0])) substitute = substitute.replace('elem', str(mesh[0]))
substitute = substitute.replace('node', str(mesh[1])) 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('x', '%.6g'%coords[0])
substitute = substitute.replace('y', '%.6g'%coords[1]) substitute = substitute.replace('y', '%.6g'%coords[1])
substitute = substitute.replace('z', '%.6g'%coords[2]) 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'. 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: Example:
1) get averaged results in slices perpendicular to x for all positive y coordinates 1) get averaged results in slices perpendicular to x for all positive y coordinates
--filter 'y >= 0.0' --separation x --map 'avg' --filter 'y >= 0.0' --separation x --map 'avg'
@ -783,84 +795,88 @@ if options.nodalScalar:
myNodeID = p.node_id(n) myNodeID = p.node_id(n)
myNodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z] myNodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z]
myElemID = 0 myElemID = 0
myIpID = 0
myGrainID = 0 myGrainID = 0
# --- filter valid locations # --- 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:... if filter != '' and not eval(filter): # for all filter expressions that are not true:...
continue # ... ignore this data point and continue with next continue # ... ignore this data point and continue with next
# --- group data locations # --- 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 if grp not in index: # create a new group if not yet present
index[grp] = groupCount 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 groupCount += 1
groups[index[grp]][0][:3] = mapIncremental('','unique', groups[index[grp]][0][:4] = mapIncremental('','unique',
len(groups[index[grp]])-1, len(groups[index[grp]])-1,
groups[index[grp]][0][:3], groups[index[grp]][0][:4],
[myElemID,myNodeID,myGrainID]) # keep only if unique average location [myElemID,myNodeID,myIpID,myGrainID]) # keep only if unique average location
groups[index[grp]][0][3:] = mapIncremental('','avg', groups[index[grp]][0][4:] = mapIncremental('','avg',
len(groups[index[grp]])-1, len(groups[index[grp]])-1,
groups[index[grp]][0][3:], groups[index[grp]][0][4:],
myNodeCoordinates) # incrementally update average location myNodeCoordinates) # incrementally update average location
groups[index[grp]].append([myElemID,myNodeID,myGrainID,0]) # append a new list defining each group member groups[index[grp]].append([myElemID,myNodeID,myIpID,myGrainID,0]) # append a new list defining each group member
memberCount += 1 memberCount += 1
else: else:
for e in xrange(stat['NumberOfElements']): 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: if e%1000 == 0:
bg.set_message('scan elem %i...'%e) bg.set_message('scan elem %i...'%e)
myElemID = p.element_id(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)))) 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))))
for n,myNodeID in enumerate(myNodeIDs): 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)) for g in range(('GrainCount' in stat['IndexOfLabel'] and int(p.element_scalar(e, stat['IndexOfLabel']['GrainCount'])[0].value))
or 1): or 1):
myGrainID = g + 1 myGrainID = g + 1
# --- filter valid locations # --- 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:... if filter != '' and not eval(filter): # for all filter expressions that are not true:...
continue # ... ignore this data point and continue with next continue # ... ignore this data point and continue with next
# --- group data locations # --- 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 if grp not in index: # create a new group if not yet present
index[grp] = groupCount 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 groupCount += 1
groups[index[grp]][0][:3] = mapIncremental('','unique', groups[index[grp]][0][:4] = mapIncremental('','unique',
len(groups[index[grp]])-1, len(groups[index[grp]])-1,
groups[index[grp]][0][:3], groups[index[grp]][0][:4],
[myElemID,myNodeID,myGrainID]) # keep only if unique average location [myElemID,myNodeID,myIpID,myGrainID]) # keep only if unique average location
groups[index[grp]][0][3:] = mapIncremental('','avg', groups[index[grp]][0][4:] = mapIncremental('','avg',
len(groups[index[grp]])-1, len(groups[index[grp]])-1,
groups[index[grp]][0][3:], groups[index[grp]][0][4:],
myIpCoordinates[n]) # incrementally update average location 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]].append([myElemID,myNodeID,myIpID,myGrainID,n]) # append a new list defining each group member
memberCount += 1 memberCount += 1
# --------------------------- sort groups -------------------------------- # --------------------------- sort groups --------------------------------
where = { where = {
'elem': 0, 'elem': 0,
'node': 1, 'node': 1,
'grain': 2, 'ip': 2,
'x': 3, 'grain': 3,
'y': 4, 'x': 4,
'z': 5, 'y': 5,
'z': 6,
} }
sortProperties = [] sortProperties = []
@ -884,7 +900,7 @@ header = []
standard = ['inc'] + \ standard = ['inc'] + \
{True: ['time'], {True: ['time'],
False:[]}[options.time] + \ False:[]}[options.time] + \
['elem','node','grain'] + \ ['elem','node','ip','grain'] + \
{True: ['node.x','node.y','node.z'], {True: ['node.x','node.y','node.z'],
False:['ip.x','ip.y','ip.z']}[options.nodalScalar != []] 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 -------------------------------- # --------------------------- read and map data per group --------------------------------
member = 0 member = 0
for i,group in enumerate(groups): for group in groups:
N = 0 # group member counter 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 member += 1
if member%1000 == 0: if member%1000 == 0:
time_delta = ((len(increments)*memberCount)/float(member+incCount*memberCount)-1.0)*(time.time()-time_start) time_delta = ((len(increments)*memberCount)/float(member+incCount*memberCount)-1.0)*(time.time()-time_start)