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DAMASK_EICMD
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1) speed-up of elementTensor output 

2) added estimate of remaining time
This commit is contained in:
Philip Eisenlohr 2010-11-02 15:45:23 +00:00
parent e80e055c75
commit 796bffee2e
1 changed files with 49 additions and 20 deletions
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69
processing/post/postResults
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@ -540,33 +540,61 @@ fileOpen = False
assembleHeader = True
header = []
for increment in increments:
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 = [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)
bg.set_message('read data from increment %i...'%increment)
time_delta = (len(increments)-incCount)*(time.time()-time_start)/(incCount+1)
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']):
nodeID = p.node_id(n)
nodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z]
elemID = 0
grainID = 0
myNodeID = nodeID[n]
myNodeCoordinates = nodeCoordinates[n]
myElemID = 0
myGrainID = 0
# --- filter valid locations
filter = substituteLocation(options.filter, [elemID,nodeID,grainID], nodeCoordinates) # generates an expression that is only true for the locations specified by options.filter
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), [elemID,nodeID,grainID], nodeCoordinates) # generates a unique key for a group of separated data based on the separation criterium for the location
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': [elemID,nodeID,grainID] + nodeCoordinates,
'content': [myElemID,myNodeID,myGrainID] + myNodeCoordinates,
}) # first entry in this list always contains the location data
# --- get data from t16 file
@ -583,30 +611,28 @@ for increment in increments:
else:
for e in range(stat['NumberOfElements']):
nodeCoordinates = map(lambda node: [node.x, node.y, node.z], map(p.node, map(p.node_sequence,p.element(e).items)))
ipCoordinates = ipCoords(p.element(e).type, nodeCoordinates)
elemID = p.element_id(e)
for n in range(p.element(e).len):
nodeID = p.element(e).items[n]
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):
grainID = g + 1
myGrainID = g + 1
# --- filter valid locations
filter = substituteLocation(options.filter, [elemID,nodeID,grainID], ipCoordinates[n]) # generates an expression that is only true for the locations specified by options.filter
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), [elemID,nodeID,grainID], ipCoordinates[n]) # generates a unique key for a group of separated data based on the separation criterium for the location
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': [elemID,nodeID,grainID] + ipCoordinates[n],
'content': [myElemID,myNodeID,myGrainID] + myIpCoordinates[n],
}) # first entry in this list always contains the location data
# --- get data from t16 file
@ -624,10 +650,13 @@ for increment in increments:
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': [ eval("p.element_tensor(e,stat['IndexOfLabel'][label])[n].%s"%component)
for component in ['intensity','t11','t22','t33','t12','t23','t13'] ],
'content': [ myTensor.intensity,
myTensor.t11, myTensor.t22, myTensor.t33,
myTensor.t12, myTensor.t23, myTensor.t13,
],
})
if options.homogenizationResult: