DAMASK_EICMD/processing/post/postResults.py

1174 lines
48 KiB
Python
Executable File

#!/usr/bin/env python2.7
# -*- coding: UTF-8 no BOM -*-
import os,sys,math,re,time,struct
import damask
from optparse import OptionParser, OptionGroup
scriptName = os.path.splitext(os.path.basename(__file__))[0]
scriptID = ' '.join([scriptName,damask.version])
fileExtensions = { \
'marc': ['.t16',],
'spectral': ['.spectralOut',],
}
# -----------------------------
class vector: # mimic py_post node object
x,y,z = [None,None,None]
def __init__(self,coords):
self.x = coords[0]
self.y = coords[1]
self.z = coords[2]
# -----------------------------
class element: # mimic py_post element object
items = []
type = None
def __init__(self,nodes,type):
self.items = nodes
self.type = type
# -----------------------------
class elemental_scalar: # mimic py_post element_scalar object
id = None
value = None
def __init__(self,node,value):
self.id = node
self.value = value
# -----------------------------
class MPIEspectral_result: # mimic py_post result object
file = None
dataOffset = 0
N_elemental_scalars = 0
grid = [0,0,0]
size = [0.0,0.0,0.0]
theTitle = ''
wd = ''
geometry = ''
extrapolate = ''
N_loadcases = 0
N_increments = 0
N_positions = 0
_frequencies = []
_increments = []
_times = []
increment = 0
startingIncrement = 0
position = 0
# this is a dummy at the moment, we need to parse the load file and figure out what time a particular increment corresponds to
time = 0.0
N_nodes = 0
N_node_scalars = 0
N_elements = 0
N_element_scalars = 0
N_element_tensors = 0
def __init__(self,filename):
self.file = open(filename, 'rb')
self.filesize = os.path.getsize(filename)
self.dataOffset = 0
while self.dataOffset < self.filesize:
self.file.seek(self.dataOffset)
if self.file.read(3) == 'eoh': break
self.dataOffset += 1
self.dataOffset += 7
#search first for the new keywords with ':', if not found try to find the old ones
self.theTitle = self._keyedString('load:')
if self.theTitle is None:
self.theTitle = self._keyedString('load')
self.wd = self._keyedString('workingdir:')
if self.wd is None:
self.wd = self._keyedString('workingdir')
self.geometry = self._keyedString('geometry:')
if self.geometry is None:
self.geometry = self._keyedString('geometry')
self.N_loadcases = self._keyedPackedArray('loadcases:',count=1,type='i')[0]
if self.N_loadcases is None:
self.N_loadcases = self._keyedPackedArray('loadcases',count=1,type='i')[0]
self._frequencies = self._keyedPackedArray('frequencies:',count=self.N_loadcases,type='i')
if all ( i is None for i in self._frequencies):
self._frequencies = self._keyedPackedArray('frequencies',count=self.N_loadcases,type='i')
self._increments = self._keyedPackedArray('increments:',count=self.N_loadcases,type='i')
if all (i is None for i in self._increments):
self._increments = self._keyedPackedArray('increments',count=self.N_loadcases,type='i')
self.startingIncrement = self._keyedPackedArray('startingIncrement:',count=1,type='i')[0]
if self.startingIncrement is None:
self.startingIncrement = self._keyedPackedArray('startingIncrement',count=1,type='i')[0]
self._times = self._keyedPackedArray('times:',count=self.N_loadcases,type='d')
if all (i is None for i in self._times):
self._times = self._keyedPackedArray('times',count=self.N_loadcases,type='d')
self._logscales = self._keyedPackedArray('logscales:',count=self.N_loadcases,type='i')
if all (i is None for i in self._logscales):
self._logscales = self._keyedPackedArray('logscales',count=self.N_loadcases,type='i')
self.size = self._keyedPackedArray('size:',count=3,type='d')
if self.size == [None,None,None]: # no 'size' found, try legacy alias 'dimension'
self.size = self._keyedPackedArray('dimension',count=3,type='d')
self.grid = self._keyedPackedArray('grid:',count=3,type='i')
if self.grid == [None,None,None]: # no 'grid' found, try legacy alias 'resolution'
self.grid = self._keyedPackedArray('resolution',count=3,type='i')
self.N_nodes = (self.grid[0]+1)*(self.grid[1]+1)*(self.grid[2]+1)
self.N_elements = self.grid[0] * self.grid[1] * self.grid[2]
self.N_element_scalars = self._keyedPackedArray('materialpoint_sizeResults:',count=1,type='i')[0]
if self.N_element_scalars is None:
self.N_element_scalars = self._keyedPackedArray('materialpoint_sizeResults',count=1,type='i')[0]
self.N_positions = (self.filesize-self.dataOffset)/(self.N_elements*self.N_element_scalars*8)
self.N_increments = 1 # add zero'th entry
for i in range(self.N_loadcases):
self.N_increments += self._increments[i]//self._frequencies[i]
# parameters for file handling depending on output format
if options.legacy:
self.tagLen=8
self.fourByteLimit = 2**31 -1 -8
else:
self.tagLen=0
self.expectedFileSize = self.dataOffset+self.N_increments*(self.tagLen+self.N_elements*self.N_element_scalars*8)
if options.legacy: self.expectedFileSize+=self.expectedFileSize//self.fourByteLimit*8 # add extra 8 bytes for additional headers at 4 GB limits
if self.expectedFileSize != self.filesize:
print('\n**\n* Unexpected file size. Incomplete simulation or file corrupted!\n**')
def __str__(self):
"""Summary of results file"""
return '\n'.join([
'workdir: %s'%self.wd,
'geometry: %s'%self.geometry,
'loadcases: %i'%self.N_loadcases,
'grid: %s'%(','.join(map(str,self.grid))),
'size: %s'%(','.join(map(str,self.size))),
'header size: %i'%self.dataOffset,
'actual file size: %i'%self.filesize,
'expected file size: %i'%self.expectedFileSize,
'positions in file : %i'%self.N_positions,
'starting increment: %i'%self.startingIncrement,
]
)
def locateKeyValue(self,identifier):
key = {'name':None,'pos':None}
name = ''
filepos=0 # start at the beginning
while name != identifier and filepos < self.dataOffset: # stop searching when found or when reached end of header
self.file.seek(filepos)
# read the starting tag in front of the keyword (Fortran indicates start and end of writing by a 4 byte tag indicating the length of the following data)
dataLen=struct.unpack('i',self.file.read(4))[0]
name = self.file.read(len(identifier)) # anticipate identifier
start=filepos+(4+len(identifier)) # position of the values for the found key
filepos=filepos+(4+dataLen+4) # forward to next keyword
if name==identifier: # found the correct name
key['pos'] = start # save position
key['name'] = name
return key
def _keyedPackedArray(self,identifier,count = 3,type = 'd',default = None):
bytecount = {'d': 8,'i': 4}
values = [default]*count
key = self.locateKeyValue(identifier)
if key['name'] == identifier and key['pos'] is not None:
self.file.seek(key['pos'])
for i in range(count):
values[i] = struct.unpack(type,self.file.read(bytecount[type]))[0]
return values
def _keyedString(self,identifier,default=None):
value = default
self.file.seek(0)
m = re.search(r'(.{4})%s(.*?)\1'%identifier,self.file.read(self.dataOffset),re.DOTALL)
if m:
value = m.group(2)
return value
def title(self):
return self.theTitle
def moveto(self,pos):
self.position = pos
self.increment = 0
self.time = 0.0
p = pos
for l in range(self.N_loadcases):
if p <= self._increments[l]//self._frequencies[l]:
break
else:
self.increment += self._increments[l]
self.time += self._times[l]
p -= self._increments[l]//self._frequencies[l]
self.increment += self._frequencies[l] * p
if self._logscales[l] > 0: # logarithmic time scale
if l == 0: self.time = 2**(self._increments[l] - (1+self._frequencies[l]*p)) * self._times[l] # first loadcase
else: self.time *= ((self.time + self._times[l])/self.time)**((1+self._frequencies[l]*p)/self._increments[l]) # any subsequent loadcase
else: # linear time scale
self.time += self._times[l]/self._increments[l] * self._frequencies[l] * p
def extrapolation(self,value):
self.extrapolate = value
def node_sequence(self,n):
return n-1
def node_id(self,n):
return n+1
def node(self,n):
a = self.grid[0]+1
b = self.grid[1]+1
c = self.grid[2]+1
return vector([self.size[0] * (n%a) / self.grid[0],
self.size[1] * ((n/a)%b) / self.grid[1],
self.size[2] * ((n/a/b)%c) / self.grid[2],
])
def element_sequence(self,e):
return e-1
def element_id(self,e):
return e+1
def element(self,e):
a = self.grid[0]+1
b = self.grid[1]+1
basenode = 1 + e+e/self.grid[0] + e/self.grid[0]/self.grid[1]*a
basenode2 = basenode+a*b
return (element([basenode ,basenode +1,basenode +a+1,basenode +a,
basenode2 ,basenode2+1,basenode2+a+1,basenode2+a,
],117))
def increments(self):
return self.N_positions
def nodes(self):
return self.N_nodes
def node_scalars(self):
return self.N_node_scalars
def elements(self):
return self.N_elements
def element_scalars(self):
return self.N_element_scalars
def element_scalar(self,e,idx):
if not options.legacy:
incStart = self.dataOffset \
+ self.position*8*self.N_elements*self.N_element_scalars
where = (e*self.N_element_scalars + idx)*8
try:
self.file.seek(incStart+where)
value = struct.unpack('d',self.file.read(8))[0]
except:
print('seeking {}'.format(incStart+where))
print('e {} idx {}'.format(e,idx))
sys.exit(1)
else:
self.fourByteLimit = 2**31 -1 -8
# header & footer + extra header and footer for 4 byte int range (Fortran)
# values
incStart = self.dataOffset \
+ self.position*8*( 1 + self.N_elements*self.N_element_scalars*8//self.fourByteLimit \
+ self.N_elements*self.N_element_scalars)
where = (e*self.N_element_scalars + idx)*8
try:
if where%self.fourByteLimit + 8 >= self.fourByteLimit: # danger of reading into fortran record footer at 4 byte limit
data=''
for i in range(8):
self.file.seek(incStart+where+(where//self.fourByteLimit)*8+4)
data += self.file.read(1)
where += 1
value = struct.unpack('d',data)[0]
else:
self.file.seek(incStart+where+(where//self.fourByteLimit)*8+4)
value = struct.unpack('d',self.file.read(8))[0]
except:
print('seeking {}'.format(incStart+where+(where//self.fourByteLimit)*8+4))
print('e {} idx {}'.format(e,idx))
sys.exit(1)
return [elemental_scalar(node,value) for node in self.element(e).items]
def element_scalar_label(elem,idx):
return 'User Defined Variable %i'%(idx+1)
def element_tensors(self):
return self.N_element_tensors
# -----------------------------
def ipCoords(elemType, nodalCoordinates):
"""Returns IP coordinates for a given element"""
nodeWeightsPerNode = {
7: [ [27.0, 9.0, 3.0, 9.0, 9.0, 3.0, 1.0, 3.0],
[ 9.0, 27.0, 9.0, 3.0, 3.0, 9.0, 3.0, 1.0],
[ 3.0, 9.0, 27.0, 9.0, 1.0, 3.0, 9.0, 3.0],
[ 9.0, 3.0, 9.0, 27.0, 3.0, 1.0, 3.0, 9.0],
[ 9.0, 3.0, 1.0, 3.0, 27.0, 9.0, 3.0, 9.0],
[ 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],
[ 3.0, 1.0, 3.0, 9.0, 9.0, 3.0, 9.0, 27.0] ],
57: [ [27.0, 9.0, 3.0, 9.0, 9.0, 3.0, 1.0, 3.0],
[ 9.0, 27.0, 9.0, 3.0, 3.0, 9.0, 3.0, 1.0],
[ 3.0, 9.0, 27.0, 9.0, 1.0, 3.0, 9.0, 3.0],
[ 9.0, 3.0, 9.0, 27.0, 3.0, 1.0, 3.0, 9.0],
[ 9.0, 3.0, 1.0, 3.0, 27.0, 9.0, 3.0, 9.0],
[ 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],
[ 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] ],
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],],
127: [ [ 45.0, 17.0, 17.0, 17.0],
[ 17.0, 45.0, 17.0, 17.0],
[ 17.0, 17.0, 45.0, 17.0],
[ 17.0, 17.0, 17.0, 45.0],],
136: [ [42.0, 15.0, 15.0, 14.0, 5.0, 5.0],
[15.0, 42.0, 15.0, 5.0, 14.0, 5.0],
[15.0, 15.0, 42.0, 5.0, 5.0, 14.0],
[14.0, 5.0, 5.0, 42.0, 15.0, 15.0],
[ 5.0, 14.0, 5.0, 15.0, 42.0, 15.0],
[ 5.0, 5.0, 14.0, 15.0, 15.0, 42.0] ],
}
Nips = len(nodeWeightsPerNode[elemType])
ipCoordinates = [[0.0,0.0,0.0] for i in range(Nips)]
for ip in range(Nips):
for node in range(len(nodeWeightsPerNode[elemType][ip])):
for i in range(3):
ipCoordinates[ip][i] += nodeWeightsPerNode[elemType][ip][node] * nodalCoordinates[node][i]
for i in range(3):
ipCoordinates[ip][i] /= sum(nodeWeightsPerNode[elemType][ip])
return ipCoordinates
# -----------------------------
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 ],
127: [ 1, 2, 3, 4 ],
136: [ 1, 2, 3, 4, 5, 6 ],
}
return ipPerNode[elemType]
# -----------------------------
def substituteLocation(string, mesh, coords):
"""Do variable interpolation in group and filter strings"""
substitute = string
substitute = substitute.replace('elem', str(mesh[0]))
substitute = substitute.replace('node', str(mesh[1]))
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])
return substitute
# -----------------------------
def heading(glue,parts):
"""Joins pieces from parts by glue. second to last entry in pieces tells multiplicity"""
header = []
for pieces in parts:
if pieces[-2] == 0:
del pieces[-2]
header.append(glue.join(map(str,pieces)))
return header
# -----------------------------
def mapIncremental(label, mapping, N, base, new):
"""
Applies the function defined by "mapping"
(can be either 'min','max','avg', 'sum', or user specified)
to a list of data
"""
theMap = { 'min': lambda n,b,a: a if n==0 else min(b,a),
'max': lambda n,b,a: a if n==0 else max(b,a),
'avg': lambda n,b,a: (n*b+a)/(n+1),
'avgabs': lambda n,b,a: (n*b+abs(a))/(n+1),
'sum': lambda n,b,a: a if n==0 else b+a,
'sumabs': lambda n,b,a: abs(a) if n==0 else b+abs(a),
'unique': lambda n,b,a: a if n==0 or b==a else 'nan'
}
if mapping in theMap:
mapped = map(theMap[mapping],[N]*len(base),base,new) # map one of the standard functions to data
if label.lower() == 'orientation': # orientation is special case:...
orientationNorm = math.sqrt(sum([q*q for q in mapped])) # ...calc norm of average quaternion
mapped = map(lambda x: x/orientationNorm, mapped) # ...renormalize quaternion
else:
try:
mapped = eval('map(%s,[N]*len(base),base,new)'%mapping) # map user defined function to colums in chunks
except:
mapped = ['nan']*len(base)
return mapped
# -----------------------------
def OpenPostfile(name,type,nodal = False):
"""Open postfile with extrapolation mode 'translate'"""
p = {\
'spectral': MPIEspectral_result,\
'marc': post_open,\
}[type](name)
p.extrapolation({True:'linear',False:'translate'}[nodal])
p.moveto(1)
return p
# -----------------------------
def ParseOutputFormat(filename,what,me):
"""Parse .output* files in order to get a list of outputs"""
content = []
format = {'outputs':{},'specials':{'brothers':[]}}
for prefix in ['']+map(str,range(1,17)):
if os.path.exists(prefix+filename+'.output'+what):
try:
file = open(prefix+filename+'.output'+what)
content = file.readlines()
file.close()
break
except:
pass
if content == []: return format # nothing found...
tag = ''
tagID = 0
for line in content:
if re.match("\s*$",line) or re.match("#",line): # skip blank lines and comments
continue
m = re.match("\[(.+)\]",line) # look for block indicator
if m: # next section
tag = m.group(1)
tagID += 1
format['specials']['brothers'].append(tag)
if tag == me or (me.isdigit() and tagID == int(me)):
format['specials']['_id'] = tagID
format['outputs'] = []
tag = me
else: # data from section
if tag == me:
(output,length) = line.split()
output.lower()
if length.isdigit():
length = int(length)
if re.match("\((.+)\)",output): # special data, e.g. (Ngrains)
format['specials'][output] = length
elif length > 0:
format['outputs'].append([output,length])
return format
# -----------------------------
def ParsePostfile(p,filename, outputFormat):
"""
Parse postfile in order to get position and labels of outputs
needs "outputFormat" for mapping of output names to postfile output indices
"""
stat = { \
'IndexOfLabel': {}, \
'Title': p.title(), \
'Extrapolation': p.extrapolate, \
'NumberOfIncrements': p.increments(), \
'NumberOfNodes': p.nodes(), \
'NumberOfNodalScalars': p.node_scalars(), \
'LabelOfNodalScalar': [None]*p.node_scalars() , \
'NumberOfElements': p.elements(), \
'NumberOfElementalScalars': p.element_scalars(), \
'LabelOfElementalScalar': [None]*p.element_scalars() , \
'NumberOfElementalTensors': p.element_tensors(), \
'LabelOfElementalTensor': [None]*p.element_tensors(), \
}
# --- find labels
for labelIndex in range(stat['NumberOfNodalScalars']):
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']: # output format without dedicated names?
stat['IndexOfLabel']['HomogenizationCount'] = stat['IndexOfLabel']['User Defined Variable 1'] # adjust first named entry
if 'HomogenizationCount' in stat['IndexOfLabel']: # does the result file contain relevant user defined output at all?
startIndex = stat['IndexOfLabel']['HomogenizationCount']
stat['LabelOfElementalScalar'][startIndex] = 'HomogenizationCount'
# 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 = 1
for (name,N) in outputFormat['Homogenization']['outputs']:
for i in range(N):
label = {False: '%s'%( name),
True:'%i_%s'%(i+1,name)}[N > 1]
stat['IndexOfLabel'][label] = startIndex + offset
stat['LabelOfElementalScalar'][startIndex + offset] = label
offset += 1
stat['IndexOfLabel']['GrainCount'] = startIndex + offset
stat['LabelOfElementalScalar'][startIndex + offset] = 'GrainCount' # add GrainCount
offset += 1
if '(ngrains)' in outputFormat['Homogenization']['specials']:
for grain in range(outputFormat['Homogenization']['specials']['(ngrains)']):
stat['IndexOfLabel']['%i_CrystalliteCount'%(grain+1)] = startIndex + offset # report crystallite count
stat['LabelOfElementalScalar'][startIndex + offset] = '%i_CrystalliteCount'%(grain+1) # add GrainCount
offset += 1
for (name,N) in outputFormat['Crystallite']['outputs']: # add crystallite outputs
for i in range(N):
label = '%i_'%(grain+1) + ('%i_'%(i+1) if N>1 else '') + name
stat['IndexOfLabel'][label] = startIndex + offset
stat['LabelOfElementalScalar'][startIndex + offset] = label
offset += 1
stat['IndexOfLabel']['%i_ConstitutiveCount'%(grain+1)] = startIndex + offset # report constitutive count
stat['LabelOfElementalScalar'][startIndex + offset] = '%i_ConstitutiveCount'%(grain+1) # add GrainCount
offset += 1
for (name,N) in outputFormat['Constitutive']['outputs']: # add constitutive outputs
for i in range(N):
label = '%i_'%(grain+1) + ('%i_'%(i+1) if N>1 else '') + name
stat['IndexOfLabel'][label] = startIndex + offset
try:
stat['LabelOfElementalScalar'][startIndex + offset] = label
except IndexError:
print('trying to assign {} at position {}+{}'.format(label,startIndex,offset))
sys.exit(1)
offset += 1
return stat
# -----------------------------
def SummarizePostfile(stat,where=sys.stdout,format='marc'):
where.write('\n\n')
where.write('title:\t%s'%stat['Title'] + '\n\n')
where.write('extraplation:\t%s'%stat['Extrapolation'] + '\n\n')
where.write('increments:\t%i'%(stat['NumberOfIncrements']) + '\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=damask.extendableOption, usage='%prog options [file[s]]', 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'.
Filters 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 negative 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 >= 0.0 and y >= 0.0 and x*x + y*y >= R1*R1 and x*x + y*y <= R2*R2'
--map 'lambda n,b,a: n*b+a*a'
User mappings need to be formulated in an incremental fashion for each new data point, a(dd),
and may use the current (incremental) result, b(ase), as well as the number, n(umber),
of already processed data points for evaluation.
""", version = scriptID)
parser.add_option('-i','--info', action='store_true', dest='info',
help='list contents of resultfile')
parser.add_option('-l','--legacy', action='store_true', dest='legacy',
help='data format of spectral solver is in legacy format (no MPI out)')
parser.add_option('-n','--nodal', action='store_true', dest='nodal',
help='data is extrapolated to nodal value')
parser.add_option( '--prefix', dest='prefix',
metavar='string',
help='prefix to result file name')
parser.add_option( '--suffix', dest='suffix',
metavar='string',
help='suffix to result file name')
parser.add_option('-d','--dir', dest='dir',
metavar='string',
help='name of subdirectory to hold output [%default]')
parser.add_option('-s','--split', action='store_true', dest='separateFiles',
help='split output per increment')
parser.add_option('-r','--range', dest='range', type='int', nargs=3,
metavar='int int int',
help='range of positions (or increments) to output (start, end, step) [all]')
parser.add_option('--increments', action='store_true', dest='getIncrements',
help='switch to increment range')
parser.add_option('-m','--map', dest='func',
metavar='string',
help='data reduction mapping [%default] out of min, max, avg, avgabs, sum, sumabs or user-lambda')
parser.add_option('-p','--type', dest='filetype',
metavar = 'string',
help = 'type of result file [auto]')
group_material = OptionGroup(parser,'Material identifier')
group_material.add_option('--homogenization', dest='homog',
help='homogenization identifier (as string or integer [%default])', metavar='string')
group_material.add_option('--crystallite', dest='cryst',
help='crystallite identifier (as string or integer [%default])', metavar='string')
group_material.add_option('--phase', dest='phase',
help='phase identifier (as string or integer [%default])', metavar='string')
group_special = OptionGroup(parser,'Special outputs')
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',
help='condition(s) to filter results [%default]', metavar='string')
group_special.add_option('--separation', action='extend', dest='sep',
help='properties to separate results [%default]', metavar='<string LIST>')
group_special.add_option('--sort', action='extend', dest='sort',
help='properties to sort results [%default]', metavar='<string LIST>')
group_general = OptionGroup(parser,'General outputs')
group_general.add_option('--ns', action='extend', dest='nodalScalar',
help='nodal scalars to extract', metavar='<string LIST>')
group_general.add_option('--es', action='extend', dest='elemScalar',
help='elemental scalars to extract', metavar='<string LIST>')
group_general.add_option('--et', action='extend', dest='elemTensor',
help='elemental tensors to extract', metavar='<string LIST>')
group_general.add_option('--ho', action='extend', dest='homogenizationResult',
help='homogenization results to extract', metavar='<string LIST>')
group_general.add_option('--cr', action='extend', dest='crystalliteResult',
help='crystallite results to extract', metavar='<string LIST>')
group_general.add_option('--co', action='extend', dest='constitutiveResult',
help='constitutive results to extract', metavar='<string LIST>')
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(legacy = False)
parser.set_defaults(nodal = False)
parser.set_defaults(prefix = '')
parser.set_defaults(suffix = '')
parser.set_defaults(dir = 'postProc')
parser.set_defaults(filetype = None)
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(sep = [])
parser.set_defaults(sort = [])
parser.set_defaults(inc = False)
parser.set_defaults(time = False)
parser.set_defaults(separateFiles = False)
parser.set_defaults(getIncrements= False)
(options, files) = parser.parse_args()
# --- basic sanity checks
if files == []:
parser.print_help()
parser.error('no file specified...')
if not os.path.exists(files[0]):
parser.print_help()
parser.error('invalid file "%s" specified...'%files[0])
# --- figure out filetype
if options.filetype is None:
ext = os.path.splitext(files[0])[1]
for theType in fileExtensions.keys():
if ext in fileExtensions[theType]:
options.filetype = theType
break
if options.filetype is not None: options.filetype = options.filetype.lower()
if options.filetype == 'marc': offset_pos = 1
else: offset_pos = 0
# --- more sanity checks
if options.filetype not in ['marc','spectral']:
parser.print_help()
parser.error('file type "%s" not supported...'%options.filetype)
if options.filetype == 'marc':
sys.path.append(damask.solver.Marc().libraryPath())
try:
from py_post import post_open
except:
print('error: no valid Mentat release found')
sys.exit(-1)
else:
def post_open():
return
if options.constitutiveResult and not options.phase:
parser.print_help()
parser.error('constitutive results require phase...')
if options.nodalScalar and ( options.elemScalar or options.elemTensor\
or options.homogenizationResult or options.crystalliteResult or options.constitutiveResult ):
parser.print_help()
parser.error('not allowed to mix nodal with elemental results...')
if not options.nodalScalar: options.nodalScalar = []
if not options.elemScalar: options.elemScalar = []
if not options.elemTensor: options.elemTensor = []
if not options.homogenizationResult: options.homogenizationResult = []
if not options.crystalliteResult: options.crystalliteResult = []
if not options.constitutiveResult: options.constitutiveResult = []
options.sort.reverse()
options.sep.reverse()
# --- start background messaging
bg = damask.util.backgroundMessage()
bg.start()
# --- parse .output and .t16 files
if os.path.splitext(files[0])[1] == '':
filename = files[0]
extension = fileExtensions[options.filetype]
else:
filename = os.path.splitext(files[0])[0]
extension = os.path.splitext(files[0])[1]
outputFormat = {}
me = {
'Homogenization': options.homog,
'Crystallite': options.cryst,
'Constitutive': options.phase,
}
bg.set_message('parsing .output files...')
for what in me:
outputFormat[what] = ParseOutputFormat(filename, what, me[what])
if '_id' not in outputFormat[what]['specials']:
print("\nsection '{}' not found in <{}>".format(me[what], what))
print('\n'.join(map(lambda x:' [%s]'%x, outputFormat[what]['specials']['brothers'])))
bg.set_message('opening result file...')
p = OpenPostfile(filename+extension,options.filetype,options.nodal)
bg.set_message('parsing result file...')
stat = ParsePostfile(p, filename, outputFormat)
if options.filetype == 'marc':
stat['NumberOfIncrements'] -= 1 # t16 contains one "virtual" increment (at 0)
# --- sanity check for output variables
# for mentat variables (nodalScalar,elemScalar,elemTensor) 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','elemScalar','elemTensor','homogenizationResult','crystalliteResult','constitutiveResult']:
if eval('options.%s'%opt):
for label in eval('options.%s'%opt):
if (opt in ['nodalScalar','elemScalar','elemTensor'] and label not in stat['IndexOfLabel'] and label not in ['elements',]) \
or (opt in ['homogenizationResult','crystalliteResult','constitutiveResult'] \
and (not outputFormat[opt[:-6].capitalize()]['outputs'] \
or label not in zip(*outputFormat[opt[:-6].capitalize()]['outputs'])[0])):
parser.error('%s "%s" unknown...'%(opt,label))
# --- output info
if options.info:
if options.filetype == 'marc':
print('\n\nMentat release {}'.format(damask.solver.Marc().version()))
if options.filetype == 'spectral':
print('\n\n{}'.format(p))
SummarizePostfile(stat)
print('\nUser Defined Outputs')
for what in me:
print('\n {}:'.format(what))
for output in outputFormat[what]['outputs']:
print(' {}'.format(output))
sys.exit(0)
# --- build connectivity maps
elementsOfNode = {}
for e in range(stat['NumberOfElements']):
if e%1000 == 0:
bg.set_message('connect elem %i...'%e)
for n in map(p.node_sequence,p.element(e).items):
if n not in elementsOfNode:
elementsOfNode[n] = [p.element_id(e)]
else:
elementsOfNode[n] += [p.element_id(e)]
maxCountElementsOfNode = 0
for l in elementsOfNode.values():
maxCountElementsOfNode = max(maxCountElementsOfNode,len(l))
# --------------------------- build group membership --------------------------------
p.moveto(offset_pos)
index = {}
groups = []
groupCount = 0
memberCount = 0
if options.nodalScalar:
for n in range(stat['NumberOfNodes']):
if n%1000 == 0:
bg.set_message('scan node %i...'%n)
myNodeID = p.node_id(n)
myNodeCoordinates = [p.node(n).x, p.node(n).y, p.node(n).z]
myElemID = 0
myIpID = 0
myGrainID = 0
# generate an expression that is only true for the locations specified by options.filter
filter = substituteLocation(options.filter, [myElemID,myNodeID,myIpID,myGrainID], myNodeCoordinates)
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
# generate a unique key for a group of separated data based on the separation criterium for the location
grp = substituteLocation('#'.join(options.sep), [myElemID,myNodeID,myIpID,myGrainID], myNodeCoordinates)
if grp not in index: # create a new group if not yet present
index[grp] = groupCount
groups.append([[0,0,0,0,0.0,0.0,0.0]]) # initialize with avg location
groupCount += 1
groups[index[grp]][0][:4] = mapIncremental('','unique',
len(groups[index[grp]])-1,
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][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 range(stat['NumberOfElements']):
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, 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
# generates an expression that is only true for the locations specified by options.filter
filter = substituteLocation(options.filter, [myElemID,myNodeID,myIpID,myGrainID], myIpCoordinates[n])
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
# generates a unique key for a group of separated data based on the separation criterium for the location
grp = substituteLocation('#'.join(options.sep), [myElemID,myNodeID,myIpID,myGrainID], myIpCoordinates[n])
if grp not in index: # create a new group if not yet present
index[grp] = groupCount
groups.append([[0,0,0,0,0.0,0.0,0.0]]) # initialize with avg location
groupCount += 1
groups[index[grp]][0][:4] = mapIncremental('','unique',
len(groups[index[grp]])-1,
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][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,
'ip': 2,
'grain': 3,
'x': 4,
'y': 5,
'z': 6,
}
sortProperties = []
for item in options.sep:
if item not in options.sort:
sortProperties.append(item)
theKeys = []
if 'none' not in map(str.lower, options.sort):
for criterium in options.sort + sortProperties:
if criterium in where:
theKeys.append('x[0][%i]'%where[criterium])
sortKeys = eval('lambda x:(%s)'%(','.join(theKeys)))
bg.set_message('sorting groups...')
groups.sort(key = sortKeys) # in-place sorting to save mem
# --------------------------- create output dir --------------------------------
dirname = os.path.abspath(os.path.join(os.path.dirname(filename),options.dir))
if not os.path.isdir(dirname):
os.mkdir(dirname,0o755)
fileOpen = False
assembleHeader = True
header = []
standard = ['inc'] + \
(['time'] if options.time else []) + \
['elem','node','ip','grain','1_pos','2_pos','3_pos']
# --------------------------- loop over positions --------------------------------
bg.set_message('getting map between positions and increments...')
incAtPosition = {}
positionOfInc = {}
for position in range(stat['NumberOfIncrements']):
p.moveto(position+offset_pos)
incAtPosition[position] = p.increment # remember "real" increment at this position
positionOfInc[p.increment] = position # remember position of "real" increment
if not options.range:
options.getIncrements = False
locations = range(stat['NumberOfIncrements']) # process all positions
else:
options.range = list(options.range) # convert to list
if options.getIncrements:
locations = [positionOfInc[x] for x in range(options.range[0],options.range[1]+1,options.range[2])
if x in positionOfInc]
else:
locations = range( max(0,options.range[0]),
min(stat['NumberOfIncrements'],options.range[1]+1),
options.range[2] )
increments = [incAtPosition[x] for x in locations] # build list of increments to process
time_start = time.time()
for incCount,position in enumerate(locations): # walk through locations
p.moveto(position+offset_pos) # wind to correct position
# --------------------------- file management --------------------------------
if options.separateFiles:
if fileOpen:
file.close() # noqa
fileOpen = False
outFilename = eval('"'+eval("'%%s_inc%%0%ii%%s.txt'%(math.log10(max(increments+[1]))+1)")\
+'"%(dirname + os.sep + options.prefix + os.path.split(filename)[1],increments[incCount],options.suffix)')
else:
outFilename = '%s.txt'%(dirname + os.sep + options.prefix + os.path.split(filename)[1] + options.suffix)
if not fileOpen:
file = open(outFilename,'w')
fileOpen = True
file.write('2\theader\n')
file.write(scriptID + '\t' + ' '.join(sys.argv[1:]) + '\n')
headerWritten = False
file.flush()
# --------------------------- read and map data per group --------------------------------
member = 0
for group in groups:
N = 0 # group member counter
for (e,n,i,g,n_local) in group[1:]: # loop over group members
member += 1
if member%1000 == 0:
time_delta = ((len(locations)*memberCount)/float(member+incCount*memberCount)-1.0)*(time.time()-time_start)
bg.set_message('(%02i:%02i:%02i) processing point %i of %i from increment %i (position %i)...'
%(time_delta//3600,time_delta%3600//60,time_delta%60,member,memberCount,increments[incCount],position))
newby = [] # current member's data
if options.nodalScalar:
for label in options.nodalScalar:
if label == 'elements':
length = maxCountElementsOfNode
content = elementsOfNode[p.node_sequence(n)]+[0]*(length-len(elementsOfNode[p.node_sequence(n)]))
else:
length = 1
content = [ p.node_scalar(p.node_sequence(n),stat['IndexOfLabel'][label]) ]
if assembleHeader:
header += heading('_',[[component,''.join( label.split() )]
for component in range(int(length>1),length+int(length>1))])
newby.append({'label':label,
'len':length,
'content':content })
if options.elemScalar:
for label in options.elemScalar:
if assembleHeader:
header += [''.join( label.split() )]
newby.append({'label':label,
'len':1,
'content':[ p.element_scalar(p.element_sequence(e),stat['IndexOfLabel'][label])[n_local].value ]})
if options.elemTensor:
for label in options.elemTensor:
if assembleHeader:
header += heading('.',[[''.join( label.split() ),component]
for component in ['intensity','t11','t22','t33','t12','t23','t13']])
myTensor = p.element_tensor(p.element_sequence(e),stat['IndexOfLabel'][label])[n_local]
newby.append({'label':label,
'len':7,
'content':[ myTensor.intensity,
myTensor.t11, myTensor.t22, myTensor.t33,
myTensor.t12, myTensor.t23, myTensor.t13,
]})
if options.homogenizationResult or \
options.crystalliteResult or \
options.constitutiveResult:
for (label,resultType) in zip(options.homogenizationResult +
options.crystalliteResult +
options.constitutiveResult,
['Homogenization']*len(options.homogenizationResult) +
['Crystallite']*len(options.crystalliteResult) +
['Constitutive']*len(options.constitutiveResult)
):
outputIndex = list(zip(*outputFormat[resultType]['outputs'])[0]).index(label) # find the position of this output in the outputFormat
length = int(outputFormat[resultType]['outputs'][outputIndex][1])
thisHead = heading('_',[[component,''.join( label.split() )] for component in range(int(length>1),length+int(length>1))])
if assembleHeader: header += thisHead
if resultType != 'Homogenization':
thisHead = heading('_',[[g,component,label] for component in range(int(length>1),length+int(length>1))])
try:
newby.append({'label':label,
'len':length,
'content':[ p.element_scalar(p.element_sequence(e),stat['IndexOfLabel'][head])[n_local].value
for head in thisHead ]})
except KeyError:
print('\nDAMASK outputs seem missing from "post" section of the *.dat file!')
sys.exit()
assembleHeader = False
if N == 0:
mappedResult = [float(x) for x in range(len(header))] # init with debug data (should get deleted by *N at N=0)
pos = 0
for chunk in newby:
mappedResult[pos:pos+chunk['len']] = mapIncremental(chunk['label'],options.func,
N,mappedResult[pos:pos+chunk['len']],chunk['content'])
pos += chunk['len']
N += 1
# --- write data row to file ---
if not headerWritten:
file.write('\t'.join(standard + header) + '\n')
headerWritten = True
file.write('\t'.join(map(str,[p.increment] + \
{True:[p.time],False:[]}[options.time] + \
group[0] + \
mappedResult)
) + '\n')
if fileOpen:
file.close()
# --------------------------- DONE --------------------------------