289 lines
10 KiB
Python
Executable File
289 lines
10 KiB
Python
Executable File
#!/usr/bin/python
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# -*- coding: UTF-8 no BOM -*-
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import threading,time,os,subprocess,shlex,string
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import numpy as np
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from scipy.optimize import curve_fit
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from scipy.linalg import svd
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from optparse import OptionParser
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import damask
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scriptID = '$Id$'
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scriptName = scriptID.split()[1]
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def execute(cmd,dir='./'):
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initialPath=os.getcwd()
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os.chdir(dir)
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out = ''
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line = True
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process = subprocess.Popen(shlex.split(cmd),stdout=subprocess.PIPE,stderr = subprocess.STDOUT)
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while line:
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line = process.stdout.readline()
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out += line
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os.chdir(initialPath)
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def Hill48(x, F,G,H,L,M,N):
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a = F*(x[4]-x[8])**2.0 + G*(x[8]-x[0])**2.0 + H*(x[0]-x[4])**2.0 + \
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2.0*L*x[1]**2.0 + 2.0*M*x[2]**2.0 + 2.0*N*x[5]**2.0 -1.0
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return a.ravel()
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def vonMises(x, S_y):
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sv=np.zeros(0,'d')
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for i in xrange(np.shape(x)[1]):
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U, l, Vh = svd(np.array(x[:,i]).reshape(3,3))
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sv = np.append(sv,l)
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sv = sv.reshape(np.shape(x)[1],3)
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ooo = (sv[:,2]-sv[:,1])**2+(sv[:,1]-sv[:,0])**2+(sv[:,0]-sv[:,2])**2-2*S_y**2
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return ooo.ravel()
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fittingCriteria = {
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'vonMises':{'fit':np.ones(1,'d'),'err':np.inf},
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'hill48' :{'fit':np.ones(6,'d'),'err':np.inf},
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'worst' :{'err':np.inf},
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'best' :{'err':np.inf}
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}
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thresholdParameter = ['totalshear','equivalentStrain']
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#---------------------------------------------------------------------------------------------------
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class Loadcase():
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#---------------------------------------------------------------------------------------------------
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'''
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Class for generating load cases for the spectral solver
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'''
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# ------------------------------------------------------------------
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def __init__(self,finalStrain,incs,time):
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print('using the random load case generator')
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self.finalStrain = finalStrain
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self.incs = incs
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self.time = time
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def getLoadcase(self,N=0):
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defgrad=['*']*9
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stress =[0]*9
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values=(np.random.random_sample(9)-.5)*self.finalStrain*2
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main=np.array([0,4,8])
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np.random.shuffle(main)
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for i in main[:2]: # fill 2 out of 3 main entries
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defgrad[i]=1.+values[i]
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stress[i]='*'
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for off in [[1,3,0],[2,6,0],[5,7,0]]: # fill 3 off-diagonal pairs of defgrad (1 or 2 entries)
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off=np.array(off)
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np.random.shuffle(off)
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for i in off[0:2]:
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if i != 0:
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defgrad[i]=values[i]
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stress[i]='*'
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return 'f '+' '.join(str(c) for c in defgrad)+\
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' p '+' '.join(str(c) for c in stress)+\
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' incs %s'%self.incs+\
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' time %s'%self.time
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#---------------------------------------------------------------------------------------------------
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class Criterion(object):
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#---------------------------------------------------------------------------------------------------
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'''
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Fitting to certain criterion
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'''
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def __init__(self,name='worst'):
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self.name = name
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self.results = fittingCriteria
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if self.name.lower() not in map(str.lower, self.results.keys()):
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raise Exception('no suitable fitting criterion selected')
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else:
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print('fitting to the %s criterion'%name)
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def fit(self,stress):
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try:
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#popt1[0], pcov = curve_fit(self.vonMises, stress, np.zeros(np.shape(stress)[1]),p0=popt1[0]) #use guessing?
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popt, pcov = curve_fit(vonMises, stress, np.zeros(np.shape(stress)[1]))
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perr = np.sqrt(np.diag(pcov))
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print 'Mises', popt, 'normalized Standard deviation', np.array(perr)/np.array(popt) #Variationskoeffizient
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except Exception as detail:
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print detail
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pass
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try:
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popt, pcov = curve_fit(Hill48, stress, np.zeros(np.shape(stress)[1]))
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#popt1[1], pcov = curve_fit(self.Hill48, stress, np.zeros(np.shape(stress)[1]),p0=popt1[1]) #use guessing?
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perr = np.sqrt(np.diag(pcov))
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print 'Hill48', popt, 'normalized Standard deviation', np.array(perr)/np.array(popt) #Variationskoeffizient
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except Exception as detail:
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print detail
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pass
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#---------------------------------------------------------------------------------------------------
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class myThread (threading.Thread):
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#---------------------------------------------------------------------------------------------------
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'''
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Runner class
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'''
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def __init__(self, threadID):
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threading.Thread.__init__(self)
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self.threadID = threadID
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def run(self):
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s.acquire()
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conv=converged()
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s.release()
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while not conv:
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doSim(4.,self.name)
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s.acquire()
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conv=converged()
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s.release()
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def doSim(delay,thread):
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s.acquire()
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me=getLoadcase()
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if not os.path.isfile('%s.load'%me):
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print('generating loadcase for sim %s from %s'%(me,thread))
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f=open('%s.load'%me,'w')
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f.write(myLoad.getLoadcase(me))
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f.close()
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s.release()
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else: s.release()
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s.acquire()
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if not os.path.isfile('%s_%i.spectralOut'%(options.geometry,me)):
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print('starting simulation %s from %s'%(me,thread))
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s.release()
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execute('DAMASK_spectral -g %s -l %i'%(options.geometry,me))
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else: s.release()
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s.acquire()
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if not os.path.isfile('./postProc/%s_%i.txt'%(options.geometry,me)):
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print('starting post processing for sim %i from %s'%(me,thread))
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s.release()
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try:
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execute('postResults --cr f,p --co totalshear %s_%i.spectralOut'%(options.geometry,me))
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except:
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execute('postResults --cr f,p %s_%i.spectralOut'%(options.geometry,me))
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execute('addCauchy ./postProc/%s_%i.txt'%(options.geometry,me))
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execute('addStrainTensors -l -v ./postProc/%s_%i.txt'%(options.geometry,me))
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execute('addMises -s Cauchy -e ln(V) ./postProc/%s_%i.txt'%(options.geometry,me))
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else: s.release()
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s.acquire()
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print('reading values for sim %i from %s'%(me,thread))
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s.release()
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refFile = open('./postProc/%s_%i.txt'%(options.geometry,me))
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table = damask.ASCIItable(refFile)
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table.head_read()
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if options.fitting =='equivalentStrain' :
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for l in ['Mises(ln(V))','1_Cauchy']:
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if l not in table.labels: print '%s not found'%l
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while table.data_read():
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if float(table.data[table.labels.index('Mises(ln(V))')]) > options.yieldValue:
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yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d')/10.e8
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break
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elif options.fitting =='totalshear' :
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for l in ['totalshear','1_Cauchy']:
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if l not in table.labels: print '%s not found'%l
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while table.data_read():
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if float(table.data[table.labels.index('totalshear')]) > options.yieldValue:
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yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d')/10.e8
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break
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s.acquire()
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try:
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yieldStress
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except NameError:
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print('could not fit for sim %i from %s'%(me,thread))
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s.release()
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return
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global stressAll
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stressAll=np.append(yieldStress,stressAll)
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print('starting fitting for sim %i from %s'%(me,thread))
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myFit.fit(stressAll.reshape(len(stressAll)//9,9).transpose())
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s.release()
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def getLoadcase():
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global N_simulations
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N_simulations+=1
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return N_simulations
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def converged():
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global N_simulations
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if N_simulations < options.max:
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return False
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else:
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return True
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# --------------------------------------------------------------------
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# MAIN
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# --------------------------------------------------------------------
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parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """
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Performs calculations with various loads on given geometry file and fits yield surface.
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""", version=string.replace(scriptID,'\n','\\n')
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)
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parser.add_option('-l','--load' , dest='load', type='float', nargs=3,
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help='load: final strain; increments; time %default', metavar='float int float')
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parser.add_option('-g','--geometry', dest='geometry', type='string',
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help='name of the geometry file [%default]', metavar='string')
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parser.add_option('--criterion', dest='criterion', action='store', choices=fittingCriteria.keys(),
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help='criterion for stopping simulations [%default]', metavar='string')
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parser.add_option('--fitting', dest='fitting', action='store', choices=thresholdParameter,
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help='yield criterion [%default]', metavar='string')
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parser.add_option('--yieldvalue', dest='yieldValue', action='store', type='float',
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help='yield criterion [%default]', metavar='float')
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parser.add_option('--min', dest='min', type='int',
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help='minimum number of simulations [%default]', metavar='int')
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parser.add_option('--max', dest='max', type='int',
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help='maximum number of iterations [%default]', metavar='int')
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parser.add_option('--threads', dest='threads', type='int',
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help='number of parallel executions [%default]', metavar='int')
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parser.set_defaults(min = 12)
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parser.set_defaults(max = 30)
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parser.set_defaults(threads = 4)
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parser.set_defaults(yieldValue = 0.002)
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parser.set_defaults(load = [0.010,100,100.0])
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parser.set_defaults(criterion = 'worst')
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parser.set_defaults(fitting = 'totalshear')
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parser.set_defaults(geometry = '20grains16x16x16')
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options = parser.parse_args()[0]
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if not os.path.isfile(options.geometry+'.geom'):
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parser.error('geometry file %s.geom not found'%options.geometry)
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if not os.path.isfile('material.config'):
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parser.error('material.config file not found')
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if options.threads<1:
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parser.error('invalid number of threads %i'%options.threads)
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if options.min<0:
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parser.error('invalid minimum number of simulations %i'%options.min)
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if options.max<options.min:
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parser.error('invalid maximumb number of simulations (below minimum)')
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if not os.path.isfile('numerics.config'):
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print('numerics.config file not found')
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N_simulations=0
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s=threading.Semaphore(1)
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stressAll=np.zeros(0,'d').reshape(0,0)
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myLoad = Loadcase(options.load[0],options.load[1],options.load[2])
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myFit = Criterion(options.criterion)
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threads=[]
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for i in range(options.threads):
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threads.append(myThread(i))
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threads[i].start()
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for i in range(options.threads):
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threads[i].join()
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print 'finished fitting to yield criteria'
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