added possibility to fit to more than one yield point, now using linear interpolation between around given threshold value
This commit is contained in:
Martin Diehl
parent
1dc14ccc85
commit
e637f6344c
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@ -39,7 +39,6 @@ def vonMises(x, S_y):
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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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@ -102,18 +101,14 @@ class Criterion(object):
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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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print 'Mises', popt
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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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print 'Hill48', popt
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except Exception as detail:
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print detail
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pass
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@ -177,33 +172,40 @@ def doSim(delay,thread):
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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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if options.fitting =='equivalentStrain':
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thresholdKey = 'Mises(ln(V))'
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elif options.fitting =='totalshear':
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thresholdKey = 'totalshear'
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s.acquire()
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for l in [thresholdKey,'1_Cauchy']:
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if l not in table.labels: print '%s not found'%l
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s.release()
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table.data_readArray(['%i_Cauchy'%(i+1) for i in xrange(9)]+[thresholdKey])
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line = 0
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lines = np.shape(table.data)[0]
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yieldStress=[None for i in xrange(int(options.yieldValue[2]))]
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for i,j in enumerate(np.linspace(options.yieldValue[0],options.yieldValue[1],options.yieldValue[2])):
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while line < lines:
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if table.data[line,9]>= j:
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upper,lower = table.data[line,9],table.data[line-1,9] # weights for linear interpolation
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yieldStress[i] = table.data[line, 0:9] * (upper-j)/(upper-lower) \
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+ table.data[line-1,0:9] * (j-lower)/(upper-lower) # linear interpolation of stress values
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break
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else:
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line+=1
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s.acquire()
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global stressAll
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print('starting fitting for sim %i from %s'%(me,thread))
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try:
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yieldStress
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except NameError:
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for i in xrange(int(options.yieldValue[2])):
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stressAll[i]=np.append(yieldStress[i]/10.e8,stressAll[i])
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myFit.fit(stressAll[i].reshape(len(stressAll[i])//9,9).transpose())
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except Exception:
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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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@ -232,12 +234,12 @@ 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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parser.add_option('--criterion', dest='criterion', 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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parser.add_option('--fitting', dest='fitting', 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('--yieldvalue', dest='yieldValue', type='float', nargs=3,
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help='yield points: start; end; count %default', metavar='float float int')
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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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@ -247,8 +249,8 @@ parser.add_option('--threads', dest='threads', type='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(yieldValue = (0.002,0.002,1))
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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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@ -264,15 +266,21 @@ if options.threads<1:
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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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parser.error('invalid maximum number of simulations (below minimum)')
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if options.yieldValue[0]>options.yieldValue[1]:
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parser.error('invalid yield start (below yield end)')
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if options.yieldValue[2] != int(options.yieldValue[2]):
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parser.error('count must be an integer')
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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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if not os.path.isfile('material.config'):
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print('material.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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stressAll=[np.zeros(0,'d').reshape(0,0) for i in xrange(int(options.yieldValue[2]))]
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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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