#!/usr/bin/env python from optparse import OptionParser import damask import os,sys,math,re,random,string import numpy as np scriptID = string.replace('$Id$','\n','\\n') scriptName = scriptID.split()[1] # --- helper functions --- def binAsBins(bin,intervals): """ explode compound bin into 3D bins list """ bins = [0]*3 bins[0] = (bin//(intervals[1] * intervals[2])) % intervals[0] bins[1] = (bin//intervals[2]) % intervals[1] bins[2] = bin % intervals[2] return bins def binsAsBin(bins,intervals): """ implode 3D bins into compound bin """ return (bins[0]*intervals[1] + bins[1])*intervals[2] + bins[2] def EulersAsBins(Eulers,intervals,deltas,center): """ return list of Eulers translated into 3D bins list """ return [int((euler+(0.5-center)*delta)//delta)%interval \ for euler,delta,interval in zip(Eulers,deltas,intervals) \ ] def binAsEulers(bin,intervals,deltas,center): """ compound bin number translated into list of Eulers """ Eulers = [0.0]*3 Eulers[2] = (bin%intervals[2] + center)*deltas[2] Eulers[1] = (bin//intervals[2]%intervals[1] + center)*deltas[1] Eulers[0] = (bin//(intervals[2]*intervals[1]) + center)*deltas[0] return Eulers def directInvRepetitions(probability,scale): """ calculate number of samples drawn by direct inversion """ nDirectInv = 0 for bin in range(len(probability)): # loop over bins nDirectInv += int(round(probability[bin]*scale)) # calc repetition return nDirectInv # ---------------------- sampling methods ----------------------------------------------------------------------- # ----- efficient algorithm --------- def directInversion (ODF,nSamples): """ ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """ nOptSamples = max(ODF['nNonZero'],nSamples) # random subsampling if too little samples requested nInvSamples = 0 repetition = [None]*ODF['nBins'] probabilityScale = nOptSamples # guess scaleLower = 0.0 nInvSamplesLower = 0 scaleUpper = float(nOptSamples) incFactor = 1.0 nIter = 0 nInvSamplesUpper = directInvRepetitions(ODF['dV_V'],scaleUpper) while (\ (scaleUpper-scaleLower > scaleUpper*1e-15 or nInvSamplesUpper < nOptSamples) and \ nInvSamplesUpper != nOptSamples \ ): # closer match required? if nInvSamplesUpper < nOptSamples: scaleLower,scaleUpper = scaleUpper,scaleUpper+incFactor*(scaleUpper-scaleLower)/2.0 incFactor *= 2.0 nInvSamplesLower,nInvSamplesUpper = nInvSamplesUpper,directInvRepetitions(ODF['dV_V'],scaleUpper) else: scaleUpper = (scaleLower+scaleUpper)/2.0 incFactor = 1.0 nInvSamplesUpper = directInvRepetitions(ODF['dV_V'],scaleUpper) nIter += 1 file['croak'].write('%i:(%12.11f,%12.11f) %i <= %i <= %i\n'\ %(nIter,scaleLower,scaleUpper,nInvSamplesLower,nOptSamples,nInvSamplesUpper)) nInvSamples = nInvSamplesUpper scale = scaleUpper file['croak'].write('created set of %i samples (%12.11f) with scaling %12.11f delivering %i\n'\ %(nInvSamples,float(nInvSamples)/nOptSamples-1.0,scale,nSamples)) repetition = [None]*ODF['nBins'] # preallocate and clear for bin in range(ODF['nBins']): # loop over bins repetition[bin] = int(round(ODF['dV_V'][bin]*scale)) # calc repetition # build set set = [None]*nInvSamples i = 0 for bin in range(ODF['nBins']): set[i:i+repetition[bin]] = [bin]*repetition[bin] # fill set with bin, i.e. orientation i += repetition[bin] # advance set counter orientations = [None]*nSamples reconstructedODF = [0.0]*ODF['nBins'] unitInc = 1.0/nSamples for j in range(nSamples): if (j == nInvSamples-1): ex = j else: ex = int(round(random.uniform(j+0.5,nInvSamples-0.5))) bin = set[ex] bins = binAsBins(bin,ODF['interval']) Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center']) orientations[j] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) ) reconstructedODF[bin] += unitInc set[ex] = set[j] # exchange orientations return orientations, reconstructedODF # ----- trial and error algorithms --------- def MonteCarloEulers (ODF,nSamples): """ ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """ countMC = 0 maxdV_V = max(ODF['dV_V']) orientations = [None]*nSamples reconstructedODF = [0.0]*ODF['nBins'] unitInc = 1.0/nSamples for j in range(nSamples): MC = maxdV_V*2.0 bin = 0 while MC > ODF['dV_V'][bin]: countMC += 1 MC = maxdV_V*random.random() Eulers = [limit*random.random() for limit in ODF['limit']] bins = EulersAsBins(Eulers,ODF['interval'],ODF['delta'],ODF['center']) bin = binsAsBin(bins,ODF['interval']) orientations[j] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) ) reconstructedODF[bin] += unitInc return orientations, reconstructedODF, countMC def MonteCarloBins (ODF,nSamples): """ ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """ countMC = 0 maxdV_V = max(ODF['dV_V']) orientations = [None]*nSamples reconstructedODF = [0.0]*ODF['nBins'] unitInc = 1.0/nSamples for j in range(nSamples): MC = maxdV_V*2.0 bin = 0 while MC > ODF['dV_V'][bin]: countMC += 1 MC = maxdV_V*random.random() bin = int(ODF['nBins'] * random.random()) Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center']) orientations[j] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) ) reconstructedODF[bin] += unitInc return orientations, reconstructedODF def TothVanHoutteSTAT (ODF,nSamples): """ ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """ orientations = [None]*nSamples reconstructedODF = [0.0]*ODF['nBins'] unitInc = 1.0/nSamples selectors = [random.random() for i in range(nSamples)] selectors.sort() indexSelector = 0 cumdV_V = 0.0 countSamples = 0 for bin in range(ODF['nBins']) : cumdV_V += ODF['dV_V'][bin] while indexSelector < nSamples and selectors[indexSelector] < cumdV_V: Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center']) orientations[countSamples] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) ) reconstructedODF[bin] += unitInc countSamples += 1 indexSelector += 1 file['croak'].write('created set of %i when asked to deliver %i\n'%(countSamples,nSamples)) return orientations, reconstructedODF # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ Transform linear binned data into Euler angles. """, version = scriptID) parser.add_option('-n', '--number', dest='number', type='int', metavar = 'int', help='number of orientations to be generated [%default]') parser.add_option('-a','--algorithm', dest='algorithm', type='string', metavar = 'string', help='sampling algorithm. IA: direct inversion, STAT: Van Houtte, MC: Monte Carlo. [%default].') #make choice parser.add_option('-p','--phase', dest='phase', type='int', metavar = 'int', help='phase index to be used [%default]') parser.add_option('--crystallite', dest='crystallite', type='int', metavar = 'int', help='crystallite index to be used [%default]') parser.add_option('-r', '--rnd', dest='randomSeed', type='int', metavar='int', \ help='seed of random number generator [%default]') parser.set_defaults(randomSeed = None) parser.set_defaults(number = 500) parser.set_defaults(algorithm = 'IA') parser.set_defaults(phase = 1) parser.set_defaults(crystallite = 1) (options,filenames) = parser.parse_args() nSamples = options.number methods = [options.algorithm] if options.randomSeed == None: options.randomSeed = int(os.urandom(4).encode('hex'), 16) random.seed(options.randomSeed) #--- setup file handles --------------------------------------------------------------------------- files = [] if filenames == []: files.append({'name':'STDIN','input':sys.stdin,'output':sys.stdout,'croak':sys.stderr}) else: for name in filenames: if os.path.exists(name): files.append({'name':name,'input':open(name),'output':open(name+'_tmp','w'),'croak':sys.stdout}) #--- loop over input files ------------------------------------------------------------------------ for file in files: file['croak'].write('\033[1m' + scriptName + '\033[0m: ' + (file['name'] if file['name'] != 'STDIN' else '') + '\n') table = damask.ASCIItable(file['input'],file['output'],buffered = False) table.head_read() # --------------- figure out columns in table ----------- ----------------------------------------- column = {} pos = 0 keys = ['phi1','Phi','phi2','intensity'] for key in keys: if key not in table.labels: file['croak'].write('column %s not found...\n'%key) else: column[key] = pos pos+=1 if pos != 4: continue binnedODF = table.data_readArray(keys) # --------------- figure out limits (left/right), delta, and interval ----------------------------- ODF = {} limits = np.array([[np.min(table.data[:,column['phi1']]),\ np.min(table.data[:,column['Phi']]),\ np.min(table.data[:,column['phi2']])],\ [np.max(table.data[:,column['phi1']]),\ np.max(table.data[:,column['Phi']]),\ np.max(table.data[:,column['phi2']])]]) ODF['limit'] = np.radians(limits[1,:]) if all(limits[0,:]<1e-8): # vertex centered ODF['center'] = 0.0 else: # cell centered ODF['center'] = 0.5 eulers = [{},{},{}] for i in xrange(table.data.shape[0]): for j in xrange(3): eulers[j][str(table.data[i,column[keys[j]]])] = True # remember eulers along phi1, Phi, and phi2 ODF['interval'] = np.array([len(eulers[0]),len(eulers[1]),len(eulers[2]),],'i') # steps are number of distict values ODF['nBins'] = ODF['interval'].prod() ODF['delta'] = np.radians(np.array(limits[1,0:3]-limits[0,0:3])/(ODF['interval']-1)) if binnedODF[0] != ODF['nBins']: file['croak'].write('expecting %i values but got %i'%(ODF['nBins'],len(linesBinnedODF))) continue # build binnedODF array sumdV_V = 0.0 ODF['dV_V'] = [None]*ODF['nBins'] ODF['nNonZero'] = 0 dg = ODF['delta'][0]*2.0*math.sin(ODF['delta'][1]/2.0)*ODF['delta'][2] for b in range(ODF['nBins']): ODF['dV_V'][b] = \ max(0.0,table.data[b,column['intensity']]) * dg * \ math.sin(((b//ODF['interval'][2])%ODF['interval'][1]+ODF['center'])*ODF['delta'][1]) if ODF['dV_V'][b] > 0.0: sumdV_V += ODF['dV_V'][b] ODF['nNonZero'] += 1 for b in range(ODF['nBins']): ODF['dV_V'][b] /= sumdV_V # normalize dV/V file['croak'].write('non-zero fraction: %12.11f (%i/%i)\n'\ %(float(ODF['nNonZero'])/ODF['nBins'],ODF['nNonZero'],ODF['nBins'])) file['croak'].write('Volume integral of ODF: %12.11f\n'%sumdV_V) file['croak'].write('Reference Integral: %12.11f\n'\ %(ODF['limit'][0]*ODF['limit'][2]*(1-math.cos(ODF['limit'][1])))) # call methods Functions = {'IA': 'directInversion', 'STAT': 'TothVanHoutteSTAT', 'MC': 'MonteCarloBins'} method = Functions[options.algorithm] Orientations, ReconstructedODF = (globals()[method])(ODF,nSamples) # calculate accuracy of sample squaredDiff = {'orig':0.0,method:0.0} squaredRelDiff = {'orig':0.0,method:0.0} mutualProd = {'orig':0.0,method:0.0} indivSum = {'orig':0.0,method:0.0} indivSquaredSum = {'orig':0.0,method:0.0} for bin in range(ODF['nBins']): squaredDiff[method] += (ODF['dV_V'][bin] - ReconstructedODF[bin])**2 if ODF['dV_V'][bin] > 0.0: squaredRelDiff[method] += (ODF['dV_V'][bin] - ReconstructedODF[bin])**2/ODF['dV_V'][bin]**2 mutualProd[method] += ODF['dV_V'][bin]*ReconstructedODF[bin] indivSum[method] += ReconstructedODF[bin] indivSquaredSum[method] += ReconstructedODF[bin]**2 indivSum['orig'] += ODF['dV_V'][bin] indivSquaredSum['orig'] += ODF['dV_V'][bin]**2 file['croak'].write('sqrt(N*)RMSD of ODFs:\t %12.11f\n'% math.sqrt(nSamples*squaredDiff[method])) file['croak'].write('RMSrD of ODFs:\t %12.11f\n'%math.sqrt(squaredRelDiff[method])) file['croak'].write('rMSD of ODFs:\t %12.11f\n'%(squaredDiff[method]/indivSquaredSum['orig'])) file['croak'].write('nNonZero correlation slope:\t %12.11f\n'\ %((ODF['nNonZero']*mutualProd[method]-indivSum['orig']*indivSum[method])/\ (ODF['nNonZero']*indivSquaredSum['orig']-indivSum['orig']**2))) file['croak'].write( 'nNonZero correlation confidence:\t %12.11f\n'\ %((mutualProd[method]-indivSum['orig']*indivSum[method]/ODF['nNonZero'])/\ (ODF['nNonZero']*math.sqrt((indivSquaredSum['orig']/ODF['nNonZero']-(indivSum['orig']/ODF['nNonZero'])**2)*\ (indivSquaredSum[method]/ODF['nNonZero']-(indivSum[method]/ODF['nNonZero'])**2))))) if method == 'IA' and nSamples < ODF['nNonZero']: strOpt = '(%i)'%ODF['nNonZero'] formatwidth = 1 file['output'].write('#-------------------#') file['output'].write('\n\n') file['output'].write('#-------------------#\n') for i,ID in enumerate(xrange(nSamples)): file['output'].write('[Grain%s]\n'%(str(ID+1).zfill(formatwidth)) + \ 'crystallite %i\n'%options.crystallite + \ '(constituent) phase %i texture %s fraction 1.0\n'%(options.phase,str(ID+1).rjust(formatwidth))) file['output'].write('\n#-------------------#') file['output'].write('\n\n') file['output'].write('#-------------------#\n') for ID in xrange(nSamples): eulers = re.split(r'[\t]', Orientations[ID].strip()) file['output'].write('[Grain%s]\n'%(str(ID+1).zfill(formatwidth)) + \ '(gauss) phi1 %10.5f Phi %10.5f phi2 %10.6f scatter 0.0 fraction 1.0\n'\ %(float(eulers[0]),float(eulers[1]),float(eulers[2]))) #--- output finalization -------------------------------------------------------------------------- if file['name'] != 'STDIN': file['output'].close() os.rename(file['name']+'_tmp', os.path.splitext(file['name'])[0] +'_'+method+'%s'%('_material.config'))