#!/usr/bin/python # -*- coding: UTF-8 no BOM -*- from optparse import OptionParser import damask import os,sys,math,re,random,string import numpy as np scriptID = string.replace('$Id$','\n','\\n') scriptName = os.path.splitext(scriptID.split()[1])[0] # --- helper functions --- def integerFactorization(i): j = int(math.floor(math.sqrt(float(i)))) while j>1 and int(i)%j != 0: j -= 1 return j 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 damask.util.croak('%i:(%12.11f,%12.11f) %i <= %i <= %i'%(nIter,scaleLower,scaleUpper, nInvSamplesLower,nOptSamples,nInvSamplesUpper)) nInvSamples = nInvSamplesUpper scale = scaleUpper damask.util.croak('created set of %i samples (%12.11f) with scaling %12.11f delivering %i'%(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 = np.zeros((nSamples,3),'f') reconstructedODF = np.zeros(ODF['nBins'],'f') 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']) # PE: why are we doing this?? Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center']) orientations[j] = np.degrees(Eulers) 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 = np.zeros((nSamples,3),'f') reconstructedODF = np.zeros(ODF['nBins'],'f') 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] = np.degrees(Eulers) 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 = np.zeros((nSamples,3),'f') reconstructedODF = np.zeros(ODF['nBins'],'f') 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] = np.degrees(Eulers) reconstructedODF[bin] += unitInc return orientations, reconstructedODF def TothVanHoutteSTAT (ODF,nSamples): """ ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """ orientations = np.zeros((nSamples,3),'f') reconstructedODF = np.zeros(ODF['nBins'],'f') 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] = np.degrees(Eulers) reconstructedODF[bin] += unitInc countSamples += 1 indexSelector += 1 damask.util.croak('created set of %i when asked to deliver %i'%(countSamples,nSamples)) return orientations, reconstructedODF # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [file[s]]', description = """ Transform linear binned ODF data into given number of orientations. """, version = scriptID) parser.add_option('-n', '--nsamples', 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: integral approximation, 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, number = 500, algorithm = 'IA', phase = 1, crystallite = 1, ang = True, ) (options,filenames) = parser.parse_args() nSamples = options.number methods = [options.algorithm] # --- loop over input files ------------------------------------------------------------------------- if filenames == []: filenames = [None] for name in filenames: try: table = damask.ASCIItable(name = name, buffered = False, readonly=True) except: continue damask.util.report(scriptName,name) randomSeed = int(os.urandom(4).encode('hex'), 16) if options.randomSeed == None else options.randomSeed # random seed per file for second phase random.seed(randomSeed) # ------------------------------------------ read header and data --------------------------------- table.head_read() errors = [] labels = ['phi1','Phi','phi2','intensity'] for i,index in enumerate(table.label_index(labels)): if index < 0: errors.append('label {} not present.'.format(labels[i])) if errors != []: damask.util.croak(errors) table.close(dismiss = True) continue table.data_readArray(labels) # --------------- figure out limits (left/right), delta, and interval ----------------------------- ODF = {} limits = np.array([np.min(table.data[:,0:3],axis=0), np.max(table.data[:,0:3],axis=0)]) # min/max euler angles in degrees ODF['limit'] = np.radians(limits[1,:]) # right hand limits in radians ODF['center'] = 0.0 if all(limits[0,:]<1e-8) else 0.5 # vertex or cell centered ODF['interval'] = np.array(map(len,[np.unique(table.data[:,i]) for i in xrange(3)]),'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)) # step size if table.data.shape[0] != ODF['nBins']: damask.util.croak('expecting %i values but got %i'%(ODF['nBins'],table.data.shape[0])) continue # ----- build binnedODF array and normalize ------------------------------------------------------ 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,table.label_index('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 damask.util.croak(['non-zero fraction: %12.11f (%i/%i)'%(float(ODF['nNonZero'])/ODF['nBins'], ODF['nNonZero'], ODF['nBins']), 'Volume integral of ODF: %12.11f\n'%sumdV_V, '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 damask.util.croak(['sqrt(N*)RMSD of ODFs:\t %12.11f'% math.sqrt(nSamples*squaredDiff[method]), 'RMSrD of ODFs:\t %12.11f'%math.sqrt(squaredRelDiff[method]), 'rMSD of ODFs:\t %12.11f'%(squaredDiff[method]/indivSquaredSum['orig']), 'nNonZero correlation slope:\t %12.11f'\ %((ODF['nNonZero']*mutualProd[method]-indivSum['orig']*indivSum[method])/\ (ODF['nNonZero']*indivSquaredSum['orig']-indivSum['orig']**2)), 'nNonZero correlation confidence:\t %12.11f'\ %((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+int(math.log10(nSamples)) materialConfig = [ '#' + scriptID + ' ' + ' '.join(sys.argv[1:]), '# random seed %i'%randomSeed, '#-------------------#', '', '#-------------------#', ] for i,ID in enumerate(xrange(nSamples)): materialConfig += ['[Grain%s]'%(str(ID+1).zfill(formatwidth)), 'crystallite %i'%options.crystallite, '(constituent) phase %i texture %s fraction 1.0'%(options.phase,str(ID+1).rjust(formatwidth)), ] materialConfig += [ '#-------------------#', '', '#-------------------#', ] for ID in xrange(nSamples): eulers = Orientations[ID] materialConfig += ['[Grain%s]'%(str(ID+1).zfill(formatwidth)), '(gauss) phi1 {} Phi {} phi2 {} scatter 0.0 fraction 1.0'.format(*eulers), ] #--- output finalization -------------------------------------------------------------------------- with (open(os.path.splitext(name)[0]+'_'+method+'_'+str(nSamples)+'_material.config','w')) as outfile: outfile.write('\n'.join(materialConfig)+'\n') table.close()