449 lines
18 KiB
Python
Executable File
449 lines
18 KiB
Python
Executable File
#!/usr/bin/env python
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from optparse import OptionParser
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import damask
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import os,sys,math,re,random,string
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import numpy as np
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scriptID = string.replace('$Id$','\n','\\n')
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scriptName = scriptID.split()[1]
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# --- helper functions ---
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def integerFactorization(i):
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j = int(math.floor(math.sqrt(float(i))))
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while j>1 and int(i)%j != 0:
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j -= 1
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return j
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def TSLheader(sizeX,sizeY,step):
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return [
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'# TEM_PIXperUM 1.000000',
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'# x-star 0.509548',
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'# y-star 0.795272',
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'# z-star 0.611799',
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'# WorkingDistance 18.000000',
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'#',
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'# Phase 1',
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'# MaterialName Al',
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'# Formula Fe',
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'# Info',
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'# Symmetry 43',
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'# LatticeConstants 2.870 2.870 2.870 90.000 90.000 90.000',
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'# NumberFamilies 4',
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'# hklFamilies 1 1 0 1 0.000000 1',
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'# hklFamilies 2 0 0 1 0.000000 1',
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'# hklFamilies 2 1 1 1 0.000000 1',
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'# hklFamilies 3 1 0 1 0.000000 1',
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'# Categories 0 0 0 0 0 ',
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'#',
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'# GRID: SquareGrid',
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'# XSTEP: ' + str(step),
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'# YSTEP: ' + str(step),
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'# NCOLS_ODD: ' + str(sizeX),
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'# NCOLS_EVEN: ' + str(sizeX),
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'# NROWS: ' + str(sizeY),
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'#',
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'# OPERATOR: ODFsammpling',
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'#',
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'# SAMPLEID: ',
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'#',
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'# SCANID: ',
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'#',
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]
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def binAsBins(bin,intervals):
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""" explode compound bin into 3D bins list """
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bins = [0]*3
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bins[0] = (bin//(intervals[1] * intervals[2])) % intervals[0]
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bins[1] = (bin//intervals[2]) % intervals[1]
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bins[2] = bin % intervals[2]
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return bins
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def binsAsBin(bins,intervals):
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""" implode 3D bins into compound bin """
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return (bins[0]*intervals[1] + bins[1])*intervals[2] + bins[2]
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def EulersAsBins(Eulers,intervals,deltas,center):
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""" return list of Eulers translated into 3D bins list """
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return [int((euler+(0.5-center)*delta)//delta)%interval \
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for euler,delta,interval in zip(Eulers,deltas,intervals) \
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]
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def binAsEulers(bin,intervals,deltas,center):
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""" compound bin number translated into list of Eulers """
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Eulers = [0.0]*3
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Eulers[2] = (bin%intervals[2] + center)*deltas[2]
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Eulers[1] = (bin//intervals[2]%intervals[1] + center)*deltas[1]
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Eulers[0] = (bin//(intervals[2]*intervals[1]) + center)*deltas[0]
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return Eulers
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def directInvRepetitions(probability,scale):
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""" calculate number of samples drawn by direct inversion """
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nDirectInv = 0
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for bin in range(len(probability)): # loop over bins
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nDirectInv += int(round(probability[bin]*scale)) # calc repetition
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return nDirectInv
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# ---------------------- sampling methods -----------------------------------------------------------------------
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# ----- efficient algorithm ---------
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def directInversion (ODF,nSamples):
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""" ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """
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nOptSamples = max(ODF['nNonZero'],nSamples) # random subsampling if too little samples requested
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nInvSamples = 0
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repetition = [None]*ODF['nBins']
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probabilityScale = nOptSamples # guess
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scaleLower = 0.0
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nInvSamplesLower = 0
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scaleUpper = float(nOptSamples)
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incFactor = 1.0
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nIter = 0
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nInvSamplesUpper = directInvRepetitions(ODF['dV_V'],scaleUpper)
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while (\
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(scaleUpper-scaleLower > scaleUpper*1e-15 or nInvSamplesUpper < nOptSamples) and \
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nInvSamplesUpper != nOptSamples \
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): # closer match required?
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if nInvSamplesUpper < nOptSamples:
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scaleLower,scaleUpper = scaleUpper,scaleUpper+incFactor*(scaleUpper-scaleLower)/2.0
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incFactor *= 2.0
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nInvSamplesLower,nInvSamplesUpper = nInvSamplesUpper,directInvRepetitions(ODF['dV_V'],scaleUpper)
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else:
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scaleUpper = (scaleLower+scaleUpper)/2.0
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incFactor = 1.0
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nInvSamplesUpper = directInvRepetitions(ODF['dV_V'],scaleUpper)
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nIter += 1
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table.croak('%i:(%12.11f,%12.11f) %i <= %i <= %i'%(nIter,scaleLower,scaleUpper,
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nInvSamplesLower,nOptSamples,nInvSamplesUpper))
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nInvSamples = nInvSamplesUpper
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scale = scaleUpper
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table.croak('created set of %i samples (%12.11f) with scaling %12.11f delivering %i'%(nInvSamples,
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float(nInvSamples)/nOptSamples-1.0,
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scale,nSamples))
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repetition = [None]*ODF['nBins'] # preallocate and clear
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for bin in range(ODF['nBins']): # loop over bins
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repetition[bin] = int(round(ODF['dV_V'][bin]*scale)) # calc repetition
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# build set
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set = [None]*nInvSamples
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i = 0
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for bin in range(ODF['nBins']):
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set[i:i+repetition[bin]] = [bin]*repetition[bin] # fill set with bin, i.e. orientation
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i += repetition[bin] # advance set counter
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orientations = np.zeros((nSamples,3),'f')
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reconstructedODF = np.zeros(ODF['nBins'],'f')
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unitInc = 1.0/nSamples
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for j in range(nSamples):
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if (j == nInvSamples-1): ex = j
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else: ex = int(round(random.uniform(j+0.5,nInvSamples-0.5)))
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bin = set[ex]
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bins = binAsBins(bin,ODF['interval']) # PE: why are we doing this??
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Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center'])
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orientations[j] = np.degrees(Eulers)
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reconstructedODF[bin] += unitInc
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set[ex] = set[j] # exchange orientations
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return orientations, reconstructedODF
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# ----- trial and error algorithms ---------
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def MonteCarloEulers (ODF,nSamples):
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""" ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """
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countMC = 0
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maxdV_V = max(ODF['dV_V'])
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orientations = np.zeros((nSamples,3),'f')
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reconstructedODF = np.zeros(ODF['nBins'],'f')
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unitInc = 1.0/nSamples
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for j in range(nSamples):
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MC = maxdV_V*2.0
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bin = 0
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while MC > ODF['dV_V'][bin]:
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countMC += 1
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MC = maxdV_V*random.random()
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Eulers = [limit*random.random() for limit in ODF['limit']]
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bins = EulersAsBins(Eulers,ODF['interval'],ODF['delta'],ODF['center'])
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bin = binsAsBin(bins,ODF['interval'])
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orientations[j] = np.degrees(Eulers)
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reconstructedODF[bin] += unitInc
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return orientations, reconstructedODF, countMC
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def MonteCarloBins (ODF,nSamples):
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""" ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """
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countMC = 0
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maxdV_V = max(ODF['dV_V'])
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orientations = np.zeros((nSamples,3),'f')
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reconstructedODF = np.zeros(ODF['nBins'],'f')
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unitInc = 1.0/nSamples
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for j in range(nSamples):
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MC = maxdV_V*2.0
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bin = 0
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while MC > ODF['dV_V'][bin]:
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countMC += 1
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MC = maxdV_V*random.random()
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bin = int(ODF['nBins'] * random.random())
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Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center'])
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orientations[j] = np.degrees(Eulers)
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reconstructedODF[bin] += unitInc
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return orientations, reconstructedODF
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def TothVanHoutteSTAT (ODF,nSamples):
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""" ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """
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orientations = np.zeros((nSamples,3),'f')
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reconstructedODF = np.zeros(ODF['nBins'],'f')
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unitInc = 1.0/nSamples
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selectors = [random.random() for i in range(nSamples)]
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selectors.sort()
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indexSelector = 0
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cumdV_V = 0.0
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countSamples = 0
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for bin in range(ODF['nBins']) :
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cumdV_V += ODF['dV_V'][bin]
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while indexSelector < nSamples and selectors[indexSelector] < cumdV_V:
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Eulers = binAsEulers(bin,ODF['interval'],ODF['delta'],ODF['center'])
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orientations[countSamples] = np.degrees(Eulers)
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reconstructedODF[bin] += unitInc
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countSamples += 1
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indexSelector += 1
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table.croak('created set of %i when asked to deliver %i'%(countSamples,nSamples))
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return orientations, reconstructedODF
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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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Transform linear binned ODF data into given number of orientations.
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""", version = scriptID)
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parser.add_option('-n', '--nsamples',
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dest = 'number',
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type = 'int', metavar = 'int',
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help = 'number of orientations to be generated [%default]')
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parser.add_option('-a','--algorithm',
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dest = 'algorithm',
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type = 'string', metavar = 'string',
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help = 'sampling algorithm. IA: integral approximation, STAT: Van Houtte, MC: Monte Carlo. [%default].') #make choice
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parser.add_option('-p','--phase',
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dest = 'phase',
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type = 'int', metavar = 'int',
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help = 'phase index to be used [%default]')
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parser.add_option('--crystallite',
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dest = 'crystallite',
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type = 'int', metavar = 'int',
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help = 'crystallite index to be used [%default]')
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parser.add_option('-r', '--rnd',
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dest = 'randomSeed',
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type = 'int', metavar = 'int', \
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help = 'seed of random number generator [%default]')
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parser.add_option('--ang',
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dest = 'ang',
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action = 'store_true',
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help = 'write TSL/EDAX .ang file [%default]')
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parser.set_defaults(randomSeed = None,
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number = 500,
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algorithm = 'IA',
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phase = 1,
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crystallite = 1,
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ang = True,
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)
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(options,filenames) = parser.parse_args()
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nSamples = options.number
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methods = [options.algorithm]
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# --- loop over input files -------------------------------------------------------------------------
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if filenames == []: filenames = ['STDIN']
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for name in filenames:
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if not (name == 'STDIN' or os.path.exists(name)): continue
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table = damask.ASCIItable(name = name, outname = None,
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buffered = False, readonly = True)
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table.croak('\033[1m'+scriptName+'\033[0m'+(': '+name if name != 'STDIN' else ''))
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randomSeed = int(os.urandom(4).encode('hex'), 16) if options.randomSeed == None else options.randomSeed # random seed per file for second phase
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random.seed(randomSeed)
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# ------------------------------------------ read header ---------------------------------------
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table.head_read()
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errors = []
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labels = ['phi1','Phi','phi2','intensity']
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for i,index in enumerate(table.label_index(labels)):
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if index < 0: errors.append('label {} not present.'.format(labels[i])
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if errors != []:
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table.croak(errors)
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table.close(dismiss = True)
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continue
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# ------------------------------------------ read data ---------------------------------------
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binnedODF = table.data_readArray(labels)
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# --------------- figure out limits (left/right), delta, and interval -----------------------------
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ODF = {}
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limits = np.array([np.min(table.data,axis=0),
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np.max(table.data,axis=0)])
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ODF['limit'] = np.radians(limits[1,:])
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if all(limits[0,:]<1e-8): # vertex centered
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ODF['center'] = 0.0
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else: # cell centered
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ODF['center'] = 0.5
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eulers = [{},{},{}]
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for i in xrange(table.data.shape[0]):
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for j in xrange(3):
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eulers[j][str(table.data[i,j]])] = True # remember eulers along phi1, Phi, and phi2
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ODF['interval'] = np.array([len(eulers[0]),len(eulers[1]),len(eulers[2]),],'i') # steps are number of distict values
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ODF['nBins'] = ODF['interval'].prod()
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ODF['delta'] = np.radians(np.array(limits[1,0:3]-limits[0,0:3])/(ODF['interval']-1))
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if binnedODF[0] != ODF['nBins']:
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table.croak('expecting %i values but got %i'%(ODF['nBins'],len(linesBinnedODF)))
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continue
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# build binnedODF array
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sumdV_V = 0.0
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ODF['dV_V'] = [None]*ODF['nBins']
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ODF['nNonZero'] = 0
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dg = ODF['delta'][0]*2.0*math.sin(ODF['delta'][1]/2.0)*ODF['delta'][2]
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for b in range(ODF['nBins']):
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ODF['dV_V'][b] = \
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max(0.0,table.data[b,column['intensity']]) * dg * \
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math.sin(((b//ODF['interval'][2])%ODF['interval'][1]+ODF['center'])*ODF['delta'][1])
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if ODF['dV_V'][b] > 0.0:
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sumdV_V += ODF['dV_V'][b]
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ODF['nNonZero'] += 1
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for b in range(ODF['nBins']): ODF['dV_V'][b] /= sumdV_V # normalize dV/V
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table.croak(['non-zero fraction: %12.11f (%i/%i)'%(float(ODF['nNonZero'])/ODF['nBins'],
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ODF['nNonZero'],
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ODF['nBins']),
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'Volume integral of ODF: %12.11f\n'%sumdV_V,
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'Reference Integral: %12.11f\n'%(ODF['limit'][0]*ODF['limit'][2]*(1-math.cos(ODF['limit'][1]))),
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])
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# call methods
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Functions = {'IA': 'directInversion', 'STAT': 'TothVanHoutteSTAT', 'MC': 'MonteCarloBins'}
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method = Functions[options.algorithm]
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Orientations, ReconstructedODF = (globals()[method])(ODF,nSamples)
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# calculate accuracy of sample
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squaredDiff = {'orig':0.0,method:0.0}
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squaredRelDiff = {'orig':0.0,method:0.0}
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mutualProd = {'orig':0.0,method:0.0}
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indivSum = {'orig':0.0,method:0.0}
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indivSquaredSum = {'orig':0.0,method:0.0}
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for bin in range(ODF['nBins']):
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squaredDiff[method] += (ODF['dV_V'][bin] - ReconstructedODF[bin])**2
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if ODF['dV_V'][bin] > 0.0:
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squaredRelDiff[method] += (ODF['dV_V'][bin] - ReconstructedODF[bin])**2/ODF['dV_V'][bin]**2
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mutualProd[method] += ODF['dV_V'][bin]*ReconstructedODF[bin]
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indivSum[method] += ReconstructedODF[bin]
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indivSquaredSum[method] += ReconstructedODF[bin]**2
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indivSum['orig'] += ODF['dV_V'][bin]
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indivSquaredSum['orig'] += ODF['dV_V'][bin]**2
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table.croak(['sqrt(N*)RMSD of ODFs:\t %12.11f'% math.sqrt(nSamples*squaredDiff[method]),
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'RMSrD of ODFs:\t %12.11f'%math.sqrt(squaredRelDiff[method]),
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'rMSD of ODFs:\t %12.11f'%(squaredDiff[method]/indivSquaredSum['orig']),
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'nNonZero correlation slope:\t %12.11f'\
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%((ODF['nNonZero']*mutualProd[method]-indivSum['orig']*indivSum[method])/\
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(ODF['nNonZero']*indivSquaredSum['orig']-indivSum['orig']**2)),
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'nNonZero correlation confidence:\t %12.11f'\
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%((mutualProd[method]-indivSum['orig']*indivSum[method]/ODF['nNonZero'])/\
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(ODF['nNonZero']*math.sqrt((indivSquaredSum['orig']/ODF['nNonZero']-(indivSum['orig']/ODF['nNonZero'])**2)*\
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(indivSquaredSum[method]/ODF['nNonZero']-(indivSum[method]/ODF['nNonZero'])**2)))),
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])
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if method == 'IA' and nSamples < ODF['nNonZero']:
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strOpt = '(%i)'%ODF['nNonZero']
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formatwidth = 1+int(math.log10(nSamples))
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materialConfig = [
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'#' + scriptID + ' ' + ' '.join(sys.argv[1:]),
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'# random seed %i'%randomSeed
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'#-------------------#',
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'<microstructure>',
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'#-------------------#',
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]
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for i,ID in enumerate(xrange(nSamples)):
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materialConfig += ['[Grain%s]'%(str(ID+1).zfill(formatwidth)),
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'crystallite %i'%options.crystallite,
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'(constituent) phase %i texture %s fraction 1.0'%(options.phase,str(ID+1).rjust(formatwidth)),
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]
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materialConfig += [
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'#-------------------#',
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'<texture>',
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'#-------------------#',
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]
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for ID in xrange(nSamples):
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eulers = Orientations[ID]
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materialConfig += ['[Grain%s]'%(str(ID+1).zfill(formatwidth)),
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'(gauss) phi1 %10.5f Phi %10.5f phi2 %10.6f scatter 0.0 fraction 1.0'%(*eulers),
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]
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#--- output finalization --------------------------------------------------------------------------
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with (open(os.path.splitext(name)[0]+'_'+method+'_'+str(nSamples)+'_material.config','w') as outfile:
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outfile.write('\n'.join(materialConfig)+'\n')
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# write ang file
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if options.ang:
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with open(os.path.splitext(name)[0]+'_'+method+'_'+str(nSamples)+'.ang','w') as outfile:
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sizeY = integerFactorization(nSamples)
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sizeX = nSamples / sizeY
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table.croak('Writing .ang file: %i * %i = %i (== %i)'%(sizeX,sizeY,sizeX*sizeY,nSamples))
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# write header
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outfile.write('\n'.join(TSLheader(sizeX,sizeY,1.0))+'\n')
|
|
|
|
# write data
|
|
counter = 0
|
|
for eulers in Orientations:
|
|
outfile.write('%10.5f %10.5f %10.5f '%(*np.radians(eulers)) +
|
|
'%10.5f %10.5f '%(counter%sizeX,counter//sizeX) +
|
|
'100.0 1.0 0 1 1.0\n')
|
|
counter += 1
|
|
|
|
#--- output finalization --------------------------------------------------------------------------
|
|
|
|
table.close()
|