copied some stcopied some scripts from the Code folder that are of interest for DAMASK
This commit is contained in:
Martin Diehl
parent
70eaa9f8b8
commit
f1df6cf40f
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#!/usr/bin/env python
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import os,sys,re
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try:
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inName = sys.argv[1]
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outName = os.path.splitext(inName)[0]+'.linearODF'
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nPhi1,nPHI,nPhi2 = map(int,sys.argv[2:5])
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except:
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print "\nusage:",sys.argv[0],"file nPhi1 nPHI nPhi2\n"
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sys.exit(1)
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N = (nPhi1-1)*(nPHI-1)*(nPhi2-1)
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try:
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inFile = open(inName,'r')
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content = inFile.readlines()
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except:
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print 'unable to read:',inName
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sys.exit(1)
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try:
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outFile = open(outName,'w')
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except:
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print 'unable to write:',outName
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sys.exit(1)
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ODF = [[[[None] for k in range(nPhi2)] for j in range(nPHI)] for i in range(nPhi1)]
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linear = [None]*N
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line = 0
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while (content[line].startswith('#')): # skip comments at start of file
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line += 1
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for iPhi1 in range(nPhi1):
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for iPHI in range(nPHI):
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for iPhi2 in range(nPhi2):
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words = content[line].split()
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ODF[iPhi1][iPHI][iPhi2] = float(words[3]) # extract intensity (in column 4)
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line += 1
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for iPhi1 in range(nPhi1-1):
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for iPHI in range(nPHI-1):
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for iPhi2 in range(nPhi2-1):
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linear[iPhi1*(nPHI-1)*(nPhi2-1)+iPHI*(nPhi2-1)+iPhi2] = (\
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ODF[iPhi1 ][iPHI ][iPhi2 ] + \
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ODF[iPhi1 ][iPHI ][iPhi2+1] + \
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ODF[iPhi1 ][iPHI+1][iPhi2 ] + \
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ODF[iPhi1 ][iPHI+1][iPhi2+1] + \
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ODF[iPhi1+1][iPHI ][iPhi2 ] + \
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ODF[iPhi1+1][iPHI ][iPhi2+1] + \
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ODF[iPhi1+1][iPHI+1][iPhi2 ] + \
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ODF[iPhi1+1][iPHI+1][iPhi2+1] \
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) / 8.0
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inFile.close()
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outFile.write('rangePhi1?\trangePHI?\trangePhi2?\n')
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outFile.write('%i\t%i\t%i needs to be converted to angular steps\n'%(nPhi1-1,nPHI-1,nPhi2-1))
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outFile.write('cell-centered data\n')
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outFile.write('\n')
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for i in range(N):
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outFile.write('%g\n'%(linear[i]))
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outFile.close()
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#!/usr/bin/env python
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import os,sys,math,re
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# --- helper functions ---
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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 [\
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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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# check usage
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try:
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inName = sys.argv[1]
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outLimits = sys.argv[2:5]
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except:
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print "usage:",sys.argv[0],"nameLinearODF limitPhi1 limitPHI limitPhi2"
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sys.exit(1);
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#open binned ODF
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try:
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inFile = open(inName,'r')
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except:
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print 'unable to open binnedODF:', inName;
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sys.exit(1);
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# process header info
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ODF = {}
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inLimits = [math.radians(int(float(limit))) for limit in inFile.readline().split()]
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outLimits = [math.radians(int(float(limit))) for limit in outLimits]
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deltas = [math.radians(float(delta)) for delta in inFile.readline().split()]
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inIntervals = [int(limit/delta) for limit,delta in zip(inLimits,deltas)]
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outIntervals = [int(limit/delta) for limit,delta in zip(outLimits,deltas)]
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inBins = inIntervals[0]*inIntervals[1]*inIntervals[2]
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print 'Limit:', [math.degrees(limit) for limit in inLimits]
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print 'Crop:', [math.degrees(limit) for limit in outLimits]
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print 'Delta:', [math.degrees(delta) for delta in deltas]
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print 'Interval:', inIntervals
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print 'Interval:', outIntervals
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centering = inFile.readline()
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if re.search('cell',centering.lower()):
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ODF['center'] = 0.5
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print 'cell-centered data (offset %g)'%ODF['center']
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else:
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ODF['center'] = 0.0
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print 'vertex-centered data (offset %g)'%ODF['center']
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inFile.readline() # skip blank delimiter
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# read linear binned data
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inODF = map(float,inFile.readlines())
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inFile.close()
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if len(inODF) != inBins:
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print 'expecting', inBins, 'values but got', len(inODF)
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sys.exit(1)
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try:
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outName = os.path.splitext(inName)[0]+'_%ix%ix%i'%(outIntervals[0],outIntervals[1],outIntervals[2])+'.linearODF'
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outFile = open(outName,'w')
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except:
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print 'unable to write:',outName
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sys.exit(1)
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outFile.write('%g\t%g\t%g\n'%(\
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math.degrees(outIntervals[0]*deltas[0]),\
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math.degrees(outIntervals[1]*deltas[1]),\
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math.degrees(outIntervals[2]*deltas[2]) ))
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outFile.write('%i\t%i\t%i\n'%(math.degrees(deltas[0]),math.degrees(deltas[1]),math.degrees(deltas[2])))
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outFile.write('%s-centered data\n'%{True:'vertex',False:'cell'}[ODF['center']==0.0])
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outFile.write('\n')
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for phi1 in range(outIntervals[0]):
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for Phi in range(outIntervals[1]):
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for phi2 in range(outIntervals[2]):
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outFile.write('%g\n'%(inODF[((phi1*inIntervals[1])+Phi)*inIntervals[2]+phi2]))
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outFile.close()
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@ -0,0 +1,331 @@
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#!/usr/bin/env python
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import os,sys,math,re,random
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random.seed()
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# --- helper functions ---
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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 [\
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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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nBins = ODF['intervals'][0]*ODF['intervals'][1]*ODF['intervals'][2]
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deltas = [limit/intervals for limit,intervals in zip(ODF['limits'],ODF['intervals'])]
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# calculate repetitions of each orientation
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if re.search(r'hybrid',sys.argv[0],re.IGNORECASE): # my script's name contains "hybrid"
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nOptSamples = max(ODF['nNonZero'],nSamples) # random subsampling if too little samples requested
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else: # blunt integer approximation
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nOptSamples = nSamples
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nInvSamples = 0
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repetition = [None]*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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print '%i:(%12.11f,%12.11f) %i <= %i <= %i'%(nIter,scaleLower,scaleUpper,nInvSamplesLower,nOptSamples,nInvSamplesUpper)
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nInvSamples = nInvSamplesUpper
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scale = scaleUpper
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print 'created set of',nInvSamples,'samples (',float(nInvSamples)/nOptSamples-1.0,') with scaling',scale,'delivering',nSamples
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repetition = [None]*nBins # preallocate and clear
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for bin in range(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(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 = [None]*nSamples
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reconstructedODF = [0.0]*nBins
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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['intervals'])
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Eulers = binAsEulers(bin,ODF['intervals'],deltas,ODF['center'])
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orientations[j] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) )
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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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nBins = ODF['intervals'][0]*ODF['intervals'][1]*ODF['intervals'][2]
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deltas = [limit/intervals for limit,intervals in zip(ODF['limits'],ODF['intervals'])]
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orientations = [None]*nSamples
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reconstructedODF = [0.0]*nBins
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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['limits']]
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bins = EulersAsBins(Eulers,ODF['intervals'],deltas,ODF['center'])
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bin = binsAsBin(bins,ODF['intervals'])
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orientations[j] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) )
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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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nBins = ODF['intervals'][0]*ODF['intervals'][1]*ODF['intervals'][2]
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deltas = [limit/intervals for limit,intervals in zip(ODF['limits'],ODF['intervals'])]
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orientations = [None]*nSamples
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reconstructedODF = [0.0]*nBins
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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(nBins * random.random())
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Eulers = binAsEulers(bin,ODF['intervals'],deltas,ODF['center'])
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orientations[j] = '%g\t%g\t%g' %( math.degrees(Eulers[0]),math.degrees(Eulers[1]),math.degrees(Eulers[2]) )
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reconstructedODF[bin] += unitInc
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|
return orientations, reconstructedODF
|
||||||
|
|
||||||
|
|
||||||
|
def TothVanHoutteSTAT (ODF,nSamples):
|
||||||
|
""" ODF contains 'dV_V' (normalized to 1), 'center', 'intervals', 'limits' (in radians) """
|
||||||
|
|
||||||
|
nBins = ODF['intervals'][0]*ODF['intervals'][1]*ODF['intervals'][2]
|
||||||
|
deltas = [limit/intervals for limit,intervals in zip(ODF['limits'],ODF['intervals'])]
|
||||||
|
orientations = [None]*nSamples
|
||||||
|
reconstructedODF = [0.0]*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(nBins) :
|
||||||
|
cumdV_V += ODF['dV_V'][bin]
|
||||||
|
while indexSelector < nSamples and selectors[indexSelector] < cumdV_V:
|
||||||
|
Eulers = binAsEulers(bin,ODF['intervals'],deltas,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
|
||||||
|
|
||||||
|
print 'created set of',countSamples,'when asked to deliver',nSamples
|
||||||
|
|
||||||
|
return orientations, reconstructedODF
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# check usage
|
||||||
|
try:
|
||||||
|
nameBinnedODF = sys.argv[1]
|
||||||
|
nSamples = int(float(sys.argv[2]))
|
||||||
|
except:
|
||||||
|
print "\nusage:",os.path.basename(sys.argv[0]),"nameLinearODF nSamples [nameSampledODF]\n"
|
||||||
|
sys.exit(1);
|
||||||
|
|
||||||
|
methods = ['IA','STAT']
|
||||||
|
|
||||||
|
#open binned ODF
|
||||||
|
try:
|
||||||
|
fileBinnedODF = open(nameBinnedODF,'r')
|
||||||
|
except:
|
||||||
|
print 'unable to open binnedODF:', nameBinnedODF;
|
||||||
|
sys.exit(1);
|
||||||
|
|
||||||
|
# process header info
|
||||||
|
ODF = {}
|
||||||
|
ODF['limits'] = [math.radians(float(limit)) for limit in fileBinnedODF.readline().split()]
|
||||||
|
ODF['deltas'] = [math.radians(float(delta)) for delta in fileBinnedODF.readline().split()]
|
||||||
|
ODF['intervals'] = [int(round(limit/delta)) for limit,delta in zip(ODF['limits'],ODF['deltas'])]
|
||||||
|
nBins = ODF['intervals'][0]*ODF['intervals'][1]*ODF['intervals'][2]
|
||||||
|
|
||||||
|
print 'Limit:', [math.degrees(limit) for limit in ODF['limits']]
|
||||||
|
print 'Delta:', [math.degrees(delta) for delta in ODF['deltas']]
|
||||||
|
print 'Interval:', ODF['intervals']
|
||||||
|
|
||||||
|
centering = fileBinnedODF.readline()
|
||||||
|
if re.search('cell',centering.lower()):
|
||||||
|
ODF['center'] = 0.5
|
||||||
|
print 'cell-centered data (offset %g)'%ODF['center']
|
||||||
|
else:
|
||||||
|
ODF['center'] = 0.0
|
||||||
|
print 'vertex-centered data (offset %g)'%ODF['center']
|
||||||
|
|
||||||
|
fileBinnedODF.readline() # skip blank delimiter
|
||||||
|
|
||||||
|
# read linear binned data
|
||||||
|
linesBinnedODF = fileBinnedODF.readlines()
|
||||||
|
fileBinnedODF.close()
|
||||||
|
|
||||||
|
if len(linesBinnedODF) != nBins:
|
||||||
|
print 'expecting', nBins, 'values but got', len(linesBinnedODF)
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
# build binnedODF array
|
||||||
|
print 'reading',nBins,'values'
|
||||||
|
sumdV_V = 0.0
|
||||||
|
ODF['dV_V'] = [None]*nBins
|
||||||
|
ODF['nNonZero'] = 0
|
||||||
|
dg = ODF['deltas'][0]*2*math.sin(ODF['deltas'][1]/2.0)*ODF['deltas'][2]
|
||||||
|
for bin in range(nBins):
|
||||||
|
ODF['dV_V'][bin] = \
|
||||||
|
max(0.0,float(linesBinnedODF[bin])) * dg * \
|
||||||
|
math.sin(((bin//ODF['intervals'][2])%ODF['intervals'][1]+ODF['center'])*ODF['deltas'][1])
|
||||||
|
if ODF['dV_V'][bin] > 0.0:
|
||||||
|
sumdV_V += ODF['dV_V'][bin]
|
||||||
|
ODF['nNonZero'] += 1
|
||||||
|
|
||||||
|
for bin in range(nBins): ODF['dV_V'][bin] /= sumdV_V # normalize dV/V
|
||||||
|
|
||||||
|
print 'non-zero fraction:', float(ODF['nNonZero'])/nBins,'(%i/%i)'%(ODF['nNonZero'],nBins)
|
||||||
|
print 'Volume integral of ODF:', sumdV_V
|
||||||
|
print 'Reference Integral:', ODF['limits'][0]*ODF['limits'][2]*(1-math.cos(ODF['limits'][1]))
|
||||||
|
|
||||||
|
# call methods
|
||||||
|
Functions = {'IA': 'directInversion', 'STAT': 'TothVanHoutteSTAT', 'MC': 'MonteCarloBins'}
|
||||||
|
Orientations = {}
|
||||||
|
ReconstructedODF = {}
|
||||||
|
for method in methods:
|
||||||
|
Orientations[method], ReconstructedODF[method] = (globals()[Functions[method]])(ODF,nSamples)
|
||||||
|
|
||||||
|
# calculate accuracy of sample
|
||||||
|
squaredDiff = {}
|
||||||
|
squaredRelDiff = {}
|
||||||
|
mutualProd = {}
|
||||||
|
indivSum = {}
|
||||||
|
indivSquaredSum = {}
|
||||||
|
for method in ['orig']+methods:
|
||||||
|
squaredDiff[method] = 0.0
|
||||||
|
squaredRelDiff[method] = 0.0
|
||||||
|
mutualProd[method] = 0.0
|
||||||
|
indivSum[method] = 0.0
|
||||||
|
indivSquaredSum[method] = 0.0
|
||||||
|
|
||||||
|
for bin in range(nBins):
|
||||||
|
for method in methods:
|
||||||
|
squaredDiff[method] += (ODF['dV_V'][bin] - ReconstructedODF[method][bin])**2
|
||||||
|
if ODF['dV_V'][bin] > 0.0:
|
||||||
|
squaredRelDiff[method] += (ODF['dV_V'][bin] - ReconstructedODF[method][bin])**2/ODF['dV_V'][bin]**2
|
||||||
|
mutualProd[method] += ODF['dV_V'][bin]*ReconstructedODF[method][bin]
|
||||||
|
indivSum[method] += ReconstructedODF[method][bin]
|
||||||
|
indivSquaredSum[method] += ReconstructedODF[method][bin]**2
|
||||||
|
indivSum['orig'] += ODF['dV_V'][bin]
|
||||||
|
indivSquaredSum['orig'] += ODF['dV_V'][bin]**2
|
||||||
|
|
||||||
|
print 'sqrt(N*)RMSD of ODFs:\t', [math.sqrt(nSamples*squaredDiff[method]) for method in methods]
|
||||||
|
print 'RMSrD of ODFs:\t', [math.sqrt(squaredRelDiff[method]) for method in methods]
|
||||||
|
print 'rMSD of ODFs:\t', [squaredDiff[method]/indivSquaredSum['orig'] for method in methods]
|
||||||
|
#print 'correlation slope:\t', [(nBins*mutualProd[method]-indivSum['orig']*indivSum[method])/(nBins*indivSquaredSum['orig']-indivSum['orig']**2) for method in ('IA','STAT','MC')]
|
||||||
|
#print 'correlation confidence:\t', [(mutualProd[method]-indivSum['orig']*indivSum[method]/nBins)/\
|
||||||
|
# (nBins*math.sqrt((indivSquaredSum['orig']/nBins-(indivSum['orig']/nBins)**2)*(indivSquaredSum[method]/nBins-(indivSum[method]/nBins)**2))) for method in ('IA','STAT','MC')]
|
||||||
|
print 'nNonZero correlation slope:\t', [(ODF['nNonZero']*mutualProd[method]-indivSum['orig']*indivSum[method])/(ODF['nNonZero']*indivSquaredSum['orig']-indivSum['orig']**2) for method in methods]
|
||||||
|
print 'nNonZero correlation confidence:\t', [(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))) for method in methods]
|
||||||
|
|
||||||
|
# write result
|
||||||
|
try:
|
||||||
|
nameSampledODF = sys.argv[3]
|
||||||
|
except:
|
||||||
|
sys.exit(0) # that's it folks
|
||||||
|
|
||||||
|
for method in methods:
|
||||||
|
if method == 'IA' and nSamples < ODF['nNonZero']:
|
||||||
|
strOpt = '(%i)'%ODF['nNonZero']
|
||||||
|
else:
|
||||||
|
strOpt = ''
|
||||||
|
try:
|
||||||
|
fileSampledODF = open(nameSampledODF+'.'+method+'sampled_'+str(nSamples)+strOpt, 'w')
|
||||||
|
fileSampledODF.write('%i\n'%nSamples)
|
||||||
|
fileSampledODF.write('\n'.join(Orientations[method])+'\n')
|
||||||
|
fileSampledODF.close()
|
||||||
|
except:
|
||||||
|
print 'unable to write sampledODF:', nameSampledODF+'.'+method+'sampled_'+str(nSamples)+strOpt
|
||||||
|
|
||||||
|
try:
|
||||||
|
fileRegressionODF = open(nameSampledODF+'.'+method+'regression_'+str(nSamples)+strOpt, 'w')
|
||||||
|
fileRegressionODF.write('\n'.join([a+'\t'+b for (a,b) in zip(map(str,ReconstructedODF[method]),map(str,ODF['dV_V']))])+'\n')
|
||||||
|
fileRegressionODF.close()
|
||||||
|
except:
|
||||||
|
print 'unable to write RegressionODF:', nameSampledODF+'.'+method+'regression_'+str(nSamples)+strOpt
|
||||||
|
|
@ -0,0 +1,121 @@
|
||||||
|
#!/usr/bin/env python
|
||||||
|
|
||||||
|
import os,sys,math,re
|
||||||
|
from optparse import OptionParser
|
||||||
|
|
||||||
|
def integerFactorization(i):
|
||||||
|
|
||||||
|
j = int(math.floor(math.sqrt(float(i))))
|
||||||
|
while (j>1 and int(i)%j != 0):
|
||||||
|
j -= 1
|
||||||
|
return j
|
||||||
|
|
||||||
|
def positiveRadians(angle):
|
||||||
|
|
||||||
|
angle = math.radians(float(angle))
|
||||||
|
while angle < 0.0:
|
||||||
|
angle += 2.0*math.pi
|
||||||
|
|
||||||
|
return angle
|
||||||
|
|
||||||
|
|
||||||
|
def getHeader(sizeX,sizeY,step):
|
||||||
|
|
||||||
|
return [ \
|
||||||
|
'# TEM_PIXperUM 1.000000', \
|
||||||
|
'# x-star 0.509548', \
|
||||||
|
'# y-star 0.795272', \
|
||||||
|
'# z-star 0.611799', \
|
||||||
|
'# WorkingDistance 18.000000', \
|
||||||
|
'#', \
|
||||||
|
'# Phase 1', \
|
||||||
|
'# MaterialName Al', \
|
||||||
|
'# Formula Fe', \
|
||||||
|
'# Info', \
|
||||||
|
'# Symmetry 43', \
|
||||||
|
'# LatticeConstants 2.870 2.870 2.870 90.000 90.000 90.000', \
|
||||||
|
'# NumberFamilies 4', \
|
||||||
|
'# hklFamilies 1 1 0 1 0.000000 1', \
|
||||||
|
'# hklFamilies 2 0 0 1 0.000000 1', \
|
||||||
|
'# hklFamilies 2 1 1 1 0.000000 1', \
|
||||||
|
'# hklFamilies 3 1 0 1 0.000000 1', \
|
||||||
|
'# Categories 0 0 0 0 0 ', \
|
||||||
|
'#', \
|
||||||
|
'# GRID: SquareGrid', \
|
||||||
|
'# XSTEP: ' + str(step), \
|
||||||
|
'# YSTEP: ' + str(step), \
|
||||||
|
'# NCOLS_ODD: ' + str(sizeX), \
|
||||||
|
'# NCOLS_EVEN: ' + str(sizeX), \
|
||||||
|
'# NROWS: ' + str(sizeY), \
|
||||||
|
'#', \
|
||||||
|
'# OPERATOR: ODFsammpling', \
|
||||||
|
'#', \
|
||||||
|
'# SAMPLEID: ', \
|
||||||
|
'#', \
|
||||||
|
'# SCANID: ', \
|
||||||
|
'#', \
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
parser = OptionParser(usage='%prog [options] datafile(s)')
|
||||||
|
parser.add_option("-c", "--column", type="int",\
|
||||||
|
dest="column",\
|
||||||
|
help="starting column of Euler triplet")
|
||||||
|
parser.add_option("-s", "--skip", type="int",\
|
||||||
|
dest="skip",\
|
||||||
|
help="skip this many lines of heading info [%default]")
|
||||||
|
|
||||||
|
parser.set_defaults (column = 1)
|
||||||
|
parser.set_defaults (skip = 0)
|
||||||
|
|
||||||
|
(options, files) = parser.parse_args()
|
||||||
|
options.column -= 1
|
||||||
|
|
||||||
|
if files == []:
|
||||||
|
parser.error('no input file specified...')
|
||||||
|
sys.exit(1)
|
||||||
|
if options.column < 0:
|
||||||
|
parser.error('column needs to be 1,2,...')
|
||||||
|
sys.exit(1)
|
||||||
|
|
||||||
|
while len(files) > 0:
|
||||||
|
textureFilename = files.pop()
|
||||||
|
baseName = os.path.splitext(textureFilename)[0]
|
||||||
|
|
||||||
|
# open texture file and read content
|
||||||
|
textureFile = open(textureFilename)
|
||||||
|
content = textureFile.readlines()
|
||||||
|
textureFile.close()
|
||||||
|
|
||||||
|
m = re.match('(\d+)\s+head',content[0],re.I)
|
||||||
|
if m != None and options.skip == 0:
|
||||||
|
options.skip = int(m.group(1))+1
|
||||||
|
|
||||||
|
# extract orientation angles
|
||||||
|
angles = [map(positiveRadians,line.split()[options.column:options.column+3]) for line in content[options.skip:]]
|
||||||
|
|
||||||
|
nPoints = len(angles)
|
||||||
|
sizeY = integerFactorization(nPoints)
|
||||||
|
sizeX = nPoints / sizeY
|
||||||
|
print '%s: %i * %i = %i (== %i)'%(baseName,sizeX,sizeY,sizeX*sizeY,nPoints)
|
||||||
|
# write ang file
|
||||||
|
|
||||||
|
try:
|
||||||
|
|
||||||
|
# write header
|
||||||
|
angFile = open(baseName + '.ang','w')
|
||||||
|
for line in getHeader(sizeX,sizeY,1.0):
|
||||||
|
angFile.write(line + '\n')
|
||||||
|
|
||||||
|
# write data
|
||||||
|
counter = 0
|
||||||
|
for point in angles:
|
||||||
|
angFile.write(''.join(['%10.5f'%angle for angle in point])+
|
||||||
|
''.join(['%10.5f'%coord for coord in [counter%sizeX,counter//sizeX]])+
|
||||||
|
' 100.0 1.0 0 1 1.0\n')
|
||||||
|
counter += 1
|
||||||
|
|
||||||
|
angFile.close()
|
||||||
|
|
||||||
|
except:
|
||||||
|
print 'unable to write',baseName
|
||||||
Loading…
Reference in New Issue