def check_euler_angles(inputf):
file1 = inputf
grains1 = multigrainOutputParser.parseGrains(file1)
ngrains1 = len(grains1)
print "Parsed %s, found %d grains" % (file1, len(grains1))
unity = numpy.identity(3)
print "Comparing Euler angles and U matrices. Making sure they match."
for grain in grains1:
angles = grain.geteulerangles()
U = grain.getU()
cphi1 = math.cos(math.radians(angles[0]))
sphi1 = math.sin(math.radians(angles[0]))
cPhi = math.cos(math.radians(angles[1]))
sPhi = math.sin(math.radians(angles[1]))
cphi2 = math.cos(math.radians(angles[2]))
sphi2 = math.sin(math.radians(angles[2]))
U2 = numpy.matrix([[
cphi1 * cphi2 - sphi1 * sphi2 * cPhi,
-cphi1 * sphi2 - sphi1 * cphi2 * cPhi, sphi1 * sPhi
],
[
sphi1 * cphi2 + cphi1 * sphi2 * cPhi,
-sphi1 * sphi2 + cphi1 * cphi2 * cPhi,
-cphi1 * sPhi
], [sphi2 * sPhi, cphi2 * sPhi, cPhi]])
C = U.dot(numpy.linalg.inv(U2))
if (not numpy.allclose(C, unity)):
print "Problem with grain %s" % grain.getName()
print C
print "Done."
return
def test():
# here = os.path.dirname(__file__)
# logfile = os.path.join(sys.prefix, 'TIMEleSS', 'data', 'gve-62-3.log')
logfile = pkg_resources.resource_filename(__name__, '../data/gve-62-3.log')
gfffile = pkg_resources.resource_filename(__name__, '../data/fcc_10bckg_.gff')
print ("Ok, we made it. We loaded the various files")
print ("\nTest GrainSpotter parsing:")
grains1 = multigrainOutputParser.parseGrains(logfile)
print ("Parsed %s, read %d grains" % (logfile, len(grains1)))
print ("\nTest GFF parsing:")
grains2 = multigrainOutputParser.parseGrains(gfffile)
print ("Parsed %s, read %d grains" % (gfffile, len(grains2)))
def extract_euler_angles(inputf, outputf):
file1 = inputf
grains1 = multigrainOutputParser.parseGrains(file1)
ngrains1 = len(grains1)
print "Parsed %s, found %d grains" % (file1, len(grains1))
f = open(outputf, "w+")
for grain in grains1:
angles = grain.geteulerangles()
f.write("%.2f %.2f %.2f\n" % (angles[0], angles[1], angles[2]))
f.close()
print "Euler angles saved in %s" % (outputf)
return
def grainSpotterClean(inputfile, outputfile):
"""
Cleans output from GrainSpotter indexings.
In particular, removes grains with no assigned peaks that occur from time to time.
Generates a new output file with no such grain.
Parameters:
inputfile - GrainSpotter log file
outputfile - name of output file with bogus grains removed
"""
# Reading list of grains from all files
grains = multigrainOutputParser.parseGrains(inputfile, False)
print("Parsed %s, found %d grains" % (inputfile, len(grains)))
multigrainOutputParser.saveGrainSpotter(outputfile, grains)
print("Saved %d grains in %s" % (len(grains), outputfile))
def comparaison(file1, file2, crystal_system, cutoff, outputstem, verbose):
"""
Function designed to compare the orientations of 2 collections of grains.
Usually used to compare an output file from GrainSpotter and that of a simulation with PolyXSim to make sure
the indexing makes sense.
- try to match grains between both
- maybe more
-
crystal_system can be one of the following values
1: Triclinic
2: Monoclinic
3: Orthorhombic
4: Tetragonal
5: Trigonal
6: Hexagonal
7: Cubic
Parameters:
file1 - file with the first list of grains (used as a reference)
file2 - file with the second list of grains
crystal_system - see abover
outputstem - stem for output file for the grain comparison
cutoff - mis-orientation below which the two grains are considered identical, in degrees
verbose - save all grain comparison into an output file rather than only matching or non matching grain
"""
# Prepare output files
filename1 = "%s-%s" % (outputstem, "log.dat")
logfile = open(filename1, 'w')
filename2 = "%s-%s" % (outputstem, "matching-grains.dat")
logmatching = open(filename2, 'w')
filename3 = "%s-%s" % (outputstem, "erroneous-grains.dat")
logerroneous = open(filename3, 'w')
filename4 = "%s-%s" % (outputstem, "missing-grains.dat")
logmissing = open(filename4, 'w')
if (not verbose):
print(
"4 output files will be generated: \n- %s,\n- %s,\n- %s,\n- %s\n" %
(filename1, filename2, filename3, filename4))
else:
filename5 = "%s-%s" % (outputstem, "verbose.dat")
logverbose = open(filename5, 'w')
# Counting number of grains
grains1 = multigrainOutputParser.parseGrains(file1)
grains2 = multigrainOutputParser.parseGrains(file2)
ngrains1 = len(grains1)
ngrains2 = len(grains2)
logit(logfile, "Parsed %s, found %d grains" % (file1, len(grains1)))
logit(logfile, "Parsed %s, found %d grains" % (file2, len(grains2)))
logit(
logfile,
"\nMisorientation below which the two grains are considered identical, in degrees: %.1f\n"
% cutoff)
# Check for doubles in list 1
logit(logfile, "Check for doubles in %s" % file1)
grains1clean = removeDoubleGrains(grains1, crystal_system, cutoff, logfile)
logit(logfile, "")
# Check for doubles in list 2
logit(logfile, "Check for doubles in %s" % file2)
grains2clean = removeDoubleGrains(grains2, crystal_system, cutoff, logfile)
logit(logfile, "")
# Loop in unique grains in list 1, trying to find pairs in list 2
goodGrains = [] # grains in list 2 that exist in list 1
erroneousGrains = [] # grains in list 2 that do not exist in list 1
grains1cleanFound = numpy.full((len(grains1clean), 1), False, dtype=bool)
logit(logfile, "Trying to match grains between the 2 collections...")
for i in range(0, len(grains2clean)):
grainMatched = []
grain2 = grains2clean[i]
U2 = grain2.getU()
for j in range(0, len(grains1clean)):
grain1 = grains1clean[j]
U1 = grain1.getU()
if (verbose): # We provide an output file all comparisons
angle = minMisorientation(U1, U2, crystal_system)
logverbose.write("Grain %s of %s\n" %
(grain1.getName(), file1))
logverbose.write("\tcompared with grain %s of %s\n" %
(grain2.getName(), file2))
logverbose.write("\tmisorientation: %.2f°\n" % (angle))
logverbose.write("U grain 1: \n" + numpy.array2string(U1) +
"\n")
logverbose.write("U grain 2: \n" + numpy.array2string(U2) +
"\n")
logverbose.write("\n\n")
if (matchGrains(U1, U2, crystal_system, cutoff)):
grainMatched.append(j)
grains1cleanFound[j] = True
if len(grainMatched) > 1:
logit(
logfile,
"- Found more than 1 pair for grain %d. Something is wrong" %
i)
sys.exit(2)
elif len(grainMatched) > 0:
goodGrains.append(i)
grain1 = grains1clean[grainMatched[0]]
U1 = grain1.getU()
angle = minMisorientation(U1, U2, crystal_system)
logit(
logfile,
"- Grain %s of %s matches %s of %s with a misorientation of %.2f°"
% (grain1.getName(), file1, grain2.getName(), file2, angle))
logmatching.write("Grain %s of %s\n" % (grain1.getName(), file1))
logmatching.write("\tmatches grain %s of %s\n" %
(grain2.getName(), file2))
logmatching.write("\tmisorientation: %.2f°\n" % (angle))
logmatching.write("U grain 1: \n" + numpy.array2string(U1) + "\n")
logmatching.write("U grain 2: \n" + numpy.array2string(U2) + "\n")
logmatching.write("\n\n")
else:
erroneousGrains.append(i)
logit(logfile,
"- Grain %s of %s has no match" % (grain2.getName(), file2))
logerroneous.write("\nGrain %s of %s: no match\n" %
(grain2.getName(), file2))
for j in range(0, len(grains1clean)):
grain1 = grains1clean[j]
U1 = grain1.getU()
angle = minMisorientation(U1, U2, crystal_system)
logerroneous.write("- Min angle with grain %s: %.2f°\n" %
(grain1.getName(), angle))
logit(logfile, "End of run\n")
for i in range(0, len(grains1clean)):
if (not grains1cleanFound[i]):
grain1 = grains1clean[i]
logmissing.write("Grain %s of %s has no match\n" %
(grain1.getName(), file1))
logit(logfile, "N. of grains in %s: %d" % (file1, ngrains1))
logit(logfile,
"N. of unique grains in %s: %d" % (file1, len(grains1clean)))
logit(logfile, "N. of grains in %s: %d" % (file2, ngrains2))
logit(logfile,
"N. of unique grains in %s: %d" % (file2, len(grains2clean)))
logit(
logfile, "N. of unique grains found in both %s and %s: %d" %
(file1, file2, len(goodGrains)))
logit(
logfile, "N. of unique grains found only in %s: %d" %
(file2, len(erroneousGrains)))
logit(
logfile, "N. of unique grains found in %s but not in %s: %d" %
(file1, file2, len(grains1clean) - len(goodGrains)))
logit(logfile, "\nIndexing capability")
logit(
logfile, "- %.1f pc of %s grains indexed" %
(100. * len(goodGrains) / len(grains1clean), file1))
logit(
logfile, "- %.1f pc of %s grains not indexed" %
(100. - 100. * len(goodGrains) / len(grains1clean), file1))
logit(
logfile, "- %.1f pc of erroneous grains in %s" %
(100. * len(erroneousGrains) / len(grains2clean), file2))
if (verbose):
print(
"\n5 output files were generated: \n- %s,\n- %s with the matching grains,\n- %s with erroneous grains,\n- %s with missing grains,\n- %s with verbose grain comparison \n"
% (filename1, filename2, filename3, filename4, filename5))
else:
print(
"\n4 output files were generated: \n- %s,\n- %s with the matching grains,\n- %s with erroneous grains,\n- %s with missing grains\n"
% (filename1, filename2, filename3, filename4))
logmatching.close()
logfile.close()
logerroneous.close()
logmissing.close()
if (verbose):
logverbose.close()
def grainSpotterMerge(files, crystal_system, cutoff, outputstem):
"""
Function designed to merge output from multiple GrainSpotter indexing
crystal_system can be one of the following values
1: Triclinic
2: Monoclinic
3: Orthorhombic
4: Tetragonal
5: Trigonal
6: Hexagonal
7: Cubic
Parameters:
files - list of GrainSpotter log files
crystal_system - see above
outputstem - stem for output file for the grain comparison
cutoff - mis-orientation below which the two grains are considered identical, in degrees
"""
filename1 = "%s-%s" % (outputstem , "log.dat")
logfile = open(filename1,'w')
# Reading list of grains from all files
grainLists = []
for filename in files:
grains = multigrainOutputParser.parseGrains(filename)
grainLists.append(grains)
logit(logfile, "Parsed %s, found %d grains" % (filename, len(grains)))
logit(logfile, "\nMisorientation below which the two grains are considered identical, in degrees: %.1f\n" % cutoff)
# Looking for unique grains
mergeGrains = []
for grains in grainLists:
mergeGrains += grains
logit(logfile, "Looking for unique grains")
grainsUnique = grainComparison.removeDoubleGrains(mergeGrains, crystal_system, cutoff, logfile)
logit(logfile, "")
# Getting some stats for each for those grains
logit(logfile, "Indexing statistics")
nIndexed = []
for i in range(0,len(grainsUnique)):
grain1 = grainsUnique[i]
n = 0
for grain2 in mergeGrains:
U1 = (grain1).getU()
U2 = (grain2).getU()
if (grainComparison.matchGrains(U1,U2,crystal_system,cutoff)):
# We have a match. Keep the grain with the largest number of peaks
n += 1
if (grain2.getNPeaks() > grain1.getNPeaks()):
grainsUnique[i] = grain2
nIndexed.append(n)
nn = grainComparison.unique(nIndexed)
nn.sort(reverse=True)
for i in nn:
nGnTimes = sum(1 for j in nIndexed if i == j)
logit(logfile,"- %d grains were indexed %d times" % (nGnTimes,i))
# Saving new files (in GrainSpotter format), based on the number of time each grain was indexed
logit(logfile, "\nSaving unique grains")
filename = ("%s-grains.log" % (outputstem))
multigrainOutputParser.saveGrainSpotter(filename,grainsUnique)
logit(logfile,"- all %d unique grains saved in %s" % (len(grainsUnique), filename))
for i in nn:
filename = ("%s-grains-%d.log" % (outputstem, i))
# Looking for grains that have been index i times
indexlist = [j for j,x in enumerate(nIndexed) if x==i]
# Save them
tosave = []
for j in range(0,len(indexlist)):
tosave.append(grainsUnique[j])
multigrainOutputParser.saveGrainSpotter(filename,tosave)
logit(logfile,"- %d grains indexed %d times saved in %s" % (len(tosave),i, filename))
logfile.close()
def gs_indexing_statistics(logfile, gve, gsinputfile, wavelength):
"""
Checks a grainspotter indexing performance
Send the final GrainSpotter log, the list of g-vectors, the GS input file (with the loosest conditions), and the wavelength
"""
nphases = len(logfile)
grains = []
ngrains = []
gsinput = []
nindexed = []
totalngrains = 0
totalindexedpeaks = 0
i = 0
# Parsing grain spotter output in input files
# Extracting grain information
for thislog in logfile:
grains.append(multigrainOutputParser.parseGrains(thislog))
print("Parsed %s, found %d grains" % (thislog, len(grains[i])))
print("Parsing grain spotter input file %s." % (gsinputfile[i]))
# Load the grain spotter input file
gsinput.append(multigrainOutputParser.parseGSInput(gsinputfile[i]))
print("\nGrainSpotter results for phase %d" % i)
print("\t%d grains indexed" % (len(grains[i])))
nindexed.append(0)
ngrains.append(len(grains[i]))
totalngrains += len(grains[i])
for grain in grains[i]:
nindexed[i] += grain.getNPeaks()
print("\t%d g-vectors indexed" % (nindexed[i]))
totalindexedpeaks += nindexed[i]
tt = 1.0 * nindexed[i] / ngrains[i]
print("\t%.1f g-vectors per grain in average" % (tt))
i = i + 1
print("")
print("Done parsing grain files.\n")
# Extract peak list and ds ranges in which to look for peak
# For each phase, the list of peaks is on top of the gve file
# Then, need keep a record of the ds tolerance for the peak (which could different for each phase)
peakssample = []
peaksgve = [None] * nphases
idlist = [None] * nphases
for i in range(0, nphases):
[peaksgve[i], idlist[i],
header] = multigrainOutputParser.parseGVE(gve[i])
print(
"Parsing header from GVE files %s to extract predicted sample peaks for phase %i"
% (gve[i], i))
tttol = gsinput[i]["sigma_tth"] * gsinput[i]["nsigmas"]
recordpeaks = False
for line in header.split("\n"):
if ((line.strip() ==
"# gx gy gz xc yc ds eta omega spot3d_id xl yl zl"
)):
recordpeaks = False
# We reached the end of the header...
if recordpeaks:
try:
tt = line.split()
ds = float(tt[0])
h = int(tt[1])
k = int(tt[2])
l = int(tt[3])
tt = 2. * numpy.degrees(numpy.arcsin(wavelength * ds / 2.))
dsmin = 2. * numpy.sin(numpy.radians(
(tt - tttol) / 2.)) / (wavelength)
dsmax = 2. * numpy.sin(numpy.radians(
(tt + tttol) / 2.)) / (wavelength)
except ValueError:
print(
"Conversion error when reading predicted sample peaks from %s."
% (gve[i]))
print("Was trying to convert %s to ds, h, k, and l" %
(line))
sys.exit(1)
peakssample.append([ds, h, k, l, tt, dsmin, dsmax, tttol])
if ((line.strip() == "# ds h k l")):
recordpeaks = True
print(
"\nRead theoretical peak positions in 2theta for all phases.\nI have a list of %d potential peaks for all %d phases.\n"
% (len(peakssample), nphases))
# Merging peaks from GVE files, removing doubles
allgves = peaksgve[0]
allPeakIds = idlist[0]
for i in range(1, nphases):
missinggveID = set(idlist[i]).symmetric_difference(set(allPeakIds))
for peakid in missinggveID:
ID_idlist = (idlist[i]).index(peakid)
allPeakIds.append(peakid)
allgves.append(peaksgve[i][ID_idlist])
print(
"Merged unique g-vectors of all %d gve files. I now have %d experimental g-vectors."
% (nphases, len(allgves)))
# Loop on all experimental g-vectors
# Are they in one of the 2 theta, omega, and eta ranges defined in grain spotter?
# Need to check for all phases
ds = []
eta = []
omega = []
keepPeak = [False] * len(allgves)
for i in range(1, nphases): # Loop on phase
gsinput[i]["dsranges"] = []
for tthrange in gsinput[i][
"tthranges"]: # Convert 2theta range to ds range for easier comparison
ds0 = 2. * numpy.sin(numpy.radians(
tthrange[0] / 2.)) / (wavelength)
ds1 = 2. * numpy.sin(numpy.radians(
tthrange[1] / 2.)) / (wavelength)
(gsinput[i]["dsranges"]).append([ds0, ds1])
# Loop on peaks. If the peak is within the range, we keep it for later
for j in range(0, len(allgves)):
peak = allgves[j]
thisds = float(peak['ds'])
thiseta = normalizedAngle360(float(
peak['eta'])) # In GrainSpotter, eta is in [0;360]
thisomega = normalizedAngle180(float(
peak['omega'])) # In GrainSpotter, omega is in [-180;180]
test1 = 0
test2 = 0
test3 = 0
for dsrange in gsinput[i]["dsranges"]:
if ((thisds >= dsrange[0]) and (thisds <= dsrange[1])):
test1 = 1
for etarange in gsinput[i]["etaranges"]:
if ((thiseta >= etarange[0]) and (thiseta <= etarange[1])):
test2 = 1
#else:
# print "Not for eta %.1f < %.1f < %.1f" % (etarange[0],thiseta,etarange[1])
for omegarange in gsinput[i]["omegaranges"]:
if ((thisomega >= omegarange[0])
and (thisomega <= omegarange[1])):
test3 = 1
#else:
# print "Not for omega %.1f < %.1f < %.1f" % (omegarange[0],thisomega,omegarange[1])
if (
test1 * test2 * test3 == 1
): # The peak is within the range of ttheta, eta, and omega for phase i. It could have been indexed.
keepPeak[j] = True
for j in range(0, len(allgves)):
if (keepPeak[j]):
peak = allgves[j]
ds.append(float(peak['ds']))
eta.append(float(peak['eta']))
omega.append(float(peak['omega']))
print(
"%d g-vectors within eta, omega, and 2theta ranges and could have been indexed."
% (len(ds)))
# Counting peak, within 2 theta range, and that can be assigned to the sample
nassigned = 0
for thisds in ds:
append = 0
for i in range(0, len(peakssample)):
if ((thisds <= peakssample[i][6])
and (thisds >= peakssample[i][5])):
append = 1
nassigned += append
print(
"%d g-vectors assigned to one of the sample peaks within these ranges."
% (nassigned))
print("\nGlobal indexing performance")
print(
"\tOut of %d possible g-vectors, %d have been assigned to %d grains" %
(nassigned, totalindexedpeaks, totalngrains))
tt = nassigned - totalindexedpeaks
print("\t%d remaining g-vectors" % (tt))
tt = 100. * totalindexedpeaks / nassigned
print("\t%.1f percents of g-vectors indexed" % (tt))
print()
return
def test_grains(grainfile,gvefile,wavelength):
"""
Check a GrainSpotter log file against a GVE file
This can be used to make sure that this exact GVE file was actually used to index the grains
"""
grains = multigrainOutputParser.parseGrains(grainfile)
print ("Parsed %s, found %d grains" % (grainfile, len(grains)))
# Load .gve file from ImageD11 :
[peaksgve,idlist,header] = multigrainOutputParser.parseGVE(gvefile)
# Try to see if all peaks in the indexed grains are in the gve
print ("Making sure all indexed peak ID's are in the GVE file...")
npeakstotal = 0
npeakserror = 0
for grain in grains :
peaks = grain.peaks
for indexedPeak in peaks :
npeakstotal += 1
try:
ID_grains = indexedPeak.getPeakID()
ID_idlist = idlist.index(ID_grains)
ID_gve = peaksgve[ID_idlist]
except ValueError:
print ("Peak %d of grain %s not found" % (ID_grains, grain.getName() ))
npeakserror += 1
if (npeakserror > 10):
print ("Too many errors, I stop here")
return
print ("I was looking for %d peaks from %d grains and got %d errors" % (npeakstotal, len(grains), npeakserror))
if (npeakserror > 0):
print ("%s and %s do not seem to match" % (grainfile, gvefile))
return
print ("All peaks in the grain file are in the GVE file. Now, looking for 2theta, eta, omega to see if they match...")
for grain in grains :
peaks = grain.peaks
for indexedPeak in peaks :
npeakstotal += 1
ID_grains = indexedPeak.getPeakID()
omegaG = normalizedAngle(indexedPeak.getOmegaMeasured())
etaG = normalizedAngle(indexedPeak.getEtaMeasured())
tthetaG = indexedPeak.getTThetaMeasured()
dsG = 2.*numpy.sin(numpy.radians(tthetaG/2.))/(wavelength)
ID_idlist = idlist.index(ID_grains)
#print peaksgve[ID_idlist]
#return
dsPeak = float(peaksgve[ID_idlist]['ds'])
etaPeak = normalizedAngle(float(peaksgve[ID_idlist]['eta']))
omegaPeak = normalizedAngle(float(peaksgve[ID_idlist]['omega']))
if ((abs(etaG-etaPeak) > 0.01) or (abs(omegaPeak-omegaG) > 0.01) or (abs(dsG-dsPeak) > 0.001)):
print ("Problem with peak %d of grain %s not found" % (ID_grains, grain.getName() ))
print ("Expected: eta = %.2f , omega = %.2f, ds = %.4f" % (etaG, omegaG, dsG))
print ("Found: eta = %.2f , omega = %.2f, ds = %.4f" % (etaPeak, omegaPeak, dsPeak))
print ("Differences: eta = %.4f , omega = %.4f, ds = %.6f" % (abs(etaPeak-etaG), abs(omegaPeak-omegaG), abs(dsG-dsPeak)))
npeakserror += 1
if (npeakserror > 10):
print ("Too many errors, I stop here")
return
if (npeakserror > 0):
print ("%s and %s do not seem to match" % (grainfile, gvefile))
return
print ("All peaks in the grain file are in the GVE file and angles fully match.")
return
def gs_indexing_statistics(logfile, gve, gsinput, wavelength):
"""
Checks a grainspotter indexing performance
Send the final GrainSpotter log, the list of g-vectors, the GS input file (with the loosest conditions), and the wavelength
"""
grains = multigrainOutputParser.parseGrains(logfile)
print "Parsed %s, found %d grains" % (logfile, len(grains))
# Load .gve file from ImageD11 :
[peaksgve,idlist,header] = multigrainOutputParser.parseGVE(gve)
# Extracting list of g-vectors from the header
peakssample = []
recordpeaks = False
#print header
for line in header.split("\n"):
if ((line.strip() == "# gx gy gz xc yc ds eta omega spot3d_id xl yl zl")):
recordpeaks = False
if recordpeaks:
tt = line.split()
ds = float(tt[0])
h = int(tt[1])
k = int(tt[2])
l = int(tt[3])
peakssample.append([ds,h,k,l])
#print h, k, l
if ((line.strip() == "# ds h k l")):
recordpeaks = True
# Load the grain spotter input file
gsinput = multigrainOutputParser.parseGSInput(gsinput)
# Try to see if all g-vectors in the indexed grains are in the gve
nindexed = 0
ngrains = len(grains)
for grain in grains :
nindexed += grain.getNPeaks()
print "\nGrainSpotter results"
print "\t%d grains indexed" % (len(grains))
print "\t%d g-vectors indexed" % (nindexed)
tt = 1.0*nindexed/ngrains
print "\t%.1f g-vectors per grain in average" % (tt)
# Matching conditions in angle ranges
ds = []
eta = []
omega = []
gsinput["dsranges"] = []
for tthrange in gsinput["tthranges"]:
ds0 = 2.*numpy.sin(numpy.radians(tthrange[0]/2.))/(wavelength)
ds1 = 2.*numpy.sin(numpy.radians(tthrange[1]/2.))/(wavelength)
gsinput["dsranges"].append([ds0,ds1])
#print gsinput
for peak in peaksgve:
thisds = float(peak['ds'])
thiseta = normalizedAngle360(float(peak['eta'])) # In GrainSpotter, eta is in [0;360]
thisomega = normalizedAngle180(float(peak['omega'])) # In GrainSpotter, omega is in [-180;180]
test1 = 0
test2 = 0
test3 = 0
for dsrange in gsinput["dsranges"]:
if ((thisds >= dsrange[0]) and (thisds <= dsrange[1])):
test1 = 1
for etarange in gsinput["etaranges"]:
if ((thiseta >= etarange[0]) and (thiseta <= etarange[1])):
test2 = 1
#else:
# print "Not for eta %.1f < %.1f < %.1f" % (etarange[0],thiseta,etarange[1])
for omegarange in gsinput["omegaranges"]:
if ((thisomega >= omegarange[0]) and (thisomega <= omegarange[1])):
test3 = 1
#else:
# print "Not for omega %.1f < %.1f < %.1f" % (omegarange[0],thisomega,omegarange[1])
if (test1*test2*test3 == 1):
ds.append(float(peak['ds']))
eta.append(float(peak['eta']))
omega.append(float(peak['omega']))
print "\nPeaks"
print "\t%d g-vectors in GVE file" % (len(peaksgve))
print "\t%d g-vectors within eta, omega, and 2theta ranges" % (len(ds))
# Counting peak, within 2 theta range, and that can be assigned to the sample
tttol = gsinput["sigma_tth"]*gsinput["nsigmas"]
dsmin = []
dsmax = []
#print tttol
for samplepeak in peakssample:
thisds = samplepeak[0]
#print thisds
tt = 2.*numpy.degrees(numpy.arcsin(wavelength*thisds/2.))
#print 2.*numpy.sin(numpy.radians((tt-tttol)/2.))/(wavelength)
#print 2.*numpy.sin(numpy.radians((tt+tttol)/2.))/(wavelength)
dsmin.append(2.*numpy.sin(numpy.radians((tt-tttol)/2.))/(wavelength))
dsmax.append(2.*numpy.sin(numpy.radians((tt+tttol)/2.))/(wavelength))
nassigned = 0
for thisds in ds:
append = 0
for i in range(0,len(dsmin)):
if ((thisds <= dsmax[i]) and (thisds >= dsmin[i])):
append = 1
nassigned += append
print "\t%d g-vectors assigned to sample within these ranges" % (nassigned)
print "\nIndexing performance"
print "\tOut of %d possible g-vectors, %d have been assigned to %d grains" % (nassigned, nindexed, len(grains))
tt = nassigned-nindexed
print "\t%d remaining g-vectors" % (tt)
tt = 100.*nindexed/nassigned
print "\t%.1f percents of g-vectors indexed" % (tt)
print
return
def comparaison(file1, file2, crystal_system, cutoff, outputstem):
"""
Function designed to compare the orientations of 2 collections of grains.
Usually used to compare an output file from GrainSpotter and that of a simulation with PolyXSim to make sure
the indexing makes sense.
- try to match grains between both
- maybe more
-
crystal_system can be one of the following values
1: Triclinic
2: Monoclinic
3: Orthorhombic
4: Tetragonal
5: Trigonal
6: Hexagonal
7: Cubic
Parameters:
file1 - file with the first list of grains (used as a reference)
file2 - file with the second list of grains
crystal_system - see abover
outputstem - stem for output file for the grain comparison
cutoff - mis-orientation below which the two grains are considered identical, in degrees
"""
# Prepare output files
filename1 = "%s-%s" % (outputstem, "log.dat")
logfile = open(filename1, 'w')
print("1 output files will be generated: \n- %s\n" % (filename1))
# Counting number of grains
grains1 = multigrainOutputParser.parseGrains(file1)
grains2 = multigrainOutputParser.parseGrains(file2)
ngrains1 = len(grains1)
ngrains2 = len(grains2)
logit(logfile, "Parsed %s, found %d grains" % (file1, len(grains1)))
logit(logfile, "Parsed %s, found %d grains" % (file2, len(grains2)))
logit(
logfile,
"\nMisorientation below which the two grains are considered identical, in degrees: %.1f\n"
% cutoff)
# Check for doubles in list 1
logit(logfile, "Check for doubles in %s" % file1)
grains1clean = removeDoubleGrains(grains1, crystal_system, cutoff, logfile)
logit(logfile, "")
# Check for doubles in list 2
logit(logfile, "Check for doubles in %s" % file2)
grains2clean = removeDoubleGrains(grains2, crystal_system, cutoff, logfile)
logit(logfile, "")
# Will hold grains that match a peak
peaksInGrains = {}
# Loop in unique grains in list 1, trying to grains in list 2 that share the same peaks
for i in range(0, len(grains1clean)):
grain1 = grains1clean[i]
grains1GVEID = grain1.getPeaksGVEID()
for j in range(0, len(grains2clean)):
grain2 = grains2clean[j]
grains2GVEID = grain2.getPeaksGVEID()
matches = set(grains1GVEID).intersection(grains2GVEID)
if (len(matches) > 0):
logit(
logfile, "- Grain %s of %s shares %d peaks with %s of %s" %
(grain1.getName(), file1, len(matches), grain2.getName(),
file2))
logit(logfile,
"Matching peaks (GVE ID): " + str(list(matches)))
for peak in matches:
try:
grains = peaksInGrains[peak]
grains.append(grain1.getName() + " in " + file1)
grains.append(grain2.getName() + " in " + file2)
peaksInGrains[peak] = grains
except KeyError:
# Key is not present
peaksInGrains[peak] = [
grain1.getName() + " in " + file1,
grain2.getName() + " in " + file2
]
logit(logfile, "\nPeaks information\n")
for peak in peaksInGrains:
logit(
logfile, "- peak %s is seen in %d grains: " %
(peak, len(peaksInGrains[peak])) + str(peaksInGrains[peak]))
return