def cropGVE(grainfile, oldgvefile, newgvefile, verbose): grains = multigrainOutputParser.parse_GrainSpotter_log(grainfile) print "Parsed grains from %s" % grainfile print "Number of grains: %d" % len(grains) [peaksgve,idlist,header] = multigrainOutputParser.parseGVE(oldgvefile) print "Removing peaks which have been assigned to grains in %s" % grainfile for grain in grains: if (verbose): print "Looking at grain %s" % grain.getName() peaks = grain.getPeaks() # Sometimes, GrainSpotter indexes the same peak twice. We need to remove those double indexings peakid = [] for peak in peaks: peakid.append(peak.getPeakID()) peakid = list(set(peakid)) # remove duplicates, may loose the ordering but we do not care # Removing assign peaks from the list of g-vectors for thisid in peakid: try: index = idlist.index(thisid) except: print "Failed removing g-vector ID %d which was found in grain %s" % (thisid, grain.getName()) return if (verbose): print "Trying to remove peak %d from the list of g-vectors" % thisid del idlist[index] del peaksgve[index] multigrainOutputParser.saveGVE(peaksgve, header, newgvefile)
def setGVE(self, gvefile):
[peaksgve, idlist, header] = multigrainOutputParser.parseGVE(gvefile)
self.peakList = peaksgve
self.npeaks = len(peaksgve)
self.gvefile = gvefile
self.twotheta = []
for peak in self.peakList:
dsPeak = float(peak['ds'])
self.twotheta.append(
2. *
numpy.degrees(numpy.arcsin(dsPeak * self.wavelength / 2.)))
def RemoveUsedGVE(logfile, gve_input, gve_output):
# Upload .log file from GrainSpotter :
grains = multigrainOutputParser.parse_GrainSpotter_log(logfile)
# Upload .gve file from ImageD11 :
[peaksgve, idlist, header] = multigrainOutputParser.parseGVE(gve_input)
# Display peak information :
nb_start = len(peaksgve)
gveToRemove = []
for grain in grains:
peaks = grain.peaks
for indexedPeak in peaks:
#print 'Peak in grainFile'
#print indexedPeak.getPeakID()
#print indexedPeak.getTThetaMeasured()
#print indexedPeak.getOmegaMeasured()
#print indexedPeak.getEtaMeasured()
#print 'Peak in gveFile'
ID_grains = indexedPeak.getPeakID()
ID_idlist = idlist.index(ID_grains)
ID_gve = peaksgve[ID_idlist]
#print ID_gve
#print ID_gve['eta']
# Eliminate g-vectors which correspond to already indexed peaks :
gveToRemove.append(ID_gve)
#print nb_start
#print len(peaksgve)
#print len(gveToRemove)
# Save the new list of (not indexed) peaks in .gve format :
newPeakslist = [peaks for peaks in peaksgve if peaks not in gveToRemove]
OutputPeaks = multigrainOutputParser.saveGVE(newPeakslist, header,
gve_output)
print '\n%s g-vectors were removed.' % len(gveToRemove)
print '\nThe new list contains %s g-vectors.' % len(newPeakslist)
print '\nSaved'
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
