def plotMultiWavelength(data, showErr=True): hklNames = bf.getKeyList(data) for hkl in hklNames: plotSin2Psi(data[hkl], showErr)
def multiWavelengthAnalysis(data, maxPsi=None):
keyList = bf.getKeyList(data)
peakCount = int(
bf.replace(keyList[-1].split('_')[0],
'pv')) + 1 # must be adapted in further versions!!!
phiVals = data['phi']
psiVals = data['psi']
tauMean = bf.zeros(peakCount)
hklList = bf.zeros(peakCount, dtypeVal=np.int)
s1Dec = bf.zeros(peakCount)
hs2Dec = bf.zeros(peakCount)
dStarVals = bf.zeros(peakCount)
dStarErrVals = bf.zeros(peakCount)
stresses = np.zeros((peakCount, 6)) # s11-33 s22-33 s13 s23 s12 s33
accuracy = np.zeros((peakCount, 6))
integralWidth = np.zeros(
(peakCount, 6)) # phi0 phi90 phi180 phi270 phi45 phi225
plotData = dict()
for p in range(peakCount): # perform this for all peaks
ibVals = data['pv' + str(p) + '_sigma']
centerVals = data['pv' + str(p) + '_center']
centerErrVals = data['pv' + str(p) + '_center_err']
dMinVals = conv.energies2latticeDists(centerVals - centerErrVals,
data['tth'])
dMaxVals = conv.energies2latticeDists(centerVals + centerErrVals,
data['tth'])
dErrVals = np.abs(dMaxVals - dMinVals) / 2
tauVals = data['pv' + str(p) + '_depth']
dVals = data['pv' + str(p) + '_dspac'] / 10
#hklVals = data['pv' + str(p) + '_hklList'] # first version with adaptions
#s1Vals = data['pv' + str(p) + '_s1List'] # first version with adaptions
#hs2Vals = data['pv' + str(p) + '_s2List'] # first version with adaptions
#hklList[p] = hklVals[0]
hVals = data['pv' + str(p) + '_h'] # second version
kVals = data['pv' + str(p) + '_k'] # second version
lVals = data['pv' + str(p) + '_l'] # second version
s1Vals = data['pv' + str(p) + '_s1'] # second version
#hs2Vals = data['pv' + str(p) + '_s2'] # second version
hs2Vals = data['pv' + str(p) + '_hs2'] # third version
hklList[p] = conv.mergeHkl(hVals[0], kVals[0], lVals[0])
s1Dec[p] = s1Vals[0]
#hs2Dec[p] = hs2Vals[0] * 0.5 # test valid for first and second version!!!!!
hs2Dec[p] = hs2Vals[0]
curData = {
'dVals': dVals,
'dErr': dErrVals,
'tauVals': tauVals,
'phiVals': phiVals,
'psiVals': psiVals,
'hklVal': hklList[p],
's1Val': s1Dec[p],
'hs2Val': hs2Dec[p],
'ibVals': ibVals
}
bf.extendDictionary(curData, data, ('a0Val', ))
# perform sin2psi analysis for current peak data
curResData, curPlotData = sin2PsiAnalysis(curData, maxPsi)
# remember results
tauMean[p] = curResData['tauMean']
dStarVals[p] = curResData['dStar100']
dStarErrVals[p] = curResData['dStar100Err']
stresses[p] = curResData['stresses']
accuracy[p] = curResData['accuracy']
curMeanIB = curResData['meanIB']
integralWidth[p, 0:len(curMeanIB)] = curMeanIB
plotData[str(hklList[p])] = curPlotData
# resData: hklList, s1Dec, hs2Dec, tauMean, aStar, aStarErr, stresses, accuracy, mVals???, bVals???
resData = {
'hklList': hklList,
's1Dec': s1Dec,
'hs2Dec': hs2Dec,
'tauMean': tauMean,
'dStar100': dStarVals,
'dStar100Err': dStarErrVals,
'stresses': stresses,
'accuracy': accuracy,
'integralWidth': integralWidth
}
return resData, plotData
def multiUniversalPlotAnalysis(data,
maxPsi=None,
minDistPsiStar=0.15,
minValPsiNormal=0.08,
minValPsiShear=0.8):
keyList = bf.getKeyList(data)
peakCount = int(
bf.replace(keyList[-1].split('_')[0],
'pv')) + 1 # must be adapted in further versions!!!
tthVal = data['tth']
phiVals = data['phi']
psiVals = data['psi']
psiUni = np.unique(psiVals)
psiUni = psiUni[psiUni != 0] # no zero value
psiSign = np.sign(psiUni[-1]) # sign of last psi value
psiUni = psiUni[np.sign(psiUni) ==
psiSign] # only negative or positive values
sinpsi2Uni = bc.sind(psiUni)**2
sin2psiUni = bc.sind(np.abs(2 * psiUni))
tauRes = np.zeros((peakCount, len(psiUni)))
hklRes = np.zeros((peakCount, len(psiUni)))
psiRes = np.zeros((peakCount, len(psiUni)))
stresses = np.zeros((peakCount, len(psiUni), 4))
errVals = np.zeros((peakCount, len(psiUni), 4))
tauS33 = np.zeros(peakCount)
aStarVals = np.zeros(peakCount)
aStarErrVals = np.zeros(peakCount)
s33 = np.zeros(peakCount)
dev_s33 = np.zeros(peakCount)
hklList = np.zeros(peakCount)
phi4 = len(np.unique(phiVals)) == 4
validCounter = 0
for p in range(peakCount): # for all peaks create one plot
centerVals = data['pv' + str(p) + '_center']
centerErrVals = data['pv' + str(p) + '_center_err']
dMinVals = conv.energies2latticeDists(centerVals - centerErrVals,
tthVal)
dMaxVals = conv.energies2latticeDists(centerVals + centerErrVals,
tthVal)
dErrVals = np.abs(dMaxVals - dMinVals) / 2
tauVals = data['pv' + str(p) + '_depth']
dVals = data['pv' + str(p) + '_dspac'] / 10 # in nm
#hklVals = data['pv' + str(p) + '_hklList'] # first version with adaptions
#s1Vals = data['pv' + str(p) + '_s1List'] # first version with adaptions
#hs2Vals = data['pv' + str(p) + '_s2List'] # first version with adaptions
#hklVal = hklVals[0]
hVals = data['pv' + str(p) + '_h'] # second version
kVals = data['pv' + str(p) + '_k'] # second version
lVals = data['pv' + str(p) + '_l'] # second version
s1Vals = data['pv' + str(p) + '_s1'] # second version
#hs2Vals = data['pv' + str(p) + '_s2'] # second version
hs2Vals = data['pv' + str(p) + '_hs2'] # third version
hklVal = conv.mergeHkl(hVals[0], kVals[0], lVals[0])
hklList[p] = hklVal
hklRes[p] = hklVal * np.ones(len(psiUni))
psiRes[p] = psiUni
s1Val = s1Vals[0]
#hs2Val = hs2Vals[0] * 0.5 # test valid for first and second version!!!!!
hs2Val = hs2Vals[0]
curData = {
'tauVals': tauVals,
'dVals': dVals,
'dErrVals': dErrVals,
'psiVals': psiVals,
'phiVals': phiVals,
'psiUni': psiUni,
'sin2psiUni': sin2psiUni,
'sinpsi2Uni': sinpsi2Uni,
'phi4': phi4,
'hklVal': hklVal,
's1Val': s1Val,
'hs2Val': hs2Val
}
bf.extendDictionary(curData, data, ('a0Val', ))
# perform universal plot analysis for current peak data
curResData = universalPlotAnalysis(curData, maxPsi, minDistPsiStar,
minValPsiNormal, minValPsiShear)
# remember results
tauRes[p] = curResData['tauRes']
stresses[p] = curResData['stresses']
errVals[p] = curResData['errVals']
aStarVals[p] = curResData['dStar100']
aStarErrVals[p] = curResData['dStar100Err']
tauS33[p] = curResData['tauS33']
s33[p] = curResData['s33']
dev_s33[p] = curResData['dev_s33']
validCounter += curResData['validCounter']
# reshape data
tauRes = np.reshape(tauRes, np.prod(bf.size(tauRes)))
hklRes = np.reshape(hklRes, np.prod(bf.size(hklRes)))
psiRes = np.reshape(psiRes, np.prod(bf.size(psiRes)))
stresses = np.reshape(
stresses,
(bf.size(stresses, 0) * bf.size(stresses, 1), bf.size(stresses, 2)))
errVals = np.reshape(
errVals,
(bf.size(errVals, 0) * bf.size(errVals, 1), bf.size(errVals, 2)))
# remove values with tau = 0
hklRes = hklRes[tauRes > 0]
psiRes = psiRes[tauRes > 0]
stresses = stresses[tauRes > 0]
errVals = errVals[tauRes > 0]
tauRes = tauRes[tauRes > 0]
# sort data concerning increasing information depth
hklRes = hklRes[np.argsort(tauRes)]
psiRes = psiRes[np.argsort(tauRes)]
stresses = stresses[np.argsort(tauRes)]
errVals = errVals[np.argsort(tauRes)]
tauRes = tauRes[np.argsort(tauRes)]
resData = {
'tauVals': tauRes,
'stresses': stresses,
'accuracy': errVals,
'hklVals': hklRes,
'psiVals': psiRes,
'validCount': validCounter
}
resDataS33 = {
'tauMean': tauS33,
'dStar100': aStarVals,
'dStar100Err': aStarErrVals,
's33': s33,
'dev_s33': dev_s33,
'hklList': hklList
}
return resData, resDataS33
plotResUvp = True
showErr = True
# showErr = False
checkUvpVals = True
fileNames = fg.requestFiles((("Data files", "*.dat"), ),
"Select P61A data file", "on")
# import all data
allData = dict()
allMetaInfo = dict()
for fileName in fileNames:
data, metaInfo = fs.loadFileP61A2(fileName)
allData[fileName] = data
allMetaInfo[fileName] = metaInfo
# combine all data for one analysis
combinedData = allData[fileNames[0]]
keyList = bf.getKeyList(combinedData)
for fileName in fileNames[1:]:
for key in keyList:
combinedData[key] = np.concatenate(
(combinedData[key], allData[fileName][key]))
# prepare data for multi wavelength and universal plot analysis
a0Val = 0.289
tthVal = 7
# tthVal = 15
inputData = bf.combineDictionaries({
'a0Val': a0Val,
'tth': tthVal
}, combinedData)
# perform multi wavelength analysis
maxPsi = 45
resDataMwl, plotDataMwl = dc.multiWavelengthAnalysis(inputData, maxPsi)