def fit(data,initialGuess,peakList,verbose=1):
print
print 'Performing the image calibration'
# create a nice Numeric data strucure to store all of the peak stuff in.
# This lets us read our data easily once we get into C
length = len(peakList)
xValues = Numeric.zeros( (length), Numeric.Float64 )
yValues = Numeric.zeros( (length), Numeric.Float64 )
qReal = Numeric.zeros( (length), Numeric.Float64 )
intensity = Numeric.zeros( (length), Numeric.Float64 )
for peakIndex in range(len(peakList)):
xPeak,yPeak,Qreal,Qfit,chi,width = peakList[peakIndex]
xValues[peakIndex] = xPeak
yValues[peakIndex] = yPeak
qReal[peakIndex] = Qreal
intensity[peakIndex] = data[int(xPeak)][int(yPeak)]
# make C do the hard work :)
centerX,centerY,distance,energy,alpha,beta,rotation,covariance = FitWrap.fitCalibrationParameters(
xValues,yValues,qReal,intensity,
initialGuess.getCenterX()['val'],
initialGuess.getCenterX()['fixed'],
initialGuess.getCenterY()['val'],
initialGuess.getCenterY()['fixed'],
initialGuess.getDistance()['val'],
initialGuess.getDistance()['fixed'],
initialGuess.getEnergy()['val'],
initialGuess.getEnergy()['fixed'],
initialGuess.getAlpha()['val'],
initialGuess.getAlpha()['fixed'],
initialGuess.getBeta()['val'],
initialGuess.getBeta()['fixed'],
initialGuess.getRotation()['val'],
initialGuess.getRotation()['fixed'],
initialGuess.getPixelLength()['val'],
initialGuess.getPixelHeight()['val'])
print 'Covariance Matrix'
print Numeric.array2string(covariance,precision=2)
print 'Root of the diagonal of the covariance matrix (I think these are uncertanties)'
print 'xc: ',sqrt(covariance[0][0])
print 'yc: ',sqrt(covariance[1][1])
print 'd: ',sqrt(covariance[2][2])
print 'E: ',sqrt(covariance[3][3])
print 'alpha: ',sqrt(covariance[4][4])
print 'beta: ',sqrt(covariance[5][5])
print 'rotation: ',sqrt(covariance[6][6])
# add the fit data to a new calibration file
ret = CalibrationData.CalibrationData()
ret.setCenterX(centerX, fixed = initialGuess.getCenterX()['fixed'] )
ret.setCenterY(centerY, fixed = initialGuess.getCenterY()['fixed'] )
ret.setDistance(distance, fixed = initialGuess.getDistance()['fixed'] )
ret.setEnergy(energy, fixed = initialGuess.getEnergy()['fixed'] )
ret.setAlpha(alpha, fixed = initialGuess.getAlpha()['fixed'] )
ret.setBeta(beta, fixed = initialGuess.getBeta()['fixed'] )
ret.setRotation(rotation, fixed = initialGuess.getRotation()['fixed'] )
# pixel length & height don't change
ret.setPixelLength(initialGuess.getPixelLength()['val'])
ret.setPixelHeight(initialGuess.getPixelHeight()['val'])
return ret,peakList
def fit(data, initialGuess, peakList, verbose=1):
print
print 'Performing the image calibration'
# create a nice Numeric data strucure to store all of the peak stuff in.
# This lets us read our data easily once we get into C
length = len(peakList)
xValues = Numeric.zeros((length), Numeric.Float64)
yValues = Numeric.zeros((length), Numeric.Float64)
qReal = Numeric.zeros((length), Numeric.Float64)
intensity = Numeric.zeros((length), Numeric.Float64)
for peakIndex in range(len(peakList)):
xPeak, yPeak, Qreal, Qfit, chi, width = peakList[peakIndex]
xValues[peakIndex] = xPeak
yValues[peakIndex] = yPeak
qReal[peakIndex] = Qreal
intensity[peakIndex] = data[int(xPeak)][int(yPeak)]
# make C do the hard work :)
centerX, centerY, distance, energy, alpha, beta, rotation, covariance = FitWrap.fitCalibrationParameters(
xValues, yValues, qReal, intensity,
initialGuess.getCenterX()['val'],
initialGuess.getCenterX()['fixed'],
initialGuess.getCenterY()['val'],
initialGuess.getCenterY()['fixed'],
initialGuess.getDistance()['val'],
initialGuess.getDistance()['fixed'],
initialGuess.getEnergy()['val'],
initialGuess.getEnergy()['fixed'],
initialGuess.getAlpha()['val'],
initialGuess.getAlpha()['fixed'],
initialGuess.getBeta()['val'],
initialGuess.getBeta()['fixed'],
initialGuess.getRotation()['val'],
initialGuess.getRotation()['fixed'],
initialGuess.getPixelLength()['val'],
initialGuess.getPixelHeight()['val'])
print 'Covariance Matrix'
print Numeric.array2string(covariance, precision=2)
print 'Root of the diagonal of the covariance matrix (I think these are uncertanties)'
print 'xc: ', sqrt(covariance[0][0])
print 'yc: ', sqrt(covariance[1][1])
print 'd: ', sqrt(covariance[2][2])
print 'E: ', sqrt(covariance[3][3])
print 'alpha: ', sqrt(covariance[4][4])
print 'beta: ', sqrt(covariance[5][5])
print 'rotation: ', sqrt(covariance[6][6])
# add the fit data to a new calibration file
ret = CalibrationData.CalibrationData()
ret.setCenterX(centerX, fixed=initialGuess.getCenterX()['fixed'])
ret.setCenterY(centerY, fixed=initialGuess.getCenterY()['fixed'])
ret.setDistance(distance, fixed=initialGuess.getDistance()['fixed'])
ret.setEnergy(energy, fixed=initialGuess.getEnergy()['fixed'])
ret.setAlpha(alpha, fixed=initialGuess.getAlpha()['fixed'])
ret.setBeta(beta, fixed=initialGuess.getBeta()['fixed'])
ret.setRotation(rotation, fixed=initialGuess.getRotation()['fixed'])
# pixel length & height don't change
ret.setPixelLength(initialGuess.getPixelLength()['val'])
ret.setPixelHeight(initialGuess.getPixelHeight()['val'])
return ret, peakList
def fit(self,initialGuess,qData,maskedPixelInfo,
numberOfChi=None,stddev=None,peakList=None):
if (numberOfChi==None and peakList==None):
raise Exception("Cannot fit the calibration data \
unless either the number of chi values or a peak list are given.")
# make peak list
print
print "Finding diffraction peaks..."
if peakList == None:
peakList = Fit.getPeakList(self.theDiffractionData.data,qData,
initialGuess,numberOfChi,stddev)
print
print "Performing image calibration..."
# do fitting
bestGuess,peakList,covariance,initialResidual,finalResidual, \
reasonForQuitting = Fit.fit(self.theDiffractionData.data,
initialGuess,peakList,maskedPixelInfo)
print " - Before fitting, the calculated residual \
per peak is ",initialResidual
print " - After fitting, the calculated residual \
per peak is ",finalResidual
print " - Reason for quitting the fit: %d, " % reasonForQuitting,
if reasonForQuitting == 2:
print "stopped by small gradient J^T e\n"
elif reasonForQuitting == 2:
print "stopped by small Dp\n"
elif reasonForQuitting == 3:
print "stopped by itmax\n"
elif reasonForQuitting == 4:
print "singular matrix. Restart from current p with increased \\mu\n"
elif reasonForQuitting == 5:
print "no further error reduction is possible. Restart with increased mu\n"
elif reasonForQuitting == 6:
print "stopped by small ||e||_2\n"
else:
print "Reason for quitting unknown\n"
print "Covariance Matrix:"
print Numeric.array2string(covariance,max_line_width=1000,precision=3)
print
print "Root of the diagonal of the covariance matrix (I think these are uncertainties)"
print " - xc %17.10f" % sqrt(covariance[0][0])
print " - yc %17.10f" % sqrt(covariance[1][1])
print " - d %17.10f" % sqrt(covariance[2][2])
print " - E %17.10f" % sqrt(covariance[3][3])
print " - alpha %17.10f" % sqrt(covariance[4][4])
print " - beta %17.10f" % sqrt(covariance[5][5])
print " - R %17.10f" % sqrt(covariance[6][6])
# create the string to save to a file (if requested)
self.lastFitString = ""
self.lastFitString += "Fit done of: %s\n" % self.theDiffractionData.filename
self.lastFitString += "\n"
self.lastFitString += "Initial Guess at calibration parameters:\n"
self.lastFitString += str(initialGuess)
self.lastFitString += "\n"
self.lastFitString += "Before fitting, the calculated residual \
per peak is "+str(initialResidual)+"\n"
self.lastFitString += "\n"
self.lastFitString += "Refined calibration parameters:\n"
self.lastFitString += str(bestGuess)
self.lastFitString += "\n"
self.lastFitString += "After fitting, the calculated residual \
per peak is "+str(finalResidual)+"\n"
self.lastFitString += "\n"
self.lastFitString += "Reason for quitting the fit: %d-" % reasonForQuitting
if reasonForQuitting == 2:
self.lastFitString += "stopped by small gradient J^T e\n"
elif reasonForQuitting == 2:
self.lastFitString += "stopped by small Dp\n"
elif reasonForQuitting == 3:
self.lastFitString += "stopped by itmax\n"
elif reasonForQuitting == 4:
self.lastFitString += "singular matrix. Restart from current p with increased \\mu\n"
elif reasonForQuitting == 5:
self.lastFitString += "no further error reduction is possible. Restart with increased mu\n"
elif reasonForQuitting == 6:
self.lastFitString += "stopped by small ||e||_2\n"
else:
self.lastFitString += "Reason for quitting unknown\n"
self.lastFitString += "\n"
self.lastFitString += "Covariance Matrix:\n"
self.lastFitString += "%s\n" % Numeric.array2string(covariance,
max_line_width=1000,precision=10)
self.lastFitString += "\n"
self.lastFitString += "Root of the diagonal of the \
covariance matrix (I think these are uncertainties)\n"
self.lastFitString += "xc %17.10f\n" % sqrt(covariance[0][0])
self.lastFitString += "yc %17.10f\n" % sqrt(covariance[1][1])
self.lastFitString += "d %17.10f\n" % sqrt(covariance[2][2])
self.lastFitString += "E %17.10f\n" % sqrt(covariance[3][3])
self.lastFitString += "alpha %17.10f\n" % sqrt(covariance[4][4])
self.lastFitString += "beta %17.10f\n" % sqrt(covariance[5][5])
self.lastFitString += "R %17.10f\n" % sqrt(covariance[6][6])
self.lastFitString += "\n"
self.lastFitString += "Q Data used when calibrating:\n"
self.lastFitString += str(qData)
return bestGuess,peakList
