def Fit(self,showLog=False,showGuess=False):
if self.silent:
print "Fitting with "+self.funcName
if self.silent:
resultFig = plt.figure(self.plotNum)
if not self.twin:
resultAx = resultFig.add_subplot(111)
else:
resultAx = self.twinAx.twinx()
xRange = linspace(self.xData.min(),self.xData.max(),1000)
yGuess = self.fitFunc(xRange,*self.iVals)
if self.silent:
if showLog:
resultAx.loglog(self.xData,self.yData,color=self.dataColor,marker=self.dataMarker,linestyle='.')
if showGuess:
resultAx.loglog(xRange,yGuess,color=self.guessColor,linestyle=self.fitLineStyle,linewidth=self.fitLineThick)
if showGuess:
resultAx.plot(xRange,yGuess,color=self.guessColor,linestyle=self.fitLineStyle)
self.legData = resultAx.errorbar(self.xData,self.yData,linestyle='.',marker=self.dataMarker, color=self.dataColor,yerr=self.yErr,elinewidth=self.errorbarThick,capsize=self.capsize)
fit = mpCurveFit(self.fitFunc, self.xData, self.yData, p0 = self.iVals, sigma = self.yErr, fixed = self.fixed,limits=self.limits,quiet=1)
params, errors = [fit.params, fit.errors]
if self.silent:
print "\nFit results: "
try:
for x,y,z in zip(self.params, params, errors):
if self.silent:
print x+": "+str(y)+" +/- "+str(z)
if self.rDisp:
self._ResultsDisplay(resultAx,params,errors)
except TypeError:
print "\n\n\n-----------> FIT FAILED!!!!!\n\n\n"
self.redChi2 = 1E3
self.chi2 = [1E4]
if self.silent:
self.ax=resultAx
return
xRange = linspace(self.xData.min(),self.xData.max(),100)
yResult = self.fitFunc(xRange,*params)
self.result = zip(params,errors)
self.result.append([fit.chi2,fit.dof])
self.result=array(self.result)
self.covar = fit.covar
self.chi2 = [fit.chi2,fit.dof]
self.redChi2 = fit.chi2/fit.dof
if self.silent:
if showLog:
resultAx.loglog(xRange,yResult,color=self.fitColor,linestyle=self.fitLineStyle,linewidth=self.fitLineThick)
else:
resultAx.plot(xRange,yResult,color=self.fitColor,linestyle=self.fitLineStyle,linewidth=self.fitLineThick)
#resultAx.errorbar(self.xData,self.yData,fmt=self.dataMarker, color=self.dataColor,yerr=self.yErr)
resultAx.set_xlabel(self.xName,fontsize=self.fontsize, weight = self.fontWeight, family = self.fontFam)
resultAx.set_ylabel(self.yName,fontsize=self.fontsize, weight = self.fontWeight, family = self.fontFam)
resultAx.set_title(self.title,fontsize=self.fontsize, weight = self.fontWeight, family = self.fontFam)
self.ax= resultAx
def FitPulse(self, event=0):
if self.pms:
#If there is pms then we need to set the right models
pulseMod = self.pms.GetPulseMod()
func = pulseMod.pulseLookup[self.numPulse - 1]
# func = self.pulseLookup[self.numPulse-1]
initialValues = pulseMod.GetInitialValues()
self.tmax = self.tMaxSelector.GetData()
if self.tmax == []:
fixPar = pulseMod.GetFixedParams()
limits = pulseMod.limits
initialValues = pulseMod.GetInitialValues()
else:
print "\n_________________________________\n"
print "Initial guess(es) for Tmax"
for x in self.tmax:
print x
print "\n_________________________________\n"
limits = []
tmp1 = []
tmp3 = []
for x in self.tmax:
tmp2 = initialValues
if self.useSelector:
tmp2[0] = x
tmp1.extend(tmp2)
tmp3.extend(pulseMod.GetFixedParams())
limits.extend(pulseMod.limits)
intialValues = tmp1
fixPar = tmp3
# print limits
# print initialValues
# print fixPar
#### Testing!!!!!
#dT = array(map(lambda x: x[1] - x[0] ,self.tBins))
#wErrors = self.errors*dT
#wData = self.data*dT
#fit = mpPulseFitEngine(func, array(map(mean,self.tBins))+self.timeOffset, self.data.tolist(), sigma=wErrors,p0=initialValues,fixed=fixPar,maxiter=400, limits=limits,tBins = self.tBins)
# place back if this does not work
fit = mpCurveFit(func,
array(map(mean, self.tBins)) + self.timeOffset,
self.data.tolist(),
sigma=self.errors,
p0=initialValues,
fixed=fixPar,
maxiter=400,
limits=limits)
self.fitResults = fit.params
self.fitCov = fit.errors
self.fitResults[3] -= self.timeOffset
self.GoodnessOfFit()
self.DisplayFitResults()
self.DisplayFitPlot()
def FitPulse(self,event=0):
if self.pms:
#If there is pms then we need to set the right models
pulseMod = self.pms.GetPulseMod()
func = pulseMod.pulseLookup[self.numPulse-1]
# func = self.pulseLookup[self.numPulse-1]
initialValues=pulseMod.GetInitialValues()
self.tmax=self.tMaxSelector.GetData()
if self.tmax == []:
fixPar = pulseMod.GetFixedParams()
limits = pulseMod.limits
initialValues = pulseMod.GetInitialValues()
else:
print "\n_________________________________\n"
print "Initial guess(es) for Tmax"
for x in self.tmax:
print x
print "\n_________________________________\n"
limits = []
tmp1 = []
tmp3=[]
for x in self.tmax:
tmp2 = initialValues
if self.useSelector:
tmp2[0] = x
tmp1.extend(tmp2)
tmp3.extend(pulseMod.GetFixedParams())
limits.extend(pulseMod.limits)
intialValues = tmp1
fixPar=tmp3
# print limits
# print initialValues
# print fixPar
#### Testing!!!!!
#dT = array(map(lambda x: x[1] - x[0] ,self.tBins))
#wErrors = self.errors*dT
#wData = self.data*dT
#fit = mpPulseFitEngine(func, array(map(mean,self.tBins))+self.timeOffset, self.data.tolist(), sigma=wErrors,p0=initialValues,fixed=fixPar,maxiter=400, limits=limits,tBins = self.tBins)
# place back if this does not work
fit = mpCurveFit(func, array(map(mean,self.tBins))+self.timeOffset, self.data.tolist(), sigma=self.errors,p0=initialValues,fixed=fixPar,maxiter=400, limits=limits)
self.fitResults = fit.params
self.fitCov = fit.errors
self.fitResults[3]-=self.timeOffset
self.GoodnessOfFit()
self.DisplayFitResults()
self.DisplayFitPlot()