def SimInt_LAM1(FITPAR):
TPARs=np.zeros([Trapnumber+1,2])
TPARs[:,0:2]=np.reshape(FITPAR[0:(Trapnumber+1)*2],(Trapnumber+1,2))
SPAR=FITPAR[Trapnumber*2+2:Trapnumber*2+5]
(Coord)= CD.LAM1CoordAssign(TPAR,SLD,Trapnumber,Pitch)
F1 = CD.FreeFormTrapezoid(Coord[:,:,0],Qx,Qz,Trapnumber)
return SimIntM=np.power(np.exp(-1*(np.power(Qx,2)+np.power(Qz,2))*np.power(SPAR[0],2)),0.5)
Formfactor=F1*M
Formfactor=abs(Formfactor)
SimInt = np.power(Formfactor,2)*SPAR[1]+SPAR[2]
TPAR[2, 0] = 66.7 TPAR[2, 1] = 1.8 SLD[2, 0] = SLD2 TPAR[3, 0] = 72.6 TPAR[3, 1] = 1.6 SLD[3, 0] = SLD2 TPAR[4, 0] = 70.4 TPAR[4, 1] = 30.8 SLD[4, 0] = SLD2 TPAR[5, 0] = 58.3 TPAR[5, 1] = 5.1 SLD[5, 0] = SLD2 TPAR[6, 0] = 36.5 TPAR[6, 1] = 0 Coord = CD.LAM1CoordAssign(TPAR, SLD, Trapnumber, Pitch) CDp.plotLAM1(Coord, Trapnumber, Pitch) (FITPAR, FITPARLB, FITPARUB) = CD.PBA_LAM1(TPAR, SPAR, Trapnumber) MCPAR = np.zeros([7]) MCPAR[0] = 1 # Chainnumber MCPAR[1] = len(FITPAR) MCPAR[2] = 5000 #stepnumber MCPAR[3] = 1 #randomchains MCPAR[4] = 1 # Resampleinterval MCPAR[5] = 20 # stepbase MCPAR[6] = 20 # steplength def SimInt_LAM1(FITPAR):
def Uncertainty1T(ReSampledMatrix):
Xi = np.zeros([101,2,len(ReSampledMatrix[:,0])])
Yi = np.zeros([101,1,len(ReSampledMatrix[:,0])])
PopWidth= np.zeros([len(ReSampledMatrix[:,0])])
PopHeight=np.zeros([len(ReSampledMatrix[:,0])])
TrapHeight=np.zeros([Trapnumber+1,len(ReSampledMatrix[:,0])])
for PopNumber in range(len(ReSampledMatrix[:,0])):
TPARU=np.zeros([Trapnumber+1,2])
TPARU[:,0:2]=np.reshape(ReSampledMatrix[PopNumber,0:(Trapnumber+1)*2],(Trapnumber+1,2))
(CoordUnc)= CD.LAM1CoordAssign(TPARU,SLD,Trapnumber,Pitch)
for i in np.arange(1,Trapnumber+1,1):
TrapHeight[i,PopNumber]=TrapHeight[i-1,PopNumber]+TPARU[i-1,1]
for LineNumber in range(1):
EffTrapnumber=0
#LeftSide
X1L = CoordUnc[EffTrapnumber,0,LineNumber]
X2L = CoordUnc[EffTrapnumber+1,0,LineNumber]
X1R = CoordUnc[EffTrapnumber,1,LineNumber]
X2R = CoordUnc[EffTrapnumber+1,1,LineNumber]
Y1 = TrapHeight[EffTrapnumber,PopNumber]
Y2 = TrapHeight[EffTrapnumber+1,PopNumber]
Disc=0
for c in np.arange(0,101,1):
if Disc > Y2 and Disc <TrapHeight[Trapnumber,PopNumber]:
EffTrapnumber=EffTrapnumber+1
X1L = CoordUnc[EffTrapnumber,0,LineNumber]
X2L = CoordUnc[EffTrapnumber+1,0,LineNumber]
X1R = CoordUnc[EffTrapnumber,1,LineNumber]
X2R = CoordUnc[EffTrapnumber+1,1,LineNumber]
Y1 = TrapHeight[EffTrapnumber,PopNumber]
Y2 = TrapHeight[EffTrapnumber+1,PopNumber]
ML = (Y2-Y1)/(X2L-X1L)
MR= (Y2-Y1)/(X2R-X1R)
BL = Y1-ML*X1L
BR = Y1-MR*X1R
Xi[c,0,PopNumber]=(Disc-BL)/ML
Xi[c,1,PopNumber]=(Disc-BR)/MR
Yi[c,0,PopNumber]=Disc
Disc=Disc+TrapHeight[Trapnumber,PopNumber]/100
Xi[:,:,PopNumber]=Xi[:,:,PopNumber]-(Xi[0,1,PopNumber]-Xi[0,0,PopNumber])/2
PopWidth[PopNumber]=Xi[50,1,PopNumber]-Xi[50,0,PopNumber]
PopHeight[PopNumber]=Yi[100,0,PopNumber]
S=np.std(Xi,2)*1.96
Center = np.average(Xi,2)
Sy=np.std(Yi,2)*1.96
YC=np.average(Yi,2)
OuterEdge=Center
YInner=YC-Sy
YOuter=YC+Sy
OuterEdge[:,0]=OuterEdge[:,0]-S[:,0]
OuterEdge[:,1]=OuterEdge[:,1]+S[:,1]
InnerEdge=Center
InnerEdge[:,0]=InnerEdge[:,0]+S[:,0]
InnerEdge[:,1]=InnerEdge[:,1]-S[:,1]
LinePlot=np.zeros([2*101,2])
InnerPlot=np.zeros([2*101,2])
OuterPlot=np.zeros([2*101,2])
LinePlot[0:101,0]=Center[:,0]
LinePlot[101:202,0]=np.flipud(Center[:,1])
LinePlot[0:101,1]=YC[:,0]
LinePlot[101:202,1]=np.flipud(YC[:,0])
InnerPlot[0:101,0]=InnerEdge[:,0]
InnerPlot[101:202,0]=np.flipud(InnerEdge[:,1])
InnerPlot[0:101,1]=YInner[:,0]
InnerPlot[101:202,1]=np.flipud(YInner[:,0])
OuterPlot[0:101,0]=OuterEdge[:,0]
OuterPlot[101:202,0]=np.flipud(OuterEdge[:,1])
OuterPlot[0:101,1]=YOuter[:,0]
OuterPlot[101:202,1]=np.flipud(YOuter[:,0])
vi=np.zeros([100,1])
vo=np.zeros([100,1])
vc=np.zeros([100,1])
for l in np.arange(1,1+0.0001,2):
for h in np.arange(1,101,1):
vi[h-1,l/2]=0.5*(YInner[h,l-1]-YInner[h-1,l-1])*((InnerEdge[h-1,l]-InnerEdge[h-1,l-1])+(InnerEdge[h,l]-InnerEdge[h,l-1]))
vo[h-1,l/2]=0.5*(YOuter[h,l-1]-YOuter[h-1,l-1])*((OuterEdge[h-1,l]-OuterEdge[h-1,l-1])+(OuterEdge[h,l]-OuterEdge[h,l-1]))
vc[h-1,l/2]=0.5*(YC[h,l-1]-YC[h-1,l-1])*((Center[h-1,l]-Center[h-1,l-1])+(Center[h,l]-Center[h,l-1]))
vd=vo-vi
vt=np.sum(vd)
vct=np.sum(vc)
WidthAvg=np.average(PopWidth)
HeightAvg=np.average(PopHeight)
WidthStd=np.std(PopWidth)
HeightStd=np.std(PopHeight)
viLower=np.zeros([10,1])
voLower=np.zeros([10,1])
vcLower=np.zeros([10,1])
for l in np.arange(1,1+0.0001,2):
for h in np.arange(1,11,1):
viLower[h-1,l/2]=0.5*(YInner[h,l-1]-YInner[h-1,l-1])*((InnerEdge[h-1,l]-InnerEdge[h-1,l-1])+(InnerEdge[h,l]-InnerEdge[h,l-1]))
voLower[h-1,l/2]=0.5*(YOuter[h,l-1]-YOuter[h-1,l-1])*((OuterEdge[h-1,l]-OuterEdge[h-1,l-1])+(OuterEdge[h,l]-OuterEdge[h,l-1]))
vcLower[h-1,l/2]=0.5*(YC[h,l-1]-YC[h-1,l-1])*((Center[h-1,l]-Center[h-1,l-1])+(Center[h,l]-Center[h,l-1]))
vdLower=voLower-viLower
vtLower=np.sum(vdLower)
vctLower=np.sum(vcLower)
viUpper=np.zeros([10,1])
voUpper=np.zeros([10,1])
vcUpper=np.zeros([10,1])
for l in np.arange(1,1+0.0001,2):
for h in np.arange(91,101,1):
viUpper[h-91,l/2]=0.5*(YInner[h,l-1]-YInner[h-1,l-1])*((InnerEdge[h-1,l]-InnerEdge[h-1,l-1])+(InnerEdge[h,l]-InnerEdge[h,l-1]))
voUpper[h-91,l/2]=0.5*(YOuter[h,l-1]-YOuter[h-1,l-1])*((OuterEdge[h-1,l]-OuterEdge[h-1,l-1])+(OuterEdge[h,l]-OuterEdge[h,l-1]))
vcUpper[h-91,l/2]=0.5*(YC[h,l-1]-YC[h-1,l-1])*((Center[h-1,l]-Center[h-1,l-1])+(Center[h,l]-Center[h,l-1]))
vdUpper=voUpper-viUpper
vtUpper=np.sum(vdUpper)
vctUpper=np.sum(vcUpper)
return(vt,vct,vtLower,vctLower,vtUpper,vctUpper,WidthAvg,HeightAvg,WidthStd,HeightStd,LinePlot,InnerPlot,OuterPlot)
