def MCMCInit_ID1(FITPAR, FITPARLB, FITPARUB, MCPAR):
MCMCInit = np.zeros([int(MCPAR[0]), int(MCPAR[1]) + 1])
for i in range(int(MCPAR[0])):
if i < MCPAR[
3]: #reversed from matlab code assigns all chains below randomnumber as random chains
for c in range(int(MCPAR[1])):
MCMCInit[i, c] = FITPARLB[c] + (
FITPARUB[c] - FITPARLB[c]) * np.random.random_sample()
(SimInt, Amplitude) = SimInt_ID1(MCMCInit[i, :])
C = np.sum(CD.Misfit(Intensity2, SimInt))
MCMCInit[i, int(MCPAR[1])] = C
else:
MCMCInit[i, 0:int(MCPAR[1])] = FITPAR
(SimInt, Amplitude) = SimInt_ID1(MCMCInit[i, :])
C = np.sum(CD.Misfit(Intensity2, SimInt))
MCMCInit[i, int(MCPAR[1])] = C
return MCMCInit
def MCMC_LAM1(MCMC_List):
MCMCInit=MCMC_List
L = int(MCPAR[1])
Stepnumber= int(MCPAR[2])
SampledMatrix=np.zeros([Stepnumber,L+1])
SampledMatrix[0,:]=MCMCInit
Move = np.zeros([L+1])
ChiPrior = MCMCInit[L]
for step in np.arange(1,Stepnumber,1):
Temp = SampledMatrix[step-1,:].copy()
for p in range(L-1):
StepControl = MCPAR[5]+MCPAR[6]*np.random.random_sample()
Move[p] = (FITPARUB[p]-FITPARLB[p])/StepControl*(np.random.random_sample()-0.5) # need out of bounds check
Temp[p]=Temp[p]+Move[p]
if Temp[p] < FITPARLB[p]:
Temp[p]=FITPARLB[p]+(FITPARUB[p]-FITPARLB[p])/1000
elif Temp[p] > FITPARUB[p]:
Temp[p]=FITPARUB[p]-(FITPARUB[p]-FITPARLB[p])/1000
SimPost=SimInt_LAM1(Temp)
ChiPost=np.sum(CD.Misfit(Intensity,SimPost))
if ChiPost < ChiPrior:
SampledMatrix[step,0:L]=Temp[0:L]
SampledMatrix[step,L]=ChiPost
ChiPrior=ChiPost
else:
MoveProb = np.exp(-0.5*np.power(ChiPost-ChiPrior,2))
if np.random.random_sample() < MoveProb:
SampledMatrix[step,0:L]=Temp[0:L]
SampledMatrix[step,L]=ChiPost
ChiPrior=ChiPost
else:
SampledMatrix[step,:]=SampledMatrix[step-1,:]
AcceptanceNumber=0;
Acceptancetotal=len(SampledMatrix[:,1])
for i in np.arange(1,len(SampledMatrix[:,1]),1):
if SampledMatrix[i,0] != SampledMatrix[i-1,0]:
AcceptanceNumber=AcceptanceNumber+1
AcceptanceProbability=AcceptanceNumber/Acceptancetotal
print(AcceptanceProbability)
ReSampledMatrix=np.zeros([int(MCPAR[2])/int(MCPAR[4]),len(SampledMatrix[1,:])])
c=-1
for i in np.arange(0,len(SampledMatrix[:,1]),MCPAR[4]):
c=c+1
ReSampledMatrix[c,:]=SampledMatrix[i,:]
return (ReSampledMatrix)
def MCMC_ID1(MCMC_List):
MCMCInit = MCMC_List
L = int(MCPAR[1])
Stepnumber = int(MCPAR[2])
SampledMatrixI = np.zeros([Stepnumber, L + 1])
SampledMatrixI[0, :] = MCMCInit
Move = np.zeros([L + 1])
ChiPrior = MCMCInit[L]
for step in np.arange(1, Stepnumber, 1):
Temp = SampledMatrixI[step - 1, :].copy()
for p in range(L - 1):
StepControl = MCPAR[5] + MCPAR[6] * np.random.random_sample()
Move[p] = (FITPARUB[p] - FITPARLB[p]) / StepControl * (
np.random.random_sample() - 0.5) # need out of bounds check
Temp[p] = Temp[p] + Move[p]
if Temp[p] < FITPARLB[p]:
Temp[p] = FITPARLB[p] + (FITPARUB[p] - FITPARLB[p]) / 1000
elif Temp[p] > FITPARUB[p]:
Temp[p] = FITPARUB[p] - (FITPARUB[p] - FITPARLB[p]) / 1000
(SimPost, AmpPost) = SimInt_ID1(Temp)
ChiPost = np.sum(CD.Misfit(Intensity2, SimPost))
if ChiPost < ChiPrior:
SampledMatrixI[step, 0:L] = Temp[0:L]
SampledMatrixI[step, L] = ChiPost
ChiPrior = ChiPost
else:
MoveProb = np.exp(-0.5 * np.power(ChiPost - ChiPrior, 2))
if np.random.random_sample() < MoveProb:
SampledMatrixI[step, 0:L] = Temp[0:L]
SampledMatrixI[step, L] = ChiPost
ChiPrior = ChiPost
else:
SampledMatrixI[step, :] = SampledMatrixI[step - 1, :]
ReSampledMatrixI = np.zeros(
[int(MCPAR[2]) / int(MCPAR[4]),
len(SampledMatrixI[1, :])])
c = -1
for i in np.arange(0, len(SampledMatrixI[:, 1]), MCPAR[4]):
c = c + 1
ReSampledMatrixI[c, :] = SampledMatrixI[i, :]
(UNCT_Param) = Uncertainty1T(ReSampledMatrixI)
return (UNCT_Param) #ReSampledMatrixI
def SimInt_PSPVP(FITPAR):
T=int(FITPAR[len(FITPAR)-3])
Disc=int(FITPAR[len(FITPAR)-2])
Pitch=int(FITPAR[len(FITPAR)-4])
Spline=np.reshape(FITPAR[0:T*5],(T,5))
Spline
Offset=FITPAR[T*5:T*5+7]
SPAR=FITPAR[T*5+7:T*5+11]
Coord=CD.PSPVPCoord(Spline,MCoord,Trapnumber, Disc,Pitch, Offset)
F1 = CD.FreeFormTrapezoid(Coord[:,:,0],Qx,Qz,Disc+1)
F2 = CD.FreeFormTrapezoid(Coord[:,:,1],Qx,Qz,Disc+1)
F3 = CD.FreeFormTrapezoid(Coord[:,:,2],Qx,Qz,Disc+1)
F4 = CD.FreeFormTrapezoid(Coord[:,:,3],Qx,Qz,Disc+1)
F5 = CD.FreeFormTrapezoid(Coord[:,:,4],Qx,Qz,Disc+1)
F6 = CD.FreeFormTrapezoid(Coord[:,:,5],Qx,Qz,Disc+1)
F7 = CD.FreeFormTrapezoid(Coord[:,:,6],Qx,Qz,Disc+1)
F8 = CD.FreeFormTrapezoid(Coord[:,:,7],Qx,Qz,Disc+1)
Formfactor=(F1+F2+F3+F4+F5+F6+F7+F8)
M=np.power(np.exp(-1*(np.power(Qx,2)+np.power(Qz,2))*np.power(SPAR[0],2)),0.5);
Formfactor=Formfactor*M
SimInt = np.power(abs(Formfactor),2)*SPAR[1]+SPAR[2]
return SimInt
def MCMCInit_PSPVPUniform(FITPAR,FITPARLB,FITPARUB,MCPAR):
MCMCInit=np.zeros([int(MCPAR[0]),int(MCPAR[1])+1])
for i in range(int(MCPAR[1])-3):
if FITPARUB[i]==FITPARLB[i]:
MCMCInit[:,i]=FITPAR[i]
else:
A= np.arange(FITPARLB[i],FITPARUB[i]+0.0001,(FITPARUB[i]-FITPARLB[i])/(int(MCPAR[0])-1))
R=np.random.rand(int(MCPAR[0]))
ind=R.argsort()
A=A[ind]
MCMCInit[:,i]=A
MCMCInit[:,int(MCPAR[1])-3:int(MCPAR[1])]=FITPAR[int(MCPAR[1])-3:int(MCPAR[1])]
for i in range(int(MCPAR[0])):
SimInt=SimInt_PSPVP(MCMCInit[i,:])
C=np.sum(CD.Misfit(Intensity,SimInt))
MCMCInit[i,int(MCPAR[1])]=C
return MCMCInit
def MCMC_PSPVP(MCMC_List):
MCMCInit = MCMC_List
L = int(MCPAR[1])
Stepnumber = int(MCPAR[2])
SampleMatrix = np.zeros([Stepnumber, L + 1])
SampleMatrix[0, :] = MCMCInit
Move = np.zeros([L + 1])
ChiPrior = MCMCInit[L]
for step in np.arange(1, Stepnumber, 1):
Temp = SampleMatrix[step - 1, :]
for p in range(L - 3):
StepControl = MCPAR[5] + MCPAR[6] * np.random.random_sample()
Move[p] = (FITPARUB[p] - FITPARLB[p]) / StepControl * (
np.random.random_sample() - 0.5) # need out of bounds check
Temp[0:L - 3] = Temp[0:L - 3] + Move[0:L - 3]
SimPost = SimInt_PSPVP(Temp)
ChiPost = np.sum(CD.Misfit(Intensity, SimPost))
if ChiPost < ChiPrior:
SampleMatrix[step, 0:L] = Temp[0:L]
SampleMatrix[step, L] = ChiPost
ChiPrior = ChiPost
else:
MoveProb = np.exp(-0.5 * np.power(ChiPost - ChiPrior, 2))
if np.random.random_sample() < MoveProb:
SampleMatrix[step, 0:L] = Temp[0:L]
SampleMatrix[step, L] = ChiPost
ChiPrior = ChiPost
else:
SampleMatrix[step, :] = SampleMatrix[step - 1, :]
return SampleMatrix
def SimInt_ID6M(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]
X1 = FITPAR[Trapnumber * 2 + 5]
(Coord) = CD.ID6MCoordAssign(TPARs, SLD, Trapnumber, Pitch, X1)
F1 = CD.FreeFormTrapezoid(Coord[:, :, 0], Qx, Qz, Trapnumber)
F2 = CD.FreeFormTrapezoid(Coord[:, :, 1], Qx, Qz, Trapnumber)
F3 = CD.FreeFormTrapezoid(Coord[:, :, 2], Qx, Qz, Trapnumber)
F4 = CD.FreeFormTrapezoid(Coord[:, :, 3], Qx, Qz, Trapnumber)
F5 = CD.FreeFormTrapezoid(Coord[:, :, 4], Qx, Qz, Trapnumber)
M = np.power(
np.exp(-1 * (np.power(Qx, 2) + np.power(Qz, 2)) *
np.power(SPAR[0], 2)), 0.5)
Formfactor = (F1 + F2 + F3 + F4 + F5) * M
Formfactor = abs(Formfactor)
SimInt = np.power(Formfactor, 2) * SPAR[1] + SPAR[2]
return (SimInt, Formfactor)
X1=Param[SampleNumber,4]
W=Param[SampleNumber,0]
H=Param[SampleNumber,1]
WidthLoad='W'+str(int(W))+'.txt'
HeightLoad='H'+str(int(H))+'.txt'
Width=np.loadtxt(WidthLoad)
Height=np.loadtxt(HeightLoad)
TPAR[:,0]=Width
TPAR[:,1]=Height
SLD[0,0]=SLD1;
SLD[1,0]=SLD1;
SLD[2,0]=SLD1;
SLD[3,0]=SLD1;
Coord=CD.ID2CoordAssign(TPAR,SLD,Trapnumber,Pitch,X1)
#CDp.plotID1(Coord,Trapnumber,Pitch)
(FITPAR,FITPARLB,FITPARUB)=CD.PBA_ID2(TPAR,SPAR,Trapnumber,X1)
R = np.random.normal(0, 0.225, [len(Qx[:,0]),len(Qx[0,:])])
(DummyIntensity,Amplitude)=SimInt_ID2(FITPAR)
PreInt=abs(Amplitude)
PreInt=np.power(PreInt,2)
M=np.amax(PreInt)
I0=(20000)/M # Scales the intensity so that the max is 20000
SPAR[1]=I0
Intensity=PreInt*I0+Bk
(FITPAR,FITPARLB,FITPARUB)=CD.PBA_ID2(TPAR,SPAR,Trapnumber,X1) #regenerates FITPAR with proper intensity scaling
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.ID1CoordAssign(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)
RA = vt / vct
RAL = vtLower / vctLower
RAU = vtUpper / vctUpper
return (vt, vct, RA, vtLower, vctLower, RAL, vtUpper, vctUpper, RAU,
WidthAvg, HeightAvg, WidthStd, HeightStd, LinePlot, InnerPlot,
OuterPlot)
SPAR[2] = Bk
W = Param[SampleNumber, 0]
H = Param[SampleNumber, 1]
WidthLoad = 'W' + str(int(W)) + '.txt'
HeightLoad = 'H' + str(int(H)) + '.txt'
Width = np.loadtxt(WidthLoad)
Height = np.loadtxt(HeightLoad)
TPAR[:, 0] = Width
TPAR[:, 1] = Height
SLD[0, 0] = SLD1
SLD[1, 0] = SLD1
SLD[2, 0] = SLD1
SLD[3, 0] = SLD1
Coord = CD.ID1CoordAssign(TPAR, SLD, Trapnumber, Pitch)
#CDp.plotID1(Coord,Trapnumber,Pitch)
(FITPAR, FITPARLB, FITPARUB) = CD.PBA_ID1(TPAR, SPAR, Trapnumber)
R = np.random.normal(0, 0.225, [len(Qx[:, 0]), len(Qx[0, :])])
(Intensity, Amplitude) = SimInt_ID1(FITPAR)
N = (1 / (np.power(Intensity, 0.5))) * Intensity # Generates noise
N = N * R
Intensity2 = Intensity + R
# Applies Noise
C = CD.Misfit(Intensity, Intensity2)
Chi2 = np.sum((C))
MCPAR = np.zeros([7])
SPAR[2] = Bk
W = Param[SampleNumber, 0]
H = Param[SampleNumber, 1]
WidthLoad = 'W' + str(int(W)) + '.txt'
HeightLoad = 'H' + str(int(H)) + '.txt'
Width = np.loadtxt(WidthLoad)
Height = np.loadtxt(HeightLoad)
TPAR[:, 0] = Width
TPAR[:, 1] = Height
SLD[0, 0] = SLD1
SLD[1, 0] = SLD1
SLD[2, 0] = SLD1
SLD[3, 0] = SLD1
Coord = CD.ID1CoordAssign(TPAR, SLD, Trapnumber, Pitch)
#CDp.plotID1(Coord,Trapnumber,Pitch)
(FITPAR, FITPARLB, FITPARUB) = CD.PBA_ID1(TPAR, SPAR, Trapnumber)
R = np.random.normal(0, 0.225, [len(Qx[:, 0]), len(Qx[0, :])])
(Intensity, Amplitude) = SimInt_ID1(FITPAR)
N = (1 / (np.power(Intensity, 0.5))) * Intensity # Generates noise
N = N * R
Intensity2 = Intensity + R
# Applies Noise
C = CD.Misfit(Intensity, Intensity2)
Chi2 = np.sum((C))
MCPAR = np.zeros([7])
X1 = Pitch / 3
W = Param[SampleNumber, 0]
H = Param[SampleNumber, 1]
WidthLoad = 'W' + str(int(W)) + '.txt'
HeightLoad = 'H' + str(int(H)) + '.txt'
Width = np.loadtxt(WidthLoad)
Height = np.loadtxt(HeightLoad)
TPAR[:, 0] = Width
TPAR[:, 1] = Height
SLD[0, 0] = SLD1
SLD[1, 0] = SLD1
SLD[2, 0] = SLD1
SLD[3, 0] = SLD1
Coord = CD.ID3MCoordAssign(TPAR, SLD, Trapnumber, Pitch, X1)
#CDp.plotID1(Coord,Trapnumber,Pitch)
(FITPAR, FITPARLB, FITPARUB) = CD.PBA_ID2(TPAR, SPAR, Trapnumber,
X1) #Does not need to be changed
R = np.random.normal(0, 0.225, [len(Qx[:, 0]), len(Qx[0, :])])
(Intensity, Amplitude) = SimInt_ID3M(FITPAR)
N = (1 / (np.power(Intensity, 0.5))) * Intensity # Generates noise
N = N * R
Intensity2 = Intensity + R
# Applies Noise
C = CD.Misfit(Intensity, Intensity2)
Chi2 = np.sum((C))
SPAR[0]=DW; SPAR[1]=I0; SPAR[2]=Bk;
W=Param[SampleNumber,0]
H=Param[SampleNumber,1]
WidthLoad='W'+str(int(W))+'.txt'
HeightLoad='H'+str(int(H))+'.txt'
Width=np.loadtxt(WidthLoad)
Height=np.loadtxt(HeightLoad)
TPAR[:,0]=Width
TPAR[:,1]=Height
SLD[0,0]=SLD1;
SLD[1,0]=SLD1;
SLD[2,0]=SLD1;
SLD[3,0]=SLD1;
Coord=CD.ID1CoordAssign(TPAR,SLD,Trapnumber,Pitch)
#CDp.plotID1(Coord,Trapnumber,Pitch)
(FITPAR,FITPARLB,FITPARUB)=CD.PBA_ID1(TPAR,SPAR,Trapnumber)
R = np.random.normal(0, 0.225, [len(Qx[:,0]),len(Qx[0,:])])
(DummyIntensity,Amplitude)=SimInt_ID1(FITPAR)
PreInt=abs(Amplitude)
PreInt=np.power(PreInt,2)
M=np.amax(PreInt)
I0=(20000)/M # Scales the intensity so that the max is 20000
SPAR[1]=I0
Intensity=PreInt*I0+Bk
(FITPAR,FITPARLB,FITPARUB)=CD.PBA_ID1(TPAR,SPAR,Trapnumber) #regenerates FITPAR with proper intensity scaling
Spline[2, 2] = 1
Spline[2, 3] = 1
Spline[2, 4] = 1
Spline[3, 0] = 21
Spline[3, 1] = 3
Spline[3, 2] = -1
Spline[3, 3] = -0.5
Spline[3, 4] = -1
Spline[4, 0] = 24
Spline[4, 1] = 3
Spline[4, 2] = -1
Spline[4, 3] = -0.5
Spline[4, 4] = -1
MCoord = np.loadtxt('MCOORDPSPVP1.txt')
(Coord) = CD.PSPVPCoord(Spline, MCoord, Trapnumber, Disc, Pitch, Offset)
(FITPAR, FITPARLB, FITPARUB) = CD.PSPVP_PB(Offset, Spline, SPAR, Trapnumber,
Disc)
MCPAR = np.zeros([7])
MCPAR[0] = 1000 # Chainnumber
MCPAR[1] = len(FITPAR)
MCPAR[2] = 100 #stepnumber
MCPAR[3] = 0 #randomchains
MCPAR[4] = 1 # Resampleinterval
MCPAR[5] = 40 # stepbase
MCPAR[6] = 200 # steplength
def SimInt_PSPVP(FITPAR):
T = int(FITPAR[len(FITPAR) - 3])
# -*- coding: utf-8 -*-
"""
This program will be used to analyse CDSAXS data from qz slices, including MCMC program for uncertainty analysis
"""
import numpy as np
import CDSAXSfunctions as CD
import matplotlib.pyplot as plt
[Qz,Qx,Intensity]=CD.CDSAXSimport('test.txt','212SCANQx.txt',121,26)
Intensity=Intensity*1000000
Trapnumber = 6
Discretization = 6
tpar=np.zeros([Trapnumber+1,3])
tpar[0,0] = 35; tpar[0,1] = 37; tpar[0,2] = 1;
tpar[1,0] = 24; tpar[1,1] = 40; tpar[1,2] = 1;
tpar[2,0] = 20; tpar[2,1] = 50; tpar[2,2] = 1;
tpar[3,0] = 15; tpar[3,1] = 50; tpar[3,2] = 1;
tpar[4,0] = 13; tpar[4,1] = 7; tpar[4,2] = 1;
tpar[5,0] = 12; tpar[5,1] = 15; tpar[5,2] = 1;
tpar[6,0] = 10; tpar[6,1] = 0; tpar[6,2] = 1;
X1 = 32; X2 = 35; X3 = 33;
X=np.zeros([2,1])
X[0,0]=X1;X[1,0]=X2
ppar=np.zeros([3,3])
ppar[0,0]=0.5; ppar[0,1]=0.2; ppar[0,2]=0;
ppar[1,0]=0.5; ppar[1,1]=0.2; ppar[1,2]=0;
ppar[2,0]=0.5; ppar[2,1]=0.2; ppar[2,2]=0;
Pitch = 135.7;
DW = 1.5
Spline[2, 2] = 1
Spline[2, 3] = 1
Spline[2, 4] = 1
Spline[3, 0] = 21
Spline[3, 1] = 3
Spline[3, 2] = -1
Spline[3, 3] = -0.5
Spline[3, 4] = -1
Spline[4, 0] = 24
Spline[4, 1] = 3
Spline[4, 2] = -1
Spline[4, 3] = -0.5
Spline[4, 4] = -1
MCoord = np.loadtxt('MCOORDPSPVP1.txt')
(Coord) = CD.PSPVPCoord(Spline, MCoord, Trapnumber, Disc, Pitch, Offset)
(FITPAR, FITPARLB, FITPARUB) = CD.PSPVP_PB(Offset, Spline, SPAR, Trapnumber,
Disc)
#%%
MCPAR = np.zeros([7])
MCPAR[0] = 2 # Chainnumber
MCPAR[1] = len(FITPAR)
MCPAR[2] = 10 #stepnumber
MCPAR[3] = 0 #randomchains
MCPAR[4] = 5 # Resampleinterval
MCPAR[5] = 40 # stepbase
MCPAR[6] = 200 # steplength
def SimInt_PSPVP(FITPAR):
SLD1 = 1; SLD2 = 1.4;
TPAR=np.zeros([Trapnumber+1,2])
SLD=np.zeros([Trapnumber+1,1])
SPAR=np.zeros(3)
SPAR[0]=DW; SPAR[1]=I0; SPAR[2]=Bk;
TPAR[0,0]=84.2; TPAR[0,1]=4.4; SLD[0,0]=SLD1;
TPAR[1,0]=71.8; TPAR[1,1]=38.8;SLD[1,0]=SLD1;
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] = 12 # Chainnumber
MCPAR[1] = len(FITPAR)
MCPAR[2] = 1000 #stepnumber
MCPAR[3] = 1 #randomchains
MCPAR[4] = 1 # Resampleinterval
MCPAR[5] = 100 # stepbase
MCPAR[6] = 100 # steplength
def SimInt_LAM1(FITPAR):
TPARs=np.zeros([Trapnumber+1,2])
