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Copy pathDefect55_StepsizeCalibration.py
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Defect55_StepsizeCalibration.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 6 15:03:39 2016
@author: dfs1
"""
import numpy as np
import CDSAXSfunctions as CD
import CDplot as CDp
Intensity=np.loadtxt('Defect55_Center_16_Int.txt')
Qx = np.loadtxt('Defect55_Center_16_Qx.txt')
Qz = np.loadtxt('Defect55_Center_16_Qz.txt')
Trapnumber = 6
DW = 1.1
I0 = 9e-5
Bk =1
Pitch = 110
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] = 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):
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)
M=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]
return SimInt
def MCMCInit_LAM1(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=SimInt_LAM1(MCMCInit[i,:])
C=np.sum(CD.Misfit(Intensity,SimInt))
MCMCInit[i,int(MCPAR[1])]=C
else:
MCMCInit[i,0:int(MCPAR[1])]=FITPAR
SimInt=SimInt_LAM1(MCMCInit[i,:])
C=np.sum(CD.Misfit(Intensity,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)
MCMCInitial=MCMCInit_LAM1(FITPAR,FITPARLB,FITPARUB,MCPAR)
Acceptprob=0;
while Acceptprob < 0.3 or Acceptprob > 0.4:
L = int(MCPAR[1])
Stepnumber= int(MCPAR[2])
SampledMatrix=np.zeros([Stepnumber,L+1])
SampledMatrix[0,:]=MCMCInitial[0,:]
Move = np.zeros([L+1])
ChiPrior = MCMCInitial[0,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
Acceptprob=AcceptanceNumber/Acceptancetotal
print(Acceptprob,MCPAR[5],MCPAR[6])
if Acceptprob < 0.3:
MCPAR[5]=MCPAR[5]+1
MCPAR[6]=MCPAR[6]+1
if Acceptprob > 0.4:
MCPAR[5]=MCPAR[5]-1
MCPAR[6]=MCPAR[6]-1