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ContactHoleTest.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Feb 16 14:12:44 2017
Contact Hole Analysis code
@author: dfs1
"""
import numpy as np
import ContactHoleFunctions as CD
import ContactPlotting as CDplot
from multiprocessing import Pool
import time
import scipy.special as sp
# Imports Intensity and Qx/Qr positions, normalizes lowest intensity to 1
Intensity=np.loadtxt('AInt.txt')
Qr = np.loadtxt('AQr.txt')
Qz = np.loadtxt('AQz.txt')
Intensity[np.isnan(Intensity)]=1
IM=np.min(Intensity)
Intensity=np.loadtxt('AInt.txt')
Intensity=Intensity/IM
# end Data import section
ConeNumber = 2
CPAR=np.zeros([ConeNumber+1,2])
SLD=np.zeros(ConeNumber+1)
Discretization = np.zeros(ConeNumber)
Pitch = 120
CPAR[0,0]= 22; CPAR[0,1]=160; SLD[0]=1;
CPAR[1,0]= 27; CPAR[1,1]= 33; SLD[1]=2.2;
CPAR[2,0]=30; CPAR[2,1]=0;
Coord=CD.CoordAssign(CPAR,SLD,ConeNumber,Pitch)
#CDplot.plotCone(Coord,ConeNumber,Pitch)
#CDplot.PlotQzCut(Qz,Intensity,SimInt,9)
Discretization[0]=40
Discretization[1]=10
I0=1
Bk=1
DW=1
SPAR=np.zeros(3)
SPAR[0]=DW; SPAR[1]=I0; SPAR[2]=Bk;
(FITPAR,FITPARLB,FITPARUB)=CD.PBA_Cone(CPAR,SPAR,ConeNumber)
MCPAR=np.zeros([7])
MCPAR[0] = 1 # Chainnumber
MCPAR[1] = len(FITPAR)
MCPAR[2] = 100 #stepnumber
MCPAR[3] = 0 #randomchains
MCPAR[4] = 1 # Resampleinterval
MCPAR[5] = 100 # stepbase
MCPAR[6] = 100 # steplength
H1 = 0
H2 = 0
Form=np.zeros([int(len(Qr[:,0])),int(len(Qr[0,:]))])
for i in range(ConeNumber):
H2=H2+CPAR[i,1]
z=np.zeros([int(Discretization[i])])
stepsize=CPAR[i,1]/Discretization[i]
z=np.arange(H1,H2+0.01,stepsize)
if i > 0 :
H1=H1+CPAR[i-1,1]
z=np.arange(H1,H2+0.01,stepsize)
R1=CPAR[i,0]
R2=CPAR[i+1,0]
if R1==R2:
R1=R1+0.000001
Slope=(H2-H1)/(R2-R1)
for ii in range(len(z)-1):
RI1=(z[ii]-H1)/Slope+R1
RI2=(z[ii+1]-H1)/Slope+R1
fa=2*np.pi*RI1/Qr*sp.jv(1,Qr*RI1)*np.exp(1j*Qz*z[ii])
fb=2*np.pi*RI2/Qr*sp.jv(1,Qr*RI2)*np.exp(1j*Qz*z[ii+1])
Form=Form+stepsize*(fb+fa)/2*SLD[i]
F1=CD.ConeFourierTransform(CPAR,ConeNumber,Qr,Qz,Discretization,SLD)
SimInt = np.power(abs(Form),2)
S=CD.ConeIntensitySim(CPAR,ConeNumber,Qr,Qz,Discretization,SLD,SPAR)
def ConeIntensitySim(FITPAR):
H1 = 0
H2 = 0
Form=np.zeros([int(len(Qr[:,0])),int(len(Qr[0,:]))])
CPAR=np.zeros([ConeNumber+1,2])
CPAR[:,0:2]=np.reshape(FITPAR[0:(ConeNumber+1)*2],(ConeNumber+1,2))
SPAR=FITPAR[ConeNumber*2+2:ConeNumber*2+5]
for i in range (ConeNumber):
H2=H2+CPAR[i,1]
z=np.zeros([int(Discretization[i])])
stepsize=CPAR[i,1]/Discretization[i]
z=np.arange(H1,H2+0.01,stepsize)
if i > 0 :
H1=H1+CPAR[i-1,1]
z=np.arange(H1,H2+0.01,stepsize)
R1=CPAR[i,0]
R2=CPAR[i+1,0]
if R1==R2:
R1=R1+0.000001
Slope=(H2-H1)/(R2-R1)
for ii in range(len(z)-1):
RI1=(z[ii]-H1)/Slope+R1
RI2=(z[ii+1]-H1)/Slope+R1
fa=2*np.pi*RI1/Qr*sp.jv(1,Qr*RI1)*np.exp(1j*Qz*z[ii])
fb=2*np.pi*RI2/Qr*sp.jv(1,Qr*RI2)*np.exp(1j*Qz*z[ii+1])
Form=Form+stepsize*(fb+fa)/2*SLD[i]
M=np.power(np.exp(-1*(np.power(Qr,2)+np.power(Qz,2))*np.power(SPAR[0],2)),0.5)
Formfactor=Form*M
Formfactor=abs(Formfactor)
SimInt = np.power(Formfactor,2)*SPAR[1]+SPAR[2]
return (SimInt)
S1=ConeIntensitySim(FITPAR)
def MCMCInit_Cone(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=ConeIntensitySim(MCMCInit[i,:])
C=np.sum(CD.Misfit(Intensity,SimInt))
MCMCInit[i,int(MCPAR[1])]=C
else:
MCMCInit[i,0:int(MCPAR[1])]=FITPAR
SimInt=ConeIntensitySim(MCMCInit[i,:])
C=np.sum(CD.Misfit(Intensity,SimInt))
MCMCInit[i,int(MCPAR[1])]=C
return MCMCInit
def MCMC_Cone(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=ConeIntensitySim(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_Cone(FITPAR,FITPARLB,FITPARUB,MCPAR)
MCMC_List=[0]*int(MCPAR[0])
for i in range(int(MCPAR[0])):
MCMC_List[i]=MCMCInitial[i,:]
start_time = time.perf_counter()
if __name__ =='__main__':
pool = Pool(processes=1)
F=pool.map(MCMC_Cone,MCMC_List)
F=tuple(F)
np.save('LAMtest',F) # add savedfilename here
end_time=time.perf_counter()
print(end_time-start_time)
ReSampledMatrix=F[0]