Hidden Markov Models for Molecular Motors: xs=Step125(x) - File Exchange

Code covered by the BSD License

DisplayModel(M, L, iterations... Display the HMM described by M, the log likelihood value,
ForBackF(M,Y) Forward-backward algorithm for step-HMM
ForwardBackward_3(M,Y,I0,test... function [LL newM gamma] = ForwardBackward_3(M,Y,I0,test,BigB)
M=MakeMonotonicModel(nu, yQua... function M=MakeMonotonicModel(nu, yQuantum, noiseSigma, transProb, stepSizes, stepSigma)
ModelChange(M1,M0) Check the changes in the transition probabilities. Looks at the
PlotAndLabelPeaks(x,y,minsize... function PlotAndLabelPeaks(x,y,minsize,xscale)
RestorationPlot(Y, EstY, wrap... function RestorationPlot(Y, EstY, wrap, yquantum, EYbase,markerstr) Fancy
WtConvol(a,b,m)
[EstY EstI LP Wrap]=ViterbiRe... % Viterbi algorithm for step-HMM
[X,Y]=StepSimulator(M,N) function [X,Y]=StepSimulator(M,N)
[X,Y]=StepSimulatorC(M,N) Given the number of time points N and the model M, simulate a staircase
[sigma nu]=EstSigmaAndNu(Y,I0) Find reasonable values for these variables based on the step data vector
b=Makeb(nu,sigma,DutyCycle,Er...
ms=QuickShift(m,shift) Faster replacement for circshift for time-critical applications.
ms=QuickShiftMex(m,shift) Faster replacement for circshift for time-critical applications.
n=NextNiceNumber(x,f) function n=NextNiceNumber(x)
xs=Step125(x)
MotorHMM1.m
MotorHMM2.m
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from Hidden Markov Models for Molecular Motors by Fred Sigworth
A set of functions for analysing noisy recordings of the random stepping of molecular motors

xs=Step125(x)
function xs=Step125(x) % Find the value in a 1..2..5 x 10^n sequence that is >= x. % n=floor(log10(x)); x0=x/10^n; testvals=[0.5 1 2 5 10]; index=find((x0<=testvals),1); xs=testvals(index)*10^n;