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2

I have previously used the following two helper functions to generate the format of covariance and correlation matrices: covariancematrix[n_] := Table[ σ[i] σ[j] ρ[i, j]^(1 - KroneckerDelta[i, j]), {i, 1, n, 1}, {j, 1, n, 1} ] /. {ρ[i_, j_] :> ρ[j, i] /; i > j} correlationmatrix[n_] := Table[ σ[i] σ[j] ρ[i, j]^(1 - KroneckerDelta[i, ...


2

Here are solutions to both boundary protocols. They are built on the same basic framework -- mainly the function that generates the moves for the walker is what differs between the two. There is a little adjustment in the way the lines and walker point is drawn because of discontinuities in the path generated by the wrap-arround protocol, Path clips at the ...


6

You could use Region functionality (for simpler regions), e.g. rw[pt_, s_, n_, reg_] := Module[{ch = {{0, 0}, {1, 0}, {-1, 0}, {0, 1}, {0, -1}}, np, st}, st = RandomChoice[ch, n]; FoldList[If[RegionMember[reg, #1 + s #2], #1 + s #2, #1] &, pt, st] ] an[p_, step_, num_, regn_] := With[{pnts = rw[p, step, num, regn]}, ListAnimate[ ...


12

Your code shows you are somewhat confused about the indices. Here is some code that is more Mathematica idiomatic and which makes keeping the indices straight much easier. I am running the simulation with a 24 hour step to cut down the data plotted. Should work just a well with your time step. maxk = 10; mink = 2; steps = 365; batterylevel = ...



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