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simulate.jl
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function simulate(model::Model,n::Int64,period::Int64;δ=1.0)::DataFrame
period_mp=model.mp.period*δ
τ = period_mp
Δt=period*δ
n_mp=Int(floor((n*period ) / (model.mp.period )))
#n_real=n-n_mp
Xt=zeros(n,2)
Xt_mp=zeros(n_mp+1,2) #nb maintenance + 0
time=zeros(n+2*n_mp)
Yt=zeros(n+2*n_mp)
j , t , k = 2, Δt , 2
Xt1, Xt2 = 0.0 , 0.0
for i in 2:n
if (t-Δt < τ) & (τ < t)
#println("($(t-Δt) < $τ) & ($τ < $t)")
# between t-Δt and τ
ΔX12 = ΔX(model, τ - t + Δt)
Xt_mp[k,1]=Xt1+ΔX12[1]
Xt_mp[k,2]=Xt2+ΔX12[2]
time[j]=τ
Yt[j]=Xt_mp[k,1]
## reduction
time[j+1]=τ
Yt[j+1]= Xt_mp[k,1]-model.mp.ρ*(Xt_mp[k,2] - Xt_mp[k-1,2])
k+=1
# between τ and t
ΔX12 = ΔX(model, t-τ)
time[j+2]=t
Yt[j+2]=Yt[j]+ΔX12[1]
j+=3
τ += period_mp
else
ΔX12 = ΔX(model,Δt)
Xt1 += ΔX12[1]
Xt2 += ΔX12[2]
Xt[i,1]=Xt1
Xt[i,2]=Xt2
#println("$i: j=$j k=$k $(n+3*n_mp)")
time[j]=t
Yt[j]=Xt1
j += 1
if τ==t
time[j]=t
Yt[j]= Xt[i,1]-model.mp.ρ*(Xt[i,2] - Xt[i-model.mp.period,2])
j += 1
τ += period_mp
end
end
t += Δt
end
return DataFrame(Time=time[1:n],Degradation=Yt[1:n])
end
# function simulate3(model::Model,n::Int64,p::Int64;δ=1.0)::DataFrame
# τ_mp=model.mp.τ
# Δt=p*δ
# Xt=zeros(n-nb_maint,2) ; Yt=zeros(n,1)
# Xt1, Xt2=0.0, 0.0
# t=0.0
# time=zeros(n)
# nb_maint=floor(((n-1)*Δt)/τ_mp)
# ind_τ=[0;(ceil(τ_mp/Δt)+1):(ceil(τ_mp/Δt)):(n-nb_maint)] #indices des temps de maintenance sur Xt
# N=n-nb_maint
# i=2
# for l in 2:N
# Xt[l,1]=Xt1
# Xt[l,2]=Xt2
# if τ_mp < t #min temps
# Δt1=Δt-(t-τ_mp)
# Δt2=t-τ_mp
# time[i]=τ_mp
# Δy = ΔX(model,Δt1)
# Yt[i] = Xt1
# t = τ_mp
# i += 1
# if τ_mp == t
# time[i]=τ_mp
# t += Δt2
# Yt[i] = Xt1 - model.mp.ρ*(Xt2-Xt[ind_τ[k],2])
# Δy = ΔX(model,Δt2)
# i += 1
# end
# τ_mp += τ_mp
# else
# if τ_mp == t
# time[i]=τ_mp
# Yt[i] = Xt1 - model.mp.ρ*(Xt2-Xt[ind_τ[k],2])
# i += 1
# τ_mp += τ_mp
# end
# time[i]=t
# t += Δt
# Δy = ΔX(model,Δt)
# Xt1 += Δy[1]
# Xt2 += Δy[2]
# Yt[i]=Xt1
# i += 1
# end
# end
# Xt1 += Δy[1]
# Xt2 += Δy[2]
# end
# return DataFrame(Time=time,Degradation=Yt)
# end
# function simulate2(model::Model,n::Int64,p::Int64)::DataFrame
# tau_periode=model.mp.τ
# nb_maint=floor(Int64,(n - 1) / (tau_periode + 1))
# n=n-nb_maint
# t=0:p:n*p-1
# taus=Float64[0;t[tau_periode+1:tau_periode:length(t)-1];t[length(t)]]
# tausm=t[range(tau_periode+1,length=nb_maint,step=tau_periode)]
# Random.seed!(ale)
# inc=[0 0]
# cov=model.r*sqrt(model.sigma21*model.sigma22)
# for i in 1:length(diff(t))
# inc=[inc;reshape(rand(MvNormal([model.mu1*diff(t)[i],mu2*diff(t)[i]],[model.sigma21*diff(t)[i] cov*diff(t)[i] ; cov*diff(t)[i] sigma22*diff(t)[i]])),1,2)]
# end
# Xt=cumsum(inc,dims=1)
# Xt0=Xt[findall(t.<=taus[2]),:]
# Xts=Float64[] ; X1=Float64[] ; X2=Float64[]
# for i in 1:(length(taus)-1)
# Xt1=Xt[intersect(findall(t.<=taus[i+1]),findall(t.>=taus[i])),1]
# Xt2=Xt[intersect(findall(t.<=taus[i+1]),findall(t.>=taus[i])),2]
# Xtf=Xt1.-model.mp.ρ*Xt2[1]
# Xts=append!(Xts,Xtf)
# X1=append!(X1,Xt1)
# X2=append!(X2,Xt2)
# end
# if length(taus)==(length(tausm)+1)
# Xtp=X1[length(X1)].-model.mp.ρ *X2[length(X2)]
# Xts=append!(Xts,Xtp)
# end
# ti=sort([tausm;t])
# dfxt=DataFrame(Temps=ti, Degradation=Xts)
# return dfxt, taus, tausm, t, Xt , X1, X2
# end
#= function ARD1(al,ale,mu1,mu2,sigma21,sigma22,r,rho,n::Int64,p::Float64,tau_periode::Int64,perio::Bool)
nb_maint=floor(Int64,(n - 1) / (tau_periode + 1))
n=n-nb_maint
if perio
t=0:p:n*p-1
else
Random.seed!(al)
t=sort(sample(1:(n*5),n-1,replace=false))
t=append!(zeros(1),t)
Random.seed!()
end
taus=Float64[0;t[tau_periode+1:tau_periode:length(t)-1];t[length(t)]]
tausm=t[range(tau_periode+1,length=nb_maint,step=tau_periode)]
Random.seed!(ale)
inc=[0 0]
cov=r*sqrt(sigma21*sigma22)
for i in 1:length(diff(t))
inc=[inc;reshape(rand(MvNormal([mu1*diff(t)[i],mu2*diff(t)[i]],[sigma21*diff(t)[i] cov*diff(t)[i] ; cov*diff(t)[i] sigma22*diff(t)[i]])),1,2)]
end
Xt=cumsum(inc,dims=1)
Xt0=Xt[findall(t.<=taus[2]),:]
Xts=Float64[] ; X1=Float64[] ; X2=Float64[]
for i in 1:(length(taus)-1)
Xt1=Xt[intersect(findall(t.<=taus[i+1]),findall(t.>=taus[i])),1]
Xt2=Xt[intersect(findall(t.<=taus[i+1]),findall(t.>=taus[i])),2]
Xtf=Xt1.-rho*Xt2[1]
Xts=append!(Xts,Xtf)
X1=append!(X1,Xt1)
X2=append!(X2,Xt2)
end
if length(taus)==(length(tausm)+1)
Xtp=X1[length(X1)].-rho*X2[length(X2)]
Xts=append!(Xts,Xtp)
end
ti=sort([tausm;t])
dfxt=DataFrame(Temps=ti, Degradation=Xts)
return dfxt, taus, tausm, t, Xt , X1, X2
end
=#