Nothing
## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "##"
)
## ---- eval=TRUE----------------------------------------------------------
# parameters
a = 0.52
b = 2
c = 4
# equations
Eq1 <- c(0,-1, 0,-1, 0, 0, 0, 0, 0, 0)
Eq2 <- c(0, 0, 0, a, 0, 0, 1, 0, 0, 0)
Eq3 <- c(b,-c, 0, 0, 0, 0, 0, 1, 0, 0)
K = cbind(Eq1, Eq2, Eq3)
## ---- eval=TRUE----------------------------------------------------------
visuEq(nVar = 3, dMax = 2, K = K, substit = c("x", "y", "z"))
## ---- eval=TRUE----------------------------------------------------------
# The dynamical system equations (just defined by matrix K)
#
# The number of variables (it can be deduced from matrix K)
nVar = dim(K)[2]
# The maximum polynomial degree of the formulation (also deduced from K)
pMax = dim(K)[1]
dMax = p2dMax(nVar, pMax)
# The initial conditions
inicond <- c(-0.04298734, 1.025536, 0.09057987)
# The integration time step
timeStep <- 0.01
# The initial and ending integration time
tDeb <- 0
tFin <- 10
## ---- eval=FALSE---------------------------------------------------------
# # Prepare the output file
# outLyapFD <- NULL
# # Method 1
# outLyapFD$Wolf <- lyapFDWolf(outLyapFD$Wolf, nVar= nVar, dMax = dMax,
# coeffF = K,
# tDeb = tDeb, dt = timeStep, tFin = tFin,
# yDeb = inicond)
# # Method 2
# outLyapFD$Grond <- lyapFDGrond(outLyapFD$Grond, nVar= nVar, dMax = dMax,
# coeffF = K,
# tDeb = tDeb, dt = timeStep, tFin = tFin,
# yDeb = inicond)
## ---- echo = FALSE, eval=TRUE--------------------------------------------
# To avoid a long time computing, the results are directly loaded
load("../data/outLyapFD.rda")
outLyapFD <- outLyapFD$Ro76
## ---- eval=TRUE----------------------------------------------------------
names(outLyapFD$Wolf)
## ---- eval=TRUE, fig.align = 'center', fig.width = 4, fig.height = 4----
plot(outLyapFD$Wolf$y[,1], outLyapFD$Wolf$y[,2], type ='l', xlab = 'x', ylab = 'y')
## ---- eval=TRUE----------------------------------------------------------
# For method 1 (Wolf et al. 1985)
outLyapFD$Wolf$meanExp
#
# For method 2 (Grond et al. 2003)
outLyapFD$Grond$meanExp
## ---- eval=TRUE----------------------------------------------------------
# For method 1 (Wolf et al. 1985)
outLyapFD$Wolf$stdExp
#
# For method 2 (Grond et al. 2003)
outLyapFD$Grond$stdExp
## ---- eval=TRUE, fig.align = 'center'-----------------------------------
plotMeanExponents(nVar, outLyapFD$Wolf, nIterStats = 400, xlim = c(1,9999), legend=TRUE)
## ---- eval=TRUE, fig.align = 'center'-----------------------------------
plotMeanExponents(nVar, outLyapFD$Wolf, nIterStats = 1000, expList = c(TRUE, FALSE, FALSE), legend=TRUE)
## ---- eval=TRUE, fig.align = 'center'-----------------------------------
plotMeanExponents(nVar, outLyapFD$Grond, nIterStats = 1000, expList = c(TRUE, TRUE, FALSE), legend=TRUE)
## ---- eval=FALSE---------------------------------------------------------
# # For method 1 (Wolf et al. 1985)
# outLyapFD$Wolf$meanDky
# #
# # For method 2 (Grond et al. 2003)
# outLyapFD$Grond$meanDky
## ---- eval=FALSE---------------------------------------------------------
# # For method 1 (Wolf et al. 1985)
# outLyapFD$Wolf$stdDky
# #
# # For method 2 (Grond et al. 2003)
# outLyapFD$Grond$stdDky
## ---- eval=TRUE, fig.align = 'center'-----------------------------------
plotLocalExponents(nVar, outLyapFD$Grond, legend=TRUE)
## ---- eval=FALSE---------------------------------------------------------
# shiny::runApp('../inst/shiny-examples/FDLyapu-app')
## ---- echo = FALSE, eval=TRUE, fig.align = 'center'---------------------
# load the model
data("allMod_nVar3_dMax2")
K <- allMod_nVar3_dMax2$L63
# Edit the equations
visuEq(nVar = 3, dMax = 2, K = allMod_nVar3_dMax2$L63, substit = 1, approx = 3)
## ---- echo = FALSE, eval=TRUE, fig.align = 'center'---------------------
# load the model
source("../inst/shiny-examples/FDLyapu-app/examples/ex4D_Rossler_1979.R")
visuEq(nVar = 4, dMax = 2, K = K, substit = 1)
## ---- eval=FALSE---------------------------------------------------------
# # Prepare the output file
# outLyapFD <- NULL
# # Method 2
# outLyapFD$Grond <- lyapFDGrond(outLyapFD$Grond, nVar= 4, dMax = 2, coeffF = K,
# tDeb = 0, dt = timeStep, tFin = 250, yDeb = c(-10,-6,0,10))
## ---- echo = FALSE, eval=TRUE--------------------------------------------
# To avoid a long time computing, the results are directly loaded
load("../data/outLyapFD.rda")
outLyapFD <- outLyapFD$Ro79
## ---- eval=TRUE, fig.align = 'center'-----------------------------------
# Plot the results
plotMeanExponents(nVar, outLyapFD$Grond, nIterStats = 1000, expList = c(TRUE, TRUE, TRUE, FALSE), legend=TRUE)
## ---- eval = TRUE--------------------------------------------------------
outLyapFD$Grond$meanExp
outLyapFD$Grond$stdExp
## ---- eval = TRUE--------------------------------------------------------
outLyapFD$Grond$meanDky
outLyapFD$Grond$stdDky
## ---- echo = TRUE, eval=TRUE, fig.align = 'center'----------------------
# load the model
data(Plague)
# Model 0 (10-term)
# tuning
KL0 <- Plague$models$model93
KL0[7,1] <- KL0[7,1]*0.598
visuEq(nVar = 3, dMax = 2, KL0, approx = 2, substit = 1)
# Model 1 (11-term) directly chaotic
KL1 <- Plague$models$model129
visuEq(nVar = 3, dMax = 2, KL1, approx = 2, substit = 1)
## ---- echo = FALSE, eval=TRUE, fig.align = 'center'---------------------
# load the model
source("../inst/shiny-examples/FDLyapu-app/examples/ex4D_EbolaModel_2016.R")
visuEq(nVar = 4, dMax = 2, K = KL, substit = c("I", "D1", "D2", "D3"), approx = 2)
## ---- echo = FALSE, eval=TRUE, fig.align = 'center'---------------------
# load the model
source("../inst/shiny-examples/FDLyapu-app/examples/ex5D_Dynamo_2015.R")
visuEq(nVar = 5, dMax = 2, K = KL, approx = 3)
## ---- echo = FALSE, eval=TRUE, fig.align = 'center'---------------------
# load the model
source("../inst/shiny-examples/FDLyapu-app/examples/ex9D_RayleighBenard_1998.R")
visuEq(nVar = 9, dMax = 2, K = KL, approx = 2,
substit = c("C1", "C2", "C3", "C4", "C5", "C6", "C7", "C8", "C9"))
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