#colourvision simulation example
#Optimum set of photoreceptors
#Maximum deltaS

library(colourvision)
#photoreceptors
data("bee")
photoreceptor.bee <- bee

#background
data(Rb)
cerrado<-Rb

## Illuminant ##
data("D65")
I<-D65

#flower data
setwd("~/Documents/Felipe Virtual/UFG/Manuscripts/Colour Vision Model Simulations")
flower<-read.csv("fred.flowers.csv")
flower<-spec.denoise(flower)
flower[flower<0]<-0

cols<-sample(2:length(flower), size=50)
flower<-flower[,c(1,cols)]


#photoreceptors
lambda.max<-seq(330,630,30)

#dichromat
#dichromat
#dichromat
lambda.max.cbn<-combn(x=lambda.max, m=2, fun=NULL, simplify=TRUE)
CTTK2<-list()
CTTK2edge<-list()
RNL2<-list()
for (i in 1:ncol(lambda.max.cbn)) {

  C<-photor(lambda.max = lambda.max.cbn[,i])
  CTTK2[[i]]<-CTTKmodel(R=flower, I=I, Rb=cerrado, C=C)
  CTTK2edge[[i]]<-GENmodel(R=flower, type="edge", edge=sqrt(3), I=I, Rb=cerrado, C=C,
                           func=function(x){x<-x/(x+1)})
  RNL2[[i]]<-RNLmodel(model = "log", R1=flower, I=I, Rb=cerrado, C=C,
                      noise = TRUE, e = rep(0.1,length(lambda.max.cbn[,i])))
  
}


###trichromat
###trichromat
###trichromat
###trichromat
lambda.max.cbn<-combn(x=lambda.max, m=3, fun=NULL, simplify=TRUE)
CTTK3<-list()
CTTK3edge<-list()
RNL3<-list()
for (i in 1:ncol(lambda.max.cbn)) {
  
  C<-photor(lambda.max = lambda.max.cbn[,i])
  CTTK3[[i]]<-CTTKmodel(R=flower, I=I, Rb=cerrado, C=C)
  CTTK3edge[[i]]<-GENmodel(R=flower, type="edge", edge=sqrt(3), I=I, Rb=cerrado, C=C,
                           func=function(x){x<-x/(x+1)})
  RNL3[[i]]<-RNLmodel(model = "log", R1=flower, I=I, Rb=cerrado, C=C,
                      noise = TRUE, e = rep(0.1,length(lambda.max.cbn[,i])))
  
}


###tetrachromat
###tetrachromat
###tetrachromat
###tetrachromat
lambda.max.cbn<-combn(x=lambda.max, m=4, fun=NULL, simplify=TRUE)
CTTK4<-list()
CTTK4edge<-list()
RNL4<-list()
for (i in 1:ncol(lambda.max.cbn)) {
  
  C<-photor(lambda.max = lambda.max.cbn[,i])
  CTTK4[[i]]<-CTTKmodel(R=flower, I=I, Rb=cerrado, C=C)
  CTTK4edge[[i]]<-GENmodel(R=flower, type="edge", edge=sqrt(3), I=I, Rb=cerrado, C=C,
                           func=function(x){x<-x/(x+1)})
  RNL4[[i]]<-RNLmodel(model = "log", R1=flower, I=I, Rb=cerrado, C=C,
                      noise = TRUE, e = rep(0.1,length(lambda.max.cbn[,i])))
  
}



###pentachromat
###pentachromat
###pentachromat
###pentachromat
lambda.max.cbn<-combn(x=lambda.max, m=5, fun=NULL, simplify=TRUE)
CTTK5<-list()
CTTK5edge<-list()
RNL5<-list()
for (i in 1:ncol(lambda.max.cbn)) {
  
  C<-photor(lambda.max = lambda.max.cbn[,i])
  CTTK5[[i]]<-CTTKmodel(R=flower, I=I, Rb=cerrado, C=C)
  CTTK5edge[[i]]<-GENmodel(R=flower, type="edge", edge=sqrt(3), I=I, Rb=cerrado, C=C,
                           func=function(x){x<-x/(x+1)})
  RNL5[[i]]<-RNLmodel(model = "log", R1=flower, I=I, Rb=cerrado, C=C,
                      noise = TRUE, e = rep(0.1,length(lambda.max.cbn[,i])))
  
}



#Results
CTTK2.mean.deltaS<-NULL
CTTK3.mean.deltaS<-NULL
CTTK4.mean.deltaS<-NULL
CTTK5.mean.deltaS<-NULL
CTTK2edge.mean.deltaS<-NULL
CTTK3edge.mean.deltaS<-NULL
CTTK4edge.mean.deltaS<-NULL
CTTK5edge.mean.deltaS<-NULL
RNL2.mean.deltaS<-NULL
RNL3.mean.deltaS<-NULL
RNL4.mean.deltaS<-NULL
RNL5.mean.deltaS<-NULL

for (i in 1:length(CTTK2)) {
  CTTK2.mean.deltaS[[i]]<-mean(CTTK2[[i]]$deltaS)
  CTTK3.mean.deltaS[[i]]<-mean(CTTK3[[i]]$deltaS)
  CTTK4.mean.deltaS[[i]]<-mean(CTTK4[[i]]$deltaS)
  CTTK5.mean.deltaS[[i]]<-mean(CTTK5[[i]]$deltaS)
  CTTK2edge.mean.deltaS[[i]]<-mean(CTTK2edge[[i]]$deltaS)
  CTTK3edge.mean.deltaS[[i]]<-mean(CTTK3edge[[i]]$deltaS)
  CTTK4edge.mean.deltaS[[i]]<-mean(CTTK4edge[[i]]$deltaS)
  CTTK5edge.mean.deltaS[[i]]<-mean(CTTK5edge[[i]]$deltaS)
  RNL2.mean.deltaS[[i]]<-mean(RNL2[[i]]$deltaS)
  RNL3.mean.deltaS[[i]]<-mean(RNL3[[i]]$deltaS)
  RNL4.mean.deltaS[[i]]<-mean(RNL4[[i]]$deltaS)
  RNL5.mean.deltaS[[i]]<-mean(RNL5[[i]]$deltaS)
}

#Set of photoreceptors
#di
CTTK2.max<-match(max(CTTK2.mean.deltaS),CTTK2.mean.deltaS)
CTTK2edge.max<-match(max(CTTK2edge.mean.deltaS),CTTK2edge.mean.deltaS)
RNL2.max<-match(max(RNL2.mean.deltaS),RNL2.mean.deltaS)

lambda.max.cbn<-combn(x=lambda.max, m=2, fun=NULL, simplify=TRUE)
lambda.max.cbn[,CTTK2.max]
lambda.max.cbn[,CTTK2edge.max]
lambda.max.cbn[,RNL2.max]

#tri
CTTK3.max<-match(max(CTTK3.mean.deltaS),CTTK3.mean.deltaS)
CTTK3edge.max<-match(max(CTTK3edge.mean.deltaS),CTTK3edge.mean.deltaS)
RNL3.max<-match(max(RNL3.mean.deltaS),RNL3.mean.deltaS)

lambda.max.cbn<-combn(x=lambda.max, m=3, fun=NULL, simplify=TRUE)
lambda.max.cbn[,CTTK3.max]
lambda.max.cbn[,CTTK3edge.max]
lambda.max.cbn[,RNL3.max]

#tetra
CTTK4.max<-match(max(CTTK4.mean.deltaS),CTTK4.mean.deltaS)
CTTK4edge.max<-match(max(CTTK4edge.mean.deltaS),CTTK4edge.mean.deltaS)
RNL4.max<-match(max(RNL4.mean.deltaS),RNL4.mean.deltaS)

lambda.max.cbn<-combn(x=lambda.max, m=4, fun=NULL, simplify=TRUE)
lambda.max.cbn[,CTTK4.max]
lambda.max.cbn[,CTTK4edge.max]
lambda.max.cbn[,RNL4.max]

#penta
CTTK5.max<-match(max(CTTK5.mean.deltaS),CTTK5.mean.deltaS)
CTTK5edge.max<-match(max(CTTK5edge.mean.deltaS),CTTK5edge.mean.deltaS)
RNL5.max<-match(max(RNL5.mean.deltaS),RNL5.mean.deltaS)

lambda.max.cbn<-combn(x=lambda.max, m=5, fun=NULL, simplify=TRUE)
lambda.max.cbn[,CTTK5.max]
lambda.max.cbn[,CTTK5edge.max]
lambda.max.cbn[,RNL5.max]

#plotting
CTTK<-c(CTTK2.mean.deltaS,CTTK3.mean.deltaS,CTTK4.mean.deltaS,CTTK5.mean.deltaS)
CTTKedge<-c(CTTK2edge.mean.deltaS,CTTK3edge.mean.deltaS,CTTK4edge.mean.deltaS,CTTK5edge.mean.deltaS)
RNL<-c(RNL2.mean.deltaS,RNL3.mean.deltaS,RNL4.mean.deltaS,RNL5.mean.deltaS)

names<-c(rep("2",length(CTTK2.mean.deltaS)),
rep("3",length(CTTK3.mean.deltaS)),
rep("4",length(CTTK4.mean.deltaS)),
rep("5",length(CTTK5.mean.deltaS)))

names<-factor(names,levels(names)[c(1,4,3,2)])


quartz(width=12, height=4)
par(mfrow=c(1,3))
boxplot(CTTK~names, ylim=c(0,0.30),
ylab = expression(paste(Delta, "S")))
boxplot(CTTKedge~names, ylim=c(0,0.30), xlab= "Number of photoreceptors")
boxplot(RNL~names)