####Generation of chl
chl_obs<-runif(3000,0.03,50)
wave<-c(320, 340, 380, 412, 443, 490, 510, 555)
kw<-c(0.030150000, 0.018650000, 0.009450000, 0.007632472, 0.009321161, 0.016500001, 0.033734146, 0.060471872)

####PARAMETERS############################################################################
s<-c(0.014)
a1<-c(0.12641389 , 0.11601784 , 0.07628119,  0.06632158,  0.06952656,  0.04314418, 0.02875758,  0.01169241)
a2<-c(-0.09658631, -0.08138506,-0.04498599,-0.26038351,-0.29751409,-0.30001828,-0.25472261, -0.18135506)
a2<-1+a2
beta<-0.4
eta<-1
################################################################################################v

acdm<-rep(NA,3000)
bbp<-rep(NA,3000)
BBP<-rep(NA,3000)
data_gen<-matrix(NA,ncol=6,nrow=24000)
noise<-rnorm(100000,mean=1,sd=0.01)
for (i in 1:8){
print(i)
 for (j in 1:3000){

	acdm[j]<-(0.059*chl_obs[j]^0.72)#*noise[j]		####!!!!covariates are correlated 
	bbp[j]<-(0.00327*chl_obs[j]^0.3913)#*noise[j]		####!!!!covariates are correlated
	BBP[j]<-beta*bbp[j]
	data_gen[(j+(3000*(i-1))),1]<-(kw[i]+acdm[j]*exp(-s[1]*(wave[i]-443))+a1[i]*chl_obs[j]^a2[i]+BBP[j]*(wave[i]/443.0)^(-eta))#*noise[j]
	data_gen[(j+(3000*(i-1))),2]<-chl_obs[j]
	data_gen[(j+(3000*(i-1))),3]<-acdm[j]
	data_gen[(j+(3000*(i-1))),4]<-bbp[j]
	data_gen[(j+(3000*(i-1))),5]<-wave[i]
	data_gen[(j+(3000*(i-1))),6]<-i
}}

write.table(data_gen,'/home/sylvain/Desktop/kd_model/model/Model_comparison_bon/Estimation_test/data_gen2.txt', col.names=F, row.names=F, sep=" ", append=F)