## functions by Yvonnick Noel yvonnick.noel@uhb.fr

## TODO
## * histogramAndDensity -- get density drawn right
## probabilityCalculator -- get Type of calculation spelled correctly
## * speed up the drawing
## constructionOFNormal -- add handler to no. of variables., ... m and n
histogramAndDensity = function(container=gwindow("Window 1: idea of a density")) {
  library(gWidgets)

  availDists = c(Uniform = "unif", Normal = "norm", Gamma = "gamma")
  updatePlot = function(h, ...) {

    ### makeEmptyPlot - windows issue
    plot.new()
    plot.window(xlim=c(0,1),ylim=c(0,1))

    
    
    rfunc = paste("r",availDists[svalue(distribution)],sep="")
    dfunc = paste("d",availDists[svalue(distribution)],sep="")
    x = do.call(rfunc, list(svalue(sampleSize),svalue(param1),svalue(param2)))
    if(nchar(svalue(cutpoints))) {
      breaks = unlist(strsplit(svalue(cutpoints)," "))
      breaks = breaks[breaks!=""]
      if(!length(breaks)) breaks="sturges"
      else breaks = as.numeric(breaks)
    }
    else { breaks = "sturges" }

    hh=hist(x,breaks=breaks,plot=FALSE)
    ## now plot histogram
    if(svalue(displayWhat) == "Counts") {
      plot(hh,
           main = paste("Distribution",svalue(distribution)),
           ylab=svalue(displayWhat)
           ) 
    } else if(svalue(displayWhat) == "Frequencies") {
      hh$counts = hh$counts / svalue(sampleSize)
      plot(hh,
           main = paste("Distribution",svalue(distribution)),
           ylab=svalue(displayWhat)
           ) 
    } else {
      hist(x,probability=TRUE,
           main = paste("Distribution",svalue(distribution)),
           ylab=svalue(displayWhat)
           ) 
      addDensity()
    }
  }

  addDensity = function(...) {
    dfunc = paste("d",availDists[svalue(distribution)],sep="")
    dFunc = get(dfunc)
    curve(dFunc(x, svalue(param1), svalue(param2)), lwd=2, col="red",add=T)
  }
distribution = gdroplist(names(availDists),horizontal=FALSE,handler=updatePlot)
sampleSize = gradio(c(500, 5000, 50000),handler=updatePlot,coerce.with=as.numeric)
displayWhat = gradio(c("Counts","Frequencies","Density of frequencies"),handler=updatePlot)
displayFunc = gcheckbox("Theoretical density",handler=addDensity)
param1 = gedit(width=5,coerce.with=as.numeric)
param2 = gedit(width=5,coerce.with=as.numeric)
cutpoints = gedit("")


BigGroup = ggroup(cont = container)
group = ggroup(horizontal = FALSE, container = BigGroup)


tmp = gframe("Distribution", container = group)
distribGroup = glayout(container=tmp)
distribGroup[1,1]=glabel("Law")
distribGroup[1,2]=distribution
distribGroup[2,1]=glabel("Parameter 1")
distribGroup[2,2]=param1
distribGroup[3,1]=glabel("[Parameter 2]")
distribGroup[3,2]=param2
visible(distribGroup)=TRUE

tmp = gframe("Sample size", container = group)
add(tmp, sampleSize)

tmp = gframe("Display", container = group)
add(tmp,displayWhat)
#add(tmp,displayFunc)

tmp = gframe("Cutpoints", container = group)
add(tmp,cutpoints,expand=TRUE)

addSpring(group)

buttonGroup=ggroup(container=group)
gbutton("cancel", container=buttonGroup, handler = function(h,...) dispose(container))
addSpring(buttonGroup)
gbutton("display",container=buttonGroup, handler=updatePlot)

add(BigGroup, ggraphics())

}



## probability calculator
probabilityCalculator = function(container=gwindow("Probability caculator")) {

  library(gWidgets)

availDists = c(Normal="norm",Student="t","Chi-2"="chisq",Fisher="f",Binomial="binom",Poisson="pois",Gamma="gamma",Beta="beta")

  theParams = list(
    "norm" = c("mean","sd",0,1),
    "t" = c("df","ncp","",0),
    "chisq" = c("df","ncp","",0),
    "f" = c("df1","df2","",""),
    "binom"=c("size","prob",1,.5),
    "pois" = c("lambda","",1,""),
    "gamma" = c("shape","rate","",1),
    "beta" = c("shape1","shape2","","")
    )
  
initOptions = function(h, ...) {

 r2s.distrib = svalue(distribution)
 r2s.is1P = r2s.distrib %in% c("Student","Chi-2","Poisson")
 if(r2s.is1P) svalue(param2)=""
 svalue(result)=""
 svalue(value)=""

}

updatePlot = function(h, ...) {

 r2s.distrib = svalue(distribution)
 r2s.param1 = svalue(param1)
 r2s.param2 = svalue(param2)
 r2s.value = svalue(value)
 r2s.p = svalue(calcWhat) == "Find quantile"
 r2s.right = svalue(side)=="to right"

 r2s.isDiscrete = r2s.distrib %in% c("Binomial","Poisson")
 r2s.is1P = r2s.distrib %in% c("Student","Chi-2","Poisson")
 r2s.is01 = function(x) (x>=0)&&(x<=1)
 r2s.isInteger = function(x) abs(x)==round(x)
 r2s.probf = availDists

 r2s.dfunction = eval(parse(text=paste("d",r2s.probf[r2s.distrib],sep="")))
 r2s.pfunction = eval(parse(text=paste("p",r2s.probf[r2s.distrib],sep="")))
 r2s.qfunction = eval(parse(text=paste("q",r2s.probf[r2s.distrib],sep="")))
 r2s.rfunction = eval(parse(text=paste("r",r2s.probf[r2s.distrib],sep="")))

 # Chosen distribution has two parameters
 if(!r2s.is1P) {
   # Check parameter values
   if(r2s.distrib=="Binomial") {
     stopifnot(r2s.isInteger(r2s.param1) && r2s.is01(r2s.param2)) }
   if(r2s.distrib=="Fisher") {
     stopifnot(r2s.isInteger(r2s.param1) && r2s.isInteger(r2s.param2)) }

   if(r2s.p) {
     r2s.prob = r2s.value
     if(!r2s.isDiscrete) {
       if(r2s.right) r2s.value = r2s.qfunction(1-r2s.prob,r2s.param1,r2s.param2)
       else          r2s.value = r2s.qfunction(r2s.prob,r2s.param1,r2s.param2)
     } else {
       if(r2s.right) r2s.value = r2s.qfunction(1-r2s.prob,r2s.param1,r2s.param2)
       else          r2s.value = r2s.qfunction(r2s.prob,r2s.param1,r2s.param2)
     }} else {
       if(!r2s.isDiscrete) {
         if(r2s.right) { r2s.prob = 1-r2s.pfunction(r2s.value,r2s.param1,r2s.param2) } else {
                         r2s.prob = r2s.pfunction(r2s.value,r2s.param1,r2s.param2)}} else {
         if(r2s.right) { r2s.prob = 1-r2s.pfunction(r2s.value-1,r2s.param1,r2s.param2) } else {
                         r2s.prob = r2s.pfunction(r2s.value,r2s.param1,r2s.param2)}}}

   r2s.dens = r2s.dfunction(r2s.value,r2s.param1,r2s.param2)
 # Chosen distribution has only one parameter
 } else {

   svalue(param2)=""

   if(r2s.distrib=="Student") {
     stopifnot(r2s.isInteger(r2s.param1)) }
   if(r2s.distrib=="Chi-2") {
     stopifnot(r2s.isInteger(r2s.param1)) }

   if(r2s.p) {
     r2s.prob = r2s.value
     if(!r2s.isDiscrete) {
       if(r2s.right) r2s.value = r2s.qfunction(1-r2s.prob,r2s.param1)
       else          r2s.value = r2s.qfunction(r2s.prob,r2s.param1)
     } else {
       if(r2s.right) r2s.value = r2s.qfunction(1-r2s.prob,r2s.param1)
       else          r2s.value = r2s.qfunction(r2s.prob,r2s.param1)
     }} else {
       if(!r2s.isDiscrete) {
         if(r2s.right) { r2s.prob = 1-r2s.pfunction(r2s.value,r2s.param1) } else {
                         r2s.prob = r2s.pfunction(r2s.value,r2s.param1)}} else {
         if(r2s.right) { r2s.prob = 1-r2s.pfunction(r2s.value-1,r2s.param1) } else {
                         r2s.prob = r2s.pfunction(r2s.value,r2s.param1)}}}

   r2s.dens = r2s.dfunction(r2s.value,r2s.param1)
 }

 # Result
 svalue(result)=ifelse(r2s.p,
         paste("x =",format(r2s.value,digits=4, nsmall=4)),
         paste("p =",format(r2s.prob,digits=4, nsmall=4)))

 # Affichage
 r2s.xlab="X"
 r2s.title = paste("Distribution :",r2s.distrib)
 r2s.ylab = expression(f(X==x))
 from = 0

 if(!r2s.is1P) {
   if(!r2s.isDiscrete) {
     from = ifelse(r2s.distrib=="Normal",r2s.param1-4*r2s.param2,0)
     to = ifelse(r2s.distrib=="Normal",r2s.param1+4*r2s.param2,max(r2s.rfunction(1000,r2s.param1,r2s.param2)))
     curve(r2s.dfunction(x,r2s.param1,r2s.param2),n=1000,from=from,to=to,lwd=2,main=r2s.title,xlab=r2s.xlab,ylab=r2s.ylab)
     if(!r2s.right) {
       r2s.z = seq(from,r2s.value,len=1000)
       for(i in r2s.z) lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1,r2s.param2))),lwd=2,col="red")
     } else {
         r2s.z = seq(r2s.value,to,len=1000)
         for(i in r2s.z) lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1,r2s.param2))),lwd=2,col="red") }
     r2s.dum=curve(r2s.dfunction(x,r2s.param1,r2s.param2),add=TRUE,n=1000,from=from,to=to,lwd=2,main=r2s.title,xlab=r2s.xlab,ylab=r2s.ylab)
   } else {
     from = 0
     to = ifelse(r2s.distrib=="Binomial",r2s.param1,max(r2s.rfunction(1000,r2s.param1,r2s.param2)))
     r2s.z = 0:to
     plot(r2s.z,r2s.dfunction(r2s.z,r2s.param1,r2s.param2),type="h",lwd=2,main=r2s.title,xlab=r2s.xlab,ylab=r2s.ylab)
     if(!r2s.right) {
       for(i in 0:(r2s.value-1)) { lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1,r2s.param2))),lwd=2,col="red") }
       lines(rbind(c(r2s.value,0),c(r2s.value,r2s.prob-r2s.pfunction(r2s.value-1,r2s.param1,r2s.param2))),lwd=2,col="red")
     } else {
       for(i in r2s.param1:(r2s.value+1)) {
       lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1,r2s.param2))),lwd=2,col="red") }
       lines(rbind(c(r2s.value,0),c(r2s.value,r2s.prob-1+r2s.pfunction(r2s.value,r2s.param1,r2s.param2))),lwd=2,col="red")}}
 # One parameter distributions
 } else {
   if(!r2s.isDiscrete) {
     from = ifelse(r2s.distrib=="Student",min(r2s.rfunction(1000,r2s.param1)),0)
     to = max(r2s.rfunction(1000,r2s.param1))
     curve(r2s.dfunction(x,r2s.param1),n=1000,from=from,to=to,lwd=2,main=r2s.title,xlab=r2s.xlab,ylab=r2s.ylab)
     if(!r2s.right) {
       r2s.z = seq(from,r2s.value,len=1000)
       for(i in r2s.z) lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1))),lwd=2,col="red")
     } else {
         r2s.z = seq(r2s.value,to,len=1000)
         for(i in r2s.z) lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1))),lwd=2,col="red") }
     r2s.dum=curve(r2s.dfunction(x,r2s.param1),add=TRUE,n=1000,from=from,to=to,lwd=2,main=r2s.title,xlab=r2s.xlab,ylab=r2s.ylab)
   } else {
     from = 0
     to = max(r2s.rfunction(1000,r2s.param1))
     r2s.z = 0:to
     plot(r2s.z,r2s.dfunction(r2s.z,r2s.param1),type="h",lwd=2,main=r2s.title,xlab=r2s.xlab,ylab=r2s.ylab)
     if(!r2s.right) {
       for(i in 0:(r2s.value-1)) { lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1))),lwd=2,col="red") }
       lines(rbind(c(r2s.value,0),c(r2s.value,r2s.prob-r2s.pfunction(r2s.value-1,r2s.param1))),lwd=2,col="red")
     } else {
       for(i in to:(r2s.value+1)) {
       lines(rbind(c(i,0),c(i,r2s.dfunction(i,r2s.param1))),lwd=2,col="red") }
       lines(rbind(c(r2s.value,0),c(r2s.value,r2s.prob-1+r2s.pfunction(r2s.value,r2s.param1))),lwd=2,col="red")}}
 }

}

  distribution = gdroplist(names(availDists),horizontal=FALSE,handler=initOptions)
  addhandlerchanged(distribution,handler = function(h,...) {
    theDist = availDists[svalue(h$obj)]

    svalue(param1label) = theParams[[theDist]][1]
    svalue(param1) = theParams[[theDist]][3]

    if(theParams[[theDist]][2] == "") {
      svalue(param2label) = theParams[[theDist]][2]
      svalue(param2) = ""      
      enabled(param2) <- FALSE
    } else {
      enabled(param2) <- TRUE
      svalue(param2label) = theParams[[theDist]][2]
      svalue(param2) = theParams[[theDist]][4]
    }
  })


  calcWhat = gradio(c("Find probability","Find quantile"))
side = gradio(c("to left","to right"))
  param1label = glabel("mean")
  param2label = glabel("sd")
param1 = gedit("0",width=15,coerce.with=as.numeric)
param2 = gedit("1",width=15,coerce.with=as.numeric)
value  = gedit(width=15,coerce.with=as.numeric, handler = updatePlot)
result = glabel("")

BigGroup = ggroup(cont = container)
group = ggroup(horizontal = FALSE, container = BigGroup)

tmp = gframe("Distribution", container = group)
distribGroup = glayout(container=tmp)
distribGroup[1,1]=glabel("Law")
distribGroup[1,2]=distribution
distribGroup[2,1]=param1label
distribGroup[2,2]=param1
distribGroup[3,1]=param2label
distribGroup[3,2]=param2
visible(distribGroup)=TRUE

tmp = gframe("Type of calculation", container = group)
add(tmp,calcWhat)

tmp = gframe("Cumulative", container = group)
add(tmp,side)

tmp = gframe("Result", container = group)
  resultGroup = glayout()
  resultGroup[1,1]=glabel("Value")
  resultGroup[1,2]=value
  resultGroup[2,1]=glabel("Result")
  resultGroup[2,2]=result
  visible(resultGroup) <- TRUE
  add(tmp,resultGroup)

addSpring(group)

buttonGroup=ggroup(container=group)
gbutton("cancel", container=buttonGroup, handler = function(h,...) dispose(container))
addSpring(buttonGroup)
gbutton("update",container=buttonGroup, handler=updatePlot)

add(BigGroup, ggraphics())

}

constructionOfNormal = function(container = gwindow("Construction of normal")) {
  
  library(gWidgets)
  
  availDists = c(Uniform = "unif", Binomial = "binom", Normal = "norm")
  
  updatePlot = function(h, ...) {
    
    rfunc = paste("r",availDists[svalue(distribution)],sep="")
    y = do.call(rfunc, list(svalue(sampleSize)*svalue(nvar),svalue(param1),svalue(param2)))
    z = rowSums(matrix(y,svalue(sampleSize),svalue(nvar)))
    
    xlab="Values of the variable"
    title = paste("Distribution :",svalue(distribution))
    ylab = "Densities/Probabilities"
 if(svalue(distribution)!="Binomial") {
   hist(z,freq=FALSE,main=title,xlab=xlab,ylab=ylab)
 }
 else {
   res = plot(table(z)/svalue(sampleSize),main=title,xlab=xlab,ylab=ylab)
 }
 if(svalue(displayFunc)) curve(dnorm(x,mean(z),sd(z)),from=min(z),to=max(z),add=TRUE,lwd=2,col=2)
}

distribution = gdroplist(names(availDists),horizontal=FALSE)
displayFunc = gcheckbox("Normal law",handler=updatePlot)
param1 = gedit(width=5,coerce.with=as.numeric)
param2 = gedit(width=5,coerce.with=as.numeric)
sampleSize  =  gedit("500",width=5,coerce.with=as.numeric)
nvar  =  gedit(width=5,coerce.with=as.numeric, handler=updatePlot)


BigGroup = ggroup(cont = container)
group = ggroup(horizontal = FALSE, container = BigGroup)


tmp = gframe("Distribution", container = group)
distribGroup = glayout(container=tmp)
distribGroup[1,1]=glabel("Law")
distribGroup[1,2]=distribution
distribGroup[2,1]=glabel("Parameter 1")
distribGroup[2,2]=param1
distribGroup[3,1]=glabel("Parameter 2")
distribGroup[3,2]=param2
visible(distribGroup)=TRUE

tmp = gframe("Score", container = group)
distribSample = glayout(container=tmp)
distribSample[1,1]=glabel("Number of observations")
distribSample[1,2]=sampleSize
distribSample[2,1]=glabel("Number of variables")
distribSample[2,2]=nvar
visible(distribSample)=TRUE

tmp = gframe("update", container = group)
add(tmp,displayFunc)

addSpring(group)

buttonGroup=ggroup(container=group)
gbutton("cancel", container=buttonGroup, handler = function(h,...) dispose(container))
addSpring(buttonGroup)
gbutton("update",container=buttonGroup, handler=updatePlot)

add(BigGroup, ggraphics())

}