snprma <- function(filenames, mixtureSampleSize=10^5, fitMixture=FALSE,
                    eps=0.1, verbose=TRUE, seed=1, cdfName, sns){

   if (missing(sns)) sns <- basename(filenames)
   if (missing(cdfName))
     cdfName <- read.celfile.header(filenames[1])$cdfName
   pkgname <- getCrlmmAnnotationName(cdfName)

   if(!require(pkgname, character.only=TRUE, quietly=!verbose)){

     suggCall <- paste("library(", pkgname,
                       ", lib.loc='/Altern/Lib/Loc')",
                       sep="")
     msg <- paste("If", pkgname,
                  "is installed on an alternative location,",
                  "please load it manually by using", suggCall)

     message(strwrap(msg))
     stop("Package ", pkgname, " could not be found.")
   }

   if(verbose) message("Loading annotations and mixture model parameters.")
   loader("preprocStuff.rda", .crlmmPkgEnv, pkgname)
   loader("genotypeStuff.rda", .crlmmPkgEnv, pkgname)
   loader("mixtureStuff.rda", .crlmmPkgEnv, pkgname)
   autosomeIndex <- getVarInEnv("autosomeIndex")
   pnsa <- getVarInEnv("pnsa")
   pnsb <- getVarInEnv("pnsb")
   fid <- getVarInEnv("fid")
   reference <- getVarInEnv("reference")
   aIndex <- getVarInEnv("aIndex")
   bIndex <- getVarInEnv("bIndex")
   SMEDIAN <- getVarInEnv("SMEDIAN")
   theKnots <- getVarInEnv("theKnots")
   gns <- getVarInEnv("gns")

   ##We will read each cel file, summarize, and run EM one by one
   ##We will save parameters of EM to use later
   mixtureParams <- matrix(0, 4, length(filenames))
   SNR <- vector("numeric", length(filenames))
   SKW <- vector("numeric", length(filenames))

 ##   mixtureParams <- initializeBigMatrix("crlmmMixt-", 4, length(filenames))
 ##   SNR <- initializeBigVector("crlmmSNR-", length(filenames), "numeric")
 ##   SKW <- initializeBigVector("crlmmSKW-", length(filenames), "numeric")

   ## This is the sample for the fitting of splines
   ## BC: I like better the idea of the user passing the seed,
   ##     because this might intefere with other analyses
   ##     (like what happened to GCRMA)
   set.seed(seed)
   idx <- sort(sample(autosomeIndex, mixtureSampleSize))
   ##S will hold (A+B)/2 and M will hold A-B
   ##NOTE: We actually dont need to save S. Only for pics etc...
   ##f is the correction. we save to avoid recomputing

   A <- matrix(as.integer(0), length(pnsa), length(filenames))
   B <- matrix(as.integer(0), length(pnsb), length(filenames))

   if(verbose){
     message("Processing ", length(filenames), " files.")

     pb <- txtProgressBar(min=0, max=length(filenames), style=3)

   }

   ##for skewness. no need to do everything
   idx2 <- sample(length(fid), 10^5)

   ##We start looping throug cel files
   for(i in seq(along=filenames)){
     y <- as.matrix(read.celfile(filenames[i], intensity.means.only=TRUE)[["INTENSITY"]][["MEAN"]][fid])
     x <- log2(y[idx2])
     SKW[i] <- mean((x-mean(x))^3)/(sd(x)^3)
     rm(x)
     y <- normalize.quantiles.use.target(y, target=reference)
     A[, i] <- intMedianSummaries(y[aIndex, 1, drop=FALSE], pnsa)
     B[, i] <- intMedianSummaries(y[bIndex, 1, drop=FALSE], pnsb)
     ##Now to fit the EM
     if(fitMixture){
       S <- (log2(A[idx, i])+log2(B[idx, i]))/2 - SMEDIAN
       M <- log2(A[idx, i])-log2(B[idx, i])

       ##we need to test the choice of eps.. it is not the max diff between funcs
       tmp <- fitAffySnpMixture56(S, M, theKnots, eps=eps)

       mixtureParams[, i] <- tmp[["coef"]]
       SNR[i] <- tmp[["medF1"]]^2/(tmp[["sigma1"]]^2+tmp[["sigma2"]]^2)
     }

     if (verbose) setTxtProgressBar(pb, i)

   }

   if (verbose) close(pb)

   if (!fitMixture) SNR <- mixtureParams <- NA
   ## gns comes from preprocStuff.rda
   list(A=A, B=B, sns=sns, gns=gns, SNR=SNR, SKW=SKW, mixtureParams=mixtureParams, cdfName=cdfName)

 }

 
 fitAffySnpMixture56 <- function(S, M, knots, probs=rep(1/3, 3), eps=.01, maxit=10, verbose=FALSE){
   ##56 stands for 5 and 6 arrays but will also work for Illumina
   ##Note the unfortunate choice of numbering:
   ##1 is BB, 2 AB, and 3 AA. Opposite to everything else!
   ##this is legacy code I decided not to change.
   ## this why at the end we report -coefs: F1 is the negative f
   mus <- append(quantile(M, c(1, 5)/6, names=FALSE), 0, 1)
   sigmas <- rep(mad(c(M[M<mus[1]]-mus[1], M[M>mus[3]]-mus[3])), 3)
   sigmas[2] <- sigmas[2]/2

   weights <- apply(cbind(mus, sigmas), 1, function(p) dnorm(M, p[1], p[2]))
   previousF1 <- -Inf
   change <- eps+1
   it <- 0

   if(verbose) message("Max change must be under ", eps, ".")
   matS <- stupidSplineBasis(S, knots)
   while (change > eps & it < maxit){
     it <- it+1
     ## E
     z <- sweep(weights, 2, probs, "*")
     LogLik <- rowSums(z)
     z <- sweep(z, 1, LogLik, "/")
     probs <- colMeans(z)

     ## M
     fit1 <- crossprod(chol2inv(chol(crossprod(sweep(matS, 1, z[, 1], FUN="*"), matS))), crossprod(matS, z[, 1]*M))

     fit2 <- sum(z[, 2]*M)/sum(z[, 2])
     F1 <- matS%*%fit1
     sigmas[c(1, 3)] <- sqrt(sum(z[, 1]*(M-F1)^2)/sum(z[, 1]))
     sigmas[2] <- sqrt(sum(z[, 2]*(M-fit2)^2)/sum(z[, 2]))

     weights[, 1] <- dnorm(M, F1, sigmas[1])
     weights[, 2] <- dnorm(M, fit2, sigmas[2])
     weights[, 3] <- dnorm(M, -F1, sigmas[3])

     change <- max(abs(F1-previousF1))
     previousF1 <- F1
     if(verbose) message("Iter ", it, ": ", change, ".")
   }
   medF1 <- median(-F1)
  return(list(coef= -fit1, medF1=medF1, sigma1=sigmas[1], sigma2=sigmas[2]))
 }
 

 snprma2 <- function(filenames, mixtureSampleSize=10^5, fitMixture=FALSE,
                     eps=0.1, verbose=TRUE, seed=1, cdfName, sns){
   if (missing(sns)) sns <- basename(filenames)
   if (missing(cdfName))
     cdfName <- read.celfile.header(filenames[1])[["cdfName"]]
   pkgname <- getCrlmmAnnotationName(cdfName)
   stopifnot(require(pkgname, character.only=TRUE, quietly=!verbose))

   if(verbose) message("Loading annotations and mixture model parameters.")

   obj <- loader("preprocStuff.rda", .crlmmPkgEnv, pkgname)

   pnsa <- getVarInEnv("pnsa")
   pnsb <- getVarInEnv("pnsb")
   gns <- getVarInEnv("gns")

   rm(list=obj, envir=.crlmmPkgEnv)
   rm(obj)

   ##We will read each cel file, summarize, and run EM one by one
   ##We will save parameters of EM to use later
   if(verbose) message("Initializing objects.")
   mixtureParams <- initializeBigMatrix("crlmmMixt-", 4, length(filenames), "double")
   SNR <- initializeBigVector("crlmmSNR-", length(filenames), "double")
   SKW <- initializeBigVector("crlmmSKW-", length(filenames), "double")

   ## This is the sample for the fitting of splines
   ## BC: I like better the idea of the user passing the seed,
   ##     because this might intefere with other analyses
   ##     (like what happened to GCRMA)
   ##S will hold (A+B)/2 and M will hold A-B
   ##NOTE: We actually dont need to save S. Only for pics etc...
   ##f is the correction. we save to avoid recomputing
   A <- initializeBigMatrix("crlmmA-", length(pnsa), length(filenames), "integer")
   B <- initializeBigMatrix("crlmmB-", length(pnsb), length(filenames), "integer")
 
   sampleBatches <- splitIndicesByNode(seq(along=filenames))
 
   if(verbose) message("Processing ", length(filenames), " files.")
 
   ocLapply(sampleBatches, processCEL, filenames=filenames,
            fitMixture=fitMixture, A=A, B=B, SKW=SKW, SNR=SNR,
            mixtureParams=mixtureParams, eps=eps, seed=seed,
            mixtureSampleSize=mixtureSampleSize, pkgname=pkgname,
            neededPkgs=c("crlmm", pkgname))

   close(mixtureParams)
   close(SNR)
   close(SKW)
   close(A)
   close(B)

   list(A=A, B=B, sns=sns, gns=gns, SNR=SNR, SKW=SKW, mixtureParams=mixtureParams, cdfName=cdfName)
 }
 

 processCEL <- function(i, filenames, fitMixture, A, B, SKW, SNR,
                        mixtureParams, eps, seed, mixtureSampleSize,
                        pkgname){

   obj1 <- loader("preprocStuff.rda", .crlmmPkgEnv, pkgname)
   obj2 <- loader("genotypeStuff.rda", .crlmmPkgEnv, pkgname)
   obj3 <- loader("mixtureStuff.rda", .crlmmPkgEnv, pkgname)

   autosomeIndex <- getVarInEnv("autosomeIndex")
   pnsa <- getVarInEnv("pnsa")
   pnsb <- getVarInEnv("pnsb")
   fid <- getVarInEnv("fid")
   reference <- getVarInEnv("reference")
   aIndex <- getVarInEnv("aIndex")
   bIndex <- getVarInEnv("bIndex")
   SMEDIAN <- getVarInEnv("SMEDIAN")
   theKnots <- getVarInEnv("theKnots")
   gns <- getVarInEnv("gns")

   rm(list=c(obj1, obj2, obj3), envir=.crlmmPkgEnv)
   rm(obj1, obj2, obj3)

 
   ## for mixture
   set.seed(seed)
   idx <- sort(sample(autosomeIndex, mixtureSampleSize))
   ##for skewness. no need to do everything
   idx2 <- sample(length(fid), 10^5)

   open(A)
   open(B)
   open(SKW)
   open(mixtureParams)
   open(SNR)
 
   for (k in i){
     y <- as.matrix(read.celfile(filenames[k], intensity.means.only=TRUE)[["INTENSITY"]][["MEAN"]][fid])
     x <- log2(y[idx2])
     SKW[k] <- mean((x-mean(x))^3)/(sd(x)^3)
     rm(x)
     y <- normalize.quantiles.use.target(y, target=reference)
     A[, k] <- intMedianSummaries(y[aIndex, 1, drop=FALSE], pnsa)
     B[, k] <- intMedianSummaries(y[bIndex, 1, drop=FALSE], pnsb)
     rm(y)

     if(fitMixture){
       S <- (log2(A[idx,k])+log2(B[idx, k]))/2 - SMEDIAN
       M <- log2(A[idx, k])-log2(B[idx, k])
       tmp <- fitAffySnpMixture56(S, M, theKnots, eps=eps)

       rm(S, M)

       mixtureParams[, k] <- tmp[["coef"]]
       SNR[k] <- tmp[["medF1"]]^2/(tmp[["sigma1"]]^2+tmp[["sigma2"]]^2)

       rm(tmp)

     } else {
       mixtureParams[, k] <- NA
       SNR[k] <- NA
     }
   }
   close(A)
   close(B)
   close(SKW)
   close(mixtureParams)
   close(SNR)

   rm(list=ls())
   gc(verbose=FALSE)

   TRUE
 }