snprma <- function(filenames, mixtureSampleSize=10^5, fitMixture=FALSE, eps=0.1, verbose=TRUE, seed=1, cdfName, sns){
	if (missing(sns)) sns <- basename(filenames)
	##ADD CHECK TO SEE IF LOADED
	if (missing(cdfName))
		cdfName <- read.celfile.header(filenames[1])$cdfName
	##  stuffDir <- changeToCrlmmAnnotationName(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.")
		rm(suggCall, msg)
	}
	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))

	## 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.")
		if (getRversion() > '2.7.0') pb <- txtProgressBar(min=0, max=length(filenames), style=3)
	}
	##We start looping throug cel files
	idx2 <- sample(length(fid), 10^5) ##for skewness. no need to do everything
	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)
			if (getRversion() > '2.7.0') setTxtProgressBar(pb, i)
			else cat(".")
	}
	if (verbose)
		if (getRversion() > '2.7.0') close(pb)
		else cat("\n")
	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]))
}