Mercurial > repos > marie-tremblay-metatoul > nmr_annotation
diff nmr_preprocessing/NmrPreprocessing_script.R @ 2:7304ec2c9ab7 draft
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| author | marie-tremblay-metatoul |
|---|---|
| date | Mon, 30 Jul 2018 10:33:03 -0400 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/nmr_preprocessing/NmrPreprocessing_script.R Mon Jul 30 10:33:03 2018 -0400 @@ -0,0 +1,1244 @@ +## ========================== +# Internal functions +## ========================== + +# beginTreatment +beginTreatment <- function(name, Signal_data = NULL, Signal_info = NULL, + force.real = FALSE) { + + cat("Begin", name, "\n") + + + # Formatting the Signal_data and Signal_info ----------------------- + + vec <- is.vector(Signal_data) + if (vec) { + Signal_data <- vec2mat(Signal_data) + } + if (is.vector(Signal_info)) { + Signal_info <- vec2mat(Signal_info) + } + if (!is.null(Signal_data)) { + if (!is.matrix(Signal_data)) { + stop("Signal_data is not a matrix.") + } + if (!is.complex(Signal_data) && !is.numeric(Signal_data)) { + stop("Signal_data contains non-numerical values.") + } + } + if (!is.null(Signal_info) && !is.matrix(Signal_info)) { + stop("Signal_info is not a matrix.") + } + + + Original_data <- Signal_data + + # Extract the real part of the spectrum --------------------------- + + if (force.real) { + if (is.complex(Signal_data)) { + Signal_data <- Re(Signal_data) + } else { + # The signal is numeric Im(Signal_data) is zero anyway so let's avoid + # using complex(real=...,imaginary=0) which would give a complex signal + # in endTreatment() + force.real <- FALSE + } + } + + + # Return the formatted data and metadata entries -------------------- + + return(list(start = proc.time(), vec = vec, force.real = force.real, + Original_data = Original_data, Signal_data = Signal_data, Signal_info = Signal_info)) +} + +# endTreatment +endTreatment <- function(name, begin_info, Signal_data) { + + # begin_info: object outputted from beginTreatment + + + # Formatting the entries and printing process time ----------------------- + end_time <- proc.time() # record it as soon as possible + start_time <- begin_info[["start"]] + delta_time <- end_time - start_time + delta <- delta_time[] + cat("End", name, "\n") + cat("It lasted", round(delta["user.self"], 3), "s user time,", round(delta["sys.self"],3), + "s system time and", round(delta["elapsed"], 3), "s elapsed time.\n") + + + if (begin_info[["force.real"]]) { + # The imaginary part is left untouched + i <- complex(real = 0, imaginary = 1) + Signal_data <- Signal_data + i * Im(begin_info[["Original_data"]]) + } + + if (begin_info[["vec"]]) { + Signal_data <- Signal_data[1, ] + } + + # Return the formatted data and metadata entries -------------------- + return(Signal_data) +} + +# checkArg +checkArg <- function(arg, checks, can.be.null=FALSE) { + check.list <- list(bool=c(is.logical, "a boolean"), + int =c(function(x){x%%1==0}, "an integer"), + num =c(is.numeric, "a numeric"), + str =c(is.character, "a string"), + pos =c(function(x){x>0}, "positive"), + pos0=c(function(x){x>=0}, "positive or zero"), + l1 =c(function(x){length(x)==1}, "of length 1") + ) + if (is.null(arg)) { + if (!can.be.null) { + stop(deparse(substitute(arg)), " is null.") + } + } else { + if (is.matrix(arg)) { + stop(deparse(substitute(arg)), " is not scalar.") + } + for (c in checks) { + if (!check.list[[c]][[1]](arg)) { + stop(deparse(substitute(arg)), " is not ", check.list[[c]][[2]], ".") + } + } + } +} + +# getArg +getArg <- function(arg, info, argname, can.be.absent=FALSE) { + if (is.null(arg)) { + start <- paste("impossible to get argument", argname, "it was not given directly and"); + if (!is.matrix(info)) { + stop(paste(start, "the info matrix was not given")) + } + if (!(argname %in% colnames(info))) { + if (can.be.absent) { + return(NULL) + } else { + stop(paste(start, "is not in the info matrix")) + } + } + if (nrow(info) < 1) { + stop(paste(start, "the info matrix has no row")) + } + arg <- info[1,argname] + if (is.na(arg)) { + stop(paste(start, "it is NA in the info matrix")) + } + } + return(arg) +} + +# binarySearch +binarySearch <- function(a, target, lower = TRUE) { + # search the index i in a such that a[i] == target + # if it doesn't exists and lower, it searches the closer a[i] such that a[i] < target + # if !lower, it seraches the closer a[i] such that a[i] > target + # a should be monotone but can be increasing or decreasing + + # if a is increasing INVARIANT: a[amin] < target < a[amax] + N <- length(a) + if ((a[N] - target) * (a[N] - a[1]) <= 0) { + return(N) + } + if ((a[1] - target) * (a[N] - a[1]) >= 0) { + return(1) + } + amin <- 1 + amax <- N + while (amin + 1 < amax) { + amid <- floor((amin + amax)/2) + if ((a[amid] - target) * (a[amax] - a[amid]) < 0) { + amin <- amid + } else if ((a[amid] - target) * (a[amax] - a[amid]) > 0) { + amax <- amid + } else { + # a[amid] == a[amax] or a[amid] == target In both cases, a[amid] == + # target + return(amid) + } + } + if (xor(lower, a[amin] > a[amax])) { + # (lower && a[amin] < a[amax]) || (!lower && a[min] > a[max]) + # If increasing and we want the lower, we take amin + # If decreasing and we want the bigger, we take amin too + return(amin) + } else { + return(amax) + } +} + +# Interpol +Interpol <- function(t, y) { + # y: sample + # t : warping function + + m <- length(y) + # t <= m-1 + # because if t > m-1, y[ti+1] will be NA when we compute g + valid <- 1 <= t & t <= m-1 # FIXME it was '<' in Bubble v2 + s <- (1:m)[valid] + ti <- floor(t[s]) + tr <- t[s] - ti + g <- y[ti + 1] - y[ti] + f <- y[ti] + tr * g + list(f=f, s=s, g=g) +} + +# vec2mat +vec2mat <- function(vec) { + return(matrix(vec, nrow = 1, dimnames = list(c(1), names(vec)))) + +} + +# binarySearch +binarySearch <- function(a, target, lower = TRUE) { + # search the index i in a such that a[i] == target + # if it doesn't exists and lower, it searches the closer a[i] such that a[i] < target + # if !lower, it seraches the closer a[i] such that a[i] > target + # a should be monotone but can be increasing or decreasing + + # if a is increasing INVARIANT: a[amin] < target < a[amax] + N <- length(a) + if ((a[N] - target) * (a[N] - a[1]) <= 0) { + return(N) + } + if ((a[1] - target) * (a[N] - a[1]) >= 0) { + return(1) + } + amin <- 1 + amax <- N + while (amin + 1 < amax) { + amid <- floor((amin + amax)/2) + if ((a[amid] - target) * (a[amax] - a[amid]) < 0) { + amin <- amid + } else if ((a[amid] - target) * (a[amax] - a[amid]) > 0) { + amax <- amid + } else { + # a[amid] == a[amax] or a[amid] == target In both cases, a[amid] == + # target + return(amid) + } + } + if (xor(lower, a[amin] > a[amax])) { + # (lower && a[amin] < a[amax]) || (!lower && a[min] > a[max]) + # If increasing and we want the lower, we take amin + # If decreasing and we want the bigger, we take amin too + return(amin) + } else { + return(amax) + } +} + + +# indexInterval +indexInterval <- function (a, from, to, inclusive=TRUE) { + # If inclusive and from <= to, we need to take the lower + # If not inclusive and from > to, we need to take the lower too + lowerFrom <- xor(inclusive, from > to) + fromIndex <- binarySearch(a, from, lowerFrom) + toIndex <- binarySearch(a, to, !lowerFrom) + return(fromIndex:toIndex) +} + + + +## ========================== +# GroupDelayCorrection +## ========================== +GroupDelayCorrection <- function(Fid_data, Fid_info = NULL, group_delay = NULL) { + + + # Data initialisation and checks ---------------------------------------------- + + begin_info <- beginTreatment("GroupDelayCorrection", Fid_data, Fid_info) + Fid_data <- begin_info[["Signal_data"]] + dimension_names <- dimnames(Fid_data) + Fid_info <- begin_info[["Signal_info"]] + checkArg(group_delay, c("num", "pos0"), can.be.null = TRUE) + # if Fid_info and group_delay are NULL, getArg will generate an error + + group_delay <- getArg(group_delay, Fid_info, "GRPDLY", can.be.absent = TRUE) + + if (is.null(group_delay)) { + + # See DetermineBrukerDigitalFilter.m in matNMR MATLAB library + group_delay_matrix <- matrix(c(44.75, 46, 46.311, 33.5, 36.5, 36.53, 66.625, + 48, 47.87, 59.0833, 50.1667, 50.229, 68.5625, 53.25, 53.289, 60.375, + 69.5, 69.551, 69.5313, 72.25, 71.6, 61.0208, 70.1667, 70.184, 70.0156, + 72.75, 72.138, 61.3438, 70.5, 70.528, 70.2578, 73, 72.348, 61.5052, 70.6667, + 70.7, 70.3789, 72.5, 72.524, 61.5859, 71.3333, NA, 70.4395, 72.25, NA, + 61.6263, 71.6667, NA, 70.4697, 72.125, NA, 61.6465, 71.8333, NA, 70.4849, + 72.0625, NA, 61.6566, 71.9167, NA, 70.4924, 72.0313, NA), nrow = 21, + ncol = 3, byrow = TRUE, dimnames = list(c(2, 3, 4, 6, 8, 12, 16, 24, + 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536, 2048), + c(10, 11, 12))) + decim <- Fid_info[1, "DECIM"] + dspfvs <- Fid_info[1, "DSPFVS"] + if (!(toString(decim) %in% rownames(group_delay_matrix))) { + stop(paste("Invalid DECIM", decim, "it should be one of", rownames(group_delay_matrix))) + } + if (!(toString(dspfvs) %in% colnames(group_delay_matrix))) { + stop(paste("Invalid DSPFVS", dspfvs, "it should be one of", colnames(group_delay_matrix))) + } + group_delay <- group_delay_matrix[toString(decim), toString(dspfvs)] + if (is.na(group_delay)) { + stop(paste("Invalid DECIM", decim, "for DSPFVS", dspfvs)) + } + } + m <- ncol(Fid_data) + n <- nrow(Fid_data) + + # GroupDelayCorrection ---------------------------------------------- + + # We do the shifting in the Fourier domain because the shift can be non-integer. + # That way we automatically have the circular behaviour of the shift and the + # interpolation if it is non-integer. + + Spectrum <- t(stats::mvfft(t(Fid_data))) + + # Spectrum <- FourierTransform(Fid_data, Fid_info) + p <- ceiling(m/2) + new_index <- c((p + 1):m, 1:p) + Spectrum <- Spectrum[,new_index] + Spectrum <- matrix(data = Spectrum, ncol = m, nrow = n) + + Omega <- (0:(m - 1))/m + i <- complex(real = 0, imaginary = 1) + + if (n>1) { + Spectrum <- sweep(Spectrum, MARGIN = 2, exp(i * group_delay * 2 * pi * Omega), `*`) + Spectrum <- Spectrum[,new_index] + }else { + Spectrum <- Spectrum* exp(i * group_delay * 2 * pi * Omega) + Spectrum <- Spectrum[new_index] + Spectrum <- matrix(data = Spectrum, ncol = m, nrow = n) + } + + + Fid_data <- t(stats::mvfft(t(Spectrum), inverse = TRUE))/m + colnames(Fid_data) <- dimension_names[[2]] + rownames(Fid_data) <- dimension_names[[1]] + + # Data finalisation ---------------------------------------------- + + return(endTreatment("GroupDelayCorrection", begin_info, Fid_data)) + } + +## ========================== +# SolventSuppression +## ========================== +SolventSuppression <- function(Fid_data, lambda.ss = 1e+06, ptw.ss = TRUE, + plotSolvent = F, returnSolvent = F) { + + # Data initialisation and checks ---------------------------------------------- + + begin_info <- beginTreatment("SolventSuppression", Fid_data) + Fid_data <- begin_info[["Signal_data"]] + checkArg(ptw.ss, c("bool")) + checkArg(lambda.ss, c("num", "pos0")) + + + # difsm function definition for the smoother ----------------------------------- + + if (ptw.ss) { + # Use of the function in ptw that smoothes signals with a finite difference + # penalty of order 2 + difsm <- ptw::difsm + } else { + # Or manual implementation based on sparse matrices for large data series (cf. + # Eilers, 2003. 'A perfect smoother') + difsm <- function(y, d = 2, lambda) { + + m <- length(y) + # Sparse identity matrix m x m + E <- Matrix::Diagonal(m) + D <- Matrix::diff(E, differences = d) + A <- E + lambda.ss * Matrix::t(D) %*% D + # base::chol does not take into account that A is sparse and is extremely slow + C <- Matrix::chol(A) + x <- Matrix::solve(C, Matrix::solve(Matrix::t(C), y)) + return(as.numeric(x)) + } + } + + # Solvent Suppression ---------------------------------------------- + + n <- dim(Fid_data)[1] + if (returnSolvent) { + SolventRe <- Fid_data + SolventIm <- Fid_data + } + for (i in 1:n) { + FidRe <- Re(Fid_data[i, ]) + FidIm <- Im(Fid_data[i, ]) + solventRe <- difsm(y = FidRe, lambda = lambda.ss) + solventIm <- difsm(y = FidIm, lambda = lambda.ss) + + if (plotSolvent) { + m <- length(FidRe) + graphics::plot(1:m, FidRe, type = "l", col = "red") + graphics::lines(1:m, solventRe, type = "l", col = "blue") + graphics::plot(1:m, FidIm, type = "l", col = "red") + graphics::lines(1:m, solventIm, type = "l", col = "blue") + } + FidRe <- FidRe - solventRe + FidIm <- FidIm - solventIm + Fid_data[i, ] <- complex(real = FidRe, imaginary = FidIm) + if (returnSolvent) { + SolventRe[i, ] <- solventRe + SolventIm[i, ] <- solventIm + } + } + + + # Data finalisation ---------------------------------------------- + + Fid_data <- endTreatment("SolventSuppression", begin_info, Fid_data) + if (returnSolvent) { + return(list(Fid_data = Fid_data, SolventRe = SolventRe, SolventIm = SolventIm)) + } else { + return(Fid_data) + } +} + + +## ========================== +# Apodization +# ============================= +Apodization <- function(Fid_data, Fid_info = NULL, DT = NULL, + type.apod = c("exp","cos2", "blockexp", "blockcos2", + "gauss", "hanning", "hamming"), phase = 0, rectRatio = 1/2, + gaussLB = 1, expLB = 1, plotWindow = F, returnFactor = F) { + + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("Apodization", Fid_data, Fid_info) + Fid_data <- begin_info[["Signal_data"]] + Fid_info <- begin_info[["Signal_info"]] + # Data check + type.apod <- match.arg(type.apod) + checkArg(DT, c("num", "pos"), can.be.null = TRUE) + checkArg(phase, c("num")) + + # Apodization ---------------------------------------------- + DT <- getArg(DT, Fid_info, "DT") # Dwell Time + m <- ncol(Fid_data) + t <- (1:m) * DT # Time + rectSize <- ceiling(rectRatio * m) + gaussLB <- (gaussLB/(sqrt(8 * log(2)))) + # Define the types of apodization: + switch(type.apod, exp = { + # exponential + Factor <- exp(-expLB * t) + }, cos2 = { + # cos^2 + c <- cos((1:m) * pi/(2 * m) - phase * pi/2) + Factor <- c * c + }, blockexp = { + # block and exponential + Factor <- c(rep.int(1, rectSize), rep.int(0, m - rectSize)) + # | rectSize | 1 ___________ | \ 0 \____ + Factor[(rectSize + 1):m] <- exp(-expLB * t[1:(m - rectSize)]) + }, blockcos2 = { + # block and cos^2 + Factor <- c(rep.int(1, rectSize), rep.int(0, m - rectSize)) + c <- cos((1:(m - rectSize)) * pi/(2 * (m - rectSize))) + Factor[(rectSize + 1):m] <- c * c + }, gauss = { + # gaussian + Factor <- exp(-(gaussLB * t)^2/2) + Factor <- Factor/max(Factor) + }, hanning = { + # Hanning + Factor <- 0.5 + 0.5 * cos((1:m) * pi/m - phase * pi) + }, hamming = { + # Hamming + Factor <- 0.54 + 0.46 * cos((1:m) * pi/m - phase * pi) + }) + if (plotWindow) { + graphics::plot(1:m, Factor, "l") + # dev.off() # device independent, it is the responsability of the + # caller to do it + } + # Apply the apodization factor on the spectra + Fid_data <- sweep(Fid_data, MARGIN = 2, Factor, `*`) + + # Data finalisation ---------------------------------------------- + Fid_data <- endTreatment("Apodization", begin_info, Fid_data) + if (returnFactor) { + return(list(Fid_data = Fid_data, Factor = Factor)) + } else { + return(Fid_data) + } +} + + +## ==================================================== +# FourierTransform +## ==================================================== + + +# fftshift1D2D +fftshift1D2D <- function(x) { + vec <- F + if (is.vector(x)) { + x <- vec2mat(x) + vec <- T + } + m <- dim(x)[2] + p <- ceiling(m/2) + new_index <- c((p + 1):m, 1:p) + y <- x[, new_index, drop = vec] +} + +# FourierTransform +FourierTransform <- function(Fid_data, Fid_info = NULL, SW_h = NULL, SW = NULL, O1 = NULL, reverse.axis = TRUE) { + + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("FourierTransform", Fid_data, Fid_info) + Fid_data <- begin_info[["Signal_data"]] + Fid_info <- begin_info[["Signal_info"]] + + m <- ncol(Fid_data) + n <- nrow(Fid_data) + + if (is.null(SW_h)) { + SW_h <- getArg(SW_h, Fid_info, "SW_h") + } + + if (is.null(SW)) { + SW <- getArg(SW, Fid_info, "SW") # Sweep Width in ppm (semi frequency scale in ppm) + } + + + if (is.null(O1)) { + O1 <- getArg(O1, Fid_info, "O1") + } + + + checkArg(reverse.axis, c("bool")) + + # Fourier Transformation ---------------------------------------------- + # mvfft does the unnormalized fourier transform (see ?mvfft), so we need divide + # by m. It does not matter a lot in our case since the spectrum will be + # normalized. + + # FT + RawSpect_data <- fftshift1D2D(t(stats::mvfft(t(Fid_data)))) + # recover the frequencies values + f <- ((0:(m - 1)) - floor(m/2)) * Fid_info[1, "SW_h"]/(m-1) + + if(reverse.axis == TRUE) { + revind <- rev(1:m) + RawSpect_data <- RawSpect_data[,revind] # reverse the spectrum + } + + RawSpect_data <- matrix(RawSpect_data, nrow = n, ncol = m) + colnames(RawSpect_data) <- f + rownames(RawSpect_data) <- rownames(Fid_data) + + # PPM conversion ---------------------------------------------- + + # The Sweep Width has to be the same since the column names are the same + + ppmInterval <- SW/(m-1) + + O1index = round((m+1)/2+O1*(m - 1) / SW_h) + + end <- O1index - m + start <- O1index -1 + ppmScale <- (start:end) * ppmInterval + RawSpect_data <- matrix(RawSpect_data, nrow = n, ncol = -(end - start) + 1, dimnames = + list(rownames(RawSpect_data), ppmScale)) + + + # Data finalisation ---------------------------------------------- + return(endTreatment("FourierTransform", begin_info, RawSpect_data)) +} + +## ==================================================== +# InternalReferencing +## ==================================================== + +InternalReferencing <- function(Spectrum_data, Fid_info, method = c("max", "thres"), + range = c("nearvalue", "all", "window"), ppm.value = 0, + direction = "left", shiftHandling = c("zerofilling", "cut", + "NAfilling", "circular"), c = 2, pc = 0.02, fromto.RC = NULL, + ppm.ir = TRUE, rowindex_graph = NULL) { + + + + # Data initialisation and checks ---------------------------------------------- + + begin_info <- beginTreatment("InternalReferencing", Spectrum_data, Fid_info) + Spectrum_data <- begin_info[["Signal_data"]] + Fid_info <- begin_info[["Signal_info"]] + + + ######## Check input arguments + + range <- match.arg(range) + shiftHandling <- match.arg(shiftHandling) + method <- match.arg(method) + plots <- NULL + + checkArg(ppm.ir, c("bool")) + checkArg(unlist(fromto.RC), c("num"), can.be.null = TRUE) + checkArg(pc, c("num")) + checkArg(ppm.value, c("num")) + checkArg(rowindex_graph, "num", can.be.null = TRUE) + + # fromto.RC : if range == "window", + # fromto.RC defines the spectral window where to search for the peak + if (!is.null(fromto.RC)) { + diff <- diff(unlist(fromto.RC))[1:length(diff(unlist(fromto.RC)))%%2 !=0] + for (i in 1:length(diff)) { + if (diff[i] >= 0) { + fromto <- c(fromto.RC[[i]][2], fromto.RC[[i]][1]) + fromto.RC[[i]] <- fromto + } + } + } + + + # findTMSPpeak function ---------------------------------------------- + # If method == "tresh", findTMSPpeak will find the position of the first + # peak (from left or right) which is higher than a predefined threshold + # and is computed as: c*(cumulated_mean/cumulated_sd) + findTMSPpeak <- function(ft, c = 2, direction = "left") { + ft <- Re(ft) # extraction de la partie réelle + N <- length(ft) + if (direction == "left") { + newindex <- rev(1:N) + ft <- rev(ft) + } + thres <- 99999 + i <- 1000 # Start at point 1000 to find the peak + vect <- ft[1:i] + + while (vect[i] <= (c * thres)) { + cumsd <- stats::sd(vect) + cummean <- mean(vect) + thres <- cummean + 3 * cumsd + i <- i + 1 + vect <- ft[1:i] + } + if (direction == "left") { + v <- newindex[i] + } else {v <- i} + + if (is.na(v)) { + warning("No peak found, need to lower the threshold.") + return(NA) + } else { + # recherche dans les 1% de points suivants du max trouve pour etre au sommet du + # pic + d <- which.max(ft[v:(v + N * 0.01)]) + new.peak <- v + d - 1 # nouveau pic du TMSP si d > 0 + + if (names(which.max(ft[v:(v + N * 0.01)])) != names(which.max(ft[v:(v + N * 0.03)]))) { + # recherche dans les 3% de points suivants du max trouve pour eviter un faux + # positif + warning("the TMSP peak might be located further away, increase the threshold to check.") + } + return(new.peak) + } + } + + + # Define the search zone ---------------------------------------- + + n <- nrow(Spectrum_data) + m <- ncol(Spectrum_data) + + # The Sweep Width (SW) has to be the same since the column names are the same + SW <- Fid_info[1, "SW"] # Sweep Width in ppm + ppmInterval <- SW/(m-1) # size of a ppm interval + + # range: How the search zone is defined ("all", "nearvalue" or "window") + if (range == "all") { + + Data <- Spectrum_data + + } else { # range = "nearvalue" or "window" + # Need to define colindex (column indexes) to apply indexInterval on it + + if (range == "nearvalue") { + + fromto.RC <- list(c(-(SW * pc)/2 + ppm.value, (SW * pc)/2 + ppm.value)) # automatic fromto values in ppm + colindex <- as.numeric(colnames(Spectrum_data)) + + } else { + # range == "window" + # fromto.RC is already user-defined + if (ppm.ir == TRUE) { + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + colindex <- 1:m + } + } + + # index intervals taking into account the different elements in the list fromto.RC + Int <- vector("list", length(fromto.RC)) + for (i in 1:length(fromto.RC)) { + Int[[i]] <- indexInterval(colindex, from = fromto.RC[[i]][1], + to = fromto.RC[[i]][2], inclusive = TRUE) + } + + # define Data as the cropped spectrum including the index intervals + # outside the research zone, the intensities are set to the minimal + # intensity of the research zone + + if (n > 1){ + Data <- apply(Re(Spectrum_data[,unlist(Int)]),1, function(x) rep(min(x), m)) + Data <- t(Data) + Data[,unlist(Int)] <- Re(Spectrum_data[,unlist(Int)]) + } else { + Data <- rep(min(Re(Spectrum_data)) ,m) + Data[unlist(Int)] <- Re(Spectrum_data[unlist(Int)]) + } + + } + + + # Apply the peak location search method ('thres' or 'max') on spectra + # ----------------------------------------------------------------------- + + if (method == "thres") { + TMSPpeaks <- apply(Data, 1, findTMSPpeak, c = c, direction = direction) + } else { # method == "max + TMSPpeaks <- apply(Re(Data), 1, which.max) + } + + + # Shift spectra according to the TMSPpeaks found -------------------------------- + # Depends on the shiftHandling + + # TMSPpeaks is a column index + maxpeak <- max(TMSPpeaks) # max accross spectra + minpeak <- min(TMSPpeaks) # min accross spectra + + + if (shiftHandling %in% c("zerofilling", "NAfilling", "cut")) { + fill <- NA + if (shiftHandling == "zerofilling") { + fill <- 0 + } + + start <- maxpeak - 1 + end <- minpeak - m + + # ppm values of each interval for the whole spectral range of the spectral matrix + ppmScale <- (start:end) * ppmInterval + + # check if ppm.value is in the ppmScale interval + if(ppm.value < min(ppmScale) | ppm.value > max(ppmScale)) { + warning("ppm.value = ", ppm.value, " is not in the ppm interval [", + round(min(ppmScale),2), ",", round(max(ppmScale),2), "], and is set to its default ppm.value 0") + ppm.value = 0 + } + + # if ppm.value != 0, ppmScale is adapted + ppmScale <- ppmScale + ppm.value + + # create the spectral matrix with realigned spectra + Spectrum_data_calib <- matrix(fill, nrow = n, ncol = -(end - start) + 1, + dimnames = list(rownames(Spectrum_data), ppmScale)) + + # fills in Spectrum_data_calib with shifted spectra + for (i in 1:n) { + shift <- (1 - TMSPpeaks[i]) + start + Spectrum_data_calib[i, (1 + shift):(m + shift)] <- Spectrum_data[i, ] + } + + if (shiftHandling == "cut") { + Spectrum_data_calib = as.matrix(stats::na.omit(t(Spectrum_data_calib))) + Spectrum_data_calib = t(Spectrum_data_calib) + base::attr(Spectrum_data_calib, "na.action") <- NULL + } + + + } else { + # circular + start <- 1 - maxpeak + end <- m - maxpeak + + ppmScale <- (start:end) * ppmInterval + + # check if ppm.value in is the ppmScale interval + if(ppm.value < min(ppmScale) | ppm.value > max(ppmScale)) { + warning("ppm.value = ", ppm.value, " is not in the ppm interval [", + round(min(ppmScale),2), ",", round(max(ppmScale),2), "], and is set to its default ppm.value 0") + ppm.value = 0 + } + + # if ppm.value != 0, ppmScale is adapted + ppmScale <- ppmScale + ppm.value + + # create the spectral matrix with realigned spectra + Spectrum_data_calib <- matrix(nrow=n, ncol=end-start+1, + dimnames=list(rownames(Spectrum_data), ppmScale)) + + # fills in Spectrum_data_calib with shifted spectra + for (i in 1:n) { + shift <- (maxpeak-TMSPpeaks[i]) + Spectrum_data_calib[i,(1+shift):m] <- Spectrum_data[i,1:(m-shift)] + if (shift > 0) { + Spectrum_data_calib[i,1:shift] <- Spectrum_data[i,(m-shift+1):m] + } + } + } + + + + + # Plot of the spectra (depending on rowindex_graph) --------------------------------------------------- + + ppm = xstart = value = xend = Legend = NULL # only for R CMD check + + + # with the search zone for TMSP and the location of the peaks just found + if (!is.null(rowindex_graph)) { + + if (range == "window") { + if (ppm.ir == TRUE) { + fromto <- fromto.RC + } else { + fromto <- list() + idcol <- as.numeric(colnames(Spectrum_data)) + for (i in 1:length(fromto.RC)) { + fromto[[i]] <- as.numeric(colnames(Spectrum_data))[fromto.RC[[i]]] + } + } + } else { + fromto <- fromto.RC + } + + # TMSPloc in ppm + TMSPloc <- as.numeric(colnames(Spectrum_data))[TMSPpeaks[rowindex_graph]] + + # num plot per window + num.stacked <- 6 + + # rectanglar bands of color for the search zone + rects <- data.frame(xstart = sapply(fromto, function(x) x[[1]]), + xend = sapply(fromto, function(x) x[[2]]), + Legend = "Peak search zone and location") + + # vlines for TMSP peak + addlines <- data.frame(rowname = rownames(Spectrum_data)[rowindex_graph],TMSPloc) + + nn <- length(rowindex_graph) + i <- 1 + j <- 1 + plots <- vector(mode = "list", length = ceiling(nn/num.stacked)) + + while (i <= nn) { + + last <- min(i + num.stacked - 1, nn) + + melted <- reshape2::melt(Re(Spectrum_data[i:last, ]), + varnames = c("rowname", "ppm")) + + plots[[j]] <- ggplot2::ggplot() + ggplot2::theme_bw() + + ggplot2::geom_line(data = melted, + ggplot2::aes(x = ppm, y = value)) + + ggplot2::geom_rect(data = rects, ggplot2::aes(xmin = xstart, xmax = xend, + ymin = -Inf, ymax = Inf, fill = Legend), alpha = 0.4) + + ggplot2::facet_grid(rowname ~ ., scales = "free_y") + + ggplot2::theme(legend.position = "none") + + ggplot2::geom_vline(data = addlines, ggplot2::aes(xintercept = TMSPloc), + color = "red", show.legend = TRUE) + + ggplot2::ggtitle("Peak search zone and location") + + ggplot2::theme(legend.position = "top", legend.text = ggplot2::element_text()) + + + + if ((melted[1, "ppm"] - melted[(dim(melted)[1]), "ppm"]) > 0) { + plots[[j]] <- plots[[j]] + ggplot2::scale_x_reverse() + } + + i <- last + 1 + j <- j + 1 + } + + plots + } + + + # Return the results ---------------------------------------------- + Spectrum_data <- endTreatment("InternalReferencing", begin_info, Spectrum_data_calib) + + if (is.null(plots)) { + return(Spectrum_data) + } else { + return(list(Spectrum_data = Spectrum_data, plots = plots)) + } + +} + +## ==================================================== +# ZeroOrderPhaseCorrection +## ==================================================== + +ZeroOrderPhaseCorrection <- function(Spectrum_data, type.zopc = c("rms", "manual", "max"), + plot_rms = NULL, returnAngle = FALSE, createWindow = TRUE, + angle = NULL, plot_spectra = FALSE, + ppm.zopc = TRUE, exclude.zopc = list(c(5.1,4.5))) { + + + # Data initialisation and checks ---------------------------------------------- + + # Entry arguments definition: + # plot_rms : graph of rms criterion returnAngle : if TRUE, returns avector of + # optimal angles createWindow : for plot_rms plots angle : If angle is not NULL, + # spectra are rotated according to the angle vector values + # plot_spectra : if TRUE, plot rotated spectra + + + + begin_info <- beginTreatment("ZeroOrderPhaseCorrection", Spectrum_data) + Spectrum_data <- begin_info[["Signal_data"]] + n <- nrow(Spectrum_data) + m <- ncol(Spectrum_data) + + rnames <- rownames(Spectrum_data) + + # Check input arguments + type.zopc <- match.arg(type.zopc) + checkArg(ppm.zopc, c("bool")) + checkArg(unlist(exclude.zopc), c("num"), can.be.null = TRUE) + + + # type.zopc in c("max", "rms") ----------------------------------------- + if (type.zopc %in% c("max", "rms")) { + # angle is found by optimization + + # rms function to be optimised + rms <- function(ang, y, meth = c("max", "rms")) { + # if (debug_plot) { graphics::abline(v=ang, col='gray60') } + roty <- y * exp(complex(real = 0, imaginary = ang)) # spectrum rotation + Rey <- Re(roty) + + if (meth == "rms") { + ReyPos <- Rey[Rey >= 0] # select positive intensities + POSss <- sum((ReyPos)^2, na.rm = TRUE) # SS for positive intensities + ss <- sum((Rey)^2, na.rm = TRUE) # SS for all intensities + return(POSss/ss) # criterion : SS for positive values / SS for all intensities + } else { + maxi <- max(Rey, na.rm = TRUE) + return(maxi) + } + } + + + # Define the interval where to search for (by defining Data) + if (is.null(exclude.zopc)) { + Data <- Spectrum_data + } else { + + # if ppm.zopc == TRUE, then exclude.zopc is in the colnames values, else, in the column + # index + if (ppm.zopc == TRUE) { + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + colindex <- 1:m + } + + # Second check for the argument exclude.zopc + diff <- diff(unlist(exclude.zopc))[1:length(diff(unlist(exclude.zopc)))%%2 !=0] + for (i in 1:length(diff)) { + if (ppm.zopc == TRUE & diff[i] >= 0) { + stop(paste("Invalid region removal because from <= to in ppm.zopc")) + } else if (ppm.zopc == FALSE & diff[i] <= 0) {stop(paste("Invalid region removal because from >= to in column index"))} + } + + + Int <- vector("list", length(exclude.zopc)) + for (i in 1:length(exclude.zopc)) { + Int[[i]] <- indexInterval(colindex, from = exclude.zopc[[i]][1], + to = exclude.zopc[[i]][2], inclusive = TRUE) + } + + vector <- rep(1, m) + vector[unlist(Int)] <- 0 + if (n > 1) { + Data <- sweep(Spectrum_data, MARGIN = 2, FUN = "*", vector) # Cropped_Spectrum + } else { + Data <- Spectrum_data * vector + } # Cropped_Spectrum + } + + + # angles computation + Angle <- c() + for (k in 1:n) + { + # The function is rms is periodic (period 2pi) and it seems that there is a phase + # x such that rms is unimodal (i.e. decreasing then increasing) on the interval + # [x; x+2pi]. However, if we do the optimization for example on [x-pi; x+pi], + # instead of being decreasing then increasing, it might be increasing then + # decreasing in which case optimize, thinking it is a valley will have to choose + # between the left or the right of this hill and if it chooses wrong, it will end + # up at like x-pi while the minimum is close to x+pi. + + # Supposing that rms is unimodal, the classical 1D unimodal optimization will + # work in either [-pi;pi] or [0;2pi] (this is not easy to be convinced by that I + # agree) and we can check which one it is simply by the following trick + + f0 <- rms(0, Data[k, ],meth = type.zopc) + fpi <- rms(pi, Data[k, ], meth = type.zopc) + if (f0 < fpi) { + interval <- c(-pi, pi) + } else { + interval <- c(0, 2 * pi) + } + + # graphs of rms criteria + debug_plot <- F # rms should not plot anything now, only when called by optimize + if (!is.null(plot_rms) && rnames[k] %in% plot_rms) { + x <- seq(min(interval), max(interval), length.out = 100) + y <- rep(1, 100) + for (K in (1:100)) { + y[K] <- rms(x[K], Data[k, ], meth = type.zopc) + } + if (createWindow == TRUE) { + grDevices::dev.new(noRStudioGD = FALSE) + } + graphics::plot(x, y, main = paste("Criterion maximization \n", + rownames(Data)[k]), ylim = c(0, 1.1), + ylab = "positiveness criterion", xlab = "angle ") + debug_plot <- T + } + + # Best angle + best <- stats::optimize(rms, interval = interval, maximum = TRUE, + y = Data[k,], meth = type.zopc) + ang <- best[["maximum"]] + + + if (debug_plot) { + graphics::abline(v = ang, col = "black") + graphics::text(x = (ang+0.1*ang), y = (y[ang]-0.1*y[ang]), labels = round(ang, 3)) + } + + # Spectrum rotation + Spectrum_data[k, ] <- Spectrum_data[k, ] * exp(complex(real = 0, imaginary = ang)) + Angle <- c(Angle, ang) + } + + + + + } else { + # type.zopc is "manual" ------------------------------------------------------- + # if Angle is already specified and no optimisation is needed + Angle <- angle + + if (!is.vector(angle)) { + stop("angle is not a vector") + } + + if (!is.numeric(angle)) { + stop("angle is not a numeric") + } + + if (length(angle) != n) { + stop(paste("angle has length", length(angle), "and there are", n, "spectra to rotate.")) + } + for (k in 1:n) { + Spectrum_data[k, ] <- Spectrum_data[k, ] * exp(complex(real = 0, imaginary = - angle[k])) + } + } + + + # Draw spectra + if (plot_spectra == TRUE) { + nn <- ceiling(n/4) + i <- 1 + for (k in 1:nn) { + if (createWindow == TRUE) { + grDevices::dev.new(noRStudioGD = FALSE) + } + graphics::par(mfrow = c(4, 2)) + while (i <= n) { + last <- min(i + 4 - 1, n) + graphics::plot(Re(Spectrum_data[i, ]), type = "l", ylab = "intensity", + xlab = "Index", main = paste0(rownames(Spectrum_data)[i], " - Real part")) + graphics::plot(Im(Spectrum_data[i, ]), type = "l", ylab = "intensity", + xlab = "Index", main = paste0(rownames(Spectrum_data)[i], " - Imaginary part")) + i <- i + 1 + } + i <- last + 1 + } + } + + + # Data finalisation ---------------------------------------------- + + Spectrum_data <- endTreatment("ZeroOrderPhaseCorrection", begin_info, Spectrum_data) + if (returnAngle) { + return(list(Spectrum_data = Spectrum_data, Angle = Angle)) + } else { + return(Spectrum_data) + } +} + + +## ==================================================== +# Baseline Correction +## ==================================================== +BaselineCorrection <- function(Spectrum_data, ptw.bc = TRUE, maxIter = 42, + lambda.bc = 1e+07, p.bc = 0.05, eps = 1e-08, + ppm.bc = TRUE, exclude.bc = list(c(5.1,4.5)), + returnBaseline = F) { + + # Data initialisation ---------------------------------------------- + begin_info <- beginTreatment("BaselineCorrection", Spectrum_data, force.real = T) + Spectrum_data <- begin_info[["Signal_data"]] + p <- p.bc + lambda <- lambda.bc + n <- dim(Spectrum_data)[1] + m <- dim(Spectrum_data)[2] + + + # Data check + checkArg(ptw.bc, c("bool")) + checkArg(maxIter, c("int", "pos")) + checkArg(lambda, c("num", "pos0")) + checkArg(p.bc, c("num", "pos0")) + checkArg(eps, c("num", "pos0")) + checkArg(returnBaseline, c("bool")) + checkArg(ppm.bc, c("bool")) + checkArg(unlist(exclude.bc), c("num"), can.be.null = TRUE) + + # Define the interval where to search for (by defining Data) + if (is.null(exclude.bc)) { + exclude_index <- NULL + } else { + # if ppm.bc == TRUE, then exclude.bc is in the colnames values, else, in the column + # index + if (ppm.bc == TRUE) { + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + colindex <- 1:m + } + + Int <- vector("list", length(exclude.bc)) + for (i in 1:length(exclude.bc)) { + Int[[i]] <- indexInterval(colindex, from = exclude.bc[[i]][1], + to = exclude.bc[[i]][2], inclusive = TRUE) + } + exclude_index <- unlist(Int) + } + + # Baseline Correction implementation definition ---------------------- + + # 2 Ways: either use the function asysm from the ptw package or by + # built-in functions + if (ptw.bc) { + asysm <- ptw::asysm + } else { + difsmw <- function(y, lambda, w, d) { + # Weighted smoothing with a finite difference penalty cf Eilers, 2003. + # (A perfect smoother) + # y: signal to be smoothed + # lambda: smoothing parameter + # w: weights (use0 zeros for missing values) + # d: order of differences in penalty (generally 2) + m <- length(y) + W <- Matrix::Diagonal(x=w) + E <- Matrix::Diagonal(m) + D <- Matrix::diff(E, differences = d) + C <- Matrix::chol(W + lambda * t(D) %*% D) + x <- Matrix::solve(C, Matrix::solve(t(C), w * y)) + return(as.numeric(x)) + + } + asysm <- function(y, lambda, p, eps, exclude_index) { + # Baseline estimation with asymmetric least squares + # y: signal + # lambda: smoothing parameter (generally 1e5 to 1e8) + # p: asymmetry parameter (generally 0.001) + # d: order of differences in penalty (generally 2) + # eps: 1e-8 in ptw package + m <- length(y) + w <- rep(1, m) + i <- 1 + repeat { + z <- difsmw(y, lambda, w, d = 2) + w0 <- w + p_vect <- rep((1-p), m) # if y <= z + eps + p_vect[y > z + eps | y < 0] <- p # if y > z + eps | y < 0 + if(!is.null(exclude_index)){ + p_vect[exclude_index] <- 0 # if exclude area + } + + w <- p_vect + # w <- p * (y > z + eps | y < 0) + (1 - p) * (y <= z + eps) + + if (sum(abs(w - w0)) == 0) { + break + } + i <- i + 1 + if (i > maxIter) { + warning("cannot find Baseline estimation in asysm") + break + } + } + return(z) + } + } + + # Baseline estimation ---------------------------------------------- + Baseline <- matrix(NA, nrow = nrow(Spectrum_data), ncol = ncol(Spectrum_data)) + + # for (k in 1:n) { + # Baseline[k, ] <- asysm(y = Spectrum_data[k, ], lambda = lambda, p = p, eps = eps) + + if (ptw.bc ){ + Baseline <- apply(Spectrum_data,1, asysm, lambda = lambda, p = p, + eps = eps) + }else { + Baseline <- apply(Spectrum_data,1, asysm, lambda = lambda, p = p, + eps = eps, exclude_index = exclude_index) + } + + + Spectrum_data <- Spectrum_data - t(Baseline) + # } + + # Data finalisation ---------------------------------------------- + Spectrum_data <- endTreatment("BaselineCorrection", begin_info, Spectrum_data) # FIXME create removeImaginary filter ?? + + if (returnBaseline) { + return(list(Spectrum_data = Spectrum_data, Baseline = Baseline)) + } else { + return(Spectrum_data) + } +} + + + +## ==================================================== +# NegativeValuesZeroing +## ==================================================== + +NegativeValuesZeroing <- function(Spectrum_data) { + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("NegativeValuesZeroing", Spectrum_data, force.real = T) + Spectrum_data <- begin_info[["Signal_data"]] + + # NegativeValuesZeroing ---------------------------------------------- + Spectrum_data[Spectrum_data < 0] <- 0 + + # Data finalisation ---------------------------------------------- + return(endTreatment("NegativeValuesZeroing", begin_info, Spectrum_data)) +} + +