R/exportTrackll.R

In snjy9182/smt-beta: Statistical analysis of single molecule trajectories

## exportTrackll-methods
##
##
###############################################################################
##' @name exportTrackll
##' @aliases exportTrackll
##' @title exportTrackll
##' @rdname exportTrackll-methods
##' @docType methods
##' @description take in a list of track lists (trackll) and export it into 
##' row-wise (ImageJ Particle Tracker style) .csv files in the working directory
##' @usage 
##' exportTrackll(trackll, cores = 1)
##' @param trackll A list of track lists.
##' @param cores Number of cores used for parallel computation. This can be 
##' the cores on a workstation, or on a cluster. Tip: each core will be 
##' assigned to read in a file when paralleled.
##' @return .csv file output
##' @details
##' The reason why ImageJ particle Tracker style .csv export was chosen is 
##' because it fully preserves track frame data, while maintaining short 
##' computation time and easy readability in Excel/etc.
##' 
##' In order to import this .csv export back into a trackll at any point 
##' (while preserving all information), select input = 3 in createTrackll.
##' 
##' If the track list does not have a fourth frame record column (not 
##' recommended), it will just output the start frame of each track instead 
##' and will take noticeably longer.
##'
##' It is not recommended that exportTrackll be run on merged list of track
##' lists (trackll).Also, ensure that the input trackll is a list of track lists
##' and not just a track list.
##' 
##' The naming scheme for each export is as follows:
##' 
##' [Last five characters of the file name]_[yy-MM-dd]_[HH-mm-ss].csv

##' @examples
##' folder=system.file('extdata','SWR1',package='sojourner')
##' trackll=createTrackll(folder=folder, input=3)
##' 
##' # Basic function call to exportTrackll into current directory
##' exportTrackll(trackll)
##' 
##' # Import export save back into a trackll
##' # Get current working directory
##' getwd()
##' trackll.2 <- createTrackll(folder = getwd(), input = 3)
##' 
##  @importFrom rowr cbind.fill
##' @export .exportRowWise
##' @export exportTrackll

############################################################################### 

#### .exportRowWise ####

## # Export one track list
## .exportRowWise(trackl)

.exportRowWise = function(trackl) {
    
    # Confirmation text of function call
    cat("\nWriting .csv row-wise output in current directory for", 
        getTrackFileName(trackl), 
        "...\n")
    
    # Collect track file name
    track.file.name <- getTrackFileName(trackl)
    
    # Check for frame record column
    if (ncol(trackl[[1]]) != 3) {
        
        # Rename track list as trajectory numbers
        names(trackl) <- c(seq_along(trackl))
        
        # Combine track data frames by trajectory and reorder column
        df <- bind_rows(trackl, .id = "Trajectory")[, c("Trajectory", 
            "Frame", "x", "y", "z")]
        
    } else {
        # Empty data frame df to be written into the .csv
        df <- NULL
        
        # Loop through every trajectory in input trackl
        for (i in seq_along(trackl)) {
            
            # Create a data frame temp with trajectory, frame, and track 
            # coordinate data
            temp <- data.frame(Trajectory = i, 
                Frame = getStartFrame(trackl, i), 
                trackl[[i]][seq_len(3)])
            
            # Append data frame df with data frame temp
            df <- rbind(df, temp)
        }
    }
    # Write the data frame df into the .csv and display confirmation text
    file.name = paste(track.file.name, format(Sys.time(), 
        format = "_%y-%m-%d_%H-%M-%S"), ".csv", sep = "")
    write.csv(df, file = file.name)
    cat(paste("\n", file.name, " placed in current directory.\n", sep = ""))
}

# #### .exportColWise ####

# # Function unused for now- may be helpful when lossy Diatrack .txt
# # export is neccessary

# # Install packages and dependencies library(rowr)

# .exportColWise = function(trackl) {
    
#     # Confirmation text of function call
#     cat("\nWriting .csv column-wise output in current directory for", 
#         getTrackFileName(trackl), "...\n")
    
#     frame.list <- list()
    
#     # Loop through every trajectory in input trackl
#     for (i in seq_along(trackl)) {
        
#         start.frame = getStartFrame(trackl[i])
        
#         frame.list <- c(frame.list, start.frame, 0, 0)
        
#         temp <- trackl[[i]][seq_len(3)]
        
#         if (i != 1) {
#             df <- cbind.fill(df, temp, fill = 0)
#         } else {
#             df <- temp
#         }
#     }
    
#     colnames(df) <- frame.list
    
#     # header = 'format (columnwise): Frame1 row n+1: (y(tn) x(tn) z(tn)),
#     # row n+1: (y(t(n+1)) x(t(n+1)) z(t(n+1))), row n+2: (y(t(n+2))
#     # x(t(n+2) z(t(n+2)) y(t(n+3))....  where Frame1 is the frame number
#     # where the target is seen for the first time, and the columns define
#     # trajectories. Beware!  the number of tracks is limited by the width
#     # of the widest text file on your machine.  Rowwise export preferred'
    
#     # Write the data frame df into the .csv and display confirmation text
#     file.name = paste("COL", getTrackFileName(trackl), ".csv", sep = "")
#     # write(header, file = file.name, append = TRUE)
#     write.table(df, file = file.name, row.names = FALSE, sep = ",")
#     # ,append = TRUE
    
#     cat(paste("\n", file.name, "placed in current directory.\n\n", sep = ""))
# }

#### exportTrackll ####

exportTrackll = function(trackll, cores = 1) {
    
    # detect number of cores
    max.cores = parallel::detectCores(logical = TRUE)
    
    if (cores == 1) {
        export = lapply(trackll, function(x) {
            .exportRowWise(trackl = x)
        })
    } else {
        # parallel excecute above block of code
        if (cores > max.cores) 
            stop("Number of cores specified is greater than maxium: ", 
                max.cores)
        
        cat("Initiated parallel execution on", cores, "cores\n")
        
        # use outfile="" to display result on screen
        cl <- parallel::makeCluster(spec = cores, type = "PSOCK", outfile = "")
        # register cluster
        parallel::setDefaultCluster(cl)
        
        # pass environment variables to workers
        parallel::clusterExport(cl,
                                varlist=c(".exportRowWise"),
                                envir=environment())
        
        export = parallel::parLapply(cl,trackll,function(x){
            .exportRowWise(trackl = x)
        })
        
        # stop cluster
        cat("\nStopping clusters...\n")
        parallel::stopCluster(cl)
    }
    
}


## TODO:
# identical(trackll,trackll.2)
# FALSE

# the track index is lost when export, therefore when import it has to be
# regnerated. export also the track index, so one can import it and create
# exactly the same