UPMASKfile: Run UPMASK in a file
In UPMASK: Unsupervised Photometric Membership Assignment in Stellar Clusters

Description Usage Arguments Author(s) References Examples

UPMASKfile executes the UPMASK method using a file as an input and writes another file as an output. This is a wrapper function that only reads a file into an R data frame, calls the UPMASKdata function using this data frame and the parameters passed by the user and writes the output into another file.

UPMASKfile(filenameWithPathInput, filenameWithPathOuput, 
positionDataIndexes=c(1,2), photometricDataIndexes=c(3,5,7,9,11,19,21,23,25,27),
photometricErrorDataIndexes=c(4,6,8,10,12,20,22,24,26,28), threshold=1, 
maxIter=20, starsPerClust_kmeans=50, nstarts_kmeans=50, nRuns=5, 
runInParallel=FALSE, paralelization="multicore", independent=TRUE, verbose=FALSE, 
autoCalibrated=FALSE, considerErrors=FALSE, finalXYCut=FALSE, 
fileWithHeader=FALSE, nDimsToKeep=4, dimRed="PCA", scale=TRUE)

`filenameWithPathInput`	a string indicating the file containing the data to run UPMASK on (with full path)
`filenameWithPathOuput`	a string indicating the file where the output shall be written (with full path)
`positionDataIndexes`	an array of integers indicating the columns of the file containing the spatial position measurements
`photometricDataIndexes`	an array of integers with the column numbers containing photometric measurements (or any other measurement to go into the PCA step)
`photometricErrorDataIndexes`	an array of integers with the column numbers containing the errors of the photometric measurements
`threshold`	a double indicating the thresholding level for the random field analysis
`maxIter`	an integer the maximum amount of iterations of the outer loop before giving up convergence (usually it is not necessary to modify this)
`starsPerClust_kmeans`	an integer with the average number of stars per k-means cluster
`nstarts_kmeans`	an integer the amount of random re-initializations of the k-means clustering method (usually it is not necessary to modify this)
`nRuns`	the total number of individual runs to execute the total number of outer loop runs to execute
`runInParallel`	a boolean indicating if the code should run in parallel
`paralelization`	a string with the type of paralilization to use. the paralelization can be: "multicore" or "MPIcluster". At this moment only "multicore" is implemented (defaults to multicore).
`independent`	a boolean indicating if non-parallel runs should be completely independent
`verbose`	a boolean indicating if the output to screen should be verbose
`autoCalibrated`	a boolean indicating if the number of random field realizations for the clustering check in the position space should be autocalibrated (experimental code, defaults to FALSE).
`considerErrors`	a boolean indicating if the errors should be taken into account
`finalXYCut`	a boolean indicating if a final cut in the XY space should be performed (defaults to FALSE)
`fileWithHeader`	a boolean indicating if the input file has a text header
`nDimsToKeep`	an integer with the number of dimensions to consider (defaults to 4)
`dimRed`	a string with the dimensionality reduction method to use (defaults to PCA. The only other options are LaplacianEigenmaps or None)
`scale`	a boolean indicating if the data should be scaled and centered

Alberto Krone-Martins, Andre Moitinho

Krone-Martins, A. & Moitinho, A., A&A, v.561, p.A57, 2014

## Not run: 
# Analyse a simulated open cluster using spatial and photometric data 
# Create strings with filenames
fileNameI <- "oc_12_500_1000_1.0_p019_0880_1_25km_120nR_withcolors.dat"
inputFileName <- system.file("extdata", fileNameI, package="UPMASK")
outputFileName <- file.path(tempdir(), "up-RESULTS.dat")

# Example of how to run UPMASK using data from a file
# (serious analysis require at least larger nRuns)
posIdx <- c(1,2)
photIdx <- c(3,5,7,9,11,19,21,23,25,27)
photErrIdx <- c(4,6,8,10,12,20,22,24,26,28)
UPMASKfile(inputFileName, outputFileName, posIdx, photIdx, photErrIdx, nRuns=5, 
           starsPerClust_kmeans=25, verbose=TRUE, fileWithHeader=TRUE)

# Open the resulting file to inspect the results
tempResults <- read.table(outputFileName, header=TRUE)

# Create a simple raw plot to see the results
pCols <- tempResults[,length(tempResults)]/max(tempResults[,length(tempResults)])
plot(tempResults[,1], tempResults[,2], col=rgb(0,0,0,pCols), cex=0.5, pch=19)

# Clean the environment
rm(list=c("tempResults", "inputFileName", "outputFileName", "pCols", "fileNameI"))

## End(Not run)