Description Usage Arguments Examples
run the pipeline
1 2 3 4 5 6 7 8 9 | pipeline(cruise.name='', repo=REPO.PATH, range=NULL, steps=1:4, pct=.97,
clust.concat.ct=3, resample.size=300, resamp.concat.max=10,
filter.width=1.5, filter.notch=1, filter.edge=1,
classify.func =2, classify.varnames=CHANNEL.CLMNS.SM, classify.numc=0, classify.noise=0,
map.margin=2,
concat.sds=!is.na(match(1,steps)), load.to.db=FALSE, preplot=FALSE, cleanup=TRUE,
input.path=paste(repo, '/', cruise.name, sep=''),
output.path=input.path, log.dir=output.path,
def.path=paste(input.path,'/', 'pop.def.tab',sep=''), parallel=TRUE, submit.cmd='qsub')
|
cruise.name |
Simplified cruise name (same name as the subdirectory in the seaflow data dir). |
steps |
Which steps of the pipeline to run. step 1 is filter, step 2 is classify, step 3 is census and consensus, step 4 is summarize. 1:2 will do step 1 to 2, etc. |
pct |
percentage completion (number of indicator files created vs input files) each job step should go to. |
clust.concat.ct |
Number of event file to concatenate at a time during the clustering/classification step. |
map.margin |
Margin in latitude/longitude around the map plots. |
resample.size |
Minimum number of events in a population. |
resamp.concat.max |
Maximum number of allowable event files to concatenate to generate statistics from. |
filter.notch |
the location of the x=y (by default) point to create the notch in the gated filter |
filter.width |
the margin of error for particle alignment determination in the filter step. |
filter.edge |
location of the boundary layer between water/air. Particles located at the boundary layer scatter light that can be detected by the position detectors for the filter step. |
classify.func |
Choose the clustering method, either flowClust (func = 1) or flowMeans (func = 2, by default) function |
classify.varnames |
A character vector specifying the variables (columns) to be included in clustering when choosing flowMeans. |
classify.numc |
Number of clusters when choosing flowMeans. If set to 0 (default) the value matches the number of populations defined in pop.def table . If set to NA, the optimal number of clusters will be estimated automatically. |
classify.noise |
Set up the noise threshold for phytoplankton cells. Only cells with chlorophyll value higher than the noise will be clustered |
concat.sds |
Determines if the sds files in the individual julian day directories should be concatenated together into sds.tab |
load.to.db |
Load the sds and stat files to the database. |
preplot |
Preplot the level 2 analysis plots to 'output.path'. |
cleanup |
Cleanup the submission and (non error reporting) R CMD BATCH log files. |
input.path |
Path to the directory with input data (raw evt or opp files. |
output.path |
Path to the directory where you wish to output data. |
log.dir |
Path to the directory where log file will be written. |
def.path |
Path to the file that defines how to gate & cluster the events into populations. |
parallel |
Boolean indicating if the job should be run in parallel using qsub (vs in serial) |
repo |
Full path to your SeaFlow repository |
range |
A named, two-integer vector specifying the start and end (inclusive) range for subsetting the input files used in each analysis step (with the exception of summarize). Values should be a (evt/opp) file numbers and names should be strings corresponding to the year_julianday directory names. The nv() function is useful for creating this vector. |
submit.cmd |
the command used to deploy an R CMD BATCH system call to a cluster. Must be used in conjunction with parallel=TRUE. |
1 2 3 4 5 6 7 8 9 | example.cruise.name <- 'seaflow_cruise'
temp.out.dir <- '.' #path.expand('~')
output.path <- paste(temp.out.dir,'/',example.cruise.name,sep='')
seaflow.path <- system.file("extdata", example.cruise.name, package="flowPhyto")
file.copy(from=seaflow.path, to=temp.out.dir, recursive=TRUE)
pipeline(repo= temp.out.dir, cruise.name='seaflow_cruise', steps=4, parallel=FALSE)
unlink(example.cruise.name, recursive=TRUE)
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