R/swaptoSubser.R

In XiaosuTong/drSpaceTime: Spatial Seasonal Trend Loess analysis routine using divide and recombined

# nolint start
#swaptoSubser <- function(input, output, cluster_control, model_control) {#
#
#  job <- list()
#  job$map <- expression({
#    lapply(seq_along(map.values), function(r) {
#      plyr::d_ply(
#        .data = map.values[[r]],
#        .variable = c("month"),
#        .fun = function(k, station = map.keys[[r]]) {
#          key <- c(station, unique(as.character(k$month)))
#          value <- subset(k, select = -c(month))
#          attr(value, "loc") <- attributes(map.values[[r]])$loc
#          rhcollect(key, value)
#      })
#    })
#  })
#  job$parameters <- list(
#    Clcontrol = cluster_control,
#    Mlcontrol = model_control
#  )
#  job$setup <- expression(
#    map = {
#      library(plyr, lib.loc=Clcontrol$libLoc)
#    }
#  )
#  job$mapred <- list(
#    mapreduce.task.timeout = 0,
#    mapreduce.job.reduces = cluster_control$reduceTask,  #cdh5
#    mapreduce.map.java.opts = cluster_control$map_jvm,
#    mapreduce.map.memory.mb = cluster_control$map_memory,
#    dfs.blocksize = cluster_control$BLK,
#    rhipe_reduce_buff_size = cluster_control$reduce_buffer_size,
#    rhipe_reduce_bytes_read = cluster_control$reduce_buffer_read,
#    rhipe_map_buff_size = cluster_control$map_buffer_size,
#    rhipe_map_bytes_read = cluster_control$map_buffer_read,
#    mapreduce.map.output.compress = TRUE,
#    mapreduce.output.fileoutputformat.compress.type = "BLOCK"#
#
#  )
#  job$input <- rhfmt(input, type="sequence")
#  job$output <- rhfmt(output, type="sequence")
#  job$mon.sec <- 20
#  job$jobname <- output
#  job$readback <- FALSE#
#
#  job.mr <- do.call("rhwatch", job)#
#
#}
# nolint end