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plotFlowConc <- function(eList, month = c(1:12), years = NULL, col_vec = c('red', 'green', 'blue'), ylabel = NULL, xlabel = NULL, alpha = 1, size = 1,  allflo = FALSE, ncol = NULL, grids = TRUE, scales = NULL, interp = 4, pretty = TRUE, use_bw = TRUE, fac_nms = NULL, ymin = 0){
  
  localDaily <- getDaily(eList)
  localINFO <- getInfo(eList)
  localsurfaces <- getSurfaces(eList)
  
  # plot title
  toplab <- with(eList$INFO, paste(shortName, paramShortName, sep = ', '))

  # flow, date info for interpolation surface

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# PPOTM:  Practical-Programming Of The Month (2016 January)

# iMAINloop:  Continue asking users "what file to load" until they type a zero.
iMAINloop <- 0;
while (iMAINloop < 1 ) {
  
  # Below creates a list of all files (in the current working directory) of pattern *.csv
  # Note that you can set  path="xxxx" to look in a different folder area
  filenames <- list.files(pattern = ".csv")

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# packages to use
library(SWMPr)
library(httr)
library(XML)
library(foreach)
library(doParallel)

# names of files on server
files_s3 <- httr::GET('https://s3.amazonaws.com/swmpalldata/')$content
files_s3 <- rawToChar(files_s3)

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# 'root' character string of directory to search
# 'file_typs' character vector of file types to search
# 'omit_blank' logical indicating of blank lines are counted
# 'recursive' logical indicating if all directories within 'root' are searched
# 'lns' logical indicating if lines are counted, use F for counting characters
# 'trace' logical for monitoring progress
file.lens <- function(root, file_typs, omit_blank = F, recursive = T, 
	lns = T, trace = T){
	
	require(reshape2)

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plot.nnet<-function(mod.in,nid=T,all.out=T,all.in=T,bias=T,wts.only=F,rel.rsc=5,
                    circle.cex=5,node.labs=T,var.labs=T,x.lab=NULL,y.lab=NULL,
                    line.stag=NULL,struct=NULL,cex.val=1,alpha.val=1,
                    circle.col='lightblue',pos.col='black',neg.col='grey',
                    bord.col='lightblue', max.sp = F,...){

  require(scales)
  
  #sanity checks
  if('mlp' %in% class(mod.in)) warning('Bias layer not applicable for rsnns object')

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lek.fun<-function(mod.in,var.sens=NULL,resp.name=NULL,exp.in=NULL,steps=100,split.vals=seq(0,1,by=0.2),val.out=F){
  
  require(ggplot2)
  require(reshape)
  
  ##
  #sort out exp and resp names based on object type of call to mod.in
  #get matrix for exp vars
  
  #for nnet

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#credit http://stackoverflow.com/questions/13649473/add-a-common-legend-for-combined-ggplots

require(ggplot2)
require(reshape)
require(gridExtra)

p.dat<-data.frame(step=row.names(grp.dat),grp.dat,stringsAsFactors=F)
p.dat<-melt(p.dat,id='step')
p.dat$step<-as.numeric(p.dat$step)

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plot.area<-function(x,col=NULL,horiz=F,prop=T,stp.ln=T,grp.ln=T,axs.cex=1,axs.lab=T,lab.cex=1,
                    names=c('Group','Step','Value'),...){
  
  #sort out color fector  
  if(!is.null(col)){
    if(sum(col %in% colors()) != length(col)) stop('col vector must be in "colors()"')
    col<-colorRampPalette(col)(ncol(x))
  }
  else col<-colorRampPalette(c('lightblue','green'))(ncol(x))
  

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gar.fun<-function(out.var,mod.in,bar.plot=T,struct=NULL,x.lab=NULL,
                  y.lab=NULL, wts.only = F){
  
  require(ggplot2)
  require(plyr)
  
  # function works with neural networks from neuralnet, nnet, and RSNNS package
  # manual input vector of weights also okay
  
  #sanity checks

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shinyServer(function(input, output) {
 
  myData <- reactive({
   read.table(
    input$file$datapath,
    sep='\t',
 	  header=T,
 	  stringsAsFactors=F
    ) 
   })