# commandArgs()
# script.description <- function()
#   showConnections() [as.character(eval.parent(quote(file), n = 3)),
#                        "description"]
# print((basename(script.description())))


######################################
#Inputs to enter#
####################################
#variables specifying the experiment:


foldername ="tubPKCixd42+-chaG80"
filenameprefix ="tG80_PKCi_d42g_"

             
firstpoint =1
datapoints <- 79

# figshare infos
# create the article on the first run (if no id_test is given)
# after first run, return "id", you get a number to insert as a value for the id_test
# next runs will update the existing article (only the figure in this version) 


thisfile= "analysispkcd42.r"

article_title= "<i>Drosophila</i> self-learning. Inhibition of PKC in d42Gal4 neurons, effect of additional UAS-GFP and cha-Gal80 constructs"
article_type =  "figure"   #, "dataset" #,"media", "poster", "paper", "fileset"
article_tags = c("self-learning","genetics", "FBti0010565","FBtp0017264","FBti0002759","FBtp0016755")
article_categories="Neuroscience"
article_visibility=   "draft" #"private" "public" #
article_authors= c("97229","96464" )  ## Julien Colomb = 97229 ## Björn Brembs = 96464
article_links="http://lab.brembs.net"



###rfigshare automatic inputs:
setwd(paste("D:/dokumente/data/FSdata",foldername, sep="/"))
AD= scan("experiment_description.txt", what="character")
article_description =  as.character(AD[1])

id_figure =id_data=id_fileset =id_code= NA
alreadyuploaded=0
ids= scan("id_numbers.txt", what="character", skip=1)
for (i in c(1: length(ids))){
  if ("id_figure" == ids[i]){ id_figure = ids[i+1]}
  if ("id_data" == ids[i]){ id_data = ids[i+1]}
  if ("id_fileset" == ids[i]){ id_fileset = ids[i+1]}
  if ("id_rawdata" == ids[i]){ id_rawdata = ids[i+1]}
  if ("id_code" == ids[i]){ id_code = ids[i+1]}
  if ("alreadyuploaded" == ids[i]){ alreadyuploaded = ids[i+1]}
}



#fs_update(id_test, title= article_title, description = article_description, type = article_type)

######################################################################################
#start code of analysis
#########################################



require(plotrix)
require(MASS)
require (vioplot)
require (UsingR)
require (gplots)
require (lattice)
require (gdata)

setwd(paste("D:/dokumente/data/FSdata",foldername, sep="/"))


#filenameprefix ="mipkc53e_"
#filenameprefix ="test_"
#filenameprefix ="pkcdelta_"

#take 23to27 out

#i <- c(firstpoint:46,48:datapoints)
i <- c(firstpoint:datapoints)
###only glued by me
#i <- c(40:46,74:92)

bjoerndata =F

#i <- c(40:datapoints)
#bjoerndata =FALSE
##########################
#get data with headers, selection of data has to be done on "data" for problems in samplesize calculation#
##########################
# vector with filenames change i for having all files

#i <- c(1:18,20:datapoints)
#i <- c(1:27:29)

filenames <- sapply(i,function(v) {
	paste(filenameprefix,v,".txt.TXT",sep="")
})

rawfilenames <- sapply(i,function(v) {
  paste(filenameprefix,v,".txt",sep="")
})
# filenames <- c("wtb_2.txt","wtb_3.txt")

# read all the files
#data <- lapply(filenames,function(name) {
#	read.table(name,header=FALSE,skip=22)
#})

# acces data frames with data[[i]]

dat <- lapply(filenames,function(name) {
	read.table(name,header=FALSE, sep ="\t", col.names= paste ("V",1:18),fill=TRUE)
})
# for (j in c(1: length(i)))
# dat[[j]] = dat[[j]][c(1:16,24,25)]
dat
data=do.call(rbind,dat)




headnames <- sapply(0,function(v) {
	paste(filenameprefix,v,".txt.TXT", sep ="")
})
head <- lapply(headnames,function(name) {
	read.table(name,header=TRUE, sep ="\t")
})

head=do.call(rbind,head)
#head = head[c(1:16,24,25)]
head


names(data) <- names (head)

####


data_ori=data


data2 = subset (data, is.na (data$remarks) )
E= 100* mean(summary(data2$date))/mean(summary(data$date))
plot(data$date, main=paste("efficacy of experiments: ",format(E, digits=2),"%"),las=2)

plot(data2$date, col=2, add=TRUE, axis=FALSE)
abline(h=mean(summary(data$date)))
abline(h=mean(summary(data2$date)),col=2)

###

data_ori=data
data2 = subset (data, is.na (data$remarks) )
E= 100* mean(summary(data2$date))/mean(summary(data$date))
plot(data$date, main=paste("efficacy of experiments: ",format(E, digits=2),"%"),las=2)

plot(data2$date, col=2, add=TRUE, axis=FALSE)
abline(h=mean(summary(data$date)))
abline(h=mean(summary(data2$date)),col=2)


### add bjoern data
if (bjoerndata){
  bjoerndata = read.csv ("tubPKCixc380/bjoerndata.csv", header=TRUE)
    #bjoerndata
  data= rbind(bjoerndata,data_ori)
}
#bjoerndata = read.csv ("PKCRNAi/bjoerndata.csv", header=TRUE)
bjoerndata
#data= rbind(bjoerndata,data_ori)

### subset of date
data$date[data$date =="17/4/2012"] ="17/04/2012"
data= subset(data, data$date !="17/04/2012")


data$genotype[data$genotype =="RnaiPKC_J"]="RnaiPKC_53e"
data$genotype[data$genotype =="RnaiPKC_H"]="RnaiPKC_Inac"
data$genotype[data$genotype =="RnaiPKC_K"]="CSx53eRNAi"
data$genotype[data$genotype =="csx53eRNAi"]="CSx53eRNAi"
data= drop.levels(data)

###############
#get subset of data (if additional genotypes or so...
##########

#data = subset (data, data$genotype != "PKC_elav_34°")
#data=drop.levels (data)
# data

#############
#make data readable and calculate mean before variables
#data$meanbefore = (data$X1_pretest1 +data$X2_pretest2)/2

data$meanbefore = (data$X2_pretest2)
data_ordtot <- cbind(data[gl(nrow(data), 1, 7*nrow(data)), c(1:7,17:18)], stack(data[, 9:15]))

data_ordtot$values <- data_ordtot$values/10000

data$scorerelative = (data$X8_test2 -data$meanbefore)/10000
data$scorenotrelative = (data$X8_test2 )/10000

added = "all"

source ("D:/dokumente/data/Rfiles/fs/dataanalysis3g.r")


################################################
################################################
################################################
################### subset ###################
##############################################

########################
# get data subset :without remarks or meanbefore center at 5500
#
data$remarks
data = subset (data, is.na (data$remarks) )
data = subset (data, data$meanbefore<6000)  
data

data$meanbefore = (data$X2_pretest2)
data_ordtot <- cbind(data[gl(nrow(data), 1, 7*nrow(data)), c(1:7,17:18)], stack(data[, 9:15]))

data_ordtot$values <- data_ordtot$values/10000

data$scorerelative = (data$X8_test2 -data$meanbefore)/10000
data$scorenotrelative = (data$X8_test2 )/10000
added = "subset"

## Data treatment is ready for analysis and ploting:
source ("D:/dokumente/data/Rfiles/fs/dataanalysis3g.r")   #produce a pdf with all the analysis locally

source ("D:/dokumente/data/Rfiles/fs/dataanalysisfigshare.r") #produce a png image with the principal results and publish it in figshare

data$scorenotrel = (data$X8_test2 )/10000
data$scorenotrel_asin = asin(sqrt((data$scorenotrel+1)/2))
#data$color_asin = asin(sqrt((data$X5_test1/10000 +1)/2))
#change=ddata$scorenotrel -data$Xb_test3/10000
plot(data$scorenotrel_asin~data$genotype)
abline(h=asin(sqrt((0.5))))
aov = aov(data$scorenotrel_asin~data$genotype*data$side_punished)
#aov2 = aov(data$color_asin~data$genotype)
summary(aov)
TukeyHSD(aov)

aov = aov(data$scorenotrel_asin~data$genotype*data$date)
summary(aov)
TukeyHSD(aov)

aov = aov(data$scorenotrel_asin~data$genotype)
summary(aov)
TukeyHSD(aov)
#plot(change~data$genotype)
# 
# K="PKC_elav"
# #K="CS_elav"
# #K="PKC_OK371"
# datag=subset (data, data$genotype== K)
# plot(datag$X8_test2~datag$date, main=K)
# abline(h=0)
# 
# K="CS_elav"
# #K="PKC_OK371"
# datag=subset (data, data$genotype== K)
# plot(datag$X8_test2~datag$date, main=K)
# abline(h=0)
# 
# boxplot(data$X8_test2~ data$date, boxwex = 0.25,subset = data$genotype== K, at= 1: length(levels(data$date)) -0.2, col="yellow")
# boxplot(data$X8_test2~ data$date, boxwex = 0.25,subset = data$genotype== "PKC_elav", at= 1: length(levels(data$date)) +0.2, col="orange", add=TRUE)
# legend("topleft", c(K, "PKC_elav"),
#        fill = c("yellow", "orange"))
# abline(h=0)


dev.off()
###
data_orig= data

data= subset(data_orig,data_orig$side_punished == "positive-left")
added = "_left"
data_ordtot <- cbind(data[gl(nrow(data), 1, 7*nrow(data)), c(1:7,17:18)], stack(data[, 9:15]))

data_ordtot$values <- data_ordtot$values/10000
source ("D:/dokumente/data/Rfiles/fs/dataanalysisoneside.r")
#dev.off()
data= subset(data_orig,data_orig$side_punished == "negative_right")
added = "_right"
data_ordtot <- cbind(data[gl(nrow(data), 1, 7*nrow(data)), c(1:7,17:18)], stack(data[, 9:15]))

data_ordtot$values <- data_ordtot$values/10000
source ("D:/dokumente/data/Rfiles/fs/dataanalysisoneside.r")
#dev.off()

data$date=as.Date(data$date, "%d/%m/%y")
p =ggplot(data, aes (date,scorenotrelative))
p + geom_point(aes(colour=factor(genotype)),size = 5) 
p + geom_boxplot(aes(fill=factor(genotype)))

# #############################################################################################################################
# #############################################################################################################################
# ########### on Figshare
# #####
# 
# ## you need to install Rtools first (the version which correspond to your R version)
# ## install devtools package then, rjson package
# # you need to set figshare to receive R commands: https://github.com/ropensci/rfigshare
# 
# ## Julien Colomb = 97229
# ## Björn Brembs = 96464?
# 
# ###start installing packages
# 
# 
# # colomb account
# options(FigshareKey = "jBkrLNsYbeov2oM09cXBBw")
# options(FigsharePrivateKey = "LhQkDeJaVJzAhWRIKYeobA")
# options(FigshareToken = "BYuYn4OjWjd8njBKCyeFXQOEAYY1MfLJ1Y0z80rVWj6AXYuYn4OjWjd8njXKCyeFXQ")
# options(FigsharePrivateToken = "aC2q4lBod3Xl52CeKwp7Fg")
# ###end figshare info
# #brembs lab account
# options(FigshareKey = "ABqLKahr9d8EBuBCdBBxpg")
# options(FigsharePrivateKey = "OCehduRnbLFvQBtD4KAZBA")
# options(FigshareToken = "Jr4eYBQkgEuOvJ5PdAlXpQ851uziewWIa5M3EgqpySAQJr4eYXQkgEuOvJ5PdAlXpQ")
# options(FigsharePrivateToken = "0WX9Lz0bWN5TA3fP0xnAXA")
# ###end figshare info
# 
# 
# 
# require(rfigshare)
# fs_auth()
# 
# ##need to create the article and get its id here: do it only once, then write the id and comment this part:
# 
# if (firstrun){
# article_title= "d42Gal4 UPKCi experiment: with and without UASGFP and chaGal80"
# article_description = "this is a temporary article"
# article_type =  "figure"   #, "dataset" #,"media", "poster", "paper", "fileset"
# article_tags = c("self-learning","genetics")
# article_categories="Neuroscience"
# article_files = paste(filenameprefix,"subset",".pdf",sep="")
# article_visibility=   "draft" #"private" "public" #
# article_authors= c("julien colomb")
# article_links="http://lab.brembs.net"
# 
# 
# id <- fs_new_article(title = article_title, description = article_description, 
#                      type = article_type, tags = article_tags, categories=article_categories ,
#                      files = article_files, visibility= article_visibility, #authors = article_authors,
#                      links=article_links)
# ##add bjoern as author:
# rfigshare:::fs_add_author(article_id = id_test, author_id = 96464)
# 
# id
# }
# #### uncomment this part to upgrade the graph (need to write the id of course):
# 
# id_test =97877
# 
# newfile= paste(filenameprefix,"subset",".pdf",sep="")
# fs_upload(id_test, file =newfile)
# 
# 
# 
# fs_update