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a3c427445f17d2b5a0200b06411d7591eb021b99
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5b2f016f1298c790224d83c1e17a425640fc777d
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/monod/Manuscript/MONOD_analysis_scripts/bak/S612.R
|
dbeab2c3beac6ab90ff47dce1c25c5bcf09eb1af
|
[] |
no_license
|
Shicheng-Guo/methylation2020
|
b77017a1fc3629fe126bf4adbb8f21f3cc9738a0
|
90273b1120316864477dfcf71d0a5a273f279ef9
|
refs/heads/master
| 2023-01-15T20:07:53.853771
| 2020-02-28T03:48:13
| 2020-02-28T03:48:13
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| 3
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UTF-8
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R
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| false
| 13,175
|
r
|
S612.R
|
# 2017-05-10
#source("http://www.bioconductor.org/biocLite.R")
#biocLite("impute")
#install.packages("gplots")
#install.packages("RColorBrewer")
#install.packages("grDevices")
library("gplots")
library("RColorBrewer")
library("grDevices")
library("impute")
gsi<-function(data){
group=names(table(colnames(data)))
index=colnames(data)
GSI<-c()
gmaxgroup<-c()
for(i in 1:nrow(data)){
gsit<-0
gmax<-names(which.max(tapply(as.numeric(data[i,]),index,function(x) mean(x,na.rm=T))))
for(j in 1:length(group)){
tmp<-(1-10^(mean(na.omit(as.numeric(data[i,which(index==group[j])])),na.rm=T))/10^(mean(na.omit(as.numeric(data[i,which(index==gmax)])))))/(length(group)-1)
gsit<-gsit+tmp
}
gmaxgroup<-c(gmaxgroup,gmax)
GSI<-c(GSI,gsit)
}
rlt=data.frame(region=rownames(data),group=gmaxgroup,GSI=GSI)
return(rlt)
}
TopGSIByCategory<-function(gsi,top=150){
GSIRlt<-c()
group<-names(table(gsi$group))
rank<-c(rep(top,length(group)))
for (i in 1:length(group)){
subset=gsi[which(gsi$group==group[i]),]
subset=subset[order(subset[,3],decreasing=T)[1:rank[i]],]
GSIRlt<-rbind(GSIRlt,subset)
}
return(na.omit(GSIRlt))
}
CvSampling<- function(Nobs=29,K=5){
rs <- runif(Nobs)
id <- seq(Nobs)[order(rs)]
k <- as.integer(Nobs*seq(1,K-1)/K)
k <- matrix(c(0,rep(k,each=2),Nobs),ncol=2,byrow=TRUE)
k[,1] <- k[,1]+1
l <- lapply(seq.int(K),function(x,k,d) list(train=d[!(seq(d) %in% seq(k[x,1],k[x,2]))], test=d[seq(k[x,1],k[x,2])]),k=k,d=id)
return(l)
}
topgsi2bio<-function(topgsi){
cor2bed<-function(cor){
cor<-as.character(cor)
a<-unlist(lapply(strsplit(cor,split=c(":")),function(x) strsplit(x,"-")))
bed<-matrix(a,ncol=3,byrow=T)
return(data.frame(bed))
}
bio<-data.frame(cor2bed(topgsi[,1]),topgsi[,2:3])
rownames(bio)<-topgsi[,1]
colnames(bio)<-paste("V",2:6,sep="")
return(bio)
}
rename<-function(data){
Data=data[,grep("STL|N37|ENC|SRX|age|new|centenarian|CTT|HCT|X7.T|X6.T|X6.P|RRBS.6P|X7.P|RRBS.7P",colnames(data))]
colnames(Data)[grep(".",colnames(Data))]<-unlist(lapply(colnames(Data)[grep(".",colnames(Data))],function(x) unlist(strsplit(x,".hapInfo|.sorted"))[1]))
colnames(Data)<-gsub("[.]","-",colnames(Data))
colnames(Data)[grep("age|new|centenarian",colnames(Data))]<-"WBC"
colnames(Data)[grep("X7.T|X6.T|SRX|CTT",colnames(Data))]<-"CT"
colnames(Data)[grep("N37|STL|ENC",colnames(Data))]<-as.character(saminfo[match(colnames(Data)[grep("N37|STL|ENC",colnames(Data))],saminfo[,1]),2])
colnames(Data)[grep("X6.P|RRBS.6P",colnames(Data))]<-"CCP"
colnames(Data)[grep("X7.P|RRBS.7P",colnames(Data))]<-"LCP"
return(Data)
}
frm<-function(data){
# feature reduction (WGBS,RRBS and each caterogy missing<60,Plasma missing<50%,low variation removed)
# features which are missing in whole reference samples will be omit since this will caused miss-classification (N<=4,data qualitye dependent)
rm1<-which(apply(data[,grep("X7.P|X6.P|.6P|.7P|NC.P",colnames(data))],1,function(x) sum(is.na(x))/length(x)>0.5))
rm2<-which(apply(data[,grep("X6.P|.6P",colnames(data))],1,function(x) sum(is.na(x))/length(x)>0.6))
rm3<-which(apply(data[,grep("X7.P|.7P",colnames(data))],1,function(x) sum(is.na(x))/length(x)>0.6))
rm4<-which(apply(data[,grep("NC.P",colnames(data))],1,function(x) sum(is.na(x))/length(x)>0.6))
rm5<-which(apply(data,1,function(x) sum(is.na(x))/length(x)>0.6))
rm<-unique(c(rm1,rm2,rm3,rm4,rm5))
return(rm)
}
setwd("/home/shg047/oasis/monod/hapinfo")
data<-read.table("MHL4.txt",head=T,row.names=1,sep="\t")
saminfo<-read.table("/home/shg047/oasis/monod/saminfo/N37Salk.saminfo",sep="\t")
load("/oasis/tscc/scratch/shg047/monod/hapinfo/MHL4.RData")
#load("/oasis/tscc/scratch/shg047/monod/hapinfo/Depth4.RData")
bio<-read.table("/oasis/tscc/scratch/shg047/monod/hapinfo/biomarker2.txt",head=F,row.names=1) # Download from Supplementary Table
rm<-frm(data)
data<-data[-rm,]
#depth<-depth[-rm,]
# copy our biomarker form supplementary table to: /home/sguo/Dropbox/Project/methylation/monod/Manuscript/MONOD_analysis_scripts/biomarker2.txt (download from supplementary)
Data<-rename(data)
colon<-subset(bio,V5=="Colon")
DATA<-data[match(rownames(colon),rownames(data)),]
DATA<-rename(DATA)
DATA<-DATA[,colnames(DATA)=="CCP"]
apply(DATA,1,function(x) sum(x>0.1,na.rm=T))
TDTD<-data[match(names(sort(apply(DATA,1,function(x) sum(x>0.3,na.rm=T)),decreasing=T)[1:10]),rownames(data)),]
TDTD<-rename(TDTD)
# for tissue-specific biomarkers
#library("impute")
#library("preprocessCore")
DATA<-Data[,grep("Brain|Colon|Intestine|Kidney|Liver|Lung|Pancreas|Spleen|Stomach|WBC",colnames(Data))]
DATA<-DATA[match(rownames(bio),rownames(DATA)),]
colnames(DATA)<-colnames(Data)[grep("Brain|Colon|Intestine|Kidney|Liver|Lung|Pancreas|Spleen|Stomach|WBC",colnames(Data))]
colnames(DATA)<-unlist(lapply(colnames(DATA),function(x) unlist(strsplit(x,"[.]"))[1]))
gsirlt<-gsi(DATA)
for(i in names(table(colnames(DATA)))){
}
apply(DATA,1,function(x) tapply(x,colnames(DATA),function(x) mean(x,na.rm=T)))
topgsi<-TopGSIByCategory(gsirlt,top=500)
bio<-topgsi2bio(topgsi)
Bio<-bio[sample(1:nrow(bio),0.9*nrow(bio)),] # apply parts of the tissue-specific biomarkers so that we can evalate the stability
DATA<-Data[match(rownames(bio),rownames(Data)),grep("Brain|Colon|Intestine|Kidney|Liver|Lung|Pancreas|Spleen|Stomach|WBC",colnames(Data))]
matchrlt<-apply(DATA,2,function(x) names(table(bio$V5))[which.max(tapply(x,bio$V5,function(x) sum(x>0.1,na.rm=T)))])
for(i in names(table(bio$V5))){
acc=sum(matchrlt[grep(i,names(matchrlt))]==i)/length(grep(i,names(matchrlt)))
print(c(i,acc))
}
#[1] "Brain" "1"
#[1] "Colon" "1"
#[1] "Intestine" "1"
#[1] "Kidney" "1"
#[1] "Liver" "1"
#[1] "Lung" "0.8"
#[1] "Pancreas" "1"
#[1] "Spleen" "1"
#[1] "Stomach""0.666666666666667"
#[1] "WBC" "1"
#### Figure 5C
set.seed(2)
out1<-grep(".6P|X6.P",colnames(data))[sample(1:30,5)]
out2<-grep(".7P|X7.P",colnames(data))[sample(1:30,5)]
out3<-grep("NC.P",colnames(data))[sample(1:75,5)]
test<-data[,c(out1,out2,out3)]
test<-test[match(rownames(bio),rownames(test)),]
# Cross-validation process
Data<-data[,-c(out1,out2,out3)]
# automatically select best threshold with 5-fold cross-validation for colon plasma/lung cancer/normal plasma together.
input<-Data[match(rownames(bio),rownames(Data)),]
set.seed(51)
k=2 # split the sample to two parts, one is for train and the other is for test.
acc1<-c()
acc2<-c()
acc3<-c()
Best<-c()
Samping<-CvSampling(24,k)
Lnum1<-c()
Lnum2<-c()
Lnum3<-c()
for(i in 1:k){
Num<-c()
for(j in seq(0,1,0.01)){
counts1<-apply(input[,grep(".6P|X6.P",colnames(Data))[Samping[[i]]$train]],2,function(x) tapply(x,bio$V5,function(x) sum(x>j,na.rm=T)))
counts2<-apply(input[,grep(".7P|X7.P",colnames(Data))[Samping[[i]]$train]],2,function(x) tapply(x,bio$V5,function(x) sum(x>j,na.rm=T)))
counts3<-apply(input[,grep("NC.P",colnames(Data))[Samping[[i]]$train]],2,function(x) tapply(x,bio$V5,function(x) sum(x>j,na.rm=T)))
num<-data.frame(id=j,
c1=sum(apply(counts1,2,function(x) which.max(x)==2)),
c2=sum(apply(counts2,2,function(x) which.max(x)==6)),
c3=sum(apply(counts3,2,function(x) which.max(x)==10)))
Num<-rbind(Num,num)
}
best<-Num[which.max(rowSums(Num[,2:4])),1]
countm1<-apply(input[,grep(".6P|X6.P",colnames(Data))[Samping[[i]]$test]],2,function(x) tapply(x,bio$V5,function(x) sum(x>best,na.rm=T)))
countm2<-apply(input[,grep(".7P|X7.P",colnames(Data))[Samping[[i]]$test]],2,function(x) tapply(x,bio$V5,function(x) sum(x>best,na.rm=T)))
countm3<-apply(input[,grep("NC.P",colnames(Data))[Samping[[i]]$test]],2,function(x) tapply(x,bio$V5,function(x) sum(x>best,na.rm=T)))
Lnum1<-c(Lnum1,c=sum(apply(countm1,2,function(x) which.max(x)==2)))
Lnum2<-c(Lnum2,c=sum(apply(countm2,2,function(x) which.max(x)==6)))
Lnum3<-c(Lnum3,c=sum(apply(countm3,2,function(x) which.max(x)==10)))
acc1<-rbind(acc1,c(best,sum(apply(countm1,2,function(x) which.max(x)==2))/(length(Samping[[i]]$test))))
acc2<-rbind(acc2,c(best,sum(apply(countm2,2,function(x) which.max(x)==6))/(length(Samping[[i]]$test))))
acc3<-rbind(acc3,c(best,sum(apply(countm3,2,function(x) which.max(x)==10))/(length(Samping[[i]]$test))))
Best<-c(Best,best)
}
Best
cc1<-apply(test[,grep(".6P|X6.P",colnames(test))],2,function(x) tapply(x,bio$V5,function(x) sum(x>mean(Best),na.rm=T)))
cc2<-apply(test[,grep(".7P|X7.P",colnames(test))],2,function(x) tapply(x,bio$V5,function(x) sum(x>mean(Best),na.rm=T)))
cc3<-apply(test[,grep("NC.P",colnames(test))],2,function(x) tapply(x,bio$V5,function(x) sum(x>mean(Best),na.rm=T)))
ccc1=sum(apply(cc1,2,function(x) which.max(x)==2))/5
ccc2=sum(apply(cc2,2,function(x) which.max(x)==6))/5
ccc3=sum(apply(cc3,2,function(x) which.max(x)==10))/5
ccc1
ccc2
ccc3
######################################################################
################ Model Stability ####################################
######################################################################
load("/oasis/tscc/scratch/shg047/monod/hapinfo/MHL4.RData")
starlt<-stability(data,bio,rep=200)
save(starlt,file="starlt.RData")
# starlt
# [,1] [,2] [,3] [,4]
# rlt 0.3605 0.8 0.8 0.8
# rlt 0.3390 0.8 1.0 1.0
senthresplot<-function(starlt){
acc<-data.frame(x=acc[,1],y=acc[,2])
acc$bin<- cut(acc[,1], c(seq(0,1,0.01)))
ggplot(acc) + geom_boxplot(aes(bin, y))
}
acc=starlt[,c(1,2)]
senthresplot(acc)
ggsave("colon-threshold-acc.pdf")
acc=starlt[,c(1,3)]
senthresplot(acc)
ggsave("lung-threshold-acc.pdf")
acc=starlt[,c(1,4)]
senthresplot(acc)
ggsave("normal-threshold-acc.pdf")
stability<-function(data,bio,rep){
Rlt<-c()
for(loop in 1:rep){
out1<-grep(".6P|X6.P",colnames(data))[sample(1:30,5)]
out2<-grep(".7P|X7.P",colnames(data))[sample(1:29,5)]
out3<-grep("NC.P",colnames(data))[sample(1:75,5)]
test<-data[,c(out1,out2,out3)]
test<-test[match(rownames(bio),rownames(test)),]
Data<-data[,-c(out1,out2,out3)]
# automatically select best threshold with 5-fold cross-validation for colon plasma/lung cancer/normal plasma together.
input<-Data[match(rownames(bio),rownames(Data)),]
k=2
acc1<-c()
acc2<-c()
acc3<-c()
Best<-c()
Samping<-CvSampling(24,k)
Lnum1<-c()
Lnum2<-c()
Lnum3<-c()
for(i in 1:k){
Num<-c()
for(j in seq(0,1,0.001)){
counts1<-apply(input[,grep(".6P|X6.P",colnames(input))[Samping[[i]]$train]],2,function(x) tapply(x,bio$V5,function(x) sum(x>j,na.rm=T)))
counts2<-apply(input[,grep(".7P|X7.P",colnames(input))[Samping[[i]]$train]],2,function(x) tapply(x,bio$V5,function(x) sum(x>j,na.rm=T)))
counts3<-apply(input[,grep("NC.P",colnames(input))[Samping[[i]]$train]],2,function(x) tapply(x,bio$V5,function(x) sum(x>j,na.rm=T)))
num<-data.frame(id=j,
c1=sum(apply(counts1,2,function(x) which.max(x)==2)),
c2=sum(apply(counts2,2,function(x) which.max(x)==6)),
c3=sum(apply(counts3,2,function(x) which.max(x)==10)))
Num<-rbind(Num,num)
}
best<-Num[which.max(rowSums(Num[,2:4])),1]
countm1<-apply(input[,grep(".6P|X6.P",colnames(input))[Samping[[i]]$test]],2,function(x) tapply(x,bio$V5,function(x) sum(x>best,na.rm=T)))
countm2<-apply(input[,grep(".7P|X7.P",colnames(input))[Samping[[i]]$test]],2,function(x) tapply(x,bio$V5,function(x) sum(x>best,na.rm=T)))
countm3<-apply(input[,grep("NC.P",colnames(input))[Samping[[i]]$test]],2,function(x) tapply(x,bio$V5,function(x) sum(x>best,na.rm=T)))
Lnum1<-c(Lnum1,c=sum(apply(countm1,2,function(x) which.max(x)==2)))
Lnum2<-c(Lnum2,c=sum(apply(countm2,2,function(x) which.max(x)==6)))
Lnum3<-c(Lnum3,c=sum(apply(countm3,2,function(x) which.max(x)==10)))
acc1<-rbind(acc1,c(best,sum(apply(countm1,2,function(x) which.max(x)==2))/(length(Samping[[i]]$test))))
acc2<-rbind(acc2,c(best,sum(apply(countm2,2,function(x) which.max(x)==6))/(length(Samping[[i]]$test))))
acc3<-rbind(acc3,c(best,sum(apply(countm3,2,function(x) which.max(x)==10))/(length(Samping[[i]]$test))))
Best<-c(Best,best)
}
cc1<-apply(test[,grep(".6P|X6.P",colnames(test))],2,function(x) tapply(x,bio$V5,function(x) sum(x>mean(Best),na.rm=T)))
cc2<-apply(test[,grep(".7P|X7.P",colnames(test))],2,function(x) tapply(x,bio$V5,function(x) sum(x>mean(Best),na.rm=T)))
cc3<-apply(test[,grep("NC.P",colnames(test))],2,function(x) tapply(x,bio$V5,function(x) sum(x>mean(Best),na.rm=T)))
ccc1=sum(apply(cc1,2,function(x) which.max(x)==2))/5
ccc2=sum(apply(cc2,2,function(x) which.max(x)==6))/5
ccc3=sum(apply(cc3,2,function(x) which.max(x)==10))/5
ccc1
ccc2
ccc3
rlt<-c(mean(Best),ccc1,ccc2,ccc3)
Rlt<-rbind(Rlt,rlt)
}
return(Rlt)
}
|
9e0b06a392cb5a7eaaa885a7e1f264ebd7c0f0f6
|
9f2f9ef625d991219cd1bd22c9a9daf34cb717a7
|
/man/diagnostic-class.Rd
|
6fd413aaa5c68c685401fd3a5f6f47db8cdd6749
|
[
"BSD-3-Clause"
] |
permissive
|
jan-glx/gmo
|
0910fe3cdf5d5506281a5bc41178464f3fde70b0
|
a3c466c553aaa487619b7baf7e36f0c35999e262
|
refs/heads/master
| 2021-05-12T01:11:38.900509
| 2018-01-15T14:53:31
| 2018-01-15T14:53:31
| 117,553,351
| 0
| 0
| null | 2018-01-15T14:08:02
| 2018-01-15T14:08:02
| null |
UTF-8
|
R
| false
| true
| 958
|
rd
|
diagnostic-class.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/util.R
\docType{class}
\name{diagnostic-class}
\alias{Diagnostic}
\alias{diagnostic-class}
\title{A Reference Class for diagnosing convergence.}
\description{
A Reference Class for diagnosing convergence.
}
\section{Fields}{
\describe{
\item{\code{par_prev}}{A numeric vector, previous checked parameter value.}
\item{\code{fn_prev}}{Double, previous checked function value.}
\item{\code{grad_prev}}{A numeric vector, previous checked gradient value.}
\item{\code{tol_rel}}{Double, relative tolerance for signaling convergence.}
}}
\section{Methods}{
\describe{
\item{\code{check_converge(par, fn, grad)}}{Check if the difference in parameters, function value, or
gradient values reached below tolerances.}
\item{\code{initialize(tol)}}{ Initializes previous checked values at 0.
@param tol
Tolerance threshold used for all tolerance checks.
}
}}
|
68d2ce9b5645d16ce734e43d3f6bdd8772ca569f
|
2e8fcc79e61ed9f80673834834fcf2abb4b8ac75
|
/man/getVariableIndex.Rd
|
d0c19e082191337831da9a286407c3248f68703f
|
[
"MIT"
] |
permissive
|
nickmckay/GeoChronR
|
893708e6667ee898165c208d200f002063e6d83f
|
f37236e1fa6616f55798bbd4e1530b5b564d0f53
|
refs/heads/master
| 2023-05-24T01:32:59.690518
| 2023-01-17T23:16:47
| 2023-01-17T23:16:47
| 32,468,418
| 30
| 2
|
MIT
| 2023-01-19T17:46:24
| 2015-03-18T15:50:12
|
R
|
UTF-8
|
R
| false
| true
| 1,409
|
rd
|
getVariableIndex.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lipd.manipulation.R
\name{getVariableIndex}
\alias{getVariableIndex}
\title{Get the index of variable list}
\usage{
getVariableIndex(
table,
var.name = NA,
alt.names = var.name,
ignore = NA,
always.choose = FALSE,
ask = TRUE,
strict.search = FALSE
)
}
\arguments{
\item{table}{a LiPD measurement, ensemble or summary Table}
\item{var.name}{string name of the variable to extract}
\item{alt.names}{A vector of strings for alternative names to search for}
\item{ignore}{A vector of strings of variableNames to ignore}
\item{always.choose}{Force selection of the variable from a list}
\item{ask}{If there is only one option, do you want to be asked whether to use it? (default = TRUE)}
\item{strict.search}{Use a strict.search to look for the ageEnsemble and depth variables. TRUE(default) or FALSE.}
}
\value{
An integer index
}
\description{
Gets the index for a LiPD "variable list"
}
\seealso{
Other LiPD manipulation:
\code{\link{createConcatenatedEnsembleMeasurementTable}()},
\code{\link{createModel}()},
\code{\link{createMultiModelEnsemble}()},
\code{\link{createSummaryTableFromEnsembleTable}()},
\code{\link{createTSid}()},
\code{\link{estimateUncertaintyFromRange}()},
\code{\link{mapAgeEnsembleToPaleoData}()},
\code{\link{pullTsVariable}()},
\code{\link{selectData}()}
}
\concept{LiPD manipulation}
|
34d622bd1d502065fda78ba4005b6e96ab36b587
|
f5b7db353ee4783f15f12fbb5b8fd1b36eae8bb6
|
/plot1.R
|
081aa39bf87bbe69c610b0c0a240a6f3a26f1dc5
|
[] |
no_license
|
LloydNarciso/dataScienceCourse4Week4
|
6faf658beb3495ed96038ea35e098afaed7da10c
|
33bbff65fe411983cd4830d117361d54992e1eb0
|
refs/heads/master
| 2021-01-12T07:33:47.779457
| 2016-12-20T18:45:53
| 2016-12-20T18:45:53
| 76,979,450
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 547
|
r
|
plot1.R
|
## This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
totalEmissions<-with(NEI,tapply(Emissions,year,sum,na.rm=TRUE))
x<-c("1999","2002","2005","2008")
png(filename="plot1.png",width=720,height=480)
plot(x,totalEmissions,main=expression(paste("Total ", PM[2.5], " Emissions from 1999-2008")),xlab="Year",
ylab=expression(paste(PM[2.5], " Emissions (in tons)")),pch=15,cex=2,lwd=2,col="blue")
lines(x,totalEmissions,type="l",col="blue")
dev.off()
|
2bf7130413c84761821ca3e8bd5bba2f96d93e85
|
7daf72d1abe4b13d1e26dc46abddfebcfc42d9e8
|
/man/pca_count.Rd
|
191fdd2adcf7ae5a99956fdec23efc949ffb6460
|
[
"MIT"
] |
permissive
|
farcego/rbl
|
6c39a7f2e63564c75860aa6a7887b2b49ffb73fb
|
b1cfa946b978dae09bf4d4b79267c4269e067627
|
refs/heads/master
| 2020-03-21T15:25:49.368438
| 2017-06-15T09:22:11
| 2017-06-15T09:22:11
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 544
|
rd
|
pca_count.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/funs_behavior.r
\name{pca_count}
\alias{pca_count}
\title{Count the number of Prey Catch Attemps (PCA)}
\usage{
pca_count(x)
}
\arguments{
\item{x}{a logical vector indicating at each timestamp if it was associated
to a PCA event.}
}
\description{
Count the number of Prey Catch Attemps (PCA)
}
\details{
A continuous succession of \code{TRUE} is considered as a single PCA.
}
\examples{
data(exses)
btt_pca <- tdrply(pca_count, "is_pca", ty = "_", obj = exses)
}
|
bdc0674feda68af0a1008d7be978b253a71d3b8d
|
05c251183d6de3e1447079da895a16e3c1d9bafa
|
/codes/Fig1A.R
|
11f6b12f7f2381b1e0b41cefb48f073b57e7eb9d
|
[] |
no_license
|
joycewang914/Genomic_and_Patient_Transfer_Analyasis_of_Resistant_Bacteria
|
42fb7d2597a5d61372253666c5447f88c05f13c3
|
21e74f522a23350700a068dd2359342db233bbcd
|
refs/heads/master
| 2022-11-15T22:32:58.696067
| 2020-07-06T08:49:06
| 2020-07-06T08:49:06
| 277,387,877
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,329
|
r
|
Fig1A.R
|
# Plot Fig 1A showing the prevalence of antibiotic-resistant organisms (AROs) in regional nursing facilities due to endemic spread of epidemic lineages.
# load dna matrix data (MGE removed)
# Fig 1B and 1C are a sample of Supplemental Figure S6. Please refer to FigS6.R for details.
library(ape)
library(wesanderson)
mrsa = read.dna("2019-07-19_Filtered_Pathways_MRSA", format = "interleaved")
vrefm = read.dna("2019-07-19_Filtered_Pathways_VREfm", format = "interleaved")
vrefc = read.dna("2019-07-19_Filtered_Pathways_VREfc", format = "interleaved")
ecol = read.dna("2019-07-19_Filtered_Pathways_CipREc", format = "interleaved")
path_aro_clusters = list("MRSA" = mrsa, "VREfm" = vrefm, "VREfc" = vrefc, "CipREc" = ecol)
path_facils = as.character(1:6)
total_enrol_pt_per_facil = structure(c(133, 82, 169, 137, 55, 76), names = 1:6)
path_aro_cluster_info = sapply(names(path_aro_clusters), FUN = function(x){
t(sapply(rownames(path_aro_clusters[[x]]), FUN = function(s){
info = strsplit(as.character(s), "-")[[1]];
id = info[1]
pt = info[2]
fac = substr(pt, 1, 1)
visit = info[3]
site = substr(info[4], 1, 1)
cbind(paste(id, pt, visit, site, sep = "-"), id, pt, visit, site, fac)
}))
})
# ARO distribution by NH
aro_prev_by_fac = sapply(path_aro_clusters, FUN = function(y){
pt = sapply(rownames(y), FUN = function(x){strsplit(x, "-")[[1]][2]})
fac = substr(pt, 1, 1)
table(fac)/total_enrol_pt_per_facil
})
# Plot barplot showing prevalence by facility
nh_cols = wes_palette(name = "Darjeeling1", length(path_aro_clusters))
nh_cols[nh_cols %in% "#F2AD00"] = "lightgrey"
par(mar = c(5.1, 8, 4.1, 4.1))
barplot(t(aro_prev_by_fac),
col = nh_cols,
border="white",
font.axis=1,
cex.lab = 2,
cex.axis = 2,
cex.names = 2,
beside= T,
xlab="Nursing Facility",
font.lab=1,
ylab = "Percentage of\ncolonized patients",
yaxt = "n",
ylim = c(0, 0.3))
axis(side = 2, font = 1, at = seq(0, .3, 0.15),
labels = seq(0, .3*100, 15), las = 2, cex.axis = 2, cex.lab = 2)
legend("top", legend = colnames(aro_prev_by_fac),
fill = nh_cols, col = nh_cols, xpd = TRUE, inset = -0.2, text.font = 2,
cex = 2, bty = "n", horiz = TRUE, border = NA, text.width = 3, x.intersp = 0.25)
|
00777bf48e9b0cd6ca14576061ccd176537aa2e4
|
320bb31bba3f88ad0e940553db4f7f54e0c4c920
|
/R/BIOMOD_Projection.R
|
25740e7b38a969554d13a1f2c540160b1e2f3c7d
|
[] |
no_license
|
biomodhub/biomod2
|
519d120381332c719fc23d1a5d0a4d1030fd2a01
|
ee9734d7dd9455cc8b76a000f74785512a119e2f
|
refs/heads/master
| 2023-08-31T03:29:40.910990
| 2023-08-28T14:10:59
| 2023-08-28T14:10:59
| 122,992,854
| 61
| 21
| null | 2023-09-12T12:29:52
| 2018-02-26T15:55:28
|
R
|
UTF-8
|
R
| false
| false
| 31,350
|
r
|
BIOMOD_Projection.R
|
###################################################################################################
##' @name BIOMOD_Projection
##' @author Wilfried Thuiller, Damien Georges
##'
##' @title Project a range of calibrated species distribution models onto new environment
##'
##' @description This function allows to project a range of models built with the
##' \code{\link{BIOMOD_Modeling}} function onto new environmental data (\emph{which can
##' represent new areas, resolution or time scales for example}).
##'
##'
##' @param bm.mod a \code{\link{BIOMOD.models.out}} object returned by the
##' \code{\link{BIOMOD_Modeling}} function
##' @param proj.name a \code{character} corresponding to the name (ID) of the projection set
##' (\emph{a new folder will be created within the simulation folder with this name})
##' @param new.env A \code{matrix}, \code{data.frame} or
##' \code{\link[terra:rast]{SpatRaster}} object containing the new
##' explanatory variables (in columns or layers, with names matching the
##' variables names given to the \code{\link{BIOMOD_FormatingData}} function to build
##' \code{bm.mod}) that will be used to project the species distribution model(s)
##' \cr \emph{Note that old format from \pkg{raster} are still supported such as
##' \code{RasterStack} objects. }
##'
##' @param new.env.xy (\emph{optional, default} \code{NULL}) \cr
##' If \code{new.env} is a \code{matrix} or a \code{data.frame}, a 2-columns \code{matrix} or
##' \code{data.frame} containing the corresponding \code{X} and \code{Y} coordinates that will be
##' used to project the species distribution model(s)
##' @param models.chosen a \code{vector} containing model names to be kept, must be either
##' \code{all} or a sub-selection of model names that can be obtained with the
##' \code{\link{get_built_models}} function
##'
##' @param metric.binary (\emph{optional, default} \code{NULL}) \cr
##' A \code{vector} containing evaluation metric names to be used to transform prediction values
##' into binary values based on models evaluation scores obtained with the
##' \code{\link{BIOMOD_Modeling}} function. Must be among \code{all} (same evaluation metrics than
##' those of \code{bm.mod}) or \code{ROC}, \code{TSS}, \code{KAPPA}, \code{ACCURACY},
##' \code{BIAS}, \code{POD}, \code{FAR}, \code{POFD}, \code{SR}, \code{CSI}, \code{ETS},
##' \code{HK}, \code{HSS}, \code{OR}, \code{ORSS}
##' @param metric.filter (\emph{optional, default} \code{NULL}) \cr
##' A \code{vector} containing evaluation metric names to be used to transform prediction values
##' into filtered values based on models evaluation scores obtained with the
##' \code{\link{BIOMOD_Modeling}} function. Must be among \code{all} (same evaluation metrics than
##' those of \code{bm.mod}) or \code{ROC}, \code{TSS}, \code{KAPPA}, \code{ACCURACY},
##' \code{BIAS}, \code{POD}, \code{FAR}, \code{POFD}, \code{SR}, \code{CSI}, \code{ETS},
##' \code{HK}, \code{HSS}, \code{OR}, \code{ORSS}
##'
##' @param compress (\emph{optional, default} \code{TRUE}) \cr
##' A \code{logical} or a \code{character} value defining whether and how objects should be
##' compressed when saved on hard drive. Must be either \code{TRUE}, \code{FALSE}, \code{xz} or
##' \code{gzip} (see Details)
##' @param build.clamping.mask (\emph{optional, default} \code{TRUE}) \cr
##' A \code{logical} value defining whether a clamping mask should be built and saved on hard
##' drive or not (see Details)
##'
##' @param nb.cpu (\emph{optional, default} \code{1}) \cr
##' An \code{integer} value corresponding to the number of computing resources to be used to
##' parallelize the single models computation
##' @param seed.val (\emph{optional, default} \code{NULL}) \cr
##' An \code{integer} value corresponding to the new seed value to be set
##'
##' @param \ldots (\emph{optional, see Details)})
##'
##'
##' @return
##'
##' A \code{BIOMOD.projection.out} object containing models projections, or links to saved
##' outputs. \cr Models projections are stored out of \R (for memory storage reasons) in
##' \code{proj.name} folder created in the current working directory :
##' \enumerate{
##' \item the output is a \code{data.frame} if \code{new.env} is a \code{matrix} or a
##' \code{data.frame}
##' \item it is a \code{\link[terra:rast]{SpatRaster}} if \code{new.env} is a
##' \code{\link[terra:rast]{SpatRaster}} (or several \code{\link[terra:rast]{SpatRaster}}
##' objects, if \code{new.env} is too large)
##' \item raw projections, as well as binary and filtered projections (if asked), are saved in
##' the \code{proj.name} folder
##' }
##'
##'
##' @details
##'
##' If \code{models.chosen = 'all'}, projections are done for all calibration and pseudo absences
##' runs if applicable. \cr These projections may be used later by the
##' \code{\link{BIOMOD_EnsembleForecasting}} function. \cr \cr
##'
##' If \code{build.clamping.mask = TRUE}, a raster file will be saved within the projection folder.
##' This mask values will correspond to the number of variables in each pixel that are out of their
##' calibration / validation range, identifying locations where predictions are uncertain. \cr \cr
##'
##' \code{...} can take the following values :
##' \itemize{
## \item{\code{clamping.level} : }{a \code{logical} value defining whether \code{clamping.mask}
## cells with at least one variable out of its calibration range are to be removed from the
## projections or not
##' \item{\code{omit.na} : }{a \code{logical} value defining whether all not fully referenced
##' environmental points will get \code{NA} as predictions or not}
##' \item{\code{on_0_1000} : }{a \code{logical} value defining whether \code{0 - 1} probabilities
##' are to be converted to \code{0 - 1000} scale to save memory on backup}
##' \item{\code{do.stack} : }{a \code{logical} value defining whether all projections are to be
##' saved as one \code{\link[terra:rast]{SpatRaster}} object or several
##' \code{\link[terra:rast]{SpatRaster}} files (\emph{the default if projections are too heavy to
##' be all loaded at once in memory})}
##' \item{\code{keep.in.memory} : }{a \code{logical} value defining whether all projections are
##' to be kept loaded at once in memory, or only links pointing to hard drive are to be returned}
##' \item{\code{output.format} : }{a \code{character} value corresponding to the projections
##' saving format on hard drive, must be either \code{.grd}, \code{.img}, \code{.tif} or \code{.RData} (the
##' default if \code{new.env} is given as \code{matrix} or \code{data.frame})}
##' }
##'
##'
##' @keywords models projection
##'
##'
##' @seealso \code{\link{BIOMOD_Modeling}}, \code{\link{BIOMOD_EnsembleModeling}},
##' \code{\link{BIOMOD_RangeSize}}
##' @family Main functions
##'
##'
##' @examples
##' library(terra)
##'
##' # Load species occurrences (6 species available)
##' data(DataSpecies)
##' head(DataSpecies)
##'
##' # Select the name of the studied species
##' myRespName <- 'GuloGulo'
##'
##' # Get corresponding presence/absence data
##' myResp <- as.numeric(DataSpecies[, myRespName])
##'
##' # Get corresponding XY coordinates
##' myRespXY <- DataSpecies[, c('X_WGS84', 'Y_WGS84')]
##'
##' # Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
##' data(bioclim_current)
##' myExpl <- terra::rast(bioclim_current)
##'
##' \dontshow{
##' myExtent <- terra::ext(0,30,45,70)
##' myExpl <- terra::crop(myExpl, myExtent)
##' }
##'
##' # ---------------------------------------------------------------#
##' file.out <- paste0(myRespName, "/", myRespName, ".AllModels.models.out")
##' if (file.exists(file.out)) {
##' myBiomodModelOut <- get(load(file.out))
##' } else {
##'
##' # Format Data with true absences
##' myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
##' expl.var = myExpl,
##' resp.xy = myRespXY,
##' resp.name = myRespName)
##'
##' # Create default modeling options
##' myBiomodOptions <- BIOMOD_ModelingOptions()
##'
##' # Model single models
##' myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
##' modeling.id = 'AllModels',
##' models = c('RF', 'GLM'),
##' bm.options = myBiomodOptions,
##' CV.strategy = 'random',
##' CV.nb.rep = 2,
##' CV.perc = 0.8,
##' metric.eval = c('TSS','ROC'),
##' var.import = 3,
##' seed.val = 42)
##' }
##'
##'
##' # ---------------------------------------------------------------#
##' # Project single models
##' file.proj <- paste0(myRespName, "/proj_Current/", myRespName, ".Current.projection.out")
##' if (file.exists(file.proj)) {
##' myBiomodProj <- get(load(file.proj))
##' } else {
##' myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
##' proj.name = 'Current',
##' new.env = myExpl,
##' models.chosen = 'all')
##' }
##' myBiomodProj
##' plot(myBiomodProj)
##'
##'
##' @importFrom foreach foreach %dopar%
## @importFrom doParallel registerDoParallel
##' @importFrom terra rast subset nlyr writeRaster terraOptions wrap unwrap
##' mem_info app is.factor mask
##' @importFrom utils capture.output
##' @importFrom abind asub
##'
##' @export
##'
##'
###################################################################################################
BIOMOD_Projection <- function(bm.mod,
proj.name,
new.env,
new.env.xy = NULL,
models.chosen = 'all',
metric.binary = NULL,
metric.filter = NULL,
compress = TRUE,
build.clamping.mask = TRUE,
nb.cpu = 1,
seed.val = NULL,
...) {
.bm_cat("Do Single Models Projection")
## 0. Check arguments ---------------------------------------------------------------------------
args <- .BIOMOD_Projection.check.args(bm.mod, proj.name, new.env, new.env.xy
, models.chosen, metric.binary, metric.filter, compress, seed.val, ...)
for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
rm(args)
## 1. Create output object ----------------------------------------------------------------------
proj_out <- new('BIOMOD.projection.out',
proj.name = proj.name,
dir.name = bm.mod@dir.name,
sp.name = bm.mod@sp.name,
expl.var.names = bm.mod@expl.var.names,
models.projected = models.chosen,
scale.models = bm.mod@scale.models,
coord = new.env.xy,
modeling.id = bm.mod@modeling.id)
proj_out@models.out@link = bm.mod@link
proj_is_raster <- FALSE
if (inherits(new.env, 'SpatRaster')) {
proj_is_raster <- TRUE
}
if (proj_is_raster) {
proj_out@proj.out <- new('BIOMOD.stored.SpatRaster')
} else {
proj_out@proj.out <- new('BIOMOD.stored.data.frame')
}
## 2. Create simulation directories -------------------------------------------------------------
nameProj <- paste0("proj_", proj.name)
nameProjSp <- paste0(nameProj, "_", bm.mod@sp.name)
namePath <- file.path(bm.mod@dir.name, bm.mod@sp.name, nameProj)
dir.create(namePath, showWarnings = FALSE, recursive = TRUE, mode = "777")
if (!do.stack) {
dir.create(file.path(namePath, "individual_projections"),
showWarnings = FALSE, recursive = TRUE, mode = "777")
}
## 3. Define the clamping mask ------------------------------------------------------------------
if (build.clamping.mask) {
cat("\n\t> Building clamping mask\n")
nameMask <- paste0(nameProjSp, "_ClampingMask")
MinMax <- get_formal_data(bm.mod, 'MinMax')
assign(x = nameMask, value = .build_clamping_mask(new.env, MinMax))
if (output.format == '.RData') {
if (proj_is_raster) {
save(list = wrap(nameMask),
file = file.path(namePath,
paste0(nameProj, "_ClampingMask", output.format)),
compress = compress)
} else {
save(list = nameMask,
file = file.path(namePath,
paste0(nameProj, "_ClampingMask", output.format)),
compress = compress)
}
} else {
writeRaster(x = get(nameMask),
filename = file.path(namePath, paste0(nameProj, "_ClampingMask", output.format)),
datatype = "INT2S",
NAflag = -9999,
overwrite = TRUE)
}
}
## 4. MAKING PROJECTIONS ------------------------------------------------------------------------
if (nb.cpu > 1) {
if (.getOS() != "windows") {
if (!isNamespaceLoaded("doParallel")) {
if(!requireNamespace('doParallel', quietly = TRUE)) stop("Package 'doParallel' not found")
}
doParallel::registerDoParallel(cores = nb.cpu)
} else {
warning("Parallelisation with `foreach` is not available for Windows. Sorry.")
}
}
if (proj_is_raster) {
new.env.wrap <- wrap(new.env) # ensure parallel run compatibility
}
proj <- foreach(mod.name = models.chosen) %dopar% {
cat("\n\t> Projecting", mod.name, "...")
if (proj_is_raster) {
new.env <- unwrap(new.env.wrap) # ensure parallel run compatibility
}
if (do.stack) {
filename <- NULL
} else {
filename <- file.path(namePath, "individual_projections",
paste0(nameProj, "_", mod.name,
ifelse(output.format == ".RData"
, ".tif", output.format)))
}
mod <- get(BIOMOD_LoadModels(bm.out = bm.mod, full.name = mod.name))
temp_workdir = NULL
if (length(grep("MAXENT$", mod.name)) == 1) {
temp_workdir = mod@model_output_dir
}
pred.tmp <- predict(mod, new.env, on_0_1000 = on_0_1000,
filename = filename, omit.na = omit.na,
temp_workdir = temp_workdir, seedval = seed.val,
overwrite = TRUE, mod.name = mod.name)
if (do.stack) {
if (proj_is_raster) {
return(wrap(pred.tmp))
} else {
return(pred.tmp)
}
} else {
cat(filename)
return(filename)
}
}
## Putting predictions into the right format
if (do.stack) {
if (proj_is_raster) {
proj <- rast(lapply(proj, unwrap)) # SpatRaster needs to be wrapped before saving
names(proj) <- models.chosen
proj <- wrap(proj)
proj.trans <- proj
} else {
proj <- as.data.frame(proj)
names(proj) <- models.chosen
proj.trans <- proj
proj <- .format_proj.df(proj, obj.type = "mod")
}
if (keep.in.memory) {
proj_out@proj.out@val <- proj
proj_out@proj.out@inMemory <- TRUE
}
}
## save projections
proj_out@type <- .get_env_class(new.env)
if (!do.stack){
saved.files = unlist(proj)
} else {
assign(x = nameProjSp, value = proj)
saved.files <- file.path(namePath, paste0(nameProjSp, output.format))
if (output.format == '.RData') {
save(list = nameProjSp, file = saved.files, compress = compress)
} else {
writeRaster(x = rast(get(nameProjSp)), filename = saved.files,
overwrite = TRUE, datatype = ifelse(on_0_1000, "INT2S", "FLT4S"), NAflag = -9999)
}
}
proj_out@proj.out@link <- saved.files
# now that proj have been saved, it can be unwrapped if it is a SpatRaster
if (proj_is_raster && do.stack) {
proj.trans <- rast(proj.trans)
}
## 5. Compute binary and/or filtered transformation ---------------------------------------------
if (!is.null(metric.binary) | !is.null(metric.filter)) {
cat("\n")
saved.files.binary <- NULL
saved.files.filtered <- NULL
thresholds <- get_evaluations(bm.mod, full.name = models.chosen)
if (!on_0_1000) { thresholds[, "cutoff"] <- thresholds[, "cutoff"] / 1000 }
## Do binary/filtering transformation
for (eval.meth in unique(c(metric.binary, metric.filter))) {
thres.tmp <- thresholds[which(thresholds$metric.eval == eval.meth), ]
rownames(thres.tmp) <- thres.tmp$full.name
thres.tmp <- thres.tmp[models.chosen, "cutoff"]
cat("\n\t> Building", eval.meth, "binaries / filtered")
if (!do.stack) {
for (i in 1:length(proj_out@proj.out@link)) {
file.tmp <- proj_out@proj.out@link[i]
if (eval.meth %in% metric.binary) {
file.tmp.binary <- sub(output.format,
paste0("_", eval.meth, "bin", output.format),
file.tmp)
saved.files.binary <- c(saved.files.binary, file.tmp.binary)
writeRaster(x = bm_BinaryTransformation(rast(file.tmp), thres.tmp[i]),
filename = file.tmp.binary,
overwrite = TRUE,
datatype = "INT2S",
NAflag = -9999)
}
if (eval.meth %in% metric.filter) {
file.tmp.filtered <- sub(output.format,
paste0("_", eval.meth, "filt", output.format),
file.tmp)
saved.files.filtered <- c(saved.files.filtered, file.tmp.filtered)
writeRaster(x = bm_BinaryTransformation(rast(file.tmp), thres.tmp[i],
do.filtering = TRUE),
filename = file.tmp.filtered,
overwrite = TRUE,
datatype = ifelse(on_0_1000, "INT2S", "FLT4S"),
NAflag = -9999)
}
}
} else {
if (eval.meth %in% metric.binary) {
nameBin <- paste0(nameProjSp, "_", eval.meth, "bin")
assign(x = nameBin, value = bm_BinaryTransformation(proj.trans, thres.tmp))
file.tmp.binary <- file.path(namePath, paste0(nameBin, output.format))
saved.files.binary <- c(saved.files.binary, file.tmp.binary)
if (output.format == '.RData') {
if (proj_is_raster) {
assign(x = nameBin, value = wrap(get(nameBin)))
} else {
assign(x = nameBin, value = .format_proj.df((get(nameBin)), obj.type = "mod"))
}
save(list = nameBin,
file = file.tmp.binary,
compress = compress)
} else {
writeRaster(x = get(nameBin),
filename = file.tmp.binary,
overwrite = TRUE,
datatype = "INT2S",
NAflag = -9999)
}
}
if (eval.meth %in% metric.filter) {
nameFilt <- paste0(nameProjSp, "_", eval.meth, "filt")
assign(x = nameFilt,
value = bm_BinaryTransformation(proj.trans, thres.tmp,
do.filtering = TRUE))
file.tmp.filtered <- file.path(namePath, paste0(nameFilt, output.format))
saved.files.filtered <- c(saved.files.filtered, file.tmp.filtered)
if (output.format == '.RData') {
if (proj_is_raster) {
assign(x = nameFilt, value = wrap(get(nameFilt)))
} else {
assign(x = nameFilt, value = .format_proj.df((get(nameFilt)), obj.type = "mod"))
}
save(list = nameFilt,
file = file.path(namePath, paste0(nameFilt, output.format)),
compress = compress)
} else {
writeRaster(x = get(nameFilt),
filename = file.tmp.filtered,
overwrite = TRUE ,
datatype = ifelse(on_0_1000, "INT2S", "FLT4S"),
NAflag = -9999)
}
}
}
}
### save binary/filtered file link into proj_out ----------------------------
if (!is.null(metric.binary)) {
proj_out@proj.out@link <- c(proj_out@proj.out@link, saved.files.binary)
}
if (!is.null(metric.filter)) {
proj_out@proj.out@link <- c(proj_out@proj.out@link, saved.files.filtered)
}
cat("\n")
}
## 6. SAVE MODEL OBJECT ON HARD DRIVE -----------------------------------------------------------
## save a copy of output object without value to be lighter
nameOut <- paste0(bm.mod@sp.name, ".", proj.name, ".projection.out")
if (!keep.in.memory) { proj_out <- free(proj_out) }
assign(nameOut, proj_out)
save(list = nameOut, file = file.path(namePath, nameOut))
.bm_cat("Done")
return(proj_out)
}
# .BIOMOD_Projection.check.args---------------------------------------------
.BIOMOD_Projection.check.args <- function(bm.mod, proj.name, new.env, new.env.xy,
models.chosen, metric.binary, metric.filter, compress, seed.val, ...)
{
args <- list(...)
## 1. Check bm.mod ----------------------------------------------------------
.fun_testIfInherits(TRUE, "bm.mod", bm.mod, "BIOMOD.models.out")
## 2. Check proj.name -------------------------------------------------------
if (is.null(proj.name)) {
stop("\nYou must define a name for Projection Outputs")
} else {
dir.create(paste0(bm.mod@sp.name, '/proj_', proj.name, '/'), showWarnings = FALSE)
}
## 3. Check new.env ---------------------------------------------------------
.fun_testIfInherits(TRUE, "new.env", new.env, c('matrix', 'data.frame', 'SpatRaster','Raster'))
if (inherits(new.env, 'matrix')) {
if (any(sapply(get_formal_data(bm.mod, "expl.var"), is.factor))) {
stop("new.env cannot be given as matrix when model involves categorical variables")
}
new.env <- data.frame(new.env)
} else if (inherits(new.env, 'data.frame')) {
# ensure that data.table are coerced into classic data.frame
new.env <- as.data.frame(new.env)
}
if (inherits(new.env, 'Raster')) {
# conversion into SpatRaster
if (any(raster::is.factor(new.env))) {
new.env <- .categorical_stack_to_terra(raster::stack(new.env),
expected_levels = head(get_formal_data(bm.mod, subinfo = "expl.var"))
)
} else {
new.env <- rast(new.env)
}
}
if (inherits(new.env, 'SpatRaster')) {
.fun_testIfIn(TRUE, "names(new.env)", names(new.env), bm.mod@expl.var.names)
new.env.mask <- .get_data_mask(new.env, value.out = 1)
new.env <- mask(new.env, new.env.mask)
} else {
.fun_testIfIn(TRUE, "colnames(new.env)", colnames(new.env), bm.mod@expl.var.names)
new.env <- new.env[ , bm.mod@expl.var.names, drop = FALSE]
}
which.factor <- which(sapply(new.env, is.factor))
if (length(which.factor) > 0) {
new.env <- .check_env_levels(new.env,
expected_levels = head(get_formal_data(bm.mod, subinfo = "expl.var")))
}
## 4. Check new.env.xy ------------------------------------------------------
if (!is.null(new.env.xy) & !inherits(new.env, 'SpatRaster')) {
new.env.xy = as.data.frame(new.env.xy)
if (ncol(new.env.xy) != 2 || nrow(new.env.xy) != nrow(new.env)) {
stop("invalid xy coordinates argument given -- dimensions mismatch !")
}
} else {
new.env.xy = data.frame()
}
## 5. Check models.chosen ---------------------------------------------------
if (models.chosen[1] == 'all') {
models.chosen <- bm.mod@models.computed
} else {
models.chosen <- intersect(models.chosen, bm.mod@models.computed)
}
if (length(models.chosen) < 1) {
stop('No models selected')
}
## check that given models exist
files.check <- paste0(bm.mod@dir.name, "/", bm.mod@sp.name, "/models/",
bm.mod@modeling.id, "/", models.chosen)
not.checked.files <- grep('MAXENT|SRE', files.check)
if (length(not.checked.files) > 0) {
files.check <- files.check[-not.checked.files]
}
missing.files <- files.check[!file.exists(files.check)]
if (length(missing.files) > 0) {
stop(paste0("Projection files missing : ", toString(missing.files)))
if (length(missing.files) == length(files.check)) {
stop("Impossible to find any models, might be a problem of working directory")
}
}
## 6. Check metric.binary & metric.filter -----------------------------------
if (!is.null(metric.binary) | !is.null(metric.filter)) {
models.evaluation <- get_evaluations(bm.mod)
if (is.null(models.evaluation)) {
warning("Binary and/or Filtered transformations of projection not ran because of models evaluation information missing")
} else {
available.evaluation <- unique(models.evaluation$metric.eval)
if (!is.null(metric.binary) && metric.binary[1] == 'all') {
metric.binary <- available.evaluation
} else if (!is.null(metric.binary) && sum(!(metric.binary %in% available.evaluation)) > 0) {
warning(paste0(toString(metric.binary[!(metric.binary %in% available.evaluation)]),
" Binary Transformation were switched off because no corresponding evaluation method found"))
metric.binary <- metric.binary[metric.binary %in% available.evaluation]
}
if (!is.null(metric.filter) && metric.filter[1] == 'all') {
metric.filter <- available.evaluation
} else if (!is.null(metric.filter) && sum(!(metric.filter %in% available.evaluation)) > 0) {
warning(paste0(toString(metric.filter[!(metric.filter %in% available.evaluation)]),
" Filtered Transformation were switched off because no corresponding evaluation method found"))
metric.filter <- metric.filter[metric.filter %in% available.evaluation]
}
}
}
## 7. Check compress --------------------------------------------------------
if (compress == 'xz') {
compress <- ifelse(.Platform$OS.type == 'windows', 'gzip', 'xz')
}
## 9. Check output.format ---------------------------------------------------
output.format <- args$output.format # raster output format
if (!is.null(output.format)) {
if (!output.format %in% c(".img", ".grd", ".tif", ".RData")) {
stop(paste0("output.format argument should be one of '.img','.grd', '.tif' or '.RData'\n"
, "Note : '.img','.grd', '.tif' are only available if you give environmental condition as a SpatRaster object"))
}
if (output.format %in% c(".img", ".grd", ".tif") && !inherits(new.env, "SpatRaster")) {
warning("output.format was automatically set to '.RData' because environmental conditions are not given as a raster object")
}
} else {
output.format <- ifelse(!inherits(new.env, "SpatRaster"), ".RData", ".tif")
}
## 9. Check do.stack --------------------------------------------------------
do.stack <- ifelse(is.null(args$do.stack), TRUE, args$do.stack)
if (!inherits(new.env, 'SpatRaster')) {
if (!do.stack) {
cat("\n\t\t! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset")
}
do.stack <- TRUE
} else if (do.stack) {
# test if there is enough memory to work with multilayer SpatRaster
capture.output({
test <-
mem_info(
subset(new.env, 1),
n = 2 * length(models.chosen) + nlyr(new.env)
)
})
if (test["needed"] >= test["available"]) {
terraOptions(todisk = TRUE)
}
# option without capture.output but with :::
# ncopies = 2 * length(models.chosen) + nlyr(new.env)
# test <- new.env@ptr$mem_needs(terra:::spatOptions(ncopies = ncopies))
# if (test[1] > test[2]) {
# terraOptions(todisk = TRUE)
# }
} else if (output.format == "RData"){
cat("\n\t\t! 'do.stack' arg is always set as TRUE for .RData output format")
do.stack <- TRUE
}
return(list(proj.name = proj.name,
new.env = new.env,
new.env.xy = new.env.xy,
models.chosen = models.chosen,
metric.binary = metric.binary,
metric.filter = metric.filter,
compress = compress,
do.stack = do.stack,
output.format = output.format,
omit.na = ifelse(is.null(args$omit.na), TRUE, args$omit.na),
do.stack = do.stack,
keep.in.memory = ifelse(is.null(args$keep.in.memory), TRUE, args$keep.in.memory),
on_0_1000 = ifelse(is.null(args$on_0_1000), TRUE, args$on_0_1000),
seed.val = seed.val))
}
### .build_clamping_mask -------------------------------------------------------
.build_clamping_mask <- function(env, MinMax)
{
if (inherits(env, 'SpatRaster')) {
## raster case ------------------------------------------------------------
env <- subset(env, names(MinMax))
## create an empty mask
clamp.mask <- app(subset(env, 1), function(x) ifelse(is.na(x), NA, 0))
ref.mask <- app(subset(env, 1), function(x) ifelse(is.na(x), NA, 1))
for (e.v in names(env)) {
if (!is.null(MinMax[[e.v]]$min)) { # numeric variable
clamp.mask <- clamp.mask +
bm_BinaryTransformation(subset(env, e.v), MinMax[[e.v]]$max) + ## pix with values outside of calib range
(ref.mask - bm_BinaryTransformation(subset(env, e.v), MinMax[[e.v]]$min)) ## pix with no values (within env[[1]] area)
} else if (!is.null(MinMax[[e.v]]$levels)) { # factorial variable
clamp.mask <-
clamp.mask +
app(subset(env, e.v),
function(x) {
ifelse(as.character(x) %in% MinMax[[e.v]]$levels,
1,
0)
}) ## pix with values outside of calib range
}
}
} else if (is.data.frame(env) | is.matrix(env) | is.numeric(env)) { ## matrix and data.frame case ---------------------------------------------
env <- as.data.frame(env)
# create an empty mask
clamp.mask <- rep(0, nrow(env))
for (e.v in names(MinMax)) {
if (!is.null(MinMax[[e.v]]$min)) { # numeric variable
clamp.mask <- clamp.mask +
bm_BinaryTransformation(env[, e.v], MinMax[[e.v]]$max) + ## pix with values outside of calib range
(1 - bm_BinaryTransformation(env[, e.v], MinMax[[e.v]]$min)) ## pix with no values (within env[[1]] area)
} else if (!is.null(MinMax[[e.v]]$levels)) { # factorial variable
clamp.mask <- clamp.mask + (env[, e.v] %in% MinMax[[e.v]]$levels)
}
}
} else {
stop("Unsupported env arg")
}
return(clamp.mask)
}
|
d658b2de576625cfac341efbe61d6eda711bd867
|
8c47fdfcb462799c38da6124cea82a7c578a7a06
|
/EV/EV1215.R
|
632b34705829fbac81bfd590de8801309c9d0903
|
[] |
no_license
|
QI0222/MA_615
|
c90df130a277194d08e3cbdd9f110229fcf9ea8c
|
adc114ed3b4bc889c21995cc527ed630ab40882d
|
refs/heads/master
| 2020-07-27T06:00:12.124158
| 2020-03-07T00:41:09
| 2020-03-07T00:41:09
| 208,893,881
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,157
|
r
|
EV1215.R
|
#research question: is there any supply for the electric vehicle charging stations?
#1. EDA on existing electric vehicles
#2. Are the charging stations in areas with restaurants and stores? (MAP)
#3. Distance between charging stations?
#4. Growth rate of the electric vehicles
#5. Growth rate of the charging stations
#reference:https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/nearby-route/#csv-output-format
#plots, maps, text analysis, PCA or EFA, appropriate plots, sturectured text with tables, images, links
#pacman::p_load("RSocrata","httr",ggplot2,leaflet,maps,stringr,jsonlite,dplyr,tidyverse,magrittr,rjson,geosphere,
#NLP,tm,RColorBrewer,wordcloud,wordcloud2,pdftools,tidytext,
#SentimentAnalysis,syuzhet)
library(RSocrata)
library(httr)
library(ggplot2)
library(leaflet)
library(maps)
library(stringr)
library(jsonlite)
library(dplyr)
library(tidyverse)
library(magrittr)
library(rjson)
library(geosphere)
library(NLP)
library(tm)
library(RColorBrewer)
library(wordcloud)
library(wordcloud2)
library(pdftools)
library(tidytext)
library(SentimentAnalysis)
library(syuzhet)
library(usmap)
library(yelpr)
#Data acquisation
#NY charging station
df <- read.socrata(
"https://data.ny.gov/resource/bpkx-gmh7.json",
app_token = "LK05q1lMNPWAqZ7My1YlIGCkr",
email = "hayleyformer@gmail.com",
password = "Hailey07#"
)
#California charging station
url <-"https://developer.nrel.gov/api/alt-fuel-stations/v1.json?fuel_type=ELEC&state=CA&api_key=UI8ftsbXoXhViwIMWdzStMcwdpyZPqyhHBOpv5sP"
df1 <- jsonlite::fromJSON(url,simplifyDataFrame = TRUE,flatten = TRUE)
df1 <- df1$fuel_stations
#All states charging station
url2 <-"https://developer.nrel.gov/api/alt-fuel-stations/v1.json?fuel_type=ELEC&state=all&api_key=UI8ftsbXoXhViwIMWdzStMcwdpyZPqyhHBOpv5sP"
df2 <- jsonlite::fromJSON(url2,simplifyDataFrame = TRUE,flatten = TRUE)
df2 <- df2$fuel_stations
#percent share of US EV sales by State
g <- readxl::read_xlsx("Percent share of US EV Sales by State.xlsx")
#Yelp data
yelp_long <- head(df$longitude,10)
yelp_lat <- head(df$latitude,10)
location <- as.data.frame(cbind(yelp_long,yelp_lat))
key <- "QOk02yFuAi8h5EBi-4eq1VO88CYP_pCw-kyej_XDucODNmTPOsqubieFDm0hmLqM16sjksD8aVEHuw4F00GWoIl9tavU7T4LmRRm2wob9rJVQZ1RYcNQCAKhVRX3XXYx"
yelpdata1 <- business_search(api_key = key, longitude = yelp_long[1], latitude = yelp_lat[1])
yelpdata1 <- as.data.frame(yelpdata1)
#yelpdata2 <- business_search(api_key = key, longitude = yelp_long[2], latitude = yelp_lat[2])
#as.data.frame(yelpdata2)
#yelpdata3 <- business_search(api_key = key, longitude = yelp_long[3], latitude = yelp_lat[3])
#as.data.frame(yelpdata3)
#yelpdata4 <- business_search(api_key = key, longitude = yelp_long[4], latitude = yelp_lat[4])
#as.data.frame(yelpdata4)
#yelpdata5 <- business_search(api_key = key, longitude = yelp_long[5], latitude = yelp_lat[5])
#as.data.frame(yelpdata5)
#Data cleaning
#choose variables to use
df <- df %>% select(1,2,3,5,6,7,8,11,12,16,17,21,28,42,44,45,46)
#clean the access_days_time
df%<>%mutate(access_time_1 = if_else(str_detect(access_days_time,"24 hours daily"),
"24 hours daily",
if_else(str_detect(access_days_time,"Dealership business hours"),
"Dealership business hours",
if_else(str_detect(access_days_time,"24 hours"),
"Office business hours",
if_else(str_detect(access_days_time," MON"),
"24 hours daily",
"others")))))
#clean the facility type
df%<>%dplyr::mutate(facility_type_1 = if_else(str_detect(facility_type,"PAY_GARAGE"),
"PAY_GARAGE",
if_else(str_detect(facility_type,"HOTEL"),
"HOTEL",
if_else(str_detect(facility_type,"CAR DEALER"),
"CAR DEALER",
if_else(str_detect(facility_type,"MUNI_GOV"),
"GOVERNMENT",
if_else(str_detect(facility_type,"COVENIENCE_STORE"),
"CONVENIENCE_STORE",
"others"))))))
#1. EDA on existing electric vehicles
#1.1 fuel type
plot1 <- df %>% ggplot(aes(x=fuel_type_code))+
geom_bar(fill = "steelblue")+
geom_text(stat = 'count',aes(label = ..count..),vjust=-.5)+
theme_bw()
plot1
#1.2 access time
plot2 <- df %>% ggplot(aes(x=access_time_1))+
geom_bar(fill = "steelblue")+
geom_text(stat = 'count',aes(label = ..count..),vjust=-.5)+
theme_bw()
plot2
#1.3 facility type
fac <- as.data.frame(df$facility_type_1)
fac <- na.omit(fac)
names(fac) <- "facility"
slices <- fac %>% group_by(facility) %>% summarise(n())
names(slices) <- c("facility","count")
lbls <- c("GOVERNMENT","HOTEL","OTHERS","PAY_GARAGE")
pct <- round(slices$count/sum(slices$count)*100)
lbls <- paste(lbls,pct)
lbls <- paste(lbls,"%",sep="")
plot3 <- pie(pct,labels = lbls,main = "Pie Chart of Facility Type")
#1.5 mapping
#mapStates = map("state",fill = TRUE,plot = FALSE)
#U.S. state map with numbers of charging stations
#map1 <- leaflet(data = mapStates) %>% addTiles() %>%
#addPolygons(fillColor = topo.colors(10,alpha = NULL),stroke = FALSE)
number <- df2 %>% group_by(state) %>% summarise(n())
colnames(number) <- c("state","number")
map1 <- plot_usmap(data = number,values = "number",color = "red")+
scale_fill_continuous(
low = "white", high = "red", name = "charging station", label = scales::comma)+
theme(legend.position = "right")
map1
#New York charging station
map2 <- leaflet(df) %>% addTiles() %>%
addMarkers(~as.numeric(longitude),~as.numeric(latitude))
map2
#CA charging station
map3 <- leaflet(df1) %>% addTiles() %>%
addMarkers(~as.numeric(longitude),~as.numeric(latitude))
map3
#All states charging station with clustering
map4 <- leaflet(df2) %>% addTiles() %>% addMarkers(
clusterOptions = markerClusterOptions()
)
# charging station only for Tesla
tesla <- df %>% filter(str_detect(access_days_time,"Tesla"))
non_tesla <- df %>% filter(!str_detect(access_days_time,"Tesla"))
icons_tesla <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = "green"
)
icons_all <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = "red"
)
map5 <- leaflet() %>% addTiles() %>%
addAwesomeMarkers(data = tesla,lng = ~as.numeric(longitude),lat = ~as.numeric(latitude),group = "Tesla - Only",icon = icons_tesla)%>%
addAwesomeMarkers(data = non_tesla,lng = ~as.numeric(longitude),lat = ~as.numeric(latitude),group = "For - All",icon = icons_all)
map5%>%addLayersControl(overlayGroups = c("Tesla - Only","For - All"),options = layersControlOptions(collapsed = FALSE))%>%hideGroup("For - All")
#test<- yelpdata$businesses
#test_coor <- test$coordinates
#test%<>%dplyr::select(name,rating,price,display_phone)
#test <- dplyr::bind_cols(test_coor,test)
#Growth rate of the electric vehicles
g[,6] <- g[,4]/g[,2] - 1
colnames(g)[6] <- "growth"
table1 <- as.data.frame(g)
#Grwoth rate of the charging station
#As noted previously, charging infrastructure increased by 36%–46% across these charging types for the same 100 markets from 2016 to 2017.
#https://theicct.org/sites/default/files/publications/US_charging_Gap_20190124.pdf
#Connected with YELP data
yelpdata1$businesses.coordinates
map6 <- leaflet(yelpdata1$businesses.coordinates) %>% addTiles() %>%
addMarkers(~as.numeric(longitude),~as.numeric(latitude)) %>%
addMarkers(lng = -73.932309, lat = 40.718037, icon = list(
iconUrl = "https://img.icons8.com/pastel-glyph/64/000000/cat--v3.png",
iconSize = c(75, 75)
))
map6 #the place with the cat icon is the charging station.
#distances between the charging station and the resutaurants around
p1 <- yelpdata1$businesses.coordinates
p2 <- as.data.frame(cbind(rep(40.718037,20),rep(-73.932309,20)))
distance <- as.data.frame(geosphere::distHaversine(p1,p2))
colnames(distance) <- "distance for charging station CAT"
#Opinion about charging station
#text mining
pdf_path <- "./energies-11-02174.pdf"
text <- pdftools::pdf_text(pdf_path)
cat(text[1])
text <- read_lines(text)
#cleaning the text
clean_text <- tolower(text) #make text to lower case using tolower() function
clean_text <- gsub(pattern = "\\W",replace=" ", clean_text) #remove puncations
clean_text <- gsub(pattern = "\\d",replace=" ", clean_text) #remove digits
clean_text <- removeWords(clean_text,words = c(stopwords(),"ai")) #remove stop words
clean_text <- gsub(pattern = "\\b[A-z]\\b{1}",replace=" ", clean_text) #remove single letters
clean_text <- stripWhitespace(clean_text) #remove white spaces
clean_text <- strsplit(clean_text," ") #split individual words and add space between them as split
#word_cloud
word_cloud <- unlist(clean_text)
tm1 <- wordcloud(word_cloud,min.freq = 5,random.order = FALSE,rot.per = 0.2,
colors = brewer.pal(5,"Dark2"),scale = c(4,0.2))
tm1
#sentiment analysis
sent <- analyzeSentiment(text,language = "english")
sent <- as.data.frame(sent[,1:4])
head(sent)
summary(sent$SentimentGI)
sent2 <- get_nrc_sentiment(text)
sent3 <- as.data.frame(colSums(sent2))
sent3 <- rownames_to_column(sent3)
colnames(sent3) <- c("emotion","count")
tm2 <- ggplot(sent3, aes(x = emotion, y = count, fill = emotion)) +
geom_bar(stat = "identity") + theme_minimal() +
theme(legend.position="none",
panel.grid.major = element_blank()) + labs( x = "Emotion", y = "Total Count") +
ggtitle("Sentiment of Electric Vehicle") + theme(plot.title = element_text(hjust=0.5))
tm2
|
dabc5f96c4133eaf423434aeb5cb24058b51a732
|
0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb
|
/cran/paws.management/man/ssm_delete_parameters.Rd
|
07a3bda2c33cb7f8160cd7f011574913c5aceac3
|
[
"Apache-2.0"
] |
permissive
|
paws-r/paws
|
196d42a2b9aca0e551a51ea5e6f34daca739591b
|
a689da2aee079391e100060524f6b973130f4e40
|
refs/heads/main
| 2023-08-18T00:33:48.538539
| 2023-08-09T09:31:24
| 2023-08-09T09:31:24
| 154,419,943
| 293
| 45
|
NOASSERTION
| 2023-09-14T15:31:32
| 2018-10-24T01:28:47
|
R
|
UTF-8
|
R
| false
| true
| 659
|
rd
|
ssm_delete_parameters.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ssm_operations.R
\name{ssm_delete_parameters}
\alias{ssm_delete_parameters}
\title{Delete a list of parameters}
\usage{
ssm_delete_parameters(Names)
}
\arguments{
\item{Names}{[required] The names of the parameters to delete. After deleting a parameter, wait
for at least 30 seconds to create a parameter with the same name.}
}
\description{
Delete a list of parameters. After deleting a parameter, wait for at least 30 seconds to create a parameter with the same name.
See \url{https://www.paws-r-sdk.com/docs/ssm_delete_parameters/} for full documentation.
}
\keyword{internal}
|
8c5b2cf84a0d4896f2d76646eca163d76ea67498
|
355aedf0b2b57e92c1e379727ba8c0e7a7296fa3
|
/process/Amniote_Database_References_Aug_2015_pre.R
|
02b11428082aa239f66b01188a66ba8f8fc9f03e
|
[] |
no_license
|
annakrystalli/bird_trait_networks
|
a2be0342bd84b1bb242d50a73727379518dadac5
|
81c56555147a56deb14af876e93996ef56330098
|
refs/heads/master
| 2021-01-17T00:59:06.585208
| 2018-02-01T18:50:00
| 2018-02-01T18:50:00
| 47,277,564
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 377
|
r
|
Amniote_Database_References_Aug_2015_pre.R
|
## ---- prR1-subset ----
data <- data[data$class == "Aves",]
## ---- prR1-derive_repro.age.diff ----
add_keep.dat <- data.frame(repro.age.diff = paste(data$female_maturity_d,
data$male_maturity_d, sep = "; "))
add_keep.dat[is.na(data$female_maturity_d) | is.na(data$male_maturity_d),] <- NA
add_keep.vars <- "repro.age.diff"
|
ea99c5e50c357997fc0635587aa49561ab355c69
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/umx/examples/umxConfint.Rd.R
|
51a25bdf369d9ec26d086fcf7dc1caaba851dd69
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,307
|
r
|
umxConfint.Rd.R
|
library(umx)
### Name: umxConfint
### Title: Get confidence intervals from a umx model
### Aliases: umxConfint
### ** Examples
require(umx)
data(demoOneFactor)
latents = c("G")
manifests = names(demoOneFactor)
m1 <- umxRAM("One Factor", data = mxData(cov(demoOneFactor), type = "cov", numObs = 500),
umxPath(from = latents, to = manifests),
umxPath(var = manifests),
umxPath(var = latents, fixedAt = 1)
)
m1 = umxConfint(m1, run = TRUE) # There are no existing CI requests...
# Add a CI request for "G_to_x1", run, and report. Save with this CI computed
m2 = umxConfint(m1, parm = "G_to_x1", run = TRUE)
# Just print out any existing CIs
umxConfint(m2)
# CI requests added for free matrix parameters. User prompted to set run = TRUE
m3 = umxConfint(m1, "all")
# Run the requested CIs
m3 = umxConfint(m3, run = TRUE)
# Run CIs for free one-headed (asymmetric) paths in RAM model.
# note: Deletes other existing requests,
tmp = umxConfint(m1, parm = "A", run = TRUE)
# Wipe existing CIs, add G_to_x1
tmp = umxConfint(m1, parm = "G_to_x1", run = TRUE, wipeExistingRequests = TRUE)
## Not run:
##D # For complex twin models, where algebras have parameters in some cells, smart might help
##D # note: only implemented for umxCP so far
##D m2 = umxConfint(m1, "smart")
## End(Not run)
|
cf05020c5202267f2a1a37e9b382b0f4db92904b
|
45f602f6af1ba44934ef8c2d928ecc90bf849b2d
|
/Oakland/Oakland Code.R
|
db0aba5771778f2f1a86ebee703ce3f5651b251b
|
[
"Apache-2.0"
] |
permissive
|
Howard-Center-Investigations/homelessness-in-the-us
|
e1e3690178d9d5241cf393bee2b371bc39fb5f3a
|
989b40c105fe4c5b8a1a93b0857758c805594178
|
refs/heads/master
| 2022-11-24T14:23:15.394426
| 2020-07-21T19:30:55
| 2020-07-21T19:30:55
| 212,883,711
| 3
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,259
|
r
|
Oakland Code.R
|
## Install packages
library(tidyverse)
#helps with cleaning data
library(janitor)
library(lubridate)
# Read in oakland data, read_csv is "a bit smarter" than read.csv
oakdata <- read_csv("oaklanddata.csv")
# Look at columns to see how each variable is interpreted (factors, characters, dates,
#etc.) problem is that date was read in as a character, not a date
glimpse(oakdata)
# Cleaning
#this makes the names lowercase, seperates the date variable base don where there is a space
#changes the date character strong into a month-day-year date, and then pulls out the year
#in a seperate column
#didn't work :round_date(date, "month") #gives error "subcript out of bounds"
oakdata <- oakdata %>%
clean_names() %>%
separate(datetimeinit, c("date", "time", "am_pm"), sep=" ") %>%
mutate(date = mdy(date)) %>%
mutate(year = year(date)) %>%
mutate(month= month(date)) %>%
separate(reqaddress, c("latitude", "longitude"), sep=" ")
oakdata$month_yr <- format(as.Date(oakdata$date), "%Y-%m")
#remove commas and parentheses
oakdata$latitude <- substring(oakdata$latitude, 2,11)
oakdata$longitude<- substring(oakdata$longitude, 1, 12)
options(digits=11)
oakdata$latitude <- as.numeric(as.character(oakdata$latitude))
options(digits=12)
oakdata$longitude <- as.numeric(as.character(oakdata$longitude))
glimpse(oakdata)
#Identify pertinent variables
issue<- oakdata$description
reqcategory<- oakdata$reqcategory
#this is too messy- need to find a way to group some addresses together based on proximity
#Counts/Descriptions
library (plyr)
library(dplyr)
#count based on one variable
issuecount = count(oakdata, "oakdata$description")
categorycount = count(oakdata, "oakdata$reqcategory")
yearcount= count(oakdata, "oakdata$year")
#not including 2020 because only january and february right now
completeyears <- oakdata %>%
filter(year != "2020" )
monthcount = count(completeyears, "completeyears$month")
#new dataframe with only homeless encampment data
he_data <- oakdata %>%
filter(oakdata$description == "Homeless Encampment")
he_completeyears<- he_data %>%
filter(year != "2020")
he_year= count(he_data, "he_data$year")
he_month = count(he_data, "he_data$month")
he_month_yr = count(he_data, "he_data$month_yr")
#many more homeless reports in july, august, sept, than jan, feb, march, april
#graph data on homeless encampments over the years
month_year<- he_month_yr$`he_data$month_yr`
library(ggplot2)
heyear<- he_year$he_data.year
heyearcount<- he_year$freq
plot(x=heyear,
y= heyearcount,
type = "b",
xlab="Year",
ylab="# of Homeless Encampment Reports",
main="Homeless Encampment Reports: Oakland 2009-2020",
xlim = c(2009, 2020),
ylim = c(0, 3500),
col="steelblue")
library(chron)
#graph data on homeless encampments by month-year date
install.packages("zoo")
library(zoo)
he_over_month_yr <- read.zoo(he_month_yr, FUN= as.yearmon)
plot (he_over_month_yr,
xlab= "Month-Year",
ylab= "# of Homeless Encampment Reports",
main="Homeless Encampment Reports: Oakland 2009-2020",
col = "steelblue")
# had a hard time converting "month_yr" to date format, so I took the shortcut above
# to get the graph, below are my failed attempts
#as.Date(as.yearmon(he_month_yr$he_data.month_yr))
#parse_date_time(he_month_yr$he_data.month_yr, "ym")
#glimpse(he_month_yr) #month_yr still in character format
#mapping the data
#testing out some leaflet functions
#library (leaflet)
#testmap <- leaflet() %>%
#addTiles() %>% # Add default OpenStreetMap map tiles
#setView(lng=37.813, lat=-122.288, zoom =12)
#testmap
#split the lat/long coordinates into seperate columns in the initial function at the
#top of the script
library(leaflet)
#map of all homeless encampment reports
he_data_map<- leaflet(data = he_data) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude)
# 2363 missing or invalud lat/lon values- may want to skim data to make sure this
#sounds right
he_data_map
addTiles(he_data_map,
urlTemplate = "//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",
attribution = NULL, layerId = NULL, group = NULL,
options = tileOptions(), data = he_data)
#are the (30, 140) coordinates mistakes?
#removing them and the NA coordinates
#only filtering out the <37 latitudes also filtered out the outlier longitudes
he_data_cleaned <- he_data %>%
filter(latitude > 37)
#cleaned map of all homeless encampment reports
he_data_map_cleaned<- leaflet(data = he_data_cleaned) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude)
he_data_map_cleaned
#trying to get all the data onto one map in different colors
getColor <- function(he_data_cleaned) {
sapply(he_data_cleaned$year, function(year) {
if(year = 2009) {
"green"
} else if(year = 2010) {
"orange"
} else {
"red"
} })
}
getColor <- function(he_data_cleaned) {
sapply(he_data_cleaned$year, function(year) {
if(year = 2009) {
"green"
} else if(year = 2010) {
"blue"
} else if(year = 2011) {
"orange"
} else if(year = 2012) {
"yellow"
} else if(year = 2013) {
"red"
} else if(year = 2014) {
"black"
} else if(year = 2015) {
"pink"
} else if(year = 2016) {
"green"
} else if(year = 2017) {
"white"
} else if(year = 2018) {
"purple"
} else if(year = 2019) {
"gray"
} else if(year = 2020) {
"brown"
} })
}
icons <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = getColor(he_data_cleaned)
)
he_by_year_map<- leaflet(he_data_cleaned) %>% addTiles() %>%
addAwesomeMarkers(~long, ~lat, icon=icons, label=~as.character(year))
#divide the homeless encampment data by year
he_2009<- he_data_cleaned %>%
filter(year == "2009")
he_2010<- he_data_cleaned %>%
filter(year == "2010")
he_2011<- he_data_cleaned %>%
filter(year == "2011")
he_2012<- he_data_cleaned %>%
filter(year == "2012")
he_2013<- he_data_cleaned %>%
filter(year == "2013")
he_2014<- he_data_cleaned %>%
filter(year == "2014")
he_2015<- he_data_cleaned %>%
filter(year == "2015")
he_2016<- he_data_cleaned %>%
filter(year == "2016")
he_2017<- he_data_cleaned %>%
filter(year == "2017")
he_2018<- he_data_cleaned %>%
filter(year == "2018")
he_2019<- he_data_cleaned %>%
filter(year == "2019")
he_2020<- he_data_cleaned %>%
filter(year == "2020")
#Map of 2019 homeless encampments
he_2019_map<-leaflet(data = he_2019) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude)
he_2019_map
#heat map of 2019 homeless encampments ?
#map of 2009 homeless encampments
he_2009_map<- leaflet(data = he_2009) %>%
addTiles() %>%
addMarkers(~longitude, ~latitude)
he_2009_map
#Goal: create a map that shows each year of data as a layer that a user can easily control
experiment_map<- leaflet() %>%
addTiles() %>%
addMarkers(data = he_2019, group = "2019") %>%
addMarkers(data = he_2014, group = "2014") %>%
addMarkers(data = he_2009, group = "2009")
experiment_map
experiment_map_2 <- leaflet() %>%
# Base groups
addTiles() %>%
addProviderTiles(providers$Stamen.Toner, group = "Toner") %>%
addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
# Overlay groups
addCircles(~long, ~lat, ~10^mag/5, stroke = F, group = "Quakes") %>%
addPolygons(data = outline, lng = ~long, lat = ~lat,
fill = F, weight = 2, color = "#FFFFCC", group = "Outline") %>%
# Layers control
addLayersControl(
baseGroups = c("OSM (default)", "Toner", "Toner Lite"),
overlayGroups = c("Quakes", "Outline"),
options = layersControlOptions(collapsed = FALSE)
)
map
#Goal: have data show up when you click and/or hover over a point
#what data do i want to show up though?
#Goal: match each call to a census tract by matching the lat/long values
# assign each call to a census tract
library(sf)
library(tidycensus)
library(tigris)
# select only homeless encampments, longitude, latitude
#he_location_data <- he_data %>%
#select( latitude, longitude)
#ca <- tidycensus::get_acs(state = "CA", geography = "tract",
# variables = "B19013_001", geometry = TRUE)
#code from stack overflow https://stackoverflow.com/questions/52248394/get-census-tract-from-lat-lon-using-tigris
#ca_tracts <- tracts("CA", class = "sf") %>%
#select(GEOID, TRACTCE)
#bbox <- st_bbox(ca_tracts)
#?st_bbox
#my_points <- data.frame(
#x = runif(200000, bbox[1], bbox[3]),
#y = runif(200000, bbox[2], bbox[4])
#) %>%
# convert the points to same CRS
#st_as_sf(coords = c("x", "y"),
#crs = st_crs(ca_tracts))
#my_points_tract <- st_join(my_points, ca_tracts)
#another tactic
#?tracts
#CA FIPS code = 06
#CA<- tracts(state = "06") %>%
#st_as_sf()
#CA_join <- st_join(he_data, CA)
#?st_join
#class(he_data)
#he_data_expmt <- he_location_data %>%
# st_as_sf(he_location_data, coords= c("latitude", "longitude"))
#another tactic
#library(devtools)
#he_data$census_code <- apply(he_location_data, 2, function(column) call_geolocator_latlon(c('lat'), c('long')))
#?apply
#call_geolocator_latlon(he_data$latitude, he_data$longitude)
#call_geolocator_latlon(lat,long)
#he_data_expmt <- he_data %>%
#filter(is.na(latitude) == F & is.na(longitude) == F) %>%
#st_as_sf(coords= c("latitude", "longitude"))
#?data.frame
#lat<- he_data
#he_data$census_code <- apply(he_data, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude']))
#?apply
#?row
#another tactic
#this data frame gives the GEOID, Tract ID, etc
#CA <- tracts(state = 06)
#CA <- st_as_sf(CA)
#plot(CA)
#CA<- CA%>%
# clean_names()
#clean the lat lon columns
#names(CA)[11] <- "latitude"
#names(CA)[12] <- "longitude"
#CA$latitude <- substring(CA$latitude, 2,11)
#glimpse(CA)
#options(digits=5)
#CA$latitude <- substring(CA$latitude, 1,11)
#CA$latitude <- as.numeric(as.character(CA$latitude))
#options(digits=8)
#CA$longitude <- substring(CA$longitude, 1,8)
#CA$longitude <- as.numeric(as.character(CA$longitude))
#now we need to match up this data with the oakland homeless data
# the struggle will be getting the digits in each latitude column to match. the CA data
#has 7 digits
#it appears there are no matches....
#only use three digits of long/lat
#he_tract_data<- merge(he_datamerge_test, CA, by = c("latitude" = "latitude", "longitude" = "longitude"))
#there are matches! roughly 11,000 in the initial homeless encampment data and roughly 117 matches
#trying to map the CA census tracts
#back to square 1
library(tigris)
library(acs)
library(stringr) # to pad fips codes
?tracts
tracts(state= "CA")
tracts <- tracts(state = 'CA', county= '001')
api.key.install(key="YOUR API KEY")
#using geo locator function but with the data by year so that it will work
glimpse(coord_2009)
?group_by
coord_2009 <- he_2009 %>%
mutate(GEOID = apply(he_2009, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
#coord_2009_test<- coord_2009%>%
# count("GEOID")
#?lapply
#coord_2009[, .(count = .N, var = sum(VAR)), by = GEOID]
#coord_2009[, .N, by=.(GEOID)]
coord_2010 <- he_2010 %>%
mutate(GEOID = apply(he_2010, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2011 <- he_2011 %>%
mutate(GEOID = apply(he_2011, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2012 <- he_2012 %>%
mutate(GEOID = apply(he_2012, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2013 <- he_2013 %>%
mutate(GEOID = apply(he_2013, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2014 <- he_2014 %>%
mutate(GEOID = apply(he_2014, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2015 <- he_2015 %>%
mutate(GEOID = apply(he_2015, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2016 <- he_2016 %>%
mutate(GEOID = apply(he_2016, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2017 <- he_2017 %>%
mutate(GEOID = apply(he_2017, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2018 <- he_2018 %>%
mutate(GEOID = apply(he_2018, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2019 <- he_2019 %>%
mutate(GEOID = apply(he_2019, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
coord_2020 <- he_2020 %>%
mutate(GEOID = apply(he_2020, 1, function(row) call_geolocator_latlon(row['latitude'], row['longitude'])))
#saving the global environment
save.image(file='oakenvironment.RData')
#combinging all geolocated homeless ancampments data again
#checked- 10870 rows which is what the original he_data_cleaned dataset had
he_with_geoid<- rbind(coord_2009, coord_2010, coord_2011, coord_2012, coord_2013, coord_2014,
coord_2015, coord_2016, coord_2017, coord_2018, coord_2019, coord_2020)
#get rid of the last four digits so it matches the gentrification data
he_with_geoid$GEOID<- substring(he_with_geoid$GEOID, 1,11)
glimpse(he_with_geoid)
#Importing Gentrification data:
gentdata<- read_csv("US_tr_GentDecline.csv")
#filter out for oakland
gentdata<- gentdata%>%
filter(CountyName== "Alameda County")
#add column showing percent change in number of low income people
gentdata$'% expansion'<- gentdata$StrongExpansionDecline/gentdata$TotPop16
gentdata$percent_expansion<-NULL
gentdatasimple<- gentdata%>%
select(GEOID, StrongExpansionDecline, TotPop16, '% expansion')
#all homeless encampment data matched with gentrification data- the rows without lat/long
#values are tracts that didn't have any homeless encampments called in them
#may want to make sure this makes sense
he_gent<-full_join(gentdata, he_with_geoid, by = "GEOID")
#counts of how many homeless reports happened in each GEOID
geoid_counts<- count(he_gent, "GEOID")
#all homeless encampment data matched with gentrification data- only GEOIDs with reports
he_gent2<-left_join(he_with_geoid, gentdata, by ="GEOID")
he_gent3<- inner_join(he_with_geoid, gentdata, by = "GEOID")
#have to eliminate the last four numbers of the GEOID in order to macth to gentdata-
#11 numbers specifies census tract, the extra four are for block(unnecessary i think)
coord_2009_tract<- coord_2009
coord_2009_tract$GEOID<- substring(coord_2009_tract$GEOID, 1,11)
coord_2009_count<- count(coord_2009_tract, "GEOID")
names(coord_2009_count)[2] <- "freq 2009"
coord_2019_tract<- coord_2019
coord_2019_tract$GEOID<- substring(coord_2019_tract$GEOID, 1,11)
coord_2019_count<- count(coord_2019_tract, "GEOID")
names(coord_2019_count)[2] <- "freq 2019"
count_09_19<- full_join(coord_2009_count, coord_2019_count, by = "GEOID")
#make NA values 0
count_09_19[is.na(count_09_19)] <- 0
#one way we could get around the baseline 0 issue:
#count_09_19[is.na(count_09_19)] <- 0.00000000000000000000001
count_09_19$'change in calls'<- count_09_19$`freq 2019`-count_09_19$`freq 2009`
count_09_19$'%change in calls'<- (count_09_19$'change in calls'/count_09_19$`freq 2009`)
#table with only certain variables
#so only 113/361 census tracts have reported calls
#there is one census tract that shows up in our data but not in the gent data
#seems like a mistake: 06013353001 should probably be "060013353001" to follow th pattern
#manually fix it ?
he_gent_combined<- full_join(count_09_19, gentdatasimple, by ="GEOID")
names(he_gent_combined)[6] <- "StrongExpDec"
#some findings- homeless calls in only 25 tracts in 2009 -> 113 tracts in 2019
#(including the same 25)
plot(he_gent_combined$`%change in calls`, he_gent_combined$`% expansion`, xlab =
"% Change in Homeless Encampment Calls 2009-2019", ylab =
"% Change in Low Income Pop 2000-2016")
ggplot(he_gent_combined, aes(x = '% change in calls', y =
'% expansion')) +geom_point()
#hard to read a lot form this map because so much has changed over the 2009-2019 period-
#encampment calls have increased in every single tract, and it's hard to distill that
#data in this scatterplot and with only 2 years
|
4a1a8d4a0bda9aba700c52cf02cf8e845b910572
|
c23216384c211a86d6fa2d04211d54da21693978
|
/Submissions/me2685@columbia.edu/SIS_Challenge.R
|
5b8633423856ef46a13166d4ab4aec885654f1d8
|
[] |
no_license
|
SportsInfoSolutions/AnalyticsChallenge2020
|
e49a239fbb00ba38d4e192726d2b64c62eded5c5
|
cd12a9caaec5d49f8f12c805d381d9619bd298d4
|
refs/heads/master
| 2023-06-05T03:18:33.617477
| 2021-06-24T20:54:41
| 2021-06-24T20:54:41
| 274,144,333
| 17
| 62
| null | 2020-07-22T14:03:21
| 2020-06-22T13:27:05
|
HTML
|
UTF-8
|
R
| false
| false
| 15,746
|
r
|
SIS_Challenge.R
|
library(tidyverse)
library(readr)
library(RCurl)
library(glue)
pbp <- read_csv("Data/AnalyticsChallenge2020Data.csv",
na = c("NULL", "NA"))
# Clean Data
pbp_clean <- pbp %>%
# Remove kneels and spikes
filter(!str_detect(PlayDesc, "kneel") & !str_detect(PlayDesc, "spike")) %>%
# Classify Scrambles using SIS data (more reliable than GSIS-provided play descriptions)
left_join(pbp %>%
group_by(GameID, EventID) %>%
summarize(run = max(if_else(str_detect(EventType, "designed run"), 1, 0)),
pass = max(if_else(str_detect(EventType, "pass"), 1, 0)),
pass_rush = max(if_else(IsRushing == 1, 1, 0)),
PlayDesc = paste(unique(PlayDesc), collapse = '-')) %>%
filter((run == 1 & pass_rush == 1)|str_detect(PlayDesc, "scramble")) %>%
select(GameID, EventID) %>%
mutate(scramble = 1)) %>%
mutate(scramble = replace_na(scramble, 0),
event_type = if_else(str_detect(EventType, "pass") | scramble == 1, "dropback", "designed run"),
yardline_100 = if_else(SideOfField == "Oppo", StartYard, 100 - StartYard),
tech_side = case_when(TechniqueName %in% c("0", "Off Ball") ~ TechniqueName,
TechniqueName == "Outside" ~ as.character(glue("{SideOfBall}OLB")),
TRUE ~ as.character(glue("{SideOfBall}{TechniqueName}"))),
in_designed_gap = case_when(event_type == "dropback" ~ NA_real_,
RunDirection == "Middle" ~
if_else(tech_side %in% c("0", "L1", "R1", "L2i", "R2i"), 1, 0),
RunDirection == "Right A Gap" ~
if_else(tech_side %in% c("0", "L1", "L2", "L2i"), 1, 0),
RunDirection == "Left A Gap" ~
if_else(tech_side %in% c("0", "R1", "R2", "R2i"), 1, 0),
RunDirection == "Right Off-Tackle B Gap" ~
if_else(tech_side %in% c("L2", "L3", "L4", "L4i"), 1, 0),
RunDirection == "Left Off-Tackle B Gap" ~
if_else(tech_side %in% c("R2", "R3", "R4", "R4i"), 1, 0),
RunDirection == "Right Off-Tackle C Gap" ~
if_else(tech_side %in% c("L4", "L5", "L7", "L6"), 1, 0),
RunDirection == "Left Off-Tackle C Gap" ~
if_else(tech_side %in% c("R4", "R5", "R7", "R6"), 1, 0),
RunDirection == "Right D Gap" ~
if_else(tech_side %in% c("L6", "L9", "LOLB"), 1, 0),
RunDirection == "Left D Gap" ~
if_else(tech_side %in% c("R6", "R9", "ROLB"), 1, 0)),
gap_force = if_else(UsedDesignedGap == 0 & in_designed_gap == 1, 1, 0),
success = if_else(EPA > 0, 1, 0),
position_group = case_when(TechniqueName %in% c("7", "Outside", "9", "6", "5") ~ "Edge",
TechniqueName == "Off Ball" ~ TechniqueName,
TRUE ~ "iDL"),
dl_tackle = if_else((SoloTackle == 1 | AssistedTackle == 1) &
position_group %in% c("Edge", "iDL") &
(SackOnPlay == 0 | is.na(SackOnPlay)), 1, 0))
# pbp_clean %>%
# filter(event_type == "designed run" & UsedDesignedGap == 0) %>%
# mutate(TimeLeft = glue("{floor(TimeLeft/60)}:{TimeLeft - floor(TimeLeft/60)*60}")) %>%
# select(Week, OffensiveTeam, Quarter, TimeLeft, Down, ToGo, RunDirection) %>%
# distinct()
# # Look at pressure rates from edge players vs interior players
DEDT <- pbp_clean %>%
filter (event_type == "dropback" & IsRushing == 1) %>%
group_by(PlayerId) %>%
summarise(Player = paste(unique(Name), collapse = '-'),
Position = paste(unique(RosterPosition), collapse = '-'),
RushSnaps = n(),
Pressure = sum(Pressure),
PressureRate = Pressure/RushSnaps,
meanEPA = mean(EPA),
EdgeRate = sum(if_else(position_group == "Edge", 1, 0))/RushSnaps,
EPA = sum(EPA)) %>%
ungroup() %>%
filter(RushSnaps >= 100) %>%
mutate(dl_pos = as.factor(if_else(EdgeRate >= 0.5, "Edge", "iDL")))
DEDT %>%
ggplot(aes(x = PressureRate, fill = dl_pos)) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = c("red", "blue")) +
scale_y_continuous(limits = c(0, 15), expand = c(0, 0)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1),
limits = c(0, 0.23), expand = c(0, 0)) +
labs(x = "Pressure Rate",
y = "Density",
title = "Distribution of Player Pressure Rates by Position Group",
fill = "Position Group") +
theme_bw() +
theme(legend.position = c(0.915, 0.885),
axis.title.x = element_text(size = 14),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 12, margin = margin(5, 0, 10, 0)),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(size = 18, hjust = 0.5, face = "bold",
margin = margin(b = 8, unit = "pt")),
plot.subtitle = element_text(size = 14, hjust = 0.5, face = "italic",
margin = margin(b = 16, unit = "pt")),
plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"))
#Look at pressure rates by technique - league wide
techs <- pbp_clean %>%
filter(event_type == "dropback" & IsRushing == 1) %>%
group_by(tech_side) %>%
summarise(RushSnaps = n(),
Pressure = sum(Pressure),
PressureRate = Pressure/RushSnaps,
meanEPA = mean(EPA),
EPA = sum(EPA)) %>%
ungroup()
tech_names <- c("LOLB", "L9","L6", "L7", "L5", "L4", "L4i", "L3", "L2", "L2i", "L1", "0",
"R1", "R2i", "R2", "R3", "R4i", "R4", "R5", "R7", "R6", "R9", "ROLB")
techs %>%
filter(tech_side != "Off Ball") %>%
ggplot(aes(x = tech_side, y = PressureRate)) +
geom_bar(stat = "identity", fill = "steelblue") +
scale_x_discrete(limits = tech_names, expand = c(0.025, 0)) +
scale_y_continuous(limits = c(0, 0.13), expand = c(0, 0)) +
#scale_x_continuous(limits = c(-5, 5)) +
labs(x = "Technique",
y = "Pressure Rate",
title = "Pressure Rate by DL Technique") +
theme_bw() +
theme(axis.title = element_text(size = 14),
axis.text.x = element_text(size = 11, margin = margin(5, 0, 10, 0)),
axis.text.y = element_text(size = 12, margin = margin(0, 5, 0, 10)),
plot.title = element_text(size = 18, hjust = 0.5, face = "bold",
margin = margin(b = 8, unit = "pt")),
plot.subtitle = element_text(size = 14, hjust = 0.5, face = "italic",
margin = margin(b = 16, unit = "pt")),
plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"))
# Create Dataframe of Pass plays - Scrambles
dfpass <- pbp_clean %>%
filter(event_type == "dropback" & scramble == 0) %>%
mutate(edge_pressure = if_else(position_group == "Edge" & Pressure == 1, 1, 0),
idl_pressure = if_else(position_group == "iDL" & Pressure == 1, 1, 0)) %>%
group_by(GameID, EventID) %>%
summarise(Quarter = mean(Quarter),
TimeLeft = mean(TimeLeft),
Down = mean(Down),
ToGo = mean(ToGo),
StartYard = mean(yardline_100),
Completion = mean(Completion),
OffensiveYardage = mean(OffensiveYardage),
EPA = mean(EPA),
success = mean(success),
Touchdown = mean(Touchdown),
PressureOnPlay = mean(PressureOnPlay),
PassBreakupOnPlay = mean(PassBreakupOnPlay),
SackOnPlay = mean(SackOnPlay),
dl_tackle = max(dl_tackle),
edge_p = max(edge_pressure),
idl_p = max(idl_pressure)) %>%
mutate(pressure_type = case_when(edge_p == 1 & idl_p == 1 ~ "Both",
edge_p == 1 ~ "Edge",
idl_p == 1 ~ "iDL",
edge_p == 0 & idl_p == 0 & PressureOnPlay == 1 ~ "Other",
TRUE ~ "No Pressure")) %>%
select(-edge_p, -idl_p) %>% na.omit()
#Look at EPA distributions by Pressure Type
dfpass %>% filter(pressure_type != "Other") %>%
ggplot(aes(x = EPA, fill = pressure_type)) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = c("red", "blue",
"green",
"yellow")) +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(limits = c(-5, 5), expand = c(0, 0)) +
labs(x = "EPA",
y = "Density",
title = "Distribution of EPA by Pressure Type",
fill = "Pressure Type") +
theme_bw() +
theme(legend.position = c(0.915, 0.885),
axis.title.x = element_text(size = 14),
axis.title.y = element_blank(),
axis.text.x = element_text(size = 12, margin = margin(5, 0, 10, 0)),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
plot.title = element_text(size = 18, hjust = 0.5, face = "bold",
margin = margin(b = 8, unit = "pt")),
plot.subtitle = element_text(size = 14, hjust = 0.5, face = "italic",
margin = margin(b = 16, unit = "pt")),
plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm")) + facet_wrap(~pressure_type)
#Kolmogorov-Smirnov Test, cannot reject null hyptohesis that two samples come from the same distribution
ks.test(Pressures$EPA[Pressures$pressure_type == "Edge"], Pressures$EPA[Pressures$pressure_type == "iDL"])
#Independent Sammples T-Test, cannot reject null hypothesis that the two sample means are equal
t.test(Pressures$EPA[Pressures$pressure_type == "Edge"], Pressures$EPA[Pressures$pressure_type == "iDL"])
#Create dataframe of run plays
dfrun <- pbp_clean %>%
filter(event_type == "designed run") %>%
group_by(GameID, EventID) %>%
mutate(in_designed_gap = max(in_designed_gap)) %>%
summarise(Quarter = mean(Quarter),
TimeLeft = mean(TimeLeft),
Down = mean(Down),
ToGo = mean(ToGo),
StartYard = mean(yardline_100),
OffensiveYardage = mean(OffensiveYardage),
EPA = mean(EPA),
success = mean(success),
Touchdown = mean(Touchdown),
RunDirection= paste(unique(RunDirection), collapse = '-'),
UsedDesignedGap = paste(unique(UsedDesignedGap), collapse = '-'),
in_designed_gap = paste(unique(in_designed_gap), collapse = '-'),
gap_force = max(gap_force),
dl_tackle = max(dl_tackle),
PlayDesc = paste(unique(PlayDesc), collapse = '-'))
#Look at whether gap forces have a significant effect on run plays
dfrun %>% na.omit() %>% ggplot(aes(x = as.factor(gap_force), y = EPA)) +
geom_boxplot()
ks.test(dfrun$EPA[dfrun$gap_force ==1], dfrun$EPA[dfrun$gap_force == 0])
t.test(dfrun$EPA[dfrun$gap_force ==1], dfrun$EPA[dfrun$gap_force == 0])
#We can conclude gap forces have no significant effect on the outcome of run plays
#Look at whether DL tackles have a significant effect on run plays with tackles by other position groups
# or runner out of bounds
dfrun %>% filter(Touchdown == 0) %>% na.omit() %>% ggplot(aes(x = as.factor(dl_tackle), y = EPA)) +
geom_boxplot()
ks.test(dfrun$EPA[dfrun$dl_tackle ==1], dfrun$EPA[dfrun$dl_tackle == 0 & dfrun$Touchdown == 0])
t.test(dfrun$EPA[dfrun$dl_tackle ==1], dfrun$EPA[dfrun$dl_tackle == 0 & dfrun$Touchdown == 0])
#We can conclude that a DL tackle has a significant effect on the outcome of run plays
#Compare distribution of EPA and Offensive Yards Gained and success rate
#of plays with a defender in the run gap and plays without
dfrun %>%
ggplot(aes(x = EPA, fill = as.factor(gap_force))) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = c("red","blue"))
dfrun %>%
ggplot(aes(x = OffensiveYardage, fill = in_designed_gap)) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = c("red","blue"))
dfrun %>%
ggplot(aes(x = EPA, fill = as.factor(dl_tackle))) +
geom_density(alpha = 0.5) +
scale_fill_manual(values = c("red","blue"))
# The most successful run type is when the offense targets an unoccupied gap and is able to run
# through that gap
dfrun %>%
group_by(in_designed_gap, UsedDesignedGap) %>%
summarize(runs = n(),
epa = mean(EPA, na.rm = T),
sr = mean(success))
#Fit a linear model to determine the effects of pressures, pass breakups on pass plays
#Control for TDs + Downs + Yardline
passmodel <- lm(EPA ~ PassBreakupOnPlay + PressureOnPlay + Touchdown + as.factor(Down) + StartYard +
ToGo,
data = dfpass)
PressureValue <- as.numeric(passmodel$coefficients["PressureOnPlay"])
PbuValue <- as.numeric(passmodel$coefficients["PassBreakupOnPlay"])
#Fit a linear model to determine the effects of DL tackles on run plays
#Control for TDs + Downs + Yardline
runmodel <- lm(EPA ~ dl_tackle + Touchdown + as.facor(Down) + StartYard + ToGo, data = dfrun)
TackleValue <- as.numeric(runmodel$coefficients["dl_tackle"])
pbp_clean$dEPA <- 0
for (i in 1:nrow(pbp_clean)){
if(pbp_clean$event_type[i] == "dropback" & pbp_clean$scramble[i] == 0) {
pbp_clean$dEPA[i] <- pbp_clean$Pressure[i] * PressureValue + pbp_clean$PassBreakup[i] * PbuValue
} else if (pbp_clean$event_type[i] == "designed run") {
pbp_clean$dEPA[i] <- pbp_clean$SoloTackle[i] * TackleValue + (pbp_clean$AssistedTackle[i] * TackleValue)/2
} else {
pbp_clean$dEPA[i] <- 0
}
}
pbp_clean$dEPA <- pbp_clean$dEPA * -1
TechValues <- pbp_clean %>%
group_by(TechniqueName) %>%
summarise(Snaps = n(),
dEPA = sum(dEPA),
dEPA100 = (dEPA/Snaps)*100) %>%
ungroup()
TalentDistributions <- pbp_clean %>%
group_by(TechniqueName, Name, PlayerId) %>%
summarise(Snaps = n(),
dEPA = sum(dEPA),
dEPA100 = (dEPA/Snaps)*100) %>%
ungroup() %>% filter(Snaps >= 20)
TalentDistributions %>%
ggplot(aes(x = dEPA100, fill = TechniqueName)) + geom_density(alpha = 0.5) +
facet_wrap(~TechniqueName)
TalentDistributions %>%
ggplot(aes(x = dEPA, fill = TechniqueName)) + geom_density(alpha = 0.5) +
facet_wrap(~TechniqueName)
TechValues %>%
filter(TechniqueName != "Off Ball") %>%
ggplot(aes(x = TechniqueName, y = dEPA100)) +
geom_bar(stat = "identity", fill = "steelblue") +
scale_x_discrete(limits = c("0","1","2","2i","3","4i","4","5","6","7","9","Outside")) +
#scale_x_continuous(limits = c(-5, 5)) +
labs(x = "Technique",
y = "dEPA/100",
title = "dEPA per 100 Snaps by DL Technique") +
theme_bw() +
theme(axis.title = element_text(size = 14),
axis.text.x = element_text(size = 11, margin = margin(5, 0, 10, 0)),
axis.text.y = element_text(size = 12, margin = margin(0, 5, 0, 10)),
plot.title = element_text(size = 18, hjust = 0.5, face = "bold",
margin = margin(b = 8, unit = "pt")),
plot.subtitle = element_text(size = 14, hjust = 0.5, face = "italic",
margin = margin(b = 16, unit = "pt")),
plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "cm"))
|
cdcbcaea478db72bec1acd36b47ca1abbf5c3dd3
|
3a268ed108e371a7e5039a60b29a26e0c5c2a78e
|
/R/crossplot.R
|
e83d96b6a6310a6207c23a0702404c4c4fc3e966
|
[] |
no_license
|
ChandlerLutz/crossplotr
|
1b0608b0cb09f5d86a1f4928a049f8e7e103a148
|
80e4407aed1e447a3672063f9332650234b94cf6
|
refs/heads/master
| 2021-01-12T06:37:10.985067
| 2018-12-16T22:09:31
| 2018-12-16T22:09:31
| 77,396,652
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 7,249
|
r
|
crossplot.R
|
## c:/Dropbox/Rpackages/crossplotr/R/plot_basic.R
## Chandler Lutz
## Questions/comments: cl.eco@cbs.dk
## $Revisions: 1.0.0 $Date: 2016-12-26
#' Create a basic cross-sectional plot using standard evaluation
#' of the variable names
#'
#' @param data the data to be plotted
#' @param x.var string with the x axis variable
#' @param y.var string with the y axis variable
#' @param size.var string with the size (weighting) variable. Defaults
#' to \code{NULL}
#' @param shapes.var string with the shapes (different shapes for
#' different) points variable. Defaults to \code{NULL}
#' @param label.var string with the names of the points that can be
#' used with \code{geom_text} or \code{geom_text_repel}
#' @param control.vars a character vector with the names of variables
#' that will be used as controls via the Frisch–Waugh–Lovell
#' theorem. The result will be a plot (an AV Plot) and statistics
#' where the variables in \code{control.vars} are partialed
#' out. These variables must be present in the original
#' dataset. Note: if \code{control.vars} is set, an attribute will
#' be added to the returned plot and will be named
#' \code{control.vars}
#' @param title string with the the title for plot
#' @param xlabel string with the xlabel. Defualts to \code{NULL}
#' @param ylabel string with the ylabel
#' @param shapes numeric vector with the shapes of the points. For
#' shape options, see
#' \url{http://www.cookbook-r.com/Graphs/Shapes_and_line_types/}
#' @param colors character vector with the colors for different
#' points. Defaults to \code{c("blue", "red", "green")}
#' @param points.alpha \code{numeric(1)} indicating the alpha to be
#' applied to the points. Default value is \code{1}. Setting
#' \code{points.alpha = 0.7} is helpful when plotting labels with
#' the points
#' @return a \code{ggplot2} plot with the cross-sectional output
#' @examples
#' data(mtcars)
#' crossplot(mtcars, x.var = "mpg", y.var = "hp", size.var = "wt",
#' shapes.var = "cyl")
#' ##Control for (partial out) qsec
#' p <- crossplot(mtcars, x.var = "hp", y.var = "mpg", size.var = "wt",
#' shapes.var = "cyl", control.vars = "qsec")
#' print(p)
#' p$control.vars
#' @export
crossplot <- function(data, x.var, y.var, size.var = NULL, shapes.var = NULL,
label.var = NULL, control.vars = NULL,
title = NULL, xlabel = NULL,
ylabel = NULL, shapes = c(1, 2, 0, 5, 6),
colors = c("blue", "red", "darkgreen"), points.alpha = 1) {
##Make sure data is a dataframe
if (!is.data.frame(data)) stop("Error: data is not a data frame")
data <- as.data.frame(data)
##Make sure x.var, y.var, size.var, shapes.var, label.var, and control.vars
##are character strings
if (!is.character(x.var) || !is.character(y.var))
stop("Error: x.var and y.var need to be character strings")
if (!is.null(size.var) && !is.character(size.var))
stop("Error: size.var needs to be a character string")
if (!is.null(shapes.var) && !is.character(shapes.var))
stop("Error: shapes.var needs to be a character string")
if (!is.null(label.var) && !is.character(label.var))
stop("Error: shapes.var needs to be a character string")
if (!is.null(control.vars) && !is.character(control.vars))
stop("Error: control.vars needs to be a character vector")
##if xlabel or ylabel are null, use the x and y variable names
if (is.null(xlabel)) xlabel <- x.var
if (is.null(ylabel)) ylabel <- y.var
##Only complete cases for the variables of interest -- see utils.R
data <- data_no_na_values(data)
##If necessary, update x and y using control.vars and the
##Frisch–Waugh–Lovell thm
if (!is.null(control.vars)) {
if (!(control.vars %in% names(data)))
stop("Error: each member of control.vars needs to be in data")
if (is.null(size.var)) {
##Not weighted
data[[x.var]] <- residuals(lm(data[, x.var] ~ data[, control.vars]))
data[[y.var]] <- residuals(lm(data[, y.var] ~ data[, control.vars]))
} else {
##Weighted
data[[x.var]] <- residuals(lm(data[, x.var] ~ data[, control.vars],
weights = data[, size.var]))
data[[y.var]] <- residuals(lm(data[, y.var] ~ data[, control.vars],
weights = data[, size.var]))
}
}
##Update shapes.var and factor ordering to make sure the bottom-positioned
##legend has space
if (!is.null(shapes.var)) {
if (is.factor(data[[shapes.var]])) {
##factor -- retain the levels
fact.levels <- levels(data[[shapes.var]]) %>% as.character
} else {
fact.levels <- data[[shapes.var]] %>% unique %>% as.character
}
fact.levels <- stringr::str_trim(fact.levels)
data[[shapes.var]] <- factor(paste0(stringr::str_trim(data[[shapes.var]]), " "),
levels = paste0(fact.levels, " "))
}
##Do the basic plot
if (is.null(label.var)) {
p <- ggplot(data, aes_string(x.var, y.var))
} else {
p <- ggplot(data, aes_string(x.var, y.var, label = label.var))
}
##Depending on shapes.var is set
if (!is.null(shapes.var)) {
p <- p + geom_point(aes_string(size = size.var,
color = shapes.var,
shape = shapes.var),
alpha = points.alpha)
} else if (is.null(shapes.var)) {
p <- p + geom_point(aes_string(size = size.var), shape = 1, alpha = points.alpha)
}
##The aestetics of the plot
p <- p +
background_grid(minor='none') +
##theme_bw() +
##The colors -- black and red
scale_colour_manual(values = colors) +
##The size of the shapes -- range from 2 to 20 and remove legend
scale_size_continuous(range = c(2, 20), guide = "none") +
##For shapes across the metros
scale_shape_manual(values = shapes) +
labs(x = xlabel, y = ylabel)
##If necessary, add the title
if (!is.null(title)) {
p <- p + ggtitle(title)
}
p <- p +
##remove legend title and add other aesthetics to the legend
theme(legend.text = element_text(size = 16),
legend.title = element_blank(),
##legend.background = element_rect(fill="gray90",
## size=.5,linetype="dotted"),
legend.background = element_rect(color="black",
size=.5,linetype="solid"),
panel.grid.minor = element_blank()) +
##make symbols in legend bigger. see
##http://stackoverflow.com/a/20416049/1317443
guides(colour = guide_legend(override.aes = list(size=6)))
##Add control.vars as an attribute to p
if (!is.null(control.vars)) {
p$control.vars <- control.vars
} else {
p$control.vars <- NA
}
return(p)
}
|
5864f9a28afd673765a5118d193c832ab9dfbef0
|
fa2ed649ad6cda9eaa3a2fc3a89b30a6e31ef8a8
|
/single_cell_RNA_sequencing/QC.R
|
91ad580b47169d4de199bddb86149deca64ce846
|
[] |
no_license
|
boxiangliu/onek1k_phase1
|
984095b5d4f419adecc8093080773493b363cb1f
|
d91eecb08ff1f7693712274de39d87a5215a7131
|
refs/heads/main
| 2023-09-01T17:12:16.981311
| 2021-10-13T08:11:08
| 2021-10-13T08:11:08
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 10,672
|
r
|
QC.R
|
# Script information ------------------------------------------------------
#' title: Quality control for Onek1k data
#' author: Jose Alquicira Hernandez
#' date: 2019/05/20
#' description: Removes outlier cells based on the expression of mitochondrial
#' gene expression, number of UMIs, and genes detected per cell. Data is read
#' from multiple directories and integrated to create a single gene expression
#' matrix. A Seurat object is created and quality control is applied to remove
#' outlier cells based on number of genes, counts, and mitochondrial expression.
#' For the percentage of mitochondrial gene expression, Cells that deviate 2 negative
#' and 3 positive SDs are considered as outliers and removed. For the total
#' number of UMIs and features (genes) per cell, only the low threshold is applied.
#' Doublets are filtered out based on demuxlet results prior QC.
# Import libraries --------------------------------------------------------
# Primary
library("tidyverse")
library("here")
library("dsLib")
library("data.table")
# Secondary
library("Seurat")
library("bestNormalize")
# Set output --------------------------------------------------------------
output <- set_output("2019-06-16", "QC")
# Read data ---------------------------------------------------------------
# Input directory
input <- file.path("",
"share",
"ScratchGeneral",
"annsen",
"data",
"experimental_data",
"CLEAN",
"OneK1K_scRNA")
# Get file names for all pools
samples <- list.dirs(input, full.names = FALSE, recursive = FALSE) %>%
str_subset("OneK1K_scRNA_Sample")
# rename pools
pools <- samples %>%
str_split("_") %>%
map(~ .[3]) %>%
unlist() %>%
str_remove("Sample")
# Create helper function to read data
readData <- function(dirSample, pool){
# Input
dirSample <- file.path(input, dirSample, "outs", "filtered_gene_bc_matrices", "hg19")
# Read file
x <- Read10X(data.dir = dirSample, gene.column = 2)
# Assign pool id to barcode
colnames(x) <- paste0(colnames(x),"-", pool)
x
}
# Read gene expression matrices
inicio("Reading gene expression data")
data <- map2(samples, pools, readData)
fin()
# Get gene and cell counts
data %>%
map(dim) %>%
reduce(rbind) %>%
as.data.frame() %>%
set_names(c("genes", "cells")) %>%
mutate(pool = paste0("pool_",pools)) -> geneCellInfo
# Save data summary per pool
write_delim(geneCellInfo, path = here(output, "pool_genes_cells.txt"), delim = "\t")
# Get gene intersection from all pools
data %>%
map(row.names) %>%
reduce(intersect) -> geneIntersection
# Validate that all genes are shared among all pools and in the same order
cat("\nAre all genes shared among all pools?\n")
data %>%
map(row.names) %>%
map_lgl(~all(geneIntersection == .)) %>%
all() %>%
cat("\n")
# The intersection of all gene names is equal to the number of genes
# in each pool. Therefore, all genes are shared among all btches.
# All gene names are in the same order in all matrices.
# Create pool info
inicio("Creating metadata")
pmap(list(geneCellInfo$pool, geneCellInfo$cells), rep) %>%
unlist() -> poolInfo
data %>%
map(colnames) %>%
unlist() %>%
data.frame(row.names = ., pool = poolInfo) -> poolInfo
fin()
# Merge cells into a single gene expression matrix
inicio("Integrating pools")
data <- do.call(cbind, data)
fin()
# Create Seurat object
inicio("Creating Seurat object")
data <- CreateSeuratObject(data,
project = "onek1k",
meta.data = poolInfo)
fin()
inicio("Saving pre-QC metadata")
saveRDS(data@meta.data, file = here(output, "preQC_metadata.RDS"))
fin()
# Keep singlets -----------------------------------------------------------
# Keep singlets only as determined by demuxlet
inicio("Extracting singlets")
path <- file.path("data", "singlets", "barcodes_assigned_to_ppl.txt")
singlets <- fread(file = here(path), data.table = FALSE)
singlets$id <- paste(str_remove(singlets$BARCODE, "-.*$"), str_remove(singlets$batch, "sample"), sep = "-")
i <- colnames(data) %in% singlets$id
table(i)
data <- data[,i]
fin()
# Add individul information to metadata
inicio("Adding individual metadata")
i <- singlets$id %in% colnames(data)
singlets <- singlets[i,]
singlets %>%
`rownames<-`(NULL) %>%
column_to_rownames("id") %>%
select(-BARCODE, -batch) -> singlets
data <- AddMetaData(data, metadata = singlets, col.name = "individual")
levs <- fct_drop(data@meta.data$pool)
i <- levs %>%
levels() %>%
str_split("_") %>%
map(2) %>%
flatten_chr() %>%
as.integer() %>%
order()
data$pool <- factor(levs, levels = levels(levs)[i])
fin()
inicio("Saving pre-QC metadata after doublet removal")
saveRDS(data@meta.data, file = here(output, "preQC_singlets_metadata.RDS"))
fin()
# QC ----------------------------------------------------------------------
# Get percentage expression of mitochondrial and ribosomal genes
data <- PercentageFeatureSet(data, pattern = "^MT-", col.name = "percent.mt")
# Extract metadata
meta.data <- data@meta.data
# Define QC metrics to evaluate to remove outliers
metricsLabels <- list("% Mitochondrial gene expression", "Number of UMIs", "Number of features")
metrics <- list("percent.mt", "nCount_RNA", "nFeature_RNA")
names(metrics) <- metrics
# Create function to get the cutoffs based on normalized data
getCutoffs <- function(object, group, metric, lowSD = 3, highSD = 2){
# Normalize QC metric across pools
res <- object %>%
split(.[[group]]) %>% # Split data by pool
map(~pull(., !!sym(metric))) %>% # Extract pool information only
map(quietly(orderNorm)) %>% # Transform to a normal distribution
map("result") # Extract results from `orderNorm`
# Extract higher threshold based on `lowSD` standard deviations from the mean
res %>% map(~mean(.$x.t) + sd(.$x.t) * highSD) -> higher
# Extract lower threshold based on `highSD` standard deviations from the mean
res %>% map(~mean(.$x.t) - sd(.$x.t) * lowSD) -> lower
# Transform Z-scores to original distributions
higherTransform <- map2(res, higher, predict, inverse = TRUE)
lowerTransform <- map2(res, lower, predict, inverse = TRUE)
# Bind results and merge into a tibble
higherTransform <- bind_cols(higherTransform) %>%
gather(key = !!sym(group), value = higher)
lowerTransform <- bind_cols(lowerTransform) %>%
gather(key = !!sym(group), value = lower)
# Combine lower and higher thresholds into a single tibble
cutoffs <- bind_cols(higherTransform, lowerTransform) %>%
gather(key = "type", value = metric, c(2,4)) %>%
select(-2)
# Combine lower and higher Z-score thresholds into a single tibble
higher <- bind_cols(higher) %>% gather(key = group, value = higher)
lower <- bind_cols(lower) %>% gather(key = group, value = lower)
cutoffsTrans <- bind_cols(higher, lower) %>%
gather(key = "type", value = metric, c(2,4)) %>%
select(-2)
# Extract original and Z-score distributions
res %>%
map(~.[c("x", "x.t")]) %>% # Extract original and transformed distributions
map(as.data.frame) %>% # Bind into a data.frame
map(`names<-`, c(metric, paste0(metric, "_norm"))) %>% # Rename columns
bind_rows(.id = group) -> res # Combine distributions across pools
# Returns:
# - cutoffs: A data.frame containing the group (pool), type of threhold (higher or lower), and the value
# - normCutoffs: Normalized cutoffs
# - trans: A data.frame including the original and the normalized distribution by group (pool)
list(cutoffs = as.data.frame(cutoffs), normCutoffs = as.data.frame(cutoffsTrans), trans = res)
}
# Get cutoffs
inicio("Obtaining cutoffs across all pools")
cutoffs <- map(metrics, ~getCutoffs(meta.data, "pool", .))
names(cutoffs) <- names(metrics)
fin()
# Filter data -------------------------------------------------------------
# Get a list of cutoffs by pool and metric
# Sets Inf higher cutoffs for "nCount_RNA" and "nFeature_RNA"
setHighCutoff <- function(x, inf = TRUE){
x %>%
map("cutoffs") %>%
map(spread, key = "type", value = "metric", 3) -> x
if(inf){
x <- x %>% map(~mutate(.x, higher = Inf))
}
x %>%
map(~split(., .["pool"]))
}
cutoffsBypool <- setHighCutoff(cutoffs[c("nCount_RNA", "nFeature_RNA")])
cutoffsBypool <- append(cutoffsBypool, setHighCutoff(cutoffs[c("percent.mt")], inf = FALSE))
findOutliers <- function(cutoff, metric, group){
# Iterate for each value in `group` variable and filter data based on
# corresponding filters. Data for each value in group is stored in `res` list
res <- map(names(cutoff), function(val){
meta.data %>%
rownames_to_column("barcode") %>%
filter(UQ(as.name(group)) == !!quo(val)) -> subMeta
lower <- subMeta %>% filter(!!sym(metric) < cutoff[[val]]$lower)
higher <- subMeta %>% filter(!!sym(metric) > cutoff[[val]]$higher)
list(lower = lower, higher = higher) %>%
bind_rows(.id = "type")
})
# Combine
res %>%
map(bind_rows)
}
# Get outliers
inicio("Extracting outliers")
res <- map2(cutoffsBypool, names(cutoffsBypool), findOutliers, group = "pool")
fin()
outliers <- res %>%
map(bind_rows) %>%
bind_rows(.id = "origin") %>%
distinct(barcode, .keep_all = TRUE)
# Get number of outliers by QC metric and type of cutoff
outliers %>%
group_by(type, origin) %>%
summarise(n = n())
# Get number of outliers by QC metric
outliers %>%
group_by(origin) %>%
summarise(n = n())
# Filter data
inicio("Removing outliers")
data <- data[,!colnames(data) %in% outliers$barcode]
fin()
# Set latent variable -----------------------------------------------------
b1 <- levels(data@meta.data$pool)[1:33]
latent <- as.factor(ifelse(data@meta.data$pool %in% b1, "b1", "b2"))
meta.data.latent <- data.frame(latent, row.names = colnames(data))
data <- AddMetaData(data, meta.data.latent)
# Save data ---------------------------------------------------------------
inicio("Saving results")
saveRDS(data, file = here(output, "QC.RDS"))
saveRDS(outliers, file = here(output, "outliers.RDS"))
saveRDS(cutoffs, file = here(output, "cutoffs.RDS"))
saveRDS(data@meta.data, file = here(output, "QC_metadata.RDS"))
fin()
# Session info ------------------------------------------------------------
print_session(here(output))
|
488a8b93da1c3030816f44ce0e31d3f90ac138e6
|
c7a896607481d06dd0aac96dddbd9d36e822ac8d
|
/tests/testthat/test-import-competitions.R
|
8ad278da64c42c896914e83f48c8eaf3e996e52a
|
[] |
no_license
|
IsaacVerm/penalty
|
49a046cb5939e693935fdcf45bf7bc5c4f24f3f2
|
3f00f72f9fbd1b3632a58f7163171d4d89e683be
|
refs/heads/master
| 2021-03-30T21:18:30.662954
| 2018-03-23T09:09:21
| 2018-03-23T12:43:41
| 124,901,887
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 982
|
r
|
test-import-competitions.R
|
context("get_competitions")
test_that("returns response succesfully", {
response <- get_competitions()
expect_equal(response$status_code, 200)
expect_is(response, "response")
})
context("extract_competition_ids")
test_that("returns list", {
response <- get_competitions()
ids <- extract_competition_ids(response, "Premier League")
expect_is(ids, "list")
})
test_that("returns ids labelled by season", {
response <- get_competitions()
ids <- extract_competition_ids(response, "Premier League")
id_regex <- "\\d{1,2}"
label_regex <- "\\d{4}\\/\\d{2}"
season_ids <- ids[["season"]]
season_labels <- names(season_ids)
expect_match(season_ids, id_regex)
expect_match(season_labels, label_regex)
})
test_that("returns competition id", {
response <- get_competitions()
ids <- extract_competition_ids(response, "Premier League")
id_regex <-"\\d{1}"
competition_id <- ids[["competition"]]
expect_match(competition_id, id_regex)
})
|
9cc43e425225b0b0b15010269bd44138ed825648
|
ac961f7c20e60c955720e0d93483b4d92ce01726
|
/unsorted_pantherJson2df.R
|
dd2c0b9a504a44d8c2c426f49117e409c5b13253
|
[] |
no_license
|
mengchen18/RFunctionCollection
|
01b2936ea794297a87cf277ef0f583d02a1bb4ac
|
222d22fc8f12a622af01b91711a66c6e9da08be8
|
refs/heads/master
| 2022-05-24T17:49:05.385208
| 2022-05-17T12:58:42
| 2022-05-17T12:58:42
| 127,109,330
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,633
|
r
|
unsorted_pantherJson2df.R
|
pantherJson2df <- function(js) {
getfixwidth <- function(x) {
if (is.null(x))
x <- NA
x
}
einfo <- function(v, cat) {
c(GO_group = cat,
GO_level = getfixwidth(v$term$level),
GO_ID = getfixwidth(v$term$id),
GO_Desc = getfixwidth(v$term$label),
N_reference = getfixwidth(v$number_in_reference),
N_observed = getfixwidth(v$input_list$number_in_list),
N_expected = getfixwidth(v$input_list$expected),
Fold_enrichment = getfixwidth(v$input_list$fold_enrichment),
Direction = getfixwidth(v$input_list$plus_minus),
Q_value = getfixwidth(v$input_list$pValue),
Genes = getfixwidth(paste(unlist(v$input_list$mapped_id_list), collapse = ";"))
)
}
ii <- js$overrepresentation$group
lt <- lapply(seq_along(ii), function(i) {
x <- ii[[i]][[1]]
if (!is.null(names(x)))
r <- einfo(x, cat = i) else {
r <- sapply(x, einfo, cat = i)
r <- t(r)
}
r
})
sapply(lt, dim)
tab <- do.call(rbind, lt)
tab <- as.data.frame(tab, stringsAsFactors = FALSE)
numcol <- c("GO_group", "GO_level", "N_reference", "N_observed", "N_expected",
"Fold_enrichment", "Q_value")
tab[numcol] <- lapply(tab[numcol], as.numeric)
attr(t, "tool_release_date") <- js$overrepresentation$tool_release_date
attr(t, "data_version_release_date") <- js$overrepresentation$data_version_release_date
attr(t, "test_type") <- js$overrepresentation$test_type
attr(t, "correction") <- js$overrepresentation$correction
attr(t, "annotation_type") <- js$overrepresentation$annotation_type
tab
}
|
846f2c95c57c83d158eae5d780ae452009de01e9
|
3a4c4c729d37c10c6db2d4de24a7d8cb8115949b
|
/R/get_attrs_desc.R
|
46b18ad61a6db92113eb8f4c17b1562704704e6f
|
[
"MIT"
] |
permissive
|
twang2218/pmap
|
9155e2b5591e3fc100200734b24b11b0006a8c4f
|
1cd62ff0b2c02faff3b2e20cc0e5c98dff871582
|
refs/heads/master
| 2021-11-25T16:24:07.288289
| 2021-10-31T16:02:08
| 2021-11-02T08:46:04
| 116,099,299
| 19
| 3
|
NOASSERTION
| 2021-11-02T08:46:05
| 2018-01-03T06:16:51
|
R
|
UTF-8
|
R
| false
| false
| 1,802
|
r
|
get_attrs_desc.R
|
#' @title Get an attribute key-pair string from an object
#' @usage get_attrs_desc(object)
#' @param object Given object, can be `list`, `matrix`, or `data.frame`
#' @description Get an attribute list string from an object
#' @examples
#' print(df)
#' # id name is_manager
#' # 1 1 Jane FALSE
#' # 2 2 John FALSE
#' # 3 3 Eric FALSE
#' # 4 4 Selena TRUE
#' get_attrs_desc(df)
#' # [1] "id: 1\nname: Jane\nis_manager: FALSE"
#' # [2] "id: 2\nname: John\nis_manager: FALSE"
#' # [3] "id: 3\nname: Eric\nis_manager: FALSE"
#' # [4] "id: 4\nname: Selena\nis_manager: TRUE"
#' @export
get_attrs_desc <- function(object) {
cls <- class(object)
# Get attribute names
# `names()` can only support `list` and `data.frame`;
# and `colnames()` cannot support `list`.
attrs.name <- NULL
if (inherits(object, "list")) {
attrs.name <- names(object)
} else {
attrs.name <- colnames(object)
}
if (any(is.null(attrs.name)) || any(is.na(attrs.name)) || length(attrs.name) == 0) {
return("")
}
# Get attributes key-pair
# `matrix` do not support `object[[name]]`, so it has to be treated specially.
attrs <- NULL
if (inherits(object, "matrix")) {
attrs <- sapply(
attrs.name,
function(name) {
paste0(name, ": ", object[, name])
}
)
} else {
attrs <- sapply(
attrs.name,
function(name) {
paste0(name, ": ", object[[name]])
}
)
}
# Combine attributes key-pairs to a single string contains the list
# `paste()` is only works for columns oriented direction, so `apply()`
# is used for `matrix` and `data.frame`
if (inherits(attrs, c("matrix", "data.frame"))) {
apply(attrs, 1, paste, collapse = "\n")
} else {
paste(attrs, collapse = "\n")
}
}
|
bec599c49867ebca72deffeee00b680956e006d5
|
096fe210156cd34fec1743fc8be582d3903721cb
|
/man/print.striprawdata.Rd
|
a8009db3c7f113b9830e5d7f44cd802c08931874
|
[] |
no_license
|
Lifebrain/metagam
|
43d383fcaa331562a0899651fd9468708c188af0
|
37d9f75a3cc97322d0ae2957e0fdeaa49ef54dfc
|
refs/heads/master
| 2023-09-01T07:34:58.376500
| 2023-05-05T18:06:03
| 2023-05-05T18:06:03
| 235,595,959
| 8
| 2
| null | 2022-01-24T10:18:37
| 2020-01-22T14:54:25
|
R
|
UTF-8
|
R
| false
| true
| 432
|
rd
|
print.striprawdata.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary_strip_rawdata.R
\name{print.striprawdata}
\alias{print.striprawdata}
\title{Print method for striprawdata}
\usage{
\method{print}{striprawdata}(x, ...)
}
\arguments{
\item{x}{Object of class \code{striprawdata}.}
\item{...}{Other arguments.}
}
\value{
The function invisibly returns its argument.
}
\description{
Print method for striprawdata
}
|
15d22b232fffec1a93d83dd5d8635c5f7c3e3d2a
|
9e758a1fd686a06c99eccf25e02bf736640531c7
|
/R/quotas_errors.R
|
bbc15bccea731ba1675ff08ad6081676c61d6787
|
[] |
no_license
|
addixvietnam/googleAnalyticsR_v0.4.2
|
2873c59bd23c76a06deaa036d676e6675c275869
|
d60ff8f8c6f6748b1fc985b9b079ac6b4738f8b3
|
refs/heads/master
| 2020-08-26T12:54:05.693373
| 2019-10-23T09:28:49
| 2019-10-23T09:28:49
| 217,016,500
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,319
|
r
|
quotas_errors.R
|
default_project_message <- function(){
is_default_project <-
getOption("googleAuthR.client_id") %in% c("289759286325-da3fr5kq4nl4nkhmhs2uft776kdsggbo.apps.googleusercontent.com",
"289759286325-42j8nmkeq5n9v9eb1kiuj2i97v9oea1f.apps.googleusercontent.com",
"201908948134-rm1ij8ursrfcbkv9koc0aqver84b04r7.apps.googleusercontent.com",
"201908948134-cjjs89cffh3k429vi7943ftpk3jg36ed.apps.googleusercontent.com")
if(is_default_project){
myMessage("Default Google Project for googleAnalyticsR is now set. This is shared with all googleAnalyticsR users. \n If making a lot of API calls, please: \n 1) create your own Google Project at https://console.developers.google.com \n 2) Activate the Google Analytics Reporting API \n 3) set options(googleAuthR.client_id) and options(googleAuthR.client_secret) \n 4) Reload the package.", level = 3)
}
}
error_check <- function(x){
if(is.error(x)){
if(grepl("insufficient tokens for quota",error.message(x))){
default_project_message()
warning("The Google Project ", getOption("googleAuthR.client_id") ," has run out of quota (typically 50,000 API calls per day)")
}
stop(error.message(x))
}
x
}
|
68d68df1aaac0d776f1c9740fd39f032f7074a98
|
319c8effd49600b5796cd1759063b0b8f10aeac1
|
/workspace/crispr/data_P101SC17051084-01-B1-7/cdf.r.2018052114
|
c902896223d896a607453359ffdd755792934900
|
[] |
no_license
|
ijayden-lung/hpc
|
94ff6b8e30049b1246b1381638a39f4f46df655c
|
6e8efdebc6a070f761547b0af888780bdd7a761d
|
refs/heads/master
| 2021-06-16T14:58:51.056045
| 2021-01-27T02:51:12
| 2021-01-27T02:51:12
| 132,264,399
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,697
|
2018052114
|
cdf.r.2018052114
|
#!/usr/bin/env Rscript
library(grid)
library(ggplot2)
args<-commandArgs(T)
data = read.table(args[1],header=TRUE,row.names=1)
pdf(args[2],width=12,height=21)
grid.newpage()
pushViewport(viewport(layout = grid.layout(3, 1)))
vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
df <- data.frame(x = c(data$mKO293RGFPPos.1,data$mKO293RGFPNeg.1,data$mKO293Rtotal.1),g = gl(3,labels =c('mKO293RGFPPos.1','mKO293RGFPNeg.1','mKO293Rtotal.1'), length(data$mKO293RGFPPos.1)))
a<-ggplot(df, aes(log10(x),colour = g))+stat_ecdf(geom = "step",pad=FALSE)+scale_x_continuous("log10 Number of reads per sgRNA",limits=c(0,4))+scale_y_continuous("Cumulative percentage of mKO library",breaks=0.1*(0:10),labels=scales::percent)
df <- data.frame(x = c(data$mKO293RGFPPos.2,data$mKO293RGFPNeg.2,data$mKO293Rtotal.2),g = gl(3,labels =c('mKO293RGFPPos.2','mKO293RGFPNeg.2','mKO293Rtotal.2'), length(data$mKO293RGFPPos.1)))
b<-ggplot(df, aes(log10(x),colour = g))+stat_ecdf(geom = "step",pad=FALSE)+scale_x_continuous("log10 Number of reads per sgRNA",limits=c(0,4))+scale_y_continuous("Cumulative percentage of mKO library",breaks=0.1*(0:10),labels=scales::percent)
df <- data.frame(x = c(data$mKO293RGFPPos.3,data$mKO293RGFPNeg.3,data$mKO293Rtotal.3),g = gl(3,labels =c('mKO293RGFPPos.3','mKO293RGFPNeg.3','mKO293Rtotal.3'), length(data$mKO293RGFPPos.1)))
c<-ggplot(df, aes(log10(x),colour = g))+stat_ecdf(geom = "step",pad=FALSE)+scale_x_continuous("log10 Number of reads per sgRNA",limits=c(0,4))+scale_y_continuous("Cumulative percentage of mKO library",breaks=0.1*(0:10),labels=scales::percent)
print(a, vp = vplayout(1,1))
print(b, vp = vplayout(2,1))
print(c, vp = vplayout(3,1))
|
618abbb80952d420fad609d3dee369be0db53927
|
4dfb7d79f898a0bb8819129b1c34ad7e2c75d352
|
/Untitled.r
|
0b406b598d3b806ce687e65c124d85a61e700e1e
|
[] |
no_license
|
AmandaJunqueira/gittutorial
|
080147e0472722941dae305beff2631b9273ed26
|
b242f87980e7f3f160f610d92d6b5a70d6d190eb
|
refs/heads/master
| 2021-01-02T04:48:05.774199
| 2020-02-10T11:50:25
| 2020-02-10T11:50:25
| 239,494,927
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 13
|
r
|
Untitled.r
|
bla bla <- 1;
|
da257424fbb07c060066307f7b6a423b0602e342
|
200d05f1571ea8ddbbc37ced7eb926d43d571f5a
|
/man/build_model_objects.Rd
|
d0f2be47e0c2e2fbd4d4e4e36c8fd6053708122e
|
[] |
no_license
|
STAT545-UBC-hw-2018-19/hw07-shreeramsenthi
|
0982b633c94d8c4ebfd40acada3ae6ad11fd268e
|
a71f1a1e3bd20fc120a8eaa0c995c322ff52035f
|
refs/heads/master
| 2020-04-05T06:01:21.970231
| 2018-11-15T08:22:27
| 2018-11-15T08:22:27
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 587
|
rd
|
build_model_objects.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/build_model_objects.R
\name{build_model_objects}
\alias{build_model_objects}
\title{Build list of model objects}
\usage{
build_model_objects(formulas, data, model = stats::lm, ...)
}
\arguments{
\item{formulas}{List of model formulas}
\item{data}{Dataframe with variables corresponding to the components of `formulas`}
\item{model}{Function that generates model objects from formulas}
\item{...}{args to be passed to `model`}
}
\value{
A list of model objects
}
\description{
Build list of model objects
}
|
170f671cfb5209469a974d45329e973230534857
|
ab84adde7680f319f025396f156adb7d1df66b65
|
/data-raw/ucdp_acd.R
|
e9d82a15516badadd070a9cb0d04595fd790b405
|
[] |
no_license
|
Louis8102/peacesciencer
|
9492d44c0749ef484611003e1c7518293a4d73c4
|
cc6f9b6130779b451a1300a7b199bc87904fb5b2
|
refs/heads/master
| 2023-04-15T11:30:23.807519
| 2021-04-28T14:56:46
| 2021-04-28T14:56:46
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,998
|
r
|
ucdp_acd.R
|
library(tidyverse)
ucdp_acd <- readxl::read_excel("~/Dropbox/data/ucdpprio/acd/ucdp-prio-acd-201.xlsx")
# I'mma select just what I want
ucdp_acd %>%
select(conflict_id, year, gwno_a, gwno_a_2nd, gwno_b, gwno_b_2nd,
incompatibility, intensity_level, type_of_conflict:ep_end_date) -> ucdp_acd
# Oof, I hate how UCDP does this. Alas, let's do it. I'd think this work.
# conflict_id == 420, 252, and 412 should be a nice trial balloon to how well this works.
# conflict_id == 218 = Kashmir. Also a good trial balloon for how well I'm capturing recurring conflicts and conflicts that span years.
# conflict_id == 11345 = South Sudan, which has no end date. Also a good trial balloon.
ucdp_acd %>%
mutate(gwno_a = strsplit(as.character(gwno_a), ",")) %>%
unnest(gwno_a) %>%
mutate(gwno_a_2nd = strsplit(as.character(gwno_a_2nd), ",")) %>%
unnest(gwno_a_2nd) %>%
mutate(gwno_b = strsplit(as.character(gwno_b), ",")) %>%
unnest(gwno_b) %>%
mutate(gwno_b_2nd = strsplit(as.character(gwno_b_2nd), ",")) %>%
unnest(gwno_b_2nd) -> ucdp_acd
ucdp_acd %>%
mutate_at(vars("start_date", "start_date2", "ep_end_date"), ~lubridate::ymd(.)) %>%
mutate_at(vars("gwno_a", "gwno_a_2nd", "gwno_b", "gwno_b_2nd"), ~as.numeric(.)) -> ucdp_acd
save(ucdp_acd, file="data/ucdp_acd.rda")
# Here is where I fiddle with things...
ucdp_acd %>%
mutate(gwcode = gwno_a) %>%
bind_rows(., ucdp_acd %>% mutate(gwcode = gwno_a_2nd)) %>%
bind_rows(., ucdp_acd %>% mutate(gwcode = gwno_b)) %>%
bind_rows(., ucdp_acd %>% mutate(gwcode = gwno_b_2nd)) %>%
filter(!is.na(gwcode)) %>%
mutate(sidea = case_when(gwcode == gwno_a ~ 1,
gwcode == gwno_a_2nd ~ 1,
TRUE ~ 0)) %>%
select(conflict_id, year, gwcode, sidea, everything()) %>%
arrange(year, conflict_id)
filter(type_of_conflict == 2) %>% summary
ucdp_acd %>%
filter(type_of_conflict %in% c(1, 3)) %>% summary
ucdp_acd %>%
filter(conflict_id == 11345) %>%
data.frame
|
f2f5fc93757815f4209a102649c8f0c8fd75b86a
|
fc087468a9a228a59f819285eb0fc4c2b726934c
|
/week 9 notes.R
|
61f7ee3753ca5df4b783f6253225872719ce15e7
|
[] |
no_license
|
jmsahakian/R_DAVIS_in_class_project
|
16475f15c78bb20ffb27d61bcd01feb04fc9f468
|
312481656b104706c692c59c501e658ff020eca3
|
refs/heads/master
| 2020-11-25T05:17:13.101369
| 2020-05-01T02:33:35
| 2020-05-01T02:33:35
| 228,517,141
| 0
| 0
| null | 2019-12-17T02:36:09
| 2019-12-17T02:36:09
| null |
UTF-8
|
R
| false
| false
| 1,667
|
r
|
week 9 notes.R
|
#Week 9 Notes
#Date Times using 'lubridate' package
#load packages
library(tidyverse)
library(lubridate)
nyf1 <- read_csv("data/raw_data/2015_NFY_solinst.csv", skip = 12)
sample_dates <- c("07-15-2019", "12-24-2018", "03-07-2013", "04-04-2019")
sample_dates <- as.Date(sample_dates, format = "%m-%d-%Y")
dt <- c("07-15-2019 14:32:09", "12-24-2018 12:40:01")
dt <- as.POSIXct(dt, format = "%m-%d-%Y %H:%M:%OS")
?as.POSIXct
dt
#have to originally rerun dt
dt <- as.POSIXct(dt, format = "%m-%d-%Y %H:%M:%OS", tz = "GMT")
dt
#sometimes you need to explicit tell R Studio which package to use lubridate::
dates_lub <- mdy(sample_dates)
sample_dates2 <- c("5A06A17 14:22", "8A17A13 06:33")
sample_dates2_lub <- mdy_hm(sample_dates2, tz = "GMT")
sample_dates2_lub
head(nfy1)
#automically converted the date and time varibles
#typically do not want tidyversie to do these conversions
#want to force tidyverse to see these fields as characters
nyf1 <- read_csv("data/raw_data/2015_NFY_solinst.csv", skip = 12, col_types = "ccidd")
head(nyf1)
#quick way to make a new column
nyf1$Datetime <- paste(nyf1$Date, nyf1$Time, sep = " ")
head(nyf1)
nyf1$Datetime2 <- ymd_hms(nyf1$Datetime, tz = "GMT")
tz(nyf1$Datetime2)
load("data/raw_data/mauna_loa_met_2001_minute.rda")
#RDS has only one object and RDA has multiple objects
summary(mloa_2001)
#will need to use the paste function to create the dates variables we want
mloa_2001$Datetime <- paste0(mloa_2001$year, "-", mloa_2001$month, "-", mloa_2001$day, " ", mloa_2001$hour24, ":", mloa_2001$min)
head(mloa_2001)
mloa_2001$Datetime <- ymd_hm(mloa_2001$Datetime, tz = "HST")
tz(mloa_2001$Datetime)
|
ef9a13e39da1e578edc10c5ee772bf28b27581fd
|
98f35573ae5ccec324f57f5752e5798c82445ca2
|
/inst/predictive/predictive.R
|
3958d5777368992fb23e3988a0c17e9e7f92b36b
|
[] |
no_license
|
cran/AOV1R
|
fd23065a2b8649d10695ebefe289cba930c751ca
|
230941f1c4d8b55a83e4aadf2946d97b65f86d93
|
refs/heads/master
| 2023-01-07T07:59:10.998185
| 2020-11-10T09:00:08
| 2020-11-10T09:00:08
| 312,225,640
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,590
|
r
|
predictive.R
|
library(AOV1R)
nsims <- 1000
test <- logical(nsims)
for(i in 1:nsims){
dat <- simAV1R(I=2, J=3, mu=10, sigmab=1, sigmaw=1)
fit <- aov1r(y ~ group, dat)
pred <- predict(fit)
newy <- simAV1R(I=1, J=1, mu=10, sigmab=1, sigmaw=1)$y
test[i] <- newy > pred[1] && newy < pred[2]
}
mean(test)
# unbalanced
nsims <- 1000
test <- logical(nsims)
for(i in 1:nsims){
dat <- simAV1R(I=3, J=4, mu=10, sigmab=1, sigmaw=1)[-c(1,2),]
fit <- aov1r(y ~ group, dat)
pred <- predict(fit)
newy <- simAV1R(I=1, J=1, mu=10, sigmab=1, sigmaw=1)$y
test[i] <- newy > pred[1] && newy < pred[2]
}
mean(test)
n <- 20000
Z <- rnorm(n)
I <- 6; J <- 4
U2b <- rchisq(n, I-1)
U2w <- rchisq(n, I*(J-1))
nsims <- 1000
test <- logical(nsims)
for(i in 1:nsims){
dat <- simAV1R(I=I, J=J, mu=10, sigmab=1, sigmaw=1)
fit <- aov1r(y ~ group, dat)
pivots <- AOV1R:::pivotal0(fit, Z, U2b, U2w)
# sims <- numeric(n)
# for(j in 1:n){
# sims[j] <- simAV1R(I=1, J=1,
# mu=pivots$G_mu[j],
# sigmab=sqrt(max(0,pivots$G_sigma2b[j])),
# sigmaw=sqrt(pivots$G_sigma2w[j]))$y
# }
sims <- rnorm(n, pivots$G_mu, sqrt(pivots$G_sigma2b+pivots$G_sigma2w))
pred <- quantile(sims, c(0.025, 0.975))
newy <- simAV1R(I=1, J=1, mu=10, sigmab=1, sigmaw=1)$y
test[i] <- newy > pred[1] && newy < pred[2]
}
mean(test) # 0.99 avec la 1ère méthode, pour nsims=100
# 0.985 avec la deuxième pour large nsims (I=2 J=3)
####
set.seed(666)
dat <- simAV1R(I=6, J=2, mu=10, sigmab=2, sigmaw=2)
fit <- aov1r(y~group, dat)
predict(fit)
library(rstanarm)
options(mc.cores = parallel::detectCores())
sfit <- stan_lmer(y ~ (1|group), data=dat,
prior_covariance = decov(1, 1, 0.01, 100),
iter = 3500, warmup=1000,
adapt_delta = 0.98, prior_PD=FALSE)
predictive_interval(sfit, newdata=data.frame(group="xxx"), prob=0.95)
predictive_interval(sfit, newdata=data.frame(group="xxx"), re.form=NA, prob=0.95)
samples <- rstan::extract(sfit$stanfit)
# aux is sigma and theta_L is sigma²_b
psims <- rnorm(10000, samples$alpha, sqrt(samples$theta_L[,1]+samples$aux^2))
quantile(psims, c(0.025, 0.975))
pivotals <- AOV1R:::pivotal(fit)
plot(density(pivotals$G_mu))
lines(density(samples$alpha), col="red")
plot(density(pivotals$G_sigma2b))
lines(density(samples$theta_L[,1]), col="red")
plot(density(pivotals$G_sigma2w))
lines(density(samples$aux^2), col="red")
library(brms)
options(mc.cores = parallel::detectCores())
bfit <- brm(y ~ (1|group), data = dat, control = list(adapt_delta = 0.95),
prior = c(prior(cauchy(0,5),class="sd")),
iter = 3500, warmup = 1000)
pred <- posterior_predict(bfit, newdata=data.frame(group="xxx"), allow_new_levels=TRUE)
quantile(pred, c(0.025, 0.975))
samples <- posterior_samples(bfit)
names(samples)
psims <- rnorm(10000, samples$b_Intercept,
sqrt(samples$sd_group__Intercept^2 + samples$sigma^2))
quantile(psims, c(0.025, 0.975))
pivotals <- AOV1R:::pivotal(fit)
plot(density(pivotals$G_mu))
lines(density(samples$b_Intercept), col="red")
plot(density(pivotals$G_sigma2b))
lines(density(samples$sd_group__Intercept^2), col="red")
plot(density(pivotals$G_sigma2w))
lines(density(samples$sigma^2), col="red")
plot(density(pivotals$G_sigma2b+pivotals$G_sigma2w, from=0, to=200))
lines(density(samples$sd_group__Intercept^2+samples$sigma^2), col="red")
# assez nickel !
plot(pivotals$G_mu[1:2000], pivotals$G_sigma2w[1:2000])
points(samples$b_Intercept, samples$sigma^2, col="red")
library(lme4)
lfit <- lmer(y ~ (1|group), dat)
|
18bfee5f4f45bd6cc6888326d6e7c5a075a9998f
|
e434738314e1ac1c97247e5395e044357a7c0eb6
|
/tests/testthat/test_shiny_reactive.R
|
aad37bfb7e4198e399e5e4cc3019b5f6fcf7f137
|
[] |
no_license
|
edanen/cytomapper
|
39e1063ded2e339bbc31fe584c28727ef75f19b8
|
60c4685887a33072ffae2637eb69a9c0dfcdbb29
|
refs/heads/master
| 2022-12-16T16:52:42.941716
| 2020-09-13T16:41:11
| 2020-09-13T16:41:11
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 280
|
r
|
test_shiny_reactive.R
|
data("pancreasImages")
data("pancreasMasks")
data("pancreasSCE")
shiny::testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), {
session$setInputs(sample = 1,
plotCount = 1)
expect_equal(input$sample, 1)
})
|
3c564e548d5105772e119eafea45733869e111b0
|
1cd07d10573cbf1f63ee64d9634d29498437bc2d
|
/Intro File.R
|
a176e9f8d61271e9d4e5a6bf7ba5759f737d84b0
|
[] |
no_license
|
anshikadhingra/R-programming
|
405bc159d6bc069bcf540cbe6c6cd70d4d8c304d
|
6c88fc2c01e388dfb67379eb366956990d1c6ee4
|
refs/heads/main
| 2023-08-24T10:37:03.965081
| 2021-10-30T10:08:40
| 2021-10-30T10:08:40
| 303,133,108
| 3
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 131
|
r
|
Intro File.R
|
#Introduction
print("Welcome to Analytics Edge class")
a<-(print("Welcome to Analytics Edge class"))
a
X=1:5
Y=6:10
plot(X,Y)
|
d7d9d50aae8d05349c95fe22b389046d2b985339
|
6efad62861b49cb755623da49bd5725bcfe28e27
|
/LAB1/Part3/Shiny/ui.R
|
0dde9e90af6542399f07309b6ccdbef0821da84d
|
[] |
no_license
|
Keerthu-Baskar/Data-Intensive-Computing-UB-CSE587
|
a37a740bffa8a463e3d2a75e6588d608c8c2969b
|
e477d37df5376a28b35da5a09c5b066fd5a1f353
|
refs/heads/master
| 2020-07-07T17:03:58.668115
| 2020-01-01T21:13:59
| 2020-01-01T21:13:59
| 203,415,040
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 651
|
r
|
ui.R
|
library(shiny)
ui <- pageWithSidebar(
# App title ----
headerPanel("Twitter Vs CDC Flu"),
# Sidebar panel for inputs ----
wellPanel(
fluidRow(
column(3, selectInput("populatie", label = h4("Maps"),
choices = list("Twitter" = "tw", "CDC" = "cd", "CDC vs Twitter" = "cdt"),
selected = "tw"),
uiOutput("img1")), # here is the image
column(9, plotOutput("plot2"))
)
),
# Main panel for displaying outputs ----
mainPanel()
)
#ui <- fluidPage(
# titlePanel("My Shiny App"),
#tags$div(img(src = "rstudio.png"))
#)
|
04cf676a9d99412e0d81deb8b571abe12cd66f33
|
b5822b9c2a756f4e540c426e7e84af35dae8caec
|
/rockchalk/R/vech2mat.R
|
e8d273f19a1e596843944afa2b644dc7d5c828aa
|
[] |
no_license
|
pauljohn32/rockchalk
|
0c75b7a7bc142669efcfabbc70d511f60c3f47e0
|
fc2d3d04396bf89ef020e824f50db3c348e3e226
|
refs/heads/master
| 2022-08-20T02:49:56.898990
| 2022-07-26T01:20:12
| 2022-07-26T01:20:12
| 8,965,635
| 8
| 5
| null | 2022-07-18T00:36:58
| 2013-03-23T04:07:35
|
R
|
UTF-8
|
R
| false
| false
| 2,326
|
r
|
vech2mat.R
|
##' Convert a half-vector (vech) into a matrix.
##'
##' Fills a matrix from a vector that represents the lower triangle.
##' If user does not supply a value for diag, then the vech will fill
##' in the diagonal as well as the strictly lower triangle. If diag
##' is provided (either a number or a vector), then vech is for the
##' strictly lower triangular part. The default value for lowerOnly
##' is FALSE, which means that a symmetric matrix will be created. See
##' examples for a demonstration of how to fill in the lower triangle
##' and leave the diagonal and the upper triangle empty.
##'
##' @param vech A vector
##' @param diag Optional. A single value or a vector for the
##' diagonal. A vech is a strictly lower triangluar vech, it
##' does not include diagonal values. diag can be either a single
##' value (to replace all elements along the diagonal) or a vector of
##' the correct length to replace the diagonal.
##' @param lowerOnly Default = FALSE.
##' @seealso Similar functions exist in many packages, see
##' \code{vec2sm} in corpcor, \code{xpnd} in MCMCpack
##' @export
##' @examples
##' x <- 1:6
##' vech2mat(x)
##' vech2mat(x, diag = 7)
##' vech2mat(x, diag = c(99, 98, 97, 96))
##' vech2mat(x, diag = 0, lowerOnly = TRUE)
vech2mat <-
function(vech, diag = NULL, lowerOnly = FALSE)
{
## Must calculate correct number of rows from vech, if
## vech implies a non-square, stop.
## If no diag, then vech provides the diagonal values
if (!is.null(diag)){
d <- (sqrt(1 + 8 * length(vech)) + 1)/2
if (!as.integer(d) == d)
stop(deparse(substitute(vech)), " must have the correct number of elements to fill a stricly lower triangle.")
X <- matrix(0, nrow = d, ncol = d)
X[lower.tri(X, diag = FALSE)] <- vech
diag(X) <- makeVec(diag, d)
if (!lowerOnly) X[upper.tri(X)] <- t(X)[upper.tri(X)]
} else {
d <- (sqrt(1 + 8 * length(vech)) - 1)/2
if (!as.integer(d) == d)
stop(paste("You supplied diag. So ", deparse(substitute(vech)), " must have the correct number of elements to fill in a lower triangle, including the diagonal.."))
X <- matrix(0, nrow = d, ncol = d)
X[lower.tri(X, diag = TRUE)] <- vech
if (!lowerOnly) X[upper.tri(X)] <- t(X)[upper.tri(X)]
}
X
}
NULL
|
4dc463467a7548a15ee1d47024fe115402d1cad8
|
aae4c7348a09c650369468b0cc20d3e10cfaeb60
|
/GenericClustering.R
|
89e33d8f03232ef8111ddfc6fceb30aeef4a394a
|
[] |
no_license
|
Prashant0701/PracticeAnalytics
|
112f4100015ee5dbb6b405ffdfae76f1e4b23309
|
4a3c8b67cc1573785b82dea90a81aa2b4dc56329
|
refs/heads/master
| 2021-08-23T19:40:01.461759
| 2017-12-06T08:02:22
| 2017-12-06T08:02:22
| 113,286,117
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,591
|
r
|
GenericClustering.R
|
rm(list=ls())
library(caret)
library(RWeka)
set.seed(1234)
# separate data into test and train sets, 70/30 split in this case
splitIndex <- createDataPartition(iris$Species, p = 0.7, list = FALSE)
train <- iris[splitIndex, ]
test <- iris[-splitIndex, ]
testInd <- test[ ,!colnames(test) %in% "Species"]
testDep <- as.factor(test[, names(test) == "Species"])
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]
#First Model
jripFit1 <- train(TrainData, TrainClasses,method = "JRip")
jripFit1
plot(jripFit1)
#Second Model
jripFit2 <- train(TrainData, TrainClasses,method = "JRip",preProcess = c("center", "scale"),tuneLength = 10,trControl = trainControl(method = "cv"))
jripFit2
plot(jripFit2)
# K means
neighborCount=2
modelKNN <- knn3(Species ~ ., data = train, k = neighborCount, prob = TRUE)
predKNN <- predict(modelKNN, testInd, type = "prob")
confKNN <- confusionMatrix(testDep, predKNN)
#Another Round
km <- kmeans(iris[,1:4], 3)
plot(iris[,1], iris[,2], col=km$cluster)
points(km$centers[,c(1,2)], col=1:3, pch=19, cex=2)
table(km$cluster, iris$Species)
#Another Way
km2 <- kmeans(iris[,1:4], 3)
plot(iris[,1], iris[,2], col=km2$cluster)
points(km2$centers[,c(1,2)], col=1:3, pch=19, cex=2)
table(km2$cluster, iris$Species)
#heir
m <- matrix(1:15,5,3)
dist(m) # computes the distance between rows of m (since there are 3 columns, it is the euclidian distance between tri-dimensional points)
dist(m,method="manhattan") # using the manhattan metric
sampleiris <- iris[sample(1:150, 40),] # get samples from iris dataset
# each observation has 4 variables, ie, they are interpreted as 4-D points
distance <- dist(sampleiris[,-5], method="euclidean")
cluster <- hclust(distance, method="average")
plot(cluster, hang=-1, label=sampleiris$Species)
plot(as.dendrogram(cluster), edgePar=list(col="darkgreen", lwd=2), horiz=T)
str(as.dendrogram(cluster)) # Prints dendrogram structure as text.
cluster$labels[cluster$order] # Prints the row labels in the order they appear in the tree.
#Prune by cluster
par(mfrow=c(1,2))
group.3 <- cutree(cluster, k = 3) # prune the tree by 3 clusters
table(group.3, sampleiris$Species) # compare with known classes
plot(sampleiris[,c(1,2)], col=group.3, pch=19, cex=2.5, main="3 clusters")
points(sampleiris[,c(1,2)], col=sampleiris$Species, pch=19, cex=1)
group.6 <- cutree(cluster, k = 6) # we can prune by more clusters
table(group.6, sampleiris$Species)
plot(sampleiris[,c(1,2)], col=group.6, pch=19, cex=2.5, main="6 clusters")
points(sampleiris[,c(1,2)], col=sampleiris$Species, pch=19, cex=1) # the little points are the true classes
par(mfrow=c(1,1))
plot(cluster, hang=-1, label=sampleiris$Species)
abline(h=0.9,lty=3,col="red")
height.0.9 <- cutree(cluster, h = 0.9)
table(height.0.9, sampleiris$Species) # compare with known classes
plot(sampleiris[,c(1,2)], col=height.0.9, pch=19, cex=2.5, main="3 clusters")
points(sampleiris[,c(1,2)], col=sampleiris$Species, pch=19, cex=1)
# Calculate the dissimilarity between observations using the Euclidean distance
dist.iris <- dist(iris, method="euclidean")
# Compute a hierarchical cluster analysis on the distance matrix using the complete linkage method
h.iris <- hclust(dist.iris, method="complete")
h.iris
head(h.iris$merge, n=10)
plot(h.iris)
h.iris.heights <- h.iris$height # height values
h.iris.heights[1:10]
subs <- round(h.iris.heights - c(0,h.iris.heights[-length(h.iris.heights)]), 3) # subtract next height
which.max(subs)
# Cuts dendrogram at specified level and draws rectangles around the resulting clusters
plot(cluster); rect.hclust(cluster, k=6, border="red")
|
77acddf84a17de77233e433287d95029045b0990
|
29d34e3302b71d41d77af715727e963aea119392
|
/R/rtPalette.R
|
56f047c2616aeb710c80b9e0ee830dd9aa37fab2
|
[] |
no_license
|
bakaibaiazbekov/rtemis
|
1f5721990d31ec5000b38354cb7768bd625e185f
|
a0c47e5f7fed297af5ad20ae821274b328696e5e
|
refs/heads/master
| 2020-05-14T20:21:40.137680
| 2019-04-17T15:42:33
| 2019-04-17T15:42:33
| 181,943,092
| 1
| 0
| null | 2019-04-17T18:00:09
| 2019-04-17T18:00:09
| null |
UTF-8
|
R
| false
| false
| 12,824
|
r
|
rtPalette.R
|
# rtPalette.R
# ::rtemis::
# 2016 Efstathios D. Gennatas egenn.github.io
# Penn ====
#' rtemis Color Palettes
#'
#' \code{pennCol}: Penn color palette (http://www.upenn.edu/about/styleguide-color-type)
#' @name rtPalettes
#' @export
pennCol <- list(darkestBlue = "#000f3a",
darkerBlue = "#00144d",
blue = "#01256e",
lighterBlue = "#045ea7",
lightestBlue = "#82afd3",
darkestRed = "#57000a",
darkerRed = "#74000e",
red = "#95001a",
lighterRed = "#c2004d",
lightestRed = "#e180a6",
darkestYellow = "#af7f00",
darkerYellow = "#eaa900",
yellow = "#f2c100",
lighterYellow = "#f8de00",
lightestYellow = "#fcef80",
darkestGreen = "#005200",
darkerGreen = "#006e00",
green = "#008e00",
lighterGreen = "#00be00",
lightestGreen = "#80df80",
darkestOrange = "#812d00",
darkerOrange = "#ac3c00",
orange = "#c35a00",
lighterOrange = "#df9700",
lightestOrange = "#efcb80",
darkestPurple = "#23001f",
darkerPurple = "#2f0029",
purple = "#4a0042",
lighterPurple = "#890082",
lightestPurple = "#c480c1")
#' \code{pennPalette}: Subset of \code{pennCol}. This is the default palette of the \link{mplot3} family
#'
#' @name rtPalettes
#' @export
pennPalette <- pennCol[c("lighterBlue", "red", "green", "yellow", "lighterPurple", "orange",
"lightestBlue", "lighterRed", "lighterGreen", "lightestPurple",
"lighterOrange")]
#' \code{pennLightPalette}: Subset of \code{pennCol}. This is the lighter Penn palette for use with the dark themes
#' @name rtPalettes
#' @export
pennLightPalette <- pennCol[c("lightestBlue", "lightestRed", "lightestGreen",
"lightestYellow", "lightestPurple")]
# Imperial ====
#' Imperial Colors
#'
#' \code{imperialCol}: Imperial College London color palette (https://www.imperial.ac.uk/brand-style-guide/visual-identity/brand-colours/)
#'
#' @name rtPalettes
#' @export
imperialCol <- list(navy = "#002147",
imperialBlue = "#003E74",
lightGrey = "#EBEEEE",
coolGrey = "#9D9D9D",
lightBlue = "#D4EFFC",
blue = "#006EAF",
processBlue = "#0091D4",
poolBlue = "#00ACD7",
darkTeal = "#0F8291",
teal = "#009CBC",
seaglass = "#379f9f",
darkGreen = "#02893B",
kermitGreen = "#66A40A",
lime = "#BBCE00",
orange = "#D24000",
tangerine = "#EC7300",
lemonYellow = "#FFDD00",
brick = "#A51900",
red = "#DD2501",
cherry = "#E40043",
raspberry = "#9F004E",
magentaPink = "#C81E78",
iris = "#751E66",
violet = "#960078",
plum = "#321E6D",
purple = "#653098")
# UCSF ====
#' UCSF Colors
#'
#' \code{ucsfCol}: UCSF color palette (http://identity.ucsf.edu/color)
#'
#' @name rtPalettes
#' @export
ucsfCol <- list(navy = "#052049",
teal = "#18A3AC",
green = "#90BD31",
blue = "#178CCB",
orange = "#F48024",
purple = "#716FB2",
red = "#EC1848",
yellow = "#FFDD00",
iTeal = "#058488",
iGreen = "#6EA400",
iBlue = "#007CBE",
iOrange = "#F26D04",
iRed = "#EB093C")
#' UCSF Color Palette
#'
#' \code{ucsfPalette}: Subset of \code{ucsfCol} for use with \link{mplot3}, etc
#'
#' @name rtPalettes
#' @export
# ucsfPalette <- ucsfCol[c("iTeal", "iRed", "iBlue", "yellow", "purple", "iOrange", "iGreen")]
ucsfPalette <- ucsfCol[c("teal", "orange", "blue", "yellow", "purple", "red", "navy", "green")]
# Berkeley ====
#' Berkeley Colors
#'
#' \code{berkeleyCol}: Berkeley color palette (https://brand.berkeley.edu/colors/)
#'
#' @name rtPalettes
#' @export
berkeleyCol <- list(berkeleyBlue = "#003262",
foundersRock = "#3B7EA1",
californiaGold = "#FDB515",
medalist = "#C4820E",
wellmanTile = "#D9661F",
roseGarden = "#EE1F60",
goldenGate = "#ED4E33",
southHall = "#6C3302",
bayFog = "#DDD5C7",
lawrence = "#00B0DA",
lapLane = "#00A598",
pacific = "#46535E",
satherGate = "#B9D3B6",
ion = "#CFDD45",
soyBean = "#859438",
stonePine = "#584F29",
grey = "#EEEEEE",
webGrey = "#888888")
# Stanford ====
#' Stanford Colors
#'
#' \code{stanfordCol}: Stanford color palette (https://identity.stanford.edu/color.html#digital-color)
#'
#' @name rtPalettes
#' @export
stanfordCol <- list(cardinal = "#8c1515",
coolGrey = "#4d4f53",
birghtRed = "#B1040E",
chocolate = "#2F2424",
stone = "#544948",
fog = "#F4F4F4",
lightSandstone = "#F9F6EF",
sandstone = "#d2c295",
warmGrey = "#3f3c30",
beige = "#9d9573",
lightSage = "#c7d1c5",
clay = "#5f574f",
cloud = "#dad7cb",
driftwood = "#b6b1a9",
sandhill = "#b3995d",
paloAlto = "#175e54",
teal = "#00505c",
purple = "#53284f",
redwood = "#8d3c1e",
brown = "#5e3032",
sky = "#0098db",
lagunita = "#007c92",
mint = "#009b76",
gold = "#b26f16",
sun = "#eaab00",
poppy = "#e98300")
# USF ====
#' USF Colors
#'
#' \code{usfCol}: USF color palette (https://myusf.usfca.edu/marketing-communications/resources/graphics-resources/brand-standards/color-palette)
#' Color conversions performed using https://www.pantone.com/color-finder/
#' @name rtPalettes
#' @export
usfCol <- list(green = "#205C40",
yellow = "#ffb81c",
gray = "#75787B")
# UC San Diego ====
#' UC San Diego Colors
#'
#' \code{ucsdCol}: UC San Diego color palette (https://ucpa.ucsd.edu/brand/elements/color-palette/)
#' @name rtPalettes
#' @export
ucsdCol <- list(blue = "#182B49",
mediumBlue = "#006A96",
gold = "#C69214",
yellow = "#FFCD00",
cyan = "#00C6D7",
green = "#6E963B",
lightYellow = "#F3E500",
orange = "#FC8900",
coolGray = "#747678",
lightGray = "#B6B1A9",
darkGold = "#84754E")
# UCLA ====
#' UCLA Colors
#'
#' \code{uclaCol}: UCLA color palette (http://brand.ucla.edu/identity/colors)
#' @name rtPalettes
#' @export
uclaCol <- list(blue = "#2774AE",
gold = "#FFD100",
darkestBlue = "#003B5C",
darkerBlue = "#005587",
lighterBlue = "#8BB8E8",
lightestBlue = "#C3D7EE",
darkestGold = "#FFB81C",
darkerGold = "#FFC72C",
yellow = "#FFFF00",
green = "#00FF87",
magenta = "#FF00A5",
cyan = "#00FFFF",
purple = "#8237FF")
# University of California ====
#' University of California Colors
#'
#' \code{ucCol}: University of California color palette
#' (http://brand.universityofcalifornia.edu/guidelines/color.html#!primary-colors)
#' @name rtPalettes
#' @export
ucCol <- list(ucBlue = "#1295D8",
ucGold = "#FFB511",
blue = "#005581",
lightBlue = "#72CDF4",
gold = "#FFD200",
lightgold = "#FFE552",
orange = "#FF6E1B",
lightOrange = "#FF8F28",
pink = "#E44C9A",
lightPink = "#FEB2E0",
teal = "#00778B",
lightTeal = "#00A3AD",
ucGray = "#7C7E7F",
warmGray8 = "#8F8884",
warmGray3 = "#BEB6AF",
warmGray1 = "#DBD5CD",
metallicGold = "#B4975A")
# Washington ====
#' University of Washington Colors
#'
#' \code{uwCol}: University of Washington color palette
#' (http://www.washington.edu/brand/graphic-elements/primary-color-palette/)
#' @name rtPalettes
#' @export
uwCol <- list(purple = "#4b2e83",
gold = "#b7a57a",
metallicGold = "#85754d")
# NIH ====
#' NIH Colors
#'
#' \code{nihCol}: NIH color palette (https://www.nlm.nih.gov/about/nlm_logo_guidelines_030414_508.pdf)
#' @name rtPalettes
#' @export
nihCol <- list(blue = "#20558a",
gray = "#616265")
# Apple ====
#' Apple Colors
#'
#' \code{appleCol}: Apple Human Interface Guidelines color palette
#' (https://developer.apple.com/design/human-interface-guidelines/ios/visual-design/color/)
#' @name rtPalettes
#' @export
appleCol <- list(red = "#FF3B30",
orange = "#FF9500",
yellow = "#FFCC00",
green = "#4CD964",
tealBlue = "#5AC8FA",
blue = "#007AFF",
purple = "#5856D6",
pink = "#FF2D55")
# Google ====
#' Google Colors
#'
#' \code{googleCol}: Google brand palette (https://brandpalettes.com/google-colors/)
#' @name rtPalettes
#' @export
googleCol <- list(blue = "#4285F4",
red = "#DB4437",
yellow = "#F4B400",
green = "#0F9D58")
# Amazon ====
#' Amazon Colors
#'
#' \code{amazonCol}: Amazon brand palette
#' (https://images-na.ssl-images-amazon.com/images/G/01/AdvertisingSite/pdfs/AmazonBrandUsageGuidelines.pdf)
#' @name rtPalettes
#' @export
amazonCol <- list(orange = "#FF9900",
blue = "#146EB4")
# Microsoft ====
#' Microsoft Colors
#'
#' \code{microsoftCol}: Microsoft brand palette
#' (https://brandcolors.net/b/microsoft)
#' @name rtPalettes
#' @export
microsoftCol <- list(orange = "#f65314",
green = "#7cbb00",
blue = "#00a1f1",
yellow = "#ffbb00")
rtPalettes <- list(pennCol = pennCol,
imperialCol = imperialCol,
ucsfCol = ucsfCol,
ucsfPalette = ucsfPalette,
berkeleyCol = berkeleyCol,
stanfordCol = stanfordCol,
usfCol = usfCol,
ucsdCol = ucsdCol,
uclaCol = uclaCol,
uwCol = uwCol,
nihCol = nihCol,
appleCol = appleCol,
googleCol = googleCol,
amazonCol = amazonCol,
microsoftCol = microsoftCol)
#' \pkg{rtemis} Color Palettes
#'
#' \code{rtPalettes} prints names of available color palettes
#' Each palette is a named list of hexadecimal color definitions which can be used with any
#' graphics function.
#' @param palette String: Name of palette to return. Default = NULL: available palette names
#' are printed and no palette is returned
#' @return
#' A list of available palettes, invisibly
#' @examples
#' rtPalette()
#' @export
rtPalette <- function(palette = NULL) {
if (is.null(palette)) {
msg(crayon::cyan("The following palettes are available:"))
print(paste(c("pennCol", "imperialCol", "ucsfCol", "berkeleyCol",
"stanfordCol", "ucsdCol", "uclaCol", "usfCol", "nihCol")))
} else {
palette <- match.arg(palette,
c("pennCol", "imperialCol", "ucsfCol", "ucsfPalette",
"berkeleyCol", "stanfordCol", "usfCol", "ucsdCol", "uclaCol", "nihCol"))
rtPalettes[[palette]]
}
} # rtemis::rtPalettes
# Custom crayon styles ====
teal <- make_style(teal = "#18A3AC")
rtBlue <- make_style(rtBlue = "#005581")
rtOrange <- make_style(rtOrange = "#F48024")
rtHighlight.color <- getOption("rt.highlight.color", "#18A3AC")
rtHighlight <- make_style(rtHighlight = rtHighlight.color)
|
fa1c455d6a4528c64bf0307ed2ca3e1ab7565075
|
11615afb4c52f1acbf0b5c07501bdc0fdff76e4d
|
/run_analysis.R
|
1e580cda32060eb3de3e11cf5c24ae0ffa3e0aea
|
[] |
no_license
|
michaelpboyle/Proj03GetClean
|
e37290c9f3a4ddcae432f6bd6bdf8701b6c2961d
|
db158694b19c289d2ab02aea0d0e2705fef77d4c
|
refs/heads/master
| 2021-01-18T15:14:47.450345
| 2015-04-25T21:49:55
| 2015-04-25T21:49:55
| 34,489,533
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 5,451
|
r
|
run_analysis.R
|
####################################################################################################
## Get Data
####################################################################################################
## Download files (Using wget method for Linux OS)
projUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(projUrl,destfile = "./03Data/projData.zip",method="wget")
dateDownloaded <- date()
print(dateDownloaded) ## Record download date
## Unzip (extract) files
unzip("./03Data/projData.zip",list=FALSE)
####################################################################################################
####################################################################################################
## Read features.txt and modify values to remove punctuation marks. Keeping names mixed-case
## for better readability.
## Resulting vector "features" will be used as column names for
## test and train dataframes.
####################################################################################################
## Requirement #4
## Appropriately labels the data set with descriptive variable names.
featureData <- read.table("./UCI HAR Dataset/features.txt")
features <- gsub("[[:punct:]]", "", featureData$V2)
####################################################################
## Create df_test (Test dataset "dataframe")
####################################################################
## Read in 3 tables making up test data.
testData <- read.table("./UCI HAR Dataset/test/X_test.txt"
,col.names=features)
testSubjData <- read.table("./UCI HAR Dataset/test/subject_test.txt"
,col.names='Subject')
testActvtyData <- read.table("./UCI HAR Dataset/test/y_test.txt"
,col.names='Activity')
## Combine 3 dataframes so that we have columns:
## Subject, Activity, followed by 561 features
testDescription <- cbind(testSubjData,testActvtyData)
str(testDescription)
table(testDescription)
df_test <- cbind(testDescription,testData)
####################################################################
####################################################################
## Create df_train (Train dataset "dataframe")
####################################################################
## Read in 3 tables making up train data.
trainData <- read.table("./UCI HAR Dataset/train/X_train.txt"
,col.names=features)
trainSubjData <- read.table("./UCI HAR Dataset/train/subject_train.txt"
,col.names='Subject')
trainActvtyData <- read.table("./UCI HAR Dataset/train/y_train.txt"
,col.names='Activity')
## Combine 3 dataframes so that we have columns:
## Subject, Activity, followed by 561 features
trainDescription <- cbind(trainSubjData,trainActvtyData)
str(trainDescription)
table(trainDescription)
df_train <- cbind(trainDescription,trainData)
####################################################################
################################################################################
## Create df_HAR (Human Activity Recognition dataframe).
################################################################################
## Requirement #1
## Merge (append) the training and the test sets to create one data set.
df_HAR <- rbind(df_train,df_test)
## Requirement #2
## Extract only the measurements on the mean and standard deviation for each measurement.
## Merge (append) the training and the test sets to create one data set.
f_keep <- features[grep("mean|std",features)]
df_HAR1 <- df_HAR[,c("Subject","Activity",f_keep)]
################################################################################
################################################################################
## Add ActivityDescription field to dataset
################################################################################
## Requirement #3
## Use descriptive activity names to name the activities in the data set
activityDescription <- read.table("./UCI HAR Dataset/activity_labels.txt"
,col.names=c('Activity','ActivityDescription'))
df_HAR2 <- merge(df_HAR1,activityDescription,by.x="Activity",by.y="Activity",all=FALSE)
################################################################################
## Remove large temporary dataframes
rm(testData)
rm(trainData)
rm(df_train)
rm(df_test)
rm(df_HAR)
################################################################################
## Summarize dataset on Activity and Subject, Provide means for remaining variables.
################################################################################
## Requirement #5
## From the data set in step 4, creates a second, independent tidy data set with the average
## of each variable for each activity and each subject.
averages_HCI <- aggregate(by=list(Activity=df_HAR2$ActivityDescription
,Subject=df_HAR2$Subject)
,df_HAR2[,f_keep],FUN="mean")
str(averages_HCI) ## Show structure of final dataset
###########################################################################################
## Upload the tidy data set created in step 5 of the instructions as a txt file
## with write.table() using row.name=FALSE
write.table(averages_HCI,file="./averages_HCI.txt",row.name=FALSE)
q()
|
99de9dc45d6cc75a7a8b890bbe4e96602951386b
|
04c91c0754a70d50c8e82dcd1378ecc77ee5b1f7
|
/positive_test.R
|
4feaf66faac9913d1478bdd3eba569358d77498a
|
[] |
no_license
|
jbsalomond/BaDEst-Test
|
d28878b1b9f506286f20475457a02a75f10a6f73
|
abe6760c5ddb82c3b4637fe66fc162f52fac7ea8
|
refs/heads/master
| 2021-03-24T13:32:53.946583
| 2017-07-06T11:38:07
| 2017-07-06T11:38:07
| 78,019,719
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,147
|
r
|
positive_test.R
|
library(dplyr)
library(MASS)
library(invgamma)
library(pbapply)
library(parallel)
cl = cl <- makeCluster(10)
clusterExport(cl, ls())
clusterEvalQ(cl, library(MASS))
clusterEvalQ(cl, library(dplyr))
clusterEvalQ(cl, library(invgamma))
clusterEvalQ(cl, library(pbapply))
# Function H
H = function(omega){
(- min(omega))
}
# sampling k
## Computing the log prob
Cxk = function(k,x,y,prior,alpha,beta,mu,lambda){
n = length(x)
TDC = mutate(data.frame(x), I = cut(x,breaks = seq(0,k)/k,right = F),Z = 1,Y = y)
Yi2 = aggregate(Y ~ I, function(z){sum((z - mean(z))^2)},data = TDC)$Y
#Yim = aggregate(Y ~ I, mean,data = TDC)$Y
ni = aggregate(Z ~ I, sum,data = TDC)$Z
#Y3 = ni*mu*(Yim - m)^2/(ni + mu)
#beta = 2*mean(Yi)
btilde = beta + 0.5*sum(Yi2) #+ 0.5*sum(Y3)
if(prior == "pois"){ logprior = dpois(k-2,lambda,log = T) }
if(prior == "geom"){ logprior = dgeom(k-1,lambda,log = T) }
lpik = -log(btilde)*(alpha + n/2) - 0.5*sum(log(ni + mu)) + 0.5*k*log(mu/(2*pi)) +
logprior
return(lpik)
}
# sample k
sampleK00 = function(x,y,prior,alpha,beta,mu,lambda,Kmax,Nk){
kliste = seq(2,Kmax)
logpik = sapply(X = kliste,FUN = Cxk,
x = x,y = y,prior = prior,alpha = alpha,beta = beta,mu = mu,lambda = lambda)
pik = exp(logpik)/sum(exp(logpik))
sample(2:Kmax,prob = pik,replace = T,size = Nk)
}
sampleK = function(x,y,prior,alpha,beta,mu,lambda,Kmax,Nk){
k0 = ceiling(sqrt(length(x)))+2
kliste = seq(2,k0)
logpik = sapply(X = kliste,FUN = Cxk,
x = x,y = y,prior = prior,alpha = alpha,beta = beta,mu = mu,lambda = lambda)
test = TRUE
i = k0
while(test){
logpik = c(logpik,Cxk(k = i+1,x=x,y=y,prior = prior,alpha = alpha,beta = beta,mu=mu,lambda = lambda))
#print(length(logpik)-k0)
#print((logpik[i] - max(logpik)))
test = (logpik[i] - max(logpik)) > -300
#print(test)
test = test&(i<Kmax)
i = i+1
}
center = logpik - max(logpik)
#print(center)
pik = exp(center)/sum(exp(center))
sample(2:i,prob = pik,replace = T,size = Nk)
}
test = function(x,y,prior,alpha,beta,mu,lambda,Kmax,Nk,M0,verbose=F,autoM0 = F){
# Sample k first
n = length(x)
k = sampleK(x,y,prior,alpha,beta,mu,lambda,Kmax,Nk)
df = data.frame(k = k, ni = 1)
ktable = aggregate(ni~k,data = df,FUN = sum)
#print(ktable)
K = dim(ktable)[1]
out = rep(0,K)
if(verbose) print(ktable)
for(i in 1:K){
j = ktable[i,1]
nj = ktable[i,2]
outk = rep(0,nj)
TDC = mutate(data.frame(x), I = cut(x,breaks = seq(0,j)/j,right = F),Z = 1,Y = y)
Yi = aggregate(Y~I, data = TDC, mean)$Y
ni = aggregate(Z~I, data = TDC, sum)$Z
Yi2 = aggregate(Y ~ I, function(z){sum((z - mean(z))^2)},data = TDC)$Y
#Yim = aggregate(Y ~ I, mean,data = TDC)$Y
#Y3 = ni*mu*(Yim - m)^2/(ni + mu)
btilde = beta + 0.5*sum(Yi2) #+ 0.5*sum(Y3)
sigma = rinvgamma(nj,shape = alpha + n/2, rate = btilde)
#print(median(sqrt(sigma)))
if(verbose) print(median(sigma))
for(l in 1:nj){
if(autoM0) tau = 2*M0*sqrt(log(n/j))*(sqrt(j*median(sigma)/(n+j*mu)))
else tau = (M0*sqrt(j*median(sigma)*log(n)/(n)))
postmean = Yi
postvar = sigma[l]/(ni + mu)
omega = rnorm(n = j, m = postmean,sd = sqrt(postvar))
#print(j)
#print(omega)
#print(H(omega))
#print(tau)
outk[l] = H(omega)>tau
}
out[i] = sum(outk)
}
return(sum(out)/Nk>=0.5)
}
run = function(n,sd = 0.1,verbose = T,autoM0 = F,prior = "geom",lambda = 0.4,M0=1,mu=1,Kmax=100){
sd = 0.1
ro = 6*(log(n)/n)^(1/3)
f = function(x){
ro*(abs(x-0.5)-0.1)*(abs(x-0.5)<0.1)
}
X = seq(0,1,length = n+1)[-(n+1)]
y = f(X) + rnorm(n,sd = sd)
return(test(X,y,prior,alpha = 100, beta = .5,mu = mu,lambda = lambda,Kmax= Kmax,Nk = 2500,M0 = M0,verbose = verbose,autoM0 = autoM0) )
}
positive = function(spsize){
mean(replicate(run(n = spsize,
verbose = F,prior = "geom",lambda = .4,M0 = 1,mu = 1,autoM0 = T),n = 250))
}
positive(100)
nlist = c(100,250,500,1000,2500)
clusterExport(cl, ls())
start.time = proc.time()
resutl <- parLapply(cl,nlist,positive)
print(proc.time() - start.time)
t(unlist(resutl))
|
765754535c42758b0d89335ea2c43bfda287c85b
|
80013fca745bae4e41b2d185e82ffa3e602baab2
|
/SGP_CONFIG/configToSGPNormGroup.R
|
bf160ae0c5bdf89250141301067e10219aa10f6e
|
[] |
no_license
|
CenterForAssessment/Washington
|
cc581196abf1aa292a987e5302d7c650a7941f60
|
38057ecb2f9ce15626984d7e528f86c92e437e46
|
refs/heads/master
| 2023-08-16T18:28:01.700164
| 2023-08-10T19:22:46
| 2023-08-10T19:22:46
| 5,937,904
| 1
| 0
| null | 2014-08-13T10:44:18
| 2012-09-24T17:15:35
|
R
|
UTF-8
|
R
| false
| false
| 3,539
|
r
|
configToSGPNormGroup.R
|
###################################################################################################
###
### Script to convert SGP configurations for EOCT analyses to SGP_NORM_GROUP preference tables
###
###################################################################################################
### Load packages
require("data.table")
options(error=recover)
### utility function
configToSGPNormGroup <- function(sgp.config) {
if ("sgp.norm.group.preference" %in% names(sgp.config)) {
tmp.data.all <- data.table()
for (g in 1:length(sgp.config$sgp.grade.sequences)) {
l <- length(sgp.config$sgp.grade.sequences[[g]])
tmp.norm.group <- paste(tail(sgp.config$sgp.panel.years, l), paste(tail(sgp.config$sgp.content.areas, l), unlist(sgp.config$sgp.grade.sequences[[g]]), sep="_"), sep="/") #tmp.norm.group.baseline <-
tmp.data <- data.table(
SGP_NORM_GROUP=paste(tmp.norm.group, collapse="; "),
# SGP_NORM_GROUP_BASELINE=paste(tmp.norm.group.baseline, collapse="; "),
PREFERENCE= sgp.config$sgp.norm.group.preference*100)
if (length(tmp.norm.group) > 2) {
for (n in 1:(length(tmp.norm.group)-2)) {
tmp.data <- rbind(tmp.data, data.table(
SGP_NORM_GROUP=paste(tail(tmp.norm.group, -n), collapse="; "),
# SGP_NORM_GROUP_BASELINE=paste(tmp.norm.group.baseline, collapse="; "),
PREFERENCE= (sgp.config$sgp.norm.group.preference*100)+n))
}
}
tmp.data.all <- rbind(tmp.data.all, tmp.data)
}
return(unique(tmp.data.all))
} else {
return(NULL)
}
}
### Load and create 2012_2013 EOC Configuration
source("EOCT/2010_2011/MATHEMATICS.R")
source("EOCT/2011_2012/MATHEMATICS.R")
# source("EOCT/2010_2011/BIOLOGY.R") # BIO starts in 2012
source("EOCT/2011_2012/BIOLOGY.R") # not calculating Science or Biology in 2013, but leave this in for WA_SGP_Norm_Group_Preference.Rdata
source("EOCT/2012_2013/MATHEMATICS.R")
WA_EOCT_2010_2011.config <- c(
EOC_MATHEMATICS_1.2010_2011.config,
EOC_MATHEMATICS_2.2010_2011.config)
WA_EOCT_2011_2012.config <- c(
EOC_MATHEMATICS_1.2011_2012.config,
EOC_MATHEMATICS_2.2011_2012.config,
BIOLOGY.2011_2012.config)
WA_EOC_2012_2013.config <- c(
EOC_MATHEMATICS_1.2012_2013.config,
EOC_MATHEMATICS_2.2012_2013.config)
### Create configToNormGroup data.frame
tmp.configToNormGroup <- lapply(WA_EOCT_2010_2011.config, configToSGPNormGroup)
WA_SGP_Norm_Group_Preference_2010_2011 <- data.table(
YEAR="2010_2011",
rbindlist(tmp.configToNormGroup))
tmp.configToNormGroup <- lapply(WA_EOCT_2011_2012.config, configToSGPNormGroup)
WA_SGP_Norm_Group_Preference_2011_2012 <- data.table(
YEAR="2011_2012",
rbindlist(tmp.configToNormGroup))
tmp.configToNormGroup <- lapply(WA_EOC_2012_2013.config, configToSGPNormGroup)
WA_SGP_Norm_Group_Preference_2012_2013 <- data.table(
YEAR="2012_2013",
rbindlist(tmp.configToNormGroup))
WA_SGP_Norm_Group_Preference <- rbind(
WA_SGP_Norm_Group_Preference_2010_2011,
WA_SGP_Norm_Group_Preference_2011_2012,
WA_SGP_Norm_Group_Preference_2012_2013
)
WA_SGP_Norm_Group_Preference$SGP_NORM_GROUP <- as.factor(WA_SGP_Norm_Group_Preference$SGP_NORM_GROUP)
### Save result
setkey(WA_SGP_Norm_Group_Preference, YEAR, SGP_NORM_GROUP)
save(WA_SGP_Norm_Group_Preference, file="WA_SGP_Norm_Group_Preference.Rdata")
|
3b3caf8386ba4fe774b8ea4f230b86b19f77e9cb
|
f095bf142246d6d1785e419333aa6b06453b92e8
|
/Get & Clean Data/week4.R
|
09fe40b73ea47fc8c2e604e07e1a47af60403da3
|
[] |
no_license
|
mornkey/datasciencecoursera
|
82f0fc087b7d441240e04e649332d18b1f46674a
|
6951a03bafca8dad2b77aceb294936e38d84d572
|
refs/heads/master
| 2020-05-27T06:28:49.788246
| 2019-08-07T08:28:34
| 2019-08-07T08:28:34
| 188,522,360
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 2,817
|
r
|
week4.R
|
# fixing character variables
URL <- 'https://data.baltimorecity.gov/api/views/dz54-2aru/rows.csv?accessType=DOWNLOAD'
download.file(URL,destfile = './camera.csv',method = 'curl')
data <- read.csv('camera.csv')
names(data)
tolower(names(data)) # convert characters to lowercases
toupper(names(data))# convert characters to uppercases
x <-strsplit(names(data),'\\.')
x[6] # split 'location.1' to 'location' and '1' , output as list
x[[6]][1] # call the first string which is 'location'
x[[6]][2]
# useful string functions
library(stringr)
nchar('nontapat sumalnop') # 17
nchar('nontapatsumalnop') # 16, spaces also counted as 1 cha
substr('nontapat sumalnop',1,8) # "nontapat", take out only pos 1:8
paste('nontapat','sumalnop',sep = '-') # "nontapat-sumalnop"
paste0('nontapat','sumalnop') # "nontapatsumalnop" , default to no space
str_trim(' nontapat ') # "nontapat" , trim out extra spaces at the beginning and the end
# names of variables should be !
# lower cases when possible, easy to read and understand, no duplicate, dont have characters like _ . , or white spaces
# variables with characer values : should be made into factor variables
### working with DATES
d <- date() # gives date and time : "Wed Jul 17 13:44:23 2019"
class(d) # character
d2 <- Sys.Date() # gives only date : "2019-07-17"
class(d2) # class : Date
# formatting dates
# %d : day as number(0-31), %a : abbreviated weekday (Mon,Wed etc.), %A : unabbreviated weekday
# %m : month as number (00-12), %b : abbreviated month, %B : unabbreviated month
# %y : 2 digits year, %Y : four digits year
format(d2,'%a%b%d') # can use with or without spaces
format(d2,'%a %d %b %Y')
# take vector of strings and convert as date
x <- c('1jan1960','2jan1995','27oct1997');z <- as.Date(x,'%d%b%Y') # as.Date takes input as pattern of text you want to convert but the output is formatted in the form of %Y-%m-%d
format(z,'%a %d %b %Y') # take output from as.Date as input of format()
x <- c('1jan1960','2jan1995','27oct1997');z <- as.Date(x,'%d%b%Y');format(z,'%a %d %b %Y')
y <- c('1-oct-2001','7-aug-1997');z <- as.Date(y,'%d-%b-%Y');format(z,'%a %d %b %Y')
# date objects can perform these operation like ...
z[1]-z[2] # Time difference of 1516 days
as.numeric(z[1]-z[2]) # 1516
z[2]-z[1] # Time difference of -1516 days
as.numeric(z[2]-z[1]) # -1516
# converting to Julian ...
weekdays(z)
months(z)
julian(z) # number of days since the origin
# Lubridate package
library(lubridate)
x <- c('2014.01.08','2010-10-22'); ymd(x) # any . / - or spaces will no longer troubles you anymore
x <- c('08/04/1989');mdy(x)
x <- c('27-10-1997','07 08 1997') ; dmy(x)
x <- Sys.time();ymd_hms(x)
ymd_hms('2011 08 03 10:15:03',tz='Asia/Bangkok') # can also set time zone
Sys.timezone() # find your time zone
# Free Data Resource : see in dir
|
b5ef122606879f8d2a82cf7f1585beb3d0f758af
|
4755427593f4e0f5a162640d6de1041110e63763
|
/cursus/data/matrix.R
|
5885404eb0c286c7a45517bdc03bce211be0f10b
|
[] |
no_license
|
HoGentTIN/onderzoekstechnieken-cursus
|
5e642d984ab422f1d001984463f0e693f89e9637
|
bd7e61aa8d2a0a4525de82774568954c76dd33ae
|
refs/heads/master
| 2022-06-28T05:09:34.694920
| 2022-06-21T13:35:59
| 2022-06-21T13:35:59
| 80,239,413
| 21
| 59
| null | 2020-05-25T06:56:06
| 2017-01-27T19:35:24
|
HTML
|
UTF-8
|
R
| false
| false
| 485
|
r
|
matrix.R
|
> A = matrix(
+ c(2, 4, 3, 1, 5, 7), # the data elementen
+ nrow=2, # aantal rijen
+ ncol=3, # aantal kolommen
+ byrow = TRUE) # vul de matrix aan per rij
> A # print de matrix
[,1] [,2] [,3]
[1,] 2 4 3
[2,] 1 5 7
> A[2, 3] # element op 2de rij, 3de kolom
[1] 7
> A[2, ] # de 2de rij
[1] 1 5 7
> A[ ,c(1,3)] # de eerste en de derde kolom
[,1] [,2]
[1,] 2 3
[2,] 1 7
|
7846b63dc7d37468734c5ffc4816be4b5390d56b
|
86772a78af6ca3567ed333c9a4cd68c5af73848d
|
/examples/Digits recognition 10 logistics/all_digits_new.r
|
861e1a4747a4f54969902f4cf8f28a0024f4a9ab
|
[] |
no_license
|
aliaksah/EMJMCMC2016
|
077170db8ca4a21fbf158d182f551b3814c6c702
|
3954d55fc45296297ee561e0f97f85eb5048c39e
|
refs/heads/master
| 2023-07-19T16:52:43.772170
| 2023-07-15T16:05:37
| 2023-07-15T16:05:37
| 53,848,643
| 17
| 5
| null | 2021-11-25T14:53:35
| 2016-03-14T10:51:06
|
R
|
UTF-8
|
R
| false
| false
| 6,754
|
r
|
all_digits_new.r
|
source("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/R/the_mode_jumping_package4.r")
estimate.glm.cpen.slow <- function(formula, data, family, logn,r = 0.1,relat =c("gone","gthird","sigmoid","tanh","atan","erf","gfifth","grelu"))
{
capture.output({out <- glm(family = family,formula = formula,data = data)})
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
sj<-2*(stri_count_fixed(str = fmla.proc[2], pattern = "*"))
sj<-sj+1*(stri_count_fixed(str = fmla.proc[2], pattern = "+"))
for(rel in relat)
sj<-sj+2*(stri_count_fixed(str = fmla.proc[2], pattern = rel))
mlik = ((-out$deviance +2*log(r)*sum(sj)))/2
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + logn*out$rank),summary.fixed =list(mean = coefficients(out))))
}
estimate.glm.cpen <- function(formula, data, family, logn,r = 0.1,relat =c("gone","gthird","sigmoid","tanh","atan","erf","gfifth","grelu"))
{
#capture.output({out <- speedglm::speedglm(family = family,formula = formula,data = data)})
capture.output({out <- glm(family = family,formula = formula,data = data)})
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
sj<-2*(stri_count_fixed(str = fmla.proc[2], pattern = "*"))
sj<-sj+1*(stri_count_fixed(str = fmla.proc[2], pattern = "+"))
for(rel in relat)
sj<-sj+2*(stri_count_fixed(str = fmla.proc[2], pattern = rel))
mlik = ((-out$deviance +2*log(r)*sum(sj)))/2
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + logn*out$rank),summary.fixed =list(mean = coefficients(out))))
}
parall.gmj <<- mclapply
digits <- read.table(file = "train.csv",sep = ",",header = T,fill=TRUE)
digits$Y1<-as.integer(digits$label==1)
digits$Y2<-as.integer(digits$label==2)
digits$Y3<-as.integer(digits$label==3)
digits$Y4<-as.integer(digits$label==4)
digits$Y5<-as.integer(digits$label==5)
digits$Y6<-as.integer(digits$label==6)
digits$Y7<-as.integer(digits$label==7)
digits$Y8<-as.integer(digits$label==8)
digits$Y9<-as.integer(digits$label==9)
digits$Y10<-as.integer(digits$label==10)
#digits<-digits[,-which(colSums(digits)==0)]
g<-function(x)
{
return((x = 1/(1+exp(-x))))
}
index <- sample.int(n = dim(digits)[1],size = dim(digits)[1]*0.025,replace = F)
test <- digits[-index, ]
train <- digits[index, ]
data.example <- as.data.frame(train,stringsAsFactors = T)
runpar<-function(vect)
{
set.seed(as.integer(vect[23]))
do.call(runemjmcmc, vect[1:22])
ppp<-mySearch$post_proceed_results_hash(hashStat = hashStat)
ppp$p.post
Nvars<-mySearch$Nvars
linx <-mySearch$Nvars+4
lHash<-length(hashStat)
mliks <- values(hashStat)[which((1:(lHash * linx)) %% linx == 1)]
betas <- values(hashStat)[which((1:(lHash * linx)) %% linx == 4)]
cterm<-max(values(hashStat)[1,],na.rm = T)
post.populi<-sum(exp(values(hashStat)[1,][1:1000]-cterm),na.rm = T)
for(i in 1:(Nvars-1))
{
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (4+i))])
}
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (0))])
t<-system.time({
res<-mySearch$forecast.matrix.na(link.g = g, covariates = (vect$test),betas = betas,mliks.in = mliks)$forecast
})
rm(betas)
rm(mliks)
clear(hashStat)
rm(hashStat)
return(list(p.post = ppp$p.post, fparam = mySearch$fparam, res = res, post.populi = post.populi, cterm = cterm))
}
gc()
gone<-function(x)as.integer(x>1)
gthird<-function(x)as.integer(abs(x)^(1/3))
gfifth<-function(x)as.integer(abs(x)^(1/5))
grelu<-function(x)as.integer(x*(x>0))
total = array(0,dim = c(10,10,3))
M<-32
for(dig in 4:1)
{
print(dig)
idss<-which(abs(cor(x = train[1:785],y=train[785+dig]))>0.01)
formula1 = as.formula(paste(colnames(train)[785+dig],"~ 1 +",paste0(colnames(train)[idss][-1],collapse = "+")))
vect<-list(formula = formula1,data = data.example,estimator =estimate.glm.cpen,estimator.args = list(data = data.example,family = binomial(), logn = log(dim(train)[1]),r=exp(-0.5)),recalc_margin = 95,locstop=T,presearch=F,save.beta = T,interact = T,relations = c("gone","gthird","sigmoid","tanh","atan","erf","gfifth","grelu"),relations.prob =c(0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1),interact.param=list(allow_offsprings=3,mutation_rate = 100,last.mutation=2500, max.tree.size = 20, Nvars.max =200,p.allow.replace=0.7,p.allow.tree=0.5,p.nor=0.3,p.and = 0.7),n.models = 30000,unique =F,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 100,print.freq = 1000,advanced.param = list(
max.N.glob=as.integer(10),
min.N.glob=as.integer(5),
max.N=as.integer(3),
min.N=as.integer(1),
printable = F))
length(vect)
params <- list(vect)[rep(1,M)]
for(jj in 1:M)
{
params[[jj]]$cpu<-jj
params[[jj]]$test<-test
}
gc()
length(params[[1]])
results<-parall.gmj(X = params,FUN = runpar,mc.preschedule = F, mc.cores = M,mc.cleanup = T)
cbind(results[[1]]$fparam,results[[1]]$p.post)
post.popul <- array(0,M)
max.popul <- array(0,M)
nulls = NULL
for(k in 1:M)
{
if(length(results[[k]])==1||length(results[[k]]$cterm)==0)
{
nulls<-c(nulls,k)
next
}
else
{
not.null <- k
}
}
for(k in 1:M)
{
if(k %in% nulls)
{
results[[k]]<-results[[not.null]]
}
max.popul[k]<-results[[k]]$cterm
post.popul[k]<-results[[k]]$post.populi
}
ml.max<-max(max.popul)
post.popul<-post.popul*exp(-ml.max+max.popul)
p.gen.post<-post.popul/sum(post.popul)
res1 = array(0,dim = length(results[[1]]$res))
for(i in 1:M)
{
if(length(which(is.na(results[[i]]$res)))>0)
next
res1<-res1+results[[i]]$res*p.gen.post[i]
}
for(jjjj in 1:10)
{
res = as.integer(res1>=0.1*jjjj)
prec<-(1-sum(abs(res-test[,785+dig]),na.rm = T)/length(res))
#FNR
ps<-which(test[,785+dig]==1)
fnr<-sum(abs(res[ps]-test[,785+dig][ps]))/(sum(abs(res[ps]-test[,785+dig][ps]))+length(ps))
#FPR
ns<-which(test[,785+dig]==0)
fpr<-sum(abs(res[ns]-test[,785+dig][ns]))/(sum(abs(res[ns]-test[,785+dig][ns]))+length(ns))
total[dig,jjjj,1]=prec
total[dig,jjjj,2]=fnr
total[dig,jjjj,3]=fpr
print(prec)
}
write.csv(file ="results.csv",x=total)
write.csv(file =paste0("res",dig,".csv"),x=res1)
rm(results)
gc()
}
|
d56b528e942421dfcc2c52783a1a374a9f03702e
|
1b840fb7602d2bb94adb38f0500a85fd9027bc89
|
/Excluded_from_manuscript/Scripts/RR_cv_by_tree.R
|
37cb44bd9cd4252aff47d6e3662ca9851223deb3
|
[
"MIT"
] |
permissive
|
sgraham9319/TreeGrowth
|
d23be1521050082015c44db227060e953ee1adec
|
703fb7fc70536ba9b0549100bb14d713dcedea2b
|
refs/heads/main
| 2023-04-12T13:31:24.377864
| 2022-02-12T16:32:05
| 2022-02-12T16:32:05
| 353,162,167
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,907
|
r
|
RR_cv_by_tree.R
|
library(dplyr)
# Define required functions
z_trans <- function(x){(x - mean(x)) / sd(x)}
coef_det <- function(x){
1 - (sum((x$observations - x$predictions)^2) /
sum((x$observations - mean(x$observations))^2))
}
# Define focal species
focal_sps <- "ABAM"
# Load training data
train <- read.csv(paste("Data/Output_data/training1.csv", sep = ""),
stringsAsFactors = F)
# Subset to focal species
train <- train %>%
filter(species == focal_sps)
# Create vector of common competitors
comm_comps <- names(which(table(train$sps_comp) > 100))
# Reduce to one row per tree id
train <- train %>%
group_by(tree_id) %>%
filter(row_number() == 1) %>%
ungroup()
# Extract observations
obs <- train %>%
select(tree_id, size_corr_growth)
# Create column for density of rare species
train <- train %>%
mutate(rare_density = abs(
all_density - apply(train %>%
select(paste(comm_comps, "density", sep = "_")),
1, sum)))
# Select columns required by model
train <- train %>%
select(size_corr_growth, all_density,
paste(comm_comps, "density", sep = "_"), rare_density,
precip_mm, temp_C, elev_m, aet_mm, pet_mm)
# Remove any columns containing only zeros
train <- train %>%
select(-which(apply(train, 2, sum) == 0))
# Define outcome variable
outcome_var <- "size_corr_growth"
# Create model formula object
mod_form <- as.formula(paste(outcome_var, "~",
paste(setdiff(names(train), outcome_var),
collapse = "+")))
# Create design matrix
dm <- model.matrix(mod_form, train)
# Standardize variables except for first column (intercept)
dm[, 2:ncol(dm)] <- apply(dm[, 2:ncol(dm)], 2, z_trans)
# Change any columns of NaNs (no variation) to zeros
dm[, which(is.nan(dm[1, ]))] <- 0
# Fit glmnet model
mod <- glmnet::cv.glmnet(x = dm, y = train$size_corr_growth,
family = "gaussian")
# Plot lambda vs. MSE
plot(mod)
# Make predictions for training data
preds <- predict(mod, newx = dm, s = "lambda.1se")
# Combine predictions with observations
obs_pred <- cbind(obs, preds)
names(obs_pred) <- c("tree_id", "observations", "predictions")
# Calculate coefficient of determination
R_squared <- coef_det(obs_pred)
# Load test data
test <- read.csv(paste("Data/Output_data/test1.csv", sep = ""),
stringsAsFactors = F)
# Reduce to one row per tree id and subset to focal species
test <- test %>%
group_by(tree_id) %>%
filter(row_number() == 1 & species == focal_sps) %>%
ungroup()
# Extract observations
obs_test <- test %>%
select(tree_id, size_corr_growth)
# Create column for density of rare species
test <- test %>%
mutate(rare_density = abs(
all_density - apply(test %>%
select(paste(comm_comps, "density", sep = "_")),
1, sum)))
# Select columns required by model
test <- test %>%
select(size_corr_growth, all_density,
paste(comm_comps, "density", sep = "_"), rare_density,
precip_mm, temp_C, elev_m, aet_mm, pet_mm)
# Remove any columns containing only zeros
test <- test %>%
select(-which(apply(test, 2, sum) == 0))
# Create test design matrix
dm_test <- model.matrix(mod_form, test)
# Standardize variables except for first column (intercept)
dm_test[, 2:ncol(dm_test)] <- apply(dm_test[, 2:ncol(dm_test)], 2, z_trans)
# Change any columns of NaNs (no variation) to zeros
dm_test[, which(is.nan(dm_test[1, ]))] <- 0
# Make predictions for test data
preds_test <- predict(mod, newx = dm_test, s = "lambda.1se")
# Combine predictions with observations
obs_pred_test <- cbind(obs_test, preds_test)
names(obs_pred_test) <- c("tree_id", "observations", "predictions")
# Calculate coefficient of determination
R_squared_test <- coef_det(obs_pred_test)
# Check coefficients of 1se model
coef(mod)
|
c3c01896378fefc936357905e40702dcfb803dee
|
71a1c5fc44d44efde09b576b0a8709694619b609
|
/man/state_opinion.Rd
|
ba0f6c6c27be84196c01becc82c47300af3f4f46
|
[] |
no_license
|
cwarshaw/dgirt
|
d3cb7650636cc077125c1897f27c8b07b7776002
|
0c2b334490b998ba3cf6bf06219e96124a39b34c
|
refs/heads/master
| 2021-01-18T18:44:38.609943
| 2016-03-18T16:42:36
| 2016-03-18T16:42:36
| 54,498,307
| 0
| 0
| null | 2016-03-22T18:14:15
| 2016-03-22T18:14:14
| null |
UTF-8
|
R
| false
| true
| 529
|
rd
|
state_opinion.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-state_opinion.r
\docType{data}
\name{state_opinion}
\alias{state_opinion}
\title{State-level policy item responses}
\format{A `dplyr` `tbl_df` with 295,248 rows and 33 columns.}
\usage{
state_opinion
}
\description{
A dataset of survey item responses and characteristics of respondents.
Data are from the Cooperative Congressional Election Study (CCES) 2006-2014.
`Q_` prepends the names of item response variables.
}
\examples{
state_opinion
}
|
6e22697f510e6b14950d551896942e1f7d1ed87c
|
5021ebeb63a466032093215e7f7f098c5f49344a
|
/plot1.R
|
21bbe57ca5fc0fcdce555a73897f87e2ffee3a19
|
[] |
no_license
|
abolick/ExData_Plotting1
|
ec41592ccfc04a44432d4ff21d4e6a1dd62a9538
|
8b817cd017158a6796b195c176b6b6b9ccd6d2a9
|
refs/heads/master
| 2021-01-24T23:51:44.484929
| 2015-01-12T00:04:20
| 2015-01-12T00:04:20
| 29,080,835
| 0
| 0
| null | 2015-01-11T04:05:10
| 2015-01-11T04:05:09
| null |
UTF-8
|
R
| false
| false
| 578
|
r
|
plot1.R
|
data <- read.table(file = "household_power_consumption.txt",
sep = ";",
skip = 66637,
nrows = 2880,
na.strings= "?")
a <- colnames(read.table("household_power_consumption.txt", sep = ";",nrow = 1,
header = TRUE))
# assign column names to data
names(data) <- a
str(data)
#creates png file
png("plot1.png",width =480,height=480)
hist(data$Global_active_power,col="red", main ="Global Active Power",
xlab="Global Active Power (kilowatts)")
dev.off()
|
7ed5dee5bb09f1fb45f6b9ec05e5f14d8485a13b
|
2bec5a52ce1fb3266e72f8fbeb5226b025584a16
|
/ProFound/man/profoundSegimGroup.Rd
|
ef3702e0adbf54f0fd0510adb5c13f4b4175429f
|
[] |
no_license
|
akhikolla/InformationHouse
|
4e45b11df18dee47519e917fcf0a869a77661fce
|
c0daab1e3f2827fd08aa5c31127fadae3f001948
|
refs/heads/master
| 2023-02-12T19:00:20.752555
| 2020-12-31T20:59:23
| 2020-12-31T20:59:23
| 325,589,503
| 9
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,094
|
rd
|
profoundSegimGroup.Rd
|
\name{profoundSegimGroup}
\alias{profoundSegimGroup}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Create Segmentation Groups
}
\description{
Given an input segmentation map, returns a map of groups of touching segments as well as the IDs of segments within each group.
}
\usage{
profoundSegimGroup(segim = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{segim}{
Integer matrix; required, the segmentation map.
}
}
\details{
To use this function you will need to have EBImage installed. Since this can be a bit cumbersome on some platforms (given its dependencies) this is only listed as a suggested package. You can have a go at installing it by running:
> source("http://bioconductor.org/biocLite.R")
> biocLite("EBImage")
Linux users might also need to install some non-standard graphics libraries (depending on your install). If you do not have them already, you should look to install **jpeg** and **tiff** libraries (these are apparently technically not entirely free, hence not coming by default on some strictly open source Linux variants).
\code{profoundSegimGroup} uses the \code{bwlabel} function from EBImage.
}
\value{
A list containting the following structures:
\item{groupim}{An map of the unique groups identified in the input \option{segim}, where the groupID is the same as the lowest valued segID in the group.}
\item{groupsegID}{A data.frame of lists giving the segIDs of segments in each group.}
The data.frame returned by \option{groupsegID} is a slightly unusal structure to see in R, but it allows for a compact manner of storing uneven vectors of grouped segments. E.g. you might have a massive group containing 30 other segments and many groups containing a single segment. Padding a normal matrix out to accommodate the larger figure would be quite inefficient. It contains the following:
\item{groupID}{Group ID, which can be matched against values in \option{groupim}}
\item{segID}{An embedded list of segmentation IDs for segments in the group. I.e. each list element of \option{segID} is a vector (see Examples for clarity).}
\item{Ngroup}{The total number of segments that are in the group.}
\item{Npix}{The total number of pixels that are in the group.}
}
\author{
Aaron Robotham
}
\seealso{
\code{\link{profoundSegimNear}}, ~~~
}
\examples{
\dontrun{
image=readFITS(system.file("extdata", 'VIKING/mystery_VIKING_Z.fits', package="ProFound"))
profound=profoundProFound(image, skycut=1.5, magzero=30, verbose=TRUE)
#Look for nearby (in this case touching) neighbours
group=profoundSegimGroup(profound$segim)
#Look at the first few rows (groups 1:5):
group$groupsegID[1:5,]
#To access the embedded vectors you have to use unlist:
unlist(group$groupsegID[1,2])
#We can check to see which segments are in group number 1:
profoundSegimPlot(image$imDat, profound$segim)
magimage(group$groupim==1, col=c(NA,'red'), add=TRUE)
}
}
% Add one or more standard keywords, see file 'KEYWORDS' in the
% R documentation directory.
\concept{ segments }% use one of RShowDoc("KEYWORDS")
|
3206bcb3d8f46c110a763816be879ae81197e80a
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/RPPanalyzer/examples/select.sample.group.Rd.R
|
3b9b4844eed7142abe9096cab2b3e4a57f470454
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 289
|
r
|
select.sample.group.Rd.R
|
library(RPPanalyzer)
### Name: select.sample.group
### Title: Selects samples from RPPA data
### Aliases: select.sample.group
### Keywords: manip
### ** Examples
library(RPPanalyzer)
data(dataII)
selectedData <- select.sample.group(dataII,params=list("stimulation"=c("A","B")))
|
5fd06a106dc59ecea4804092bbcad1b74283d77c
|
0273c7facf88bc6c4320f368063d9545b1523f8a
|
/src/lemma_ngrams.R
|
1e1224bba47fbcfc2a8d94ca789611daab4a0003
|
[] |
no_license
|
mnbram/boardgameanalysis
|
a5f7d169e813c2481165909497dbf24ba3025186
|
292a0b4da4bd8246636239ebc44022b736b57df5
|
refs/heads/master
| 2021-01-16T18:10:48.364226
| 2017-08-11T16:10:40
| 2017-08-11T16:10:40
| 100,047,861
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,205
|
r
|
lemma_ngrams.R
|
library(tidyverse)
library(tidytext)
library(stringr)
library(glmnet)
library(doParallel)
setwd("~/bgg/kickstarter")
lemma_files <- list.files("data/lemmas", full.names = TRUE)
texts <- lapply(lemma_files, readLines)
text_lengths <- map_int(texts, length)
kids <- map_int(lemma_files, function(x)
as.integer(str_match(x, "[0-9]+")))
lemmas <- tibble(
k_id = rep(kids, text_lengths),
text = unlist(texts)
)
rm(texts)
# Remove lemmatized pronouns and parsing mistakes as stop words
stop_plus <- c(stop_words$word, "pron", "â")
unigrams_bow <- lemmas %>%
unnest_tokens(type, text) %>%
count(k_id, type, sort = TRUE) %>%
ungroup() %>%
filter(!type %in% stop_plus) %>%
mutate(type = str_replace_all(type, fixed("_"), "-"))
bigrams_bow <- lemmas %>%
unnest_tokens(type, text, token = "ngrams", n = 2) %>%
count(k_id, type, sort = TRUE) %>%
ungroup() %>%
separate(type, c("word1", "word2"), sep = " ") %>%
filter(
!word1 %in% stop_plus,
!word2 %in% stop_plus
) %>%
unite(type, word1, word2, sep = "__")
common <- rbind(unigrams_bow, bigrams_bow) %>%
group_by(type) %>%
summarize(freq_in_campaigns = length(unique(k_id))/length(lemma_files)) %>%
filter(freq_in_campaigns >= 0.001)
sparse_unibi_bow <- rbind(unigrams_bow, bigrams_bow) %>%
filter(type %in% common$type) %>%
cast_sparse(k_id, type)
br_data <- read_delim("data/benrugg.csv", delim = ",",
escape_backslash = TRUE, escape_double = FALSE) %>%
filter(
sub_category == "Tabletop Games",
kickstarter_id %in% kids
) %>%
select(kickstarter_id, state) %>%
slice(match(as.integer(rownames(sparse_unibi_bow)), kickstarter_id))
states <- setNames(ifelse(br_data$state == "successful", 1, 0),
br_data$kickstarter_id) %>% as.matrix()
colnames(states) <- "success_"
sparse_results_bow <- cbind(states, sparse_unibi_bow)
registerDoParallel(cores = 6)
penalties <- rep(1, ncol(sparse_results_bow)-1)
penalties[grep("__", colnames(sparse_results_bow[,-1]))] <- 0.5
glm_coefs <- function() {
glm_bow <- cv.glmnet(
sparse_results_bow[,-1], as.factor(sparse_results_bow[,1]),
alpha = 1, family = "binomial", penalty.factor = penalties,
standardize = FALSE, type.measure = "auc", parallel = TRUE)
coef(glm_bow, s = "lambda.min") %>%
tidy() %>%
filter(value != 0)
}
set.seed(984)
glm_coefs_cv <- lapply(1:100, function(x) glm_coefs())
glm100_coef <- lapply(glm_coefs_cv, function(x) {
x %>%
filter(row != "(Intercept)") %>%
select(row, value) %>%
as_tibble()
}) %>%
bind_rows() %>%
group_by(row) %>%
summarize(n = n(), mean = mean(value)) %>%
arrange(desc(n), desc(abs(mean)))
term_counts <- apply(sparse_unibi_bow, 2, sum)
term_freqs <- tidy(term_counts) %>%
filter(names %in% glm100_coef$row) %>%
mutate(freq = x/nrow(sparse_unibi_bow))
glm100_coef_full <- glm100_coef %>%
left_join(term_freqs, by = c("row" = "names")) %>%
rename(term = row, nmodels = n, coef = mean) %>%
select(term, nmodels, coef, freq)
write_csv(glm100_coef_full, "glm100_coef_all.csv")
|
7868deaf2b701ffd634651d6e0fdea21e6c3fe0a
|
6dbd098f38a9dc01a837a65a4ce633282c59c108
|
/scripts/mtbls520_18_phylogeny.r
|
cd3e669ef0b1ad9b2850ad5e0438e9d7b17b0fc2
|
[
"Apache-2.0"
] |
permissive
|
korseby/container-mtbls520
|
82b656267c2fdfc6beffe8c312395bc304945e25
|
04b55283e3698a82111fa4531198915e556ba2a4
|
refs/heads/master
| 2021-06-17T06:00:45.219498
| 2019-08-09T06:06:58
| 2019-08-09T06:06:58
| 107,677,250
| 1
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,154
|
r
|
mtbls520_18_phylogeny.r
|
#!/usr/bin/env Rscript
# ---------- Load R environment ----------
# Setup R error handling to go to stderr
options(show.error.messages=F, error=function() { cat(geterrmessage(), file=stderr()); q("no",1,F) } )
# Set proper locale
loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
loc <- Sys.setlocale(category="LC_ALL", locale="C")
# Set options
options(encoding="UTF-8")
options(stringAsfactors=FALSE, useFancyQuotes=FALSE)
# Take in trailing command line arguments
args <- commandArgs(trailingOnly=TRUE)
if (length(args) < 4) {
print("Error! No or not enough arguments given.")
print("Usage: $0 input.rdata moss_phylo.tre procrustes.pdf phylogeny.pdf")
quit(save="no", status=1, runLast=FALSE)
}
# Load R environment
load(file=args[1])
args <- commandArgs(trailingOnly=TRUE)
# Load libraries
library(vegan)
library(ape)
library(pvclust)
library(dendextend)
library(cba)
library(phangorn)
# ---------- Phylogeny ----------
# Read phylogenetic tree
phylo_tree <- read.tree(args[2])
# Replace names with species codes
phylo_index <- match(substr(phylo_tree$tip.label,1,3), as.character(lapply(X=species_names, FUN=function(x) { x <- substr(x,1,3) })))
phylo_tree$tip.label <- as.character(species_names[phylo_index])
# Cophenetic distance matrix, needed for mpd and mntd calculations below
phylo_dist <- cophenetic.phylo(phylo_tree)
# Distance matrix of phylogenetic tree using Bray-Curtis
phylo_dist <- vegdist(phylo_dist, method="bray")
# Hierarchical clustering
phylo_hclust <- hclust(phylo_dist, method="complete")
# Merge feat_list for species from samples
feat_list_species <- NULL
for (i in species_names) feat_list_species <- rbind(feat_list_species, apply(X=feat_list[species==i,], MARGIN=2, FUN=function(x) { median(x) } ))
rownames(feat_list_species) <- species_names
# Reorder rows according to phylogenetic tree order
feat_list_species <- feat_list_species[phylo_index,]
# Distance matrix of feat_list using Bray-Curtis
feat_dist <- vegdist(feat_list_species, method="bray")
# Hierarchical clustering
feat_hclust <- hclust(feat_dist, method="complete")
# Optimal order
feat_opti <- order.optimal(feat_dist, feat_hclust$merge)
feat_oclust <- feat_hclust
feat_oclust$merge <- feat_opti$merge
feat_oclust$order <- feat_opti$order
# pos-mode: Manually rotate "Polstr" + "Plaund" branches
if (polarity == "positive") {
feat_oclust <- reorder(feat_oclust, c(1, 2, 4, 5, 3, 9, 6, 8, 7))
}
# neg-mode: Manually rotate branches
if (polarity == "negative") {
feat_oclust <- reorder(feat_oclust, c(1, 2, 3, 6, 4, 8, 7, 5, 9))
}
# Procrustes analysis
model_procrust <- protest(X=phylo_dist, Y=feat_dist, permutations=10000)
pdf(file=args[3], encoding="ISOLatin1", pointsize=10, width=5, height=5, family="Helvetica")
plot(model_procrust)
dev.off()
# Mantel test
model_mantel <- mantel(xdis=phylo_dist, ydis=feat_dist, method="pearson", permutations=10000)
# Correlation tests
model_cor <- cor(phylo_dist, feat_dist, method="pearson")
model_cop <- cor_cophenetic(hclust(phylo_dist), hclust(feat_dist), method="pearson")
# Robinson-Foulds metric
RF.dist(phylo_tree, as.phylo(feat_oclust), normalize=TRUE, check.labels=TRUE, rooted=TRUE)
# Plot phylogenetic tree
pdf(args[4], encoding="ISOLatin1", pointsize=12, width=8, height=5, family="Helvetica")
par(mfrow=c(1,2), mar=c(1,1,2,1), cex=1.0)
plot(phylo_tree, type="phylogram", direction="rightwards", x.lim=c(0,11), label.offset=0.4, use.edge.length=TRUE, show.tip.label=TRUE, tip.color=species_colors[phylo_index], font=2, main="")
mtext(text="(a)", adj=0, line=0.5, font=2, cex=1.2)
plot(as.phylo(feat_oclust), type="phylogram", direction="leftwards", x.lim=c(0,0.5), label.offset=0.01, use.edge.length=TRUE, show.tip.label=TRUE, tip.color=species_colors[phylo_index], font=2, main="")
mtext(text="(b)", adj=0, line=0.5, font=2, cex=1.2)
dev.off()
# r = Mantel
print(paste("Mantel statistic:", round(model_mantel$statistic,3)))
# c = cor_cophenetic
print(paste("Correlation:", round(model_cop,3)))
# rf = Robinson-Foulds
print(paste("Robinson-Foulds metric:", round(RF.dist(phylo_tree, as.phylo(feat_oclust), normalize=TRUE, check.labels=TRUE, rooted=TRUE), 3)))
|
6c6d9b21114f1cc4d6445d296450f5e6b788ae7e
|
a614846a4bfbb4432aaff0aca0105682e95e9167
|
/R/supplements/preprocess-Senkler2018.R
|
039af5a6dc7162be9ef9859ea8cd75af27abc3ea
|
[] |
no_license
|
skinnider/CF-MS-searches
|
58044f1363186b43878b2971927e6da738b63663
|
0851000f90e0324f9363b50ed779af5a68a16e76
|
refs/heads/master
| 2023-04-09T09:35:33.057903
| 2020-10-10T21:35:41
| 2020-10-10T21:35:41
| 302,994,048
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 711
|
r
|
preprocess-Senkler2018.R
|
# Preprocess data originally reported in the supporting materials of
# Senkler et al., Curr Biol 2018.
setwd("~/git/CF-MS-searches")
options(stringsAsFactors = F)
library(tidyverse)
library(magrittr)
library(readxl)
# read file
dat = read.delim(
"data/supplements/Senkler2018/proteinGroups_results.txt.gz") %>%
filter(Potential.contaminant != '+',
Reverse != '+',
Only.identified.by.site != '+')
# extract iBAQ
mat = dat %>%
dplyr::select(starts_with('iBAQ.')) %>%
as.matrix() %>%
set_rownames(dat[[1]])
# remove proteins never quantified
mat %<>% extract(rowSums(. > 0) > 0, )
# save as RDS
replicates = list(map = mat)
saveRDS(replicates, 'data/supplements/PXD008974.rds')
|
98f568cd69893a6da8a4653d35547413dcd4d4a0
|
9aafde089eb3d8bba05aec912e61fbd9fb84bd49
|
/codeml_files/newick_trees_processed_and_cleaned/11551_0/rinput.R
|
f96591cce4ca3898c88e62290dfb378807eacc78
|
[] |
no_license
|
DaniBoo/cyanobacteria_project
|
6a816bb0ccf285842b61bfd3612c176f5877a1fb
|
be08ff723284b0c38f9c758d3e250c664bbfbf3b
|
refs/heads/master
| 2021-01-25T05:28:00.686474
| 2013-03-23T15:09:39
| 2013-03-23T15:09:39
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 137
|
r
|
rinput.R
|
library(ape)
testtree <- read.tree("11551_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="11551_0_unrooted.txt")
|
db66ed94c7c2c06dad6d215c70e4d96b50e89c05
|
df7b3a1eb6f65ac3e576f8b49ad385cb0303f2b0
|
/R/cbat.R
|
5838e3489b7d5a6769bc75895213a5424d4f4245
|
[
"MIT"
] |
permissive
|
ykunisato/senshuRmd
|
0f974443ae343fc8f963e718f1d3b1102dbd3ae5
|
2f60fda7269985917f7d3e0dacb47a8666eb4590
|
refs/heads/master
| 2022-04-01T17:43:42.117858
| 2022-03-15T03:54:14
| 2022-03-15T03:54:14
| 217,082,796
| 7
| 2
| null | null | null | null |
UTF-8
|
R
| false
| false
| 14,669
|
r
|
cbat.R
|
#' @title Set template files for CBAT
#' @importFrom utils download.file
#' @importFrom utils unzip
#' @param task_name name of task
#' @param jsPsych_version If you set a specific version number of jsPsych,
#' set_jsPsych prepare a file with that version of jsPsych.
#' @param use_rc If you don"t use the RC, set FALSE.
#' @examples # set_cbat("stroop")
#' @export
set_cbat <- function(task_name = "task_name",
jsPsych_version = "7.1.2",
use_rc = TRUE){
#check exercises directory
if(use_rc == TRUE){
dir_names_cwd = basename(list.dirs())
if(sum(dir_names_cwd == "exercises") >= 1){
path = paste0(getwd(),"/exercises")
dir.create(file.path(path, task_name), showWarnings = FALSE)
path = paste0(path,"/",task_name)
}else{
stop(paste("Error! Run the code in the directory where the 'exercises' directory is located."))
}
}else{
path = getwd()
dir.create(file.path(path, task_name), showWarnings = FALSE)
path = paste0(path,"/",task_name)
}
# prepare the files and directories
if(jsPsych_version == "6.3.1"){
## make demo-.html file
tmp_html <- file(file.path(path, paste0("demo_",task_name,".html")), "w")
writeLines("<!DOCTYPE html>", tmp_html)
writeLines("<html>", tmp_html)
writeLines(" <head>", tmp_html)
writeLines(' <meta charset="UTF-8" />', tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/jspsych.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-fullscreen.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-html-keyboard-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-html-button-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-likert.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-multi-choice.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-multi-select.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-text.js"></script>'), tmp_html)
writeLines(paste0(' <link rel="stylesheet" href="',task_name,'/jspsych-',jsPsych_version,'/css/jspsych.css" />'), tmp_html)
writeLines(" </head>", tmp_html)
writeLines(" <body></body>", tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/demo_fullscreen.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/task.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/demo_run.js"></script>'), tmp_html)
writeLines("</html>", tmp_html)
close(tmp_html)
## make -.html file
tmp_html <- file(file.path(path, paste0(task_name,".html")), "w")
writeLines("<!DOCTYPE html>", tmp_html)
writeLines("<html>", tmp_html)
writeLines(" <head>", tmp_html)
writeLines(' <meta charset="UTF-8" />', tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/jspsych.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-fullscreen.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-html-keyboard-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-html-button-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-likert.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-multi-choice.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-multi-select.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych-',jsPsych_version,'/plugins/jspsych-survey-text.js"></script>'), tmp_html)
writeLines(' <script src="jatos.js"></script>', tmp_html)
writeLines(paste0(' <link rel="stylesheet" href="',task_name,'/jspsych-',jsPsych_version,'/css/jspsych.css" />'), tmp_html)
writeLines(" </head>", tmp_html)
writeLines(" <body></body>", tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/jatos_fullscreen.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/task.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/jatos_run.js"></script>'), tmp_html)
writeLines("</html>", tmp_html)
close(tmp_html)
## make directory of repository
dir.create(file.path(path, task_name), showWarnings = FALSE)
## download jsPsych
temp_jsPsych <- tempfile()
download.file(paste0('https://github.com/jspsych/jsPsych/releases/download/v',jsPsych_version,'/jspsych-',jsPsych_version,'.zip'),temp_jsPsych)
unzip(temp_jsPsych, exdir = file.path(path, task_name))
unlink(temp_jsPsych)
## download js files
file_path <- paste0(path,"/",task_name)
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/task.js",paste0(file_path,"/task.js"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/jatos_run.js",paste0(file_path,"/jatos_run.js"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/jatos_fullscreen.js",paste0(file_path,"/jatos_fullscreen.js"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/demo_run.js",paste0(file_path,"/demo_run.js"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/demo_fullscreen.js",paste0(file_path,"/demo_fullscreen.js"))
## make stimli directory and picture
dir.create(file.path(file_path, "stimuli"), showWarnings = FALSE)
stim_path <- paste0(file_path,"/stimuli")
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/stimuli/reward.jpeg",paste0(stim_path,"/reward.jpeg"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/stimuli/punishment.jpeg",paste0(stim_path,"/punishment.jpeg"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/stimuli/s1.jpeg",paste0(stim_path,"/s1.jpeg"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/stimuli/s1s.jpeg",paste0(stim_path,"/s1s.jpeg"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/stimuli/s2.jpeg",paste0(stim_path,"/s2.jpeg"))
download.file("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych6.3/name_of_repository/stimuli/s2s.jpeg",paste0(stim_path,"/s2s.jpeg"))
}else if(substr(jsPsych_version, 1, 1)=="7"){
## make demo-.html file
tmp_html <- file(file.path(path, paste0("demo_",task_name,".html")), "w")
writeLines("<!DOCTYPE html>", tmp_html)
writeLines("<html>", tmp_html)
writeLines(" <head>", tmp_html)
writeLines(' <meta charset="UTF-8" />', tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/jspsych.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-fullscreen.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-html-keyboard-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-html-button-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-likert.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-multi-choice.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-multi-select.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-text.js"></script>'), tmp_html)
writeLines(paste0(' <link href="',task_name,'/jspsych/dist/jspsych.css" rel="stylesheet" type="text/css" />'), tmp_html)
writeLines(" </head>", tmp_html)
writeLines(" <body></body>", tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/demo_jspsych_init.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/task.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/demo_jspsych_run.js"></script>'), tmp_html)
writeLines("</html>", tmp_html)
close(tmp_html)
## make -.html file
tmp_html <- file(file.path(path, paste0(task_name,".html")), "w")
writeLines("<!DOCTYPE html>", tmp_html)
writeLines("<html>", tmp_html)
writeLines(" <head>", tmp_html)
writeLines(' <meta charset="UTF-8" />', tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/jspsych.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-fullscreen.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-html-keyboard-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-html-button-response.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-likert.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-multi-choice.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-multi-select.js"></script>'), tmp_html)
writeLines(paste0(' <script src="',task_name,'/jspsych/dist/plugin-survey-text.js"></script>'), tmp_html)
writeLines(' <script src="jatos.js"></script>', tmp_html)
writeLines(paste0(' <link href="',task_name,'/jspsych/dist/jspsych.css" rel="stylesheet" type="text/css" />'), tmp_html)
writeLines(" </head>", tmp_html)
writeLines(" <body></body>", tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/jatos_jspsych_init.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/task.js"></script>'), tmp_html)
writeLines(paste0(' <script type="text/javascript" src="',task_name,'/jatos_jspsych_run.js"></script>'), tmp_html)
writeLines("</html>", tmp_html)
close(tmp_html)
## make directory of repository
dir.create(file.path(path, task_name), showWarnings = FALSE)
## download jsPsych
temp_jsPsych <- tempfile()
if(jsPsych_version=="7.0.0"){
download.file(paste0('https://github.com/jspsych/jsPsych/releases/download/jspsych@7.0.0/jspsych-7.0.0-dist.zip'),temp_jsPsych)
}else{
download.file(paste0('https://github.com/jspsych/jsPsych/releases/download/jspsych@',jsPsych_version,'/jspsych.zip'),temp_jsPsych)
}
unzip(temp_jsPsych, exdir = file.path(path, task_name,"jspsych"))
unlink(temp_jsPsych)
## download js files
file_path <- paste0(path,"/",task_name)
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/task.js"),paste0(file_path,"/task.js"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/demo_jspsych_init.js"),paste0(file_path,"/demo_jspsych_init.js"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/demo_jspsych_run.js"),paste0(file_path,"/demo_jspsych_run.js"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/jatos_jspsych_init.js"),paste0(file_path,"/jatos_jspsych_init.js"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/jatos_jspsych_run.js"),paste0(file_path,"/jatos_jspsych_run.js"))
## make stimli directory and picture
dir.create(file.path(file_path, "stimuli"), showWarnings = FALSE)
stim_path <- paste0(file_path,"/stimuli")
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/stimuli/reward.jpeg"),paste0(stim_path,"/reward.jpeg"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/stimuli/punishment.jpeg"),paste0(stim_path,"/punishment.jpeg"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/stimuli/s1.jpeg"),paste0(stim_path,"/s1.jpeg"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/stimuli/s1s.jpeg"),paste0(stim_path,"/s1s.jpeg"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/stimuli/s2.jpeg"),paste0(stim_path,"/s2.jpeg"))
download.file(paste0("https://raw.githubusercontent.com/ykunisato/template-jsPsych-task/main/template-jsPsych",substr(jsPsych_version, 1, 3),"/name_of_repository/stimuli/s2s.jpeg"),paste0(stim_path,"/s2s.jpeg"))
}
}
|
17b8ae0afd75db9df279b75b2b82291b1eeae8e3
|
98c40fe72bfe9caafc3db5ca0a2c0944cad33988
|
/man/cutoff.Rd
|
6e26c2776cef2da629ff32393dd68ee41bd636d1
|
[] |
no_license
|
datawookie/emayili
|
c91d38dc5bf0fc38cff45260dc0ba99bce7e754f
|
cb0f2f7e6c8738a30ddd88834abd88b63585244c
|
refs/heads/master
| 2023-09-01T00:02:36.105432
| 2023-08-30T10:49:01
| 2023-08-30T10:49:01
| 187,310,940
| 158
| 41
| null | 2023-08-03T06:52:34
| 2019-05-18T03:45:55
|
R
|
UTF-8
|
R
| false
| true
| 834
|
rd
|
cutoff.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/header-expires.R
\name{cutoff}
\alias{cutoff}
\alias{expires}
\alias{replyby}
\title{Set or query message expiry or reply-by time}
\usage{
expires(msg, datetime = NULL, tz = "")
replyby(msg, datetime = NULL, tz = "")
}
\arguments{
\item{msg}{A message object.}
\item{datetime}{Date and time.}
\item{tz}{A character string specifying the time zone.}
}
\value{
A message object.
}
\description{
Functions to specify the time at which a message expires or by which a reply
is requested.
}
\details{
Manipulate the \code{Expires} and \code{Reply-By} fields as specified in
\href{https://www.ietf.org/rfc/rfc2156.txt}{RFC 2156}.
}
\examples{
envelope() \%>\%
expires("2030-01-01 13:25:00", "UTC")
envelope() \%>\%
replyby("2021-12-25 06:00:00", "GMT")
}
|
ab6eec1483b40483671588d84da8f92ed94bfdaa
|
9cc1af02c31ab3bdccd3822767121b56e6da4e68
|
/index_hoping_functions.R
|
d30e9cd276fe474f93ce95ea5ba603ccf2a294cd
|
[] |
no_license
|
yy930/test
|
4e4146c889142909a9de0f1c218c36600a7e3e92
|
8558ee7544446e81cbaadc76c61bcee40ad89d64
|
refs/heads/master
| 2020-05-23T06:36:06.265372
| 2017-09-13T12:51:22
| 2017-09-13T12:51:22
| 56,673,274
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 33,501
|
r
|
index_hoping_functions.R
|
#heatmap for plate 1
visheatmap1<-function(.data, .title = "Number of shared clonotypes", .labs = c("Sample", "Sample"),
.legend = "Shared clonotypes", .na.value = NA, .text = T, .scientific = FALSE,
.signif.digits = 4, .size.text = 4, .no.legend = F, .no.labs = F)
{
if (has.class(.data, "data.frame")) {
names <- .data[, 1]
.data <- as.matrix(.data[, -1])
row.names(.data) <- names
}
if (is.null(colnames(.data))) {
colnames(.data) <- paste0("C", 1:ncol(.data))
}
if (is.null(row.names(.data))) {
row.names(.data) <- paste0("C", 1:nrow(.data))
}
.data[is.na(.data)] <- .na.value
tmp <- as.data.frame(.data)
tmp$name <- row.names(.data)
m <- melt(tmp, id.var = c("name"))
m[, 1] <- factor(m[, 1], levels = rev(rownames(.data)))
m[, 2] <- factor(m[, 2], levels = colnames(.data))
.cg <- .colourblind.gradient(min(m$value), max(m$value))
#m$label <- format(m$value, scientific = .scientific, digits = .signif.digits)
m$label <- as.vector(matrix(celltype$cell.type[1:96],nrow = 8, ncol=12, byrow=T))#!!!!!!
p <- ggplot(m, aes(x = variable, y = name, fill = value))
p <- p + geom_tile(aes(fill = value), colour = "white")
if (.text) {
p <- p + geom_text(aes(fill = value, label = label), size = .size.text)#!!!!!!!!!!
}
p <- p + .cg
p <- p + ggtitle(.title) + guides(fill = guide_colourbar(title = .legend)) +
xlab(.labs[1]) + ylab(.labs[2]) + coord_fixed() + theme_linedraw() +
theme(axis.text.x = element_text(angle = 90)) + scale_x_discrete(expand = c(0,
0)) + scale_y_discrete(expand = c(0, 0))
if (.no.legend) {
p <- p + theme(legend.position = "none")
}
if (.no.labs) {
p <- p + theme(axis.title.x = element_blank(), axis.title.y = element_blank())
}
p
}
#heatmap for plate 2+3
visheatmap23<-function(.data, .title = "Number of shared clonotypes", .labs = c("Sample", "Sample"),
.legend = "Shared clonotypes", .na.value = NA, .text = T, .scientific = FALSE,
.signif.digits = 4, .size.text = 4, .no.legend = F, .no.labs = F)
{
if (has.class(.data, "data.frame")) {
names <- .data[, 1]
.data <- as.matrix(.data[, -1])
row.names(.data) <- names
}
if (is.null(colnames(.data))) {
colnames(.data) <- paste0("C", 1:ncol(.data))
}
if (is.null(row.names(.data))) {
row.names(.data) <- paste0("C", 1:nrow(.data))
}
.data[is.na(.data)] <- .na.value
tmp <- as.data.frame(.data)
tmp$name <- row.names(.data)
m <- melt(tmp, id.var = c("name"))
m[, 1] <- factor(m[, 1], levels = rev(rownames(.data)))
m[, 2] <- factor(m[, 2], levels = colnames(.data))
.cg <- .colourblind.gradient(min(m$value), max(m$value))
#m$label <- format(m$value, scientific = .scientific, digits = .signif.digits)
m$label <- as.vector(matrix(celltype$cell.type[97:288],nrow = 16, ncol=12, byrow=T))#!!!!!!
p <- ggplot(m, aes(x = variable, y = name, fill = value))
p <- p + geom_tile(aes(fill = value), colour = "white")
if (.text) {
p <- p + geom_text(aes(fill = value, label = label), size = .size.text)#!!!!!!!!!!
}
p <- p + .cg
p <- p + ggtitle(.title) + guides(fill = guide_colourbar(title = .legend)) +
xlab(.labs[1]) + ylab(.labs[2]) + coord_fixed() + theme_linedraw() +
theme(axis.text.x = element_text(angle = 90)) + scale_x_discrete(expand = c(0,
0)) + scale_y_discrete(expand = c(0, 0))
if (.no.legend) {
p <- p + theme(legend.position = "none")
}
if (.no.labs) {
p <- p + theme(axis.title.x = element_blank(), axis.title.y = element_blank())
}
p
}
#heatmap for plate 4+5
visheatmap45<-function(.data, .title = "Number of shared clonotypes", .labs = c("Sample", "Sample"),
.legend = "Shared clonotypes", .na.value = NA, .text = T, .scientific = FALSE,
.signif.digits = 4, .size.text = 4, .no.legend = F, .no.labs = F)
{
if (has.class(.data, "data.frame")) {
names <- .data[, 1]
.data <- as.matrix(.data[, -1])
row.names(.data) <- names
}
if (is.null(colnames(.data))) {
colnames(.data) <- paste0("C", 1:ncol(.data))
}
if (is.null(row.names(.data))) {
row.names(.data) <- paste0("C", 1:nrow(.data))
}
.data[is.na(.data)] <- .na.value
tmp <- as.data.frame(.data)
tmp$name <- row.names(.data)
m <- melt(tmp, id.var = c("name"))
m[, 1] <- factor(m[, 1], levels = rev(rownames(.data)))
m[, 2] <- factor(m[, 2], levels = colnames(.data))
.cg <- .colourblind.gradient(min(m$value), max(m$value))
#m$label <- format(m$value, scientific = .scientific, digits = .signif.digits)
m$label <- as.vector(matrix(celltype$cell.type[289:480],nrow = 16, ncol=12, byrow=T))#!!!!!!
p <- ggplot(m, aes(x = variable, y = name, fill = value))
p <- p + geom_tile(aes(fill = value), colour = "white")
if (.text) {
p <- p + geom_text(aes(fill = value, label = label), size = .size.text)#!!!!!!!!!!
}
p <- p + .cg
p <- p + ggtitle(.title) + guides(fill = guide_colourbar(title = .legend)) +
xlab(.labs[1]) + ylab(.labs[2]) + coord_fixed() + theme_linedraw() +
theme(axis.text.x = element_text(angle = 90)) + scale_x_discrete(expand = c(0,
0)) + scale_y_discrete(expand = c(0, 0))
if (.no.legend) {
p <- p + theme(legend.position = "none")
}
if (.no.labs) {
p <- p + theme(axis.title.x = element_blank(), axis.title.y = element_blank())
}
p
}
.colourblind.gradient <- function (.min = NA, .max = NA, .colour = F) {
# cs <- c("#FFFFD9", "#41B6C4", "#225EA8")
# cs <- c("#FFFFBB", "#41B6C4", "#225EA8")
# cs <- c("#FFBB00", "#41B6C4", "#225EA8") <- old version
# cs <- c("#FF4B20", "#FFB433", "#C6EDEC", "#85CFFF", "#0348A6")
# cs <- c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6")
# scale_fill_gradientn(guide='colourbar', colours=c("#0072B2", "#EEEEEE", "#D55E00")
cs <- c(c("#0072B2", "#EEEEEE", "#D55E00"))
if (!is.na(.min)) {
if (.colour) {
scale_colour_gradientn(limits = c(.min, .max), guide='colorbar', colours = cs, na.value = 'grey60')
} else {
scale_fill_gradientn(limits = c(.min, .max), guide='colorbar', colours = cs, na.value = 'grey60')
}
} else {
if (.colour) {
scale_colour_gradientn(colours = cs, na.value = 'grey60')
} else {
scale_fill_gradientn(colours = cs, na.value = 'grey60')
}
}
}
#function to rename files
getfile <- function(taskid){
letter = c("A","B","C","D","E","F","G","H")
num = c("1","2","3","4","5","6","7","8","9","10","11","12")
if (as.numeric(taskid)<97)
{if (as.numeric(taskid) %% 12 == 0)
{ l=letter[as.numeric(taskid)/12]
n="12"}
else{
l = letter[as.numeric(taskid)/12+1]
n = num[as.numeric(taskid)%%12]}
return (paste("1",l,n,sep=""))
}
if (as.numeric(taskid)>96){
smalln=as.numeric(taskid)-96
if (smalln%%12 == 0){
l = letter[smalln/12]
n="12"
}
else{
l = letter[smalln/12+1]
n = num[smalln%%12]
}
return (paste("2",l,n,sep=""))
}
}
#function to get a list of cells with converted names using function "getfile"
getfilelist<-function(input_file_list){
out_file_list=vector('character')
for (infile in input_file_list){
out_file_list<-c(out_file_list,getfile(infile))
}
return (out_file_list)
}
#function to aggregate V_GENEs
agg_v_gene<-function(v_cell_tpm){
v_cell_tpm$V_GENE<-str_split_fixed(v_cell_tpm$V_GENE,pattern ="[*]",n=2)[,1]
v_cell_tpm$flag<-paste(v_cell_tpm$V_GENE,v_cell_tpm$CellID,sep='|')
agg_v<-aggregate(TPM~flag,data=v_cell_tpm,FUN=sum)
agg_v$V_GENE<-str_split_fixed(agg_v$flag,pattern ="[|]",n=2)[,1]
agg_v$CellID<-str_split_fixed(agg_v$flag,pattern ="[|]",n=2)[,2]
agg_v<-agg_v[,c("V_GENE","CellID","TPM")]
return(agg_v)
}
#function to get expression in plate use={"tcr","v","bcr","vj","aa"}
expr_mat<-function(plateID,geneID,use){
if (use=="v") {
cells<-vlist[[plateID]][geneID,3]
tpms<-vlist[[plateID]][geneID,4]
}
if (use=="tcr"){
cells<-tcrlist[[plateID]][tcrID,3]
tpms<-tcrlist[[plateID]][tcrID,4]
}
if (use=="vj"){
cells<-vjlist[[plateID]][geneID,3]
tpms<-vjlist[[plateID]][geneID,4]
}
if (use=="bcr") {
cells<-bcrlist[[plateID]][geneID,3]
tpms<-bcrlist[[plateID]][geneID,4]
}
if (use=="aa") {
cells<-sbcrlist[[plateID]][geneID,3]
tpms<-sbcrlist[[plateID]][geneID,4]
}
mat_plate<-matrix(nrow=8,ncol=12,dimnames=list(c("A","B","C","D","E","F","G","H"),as.character(1:12)))
vec_cell<-strsplit(cells,split = ",")[[1]]
vec_cellrow<-gsub("([0-9]+)", "",vec_cell)
vec_cellcol<-gsub(".*[A-Z]", "", vec_cell)
vec_tpm <-as.numeric(strsplit(tpms,split = ",")[[1]])
#cell_tpm_mat<-data.frame(vec_cellrow,vec_cellcol,vec_tpm)
for (i in (1:length(vec_cell))){
row_id=which(rownames(mat_plate)==vec_cellrow[i])
col_id=which(colnames(mat_plate)==vec_cellcol[i])
mat_plate[row_id,col_id]<-vec_tpm[i]
}
mat_plate<-as.numeric(mat_plate)
mat<-matrix(log2(as.numeric(mat_plate)+1),8,12)
rownames(mat)<-c("A","B","C","D","E","F","G","H")
colnames(mat)<-as.character(1:12)
return (mat)
}
expr_matrix<-function(vec_cell,vec_ratio,plate=1){
mat_plate<-matrix(nrow=8,ncol=12,dimnames=list(c("A","B","C","D","E","F","G","H"),as.character(1:12)))
vec_cellrow<-gsub("([0-9]+)$", "", vec_cell)
vec_cellcol<-gsub("[^\\d]+", "", vec_cell, perl=TRUE)
for (i in (1:length(vec_cell))){
row_id=which(rownames(mat_plate)==vec_cellrow[i])
col_id=which(colnames(mat_plate)==vec_cellcol[i])
mat_plate[row_id,col_id]<-vec_ratio[i]
}
mat_plate[mat_plate==1]<-0
# mat_plate[is.na(mat_plate)|]<-0
return (mat_plate)
}
expr_2matrix<-function(vec_cell,vec_ratio,plate=23){
ifelse(plate==23,
mat_plate<-matrix(nrow=16,ncol=12,dimnames=list(c("1A","1B","1C","1D","1E","1F","1G","1H","2A","2B","2C","2D","2E","2F","2G","2H"),as.character(1:12))),
mat_plate<-matrix(nrow=16,ncol=12,dimnames=list(c("3A","3B","3C","3D","3E","3F","3G","3H","4A","4B","4C","4D","4E","4F","4G","4H"),as.character(1:12))))
vec_cellrow<-gsub("([0-9]+)$", "", vec_cell)
vec_cellcol<-gsub("[^\\d]+", "", gsub("^([0-9]+)", "",vec_cell), perl=TRUE)
for (i in (1:length(vec_cell))){
row_id=which(rownames(mat_plate)==vec_cellrow[i])
col_id=which(colnames(mat_plate)==vec_cellcol[i])
mat_plate[row_id,col_id]<-vec_ratio[i]
}
mat_plate[mat_plate==1]<-0
return (mat_plate)
}
#function to get input variable name
get_input_name <- function(v1) {
deparse(substitute(v1))
}
addstar<-function(x){a=paste("*",x,sep = "")
return(a)}
cat<-function(x){return (paste(x,collapse = ","))}
#function to identify sources!crosspos,celltype=T,tpm>2^9)
# find_source_1plate<-function(tcr){
# tcr$source<-""
# cell_list<-str_split(tcr$cells,",")
# tpm_list<-str_split(tcr$TPMs,",")
# for (tcrind in 1:length(cell_list)){
# rows<-substr(cell_list[[tcrind]],1,1)
# columns<-substring(cell_list[[tcrind]],2)
# freqtable_row<-table(rows)>1
# common_rows<-names(freqtable_row[freqtable_row=="TRUE"])
# freqtable_col<-table(columns)>1
# common_columns<-names(freqtable_col[freqtable_col=="TRUE"])
# if (length(common_columns)==0|length(common_rows)==0){
# maxcellind<-which.max(tpm_list[[tcrind]])
# cell_type<-celltype$cell.type[celltype$well==paste(rows[maxcellind],columns[maxcellind],sep = "")]##!!
# if (cell_type=="T"){
# tcr$source[tcrind]<-paste(tcr$source[tcrind],paste(rows[maxcellind],columns[maxcellind],sep = ""),sep = ",")
# }
# }else{
# # for (cellind in 1:length(cell_list[[tcrind]])){
# # cell_type<-celltype$cell.type[celltype$well==paste(rows[cellind],columns[cellind],sep = "")]##!!
# # tpm<-tpm_list[[tcrind]][cellind]
# # if (rows[cellind]%in%common_rows && columns[cellind]%in%common_columns &&
# # cell_type=="T" &&
# # tpm%in%as.character(sort(as.numeric(tpm_list[[tcrind]]), decreasing=TRUE)[1:(length(cell_list[[tcrind]])*0.3)]))
# # {
# # tcr$source[tcrind]<-paste(tcr$source[tcrind],paste(common_rows,common_columns,sep = ""),sep = ",")
# # }
# # }
# cross_pos<-sort(apply(expand.grid(common_rows,common_columns), 1, paste, collapse = "", sep = ""))
# cross_cells<-cell_list[[tcrind]][cell_list[[tcrind]]%in%cross_pos]
# source_cells<-cross_cells[as.numeric(tpm_list[[tcrind]][match(cross_cells,cell_list[[tcrind]])])>2^6]#512
# source_cells<-source_cells[celltype$cell.type[match(source_cells,celltype$well)]=="T"]
# tcr$source[tcrind]<-paste(tcr$source[tcrind],paste(source_cells, collapse = ','),sep = ",")
# }
# }
# tcr$source<-gsub("^,","",tcr$source)
# return(tcr)
# }
#function to identify sources
find_source_plate<-function(tcr,type="T")#type="P"
{tcr$source<-""
for (tcrind in 1:length(tcr$cells)){
source_cells=""
cell_vec<-unlist(str_split(tcr$cells[tcrind],","))
tpm_vec<-as.numeric(unlist(str_split(tcr$TPMs[tcrind],",")))
rows<-gsub("([0-9]+)$", "",cell_vec)
freqtable_row<-table(rows)>1
common_rows<-names(freqtable_row[freqtable_row=="TRUE"])
columns<-gsub("[^\\d]+", "",gsub("^([0-9]+)","",cell_vec),perl=TRUE)
freqtable_col<-table(columns)>1
common_columns<-names(freqtable_col[freqtable_col=="TRUE"])
if (length(common_columns)+length(common_rows)>0){#number of common lines
if (length(common_columns)==0|length(common_rows)==0){#cells in the same column(row)
maxcellind<-which.max(tpm_vec)
tpm_source<-as.numeric(tpm_vec[maxcellind])
well<-paste(rows[maxcellind],columns[maxcellind],sep = "")
cell_type<-celltype$cell.type[celltype$well==well]##!!
if (type=="T"){
if (cell_type=="T" && tpm_source>2^5 &&
all(tpm_source>5*tpm_vec[!cell_vec %in% c(well,"A1","1A1","2A1","3A1","4A1")]))
{tcr$source[tcrind]<-paste(tcr$source[tcrind],well,sep = ",")}}
else if(type=="P"){
if (cell_type=="P" && tpm_source>2^5 &&
all(tpm_source>5*tpm_vec[!cell_vec %in% c(well,"A1","1A1","2A1","3A1","4A1")]))
{tcr$source[tcrind]<-paste(tcr$source[tcrind],well,sep = ",")}}
}else{#cells in multiple columns(rows)
cross_pos<-sort(apply(expand.grid(common_rows,common_columns), 1, paste, collapse = "", sep = ""))
cross_cells<-cell_vec[cell_vec%in%cross_pos]
source_cells<-cross_cells[as.numeric(tpm_vec[match(cross_cells,cell_vec)])>2^5]#512
if (length(source_cells)>0){
scells<-source_cells
source_tpms<-tpm_vec[match(source_cells,cell_vec)]
if(exists("cells_in_line_sources")){rm(cells_in_line_sources)}
for (i in 1:length(source_cells)){
ifelse(length(source_cells)==1,scell<-source_cells,scell<-source_cells[i])
ifelse(length(source_cells)==1,source_tpm<-as.numeric(source_tpms),source_tpm<-as.numeric(source_tpms[i]))
source_row<-gsub("([0-9]+)$", "",scell)
source_column<-gsub("[^\\d]+", "",gsub("^([0-9]+)","",scell),perl=TRUE)
is_cells_in_line<-(rows==source_row|columns==source_column)
cells_in_line<-cell_vec[is_cells_in_line]
ifelse(exists("cells_in_line_sources"),cells_in_line_sources<-c(cells_in_line_sources,cells_in_line),cells_in_line_sources<-cells_in_line)
tpm_in_line<-as.numeric(tpm_vec[match(cells_in_line,cell_vec)])
if (!all(source_tpm>5*tpm_in_line[!cells_in_line %in% c(cells_in_line_sources,"A1","1A1","2A1","3A1","4A1")]) | source_tpm<2^5){
scells<-scells[scells != scell]#remove scell from source_cells
cells_in_line_sources[!cells_in_line_sources%in%cells_in_line]
}
}
ifelse(type=="T",source_cells<-scells[celltype$cell.type[match(scells,celltype$well)]=="T"],
source_cells<-scells[celltype$cell.type[match(scells,celltype$well)]=="P"])
}
}
}
tcr$source[tcrind]<-paste(source_cells,collapse = ",")
}
return(tcr)
}
#add a list of source_cells for each V_genes in v1
append_subset_v<-function(tcr_source,v){
v_gene_vec<-str_split_fixed(tcr_source$TCR,"_",2)[,1]#[nchar(tcr1_source$source)!=0]
tcr_source$V_GENE<-v_gene_vec
sv<-v[str_split_fixed(v$V_GENE,"_",2)[,1] %in% v_gene_vec,]
sv$V_GENE<-str_split_fixed(sv$V_GENE,"_",2)[,1]
sv<-merge(sv,aggregate(source~V_GENE,data = tcr_source,FUN = c),by="V_GENE")
sv$source<-lapply(sv$source,function(x){paste(x,collapse = ",")})
return(sv)
}
get_potential_source1<-function(tcr_source,sv)
{potential_source<-rep(NA,dim(sv)[1])
v_vec<-unique(str_split_fixed(tcr_source$TCR,"_",2)[,1])
v_vec[1]<-"TRAV38-2_DV8"########only plate1
for (i in 1:length(v_vec)){
acell<-sv$cells[which(sv$V_GENE==v_vec[i])]
acell<-unlist(str_split(acell,","))
is_t<-celltype$cell.type[match(acell,celltype$well)]=="T"
atpm<-sv$TPMs[which(sv$V_GENE==v_vec[i])]
atpm<-as.numeric(unlist(str_split(atpm,",")))
is_hightpm<-atpm>2^5
rows<-gsub("([0-9]+)$", "", acell)
columns<-gsub("[^\\d]+", "", acell, perl=TRUE)
is_spread<-unlist(lapply(rows,function(x) length(x[rows==x])>1))&unlist(lapply(columns,function(x) length(x[columns==x])>1))
potential_source[i]<-paste(acell[is_t&is_hightpm&is_spread],collapse = ",")
}
return(potential_source)
}
get_potential_source23<-function(tcr_source,sv)
{potential_source<-rep(NA,dim(sv)[1])
v_vec<-unique(str_split_fixed(tcr_source$TCR,"_",2)[,1])
for (i in 1:length(v_vec)){
acell<-sv$cells[which(sv$V_GENE==v_vec[i])]
acell<-unlist(str_split(acell,","))
# acell[!grepl("[*]",acell)]<-paste("1",acell[!grepl("[*]",acell)],sep = "")
# acell<-gsub("[*]","2",acell)
is_t<-celltype$cell.type[match(acell,celltype$well)]=="T"
atpm<-sv$TPMs[which(sv$V_GENE==v_vec[i])]
atpm<-as.numeric(unlist(str_split(atpm,",")))
is_hightpm<-atpm>2^5
rows<-gsub("([0-9]+)$", "", acell)
columns<-gsub("[^\\d]+", "", acell, perl=TRUE)
is_spread<-unlist(lapply(rows,function(x) length(x[rows==x])>1))&unlist(lapply(columns,function(x) length(x[columns==x])>1))
potential_source[i]<-paste(acell[is_t&is_hightpm&is_spread],collapse = ",")
}
return(potential_source)
}
get_potential_source45<-function(tcr_source,sv)
{potential_source<-rep(NA,dim(sv)[1])
v_vec<-unique(str_split_fixed(tcr_source$TCR,"_",2)[,1])
for (i in 1:length(v_vec)){
acell<-sv$cells[which(sv$V_GENE==v_vec[i])]
acell<-unlist(str_split(acell,","))
# acell[!grepl("[*]",acell)]<-paste("3",acell[!grepl("[*]",acell)],sep = "")
# acell<-gsub("[*]","4",acell)
is_t<-celltype$cell.type[match(acell,celltype$well)]=="T"
atpm<-sv$TPMs[which(sv$V_GENE==v_vec[i])]
atpm<-as.numeric(unlist(str_split(atpm,",")))
is_hightpm<-atpm>2^5
rows<-gsub("([0-9]+)$", "", acell)
columns<-gsub("[^\\d]+", "", acell, perl=TRUE)
is_spread<-unlist(lapply(rows,function(x) length(x[rows==x])>1))&unlist(lapply(columns,function(x) length(x[columns==x])>1))
potential_source[i]<-paste(acell[is_t&is_hightpm&is_spread],collapse = ",")
}
return(potential_source)
}
#to check if a cell is source for v gene
check_source<-function(gene,cell_list,use="vsourcelist")#use=tcrsourcelist,vsourcelist,bcrsourcelist,sbcrsourcelist,ntbcrsourcelist
{cell_list<-unlist(strsplit(cell_list,split=","))
n=length(cell_list)
ifs<-rep(NA,n)
for (i in 1:n){
if (!is.na(cell_list[1])){
scells<-NA
if(use=="vsourcelist"){ifs[i]<-gene%in%sourcelist[cell_list[i],]}
else if(use=="tcrsourcelist"){
ifelse(grepl("^TRA",gene),ab<-2,ab<-3)
ifs[i]<-gene%in%tcrsourcelist[cell_list[i],]|is.na(tcrsourcelist[cell_list[i],ab])
}
else if(use=="bcrsourcelist"){ifs[i]<-gene%in%bcrsourcelist[cell_list[i],]}
else if(use=="sbcrsourcelist"){ifs[i]<-gene%in%sbcrsourcelist[cell_list[i],]}
else if(use=="ntbcrsourcelist"){ifs[i]<-gene%in%ntbcrsourcelist[cell_list[i],]}
scells<-paste(cell_list[ifs],collapse = ",")
}
}
return (scells)
}
#filter non_source
filter_non_source<-function(gene,cell_list,use)#use=tcrsourcelist,vsourcelist,bcrsourcelist,sbcrsourcelist,ntbcrsourcelist
{#cell_list<-unlist(strsplit(cell_list,split=","))
n=length(cell_list)
ifs<-rep(NA,n)
for (i in 1:n){
if (!is.na(cell_list[1])){
scells<-NA
if(use=="v"){ifs[i]<-!gene%in%sourcelist[cell_list[i],]}
else if(use=="tcr"){
ifelse(grepl("^TRA",gene),ab<-2,ab<-3)
ifs[i]<-!gene%in%tcrsourcelist[cell_list[i],]
}
else if(use=="bcr"){!ifs[i]<-gene%in%bcrsourcelist[cell_list[i],]}
else if(use=="aa"){!ifs[i]<-gene%in%sbcrsourcelist[cell_list[i],]}
else if(use=="nt"){!ifs[i]<-gene%in%ntbcrsourcelist[cell_list[i],]}
scells<-paste(cell_list[ifs],collapse = ",")
}
}
return (scells)
}
#get expression array from v gene$filtered_source
get_array<-function(sv,n_plate="T"){
source_cells<-unlist(str_split(paste(sv$filted_source[sv$filted_source!=""],collapse = ","),","))
ifelse(n_plate=="T",vexpr<-array(0,dim = c(16,12,length(source_cells))),vexpr<-array(0,dim = c(8,12,length(source_cells))))
k=1
for (i in 1:length(sv$filted_source)){#loop through all v genes in sv
if(!is.na(sv$filted_source[i])&&sv$filted_source[i]!=""){
cell_vec<-unlist(str_split(sv$cells[[i]],","))
tpm_vec<-unlist(str_split(sv$TPMs[[i]],","))
source_cell_vec<-unlist(str_split(sv$filted_source[[i]],","))
source_cell_vec<-gsub("^[1,3]","",source_cell_vec)
source_cell_vec<-gsub("^[2,4]","*",source_cell_vec)
source_tpm_vec<-tpm_vec[cell_vec%in%source_cell_vec]
rows<-gsub("([0-9]+)$", "",cell_vec)
columns<-gsub("[^\\d]+", "",cell_vec,perl=TRUE)
if (length(source_cell_vec)==1){#only one source cell
id<-which(cell_vec==source_cell_vec)
tpm_source<-tpm_vec[id]
l<-rows%in%(gsub("([0-9]+)$", "",source_cell_vec))|columns%in%gsub("[^\\d]+", "",source_cell_vec,perl=TRUE)
cells_line<-cell_vec[l]#cells in the same row|column with the source
allrowcells<-paste(gsub("([0-9]+)$", "",source_cell_vec),1:12,sep="")
ifelse(n_plate=="F",
allcolumncells<-paste(c("A","B","C","D","E","F","G","H"),gsub("[^\\d]+", "",source_cell_vec,perl=TRUE),sep=""),
allcolumncells<-paste(c("A","B","C","D","E","F","G","H","*A","*B","*C","*D","*E","*F","*G","*H"),gsub("[^\\d]+", "",source_cell_vec,perl=TRUE),sep=""))
tpm_vec_line<-c(tpm_vec[l],rep(0,length(setdiff(union(allcolumncells,allrowcells),cells_line))))
cells_line<-c(cells_line,setdiff(union(allcolumncells,allrowcells),cells_line))
ratios<-as.numeric(tpm_vec_line)/as.numeric(tpm_source)
ifelse(n_plate=="F",vexpr[,,k]<-expr_matrix(cells_line,ratios),vexpr[,,k]<-expr_2matrix(cells_line,ratios))
k=k+1
}
else{#multiple sources
for (j in 1:length(source_cell_vec)){
tpm_vec<-unlist(str_split(sv$TPMs[[i]],","))
id<-which(cell_vec==source_cell_vec[j])
tpm_source<-tpm_vec[id]
is_cells_in_row<-rows==(gsub("([0-9]+)$", "",source_cell_vec[j]))
is_cells_in_col<-columns==gsub("[^\\d]+", "",source_cell_vec[j],perl=TRUE)
is_cells_in_line<-is_cells_in_row|is_cells_in_col
cell_in_line<-cell_vec[is_cells_in_line]#cells in the same row|column with the source:source_cell_vec[j]
tpm_in_line<-tpm_vec[is_cells_in_line]
for (t in cell_in_line) {
if (t!=source_cell_vec[j]){
t_row<-gsub("([0-9]+)$", "",t)
t_col<-regmatches(t, regexpr("\\d$", t))
is_sources_in_row<-gsub("([0-9]+)$", "",source_cell_vec)==t_row
is_sources_in_col<-gsub("[^\\d]+", "",source_cell_vec, perl=TRUE)==t_col
if ((sum(is_sources_in_row)+sum(is_sources_in_col))>1)
{
perc_tpm<-as.numeric(tpm_source)/sum(as.numeric(source_tpm_vec[is_sources_in_row|is_sources_in_col]))
tpm_in_line[match(t,cell_in_line)]<-as.numeric(tpm_in_line[match(t,cell_in_line)])*perc_tpm
}
}
}
allrowcells<-paste(gsub("([0-9]+)$", "",source_cell_vec[j]),1:12,sep="")
ifelse(n_plate=="F",
allcolumncells<-paste(c("A","B","C","D","E","F","G","H"),gsub("[^\\d]+", "",source_cell_vec[j],perl=TRUE),sep=""),
allcolumncells<-paste(c("A","B","C","D","E","F","G","H","*A","*B","*C","*D","*E","*F","*G","*H"),gsub("[^\\d]+", "",source_cell_vec[j],perl=TRUE),sep=""))
app_tpm_in_line<-c(tpm_in_line,rep(0,length(setdiff(union(allcolumncells,allrowcells),cell_in_line))))
app_cell_in_line<-c(cell_in_line,setdiff(union(allrowcells,allcolumncells),cell_in_line))
ratios<-as.numeric(app_tpm_in_line)/as.numeric(tpm_source)
ratios[which(app_cell_in_line==source_cell_vec[j])]<-0
ifelse(n_plate=="F",vexpr[,,k]<-expr_matrix(app_cell_in_line,ratios),vexpr[,,k]<-expr_2matrix(app_cell_in_line,ratios))
k=k+1
}
}
}
}
return(vexpr)
}
#get expression array from tcr_source$source
get_array_tcr<-function(tcr_source,n_plate="T")
{source_cells<-unlist(str_split(paste(tcr_source$source[tcr_source$source!=""],collapse = ","),","))
ifelse(n_plate=="T",tcr_array<-array(0,dim = c(16,12,length(source_cells))),tcr_array<-array(0,dim = c(8,12,length(source_cells))))
k=1
for (i in 1:length(tcr_source$source)){
cell_vec<-unlist(str_split(tcr_source$cells[[i]],","))
tpm_vec<-unlist(str_split(tcr_source$TPMs[[i]],","))
source_cell_vec<-unlist(str_split(tcr_source$source[[i]],","))
source_cell_vec<-gsub("^[1,3]","",source_cell_vec)
source_cell_vec<-gsub("^[2,4]","*",source_cell_vec)
source_tpm_vec<-tpm_vec[cell_vec%in%source_cell_vec]
rows<-gsub("([0-9]+)$", "",cell_vec)
columns<-gsub("[^\\d]+", "",cell_vec,perl=TRUE)
if (source_cell_vec!=""&&length(source_cell_vec)==1){#only one source cell
id<-which(cell_vec==source_cell_vec)
tpm_source<-tpm_vec[id]
l<-rows%in%(gsub("([0-9]+)$", "",source_cell_vec))|columns%in%gsub("[^\\d]+", "",source_cell_vec,perl=TRUE)
cells_line<-cell_vec[l]#cells in the same row|column with the source
allrowcells<-paste(gsub("([0-9]+)$", "",source_cell_vec),1:12,sep="")
ifelse(n_plate=="F",
allcolumncells<-paste(c("A","B","C","D","E","F","G","H"),gsub("[^\\d]+", "",source_cell_vec,perl=TRUE),sep=""),
allcolumncells<-paste(c("A","B","C","D","E","F","G","H","*A","*B","*C","*D","*E","*F","*G","*H"),gsub("[^\\d]+", "",source_cell_vec,perl=TRUE),sep=""))
tpm_vec_line<-c(tpm_vec[l],rep(0,length(setdiff(union(allcolumncells,allrowcells),cells_line))))
cells_line<-c(cells_line,setdiff(union(allcolumncells,allrowcells),cells_line))
ratios<-as.numeric(tpm_vec_line)/as.numeric(tpm_source)
ifelse(n_plate=="F",tcr_array[,,k]<-expr_matrix(cells_line,ratios),tcr_array[,,k]<-expr_2matrix(cells_line,ratios))
k=k+1
}else if(source_cell_vec!=""&&length(source_cell_vec)>1){#multiple source cell
for (j in 1:length(source_cell_vec)){
tpm_vec<-unlist(str_split(tcr_source$TPMs[[i]],","))
id<-which(cell_vec==source_cell_vec[j])
tpm_source<-tpm_vec[id]
is_cells_in_row<-rows==(gsub("([0-9]+)$", "",source_cell_vec[j]))
is_cells_in_col<-columns==gsub("[^\\d]+", "",source_cell_vec[j],perl=TRUE)
is_cells_in_line<-is_cells_in_row|is_cells_in_col
cell_in_line<-cell_vec[is_cells_in_line]#cells in the same row|column with the source:source_cell_vec[j]
tpm_in_line<-tpm_vec[is_cells_in_line]
for (t in cell_in_line) {
if (t!=source_cell_vec[j]){
t_row<-gsub("([0-9]+)$", "",t)
t_col<-regmatches(t, regexpr("\\d$", t))
is_sources_in_row<-gsub("([0-9]+)$", "",source_cell_vec)==t_row
is_sources_in_col<-gsub("[^\\d]+", "",source_cell_vec)==t_col
if ((sum(is_sources_in_row)+sum(is_sources_in_col))>1)
{
perc_tpm<-as.numeric(tpm_source)/sum(as.numeric(source_tpm_vec[is_sources_in_row|is_sources_in_col]))
tpm_in_line[match(t,cell_in_line)]<-as.numeric(tpm_in_line[match(t,cell_in_line)])*perc_tpm
}
}
}
allrowcells<-paste(gsub("([0-9]+)$", "",source_cell_vec[j]),1:12,sep="")
ifelse(n_plate=="F",
allcolumncells<-paste(c("A","B","C","D","E","F","G","H"),gsub("[^\\d]+", "",source_cell_vec[j],perl=TRUE),sep=""),
allcolumncells<-paste(c("A","B","C","D","E","F","G","H","*A","*B","*C","*D","*E","*F","*G","*H"),gsub("[^\\d]+", "",source_cell_vec[j],perl=TRUE),sep=""))
app_tpm_in_line<-c(tpm_in_line,rep(0,length(setdiff(union(allcolumncells,allrowcells),cell_in_line))))
app_cell_in_line<-c(cell_in_line,setdiff(union(allrowcells,allcolumncells),cell_in_line))
ratios<-as.numeric(app_tpm_in_line)/as.numeric(tpm_source)
ratios[which(app_cell_in_line==source_cell_vec[j])]<-0
ifelse(n_plate=="F",tcr_array[,,k]<-expr_matrix(app_cell_in_line,ratios),tcr_array[,,k]<-expr_2matrix(app_cell_in_line,ratios))
k=k+1
}
}
}
return(tcr_array)
}
collect_rows<-function(array,array_use=""){
ifelse(array_use=="",n<-dim(array)[3],n<-length(array_use))
target_rows<-matrix(NA,nrow = n,ncol = 12)
row_freq<-rep(0,dim(array)[1])
row_ids<-NA
for (i in 1:n){
row_idx<-which(rowSums(array[,,i])>=0)
row_freq[row_idx]=row_freq[row_idx]+1
target_rows[i,]<-array[,,i][row_idx,]
ifelse(is.na(row_ids),row_ids<-row_idx,row_ids<-c(row_ids,row_idx))
}
return(list(target_rows,row_freq,row_ids))
}
collect_cols<-function(array,array_use=""){
ifelse(array_use=="",n<-dim(array)[3],n<-length(array_use))
k<-dim(array)[1]
target_columns<-matrix(NA,nrow = k,ncol = n)
col_freq<-rep(0,12)
col_ids<-NA
for (i in 1:n){
col_idx<-which(colSums(array[,,i])>=0)
col_freq[col_idx]=col_freq[col_idx]+1
target_columns[,i]<-array[,,i][,col_idx]
ifelse(is.na(col_ids),col_ids<-col_idx,col_ids<-c(col_ids,col_idx))
}
return(list(target_columns,col_freq,col_ids))
}
#correct the sequences such that allow one nt mismatch if BCRs with the same V_J
collapse_xnt_mismatch<-function(bcr,num_nt){
bcr$V<-str_split_fixed(bcr$ReceptorID, "_", 3)[,1]
bcr$N<-str_split_fixed(bcr$ReceptorID, "_", 3)[,2]
bcr$J<-str_split_fixed(bcr$ReceptorID, "_", 3)[,3]
bcr$V_J<-paste(bcr$V,bcr$J,sep='_')
#bcr$topTPM<-unlist(lapply(str_split(bcr$TPM,","),function(x) max(as.numeric(x))))
for (i in unique(bcr$V_J)){
ids<-which(bcr$V_J==i)
if (length(ids)>1){
# pairp=combn(bcr$ReceptorID[ids],2)
#ind_maxtpm<-which.max(bcr[ids,]$topTPM)
ind_maxtpm<-which.max(bcr[ids,]$TPM)
# ref<-bcr[ids,]$N[ind_maxtpm]
#ref<-bcr[ind_maxtpm,]$full_sequence??????
ref<-bcr[ids,]$CDR3nt[ind_maxtpm]
refaa<-bcr[ids,]$CDR3aa[ind_maxtpm]
ids <- ids[-ind_maxtpm]
for (j in ids)
{
# if (adist(bcr$N[j],ref)<=num_nt)
if (adist(bcr$CDR3nt[j],ref)<=num_nt)
{
# bcr$N[j]<-ref #change the nt seq
bcr$CDR3nt[j]<-ref
bcr$CDR3aa[j]<-refaa
}
}
}
}
bcr<-bcr[,-which(names(bcr)%in%c("V","N","J","V_J"))]
return(bcr)}
#
# bcr$ReceptorID<-paste(bcr$V,bcr$N,bcr$J,sep = "_")
# bcr$Freq<-as.numeric(bcr$Freq)
# # v1 <- aggregate(Freq~ReceptorID,data=bcr,FUN=sum)
# # v2 <- aggregate(cells~ReceptorID,data=bcr,FUN=c)
# # v3 <- aggregate(TPMs~ReceptorID,data=bcr,FUN=c)
# # bcr <- merge(v1, v2, by="ReceptorID")
# # bcr <- merge(bcr, v3, by="ReceptorID")
# v1 <- aggregate(Freq~full_sequence,data=bcr,FUN=sum)
# v2 <- aggregate(cells~full_sequence,data=bcr,FUN=c)
# v3 <- aggregate(TPMs~full_sequence,data=bcr,FUN=c)
# bcr <- merge(v1, v2, by="full_sequence")
# bcr <- merge(bcr, v3, by="full_sequence")
# for (i in 1:dim(bcr)[1]){
# if (length(unlist(bcr$cells[i]))>1){
# cells<-unlist(str_split(paste(unlist(bcr$cells[i]),collapse =","),","))
# if(length(unique(cells))<length(cells)){
# tpms<-as.numeric(unlist(str_split(paste(unlist(bcr$TPMs[i]),collapse =","),",")))
# df<-data.frame(cells,tpms)
# adf<-aggregate(tpms~cells,data = df, FUN=sum)
# bcr$cells[i]<-paste((adf$cells),collapse =",")
# bcr$TPMs[i]<-paste((adf$tpms),collapse =",")
# }else{
# bcr$cells[i]<-paste(unlist(bcr$cells[i]),collapse =",")
# bcr$TPMs[i]<-paste(unlist(bcr$TPMs[i]),collapse =",")
# }
# }
# else
# { bcr$cells[i]<-unlist(bcr$cells[i])
# bcr$TPMs[i]<-unlist(bcr$TPMs[i])
# }
# }
# bcr$cells<-unlist(bcr$cells)
# bcr$TPMs<-unlist(bcr$TPMs)
# return(bcr)
# }
merge_seq<-function(bracer,bracer_seq){
bracer<-bracer[order(bracer[,1], bracer[,2]), ]
bracer_seq<-bracer_seq[order(bracer_seq[,1], bracer_seq[,2]), ]
bracer<-cbind(bracer,bracer_seq$Sequence)
colnames(bracer)[4]<-"sequence"
return(bracer)
}
|
65c8fa5f386413800dcae68bdc4df37ff4eedcbf
|
f3fbe9e4f49764e088531485f14c8f0962569420
|
/R-code/do_crisscross_hanning_50_wosa.R
|
9f2ba5096cc91b91f053a45436b9013d65652d84
|
[] |
no_license
|
dmn001/sauts
|
d3a678091a081679561db2ada077a7dc9630847d
|
2797e0ab943fb02ebea82df32bd1cc2748c54dcc
|
refs/heads/master
| 2023-04-30T12:13:14.502267
| 2021-05-22T16:11:15
| 2021-05-22T16:11:15
| 369,851,701
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 386
|
r
|
do_crisscross_hanning_50_wosa.R
|
### generate crisscross for WOSA with Hanning data taper and 50% overlap
do_crisscross_hanning_50_wosa <- function(the_taper,N_B,n_or_block_starts,delta_t=1)
{
edof <- 36*N_B^2/(19*N_B-1)
return(list(up = 10*log10(edof/qchisq(0.025,edof)),
down = -10*log10(edof/qchisq(0.975,edof)),
width = B_H(the_taper,delta_t),
edof=edof))
}
|
00f5e526179ed11de4ee6b7e06d9cc8ddb0432bd
|
1f19663b2f7daf18abda91a926fd8e08cb83dc8d
|
/R/createConnection.R
|
e83cb0c77622eced30e84993faf865d405e2230f
|
[] |
no_license
|
horlar1/R.Deploymodel
|
335dbaffb3a06382ceb3223cfc2ac84ca94955cd
|
01c4bdb670580f36e4eeb1625c9ef926f7be149f
|
refs/heads/master
| 2020-12-05T13:19:54.807811
| 2020-01-07T12:14:32
| 2020-01-07T12:14:32
| 232,123,601
| 2
| 0
| null | 2020-01-07T12:14:33
| 2020-01-06T14:56:57
|
R
|
UTF-8
|
R
| false
| false
| 1,046
|
r
|
createConnection.R
|
#' Create a connection to the server
#' @param server server name for authentication (charcter string)
#' @param database database name
#' @param username username for authentication
#' @param password for authenitication (if required)
#' @export
#' @importFrom RODBC odbcDriverConnect
#' @seealso \code{\link[RODBC]{odbcConnect}}
#' @examples \dontrun{
#' # don't run this sript
#' con = createConnection(server = "",database = "",username="",password="")
#' }
createConnection <- function(server,database,username,password){
if(missing(server))
stop("missing argument 'server name'")
if(missing(database))
stop("missing argument 'database name'")
if(missing(username))
stop("missing argument 'username'")
if(missing(password))
stop("missing argument 'password'")
dbconnection<- odbcDriverConnect(paste0("Driver=ODBC Driver 13 for SQL Server;
Server=",server,"; Database=",database,";
Uid=",username,"; Pwd=",password,""))
return(dbconnection)
}
|
c732858b6414c90f00b0a0d35a9f3659028f4609
|
1c4caaa37a9aaeac8c0fa73a417503d6ed05ef53
|
/inst/scripts/fig2a.R
|
7a1cd60a5d008c4db093699d8218f3c9fe0e9a03
|
[] |
no_license
|
metamaden/recountmethylation_manuscript_supplement
|
08996bf66fa0ad920b35107105d27b94663fb275
|
4a254783b8933411a80032e33549ae94c1544ad6
|
refs/heads/master
| 2020-12-27T09:22:39.405834
| 2020-10-09T06:27:08
| 2020-10-09T06:27:08
| 237,849,928
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,656
|
r
|
fig2a.R
|
#!/usr/bin/env R
# Make figure 2A, BeadArray controls stacked barplot
# of sample performance outcomes.
library(ggplot2)
library(ggforce)
library(data.table)
library(recountmethylationManuscriptSupplement)
pkgname <- "recountmethylationManuscriptSupplement"
tables.dir <- system.file("extdata", "tables", package = pkgname)
#----------
# load data
#----------
fn <- "table-s2_qcmd-allgsm.csv"
qcmd <- fread(file.path(tables.dir, fn), sep = ",", data.table = FALSE)
which.bacol <- which(grepl("^ba.*", colnames(qcmd)))
bat <- qcmd[,c(1, which.bacol)]
colnames(bat) <- gsub("^ba\\.", "", colnames(bat))
#---------------
# make plot data
#---------------
tr <- bathresh(bat)
dfplot <- matrix(nrow = 0, ncol = 3)
for(c in colnames(tr)[2:18]){
num.pass <- length(which(tr[,c] == "PASS"))
num.fail <- length(which(tr[,c] == "FAIL"))
dfplot = rbind(dfplot, matrix(c(num.pass, "ABOVE", c), nrow = 1))
dfplot = rbind(dfplot, matrix(c(num.fail, "BELOW", c), nrow = 1))
}
dfplot = as.data.frame(dfplot, stringsAsFactors = F)
dfplot[,1] = as.numeric(dfplot[,1])
colnames(dfplot) = c("Num. Samples", "Thresh. Count", "Metric")
dfi = dfplot[dfplot[,2] == "BELOW",]
dfplot$Metric = factor(dfplot$Metric, levels = dfi[,3][rev(order(dfi[,1]))])
#----------
# make plot
#----------
fig2a <- ggplot(dfplot, aes(x = `Metric`, y = `Num. Samples`, fill = `Thresh. Count`)) +
geom_bar(stat = 'identity') + scale_fill_manual(values = c("limegreen", "red")) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
facet_zoom(ylim = c(0, 2500), zoom.data = ifelse(a <= 10, NA, FALSE)) +
xlab("BeadArray Metric") + ylab("Number of\nSamples")
|
9fc80399c7534fd73f76b00ab61f1c964470c154
|
3ea675eb05c11dd66e095eb4713308616e0036ed
|
/cachematrix.R
|
1ba52409b7b2df3e44e648314daca015de6a1c06
|
[] |
no_license
|
Khizermark/ProgrammingAssignment2
|
a8a7c4d63f6c205319f22e05d0e0e3df47125097
|
3b033a9f148593a80d5db81e3a63e99ba2e59e3e
|
refs/heads/master
| 2021-01-15T22:28:23.136012
| 2015-02-22T20:14:42
| 2015-02-22T20:14:42
| 31,175,968
| 0
| 0
| null | 2015-02-22T19:20:12
| 2015-02-22T19:20:10
| null |
UTF-8
|
R
| false
| false
| 1,833
|
r
|
cachematrix.R
|
#######################################################################
############ Describing Each Steps as it goes!!########################
#######################################################################
makeCacheMatrix <- function(x = matrix()) {
inverse <- matrix() ## Creating variable for storing inverse...
## By default inverse will have NAs
setmatrix <- function(y){ ## Storing the Matrix in X
x <<- y
m <<- matrix() ## Variable for inverse
}
getmatrix <- function() x ## Function to get the value of Matrix
setinverse <- function(inverse) m <<- inverse ## Function to store the value of inverse
getinverse <- function() inverse ## Function to get value of inverse
list(setmatrix = setmatrix, getmatrix = getmatrix,
setinverse = setinverse, ## List which returns all the above
getinverse = getinverse)
}
cacheSolve <- function(x, ...) {
## Returns a matrix that is the inverse of 'x'
m <- x$getinverse() ## Getting the value of inverse....
if(!is.na(m)) { ## Checking whether m is empty matrix
message("getting inverse of required matrix")
return(m) ## if m is not empty, its value is returned, X
} ## Otherwise, it is computed in following lines
data <- x$getmatrix() ## Getting Matrix Data
m <- solve(data, ...) ## Solving for inverse of required matrix
x$setinverse(m) ## Storing that inverse value in m
m ## Returning the inverse matrix m
}
|
7c6c2abd44dea43bd28158f83ec8414079101386
|
0b5d4f07fb7c6c1b574503877389f1ca4949a23d
|
/tests/testthat/test_aresimcn.R
|
29c143b519bd38850463cab74a58248f0ae7afde
|
[] |
no_license
|
joemckean/mathstat
|
0673c5f97064b8f4ac77d21ce3f10062e98b91f6
|
5e008df47b5549730c5e219b62c2cec55ecf3c3f
|
refs/heads/master
| 2020-04-08T14:34:52.117179
| 2019-04-22T17:58:40
| 2019-04-22T17:58:40
| 159,443,068
| 2
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,978
|
r
|
test_aresimcn.R
|
# Test for aresimcn function Returns: An error message if any
# tests fail
context("aresimcn")
test_that("edge cases", {
expect_error(aresimcn(Inf), "argument 1 cannot include an Inf or -Inf")
expect_error(aresimcn(n = -1), "argument 1 must be positive")
expect_error(aresimcn(n = 50, nsims = Inf), "argument 2 cannot include an Inf or -Inf")
expect_error(aresimcn(n = 50, nsims = -1), "argument 2 must be positive")
expect_error(aresimcn(n = 50, eps = -1), "input argument 'eps' must be between zero and one")
expect_error(aresimcn(n = 50, eps = 1), "input argument 'eps' must be between zero and one")
expect_error(aresimcn(n = 50, nsims = 100, eps = Inf), "argument 3 cannot include an Inf or -Inf")
expect_error(aresimcn(n = 50, nsims = 100, vc = -1), "argument 4 must be positive")
expect_error(aresimcn(n = 50, vc = Inf), "argument 4 cannot include an Inf or -Inf")
})
test_that("input", {
# Checking invalid input for n
expect_error(aresimcn(n = 0), "argument 1 must be numeric and non-zero")
expect_error(aresimcn(n = NA), "argument 1 must be a number")
expect_error(aresimcn(n = NA), "argument 1 must be numeric and non-zero")
expect_error(aresimcn(n = NA), "argument 1 must be positive")
# Checking invalid input for nsims
expect_error(aresimcn(nsims = NA), "argument 2 must be a number")
expect_error(aresimcn(nsims = NA), "argument 2 must be numeric and non-zero")
expect_error(aresimcn(nsims = NA), "argument 2 must be positive")
# Checking invalid input for eps
expect_error(aresimcn(n = 50, eps = NA), "argument 3 must be a number")
# Checking invalid input for vc
expect_error(aresimcn(n = 50, vc = NA), "argument 4 must be positive")
expect_error(aresimcn(n = 50, vc = NA), "argument 4 must be a number")
})
test_that("output", {
expect_equal(is.numeric(aresimcn(30, 100, 0.25, 3)), TRUE)
expect_equal(is.vector(aresimcn(30, 100, 0.25, 3)), TRUE)
expect_equal(length(aresimcn(30, 100, 0.25, 3)), 1)
})
|
1cc26a66aef7c2154365e53da1b841f98640b355
|
ffdea92d4315e4363dd4ae673a1a6adf82a761b5
|
/data/genthat_extracted_code/spdep/examples/autocov_dist.Rd.R
|
0de782b567262c5042512492b31b51082d8d3f52
|
[] |
no_license
|
surayaaramli/typeRrh
|
d257ac8905c49123f4ccd4e377ee3dfc84d1636c
|
66e6996f31961bc8b9aafe1a6a6098327b66bf71
|
refs/heads/master
| 2023-05-05T04:05:31.617869
| 2019-04-25T22:10:06
| 2019-04-25T22:10:06
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,477
|
r
|
autocov_dist.Rd.R
|
library(spdep)
### Name: autocov_dist
### Title: Distance-weighted autocovariate
### Aliases: autocov_dist
### Keywords: spatial
### ** Examples
if (require(rgdal, quietly=TRUE)) {
example(columbus, package="spData")
xy <- cbind(columbus$X, columbus$Y)
ac1a <- autocov_dist(columbus$CRIME, xy, nbs=10, style="B",
type="one")
acinva <- autocov_dist(columbus$CRIME, xy, nbs=10, style="B",
type="inverse")
acinv2a <- autocov_dist(columbus$CRIME, xy, nbs=10, style="B",
type="inverse.squared")
plot(ac1a ~ columbus$CRIME, pch=16, asp=1)
points(acinva ~ columbus$CRIME, pch=16, col="red")
points(acinv2a ~ columbus$CRIME, pch=16, col="blue")
abline(0,1)
nb <- dnearneigh(xy, 0, 10)
lw <- nb2listw(nb, style="B")
ac1b <- lag(lw, columbus$CRIME)
all.equal(ac1b, ac1a)
nbd <- nbdists(nb, xy)
gl <- lapply(nbd, function(x) 1/x)
lw <- nb2listw(nb, glist=gl)
acinvb <- lag(lw, columbus$CRIME)
all.equal(acinvb, acinva)
gl2 <- lapply(nbd, function(x) 1/(x^2))
lw <- nb2listw(nb, glist=gl2)
acinv2b <- lag(lw, columbus$CRIME)
all.equal(acinv2b, acinv2a)
glm(CRIME ~ HOVAL + ac1b, data=columbus, family="gaussian")
spautolm(columbus$CRIME ~ HOVAL, data=columbus,
listw=nb2listw(nb, style="W"))
xy <- SpatialPoints(xy)
acinva <- autocov_dist(columbus$CRIME, xy, nbs=10, style="W",
type="inverse")
nb <- dnearneigh(xy, 0, 10)
nbd <- nbdists(nb, xy)
gl <- lapply(nbd, function(x) 1/x)
lw <- nb2listw(nb, glist=gl)
acinvb <- lag(lw, columbus$CRIME)
all.equal(acinvb, acinva)
}
|
a439023752a853ca1363c26525a8fe02feec7727
|
13ce5625673daf9a82cda276f5a1bee58ba1dcd1
|
/man/reduce_space.Rd
|
2cc33a880cdf5de2cbb7abf2a025d83e55b57571
|
[
"Apache-2.0"
] |
permissive
|
edzer/gdalcubes_R
|
afe19d9e97f695e7838fb6ab4719fc50f2a04d34
|
ed4b705a095eb23fb47017e339b8fb0e0a7a9ec9
|
refs/heads/master
| 2020-04-16T04:26:03.878484
| 2019-01-10T15:36:47
| 2019-01-10T15:36:47
| 165,266,870
| 0
| 1
| null | 2019-01-11T15:37:54
| 2019-01-11T15:37:54
| null |
UTF-8
|
R
| false
| true
| 1,178
|
rd
|
reduce_space.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/streaming.R
\name{reduce_space}
\alias{reduce_space}
\title{Apply a function over space and bands in a four-dimensional (band, time, y, x) array}
\usage{
reduce_space(x, FUN, ...)
}
\arguments{
\item{x}{four-dimensional input bands with dimension order band, time, y, x}
\item{FUN}{function which receives one spatial slice in a three-dimensional array with dimensions bands, y, x as input}
\item{...}{further arguments passed to FUN}
}
\description{
Apply a function over space and bands in a four-dimensional (band, time, y, x) array
}
\details{
FUN is expected to produce a numeric vector (or scalar) where elements are interpreted as new bands in the result
}
\note{
This is a helper function that uses the same dimension ordering as gdalcubes streaming. It can be used to simplify
the application of R functions e.g. over spatial slices in a data cube.
}
\examples{
\dontrun{
load(system.file("extdata","sample_chunk.Rdata", package="gdalcubes"))
y = reduce_space(sample_chunk, function(x) {
ndvi <- (x[8,,]-x[4,,])/(x[8,,]+x[4,,])
return(c(min(ndvi, na.rm=TRUE),max(ndvi, na.rm=T)))
})}
}
|
7b494e17cbb7fb8117e9c69635edce784a19872e
|
41f4fdf3a3bedacc3b5905169dbf5af1e4fc44b6
|
/6205_nsamplemean.R
|
885fa399ff8ab9b435e1dd0f6d84a7d42ae2dec0
|
[] |
no_license
|
henrylankin/stat6205
|
a62867099ca8731a01c31b2f28190bbc7a22c1d7
|
c846a4f25c74e255a9277102195c2c2598a6b9d8
|
refs/heads/master
| 2021-01-23T05:15:14.237114
| 2017-03-27T04:47:22
| 2017-03-27T04:47:22
| 86,292,289
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 410
|
r
|
6205_nsamplemean.R
|
# 6205 normal sample mean distribution plot
mu <- 50
sigma <- 6
x <- seq(mu-3*sigma, mu+3*sigma, by=1)
plot(x, dnorm(x,50,6), type = 'l', lty = 'dotted', col = 'red', ylim = c(0,0.5), ylab = 'density')
x1 <- seq(50-6, 50+6, by=0.1)
lines(x1, dnorm(x1, 50, 6/3), type = 'l', lty = 'dashed', col = 'blue')
x2 <- seq(50-3, 50+3, by = 0.005)
lines(x2, dnorm(x2, 50, 1), type = "l", lty = "twodash", col = "green")
|
3ead8fa5b4bd1058f8fb5c5207a71f8e69086f22
|
1b850eb94c23ee80867279b7ce834e6e9502acc2
|
/Aries_Web/public/recommend/zxz/diazo/js-built/interaction/cbtview-build2013031.js
|
4254a6ff54998a6cc648c0aeb33ab6f9dda471d0
|
[] |
no_license
|
zoeyYan/pro1
|
b64dc8b655224d7f5f450ea63541016c929097c9
|
4fe3571cba6e1143d0ad11297b52e8b3b1dc6d81
|
refs/heads/master
| 2021-01-10T01:51:23.920569
| 2016-01-18T02:46:53
| 2016-01-18T02:46:53
| 49,711,000
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 624,901
|
js
|
cbtview-build2013031.js
|
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* http://jqueryui.com
* Includes: jquery.ui.core.js, jquery.ui.widget.js, jquery.ui.mouse.js, jquery.ui.position.js, jquery.ui.draggable.js, jquery.ui.droppable.js, jquery.ui.resizable.js, jquery.ui.selectable.js, jquery.ui.sortable.js, jquery.ui.effect.js, jquery.ui.effect-blind.js, jquery.ui.effect-bounce.js, jquery.ui.effect-clip.js, jquery.ui.effect-drop.js, jquery.ui.effect-explode.js, jquery.ui.effect-fade.js, jquery.ui.effect-fold.js, jquery.ui.effect-highlight.js, jquery.ui.effect-pulsate.js, jquery.ui.effect-scale.js, jquery.ui.effect-shake.js, jquery.ui.effect-slide.js, jquery.ui.effect-transfer.js
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class="controls" style="margin-left: 110px;">',' <input type="text" value="<%=player%>" id="inc_html" style="width:360px;font-family: \'microsoft yahei\', tahoma, arial, sans-serif;font-size: 14px;">'," </div>"," </div>"," </div>",' <div id="small_share" style="position:absolute; display: none; width:100%; height:100%">',' <table style="width:100%; height:100%">',' <tr style="height:30%"><td style="text-align: center;">'," </td></tr>",' <tr style="height:20px"><td style="text-align: center;">',' <lable for="copylink" style="color: white; width: auto; font-size: 14px;font-family: \'microsoft yahei\', tahoma, arial, sans-serif;">鼠标移入，按下Ctrl+C复制地址：</label>'," </td></tr>",' <tr><td style="text-align: center;">',' <input type="text" value="<%=url%>" id="copylink" style="width: 80%;font-size: 14px;font-family: \'microsoft yahei\', tahoma, arial, sans-serif;"/>'," </td></tr>",' <tr style="height:20%"><td style="text-align: center;">'," </td></tr>"," </table>"," </div>"," 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class="iText nanoscrollbar Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%=iDetail.iText%>\n </div> \n <%}%>\n <%if(iType==\'Image\'){%>\n <div class="iImage Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%if(iDetail.iImg&&iDetail.iImg[0]){%>\n <%if(_g.getUrlParameterByName("-playmode")=="1"){%>\n <img class="Context" src="<%=iDetail.iImg[0].thumbnail%>" style="width:100%;height:100%;position:absolute;">\n <%}else{ %>\n <img class="Context" src="<%=iDetail.iImg[0].picture%>" style="width:100%;height:100%;position:absolute;">\n <%}%>\n <%}%>\n </div>\n <%}%>\n <%if(iType==\'Link\'){%>\n <div class="iLink Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;"></div>\n <%}%>\n <%if(iType==\'Slide\'){%>\n <div class="iSlide Element" style="overflow:hidden;position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <div class="slidecontent" style="position:absolute;width:100%;height:100%;text-align:center;line-height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%if(iDetail.iImg){%>\n <%_.each(iDetail.iImg,function(i){%>\n <%if(_g.getUrlParameterByName("-playmode")=="1"){%>\n <img data-index="<%=iDetail.iImg.indexOf(i)%>" class="Context" src="<%=i.thumbnail%>" style="width:100%;height:100%;position:absolute;margin:auto;display:inline;verticle-align:middle;">\n <%}else{ %>\n <img data-index="<%=iDetail.iImg.indexOf(i)%>" class="Context" src="<%=i.picture%>" style="width:100%;height:100%;position:absolute;margin:auto;display:inline;verticle-align:middle;">\n <%}%> \n <%})%>\n <%}%>\n </div>\n </div>\n <%}%>\n <%if(iType==\'CycleImage\'){%>\n <div class="iCycleImage Element" style="overflow:hidden;position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <div class="slidecontent" style="position:absolute;width:100%;height:100%;line-height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%if(iDetail.iImg){%>\n <%_.each(iDetail.iImg,function(i){%>\n <%if(_g.getUrlParameterByName("-playmode")=="1"){%>\n <img data-index="<%=iDetail.iImg.indexOf(i)%>" class="Context" src="<%=i.thumbnail%>" style="width:100%;height:100%;position:absolute;margin:auto;display:inline;verticle-align:middle;">\n <%}else{ %>\n <img data-index="<%=iDetail.iImg.indexOf(i)%>" class="Context" src="<%=i.picture%>" style="width:100%;height:100%;position:absolute;margin:auto;display:inline;verticle-align:middle;">\n <%}%>\n <%})%>\n <%}%>\n </div>\n </div>\n <%}%>\n <%if(iType==\'Video\'){%>\n <div class="iVideo Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%if(iDetail.iFile&&iDetail.iFile[0]){%>\n <%if(typeof cbt_data=="undefined"){ %>\n <video id="Video_<%=id%>" style="position:absolute;" src="<%=iDetail.iFile[0].file||iDetail.iFile[0].content%>_origin.mp4" poster="<%=iDetail.iFile[0].picture%>" width="<%=iCommon[iPageDirection].iWidth%>" height="<%=iCommon[iPageDirection].iHeight%>">\n </video>\n <% }else{ %>\n <video id="Video_<%=id%>" style="position:absolute;" src="<%=iDetail.iFile[0].file||iDetail.iFile[0].content%>" poster="<%=iDetail.iFile[0].picture%>" width="<%=iCommon[iPageDirection].iWidth%>" height="<%=iCommon[iPageDirection].iHeight%>">\n </video> \n <% } %>\n <%}%>\n </div>\n <%}%>\n <%if(iType==\'Button\'){%>\n <div class="iButton Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%if(iDetail.iconNormal&&iDetail.iconNormal[0]){%>\n <img class="Context iconNormal" src="<%=iDetail.iconNormal[0].picture%>" style="width:100%;height:100%;">\n <%}%>\n <%if(iDetail.iconActive&&iDetail.iconActive[0]){%>\n <img class="Context iconActive" src="<%=iDetail.iconActive[0].picture%>" style="width:100%;height:100%;display:none;">\n <%}%>\n </div>\n <%}%>\n <%if(iType=="Map"){%>\n <div class="iMap nanoscrollbar Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%=iDetail.iText%>\n </div> \n <%}%>\n <%if(iType=="Pay"){%>\n <div class="iPay nanoscrollbar Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <%=iDetail.iText%>\n </div> \n <%}%>\n <%if(iType=="LayerRef"||iType=="LayerSlide"){%>\n <div class="iLayerRef Element" style="position:absolute;left: -<%=iCommon[iPageDirection].iWidth/2%>px; top: -<%=iCommon[iPageDirection].iHeight/2%>px;background-color:transparent;width:<%=iCommon[iPageDirection].iWidth%>px;height:<%=iCommon[iPageDirection].iHeight%>px;">\n <div class="layer-mask" style="position:absolute;width:100%;height:100%;overflow:hidden;">\n <%if(iDetail.layer_ids&&iDetail.layer_ids.length>0){%>\n <div class="layer-content" style="position:absolute;left:<%=iDetail.x%>px;top:<%=iDetail.y%>px;width:100%;height:100%;">\n <% _.each(iDetail.layer_ids,function(layerid){ %>\n <% if(interaction_view.ilayerlist.get(layerid)){ %>\n <div class="layer-item" data-index="<%=iDetail.layer_ids.indexOf(layerid)%>" data-id="<%=layerid%>" style="position:absolute;opacity:0;width:<%=interaction_view.ilayerlist.get(layerid).toJSON().layer_width%>px;height:<%=interaction_view.ilayerlist.get(layerid).toJSON().layer_height%>px;left:0px;right:0px;">\n <div href="" class="layer-bg" style="position:absolute;width:100%;height:100%;text-align:center;line-height:<%=interaction_view.ilayerlist.get(layerid).toJSON().layer_height%>px;background-color:<%=interaction_view.ilayerlist.get(layerid).toJSON().bg_color%>;opacity:<%=interaction_view.ilayerlist.get(layerid).toJSON().bg_opacity%>">\n <% if(interaction_view.ilayerlist.get(layerid).get("picture")){ %>\n <img src="<%=interaction_view.ilayerlist.get(layerid).get("picture")%>" style="<%=(interaction_view.ilayerlist.get(layerid).toJSON().layer_width>=interaction_view.ilayerlist.get(layerid).toJSON().layer_height)?"width:auto;height:100%;":"width:100%;height:auto;"%>margin:auto">\n <% } %>\n </div> \n </div>\n <% } %>\n <% }) %> \n </div>\n <% } %>\n </div> \n </div> \n <%}%>\n</div>\n'}),define("text!interaction_view/template/message.js",[],function(){return'<div class="status <%=type%>">\n<p><%=msg%></p>\n<a href="#" class="close">Close</a>\n</div>'}),define("interaction_view/model/base",["jquery","backbone","greensock/TweenMax","text!interaction_view/template/animateitem_view.js","text!interaction_view/template/message.js"],function(){var AnimateElementTemplate=require("text!interaction_view/template/animateitem_view.js"),MessageTemplate=require("text!interaction_view/template/message.js");window.interaction_view={model:{Base:Backbone.Model.extend({defaults:{iType:"iBasetype",iLock:!0,iVisibility:!0,iCommon:null,iDetail:null,iOptions:{iParent:null,iParentModel:null,iDraggable:!0,iResizable:!0,template:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iKeyscontrol:!0},iPage:null,iParentModel:null,iDraggable:!0,iResizable:!0,template:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iAutofocus:!1,iKeyscontrol:!0,iUrl:null,callback:null,iBackground:"",iResources:null,iResourcesType:null,iResourcesProperties:null},options:{createwait:!0},initialize:function(){this.options=this.get("iOptions");if(!this.id){this.beforecreate();var e=this;this.get("iAutoindex")?(this.set("id",this.get("iType")+"_"+this.cid+(new Date).getTime()),this.setcollection(),this.setmodel()):(this.options.createwait&&jqwait_simple(),this.savemodel(function(t){e.set("id",t.ID.toString()),e.setcollection(),e.setmodel()}))}else this.setcollection(),this.setmodel()},beforecreate:function(){},savemodel:function(callback){this.save({},{wait:!0,success:function(model,response){returned=eval(response),returned.Status=="Success"?(jqwait_simple_close(),callback&&callback(returned)):model_error({method:"create",status:returned.Status,msg:returned.Message,model:model})},error:function(){model_error({method:"create",status:"ServerError",msg:"server error or server is not available now,please try again!",model:model})}})},setmodel:function(){this.page=interaction_view.ipagelist.getPage(this.get("pageid"))||interaction_view.imasterlist.get(this.get("pageid")),this.setlist(),this.setview(),this.setsyncmodel()},setlist:function(){},setresourcesmodel:function(){},validate:function(e){},setsyncmodel:function(){},setcollection:function(){},setview:function(){},url:function(){},sync:function(e,t,n){}})},collection:{Base:Backbone.Collection.extend({iSync:!1,initialize:function(){this.setonadd(),this.setonremove()},setonadd:function(){},setonremove:function(){},url:"",sync:function(e,t,n){},parse:function(e){},resetViewStatus:function(){this.each(function(e){e.iview.resetStatus()})}}),list:Backbone.Collection.extend({iSync:!0,url:"",setonadd:function(){this.on("add",function(e){})},setonremove:function(){},sync:function(e,t,n){e=="read"&&(n.url=context_url+"/overlayinit.json"),Backbone.emulateHTTP=!0,Backbone.emulateJSON=!0,Backbone.sync(e,t,n)},parse:function(e){},resetViewStatus:function(){this.each(function(e){e.iview.resetStatus()})}})},view:{Base:Backbone.View.extend({initialize:function(e){_.bindAll(this),this.model.bind("destroy",this.destroy),this.model.bind("change",this.change),this.initstart()},initstart:function(){this.preload(),this.renderDynamicElement(),this.render()},events:{},options:{},change:function(e){},renderStaticElement:function(){},renderDynamicElement:function(){var e=this.model.get("pageid");this.model.set("isMasterOverlay",!1);if(this.model.get("pagetype")=="LayerRef"){var t=this.model.get("parentpageid"),n=this.model.get("layerid"),r=interaction_view.ipagelist.getPage(t)||interaction_view.imasterlist.get(t);this.model.page=r.iOverlaylist.get(e).layer[n];var i=r.iOverlaylist.get(e);this.container=i.iview.$el.find(".layer-content").children(".layer-item[data-id="+n+"]")}else this.model.get("pagetype")=="master"?(this.container=$('.Presentation[id="'+interaction_view.currentPage+'"]').find(".interaction-view"),this.model.set("isMasterOverlay",!0)):this.model.get("pageid")?this.container=$('.Presentation[id="'+this.model.get("pageid")+'"]').find(".interaction-view"):this.container=$(".interaction-view");this.container.append(_.template(AnimateElementTemplate,this.model.toJSON())),this.setElement(this.container.find('div.iView.dynamic[id="'+this.model.id+'"]'));var s=this.getcommon();this.x=s.iStartx+s.iWidth/2,this.y=s.iStarty+s.iHeight/2,TweenMax.to(this.$el[0],0,{x:s.iStartx+s.iWidth/2,y:s.iStarty+s.iHeight/2}),this.model.get("pagetype")=="master"&&this.$el.addClass("master masterhide"),_.include(["CycleImage","Slide","Button"],this.model.get("iType"))&&this.setRatiofixxed()},preload:function(){},afterpreload:function(){this.setZindex(),this.setElementDisplay(),this.setOtherDisplay(),this.copyelstyle=this.$el.attr("style"),this.copyelementstyle=this.$el.children(".Element").attr("style"),this.preloaded=!0},setRatiofixxed:function(){var e=this,t=this.getdetail();if(!t.ratiofixxed||!t.iImg||t.iImg.length==0)return;var n=this.getcommon();_.each(t.iImg,function(r){var i=_g.ui.centerImage({containment:e.$el.find(".slidecontent"),Item:e.$el.find(".slidecontent").children()[t.iImg.indexOf(r)],parentWidth:n.iWidth,parentHeight:n.iHeight,width:r.width,height:r.height,setParent:!1});console.log(i),i.width<n.iWidth&&(e.$el.find(".slidecontent").children("[data-index="+t.iImg.indexOf(r)+"]").css("margin-left",(n.iWidth-i.width)/2+"px"),e.$el.find(".slidecontent").children("[data-index="+t.iImg.indexOf(r)+"]").css("margin-right",(n.iWidth-i.width)/2+"px")),i.height<n.iHeight&&(e.$el.find(".slidecontent").children("[data-index="+t.iImg.indexOf(r)+"]").css("margin-top",(n.iHeight-i.height)/2+"px"),e.$el.find(".slidecontent").children("[data-index="+t.iImg.indexOf(r)+"]").css("margin-bottom",(n.iHeight-i.height)/2+"px"))})},setElementDisplay:function(){this.$el.length>0,this.$el.children(".Element").length>0&&TweenMax.to(this.$el.children(".Element")[0],0,{scale:1});var e=this.model.toJSON().iDetail.iHidden||!1,t=this.model.page.iAnimationlist.where({overlay_id:this.model.id});if(t&&t[0]&&_.include([101,102,103],t[0].toJSON().iType)){var n=-9999,r=-9999;t[0]._animation&&t[0]._animation.css&&(n=t[0]._animation.css.x,r=t[0]._animation.css.y);if(t[0].toJSON().iType==101){e&&this.$el.addClass("hide");var i=this.getcommon().iWidth,s=this.getcommon().iHeight;TweenMax.to(this.$el[0],0,{x:n,y:r})}t[0].toJSON().iType==102&&(e&&this.$el.addClass("hide"),TweenMax.to(this.$el.children(".Element")[0],0,{scale:0})),t[0].toJSON().iType==103&&(e&&this.$el.addClass("hide"),TweenMax.to(this.$el[0],0,{opacity:0}))}else e?this.$el.addClass("hide"):this.$el.removeClass("hide");this.Clone=this.$el.clone()},setOtherDisplay:function(){},setZindex:function(){if(this.model.get("iType")=="Audio")return!1;var e=this.model.page.iOverlaylist.indexOf(this.model);this.model.get("pagetype")=="master"&&(e+=1e3),this.$el&&this.$el.css("z-index",e)},render:function(){var e=this.model.toJSON(),t=interaction.template.element(this.model.toJSON()),n=interaction.template.element(this.model.toJSON());return $(this.model.get("iParentdiv")).append(n),this.setElement(div_find(this.model.id)),this.updatebackground(),this.infoel=$("#info_attributes").children(".setting-body"),this},renderCentrolPos:function(e){document.getElementById(this.model.id).style.left=e.x-100+"px",document.getElementById(this.model.id).style.top=e.x-100+"px"},destroy:function(e,t){},updateposition:function(){var e=this.getcommon();e&&(this.$el.css("left",e.iStartx+"px"),this.$el.css("top",e.iStarty+"px"),this.$el.width(e.iWidth),this.$el.height(e.iHeight))},updateel:function(){this.model.get("iTemplate")&&this.$el.html(this.model.get("iTemplate")(this.model.toJSON()))},getcommon:function(){var e=null,t=this.model.toJSON();return this.model.get("iCommon")&&(e=t.iCommon[iPageDirection]),e},getdetail:function(){var e=this.model.toJSON();return e.iDetail},getCentralPos:function(){var e={},t=this.getcommon();return e.x=t.iStartx+t.iWidth/2,e.y=t.iStarty+t.iHeight/2,e},getCommonByCentralPos:function(e){var t=this.getcommon();return t.iStartx=e.x-t.iWidth/2,t.iStarty=e.y-t.iHight/2,t},setcommon:function(e){var t=this.model.toJSON(),n=t.iCommon;!_.isNaN(e.iStartx)&&!_.isNaN(e.iStarty)&&!_.isNaN(e.iWidth)&&!_.isNaN(e.iHeight)&&(n[iPageDirection]=e),this.model.set("iCommon",n)},setdetail:function(e){var t=this.model.toJSON(),n=t.iDetail;_.each(_.keys(e),function(t){n[t]=e[t]}),this.model.set("iDetail",n),this.updateinfo(),this.updatebackground(),this.updateresources()},settext:function(){var e=tinyMCE.getInstanceById("tEditor_"+this.model.id).getBody().innerHTML;this.setdetail({iText:e}),tinyMCE.getInstanceById("tEditor_"+this.model.id)&&(tinyMCE.execCommand("mceFocus",!1,"tEditor_"+this.model.id),tinyMCE.execCommand("mceRemoveControl",!1,"tEditor_"+this.model.id))},setCentralPos:function(e){var t=this.getcommon();t.iStartx=e.x-t.iWidth/2,t.iStarty=e.y-t.iHight/2,this.setcommon(t)},showdiv:function(){this.$el.removeClass("hide"),this.model.set("iVisibility",!0)},hidediv:function(){this.$el.addClass("hide"),this.model.set("iVisibility",!1)},resetStatus:function(){this.$el.attr("style",this.copyelstyle),this.$el.children(".Element").attr("style",this.copyelementstyle),this.setElementDisplay(),this.resetOtherStatus&&this.resetOtherStatus()},sendToPreload:function(e){var t={parentpageid:this.model.get("parentpageid"),overlay_id:this.model.id,elementtype:this.model.get("iType"),pageid:this.model.get("pageid"),pagetype:this.model.get("pagetype"),layerid:this.model.get("layerid")};t.file=e.file;var n=new interaction_view.model.Preload(t)},playTimeline:function(){console.log("begin to play timeline"),this.autoplay?this.timeline&&this.timeline.play():(this.autoplay=!0,this.slideel.children().show(),this.timeline.kill(),this.timeline=new TimelineMax({paused:!0,repeat:this.repeat,onComplete:this.setRewind()}),this.setTimeline(),this.timeline.play())},pauseTimeline:function(){this.timeline&&this.timeline.pause()},stopTimeline:function(){this.autoplay=!1,this.timeline.kill(),this.timeline=new TimelineMax({paused:!0,repeat:this.repeat}),this.setTimeline(),this.timeline.pause(1e-5)},setRewind:function(){var e=this,t=this.getdetail(),n=this.delay,r=function(){setTimeout(function(){e.timeline.pause(1e-5)},n*1e3)},i=t.iRepeat?-1:0,s=t.iRewind;return!i&&s?r:null},setLoopSwitch:function(){var e=this,t=0,n=this.delay;this.autoplay||(n=2);for(i=0;i<this.slidelength;i++){if(i==this.slidelength-1&&!this.repeat)return;var r=this.slideel.children('[data-index="'+i+'"]');this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:0},onUpdateParams:[i+1<this.slidelength?i+1:0],onUpdate:this.onTimelineUpdate}),t+n);var s=this.slideel.children('[data-index="'+(i+1<this.slidelength?i+1:0)+'"]');this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:0}}),t),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1}}),t+n),t+=n+2e-5}},setNone:function(){var e=this,t=0,n=this.delay;this.autoplay||(n=.5);for(i=0;i<this.slidelength;i++){if(i==this.slidelength-1&&!this.repeat)return;var r=this.slideel.children('[data-index="'+i+'"]');this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:0},onUpdateParams:[i+1<this.slidelength?i+1:0],onUpdate:this.onTimelineUpdate}),t+n);var s=this.slideel.children('[data-index="'+(i+1<this.slidelength?i+1:0)+'"]');this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:0}}),t),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1}}),t+n),t+=n+2e-5}},setFade:function(){var e=this,t=0,n=this.delay,r,s;this.autoplay||(n=0);for(i=0;i<this.slidelength;i++){i==0&&(r=this.slideel.children('[data-index="'+i+'"]'),this.timeline.add(TweenMax.to(r,0,{css:{opacity:1},onCompleteParams:[0],onComplete:e.onChangeSlideTo}),t),t+=n);if(i==this.slidelength-1){if(!this.repeat)break;r=this.slideel.children('[data-index="'+i+'"]'),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.fromTo(r,.5,{opacity:1,ease:"Linear.easeNone"},{opacity:0,onUpdateParams:[i+1<this.slidelength?i+1:0],onUpdate:this.onTimelineUpdate}),t+1e-5),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:0}}),t+.50001),s=this.slideel.children('[data-index="'+(i+1<this.slidelength?i+1:0)+'"]'),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.fromTo(s,.5,{opacity:1},{opacity:1,ease:"Linear.easeNone"}),t+1e-5),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1},onCompleteParams:[i+1<this.slidelength?i+1:0],onComplete:e.onChangeSlideTo}),t+.50001),t+=n+.50002}else r=this.slideel.children('[data-index="'+i+'"]'),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.fromTo(r,.5,{opacity:1,ease:"Linear.easeNone"},{opacity:1,onUpdateParams:[i+1<this.slidelength?i+1:0],onUpdate:this.onTimelineUpdate}),t+1e-5),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:0}}),t+.50001),s=this.slideel.children('[data-index="'+(i+1<this.slidelength?i+1:0)+'"]'),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:0}}),t),this.timeline.add(TweenMax.fromTo(s,.5,{opacity:0},{opacity:1,ease:"Linear.easeNone"}),t+1e-5),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1},onCompleteParams:[i+1<this.slidelength?i+1:0],onComplete:e.onChangeSlideTo}),t+.50001),t+=n+.50002}},setSlip:function(){var e=this,t=0,n=this.delay,r,s;this.autoplay||(n=0);for(i=0;i<this.slidelength;i++){if(i==this.slidelength-1&&!this.repeat)break;var o=this.getParamsForSlide(i).param,u=this.getParamsForSlide(i+1<this.slidelength?i+1:0).param1;o.onUpdateParams=[i+1<this.slidelength?i+1:0],o.onUpdate=this.onTimelineUpdate,i==0&&(r=this.slideel.children('[data-index="'+i+'"]'),this.timeline.add(TweenMax.to(r,0,{css:{opacity:1},onCompleteParams:[0],onComplete:e.onChangeSlideTo}),t),t+=n),r=this.slideel.children('[data-index="'+i+'"]'),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.fromTo(r,.5,{left:"0px",top:"0px",ease:"Linear.easeNone"},_.clone(o)),t+1e-5),this.timeline.add(TweenMax.to(r,1e-5,{css:{opacity:0}}),t+.50001),s=this.slideel.children('[data-index="'+(i+1<this.slidelength?i+1:0)+'"]'),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1}}),t),this.timeline.add(TweenMax.fromTo(s,.5,_.clone(u),{top:"0px",left:"0px",ease:"Linear.easeNone"}),t+1e-5),this.timeline.add(TweenMax.to(s,1e-5,{css:{opacity:1},onCompleteParams:[i+1<this.slidelength?i+1:0],onComplete:e.onChangeSlideTo}),t+.50001),t+=n+.50002}},onBindingChangeTo:function(e){interaction_view.events.onBingChangeTo({type:e.type,page_id:this.model.get("pageid"),id:this.model.id,delta:e.delta,deltarate:e.deltarate,cdirection:e.cdirection})},onTimelineUpdate:function(e){var t=this.timeline.time();interaction_view.events.onBingChangeTo({type:0,page_id:this.model.get("pageid"),id:this.model.id,delta:-1,deltarate:t/this.timeline.duration(),cdirection:null})},onChangeSlideTo:function(e){this.currentIndex=e,this.model.trigger("slideTo",e)},onExecuteBinding:function(e){if(this.model.get("iType")=="LayerRef"){this.onExecuteBindingForLayerRef(e);return}var t=this;if(this.slideel&&this.slidelength>1&&this.timeline){var n=this.timeline.duration();this.repeat?singletime=n/this.slidelength:singletime=n/(this.slidelength-1);if(e.type==0){t.setSlideDisplayAll(),i=e.deltarate*n;var r=parseInt(i/singletime,10);t.timeline.seek(i),t.slideChangePatch(e.delta,r),t.onChangeSlideTo(r)}else{var i=t.timeline.time();console.log("stop at delta"+e.delta),t.model.get("iType")!="CycleImage"&&(i=parseInt(i/singletime,10)*singletime,e.delta<0&&i<n&&(i+=singletime)),i==0&&(i=1e-5),t.timeline.seek(i);var r=parseInt(i/singletime,10);r>t.slidelength-1&&(r=0),t.setSlideDisplay(r),t.onChangeSlideTo(r)}}},setSlideDisplay:function(e){this.autoplay||(this.slideel.children('[data-index="'+e+'"]').show().css("display","block"),this.slideel.children('[data-index="'+e+'"]').siblings().hide())},setSlideDisplayAll:function(){this.slideel.children().show()},setElementTo:function(e){if(Number(e)==NaN||this.currentIndex==Number(e))return;e=Number(e);var t=this.getdetail(),n=t.iRepeat,r=null,i;if(!this.slidelength||e>=this.slidelength)return;var s=this.timeline.duration();n?i=s/this.slidelength:i=s/(this.slidelength-1),console.log(this.timeline.time());if(e=="first")r=1e-5;else if(e=="last")r=(this.slidelength-1)*i;else if(e=="prev"){if(!n&&this.currentIndex==0)return!1;r=this.timeline.time()-i>=0?parseInt(this.timeline.time()/i-1,10)*i:i*(this.slidelength-1)}else if(e=="next"){if(!n&&this.currentIndex==this.slidelength-1)return!1;console.log("singletime is "+i),console.log("duration is "+s),console.log("time now is "+this.timeline.time()),r==1e-5&&(r=0),r=this.timeline.time()+i<s?parseInt(this.timeline.time()/i+1,10)*i:0}else{if(!(Number(e)>=0&&Number(e)<this.slidelength))return!1;r=i*parseInt(e,10)}console.log(r),r==0&&(r=1e-5),e=parseInt(r/i,10),e>this.slidelength-1&&(e=0),this.timeline.seek(r),this.setSlideDisplay(e),this.onChangeSlideTo(e)},slideChangePatch:function(e,t){var n=this;if(e>0)for(i=n.currentIndex;i>t;i--)i<0&&(i=n.slidelength-1),i!=n.currentIndex&&n.onChangeSlideTo(i);if(e<0)for(i=n.currentIndex;i<t;i++)i>n.slidelength-1&&(i=0),i!=n.currentIndex&&n.onChangeSlideTo(i)},control:function(){var e=_g.hasTouch,t=e?"touchstart":"mousedown",n=e?"touchmove":"mousemove",r=e?"touchend":"mouseup";eCancel=e?"touchcancel":"mouseleave";var i=this,s=this.getdetail(),o=0,u=0,a,f,l,c=0,h,p=!1,d=null,v=null,m=null,g=this.$el.children(".Element").width(),y=s.iSlidetype?Number(s.iSlidetype):0,b=this.timeline.duration(),w=y=="横向"||Number(y)==0?0:1;this.repeat?a=b/this.slidelength:a=b/(this.slidelength-1);var E=this.repeat,S=function(e){h=0,o=i.timeline.time(),p=!0,m=i.slideel.children(":visible").first().index(),console.log("control start")},x=function(e,t){i.setSlideDisplayAll(),e>b&&(E?e-=b:e=b),e<0&&(E?e=b+e:e=1e-5);var n=e/b;i.onBindingChangeTo({type:0,delta:t,deltarate:n,cdirection:w});if(i.model.get("iType")=="CycleImage"){var r=parseInt(e/a);r>i.slidelength-1&&(r=0),i.slideChangePatch(h,r),i.onChangeSlideTo(r)}i.timeline.seek(e)},T=function(e){o=i.timeline.time(),i.model.get("iType")!="CycleImage"&&(o=parseInt(o/a,10)*a,e<0&&o<b&&(o+=a)),o==0&&(o=1e-5),i.timeline.seek(o);var t=parseInt(o/a,10);t>i.slidelength-1&&(t=0),i.setSlideDisplay(t),i.onChangeSlideTo(t),i.onBindingChangeTo({type:1,delta:e})};this.$el.children(".Element").on(t,function(e){return i.testControl(e.target)?(e=_g.hasTouch?e.originalEvent.touches[0]:e,u=0,d=f=e.pageX,v=l=e.pageY,S(),!1):!1}),this.$el.children(".Element").on(n,function(e){e=_g.hasTouch?e.originalEvent.touches[0]:e;if(p&&d){h=y=="横向"||Number(y)==0?e.pageX-d:e.pageY-v,c=y=="横向"||Number(y)==0?e.pageX-f:e.pageY-l,f=e.pageX,l=e.pageY,i.setSlideDisplayAll();if(h==0)return;i.model.get("iType")=="CycleImage"?u=o-h/g*b:u=o-h/g*a,x(u,c)}return!1}),this.$el.children(".Element").on(r,function(e){if(p)return i.testControl(e.target)?(h!=0?T(h):(i.setSlideDisplay(m),$(e.currentTarget).trigger("click")),p=!1,console.log("control stop"),!1):!1}),this.$el.children(".Element").on(eCancel,function(e){if(p)return i.testControl(e.target)?(h!=0?T(h):i.setSlideDisplay(m),p=!1,console.log("control cancel"),!1):!1}),this.$el.children(".Element").on("wheelstart",function(e,t){u=0,d=f=0,v=l=0,h=0,o=i.timeline.time(),m=i.slideel.children(":visible").first().index(),console.log("control start")}),this.$el.children(".Element").on("wheelmove",function(e,t){var n=Math.max(-1,Math.min(1,t.wheelDeltaX||0)),r=Math.max(-1,Math.min(1,t.wheelDeltaY||-t.detail));console.log(r),f+=n/Math.abs(n)*20,l+=r/Math.abs(r)*20,console.log(l),h=y=="横向"||Number(y)==0?n:r,c=y=="横向"||Number(y)==0?n:r;var s=y=="横向"||Number(y)==0?f:l;s>1e3&&(s=999),s<-1e3&&(s=-999),i.model.get("iType")=="CycleImage"?u=o-s/1e3*b:u=o-s/1e3*a,x(u,c)}),this.$el.children(".Element").on("wheelend",function(e,t){var n=y=="横向"||Number(y)==0?f:l;T(n),f=l=0})},getParamsForFade:function(e){param.opacity=1,param1.opacity=0},getParamsForSlide:function(e){var t=this.getdetail(),n=t.iSlidetype?Number(t.iSlidetype):0,r=this.slideel.children('[data-index="'+e+'"]');if(this.model.get("iType")=="Slide"||this.model.get("iType")=="CycleImage")r=this.slideel;var i={ease:"Linear.easeNone"},s={ease:"Linear.easeNone"};return n=="横向"||Number(n)==0?(i.opacity=1,i.left="-"+r.css("width"),s.left=r.css("width"),s.opacity=1):(i.opacity=1,i.top="-"+r.css("height"),s.top=r.css("height"),s.opacity=1),{param:i,param1:s}},getSlideDuration:function(){var e=this.getdetail(),t;return this.slidelength==0?0:(this.model.get("iType")!="CycleImage"?t=(this.delay+.50002)*this.slidelength:t=(this.delay+2e-5)*this.slidelength,t)}})}},window.iOverlaylist=new interaction_view.collection.Base,interaction_view.iOverlaylist=new interaction_view.collection.list,interaction_view.currentPage=null}),define("interaction_view/ui/animation",["jquery","backbone"],function(){window.animation=function(e){this.defaults={countTime:0,currentFrame:0,path:null,raster:null,animation:null},this.options=e,this.options=$.extend(!0,{},this.defaults,typeof e=="object"&&e),this.page=this.options.collection.page,this.init()},window.animation.prototype={init:function(){_.bindAll(this),this.setparam()},cssTovalue:function(e){var t=e;return t=Number(e.split("px")[0]),t},EaseTypes:{0:"Linear.easeNone",1:"Power0.easeIn",2:"Power0.easeInOut",3:"Power0.easeOut",4:"Power1.easeIn",5:"Power1.easeInOut",6:"Power1.easeOut",7:"Power2.easeIn",8:"Power2.easeInOut",9:"Power2.easeOut",10:"Power3.easeIn",11:"Power3.easeInOut",12:"Power3.easeOut",13:"Power4.easeIn",14:"Power4.easeInOut",15:"Power4.easeOut",16:"Quad.easeIn",17:"Quad.easeInOut",18:"Quad.easeOut",19:"Cubic.easeIn",20:"Cubic.easeInOut",21:"Cubic.easeOut",22:"Quart.easeIn",23:"Quart.easeInOut",24:"Quart.easeOut",25:"Quint.easeIn",26:"Quint.easeInOut",27:"Quint.easeOut",28:"Strong.easeIn",29:"Strong.easeInOut",30:"Strong.easeOut",31:"Back.easeIn",32:"Back.easeInOut",33:"Back.easeOut",34:"Bounce.easeIn",35:"Bounce.easeInOut",36:"Bounce.easeOut",37:"Circ.easeIn",38:"Circ.easeInOut",39:"Circ.easeOut",40:"Elastic.easeIn",41:"Elastic.easeInOut",42:"Elastic.easeOut",43:"Expo.easeIn",44:"Expo.easeInOut",45:"Expo.easeOut",46:"Sine.easeIn",47:"Sine.easeInOut",48:"Sine.easeOut",49:"SlowMo.ease"},getTypeName:function(e){switch(e){case 100:return"出现动画";case 101:return"飞入";case 102:return"放大";case 103:return"渐现";case 200:return"消失动画";case 201:return"飞出";case 202:return"缩小";case 203:return"渐隐";case 300:return"运动路径";case 301:return"直线运动";case 302:return"曲线运动";case 400:return"强调动画";case 401:return"放大缩小";case 402:return"透明";case 403:return"旋转";case 500:return"媒体动画";case 501:return"播放";case 502:return"暂停";case 503:return"结束"}},play:function(){var e=this.page.iOverlaylist.get(this.options.animation.overlay_id);this.overlay_type=e.get("iType"),this.overlay_type=="Audio"?this.playmedia(this.options.animation.iType):_.include([501,502,503],this.options.animation.iType)?(console.info(this.options.animation.iType),this.playmedia(this.options.animation.iType)):_.include([101,102,103],this.options.animation.iType)?(console.info(this.param),this.tween=TweenMax.from(this.obj,this.options.animation.iTiming.duration,this.param)):this.tween=TweenMax.to(this.obj,this.options.animation.iTiming.duration,this.param)},playmedia:function(e){var t=this.page.iOverlaylist.get(this.options.animation.overlay_id);e==501&&t.loaded&&(t.get("iType")=="Audio"&&t.media&&console.log(t.media.getDuration()),t.get("iType")=="Video"&&t.iview.$el.removeClass("hide"),t.media.play()),e==502&&t.loaded&&t.media.pause(),e==503&&t.loaded&&(model.media.setCurrentTime(0),model.media.pause()),this.options.collection.onComplete(this.options.animation.id)},setpathparam:function(e){var t=this,n;this.pathoriginalpoint={},this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0];var r=this.options.animation.iDetail.autoRotate||!1;if(r){var i=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first()[0];this.param.onStart=function(){}}e==302&&(n=[],_.each(this.options.animation.iDetail.path,function(e){if(e.handleIn){var t={};t.x=e.handleIn.x+e.point.x,t.y=e.handleIn.y+e.point.y,n.push(t)}n.push({x:e.point.x,y:e.point.y});if(e.handleOut){var r={};r.x=e.handleOut.x+e.point.x,r.y=e.handleOut.y+e.point.y,n.push(r)}}),this.param.bezier={values:n,type:"cubic",autoRotate:r});if(e==301){n=[];var s;for(s in this.options.animation.iDetail.path){if(s!=0){var o=this.options.animation.iDetail.path[s-1],u=this.options.animation.iDetail.path[s],a={x:(o.point.x+u.point.x)/2,y:(o.point.y+u.point.y)/2};n.push(a)}n.push({x:this.options.animation.iDetail.path[s].point.x,y:this.options.animation.iDetail.path[s].point.y})}this.param.bezier={values:n,type:"quadratic",autoRotate:r}}},setappearparam:function(e){this.fromparam={},this.fromparam.onStart=this.showelement,this.fromparam.immediateRender=!1,this.toparam={},this.toparam.onComplete=this.param.onComplete,this.toparam.delay=this.param.delay,this.toparam.repeat=this.param.repeat,this.toparam.ease=this.param.ease;var t,n,r;if(e==101){t=this.options.animation.iDetail.direction||0;var i=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.x,s=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.y,o=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first().width(),u=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first().height();n={};switch(t){case 0:n.y="-"+u,n.x=i;break;case 1:n.x=interaction_view.size.x+o,n.y=s;break;case 2:n.y=interaction_view.size.y+u,n.x=i;break;case 3:n.x="-"+o,n.y=s}this.param.css=n,this.fromparam.css={x:Number(n.x),y:Number(n.y)},this.toparam.css={x:Number(i),y:Number(s)},this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0]}if(e==102){t=this.options.animation.iDetail.direction||0,r={},n={};var a=this.page.iOverlaylist.get(this.options.animation.overlay_id);r.scale=0,this.param.css=r,this.fromparam.css={scale:0},this.toparam.css={scale:1},this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first()[0]}e==103&&(t=this.options.animation.iDetail.direction||0,r={},this.param.css={opacity:0},this.fromparam.css={opacity:0},this.toparam.css={opacity:1},this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0])},setdisappearparam:function(e){var t={};if(e==201){var n=this.options.animation.iDetail.direction||0;this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0];var r=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first().width()*2,i=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first().height()*2;switch(n){case 0:t.y="-"+i;break;case 1:t.x=interaction_view.size.x+r;break;case 2:t.y=interaction_view.size.y+i;break;case 3:t.x="-"+r}this.param.css=t}if(e==202){var s=this.page.iOverlaylist.get(this.options.animation.overlay_id);t.scale=0,this.param.css=t,this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first()[0]}e==203&&(this.param.css={opacity:0},this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0]),this.param.onComplete=this.hideelement},setstrongparam:function(e){if(e==401){var t=this.page.iOverlaylist.get(this.options.animation.overlay_id),n={};this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.find(".Element").first()[0],n.scale=this.options.animation.iDetail.scale||2,this.param.css=n}if(e==402){this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0];var r=this.options.animation.iDetail.opacity||0;this.param.css={opacity:r}}if(e==403){this.obj=this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el[0];var i=this.options.animation.iDetail.rotation||180,s=this.options.animation.iDetail.axial||0,o="_cw",u="+";i<0&&(o="_ccw"),i<0&&(u="-");var n={};s==0&&(n.rotation=u+"="+Math.abs(i)+o),s==1&&(n.rotationX=u+"="+Math.abs(i)+o),s==2&&(n.rotationY=u+"="+Math.abs(i)+o),this.param.css=n}},setparam:function(){var e=this;this.param={},this.param.data=this.options.animation;var t=this.page.iOverlaylist.get(this.options.animation.overlay_id);this.param.onComplete=this.stop,this.param.onCompleteParams=["{self}"],this.param.ease=this.EaseTypes[this.options.animation.iTiming.ease||0];var n=this.options.animation.iTiming.repeat||0;n==0||n==-1?this.param.repeat=n:n==-2?this.param.repeat=-1:this.param.repeat=n;switch(this.options.animation.iType){case 100:return"出现动画";case 101:this.setappearparam(101);break;case 102:this.setappearparam(102);break;case 103:this.setappearparam(103);break;case 200:return"消失动画";case 201:this.setdisappearparam(201);break;case 202:this.setdisappearparam(202);break;case 203:this.setdisappearparam(203);break;case 300:return"运动路径";case 301:this.setpathparam(301);break;case 302:this.setpathparam(302);break;case 400:return"强调动画";case 401:this.setstrongparam(401);break;case 402:this.setstrongparam(402);break;case 403:this.setstrongparam(403);break;case 500:return"媒体动画";case 501:this.setmedia(501);break;case 502:this.setmedia(502);break;case 503:this.setmedia(503)}},setmedia:function(e){},pathstop:function(e){var t=this.options.animation.iTiming.rewind||!1;t&&this.options.animation.iTiming.repeat!=-1&&this.options.animation.iTiming.repeat!=-2&&this.options.animation.iDetail.path&&this.options.animation.iDetail.path[0]&&e.pause(0)},animationstop:function(e){var t=this.options.animation.iTiming.rewind||!1;t&&this.options.animation.iTiming.repeat!=-1&&this.options.animation.iTiming.repeat!=-2&&e.pause(0)},stop:function(e){this.animationstop(e),this.options.collection.onComplete(this.options.animation.id)},forcestop:function(e){var t=this.options.animation.iTiming.rewind||!1;t?e.pause(0):e.pause(this.options.animation.iTiming.duration),this.options.collection.onComplete(this.options.animation.id)},addclickcontrol:function(){this.options.collection.addclickstop(this.options.animation.id)},showelement:function(){console.log("on start show"),this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.removeClass("hide")},hideelement:function(){this.page.iOverlaylist.get(this.options.animation.overlay_id).iview.$el.addClass("hide")}}}),define("interaction_view/model/action",["interaction_view/model/base","interaction_view/ui/animation"],function(){interaction_view.model.Action=interaction_view.model.Base.extend({defaults:{page_id:null,ref_id:null,index:null,layer_id:null,overlay_id:null},setcollection:function(){},setview:function(e){this.pageid=this.get("pageid"),this.parentpageid=this.get("parentpageid");if(this.get("pagetype")=="LayerRef"){var t=this.get("parentpageid"),n=this.get("layerid"),r=interaction_view.ipagelist.getPage(t)||interaction_view.imasterlist.get(t);this.parentpage=r,this.page=r.iOverlaylist.get(this.pageid).layer[n]}else this.page=interaction_view.ipagelist.getPage(this.pageid)||interaction_view.imasterlist.get(this.pageid);this.actionpatch(),this.iview=new interaction_view.view.Action({model:this})},setsyncmodel:function(){},actionpatch:function(){var e=this,t=this.get("iDetail");this.get("pagetype")=="LayerRef"&&(this.get("layer_id")||this.set("layer_id",this.get("layerid"))),t.results[0].iAction!=5&&t.results[0].iAction<7&&(t.results[0].iAction==6&&typeof t.results[0].values=="string"&&(t.results[0].values=[{id:t.results[0].values,page_id:e.pageid}]),t.results[0].values=_.map(t.results[0].values,function(t){return typeof t!="object"?{id:t,page_id:e.pageid}:t})),this.set("iDetail",t)}}),interaction_view.collection.Action=interaction_view.collection.Base.extend({initialize:function(){this.setonadd(),this.setonremove()},model:interaction_view.model.Action,setonadd:function(){this.on("add",function(e){})},onSingleComplete:function(){},onUpdate:function(){},onTotalComplete:function(){},resetStatus:function(){this.each(function(e){e.iview.resetStatus()})}}),interaction_view.view.Action=interaction_view.view.Base.extend({initstart:function(){},getAnimationPage:function(e,t){return interaction_view.ipagelist.getPage(e)||interaction_view.imasterlist.get(e)?interaction_view.ipagelist.getPage(e)||interaction_view.imasterlist.get(e):this.model.page},renderanimations:function(){var e=this,t=this.model.toJSON();this.timeline=new TimelineMax({paused:!0}),this.timeline.addLabel("Start"),this.TotalDuration=0;var n=this.values;n&&(_.each(n,function(t){var r=t.page_id,i=e.getAnimationPage(r),s=i.iAnimationlist.get(t.id);if(!s)return;var o=s.toJSON(),u=s._animation.obj,a=$.extend(!0,{},s._animation.param),f=s.toJSON().iTiming.duration||0,l=s.toJSON().iTiming.repeat||0,c=o.iTiming.delay||0;e.TotalDuration+=c,f=f;var h=_.pluck(n,"id").indexOf(s.id);if(o.iTiming.start==0)e.timeline.addLabel(s+"_start",e.TotalDuration),e.timeline.call(i.iAnimationlist.AddClickTween,[e],this,e.TotalDuration-c);else if(o.iTiming.start==2)if(h!=0){var p=e.timeline.getLabelTime(n[h-1].id+"_start");e.timeline.addLabel(s.id+"_start",p+c),e.TotalDuration=e.timeline.getLabelTime(s.id+"_start")}else e.timeline.addLabel(s.id+"_start",e.TotalDuration);else e.timeline.addLabel(s.id+"_start",e.TotalDuration);if(_.include([501,502,503],s.get("iType"))){var d=i.iAnimationlist.getMediaTime(s);s.get("iType")==502||s.get("iType")==503?f=0:f=d,e.timeline.call(i.iAnimationlist.AddMediaTween,[s],this,e.TotalDuration)}else o.iTiming.repeat!=0?e.timeline.call(i.iAnimationlist.AddRepeatTween,[s],this,e.TotalDuration):_.include([101,102,103],s.get("iType"))?(console.log(s._animation),e.timeline.add(TweenMax.fromTo(u,f,s._animation.fromparam,s._animation.toparam),e.TotalDuration)):e.timeline.add(TweenMax.to(u,f,s._animation.param),e.TotalDuration);console.log("this time is :"+e.TotalDuration),e.TotalDuration+=f,e.timeline.addLabel(s.id+"_end",e.TotalDuration)}),e.timeline.play(0))},onComplete:function(){},events:{},renderDynamicElement:function(){},getClassification:function(){if(!this.ref_id){if(_.isNumber(this.index)&&this.overlaymodel)return 4;if(!_.isNumber(this.index)&&this.overlaymodel)return 1;if(!this.overlaymodel){if(this.overlay_id==this.model.page.id)return 2;if(interaction_view.ilayerlist.get(this.overlay_id))return 3}}if(this.ref_id&&this.model.get("pagetype")=="LayerRef"&&this.model.pageid==this.ref_id&&this.model.parentpageid==this.page_id){if(_.isNumber(this.index)&&this.overlaymodel)return 4;if(!_.isNumber(this.index)&&this.overlaymodel)return 1;if(!this.overlaymodel){if(this.overlay_id==this.model.page.id)return 2;if(interaction_view.ilayerlist.get(this.overlay_id))return 3}}return 0},render:function(){this.iType=this.model.toJSON().iType,this.overlay_id=this.model.toJSON().overlay_id,this.page_id=this.model.toJSON().page_id,this.ref_id=this.model.toJSON().ref_id,this.index=this.model.toJSON().index;var e=this.model.toJSON().iDetail;if(!e||!e.results)return;var t=this;this.actiontype=e.results[0].iAction,this.values=e.results[0].values,this.overlaymodel=this.model.page.iOverlaylist.get(this.overlay_id);var n=this.getClassification();if(!n)return;switch(n){case 1:this.renderforoverlay();break;case 2:this.renderforpage();break;case 3:this.renderforlayer();break;case 4:this.renderforslideindex()}},renderforoverlay:function(){var e=this;this.TotalDuration=0,this.overlaymodel.iview.$el.css("cursor","pointer"),this.overlaymodel.iview.$el.addClass("hasaction");var t=this.overlaymodel.iview.$el;_.include(["Slide","LayerSlide","CycleImage"],this.overlaymodel.get("iType"))&&(t=t.children(".Element")),this.iType==1&&(t=this.overlaymodel.iview.$el.find("img.iconNormal")),this.iType==2&&(t=this.overlaymodel.iview.$el.find("img.iconActive"));if(!t)return;t.on("click",function(t){if(_.include(["Slide","LayerSlide","CycleImage"],e.overlaymodel.get("iType"))&&!$(t.target).is(t.currentTarget))return!1;e.executeAction(e)})},renderforlayer:function(){var e=this,t=this.model.get("layerid"),n=this.model.parentpage.iOverlaylist.get(this.model.get("pageid")).toJSON().iDetail.layer_ids.indexOf(t);if(n==-1)return!1;this.iType==0&&this.model.parentpage&&this.model.parentpage.iOverlaylist.get(this.model.get("pageid")).iview.$el.children(".Element").on("click",function(t){if(!$(t.target).is(t.currentTarget)&&$(t.currentTarget).find(".layer-content").first().children().length>1)return!1;e.model.parentpage.iOverlaylist.get(e.model.get("pageid")).iview.currentIndex==n&&e.testControlforLayer(t)&&e.executeAction(e)}),this.iType==3&&interaction_view.events.pageStartEvents.push({type:"page",page_id:e.model.pageid,layer_id:this.model.get("layerid"),func:function(){e.executeAction(e)}}),this.iType==4&&this.model.parentpage&&this.model.parentpage.iOverlaylist.get(this.model.get("pageid")).iview.$el.children(".Element").on("dblclick",function(t){if($(t.target).closest(".layer-item").index()!=e.model.parentpage.iOverlaylist.get(e.model.get("pageid")).iview.currentIndex)return;e.model.parentpage.iOverlaylist.get(e.model.get("pageid")).iview.currentIndex==n&&e.testControlforLayer(t)&&e.executeAction(e)})},renderforpage:function(){var e=this;this.iType==0&&interaction_view.events.pageClickEvents.push({type:e.model.page.id==interaction_view.imasterlist.at(0).id?"master":"page",page_id:e.model.page.id,func:function(){e.executeAction(e)}}),this.iType==3&&interaction_view.events.pageStartEvents.push({type:"page",page_id:e.model.page.id,func:function(){e.executeAction(e)}}),this.iType==4&&interaction_view.events.pageDblClickEvents.push({type:e.model.page.id==interaction_view.imasterlist.at(0).id?"master":"page",page_id:e.model.page.id,func:function(){e.executeAction(e)}})},renderforslideindex:function(){this.overlaymodel.get("iType")!="CycleImage"&&this.overlaymodel.get("iType")!="Slide";if(!(this.overlaymodel.iview.slideel&&this.overlaymodel.iview.slideel.children().length>0&&this.overlaymodel.iview.slideel.children('[data-index="'+this.index+'"]').length>0))return!1;var e=this;this.iType==0&&(this.overlaymodel.iview.slideel.children('[data-index="'+this.index+'"]').addClass("hasaction"),this.overlaymodel.iview.slideel.children('[data-index="'+this.index+'"]').on("click",function(t){console.log(t.target),e.overlaymodel.iview.currentIndex==e.index&&e.executeAction(e)})),this.iType==5&&interaction_view.events.elementChangeToEvents.push({type:"slide",page_id:e.model.pageid,overlay_id:this.overlaymodel.id,index:this.index,func:function(){e.executeAction(e)}}),this.iType==4&&this.overlaymodel.iview.slideel.children('[data-index="'+this.index+'"]').on("dblclick",function(t){e.executeAction(e)})},executeAction:function(e){if(typeof Reveal!="undefined"&&Reveal&&Reveal.isOverview()&&e.iType!=3)return!1;e.actiontype==0&&e.renderanimations(),e.actiontype==1&&e.rendershow(),e.actiontype==2&&e.renderhide(),e.actiontype==6&&e.setElementTo(),e.actiontype==5&&(interaction_view.foredit?global.message("warning","请使用playAll模式预览该功能"):e.renderpage()),e.actiontype==7&&(interaction_view.foredit?global.message("warning","请使用playAll模式预览该功能"):e.renderoverview()),e.actiontype==8&&(interaction_view.foredit?global.message("warning","请使用playAll模式预览该功能"):e.rendershare()),e.actiontype==10&&(interaction_view.foredit?global.message("warning","请使用playAll模式预览该功能"):e.renderurl())},rendershare:function(){interaction_view.presentate.share_overlay.toggle()},renderoverview:function(){Reveal.toggleOverview(!0)},renderclick:function(){},rendershow:function(){var e=this,t=this.values;t&&_.each(t,function(t){var n=t.page_id,r=e.getAnimationPage(n),i=r.iOverlaylist.get(t.id);i&&i.iview&&i.iview.$el&&i.iview.$el.removeClass("hide")})},renderhide:function(){var e=this,t=this.values;t&&_.each(t,function(t){var n=t.page_id,r=e.getAnimationPage(n),i=r.iOverlaylist.get(t.id);i&&i.iview&&i.iview.$el&&i.iview.$el.addClass("hide")})},renderpage:function(){var e=this.model.toJSON().iDetail,t=this.values,n=null,r=null,i=e.results[0].iActionDetail?e.results[0].iActionDetail.transition?e.results[0].iActionDetail.transition:0:0;if(i==0||i)if(t!="first"&&t!="last"&&t!="prev"&&t!="next"){var s=interaction_view.ipagelist.getPage(t);console.log(i),s&&(i<4?interaction_view.ipagelist.getPage(t).iview.$el.attr("data-transition","linear"):i<8?interaction_view.ipagelist.getPage(t).iview.$el.attr("data-transition","page"):i==8?interaction_view.ipagelist.getPage(t).iview.$el.attr("data-transition","fade"):interaction_view.ipagelist.getPage(t).iview.$el.removeAttr("data-transition"))}var o=interaction_view.ipagelist.length;switch(t){case"first":Reveal.slide(0);break;case"last":Reveal.slide(o-1);break;case"prev":Reveal.prev();break;case"next":Reveal.next();break;default:if(interaction_view.ipagelist.getPage(t)){var u=interaction_view.ipagelist.getPage(t);interaction_view.doc.toJSON().play_mode==0?(n=interaction_view.ipagelist.indexOf(u),Reveal.slide(n)):(n=u.collection.parent.collection.indexOf(u.collection.parent),r=u.collection.indexOf(u),Reveal.slide(n,r))}}},renderurl:function(){if(this.values.length==0||_.isEmpty(this.values[0].url))return;var e=this.values[0].url;e.indexOf("://")==-1&&(e="http://"+e),window.open(e,"_blank")},setElementTo:function(){var e=this,t=this.model.toJSON().iDetail,n=this.values[0].id,r=this.values[0].page_id,i=e.getAnimationPage(r),s=t.results[0].iActionDetail?t.results[0].iActionDetail.index:null;if(n&&s){var o=i.iOverlaylist.get(n);o&&o.iview&&o.iview.setElementTo(s)}this.overlaymodel&&this.overlaymodel.toJSON().iType=="Button"&&(this.overlaymodel.iview.groupcontrol=!0,this.syncbuttons(i,this.model.toJSON().overlay_id,this.values[0]))},syncbuttons:function(e,t,n){var r=this,i=_.filter(this.model.page.iActionlist.toJSON(),function(e){return r.model.page.iOverlaylist.get(e.overlay_id)&&r.model.page.iOverlaylist.get(e.overlay_id).get("iType")=="Button"&&e.iDetail.results[0].iAction==6});_.each(i,function(e){if(e.overlay_id!=t){var i=r.model.page.iOverlaylist.get(e.overlay_id);i&&i.toJSON().iDetail.isSwitch&&e.iDetail.results&&e.iDetail.results[0].values[0].id==n.id&&e.iDetail.results[0].values[0].page_id==n.page_id&&i.iview.resetButtonStatus()}})},resetStatus:function(){this.overlaymodel.iview.$el.off("click")},testControlforLayer:function(e){var t=$(e.target).closest(".iView");if($(e.target).closest(".iView").hasClass("hasaction"))return!1;if(t){var n=t.attr("data-type");if(n=="Slide"||n=="CycleImage")if(t.attr("data-iSlipable"))return!1}return!0}}),interaction_view.iActionlist=new interaction_view.collection.Action}),define("interaction_view/model/animation_view",["interaction_view/model/base","interaction_view/ui/animation"],function(){interaction_view.model.Animation=interaction_view.model.Base.extend({defaults:{},setcollection:function(){},setview:function(e){this.iview=new interaction_view.view.Animation({model:this})},setsyncmodel:function(){}}),interaction_view.collection.Animation=interaction_view.collection.Base.extend({initialize:function(){this.setonadd(),this.setonremove()},model:interaction_view.model.Animation,setonadd:function(){this.on("add",function(e){})},setParams:function(e){this.repeatTweens=[];if(this.length==0)return;var t=this;if(this.pagetype=="LayerRef"){var n=this.layerid,r=interaction_view.ipagelist.getPage(this.parentpageid)||interaction_view.imasterlist.get(this.arentpageid);this.page=r.iOverlaylist.get(this.pageid).layer[n]}else this.page=interaction_view.ipagelist.getPage(this.pageid)||interaction_view.imasterlist.get(this.pageid);this.each(function(e){e.tween=null;var n=e.toJSON();if(!t.page.iOverlaylist.get(n.overlay_id))return;e._animation=new animation({collection:t,animation:e.toJSON()})})},setPlay:function(e){if(this.length==0)return;var t=this,n=function(){};this.timeline=new TimelineMax({paused:!0,onStart:n}),this.timeline.addLabel("Start"),this.TotalDuration=0,this.each(function(e){var n=e.toJSON();if(!t.page.iOverlaylist.get(n.overlay_id))return;if(n.iTiming.waitaction&&!interaction_view.SinglePreview)return;var r=e._animation.obj,i=$.extend(!0,{},e._animation.param),s=e.toJSON().iTiming.duration||0,o=e.toJSON().iTiming.delay||0;t.TotalDuration+=o,s=s;var u=t.indexOf(e);if(n.iTiming.start==0)t.timeline.addLabel(e.id+"_start",t.TotalDuration),t.timeline.call(t.AddClickTween,[t],this,t.TotalDuration-o);else if(n.iTiming.start==2)if(u!=0){var a=t.timeline.getLabelTime(t.at(u-1).id+"_start");t.timeline.addLabel(e.id+"_start",a+o),t.TotalDuration=t.timeline.getLabelTime(e.id+"_start")}else t.timeline.addLabel(e.id+"_start",t.TotalDuration);else t.timeline.addLabel(e.id+"_start",t.TotalDuration);if(_.include([501,502,503],e.get("iType"))){var f=t.getMediaTime(e);e.get("iType")==502||e.get("iType")==503?s=0:s=f,t.timeline.call(t.AddMediaTween,[e],this,t.TotalDuration)}else n.iTiming.repeat!=0?t.timeline.call(t.AddRepeatTween,[e],this,t.TotalDuration):_.include([101,102,103],e.get("iType"))?t.timeline.add(e.tween=TweenMax.fromTo(r,s,e._animation.fromparam,e._animation.toparam),t.TotalDuration):t.timeline.add(e.tween=TweenMax.to(r,s,i),t.TotalDuration);t.TotalDuration+=s,t.timeline.addLabel(e.id+"_end",t.TotalDuration)})},play:function(){if(this.length==0)return;this.setPlay(),this.timeline.play(0)},playMain:function(){if(this.length==0)return;this.setParams(),this.setPlay(),this.timeline.play(0),this.afterTimeLineCreated&&this.afterTimeLineCreated()},AddClickTween:function(e){if(interaction_view.SinglePreview)return;e.timeline.pause(),$(".interaction-view").css("cursor","pointer"),interaction_view.events.pageClickEvents.push({type:"waitaction",page_id:e.page?e.page.id:null,func:function(){$(".interaction-view").css("cursor","auto"),e.timeline.resume()}})},AddRepeatTween:function(e){var t=e._animation.obj,n=$.extend(!0,{},e._animation.param),r=e.toJSON().iTiming.duration||0,i=e.toJSON().iTiming.repeat||0;_.include([101,102,103],e.get("iType"))?e.tween=TweenMax.fromTo(t,r,e._animation.fromparam,e._animation.toparam):e.tween=TweenMax.to(t,r,n),i==-2&&$(".interaction-view").one("click",function(){e._animation.forcestop(e.tween)}),e.collection.repeatTweens.push(e.tween)},AddMediaTween:function(e){var t=e.collection.page;if(interaction_view.SinglePreview){var n=t.iAnimationlist.timeline.getLabelTime(interaction_view.PreviewItemId+"_start"),r=t.iAnimationlist.timeline.getLabelTime(e.id+"_start");if(n>r)return}var i=t.iOverlaylist.get(e.get("overlay_id")),s=e.get("iType");s==501&&(i.get("iType")=="Audio"||i.get("iType")=="Video"?i.loaded&&(i.get("iType")=="Audio"&&(console.log(i.media),i.media.play()),i.get("iType")=="Video"&&(i.iview.$el.removeClass("hide"),i.hided=!1,setTimeout(function(){i.media.play()},100))):i.iview.playTimeline()),s==502&&(i.get("iType")=="Audio"||i.get("iType")=="Video"?i.loaded&&i.media.pause():i.iview.pauseTimeline());if(s==503)if(i.get("iType")=="Audio"||i.get("iType")=="Video"){if(i.loaded){i.media.setCurrentTime(0),i.media.pause();if(i.get("iType")=="Video"){var o=i.get("iDetail").iAutohide;o==null&&(o=!0),o&&i.iview.$el.addClass("hide")}}}else i.iview.stopTimeline();t.iAnimationlist.onComplete(e.id)},getMediaTime:function(e){var t=0,n=this.page.iOverlaylist.get(e.toJSON().overlay_id);return n.media?(n.get("iType")=="Audio"&&(t=n.media.duration),n.get("iType")=="Video"&&(t=n.media.duration)):_.include(["CycleImage","Slide","LayerSlide"],n.get("iType"))&&(t=n.iview.getSlideDuration()),t},onSingleComplete:function(){},onUpdate:function(){},onTotalComplete:function(){},onComplete:function(e){},resetAnimationStatus:function(){_.each(this.repeatTweens,function(e){e.kill()}),this.repeatTweens=[],this.timeline&&this.timeline.pause(0)}}),interaction_view.view.Animation=interaction_view.view.Base.extend({events:{},renderDynamicElement:function(){},render:function(e){}}),interaction_view.iAnimationlist=new 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e=this.getdetail();this.autoplay=e.iAutoplay,this.currentIndex=0,this.timeline&&(this.timeline.pause(1e-5),this.autoplay?this.timeline.play(0):this.setSlideDisplay(0))}})}),define("interaction_view/model/image",["interaction_view/model/base"],function(){interaction_view.model.Image=interaction_view.model.Base.extend({defaults:{iType:"Image",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new 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t={eventType:0,mutiple:0,element:null,callback:null,type:0,enableControl:!1};e=e?$.extend(!0,{},t,e):$.extend(!0,{},t),e.onKeyDown=function(t,n){var r=t.keyCode?t.keyCode:t.which;r==13&&e.mutiple==0&&(e.enableClose=!0,e.$this.blur(),$(document).off("keydown",e.onKeyDown),e.result())},e.result=function(){if(e.$this.data("before")!==e.$this.html()){e.$this.data("before",e.$this.html());var t=e.type==0?e.$this.text():e.$this.html();e.callback&&e.callback(t)}},e.enableEdit=!1;if(!e.element)return!1;e.element&&(e.$this=$(e.element),e.$this.addClass("_g_contenteditable"),e.eventType==0&&$(e.element).attr("contenteditable","true"),e.eventType==1&&($(e.element).attr("contenteditable","false"),$(e.element).on("dblclick",function(){$(this).attr("contenteditable","true"),$(this).focus()})),$(e.element).on("focus",function(){return 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t={template:null,className:null,containment:null,wrap:null,wrapClassName:null,autoRender:!0,position:1,parseData:null,callback:null,bindChange:null,parseTemplate:null,initialize:function(){_.bindAll(this),this.autoRender&&this.render(),this.callback&&this.callback(this)},createEl:function(){var e=this.model.toJSON(),t;this.parseData&&(e=this.parseData()),this.parseTemplate?t=this.parseTemplate(this.template):t=_g.parseTemplate(this.template);var n=t(e);return n},render:function(e){var t=this;if(!this.model||!this.template||!this.containment)return!1;var n=$(this.containment);this.wrap&&!$(this.containment).is(this.wrap)&&($(this.containment).children(this.wrap).length==0&&$(this.containment).append(document.createElement(this.wrap)),n=$(this.containment).children(this.wrap),this.containment&&this.wrapClassName&&n.addClass(this.wrapClassName));var r=this.createEl();this.position==1?n.append(r):n.prepend(r);var i=_g.ui.findById({containment:n,id:t.model.id});return 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t={containment:null};e=e?$.extend({},t,e):t,this.length>0&&(_.each(this.at(0).iViewlist,function(t){containment=e.containment||t.containment,containment&&(t.wrap&&!$(containment).is(t.wrap)?$(containment).children(t.wrap).empty():$(containment).empty())}),this.each(function(t){e.containment&&(t.iview.containment=e.containment),t.iview.update()}))}};e=e?$.extend(!0,{},t,e):t;var n=Backbone.Collection.extend(e);return n},createTreeByData:function(e){var t={collection:null,data:null,treeLevel:0,parent:null},n=e.parent,r;e=e?$.extend(!0,{},t,e):t,e.parent=n;if(!e.collection||!e.data)return null;var i=$.extend(!0,{},e.data);if(typeof e.collection=="function")r=new e.collection;else{var s=_g.mvc.createCollection(e.collection);r=new s}return r.parent=n,r.refreshTreeView=function(e){this.refreshView(e),this.each(function(e){e.icollection.refreshTreeView({containment:e.iview.$el})})},r.getModels=function(){var e=_g.mvc.getTreeModels(r);return e},r.getModelsJSON=function(){var e=r.getModels();return e=_.map(e,function(e){return e.toJSON()}),e},r.getModel=function(e){return _g.mvc.getTreeModel(r,e)},r.on("add",function(t){t.treeLevel=e.treeLevel,t.icollection=_g.mvc.createTreeByData({collection:e.collection,data:[],treeLevel:e.treeLevel+1}),r.parent&&(r.View=typeof r.View=="object"?$.extend(!0,{},r.View,{containment:r.parent.iview.$el}):r.View.extend({containment:r.parent.iview.$el})),t.addView("iview",r.View),_g.ui.sortByIds({containment:t.iview.wrap?t.iview.$el.parent(t.iview.wrap):t.iview.$el.parent(),ids:r.pluck("id")}),t.icollection.parent=t,t.icollection.View=r.View}),r.saveAll=function(e){var t=typeof _gDebug!="undefined"&&_gDebug||this.debug,n=t?this.staticSaveUrl?this.staticSaveUrl:this.saveUrl:this.saveUrl;if(!n)return!1;var r=_g.mvc._CollectionforSaveAll();r.data=_g.mvc.getTreeData(this),r.url=n,r.save({},{wait:!0,success:function(t,n){e&&e(n)},error:function(){}})},_.each(i,function(t){var n;t.children?n=$.extend(!0,{},t.children):n=[],delete 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r={};e.collection=n;if(!e.collection||!e.view)return!1;e.containment&&(r.containment=e.containment),e.wrap&&(r.wrap=e.wrap),e.template&&(r.template=e.template),e.parseTemplate&&(r.parseTemplate=e.parseTemplate),e.parseData&&(r.parseData=e.parseData),e.callback&&(r.callback=e.callback),e.wrapClassName&&(r.wrapClassName=e.wrapClassName),e.className&&(r.className=e.className),r.update=e.update;var i=typeof e.view=="function"?e.view.extend(r):$.extend(!0,e.view,r);e.collection.View=i,e.collection.each(function(t){t.addView("iview",i);if(t.icollection){typeof e.containment!="string"&&(e.containment=e.containment.selector);var n=e.containment+(e.wrap?" >"+e.wrap:"")+' >[id="'+t.id+'"]';_g.mvc.createTreeView({collection:t.icollection,view:e.view,containment:n,wrap:e.wrap||null,template:e.template||null})}})},sortTreeByIds:function(e){var t=function(n,r,s){return n?(n.reset([],{silent:e.silent}),_.each(r,function(r){r.children=r.children||[];var o=i.get(r.id);o.treeLevel=s,o.icollection=t(o.icollection,r.children,s+1),n.add(o,{silent:e.silent}),o.collection=n}),n):null},n={collection:null,ids:null,treeLevel:0,silent:!0},r=e.collection;e=e?$.extend(!0,{},n,e):n,e.collection=r;if(!e.collection||!e.ids)return!1;var i=new Backbone.Collection;i.add(_g.mvc.getTreeModels(e.collection));var s=t(e.collection,e.ids,e.treeLevel);return s},getTreeModels:function(e){var t=[];return e.each(function(e){t.push(e);if(e.icollection){var n=_g.mvc.getTreeModels(e.icollection);_.each(n,function(e){t.push(e)})}}),t},getTreeModel:function(e,t){if(!e||!t)return!1;var n=_g.mvc.getTreeModels(e);return _.find(n,function(e){return e.id==t})},getTreeData:function(e){var t=[];return e.each(function(e){var n=$.extend(!0,{},e.toJSON());e.icollection&&(n.children=_g.mvc.getTreeData(e.icollection)),t.push(n)}),t}};typeof require=="undefined"?(window._g||(window._g={}),window._g.mvc=_g_mvc,_g_mvc=undefined):define("_g/mvc",["jquery","backbone","_g/base","_g/ui","_g/util"],function(){return window._g.mvc=_g_mvc,_g_mvc=undefined,window._g.mvc})}(window),define("text!interaction_view/template/page.js",[],function(){return'<section class="Presentation" id="<%=id%>">\n <div class="animationbk" style="text-align:center;position:absolute;width:100%;height:100%;background-color:<%=bg_color%>;opacity:<%=bg_opacity%>">\n <% if(typeof picture!="undefined"&&picture){ %>\n <img src="<%=picture%>" style="vertical-align: middle;<%=(width/height>=interaction_view.size.x/interaction_view.size.y)?"width:100%;":""%> <%=(width/height<interaction_view.size.x/interaction_view.size.y)?"height:100%;":""%>">\n <% } %>\n </div>\n <div class="interaction-area typo">\n <div class="interaction-view"></div>\n </div>\n</section>'}),define("text!interaction_view/template/pagegroup.js",[],function(){return'<section class="group" id="<%=id%>" data-transition="zoom" data-transition-speed="fast"></section>'}),define("interaction_view/view/page",["_g/mvc","text!interaction_view/template/page.js","text!interaction_view/template/pagegroup.js"],function(){var e={template:require("text!interaction_view/template/page.js"),className:null,containment:"div.slides",wrap:null,wrapClassName:null,autoRender:!0,position:1,parseData:null,callback:function(){this.model.get("type")!="page_group"&&(this.overlays=$.extend(!0,{},this.model.get("overlays")),this.animations=$.extend(!0,{},this.model.get("animations")),this.actions=$.extend(!0,{},this.model.get("actions")))},bindChange:null,parseTemplate:function(){var e=this.model.get("type");return e=="page_group"?_g.parseTemplate(require("text!interaction_view/template/pagegroup.js")):_g.parseTemplate(require("text!interaction_view/template/page.js"))},afterload:function(){var e=this;e.animations=_.map(e.animations,function(t){return t.pageid=e.model.id,t}),e.model.iAnimationlist.reset(e.animations),overlays=_.filter(e.overlays,function(t){return t.pageid=e.model.id,interaction_view.model[t.iType]}),overlays=_.map(overlays,function(e){return new interaction_view.model[e.iType](e)}),e.model.iOverlaylist.reset(overlays),e.model.iOverlaylist.each(function(e){e.iview.setZindex()}),e.actions=_.map(e.actions,function(t){return t.pageid=e.model.id,t}),e.model.iActionlist.reset(e.actions),e.startLoad()},startLoad:function(){var e=this;this.model.ipreloadlist.length==0?interaction_view.finishPagePreload(this.model.id):this.model.ipreloadlist.preload()},afterpreload:function(){this.model.iOverlaylist.each(function(e){e.iview.preloaded||e.iview.afterpreload()}),this.model.iActionlist.each(function(e){e.iview.render()})}};return e}),define("interaction_view/model/page",["_g/mvc","interaction_view/view/page"],function(){var e={defaults:{iType:"Page",position:0,description:"",title:"untitled",picture:null,preview:'<img src="/staticfs/admin-epub360/img/thumb.png">',thumbnail:null,width:null,height:null,bg_color:"#FFFFFF",bg_opacity:1,show_master:!0},autoIndex:!0,callback:function(){this.get("type")!="page_group"&&(this.iOverlaylist=new interaction_view.collection.list,this.iAnimationlist=new interaction_view.collection.Animation,this.iActionlist=new interaction_view.collection.Action,this.ipreloadlist=new interaction_view.collection.Preload,this.ipreloadlist.pageid=this.iOverlaylist.pageid=this.iAnimationlist.pageid=this.iActionlist.pageid=this.id)},overlays:[],actions:[],animations:[]};return _g.mvc.createModel(e)}),define("interaction_view/collection/page",["_g/mvc","interaction_view/model/page"],function(){var e={enableSync:!1,model:require("interaction_view/model/page"),getPages:function(){var e=_g.mvc.getTreeModels(interaction_view.ipagelist);return e=_.reject(e,function(e){return e.toJSON().type=="page_group"}),e},getPagesJSON:function(){var e=_g.mvc.getTreeModels(interaction_view.ipagelist);return e=_.reject(e,function(e){return e.toJSON().type=="page_group"}),e=_.map(e,function(e){return e.toJSON()}),e},getPage:function(e){return _g.mvc.getTreeModel(interaction_view.ipagelist,e)}};return _g.mvc.createCollection(e)}),define("interaction_view/model/layer",["interaction_view/model/page"],function(){var e=require("interaction_view/model/page"),t=e.extend({defaults:{iType:"Layer",position:0,description:"",title:"untitled",picture:null,preview:'<img src="/staticfs/admin-epub360/img/thumb.png">',thumbnail:null,width:null,height:null,layer_width:1024,layer_height:768,bg_color:"#FFFFFF",bg_opacity:1},callback:function(){this.actions=this.get("actions"),this.animations=this.get("animations"),this.overlays=this.get("overlays")}});return t}),define("interaction_view/collection/layer",["interaction_view/collection/page","interaction_view/model/layer"],function(){var e=require("interaction_view/collection/page"),t=e.extend({model:require("interaction_view/model/layer")});return t}),define("interaction_view/model/master",["interaction_view/model/page"],function(){var e=require("interaction_view/model/page"),t=e.extend({defaults:{iType:"Master",position:0,description:"",title:"untitled",picture:null,preview:'<img src="/staticfs/admin-epub360/img/thumb.png">',thumbnail:null,width:null,height:null,bg_color:"#FFFFFF",bg_opacity:1},callback:function(){this.iOverlaylist=new interaction_view.collection.list,this.iAnimationlist=new interaction_view.collection.Animation,this.iActionlist=new interaction_view.collection.Action,this.ipreloadlist=new interaction_view.collection.Preload,this.ipreloadlist.pageid=this.iOverlaylist.pageid=this.iAnimationlist.pageid=this.iActionlist.pageid=this.id}});return t}),define("interaction_view/view/master",["interaction_view/view/page"],function(){var e=require("interaction_view/view/page"),t=$.extend({},e,{template:null,className:null,containment:null,wrap:null,wrapClassName:null,autoRender:!1,position:1,parseData:null,afterload:function(){var e=this,t=this.model;if(!t)return;e.animations=_.map(e.animations,function(t){return t.pageid=e.model.id,t.pagetype="master",t}),e.model.iAnimationlist.reset(e.animations),overlays=_.filter(e.overlays,function(t){return t.pageid=e.model.id,t.pagetype="master",interaction_view.model[t.iType]}),overlays=_.map(overlays,function(e){return new interaction_view.model[e.iType](e)}),e.model.iOverlaylist.reset(overlays),e.model.iOverlaylist.each(function(e){e.iview.setZindex()}),e.actions=_.map(e.actions,function(t){return t.pageid=e.model.id,t.pagetype="master",t}),e.model.iActionlist.reset(e.actions),e.startLoad()}});return t}),define("interaction_view/collection/master",["interaction_view/collection/page","interaction_view/model/master","interaction_view/view/master"],function(){var e=require("interaction_view/collection/page"),t=e.extend({model:require("interaction_view/model/master")});return t}),define("interaction_view/model/doc",["_g/mvc","interaction_view/collection/page","interaction_view/view/page","interaction_view/collection/layer","interaction_view/collection/master"],function(){var e={defaults:{id:"doc",play_mode:0},autoUpdate:!1,fetchUrl:context_url+"index.json",staticFetchUrl:"/staticfs/common/js/interaction_view/data/index.json",parse:function(e){var t=this;console.log("fetch data:"+e);if(e.code==200){if(e.data){interaction_view.size={x:e.data.width||1024,y:e.data.height||768};if(typeof adjust_screen!="undefined"){var n=get_window_size();window.screenScale=n.x/n.y>interaction_view.size.x/interaction_view.size.y?n.y/interaction_view.size.y:n.x/interaction_view.size.x,adjust_screen(),window.screenScale=null}return this.initMasters(e),this.initLayers(e),this.initPages(e),_.pick(e.data,"id","title","play_mode","width","height")}return{}}},parseOrgnization:function(e,t){var n=this,r=[];return _.each(e,function(e){if(e.type=="page_group"){var i=n.parseOrgnization(e.children,e.id);_.each(i,function(e){r.push(e)})}else e.children||(e.children=[]),e.children.unshift($.extend(!0,{},e)),e.children&&_.each(e.children,function(n){n.id==e.id&&(n.children=[]),n.group=t}),e.type="page_group",delete e.id,delete e.animations,delete e.overlays,delete e.actions,r.push(e)}),r},initMasters:function(e){var t=require("interaction_view/collection/master");e.data.masters||(e.data.masters=[]),interaction_view.imasterlist=_g.mvc.createTreeByData({collection:t,data:e.data.masters})},initLayers:function(e){var t=require("interaction_view/collection/layer");e.data.layers||(e.data.layers=[]),interaction_view.ilayerlist=_g.mvc.createTreeByData({collection:t,data:e.data.layers})},initPages:function(e){var t=this,n,r=require("interaction_view/collection/page");e.data.play_mode==0?n=e.data.chapter:(n=_g.array.maptree({treesource:e.data.organization.children,mapdata:e.data.chapter}),n=_.reject(n,function(e){return!e.children||e.children.length==0}),n=t.parseOrgnization(n)),interaction_view.ipagelist=_g.mvc.createTreeByData({collection:r,data:n}),_g.mvc.createTreeView({collection:interaction_view.ipagelist,view:require("interaction_view/view/page"),containment:"div.slides",wrap:null})},callback:function(){}};return _g.mvc.createModel(e)}),define("text!interaction_view/template/preload.js",[],function(){return'<img src="<%=file%>" width="1" height="1" id="<%=id%>"/>'});var 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YT.Player(e.containerId,{height:e.height,width:e.width,videoId:e.videoId,playerVars:{controls:0},events:{onReady:function(){e.pluginMediaElement.pluginApi=n,mejs.MediaPluginBridge.initPlugin(e.pluginId),setInterval(function(){mejs.YouTubeApi.createEvent(n,t,"timeupdate")},250)},onStateChange:function(e){mejs.YouTubeApi.handleStateChange(e.data,n,t)}}})},createEvent:function(e,t,n){n={type:n,target:t};if(e&&e.getDuration){t.currentTime=n.currentTime=e.getCurrentTime(),t.duration=n.duration=e.getDuration(),n.paused=t.paused,n.ended=t.ended,n.muted=e.isMuted(),n.volume=e.getVolume()/100,n.bytesTotal=e.getVideoBytesTotal(),n.bufferedBytes=e.getVideoBytesLoaded();var r=n.bufferedBytes/n.bytesTotal*n.duration;n.target.buffered=n.buffered={start:function(){return 0},end:function(){return 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height="'+e.height+'" style="visibility: visible; " class="mejs-shim"><param name="allowScriptAccess" value="always"><param name="wmode" value="transparent"></object>'},flashReady:function(e){var t=this.flashPlayers[e],n=document.getElementById(e),r=t.pluginMediaElement;r.pluginApi=r.pluginElement=n,mejs.MediaPluginBridge.initPlugin(e),n.cueVideoById(t.videoId),e=t.containerId+"_callback",window[e]=function(e){mejs.YouTubeApi.handleStateChange(e,n,r)},n.addEventListener("onStateChange",e),setInterval(function(){mejs.YouTubeApi.createEvent(n,r,"timeupdate")},250)},handleStateChange:function(e,t,n){switch(e){case-1:n.paused=!0,n.ended=!0,mejs.YouTubeApi.createEvent(t,n,"loadedmetadata");break;case 0:n.paused=!1,n.ended=!0,mejs.YouTubeApi.createEvent(t,n,"ended");break;case 1:n.paused=!1,n.ended=!1,mejs.YouTubeApi.createEvent(t,n,"play"),mejs.YouTubeApi.createEvent(t,n,"playing");break;case 2:n.paused=!0,n.ended=!1,mejs.YouTubeApi.createEvent(t,n,"pause");break;case 3:mejs.YouTubeApi.createEvent(t,n,"progress")}}},window.mejs=mejs,window.MediaElement=mejs.MediaElement,function(e,t){var n={locale:{language:"",strings:{}},methods:{}};n.locale.getLanguage=function(){return n.locale.language||navigator.language},typeof mejsL10n!="undefined"&&(n.locale.language=mejsL10n.language),n.locale.INIT_LANGUAGE=n.locale.getLanguage(),n.methods.checkPlain=function(e){var t,n,r={"&":"&",'"':""","<":"<",">":">"};e=String(e);for(t in r)r.hasOwnProperty(t)&&(n=RegExp(t,"g"),e=e.replace(n,r[t]));return e},n.methods.formatString=function(e,t){for(var r in t){switch(r.charAt(0)){case"@":t[r]=n.methods.checkPlain(t[r]);break;case"!":break;default:t[r]='<em class="placeholder">'+n.methods.checkPlain(t[r])+"</em>"}e=e.replace(r,t[r])}return e},n.methods.t=function(e,t,r){return n.locale.strings&&n.locale.strings[r.context]&&n.locale.strings[r.context][e]&&(e=n.locale.strings[r.context][e]),t&&(e=n.methods.formatString(e,t)),e},n.t=function(e,t,r){if(typeof e=="string"&&e.length>0){var i=n.locale.getLanguage();return r=r||{context:i},n.methods.t(e,t,r)}throw{name:"InvalidArgumentException",message:"First argument is either not a string or empty."}},t.i18n=n}(document,mejs),function(e){typeof mejsL10n!="undefined"&&(e[mejsL10n.language]=mejsL10n.strings)}(mejs.i18n.locale.strings),function(e){e.de={Fullscreen:"Vollbild","Go Fullscreen":"Vollbild an","Turn off Fullscreen":"Vollbild aus",Close:"Schließen"}}(mejs.i18n.locale.strings),function(e){e.zh={Fullscreen:"全螢幕","Go Fullscreen":"全屏模式","Turn off Fullscreen":"退出全屏模式",Close:"關閉"}}(mejs.i18n.locale.strings),typeof jQuery!="undefined"?mejs.$=jQuery:typeof ender!="undefined"&&(mejs.$=ender),function(e){mejs.MepDefaults={poster:"",showPosterWhenEnded:!1,defaultVideoWidth:480,defaultVideoHeight:270,videoWidth:-1,videoHeight:-1,defaultAudioWidth:400,defaultAudioHeight:30,defaultSeekBackwardInterval:function(e){return e.duration*.05},defaultSeekForwardInterval:function(e){return 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mejs.MediaElementPlayer(t,n)},mejs.MediaElementPlayer.prototype={hasFocus:!1,controlsAreVisible:!0,init:function(){var t=this,n=mejs.MediaFeatures,r=e.extend(!0,{},t.options,{success:function(e,n){t.meReady(e,n)},error:function(e){t.handleError(e)}}),i=t.media.tagName.toLowerCase();t.isDynamic=i!=="audio"&&i!=="video",t.isVideo=t.isDynamic?t.options.isVideo:i!=="audio"&&t.options.isVideo;if(n.isiPad&&t.options.iPadUseNativeControls||n.isiPhone&&t.options.iPhoneUseNativeControls)t.$media.attr("controls","controls"),n.isiPad&&t.media.getAttribute("autoplay")!==null&&(t.media.load(),t.media.play());else if(!n.isAndroid||!t.options.AndroidUseNativeControls)t.$media.removeAttr("controls"),t.container=e('<div id="'+t.id+'" class="mejs-container '+(mejs.MediaFeatures.svg?"svg":"no-svg")+'"><div class="mejs-inner"><div class="mejs-mediaelement"></div><div class="mejs-layers"></div><div class="mejs-controls"></div><div class="mejs-clear"></div></div></div>').addClass(t.$media[0].className).insertBefore(t.$media),t.container.addClass((n.isAndroid?"mejs-android ":"")+(n.isiOS?"mejs-ios ":"")+(n.isiPad?"mejs-ipad ":"")+(n.isiPhone?"mejs-iphone ":"")+(t.isVideo?"mejs-video ":"mejs-audio ")),n.isiOS?(n=t.$media.clone(),t.container.find(".mejs-mediaelement").append(n),t.$media.remove(),t.$node=t.$media=n,t.node=t.media=n[0]):t.container.find(".mejs-mediaelement").append(t.$media),t.controls=t.container.find(".mejs-controls"),t.layers=t.container.find(".mejs-layers"),n=t.isVideo?"video":"audio",i=n.substring(0,1).toUpperCase()+n.substring(1),t.width=t.options[n+"Width"]>0||t.options[n+"Width"].toString().indexOf("%")>-1?t.options[n+"Width"]:t.media.style.width!==""&&t.media.style.width!==null?t.media.style.width:t.media.getAttribute("width")!==null?t.$media.attr("width"):t.options["default"+i+"Width"],t.height=t.options[n+"Height"]>0||t.options[n+"Height"].toString().indexOf("%")>-1?t.options[n+"Height"]:t.media.style.height!==""&&t.media.style.height!==null?t.media.style.height:t.$media[0].getAttribute("height")!==null?t.$media.attr("height"):t.options["default"+i+"Height"],t.setPlayerSize(t.width,t.height),r.pluginWidth=t.width,r.pluginHeight=t.height;mejs.MediaElement(t.$media[0],r),typeof t.container!="undefined"&&t.controlsAreVisible&&t.container.trigger("controlsshown")},showControls:function(e){var t=this;e=typeof e=="undefined"||e,t.controlsAreVisible||(e?(t.controls.css("visibility","visible").stop(!0,!0).fadeIn(200,function(){t.controlsAreVisible=!0,t.container.trigger("controlsshown")}),t.container.find(".mejs-control").css("visibility","visible").stop(!0,!0).fadeIn(200,function(){t.controlsAreVisible=!0})):(t.controls.css("visibility","visible").css("display","block"),t.container.find(".mejs-control").css("visibility","visible").css("display","block"),t.controlsAreVisible=!0,t.container.trigger("controlsshown")),t.setControlsSize())},hideControls:function(t){var n=this;t=typeof t=="undefined"||t,!!n.controlsAreVisible&&!n.options.alwaysShowControls&&(t?(n.controls.stop(!0,!0).fadeOut(200,function(){e(this).css("visibility","hidden").css("display","block"),n.controlsAreVisible=!1,n.container.trigger("controlshidden")}),n.container.find(".mejs-control").stop(!0,!0).fadeOut(200,function(){e(this).css("visibility","hidden").css("display","block")})):(n.controls.css("visibility","hidden").css("display","block"),n.container.find(".mejs-control").css("visibility","hidden").css("display","block"),n.controlsAreVisible=!1,n.container.trigger("controlshidden")))},controlsTimer:null,startControlsTimer:function(e){var t=this;e=typeof e!="undefined"?e:1500,t.killControlsTimer("start"),t.controlsTimer=setTimeout(function(){t.hideControls(),t.killControlsTimer("hide")},e)},killControlsTimer:function(){this.controlsTimer!==null&&(clearTimeout(this.controlsTimer),delete this.controlsTimer,this.controlsTimer=null)},controlsEnabled:!0,disableControls:function(){this.killControlsTimer(),this.hideControls(!1),this.controlsEnabled=!1},enableControls:function(){this.showControls(!1),this.controlsEnabled=!0},meReady:function(e,t){var n=this,r=mejs.MediaFeatures,i=t.getAttribute("autoplay");i=typeof i!="undefined"&&i!==null&&i!=="false";var s;if(!n.created){n.created=!0,n.media=e,n.domNode=t;if((!r.isAndroid||!n.options.AndroidUseNativeControls)&&(!r.isiPad||!n.options.iPadUseNativeControls)&&(!r.isiPhone||!n.options.iPhoneUseNativeControls)){n.buildposter(n,n.controls,n.layers,n.media),n.buildkeyboard(n,n.controls,n.layers,n.media),n.buildoverlays(n,n.controls,n.layers,n.media),n.findTracks();for(s in n.options.features){r=n.options.features[s];if(n["build"+r])try{n["build"+r](n,n.controls,n.layers,n.media)}catch(o){}}n.container.trigger("controlsready"),n.setPlayerSize(n.width,n.height),n.setControlsSize(),n.isVideo&&(mejs.MediaFeatures.hasTouch?n.$media.bind("touchstart",function(){n.controlsAreVisible?n.hideControls(!1):n.controlsEnabled&&n.showControls(!1)}):(mejs.MediaElementPlayer.prototype.clickToPlayPauseCallback=function(){n.options.clickToPlayPause&&(n.media.paused?n.media.play():n.media.pause())},n.media.addEventListener("click",n.clickToPlayPauseCallback,!1),n.container.bind("mouseenter mouseover",function(){n.controlsEnabled&&(n.options.alwaysShowControls||(n.killControlsTimer("enter"),n.showControls(),n.startControlsTimer(2500)))}).bind("mousemove",function(){n.controlsEnabled&&(n.controlsAreVisible||n.showControls(),n.options.alwaysShowControls||n.startControlsTimer(2500))}).bind("mouseleave",function(){n.controlsEnabled&&!n.media.paused&&!n.options.alwaysShowControls&&n.startControlsTimer(1e3)})),n.options.hideVideoControlsOnLoad&&n.hideControls(!1),i&&!n.options.alwaysShowControls&&n.hideControls(),n.options.enableAutosize&&n.media.addEventListener("loadedmetadata",function(e){n.options.videoHeight<=0&&n.domNode.getAttribute("height")===null&&!isNaN(e.target.videoHeight)&&(n.setPlayerSize(e.target.videoWidth,e.target.videoHeight),n.setControlsSize(),n.media.setVideoSize(e.target.videoWidth,e.target.videoHeight))},!1)),e.addEventListener("play",function(){for(var e in mejs.players){var t=mejs.players[e];t.id!=n.id&&n.options.pauseOtherPlayers&&!t.paused&&!t.ended&&t.pause(),t.hasFocus=!1}n.hasFocus=!0},!1),n.media.addEventListener("ended",function(){if(n.options.autoRewind)try{n.media.setCurrentTime(0)}catch(e){}n.media.pause(),n.setProgressRail&&n.setProgressRail(),n.setCurrentRail&&n.setCurrentRail(),n.options.loop?n.media.play():!n.options.alwaysShowControls&&n.controlsEnabled&&n.showControls()},!1),n.media.addEventListener("loadedmetadata",function(){n.updateDuration&&n.updateDuration(),n.updateCurrent&&n.updateCurrent(),n.isFullScreen||(n.setPlayerSize(n.width,n.height),n.setControlsSize())},!1),setTimeout(function(){n.setPlayerSize(n.width,n.height),n.setControlsSize()},50),n.globalBind("resize",function(){n.isFullScreen||mejs.MediaFeatures.hasTrueNativeFullScreen&&document.webkitIsFullScreen||n.setPlayerSize(n.width,n.height),n.setControlsSize()}),n.media.pluginType=="youtube"&&n.container.find(".mejs-overlay-play").hide()}i&&e.pluginType=="native"&&(e.load(),e.play()),n.options.success&&(typeof n.options.success=="string"?window[n.options.success](n.media,n.domNode,n):n.options.success(n.media,n.domNode,n))}},handleError:function(e){this.controls.hide(),this.options.error&&this.options.error(e)},setPlayerSize:function(t,n){typeof t!="undefined"&&(this.width=t),typeof n!="undefined"&&(this.height=n);if(this.height.toString().indexOf("%")>0||this.$node.css("max-width")==="100%"||parseInt(this.$node.css("max-width").replace(/px/,""),10)/this.$node.offsetParent().width()===1||this.$node[0].currentStyle&&this.$node[0].currentStyle.maxWidth==="100%"){var r=this.isVideo?this.media.videoWidth&&this.media.videoWidth>0?this.media.videoWidth:this.options.defaultVideoWidth:this.options.defaultAudioWidth,i=this.isVideo?this.media.videoHeight&&this.media.videoHeight>0?this.media.videoHeight:this.options.defaultVideoHeight:this.options.defaultAudioHeight,s=this.container.parent().closest(":visible").width();r=this.isVideo||!this.options.autosizeProgress?parseInt(s*i/r,10):i,this.container.parent()[0].tagName.toLowerCase()==="body"&&(s=e(window).width(),r=e(window).height()),r!=0&&s!=0&&(this.container.width(s).height(r),this.$media.add(this.container.find(".mejs-shim")).width("100%").height("100%"),this.isVideo&&this.media.setVideoSize&&this.media.setVideoSize(s,r),this.layers.children(".mejs-layer").width("100%").height("100%"))}else this.container.width(this.width).height(this.height),this.layers.children(".mejs-layer").width(this.width).height(this.height);s=this.layers.find(".mejs-overlay-play"),r=s.find(".mejs-overlay-button"),s.height(this.container.height()-this.controls.height()),r.css("margin-top","-"+(r.height()/2-this.controls.height()/2).toString()+"px")},setControlsSize:function(){var t=0,n=0,r=this.controls.find(".mejs-time-rail"),i=this.controls.find(".mejs-time-total");this.controls.find(".mejs-time-current"),this.controls.find(".mejs-time-loaded");var s=r.siblings();this.options&&!this.options.autosizeProgress&&(n=parseInt(r.css("width")));if(n===0||!n)s.each(function(){var n=e(this);n.css("position")!="absolute"&&n.is(":visible")&&(t+=e(this).outerWidth(!0))}),n=this.controls.width()-t-(r.outerWidth(!0)-r.width());r.width(n),i.width(n-(i.outerWidth(!0)-i.width())),this.setProgressRail&&this.setProgressRail(),this.setCurrentRail&&this.setCurrentRail()},buildposter:function(t,n,r,i){var s=e('<div class="mejs-poster mejs-layer"></div>').appendTo(r);n=t.$media.attr("poster"),t.options.poster!==""&&(n=t.options.poster),n!==""&&n!=null?this.setPoster(n):s.hide(),i.addEventListener("play",function(){s.hide()},!1),t.options.showPosterWhenEnded&&t.options.autoRewind&&i.addEventListener("ended",function(){s.show()},!1)},setPoster:function(t){var n=this.container.find(".mejs-poster"),r=n.find("img");r.length==0&&(r=e('<img width="100%" height="100%" />').appendTo(n)),r.attr("src",t),n.css({"background-image":"url("+t+")"})},buildoverlays:function(t,n,r,i){var s=this;if(t.isVideo){var o=e('<div class="mejs-overlay mejs-layer"><div class="mejs-overlay-loading"><span></span></div></div>').hide().appendTo(r),u=e('<div class="mejs-overlay mejs-layer"><div class="mejs-overlay-error"></div></div>').hide().appendTo(r),a=e('<div class="mejs-overlay mejs-layer mejs-overlay-play"><div class="mejs-overlay-button"></div></div>').appendTo(r).click(function(){s.options.clickToPlayPause&&(i.paused?i.play():i.pause())});i.addEventListener("play",function(){a.hide(),o.hide(),n.find(".mejs-time-buffering").hide(),u.hide()},!1),i.addEventListener("playing",function(){a.hide(),o.hide(),n.find(".mejs-time-buffering").hide(),u.hide()},!1),i.addEventListener("seeking",function(){o.show(),n.find(".mejs-time-buffering").show()},!1),i.addEventListener("seeked",function(){o.hide(),n.find(".mejs-time-buffering").hide()},!1),i.addEventListener("pause",function(){mejs.MediaFeatures.isiPhone||a.show()},!1),i.addEventListener("waiting",function(){o.show(),n.find(".mejs-time-buffering").show()},!1),i.addEventListener("loadeddata",function(){o.show(),n.find(".mejs-time-buffering").show()},!1),i.addEventListener("canplay",function(){o.hide(),n.find(".mejs-time-buffering").hide()},!1),i.addEventListener("error",function(){o.hide(),n.find(".mejs-time-buffering").hide(),u.show(),u.find("mejs-overlay-error").html("Error loading this resource")},!1)}},buildkeyboard:function(t,n,r,i){this.globalBind("keydown",function(e){if(t.hasFocus&&t.options.enableKeyboard)for(var n=0,r=t.options.keyActions.length;n<r;n++)for(var s=t.options.keyActions[n],o=0,u=s.keys.length;o<u;o++)if(e.keyCode==s.keys[o])return e.preventDefault(),s.action(t,i,e.keyCode),!1;return!0}),this.globalBind("click",function(n){e(n.target).closest(".mejs-container").length==0&&(t.hasFocus=!1)})},findTracks:function(){var t=this,n=t.$media.find("track");t.tracks=[],n.each(function(n,r){r=e(r),t.tracks.push({srclang:r.attr("srclang")?r.attr("srclang").toLowerCase():"",src:r.attr("src"),kind:r.attr("kind"),label:r.attr("label")||"",entries:[],isLoaded:!1})})},changeSkin:function(e){this.container[0].className="mejs-container "+e,this.setPlayerSize(this.width,this.height),this.setControlsSize()},play:function(){this.media.play()},pause:function(){try{this.media.pause()}catch(e){}},load:function(){this.media.load()},setMuted:function(e){this.media.setMuted(e)},setCurrentTime:function(e){this.media.setCurrentTime(e)},getCurrentTime:function(){return this.media.currentTime},setVolume:function(e){this.media.setVolume(e)},getVolume:function(){return this.media.volume},setSrc:function(e){this.media.setSrc(e)},remove:function(){var e,t;for(e in this.options.features){t=this.options.features[e];if(this["clean"+t])try{this["clean"+t](this)}catch(n){}}this.isDynamic?this.$node.insertBefore(this.container):(this.$media.prop("controls",!0),this.$node.clone().show().insertBefore(this.container),this.$node.remove()),this.media.pluginType!=="native"&&this.media.remove(),delete mejs.players[this.id],this.container.remove(),this.globalUnbind(),delete this.node.player}},function(){function t(t,r){var i={d:[],w:[]};return e.each((t||"").split(" "),function(e,t){var s=t+"."+r;s.indexOf(".")===0?(i.d.push(s),i.w.push(s)):i[n.test(t)?"w":"d"].push(s)}),i.d=i.d.join(" "),i.w=i.w.join(" "),i}var n=/^((after|before)print|(before)?unload|hashchange|message|o(ff|n)line|page(hide|show)|popstate|resize|storage)\b/;mejs.MediaElementPlayer.prototype.globalBind=function(n,r,i){n=t(n,this.id),n.d&&e(document).bind(n.d,r,i),n.w&&e(window).bind(n.w,r,i)},mejs.MediaElementPlayer.prototype.globalUnbind=function(n,r){n=t(n,this.id),n.d&&e(document).unbind(n.d,r),n.w&&e(window).unbind(n.w,r)}}(),typeof jQuery!="undefined"&&(jQuery.fn.mediaelementplayer=function(e){return e===!1?this.each(function(){var e=jQuery(this).data("mediaelementplayer");e&&e.remove(),jQuery(this).removeData("mediaelementplayer")}):this.each(function(){jQuery(this).data("mediaelementplayer",new mejs.MediaElementPlayer(this,e))}),this}),e(document).ready(function(){e(".mejs-player").mediaelementplayer()}),window.MediaElementPlayer=mejs.MediaElementPlayer}(mejs.$),function(e){e.extend(mejs.MepDefaults,{playpauseText:mejs.i18n.t("Play/Pause")}),e.extend(MediaElementPlayer.prototype,{buildplaypause:function(t,n,r,i){var s=e('<div class="mejs-button mejs-playpause-button mejs-play" ><button type="button" aria-controls="'+this.id+'" title="'+this.options.playpauseText+'" aria-label="'+this.options.playpauseText+'"></button></div>').appendTo(n).click(function(e){return e.preventDefault(),i.paused?i.play():i.pause(),!1});i.addEventListener("play",function(){s.removeClass("mejs-play").addClass("mejs-pause")},!1),i.addEventListener("playing",function(){s.removeClass("mejs-play").addClass("mejs-pause")},!1),i.addEventListener("pause",function(){s.removeClass("mejs-pause").addClass("mejs-play")},!1),i.addEventListener("paused",function(){s.removeClass("mejs-pause").addClass("mejs-play")},!1)}})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{stopText:"Stop"}),e.extend(MediaElementPlayer.prototype,{buildstop:function(t,n,r,i){e('<div class="mejs-button mejs-stop-button mejs-stop"><button type="button" aria-controls="'+this.id+'" title="'+this.options.stopText+'" aria-label="'+this.options.stopText+'"></button></div>').appendTo(n).click(function(){i.paused||i.pause(),i.currentTime>0&&(i.setCurrentTime(0),i.pause(),n.find(".mejs-time-current").width("0px"),n.find(".mejs-time-handle").css("left","0px"),n.find(".mejs-time-float-current").html(mejs.Utility.secondsToTimeCode(0)),n.find(".mejs-currenttime").html(mejs.Utility.secondsToTimeCode(0)),r.find(".mejs-poster").show())})}})}(mejs.$),function(e){e.extend(MediaElementPlayer.prototype,{buildprogress:function(t,n,r,i){e('<div class="mejs-time-rail"><span class="mejs-time-total"><span class="mejs-time-buffering"></span><span class="mejs-time-loaded"></span><span class="mejs-time-current"></span><span class="mejs-time-handle"></span><span class="mejs-time-float"><span class="mejs-time-float-current">00:00</span><span class="mejs-time-float-corner"></span></span></span></div>').appendTo(n),n.find(".mejs-time-buffering").hide();var s=this,o=n.find(".mejs-time-total");r=n.find(".mejs-time-loaded");var u=n.find(".mejs-time-current"),a=n.find(".mejs-time-handle"),l=n.find(".mejs-time-float"),c=n.find(".mejs-time-float-current"),h=function(e){e=e.pageX;var t=o.offset(),n=o.outerWidth(!0),r=0,s=r=0;i.duration&&(e<t.left?e=t.left:e>n+t.left&&(e=n+t.left),s=e-t.left,r=s/n,r=r<=.02?0:r*i.duration,p&&r!==i.currentTime&&i.setCurrentTime(r),mejs.MediaFeatures.hasTouch||(l.css("left",s),c.html(mejs.Utility.secondsToTimeCode(r)),l.show()))},p=!1;o.bind("mousedown",function(e){if(e.which===1)return p=!0,h(e),s.globalBind("mousemove.dur",function(e){h(e)}),s.globalBind("mouseup.dur",function(){p=!1,l.hide(),s.globalUnbind(".dur")}),!1}).bind("mouseenter",function(){s.globalBind("mousemove.dur",function(e){h(e)}),mejs.MediaFeatures.hasTouch||l.show()}).bind("mouseleave",function(){p||(s.globalUnbind(".dur"),l.hide())}),i.addEventListener("progress",function(e){t.setProgressRail(e),t.setCurrentRail(e)},!1),i.addEventListener("timeupdate",function(e){t.setProgressRail(e),t.setCurrentRail(e)},!1),s.loaded=r,s.total=o,s.current=u,s.handle=a},setProgressRail:function(e){var t=e!=undefined?e.target:this.media,n=null;t&&t.buffered&&t.buffered.length>0&&t.buffered.end&&t.duration?n=t.buffered.end(0)/t.duration:t&&t.bytesTotal!=undefined&&t.bytesTotal>0&&t.bufferedBytes!=undefined?n=t.bufferedBytes/t.bytesTotal:e&&e.lengthComputable&&e.total!=0&&(n=e.loaded/e.total),n!==null&&(n=Math.min(1,Math.max(0,n)),this.loaded&&this.total&&this.loaded.width(this.total.width()*n))},setCurrentRail:function(){if(this.media.currentTime!=undefined&&this.media.duration&&this.total&&this.handle){var e=Math.round(this.total.width()*this.media.currentTime/this.media.duration),t=e-Math.round(this.handle.outerWidth(!0)/2);this.current.width(e),this.handle.css("left",t)}}})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{duration:-1,timeAndDurationSeparator:"<span> | </span>"}),e.extend(MediaElementPlayer.prototype,{buildcurrent:function(t,n,r,i){e('<div class="mejs-time"><span class="mejs-currenttime">'+(t.options.alwaysShowHours?"00:":"")+(t.options.showTimecodeFrameCount?"00:00:00":"00:00")+"</span></div>").appendTo(n),this.currenttime=this.controls.find(".mejs-currenttime"),i.addEventListener("timeupdate",function(){t.updateCurrent()},!1)},buildduration:function(t,n,r,i){n.children().last().find(".mejs-currenttime").length>0?e(this.options.timeAndDurationSeparator+'<span class="mejs-duration">'+(this.options.duration>0?mejs.Utility.secondsToTimeCode(this.options.duration,this.options.alwaysShowHours||this.media.duration>3600,this.options.showTimecodeFrameCount,this.options.framesPerSecond||25):(t.options.alwaysShowHours?"00:":"")+(t.options.showTimecodeFrameCount?"00:00:00":"00:00"))+"</span>").appendTo(n.find(".mejs-time")):(n.find(".mejs-currenttime").parent().addClass("mejs-currenttime-container"),e('<div class="mejs-time mejs-duration-container"><span class="mejs-duration">'+(this.options.duration>0?mejs.Utility.secondsToTimeCode(this.options.duration,this.options.alwaysShowHours||this.media.duration>3600,this.options.showTimecodeFrameCount,this.options.framesPerSecond||25):(t.options.alwaysShowHours?"00:":"")+(t.options.showTimecodeFrameCount?"00:00:00":"00:00"))+"</span></div>").appendTo(n)),this.durationD=this.controls.find(".mejs-duration"),i.addEventListener("timeupdate",function(){t.updateDuration()},!1)},updateCurrent:function(){this.currenttime&&this.currenttime.html(mejs.Utility.secondsToTimeCode(this.media.currentTime,this.options.alwaysShowHours||this.media.duration>3600,this.options.showTimecodeFrameCount,this.options.framesPerSecond||25))},updateDuration:function(){this.container.toggleClass("mejs-long-video",this.media.duration>3600),this.durationD&&(this.options.duration>0||this.media.duration)&&this.durationD.html(mejs.Utility.secondsToTimeCode(this.options.duration>0?this.options.duration:this.media.duration,this.options.alwaysShowHours,this.options.showTimecodeFrameCount,this.options.framesPerSecond||25))}})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{muteText:mejs.i18n.t("Mute Toggle"),hideVolumeOnTouchDevices:!0,audioVolume:"horizontal",videoVolume:"vertical"}),e.extend(MediaElementPlayer.prototype,{buildvolume:function(t,n,r,i){if(!mejs.MediaFeatures.hasTouch||!this.options.hideVolumeOnTouchDevices){var s=this,o=s.isVideo?s.options.videoVolume:s.options.audioVolume,u=o=="horizontal"?e('<div class="mejs-button mejs-volume-button mejs-mute"><button type="button" aria-controls="'+s.id+'" title="'+s.options.muteText+'" aria-label="'+s.options.muteText+'"></button></div><div class="mejs-horizontal-volume-slider"><div class="mejs-horizontal-volume-total"></div><div class="mejs-horizontal-volume-current"></div><div class="mejs-horizontal-volume-handle"></div></div>').appendTo(n):e('<div class="mejs-button mejs-volume-button mejs-mute"><button type="button" aria-controls="'+s.id+'" title="'+s.options.muteText+'" aria-label="'+s.options.muteText+'"></button><div class="mejs-volume-slider"><div class="mejs-volume-total"></div><div class="mejs-volume-current"></div><div class="mejs-volume-handle"></div></div></div>').appendTo(n),a=s.container.find(".mejs-volume-slider, .mejs-horizontal-volume-slider"),l=s.container.find(".mejs-volume-total, .mejs-horizontal-volume-total"),c=s.container.find(".mejs-volume-current, .mejs-horizontal-volume-current"),h=s.container.find(".mejs-volume-handle, .mejs-horizontal-volume-handle"),p=function(e,t){if(!a.is(":visible")&&typeof t=="undefined")a.show(),p(e,!0),a.hide();else{e=Math.max(0,e),e=Math.min(e,1),e==0?u.removeClass("mejs-mute").addClass("mejs-unmute"):u.removeClass("mejs-unmute").addClass("mejs-mute");if(o=="vertical"){var n=l.height(),r=l.position(),i=n-n*e;h.css("top",Math.round(r.top+i-h.height()/2)),c.height(n-i),c.css("top",r.top+i)}else n=l.width(),r=l.position(),n*=e,h.css("left",Math.round(r.left+n-h.width()/2)),c.width(Math.round(n))}},d=function(e){var t=null,n=l.offset();if(o=="vertical"){t=l.height(),parseInt(l.css("top").replace(/px/,""),10),t=(t-(e.pageY-n.top))/t;if(n.top==0||n.left==0)return}else t=l.width(),t=(e.pageX-n.left)/t;t=Math.max(0,t),t=Math.min(t,1),p(t),t==0?i.setMuted(!0):i.setMuted(!1),i.setVolume(t)},v=!1,m=!1;u.hover(function(){a.show(),m=!0},function(){m=!1,!v&&o=="vertical"&&a.hide()}),a.bind("mouseover",function(){m=!0}).bind("mousedown",function(e){return d(e),s.globalBind("mousemove.vol",function(e){d(e)}),s.globalBind("mouseup.vol",function(){v=!1,s.globalUnbind(".vol"),!m&&o=="vertical"&&a.hide()}),v=!0,!1}),u.find("button").click(function(){i.setMuted(!i.muted)}),i.addEventListener("volumechange",function(){v||(i.muted?(p(0),u.removeClass("mejs-mute").addClass("mejs-unmute")):(p(i.volume),u.removeClass("mejs-unmute").addClass("mejs-mute")))},!1),s.container.is(":visible")&&(p(t.options.startVolume),t.options.startVolume===0&&i.setMuted(!0),i.pluginType==="native"&&i.setVolume(t.options.startVolume))}}})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{usePluginFullScreen:!0,newWindowCallback:function(){return""},fullscreenText:mejs.i18n.t("Fullscreen")}),e.extend(MediaElementPlayer.prototype,{isFullScreen:!1,isNativeFullScreen:!1,isInIframe:!1,buildfullscreen:function(t,n,r,i){if(t.isVideo){t.isInIframe=window.location!=window.parent.location,mejs.MediaFeatures.hasTrueNativeFullScreen&&(r=function(){t.isFullScreen&&(mejs.MediaFeatures.isFullScreen()?(t.isNativeFullScreen=!0,t.setControlsSize()):(t.isNativeFullScreen=!1,t.exitFullScreen()))},mejs.MediaFeatures.hasMozNativeFullScreen?t.globalBind(mejs.MediaFeatures.fullScreenEventName,r):t.container.bind(mejs.MediaFeatures.fullScreenEventName,r));var s=this,o=e('<div class="mejs-button mejs-fullscreen-button"><button type="button" aria-controls="'+s.id+'" title="'+s.options.fullscreenText+'" aria-label="'+s.options.fullscreenText+'"></button></div>').appendTo(n);if(s.media.pluginType==="native"||!s.options.usePluginFullScreen&&!mejs.MediaFeatures.isFirefox)o.click(function(){mejs.MediaFeatures.hasTrueNativeFullScreen&&mejs.MediaFeatures.isFullScreen()||t.isFullScreen?t.exitFullScreen():t.enterFullScreen()});else{var u=null;if(function(){var e=document.createElement("x"),t=document.documentElement,n=window.getComputedStyle;return"pointerEvents"in e.style?(e.style.pointerEvents="auto",e.style.pointerEvents="x",t.appendChild(e),n=n&&n(e,"").pointerEvents==="auto",t.removeChild(e),!!n):!1}()&&!mejs.MediaFeatures.isOpera){var a=!1,l=function(){if(a){for(var e in c)c[e].hide();o.css("pointer-events",""),s.controls.css("pointer-events",""),s.media.removeEventListener("click",s.clickToPlayPauseCallback),a=!1}},c={};n=["top","left","right","bottom"];var h,p=function(){var e=o.offset().left-s.container.offset().left,t=o.offset().top-s.container.offset().top,n=o.outerWidth(!0),r=o.outerHeight(!0),i=s.container.width(),u=s.container.height();for(h in c)c[h].css({position:"absolute",top:0,left:0});c.top.width(i).height(t),c.left.width(e).height(r).css({top:t}),c.right.width(i-e-n).height(r).css({top:t,left:e+n}),c.bottom.width(i).height(u-r-t).css({top:t+r})};s.globalBind("resize",function(){p()}),h=0;for(r=n.length;h<r;h++)c[n[h]]=e('<div class="mejs-fullscreen-hover" />').appendTo(s.container).mouseover(l).hide();o.on("mouseover",function(){if(!s.isFullScreen){var e=o.offset(),n=t.container.offset();i.positionFullscreenButton(e.left-n.left,e.top-n.top,!1),o.css("pointer-events","none"),s.controls.css("pointer-events","none"),s.media.addEventListener("click",s.clickToPlayPauseCallback);for(h in c)c[h].show();p(),a=!0}}),i.addEventListener("fullscreenchange",function(){s.isFullScreen=!s.isFullScreen,s.isFullScreen?s.media.removeEventListener("click",s.clickToPlayPauseCallback):s.media.addEventListener("click",s.clickToPlayPauseCallback),l()}),s.globalBind("mousemove",function(e){if(a){var t=o.offset();if(e.pageY<t.top||e.pageY>t.top+o.outerHeight(!0)||e.pageX<t.left||e.pageX>t.left+o.outerWidth(!0))o.css("pointer-events",""),s.controls.css("pointer-events",""),a=!1}})}else o.on("mouseover",function(){u!==null&&(clearTimeout(u),delete u);var e=o.offset(),n=t.container.offset();i.positionFullscreenButton(e.left-n.left,e.top-n.top,!0)}).on("mouseout",function(){u!==null&&(clearTimeout(u),delete u),u=setTimeout(function(){i.hideFullscreenButton()},1500)})}t.fullscreenBtn=o,s.globalBind("keydown",function(e){(mejs.MediaFeatures.hasTrueNativeFullScreen&&mejs.MediaFeatures.isFullScreen()||s.isFullScreen)&&e.keyCode==27&&t.exitFullScreen()})}},cleanfullscreen:function(e){e.exitFullScreen()},containerSizeTimeout:null,enterFullScreen:function(){var t=this;if(t.media.pluginType==="native"||!mejs.MediaFeatures.isFirefox&&!t.options.usePluginFullScreen){e(document.documentElement).addClass("mejs-fullscreen"),normalHeight=t.container.height(),normalWidth=t.container.width();if(t.media.pluginType==="native")if(mejs.MediaFeatures.hasTrueNativeFullScreen)mejs.MediaFeatures.requestFullScreen(t.container[0]),t.isInIframe&&setTimeout(function r(){t.isNativeFullScreen&&(e(window).width()!==screen.width?t.exitFullScreen():setTimeout(r,500))},500);else if(mejs.MediaFeatures.hasSemiNativeFullScreen){t.media.webkitEnterFullscreen();return}if(t.isInIframe){var n=t.options.newWindowCallback(this);if(n!==""){if(!mejs.MediaFeatures.hasTrueNativeFullScreen){t.pause(),window.open(n,t.id,"top=0,left=0,width="+screen.availWidth+",height="+screen.availHeight+",resizable=yes,scrollbars=no,status=no,toolbar=no");return}setTimeout(function(){t.isNativeFullScreen||(t.pause(),window.open(n,t.id,"top=0,left=0,width="+screen.availWidth+",height="+screen.availHeight+",resizable=yes,scrollbars=no,status=no,toolbar=no"))},250)}}t.container.addClass("mejs-container-fullscreen").width("100%").height("100%"),t.containerSizeTimeout=setTimeout(function(){t.container.css({width:"100%",height:"100%"}),t.setControlsSize()},500),t.media.pluginType==="native"?t.$media.width("100%").height("100%"):(t.container.find(".mejs-shim").width("100%").height("100%"),t.media.setVideoSize(e(window).width(),e(window).height())),t.layers.children("div").width("100%").height("100%"),t.fullscreenBtn&&t.fullscreenBtn.removeClass("mejs-fullscreen").addClass("mejs-unfullscreen"),t.setControlsSize(),t.isFullScreen=!0}},exitFullScreen:function(){clearTimeout(this.containerSizeTimeout),this.media.pluginType!=="native"&&mejs.MediaFeatures.isFirefox?this.media.setFullscreen(!1):(mejs.MediaFeatures.hasTrueNativeFullScreen&&(mejs.MediaFeatures.isFullScreen()||this.isFullScreen)&&mejs.MediaFeatures.cancelFullScreen(),e(document.documentElement).removeClass("mejs-fullscreen"),this.container.removeClass("mejs-container-fullscreen").width(normalWidth).height(normalHeight),this.media.pluginType==="native"?this.$media.width(normalWidth).height(normalHeight):(this.container.find(".mejs-shim").width(normalWidth).height(normalHeight),this.media.setVideoSize(normalWidth,normalHeight)),this.layers.children("div").width(normalWidth).height(normalHeight),this.fullscreenBtn.removeClass("mejs-unfullscreen").addClass("mejs-fullscreen"),this.setControlsSize(),this.isFullScreen=!1)}})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{startLanguage:"",tracksText:mejs.i18n.t("Captions/Subtitles"),hideCaptionsButtonWhenEmpty:!0,toggleCaptionsButtonWhenOnlyOne:!1,slidesSelector:""}),e.extend(MediaElementPlayer.prototype,{hasChapters:!1,buildtracks:function(t,n,r,i){if(t.tracks.length!=0){var s;if(this.domNode.textTracks)for(s=this.domNode.textTracks.length-1;s>=0;s--)this.domNode.textTracks[s].mode="hidden";t.chapters=e('<div class="mejs-chapters mejs-layer"></div>').prependTo(r).hide(),t.captions=e('<div class="mejs-captions-layer mejs-layer"><div class="mejs-captions-position mejs-captions-position-hover"><span class="mejs-captions-text"></span></div></div>').prependTo(r).hide(),t.captionsText=t.captions.find(".mejs-captions-text"),t.captionsButton=e('<div class="mejs-button mejs-captions-button"><button type="button" aria-controls="'+this.id+'" title="'+this.options.tracksText+'" aria-label="'+this.options.tracksText+'"></button><div class="mejs-captions-selector"><ul><li><input type="radio" name="'+t.id+'_captions" id="'+t.id+'_captions_none" value="none" checked="checked" /><label for="'+t.id+'_captions_none">'+mejs.i18n.t("None")+"</label></li></ul></div></div>").appendTo(n);for(s=n=0;s<t.tracks.length;s++)t.tracks[s].kind=="subtitles"&&n++;this.options.toggleCaptionsButtonWhenOnlyOne&&n==1?t.captionsButton.on("click",function(){t.setTrack(t.selectedTrack==null?t.tracks[0].srclang:"none")}):t.captionsButton.hover(function(){e(this).find(".mejs-captions-selector").css("visibility","visible")},function(){e(this).find(".mejs-captions-selector").css("visibility","hidden")}).on("click","input[type=radio]",function(){lang=this.value,t.setTrack(lang)}),t.options.alwaysShowControls?t.container.find(".mejs-captions-position").addClass("mejs-captions-position-hover"):t.container.bind("controlsshown",function(){t.container.find(".mejs-captions-position").addClass("mejs-captions-position-hover")}).bind("controlshidden",function(){i.paused||t.container.find(".mejs-captions-position").removeClass("mejs-captions-position-hover")}),t.trackToLoad=-1,t.selectedTrack=null,t.isLoadingTrack=!1;for(s=0;s<t.tracks.length;s++)t.tracks[s].kind=="subtitles"&&t.addTrackButton(t.tracks[s].srclang,t.tracks[s].label);t.loadNextTrack(),i.addEventListener("timeupdate",function(){t.displayCaptions()},!1),t.options.slidesSelector!=""&&(t.slidesContainer=e(t.options.slidesSelector),i.addEventListener("timeupdate",function(){t.displaySlides()},!1)),i.addEventListener("loadedmetadata",function(){t.displayChapters()},!1),t.container.hover(function(){t.hasChapters&&(t.chapters.css("visibility","visible"),t.chapters.fadeIn(200).height(t.chapters.find(".mejs-chapter").outerHeight()))},function(){t.hasChapters&&!i.paused&&t.chapters.fadeOut(200,function(){e(this).css("visibility","hidden"),e(this).css("display","block")})}),t.node.getAttribute("autoplay")!==null&&t.chapters.css("visibility","hidden")}},setTrack:function(e){var t;if(e=="none")this.selectedTrack=null,this.captionsButton.removeClass("mejs-captions-enabled");else for(t=0;t<this.tracks.length;t++)if(this.tracks[t].srclang==e){this.selectedTrack==null&&this.captionsButton.addClass("mejs-captions-enabled"),this.selectedTrack=this.tracks[t],this.captions.attr("lang",this.selectedTrack.srclang),this.displayCaptions();break}},loadNextTrack:function(){this.trackToLoad++,this.trackToLoad<this.tracks.length?(this.isLoadingTrack=!0,this.loadTrack(this.trackToLoad)):(this.isLoadingTrack=!1,this.checkForTracks())},loadTrack:function(t){var n=this,r=n.tracks[t];e.ajax({url:r.src,dataType:"text",success:function(e){r.entries=typeof e=="string"&&/<tt\s+xml/ig.exec(e)?mejs.TrackFormatParser.dfxp.parse(e):mejs.TrackFormatParser.webvvt.parse(e),r.isLoaded=!0,n.enableTrackButton(r.srclang,r.label),n.loadNextTrack(),r.kind=="chapters"&&n.media.addEventListener("play",function(){n.media.duration>0&&n.displayChapters(r)},!1),r.kind=="slides"&&n.setupSlides(r)},error:function(){n.loadNextTrack()}})},enableTrackButton:function(t,n){n===""&&(n=mejs.language.codes[t]||t),this.captionsButton.find("input[value="+t+"]").prop("disabled",!1).siblings("label").html(n),this.options.startLanguage==t&&e("#"+this.id+"_captions_"+t).click(),this.adjustLanguageBox()},addTrackButton:function(t,n){n===""&&(n=mejs.language.codes[t]||t),this.captionsButton.find("ul").append(e('<li><input type="radio" name="'+this.id+'_captions" id="'+this.id+"_captions_"+t+'" value="'+t+'" disabled="disabled" /><label for="'+this.id+"_captions_"+t+'">'+n+" (loading)</label></li>")),this.adjustLanguageBox(),this.container.find(".mejs-captions-translations option[value="+t+"]").remove()},adjustLanguageBox:function(){this.captionsButton.find(".mejs-captions-selector").height(this.captionsButton.find(".mejs-captions-selector ul").outerHeight(!0)+this.captionsButton.find(".mejs-captions-translations").outerHeight(!0))},checkForTracks:function(){var e=!1;if(this.options.hideCaptionsButtonWhenEmpty){for(i=0;i<this.tracks.length;i++)if(this.tracks[i].kind=="subtitles"){e=!0;break}e||(this.captionsButton.hide(),this.setControlsSize())}},displayCaptions:function(){if(typeof this.tracks!="undefined"){var e,t=this.selectedTrack;if(t!=null&&t.isLoaded)for(e=0;e<t.entries.times.length;e++)if(this.media.currentTime>=t.entries.times[e].start&&this.media.currentTime<=t.entries.times[e].stop){this.captionsText.html(t.entries.text[e]),this.captions.show().height(0);return}this.captions.hide()}},setupSlides:function(e){this.slides=e,this.slides.entries.imgs=[this.slides.entries.text.length],this.showSlide(0)},showSlide:function(t){if(typeof this.tracks!="undefined"&&typeof this.slidesContainer!="undefined"){var n=this,r=n.slides.entries.text[t],i=n.slides.entries.imgs[t];typeof i=="undefined"||typeof i.fadeIn=="undefined"?n.slides.entries.imgs[t]=i=e('<img src="'+r+'">').on("load",function(){i.appendTo(n.slidesContainer).hide().fadeIn().siblings(":visible").fadeOut()}):!i.is(":visible")&&!i.is(":animated")&&i.fadeIn().siblings(":visible").fadeOut()}},displaySlides:function(){if(typeof this.slides!="undefined"){var e=this.slides,t;for(t=0;t<e.entries.times.length;t++)if(this.media.currentTime>=e.entries.times[t].start&&this.media.currentTime<=e.entries.times[t].stop){this.showSlide(t);break}}},displayChapters:function(){var e;for(e=0;e<this.tracks.length;e++)if(this.tracks[e].kind=="chapters"&&this.tracks[e].isLoaded){this.drawChapters(this.tracks[e]),this.hasChapters=!0;break}},drawChapters:function(t){var n=this,r,i,s=i=0;n.chapters.empty();for(r=0;r<t.entries.times.length;r++){i=t.entries.times[r].stop-t.entries.times[r].start,i=Math.floor(i/n.media.duration*100);if(i+s>100||r==t.entries.times.length-1&&i+s<100)i=100-s;n.chapters.append(e('<div class="mejs-chapter" rel="'+t.entries.times[r].start+'" style="left: '+s.toString()+"%;width: "+i.toString()+'%;"><div class="mejs-chapter-block'+(r==t.entries.times.length-1?" mejs-chapter-block-last":"")+'"><span class="ch-title">'+t.entries.text[r]+'</span><span class="ch-time">'+mejs.Utility.secondsToTimeCode(t.entries.times[r].start)+"–"+mejs.Utility.secondsToTimeCode(t.entries.times[r].stop)+"</span></div></div>")),s+=i}n.chapters.find("div.mejs-chapter").click(function(){n.media.setCurrentTime(parseFloat(e(this).attr("rel"))),n.media.paused&&n.media.play()}),n.chapters.show()}}),mejs.language={codes:{af:"Afrikaans",sq:"Albanian",ar:"Arabic",be:"Belarusian",bg:"Bulgarian",ca:"Catalan",zh:"Chinese","zh-cn":"Chinese Simplified","zh-tw":"Chinese Traditional",hr:"Croatian",cs:"Czech",da:"Danish",nl:"Dutch",en:"English",et:"Estonian",tl:"Filipino",fi:"Finnish",fr:"French",gl:"Galician",de:"German",el:"Greek",ht:"Haitian Creole",iw:"Hebrew",hi:"Hindi",hu:"Hungarian",is:"Icelandic",id:"Indonesian",ga:"Irish",it:"Italian",ja:"Japanese",ko:"Korean",lv:"Latvian",lt:"Lithuanian",mk:"Macedonian",ms:"Malay",mt:"Maltese",no:"Norwegian",fa:"Persian",pl:"Polish",pt:"Portuguese",ro:"Romanian",ru:"Russian",sr:"Serbian",sk:"Slovak",sl:"Slovenian",es:"Spanish",sw:"Swahili",sv:"Swedish",tl:"Tagalog",th:"Thai",tr:"Turkish",uk:"Ukrainian",vi:"Vietnamese",cy:"Welsh",yi:"Yiddish"}},mejs.TrackFormatParser={webvvt:{pattern_identifier:/^([a-zA-z]+-)?[0-9]+$/,pattern_timecode:/^([0-9]{2}:[0-9]{2}:[0-9]{2}([,.][0-9]{1,3})?) --\> ([0-9]{2}:[0-9]{2}:[0-9]{2}([,.][0-9]{3})?)(.*)$/,parse:function(t){var n=0;t=mejs.TrackFormatParser.split2(t,/\r?\n/);for(var r={text:[],times:[]},i,s;n<t.length;n++)if(this.pattern_identifier.exec(t[n])){n++;if((i=this.pattern_timecode.exec(t[n]))&&n<t.length){n++,s=t[n];for(n++;t[n]!==""&&n<t.length;)s=s+"\n"+t[n],n++;s=e.trim(s).replace(/(\b(https?|ftp|file):\/\/[-A-Z0-9+&@#\/%?=~_|!:,.;]*[-A-Z0-9+&@#\/%=~_|])/ig,"<a href='$1' target='_blank'>$1</a>"),r.text.push(s),r.times.push({start:mejs.Utility.convertSMPTEtoSeconds(i[1])==0?.2:mejs.Utility.convertSMPTEtoSeconds(i[1]),stop:mejs.Utility.convertSMPTEtoSeconds(i[3]),settings:i[5]})}}return r}},dfxp:{parse:function(t){t=e(t).filter("tt");var n=0;n=t.children("div").eq(0);var r=n.find("p");n=t.find("#"+n.attr("style"));var i,s;t={text:[],times:[]};if(n.length){s=n.removeAttr("id").get(0).attributes;if(s.length){i={};for(n=0;n<s.length;n++)i[s[n].name.split(":")[1]]=s[n].value}}for(n=0;n<r.length;n++){var o;s={start:null,stop:null,style:null},r.eq(n).attr("begin")&&(s.start=mejs.Utility.convertSMPTEtoSeconds(r.eq(n).attr("begin"))),!s.start&&r.eq(n-1).attr("end")&&(s.start=mejs.Utility.convertSMPTEtoSeconds(r.eq(n-1).attr("end"))),r.eq(n).attr("end")&&(s.stop=mejs.Utility.convertSMPTEtoSeconds(r.eq(n).attr("end"))),!s.stop&&r.eq(n+1).attr("begin")&&(s.stop=mejs.Utility.convertSMPTEtoSeconds(r.eq(n+1).attr("begin")));if(i){o="";for(var u in i)o+=u+":"+i[u]+";"}o&&(s.style=o),s.start==0&&(s.start=.2),t.times.push(s),s=e.trim(r.eq(n).html()).replace(/(\b(https?|ftp|file):\/\/[-A-Z0-9+&@#\/%?=~_|!:,.;]*[-A-Z0-9+&@#\/%=~_|])/ig,"<a href='$1' target='_blank'>$1</a>"),t.text.push(s),t.times.start==0&&(t.times.start=2)}return t}},split2:function(e,t){return e.split(t)}},"x\n\ny".split(/\n/gi).length!=3&&(mejs.TrackFormatParser.split2=function(e,t){var n=[],r="",i;for(i=0;i<e.length;i++)r+=e.substring(i,i+1),t.test(r)&&(n.push(r.replace(t,"")),r="");return n.push(r),n})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{contextMenuItems:[{render:function(e){return typeof e.enterFullScreen=="undefined"?null:e.isFullScreen?mejs.i18n.t("Turn off Fullscreen"):mejs.i18n.t("Go Fullscreen")},click:function(e){e.isFullScreen?e.exitFullScreen():e.enterFullScreen()}},{render:function(e){return e.media.muted?mejs.i18n.t("Unmute"):mejs.i18n.t("Mute")},click:function(e){e.media.muted?e.setMuted(!1):e.setMuted(!0)}},{isSeparator:!0},{render:function(){return mejs.i18n.t("Download Video")},click:function(e){window.location.href=e.media.currentSrc}}]}),e.extend(MediaElementPlayer.prototype,{buildcontextmenu:function(t){t.contextMenu=e('<div class="mejs-contextmenu"></div>').appendTo(e("body")).hide(),t.container.bind("contextmenu",function(e){if(t.isContextMenuEnabled)return e.preventDefault(),t.renderContextMenu(e.clientX-1,e.clientY-1),!1}),t.container.bind("click",function(){t.contextMenu.hide()}),t.contextMenu.bind("mouseleave",function(){t.startContextMenuTimer()})},cleancontextmenu:function(e){e.contextMenu.remove()},isContextMenuEnabled:!0,enableContextMenu:function(){this.isContextMenuEnabled=!0},disableContextMenu:function(){this.isContextMenuEnabled=!1},contextMenuTimeout:null,startContextMenuTimer:function(){var e=this;e.killContextMenuTimer(),e.contextMenuTimer=setTimeout(function(){e.hideContextMenu(),e.killContextMenuTimer()},750)},killContextMenuTimer:function(){var e=this.contextMenuTimer;e!=null&&(clearTimeout(e),delete e)},hideContextMenu:function(){this.contextMenu.hide()},renderContextMenu:function(t,n){for(var r=this,i="",s=r.options.contextMenuItems,o=0,u=s.length;o<u;o++)if(s[o].isSeparator)i+='<div class="mejs-contextmenu-separator"></div>';else{var a=s[o].render(r);a!=null&&(i+='<div class="mejs-contextmenu-item" data-itemindex="'+o+'" id="element-'+Math.random()*1e6+'">'+a+"</div>")}r.contextMenu.empty().append(e(i)).css({top:n,left:t}).show(),r.contextMenu.find(".mejs-contextmenu-item").each(function(){var t=e(this),n=parseInt(t.data("itemindex"),10),i=r.options.contextMenuItems[n];typeof i.show!="undefined"&&i.show(t,r),t.click(function(){typeof i.click!="undefined"&&i.click(r),r.contextMenu.hide()})}),setTimeout(function(){r.killControlsTimer("rev3")},100)}})}(mejs.$),function(e){e.extend(mejs.MepDefaults,{postrollCloseText:mejs.i18n.t("Close")}),e.extend(MediaElementPlayer.prototype,{buildpostroll:function(t,n,r){var i=this.container.find('link[rel="postroll"]').attr("href");typeof i!="undefined"&&(t.postroll=e('<div class="mejs-postroll-layer mejs-layer"><a class="mejs-postroll-close" onclick="$(this).parent().hide();return false;">'+this.options.postrollCloseText+'</a><div class="mejs-postroll-layer-content"></div></div>').prependTo(r).hide(),this.media.addEventListener("ended",function(){e.ajax({dataType:"html",url:i,success:function(e){r.find(".mejs-postroll-layer-content").html(e)}}),t.postroll.show()},!1))}})}(mejs.$),define("mediaelementjs",["jquery"],function(){}),define("interaction_view/model/preload",["interaction_view/model/base","text!interaction_view/template/preload.js","mediaelementjs"],function(){var e=require("text!interaction_view/template/preload.js");interaction_view.model.Preload=interaction_view.model.Base.extend({defaults:{iType:"Preload",iLock:!0,iVisibility:!0,iCommon:null,iDetail:null,iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!0,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null,loaded:!1},setcollection:function(){this.pageid=this.get("pageid");if(this.get("pagetype")=="LayerRef"){var e=this.get("parentpageid"),t=this.get("layerid"),n=interaction_view.ipagelist.getPage(e)||interaction_view.imasterlist.get(e);this.page=n.iOverlaylist.get(this.pageid).layer[t]}else this.page=interaction_view.ipagelist.getPage(this.pageid)||interaction_view.imasterlist.get(this.pageid);this.page.ipreloadlist.get(this.id)||this.page.ipreloadlist.add(this)},setview:function(e){this.pageid=this.get("pageid");if(this.get("pagetype")=="LayerRef"){var t=this.get("parentpageid"),n=this.get("layerid"),r=interaction_view.ipagelist.getPage(t)||interaction_view.imasterlist.get(t);this.page=r.iOverlaylist.get(this.pageid).layer[n]}else this.page=interaction_view.ipagelist.getPage(this.pageid)||interaction_view.imasterlist.get(this.pageid);this.iview=new interaction_view.view.Preload({model:this})},setsyncmodel:function(){}}),interaction_view.collection.Preload=interaction_view.collection.Base.extend({preloadcheck:function(e,t,n){var r=this.where({loaded:!0});t==0&&(console.log("file:"+e+" load failed!"),interaction_view.message("alert","图像或媒体资源加载失败，可能会影响动画效果")),n.iview.afterpreload(e);if(this.where({loaded:false}).length==0)if(this.pagetype=="LayerRef"){var i=this.parentpageid,s=this.layerid,o=interaction_view.ipagelist.getPage(i)||interaction_view.imasterlist.get(i);refmodel=o.iOverlaylist.get(this.pageid),refmodel.iview.afterlayerload(s)}else interaction_view.finishPagePreload(this.pageid);interaction_view.setPreloadStatus&&interaction_view.setPreloadStatus()},preload:function(){this.each(function(e){e.iview.render()})}}),interaction_view.view.Preload=interaction_view.view.Base.extend({events:{},initstart:function(){$("div#preloader").append(_.template(e,{id:this.model.id,file:null}))},renderDynamicElement:function(){},render:function(){var e=this,t=this.elementmodel=e.model.page.iOverlaylist.get(this.model.get("overlay_id"));this.model.get("elementtype")=="Audio"?(console.info("Audio"),this.audioPreload()):this.model.get("elementtype")=="Video"?this.videoPreload():this.model.get("elementtype")=="LayerRef"||this.model.get("elementtype")=="LayerSlide"?this.layerPreload(this.model):$('img[id="'+this.model.id+'"]').load(function(){e.model.set("loaded",!0),e.model.page.ipreloadlist.preloadcheck(e.model.toJSON().file,!0,t)}).error(function(){e.model.set("loaded",!0),e.model.page.ipreloadlist.preloadcheck(e.model.toJSON().file,!1,t)}).attr("src",e.model.toJSON().file)},audioPreload:function(){var e=this,t=this.elementmodel,n=this.elementmodel.iview.getdetail(),r=n.iRepeat||!1,i=t.autoplay=n.iAutoplay||!1,s="Audio_"+e.model.id+this.elementmodel.id,o=this.model.toJSON().file;t.loaded=!1,$("div#preloader").append('<audio id="'+s+'" src="'+o+'"></audio>'),options={enablePluginDebug:!1,type:"",pluginPath:web_url+"common/js/mediaelementjs/",flashName:"flashmediaelement.swf",silverlightName:"silverlightmediaelement.xap",timerRate:100,pauseOtherPlayers:!1,features:[],loop:r,success:function(n,i){t.media=n,n.play();var s=!1;n.addEventListener("loadeddata",function(r){s||(n.pause(),s=!0,t.loaded=!0,console.info("audio loaded"),e.model.set("loaded",!0),e.model.page.ipreloadlist.preloadcheck(e.model.toJSON().file,!0,t))},!1),n.addEventListener("ended",function(e){r||(t.media.setCurrentTime(0),t.media.pause())},!1),n.addEventListener("timeupdate",function(e){console.log("time update")},!1)},error:function(){t.loaded=!1,e.model.set("loaded",!0),e.model.page.ipreloadlist.preloadcheck(e.model.toJSON().file,!1,t)}};if(_g.string.getUrlExt(o)==".m4a"||_g.string.getUrlExt(o)==".M4A")options.pluginVars="isvideo=true";options.plugins=["flash","silverlight"],$("#Audio_"+e.model.id+this.elementmodel.id).mediaelementplayer(options)},videoPreload:function(){var e=this,t=this.elementmodel,n=this.elementmodel.iview.getdetail(),r=n.iRepeat||!1,i=n.iControl||!1,s=t.iview.getcommon(),o=t.autoplay=n.iAutoplay||!1;$("#Video_"+this.elementmodel.id).attr("id","Video_"+e.model.id+this.elementmodel.id),t.videoloaded=!1;var u=n.iAutohide;u==null&&(u=!0),options={enablePluginDebug:!1,type:"",pluginPath:web_url+"common/js/mediaelementjs/",flashName:"flashmediaelement.swf",silverlightName:"silverlightmediaelement.xap",videoWidth:s.iWidth,videoHeight:s.iHeight,pluginWidth:-1,pluginHeight:-1,timerRate:100,pauseOtherPlayers:!1,features:i?["playpause","progress","current","duration","volume","fullscreen"]:[],loop:r,success:function(n,s){t.media=n,n.play();var a=!1;i||(t.iview.$el.find(".mejs-controls").addClass("hide"),t.iview.$el.find(".mejs-overlay-button").addClass("hide")),n.addEventListener("loadedmetadata",function(r){a||(a=!0,n.pause(),n.setCurrentTime(0),o||t.iview.$el.addClass("hide"),t.iview.$el.css("opacity",""),t.videoloaded=!0,console.info("video loaded"),t.iview.$el.children(".iVideo").removeClass("unloaded"),t.loaded=!0,e.model.set("loaded",!0),e.model.page.ipreloadlist.preloadcheck(e.model.toJSON().file,!0,t))},!1),n.addEventListener("ended",function(e){r||(n.setCurrentTime(0),n.pause(),u&&(t.iview.$el.addClass("hide"),t.hided=!0))},!1),n.addEventListener("timeupdate",function(e){console.log("time update")},!1)},error:function(){t.loaded=!1,e.model.set("loaded",!0),e.model.page.ipreloadlist.preloadcheck(e.model.toJSON().file,!1,t)}},options.plugins=["flash","silverlight"],t.iview.$el.removeClass("hide").css("opacity",0),$("#Video_"+e.model.id+this.elementmodel.id).mediaelementplayer(options)},layerPreload:function(){this.elementmodel.iview.loadLayer(this.model)},canPlayVideo:function(e){var t=document.createElement("video"),n,r,i,s;return t.canPlayType?(n=""!==t.canPlayType('video/mp4; codecs="mp4v.20.8"'),r=""!==(t.canPlayType('video/mp4; codecs="avc1.42E01E"')||t.canPlayType('video/mp4; codecs="avc1.42E01E, mp4a.40.2"')),i=""!==t.canPlayType('video/ogg; codecs="theora"'),s=""!==t.canPlayType('video/webm; codecs="vp8, vorbis"'),n||r?!0:!1):!1}}),interaction_view.ipreloadlist=new interaction_view.collection.Preload}),define("interaction_view/model/richtext",["interaction_view/model/base"],function(){interaction_view.model.RichText=interaction_view.model.Base.extend({defaults:{iType:"RichText",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.RichText({model:this})},setsyncmodel:function(){}}),interaction_view.view.RichText=interaction_view.view.Base.extend({events:{},render:function(e){interaction_view.setscrollnano(this.$el)}})}),define("interaction_view/model/slide",["interaction_view/model/base"],function(){interaction_view.model.Slide=interaction_view.model.Base.extend({defaults:{iType:"Slide",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null,iBackground:"rgba(62,189,255,0.5)",iResourcesProperties:[{id:"iImg",type:"single"}],iAnimationProperties:[{id:"iAnimation",type:"object"}]},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Slide({model:this})},setsyncmodel:function(){}}),interaction_view.view.Slide=interaction_view.view.Base.extend({events:{},render:function(e){var t=this,n=this.getdetail();this.currentSlideIndex=null,this.model.on("slideTo",function(e){if(t.currentSlideIndex!=null&&t.currentSlideIndex==e)return!1;t.preloaded&&(interaction_view.events.onChangeTo(t.model.get("pageid"),t.model.id,e),t.currentSlideIndex=e)})},preload:function(){var e=this,t=this.getdetail(),n;this.checkImglist=_.pluck(t.iImg,"picture"),this.preloaded=!1,t.iImg&&_.each(t.iImg,function(t){_g.getUrlParameterByName("-playmode")=="1"?n=t.thumbnail:n=t.picture,e.sendToPreload({file:n})})},checkImgload:function(e){return this.checkImglist=_.reject(this.checkImglist,function(t){return t==e}),this.checkImglist.length==0?!0:!1},afterpreload:function(e){if(e&&this.preloaded)return;e&&(this.preloaded=this.checkImgload(e)),this.preloaded&&(this.playAnimation(),this.setZindex(),this.setElementDisplay(),this.copyelstyle=this.$el.attr("style"),this.copyelementstyle=this.$el.children(".iElement").attr("style"),this.$el.children(".Element").on("mousewheel",function(e){var t=e.wheelDelta;t>0?console.log("up"):console.log("down")}))},playAnimation:function(){var e=this,t=0,n=this.getdetail(),r=n.iSlidetype;this.delay=Number(n.iInterval)||2,this.autoplay=n.iAutoplay;var i=n.iRepeat?-1:0;this.iFade=n.iFade||0,this.timeline=new TimelineMax({paused:!0,repeat:i,onComplete:this.setRewind()}),this.slideel=e.$el.children(".Element").find(".slidecontent").first(),this.slidelength=n.iImg.length,this.repeat=i,this.iRewind=n.iRewind,this.slipable=n.iSlipable,this.setTimeline(),this.autoplay?this.timeline.play(0):this.setSlideDisplay(0),this.onChangeSlideTo(0),this.slipable&&(this.control(),this.autoplay||this.$el.attr("data-iSlipable",!0))},setTimeline:function(){if(this.iFade=="Fade"||0)this.setFade();else if(this.iFade=="Slip")this.setSlip();else{if(this.iFade!="None")return;this.setNone()}this.slideel.children().first().css({opacity:1,left:0,top:0}).nextAll().css("opacity",0)},testControl:function(){return!0},resetOtherStatus:function(){if(!this.slideel)return;this.slideel.children().first().css({opacity:1,left:0,top:0});var e=this.getdetail();this.autoplay=e.iAutoplay,this.currentIndex=0,this.timeline&&(this.timeline.pause(1e-5),this.autoplay?this.timeline.play(0):this.setSlideDisplay(0))}})}),define("interaction_view/model/video",["interaction_view/model/base"],function(){interaction_view.model.Video=interaction_view.model.Base.extend({defaults:{iType:"Video",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null,iBackground:"rgba(62,189,255,0.5)",iResourcesProperties:[{id:"iImg",type:"single"}],iAnimationProperties:[{id:"iAnimation",type:"object"}]},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Video({model:this})},setsyncmodel:function(){},getFile:function(){var e=this.get("iDetail");return e.iFile&&e.iFile[0]?e.iFile[0].file:null}}),interaction_view.view.Video=interaction_view.view.Base.extend({events:{},render:function(e){this.afterpreload()},preload:function(){var e=this.getdetail();if(e.iFile&&e.iFile[0]){var t=e.iFile[0].file||e.iFile[0].content;this.sendToPreload({file:t})}},resetOtherStatus:function(){this.model.hided||this.model.media.setCurrentTime(0)}})}),define("interaction_view/model/button",["interaction_view/model/base"],function(){interaction_view.model.Button=interaction_view.model.Base.extend({defaults:{iType:"Button",iVisibility:!0,iCommon:null,iDetail:null,iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Button({model:this})},setsyncmodel:function(){}}),interaction_view.view.Button=interaction_view.view.Base.extend({events:{},render:function(e){},preload:function(){this.preloaded=!1,this.checkImglist=[];var e=this.getdetail();e.iconNormal&&e.iconNormal[0]&&(this.checkImglist.push(e.iconNormal[0].picture),this.sendToPreload({file:e.iconNormal[0].picture})),e.iconActive&&e.iconActive[0]&&(this.checkImglist.push(e.iconActive[0].picture),this.sendToPreload({file:e.iconActive[0].picture}))},checkImgload:function(e){return this.checkImglist=_.reject(this.checkImglist,function(t){return t==e}),this.checkImglist.length==0?!0:!1},afterpreload:function(e){if(e&&this.preloaded)return;e&&!this.preloaded&&(this.preloaded=this.checkImgload(e)),this.preloaded&&(this.setZindex(),this.setElementDisplay(),this.setOtherDisplay(),this.copyelstyle=this.$el.attr("style"),this.copyelementstyle=this.$el.children(".iElement").attr("style"))},setOtherDisplay:function(){this.active=!1,this.buttonon=!1;var e=this;this.$el.css("cursor","pointer");var t=this.getdetail();t.isSwitch?this.$el.on("click",function(t){e.changeStatus()}):(this.$el.on("mousedown",function(t){e.buttonon=!0,e.changeStatus()}),this.$el.on("mouseup",function(t){e.buttonon&&(e.buttonon=!1,t.stopPropagation(),e.changeStatus(),e.$el.trigger("click"))}))},changeStatus:function(){var e=this.getdetail(),t=this;if(!t.active)t.$el.find("img.iconActive").show(),t.$el.find("img.iconNormal").hide(),t.active=!0;else{if(t.groupcontrol&&e.isSwitch)return;t.$el.find("img.iconActive").hide(),t.$el.find("img.iconNormal").show(),t.active=!1}},resetOtherStatus:function(){this.resetButtonStatus()},resetButtonStatus:function(){this.$el.find("img.iconActive").hide(),this.$el.find("img.iconNormal").show(),this.active=!1}})}),define("interaction_view/model/map",["interaction_view/model/base"],function(){interaction_view.model.Map=interaction_view.model.Base.extend({defaults:{iType:"Map",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Map({model:this})},setsyncmodel:function(){}}),interaction_view.view.Map=interaction_view.view.Base.extend({events:{},render:function(e){this.afterpreload()}})}),define("interaction_view/model/pay",["interaction_view/model/base"],function(){interaction_view.model.Pay=interaction_view.model.Base.extend({defaults:{iType:"Pay",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Pay({model:this})},setsyncmodel:function(){}}),interaction_view.view.Pay=interaction_view.view.Base.extend({events:{},render:function(e){this.afterpreload()}})}),define("interaction_view/model/link",["interaction_view/model/base"],function(){interaction_view.model.Link=interaction_view.model.Base.extend({defaults:{iType:"Link",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Link({model:this})},setsyncmodel:function(){}}),interaction_view.view.Link=interaction_view.view.Base.extend({events:{},render:function(e){}})}),define("interaction_view/view/layer",["interaction_view/view/page"],function(){var e=require("interaction_view/view/page"),t=$.extend({},e,{template:null,className:null,containment:null,wrap:null,wrapClassName:null,autoRender:!1,position:1,parseData:null,afterload:function(){var e=this,t=this.model.get("iDetail").layer_ids;if(!t||t.length==0)return;_.each(t,function(t){var n=interaction_view.ilayerlist.get(t);if(!n)return;e.animations=_.map(n.animations,function(n){return n.pageid=e.model.id,n.parentpageid=e.model.get("pageid"),n.pagetype="LayerRef",n.layerid=t,n}),e.model.layer[t].iAnimationlist.reset(e.animations),overlays=_.filter(n.overlays,function(n){return n.pageid=e.model.id,n.parentpageid=e.model.get("pageid"),n.pagetype="LayerRef",n.layerid=t,interaction_view.model[n.iType]}),overlays=_.map(overlays,function(e){return new interaction_view.model[e.iType](e)}),e.model.layer[t].iOverlaylist.reset(overlays),e.model.layer[t].iOverlaylist.each(function(e){e.iview.setZindex()}),e.actions=_.map(n.actions,function(n){return n.pageid=e.model.id,n.parentpageid=e.model.get("pageid"),n.pagetype="LayerRef",n.layerid=t,n}),e.model.layer[t].iActionlist.reset(n.actions)}),e.startLayerLoad()},startLayerLoad:function(){var e=this;_.each(this.model.layer,function(t,n){t.ipreloadlist.length==0?e.model.iview.afterlayerload(n):t.ipreloadlist.preload()})}});return t}),function(e,t){function n(t,n){var i,s,o,u=t.nodeName.toLowerCase();return"area"===u?(i=t.parentNode,s=i.name,!t.href||!s||i.nodeName.toLowerCase()!=="map"?!1:(o=e("img[usemap=#"+s+"]")[0],!!o&&r(o))):(/input|select|textarea|button|object/.test(u)?!t.disabled:"a"===u?t.href||n:n)&&r(t)}function r(t){return e.expr.filters.visible(t)&&!e(t).parents().andSelf().filter(function(){return e.css(this,"visibility")==="hidden"}).length}var i=0,s=/^ui-id-\d+$/;e.ui=e.ui||{};if(e.ui.version)return;e.extend(e.ui,{version:"1.9.2",keyCode:{BACKSPACE:8,COMMA:188,DELETE:46,DOWN:40,END:35,ENTER:13,ESCAPE:27,HOME:36,LEFT:37,NUMPAD_ADD:107,NUMPAD_DECIMAL:110,NUMPAD_DIVIDE:111,NUMPAD_ENTER:108,NUMPAD_MULTIPLY:106,NUMPAD_SUBTRACT:109,PAGE_DOWN:34,PAGE_UP:33,PERIOD:190,RIGHT:39,SPACE:32,TAB:9,UP:38}}),e.fn.extend({_focus:e.fn.focus,focus:function(t,n){return typeof t=="number"?this.each(function(){var r=this;setTimeout(function(){e(r).focus(),n&&n.call(r)},t)}):this._focus.apply(this,arguments)},scrollParent:function(){var t;return e.ui.ie&&/(static|relative)/.test(this.css("position"))||/absolute/.test(this.css("position"))?t=this.parents().filter(function(){return/(relative|absolute|fixed)/.test(e.css(this,"position"))&&/(auto|scroll)/.test(e.css(this,"overflow")+e.css(this,"overflow-y")+e.css(this,"overflow-x"))}).eq(0):t=this.parents().filter(function(){return/(auto|scroll)/.test(e.css(this,"overflow")+e.css(this,"overflow-y")+e.css(this,"overflow-x"))}).eq(0),/fixed/.test(this.css("position"))||!t.length?e(document):t},zIndex:function(n){if(n!==t)return this.css("zIndex",n);if(this.length){var r=e(this[0]),i,s;while(r.length&&r[0]!==document){i=r.css("position");if(i==="absolute"||i==="relative"||i==="fixed"){s=parseInt(r.css("zIndex"),10);if(!isNaN(s)&&s!==0)return s}r=r.parent()}}return 0},uniqueId:function(){return this.each(function(){this.id||(this.id="ui-id-"+ ++i)})},removeUniqueId:function(){return this.each(function(){s.test(this.id)&&e(this).removeAttr("id")})}}),e.extend(e.expr[":"],{data:e.expr.createPseudo?e.expr.createPseudo(function(t){return function(n){return!!e.data(n,t)}}):function(t,n,r){return!!e.data(t,r[3])},focusable:function(t){return n(t,!isNaN(e.attr(t,"tabindex")))},tabbable:function(t){var r=e.attr(t,"tabindex"),i=isNaN(r);return(i||r>=0)&&n(t,!i)}}),e(function(){var t=document.body,n=t.appendChild(n=document.createElement("div"));n.offsetHeight,e.extend(n.style,{minHeight:"100px",height:"auto",padding:0,borderWidth:0}),e.support.minHeight=n.offsetHeight===100,e.support.selectstart="onselectstart"in n,t.removeChild(n).style.display="none"}),e("<a>").outerWidth(1).jquery||e.each(["Width","Height"],function(n,r){function i(t,n,r,i){return e.each(s,function(){n-=parseFloat(e.css(t,"padding"+this))||0,r&&(n-=parseFloat(e.css(t,"border"+this+"Width"))||0),i&&(n-=parseFloat(e.css(t,"margin"+this))||0)}),n}var s=r==="Width"?["Left","Right"]:["Top","Bottom"],o=r.toLowerCase(),u={innerWidth:e.fn.innerWidth,innerHeight:e.fn.innerHeight,outerWidth:e.fn.outerWidth,outerHeight:e.fn.outerHeight};e.fn["inner"+r]=function(n){return n===t?u["inner"+r].call(this):this.each(function(){e(this).css(o,i(this,n)+"px")})},e.fn["outer"+r]=function(t,n){return typeof t!="number"?u["outer"+r].call(this,t):this.each(function(){e(this).css(o,i(this,t,!0,n)+"px")})}}),e("<a>").data("a-b","a").removeData("a-b").data("a-b")&&(e.fn.removeData=function(t){return function(n){return arguments.length?t.call(this,e.camelCase(n)):t.call(this)}}(e.fn.removeData)),function(){var t=/msie ([\w.]+)/.exec(navigator.userAgent.toLowerCase())||[];e.ui.ie=t.length?!0:!1,e.ui.ie6=parseFloat(t[1],10)===6}(),e.fn.extend({disableSelection:function(){return this.bind((e.support.selectstart?"selectstart":"mousedown")+".ui-disableSelection",function(e){e.preventDefault()})},enableSelection:function(){return this.unbind(".ui-disableSelection")}}),e.extend(e.ui,{plugin:{add:function(t,n,r){var i,s=e.ui[t].prototype;for(i in r)s.plugins[i]=s.plugins[i]||[],s.plugins[i].push([n,r[i]])},call:function(e,t,n){var r,i=e.plugins[t];if(!i||!e.element[0].parentNode||e.element[0].parentNode.nodeType===11)return;for(r=0;r<i.length;r++)e.options[i[r][0]]&&i[r][1].apply(e.element,n)}},contains:e.contains,hasScroll:function(t,n){if(e(t).css("overflow")==="hidden")return!1;var r=n&&n==="left"?"scrollLeft":"scrollTop",i=!1;return t[r]>0?!0:(t[r]=1,i=t[r]>0,t[r]=0,i)},isOverAxis:function(e,t,n){return e>t&&e<t+n},isOver:function(t,n,r,i,s,o){return e.ui.isOverAxis(t,r,s)&&e.ui.isOverAxis(n,i,o)}})}(jQuery),function(e,t){var n=0,r=Array.prototype.slice,i=e.cleanData;e.cleanData=function(t){for(var n=0,r;(r=t[n])!=null;n++)try{e(r).triggerHandler("remove")}catch(s){}i(t)},e.widget=function(t,n,r){var i,s,o,u,a=t.split(".")[0];t=t.split(".")[1],i=a+"-"+t,r||(r=n,n=e.Widget),e.expr[":"][i.toLowerCase()]=function(t){return!!e.data(t,i)},e[a]=e[a]||{},s=e[a][t],o=e[a][t]=function(e,t){if(!this._createWidget)return new o(e,t);arguments.length&&this._createWidget(e,t)},e.extend(o,s,{version:r.version,_proto:e.extend({},r),_childConstructors:[]}),u=new n,u.options=e.widget.extend({},u.options),e.each(r,function(t,i){e.isFunction(i)&&(r[t]=function(){var e=function(){return n.prototype[t].apply(this,arguments)},r=function(e){return n.prototype[t].apply(this,e)};return function(){var t=this._super,n=this._superApply,s;return this._super=e,this._superApply=r,s=i.apply(this,arguments),this._super=t,this._superApply=n,s}}())}),o.prototype=e.widget.extend(u,{widgetEventPrefix:s?u.widgetEventPrefix:t},r,{constructor:o,namespace:a,widgetName:t,widgetBaseClass:i,widgetFullName:i}),s?(e.each(s._childConstructors,function(t,n){var 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n=t.within,r=n.isWindow?n.scrollTop:n.offset.top,i=t.within.height,o=e.top-t.collisionPosition.marginTop,u=r-o,a=o+t.collisionHeight-i-r,f;t.collisionHeight>i?u>0&&a<=0?(f=e.top+u+t.collisionHeight-i-r,e.top+=u-f):a>0&&u<=0?e.top=r:u>a?e.top=r+i-t.collisionHeight:e.top=r:u>0?e.top+=u:a>0?e.top-=a:e.top=s(e.top-o,e.top)}},flip:{left:function(e,t){var n=t.within,r=n.offset.left+n.scrollLeft,i=n.width,s=n.isWindow?n.scrollLeft:n.offset.left,u=e.left-t.collisionPosition.marginLeft,a=u-s,f=u+t.collisionWidth-i-s,l=t.my[0]==="left"?-t.elemWidth:t.my[0]==="right"?t.elemWidth:0,c=t.at[0]==="left"?t.targetWidth:t.at[0]==="right"?-t.targetWidth:0,h=-2*t.offset[0],p,d;if(a<0){p=e.left+l+c+h+t.collisionWidth-i-r;if(p<0||p<o(a))e.left+=l+c+h}else if(f>0){d=e.left-t.collisionPosition.marginLeft+l+c+h-s;if(d>0||o(d)<f)e.left+=l+c+h}},top:function(e,t){var n=t.within,r=n.offset.top+n.scrollTop,i=n.height,s=n.isWindow?n.scrollTop:n.offset.top,u=e.top-t.collisionPosition.marginTop,a=u-s,f=u+t.collisionHeight-i-s,l=t.my[1]==="top",c=l?-t.elemHeight:t.my[1]==="bottom"?t.elemHeight:0,h=t.at[1]==="top"?t.targetHeight:t.at[1]==="bottom"?-t.targetHeight:0,p=-2*t.offset[1],d,v;a<0?(v=e.top+c+h+p+t.collisionHeight-i-r,e.top+c+h+p>a&&(v<0||v<o(a))&&(e.top+=c+h+p)):f>0&&(d=e.top-t.collisionPosition.marginTop+c+h+p-s,e.top+c+h+p>f&&(d>0||o(d)<f)&&(e.top+=c+h+p))}},flipfit:{left:function(){e.ui.position.flip.left.apply(this,arguments),e.ui.position.fit.left.apply(this,arguments)},top:function(){e.ui.position.flip.top.apply(this,arguments),e.ui.position.fit.top.apply(this,arguments)}}},function(){var t,n,r,i,s,o=document.getElementsByTagName("body")[0],u=document.createElement("div");t=document.createElement(o?"div":"body"),r={visibility:"hidden",width:0,height:0,border:0,margin:0,background:"none"},o&&e.extend(r,{position:"absolute",left:"-1000px",top:"-1000px"});for(s in r)t.style[s]=r[s];t.appendChild(u),n=o||document.documentElement,n.insertBefore(t,n.firstChild),u.style.cssText="position: absolute; left: 10.7432222px;",i=e(u).offset().left,e.support.offsetFractions=i>10&&i<11,t.innerHTML="",n.removeChild(t)}(),e.uiBackCompat!==!1&&function(e){var n=e.fn.position;e.fn.position=function(r){if(!r||!r.offset)return n.call(this,r);var i=r.offset.split(" "),s=r.at.split(" ");return i.length===1&&(i[1]=i[0]),/^\d/.test(i[0])&&(i[0]="+"+i[0]),/^\d/.test(i[1])&&(i[1]="+"+i[1]),s.length===1&&(/left|center|right/.test(s[0])?s[1]="center":(s[1]=s[0],s[0]="center")),n.call(this,e.extend(r,{at:s[0]+i[0]+" 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this.helper||n.disabled||e(t.target).is(".ui-resizable-handle")?!1:(this.handle=this._getHandle(t),this.handle?(e(n.iframeFix===!0?"iframe":n.iframeFix).each(function(){e('<div class="ui-draggable-iframeFix" style="background: #fff;"></div>').css({width:this.offsetWidth+"px",height:this.offsetHeight+"px",position:"absolute",opacity:"0.001",zIndex:1e3}).css(e(this).offset()).appendTo("body")}),!0):!1)},_mouseStart:function(t){var n=this.options;return 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this._trigger("stop",t)!==!1&&this._clear();return!1},_mouseUp:function(t){return e("div.ui-draggable-iframeFix").each(function(){this.parentNode.removeChild(this)}),e.ui.ddmanager&&e.ui.ddmanager.dragStop(this,t),e.ui.mouse.prototype._mouseUp.call(this,t)},cancel:function(){return this.helper.is(".ui-draggable-dragging")?this._mouseUp({}):this._clear(),this},_getHandle:function(t){var n=!this.options.handle||!e(this.options.handle,this.element).length?!0:!1;return e(this.options.handle,this.element).find("*").andSelf().each(function(){this==t.target&&(n=!0)}),n},_createHelper:function(t){var n=this.options,r=e.isFunction(n.helper)?e(n.helper.apply(this.element[0],[t])):n.helper=="clone"?this.element.clone().removeAttr("id"):this.element;return r.parents("body").length||r.appendTo(n.appendTo=="parent"?this.element[0].parentNode:n.appendTo),r[0]!=this.element[0]&&!/(fixed|absolute)/.test(r.css("position"))&&r.css("position","absolute"),r},_adjustOffsetFromHelper:function(t){typeof t=="string"&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();this.cssPosition=="absolute"&&this.scrollParent[0]!=document&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop());if(this.offsetParent[0]==document.body||this.offsetParent[0].tagName&&this.offsetParent[0].tagName.toLowerCase()=="html"&&e.ui.ie)t={top:0,left:0};return{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if(this.cssPosition=="relative"){var e=this.element.position();return{top:e.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:e.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}}return{top:0,left:0}},_cacheMargins:function(){this.margins={left:parseInt(this.element.css("marginLeft"),10)||0,top:parseInt(this.element.css("marginTop"),10)||0,right:parseInt(this.element.css("marginRight"),10)||0,bottom:parseInt(this.element.css("marginBottom"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t=this.options;t.containment=="parent"&&(t.containment=this.helper[0].parentNode);if(t.containment=="document"||t.containment=="window")this.containment=[t.containment=="document"?0:e(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,t.containment=="document"?0:e(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,(t.containment=="document"?0:e(window).scrollLeft())+e(t.containment=="document"?document:window).width()-this.helperProportions.width-this.margins.left,(t.containment=="document"?0:e(window).scrollTop())+(e(t.containment=="document"?document:window).height()||document.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top];if(!/^(document|window|parent)$/.test(t.containment)&&t.containment.constructor!=Array){var n=e(t.containment),r=n[0];if(!r)return;var i=n.offset(),s=e(r).css("overflow")!="hidden";this.containment=[(parseInt(e(r).css("borderLeftWidth"),10)||0)+(parseInt(e(r).css("paddingLeft"),10)||0),(parseInt(e(r).css("borderTopWidth"),10)||0)+(parseInt(e(r).css("paddingTop"),10)||0),(s?Math.max(r.scrollWidth,r.offsetWidth):r.offsetWidth)-(parseInt(e(r).css("borderLeftWidth"),10)||0)-(parseInt(e(r).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(s?Math.max(r.scrollHeight,r.offsetHeight):r.offsetHeight)-(parseInt(e(r).css("borderTopWidth"),10)||0)-(parseInt(e(r).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relative_container=n}else t.containment.constructor==Array&&(this.containment=t.containment)},_convertPositionTo:function(t,n){n||(n=this.position);var r=t=="absolute"?1:-1,i=this.options,s=this.cssPosition!="absolute"||this.scrollParent[0]!=document&&!!e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(s[0].tagName);return{top:n.top+this.offset.relative.top*r+this.offset.parent.top*r-(this.cssPosition=="fixed"?-this.scrollParent.scrollTop():o?0:s.scrollTop())*r,left:n.left+this.offset.relative.left*r+this.offset.parent.left*r-(this.cssPosition=="fixed"?-this.scrollParent.scrollLeft():o?0:s.scrollLeft())*r}},_generatePosition:function(t){var n=this.options,r=this.cssPosition!="absolute"||this.scrollParent[0]!=document&&!!e.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,i=/(html|body)/i.test(r[0].tagName),s=t.pageX,o=t.pageY;if(this.originalPosition){var u;if(this.containment){if(this.relative_container){var a=this.relative_container.offset();u=[this.containment[0]+a.left,this.containment[1]+a.top,this.containment[2]+a.left,this.containment[3]+a.top]}else u=this.containment;t.pageX-this.offset.click.left<u[0]&&(s=u[0]+this.offset.click.left),t.pageY-this.offset.click.top<u[1]&&(o=u[1]+this.offset.click.top),t.pageX-this.offset.click.left>u[2]&&(s=u[2]+this.offset.click.left),t.pageY-this.offset.click.top>u[3]&&(o=u[3]+this.offset.click.top)}if(n.grid){var f=n.grid[1]?this.originalPageY+Math.round((o-this.originalPageY)/n.grid[1])*n.grid[1]:this.originalPageY;o=u?f-this.offset.click.top<u[1]||f-this.offset.click.top>u[3]?f-this.offset.click.top<u[1]?f+n.grid[1]:f-n.grid[1]:f:f;var l=n.grid[0]?this.originalPageX+Math.round((s-this.originalPageX)/n.grid[0])*n.grid[0]:this.originalPageX;s=u?l-this.offset.click.left<u[0]||l-this.offset.click.left>u[2]?l-this.offset.click.left<u[0]?l+n.grid[0]:l-n.grid[0]:l:l}}return{top:o-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+(this.cssPosition=="fixed"?-this.scrollParent.scrollTop():i?0:r.scrollTop()),left:s-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+(this.cssPosition=="fixed"?-this.scrollParent.scrollLeft():i?0:r.scrollLeft())}},_clear:function(){this.helper.removeClass("ui-draggable-dragging"),this.helper[0]!=this.element[0]&&!this.cancelHelperRemoval&&this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1},_trigger:function(t,n,r){return r=r||this._uiHash(),e.ui.plugin.call(this,t,[n,r]),t=="drag"&&(this.positionAbs=this._convertPositionTo("absolute")),e.Widget.prototype._trigger.call(this,t,n,r)},plugins:{},_uiHash:function(e){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),e.ui.plugin.add("draggable","connectToSortable",{start:function(t,n){var r=e(this).data("draggable"),i=r.options,s=e.extend({},n,{item:r.element});r.sortables=[],e(i.connectToSortable).each(function(){var n=e.data(this,"sortable");n&&!n.options.disabled&&(r.sortables.push({instance:n,shouldRevert:n.options.revert}),n.refreshPositions(),n._trigger("activate",t,s))})},stop:function(t,n){var r=e(this).data("draggable"),i=e.extend({},n,{item:r.element});e.each(r.sortables,function(){this.instance.isOver?(this.instance.isOver=0,r.cancelHelperRemoval=!0,this.instance.cancelHelperRemoval=!1,this.shouldRevert&&(this.instance.options.revert=!0),this.instance._mouseStop(t),this.instance.options.helper=this.instance.options._helper,r.options.helper=="original"&&this.instance.currentItem.css({top:"auto",left:"auto"})):(this.instance.cancelHelperRemoval=!1,this.instance._trigger("deactivate",t,i))})},drag:function(t,n){var r=e(this).data("draggable"),i=this,s=function(t){var n=this.offset.click.top,r=this.offset.click.left,i=this.positionAbs.top,s=this.positionAbs.left,o=t.height,u=t.width,a=t.top,f=t.left;return e.ui.isOver(i+n,s+r,a,f,o,u)};e.each(r.sortables,function(s){var o=!1,u=this;this.instance.positionAbs=r.positionAbs,this.instance.helperProportions=r.helperProportions,this.instance.offset.click=r.offset.click,this.instance._intersectsWith(this.instance.containerCache)&&(o=!0,e.each(r.sortables,function(){return this.instance.positionAbs=r.positionAbs,this.instance.helperProportions=r.helperProportions,this.instance.offset.click=r.offset.click,this!=u&&this.instance._intersectsWith(this.instance.containerCache)&&e.ui.contains(u.instance.element[0],this.instance.element[0])&&(o=!1),o})),o?(this.instance.isOver||(this.instance.isOver=1,this.instance.currentItem=e(i).clone().removeAttr("id").appendTo(this.instance.element).data("sortable-item",!0),this.instance.options._helper=this.instance.options.helper,this.instance.options.helper=function(){return n.helper[0]},t.target=this.instance.currentItem[0],this.instance._mouseCapture(t,!0),this.instance._mouseStart(t,!0,!0),this.instance.offset.click.top=r.offset.click.top,this.instance.offset.click.left=r.offset.click.left,this.instance.offset.parent.left-=r.offset.parent.left-this.instance.offset.parent.left,this.instance.offset.parent.top-=r.offset.parent.top-this.instance.offset.parent.top,r._trigger("toSortable",t),r.dropped=this.instance.element,r.currentItem=r.element,this.instance.fromOutside=r),this.instance.currentItem&&this.instance._mouseDrag(t)):this.instance.isOver&&(this.instance.isOver=0,this.instance.cancelHelperRemoval=!0,this.instance.options.revert=!1,this.instance._trigger("out",t,this.instance._uiHash(this.instance)),this.instance._mouseStop(t,!0),this.instance.options.helper=this.instance.options._helper,this.instance.currentItem.remove(),this.instance.placeholder&&this.instance.placeholder.remove(),r._trigger("fromSortable",t),r.dropped=!1)})}}),e.ui.plugin.add("draggable","cursor",{start:function(t,n){var r=e("body"),i=e(this).data("draggable").options;r.css("cursor")&&(i._cursor=r.css("cursor")),r.css("cursor",i.cursor)},stop:function(t,n){var r=e(this).data("draggable").options;r._cursor&&e("body").css("cursor",r._cursor)}}),e.ui.plugin.add("draggable","opacity",{start:function(t,n){var r=e(n.helper),i=e(this).data("draggable").options;r.css("opacity")&&(i._opacity=r.css("opacity")),r.css("opacity",i.opacity)},stop:function(t,n){var r=e(this).data("draggable").options;r._opacity&&e(n.helper).css("opacity",r._opacity)}}),e.ui.plugin.add("draggable","scroll",{start:function(t,n){var r=e(this).data("draggable");r.scrollParent[0]!=document&&r.scrollParent[0].tagName!="HTML"&&(r.overflowOffset=r.scrollParent.offset())},drag:function(t,n){var r=e(this).data("draggable"),i=r.options,s=!1;if(r.scrollParent[0]!=document&&r.scrollParent[0].tagName!="HTML"){if(!i.axis||i.axis!="x")r.overflowOffset.top+r.scrollParent[0].offsetHeight-t.pageY<i.scrollSensitivity?r.scrollParent[0].scrollTop=s=r.scrollParent[0].scrollTop+i.scrollSpeed:t.pageY-r.overflowOffset.top<i.scrollSensitivity&&(r.scrollParent[0].scrollTop=s=r.scrollParent[0].scrollTop-i.scrollSpeed);if(!i.axis||i.axis!="y")r.overflowOffset.left+r.scrollParent[0].offsetWidth-t.pageX<i.scrollSensitivity?r.scrollParent[0].scrollLeft=s=r.scrollParent[0].scrollLeft+i.scrollSpeed:t.pageX-r.overflowOffset.left<i.scrollSensitivity&&(r.scrollParent[0].scrollLeft=s=r.scrollParent[0].scrollLeft-i.scrollSpeed)}else{if(!i.axis||i.axis!="x")t.pageY-e(document).scrollTop()<i.scrollSensitivity?s=e(document).scrollTop(e(document).scrollTop()-i.scrollSpeed):e(window).height()-(t.pageY-e(document).scrollTop())<i.scrollSensitivity&&(s=e(document).scrollTop(e(document).scrollTop()+i.scrollSpeed));if(!i.axis||i.axis!="y")t.pageX-e(document).scrollLeft()<i.scrollSensitivity?s=e(document).scrollLeft(e(document).scrollLeft()-i.scrollSpeed):e(window).width()-(t.pageX-e(document).scrollLeft())<i.scrollSensitivity&&(s=e(document).scrollLeft(e(document).scrollLeft()+i.scrollSpeed))}s!==!1&&e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(r,t)}}),e.ui.plugin.add("draggable","snap",{start:function(t,n){var r=e(this).data("draggable"),i=r.options;r.snapElements=[],e(i.snap.constructor!=String?i.snap.items||":data(draggable)":i.snap).each(function(){var t=e(this),n=t.offset();this!=r.element[0]&&r.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:n.top,left:n.left})})},drag:function(t,n){var r=e(this).data("draggable"),i=r.options,s=i.snapTolerance,o=n.offset.left,u=o+r.helperProportions.width,a=n.offset.top,f=a+r.helperProportions.height;for(var l=r.snapElements.length-1;l>=0;l--){var c=r.snapElements[l].left,h=c+r.snapElements[l].width,p=r.snapElements[l].top,d=p+r.snapElements[l].height;if(!(c-s<o&&o<h+s&&p-s<a&&a<d+s||c-s<o&&o<h+s&&p-s<f&&f<d+s||c-s<u&&u<h+s&&p-s<a&&a<d+s||c-s<u&&u<h+s&&p-s<f&&f<d+s)){r.snapElements[l].snapping&&r.options.snap.release&&r.options.snap.release.call(r.element,t,e.extend(r._uiHash(),{snapItem:r.snapElements[l].item})),r.snapElements[l].snapping=!1;continue}if(i.snapMode!="inner"){var v=Math.abs(p-f)<=s,m=Math.abs(d-a)<=s,g=Math.abs(c-u)<=s,y=Math.abs(h-o)<=s;v&&(n.position.top=r._convertPositionTo("relative",{top:p-r.helperProportions.height,left:0}).top-r.margins.top),m&&(n.position.top=r._convertPositionTo("relative",{top:d,left:0}).top-r.margins.top),g&&(n.position.left=r._convertPositionTo("relative",{top:0,left:c-r.helperProportions.width}).left-r.margins.left),y&&(n.position.left=r._convertPositionTo("relative",{top:0,left:h}).left-r.margins.left)}var b=v||m||g||y;if(i.snapMode!="outer"){var v=Math.abs(p-a)<=s,m=Math.abs(d-f)<=s,g=Math.abs(c-o)<=s,y=Math.abs(h-u)<=s;v&&(n.position.top=r._convertPositionTo("relative",{top:p,left:0}).top-r.margins.top),m&&(n.position.top=r._convertPositionTo("relative",{top:d-r.helperProportions.height,left:0}).top-r.margins.top),g&&(n.position.left=r._convertPositionTo("relative",{top:0,left:c}).left-r.margins.left),y&&(n.position.left=r._convertPositionTo("relative",{top:0,left:h-r.helperProportions.width}).left-r.margins.left)}!r.snapElements[l].snapping&&(v||m||g||y||b)&&r.options.snap.snap&&r.options.snap.snap.call(r.element,t,e.extend(r._uiHash(),{snapItem:r.snapElements[l].item})),r.snapElements[l].snapping=v||m||g||y||b}}}),e.ui.plugin.add("draggable","stack",{start:function(t,n){var r=e(this).data("draggable").options,i=e.makeArray(e(r.stack)).sort(function(t,n){return(parseInt(e(t).css("zIndex"),10)||0)-(parseInt(e(n).css("zIndex"),10)||0)});if(!i.length)return;var s=parseInt(i[0].style.zIndex)||0;e(i).each(function(e){this.style.zIndex=s+e}),this[0].style.zIndex=s+i.length}}),e.ui.plugin.add("draggable","zIndex",{start:function(t,n){var r=e(n.helper),i=e(this).data("draggable").options;r.css("zIndex")&&(i._zIndex=r.css("zIndex")),r.css("zIndex",i.zIndex)},stop:function(t,n){var r=e(this).data("draggable").options;r._zIndex&&e(n.helper).css("zIndex",r._zIndex)}})}(jQuery),function(e,t){e.widget("ui.droppable",{version:"1.9.2",widgetEventPrefix:"drop",options:{accept:"*",activeClass:!1,addClasses:!0,greedy:!1,hoverClass:!1,scope:"default",tolerance:"intersect"},_create:function(){var t=this.options,n=t.accept;this.isover=0,this.isout=1,this.accept=e.isFunction(n)?n:function(e){return e.is(n)},this.proportions={width:this.element[0].offsetWidth,height:this.element[0].offsetHeight},e.ui.ddmanager.droppables[t.scope]=e.ui.ddmanager.droppables[t.scope]||[],e.ui.ddmanager.droppables[t.scope].push(this),t.addClasses&&this.element.addClass("ui-droppable")},_destroy:function(){var t=e.ui.ddmanager.droppables[this.options.scope];for(var n=0;n<t.length;n++)t[n]==this&&t.splice(n,1);this.element.removeClass("ui-droppable ui-droppable-disabled")},_setOption:function(t,n){t=="accept"&&(this.accept=e.isFunction(n)?n:function(e){return e.is(n)}),e.Widget.prototype._setOption.apply(this,arguments)},_activate:function(t){var n=e.ui.ddmanager.current;this.options.activeClass&&this.element.addClass(this.options.activeClass),n&&this._trigger("activate",t,this.ui(n))},_deactivate:function(t){var n=e.ui.ddmanager.current;this.options.activeClass&&this.element.removeClass(this.options.activeClass),n&&this._trigger("deactivate",t,this.ui(n))},_over:function(t){var n=e.ui.ddmanager.current;if(!n||(n.currentItem||n.element)[0]==this.element[0])return;this.accept.call(this.element[0],n.currentItem||n.element)&&(this.options.hoverClass&&this.element.addClass(this.options.hoverClass),this._trigger("over",t,this.ui(n)))},_out:function(t){var n=e.ui.ddmanager.current;if(!n||(n.currentItem||n.element)[0]==this.element[0])return;this.accept.call(this.element[0],n.currentItem||n.element)&&(this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("out",t,this.ui(n)))},_drop:function(t,n){var r=n||e.ui.ddmanager.current;if(!r||(r.currentItem||r.element)[0]==this.element[0])return!1;var i=!1;return this.element.find(":data(droppable)").not(".ui-draggable-dragging").each(function(){var t=e.data(this,"droppable");if(t.options.greedy&&!t.options.disabled&&t.options.scope==r.options.scope&&t.accept.call(t.element[0],r.currentItem||r.element)&&e.ui.intersect(r,e.extend(t,{offset:t.element.offset()}),t.options.tolerance))return i=!0,!1}),i?!1:this.accept.call(this.element[0],r.currentItem||r.element)?(this.options.activeClass&&this.element.removeClass(this.options.activeClass),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass),this._trigger("drop",t,this.ui(r)),this.element):!1},ui:function(e){return{draggable:e.currentItem||e.element,helper:e.helper,position:e.position,offset:e.positionAbs}}}),e.ui.intersect=function(t,n,r){if(!n.offset)return!1;var i=(t.positionAbs||t.position.absolute).left,s=i+t.helperProportions.width,o=(t.positionAbs||t.position.absolute).top,u=o+t.helperProportions.height,a=n.offset.left,f=a+n.proportions.width,l=n.offset.top,c=l+n.proportions.height;switch(r){case"fit":return a<=i&&s<=f&&l<=o&&u<=c;case"intersect":return a<i+t.helperProportions.width/2&&s-t.helperProportions.width/2<f&&l<o+t.helperProportions.height/2&&u-t.helperProportions.height/2<c;case"pointer":var h=(t.positionAbs||t.position.absolute).left+(t.clickOffset||t.offset.click).left,p=(t.positionAbs||t.position.absolute).top+(t.clickOffset||t.offset.click).top,d=e.ui.isOver(p,h,l,a,n.proportions.height,n.proportions.width);return d;case"touch":return(o>=l&&o<=c||u>=l&&u<=c||o<l&&u>c)&&(i>=a&&i<=f||s>=a&&s<=f||i<a&&s>f);default:return!1}},e.ui.ddmanager={current:null,droppables:{"default":[]},prepareOffsets:function(t,n){var r=e.ui.ddmanager.droppables[t.options.scope]||[],i=n?n.type:null,s=(t.currentItem||t.element).find(":data(droppable)").andSelf();e:for(var o=0;o<r.length;o++){if(r[o].options.disabled||t&&!r[o].accept.call(r[o].element[0],t.currentItem||t.element))continue;for(var u=0;u<s.length;u++)if(s[u]==r[o].element[0]){r[o].proportions.height=0;continue e}r[o].visible=r[o].element.css("display")!="none";if(!r[o].visible)continue;i=="mousedown"&&r[o]._activate.call(r[o],n),r[o].offset=r[o].element.offset(),r[o].proportions={width:r[o].element[0].offsetWidth,height:r[o].element[0].offsetHeight}}},drop:function(t,n){var r=!1;return e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(!this.options)return;!this.options.disabled&&this.visible&&e.ui.intersect(t,this,this.options.tolerance)&&(r=this._drop.call(this,n)||r),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=1,this.isover=0,this._deactivate.call(this,n))}),r},dragStart:function(t,n){t.element.parentsUntil("body").bind("scroll.droppable",function(){t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,n)})},drag:function(t,n){t.options.refreshPositions&&e.ui.ddmanager.prepareOffsets(t,n),e.each(e.ui.ddmanager.droppables[t.options.scope]||[],function(){if(this.options.disabled||this.greedyChild||!this.visible)return;var r=e.ui.intersect(t,this,this.options.tolerance),i=!r&&this.isover==1?"isout":r&&this.isover==0?"isover":null;if(!i)return;var s;if(this.options.greedy){var o=this.options.scope,u=this.element.parents(":data(droppable)").filter(function(){return e.data(this,"droppable").options.scope===o});u.length&&(s=e.data(u[0],"droppable"),s.greedyChild=i=="isover"?1:0)}s&&i=="isover"&&(s.isover=0,s.isout=1,s._out.call(s,n)),this[i]=1,this[i=="isout"?"isover":"isout"]=0,this[i=="isover"?"_over":"_out"].call(this,n),s&&i=="isout"&&(s.isout=0,s.isover=1,s._over.call(s,n))})},dragStop:function(t,n){t.element.parentsUntil("body").unbind("scroll.droppable"),t.options.refreshPositions||e.ui.ddmanager.prepareOffsets(t,n)}}}(jQuery),function(e,t){e.widget("ui.resizable",e.ui.mouse,{version:"1.9.2",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:1e3},_create:function(){var t=this,n=this.options;this.element.addClass("ui-resizable"),e.extend(this,{_aspectRatio:!!n.aspectRatio,aspectRatio:n.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:n.helper||n.ghost||n.animate?n.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/canvas|textarea|input|select|button|img/i)&&(this.element.wrap(e('<div class="ui-wrapper" style="overflow: hidden;"></div>').css({position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("resizable",this.element.data("resizable")),this.elementIsWrapper=!0,this.element.css({marginLeft:this.originalElement.css("marginLeft"),marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom")}),this.originalElement.css({marginLeft:0,marginTop:0,marginRight:0,marginBottom:0}),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css({margin:this.originalElement.css("margin")}),this._proportionallyResize()),this.handles=n.handles||(e(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se");if(this.handles.constructor==String){this.handles=="all"&&(this.handles="n,e,s,w,se,sw,ne,nw");var r=this.handles.split(",");this.handles={};for(var i=0;i<r.length;i++){var s=e.trim(r[i]),o="ui-resizable-"+s,u=e('<div class="ui-resizable-handle '+o+'"></div>');u.css({zIndex:n.zIndex}),"se"==s&&u.addClass("ui-icon ui-icon-gripsmall-diagonal-se"),this.handles[s]=".ui-resizable-"+s,this.element.append(u)}}this._renderAxis=function(t){t=t||this.element;for(var n in this.handles){this.handles[n].constructor==String&&(this.handles[n]=e(this.handles[n],this.element).show());if(this.elementIsWrapper&&this.originalElement[0].nodeName.match(/textarea|input|select|button/i)){var r=e(this.handles[n],this.element),i=0;i=/sw|ne|nw|se|n|s/.test(n)?r.outerHeight():r.outerWidth();var s=["padding",/ne|nw|n/.test(n)?"Top":/se|sw|s/.test(n)?"Bottom":/^e$/.test(n)?"Right":"Left"].join("");t.css(s,i),this._proportionallyResize()}if(!e(this.handles[n]).length)continue}},this._renderAxis(this.element),this._handles=e(".ui-resizable-handle",this.element).disableSelection(),this._handles.mouseover(function(){if(!t.resizing){if(this.className)var e=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i);t.axis=e&&e[1]?e[1]:"se"}}),n.autoHide&&(this._handles.hide(),e(this.element).addClass("ui-resizable-autohide").mouseenter(function(){if(n.disabled)return;e(this).removeClass("ui-resizable-autohide"),t._handles.show()}).mouseleave(function(){if(n.disabled)return;t.resizing||(e(this).addClass("ui-resizable-autohide"),t._handles.hide())})),this._mouseInit()},_destroy:function(){this._mouseDestroy();var t=function(t){e(t).removeClass("ui-resizable ui-resizable-disabled ui-resizable-resizing").removeData("resizable").removeData("ui-resizable").unbind(".resizable").find(".ui-resizable-handle").remove()};if(this.elementIsWrapper){t(this.element);var n=this.element;this.originalElement.css({position:n.css("position"),width:n.outerWidth(),height:n.outerHeight(),top:n.css("top"),left:n.css("left")}).insertAfter(n),n.remove()}return this.originalElement.css("resize",this.originalResizeStyle),t(this.originalElement),this},_mouseCapture:function(t){var n=!1;for(var r in this.handles)e(this.handles[r])[0]==t.target&&(n=!0);return!this.options.disabled&&n},_mouseStart:function(t){var r=this.options,i=this.element.position(),s=this.element;this.resizing=!0,this.documentScroll={top:e(document).scrollTop(),left:e(document).scrollLeft()},(s.is(".ui-draggable")||/absolute/.test(s.css("position")))&&s.css({position:"absolute",top:i.top,left:i.left}),this._renderProxy();var o=n(this.helper.css("left")),u=n(this.helper.css("top"));r.containment&&(o+=e(r.containment).scrollLeft()||0,u+=e(r.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:o,top:u},this.size=this._helper?{width:s.outerWidth(),height:s.outerHeight()}:{width:s.width(),height:s.height()},this.originalSize=this._helper?{width:s.outerWidth(),height:s.outerHeight()}:{width:s.width(),height:s.height()},this.originalPosition={left:o,top:u},this.sizeDiff={width:s.outerWidth()-s.width(),height:s.outerHeight()-s.height()},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio=typeof r.aspectRatio=="number"?r.aspectRatio:this.originalSize.width/this.originalSize.height||1;var a=e(".ui-resizable-"+this.axis).css("cursor");return e("body").css("cursor",a=="auto"?this.axis+"-resize":a),s.addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(e){var t=this.helper,n=this.options,r={},i=this,s=this.originalMousePosition,o=this.axis,u=e.pageX-s.left||0,a=e.pageY-s.top||0,f=this._change[o];if(!f)return!1;var l=f.apply(this,[e,u,a]);this._updateVirtualBoundaries(e.shiftKey);if(this._aspectRatio||e.shiftKey)l=this._updateRatio(l,e);return l=this._respectSize(l,e),this._propagate("resize",e),t.css({top:this.position.top+"px",left:this.position.left+"px",width:this.size.width+"px",height:this.size.height+"px"}),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),this._updateCache(l),this._trigger("resize",e,this.ui()),!1},_mouseStop:function(t){this.resizing=!1;var n=this.options,r=this;if(this._helper){var i=this._proportionallyResizeElements,s=i.length&&/textarea/i.test(i[0].nodeName),o=s&&e.ui.hasScroll(i[0],"left")?0:r.sizeDiff.height,u=s?0:r.sizeDiff.width,a={width:r.helper.width()-u,height:r.helper.height()-o},f=parseInt(r.element.css("left"),10)+(r.position.left-r.originalPosition.left)||null,l=parseInt(r.element.css("top"),10)+(r.position.top-r.originalPosition.top)||null;n.animate||this.element.css(e.extend(a,{top:l,left:f})),r.helper.height(r.size.height),r.helper.width(r.size.width),this._helper&&!n.animate&&this._proportionallyResize()}return e("body").css("cursor","auto"),this.element.removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updateVirtualBoundaries:function(e){var t=this.options,n,i,s,o,u;u={minWidth:r(t.minWidth)?t.minWidth:0,maxWidth:r(t.maxWidth)?t.maxWidth:Infinity,minHeight:r(t.minHeight)?t.minHeight:0,maxHeight:r(t.maxHeight)?t.maxHeight:Infinity};if(this._aspectRatio||e)n=u.minHeight*this.aspectRatio,s=u.minWidth/this.aspectRatio,i=u.maxHeight*this.aspectRatio,o=u.maxWidth/this.aspectRatio,n>u.minWidth&&(u.minWidth=n),s>u.minHeight&&(u.minHeight=s),i<u.maxWidth&&(u.maxWidth=i),o<u.maxHeight&&(u.maxHeight=o);this._vBoundaries=u},_updateCache:function(e){var t=this.options;this.offset=this.helper.offset(),r(e.left)&&(this.position.left=e.left),r(e.top)&&(this.position.top=e.top),r(e.height)&&(this.size.height=e.height),r(e.width)&&(this.size.width=e.width)},_updateRatio:function(e,t){var n=this.options,i=this.position,s=this.size,o=this.axis;return r(e.height)?e.width=e.height*this.aspectRatio:r(e.width)&&(e.height=e.width/this.aspectRatio),o=="sw"&&(e.left=i.left+(s.width-e.width),e.top=null),o=="nw"&&(e.top=i.top+(s.height-e.height),e.left=i.left+(s.width-e.width)),e},_respectSize:function(e,t){var n=this.helper,i=this._vBoundaries,s=this._aspectRatio||t.shiftKey,o=this.axis,u=r(e.width)&&i.maxWidth&&i.maxWidth<e.width,a=r(e.height)&&i.maxHeight&&i.maxHeight<e.height,f=r(e.width)&&i.minWidth&&i.minWidth>e.width,l=r(e.height)&&i.minHeight&&i.minHeight>e.height;f&&(e.width=i.minWidth),l&&(e.height=i.minHeight),u&&(e.width=i.maxWidth),a&&(e.height=i.maxHeight);var c=this.originalPosition.left+this.originalSize.width,h=this.position.top+this.size.height,p=/sw|nw|w/.test(o),d=/nw|ne|n/.test(o);f&&p&&(e.left=c-i.minWidth),u&&p&&(e.left=c-i.maxWidth),l&&d&&(e.top=h-i.minHeight),a&&d&&(e.top=h-i.maxHeight);var v=!e.width&&!e.height;return v&&!e.left&&e.top?e.top=null:v&&!e.top&&e.left&&(e.left=null),e},_proportionallyResize:function(){var t=this.options;if(!this._proportionallyResizeElements.length)return;var n=this.helper||this.element;for(var r=0;r<this._proportionallyResizeElements.length;r++){var i=this._proportionallyResizeElements[r];if(!this.borderDif){var s=[i.css("borderTopWidth"),i.css("borderRightWidth"),i.css("borderBottomWidth"),i.css("borderLeftWidth")],o=[i.css("paddingTop"),i.css("paddingRight"),i.css("paddingBottom"),i.css("paddingLeft")];this.borderDif=e.map(s,function(e,t){var n=parseInt(e,10)||0,r=parseInt(o[t],10)||0;return n+r})}i.css({height:n.height()-this.borderDif[0]-this.borderDif[2]||0,width:n.width()-this.borderDif[1]-this.borderDif[3]||0})}},_renderProxy:function(){var t=this.element,n=this.options;this.elementOffset=t.offset();if(this._helper){this.helper=this.helper||e('<div style="overflow:hidden;"></div>');var r=e.ui.ie6?1:0,i=e.ui.ie6?2:-1;this.helper.addClass(this._helper).css({width:this.element.outerWidth()+i,height:this.element.outerHeight()+i,position:"absolute",left:this.elementOffset.left-r+"px",top:this.elementOffset.top-r+"px",zIndex:++n.zIndex}),this.helper.appendTo("body").disableSelection()}else this.helper=this.element},_change:{e:function(e,t,n){return{width:this.originalSize.width+t}},w:function(e,t,n){var r=this.options,i=this.originalSize,s=this.originalPosition;return{left:s.left+t,width:i.width-t}},n:function(e,t,n){var r=this.options,i=this.originalSize,s=this.originalPosition;return{top:s.top+n,height:i.height-n}},s:function(e,t,n){return{height:this.originalSize.height+n}},se:function(t,n,r){return e.extend(this._change.s.apply(this,arguments),this._change.e.apply(this,[t,n,r]))},sw:function(t,n,r){return e.extend(this._change.s.apply(this,arguments),this._change.w.apply(this,[t,n,r]))},ne:function(t,n,r){return e.extend(this._change.n.apply(this,arguments),this._change.e.apply(this,[t,n,r]))},nw:function(t,n,r){return e.extend(this._change.n.apply(this,arguments),this._change.w.apply(this,[t,n,r]))}},_propagate:function(t,n){e.ui.plugin.call(this,t,[n,this.ui()]),t!="resize"&&this._trigger(t,n,this.ui())},plugins:{},ui:function(){return{originalElement:this.originalElement,element:this.element,helper:this.helper,position:this.position,size:this.size,originalSize:this.originalSize,originalPosition:this.originalPosition}}}),e.ui.plugin.add("resizable","alsoResize",{start:function(t,n){var r=e(this).data("resizable"),i=r.options,s=function(t){e(t).each(function(){var t=e(this);t.data("resizable-alsoresize",{width:parseInt(t.width(),10),height:parseInt(t.height(),10),left:parseInt(t.css("left"),10),top:parseInt(t.css("top"),10)})})};typeof i.alsoResize=="object"&&!i.alsoResize.parentNode?i.alsoResize.length?(i.alsoResize=i.alsoResize[0],s(i.alsoResize)):e.each(i.alsoResize,function(e){s(e)}):s(i.alsoResize)},resize:function(t,n){var r=e(this).data("resizable"),i=r.options,s=r.originalSize,o=r.originalPosition,u={height:r.size.height-s.height||0,width:r.size.width-s.width||0,top:r.position.top-o.top||0,left:r.position.left-o.left||0},a=function(t,r){e(t).each(function(){var t=e(this),i=e(this).data("resizable-alsoresize"),s={},o=r&&r.length?r:t.parents(n.originalElement[0]).length?["width","height"]:["width","height","top","left"];e.each(o,function(e,t){var n=(i[t]||0)+(u[t]||0);n&&n>=0&&(s[t]=n||null)}),t.css(s)})};typeof i.alsoResize=="object"&&!i.alsoResize.nodeType?e.each(i.alsoResize,function(e,t){a(e,t)}):a(i.alsoResize)},stop:function(t,n){e(this).removeData("resizable-alsoresize")}}),e.ui.plugin.add("resizable","animate",{stop:function(t,n){var r=e(this).data("resizable"),i=r.options,s=r._proportionallyResizeElements,o=s.length&&/textarea/i.test(s[0].nodeName),u=o&&e.ui.hasScroll(s[0],"left")?0:r.sizeDiff.height,a=o?0:r.sizeDiff.width,f={width:r.size.width-a,height:r.size.height-u},l=parseInt(r.element.css("left"),10)+(r.position.left-r.originalPosition.left)||null,c=parseInt(r.element.css("top"),10)+(r.position.top-r.originalPosition.top)||null;r.element.animate(e.extend(f,c&&l?{top:c,left:l}:{}),{duration:i.animateDuration,easing:i.animateEasing,step:function(){var n={width:parseInt(r.element.css("width"),10),height:parseInt(r.element.css("height"),10),top:parseInt(r.element.css("top"),10),left:parseInt(r.element.css("left"),10)};s&&s.length&&e(s[0]).css({width:n.width,height:n.height}),r._updateCache(n),r._propagate("resize",t)}})}}),e.ui.plugin.add("resizable","containment",{start:function(t,r){var i=e(this).data("resizable"),s=i.options,o=i.element,u=s.containment,a=u instanceof e?u.get(0):/parent/.test(u)?o.parent().get(0):u;if(!a)return;i.containerElement=e(a);if(/document/.test(u)||u==document)i.containerOffset={left:0,top:0},i.containerPosition={left:0,top:0},i.parentData={element:e(document),left:0,top:0,width:e(document).width(),height:e(document).height()||document.body.parentNode.scrollHeight};else{var f=e(a),l=[];e(["Top","Right","Left","Bottom"]).each(function(e,t){l[e]=n(f.css("padding"+t))}),i.containerOffset=f.offset(),i.containerPosition=f.position(),i.containerSize={height:f.innerHeight()-l[3],width:f.innerWidth()-l[1]};var c=i.containerOffset,h=i.containerSize.height,p=i.containerSize.width,d=e.ui.hasScroll(a,"left")?a.scrollWidth:p,v=e.ui.hasScroll(a)?a.scrollHeight:h;i.parentData={element:a,left:c.left,top:c.top,width:d,height:v}}},resize:function(t,n){var r=e(this).data("resizable"),i=r.options,s=r.containerSize,o=r.containerOffset,u=r.size,a=r.position,f=r._aspectRatio||t.shiftKey,l={top:0,left:0},c=r.containerElement;c[0]!=document&&/static/.test(c.css("position"))&&(l=o),a.left<(r._helper?o.left:0)&&(r.size.width=r.size.width+(r._helper?r.position.left-o.left:r.position.left-l.left),f&&(r.size.height=r.size.width/r.aspectRatio),r.position.left=i.helper?o.left:0),a.top<(r._helper?o.top:0)&&(r.size.height=r.size.height+(r._helper?r.position.top-o.top:r.position.top),f&&(r.size.width=r.size.height*r.aspectRatio),r.position.top=r._helper?o.top:0),r.offset.left=r.parentData.left+r.position.left,r.offset.top=r.parentData.top+r.position.top;var h=Math.abs((r._helper?r.offset.left-l.left:r.offset.left-l.left)+r.sizeDiff.width),p=Math.abs((r._helper?r.offset.top-l.top:r.offset.top-o.top)+r.sizeDiff.height),d=r.containerElement.get(0)==r.element.parent().get(0),v=/relative|absolute/.test(r.containerElement.css("position"));d&&v&&(h-=r.parentData.left),h+r.size.width>=r.parentData.width&&(r.size.width=r.parentData.width-h,f&&(r.size.height=r.size.width/r.aspectRatio)),p+r.size.height>=r.parentData.height&&(r.size.height=r.parentData.height-p,f&&(r.size.width=r.size.height*r.aspectRatio))},stop:function(t,n){var r=e(this).data("resizable"),i=r.options,s=r.position,o=r.containerOffset,u=r.containerPosition,a=r.containerElement,f=e(r.helper),l=f.offset(),c=f.outerWidth()-r.sizeDiff.width,h=f.outerHeight()-r.sizeDiff.height;r._helper&&!i.animate&&/relative/.test(a.css("position"))&&e(this).css({left:l.left-u.left-o.left,width:c,height:h}),r._helper&&!i.animate&&/static/.test(a.css("position"))&&e(this).css({left:l.left-u.left-o.left,width:c,height:h})}}),e.ui.plugin.add("resizable","ghost",{start:function(t,n){var r=e(this).data("resizable"),i=r.options,s=r.size;r.ghost=r.originalElement.clone(),r.ghost.css({opacity:.25,display:"block",position:"relative",height:s.height,width:s.width,margin:0,left:0,top:0}).addClass("ui-resizable-ghost").addClass(typeof i.ghost=="string"?i.ghost:""),r.ghost.appendTo(r.helper)},resize:function(t,n){var r=e(this).data("resizable"),i=r.options;r.ghost&&r.ghost.css({position:"relative",height:r.size.height,width:r.size.width})},stop:function(t,n){var r=e(this).data("resizable"),i=r.options;r.ghost&&r.helper&&r.helper.get(0).removeChild(r.ghost.get(0))}}),e.ui.plugin.add("resizable","grid",{resize:function(t,n){var r=e(this).data("resizable"),i=r.options,s=r.size,o=r.originalSize,u=r.originalPosition,a=r.axis,f=i._aspectRatio||t.shiftKey;i.grid=typeof i.grid=="number"?[i.grid,i.grid]:i.grid;var l=Math.round((s.width-o.width)/(i.grid[0]||1))*(i.grid[0]||1),c=Math.round((s.height-o.height)/(i.grid[1]||1))*(i.grid[1]||1);/^(se|s|e)$/.test(a)?(r.size.width=o.width+l,r.size.height=o.height+c):/^(ne)$/.test(a)?(r.size.width=o.width+l,r.size.height=o.height+c,r.position.top=u.top-c):/^(sw)$/.test(a)?(r.size.width=o.width+l,r.size.height=o.height+c,r.position.left=u.left-l):(r.size.width=o.width+l,r.size.height=o.height+c,r.position.top=u.top-c,r.position.left=u.left-l)}});var n=function(e){return parseInt(e,10)||0},r=function(e){return!isNaN(parseInt(e,10))}}(jQuery),function(e,t){e.widget("ui.selectable",e.ui.mouse,{version:"1.9.2",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch"},_create:function(){var t=this;this.element.addClass("ui-selectable"),this.dragged=!1;var n;this.refresh=function(){n=e(t.options.filter,t.element[0]),n.addClass("ui-selectee"),n.each(function(){var t=e(this),n=t.offset();e.data(this,"selectable-item",{element:this,$element:t,left:n.left,top:n.top,right:n.left+t.outerWidth(),bottom:n.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this.selectees=n.addClass("ui-selectee"),this._mouseInit(),this.helper=e("<div class='ui-selectable-helper'></div>")},_destroy:function(){this.selectees.removeClass("ui-selectee").removeData("selectable-item"),this.element.removeClass("ui-selectable ui-selectable-disabled"),this._mouseDestroy()},_mouseStart:function(t){var n=this;this.opos=[t.pageX,t.pageY];if(this.options.disabled)return;var r=this.options;this.selectees=e(r.filter,this.element[0]),this._trigger("start",t),e(r.appendTo).append(this.helper),this.helper.css({left:t.clientX,top:t.clientY,width:0,height:0}),r.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var r=e.data(this,"selectable-item");r.startselected=!0,!t.metaKey&&!t.ctrlKey&&(r.$element.removeClass("ui-selected"),r.selected=!1,r.$element.addClass("ui-unselecting"),r.unselecting=!0,n._trigger("unselecting",t,{unselecting:r.element}))}),e(t.target).parents().andSelf().each(function(){var r=e.data(this,"selectable-item");if(r){var i=!t.metaKey&&!t.ctrlKey||!r.$element.hasClass("ui-selected");return r.$element.removeClass(i?"ui-unselecting":"ui-selected").addClass(i?"ui-selecting":"ui-unselecting"),r.unselecting=!i,r.selecting=i,r.selected=i,i?n._trigger("selecting",t,{selecting:r.element}):n._trigger("unselecting",t,{unselecting:r.element}),!1}})},_mouseDrag:function(t){var n=this;this.dragged=!0;if(this.options.disabled)return;var r=this.options,i=this.opos[0],s=this.opos[1],o=t.pageX,u=t.pageY;if(i>o){var a=o;o=i,i=a}if(s>u){var a=u;u=s,s=a}return this.helper.css({left:i,top:s,width:o-i,height:u-s}),this.selectees.each(function(){var a=e.data(this,"selectable-item");if(!a||a.element==n.element[0])return;var f=!1;r.tolerance=="touch"?f=!(a.left>o||a.right<i||a.top>u||a.bottom<s):r.tolerance=="fit"&&(f=a.left>i&&a.right<o&&a.top>s&&a.bottom<u),f?(a.selected&&(a.$element.removeClass("ui-selected"),a.selected=!1),a.unselecting&&(a.$element.removeClass("ui-unselecting"),a.unselecting=!1),a.selecting||(a.$element.addClass("ui-selecting"),a.selecting=!0,n._trigger("selecting",t,{selecting:a.element}))):(a.selecting&&((t.metaKey||t.ctrlKey)&&a.startselected?(a.$element.removeClass("ui-selecting"),a.selecting=!1,a.$element.addClass("ui-selected"),a.selected=!0):(a.$element.removeClass("ui-selecting"),a.selecting=!1,a.startselected&&(a.$element.addClass("ui-unselecting"),a.unselecting=!0),n._trigger("unselecting",t,{unselecting:a.element}))),a.selected&&!t.metaKey&&!t.ctrlKey&&!a.startselected&&(a.$element.removeClass("ui-selected"),a.selected=!1,a.$element.addClass("ui-unselecting"),a.unselecting=!0,n._trigger("unselecting",t,{unselecting:a.element})))}),!1},_mouseStop:function(t){var n=this;this.dragged=!1;var r=this.options;return e(".ui-unselecting",this.element[0]).each(function(){var r=e.data(this,"selectable-item");r.$element.removeClass("ui-unselecting"),r.unselecting=!1,r.startselected=!1,n._trigger("unselected",t,{unselected:r.element})}),e(".ui-selecting",this.element[0]).each(function(){var r=e.data(this,"selectable-item");r.$element.removeClass("ui-selecting").addClass("ui-selected"),r.selecting=!1,r.selected=!0,r.startselected=!0,n._trigger("selected",t,{selected:r.element})}),this._trigger("stop",t),this.helper.remove(),!1}})}(jQuery),function(e,t){e.widget("ui.sortable",e.ui.mouse,{version:"1.9.2",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3},_create:function(){var e=this.options;this.containerCache={},this.element.addClass("ui-sortable"),this.refresh(),this.floating=this.items.length?e.axis==="x"||/left|right/.test(this.items[0].item.css("float"))||/inline|table-cell/.test(this.items[0].item.css("display")):!1,this.offset=this.element.offset(),this._mouseInit(),this.ready=!0},_destroy:function(){this.element.removeClass("ui-sortable ui-sortable-disabled"),this._mouseDestroy();for(var e=this.items.length-1;e>=0;e--)this.items[e].item.removeData(this.widgetName+"-item");return this},_setOption:function(t,n){t==="disabled"?(this.options[t]=n,this.widget().toggleClass("ui-sortable-disabled",!!n)):e.Widget.prototype._setOption.apply(this,arguments)},_mouseCapture:function(t,n){var r=this;if(this.reverting)return!1;if(this.options.disabled||this.options.type=="static")return!1;this._refreshItems(t);var i=null,s=e(t.target).parents().each(function(){if(e.data(this,r.widgetName+"-item")==r)return i=e(this),!1});e.data(t.target,r.widgetName+"-item")==r&&(i=e(t.target));if(!i)return!1;if(this.options.handle&&!n){var o=!1;e(this.options.handle,i).find("*").andSelf().each(function(){this==t.target&&(o=!0)});if(!o)return!1}return this.currentItem=i,this._removeCurrentsFromItems(),!0},_mouseStart:function(t,n,r){var i=this.options;this.currentContainer=this,this.refreshPositions(),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.scrollParent=this.helper.scrollParent(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},e.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},parent:this._getParentOffset(),relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,i.cursorAt&&this._adjustOffsetFromHelper(i.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!=this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),i.containment&&this._setContainment(),i.cursor&&(e("body").css("cursor")&&(this._storedCursor=e("body").css("cursor")),e("body").css("cursor",i.cursor)),i.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",i.opacity)),i.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",i.zIndex)),this.scrollParent[0]!=document&&this.scrollParent[0].tagName!="HTML"&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions();if(!r)for(var s=this.containers.length-1;s>=0;s--)this.containers[s]._trigger("activate",t,this._uiHash(this));return e.ui.ddmanager&&(e.ui.ddmanager.current=this),e.ui.ddmanager&&!i.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this.helper.addClass("ui-sortable-helper"),this._mouseDrag(t),!0},_mouseDrag:function(t){this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.lastPositionAbs||(this.lastPositionAbs=this.positionAbs);if(this.options.scroll){var n=this.options,r=!1;this.scrollParent[0]!=document&&this.scrollParent[0].tagName!="HTML"?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<n.scrollSensitivity?this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop+n.scrollSpeed:t.pageY-this.overflowOffset.top<n.scrollSensitivity&&(this.scrollParent[0].scrollTop=r=this.scrollParent[0].scrollTop-n.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<n.scrollSensitivity?this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft+n.scrollSpeed:t.pageX-this.overflowOffset.left<n.scrollSensitivity&&(this.scrollParent[0].scrollLeft=r=this.scrollParent[0].scrollLeft-n.scrollSpeed)):(t.pageY-e(document).scrollTop()<n.scrollSensitivity?r=e(document).scrollTop(e(document).scrollTop()-n.scrollSpeed):e(window).height()-(t.pageY-e(document).scrollTop())<n.scrollSensitivity&&(r=e(document).scrollTop(e(document).scrollTop()+n.scrollSpeed)),t.pageX-e(document).scrollLeft()<n.scrollSensitivity?r=e(document).scrollLeft(e(document).scrollLeft()-n.scrollSpeed):e(window).width()-(t.pageX-e(document).scrollLeft())<n.scrollSensitivity&&(r=e(document).scrollLeft(e(document).scrollLeft()+n.scrollSpeed))),r!==!1&&e.ui.ddmanager&&!n.dropBehaviour&&e.ui.ddmanager.prepareOffsets(this,t)}this.positionAbs=this._convertPositionTo("absolute");if(!this.options.axis||this.options.axis!="y")this.helper[0].style.left=this.position.left+"px";if(!this.options.axis||this.options.axis!="x")this.helper[0].style.top=this.position.top+"px";for(var i=this.items.length-1;i>=0;i--){var s=this.items[i],o=s.item[0],u=this._intersectsWithPointer(s);if(!u)continue;if(s.instance!==this.currentContainer)continue;if(o!=this.currentItem[0]&&this.placeholder[u==1?"next":"prev"]()[0]!=o&&!e.contains(this.placeholder[0],o)&&(this.options.type=="semi-dynamic"?!e.contains(this.element[0],o):!0)){this.direction=u==1?"down":"up";if(this.options.tolerance!="pointer"&&!this._intersectsWithSides(s))break;this._rearrange(t,s),this._trigger("change",t,this._uiHash());break}}return this._contactContainers(t),e.ui.ddmanager&&e.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,n){if(!t)return;e.ui.ddmanager&&!this.options.dropBehaviour&&e.ui.ddmanager.drop(this,t);if(this.options.revert){var r=this,i=this.placeholder.offset();this.reverting=!0,e(this.helper).animate({left:i.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]==document.body?0:this.offsetParent[0].scrollLeft),top:i.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]==document.body?0:this.offsetParent[0].scrollTop)},parseInt(this.options.revert,10)||500,function(){r._clear(t)})}else this._clear(t,n);return!1},cancel:function(){if(this.dragging){this._mouseUp({target:null}),this.options.helper=="original"?this.currentItem.css(this._storedCSS).removeClass("ui-sortable-helper"):this.currentItem.show();for(var t=this.containers.length-1;t>=0;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.options.helper!="original"&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),e.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?e(this.domPosition.prev).after(this.currentItem):e(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(t){var n=this._getItemsAsjQuery(t&&t.connected),r=[];return t=t||{},e(n).each(function(){var n=(e(t.item||this).attr(t.attribute||"id")||"").match(t.expression||/(.+)[-=_](.+)/);n&&r.push((t.key||n[1]+"[]")+"="+(t.key&&t.expression?n[1]:n[2]))}),!r.length&&t.key&&r.push(t.key+"="),r.join("&")},toArray:function(t){var n=this._getItemsAsjQuery(t&&t.connected),r=[];return t=t||{},n.each(function(){r.push(e(t.item||this).attr(t.attribute||"id")||"")}),r},_intersectsWith:function(e){var t=this.positionAbs.left,n=t+this.helperProportions.width,r=this.positionAbs.top,i=r+this.helperProportions.height,s=e.left,o=s+e.width,u=e.top,a=u+e.height,f=this.offset.click.top,l=this.offset.click.left,c=r+f>u&&r+f<a&&t+l>s&&t+l<o;return this.options.tolerance=="pointer"||this.options.forcePointerForContainers||this.options.tolerance!="pointer"&&this.helperProportions[this.floating?"width":"height"]>e[this.floating?"width":"height"]?c:s<t+this.helperProportions.width/2&&n-this.helperProportions.width/2<o&&u<r+this.helperProportions.height/2&&i-this.helperProportions.height/2<a},_intersectsWithPointer:function(t){var n=this.options.axis==="x"||e.ui.isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),r=this.options.axis==="y"||e.ui.isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width),i=n&&r,s=this._getDragVerticalDirection(),o=this._getDragHorizontalDirection();return i?this.floating?o&&o=="right"||s=="down"?2:1:s&&(s=="down"?2:1):!1},_intersectsWithSides:function(t){var n=e.ui.isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),r=e.ui.isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),i=this._getDragVerticalDirection(),s=this._getDragHorizontalDirection();return this.floating&&s?s=="right"&&r||s=="left"&&!r:i&&(i=="down"&&n||i=="up"&&!n)},_getDragVerticalDirection:function(){var e=this.positionAbs.top-this.lastPositionAbs.top;return e!=0&&(e>0?"down":"up")},_getDragHorizontalDirection:function(){var e=this.positionAbs.left-this.lastPositionAbs.left;return e!=0&&(e>0?"right":"left")},refresh:function(e){return this._refreshItems(e),this.refreshPositions(),this},_connectWith:function(){var e=this.options;return e.connectWith.constructor==String?[e.connectWith]:e.connectWith},_getItemsAsjQuery:function(t){var n=[],r=[],i=this._connectWith();if(i&&t)for(var s=i.length-1;s>=0;s--){var o=e(i[s]);for(var u=o.length-1;u>=0;u--){var a=e.data(o[u],this.widgetName);a&&a!=this&&!a.options.disabled&&r.push([e.isFunction(a.options.items)?a.options.items.call(a.element):e(a.options.items,a.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),a])}}r.push([e.isFunction(this.options.items)?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):e(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]);for(var s=r.length-1;s>=0;s--)r[s][0].each(function(){n.push(this)});return e(n)},_removeCurrentsFromItems:function(){var t=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=e.grep(this.items,function(e){for(var n=0;n<t.length;n++)if(t[n]==e.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var n=this.items,r=[[e.isFunction(this.options.items)?this.options.items.call(this.element[0],t,{item:this.currentItem}):e(this.options.items,this.element),this]],i=this._connectWith();if(i&&this.ready)for(var s=i.length-1;s>=0;s--){var o=e(i[s]);for(var u=o.length-1;u>=0;u--){var a=e.data(o[u],this.widgetName);a&&a!=this&&!a.options.disabled&&(r.push([e.isFunction(a.options.items)?a.options.items.call(a.element[0],t,{item:this.currentItem}):e(a.options.items,a.element),a]),this.containers.push(a))}}for(var s=r.length-1;s>=0;s--){var f=r[s][1],l=r[s][0];for(var u=0,c=l.length;u<c;u++){var h=e(l[u]);h.data(this.widgetName+"-item",f),n.push({item:h,instance:f,width:0,height:0,left:0,top:0})}}},refreshPositions:function(t){this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset());for(var n=this.items.length-1;n>=0;n--){var r=this.items[n];if(r.instance!=this.currentContainer&&this.currentContainer&&r.item[0]!=this.currentItem[0])continue;var i=this.options.toleranceElement?e(this.options.toleranceElement,r.item):r.item;t||(r.width=i.outerWidth(),r.height=i.outerHeight());var s=i.offset();r.left=s.left,r.top=s.top}if(this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(var n=this.containers.length-1;n>=0;n--){var s=this.containers[n].element.offset();this.containers[n].containerCache.left=s.left,this.containers[n].containerCache.top=s.top,this.containers[n].containerCache.width=this.containers[n].element.outerWidth(),this.containers[n].containerCache.height=this.containers[n].element.outerHeight()}return this},_createPlaceholder:function(t){t=t||this;var n=t.options;if(!n.placeholder||n.placeholder.constructor==String){var r=n.placeholder;n.placeholder={element:function(){var n=e(document.createElement(t.currentItem[0].nodeName)).addClass(r||t.currentItem[0].className+" ui-sortable-placeholder").removeClass("ui-sortable-helper")[0];return r||(n.style.visibility="hidden"),n},update:function(e,i){if(r&&!n.forcePlaceholderSize)return;i.height()||i.height(t.currentItem.innerHeight()-parseInt(t.currentItem.css("paddingTop")||0,10)-parseInt(t.currentItem.css("paddingBottom")||0,10)),i.width()||i.width(t.currentItem.innerWidth()-parseInt(t.currentItem.css("paddingLeft")||0,10)-parseInt(t.currentItem.css("paddingRight")||0,10))}}}t.placeholder=e(n.placeholder.element.call(t.element,t.currentItem)),t.currentItem.after(t.placeholder),n.placeholder.update(t,t.placeholder)},_contactContainers:function(t){var n=null,r=null;for(var i=this.containers.length-1;i>=0;i--){if(e.contains(this.currentItem[0],this.containers[i].element[0]))continue;if(this._intersectsWith(this.containers[i].containerCache)){if(n&&e.contains(this.containers[i].element[0],n.element[0]))continue;n=this.containers[i],r=i}else this.containers[i].containerCache.over&&(this.containers[i]._trigger("out",t,this._uiHash(this)),this.containers[i].containerCache.over=0)}if(!n)return;if(this.containers.length===1)this.containers[r]._trigger("over",t,this._uiHash(this)),this.containers[r].containerCache.over=1;else{var s=1e4,o=null,u=this.containers[r].floating?"left":"top",a=this.containers[r].floating?"width":"height",f=this.positionAbs[u]+this.offset.click[u];for(var l=this.items.length-1;l>=0;l--){if(!e.contains(this.containers[r].element[0],this.items[l].item[0]))continue;if(this.items[l].item[0]==this.currentItem[0])continue;var c=this.items[l].item.offset()[u],h=!1;Math.abs(c-f)>Math.abs(c+this.items[l][a]-f)&&(h=!0,c+=this.items[l][a]),Math.abs(c-f)<s&&(s=Math.abs(c-f),o=this.items[l],this.direction=h?"up":"down")}if(!o&&!this.options.dropOnEmpty)return;this.currentContainer=this.containers[r],o?this._rearrange(t,o,null,!0):this._rearrange(t,null,this.containers[r].element,!0),this._trigger("change",t,this._uiHash()),this.containers[r]._trigger("change",t,this._uiHash(this)),this.options.placeholder.update(this.currentContainer,this.placeholder),this.containers[r]._trigger("over",t,this._uiHash(this)),this.containers[r].containerCache.over=1}},_createHelper:function(t){var n=this.options,r=e.isFunction(n.helper)?e(n.helper.apply(this.element[0],[t,this.currentItem])):n.helper=="clone"?this.currentItem.clone():this.currentItem;return r.parents("body").length||e(n.appendTo!="parent"?n.appendTo:this.currentItem[0].parentNode)[0].appendChild(r[0]),r[0]==this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),(r[0].style.width==""||n.forceHelperSize)&&r.width(this.currentItem.width()),(r[0].style.height==""||n.forceHelperSize)&&r.height(this.currentItem.height()),r},_adjustOffsetFromHelper:function(t){typeof t=="string"&&(t=t.split(" ")),e.isArray(t)&&(t={left:+t[0],top:+t[1]||0}),"left"in t&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();this.cssPosition=="absolute"&&this.scrollParent[0]!=document&&e.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop());if(this.offsetParent[0]==document.body||this.offsetParent[0].tagName&&this.offsetParent[0].tagName.toLowerCase()=="html"&&e.ui.ie)t={top:0,left:0};return{top:t.top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if(this.cssPosition=="relative"){var 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interaction_view.collection.Preload,n.layer[e].ipreloadlist.pageid=n.layer[e].iOverlaylist.pageid=n.layer[e].iAnimationlist.pageid=n.layer[e].iActionlist.pageid=n.id,n.layer[e].ipreloadlist.pagetype=n.layer[e].iOverlaylist.pagetype=n.layer[e].iAnimationlist.pagetype=n.layer[e].iActionlist.pagetype="LayerRef",n.layer[e].ipreloadlist.parentpageid=n.layer[e].iOverlaylist.parentpageid=n.layer[e].iAnimationlist.parentpageid=n.layer[e].iActionlist.parentpageid=n.get("pageid"),n.layer[e].ipreloadlist.layerid=n.layer[e].iOverlaylist.layerid=n.layer[e].iAnimationlist.layerid=n.layer[e].iActionlist.layerid=e}),this.setiview()},setiview:function(){this.iview=new interaction_view.view.LayerRef({model:this})},setsyncmodel:function(){},filterlayers:function(){var e=this.get("iDetail"),t=e.layer_ids;e.layer_ids&&e.layer_ids.length>0&&(t=_.filter(t,function(e){return interaction_view.ilayerlist.get(e)})),e.layer_ids=t,this.set("iDetail",e)}}),interaction_view.view.LayerRef=interaction_view.view.Base.extend({events:{},render:function(e){},preload:function(){var e=this.model.get("iDetail");e.layer_ids&&e.layer_ids.length>0&&this.sendToPreload({file:null})},loadLayer:function(e){var t=this.getdetail();this.layerCheckList=_.clone(t.layer_ids),this.firstlayer=t.layer_ids?t.layer_ids[0]:null,this.preloadmodel=e;var n=require("interaction_view/view/layer");n=_g.mvc.createView(n),this.layerview=new n({model:this.model}),this.layerview.afterload()},afterlayerload:function(e){console.log(e);if(this.preloadmodel){this.layerCheckList=_.reject(this.layerCheckList,function(t){return t==e}),this.model.layer[e].iOverlaylist.each(function(e){e.iview.preloaded||e.iview.afterpreload()}),this.model.layer[e].iActionlist.each(function(e){e.iview.render()});var t=this.model.toJSON().iDetail.iHidden||!1;this.layerCheckList.length==0&&(this.slideel=this.$el.find(".layer-content").first(),this.slideel.children(".layer-item").first().show(),this.slidestyle=this.slideel.attr("style"),this.playAnimation(),this.firstlayer&&this.model.get("iType")=="LayerRef"&&(this.model.layer[this.firstlayer].isLayer=!0),this.preloadmodel.set("loaded",!0),this.preloadmodel.page.ipreloadlist.preloadcheck(null,!0,this.model))}},playLayer:function(){this.firstlayer&&interaction_view.play(this.model.layer[this.firstlayer])},playAnimation:function(){var e=this;this.$el.find(".layer-content").children().css("opacity",1);var t=this.getLayerContentSize(),n=this.getcommon();if(t.width==n.iWidth&&t.height==n.iHeight)return!1;var r=this.getMoveRange();console.log(r);var i=0,s=0,o,u;this.$el.find(".layer-content").first().draggable({start:function(e,t){o=i=t.position.left,u=s=t.position.top},drag:function(t,n){var i=n.position,u=e.getcommon(),a=e.checkMaskPos(i,{width:u.iWidth,height:u.iHeight});a&&(n.position=a);if(n.position.left-o!=0){if(r.x>0){var f=n.position.left-o;o=n.position.left;var l=Math.abs(n.position.left)/r.x;e.onBindingChangeTo({type:0,delta:f,deltarate:l,cdirection:0})}console.log("x zhou")}n.position.top-s!=0&&console.log("y zhou")},stop:function(t,n){e.onBindingChangeTo({type:1,delta:n.position.left-o,cdirection:0})}}),this.$el.addClass("hascontrols")},checkMaskPos:function(e,t){var n=this.getdetail(),r=!0,i=this.getLayerContentSize(),s=i.width,o=i.height;return e.left>0&&(e.left=0,r=!1),e.top>0&&(e.top=0,r=!1),Math.abs(e.left)+t.width>s&&(e.left=-(s-t.width),r=!1),Math.abs(e.top)+t.height>o&&(e.top=-(o-t.height),r=!1),r?null:{left:e.left,top:e.top}},onExecuteBindingForLayerRef:function(e){var t=this,n=this.getMoveRange();if(e.type==1)return;e.cdirection||n.x>0&&this.$el.find(".layer-content").first().css("left",-e.deltarate*n.x),e.cdirection&&n.y>0&&this.$el.find(".layer-content").first().css("top",-e.deltarate*n.y)},getMoveRange:function(){var e=this.getLayerContentSize(),t=e.width,n=e.height,r=this.getcommon();return{x:t-r.iWidth,y:n-r.iHeight}},getLayerContentSize:function(){var e=this.getdetail(),t={};if(!e.layer_ids||e.layer_ids.length>0){var n=_.map(e.layer_ids,function(e){return interaction_view.ilayerlist.get(e).toJSON()});return t.width=_.max(_.pluck(n,"layer_width")),t.height=_.max(_.pluck(n,"layer_height")),t}return null},stoplayer:function(){var e=this,t=this.getdetail();t.layer_ids&&t.layer_ids.length>0&&_.each(t.layer_ids,function(t){e.model.layer[t].iAnimationlist.timeline&&e.model.layer[t].iAnimationlist.timeline.pause(0),e.model.layer[t].iOverlaylist.resetViewStatus(),interaction_view.bezierPatch&&interaction_view.bezierPatch(e.model.layer[t])})},resetlayer:function(){var e=this,t=this.getdetail();t.layer_ids&&t.layer_ids.length>0&&(_.each(t.layer_ids,function(t){e.model.layer[t].iAnimationlist.resetAnimationStatus(),e.model.layer[t].iOverlaylist.resetViewStatus()}),this.slideel.attr("style",this.slidestyle),e.currentIndex=0)},resetOtherStatus:function(){this.resetlayer()}})}),define("interaction_view/model/layerslide",["interaction_view/model/layerref"],function(){interaction_view.model.LayerSlide=interaction_view.model.LayerRef.extend({defaults:{iType:"LayerSlide",iLock:!0,iVisibility:!0,iCommon:null,iDetail:null,iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setiview:function(){this.iview=new interaction_view.view.LayerSlide({model:this})}}),interaction_view.view.LayerSlide=interaction_view.view.LayerRef.extend({render:function(e){var t=this,n=this.getdetail();this.currentSlideIndex=null,this.model.on("slideTo",function(e){console.log(e);if(t.currentSlideIndex!=null&&t.currentSlideIndex==e)return!1;if(t.currentSlideIndex!=null&&t.currentSlideIndex!=e){var r=n.layer_ids[t.currentSlideIndex];interaction_view.stopPlay(t.model.layer[r])}if(t.preloaded){var i=n.layer_ids[e];if(!t.model.layer[i])return;t.model.layer[i].isLayer=!0,interaction_view.play(t.model.layer[i]),t.currentSlideIndex=e,t.model.layer[i].playFinished=!0}})},playAnimation:function(){var e=this,t=0,n=this.getdetail(),r=n.iSlidetype;this.delay=Number(n.iInterval)||2,this.autoplay=n.iAutoplay;var i=n.iRepeat?-1:0;this.iFade=n.iFade||0,this.iRewind=n.iRewind,this.timeline=new TimelineMax({paused:!0,repeat:i,onComplete:this.setRewind()}),this.slideel=e.$el.children(".Element").find(".layer-content").first(),this.slidelength=n.layer_ids.length;if(this.slidelength<2)return!1;this.repeat=i,this.slipable=n.iSlipable,this.setTimeline(),this.autoplay?this.timeline.play(0):this.setSlideDisplay(0),this.slipable?(this.control(),this.$el.addClass("hascontrols")):this.slideel&&this.slideel.on("click",function(){e.$el.children(".Element").trigger("click")})},playLayer:function(){this.autoplay&&this.timeline&&this.timeline.play(0),this.slidelength>0&&this.model.trigger("slideTo",0)},setTimeline:function(){if(this.iFade=="Fade"||0)this.setFade();else if(this.iFade=="Slip")this.setSlip();else{if(this.iFade!="None")return;this.setNone()}this.slideel.children().first().css({opacity:1,left:0,top:0}).nextAll().css("opacity",0)},getSlipeInterval:function(e){},getLayerContentSize:function(){var e=this.getdetail(),t={};if(!e.layer_ids||e.layer_ids.length>0){var n=_.map(e.layer_ids,function(e){return interaction_view.ilayerlist.get(e).toJSON()});return t.width=_.max(_.pluck(n,"layer_width")),t.height=_.max(_.pluck(n,"layer_height")),t}return null},getStartPoint:function(e){return this.delay+(this.delay+.500002)*this.delay},getEndPoint:function(e){return(this.delay+.500002)*e+1e-5},getCurrentIndex:function(e){var t=this.timeline.duration(),n=t/this.slidelength;return parseInt(e/n,10)},testControl:function(e){var t=this,n=$(e).closest(".iView");if(n.hasClass("hasaction"))return!1;if(n){var r=n.attr("data-type");if(r=="Slide"||r=="CycleImage")if(n.attr("data-iSlipable"))return!1;return!0}return!0},resetOtherStatus:function(){this.resetlayer();var e=this,t=this.getdetail();if(!t.layer_ids||t.layer_ids.length==0)return;this.slideel.children().first().css({opacity:1,left:0,top:0}),this.autoplay=t.iAutoplay,e.currentIndex=0,this.timeline&&(this.timeline.pause(1e-5),this.autoplay?this.timeline.play(0):this.setSlideDisplay(0))}})}),define("interaction_view/model/Region",["interaction_view/model/base"],function(){interaction_view.model.Region=interaction_view.model.Base.extend({defaults:{iType:"Region",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Region({model:this})},setsyncmodel:function(){}}),interaction_view.view.Region=interaction_view.view.Base.extend({events:{},render:function(e){interaction_view.setscrollnano(this.$el)}})}),define("interaction_view/model/binding",["interaction_view/model/base"],function(){interaction_view.model.Binding=interaction_view.model.Base.extend({defaults:{iType:"Binding",iLock:!0,iVisibility:!0,iCommon:{portait:{iStartx:400,iStarty:200,iWidth:200,iHeight:200},landscape:{iStartx:400,iStarty:200,iWidth:200,iHeight:200}},iDetail:{iAdapt:!1,iFullview:!1,iImg:null,iAnimation:[]},iParent:"",iChild:"",iOptions:{createfocus:!0},iParentModel:null,iDraggable:!0,iResizable:!0,iTemplate:null,iParentdiv:".interaction-view",iAutoindex:!1,iSync:!1,iZindex:"-1",iUrl:null,iAutofocus:!1,iKeyscontrol:!0,callback:null,iBackground:"rgba(62,189,255,0.5)",iResourcesProperties:[{id:"iImg",type:"single"}],iAnimationProperties:[{id:"iAnimation",type:"object"}]},setcollection:function(){iOverlaylist.get(this.id)||iOverlaylist.add(this)},setview:function(e){this.iview=new interaction_view.view.Binding({model:this})},setsyncmodel:function(){}}),interaction_view.view.Binding=interaction_view.view.Base.extend({events:{},renderDynamicElement:function(){},render:function(e){},preload:function(){},afterpreload:function(){var e=this,t=this.getdetail();this.bindings=t.iBindings;if(this.bindings.length>1){var n=this.getControlBinding();_.each(this.bindings,function(t){interaction_view.events.bindingEvents.push({id:t.id,page_id:t.page_id,direction:t.direction,func:function(t){e.executeBinding(e,t)}})})}this.preloaded=!0},getControlBinding:function(){return _.find(this.bindings,function(e){return e.isControl})?_.find(this.bindings,function(e){return e.isControl}):(this.bindings[0].isControl=!0,this.bindings[0])},getOperateBindings:function(){return _.reject(this.bindings,function(e){return e.isControl})},getBindingElement:function(e,t){var n;return interaction_view.ipagelist.getPage(e)||interaction_view.imasterlist.get(e)?n=interaction_view.ipagelist.getPage(e)||interaction_view.imasterlist.get(e):n=this.model.page,n.iOverlaylist.get(t)},getDirection:function(e,t){var n=this.getControlBinding();return t.id==n.id?e.direction?1:-1:e.id==n.id?t.direction?1:-1:t.direction&&e.direction?1:-1},executeBinding:function(e,t){var e=this;_.each(this.bindings,function(n){if(n.id==t.id)return;var r=e.getDirection(n,t),i=e.getBindingElement(n.page_id,n.id);i&&(t.direction=r,t.delta=r*t.delta,t.deltarate=r==1?t.deltarate:1-t.deltarate,i.iview.onExecuteBinding(t))})},resetStatus:function(){}})}),define("interaction_view/model/main",["interaction_view/model/action","interaction_view/model/animation_view","interaction_view/model/audio","interaction_view/model/chapter","interaction_view/model/cycleimage","interaction_view/model/image","interaction_view/model/page","interaction_view/model/doc","interaction_view/model/preload","interaction_view/model/richtext","interaction_view/model/slide","interaction_view/model/video","interaction_view/model/button","interaction_view/model/map","interaction_view/model/pay","interaction_view/model/link","interaction_view/model/layerref","interaction_view/model/layerslide","interaction_view/model/Region","interaction_view/model/binding"],function(){}),function(e,t,n){var r,i,s,o,u;o={paneClass:"pane",sliderClass:"slider",contentClass:"content",iOSNativeScrolling:!1,preventPageScrolling:!1,disableResize:!1,alwaysVisible:!1,flashDelay:1500,sliderMinHeight:20,sliderMaxHeight:null},r="Microsoft Internet Explorer"===t.navigator.appName&&/msie 7./i.test(t.navigator.appVersion)&&t.ActiveXObject,i=null,u=function(){var e,t;return e=n.createElement("div"),t=e.style,t.position="absolute",t.width="100px",t.height="100px",t.overflow="scroll",t.top="-9999px",n.body.appendChild(e),t=e.offsetWidth-e.clientWidth,n.body.removeChild(e),t},s=function(){function s(r,s){this.el=r,this.options=s,i||(i=u()),this.$el=e(this.el),this.doc=e(n),this.win=e(t),this.generate(),this.createEvents(),this.addEvents(),this.reset()}return s.prototype.preventScrolling=function(e,t){this.isActive&&("DOMMouseScroll"===e.type?("down"===t&&0<e.originalEvent.detail||"up"===t&&0>e.originalEvent.detail)&&e.preventDefault():"mousewheel"===e.type&&e.originalEvent&&e.originalEvent.wheelDelta&&("down"===t&&0>e.originalEvent.wheelDelta||"up"===t&&0<e.originalEvent.wheelDelta)&&e.preventDefault())},s.prototype.updateScrollValues=function(){var e;e=this.content[0],this.maxScrollTop=e.scrollHeight-e.clientHeight,this.contentScrollTop=e.scrollTop,this.maxSliderTop=this.paneHeight-this.sliderHeight,this.sliderTop=this.contentScrollTop*this.maxSliderTop/this.maxScrollTop},s.prototype.createEvents=function(){var e=this;this.events={down:function(t){return e.isBeingDragged=!0,e.offsetY=t.pageY-e.slider.offset().top,e.pane.addClass("active"),e.doc.bind("mousemove",e.events.drag).bind("mouseup",e.events.up),!1},drag:function(t){return e.sliderY=t.pageY-e.$el.offset().top-e.offsetY,e.scroll(),e.updateScrollValues(),e.contentScrollTop>=e.maxScrollTop?e.$el.trigger("scrollend"):0===e.contentScrollTop&&e.$el.trigger("scrolltop"),!1},up:function(){return e.isBeingDragged=!1,e.pane.removeClass("active"),e.doc.unbind("mousemove",e.events.drag).unbind("mouseup",e.events.up),!1},resize:function(){e.reset()},panedown:function(t){return e.sliderY=(t.offsetY||t.originalEvent.layerY)-.5*e.sliderHeight,e.scroll(),e.events.down(t),!1},scroll:function(t){e.isBeingDragged||(e.updateScrollValues(),e.sliderY=e.sliderTop,e.slider.css({top:e.sliderTop}),null!=t&&(e.contentScrollTop>=e.maxScrollTop?(e.options.preventPageScrolling&&e.preventScrolling(t,"down"),e.$el.trigger("scrollend")):0===e.contentScrollTop&&(e.options.preventPageScrolling&&e.preventScrolling(t,"up"),e.$el.trigger("scrolltop"))))},wheel:function(t){if(null!=t)return e.sliderY+=-t.wheelDeltaY||-t.delta,e.scroll(),!1}}},s.prototype.addEvents=function(){var e;this.removeEvents(),e=this.events,this.options.disableResize||this.win.bind("resize",e.resize),this.slider.bind("mousedown",e.down),this.pane.bind("mousedown",e.panedown).bind("mousewheel DOMMouseScroll",e.wheel),this.content.bind("scroll mousewheel DOMMouseScroll touchmove",e.scroll)},s.prototype.removeEvents=function(){var e;e=this.events,this.win.unbind("resize",e.resize),this.slider.unbind(),this.pane.unbind(),this.content.unbind("scroll mousewheel DOMMouseScroll touchmove",e.scroll).unbind("keydown",e.keydown).unbind("keyup",e.keyup)},s.prototype.generate=function(){var e,t,n,r,s;return n=this.options,r=n.paneClass,s=n.sliderClass,e=n.contentClass,!this.$el.find(""+r).length&&!this.$el.find(""+s).length&&this.$el.append('<div class="'+r+'"><div class="'+s+'" /></div>'),this.content=this.$el.children("."+e),this.content.attr("tabindex",0),this.slider=this.$el.find("."+s),this.pane=this.$el.find("."+r),i&&(t={right:-i},this.$el.addClass("has-scrollbar")),n.iOSNativeScrolling&&(null==t&&(t={}),t.WebkitOverflowScrolling="touch"),null!=t&&this.content.css(t),n.alwaysVisible&&this.pane.css({opacity:1,visibility:"visible"}),this},s.prototype.restore=function(){return this.stopped=!1,this.pane.show(),this.addEvents()},s.prototype.reset=function(){var e,t,n,s,o,u,a;return this.$el.find("."+this.options.paneClass).length||this.generate().stop(),this.stopped&&this.restore(),e=this.content[0],n=e.style,s=n.overflowY,r&&this.content.css({height:this.content.height()}),t=e.scrollHeight+i,u=this.pane.outerHeight(),a=parseInt(this.pane.css("top"),10),o=parseInt(this.pane.css("bottom"),10),o=u+a+o,a=Math.round(o/t*o),a<this.options.sliderMinHeight?a=this.options.sliderMinHeight:null!=this.options.sliderMaxHeight&&a>this.options.sliderMaxHeight&&(a=this.options.sliderMaxHeight),"scroll"===s&&"scroll"!==n.overflowX&&(a+=i),this.maxSliderTop=o-a,this.contentHeight=t,this.paneHeight=u,this.paneOuterHeight=o,this.sliderHeight=a,this.slider.height(a),this.events.scroll(),this.pane.show(),this.isActive=!0,this.pane.outerHeight(!0)>=e.scrollHeight&&"scroll"!==s?(this.pane.hide(),this.isActive=!1):this.el.clientHeight===e.scrollHeight&&"scroll"===s?this.slider.hide():this.slider.show(),this},s.prototype.scroll=function(){return this.sliderY=Math.max(0,this.sliderY),this.sliderY=Math.min(this.maxSliderTop,this.sliderY),this.content.scrollTop(-1*((this.paneHeight-this.contentHeight+i)*this.sliderY/this.maxSliderTop)),this.slider.css({top:this.sliderY}),this},s.prototype.scrollBottom=function(e){return this.reset(),this.content.scrollTop(this.contentHeight-this.content.height()-e).trigger("mousewheel"),this},s.prototype.scrollTop=function(e){return this.reset(),this.content.scrollTop(+e).trigger("mousewheel"),this},s.prototype.scrollTo=function(t){return this.reset(),t=e(t).offset().top,t>this.maxSliderTop&&(t/=this.contentHeight,this.sliderY=t*=this.maxSliderTop,this.scroll()),this},s.prototype.stop=function(){return this.stopped=!0,this.removeEvents(),this.pane.hide(),this},s.prototype.flash=function(){var e=this;return this.pane.addClass("flashed"),setTimeout(function(){e.pane.removeClass("flashed")},this.options.flashDelay),this},s}(),e.fn.nanoScroller=function(t){return this.each(function(){var n;(n=this.nanoscroller)||(n=e.extend({},o),t&&"object"==typeof t&&(n=e.extend(n,t)),this.nanoscroller=n=new s(this,n));if(t&&"object"==typeof t){e.extend(n.options,t);if(t.scrollBottom)return n.scrollBottom(t.scrollBottom);if(t.scrollTop)return n.scrollTop(t.scrollTop);if(t.scrollTo)return n.scrollTo(t.scrollTo);if("bottom"===t.scroll)return n.scrollBottom(0);if("top"===t.scroll)return n.scrollTop(0);if(t.scroll&&t.scroll instanceof e)return n.scrollTo(t.scroll);if(t.stop)return n.stop();if(t.flash)return n.flash()}return n.reset()})}}(jQuery,window,document),define("jquery.nanoscroller",["jquery"],function(){}),define("interaction_view/ui/main",["jquery","backbone","jquery.nanoscroller","text!interaction_view/template/message.js"],function(){var e=require("text!interaction_view/template/message.js");return{message:function(t,n,r){$("body").find(".msg").length==0&&$("body").append('<div 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|
5b22d5cff0b3fd262a81b1d81eb823523e212557
|
646dea688d45b8478fa63674f0d9e4b29884d4a2
|
/Data/EXO/EDGER/edgeR analysis of data.r
|
a30f4ddf11cf65786107e41e1b8816cc200c15f6
|
[] |
no_license
|
HarleyRobinson/Honours2
|
ec95c23a78cc280fc32d5462bbb25a8d0a0340d3
|
0a75eeb0a872d2b83c08d2cd1e5ac0c87c61400f
|
refs/heads/master
| 2021-03-27T13:10:31.244358
| 2016-11-14T06:37:14
| 2016-11-14T06:37:14
| 50,013,122
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,648
|
r
|
edgeR analysis of data.r
|
# Data input
# load the edgeR library
library(edgeR)
library(lumi)
# read in the raw data
rawdata <- read.delim("miRNA_raw_counts_CORRECT_NAMES.txt")
# create a DGEList object. In this case, raw data is in columns 2 to 55, and unique identifiers are in 1 (miRNA name)
y <- DGEList(counts=Cav1PN4[,1:6], genes=rownames(rawdata))
# Filtering and Normalization
# compute the effective library size by using TMM normalization
keep <- rowSums(cpm(y)>10) >=2
y <- y[keep,]
y$samples$lib.size <- colSums(y$counts)
y <- calcNormFactors(y)
#make QC plots
d <-cpm(y, normalized.lib.sizes=TRUE)
d<-t(d)
dist<-dist(d)
hc<-hclust(dist)
plot(hc)
# Export the graph to a PDF
pdf(file="dendrogramPC3Cav1Sub.pdf", paper="a4r")
plot(hc)
dev.off()
# An MDS plot shows the biological coefficient of variation between the samples. The two
# dimensions are the biggest and second biggest sources of variation within the data. Again, this plot
# seems to suggest that the biggest differentiators of these samples are not related to the properties of
# the genes tested.
d <-t(d)
plotMDS(d, col=c(rep(1,15), rep(2, 15), rep(1, 12), rep(2, 12)))
legend("topright", legend = c("Exosome", "Pellet"), col = 1:2, pch = 15)
plotMDS(d, col=c(rep(1,30), rep(2,24)))
legend("topright", legend = c("HEK", "PC3"), col = 1:2, pch = 15)
# Export the graph to a PDF
pdf(file="MDSCavin1withSubPC3exo.pdf", paper="a4r")
plotMDS(d, col=c(rep(1,15), rep(2, 15), rep(1, 12), rep(2, 12)))
legend("topright", legend = c("Exosome", "Pellet"), col = 1:2, pch = 15)
plotMDS(d, col=c(rep(1,30), rep(2,24)))
legend("topright", legend = c("HEK", "PC3"), col = 1:2, pch = 15)
dev.off()
|
1c6cf7d9a3b408e39b679b89fb5648dcc45190da
|
76ecf1a0cd569defadd07a07b60df6f5d5ab9d8e
|
/inst/doc/ReDaMoR.R
|
27339b84497acbff2a3660ca843ec4169aa9d755
|
[] |
no_license
|
cran/ReDaMoR
|
3f12268a6dd9b8f8daf7d0f448e7b809b6748553
|
abc8c94f1d1cece130907c1bb0517ba3466baf13
|
refs/heads/master
| 2023-07-23T18:12:18.564979
| 2023-07-05T23:14:14
| 2023-07-05T23:14:14
| 251,642,241
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,010
|
r
|
ReDaMoR.R
|
## ----setup, include = FALSE---------------------------------------------------
library(knitr)
library(ReDaMoR)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
cranRef <- function(x){
sprintf(
"[%s](https://CRAN.R-project.org/package=%s): %s",
x, x, packageDescription(x)$Title
)
}
## ---- eval=FALSE--------------------------------------------------------------
# install.packages("ReDaMoR")
## ---- eval=FALSE--------------------------------------------------------------
# devtools::install_github("patzaw/ReDaMoR")
## ---- eval=FALSE--------------------------------------------------------------
# library(ReDaMoR)
# m <- model_relational_data()
## ---- eval=FALSE--------------------------------------------------------------
# m <- model_relational_data(recover_RelDataModel())
## -----------------------------------------------------------------------------
hpo_model <- read_json_data_model(
system.file("examples/HPO-model.json", package="ReDaMoR")
)
plot(hpo_model)
## -----------------------------------------------------------------------------
## Edit the model
# m <- model_relational_data(hpo_model)
## ---- eval=FALSE--------------------------------------------------------------
# library(ReDaMoR)
# model_relational_data()
## -----------------------------------------------------------------------------
confrontation_report <- confront_data(
hpo_model,
path=list.files(
system.file("examples/HPO-subset", package="ReDaMoR"),
full.names=TRUE
),
returnData=TRUE
)
## ---- results='asis'----------------------------------------------------------
# view_confrontation_report(confrontation_report) # Use RStudio viewer
format_confrontation_report_md(
confrontation_report,
title="Example: Confrontation with original data",
level=1, numbered=FALSE
) %>%
cat()
## -----------------------------------------------------------------------------
hpo_tables <- confrontation_report$data
## ---- results='asis'----------------------------------------------------------
hpo_tables$HPO_diseases <- hpo_tables$HPO_diseases %>% slice(1:100)
hpo_tables$HPO_synonyms[1:10, "synonym"] <- NA
hpo_tables$HPO_hp <- hpo_tables$HPO_hp %>% mutate(level=as.character(level))
confront_data(hpo_model, hpo_tables, verbose=FALSE) %>%
format_confrontation_report_md(
title="Example: Confrontation with altered data",
level=1, numbered=FALSE
) %>%
cat()
## -----------------------------------------------------------------------------
hpo_tables <- confrontation_report$data
new_model <- df_to_model(
list=names(hpo_tables), envir=as.environment(hpo_tables)
)
new_model %>%
auto_layout(lengthMultiplier=250) %>%
plot()
## -----------------------------------------------------------------------------
# model_relational_data(new_model)
## -----------------------------------------------------------------------------
ge_model <- read_json_data_model(
system.file("examples/GE-model.json", package="ReDaMoR")
)
plot(ge_model)
|
d2826198d1f1b7251f78dec83265cacf4942acf7
|
44a919553acf39c6979767bb42f6b70fe5ecba83
|
/Perez_et_al_2022/3_optimization-criteria-LST.R
|
461a976a0a922b7391453760d29f48ee8bfac8dc
|
[
"MIT"
] |
permissive
|
farkkilab/pubs
|
cb92d67132925cbebbf9b79744ed9a0c5020ccd7
|
0e76ee49aaba6583334ba3657366507673e638e9
|
refs/heads/master
| 2022-11-17T22:10:09.355688
| 2022-11-15T14:44:40
| 2022-11-15T14:44:40
| 234,320,206
| 6
| 3
| null | null | null | null |
UTF-8
|
R
| false
| false
| 16,901
|
r
|
3_optimization-criteria-LST.R
|
library(ggplot2)
library(rpart)
library(rpart.utils)
library(rpart.plot)
library(ggpubr)
#Importing other function inside the repository
extra.functions <- list.files("R/", full.names = TRUE)
sapply(extra.functions, source)
load("sysdata.rda")
####### Defining variables ###########
outputfolder = "/home/fernpere/HRD/TCGA_analysis/find_HRD_signaturesHGSC_germlines//" #Mainly for plots
###### Reading input #######
#This are GRCh38 segments for OVA-TCGA (PanCanAtlas)
segHRDTCGA <- read.table(file = "/home/fernpere/HRD/TCGA_analysis/find_HRD_signatures/HRD_samples-TCGAsegmentsHGSC_2021.txt", header=T)
segHRPTCGA <- read.table(file = "/home/fernpere/HRD/TCGA_analysis/find_HRD_signatures/HRP_samples-TCGAsegmentsHGSC_2021.txt", header=T)
#Plody information
Ploidy_file <- read.table(file="/home/fernpere/HRD/TCGA_analysis/find_HRD_signatures/samplesHR_ploidy-purity2.txt", header = T, sep="\t")
##Preprocesing the segments from TCGA portal
chrominfo = chrominfo_grch38
ploidy <- rep(2, nrow(segHRDTCGA))
segHRDTCGA <- cbind (segHRDTCGA, ploidy)
ploidy <- rep(2, nrow(segHRPTCGA))
segHRPTCGA <- cbind (segHRPTCGA, ploidy)
preprocessed_HRD_TCGA <- preparing.input(segHRDTCGA)
preprocessed_HRP_TCGA <- preparing.input(segHRPTCGA)
### Sample TCGA-13-1511 (HRP), is an HRP outlier, we excluded###
preprocessed_HRP_TCGA <- preprocessed_HRP_TCGA[!preprocessed_HRP_TCGA[,1] %in% c("TCGA-13-1511"),]
#########################################################################################################################
#########################################################################################################################
#Plotting distribution of segments as in Popova et al. 2012 Figure 2A
#Proportion of segments equal or greater than a given segment size
##### Density calculation of events of HRD ####
Sizes <- c(seq(0.25,80, by=0.25))
all_samplesTCGAportal <- rbind(preprocessed_HRD_TCGA, preprocessed_HRP_TCGA)
#Proportion of segments of a given size
Proportions_segments_HRD <- segmentBySamples(preprocessed_HRD_TCGA, Sizes)
Proportions_segments_HRP <- segmentBySamples(preprocessed_HRP_TCGA, Sizes)
average_proportions_HRD <- log2(apply(Proportions_segments_HRD, FUN = mean, MARGIN = 1))
average_proportions_HRP <- log2(apply(Proportions_segments_HRP, FUN = mean, MARGIN = 1))
#Fitting a smoothing spline for the proportion of segments of given size
yHRD <- average_proportions_HRD[1:160]
xHRD <- as.numeric(names(average_proportions_HRD)[1:160])
loHRD <- smooth.spline(xHRD, yHRD, spar=0.5)
yHRP <- average_proportions_HRP[1:160]
xHRP <- as.numeric(names(average_proportions_HRP)[1:160])
loHRP <- smooth.spline(xHRP, yHRP, spar=0.5)
#Plotting differences in distribution of segments for Sup.Figure2b
svg(file=paste0(outputfolder, "HRP-HRD_segmentsProportions_average_HGSC.svg"), height = 6, width = 6, pointsize = 6)
plot(Sizes,average_proportions_HRD,xlim = c(0,28), ylim=c(-10.6,-4), pch=16, xaxt="n", main="",
xlab="Segment size, Mb", ylab="Log2(Average proportions)", type="p", cex=0.8, col="darkred",
cex.axis = 1.8, cex.lab=2.8)
points(Sizes,average_proportions_HRP, col="blue", pch=16, cex=0.8)
lines(loHRP, col="blue")
lines(loHRD, col="darkred")
axis(side=1,at=seq(0,90,by=1), cex.axis=1.5)
grid(nx = NULL, ny = NULL, col = "lightgray", lty = "dotted")
abline(v=2, lty=2)
dev.off()
#########################################################################################################################################################
################################################### Calculating best LSTs ###############################################################################
#########################################################################################################################################################
####### First generate vector with with size #########
chrominfo = chrominfo_grch38
LSTs_per_sampleTCGA_HRP <- LSTs(preprocessed_HRP_TCGA, chrominfo = chrominfo_grch38)
LSTs_per_sampleTCGA_HRD <- LSTs(preprocessed_HRD_TCGA, chrominfo = chrominfo_grch38)
####### Iteration of parameters that define an LST, get the number of LSTs under those parameters#########
mindistance_values <- c(1, 2, 3, 4) #In Mb, minimum AIs length for smoothing
segsizes_values <- c(1:20) #Mb minimum size of consecutive AIs after smoothing
tandemelements_Values = c(2,3) #Number of consecutive AIs after smoothing
chrominfo = chrominfo_grch38
#In the next pair of dataframes will be stored the amount of LSTs under the parameters s,m,t
#Just initialize them whit random numbers in the first two columns, letter will be removed the first first two columns
LSTs_all_parameters_HRP <- data.frame(x=rep(1,length(LSTs_per_sampleTCGA_HRP)), y=rep(1,length(LSTs_per_sampleTCGA_HRP)))
LSTs_all_parameters_HRD <- data.frame(x=rep(1,length(LSTs_per_sampleTCGA_HRD)), y=rep(1,length(LSTs_per_sampleTCGA_HRD)))
for (m in mindistance_values){
m <- m * 1e6 #To Mb
for (s in segsizes_values){
s <- s * 1e6 #To Mb
print(paste("distance=",m,"segzises=",s))
for (t in tandemelements_Values){
LSTs_per_sampleTCGA_HRP <- LSTs(preprocessed_HRP_TCGA, mindistance = m, segsizes = s, tandemelements = t)
LSTs_all_parameters_HRP <- cbind(LSTs_all_parameters_HRP,LSTs_per_sampleTCGA_HRP)
LSTs_per_sampleTCGA_HRD <- LSTs(preprocessed_HRD_TCGA, mindistance = m, segsizes = s, tandemelements = t)
LSTs_all_parameters_HRD <- cbind(LSTs_all_parameters_HRD,LSTs_per_sampleTCGA_HRD)
}
}
}
#Generate column names
#Column names have the next structure: m1_t2_s5
#m means the minimum segment size for smoothing
#t number of tandem elements
#s minimum size of AI that is included in for a LSTs
column_names <- c("x","y")
for (m in mindistance_values){
for (s in segsizes_values){
for (t in tandemelements_Values){
#Column names
name <- paste(paste("m", m, sep=""), paste("t", t, sep=""), paste("s", s, sep=""), sep="_")
print(name)
column_names <- c(column_names, name)
}
}
}
######Add column and row names######
colnames(LSTs_all_parameters_HRP) <- column_names
LSTs_all_parameters_HRP <- LSTs_all_parameters_HRP[,-c(1,2)] #Removing random numbers
colnames(LSTs_all_parameters_HRD) <- column_names
LSTs_all_parameters_HRD <- LSTs_all_parameters_HRD[,-c(1,2)] #Removing random numbers
LSTs_all_parameters_HRD$samples <- row.names(LSTs_all_parameters_HRD)
LSTs_all_parameters_HRP$samples <- row.names(LSTs_all_parameters_HRP)
############ Plotting each distribution of segments in sample by HR status ###############
#Segments sizes per LSTs, considered with a minimum of 1Mb and two tandem segments
#No a plot in manuscript
m <- 1
t <- 2
average_proportions_HRD
columns_to_get <- NULL
for (s in segsizes_values){
column <- paste(paste("m", m, sep=""), paste("t", t, sep=""), paste("s", s, sep=""), sep="_")
columns_to_get <- c(columns_to_get, column)
}
for (sample in 1:length(row.names(LSTs_all_parameters_HRD))){
if (sample == 1){
values_sample <- LSTs_all_parameters_HRD[sample,columns_to_get]
plot(segsizes_values, values_sample, type="l", lwd=1.2, ylim=c(4,86), xlim=c(5,20), pch=20, xlab="Segment size, Mb", ylab="State transitions")
}else{
values_sample <- LSTs_all_parameters_HRD[sample,columns_to_get]
lines(segsizes_values, values_sample, type="l", lwd=1.2, pch=20)
}
}
for (sample in 1:length(row.names(LSTs_all_parameters_HRP))){
values_sample <- LSTs_all_parameters_HRP[sample,columns_to_get]
lines(segsizes_values, values_sample, type="l", lwd=1.2, pch=20, col="blue")
}
legend(15,80, c("HRD","HRP"), col = c("black","blue"), lty = 1)
################# Calculus of p values and accuracy for the difference in abundance of LSTs between HRD and HRP########
######Function to use ############
calculate.pvalues.BA <- function(abundances.LSTs.HRD, abundances.LSTs.HRP, min.distances=mindistance_values, segments.sizes=segsizes_values, tandem.elements=2){
values_by_mindist <- NULL
for (m in min.distances){
Pvalues_bySegments <- NULL
ACC_bySegments <- NULL
for (s in segments.sizes){
column <- paste(paste("m", m, sep=""), paste("t", tandem.elements, sep=""), paste("s", s, sep=""), sep="_")
print(column)
all.pvals <- NULL
all.BA <- NULL
#for (i in 1:1000){ #This for loop was used for jacknifing
#HRD.samples.extract <- nrow(abundances.LSTs.HRD)-1
#HRP.samples.extract <- nrow(abundances.LSTs.HRP) - 1
HRP.samples.extract <- nrow(abundances.LSTs.HRP)
HRD.samples.extract <- nrow(abundances.LSTs.HRD)
abundances.LSTs.HRD.sample <- sample(abundances.LSTs.HRD[,column], HRD.samples.extract, replace = FALSE) #This was used for jacknifing sampling
abundances.LSTs.HRP.sample <- sample(abundances.LSTs.HRP[,column], HRP.samples.extract, replace = FALSE) #This was used for jacknifing sampling
test_u <- wilcox.test(abundances.LSTs.HRD.sample, abundances.LSTs.HRP.sample, alternative = "greater")
pvals <- test_u$p.value
all.pvals <- c(all.pvals, pvals)
acc_iteration <- get_dispertion_acc(abundances.LSTs.HRD.sample, abundances.LSTs.HRP.sample)
all.BA <- c(all.BA, acc_iteration)
#}
Pvalues_bySegments <- c(Pvalues_bySegments, mean(all.pvals))
ACC_bySegments <- c(ACC_bySegments, mean(all.BA))
}
j <- data.frame(pvalues=Pvalues_bySegments, BA=ACC_bySegments,
S_Mb = segments.sizes,
mdist_Mb = as.numeric(c(rep(m,length(Pvalues_bySegments)))),
values_by_tandem = rep(tandem.elements,length(Pvalues_bySegments)))
values_by_mindist <- rbind(values_by_mindist, j)
}
values_by_mindist$logFDR <- (-1) * log10(values_by_mindist$pvalues)
values_by_mindist$mdist_Mb <- as.factor(values_by_mindist$mdist_Mb)
values_by_mindist$S_Mb <- as.factor(values_by_mindist$S_Mb)
return(values_by_mindist)
}
#For each combination of parameters m l t#
BA.pvals.LSTs.t2 <- calculate.pvalues.BA(LSTs_all_parameters_HRD, LSTs_all_parameters_HRP, tandem.elements=2)
BA.pvals.LSTs.t3 <- calculate.pvalues.BA(LSTs_all_parameters_HRD, LSTs_all_parameters_HRP, tandem.elements=3, segments.sizes=c(1:12))
#Plotting accuracy and p.values of difference in abundance between HRD and HRP for two tandem AIs
#For Sup.Figure 2d
p <- ggplot(BA.pvals.LSTs.t2, aes(x=S_Mb, y=mdist_Mb))
p <- p + geom_point(aes(color=logFDR, size=BA)) + theme_classic()
p <- p + scale_color_gradientn(name="-log10(p.val)", colours = c("blue3", "darkcyan", "red"), values=c(0,0.90,1), limits=c(3.8,12.5))
p <- p + scale_radius(breaks = c(0.75, 0.80, 0.85, 0.90), limits=c(0.58,0.91), range = c(2,7))
p <- p + theme(axis.text=element_text(size=rel(1.1)), strip.placement = "outside",strip.background = element_blank(),
axis.text.x=element_text(size=rel(1.3)), axis.text.y=element_text(size=rel(1.3)),
legend.text=element_text(size=rel(1.1)), legend.title=element_text(size=rel(1.2)),
axis.title=element_text(size=rel(1.5)))
p <- p + guides(colour = guide_colourbar(order = 1), size = guide_legend(keyheight = 1))
p <- p + ylab("Space between AIs (Mb)\n") + xlab("\nMinimun size of AIs (Mb)")
print(p)
ggsave(p, filename = paste0(outputfolder, "LSTs-windows_pvalues_dotplot_segmentsHGSC_t2.svg"), width = 20, height = 9, units = "cm")
ggsave(p, filename = paste0(outputfolder, "LSTs-windows_pvalues_dotplot_segmentsHGSC_t2.png"), width = 20, height = 9, units = "cm")
#Plotting accuracy and p.values of difference in abundance between HRD and HRP for three tandem AIs
#For Sup.Figure 2e
p <- ggplot(BA.pvals.LSTs.t3, aes(x=S_Mb, y=mdist_Mb))
p <- p + geom_point(aes(color=logFDR, size=BA)) + theme_classic()
p <- p + scale_color_gradientn(name="-log10(p.val)", colours = c("blue3", "darkcyan", "red"), values=c(0,0.90,1), limits=c(3.8,12.5))
p <- p + scale_radius(breaks = c(0.75, 0.80, 0.85, 0.90), limits=c(0.58,0.91), range = c(2,7))
p <- p + theme(axis.text=element_text(size=rel(1.1)), strip.placement = "outside",strip.background = element_blank(),
axis.text.x=element_text(size=rel(1.3)), axis.text.y=element_text(size=rel(1.3)),
legend.text=element_text(size=rel(1.3)), legend.title=element_text(size=rel(1.3)),
axis.title=element_text(size=rel(1.5)))
p <- p + guides(colour = guide_colourbar(order = 1), size = guide_legend(keyheight = 1))
p <- p + ylab("Space between AIs (Mb)\n") + xlab("\nMinimun size of AIs (Mb)")
print(p)
ggsave(p, filename = paste0(outputfolder, "LSTs-windows_pvalues_dotplot_segmentsHGSC_t3.svg"), width = 20, height = 9, units = "cm")
ggsave(p, filename = paste0(outputfolder, "LSTs-windows_pvalues_dotplot_segmentsHGSC_t3.png"), width = 20, height = 9, units = "cm")
#Best values for distinguishing LSTs are segments longer than 9Mb and a space (smoothing) of 1Mb
BA.pvals.LSTs.t2[which.max(BA.pvals.LSTs.t2$BA * BA.pvals.LSTs.t2$logFDR),]
#Printing the accuracy of the Telli2016 for separating HRD or HRPcolum
BA.pvals.LSTs.t2[BA.pvals.LSTs.t2$S_Mb == "10" & BA.pvals.LSTs.t2$mdist_Mb == "3",]
#Printing the accuracy of the of distinguished best cutoffs (values)
BA.pvals.LSTs.t2[BA.pvals.LSTs.t2$S_Mb == "12" & BA.pvals.LSTs.t2$mdist_Mb == "1",]
####### Boxplots to compare the separation between HRP and HRD samples using the new cutoff ##########
#The best value before reported was "m3_t2_s10"
status1 <- rep("HRD", length(LSTs_all_parameters_HRD[,"m3_t2_s10"]))
status2 <- rep("HRP", length(LSTs_all_parameters_HRP[,"m3_t2_s10"]))
Scars_definition <- rep("Previous", length(c(LSTs_all_parameters_HRD[,"m3_t2_s10"], LSTs_all_parameters_HRP[,"m3_t2_s10"])))
previosLSTS <- data.frame(HRDstatus=c(status1, status2), LSTs=c(LSTs_all_parameters_HRD[,"m3_t2_s10"], LSTs_all_parameters_HRP[,"m3_t2_s10"]), Scars_def=Scars_definition)
#The best value here identified was "m1_t2_s11"
Scars_definition2 <- rep("New proposal", length(c(LSTs_all_parameters_HRD[,"m1_t2_s9"], LSTs_all_parameters_HRP[,"m1_t2_s9"])))
newLSTS <- data.frame(HRDstatus=c(status1, status2), LSTs=c(LSTs_all_parameters_HRD[,"m1_t2_s9"], LSTs_all_parameters_HRP[,"m1_t2_s9"]), Scars_def=Scars_definition2)
LSTs_scars_comparision <- rbind(previosLSTS, newLSTS)
p <- ggplot(LSTs_scars_comparision, aes (x=HRDstatus, y=LSTs)) + geom_boxplot(alpha = 0.5, outlier.shape = NA)
p <- p + facet_wrap(~Scars_def)
p <- p + ylab("LSTs scars") + xlab("")
p <- p + theme(axis.text=element_text(size=rel(1.5)), strip.placement = "outside",strip.background = element_blank(),
axis.text.x=element_text(size=rel(1.3)), axis.text.y=element_text(size=rel(1.3)), legend.text=element_text(size=rel(1.4)),
strip.text.x = element_text(size=rel(2.8)),
legend.title=element_text(size=rel(2)), axis.title=element_text(size=rel(2.5)),
panel.spacing = unit(1, "lines"),
panel.border = element_rect(linetype = "solid", fill = NA),
panel.background = element_rect(fill = "white"),
panel.grid.major = element_line(size = 0.5, linetype = 'solid', colour = "lightgrey"),
panel.grid.minor = element_line(size = 0.5, linetype = 'solid', colour = "lightgrey"),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank())
p <- p + geom_point(aes(y=LSTs, color=HRDstatus), position= position_jitter(width= .3), size= 3, alpha = 0.7, show.legend = FALSE)
p <- p + ylim(0,50)
print(p)
ggsave(p, filename = "/home/fernpere/HRD/Figures/LSTs-previous_new_scars_boxplot.svg", width = 36, height = 20, units = "cm")
ggsave(p, filename = "/home/fernpere/HRD/Figures/LSTs-previous_new_scars_boxplot.png", width = 36, height = 20, units = "cm")
########################### Get cutoff for that separe LSTs for HRPs and HRDs ###############################
LSTs_per_sampleTCGA_HRP <- LSTs(preprocessed_HRP_TCGA, segsizes=9e6, mindistance=1e6, tandemelements=2)
LSTs_per_sampleTCGA_HRD <- LSTs(preprocessed_HRD_TCGA, segsizes=9e6, mindistance=1e6, tandemelements=2)
dA <- data.frame(LSTs=LSTs_per_sampleTCGA_HRD, anyvalue=rep(2,length(LSTs_per_sampleTCGA_HRD)), status=rep("HRD",length(LSTs_per_sampleTCGA_HRD)))
dB <- data.frame(LSTs=LSTs_per_sampleTCGA_HRP, anyvalue=rep(2,length(LSTs_per_sampleTCGA_HRP)), status=rep("HRP",length(LSTs_per_sampleTCGA_HRP)))
dz <- rbind(dA,dB)
fit <- rpart(status ~ ., data=dz, method='class', control=rpart.control(minsplit = 2, minbucket = 1, cp=0.001))
#Identify cutoff
value <- rpart.subrules.table(fit)[1,5]
cutoff <- as.numeric(value)
cutoff
#Cutoff is 13.5
|
12884727235ae90697bead1a556bda8d0f06dcf1
|
19db2fd88fa5278e7427807b177de0513ddbb6a3
|
/R.functions/alltimerecord.R
|
385d1e2eb891e9f471d45c842cbaa6058abfc1a0
|
[] |
no_license
|
dmrust/engsoccerdata
|
86e6b57cc1050d42677c73cc3b8ff2cf32771241
|
1fd9e37c6637d2180837ab4460c5f4ba7547f47b
|
refs/heads/master
| 2021-01-16T22:13:08.762163
| 2014-10-10T23:43:53
| 2014-10-10T23:43:53
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 900
|
r
|
alltimerecord.R
|
### Function to List the all-time records of a team
alltimerecord<-function (df, teamname) {
library(dplyr)
library(tidyr)
hrec<-df %>%
filter(home==teamname) %>%
summarise(P = n(), W=sum(result=="H"), D=sum(result=="D"), L=sum(result=="A"),
GF = sum(hgoal), GA = sum(vgoal), GD=sum(goaldif))
vrec<-df %>%
filter(visitor==teamname) %>%
summarise(P = n(), W=sum(result=="A"), D=sum(result=="D"), L=sum(result=="H"),
GF = sum(vgoal), GA = sum(hgoal), GD=GF-GA,)
temp<-rbind(hrec,vrec,hrec+vrec)
rownames(temp)<-c("home", "away", "total")
return(temp)
}
#Examples
alltimerecord(df, "Aston Villa")
alltimerecord(df, "Arsenal")
alltimerecord(df, "Liverpool")
alltimerecord(df, "Manchester United")
alltimerecord(df, "York City")
alltimerecord(df, "Rochdale")
alltimerecord(df, "Birmingham City")
alltimerecord(df, "Leeds City")
|
e3fdd4b45b9a0c8375ae07dc96d24ae6d67859b2
|
b59cc783d2da2f32737432c1b13cf72c5802f067
|
/man/Oral.Rd
|
c389daba49bc956841522c48d8b4dcca8d7fc6fd
|
[] |
no_license
|
jdsimkin04/shinyinla
|
9a16007b375975a3f96b6ca29a1284aa6cafb180
|
e58da27a2a090557058b2a5ee63717b116216bf7
|
refs/heads/master
| 2023-06-05T08:34:34.423593
| 2021-06-24T00:27:04
| 2021-06-24T00:27:04
| 330,322,338
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 554
|
rd
|
Oral.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/INLA-package.R
\docType{data}
\name{Oral}
\alias{Oral}
\title{~~ data name/kind ... ~~}
\format{
A data frame with 544 observations on the following 3 variables.
\describe{ \item{region}{a numeric vector} \item{E}{a
numeric vector} \item{Y}{a numeric vector} }
}
\description{
~~ A concise (1-5 lines) description of the dataset. ~~
}
\references{
Rue, H and Held, L. (2005) \emph{Gaussian Markov Random Fields -
Theory and Applications} Chapman and Hall
}
\keyword{datasets}
|
087c3b7cae149070d6457bd58f3cc04f0bb2c76a
|
9f8d324923a02ceda5637d5b6b230eb2c661e820
|
/man/createVarSyntax.Rd
|
1422d5c2bf6d761bb361881e6569971c62d0aeb0
|
[] |
no_license
|
wang-ze/MplusAutomation
|
bff30512541b3ff3fddb50044c2f2d3f7666f47e
|
46e329d2ce403c88e0177906543d950947ca1ff1
|
refs/heads/master
| 2020-04-02T10:35:44.741395
| 2018-10-01T16:53:37
| 2018-10-01T16:53:37
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 608
|
rd
|
createVarSyntax.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createModels.R
\name{createVarSyntax}
\alias{createVarSyntax}
\title{Create Mplus syntax for variable names}
\usage{
createVarSyntax(data)
}
\arguments{
\item{data}{An \code{R} dataset.}
}
\value{
A character vector of the variable names for Mplus
}
\description{
This is a simple function designed to take a dataset in \code{R}
and translate it into a set of variable names for Mplus.
}
\examples{
MplusAutomation:::createVarSyntax(mtcars)
}
\seealso{
\code{\link{prepareMplusData}}
}
\keyword{internal}
|
83d6f6cb6c0738e8d1cd34c1e98ed3d25636d71d
|
5bacfd2bd5fad918a06fbe5334a8447e855495d0
|
/model/prep-data.R
|
92255635b0bf90bbe828974785b47f34d7ea2a74
|
[] |
no_license
|
CoryMcCartan/us-house-18
|
6464ec20d3ff767b3f9ba9f50cc3c2fbb014b3de
|
68ec2376e9b2001c51e8085e985618774fdd3c79
|
refs/heads/master
| 2022-04-14T07:57:50.165690
| 2020-03-11T02:06:50
| 2020-03-11T02:06:50
| 113,709,747
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 4,307
|
r
|
prep-data.R
|
# DATA PREPARATION
library(Hmisc)
library(pollstR)
library(tidyverse)
library(lubridate)
election.day = as.Date("2018-11-06")
# Past Presidential approval
president = read.csv("data/past_pres_approval_raw.csv", sep="\t") %>%
transmute(approval=Approving / 100,
date=mdy(End.Date),
president=President,
party=Party) %>%
filter(year(date) >= 1970, year(date) <= 2030) %>%
arrange(date)
write.csv(president, "data/past_pres_approval.csv", row.names=F)
# Current polling
polls = pollster_charts_polls("2018-national-house-race")[["content"]] %>%
filter(partisanship == "Nonpartisan") %>%
transmute(dem = Democrat / 100,
gop = Republican / 100,
other = Other / 100,
undecided = Undecided / 100,
n_resp = observations,
n_side = round((dem + gop) * n_resp),
n_dem = round(dem * n_resp),
pollster = survey_house,
firm.id = as.numeric(as.factor(pollster)),
date = ymd(end_date),
type = recode(sample_subpopulation, `Registered Voters`="RV",
`Likely Votesrs`="LV")) %>%
as.data.frame
# drop missing data
polls = polls[complete.cases(polls),]
# week IDs
start.day = min(polls$date)
end.day = max(polls$date)
n.weeks = ceiling(as.numeric(election.day - start.day) / 7)
polls$week = floor(as.numeric(election.day - polls$date) / 7)
# output
write.csv(polls, "data/current_polls.csv", row.names=F)
# past polling
past.polls = read.csv("data/raw_past_polling.csv") %>%
filter(type_simple == "House-G", location == "US")
election.dates = unique(mdy(past.polls$electiondate))
lgt = function(x) log(x / (1-x))
past.polls %<>% transmute(year = year,
n_resp = samplesize,
dem = cand1_pct/(cand1_pct + cand2_pct),
logit = lgt(dem),
weeks.until = round(as.numeric(
mdy(electiondate) - mdy(polldate)) / 7)) %>%
group_by(year, weeks.until) %>%
summarise(lgt = wtd.mean(logit, sqrt(n_resp)),
sd = sqrt(wtd.var(logit, sqrt(n_resp)))) %>%
filter(!is.na(sd), sd > 0) %>%
as.data.frame
# manually add more data
new.pp = data.frame(year=c(1974, 1982, 1994), weeks.until=0,
lgt=c(lgt(0.56/0.86), c(0.54/0.9), 0), sd=NA)
election.dates = c(election.dates, ymd("1974-11-01", "1982-11-01", "1994-11-01"))
past.polls = rbind(past.polls, new.pp)
# Merge
economy = read.csv("data/economy.csv", na.strings=".") %>%
transmute(date = ymd(DATE),
unemp = as.numeric(UNRATE) / 100,
gdp = as.numeric(A939RX0Q048SBEA_PC1) / 100,
infl = as.numeric(CPIAUCSL_PC1) / 100,
earn = as.numeric(AHETPI_PC1) / 100) %>%
filter(year(date) > 1970)
congress = read.csv("data/congress_approval.csv")
control = read.csv("data/party_control.csv") %>%
mutate(midterm = ifelse((elect.year %% 4) == 2, 1, 0))
fundamentals = data.frame(year=year(election.dates), approval=0, unemp=0, gdp=0, earn=0)
for (i in 1:length(election.dates)) {
d = election.dates[i]
appr = (president %>%
filter(abs(date - d) < 120 & abs(date - d) > 30) %>%
summarise(m = mean(approval)))$m
econ = economy %>%
filter(abs(date - d) < 120 & abs(date - d) > 30) %>%
summarise(unemp = mean(unemp),
gdp = mean(gdp),
earn = mean(earn))
fundamentals[i,2] = log(appr / (1 - appr))
fundamentals[i,3] = econ$unemp
fundamentals[i,4] = econ$gdp
fundamentals[i,5] = econ$earn
}
code = function(x) 2*x - 1
combined = past.polls %>%
left_join(control, by=c("year"="elect.year")) %>%
left_join(fundamentals, by=c("year")) %>%
transmute(year, weeks_until=weeks.until, logit_intent=lgt, sd_intent=sd,
seats=dem.seats, before=dem.before, pres=code(dem.pres),
house=code(dem.house), midterm=midterm, appr=approval, unemp, gdp, earn)
# manually add 1974 and fix 2014
combined$weeks_until[combined$year==2014][1] = 0
combined[combined$year==1974, c("seats", "before", "pres", "house", "midterm")] =
c(291, 242, -1, 1, 1)
write.csv(combined, "data/combined.csv", row.names=F)
|
6e30af7bce154626d05a91492075294a7baea557
|
778cb2c2de4a80efa6e7ca006961759bbd12a028
|
/zone_detailed.R
|
7b0345454b6951bfa67fa19318785fa2c9311122
|
[] |
no_license
|
victor-gallet/kaggle-kobe
|
6fb73ff522d75d5ed0e047cdea782e853721ed8c
|
0de2be4e2d058e1a1f5baaa252ce221c895f81b5
|
refs/heads/master
| 2016-09-12T09:14:10.304519
| 2016-06-02T19:09:51
| 2016-06-02T19:09:51
| 58,809,155
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,284
|
r
|
zone_detailed.R
|
library(ggplot2)
library(dplyr)
library('png')
shots = read.csv("data/data.csv", stringsAsFactors = T)
train = shots[!is.na(shots$shot_made_flag),]
test = shots[is.na(shots$shot_made_flag),]
courtplot <- function(feat) {
feat <- substitute(feat)
train %>%
ggplot(aes(x = loc_x, y = loc_y)) +
geom_point(aes_q(color = feat), alpha = 0.7, size = 3) +
theme_void() +
ggtitle(paste(feat))
}
train$shot_zone_detailed <- NA
train$shot_zone_detailed[train$loc_x <= -220 & train$loc_y <= 100 & train$shot_type == "3PT Field Goal"] = "1"
train$shot_zone_detailed[train$loc_x >= -220 & train$loc_x <= -150 & train$loc_y <= 100 & train$shot_type == "2PT Field Goal"] = "2"
train$shot_zone_detailed[train$loc_x < -90 & train$shot_type == "3PT Field Goal" & train$loc_y > 100] = "3"
train$shot_zone_detailed[train$loc_x < 90 & train$loc_x > -90 & train$shot_type == "3PT Field Goal"] = "4"
train$shot_zone_detailed[train$loc_x < 70 & train$loc_x > -70 & train$loc_y > 150 & train$shot_type == "2PT Field Goal"] = "6"
train$shot_zone_detailed[train$loc_x > 70 & train$shot_type == "3PT Field Goal"] = "7"
train$shot_zone_detailed[train$loc_x < 90 & train$loc_x > -90 & train$loc_y > 90 & train$loc_y < 150 & train$shot_type == "2PT Field Goal"] = "10"
train$shot_zone_detailed[sqrt(train$loc_x^2 + train$loc_y^2) < 90] = "12"
train$shot_zone_detailed[train$loc_x < 220 & train$loc_x > 150 & train$loc_y <= 100] = "13"
train$shot_zone_detailed[train$loc_x > 220 & train$loc_y < 100 & train$shot_type == "3PT Field Goal"] = "14"
train$shot_zone_detailed[is.na(train$shot_zone_detailed) & train$loc_y > 100 & train$loc_x > -210 & train$loc_x < 70] = "5"
train$shot_zone_detailed[is.na(train$shot_zone_detailed) & train$loc_y > 100 & train$loc_x >= 70 & train$loc_x < 210] = "8"
train$shot_zone_detailed[is.na(train$shot_zone_detailed) & train$loc_y <= 100 & train$loc_x < 0] = "9"
train$shot_zone_detailed[is.na(train$shot_zone_detailed) & train$loc_y <= 100 & train$loc_x > 0] = "11"
train$shot_zone_detailed = as.factor(train$shot_zone_detailed)
courtplot(shot_zone_detailed)
summary(train$shot_zone_detailed)
## Construction
mean_x = aggregate(train$loc_x, list(train$shot_zone_detailed), na.rm = TRUE, mean)
mean_y = aggregate(train$loc_y, list(train$shot_zone_detailed), na.rm = TRUE, mean)
mean_xy = data.frame(mean_x$Group.1, mean_x$x, mean_y$x)
pourcentage_shot = as.data.frame(prop.table(table(train$shot_made_flag, train$shot_zone_detailed), 2))
shot_made_by_zone = as.data.frame(table(train$shot_made_flag, train$shot_zone_detailed))
plot(1, type="n", xlab="", ylab="", xlim=c(-235, 235), ylim=c(-30, 400))
lim <- par()
rasterImage(courtimg, lim$usr[1], lim$usr[3], lim$usr[2], lim$usr[4])
grid()
for (zone in 1:14) {
x = mean_xy[mean_xy$mean_x.Group.1 == zone,]$mean_x.x
y = mean_xy[mean_xy$mean_x.Group.1 == zone,]$mean_y.x
text_pourcentage = pourcentage_shot[pourcentage_shot$Var1 == 1 & pourcentage_shot$Var2 == zone,]$Freq
success_shot = shot_made_by_zone[shot_made_by_zone$Var2 == zone & shot_made_by_zone$Var1 == 1,]$Freq
missed_shot = shot_made_by_zone[shot_made_by_zone$Var2 == zone & shot_made_by_zone$Var1 == 0,]$Freq
text(x, y, sprintf("%d%% \n %d / %d", round(text_pourcentage * 100), success_shot, success_shot + missed_shot))
}
|
e2a71063b9ff7232d19a769d7b1a9dbacc7b3f01
|
66103bd7268a8f6bb811141c759f58515e962428
|
/src/interface_r/R/reexports.R
|
a3724247f389a271607eba8ba26816a4ad21dcd8
|
[
"BSD-3-Clause",
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
pnijhara/h2o4gpu
|
38a6f17cfd61f712bb709de187127a67ee36e106
|
6257112c134136471420b68241f57190a445b67d
|
refs/heads/master
| 2022-07-03T03:08:53.108604
| 2020-04-29T16:44:46
| 2020-04-29T16:44:46
| 260,985,697
| 0
| 0
|
Apache-2.0
| 2020-05-04T12:23:05
| 2020-05-03T17:38:46
| null |
UTF-8
|
R
| false
| false
| 138
|
r
|
reexports.R
|
#' @export
magrittr::`%>%`
#' @export
reticulate::use_condaenv
#' @export
reticulate::use_python
#' @export
reticulate::use_virtualenv
|
63a122dda0f343fc84dc42c024a59c5fbbdb207e
|
6464efbccd76256c3fb97fa4e50efb5d480b7c8c
|
/paws/man/codecommit_get_pull_request.Rd
|
ddb380bdf8e9a5d221e66e79fddf57527580456f
|
[
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] |
permissive
|
johnnytommy/paws
|
019b410ad8d4218199eb7349eb1844864bd45119
|
a371a5f2207b534cf60735e693c809bd33ce3ccf
|
refs/heads/master
| 2020-09-14T23:09:23.848860
| 2020-04-06T21:49:17
| 2020-04-06T21:49:17
| 223,286,996
| 1
| 0
|
NOASSERTION
| 2019-11-22T00:29:10
| 2019-11-21T23:56:19
| null |
UTF-8
|
R
| false
| true
| 624
|
rd
|
codecommit_get_pull_request.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codecommit_operations.R
\name{codecommit_get_pull_request}
\alias{codecommit_get_pull_request}
\title{Gets information about a pull request in a specified repository}
\usage{
codecommit_get_pull_request(pullRequestId)
}
\arguments{
\item{pullRequestId}{[required] The system-generated ID of the pull request. To get this ID, use
ListPullRequests.}
}
\description{
Gets information about a pull request in a specified repository.
}
\section{Request syntax}{
\preformatted{svc$get_pull_request(
pullRequestId = "string"
)
}
}
\keyword{internal}
|
b9afee20218629fef1510e9541a4a76fe39cdd1d
|
17fd4de354250e4f9b5d4b00bd04bf21971c7425
|
/data_analysis.R
|
bee6eeee8b3069a15328beb46794bd7df1e7715e
|
[
"MIT"
] |
permissive
|
martinfriedrichsmanthey/Red_List_Fish_Data
|
14dd752d5902e4179a6f075f07572f51af5f92ca
|
151cde81dbdbf74d49060e2d984ff5de4d87258d
|
refs/heads/main
| 2023-07-18T14:05:55.184607
| 2021-08-31T13:19:06
| 2021-08-31T13:19:06
| 377,147,853
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,613
|
r
|
data_analysis.R
|
#### fish population trends for Red List in Germany
##### initial settings ####
### path to data
dat_dir<-"C:/Users/zf53moho/Documents/NFDI4BioDiv/Data/Fish Data/Fischdaten_Datenbank/Red_List_Fish_Data/clean_data/"
save_dir<-"C:/Users/zf53moho/Documents/NFDI4BioDiv/Data/Fish Data/Fischdaten_Datenbank/Red_List_Fish_Data/modelling_results/"
setwd(dat_dir)
### packages
library(brms)
library(rstan)
library(foreach)
library(doParallel)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores()) #8
##### load and prepare data ####
main_dat<-read.csv("clean_data.csv")
#centre year, month_year and day_year
main_dat$factor_year<- as.factor(main_dat$year)
main_dat$scaled_year <- scale(main_dat$year, scale=F)
main_dat$index_year<-main_dat$year - 2003
main_dat$scaled_year_day <- scale(main_dat$year_day)
main_dat$scaled_year_month <- scale(main_dat$year_month)
specs<- unique(main_dat$species) ### list all species
specs<-specs[order(specs)]
#### general infos
#### Rhat should be less than 1.1
#### we could predict trends for each site and species and plot them
#### plot the random slopes for each species to see the variation among sites
# sd(scaled_year) 0.19 0.03 0.14 0.25
#### use the predict function to predict the amount of species for a certain day with certain effort within a year
#### pirateplots to visualise the data
#### include seasonality --> I(year_day^2) ---> models do not converge
#### the -1 in front of the model formula removes the intercept
#### factor year only for visualization
#### the offset(log(effort_m)) indicates that we assume that we would catch the twice the amount of species if we double the effort
#setup parallel backend to use many processors
#cores=detectCores()
#cl <- makeCluster(cores[1]-3) #not to overload your computer
#registerDoParallel(cl)
#p<-foreach(i=1:length(specs), .packages="brms") %dopar%
for (i in 1:length(specs))
{
tmp_spec<-subset(main_dat,main_dat$species==specs[i])
#### model to find trends per year for each species
if(file.exists(paste0(save_dir,"factor_",specs[i],".rds"))==FALSE)
{
mod_factor_year <- brm(n_individuals ~ -1 + factor_year + year_day + offset(log(effort_m)) +(1|unique_ID), data = tmp_spec, family = negbinomial())
saveRDS(mod_factor_year, file = paste0(save_dir,"factor_",specs[i],".rds"))
rm(mod_factor_year)
}
#### model for overall trend
if(file.exists(paste0(save_dir,"index_",specs[i],".rds"))==FALSE)
{
mod_index_year <- brm(n_individuals ~ index_year + year_day + offset(log(effort_m)) + (1+scaled_year|unique_ID), data = tmp_spec, family = negbinomial())
saveRDS(mod_index_year, file = paste0(save_dir,"index_",specs[i],".rds"))
rm(mod_index_year)
}
rm(tmp_spec)
}
mod_factor_year <- brm(n_individuals ~ -1 + factor_year + year_day + offset(log(effort_m)) +(1|unique_ID), data = tmp_spec, family = negbinomial())
saveRDS(mod_factor_year, file = paste0(save_dir,"factor_",specs[3],".rds"))
mod_index_year <- brm(n_individuals ~ index_year + year_day + offset(log(effort_m)) + (1+scaled_year|unique_ID), data = tmp_spec, family = negbinomial())
saveRDS(mod_index_year, file = paste0(save_dir,"index_",specs[3],".rds"))
#### simplest model for 1 species and all years
#Family: negbinomial
#Links: mu = log; shape = identity
#Formula: n_individuals ~ scaled_year + year_day + year_day^2 + (1 + scaled_year | unique_ID)
#Data: tmp_spec (Number of observations: 1066)
#Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#total post-warmup samples = 4000
#
#Group-Level Effects:
# ~unique_ID (Number of levels: 259)
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#sd(Intercept) 2.54 0.14 2.27 2.83 1.00 1047 1957
#sd(scaled_year) 0.79 0.12 0.56 1.03 1.00 1028 2148
#cor(Intercept,scaled_year) -0.65 0.11 -0.84 -0.42 1.00 1100 2148
#
#Population-Level Effects:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#Intercept 2.01 0.31 1.42 2.61 1.00 2381 3021
#scaled_year 1.03 0.10 0.84 1.22 1.00 2469 2771
#year_day 0.00 0.00 0.00 0.01 1.00 5124 3414
#
#Family Specific Parameters:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#shape 0.47 0.03 0.41 0.53 1.01 1209 3017
#
#Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
#and Tail_ESS are effective sample size measures, and Rhat is the potential
#scale reduction factor on split chains (at convergence, Rhat = 1).
#### simplest model for 1 species and all years
fit1_all_years_method <- brm(n_individuals ~ scaled_year + year_day + offset(log(effort_m)) + (1+scaled_year|unique_ID), data = tmp_spec, family = negbinomial()) ##### random slopes for scaled year (1+scaled...)
fit1_all_years_method
plot(fit1_all_years_method)
fixef(fit1_all_years_method)
?brm
#fit1_all_years_factor_seasonality <- brm(n_individuals ~ -1 + factor_year + year_day + I(year_day^2) + offset(log(effort_m)) +(1|unique_ID), data = tmp_spec, family = negbinomial()) #### the -1 removes the intercept #### factor year only for visualization
#fit1_all_years_factor_seasonality
#plot(fit1_all_years_factor_seasonality)
#fixef(fit1_all_factor_seasonality)
##### models with seasonality do not converge
### model for eals
tmp_anguilla<- subset(main_dat,main_dat$species==specs[7])
tmp_anguilla_all_years <- brm(n_individuals ~ scaled_year, data = tmp_anguilla, family = negbinomial())
tmp_anguilla_all_years
#Family: negbinomial
#Links: mu = log; shape = identity
#Formula: n_individuals ~ scaled_year
#Data: tmp_anguilla (Number of observations: 12197)
#Samples: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#total post-warmup samples = 4000
#
#Population-Level Effects:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#Intercept 2.28 0.02 2.24 2.31 1.00 3979 2176
#scaled_year -0.33 0.02 -0.36 -0.29 1.00 3857 2893
#
#Family Specific Parameters:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#shape 0.27 0.00 0.26 0.27 1.00 4301 3320
#
#Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
#and Tail_ESS are effective sample size measures, and Rhat is the potential
#scale reduction factor on split chains (at convergence, Rhat = 1).
|
198ff94b3d6080b9d974250e8cff66c811fe6821
|
364f92a555ab90d567c425de039cb3f050956842
|
/Downstream_processing/Culex_DESeq.R
|
881de927cd183a2e3ef0a399d13c632e7afbded3
|
[] |
no_license
|
mcadamme/Culex_RNAseq_Chemosensory
|
57c79388f6be70579faf329c5eaeddf15e04285d
|
a9d379a309ddf4b7e37b22e184656fd4de4cfbd7
|
refs/heads/master
| 2021-04-26T23:16:28.358939
| 2021-03-02T15:08:42
| 2021-03-02T15:08:42
| 123,960,205
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 26,439
|
r
|
Culex_DESeq.R
|
#script to run DESeq2
#10/20/2020 MF
##Install DESeq2
#if (!requireNamespace("BiocManager", quietly = TRUE))
#install.packages("BiocManager")
#BiocManager::install("DESeq2")
#BiocManager::install('EnhancedVolcano')
#Load Libraries
library(DESeq2); library(pheatmap); library(ashr); library(EnhancedVolcano); library(magrittr); library(ggfortify); library(reshape2); library(ggplot2); library(GOplot); library(arm)
#set working directory
hiQual_Ex<-("/media/fritzlab/EE9C16C89C168AEB/Noreuil/trimmed_pairs/DGE_GenAligned_SamFiles/highQual_exon/")
setwd(hiQual_Ex)
#Load in read counts and assign them sample labels
outputPrefix<-("Culex_DEseq")
sampleFiles<-c("M1-1_S1_htseq","M1-2_S2_htseq","M1-3_S3_htseq","M1-4_S4_htseq","M2-1_S5_htseq","M2-2_S6_htseq","M2-3_S7_htseq","M2-4_S8_htseq","M4-1_S13_htseq","M4-2_S14_htseq","M4-3_S15_htseq","M4-4_S16_htseq")
sampleNames<-c("BG_Gravid1", "BG_Gravid2", "BG_Gravid3", "BG_Gravid4", "BG_Parous1", "BG_Parous2",
"BG_Parous3", "BG_Parous4", "AG1", "AG2", "AG3", "AG4")
sampleCondition<-c("CALgravidF", "CALgravidF", "CALgravidF", "CALgravidF", "CALparousF", "CALparousF", "CALparousF", "CALparousF", "PipEvanF", "PipEvanF", "PipEvanF", "PipEvanF")
sampleTable<-data.frame(sampleName=sampleNames, fileName=sampleFiles, condition=sampleCondition)
treatments<-c("CALgravidF", "CALparousF", "PipEvanF")
#Create DESeq Data - CHANGE DIRECTORY
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
directory = hiQual_Ex,
design= ~ condition)
colData(ddsHTSeq)$condition <- factor(colData(ddsHTSeq)$condition, levels=treatments)
dim(ddsHTSeq)#getting num genes in dataset and verifying num samples
#Prefilter so only genes with at least 10 reads in at least 4 samples are considered
keep <- rowSums(counts(ddsHTSeq) >= 10) >= 4
ddsHTSeq <- ddsHTSeq[keep,]
dim(ddsHTSeq)#new filtered num genes in dataset and verifying num samples
ddsHTSeq$condition <- factor(ddsHTSeq$condition, levels = treatments)
#Looking at distr of filtered count data across samples
librarySizes <- colSums(counts(ddsHTSeq))
barplot(librarySizes,
names=names(librarySizes),
las=2, ylim = c(0,1.6e+07),
main="Barplot of library sizes")
logcounts <- log2(counts(ddsHTSeq) + 1)
head(logcounts)
#Any difference between per gene counts for each of the sample groups?
statusCol <- as.numeric(factor(ddsHTSeq$condition)) + 1 # make a colour vector
boxplot(logcounts,
xlab="",
ylab="Log2(Counts)",
las=2,
col=statusCol)
#Adding median log counts
abline(h=median(as.matrix(logcounts)), col="blue")
#Looking at PCA of the data - Do treatments cluster together?
rlogcounts <- rlog(counts(ddsHTSeq))#transforming data to make it approximately homoskedastic, n < 30 so rlog is better
select = order(rowMeans(rlogcounts), decreasing=TRUE)[1:12710]
#This select variable is here because I modified the numbers of genes included in the PCA - from 500-12710.
#The percent variation explained by each PC changes with the number of genes included, but not THAT much.
#At lower numbers of genes, PhysStat can be predicted by PC1 better than PC2, but this changes as I increase to include more.
#Because I couldn't decide on a sensible cutoff for the number of genes to include, I used the full dataset.
highexprgenes_counts <- rlogcounts[select,]
colnames(highexprgenes_counts)<- ddsHTSeq$condition
data_for_PCA <- t(highexprgenes_counts)
dim(data_for_PCA)
#run PCA
pcDat <- prcomp(data_for_PCA, center = T)
#basic plot
autoplot(pcDat)
#plot for pub
pdf("Fig2_PCA_Treatments.pdf",width=6,height=6,paper='special')
autoplot(pcDat,
data = ddsHTSeq$colData,
colour=as.numeric(factor(ddsHTSeq$condition)),
shape=FALSE,
label.size=6, xlim = c(-0.4, 0.5)) + theme_bw()
dev.off()
#test of whether PCs can predict strain and physState
Treats <- as.character(c("BG_grav", "BG_grav", "BG_grav", "BG_grav", "BG_par", "BG_par", "BG_par", "BG_par", "AG2", "AG2", "AG2", "AG2"))
PC1 <- as.character(pcDat$x[,1])
PC2 <- as.character(pcDat$x[,2])
PC3 <- as.character(pcDat$x[,3])
Strain <- as.character(c("BG", "BG", "BG", "BG", "BG", "BG", "BG", "BG", "AG", "AG", "AG", "AG"))
PhysStat <- as.character(c("grav", "grav", "grav", "grav", "hs", "hs", "hs", "hs", "hs", "hs", "hs", "hs"))
dat_for_glm <- data.frame(cbind(Treats, Strain, PhysStat, PC1, PC2, PC3), row.names = NULL)
summary(dat_for_glm)
str(dat_for_glm)
dat_for_glm$PC1 <- as.numeric(as.character(dat_for_glm$PC1))
dat_for_glm$PC2 <- as.numeric(as.character(dat_for_glm$PC2))
dat_for_glm$PC3 <- as.numeric(as.character(dat_for_glm$PC3))
#Are PCs 1,2,3 capable of separating samples by strain?
Model_strain_PC1 <- bayesglm(Strain ~ PC1, data=dat_for_glm, family="binomial")
summary(Model_strain_PC1)
simulates <- coef(sim(Model_strain_PC1, n.sims = 10000))
head(simulates, 10)
plot(density(simulates[,2]), main = "Model_strain_PC1", xlab = "Posterior.open", ylab = "Density")
quantile(simulates[,2], c(0.025, 0.975))#gives the 95% credible intervals, which don't overlap with zero
#This indicates that PC1 is capable of separating our samples by strain.
Model_strain_PC2 <- bayesglm(Strain ~ PC2, data=dat_for_glm, family="binomial")
summary(Model_strain_PC2)
simulates <- coef(sim(Model_strain_PC2, n.sims = 10000))
head(simulates, 10)
plot(density(simulates[,2]), main = "Model_strain_PC2", xlab = "Posterior.open", ylab = "Density")
quantile(simulates[,2], c(0.025, 0.975))#PC2 - overlaps zero
Model_strain_PC3 <- bayesglm(Strain ~ PC3, data=dat_for_glm, family="binomial")
summary(Model_strain_PC3)
simulates <- coef(sim(Model_strain_PC3, n.sims = 10000))
head(simulates, 10)
plot(density(simulates[,2]), main = "Model_strain_PC3", xlab = "Posterior.open", ylab = "Density")
quantile(simulates[,2], c(0.025, 0.975))#PC3 - overlaps zero
#Are PCs 1,2,3 capable of separating samples by physState?
Model_PhysStat_PC1 <- bayesglm(PhysStat ~ PC1, data=dat_for_glm, family="binomial")
summary(Model_PhysStat_PC1)
simulates <- coef(sim(Model_PhysStat_PC1, n.sims = 10000))
head(simulates, 10)
plot(density(simulates[,2]), main = "Model_PhysStat_PC1", xlab = "Posterior.open", ylab = "Density")
quantile(simulates[,2], c(0.025, 0.975))#PC1 & PhysStat - overlaps zero.
Model_PhysStat_PC2 <- bayesglm(PhysStat ~ PC2, data=dat_for_glm, family="binomial")
summary(Model_PhysStat_PC2)
simulates <- coef(sim(Model_PhysStat_PC2, n.sims = 10000))
head(simulates, 10)
plot(density(simulates[,2]), main = "Model_PhysStat_PC2", xlab = "Posterior.open", ylab = "Density")
quantile(simulates[,2], c(0.025, 0.975))#PC2 & PhysStat - does not overlap zero
Model_PhysStat_PC3 <- bayesglm(PhysStat ~ PC3, data=dat_for_glm, family="binomial")
summary(Model_PhysStat_PC3)
simulates <- coef(sim(Model_PhysStat_PC3, n.sims = 10000))
head(simulates, 10)
plot(density(simulates[,2]), main = "Model_PhysStat_PC3", xlab = "Posterior.open", ylab = "Density")
quantile(simulates[,2], c(0.025, 0.975))#PC3 & PhysStat - overlaps zero
#Note that the PCA shows that BG_Parous4 is fairly different from the other BG parous treatments - should I drop it? See end of script for code to do this - in the end, I did not for the paper.
####Differential Expression Analysis
##First with all data
dds<-DESeq(ddsHTSeq)
res<-results(dds)
resultsNames(dds)
###getting normalized filtered read counts
dds <- estimateSizeFactors(dds)
Norm_counts <- counts(dds, normalized=TRUE)
cormat <- cor(Norm_counts, method = "spearman")#looking at correlation coefficients for gene expression values between treatments.
#Function to Get upper triangle of the correlation matrix
get_upper_tri <- function(cormat){
cormat[lower.tri(cormat)]<- NA
return(cormat)
}
upper_tri <- get_upper_tri(cormat)
# Melt the correlation matrix
melted_cormat <- melt(upper_tri, na.rm = TRUE)
# Create a ggheatmap
ggheatmap <- ggplot(melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "white", high = "red", mid = "pink",
midpoint = 0.95, limit = c(0.90,1), space = "Lab",
name="Spearman\nCorrelation") +
theme_minimal()+ # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
xlab("") + ylab("")+
coord_fixed()
# Print the heatmap
print(ggheatmap)
#write.csv(as.data.frame(Norm_counts),
#file="Normalized_ReadCounts_AllTreats.csv")
#Norm_Rownames <- data.frame(rownames(Norm_counts))
#Norm_Rownames$Descriptor <- "NA"
#CALsOnly <- cbind(Norm_Rownames, (data.frame(Norm_counts[,c(1:8)])))
#write.table(as.data.frame(CALsOnly),
#file="Normalized_ReadCounts_CALsOnly.txt", row.names = F, sep = "\t")
#CalParVPip<- cbind(Norm_Rownames, (data.frame(Norm_counts[,c(-1,-2,-3,-4)])))
#write.table(as.data.frame(CalParVPip),
#file="Normalized_ReadCounts_CalParVPip.txt", row.names = F, sep = "\t")
###Gravid F vs. Parous F--Use LFC for gene ranking and visualization, and use the p-values from the non-LFC
resLFC <- lfcShrink(dds, contrast = c("condition", "CALgravidF", "CALparousF"), type="ashr")
#Look at summary values
summary(resLFC)
#how many significantly DE genes? Looked at adjusted pvals of 0.1 and 0.05. Added a FC cutoff for shrunken FCs of 1.5x, as well.
sum(resLFC$padj < 0.1 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
sum(resLFC$padj < 0.05, na.rm=TRUE)
sum(resLFC$padj < 0.05 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
#getting numbers of up and down regulated genes
upReg <- subset(resLFC, padj < 0.05 & log2FoldChange > 0.58)#LFC > 1.5 or upReg in gravid
nrow(upReg)
head(upReg)
downReg <- subset(resLFC, padj < 0.05 & log2FoldChange < -0.58)#LFC < 1.5 or downReg in gravid
nrow(downReg)
head(downReg)
#Create plots based on LFCshrunken dataset, which minimizes noise from low read counts
pdf("plotMA_gravidVparous.pdf",width=6,height=6,paper='special')
plotMA(resLFC, ylim=c(-3,3))
dev.off()
#plotting differences in gene expression using lfcShrink output.
pdf("EV_gravidVparous.pdf",width=8,height=6,paper='special') #gene names after selectLab are all diff exp genes
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue',
#selectLab = c('CPIJ003142', 'CPIJ004468', 'CPIJ004690', 'CPIJ008018', 'CPIJ008747', 'CPIJ010041', 'CPIJ011084', 'CPIJ011244',
#'CPIJ015908','CPIJ018848', 'CPIJ003456', 'CPIJ004365', 'CPIJ004417', 'CPIJ008256', 'CPIJ012990', 'CPIJ014981'),
selectLab = NA,
#drawConnectors = TRUE,
xlim = c(-1.5, 1.5),
ylim = c(0,30),
pCutoff = 10e-6,
FCcutoff = 0.58,
pointSize = 2.0,
labSize = 5.0)
dev.off()
#Looking at genes using the standard frequentist framework with a false discovery rate correction.
res05 <- results(dds, alpha=0.05, contrast = c("condition", "CALgravidF", "CALparousF"))#results here are largely consistent with the shrunken LFC framework above without the LFC threshold argument.
summary(res05) #note there are quite a few with "low" counts (mean count < 88), total significant = 556 compared with the 544 above.
#non-LFC dataset filtering
sum(res05$padj < 0.05 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
# Note the small difference between LFC shrunken and "standard" analyses are normal - inconsistencies between them are typically minimal and have been described: https://support.bioconductor.org/p/110307/
#getting full gene lists with different LFCs and padj - from lfcShrink and results. Note that genes themselves are the same, what differ (mildly) are the statistics.
full_genes_CalgravidF_CalparousF <- data.frame(c(paste(resLFC@rownames, sep = "", "-RA")), resLFC@listData$log2FoldChange, resLFC@listData$padj)#paste adds VB format to IDs
write.table(full_genes_CalgravidF_CalparousF, file = "CALgravidF_v_CALparousF_AllGenes_LFCS.txt",
col.names = F, row.names = F, sep = "\t")
full_genes_CalgravidF_CalparousF <- data.frame(c(paste(res05@rownames, sep = "", "-RA")), res05@listData$log2FoldChange, res05@listData$padj)#paste adds VB format to IDs
write.table(full_genes_CalgravidF_CalparousF, file = "CALgravidF_v_CALparousF_AllGenes_nonLFCS.txt",
col.names = F, row.names = F, sep = "\t")
#Saving a list of significant DE genes
resSig <- subset(resLFC, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.table(data.frame(c(paste(resSig@rownames, sep = "", "-RA")), resSig@listData$log2FoldChange, resSig@listData$padj),
file="CALgravidF_v_CALparousF_LFCS_padj05.txt", col.names = F, row.names = F, sep = "\t")
resSig <- subset(res05, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.table(data.frame(c(paste(resSig@rownames, sep = "", "-RA")), resSig@listData$log2FoldChange, resSig@listData$padj),
file="CALgravidF_v_CALparousF_nonLFCS_padj05.txt", col.names = F, row.names = F, sep = "\t")
###Parous F vs. pipiens
#######Here looking at full dataset including the BG_parous4 outlier
resLFC <- lfcShrink(dds, contrast = c("condition", "PipEvanF", "CALparousF"), type="ashr")
#Look at summary values
summary(resLFC)
#how many significantly DE genes? The default p-value cutoff is 0.1 & adding a log2FoldChange cutoff (1.5), as well.
sum(resLFC$padj < 0.1 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
sum(resLFC$padj < 0.05, na.rm=TRUE)
sum(resLFC$padj < 0.05 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
upReg <- subset(resLFC, padj < 0.05 & log2FoldChange > 0.58)#LFC > 1.5 or upReg in Pip
nrow(upReg)
head(upReg)
downReg <- subset(resLFC, padj < 0.05 & log2FoldChange < -0.58)#LFC < 1.5 or downReg in Pip
nrow(downReg)
head(downReg)
#Create plots based on the LFC, which minimizes noise from low read counts
pdf("plotMA_parousVpip.pdf",width=6,height=6,paper='special')
plotMA(resLFC, ylim=c(-3,3))
dev.off()
pdf("EV_parousVpip.pdf",width=8,height=6,paper='special') #gene labels commented out for selectLab are sig DE sensory genes.
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue',
#selectLab = c("CPIJ001730", "CPIJ002108", "CPIJ002109", "CPIJ002111", "CPIJ004145","CPIJ007617",
#"CPIJ009568", "CPIJ010367", "CPIJ010787", "CPIJ012716","CPIJ012717", "CPIJ012719",
#"CPIJ013976", "CPIJ014525", "CPIJ016479","CPIJ016949", "CPIJ016966", "CPIJ019610",
#"CPIJ016433", "CPIJ011564", "CPIJ014330","CPIJ002605", "CPIJ002618", "CPIJ002628",
#"CPIJ007315", "CPIJ004067"),
selectLab = NA,
#drawConnectors = TRUE,
xlim = c(-10, 10),
ylim = c(0,200),
pCutoff = 10e-6,
FCcutoff = 0.58,
pointSize = 2.0,
labSize = 2.0)
dev.off()
#Looking at DGE from "standard" analysis.
res05 <- results(dds, alpha=0.05, contrast = c("condition", "PipEvanF", "CALparousF"))
summary(res05)
sum(res05$padj < 0.05 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
#getting full gene list
full_genes_CalparousF_PipF <- data.frame(c(paste(resLFC@rownames, sep = "", "-RA")), resLFC@listData$log2FoldChange, resLFC@listData$padj)#paste adds VB format to IDs
write.table(full_genes_CalparousF_PipF, file = "CALparousF_v_PipEvanF_AllGenes_LFCS.txt",
col.names = F, row.names = F, sep = "\t")
full_genes__CalparousF_PipF <- data.frame(c(paste(res05@rownames, sep = "", "-RA")), res05@listData$log2FoldChange, res05@listData$padj)#paste adds VB format to IDs
write.table(full_genes_CalparousF_PipF, file = "CALparousF_v_PipEvanF_AllGenes_nonLFCS.txt",
col.names = F, row.names = F, sep = "\t")
#Save a list of significant DE genes with 1.5x change or greater
resSig <- subset(resLFC, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.table(data.frame(c(paste(resSig@rownames, sep = "", "-RA")), resSig@listData$log2FoldChange, resSig@listData$padj),
file="CALparousF_v_PipEvanF_LFCS_padj05.txt", row.names = F, col.names = F, sep = "\t")
resSig <- subset(res05, padj < 0.05 & abs(log2FoldChange) > 0.58 )
write.table(data.frame(c(paste(resSig@rownames, sep = "", "-RA")), resSig@listData$log2FoldChange, resSig@listData$padj),
file="CALparousF_v_PipEvanF_nonLFCS_padj05.txt", row.names = F, col.names = F, sep = "\t")
#####This contrast is not that interesting, but looked at it anyway. Will be useful to verify those 16 genes that look important to suppressing host-seeking in BG gravid.
###Gravid F vs. pipiens
resLFC <- lfcShrink(dds, contrast = c("condition", "PipEvanF", "CALgravidF"), type="ashr")
#Look at summary values
summary(resLFC)
#how many significantly DE genes? The default p-value cutoff is 0.1. Added a log2foldchange cutoff of 1.5x, as well.
sum(resLFC$padj < 0.1 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
sum(resLFC$padj < 0.05, na.rm=TRUE)
sum(resLFC$padj < 0.05 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
upReg <- subset(resLFC, padj < 0.05 & log2FoldChange > 0.58)#LFC > 1.5 or upReg in Pip
nrow(upReg)
head(upReg)
downReg <- subset(resLFC, padj < 0.05 & log2FoldChange < -0.58)#LFC < 1.5 or downReg in Pip
nrow(downReg)
head(downReg)
#Create plots based on the LFC, which minimizes noise from low read counts
pdf("plotMA_gravidVPipEvanF.pdf",width=6,height=6,paper='special')
plotMA(resLFC, ylim=c(-3,3))
dev.off()
pdf("EV_gravidVPipEvanF.pdf",width=8,height=6,paper='special')
EnhancedVolcano(resLFC,
lab = rownames(resLFC),
x = 'log2FoldChange',
y = 'pvalue',
#selectLab = c('CPIJ003456'),
selectLab = NA,
xlim = c(-1.5, 1.5),
ylim = c(0,30),
pCutoff = 10e-6,
FCcutoff = 0.58,
pointSize = 2.0,
labSize = 5.0)
dev.off()
#Standard analysis - non LFC data
res05 <- results(dds, alpha=0.05, contrast = c("condition", "PipEvanF", "CALgravidF"))
summary(res05)
sum(res05$padj < 0.05 & abs(resLFC$log2FoldChange) > 0.58, na.rm=TRUE)
#getting full gene list
full_genes_CalgravidF_PipEvanF <- data.frame(c(paste(resLFC@rownames, sep = "", "-RA")), resLFC@listData$log2FoldChange, resLFC@listData$padj)#paste adds VB format to IDs
write.table(full_genes_CalgravidF_PipEvanF, file = "CALgravidF_v_PipEvanF_AllGenes_LFCS.txt",
col.names = F, row.names = F, sep = "\t")
full_genes_CalgravidF_PipEvanF <- data.frame(c(paste(res05@rownames, sep = "", "-RA")), res05@listData$log2FoldChange, res05@listData$padj)#paste adds VB format to IDs
write.table(full_genes_CalgravidF_PipEvanF, file = "CALgravidF_v_PipEvanF_AllGenes_nonLFCS.txt",
col.names = F, row.names = F, sep = "\t")
#Save a list of significant DE genes
resSig <- subset(resLFC, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.table(data.frame(c(paste(resSig@rownames, sep = "", "-RA")), resSig@listData$log2FoldChange, resSig@listData$padj),
file="CALgravidF_v_PipEvanF_LFCS_padj05.txt", row.names = F, col.names = F, sep = "\t")
resSig <- subset(res05, padj < 0.05 & abs(log2FoldChange) > 0.58 )
write.table(data.frame(c(paste(resSig@rownames, sep = "", "-RA")), resSig@listData$log2FoldChange, resSig@listData$padj),
file="CALGravidF_v_PipEvanF_nonLFCS_padj05.txt", row.names = F, col.names = F, sep = "\t")
#VennDiag DEGs
dat_1 <- read.table("CALgravidF_v_CALparousF_LFCS_padj05.txt", header = F)
dat_2 <- read.table("CALparousF_v_PipEvanF_LFCS_padj05.txt", header = F)
dat_3 <- read.table("CALgravidF_v_PipEvanF_LFCS_padj05.txt", header = F)
l1 <- dat_1[, c(1,2)]
l2 <- dat_2[, c(1,2)]
l3 <- dat_3[, c(1,2)]
VennDiag <- GOVenn(l1,l2,l3, label=c('BG1 gravid v parous','AG2 v BG1 parous','AG2 v BG1 gravid'), plot = F)
print(VennDiag$plot)
######After looking at the first PCA, I wasn't sure about whether to drop BG_parous4. In the end, I did not because it did not greatly impact the numbers of differentially expressed genes I recovered.
#Load in read counts and assign them sample labels
outputPrefix<-("Culex_DEseq_dropped")
sampleFiles<-c("M1-1_S1_htseq","M1-2_S2_htseq","M1-3_S3_htseq","M1-4_S4_htseq","M2-1_S5_htseq","M2-2_S6_htseq","M2-3_S7_htseq","M4-1_S13_htseq","M4-2_S14_htseq","M4-3_S15_htseq","M4-4_S16_htseq")
sampleNames<-c("BG_Gravid1", "BG_Gravid2", "BG_Gravid3", "BG_Gravid4", "BG_Parous1", "BG_Parous2",
"BG_Parous3", "AG1", "AG2", "AG3", "AG4")
sampleCondition<-c("CALgravidF", "CALgravidF", "CALgravidF", "CALgravidF", "CALparousF", "CALparousF", "CALparousF", "PipEvanF", "PipEvanF", "PipEvanF", "PipEvanF")
sampleTable<-data.frame(sampleName=sampleNames, fileName=sampleFiles, condition=sampleCondition)
treatments<-c("CALgravidF", "CALparousF", "PipEvanF")
#Create DESeq Data
ddsHTSeq_dropped <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
directory = hiQual_Ex,
design= ~ condition)
colData(ddsHTSeq_dropped)$condition <- factor(colData(ddsHTSeq_dropped)$condition, levels=treatments)
dim(ddsHTSeq_dropped)#getting num genes in dataset and verifying num samples
#Prefilter so only genes with atleast 10 reads in atleast 4 samples are considered.
keep <- rowSums(counts(ddsHTSeq_dropped) >= 10) >= 4
ddsHTSeq_dropped <- ddsHTSeq_dropped[keep,]
dim(ddsHTSeq_dropped)#new filtered num genes in dataset and verifying num samples
ddsHTSeq_dropped$condition <- factor(ddsHTSeq_dropped$condition, levels = treatments)
#new pca
rlogcounts_dropped <- rlog(counts(ddsHTSeq_dropped))
#run PCA
pcDat_dropped <- prcomp(t(rlogcounts_dropped))
#basic plot
autoplot(pcDat_dropped)
#plot for pub
pdf("PCA_Treatments_dropped.pdf",width=6,height=6,paper='special')
autoplot(pcDat_dropped,
data = ddsHTSeq_dropped$colData,
colour=as.numeric(factor(ddsHTSeq_dropped$condition)),
shape=FALSE,
label.size=6, xlim = c(-0.4, 0.5)) + theme_bw()
dev.off()
####This is the DeSeq2 analysis BG_gravid v BG_parous without the BG_parous4
dds_dropped<-DESeq(ddsHTSeq_dropped)
res_dropped<-results(dds_dropped)
###Gravid F vs. Parous F--Use LFC for gene ranking and visualization, and use the p-values from the non-LFC
resLFC_dropped <- lfcShrink(dds_dropped, contrast = c("condition", "CALgravidF", "CALparousF"), type="ashr")
#Look at summary values
summary(resLFC_dropped)
#how many significantly DE genes? The default p-value cutoff is 0.1
sum(resLFC_dropped$padj < 0.1 & abs(resLFC_dropped$log2FoldChange) > 0.58, na.rm=TRUE)
sum(resLFC_dropped$padj < 0.05, na.rm=TRUE)
sum(resLFC_dropped$padj < 0.05 & abs(resLFC_dropped$log2FoldChange) > 0.58, na.rm=TRUE)
#Look at genes when changing the p-value cutoff--Use p-values from the non LFC data
res05_dropped <- results(dds_dropped, alpha=0.05, contrast = c("condition", "CALgravidF", "CALparousF"))
summary(res05_dropped)
sum(res05_dropped$padj < 0.05 & abs(resLFC_dropped$log2FoldChange) > 0.58, na.rm=TRUE)
#Create plots based on the LFC, which minimizes noise from low read counts
pdf("plotMA_gravidVparous_dropped.pdf",width=6,height=6,paper='special')
plotMA(resLFC_dropped, ylim=c(-3,3))
dev.off()
#Save a list of significant DE genes
resSig_dropped <- subset(res05_dropped, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.csv(as.data.frame(resSig_dropped),
file="CALgravidF_v_CALparousF_dropped_nonLFCS_padj05.csv")
resSig_dropped <- subset(resLFC_dropped, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.csv(as.data.frame(resSig_dropped),
file="CALgravidF_v_CALparousF_dropped_LFCS_padj05.csv")
pdf("EV_gravidVparous_dropped.pdf",width=8,height=6,paper='special')
EnhancedVolcano(resLFC_dropped,
lab = rownames(resLFC_dropped),
x = 'log2FoldChange',
y = 'pvalue',
#selectLab = c('CPIJ003456'),
selectLab = NA,
xlim = c(-1.5, 1.5),
ylim = c(0,30),
pCutoff = 10e-6,
FCcutoff = 0.58,
pointSize = 2.0,
labSize = 5.0)
dev.off()
######Here rerunning DeSeq2 for Parous v pip without BG_parous 4 outlier
resLFC_dropped <- lfcShrink(dds_dropped, contrast = c("condition", "PipEvanF", "CALparousF"), type="ashr")
#Look at summary values
summary(resLFC_dropped)
#how many significantly DE genes? The default p-value cutoff is 0.1
sum(resLFC_dropped$padj < 0.1 & abs(resLFC_dropped$log2FoldChange) > 0.58, na.rm=TRUE)
sum(resLFC_dropped$padj < 0.05, na.rm=TRUE)
sum(resLFC_dropped$padj < 0.05 & abs(resLFC_dropped$log2FoldChange) > 0.58, na.rm=TRUE)
#Look at genes when changing the p-value cutoff--Use p-values from the non LFC data
res05_dropped <- results(dds_dropped, alpha=0.05, contrast = c("condition", "PipEvanF", "CALparousF"))
summary(res05_dropped)
sum(res05_dropped$padj < 0.05 & abs(resLFC_dropped$log2FoldChange) > 0.58, na.rm=TRUE)
#Create plots based on the LFC, which minimizes noise from low read counts
pdf("plotMA_parousVpip_dropped.pdf",width=6,height=6,paper='special')
plotMA(resLFC_dropped, ylim=c(-3,3))
dev.off()
pdf("EV_parousVpip_dropped.pdf",width=8,height=6,paper='special')
EnhancedVolcano(resLFC_dropped,
lab = rownames(resLFC_dropped),
x = 'log2FoldChange',
y = 'pvalue',
#selectLab = c('CPIJ003456'),
selectLab = NA,
xlim = c(-10, 10),
ylim = c(0,200),
pCutoff = 10e-6,
FCcutoff = 0.58,
pointSize = 2.0,
labSize = 5.0)
dev.off()
#Save a list of significant DE genes
resSig_dropped <- subset(res05_dropped, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.csv(as.data.frame(resSig_dropped),
file="CALparousF_v_PipEvanF_dropped_nonLFCS_padj05.csv")
resSig_dropped <- subset(resLFC_dropped, padj < 0.05 & abs(log2FoldChange) > 0.58)
write.csv(as.data.frame(resSig_dropped),
file="CALparousF_v_PipEvanF_dropped_LFC_padj05.csv")
|
d2f9dbce06db49fb92872cf48dd598fad1a430f8
|
0a4dc06265e93a689dd43d0341395bb170957122
|
/R/vis_hom_time_series.R
|
1e835037d9e84073a8a6308202607778c7b5da22
|
[] |
no_license
|
MariekeDirk/Dimming-Brightening
|
119f4feb9bda39079ad335de978370e2179c7367
|
2dd010c021cacda225f99fbcbaf3de5123306ca7
|
refs/heads/master
| 2020-09-13T22:09:42.833154
| 2020-03-16T08:57:48
| 2020-03-16T08:57:48
| 222,917,938
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,673
|
r
|
vis_hom_time_series.R
|
#'Visualize homogenized time series from Climatol
#'@description plots the homogenized time series with the interactive plotly library.
#'@param id staid of the station
#'@param brks break points from climatol
#'@param h_series homogenized time series
#'@param o_series original series from ECAD
#'@export
plot_hom_series<-function(id,brks=break_points,h_series=hom_series,o_series=series_comb){
var<-paste0("sta_id",id)
#break points for this station
I<-which(brks$Code==var)
if(length(I)==0){
message(paste0("No break points in this series, try for example ",
tail(gsub("sta_id","",unique(break_points$Code)),n=1)))
return(FALSE)
}
time_series<-h_series[,1]; names(time_series) <- "time"
time_series$time<-as.Date(time_series$time)
original_series<-data.frame(o_series[which(o_series$STAID==id),]$month_year,
o_series[which(o_series$STAID==id),]$QQm
)
names(original_series) <- c("time","original")
homogenized_series<-h_series[[var]]; names(homogenized_series) <- "homogenized"
homogenized_series <- data.frame(time_series,homogenized_series)
df<-full_join(original_series,homogenized_series,by="time")
df_long<-tidyr::gather(df,"series","measurement",-time)
df_long$series<-as.factor(df_long$series)
#plotting routine
p<- ggplot2::ggplot(df_long,aes(time,measurement,color=series)) +
ggplot2::geom_line() +
ggplot2::scale_color_manual(values = c("red", "darkgrey"))
if(length(I)!=0){
p <- p + ggplot2::geom_vline(xintercept = break_points$Date[I],colour="red",linetype=2)
}else{message("no breaks found")}
plotly::ggplotly(p,dynamicTicks = TRUE)
}
|
5887490e37583fc176dff5688731393595d12dc7
|
2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0
|
/fuzzedpackages/gasper/man/laplacian_mat.Rd
|
db1d8191194d8ed7105ef90736d3fd741d987780
|
[] |
no_license
|
akhikolla/testpackages
|
62ccaeed866e2194652b65e7360987b3b20df7e7
|
01259c3543febc89955ea5b79f3a08d3afe57e95
|
refs/heads/master
| 2023-02-18T03:50:28.288006
| 2021-01-18T13:23:32
| 2021-01-18T13:23:32
| 329,981,898
| 7
| 1
| null | null | null | null |
UTF-8
|
R
| false
| true
| 318
|
rd
|
laplacian_mat.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/laplacian_mat.R
\name{laplacian_mat}
\alias{laplacian_mat}
\title{Laplacian matrix.}
\usage{
laplacian_mat(W)
}
\arguments{
\item{W}{Adjacency matrix.}
}
\description{
Compute the (unormalized) laplacian matrix from the adjacency matrix.
}
|
38d812b4586dd42d6fbcdc51646ec4628062cdfe
|
0f0246cb0dc295f6bb01b70ff4a0182f08e82922
|
/R/FitIndices.R
|
31b2c7cf752c497293a95331f11f316f9fe1c44a
|
[] |
no_license
|
Kucharssim/LCAapp
|
4c0ee6d7a14c4ce634ff2e91a359906e8e605aad
|
69c5318c2f1c14f043a5d71c2f5ba035b98d84b5
|
refs/heads/master
| 2021-06-13T07:23:46.958035
| 2021-05-24T10:42:50
| 2021-05-24T10:42:50
| 91,227,492
| 3
| 2
| null | 2021-05-24T10:42:51
| 2017-05-14T07:36:09
|
R
|
UTF-8
|
R
| false
| false
| 3,580
|
r
|
FitIndices.R
|
nParamsDf <- function(k, tab.d){
# compute the number of estimated parameters
# and degrees of freedom
#
# Args:
# k: number of classes in the model
# tab.d: the dummy data
#
# Returns a vector of df and n of parameters
lev <- Levels(tab.d) # vector of levels
n.params <- (k-1) + k*sum(lev-1) # number of parameters
df <- prod(lev) - 1 - n.params # df
return(c(df, n.params))
}
chigSq <- function(d, theta, pi){
# Compute Chi^2 and G^2
#
# Args:
# d: dummy data
# theta: the conditional probabilities
# pi: class sizes
#
# Returns: vector of Ch^2 and G^2
n <- nrow(d)
observed <- table(d)
expected <- Expected(pi, theta, n)
chi <- sum(((observed-expected)^2)/expected)
g <- 2 * sum(observed*log(observed/expected), na.rm=TRUE)
return(c(chi, g))
}
Expected <- function(pi, theta, n=NA){
# Compute the expected counts
#
# Args:
# pi: the class sizes
# theta: the conditional probabilities
# n: sample size
#
# Returns a table with expected counts
# Loop over classes
tab <- lapply(1:length(pi), function(class) {
# Get the probabilities of responses given current class
probs <- lapply(theta, function(x){
t(t(x[class,]))
})
# Compute cross-product of the first two items
p.table <- (probs[[1]] %*% t(probs[[2]]))
# If there are more items, add dimensions
if(length(probs)>2){
for(i in 3:length(probs)){
# Compute the outer products
p.table <- drop( p.table %o% probs[[i]] )
}
}
# Multiply the resulting probabilty table by sample size to get counts
return(p.table * n * pi[class])
})
# Sum the expected counts over all classes and return
return(Reduce("+", tab))
}
aicbic <- function(llik, n.params, n){
# Compute AIC and BIC
#
# Args:
# llik: log-likelihood
# n.params: number of parameters
# n: sample size
#
# Returns: AIC and BIC
aic <- -2*llik + 2*n.params
bic <- -2*llik + log(n)*n.params
c(aic, bic)
}
entropy <- function(p){
# Compute the Entropy of class separation (Muthen & Muthen, 2006)
#
# Args:
# p: probabtilities oa class membership
#
# Returns: entropy of class separation (bounded at <0,1>)
k <- ncol(p)
if(k<=1){
warning("Cannot compute entropy for 1 class model")
return(1)
}
n <- nrow(p)
log.p <- log(p)
log.p[log.p==-Inf] <- 0
ent <- sum(p * log.p)
ent <- 1 + (ent/(n*log(k)))
# Output
return(ent)
}
fitMeasures <- function(d, rawd, model){
# Wrapper for the fit functions
#
# Args:
# d: dummy data
# rawd: raw data
# model: the output of the emLCA
#
# Output: n of classes, chi^2, G^2, df, n of parameters,
# AIC, BIC, Entropy
classes <- model$classes
df.n.params <- nParamsDf(classes, d)
chi.g <- chigSq(rawd, model$theta, model$pi)
aicbic <- aicbic(model$llik, df.n.params[2], nrow(model$posterior))
entropy <- entropy(model$posterior)
c(classes, round(chi.g, 2), df.n.params,
round(c(aicbic, entropy), 2))
}
multiFitMeasures <- function(d, rawd, models){
# Compute the fitmeasures for multiple models
#
# Args:
# d: dummy data
# rawd: raw data
# models: the optimal models
#
# Returns a table with the fit measures per model
tab <- sapply(models, function(model){
fitMeasures(d, rawd, model)
})
tab <- t(tab)
colnames(tab) <- c("classes","Chi Square", "G Square", "df", "n of params", "AIC", "BIC", "Entropy")
return(tab)
}
|
d39361f19fe6a1dde1aabd345aec07d8e1c74f22
|
2c1ab15c29ec9db51a129292a7d52e6a518f551d
|
/man/predict.ranktree.Rd
|
8eb0b679006e4317cb621b735bba743fa4086c4f
|
[] |
no_license
|
cran/ConsRankClass
|
cc65611f9a25f821d9233af298d032d80963038f
|
33f18bd53c9446c0b020042ff957798c93d1c138
|
refs/heads/master
| 2023-08-14T16:56:42.821373
| 2021-09-28T09:10:02
| 2021-09-28T09:10:02
| 368,585,748
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 1,519
|
rd
|
predict.ranktree.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict.ranktree.R
\name{predict.ranktree}
\alias{predict.ranktree}
\title{Predict the median rankings for new observations}
\usage{
\method{predict}{ranktree}(object, newx, ...)
}
\arguments{
\item{object}{An object of the class "ranktree"}
\item{newx}{A dataframe of the same nature of the predictor dataframe with which the tree has been built}
\item{\dots}{System reserved (No specific usage)}
}
\value{
A list containing:
\tabular{lll}{
rankings \tab \tab the fit in terms of rankings\cr
orderings \tab \tab the fit in terms of orderings\cr
info\tab \tab dataframe containing the terminal nodes in which the new x fall down, then the new x and the fit (in terms of rankings)
}
}
\description{
Predict the median rankings in a tree-based structure built with \code{ranktree} for new observations
}
\examples{
\donttest{
data(EVS)
EVS$rankings[is.na(EVS$rankings)] <- 3
set.seed(654)
training=sample(1911,1434)
tree <- ranktree(EVS$rankings[training,],EVS$predictors[training,],decrmin=0.001,num=50)
#use the function predict ro predict rankings for new predictors
rankfit <- predict(tree,newx=EVS$predictors[-training,])
#fit in terms of rankings
rankfit$rankings
#fit in terms of orderings
rankfit$orderings
# information about the fit (terminal node, predictor and fit (in terms of rankings))
rankfit$info
}
}
\seealso{
\code{\link{ranktree}} \code{\link{validatetree}}
}
\author{
Antonio D'Ambrosio \email{antdambr@unina.it}
}
|
bb21bd0079565daed475d071fa787f17aea179ef
|
667cd59f9059f566b5c4ac2c76b1b8b6f09f50d5
|
/ma415project2R.R
|
6e0ead74f1bcfabe9a00c152fc28da827968a768
|
[] |
no_license
|
carlyrosewilling/MA415Proj2
|
99f1161bff22a223cd63948d78b95a3fe99eb8ca
|
531bb5ed59378b587d365e7b18b0cc8f3c498c4b
|
refs/heads/master
| 2021-05-07T13:56:07.343239
| 2020-12-21T23:25:48
| 2020-12-21T23:25:48
| 109,762,378
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 27,164
|
r
|
ma415project2R.R
|
# Setting up some libraries we'll need
options(java.parameters = "- Xmx1024m")
library(tidyverse)
library(stringr)
library(lubridate)
library(xlsx)
library(gridExtra)
# For Cape May
# These sections of code will repeat for each location, so I'm only commenting once
# First we set up the urls of the buoy data tables from the NDBC website
str1 <- "http://www.ndbc.noaa.gov/view_text_file.php?filename=44009h"
str2 <- ".txt.gz&dir=data/historical/stdmet/"
years <- c(1984:2016)
urls <- str_c(str1, years, str2, sep = "")
# Now that we havve urls, let's say what the filenames for each year will be
filenames <- str_c("cm", years, sep = "")
# And how many we have
N <- length(urls)
for (i in 1:N){
# For each year of our data, get the table from that url and call it "file"
assign(filenames[i], read_table(urls[i], col_names = TRUE))
file <- get(filenames[i])
# Before 1998 the year column is only 2 digits, YY, so let's get those to all match
colnames(file)[1] <-"YYYY"
# If the first year entry is 2 digits, put 19 in front of it
if(nchar(file[1,1]) == 2 & file[1,1] > 50 ) {
file[1] <- lapply(file[1], function(x){str_c(19, x, sep = "")})
}
# Since we turned it into a string by doing that, let's get it back to a number
file$YYYY <- as.numeric(file$YYYY)
# And then make sure everything else is numbers
file$MM <- as.numeric(file$MM)
file$DD <- as.numeric(file$DD)
file$hh <- as.numeric(file$hh)
file$ATMP <- as.numeric(file$ATMP)
file$WTMP <- as.numeric(file$WTMP)
# Early years didn't have a minutes column
# For those with minutes, we want to add it in with an entry at zero
# Otherwise, we only want 0 minutes if possible
# But some years have entries at 50 minutes instead
if(is.element("mm", colnames(file))) {
file$mm <- as.numeric(file$mm)
file <- file %>% filter(mm == 00 | mm == 50)
}
else {
file$mm <- 00
}
# Pulling out just the columns we care about, then making the date column
file <- file %>% select(YYYY, MM, DD, hh, mm, ATMP, WTMP)
file$date <- make_datetime(year = file$YYYY, month = file$MM, day = file$DD,
hour = file$hh, min = file$mm)
# Putting our temporary 'file' into the main dataframe for this location
if(i == 1){
CM <- file
}
else{
CM <- rbind.data.frame(CM, file)
}
}
# Now we have a few other actions to perform
# We filter out so we have just one entry for each day, the closest to noon
CM <- filter(CM, hh == 12 & mm == 00 | hh == 11 & mm == 50)
# Then make the time difference column
CM$timediff <- make_datetime(hour = CM$hh, min = CM$mm) - make_datetime(hour = 12,
min = 00)
# Then we'll filter down to the columns we really want, recode the NAs,
# and rename the columns
CM <- select(CM, date, timediff, ATMP, WTMP)
CM$ATMP <- apply(CM[,3], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
CM$ATMP <- apply(CM[,3], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
CM$WTMP <- apply(CM[,4], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
CM$WTMP <- apply(CM[,4], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
colnames(CM)[3] <- "air_temp"
colnames(CM)[4] <- "sea_temp"
colnames(CM)[1] <- "date_time"
colnames(CM)[2] <- "time_diff"
# There are a few more columns to put in
CM$team_num <- 1
CM$reading_type <- "buoy"
CM$Lat <- 38.5
CM$Lon <- 74.7
# And then reordering the columns
CM <- CM[c("team_num", "reading_type", "date_time", "time_diff", "Lat", "Lon",
"sea_temp", "air_temp")]
# Then we do it all again for the next location
# Molasses Reef
str1 <- "http://www.ndbc.noaa.gov/view_text_file.php?filename=mlrf1h"
str2 <- ".txt.gz&dir=data/historical/stdmet/"
years <- c(1987:2016)
urls <- str_c(str1, years, str2, sep = "")
filenames <- str_c("mr", years, sep = "")
N <- length(urls)
for (i in 1:N){
assign(filenames[i], read_table(urls[i], col_names = TRUE))
file <- get(filenames[i])
colnames(file)[1] <-"YYYY"
if(nchar(file[1,1]) == 2 & file[1,1] > 50 ) {
file[1] <- lapply(file[1], function(x){str_c(19, x, sep = "")})
}
file$YYYY <- as.numeric(file$YYYY)
file$MM <- as.numeric(file$MM)
file$DD <- as.numeric(file$DD)
file$hh <- as.numeric(file$hh)
file$ATMP <- as.numeric(file$ATMP)
file$WTMP <- as.numeric(file$WTMP)
if(is.element("mm", colnames(file))) {
file$mm <- as.numeric(file$mm)
file <- file %>% filter(mm == 00 | mm == 50)
}
else {
file$mm <- 00
}
file <- file %>% select(YYYY, MM, DD, hh, mm, ATMP, WTMP)
file$date <- make_datetime(year = file$YYYY, month = file$MM, day = file$DD,
hour = file$hh, min = file$mm)
if(i == 1){
MR <- file
}
else{
MR <- rbind.data.frame(MR, file)
}
}
MR<-filter(MR, hh==12 & mm == 00 | hh == 11 & mm == 50)
MR$timediff <- make_datetime(hour = MR$hh, min = MR$mm) - make_datetime(hour = 12, min = 00)
MR<-select(MR, date, timediff, ATMP, WTMP)
MR$ATMP <- apply(MR[,3], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
MR$ATMP <- apply(MR[,3], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
MR$WTMP <- apply(MR[,4], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
MR$WTMP <- apply(MR[,4], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
colnames(MR)[3] <- "air_temp"
colnames(MR)[4] <- "sea_temp"
colnames(MR)[1] <- "date_time"
colnames(MR)[2] <- "time_diff"
MR$team_num <- 1
MR$reading_type <- "buoy"
MR$Lat <- 25.0
MR$Lon <- 80.4
MR <- MR[c("team_num", "reading_type", "date_time", "time_diff", "Lat", "Lon",
"sea_temp", "air_temp")]
# Georges Bank
str1 <- "http://www.ndbc.noaa.gov/view_text_file.php?filename=44011h"
str2 <- ".txt.gz&dir=data/historical/stdmet/"
years <- c(1984:2013, 2015, 2016)
urls <- str_c(str1, years, str2, sep = "")
filenames <- str_c("gb", years, sep = "")
N <- length(urls)
for (i in 1:N){
assign(filenames[i], read_table(urls[i], col_names = TRUE))
file <- get(filenames[i])
colnames(file)[1] <-"YYYY"
if(nchar(file[1,1]) == 2 & file[1,1] > 50 ) {
file[1] <- lapply(file[1], function(x){str_c(19, x, sep = "")})
}
file$YYYY <- as.numeric(file$YYYY)
file$MM <- as.numeric(file$MM)
file$DD <- as.numeric(file$DD)
file$hh <- as.numeric(file$hh)
file$ATMP <- as.numeric(file$ATMP)
file$WTMP <- as.numeric(file$WTMP)
if(is.element("mm", colnames(file))) {
file$mm <- as.numeric(file$mm)
file <- file %>% filter(mm == 00 | mm == 50)
}
else {
file$mm <- 00
}
file <- file %>% select(YYYY, MM, DD, hh, mm, ATMP, WTMP)
file$date <- make_datetime(year = file$YYYY, month = file$MM, day = file$DD,
hour = file$hh, min = file$mm)
if(i == 1){
GB <- file
}
else{
GB <- rbind.data.frame(GB, file)
}
}
GB<-filter(GB, hh==12 & mm == 00 | hh == 11 & mm == 50)
GB$timediff <- make_datetime(hour = GB$hh, min = GB$mm) - make_datetime(hour = 12, min = 00)
GB <- select(GB, date, timediff, ATMP, WTMP)
GB$ATMP <- apply(GB[,3], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
GB$ATMP <- apply(GB[,3], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
GB$WTMP <- apply(GB[,4], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
GB$WTMP <- apply(GB[,4], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
colnames(GB)[3] <- "air_temp"
colnames(GB)[4] <- "sea_temp"
colnames(GB)[1] <- "date_time"
colnames(GB)[2] <- "time_diff"
GB$team_num <- 1
GB$reading_type <- "buoy"
GB$Lat <- 41.1
GB$Lon <- 66.6
GB <- GB[c("team_num", "reading_type", "date_time", "time_diff", "Lat", "Lon",
"sea_temp", "air_temp")]
# Mid-Gulf
str1 <- "http://www.ndbc.noaa.gov/view_text_file.php?filename=42001h"
str2 <- ".txt.gz&dir=data/historical/stdmet/"
years <- c(1984:2016)
urls <- str_c(str1, years, str2, sep = "")
filenames <- str_c("mg", years, sep = "")
N <- length(urls)
for (i in 1:N){
assign(filenames[i], read_table(urls[i], col_names = TRUE))
file <- get(filenames[i])
colnames(file)[1] <-"YYYY"
if(nchar(file[1,1]) == 2 & file[1,1] > 50 ) {
file[1] <- lapply(file[1], function(x){str_c(19, x, sep = "")})
}
file$YYYY <- as.numeric(file$YYYY)
file$MM <- as.numeric(file$MM)
file$DD <- as.numeric(file$DD)
file$hh <- as.numeric(file$hh)
file$ATMP <- as.numeric(file$ATMP)
file$WTMP <- as.numeric(file$WTMP)
if(is.element("mm", colnames(file))) {
file$mm <- as.numeric(file$mm)
file <- file %>% filter(mm == 00 | mm == 50)
}
else {
file$mm <- 00
}
file <- file %>% select(YYYY, MM, DD, hh, mm, ATMP, WTMP)
file$date <- make_datetime(year = file$YYYY, month = file$MM, day = file$DD,
hour = file$hh, min = file$mm)
if(i == 1){
MG <- file
}
else{
MG <- rbind.data.frame(MG, file)
}
}
MG <- filter(MG, hh == 12 & mm == 00 | hh == 11 & mm == 50)
MG$timediff <- make_datetime(hour = MG$hh, min = MG$mm) - make_datetime(hour = 12, min = 00)
MG <- select(MG, date, timediff, ATMP, WTMP)
MG$ATMP <- apply(MG[,3], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
MG$ATMP <- apply(MG[,3], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
MG$WTMP <- apply(MG[,4], MARGIN = 2, function(x){ifelse(x == 999.0, NA, x)})
MG$WTMP <- apply(MG[,4], MARGIN = 2, function(x){ifelse(x == 99.0, NA, x)})
colnames(MG)[3] <- "air_temp"
colnames(MG)[4] <- "sea_temp"
colnames(MG)[1] <- "date_time"
colnames(MG)[2] <- "time_diff"
MG$team_num <- 1
MG$reading_type <- "buoy"
MG$Lat <- 25.9
MG$Lon <- 89.7
MG <- MG[c("team_num", "reading_type", "date_time", "time_diff", "Lat", "Lon",
"sea_temp", "air_temp")]
# Finally, we need to put these dataframes into an xlsx file
write.xlsx2(CM, "group1data.xlsx", sheetName = "Cape May - Buoy # 44009")
write.xlsx2(MR, "group1data.xlsx", sheetName = "Molasses Reef - Buoy # MLRF1", append = TRUE)
write.xlsx2(GB, "group1data.xlsx", sheetName = "Georges Bank - Buoy # 44011", append = TRUE)
write.xlsx2(MG, "group1data.xlsx", sheetName = "Mid Gulf - Buoy # 42001", append = TRUE)
# Seasonal Data Visualization
# First we want to create a new dataframe that has separate month columns for each buoy
CM.seasons <- mutate(CM, month = month(CM$date_time))
MR.seasons <- mutate(MR, month = month(MR$date_time))
GB.seasons <- mutate(GB, month = month(GB$date_time))
MG.seasons <- mutate(MG, month = month(MG$date_time))
# Next we want to create separate data frames for each season
# We approximated each season to 3 months:
# Winter = January, February, and March
# Spring = April, May, and June
# Summer = July, August, and September
# Fall = October, November, and December
CM.winter <- filter(CM.seasons, month == 1 | month == 2 | month == 3)
CM.spring <- filter(CM.seasons, month == 4 | month == 5 | month == 6)
CM.summer <- filter(CM.seasons, month == 7 | month == 8 | month == 9)
CM.fall <- filter(CM.seasons, month == 10 | month == 11 | month == 12)
MR.winter <- filter(MR.seasons, month == 1 | month == 2 | month == 3)
MR.spring <- filter(MR.seasons, month == 4 | month == 5 | month == 6)
MR.summer <- filter(MR.seasons, month == 7 | month == 8 | month == 9)
MR.fall <- filter(MR.seasons, month == 10 | month == 11 | month == 12)
GB.winter <- filter(GB.seasons, month == 1 | month == 2 | month == 3)
GB.spring <- filter(GB.seasons, month == 4 | month == 5 | month == 6)
GB.summer <- filter(GB.seasons, month == 7 | month == 8 | month == 9)
GB.fall <- filter(GB.seasons, month == 10 | month == 11 | month == 12)
MG.winter <- filter(MG.seasons, month == 1 | month == 2 | month == 3)
MG.spring <- filter(MG.seasons, month == 4 | month == 5 | month == 6)
MG.summer <- filter(MG.seasons, month == 7 | month == 8 | month == 9)
MG.fall <- filter(MG.seasons, month == 10 | month == 11 | month == 12)
# Then, I want to assign plots of each season for each buoy to names
winter.CM <- ggplot() + geom_point(data = CM.winter, aes(y = air_temp, x = date_time),
color = "darkslategray4") +
geom_smooth(data = CM.winter, aes(y = air_temp, x = date_time), color = "darkslategray",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Winter Months in Cape May")
spring.CM <- ggplot() + geom_point(data = CM.spring, aes(y = air_temp, x = date_time),
color = "darkorchid1") +
geom_smooth(data = CM.spring, aes(y = air_temp, x = date_time), color = "darkorchid4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Spring Months in Cape May")
summer.CM <- ggplot() + geom_point(data = CM.summer, aes(y = air_temp, x = date_time),
color = "goldenrod1") +
geom_smooth(data = CM.summer, aes(y = air_temp, x = date_time), color = "goldenrod4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Summer Months in Cape May")
fall.CM <- ggplot() + geom_point(data = CM.fall, aes(y = air_temp, x = date_time),
color = "darkorange2") +
geom_smooth(data = CM.fall, aes(y = air_temp, x = date_time), color = "darkorange4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Fall Months in Cape May")
winter.MR <- ggplot() + geom_point(data = MR.winter, aes(y = air_temp, x = date_time),
color = "darkslategray4") +
geom_smooth(data = MR.winter, aes(y = air_temp, x = date_time), color = "darkslategray",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Winter Months in Molasses Reef")
spring.MR <- ggplot() + geom_point(data = MR.spring, aes(y = air_temp, x = date_time),
color = "darkorchid1") +
geom_smooth(data = MR.spring, aes(y = air_temp, x = date_time), color = "darkorchid4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Spring Months in Molasses Reef")
summer.MR <- ggplot() + geom_point(data = MR.summer, aes(y = air_temp, x = date_time),
color = "goldenrod1") +
geom_smooth(data = MR.summer, aes(y = air_temp, x = date_time), color = "goldenrod4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Summer Months in Molasses Reef")
fall.MR <- ggplot() + geom_point(data = MR.fall, aes(y = air_temp, x = date_time),
color = "darkorange2") +
geom_smooth(data = MR.fall, aes(y = air_temp, x = date_time), color = "darkorange4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Fall Months in Molasses Reef")
winter.GB <- ggplot() + geom_point(data = GB.winter, aes(y = air_temp, x = date_time),
color = "darkslategray4") +
geom_smooth(data = GB.winter, aes(y = air_temp, x = date_time), color = "darkslategray",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Winter Months in Georges Bank")
spring.GB <- ggplot() + geom_point(data = GB.spring, aes(y = air_temp, x = date_time),
color = "darkorchid1") +
geom_smooth(data = GB.spring, aes(y = air_temp, x = date_time), color = "darkorchid4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Spring Months in Georges Bank")
summer.GB <- ggplot() + geom_point(data = GB.summer, aes(y = air_temp, x = date_time),
color = "goldenrod1") +
geom_smooth(data = GB.summer, aes(y = air_temp, x = date_time), color = "goldenrod4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Summer Months in Georges Bank")
fall.GB <- ggplot() + geom_point(data = GB.fall, aes(y = air_temp, x = date_time),
color = "darkorange2") +
geom_smooth(data = GB.fall, aes(y = air_temp, x = date_time), color = "darkorange4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Fall Months in Georges Bank")
winter.MG <- ggplot() + geom_point(data = MG.winter, aes(y = air_temp, x = date_time),
color = "darkslategray4") +
geom_smooth(data = MG.winter, aes(y = air_temp, x = date_time), color = "darkslategray",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Winter Months in Mid Gulf")
spring.MG <- ggplot() + geom_point(data = MG.spring, aes(y = air_temp, x = date_time),
color = "darkorchid1") +
geom_smooth(data = MG.spring, aes(y = air_temp, x = date_time), color = "darkorchid4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Spring Months in Mid Gulf")
summer.MG <- ggplot() + geom_point(data = MG.summer, aes(y = air_temp, x = date_time),
color = "goldenrod1") +
geom_smooth(data = MG.summer, aes(y = air_temp, x = date_time), color = "goldenrod4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Summer Months in Mid Gulf")
fall.MG <- ggplot() + geom_point(data = MG.fall, aes(y = air_temp, x = date_time),
color = "darkorange2") +
geom_smooth(data = MG.fall, aes(y = air_temp, x = date_time), color = "darkorange4",
method = "lm", se = FALSE) +
labs(x = "Year", y = "Air Temperature (°C)", title = "Year vs. Air Temperature,
Fall Months in Mid Gulf")
# Finally, we want to arrange each plot so that they appear side by side for each buoy,
# with appropriate titles and labels
grid.arrange(winter.CM, spring.CM, summer.CM, fall.CM, ncol=2)
grid.arrange(winter.MR, spring.MR, summer.MR, fall.MR, ncol=2)
grid.arrange(winter.GB, spring.GB, summer.GB, fall.GB, ncol=2)
grid.arrange(winter.MG, spring.MG, summer.MG, fall.MG, ncol=2)
# I also calculated the corellation coefficients for each season over time
# against the air temperature for each buoy
# Making sure dates are numerics
CM.winter$date_time <- as.numeric(CM.winter$date_time)
CM.spring$date_time <- as.numeric(CM.spring$date_time)
CM.summer$date_time <- as.numeric(CM.summer$date_time)
CM.fall$date_time <- as.numeric(CM.fall$date_time)
# Inserting into the correlation function
cor(CM.winter$air_temp, CM.winter$date_time, use = "complete.obs")
cor(CM.spring$air_temp, CM.spring$date_time, use = "complete.obs")
cor(CM.summer$air_temp, CM.summer$date_time, use = "complete.obs")
cor(CM.fall$air_temp, CM.fall$date_time, use = "complete.obs")
MR.winter$date_time <- as.numeric(MR.winter$date_time)
MR.spring$date_time <- as.numeric(MR.spring$date_time)
MR.summer$date_time <- as.numeric(MR.summer$date_time)
MR.fall$date_time <- as.numeric(MR.fall$date_time)
cor(MR.winter$air_temp, MR.winter$date_time, use = "complete.obs")
cor(MR.spring$air_temp, MR.spring$date_time, use = "complete.obs")
cor(MR.summer$air_temp, MR.summer$date_time, use = "complete.obs")
cor(MR.fall$air_temp, MR.fall$date_time, use = "complete.obs")
GB.winter$date_time <- as.numeric(GB.winter$date_time)
GB.spring$date_time <- as.numeric(GB.spring$date_time)
GB.summer$date_time <- as.numeric(GB.summer$date_time)
GB.fall$date_time <- as.numeric(GB.fall$date_time)
cor(GB.winter$air_temp, GB.winter$date_time, use = "complete.obs")
cor(GB.spring$air_temp, GB.spring$date_time, use = "complete.obs")
cor(GB.summer$air_temp, GB.summer$date_time, use = "complete.obs")
cor(GB.fall$air_temp, GB.fall$date_time, use = "complete.obs")
MG.winter$date_time <- as.numeric(MG.winter$date_time)
MG.spring$date_time <- as.numeric(MG.spring$date_time)
MG.summer$date_time <- as.numeric(MG.summer$date_time)
MG.fall$date_time <- as.numeric(MG.fall$date_time)
cor(MG.winter$air_temp, MG.winter$date_time, use = "complete.obs")
cor(MG.spring$air_temp, MG.spring$date_time, use = "complete.obs")
cor(MG.summer$air_temp, MG.summer$date_time, use = "complete.obs")
cor(MG.fall$air_temp, MG.fall$date_time, use = "complete.obs")
# Data Visuals for Air Temp and Sea Temp Correlation
# CM Air and Sea
CM.AirSea <- ggplot(data = CM) +
geom_point(mapping = aes(x = sea_temp, y = air_temp), color="lightblue") +
labs(title="Cape May Air Temperature vs. Sea Temperature",
x="Sea Temperature (°C)", y="Air Temperature (°C)")
# MR Air and Sea
MR.AirSea <- ggplot(data = MR) +
geom_point(mapping = aes(x = sea_temp, y = air_temp), color="darkolivegreen1") +
labs(title="Molasses Reef Air Temperature vs. Sea Temperature",
x="Sea Temperature (°C)", y="Air Temperature (°C)")
# MG Air and Sea
MG.AirSea <- ggplot(data = MG) +
geom_point(mapping = aes(x = sea_temp, y = air_temp), color="lightpink1") +
labs(title="Mid Gulf Air Temperature vs. Sea Temperature",
x="Sea Temperature (°C)", y="Air Temperature (°C)")
# GB Air and Sea
GB.AirSea <- ggplot(data = GB) +
geom_point(mapping = aes(x = sea_temp, y = air_temp), color="khaki") +
labs(title="Georges Bank Air Temperature vs. Sea Temperature",
x="Sea Temperature (°C)", y="Air Temperature (°C)")
# Make the Plots Readable and Comparable with gridExtra
grid.arrange(CM.AirSea, MR.AirSea, MG.AirSea, GB.AirSea, ncol=2)
#Descriptive Statistics
#Each descriptive statistics block contains a summary statement to retrieve the
#minumum, maximum, mean, and median. The sd() argument produces the standard deviation
#and the cor() finds the correlation between the air_temp and sea_temp for the given block
#descriptive statistics for Cape May (all years)
summary(CM$air_temp)
sd(CM$air_temp, na.rm=TRUE)
summary(CM$sea_temp)
sd(CM$sea_temp, na.rm=TRUE)
cor(CM$air_temp, CM$sea_temp, use = "complete.obs")
#descriptive statistics for Cape May (1984)
CM1984 <- CM %>% filter(date_time >= as.Date("1984-01-01") &
date_time <= as.Date("1984-12-31"))
#this filter statement takes data only from the first complete year of available data,
#and creates a new corresponding variable so that the first year can be compared to the most
#recent year of available data
summary(CM1984$air_temp)
sd(CM1984$air_temp, na.rm=TRUE)
summary(CM1984$sea_temp)
sd(CM1984$sea_temp, na.rm=TRUE)
cor(CM1984$air_temp, CM1984$sea_temp, use = "complete.obs")
#descriptive statistics for Cape May (2015)
CM2015 <- CM %>% filter(date_time >= as.Date("2015-01-01") &
date_time <= as.Date("2015-12-31"))
#this filter statement takes data only from the most recent complete year of data
#and creates a new corresponding variable so that the most recent year can be compared to the
#first year of available data
summary(CM2015$air_temp)
sd(CM2015$air_temp, na.rm=TRUE)
summary(CM2015$sea_temp)
sd(CM2015$sea_temp, na.rm=TRUE)
cor(CM2015$air_temp, CM2015$sea_temp, use = "complete.obs")
#Cape May T-Test
#Using the previously created variables, T-Tests are run for difference in means in both
#air temperature and sea temperature. This translates to seeing if there is a significant
#difference in temperature between the first year and the most recent year
t.test(CM2015$air_temp, CM1984$air_temp)
t.test(CM2015$sea_temp, CM1984$sea_temp)
#The code described above is repeated for all of the buoys.
#descriptive statistics for Molasses Reef (all years)
summary(MR$air_temp)
sd(MR$air_temp, na.rm=TRUE)
summary(MR$sea_temp)
sd(MR$sea_temp, na.rm=TRUE)
cor(MR$air_temp, MR$sea_temp, use = "complete.obs")
#descriptive statistics for Molasses Reef (1987)
MR1988 <- MR %>% filter(date_time >= as.Date("1988-01-01") &
date_time <= as.Date("1988-12-31"))
summary(MR1988$air_temp)
sd(MR1988$air_temp, na.rm=TRUE)
summary(MR1988$sea_temp)
sd(MR1988$sea_temp, na.rm=TRUE)
cor(MR1988$air_temp, MR1988$sea_temp, use = "complete.obs")
#descriptive statistics for Molasses Reef (2015)
MR2015 <- MR %>% filter(date_time >= as.Date("2015-01-01") &
date_time <= as.Date("2015-12-31"))
summary(MR2015$air_temp)
sd(MR2015$air_temp, na.rm=TRUE)
summary(MR2015$sea_temp)
sd(MR2015$sea_temp, na.rm=TRUE)
cor(MR2015$air_temp, MR2015$sea_temp, use = "complete.obs")
#Molasses Reef T-Test
t.test(MR2015$air_temp, MR1988$air_temp)
t.test(MR2015$sea_temp, MR1988$sea_temp)
#descriptive statistics for Georges Bank (all years)
summary(GB$air_temp)
sd(GB$air_temp, na.rm=TRUE)
summary(GB$sea_temp)
sd(GB$sea_temp, na.rm=TRUE)
cor(GB$air_temp, GB$sea_temp, use = "complete.obs")
#descriptive statistics for Georges Bank (1985)
GB1985 <- GB %>% filter(date_time >= as.Date("1985-01-01") &
date_time <= as.Date("1985-12-31"))
summary(GB1985$air_temp)
sd(GB1985$air_temp, na.rm=TRUE)
summary(GB1985$sea_temp)
sd(GB1985$sea_temp, na.rm=TRUE)
cor(GB1985$air_temp, GB1985$sea_temp, use = "complete.obs")
#descriptive statistics for Georges Bank (2015)
GB2015 <- GB %>% filter(date_time >= as.Date("2015-01-01") &
date_time <= as.Date("2015-12-31"))
summary(GB2015$air_temp)
sd(GB2015$air_temp, na.rm=TRUE)
summary(GB2015$sea_temp)
sd(GB2015$sea_temp, na.rm=TRUE)
cor(GB2015$air_temp, GB2015$sea_temp, use = "complete.obs")
#Georges Bank T-Test
t.test(GB2015$air_temp, GB1985$air_temp)
t.test(GB2015$sea_temp, GB1985$sea_temp)
#descriptive statistics for Mid Gulf (all years)
summary(MG$air_temp)
sd(MG$air_temp, na.rm=TRUE)
summary(MG$sea_temp)
sd(MG$sea_temp, na.rm=TRUE)
cor(MG$air_temp, MG$sea_temp, use = "complete.obs")
#descriptive statistics for Mid Gulf (1984)
MG1984 <- MG %>% filter(date_time >= as.Date("1984-01-01") &
date_time <= as.Date("1984-12-31"))
summary(MG1984$air_temp)
sd(MG1984$air_temp, na.rm=TRUE)
summary(MG1984$sea_temp)
sd(MG1984$sea_temp, na.rm=TRUE)
cor(MG1984$air_temp, MG1984$sea_temp, use = "complete.obs")
#descriptive statistics for Mid Gulf (2015)
MG2015 <- MG %>% filter(date_time >= as.Date("2015-01-01") &
date_time <= as.Date("2015-12-31"))
summary(MG2015$air_temp)
sd(MG2015$air_temp, na.rm=TRUE)
summary(MG2015$sea_temp)
sd(MG2015$sea_temp, na.rm=TRUE)
cor(MG2015$air_temp, MG2015$sea_temp, use = "complete.obs")
#Mid Gulf T-Test
t.test(MG2015$air_temp, MG1984$air_temp)
t.test(MG2015$sea_temp, MG1984$sea_temp)
|
42826ad3b868f5dd246c2901e0264403c2917595
|
7b5f3f514a0d2f0be2af7fabce40a92fc66e11e3
|
/plot1.r
|
fbc61de461c83daa479dcdd1d29d74c442db16b0
|
[] |
no_license
|
xianyang-wong/ExData_Plotting2
|
95c7d188e6d05f59614b464d8254dea63b50af29
|
07f9dae311fbdd19884ba85cd4ef85067eae9059
|
refs/heads/master
| 2021-01-19T00:13:08.150000
| 2015-04-23T13:47:00
| 2015-04-23T13:47:00
| 34,458,316
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 543
|
r
|
plot1.r
|
# Loads data if non-existent
if (!"NEI" %in% ls()) {
NEI <- readRDS("summarySCC_PM25.rds")
}
if (!"SCC" %in% ls()) {
SCC <- readRDS("Source_Classification_Code.rds")
}
par("mar"=c(5.1, 4.5, 4.1, 2.1))
png(filename = "plot1.png",
width = 480, height = 480,
units = "px")
totalEmissions <- aggregate(NEI$Emissions, list(NEI$year), FUN = "sum")
plot(totalEmissions, type = "l", xlab = "Year",
main = "Total Emissions in the United States from 1999 to 2008",
ylab = expression('Total PM'[2.5]*" Emission"))
dev.off()
|
0b11268846a576afa812d00a590feaa55579b7f9
|
0c6ff19387075346018f41f6e15b1e7e4759fbf9
|
/word2vec_eval.R
|
ac18c7cde051f708e6b4a781c47bfa95fe777d9e
|
[] |
no_license
|
Sandy-HE/Tag_analysis
|
03db4829b6608b14ae5040da3dd43ec3e7f4d2de
|
16a70d5832ed0096c842136351eae7f1b71778ce
|
refs/heads/master
| 2021-03-31T16:13:25.470609
| 2020-06-12T04:57:18
| 2020-06-12T04:57:18
| 248,118,726
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,397
|
r
|
word2vec_eval.R
|
#Date: 2020-05-18
#To compare glove performance with word2vec, meanwhile keep the same corpus.
#We use text2vec pruned_vocab to provide the input of word2vec
#read back the processing result of word2vec to do evaluation
#word2vec part run by "wvbyword2vec.py"
library("data.table")
#get pruned_vocab from text2vec analysis(combined with lastfm_tags_analysis.R)
#based pruned_vocab, clean tags dataset and save csv file
termfilter <- function(termlist){
term = termlist[termlist %in% pruned_vocab$term]
paste(term,collapse =";")
}
token1= lapply(tokens, termfilter)
#temp2 = as.data.frame(do.call(rbind,token1))
temp1 = train[,"track_id"]
temp1$prunedtag= token1
fwrite(temp1, file="allprunedtags_emo_only.csv")
#====read back the result of word2vec processing
#'phrase_emo_only_cbow_D64.csv'
wordvec_df <- fread("phrase_7685_cbow_D64.csv")
wordvec_df <- fread("phrase_7685_sg_D32.csv")
wordvec_df <- fread("phrase_emo_only_cbow_D4.csv")
wordvec_df <- fread("phrase_emo_only_sg_D32.csv")
d=32
wordvec_df <- wordvec_df[-1,]
colnames(wordvec_df)[d+1]<-"term"
word_vectors <- as.matrix(wordvec_df[,1:d])
rownames(word_vectors) <- wordvec_df$term
eterms_df <- fread("./emotion_terms_list3.csv")
sub_wordvec <- subset(word_vectors, rownames(word_vectors) %in% eterms_df$term)
sub_wordvec <- sub_wordvec[order(rownames(sub_wordvec)),]
#====Obtain ground truth data for valence-arousal-dominant rating====
#Warriner VAD rating, value range is [1,9]
baseline_df = fread("Warriner_avd_ratings.csv")
baseline_df = baseline_df[,c("Word","V.Mean.Sum","A.Mean.Sum","D.Mean.Sum")]
baseline_df = baseline_df[baseline_df$Word %in% rownames(sub_wordvec),]
#Normalized to [-1,1]. Given range [-n,n], scale to [-1,1]. (x-(-n))*2/2n-1
baseline_mt = as.matrix(baseline_df[,2:3])
baseline_mt= (baseline_mt-1)*2/8-1
rownames(baseline_mt) <- baseline_df$Word
#====nMDS -- reduce to 2D or 3D word vectors====
#calculate pairwise-rows cosine similarity and generate a similarity matrix
library(vegan)
library(lsa)
termsim_mt <-lsa::cosine(t(sub_wordvec))
termdis_mt <- max(termsim_mt)-termsim_mt
mds_model<-metaMDS(termdis_mt, k=3)
mds_terms <- mds_model$points
mds_terms <- mds_terms[rownames(mds_terms) %in% baseline_df$Word ,]
proc <- procrustes(baseline_mt,mds_terms)
#plot(proc)
summary(proc)
mds_model<-metaMDS(termdis_mt, k=2)
#stressplot(mds_model,termdis_mt)
mds_terms <- mds_model$points
mds_terms <- mds_terms[rownames(mds_terms) %in% baseline_df$Word ,]
#mds_terms_exchange <- mds_terms[,c("MDS2","MDS1")]
proc <- procrustes(baseline_mt,mds_terms)
#plot(proc)
summary(proc)
#====visualization====
plot(mds_terms, type= 'n')
text(mds_terms,rownames(mds_terms),cex=.7)
#====Scherer baseline====
scherer_cord = as.data.frame(fread("scherer_emotion_coord.csv"))
eterms_df <- fread("./emotion_terms_list.csv")
sub_wordvec <- subset(word_vectors, rownames(word_vectors) %in% eterms_df$term)
sub_wordvec <- sub_wordvec[order(rownames(sub_wordvec)),]
scherer_cord$norm1_x = (scherer_cord$x)/230
scherer_cord$norm1_y = (scherer_cord$y)/230
scherer_cord_new <- scherer_cord[scherer_cord$term %in% rownames(sub_wordvec),]
scherer_cord_new2 <- as.matrix(scherer_cord_new[,-c(1,2,3)])
mds_terms <- mds_terms[rownames(mds_terms) %in% scherer_cord_new$term ,]
proc <- procrustes(scherer_cord_new2,mds_terms)
|
fa9f57b64ce1aa40a5bfcb691992432f8ddcc159
|
f73a7a320623e36c620cdd028a335b237f51e660
|
/man/GDPR_list.Rd
|
ee68fa3352ca62f183f54753f05658fd8a3dbfea
|
[
"CC-BY-4.0",
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
jonocarroll/tidyGDPR
|
5393e6e691dbc2a3e0b2867814e4a0744d537cef
|
92f4029e110027561dcd7cc148a5fff60151541d
|
refs/heads/master
| 2020-03-18T15:42:20.935164
| 2018-05-26T03:20:15
| 2018-05-26T03:20:15
| 134,924,332
| 12
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 550
|
rd
|
GDPR_list.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stored_data.R
\docType{data}
\name{GDPR_list}
\alias{GDPR_list}
\title{GDPR Regulations Recitals as a list}
\format{An object of class \code{list} of length 11.}
\source{
\url{http://openscience.adaptcentre.ie/projects/GDPRtEXT/} Made
available under the
\href{https://creativecommons.org/licenses/by/4.0/}{CC-by-4.0} license
}
\usage{
GDPR_list
}
\description{
The GDPR full regulations stored as a list, converted from raw JSON via
\link{fromJSON}.
}
\keyword{datasets}
|
79dca4b0b90d7520e6559ada1e10b167f7a74b9e
|
c6230d85e6ee06dc26e63fc756ecfbf81f1cfe94
|
/R/theme.R
|
f77a85cad90350188c73ec001b90076a30019ad7
|
[
"Apache-2.0"
] |
permissive
|
bcgov/cccharts
|
48a3df6e09862326d08059978b92cc924f16cd2a
|
474e1360e0470728797bc304595c556b05812694
|
refs/heads/master
| 2021-01-11T05:34:05.178166
| 2020-12-16T23:02:21
| 2020-12-16T23:02:21
| 69,051,799
| 10
| 4
|
Apache-2.0
| 2020-04-15T00:37:10
| 2016-09-23T18:44:37
|
R
|
UTF-8
|
R
| false
| false
| 2,269
|
r
|
theme.R
|
# Copyright 2016 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and limitations under the License.
#' Theme
#'
#' ggplot2 theme for cccharts plots
#'
#' @param facet A flag indicating whether to use the theme for facetted graphs.
#' @param map A flag indicating whether to use the theme for maps.
#' @param base_family Base font family for plotting
#' @seealso \code{\link[envreportutils]{theme_soe}} and
#' \code{\link[envreportutils]{theme_soe_facet}}
#' @export
theme_cccharts <- function(facet = FALSE, map = FALSE, base_family = "") {
if (facet) {
theme <- envreportutils::theme_soe_facet(base_family = base_family)
} else
theme <- envreportutils::theme_soe(base_family = base_family)
theme <- theme + theme(
plot.title = element_text(size = rel(1.2)),
axis.title.y = element_text(size = 14),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 14),
axis.text.y = element_text(size = 12),
axis.line = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.border = element_rect(colour = "grey50", fill = NA),
panel.background = element_rect(colour = "grey50", fill = NA),
legend.position = ("bottom"),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 11),
legend.direction = ("horizontal"),
legend.key = element_rect(color = "white", fill = NA),
strip.text.x = element_text(size = 12)
)
if (map) {
theme <- theme + theme(
panel.grid = element_blank(),
panel.border = element_blank(),
panel.background = element_rect(color = "white", fill = NA),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank()
)
}
theme
}
|
b5f06ca51f35828f37b1fc7744092c4a8d34eebf
|
70d6e1ca3c30d64d88fff068628774cfbeb2f307
|
/listenEnv.R
|
d7f8e485472b4ba602ad27ee687c50c05aba0d89
|
[] |
no_license
|
josousa82/Course-R-Introduction-W3
|
7db9ffd343646ec31e6fa2a8e18c6c7fc9d24c2d
|
b2f64a186f847e0912287486f13233f7d68048af
|
refs/heads/master
| 2021-09-03T18:35:17.020997
| 2018-01-11T04:10:11
| 2018-01-11T04:10:11
| 115,117,724
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 144
|
r
|
listenEnv.R
|
listEnv <- function (){
g <- globalenv()
while (environmentName(g) != 'R_EmptyEnv') {
g <- parent.env(g);
cat(str(g, give.attr = F))
}
}
|
9da070a8ee1fb1705ee356ceb79cb2f55393b05b
|
fcf46fdb7479a9f3df87446a720ca301a72888d5
|
/graphLASSO.R
|
c8b7e627cbef61c5144220b1778a7abafbd2a8f8
|
[] |
no_license
|
devcao/LOCOpath_repo
|
b080ac685f04f0f22f88c5667293e2e73a797045
|
2c6b0b5553e9298e6c30cf3bfe25348e31625088
|
refs/heads/master
| 2022-12-28T19:35:24.549883
| 2020-10-19T02:53:55
| 2020-10-19T02:53:55
| null | 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 18,357
|
r
|
graphLASSO.R
|
# This file contains basic code for graphical LASSO
###### Loading some useful package, not all required ########
require(glasso)
require(CVglasso)
require(LOCOpath)
require(huge)
#source('graphLASSO.R')
source('NetTS.R')
require(igraph)
#######################################
########## A mofifiled version of the huge.plot function in huge package ##############
huge.plot_v1 = function (G, epsflag = FALSE, graph.name = "default", cur.num = 1,
location = NULL, ...)
{
# A mofifiled version of the huge.plot function in huge package
# All arguments the same as huge.plot, check huge.plot for more details
gcinfo(FALSE)
if (missing(location))
location = tempdir()
oldlocation = getwd()
a
setwd(location)
g = graph.adjacency(as.matrix(G != 0), mode = "undirected",
diag = FALSE)
layout.grid = layout.fruchterman.reingold(g)
if (epsflag == TRUE)
postscript(paste(paste(graph.name, cur.num, sep = ""),
"eps", sep = "."), width = 8, height = 8)
plot(g, layout = layout.grid, edge.color = "gray50", vertex.color = "red",
vertex.size = 2, vertex.label = NA, ...)
rm(g, location)
gc()
if (epsflag == TRUE)
dev.off()
setwd(oldlocation)
}
### 2 functoins to make the imgage function in R generate correct matrix plot ###
rotate <- function(x) t(apply(x, 2, rev)) # working function#\
image_v1 = function(mat,...){image(rotate(mat),...)} # new version of the image_funcion
###### graphical model variable screen #######
simu_graph_screen = function(n = 100, p = 50, type = 'A', Iter = 250){
# graphical model variable screen, output will be used to generete ROC curve
# Args:
# n, p; int, sample size and dimension of matrix
# type: allow 2 options ('A', 'C'), 2 types of matices
# Iter: int, number if iterations, use defualt 50
# Returns:
# A List of: Theta_list: a list of all randomly generated precision matrix
# glasso_results: a list of glasso estimates
# LOCO_results: a list of calulated LOCO variable importance
Theta_list = list()
glasso_results = list()
LOCO_results = list()
for (i in 1:Iter){
#aa = chol(Sigma)
Theta = generate_precision(p=p, type = type)
Theta_list[[i]] = Theta
Sigma=solve(Theta)
Mu=rep(0,p)
X=rmvn(n=n, mu = Mu,sigma = Sigma)
S <- var(X)
a = CVglasso(X=X, S=S)
glasso_results[[i]] = a$Omega
TS_sigma = graph_TS(S = S, n_rho = 50)
LOCO_results[[i]] = TS_sigma
#TS_sd = TS_sigma/sum(TS_sigma, na.rm=TRUE)
#diag(TS_sd) = 0
#results[i, 1] = mean( (Theta != 0) == (a$Omega != 0) )
#results[i, 2] = mean( (Theta != 0) == (ifelse(TS_sd>thresh, 1,0) != 0) )
cat('Now:',i, '\n')
}
return(list(Theta_list = Theta_list, glasso_results = glasso_results, LOCO_results = LOCO_results))
}
###### generate 2 types of precision matrix as described in GRASS paper #######
generate_precision = function(p=50, type = 'A'){
# Generate 2 types of precision matrix as described in GRASS paper
# Args:
# p: int, dimension of matrix
# type: allow 2 options ('A', 'C'), 2 types of matices
# Returns: matrix, precision matrix
#
if(type == 'A'){
A = matrix(ifelse(runif(p*p, min=0,max=1)<=0.01, 1, 0), p, p)
non_0_edge = which(A == 1, arr.ind=TRUE)
upper_edge = non_0_edge[which((non_0_edge[,2] - non_0_edge[,1]) > 0), ]
if(is.matrix(upper_edge)){
lower_edge = upper_edge
lower_edge[,1] = upper_edge[,2]; lower_edge[,2] = upper_edge[,1]
random_vector = runif(dim(upper_edge)[1], min=-0.3,max=0.7)
A_prime = matrix(0, p, p)
A_prime[upper_edge] = random_vector
A_prime[lower_edge] = random_vector
diag(A_prime) = 1
}else{
lower_edge[1] = upper_edge[2]; lower_edge[2] = upper_edge[1]
random_vector = runif(1, min=-0.3,max=0.7)
A_prime = matrix(0, p, p)
A_prime[upper_edge[1],upper_edge[2]] = random_vector
A_prime[upper_edge[2],upper_edge[1]] = random_vector
diag(A_prime) = 1
}
lam_min = min(eigen(A_prime)$values)
Theta = A_prime + (0.1-lam_min)*diag(rep(1,p))
Theta = Theta / Theta[1,1]
}else if (type == 'C'){
A_prime = matrix(0, p, p)
for(i in 1:(p-2)){
random_value = runif(2, min=-0.3,max=0.7)
A_prime[i,i+1] = random_value[1]
A_prime[i,i+2] = random_value[2]
A_prime[i+1,i] = A_prime[i,i+1]
A_prime[i+2,i] = A_prime[i,i+2]
}
lam_min = min(eigen(A_prime)$values)
Theta = A_prime + (0.1-lam_min)*diag(rep(1,p))
Theta = Theta / Theta[1,1]
}else{
stop("Only support type = 'A' or 'C' for now" )
}
return(Theta)
}
###### graphical model variable screen (my own version, not for the GRASS paper) #######
simu_graph_screen_v2 = function(Theta, Iter = 200){
# graphical model variable screen, output will be used to generete ROC curve
# Args:
# THeta: matrix, the true precision matrix
# Iter: int, number if iterations, use defualt 50
# Returns:
# A List of: Theta_list: a list of all randomly generated precision matrix
# glasso_results: a list of glasso estimates
# LOCO_results: a list of calulated LOCO variable importance
Sigma=solve(Theta)
Theta_list = list()
glasso_results = list()
LOCO_results = list()
for (i in 1:Iter){
Theta_list[[i]] = Theta
Mu=rep(0,p)
X=rmvn(n=100, mu = Mu,sigma = Sigma)
S<- var(X)
a = CVglasso(X=X, S=S)
glasso_results[[i]] = a$Omega
TS_sigma = graph_TS(S = S, n_rho = 50)
LOCO_results[[i]] = TS_sigma
#TS_sd = TS_sigma/sum(TS_sigma, na.rm=TRUE)
#diag(TS_sd) = 0
#results[i, 1] = mean( (Theta != 0) == (a$Omega != 0) )
#results[i, 2] = mean( (Theta != 0) == (ifelse(TS_sd>thresh, 1,0) != 0) )
cat('Now:',i, '\n')
}
return(list(Theta_list = Theta_list, glasso_results = glasso_results, LOCO_results = LOCO_results))
}
#### A wrapper function to calculate all precision matrix LOCO statistic for all entries of a precision matrix ###
graph_TS = function(S, ...){
# A wrapper function to calculate all LOCO statistic for all entries of a matrix
# Args:
# S: matrix, emprical covariance matrix
# ..., arguments for ExactNet.TS.Graph
# Returns: matrix of LOCO statistic
p = dim(S)[1]
TS = matrix(NA, p, p)
for (i in 1:(p-1)){
for (j in seq(i+1, p)){
#for (i in 1:(p)){
# for (j in seq(1, p)){
cat(i, j, '\n')
try({
ts = ExactNet.TS.Graph(S, c1 = i, c2 = j, ...)
})
TS[i,j] = ts; TS[j,i] = ts;
}
}
return(TS)
}
#### A wrapper function to calculate all covariance matrix LOCO statistic for all entries of of a matrix ###
graph_TS_sigma = function(S, ...){
# A wrapper function to calculate all LOCO statistic for all entries of a covariance matrix
# Args:
# S: matrix, emprical covariance matrix
# ..., arguments for ExactNet.TS.Graph.Sigma
# Returns: matrix of LOCO statistic
p = dim(S)[1]
TS = matrix(NA, p, p)
for (i in 1:(p-1)){
for (j in seq(i+1, p)){
#for (i in 1:(p)){
# for (j in seq(1, p)){
cat(i, j, '\n')
try({
ts = ExactNet.TS.Graph.Sigma(S, c1 = i, c2 = j, ...)
})
TS[i,j] = ts; TS[j,i] = ts;
}
}
return(TS)
}
#### A function to transform the outputs of graphical LASSO code
#### to a matrix so we can compute the LOCO statistic easier
trans_lars_matrix = function(obj, use.glasso = FALSE){
# Args:
# obj: cound be glasso output or graphLASSO.path output
# use.glasso: if obj is glasso output, use FALSE. Otherwise, set to be TRUE
# Return: a matrix,, each row contains the upper triangle value for a fixed lambda
if (use.glasso){
p = dim(obj$wi)[1]
n_lam = dim(obj$wi)[3]
beta_hat = matrix(0, n_lam, p*(p-1)/2)
for(i in 1:n_lam){
mat_temp = obj$wi[,,i]
beta_hat[i, ] = mat_temp[upper.tri(mat_temp, diag = FALSE)]
}
}else{
p = dim(obj$wi[[1]])[1]
n_lam = length(obj$rholist)
beta_hat = matrix(0, n_lam, p*(p-1)/2)
for(i in 1:n_lam){
mat_temp = obj$wi[[i]]
beta_hat[i, ] = mat_temp[upper.tri(mat_temp, diag = FALSE)]
}
}
return(beta_hat)
}
######### Our own version of graphical LASSO ########
graphLASSO = function(S, lambda, maxIt = 100, tol = 1e-6, use.lars=FALSE){
# Our own version of graphical LASSO
# Args:
# S: int. sample covariance matrix
# lambda: positive float, the regularization parameter
# maxIt: int, default 100, maximum itersion allowed
# tol: float, tolerance criterion, if results between 2 itersion are < tol, quit from intersion
# use.lars: use lars or glment as backend. TRUE for lars, FALSE for glmnet
# Defulat FALSE (recommened), glmnet runs faster than lars
# Returns:
# A list of: lambda: lambda value,
# W: estimated covariance matrix
# Theta: estimated precision matrix, invert of W
p = dim(S)[1]
W = S + diag(rep(lambda, p))
W_old = W
i = 0
while (i < maxIt){
i = i+1
for (j in p:1){
sub_index = (1:p)[-j]
eigen_obj = eigen(W[sub_index, sub_index], symmetric = TRUE)
V = eigen_obj$vectors
d = eigen_obj$values
X = V %*% diag(sqrt(d)) %*% t(V) # W_11^(-1/2) * s_12
Y = V %*% diag( 1/sqrt(d) ) %*% t(V) %*% S[sub_index,j]
if(use.lars){
lars_obj = lars(X, Y, type='lasso', intercept=FALSE, normalize=FALSE, use.Gram = FALSE)
b = predict(lars_obj, s=lambda, type='coefficients', mode='lambda')$coefficients
}else{
b = coef( glmnet(X,Y,lambda = lambda/(p-1), intercept = FALSE, standardize = FALSE) )[-1]
}
W[sub_index, j] = W[sub_index, sub_index] %*% b
W[j, sub_index] = t(W[sub_index, j])
}
#print(W)
if( norm(W-W_old, type = '1') < tol ){
break
}
W_old = W
}
if (i == maxIt){
warning('Maximum number of iteration reached, glasso may not converge.')
}
return(list(lambda=lambda, W = W, Theta = solve(W)))
}
###### Our own version of graphical LASSO, with constraint Sigma_{c1,c2} = 0 #######
graphLASSO.c = function(S, lambda, c1, c2, maxIt = 100, tol = 1e-6, use.lars=FALSE){
# Our own version of graphical LASSO, with constraint Sigma_{c1,c2} = 0
# Args:
# S: int. sample covariance matrix
# lambda: positive float, the regularization parameter
# c1, c2: int, specify the correspoing entry of under constraint Sigma_{c1,c2} = 0
# maxIt: int, default 100, maximum itersion allowed
# tol: float, tolerance criterion, if results between 2 itersion are < tol, quit from intersion
# use.lars: use lars or glment as backend. TRUE for lars, FALSE for glmnet
# Defulat FALSE (recommened), glmnet runs faster than lars
# Returns:
# A list of: lambda: lambda value,
# W: estimated covariance matrix
# Theta: estimated precision matrix, invert of W
if(c1==c2){
stop('wrong input of c1,c2, must be different')
}else{
c1=min(c1,c2)
c2=max(c1,c2)
}
p = dim(S)[1]
W = S + diag(rep(lambda, p))
W_old = W
i = 0
while (i < maxIt){
i = i+1
for (j in p:1){
if(j == c2){
sub_index = (1:p)[-c(c1,c2)]
eigen_obj = eigen(W[sub_index, sub_index], symmetric = TRUE)
V = eigen_obj$vectors
d = eigen_obj$values
X = V %*% diag(sqrt(d)) %*% t(V) # W_11^(-1/2) * s_12
Y = V %*% diag( 1/sqrt(d) ) %*% t(V) %*% S[sub_index,j]
if(use.lars){
lars_obj = lars(X, Y, type='lasso', intercept=FALSE, normalize=FALSE, use.Gram = FALSE)
b = predict(lars_obj, s=lambda, type='coefficients', mode='lambda')$coefficients
}else{
b = coef( glmnet(X,Y,lambda = lambda/(p-2), intercept = FALSE, standardize = FALSE) )[-1]
}
#w_12_constraint = W[sub_index, sub_index] %*% b
W[sub_index, j] = W[sub_index, sub_index] %*% b
W[j, sub_index] = t(W[sub_index, j])
W[c1, j] = 0
W[j, c1] = 0
}else if (j == c1){
sub_index = (1:p)[-c(c1,c2)]
eigen_obj = eigen(W[sub_index, sub_index], symmetric = TRUE)
V = eigen_obj$vectors
d = eigen_obj$values
X = V %*% diag(sqrt(d)) %*% t(V) # W_11^(-1/2) * s_12
Y = V %*% diag( 1/sqrt(d) ) %*% t(V) %*% S[sub_index,j]
if(use.lars){
lars_obj = lars(X, Y, type='lasso', intercept=FALSE, normalize=FALSE, use.Gram = FALSE)
b = predict(lars_obj, s=lambda, type='coefficients', mode='lambda')$coefficients
}else{
b = coef( glmnet(X,Y,lambda = lambda/(p-2), intercept = FALSE, standardize = FALSE) )[-1]
}
W[sub_index, j] = W[sub_index, sub_index] %*% b
W[j, sub_index] = t(W[sub_index, j])
W[c2, j] = 0
W[j, c2] = 0
}else{
sub_index = (1:p)[-j]
eigen_obj = eigen(W[sub_index, sub_index], symmetric = TRUE)
V = eigen_obj$vectors
d = eigen_obj$values
X = V %*% diag(sqrt(d)) %*% t(V) # W_11^(-1/2) * s_12
Y = V %*% diag( 1/sqrt(d) ) %*% t(V) %*% S[sub_index,j]
if(use.lars){
lars_obj = lars(X, Y, type='lasso', intercept=FALSE, normalize=FALSE, use.Gram = FALSE)
b = predict(lars_obj, s=lambda, type='coefficients', mode='lambda')$coefficients
}else{
b = coef( glmnet(X,Y,lambda = lambda/(p-1), intercept = FALSE, standardize = FALSE) )[-1]
}
W[sub_index, j] = W[sub_index, sub_index] %*% b
W[j, sub_index] = t(W[sub_index, j])
}
}
#print(W)
if( norm(W-W_old, type = '1') < tol ){
break
}
W_old = W
}
if (i == maxIt){
warning('Maximum number of iteration reached, glasso may not converge.')
}
return(list(lambda=lambda, W = W, Theta = solve(W)))
}
####################################################################################
##### Wrapper function the graphLASSO function, genereate graph LASSO solution path ##########
graphLASSO.path = function(S, rholist = NULL, n_rho = 10, ...){
# Wrapper function the graphLASSO function, genereate graph LASSO solution path
# Args:
# S: matrix, sample covariance matrix
# rholist: vector, sequence of rho, default NULL, if specified, need to be a vector
# n_rho: int, length of the sequnce of rho, default 10
# Returns: a list of:
# w: list of estimated covariance matrix
# wi: list of estimated precision matrix
# rholist: a vector of sequence of rho
#
if (is.null(rholist)) {
rholist = seq(max(abs(S))/n_rho, max(abs(S)), length = n_rho)
}
w = list(); wi = list()
i = 0
for (rho in rholist){
i = i + 1
glasso_obj = graphLASSO(S = S, lambda = rho, ...)
w[[i]] = glasso_obj$W
wi[[i]] = glasso_obj$Theta
}
return(list(w = w, wi = wi, rholist = rholist))
}
# Wrapper function the graphLASSO.c function, genereate graph LASSO solution path under constraint Sigma_{c1,c2} = 0 #######
graphLASSO.path.c = function(S, rholist = NULL, n_rho = 10, ...){
# Args:
# S: matrix, sample covariance matrix
# rholist: vector, sequence of rho, default NULL, if specified, need to be a vector
# n_rho: int, length of the sequnce of rho, default 10
# ..., other arguments for function graphLASSO.c
# Returns: a list of:
# w: list of estimated covariance matrix
# wi: list of estimated precision matrix
# rholist: a vector of sequence of rho
#
if (is.null(rholist)) {
rholist = seq(max(abs(S))/n_rho, max(abs(S)), length = n_rho)
}
w = list(); wi = list()
i = 0
for (rho in rholist){
i = i + 1
glasso_obj = graphLASSO.c(S = S, lambda = rho, ...)
w[[i]] = glasso_obj$W
wi[[i]] = glasso_obj$Theta
}
return(list(w = w, wi = wi, rholist = rholist))
}
# Wrapper function the glasso, genereate graph LASSO solution path #######
glassopath.g = function(S, rholist = NULL, n_rho = 10, ...){
# Args:
# S: matrix, sample covariance matrix
# rholist: vector, sequence of rho, default NULL, if specified, need to be a vector
# n_rho: int, length of the sequnce of rho, default 10
# ..., other arguments for function glasso
# Returns: a list of:
# w: list of estimated covariance matrix
# wi: list of estimated precision matrix
# rholist: a vector of sequence of rho
#
if (is.null(rholist)) {
rholist = seq(max(abs(S))/n_rho, max(abs(S)), length = n_rho)
}
p = dim(S)[1]
w = list(); wi = list()
i = 0
for (rho in rholist){
i = i + 1
glasso_obj = glasso(s = S, rho = rho, ...)
w[[i]] = glasso_obj$w
wi[[i]] = glasso_obj$wi
}
return(list(w = w, wi = wi, rholist = rholist))
}
# Constriant version: wrapper function the glasso function, genereate graph LASSO solution path under constraint Sigma_{c1,c2} = 0 #######
glassopath.c = function(S, c1 = 1, c2 = 2, large_pen = 10e4, rholist = NULL, n_rho = 10, ...){
# Args:
# S: matrix, sample covariance matrix
# c1, c2: int, specify the correspoing entry of under constraint Sigma_{c1,c2} = 0
# large_pen: the penalty on the (c1,c2) entry of S, should be large number
# just use the default 10^4 unless you are experimenting
# rholist: vector, sequence of rho, default NULL, if specified, need to be a vector
# n_rho: int, length of the sequnce of rho, default 10
# ..., other arguments for function glasso
# Returns: a list of:
# w: list of estimated covariance matrix
# wi: list of estimated precision matrix
# rholist: a vector of sequence of rho
#
if (is.null(rholist)) {
rholist = seq(max(abs(S))/n_rho, max(abs(S)), length = n_rho)
}
p = dim(S)[1]
w = list(); wi = list()
i = 0
for (rho in rholist){
i = i + 1
rho_matrix = matrix(rho, p, p)
rho_matrix[c1,c2] = large_pen
rho_matrix[c2,c1] = large_pen
glasso_obj = glasso(s = S, rho = rho_matrix, ...)
w[[i]] = glasso_obj$w
wi[[i]] = glasso_obj$wi
}
return(list(w = w, wi = wi, rholist = rholist))
}
|
0d886aa62c53258df8cc597f8ff2fc96c2996c7b
|
a4e4aa76c902101fe5a475db1a4c2fafb2e55e9d
|
/대덕 wordcloud/daedeok.R
|
90d34bfb0bb4085ad68fa6df58a2c6db74c450dc
|
[] |
no_license
|
joeychoi12/NC_Project
|
76f2777d8d9563a65e714887bacebaf313ceb040
|
36e5eac6d61b16989216d0c9ff1875b4961a6524
|
refs/heads/master
| 2020-06-16T07:51:42.060661
| 2019-08-13T08:01:04
| 2019-08-13T08:01:04
| 195,516,735
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,965
|
r
|
daedeok.R
|
#대덕특구 WordCloud
setwd("c:/Users/Joey/Documents/R/Project/NC_Project/")
# Text Crawling from Investing.com News article
setwd("d:/workspace/R_Crawling/")
library(rvest)
library(dplyr)
library(stringr)
library(wordcloud2)
trim <- function(x) gsub("^\\s+|\\s+$", "", x)
trim1 <- function(x) gsub("\r", "", x)
trim2 <- function(x) gsub("\t", "", x)
trim3 <- function(x) gsub("\n", "", x)
base_url <- "https://hellodd.com/?md=news&mt=lists&list_type=plists&j_pid=&j_name=&ymd=&sdate=&edate=&cate_code=&find=all&search=대덕특구&page="
url_page1 <- paste0(base_url, 2)
url_page1
html_test <- read_html(url_page1)
html_nodes(html_test,".article") %>% html_text() %>% trim() %>% trim1() %>% trim2()
article <- c()
for (i in 1:347) {
if(i %% 50 == 0) print(i)
url <- paste0(base_url,i,encoding="UTF-8")
html <- read_html(url)
title <- html_nodes(html, ".article") %>% html_text() %>% trim() %>% trim1() %>% trim2()
article <- c(article,title)
}
article_title <- data.frame(title = article)
write.csv(article_title,"대덕.csv")
View(article_title)
#Wordcloud
install.packages("KoNLP")
library(KoNLP)
useSejongDic()
write(article, "daedeok_data.txt")
data <- readLines('daedeok_data.txt', Encoding="UTF-8")
data1 <- sapply(data, extractNoun, USE.NAMES = F)
data3 <- unlist(data1)
data3 <- gsub("\\d+","",data3) ## 숫자 없애기
data3 <- Filter(function(x) {nchar(x) >= 2}, data3) #2글자 이상 필터
data4 <- str_replace_all(data3, "[^[:alpha:]]","") #한글, 영어이외는 삭제
txt2 <- readLines("경제gsub.txt")
for(i in 1:length(txt2)) {
data3 <- gsub(txt2[i],"",data3)
}
write(data4,"대덕특구.txt")
data5 <- read.table("대덕특구.txt")
wordcount <- table(data5)
wordcloud <-sort(wordcount,decreasing = T)
head(wordcloud)
wordcloud2(wordcloud)
Sys.getlocale()
Sys.getlocale("LC_ALL","ko_KR.UTF-8")
Sys.setlocale("LC_ALL","ko_KR.UTF-8")
Sys.setlocale("LC_ALL", "korean")
Sys.setlocale("LC_ALL", "en_US.UTF-8")
|
425d148f212d1951888a5dd60790efbb55cafdb5
|
e69b1afa76dbf248acbcea98bc2660e49e8e6805
|
/R programming/Week 3/rankall.r
|
f8f703359ec4607d3f1456fca1c9c843aef5793e
|
[] |
no_license
|
Aparna-Vijayakumar/Data-Science-Coursera-Projects
|
281c6d7548dc718857679f47e49b485ec4fdca97
|
bf531db45b35aa4d6b169aeae19acb533b0c4cc1
|
refs/heads/master
| 2020-04-12T10:21:48.898664
| 2018-12-20T13:30:57
| 2018-12-20T13:30:57
| 162,427,731
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,451
|
r
|
rankall.r
|
rankall <- function(outcome, num = "best") {
## Read the outcome data
dat <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
## Check that state and outcome are valid
states = unique(dat[, 7])
switch(outcome, `heart attack` = {
col = 11
}, `heart failure` = {
col = 17
}, pneumonia = {
col = 23
}, stop("invalid outcome"))
## Return hospital name in that state with the given rank 30-day death rate
dat[, col] = as.numeric(dat[, col])
dat = dat[, c(2, 7, col)] # leave only name, state, and death rate
dat = na.omit(dat)
# head(dat) Hospital.Name State 1 SOUTHEAST ALABAMA MEDICAL CENTER AL 2
# MARSHALL MEDICAL CENTER SOUTH AL 3 ELIZA COFFEE MEMORIAL HOSPITAL AL 7 ST
# VINCENT'S EAST AL 8 DEKALB REGIONAL MEDICAL CENTER AL 9 SHELBY BAPTIST
# MEDICAL CENTER AL
# Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack 1 14.3 2 18.5 3
# 18.1 7 17.7 8 18.0 9 15.9
rank_in_state <- function(state) {
df = dat[dat[, 2] == state, ]
nhospital = nrow(df)
switch(num, best = {
num = 1
}, worst = {
num = nhospital
})
if (num > nhospital) {
result = NA
}
o = order(df[, 3], df[, 1])
result = df[o, ][num, 1]
c(result, state)
}
output = do.call(rbind, lapply(states, rank_in_state))
output = output[order(output[, 2]), ]
rownames(output) = output[, 2]
colnames(output) = c("hospital", "state")
data.frame(output)
}
|
1d2edc9647b2c58c081479861ce5e7c428928ad6
|
119e0655c3a2e1418bb67e42083e492546998f2e
|
/scripts_queue/Reading_MODIS_LST_with_QC_09112013.R
|
263bdbd49982ae3cf6a7ded01ac9dd424d0e15ee
|
[] |
no_license
|
dahcase/space_time_lucc
|
435041b61700d481c86d02704d2151c8376e4e82
|
67fe9dca7a496714478fa1ce860e0cfbfc9fe603
|
refs/heads/master
| 2021-05-07T17:55:50.016325
| 2017-11-01T05:26:11
| 2017-11-01T05:26:11
| 108,768,679
| 0
| 1
| null | 2017-10-29T20:28:16
| 2017-10-29T20:28:16
| null |
UTF-8
|
R
| false
| false
| 6,704
|
r
|
Reading_MODIS_LST_with_QC_09112013.R
|
######################################## MODIS QC FOR MOD12Q1 and MOD11A2 #######################################
########################################### Read QC flags in R for MODIS #####################################
#This script provides an example of Quality Flag processing for twor MODIS product in R.
#MODIS currently stores information in HDF4 format. Layers to be extracted must be listed first
#using for example gdalinfo. Note that QC flags are store bitpacks of 8bits (byte) in big endian!!!
#A data frame matching flag values is created to facilate the processing.
#Much of the inspiration and code originates from Steve Mosher:
#http://stevemosher.wordpress.com/2012/12/05/modis-qc-bits/
#AUTHOR: Benoit Parmentier
#DATE: 09/11/2013
#PROJECT: Space Time project and NCEAS
###################################################################################################
###Loading R library and packages
#library(gtools) # loading some useful tools
library(sp)
library(raster)
library(rgdal)
library(BMS) #contains hex2bin and bin2hex
library(bitops)
### Parameters and arguments
in_dir<- "/Users/benoitparmentier/Dropbox/Data/NCEAS/MODIS_processing"
out_dir<- "/Users/benoitparmentier/Dropbox/Data/NCEAS/MODIS_processing"
setwd(out_dir)
infile_LC<-"MCD12Q1A2001001.h10v09.051.2012157220925.hdf"
infile_LST <- list.files(path=in_dir,pattern=".*.hdf$")
infile_IDRISI <- list.files(path=in_dir,pattern=".*.rst$")
function_analyses_paper <-"MODIS_and_raster_processing_functions_09112013.R"
script_path<-in_dir #path to script functions
source(file.path(script_path,function_analyses_paper)) #source all functions used in this script.
### BEGIN ###
rg <-GDAL.open(infile_LST[1])
getDriver(rg)
getDriverLongName(rg) #this does not work
#GDALinfo(rg)
#test<-readGDAL(rg,band=1)
GDALinfo_hdf<-GDALinfo(infile_LST[1])
str(GDALinfo_hdf)
#modis_subdataset <-attr(GDALinfo_hdf,"subdsmdata") #get modis subdataset
#GDALinfo_hdf["columns"]
#GDALinfo_hdf["rows"]
modis_subdataset <- attributes(GDALinfo_hdf)$subdsmdata
print(modis_subdataset)
###### PART I: Reading LST and Land cover layers (subset) ######
#HDF4_EOS:EOS_GRID:"MOD11A1.A2001001.h09v04.005.2006343034412.hdf":MODIS_Grid_Daily_1km_LST:LST_Day_1km
#HDF4_EOS:EOS_GRID:"MOD11A1.A2001001.h09v04.005.2006343034412.hdf":MODIS_Grid_Daily_1km_LST:QC_Day
#modis_subset_layer <- paste("HDF4_EOS:EOS_GRID:",f20,":MODIS_Grid_Daily_1km_LST:LST_Day_1km",sep='')
modis_subset_layer_LST_Day <- paste("HDF4_EOS:EOS_GRID:",infile_LST[1],":MODIS_Grid_Daily_1km_LST:LST_Day_1km",sep="")
modis_subset_layer_LST_QC <- paste("HDF4_EOS:EOS_GRID:",infile_LST[1],":MODIS_Grid_Daily_1km_LST:QC_Day",sep="")
#modis_subset_layer <- file.path(in_dir,modis_subset_layer)
r <-readGDAL(modis_subset_layer_LST_Day)
r <-raster(r)
r_qc <-readGDAL(modis_subset_layer_LST_QC)
r_qc <-raster(r_qc)
#system("gdalinfo MOD11A1.A2001020.h09v04.005.2006347194212.hdf")
#system(paste("gdalinfo"," MCD12Q1A2001001.h10v09.051.2012157220925.hdf",sep=""))
quartz(13,28)
plot(r)
plot(r_qc)
#note the storage in FLT4S, i.e. float but in effect empty for
#for anything greater than 255!!!
freq(r_qc)
#0 is good quality
#Here are the most frequent categories found in the QC lqyers...
r_qc2 <- r_qc==2 # QC not produced clouds
r_qc0 <- r_qc==0 # QC good quality
r_qc3 <- r_qc==3 # QC not produced
r_qc65 <- r_qc==65 # LST produced
r_qcl1 <- stack(r_qc0,r_qc2,r_qc3,r_qc65)
layerNames(r_qcl1) <- c("r_qc0 Good quality","r_qc2 Not produced-Cloud","r_qc3 not produced","r_qc65 LST produced")
plot(r_qcl1)
###### PART II: Reading and Handling QC flags ######
## CONVERSION TO RAW FORMAT: rawToBits,inToBits,packBits: this is in {base} package
intToBits(65) #integer INT four bytes, not that the notation include 01 for 1
#rawToBits(65) for vector
length(intToBits(65))
intToBits(65)[1:8] #integer INT four bytes
as.integer(intToBits(65)[1:8])
#[1] 1 0 0 0 0 0 1 0 #this is little endian binary notation
fg<-as.integer(intToBits(65)[1:8]) #flag reversed into big endian to match MODIS
fg[8:1] #This is number 65 in big endian format!!! BITS 0-1 : LST produced check other QA
#[1] 0 1 0 0 0 0 0 1
rev(as.integer(intToBits(65)[1:8]))
##### Quick test
unique_val<-unique(r_qc) #unique values
f_values <- as.data.frame(freq(r_qc)) # frequency values in the raster...
head(f_values)
f_values
rev(as.integer(intToBits(65)[1:8]))
val<-(as.integer(intToBits(65)[1:8]))
val<-val[8:1]
#sprintf("%x",65) #convert decimal to hexadecimal using C-style string formatting
#sprintf("%x",123)
#r_qc2 <- r_qc==2 # LST not produced due to clouds (1-0)
#r_qc0 <- r_qc==0 # LST good quality (0-0)
#r_qc3 <- r_qc==3 # LST not produced (1-1) (in hex decimal 0x03)
#r_qc65 <- r_qc==65 # LST produced check QA (0-1)
#[1] 0 1 0 0 0 0 0 1 : this is 65
val[1:2] # LST produced check QA (0-1)
val[3:4] # good quality (0-0) #this is in the second level info, data quality flag
val[5:6] # average emissivity error <= 0.01 , Emis Error Flag
val[7:8] # average LST error <= 2K , LST Error Flag
rev(as.integer(intToBits(65)[1:8]))
rev(hex2bin("0x03")) #if 110000000000 then it is not produced and should be removed...?
#if((qc_this_day & 0x03)==0, ${lst}, null())' # & is bitwise-and,
#e.g qc_this day: 00000010
#e.g. 0x03 : 00000011
# result "AND" : 00000010 hence FALSE and value is set to null in GRASS...
# Check in GRASS...
# In R use bitAnd(a,b) for the bitwise operator &
#there are 13 unique values, the most frequent one is value 2 with 3,0 and 65 following...
## REWRITE INTO A FUNCTION with options for LST,LC and NDVI/EVI
## Step 1: list values in raster
f_values <- as.data.frame(freq(r_qc)) # frequency values in the raster...
head(f_values)
## Step 2: convert integer values into relevant binary
t44 <- (sapply(f_values$value,function(x){rev(as.integer(intToBits(x)[1:8]))}))
t44 <- (lapply(f_values$value,function(x){rev(as.integer(intToBits(x)[1:8]))}))
#f_values$bin_val <- unlist(lapply(f_values$value,function(x){rev(as.integer(intToBits(x)[1:8]))}))
#This is currently created for LST (see S. Mosher blog)
QC_obj <- create_MODIS_QC_table(LST=TRUE)
names(QC_obj)
QC_data_lst <- QC_obj$LST
qc_lst_valid <- subset(x=QC_data_lst,Bit1 == 0 & Bit0 ==1 & Bit3 !=1)
names(qc_lst_valid)
qc_lst_valid$Integer_Value
qc_valid<-qc_lst_valid$Integer_Value
qc_valid_modis_fun(qc_valid,rast_qc,rast_var){
## function to download modis product??
#add here...
|
003463745c27b6d4c1da201aee13eaa41f08eef9
|
96e54a2f183ac913cd533b22560dbb6f9de98e64
|
/man/assignMultiple.Rd
|
e8c2f447a07388e4a326e9290332611cc23a16cf
|
[] |
no_license
|
cran/KarsTS
|
fe9e7cb51abd77edc1cf461b92fe86e9c760b9a8
|
a61bf7a479a7eeba1d2af68ff0fab8041b3d3fe2
|
refs/heads/master
| 2021-08-16T07:19:03.010559
| 2021-01-14T19:50:05
| 2021-01-14T19:50:05
| 92,603,787
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 592
|
rd
|
assignMultiple.Rd
|
\name{assignMultiple}
\alias{assignMultiple}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
assignMultiple: assign multiple
}
\description{
This function applies the function assign multiple times
}
\usage{
assignMultiple(namesVector, valuesList, envir = KTSEnv)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{namesVector}{
A vector containing the names to be assigned
}
\item{valuesList}{
The values to which the names will be assigned
}
\item{envir}{
The environment
}
}
\author{
Marina Saez Andreu
}
|
d4c3f6c0367bd01463fdcc23ae17d56b86b05e0d
|
641f150bdb4dfd3cb5cd28af2c36dfeef24a6776
|
/plot3.R
|
9a762ff46fcf4526e5292473cebf86da48337834
|
[] |
no_license
|
Louise222/ExData_Plotting1
|
824e5d902831148bf32fe8429979e5581365e2d5
|
85c602be91e7e9486549fea866fe5c23efb65840
|
refs/heads/master
| 2021-01-21T02:59:19.979829
| 2016-01-08T08:31:45
| 2016-01-08T08:31:45
| 49,254,300
| 0
| 0
| null | 2016-01-08T06:29:23
| 2016-01-08T06:29:23
| null |
UTF-8
|
R
| false
| false
| 446
|
r
|
plot3.R
|
source("load_data.R")
png(filename = "plot3.png", width = 480, height = 480, units = "px")
plot(newdata$DateTime,newdata$Sub_metering_1,type = "l",
col="black",xlab="",ylab = "Energy sub metering")
lines(newdata$DateTime,newdata$Sub_metering_2,col="red")
lines(newdata$DateTime,newdata$Sub_metering_3,col="blue")
legend("topright",c("Sub_metering_1","Sub_metering_2","Sub_metering_3"),
col = c("black","red","blue"),lwd = 1)
dev.off()
|
e5b304972be9a7b44a8ea8946ce17513a4c89ed1
|
1eee16736f5560821b78979095454dea33b40e98
|
/thirdParty/HiddenMarkov.mod/R/summary.mmglm1.R
|
d4381fb77ae1ea7aaaaa322be44e23390e164467
|
[] |
no_license
|
karl616/gNOMePeaks
|
83b0801727522cbacefa70129c41f0b8be59b1ee
|
80f1f3107a0dbf95fa2e98bdd825ceabdaff3863
|
refs/heads/master
| 2021-01-21T13:52:44.797719
| 2019-03-08T14:27:36
| 2019-03-08T14:27:36
| 49,002,976
| 4
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 229
|
r
|
summary.mmglm1.R
|
"summary.mmglm1" <-
function (object, ...)
{
list(delta=object$delta, Pi=object$Pi, nonstat=object$nonstat,
beta=object$beta,
sigma=object$sigma, glmfamily=object$glmfamily,
n=length(object$y))
}
|
5919d6b4d61c2ab795d9e5b3a12f9f542372bad8
|
fdb874e3f89e28d8d5e2c9a499aa9bbe46349792
|
/fitGaussianGraph/R/fitGaussianGraph.R
|
260054dab4a119a873695d4529f8cad143ce6e9e
|
[] |
no_license
|
bohaoyao/fitting-Gaussian-graphical-models
|
e3ae9f754c5902013ba9f89931ed6c696736c33d
|
9cff38b4989176dffa514af5333fe2ffb5612049
|
refs/heads/master
| 2022-08-07T06:38:30.051474
| 2022-07-20T16:35:44
| 2022-07-20T16:35:44
| 225,881,948
| 0
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,165
|
r
|
fitGaussianGraph.R
|
#outputs value of a_{i,j}
f <- function(i, j, LambdaE, S){
ans <- S[i,j]
if (i > 1) {
for (k in 1:(i-1)) {
ans <- ans - LambdaE[k,i]*S[k,j]
}
}
return(ans)
}
#outputs parents of i
parents <- function(i, LambdaA){
ans <- c()
if (i > 1) {
for (j in 1:(i-1)) {
if (LambdaA[j,i] != 0) {
ans <- c(ans, j)
}
}
}
return(ans)
}
#outputs the starting point \Tilde{\Lambda}
SolveLambdaE <- function(LambdaA, S){
p <- dim(S)[1] # dimension
LambdaE <- matrix(0, p, p)
for (i in 2:p) {
pa <- parents(i, LambdaA)
p1 <- length(pa)
if (p1 > 0) {
A <- matrix(0, p1, p1)
b <- matrix(0, p1, 1)
count1 <- 1
for (j in pa) {
count2 <- 1
for (k in pa) {
A[count1, count2] <- f(j, k, LambdaE, S)
count2 <- count2 + 1
}
b[count1, 1] <- f(j, i, LambdaE, S)
count1 <- count1 + 1
}
B <- solve(A,b)
LambdaE[pa,i] <- B
}
}
return(LambdaE)
}
#outputs the value of \Tilde{\omega_{ij}}
SolveOmega <- function(i, j, LambdaE, S){
ans <- f(i, j, LambdaE, S)
if (j > 1) {
for (k in 1:(j-1)) {
ans <- ans - LambdaE[k,j]*f(i, k, LambdaE, S)
}
}
return(ans)
}
#outputs the starting points \Tilde{\Omega}
SolveOmegaE <- function(OmegaA, LambdaE, S){
p <- dim(S)[1] # dimension
OmegaE <- matrix(0, p, p)
for (i in 1:p) {
for (j in i:p) {
if (OmegaA[i,j] != 0) {
OmegaE[i,j] <- SolveOmega(i, j, LambdaE, S)
if (i != j) {
OmegaE[j,i] <- OmegaE[i,j]
}
}
}
}
return(OmegaE)
}
#outputs the derivative of the loglikelihood against Lambda
dLambda <- function(L, O, LambdaA, S) {
p <- dim(O)[1] # dimension
A <- 2*S%*%(diag(p)-L)%*%solve(O)
for (i in 1:p) {
for (j in 1:p) {
if (LambdaA[i,j] == 0){
A[i,j] <- 0
}
}
}
return(A)
}
#outputs the derivative of the loglikelihood against Omega
dOmega <- function(L, O, OmegaA, S) {
p <- dim(O)[1] # dimension
B <- solve(O)%*%t((diag(p)-L))%*%S%*%(diag(p)-L)%*%solve(O)-solve(O)
for (i in 1:p) {
for (j in i:p) {
if (OmegaA[i,j] == 0){
B[i,j] <- 0
B[j,i] <- 0
}
}
}
return(B)
}
#outputs the loglikelihood given Lambda and Omega (divided by n)
loglikelihood <- function(L, O, S) {
p <- dim(O)[1] # dimension
return(log(det((diag(p)-L)%*%solve(O)%*%t(diag(p)-L)))-sum(diag(S%*%(diag(p)-L)%*%solve(O)%*%t(diag(p)-L))))
}
#outputs the loglikelihood given Sigma
loglikelihoodS <- function(Sigma, S, n) {
return((-n*(log(det(Sigma)))-n*sum(diag(solve(Sigma)%*%S)))/2)
}
#ouputs the number of missing edges in the graph (used for finding the degree of freedom)
count_missing_edges <- function(LambdaA, OmegaA) {
p <- dim(LambdaA)[1]
edges <- 0
for (i in 1:(p-1)) {
for (j in (i+1):p) {
if (LambdaA[i,j] != 0) {
edges <- edges + 1
}
else if (OmegaA[i,j] != 0) {
edges <- edges + 1
}
}
}
return(p*(p-1)/2 - edges)
}
#uses hill climbing algorithm to output the supremum of Lambda, Omega and Sigma
#also computes the p-value
HillClimb <- function(LambdaE, OmegaE, learning_rate, convergence, S, n) {
diff <- 1
Lambda0 <- LambdaE
Omega0 <- OmegaE
p <- dim(S)[1] # dimension
min_eig <- min(eigen(Omega0)$values)
if (min_eig <= 0) {
Omega0 <- Omega0 - (min_eig - 1)*diag(p)
}
# if (min(eigen(Omega0)$values) <= 0) {
# SigmaE <- solve(t(diag(p)-LambdaE)) %*% OmegaE %*% solve(diag(p)-LambdaE)
# message('Use a larger sample size to obtain p-value.')
# return(list(Lambdahat = LambdaE, Omegahat = OmegaE, Sigmahat = SigmaE))
# }
likelihoodvector <- c(loglikelihood(Lambda0, Omega0, S))
while (diff > convergence) {
LambdaE <- Lambda0
OmegaE <- Omega0
A <- dLambda(LambdaE, OmegaE, LambdaE, S)
B <- dOmega(LambdaE, OmegaE, OmegaE, S)
Lambda0 <- LambdaE + learning_rate*A
Omega0 <- OmegaE + learning_rate*B
diff <- loglikelihood(Lambda0, Omega0, S) - loglikelihood(LambdaE, OmegaE, S)
likelihoodvector <- c(likelihoodvector, loglikelihood(Lambda0, Omega0, S))
}
SigmaE <- solve(t(diag(p)-LambdaE)) %*% OmegaE %*% solve(diag(p)-LambdaE)
s <- 2 * (loglikelihoodS(S, S, n) - loglikelihoodS(SigmaE, S, n)) #likelihood ratio statistic
e <- count_missing_edges(LambdaE, OmegaE)
plot(likelihoodvector)
return(list(Lambdahat = LambdaE, Omegahat = OmegaE, Sigmahat = SigmaE, chisq = s, pvalue = pchisq(s, df=e, lower.tail = FALSE)))
}
#' Fitting bridgeless Gaussian graphical model
#'
#' Fits a bridgeless Gaussian graphical model as well as outputting the p-value of whether the data set fits the model.
#'
#' The p-value should be used as a guide for rejecting models only as we might no longer be working in a concave space at low p-values.
#' @param X centered data set (can provide both S and n instead).
#' @param S covariance matrix of X (optional if X is provided).
#' @param n sample size (optional if X is provided).
#' @param LambdaA adjacency matrix for the directed edges in G.
#' @param OmegaA adjacency matrix for the bidirected edges in G.
#' @param learning_rate learning rate for the hill-climbing algorithm.
#' @param convergence_rate the difference of the objective function between iterations whence we determine the hill-climbing algorithm has converged.
#'
#' @return A list with components
#' \describe{
#' \item{Lambdahat}{a square matrix of the fitted regression coefficients.}
#' \item{Omegahat}{the fitted covariance matrix of the error terms.}
#' \item{Sigmahat}{the fitted covariance matrix of all observed variables.}
#' \item{chisq}{the test statistic which converges to a chi-squared distribution.}
#' \item{pvalue}{the p-value of whether the data set X fits the graphical model.}
#' }
#'
#' @export
fitGaussianGraph <- function(X, S, n, LambdaA, OmegaA, learning_rate, convergence_rate = 0) {
if (missing(S)) {
S <- cov(X)
}
if (missing(n)) {
n <- length(X[,1])
}
LambdaE <- SolveLambdaE(LambdaA, S)
OmegaE <- SolveOmegaE(OmegaA, LambdaE, S)
return(HillClimb(LambdaE, OmegaE, learning_rate, convergence_rate, S, n))
}
|
59e73cef20325621916023866c0d85b6f8dc721b
|
b4294e86bd4aa13da2da944c3499eff23ac9e0c6
|
/clustering.R
|
d1688c6c5cc4c662ca5a6b2ae5f77a1800d1a18a
|
[] |
no_license
|
sylvansecrets/signal-coursework
|
62f0a5e7522eb478b6a6d6adc01609cebfeeeeca
|
caacf893d0e8c8fdff6c7fc0433422f54ea04309
|
refs/heads/master
| 2021-01-12T10:18:53.326551
| 2016-12-14T03:06:14
| 2016-12-14T03:06:14
| 76,419,676
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,474
|
r
|
clustering.R
|
library("readr")
library("cluster")
library("pvclust")
library("fpc")
library("ggplot2")
options(digits=3)
setwd("C:/Users/User/Documents/GitHub/Signal-Data-Science")
protein_df = read.delim("~/GitHub/Signal-Data-Science/protein.txt")
rownames(protein_df) = protein_df$Country
protein_df$Country=NULL
# Scale the data
protein_matrix = scale(protein_df)
protein_matrix
# Generate distance matrix
dist_matrix = dist(protein_matrix, method="euclidean")
dist_matrix
# Create a cluster
clust = hclust(dist_matrix, method="ward.D2")
plot(clust)
# Convenience function
print_clusters = function(labels, k) {
for(i in 1:k) {
print(paste("cluster", i))
print(protein_df[labels == i, c( "RedMeat",
"Fish", "Fr.Veg")])
}
}
# Cut trees into 5
cut_clust = cutree(clust,k=5)
print_clusters(cut_clust, k=5)
# Convenience function
hclust_plot = function(d, method,k){
scaled = scale(d)
distance = dist(scaled)
uncut = hclust(distance, method=method)
cut = cutree(uncut, k=k)
return(
clusplot(scaled, cut, color=TRUE, shade=TRUE, labels=2, lines=0)
)
}
# Plot the first two principle components
for (k in 2:5) {
hclust_plot(dist_matrix, "ward.D2", k)
}
# bootstrap sampled clusters (new with P-values!)
# transpose first, pvclust goes by columns instead of rows
pval_clust = pvclust(t(protein_matrix), method.hclust="ward.D2", method.dist="euclidean")
# K means clustering
k_mean = kmeans(protein_matrix, 5)
kmeans_plot = function(data,k){
k_mean = kmeans(data,k)
return(clusplot(data, k_mean$cluster, color=TRUE, shade=TRUE, labels=2, lines=0))
}
# fairly not stable with k=5
# extremely stable with k=2
artificial = rbind(China = rnorm(ncol(protein_matrix), sd=2), protein_matrix)
# the Chinese ruin everything
# Validating a choice of K
# NEVER EVER put plot=TRUE
k_meanrun = kmeansruns(protein_matrix, krange=1:10, criterion="ch")
k_meanrun2 = kmeansruns(protein_matrix, krange=1:10, criterion="asw")
k_mean_compare = data.frame(k=1:10, asw=scale(k_meanrun2$crit), ch=scale(k_meanrun$crit))
ggplot(k_mean_compare, aes(x=k)) + geom_point(aes(y=asw), color="darkgreen", size=5, alpha=0.5) + geom_point(aes(y=ch), color="purple", size=4, alpha=0.7)
# The criteria disagree: aws supports 3 (slight), ch supports 2(lots)
# run bootstrap k-means
k_bootstrap = clusterboot(protein_matrix, clustermethod=kmeansCBI, runs=100, iter.max=100, krange=5)
# get clusters on original data
bootstrap_clust = k_bootstrap$result$partition
bootstrap_clust[bootstrap_clust == 1] # Greece Italy Portugal Spain
bootstrap_clust[bootstrap_clust == 2] # Albania Bulgaria Romania Yugoslavia
bootstrap_clust[bootstrap_clust == 3] # Denmark Finland Norway Swede
bootstrap_clust[bootstrap_clust == 4] # Austria Belgium France Ireland Netherlands Switzerland UK W Germany
bootstrap_clust[bootstrap_clust == 5] # Czechoslovakia E Germany Hungary Poland USSR
# of times each cluster has dissolved (more is bad)
k_bootstrap$bootbrd
# [1] 40 17 23 19 51
# stability of clusters (closer to 1 is good)
k_bootstrap$bootmean
# 0.703 0.835 0.842 0.758 0.606
# first and fifth aren't great.
clusplot(protein_matrix, k_bootstrap$result$partition, color=TRUE, shade=TRUE, labels=2, lines=0)
# incidentally, 1 and 5 are the most elongated on this graph
|
ec0608ab59d19691b56e1903d53737d440150c59
|
2b96dda01e284f5df6c5f749272249ff3b1c26cd
|
/ch07/ex7-2.r
|
61b94533d0d98da095f32e452f6eb2071258a11f
|
[] |
no_license
|
freebz/The-Art-of-R-Programming
|
f9a82b42fedebc5ef0f10bfd132bdcad27600d83
|
6e51856512b945455810b1ad686bae58f0779ff7
|
refs/heads/master
| 2021-06-05T03:27:00.519420
| 2016-08-12T05:01:24
| 2016-08-12T05:01:24
| 65,524,551
| 1
| 1
| null | null | null | null |
UTF-8
|
R
| false
| false
| 268
|
r
|
ex7-2.r
|
## 7.2 산술 및 불리언 연산과 값
x <- c(TRUE, FALSE, TRUE)
y <- c(TRUE, TRUE, FALSE)
x
y
x & y
x[1] && y[1]
if (x[1] && y[1]) print("both TRUE")
if (x & y) print("both TRUE")
1 < 2
(1 < 2) * (3 < 4)
(1 < 2) * (3 < 4) * (5 < 1)
(1 < 2) == TRUE
(1 < 2) == 1
|
fea2c3f1672d2b7d211bb60f320b9d14322e7ea7
|
9b94004d73d16cb4ce13efb088f240302abf33b5
|
/classifier_EM_tskew.R
|
4ce410c91dd14de87be02b67bf6253e390f0f088
|
[] |
no_license
|
Zeroh729/MS-Thesis
|
f94b89070255851a1c261bb8d8ef04c241dfdad7
|
c4058e9444883a768a4c35876b9cb4e645c0d2f0
|
refs/heads/master
| 2022-12-31T20:45:59.786850
| 2020-10-22T07:15:45
| 2020-10-22T07:15:45
| 267,747,109
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 6,277
|
r
|
classifier_EM_tskew.R
|
source("D:/~Masters/~ MS-STAT/~THESIS/Code/utils.R")
source("D:/~Masters/~ MS-STAT/~THESIS/Code/common.R")
library(EMCluster)
library(EMMIXskew)
library(dplyr)
emclassifier_tskew <- function(vecFluo, volDrp, crit="BIC"){
res <- em_tskew(data.frame(Fluorescence=sort(vecFluo)), distr="mst",volDrp = volDrp, crit)
res_info <- get_resInfo(res, "mst")
return(res_info)
}
emclassifier_t <- function(vecFluo, volDrp, crit="BIC"){
res <- em_tskew(data.frame(Fluorescence=sort(vecFluo)), distr = "mvt", volDrp = volDrp, crit)
res_info <- get_resInfo(res, "mvt")
return(res_info)
}
get_resInfo <- function(res, distr){
res_info <- list()
G <- res$G
if(distr == "mst"){
estParam <- data.frame(Mu = c(res$em$modpts), Sigma = sqrt(c(res$em$sigma)), Df = res$em$dof, Skew = c(res$em$delta), MixProp = res$em$pro) %>%
mutate(NegThres = rep(res$negThres, nrow(.))) %>%
mutate(PosThres = rep(res$posThres, nrow(.))) %>%
arrange(Mu)
title <- bquote("Neg~"~t~"("~v==.(round(estParam[1,"Df"],2))~","~mu==.(round(estParam[1,"Mu"],2))~","~sigma==.(round(estParam[1,"Sigma"],2))~","~delta==.(round(estParam[1,"Skew"],2))~")")
subtitle <- bquote("Pos~"~t~"("~v==.(round(estParam[G,"Df"],2))~","~mu==.(round(estParam[G,"Mu"],2))~","~sigma==.(round(estParam[G,"Sigma"],2))~","~delta==.(round(estParam[G,"Skew"],2))~")")
if(G == 3){
res_info$desc <- bquote("Rain~"~t~"("~v==.(round(estParam[2,"Df"],2))~","~mu==.(round(estParam[2,"Mu"],2))~","~sigma==.(round(estParam[2,"Sigma"],2))~","~delta==.(round(estParam[2,"Skew"],2))~")")
}
}else if(distr == "mvt"){
estParam <- data.frame(Mu = c(res$em$modpts), Sigma = sqrt(c(res$em$sigma)), Df = res$em$dof, MixProp = res$em$pro) %>%
mutate(NegThres = rep(res$negThres, nrow(.))) %>%
mutate(PosThres = rep(res$posThres, nrow(.))) %>%
arrange(Mu)
title <- bquote("Neg~"~t~"("~v==.(round(estParam[1,"Df"],2))~","~mu==.(round(estParam[1,"Mu"],2))~","~sigma==.(round(estParam[1,"Sigma"],2))~")")
subtitle <- bquote("Pos~"~t~"("~v==.(round(estParam[G,"Df"],2))~","~mu==.(round(estParam[G,"Mu"],2))~","~sigma==.(round(estParam[G,"Sigma"],2))~")")
if(G == 3){
res_info$desc <- bquote("Rain~"~t~"("~v==.(round(estParam[2,"Df"],2))~","~mu==.(round(estParam[2,"Mu"],2))~","~sigma==.(round(estParam[2,"Sigma"],2))~")")
}
}
res_info$est_parameter <- estParam
res_info$classification <- res$classification
res_info$G <- G
res_info$emres <- res
res_info$title <- title
res_info$subtitle <- subtitle
return(res_info)
}
em_tskew <- function(drp, volDrp, distr, crit="BIC"){
getClusMemberProb <- function(emres){
clusterMem <- emres$tau
means <- emres$modpts
posClust <- which.max(means)
negClust <- which.min(means)
rainClust <- if(length(means)==3) which(means == median(means)) else NA
clusts <- c(posClust, negClust, rainClust)
names(clusts) <- c("pos", "neg", "rain")
clusts <- sort(clusts)
classification <- factor(names(clusts[emres$clust]), levels = c("pos", "neg", "rain"))
print(paste("Negative Mu:", means[negClust], "Positive Mu:", means[posClust]))
if(!is.na(rainClust))
print(paste("Rain Mu:", means[rainClust]))
posMemberProb <- clusterMem[,posClust]
negMemberProb <- clusterMem[,negClust]
rainMemberProb <- clusterMem[,rainClust]
return(list(negMemberProb,posMemberProb, classification))
}
getInitParams <- function(drp, G){
set.seed(1234)
initEM <- init.EM(drp, nclass = G)
initEMSkew <- list(mu = t(initEM$Mu),
sigma = array(c(initEM$LTSigma), c(1,1,G)),
pro = initEM$pi,
dof = rep(30, G),
delta = t(matrix(rep(0,G))))
writeLines(paste("Initial parameters for G=", G))
writeLines(paste("Mu = ", initEMSkew$mu))
writeLines(paste("Sigma = ", initEMSkew$sigma))
writeLines(paste("Pi = ", initEMSkew$pro))
return(initEMSkew)
}
set.seed(1234)
# Manual - https://cran.r-project.org/web/packages/EMMIXskew/EMMIXskew.pdf; ncov = 3 means general variance (1 & 2 gives me equal variances)
emres_G2 <- EmSkew(drp, init = getInitParams(drp, G = 2), g = 2, nkmeans = 2, nrandom = 2, distr = distr, ncov = 3, initloop = 20, debug = FALSE) # MAIN FUNCTION
emres_G3 <- EmSkew(drp, init = getInitParams(drp, G = 3), g = 3, nkmeans = 3, nrandom = 3, distr = distr, ncov = 3, initloop = 20, debug = FALSE) # MAIN FUNCTION
if(crit == "BIC"){
scoreG2 <- emres_G2$bic
scoreG3 <- emres_G3$bic
}else if(crit == "ICL"){
scoreG2 <- emres_G2$ICL
scoreG3 <- emres_G3$ICL
}else if(crit == "AIC"){
scoreG2 <- emres_G2$aic
scoreG3 <- emres_G3$aic
}
if(scoreG3 < scoreG2){
emres <- emres_G3
emres_lower <- emres_G2
G <- 3
}else{
emres <- emres_G2
emres_lower <- emres_G3
G <- 2
}
.g(negMemberProb, posMemberProb, classification) %=% getClusMemberProb(emres)
nneg <- sum(classification == "neg")
res <- list()
res$est <- cal_concentration(nneg, nrow(drp),volDrp)
res$member <- list(negProb = negMemberProb, posProb = posMemberProb)
res$G <- G
res$em <- emres
res$em_lower <- emres_lower
res$crit <- crit
res$critScore <- min(scoreG2, scoreG3)
res$bestmodel <- paste(paste(crit, "of G=2 is",scoreG2), paste(crit, "of G=3 is", scoreG3), sep = "\n")
res$classification <- classification
res$negThres <- drp[max(which(classification == "neg")),]
res$posThres <- drp[min(which(classification == "pos")),]
return(res)
}
# setwd("D:/~Masters/~ MS-STAT/~THESIS/Papers/(Supplementary Files) Lievens/ddPCR-master")
# if(!exists("df_orig")) df_orig <- readRDS("Dataset_t_sampled.RDS")
# flou <- df_orig %>% filter(react.ID==13) %>% select(-c(1:11))
# flou <- flou[!is.na(flou)]
# flou <- sort(as.numeric(as.character((flou))))
#
# emres <- emclassifier_tskew(flou, 0.85, crit="BIC")
# emres$emres$bestmodel
# emres$G
# emres$emres$em$mu
#
#
# emres$emres$em$aic
# emres$emres$em_lower$aic
# Comparison with 3 Targets in Umbrella tutorial data
# Umbrella (by thres) | (by robust) | Mine
# Target 1 : 6021 | 6008 | 6021
# Target 2 : 5893 | 5914 | 5111
# Target 3 : 5717 | 5697 | 4584
|
9e4de14989ef8c07b2a3f19a863510535090ac7c
|
43badf574c549135a0fc50b5054be28fdc7fb275
|
/experiment/MaFMethodology/R/hhcovshhcmoea/IGD/15/kruskalscript.R
|
7cef81c9e79f6e3f0248fddfecc00230c5bceadc
|
[] |
no_license
|
fritsche/hhco-vs-hhcmoea
|
8343a589e8efd1b0c0ffc8b0d67347fa688eb5a0
|
8cb8e1ec2ebab879d74391013d868af24d769b79
|
refs/heads/main
| 2023-02-10T15:19:20.576194
| 2021-01-09T14:00:38
| 2021-01-09T14:00:38
| 326,830,556
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 1,062
|
r
|
kruskalscript.R
|
require(PMCMRplus)
options("width"=10000)
ARRAY <- c(0.02564970475535416,0.01621768667849493,0.016800804793346218,0.016280921910048676,0.0696660740186638,0.016177777896017948,0.058561150499856586,0.01669128097678837,0.0160956658042893,0.05241825970439316,0.045721932836257646,0.03284580700929481,0.09004995630520703,0.015348621785942448,0.015844836607363782,0.019709934727626093,0.016074199028840254,0.015616036277590674,0.016449652590480437,0.017913837304476394,0.08784820446844634,0.02066736273635942,0.020069257987767073,0.11730987199401094,0.02837753926502847,0.018606337844368052,0.02708142995815993,0.15555478815849594,0.11843003497558917,0.018753892831454795,0.09747591145088905,0.023611751550549957,0.0191628505003178,0.06671959328116094,0.026136708289983955,0.01928011323808046,0.04418713139265151,0.16959652122366278,0.017343335949022196,0.19735732180574594)
categs<-as.factor(rep(c("HHcMOEA","HHCOR2LPNORM"),each=20));
result <- kruskal.test(ARRAY,categs)
print(result);pos_teste<-kwAllPairsNemenyiTest(ARRAY, categs, method='Tukey');print(pos_teste);
|
993432cbad85930bdc2a9ceb36e820b4985caae5
|
648ebf72f913f90fe9575a8325ce1d8633ac449e
|
/R/svd.triplet.R
|
c19cd38d5b246a562baa079f678cfd695ef1c62a
|
[] |
no_license
|
husson/FactoMineR
|
1b2068dc925647603899607bf2a90e926ef002d5
|
1f7d04dcf7f24798ce6b322a4dd3ab2bd8ddd238
|
refs/heads/master
| 2023-04-28T09:19:02.186828
| 2023-04-23T11:32:21
| 2023-04-23T11:32:21
| 32,511,270
| 26
| 9
| null | 2023-04-21T13:34:38
| 2015-03-19T09:08:51
|
HTML
|
UTF-8
|
R
| false
| false
| 2,931
|
r
|
svd.triplet.R
|
svd.triplet <- function (X, row.w = NULL, col.w = NULL,ncp=Inf) {
tryCatch.W.E <- function(expr){ ## function proposed by Maechler
W <- NULL
w.handler <- function(w){ # warning handler
W <<- w
invokeRestart("muffleWarning")
}
list(value = withCallingHandlers(tryCatch(expr, error = function(e) e),
warning = w.handler),
warning = W)
}
if (is.null(row.w)) row.w <- rep(1/nrow(X), nrow(X))
if (is.null(col.w)) col.w <- rep(1, ncol(X))
ncp <- min(ncp,nrow(X)-1,ncol(X))
row.w <- row.w / sum(row.w)
X <- t(t(X)*sqrt(col.w))*sqrt(row.w)
if (ncol(X)<nrow(X)){
## svd.usuelle <- svd(X,nu=ncp,nv=ncp)
## lignes suivantes pour eviter qq pb de convergence de l'algo LINPACK de svd
svd.usuelle <- tryCatch.W.E(svd(X,nu=ncp,nv=ncp))$val
if (names(svd.usuelle)[[1]]=="message"){
svd.usuelle <- tryCatch.W.E(svd(t(X),nu=ncp,nv=ncp))$val
if (names(svd.usuelle)[[1]]=="d"){
aux <- svd.usuelle$u
svd.usuelle$u <- svd.usuelle$v
svd.usuelle$v <- aux
} else{
bb <- eigen(crossprod(X,X),symmetric=TRUE)
svd.usuelle <- vector(mode = "list", length = 3)
svd.usuelle$d[svd.usuelle$d<0]<-0
svd.usuelle$d <- sqrt(svd.usuelle$d)
svd.usuelle$v <- bb$vec[,1:ncp]
# svd.usuelle$u <- sweep(X%*%svd.usuelle$v,2,svd.usuelle$d[1:ncp],FUN="/")
svd.usuelle$u <- t(t(crossprod(t(X),svd.usuelle$v))/svd.usuelle$d[1:ncp])
}
}
U <- svd.usuelle$u
V <- svd.usuelle$v
if (ncp >1){
mult <- sign(as.vector(crossprod(rep(1,nrow(V)),as.matrix(V))))
mult[mult==0] <- 1
U <- t(t(U)*mult)
V <- t(t(V)*mult)
}
U <- U/sqrt(row.w)
V <- V/sqrt(col.w)
}
else{
svd.usuelle <- tryCatch.W.E(svd(t(X),nu=ncp,nv=ncp))$val
if (names(svd.usuelle)[[1]]=="message"){
svd.usuelle <- tryCatch.W.E(svd(X,nu=ncp,nv=ncp))$val
if (names(svd.usuelle)[[1]]=="d"){
aux <- svd.usuelle$u
svd.usuelle$u <- svd.usuelle$v
svd.usuelle$v <- aux
} else{
bb <- eigen(crossprod(t(X),t(X)),symmetric=TRUE)
svd.usuelle <- vector(mode = "list", length = 3)
svd.usuelle$d[svd.usuelle$d<0]<-0
svd.usuelle$d <- sqrt(svd.usuelle$d)
svd.usuelle$v <- bb$vec[,1:ncp]
svd.usuelle$u <- t(t(crossprod(X,svd.usuelle$v))/svd.usuelle$d[1:ncp])
}
}
U <- svd.usuelle$v
V <- svd.usuelle$u
mult <- sign(as.vector(crossprod(rep(1,nrow(V)),as.matrix(V))))
mult[mult==0] <- 1
V <- t(t(V)*mult)/sqrt(col.w)
U <- t(t(U)*mult)/sqrt(row.w)
}
vs <- svd.usuelle$d[1:min(ncol(X),nrow(X)-1)]
num <- which(vs[1:ncp]<1e-15)
if (length(num)==1){
U[,num] <- U[,num,drop=FALSE]*vs[num]
V[,num] <- V[,num,drop=FALSE]*vs[num]
}
if (length(num)>1){
U[,num] <- t(t(U[,num])*vs[num])
V[,num] <- t(t(V[,num])*vs[num])
}
res <- list(vs = vs, U = U, V = V)
return(res)
}
|
6ac0235a84f9edc0855e66f9aaee6c24e63bfe47
|
a677b41109337cfc41479ee14e73884f2d75e898
|
/dsp/dwt.R
|
056296a8a374b0c874342a90c59967f8c62c76e7
|
[
"CC0-1.0"
] |
permissive
|
af12066/example-r
|
b1d0eca8a1dee37203c398631900a2a2383c881b
|
4cdd170407fcbc90b49dcd2a5e85a13a067836a4
|
refs/heads/master
| 2016-08-12T07:28:52.798354
| 2015-12-13T03:11:41
| 2015-12-13T03:11:41
| 47,903,987
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 634
|
r
|
dwt.R
|
library(wavelets)
Fs <- 100 # サンプリング周波数
sampling <- 1024 # Waveletサンプルポイント数
samplingPoint <- 0:(sampling-1) # サンプルポイント数のベクトル
t <- samplingPoint / Fs # 時間軸
wave1 <- sin(2 * pi * 1 * t) # 波形（1Hz）
wave2 <- rnorm(t) / 10 # 波形2（わずかにランダムノイズ）
wave <- wave1 + wave2
wdec <- dwt(wave, filter = "d4", n.levels = 4)
# ウェーブレットの種類...
# d : Daubechies (2,4,6,8,10,12,14,16,18,20)
# la : Least Asymetric (8,10,12,14,16,18,20)
# bl : Best Localized (14,18,20)
# c : Coiflet (6,12,18,24,30)
plot(wdec)
|
98e72b1a602ddfb70846814074ef2e2fee03ec74
|
02fe930e2f9c76fea9643d013e98ab937724579f
|
/R/fastqTools.R
|
ceaf1fc92cecf2bc68de87314f5a319157d1c9a2
|
[] |
no_license
|
sturkarslan/DuffyTools
|
ec751c1b58db5bef30ccc76f6e2afbc26c317d7d
|
10c540eaabdda27c14ddc9e27096e52444a2a67c
|
refs/heads/master
| 2016-08-04T23:58:02.110791
| 2014-11-26T18:09:35
| 2014-11-26T18:09:35
| 27,187,826
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 25,677
|
r
|
fastqTools.R
|
# fastqTools.R
`readFastqFile` <- function( filein, maxReads=NULL, verbose=TRUE) {
fileToUse <- allowCompressedFileName( filein)
if ( ! file.readable( fileToUse)) {
warning( paste( "readFastqFile: unable to read FASTQ file", fileToUse))
return( data.frame())
}
# catch compressed files
conIn <- openCompressedFile( fileToUse, open="r")
if (verbose) cat( "\nReading file: ", fileToUse)
# read in the raw text
chunkSize <- 1000000
if ( ! is.null(maxReads) && (maxReads*4) < chunkSize) chunkSize <- maxReads * 4
readIDs <- readSeqs <- scores <- vector()
repeat {
fastqText <- readLines( con=conIn, n=chunkSize, warn=FALSE)
if ( length( fastqText) < 1) break
# get the delimited lines
idLines <- grep( "^@", fastqText)
scoreMarks <- grep( "^\\+", fastqText)
if ( length( idLines) < 1 || length(scoreMarks) < 1 ) {
warning( paste( "readFastqFile: not a FASTQ format file: ", fileToUse))
return( data.frame())
}
idLines <- base::sort( intersect( idLines, seq( 1, length(fastqText), by=4)))
scoreMarks <- base::sort( intersect( scoreMarks, seq( 3, length(fastqText), by=4)))
if ( (length( idLines) != length(scoreMarks)) || any( idLines >= scoreMarks)) {
warning( paste( "readFastqFile: bad FASTQ file: ", fileToUse,
"\tMismatched readIDs and/or quality scores..."))
return( data.frame())
}
# now get the parts
readIDs <- base::append( readIDs, sub( "^@", "", fastqText[ idLines]))
readSeqs <- base::append( readSeqs, fastqText[ (idLines+1)])
scores <- base::append( scores, fastqText[ (scoreMarks+1)])
if (verbose) cat(".")
if ( ! is.null( maxReads)) {
if ( length(readIDs) >= maxReads) break
}
} # end of 'repeat' loop...
close( conIn)
if ( any( base::nchar( readSeqs) != base::nchar( scores)))
warning( "readFastqFile: some Read & Score lengths disagree")
out <- data.frame( readIDs, readSeqs, scores, stringsAsFactors=FALSE)
colnames( out) <- FASTQ_COLUMNS
rownames( out) <- 1:nrow(out)
if ( ! is.null( maxReads)) {
if ( nrow(out) >= maxReads) out <- out[ 1:maxReads, ]
}
if (verbose) cat( "\nN_Reads: ", prettyNum( nrow(out), big.mark=","), "\n")
return( out)
}
`test.readFastqFile` <- function() {
tmpFile <- build.testFastqFile()
zz <- readFastqFile( tmpFile, verbose=FALSE)
checkEquals( dim(zz), c(1000,3))
checkEquals( colnames(zz), FASTQ_COLUMNS)
remove.testFile( tmpFile)
}
# efficient writing of .fastq to a file
`writeFastqFile` <- function( x, fileout, compress=FALSE, verbose=TRUE) {
if ( ! all( colnames( x) == FASTQ_COLUMNS)) {
warning( paste( "writeFastqFile: unexpected column names: ", colnames(x)))
warning( "No file written...")
return( NULL)
}
fileToUse <- fileout
# catch compressed file
hasGZ <- (regexpr( "\\.gz$", fileout) > 0)
if ( compress || hasGZ) {
if ( ! hasGZ) fileToUse <- paste( fileout, "gz", sep=".")
conOut <- gzfile( fileToUse, open="w")
} else {
conOut <- file( fileToUse, open="w")
}
# prepend the .fastq markers to the needed spots
idText <- base::paste( "@", x$READ_ID, sep="")
out <- base::paste( idText, x$READ_SEQ, "+", x$SCORE, sep="\n")
# write it out
writeLines( out, con=conOut, sep="\n")
if( verbose) cat( "\nWrote file: \t", fileToUse, "\nN_Reads: \t", nrow( x),"\n")
close( conOut)
return( fileToUse)
}
`test.writeFastqFile` <- function() {
tmpFile <- build.testFastqFile()
zz <- readFastqFile( tmpFile, verbose=FALSE)
tmpFile2 <- writeFastqFile( zz, fileout="DuffyTools.test2.fastq", verbose=FALSE)
zz2 <- readFastqFile( tmpFile2, verbose=FALSE)
checkEquals( zz, zz2)
remove.testFile( tmpFile)
remove.testFile( tmpFile2)
}
# make shorter reads from the ends for splice discovery
`fastqToOverlapReadlets` <- function( filein, fileout, segmentLength=12, verbose=TRUE) {
fileToUse <- allowCompressedFileName( filein)
if ( ! file.exists( fileToUse)) stop( paste("Can't find input file: ", fileToUse))
conIn <- openCompressedFile( fileToUse, open="r")
if ( regexpr( ".gz$", fileout) > 0) {
conOut <- gzfile( fileout, open="w")
} else {
conOut <- file( fileout, open="w")
}
chunkSize <- 400000
nread <- 0
if (verbose) cat( "\nReading file: ", fileToUse)
repeat {
chunk <- readLines( conIn, n=chunkSize)
if ( length( chunk) < 1) break
# get the lines we want
ids <- chunk[ seq( 1, length(chunk), by=4)]
seqs <- chunk[ seq( 2, length(chunk), by=4)]
scores <- chunk[ seq( 4, length(chunk), by=4)]
N <- length( ids)
# get the actual read lengths
if ( nread == 0) {
alllens <- base::nchar( seqs)
maxlen <- max( alllens)
segmentLength <- ceiling(segmentLength/2) * 2
overlap <- segmentLength / 2
nOverlaps <- ceiling( (maxlen - segmentLength) / overlap) + 1
if (verbose) cat( "\n readLen, segLength, overlap, nSegs: ", maxlen, segmentLength, overlap, nOverlaps)
}
myStarts <- seq( 1, (maxlen-overlap), by=overlap)
myStops <- seq( segmentLength, (maxlen+overlap-1), by=overlap)
if ( length( myStarts) != nOverlaps || length( myStarts) != nOverlaps) {
cat( "bad overlap sizes...?")
stop("")
}
myStarts[nOverlaps] <- maxlen - segmentLength + 1
myStops[nOverlaps] <- maxlen
# get the substring chunks
allSeqs <- allScores <- allIDs <- matrix( nrow=N, ncol=nOverlaps)
for ( k in 1:nOverlaps) {
allSeqs[ ,k] <- base::substr( seqs, myStarts[k], myStops[k])
allScores[ ,k] <- base::substr( scores, myStarts[k], myStops[k])
allIDs[ ,k] <- base::paste( ids, "/seg", k, sep="")
}
# interleave the output to keep the pairs together
outIDs <- as.vector( t( allIDs))
outSeqs <- as.vector( t( allSeqs))
outScores <- as.vector( t( allScores))
# format as Fastq (the ID still has the '@'...)
outTxt <- base::paste( outIDs, "\n", outSeqs, "\n+\n", outScores, sep="")
writeLines( outTxt, con=conOut)
nread <- nread + length( chunk)
if (verbose) cat( ".")
}
close( conIn)
close( conOut)
if (verbose) cat( "\nN_reads: ", round( as.integer(nread)/4))
if (verbose) cat( "\nWrote file: ", fileout, "\n")
return( list( "Segments"=nOverlaps, "Overlap"=overlap))
}
`fastqToChunks` <- function( filein, fileroot=sub( "(fq|fastq)(.gz)?$","",filein),
filetail=sub( fileroot, "", filein, fixed=T),
chunk.size=1000000) {
filein <- allowCompressedFileName( filein)
if ( ! file.exists( filein)) stop( paste("Can't find input file: ", filein))
conIn <- openCompressedFile( filein, open="r")
blockSize <- min( chunk.size, 100000) * 4
nread <- 0
nchunk <- 0
cat( "\nBreaking: ", basename(filein), " into chunks of ", chunk.size, "reads.\n")
nThisChunk <- 0
makeNewFile <- TRUE
repeat {
if ( makeNewFile) {
nchunk <- nchunk + 1
fileout <- paste( fileroot, "chunk", nchunk, ".", filetail, sep="")
if ( regexpr( ".gz$", fileout) > 0) {
conOut <- gzfile( fileout, open="w")
} else {
conOut <- file( fileout, open="w")
}
makeNewFile <- FALSE
}
thisBlockSize <- min( blockSize, (chunk.size-nThisChunk)*4)
chunk <- readLines( conIn, n=thisBlockSize)
if ( length( chunk) < 1) break
nread <- nread + length(chunk)/4
nThisChunk <- nThisChunk + length(chunk)/4
# get the lines we want
who <- seq( 1, length(chunk), by=4)
ids <- chunk[ who]
seqs <- chunk[ who + 1]
scores <- chunk[ who + 3]
writeLines( base::paste( ids, seqs, "+", scores, sep="\n"), con=conOut)
cat( ".")
if ( nThisChunk >= chunk.size) {
close( conOut)
cat( " ", nread, basename(fileout), "\n")
makeNewFile <- TRUE
nThisChunk <- 0
}
}
# close the last one...
close( conOut)
cat( "\n", nread, basename(fileout))
close( conIn)
cat( "\nN_reads: ", round( as.integer(nread)/4))
return()
}
`fastqToFasta` <- function( filein, fileout, Qscores=TRUE) {
filein <- allowCompressedFileName( filein)
if ( ! file.exists( filein)) stop( paste("Can't find input file: ", filein))
conIn <- openCompressedFile( filein, open="r")
if ( regexpr( ".gz$", fileout) > 0) {
conOut <- gzfile( fileout, open="w")
} else {
conOut <- file( fileout, open="w")
}
chunkSize <- 800000
nread <- 0
repeat {
chunk <- readLines( conIn, n=chunkSize)
if ( length( chunk) < 1) break
nread <- nread + length( chunk)
# get the lines we want
ids <- chunk[ seq( 1, length(chunk), by=4)]
seqs <- chunk[ seq( 2, length(chunk), by=4)]
if ( Qscores) {
scores <- chunk[ seq( 4, length(chunk), by=4)]
avgScores <- apply( phredScoreStringToInt( scores), MARGIN=1, FUN=mean)
newIds <- base::paste( sub( "@", ">", ids, fixed=TRUE), ":Q=", formatC( avgScores, digits=2, format="f"), sep="")
} else {
newIds <- sub( "@", ">", ids, fixed=TRUE)
}
writeLines( base::paste( newIds, seqs, sep="\n"), con=conOut)
cat( ".")
}
close( conIn)
close( conOut)
cat( "\nN_reads: ", round( as.integer(nread)/4))
return()
}
`clipBases` <- function( mySEQ, myScores, laneID, clip5prime, clip3prime, scoresAsIntegers=TRUE) {
# parse the clip instructions...
if ( is.null( clip5prime)) clip5prime <- 0
if ( is.null( clip3prime)) clip3prime <- 0
clipLanes <- vector()
if ( ! is.null( names(clip5prime))) clipLanes <- names( clip5prime)
if ( ! is.null( names(clip3prime))) clipLanes <- unique.default( base::append( clipLanes, names( clip3prime)))
nClipLanes <- length( clipLanes)
doByLane <- (nClipLanes > 0)
if ( doByLane) {
# there were some named lanes, so expand to a complete set
trims <- matrix( 0, nrow=max(as.numeric(clipLanes)), ncol=2)
trims[ as.numeric( names( clip5prime)), 1] <- base::unlist( clip5prime)
trims[ as.numeric( names( clip3prime)), 2] <- base::unlist( clip3prime)
}
# get the lengths
nlines <- length( mySEQ)
nbases <- base::nchar( mySEQ)
nscoreCh <- base::nchar( myScores)
# and remove the leading spaces before the first base score
if ( scoresAsIntegers) {
repeat {
hasBlank <- which( base::substr( myScores,1,1) == " ")
if ( length( hasBlank) < 1) break
myScores[ hasBlank] <- sub( " ", "", myScores[ hasBlank], fixed=TRUE)
nscoreCh[ hasBlank] <- nscoreCh[ hasBlank] - 1
}
scoreList <- strsplit( myScores, split=" ", fixed=TRUE)
lens <- sapply( scoreList, length)
if ( any( lens != nbases)) warning("clipBases: Read -- Quality score length mis-match")
}
# ready to do the clipping...
if ( ! doByLane) {
cat( " clipping all lanes(5',3')=", clip5prime, clip3prime)
new5 <- 1 + clip5prime
for ( i in 1:nlines) {
new3 <- nbases[i] - clip3prime
mySEQ[i] <- base::substr( mySEQ[i], new5, new3)
if ( scoresAsIntegers) {
scoretmp <- scoreList[[i]][ new5:new3]
myScores[i] <- base::paste( scoretmp, collapse=" ")
} else {
myScores[i] <- base::substr( myScores[i], new5, new3)
}
}
} else {
for ( ilane in 1:nClipLanes) {
lane <- clipLanes[ ilane]
this5 <- trims[ as.integer( lane),1]
this3 <- trims[ as.integer( lane),2]
if ( all( c( this5, this3) == 0)) next
who <- which( laneID == lane)
if ( length( who) < 1) next
new5 <- 1 + this5
cat( " clipping lane",lane,"(5',3')=", this5, this3)
for ( i in who) {
new3 <- nbases[i] - this3
mySEQ[i] <- base::substr( mySEQ[i], new5, new3)
if ( scoresAsIntegers) {
scoretmp <- scoreList[[i]][ new5:new3]
myScores[i] <- base::paste( scoretmp, collapse=" ")
} else {
myScores[i] <- base::substr( myScores[i], new5, new3)
}
}
}
}
# ready.
out <- list( "seqs"=mySEQ, "scores"=myScores)
return( out)
}
clipFastqFile <- function( filein, fileout, clip5prime=0, clip3prime=0) {
# clip bases off an existing fastq file
filein <- allowCompressedFileName( filein)
if ( ! file.exists( filein)) stop( paste("Can't find input file: ", filein))
conIn <- openCompressedFile( filein, open="r")
if ( regexpr( ".gz$", fileout) > 0) {
conOut <- gzfile( fileout, open="w")
} else {
conOut <- file( fileout, open="w")
}
chunkSize <- 800000
nread <- 0
cat( "\nClipping ( 5', 3') = ", clip5prime, clip3prime, "\n")
repeat {
chunk <- readLines( conIn, n=chunkSize)
if ( length( chunk) < 1) break
nread <- nread + length( chunk)
# get the lines we want
ids <- chunk[ seq( 1, length(chunk), by=4)]
seqs <- chunk[ seq( 2, length(chunk), by=4)]
scores <- chunk[ seq( 4, length(chunk), by=4)]
ans <- clipBases( seqs, scores, ids, clip5=clip5prime, clip3=clip3prime, scoresAsIntegers=FALSE)
newSeqs <- ans$seq
newScores <- ans$scores
newLen <- base::nchar( newSeqs[1])
newIds <- sub( "=[0-9]+$", base::paste( "=",newLen, sep=""), ids)
newId2 <- sub( "@","+", newIds, fixed=TRUE)
writeLines( base::paste( newIds, newSeqs, newId2, newScores, sep="\n"), con=conOut)
cat( ".")
}
close( conIn)
close( conOut)
cat( "\nN_reads trimmed: ", round( as.integer(nread)/4))
cat( "\nNew Read Length: ", base::nchar( newSeqs[1]), "\n")
return()
}
`fastqPatternSearch` <- function( filein, patterns, max.mismatch=0, chunkSize=400000) {
require( Biostrings)
fileToUse <- allowCompressedFileName( filein)
if ( ! file.exists( fileToUse)) stop( paste("Can't find input file: ", fileToUse))
conIn <- openCompressedFile( fileToUse, open="r")
nread <- 0
cat( "\nReading file: ", fileToUse, "\n")
nPatt <- length( patterns)
findCounts <- rep( 0, times=nPatt)
repeat {
chunk <- readLines( conIn, n=chunkSize)
if ( length( chunk) < 1) break
# get the lines we want
ids <- chunk[ seq( 1, length(chunk), by=4)]
seqs <- chunk[ seq( 2, length(chunk), by=4)]
N <- length( ids)
# turn these into a serchable string
subject <- DNAString( paste( seqs, collapse="N"))
for ( k in 1:nPatt) {
v <- countPattern( patterns[k], subject, max.mismatch=max.mismatch)
findCounts[k] <- findCounts[k] + v
if ( k %% 20 == 0) cat( "\r", k, v, sum( findCounts[1:k]))
}
nread <- nread + N
cat( "\nReads: ", formatC( as.integer(nread), big.mark=","), "\tHits: ", sum( findCounts))
}
cat( "\n")
close( conIn)
out <- findCounts
names(out) <- patterns
return( out)
}
`bam2fastq` <- function( bamfile, outfile=sub( ".bam$", "", bamfile), paired.end=TRUE) {
cat( "\nConverting BAM file: ", bamfile)
cat( "\nCreating FASTQ(s): ", outfile, "\n")
cmdline <- paste( "bam2fastq.pl ", " --filter '-F 0x000' --prefix ", outfile, " ", bamfile)
if ( paired.end) {
cmdline <- paste( "bam2fastq.pl ", " --filter '-F 0x000' --yes --prefix ", outfile, " ", bamfile)
}
system( cmdline)
cat( " Done.\n")
return( )
}
`fastqToPeptides` <- function( filein, fileout, chunkSize=100000) {
filein <- allowCompressedFileName( filein)
if ( ! file.exists( filein)) stop( paste("Can't find input file: ", filein))
conIn <- openCompressedFile( filein, open="r")
#conOut <- file( fileout, open="wt")
chunkLines <- chunkSize * 4
# local function to parallelize
myDNAtoAAfunc <- function( x, peps, cnts) {
peptides <- counts <- vector()
nout <- 0
lapply( x, function(i) {
dna <- peps[ i]
lenFullAA <- floor( nchar(dna)/3)
AAs <- DNAtoAA.fast( dna)
# they have to be full length with no N's or non-AA calls
good <- setdiff( which( nchar(AAs) == lenFullAA), grep( "?", AAs, fixed=T))
if ( (ngood <- length(good)) > 0) {
nnow <- (nout+1) : (nout+ngood)
peptides[ nnow] <<- AAs[ good]
counts[ nnow] <<- cnts[i]
nout <<- nout + ngood
}
return(NULL)
})
# the mapping from DNA to AA may have generated duplicates
if ( length( peptides) < 1) return( data.frame())
tapply( 1:length(peptides), factor( peptides), FUN=function(x) {
if ( length(x) < 2) return()
totalCnt <- sum( counts[x])
counts[ x[1]] <<- totalCnt
counts[ x[2:length(x)]] <<- 0
return()
}, simplify=FALSE)
keep <- which( counts > 0)
# final ans is a table of peptides with counts
ans <- list( "Peptide"=peptides[keep], "Count"=counts[keep])
return(ans)
} # end of local function
nread <- ntotal <- nUtotal <- 0
repeat {
chunk <- readLines( conIn, n=chunkLines)
if ( length( chunk) < 1) break
nReadsNow <- length(chunk)/4
nread <- nread + nReadsNow
cat( "\nReads: ", prettyNum( as.integer( nread), big.mark=","))
# get the raw read lines we want, but only do one of each duplicate
seqs <- chunk[ seq.int( 2, length(chunk), 4)]
seqCntsTbl <- table( seqs)
uniqSeqs <- names( seqCntsTbl)
uniqCnts <- as.vector( seqCntsTbl)
N <- length(uniqCnts)
rm( seqCntsTbl)
nUtotal <- nUtotal + N
cat( " Unique: ", prettyNum( as.integer( nUtotal)))
coreOpt <- getOption("cores")
nCores <- if ( is.null(coreOpt)) 1 else as.integer( coreOpt)
if ( nCores < 2) {
ans <- myDNAtoAAfunc( 1:N, uniqSeqs, uniqCnts)
} else {
starts <- seq.int( 1, nReadsNow, (nReadsNow/nCores))
stops <- c( starts[2:length(starts)] - 1, nReadsNow)
cat( " use", nCores, "cores..")
locs <- vector( mode="list")
for ( i in 1:length(starts)) locs[[i]] <- starts[i] : stops[i]
ans <- multicore.lapply( locs, FUN=myDNAtoAAfunc, peps=uniqSeqs, cnts=uniqCnts, preschedule=TRUE)
}
# extract all the little data frame answers
bigp <- bigc <- vector()
if ( length( ans) == 2 && names(ans)[1] == "Peptide") {
bigp <- ans$Peptide
bigc <- ans$Count
} else {
cat( " merge..")
for ( k in 1:length(ans)) {
obj <- ans[[k]]
newlocs <- (length(bigp) + 1) : (length(bigp) + length(obj$Peptide))
bigp[ newlocs] <- obj$Peptide
bigc[ newlocs] <- obj$Count
}
}
N <- length(bigp)
# the mapping from DNA to AA may have generated duplicates
cat( " dups..")
tapply( 1:N, factor( bigp), FUN=function(x) {
if ( length(x) < 2) return()
totalCnt <- sum( bigc[x])
bigc[ x[1]] <<- totalCnt
bigc[ x[2:length(x)]] <<- 0
return()
}, simplify=FALSE)
keep <- which( bigc > 0)
ansDF <- data.frame( "Peptide"=bigp[keep], "Count"=bigc[keep], stringsAsFactors=F)
nout <- nrow(ansDF)
ntotal <- ntotal + nout
cat( " Peptides: ", prettyNum( as.integer( ntotal)), " ", as.percent( ntotal, big.value=nUtotal*100,
percentSign=FALSE), "per Read")
#write.table( ansDF, file=conOut, col.names=(ntotal == nout), sep="\t", quote=F, row.names=F)
if ( ntotal == nout) {
write.table( ansDF, file=fileout, append=FALSE, col.names=TRUE, sep="\t", quote=F, row.names=F)
} else {
write.table( ansDF, file=fileout, append=TRUE, col.names=FALSE, sep="\t", quote=F, row.names=F)
}
}
close( conIn)
#close( conOut)
cat( "\nTotal_Reads: ", prettyNum( as.integer(nread), big.mark=","))
cat( "\nTotal_Peptides: ", prettyNum( as.integer(ntotal), big.mark=","))
return( ntotal)
}
`fastqReader` <- function() {
filename <- ""
con <- NULL
N <- 0
initialize <- function( file) {
cat( "\nInitializing FASTQ reader for: ", file)
if ( ! file.exists( file)) {
cat( "\nFile not found: ", file)
return( "Error")
}
con <<- gzfile( file, open="rt")
filename <<- file
return( file)
}
read <- function( n=1) {
nlines <- n * 4
txt <- readLines( con, n=nlines)
nread <- length( txt)
if ( nread < 4) return(NULL)
isRID <- seq( 1, nread, by=4)
N <<- N + length(isRID)
return( list( "rid"=base::sub( "^@","",txt[isRID]), "seq"=txt[isRID+1], "score"=txt[isRID+3]))
}
finalize <- function() {
if ( !is.null(con)) base::close(con)
cat( "\nRead", N, "records from: ", filename, "\n")
return()
}
return( environment())
}
`fastqWriter` <- function() {
filename <- ""
con <- NULL
N <- 0
initialize <- function( file) {
cat( "\nInitializing FASTQ writer for: ", file)
con <<- gzfile( file, open="wt")
filename <<- file
return( file)
}
write <- function( rid, seq, score) {
txt <- paste( "@", rid, "\n", seq, "\n+\n", score, sep="")
writeLines( txt, con=con)
N <<- N + 1
return()
}
finalize <- function() {
if ( !is.null(con)) base::close(con)
cat( "\nWrote", N, "records to: ", filename, "\n")
return()
}
return( environment())
}
`fastqToPairedFiles` <- function( file, secondFile=NULL, max.buf=20000) {
options( warn=-1)
on.exit( options( warn=0))
fin <- fastqReader()
if ( fin$initialize(file) != file) return()
f1name <- sub( "fastq.gz", "1.fastq.gz", file)
fout1 <- fastqWriter()
fout1$initialize(f1name)
on.exit( fout1$finalize(), add=TRUE)
f2name <- sub( "fastq.gz", "2.fastq.gz", file)
fout2 <- fastqWriter()
fout2$initialize(f2name)
on.exit( fout2$finalize(), add=TRUE)
rids1 <- rids2 <- seqs1 <- seqs2 <- scores1 <- scores2 <- rep.int("", 100)
n1 <- n2 <- nout <- 0
find1 <- function( rid) {
if ( n1 < 1) return(0)
who <- (1:n1)[ rid == rids1[1:n1]][1]
return( if (is.na(who)) 0 else who)
}
find2 <- function( rid) {
if ( n2 < 1) return(0)
who <- (1:n2)[ rid == rids2[1:n2]][1]
return( if (is.na(who)) 0 else who)
}
store1 <- function( rid, seq, score) {
where <- if ( n1 > 0) (1:n1)[ rids1[1:n1] == ""][1] else NA
#where <- if ( n1 > 0) which( rids1[1:n1] == "")[1] else NA
if ( is.na( where)) {
n1 <<- n1 + 1
where <- n1
}
rids1[where] <<- rid
seqs1[where] <<- seq
scores1[where] <<- score
return()
}
store2 <- function( rid, seq, score) {
where <- if ( n2 > 0) (1:n2)[ rids2[1:n2] == ""][1] else NA
#where <- if ( n2 > 0) which( rids2[1:n2] == "")[1] else NA
if ( is.na( where)) {
n2 <<- n2 + 1
where <- n2
}
rids2[where] <<- rid
seqs2[where] <<- seq
scores2[where] <<- score
return()
}
squeeze1 <- function() {
isBlank <- which( rids1 == "")
if ( length( isBlank) > 0) {
rids1 <<- rids1[ -isBlank]
seqs1 <<- seqs1[ -isBlank]
scores1 <<- scores1[ -isBlank]
n1 <<- length(rids1)
if ( n1 > max.buf) {
drops <- 1 : (n1-max.buf)
rids1 <<- rids1[ -drops]
seqs1 <<- seqs1[ -drops]
scores1 <<- scores1[ -drops]
n1 <<- length(rids1)
}
}
}
squeeze2 <- function() {
isBlank <- which( rids2 == "")
if ( length( isBlank) > 0) {
rids2 <<- rids2[ -isBlank]
seqs2 <<- seqs2[ -isBlank]
scores2 <<- scores2[ -isBlank]
n2 <<- length(rids2)
if ( n2 > max.buf) {
drops <- 1 : (n2-max.buf)
rids2 <<- rids2[ -drops]
seqs2 <<- seqs2[ -drops]
scores2 <<- scores2[ -drops]
n2 <<- length(rids2)
}
}
}
# the main loop is to look for mate pairs within the file, buffering up as we go
# so read one at a time
cat("\n")
repeat {
item <- fin$read(1)
if (is.null(item)) break
rid1 <- rid2 <- item[[1]]
nc <- nchar( rid1)
mate <- substr( rid1, nc, nc)
rid <- substr( rid1, 1, nc-1)
seq <- item[[2]]
score <- item[[3]]
is1 <- (mate == '1')
hit <- FALSE
if (is1) {
where2 <- find2( rid)
if ( where2 == 0) {
store1( rid, seq, score)
} else {
fout1$write( rid1, seq, score)
rid2 <- paste( rid, "2", sep="")
fout2$write( rid2, seqs2[where2], scores2[where2])
rids2[where2] <- ""
nout <- nout + 1
hit <- TRUE
}
} else {
where1 <- find1( rid)
if ( where1 == 0) {
store2( rid, seq, score)
} else {
fout2$write( rid2, seq, score)
rid1 <- paste( rid, "1", sep="")
fout1$write( rid1, seqs1[where1], scores1[where1])
rids1[where1] <- ""
nout <- nout + 1
hit <- TRUE
}
}
if ( hit && nout %% 100 == 0) {
squeeze1()
squeeze2()
cat( "\rPairs:", nout, " Buf1:", n1, " Buf2:", n2)
}
}
# done reading the file...
fin$finalize()
cat( "\rPairs:", nout, " Buf1:", n1, " Buf2:", n2)
# if a second file was given, (the 'No Hits'), use it as a read only source of possible second mates
if ( ! is.null( secondFile)) {
if ( fin$initialize(secondFile) != secondFile) break
cat("\n")
repeat {
item <- fin$read( n=10000)
if (is.null(item)) break
ridset <- item[[1]]
seqset <- item[[2]]
scoreset <- item[[3]]
hit <- FALSE
# see if any of whats in our buffers match these
try1 <- paste( rids1, "2", sep="")
where <- match( ridset, try1, nomatch=0)
if ( any( where > 0)) {
set2 <- which( where > 0)
set1 <- where[ set2]
for (j in 1:length(set2)) {
j1 <- set1[j]
j2 <- set2[j]
fout1$write( rids1[j1], seqs1[j1], scores1[j1])
fout2$write( ridset[j2], seqset[j2], scoreset[j2])
rids1[j1] <- ""
nout <- nout + 1
}
hit <- TRUE
}
try2 <- paste( rids2, "1", sep="")
where <- match( ridset, try2, nomatch=0)
if ( any( where > 0)) {
set1 <- which( where > 0)
set2 <- where[ set1]
for (j in 1:length(set1)) {
j1 <- set1[j]
j2 <- set2[j]
fout1$write( ridset[j1], seqset[j1], scoreset[j1])
fout2$write( rids2[j2], seqs2[j2], scores2[j2])
rids2[j2] <- ""
nout <- nout + 1
}
hit <- TRUE
}
if (hit) {
squeeze1()
squeeze2()
cat( "\rPairs:", nout, " Buf1:", n1, " Buf2:", n2, " ")
if ( n1 == 0 && n2 == 0) break
}
}
fin$finalize()
}
# all done now
}
|
36ee8dcb5ec75e7c59693a236da8b4d4634cd53c
|
c06f1619dbd2007837f73cc184fdef6d821ad981
|
/man/same.Rd
|
37a2f7b229d4295cd2e8137000cc146c8f3976a4
|
[] |
no_license
|
BenGriff42/useless
|
34cba60dada4e01324cd418e38e5c8fa0cad8f2d
|
ef3686ae6cbe4095ad0bdd3ac7ed97f4785c2a35
|
refs/heads/main
| 2023-08-14T11:45:09.814151
| 2021-10-07T12:28:32
| 2021-10-07T12:28:32
| 414,521,717
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| true
| 309
|
rd
|
same.Rd
|
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/useless_functions.R
\name{same}
\alias{same}
\title{Return input}
\usage{
same(x)
}
\arguments{
\item{x}{Anything}
}
\value{
What you input
}
\description{
Function to return whatever you put in
}
\examples{
same(1)
same("same")
}
|
159a89899233f9bcdcc4d1f30ca667cbcc1ca2c7
|
4201e9b754760dc35fc0aeef9df5a8b9d801c47f
|
/bin/R-3.5.1/src/library/stats/man/pairwise.wilcox.test.Rd
|
55d858490249dedc04617eb60f834e0cdc3beec8
|
[
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"GPL-2.0-only"
] |
permissive
|
lifebit-ai/exomedepth
|
cbe59cb7fcf2f9183d187f8d466c6620fb1a0c2e
|
5a775ae5e2a247aeadc5208a34e8717c7855d080
|
refs/heads/master
| 2020-03-27T12:55:56.400581
| 2018-10-11T10:00:07
| 2018-10-11T10:00:07
| 146,578,924
| 0
| 0
|
MIT
| 2018-08-29T09:43:52
| 2018-08-29T09:43:51
| null |
UTF-8
|
R
| false
| false
| 1,543
|
rd
|
pairwise.wilcox.test.Rd
|
% File src/library/stats/man/pairwise.wilcox.test.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later
\name{pairwise.wilcox.test}
\alias{pairwise.wilcox.test}
\title{Pairwise Wilcoxon Rank Sum Tests}
\description{
Calculate pairwise comparisons between group levels with corrections
for multiple testing.
}
\usage{
pairwise.wilcox.test(x, g, p.adjust.method = p.adjust.methods,
paired = FALSE, \dots)
}
\arguments{
\item{x}{ response vector. }
\item{g}{ grouping vector or factor. }
\item{p.adjust.method}{ method for adjusting p values (see
\code{\link{p.adjust}}). Can be abbreviated.}
\item{paired}{a logical indicating whether you want a paired test.}
\item{\dots}{additional arguments to pass to \code{\link{wilcox.test}}.}
}
\details{
Extra arguments that are passed on to \code{wilcox.test} may or may
not be sensible in this context. In particular,
only the lower triangle of the matrix of possible comparisons is being
calculated, so setting \code{alternative} to anything other than
\code{"two.sided"} requires that the levels of \code{g} are ordered
sensibly.
}
\value{
Object of class \code{"pairwise.htest"}
}
\seealso{ \code{\link{wilcox.test}}, \code{\link{p.adjust}}}
\examples{
attach(airquality)
Month <- factor(Month, labels = month.abb[5:9])
## These give warnings because of ties :
pairwise.wilcox.test(Ozone, Month)
pairwise.wilcox.test(Ozone, Month, p.adj = "bonf")
detach()
}
\keyword{htest}
|
977cd77d4cc90f7d8b437cc8291f9bf6eca6a800
|
ecb246919cb876cf82dc3375b9105189b254b96e
|
/tests/testthat/test_period_dates.R
|
3492827f0ab8e4bfc65e846c3820f19ad738e99c
|
[] |
no_license
|
mickmioduszewski/dqr
|
4060cdc230c0f0a5756e93c7499b8b50e4635b56
|
9b5fd4ca824a6aade7ad5db600305360b93f124a
|
refs/heads/master
| 2020-04-16T08:48:48.559005
| 2019-01-13T02:47:14
| 2019-01-13T02:47:14
| 165,439,005
| 1
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 106
|
r
|
test_period_dates.R
|
context("Data quality for R stub")
library(lubridate)
test_that("stub", {
expect_equal(TRUE, TRUE)
})
|
8bba6b6da9e2d4018da75489ac71205f5eba71c8
|
0dac3070dd10c9329f52562cb119c5090c039fb5
|
/Worksheet_5/Worksheet_5.R
|
eee5b2320b4368cffd2157738eb68f8bcd00d83c
|
[] |
no_license
|
retodomax/CompSkills
|
9af99150e6a50296f72a69f9c70cbdbd7ca2fbfe
|
1d7d1409e5240abee4269aed1864028fbd87494d
|
refs/heads/master
| 2020-09-24T05:59:19.469927
| 2019-12-03T23:10:44
| 2019-12-03T23:10:44
| 225,681,922
| 0
| 0
| null | null | null | null |
UTF-8
|
R
| false
| false
| 3,771
|
r
|
Worksheet_5.R
|
#' ---
#' project: CompSkills #################################################
#' title: Homework 5
#' author: "[Reto Zihlmann](https://retodomax.github.io/)"
#' date: 2019-11-19 18:40:38
#' output: github_document #############################################
#' ---
# packages ----------------------------------------------------------------
library(mgcv)
# Single example ----------------------------------------------------------
data <- read.table("frequency.dat")
names(data) <- c("year", "freq")
plot(freq ~ year, data = data, ylab = "Freqency [MHz]",
ylim = c(-1000, 6000))
# Assume a true function
fpl <- function(x, A = 0, B = 3000, xmid = 2003, scal = 3){
A+(B-A)/(1+exp((xmid-x)/scal))
}
curve(fpl, from = 1970, to = 2020, add = T)
# sample new points
set.seed(1)
true_value <- fpl(data$year)
value <- abs(true_value + rnorm(length(true_value), mean = 0, sd = true_value/3))
points(data$year, value, col = 'green')
sim_data <- data.frame(year = data$year, freq = value)
# fit with GAM
fit <- gam(log(freq) ~ s(year), data = sim_data)
# fit with SSfpl()
fit_ssfpl <- nls(freq ~ SSfpl(year, A, B, xmid, scal), data = sim_data)
# plot all
plot(freq ~ year, data = sim_data, ylab = "Freqency [MHz]",
ylim = c(-1000, 6000))
lines(sim_data$year,exp(predict(fit)), col = "red", lwd = 2)
lines(sim_data$year, predict(fit_ssfpl), col = 'green', lwd = 2)
curve(fpl, lwd = 2, add = T)
legend('topleft', legend = c('True model', 'GAM fit', 'FPL fit'),
lwd = 2, col = c('black', 'red', 'green'))
abline(v = 2008, lty = 2)
abline(v = 2017, lty = 2)
# question: which model is closer to true function at year = 2008
# and year = 2017
# simulation --------------------------------------------------------------
sim_diff <- function() {
true_value <- fpl(data$year)
value <- abs(true_value + rnorm(length(true_value), mean = 0, sd = true_value/3))
sim_data <- data.frame(year = data$year, freq = value)
fit <- gam(log(freq) ~ s(year), data = sim_data)
fit_ssfpl <- nls(freq ~ SSfpl(year, A, B, xmid, scal), data = sim_data)
gam_pred <- unname(exp(predict(fit, newdata = list(year = c(2008, 2017)))))
ssfpl_pred <- predict(fit_ssfpl, newdata = list(year = c(2008, 2017)))
true_values <- fpl(c(2008, 2017))
c(gam_pred-true_values, ssfpl_pred-true_values)
}
## maybe include:
# - variate the number of data points (n)
# (at the moment n always the number of observations in real data)
# - variate the distribution of the errors
# (symmetirc(normal)/nonsymetric(chisquare, lognormal), heavy tailed,
# different support (only positive numbers),
# discrete/count, ...)
sim_diff()
out <- replicate(1000, sim_diff())
out <- t(out)
# plot simulation results -------------------------------------------------
plot(density(out[,1]), main = 'Difference to true function at 2008',
xlim = c(-1000, 1000), ylim = c(0, 0.005),
col = 'red', lwd = 2)
lines(density(out[,3]),
col = 'green', lwd = 2)
abline(v = 0, lty = 2)
legend('topleft', legend = c('True model', 'GAM fit', 'FPL fit'),
lwd = 2, col = c('black', 'red', 'green'))
plot(density(out[,2]), main = 'Difference to true function at 2008',
xlim = c(-1000, 1000), ylim = c(0, 0.005),
col = 'red', lwd = 2)
lines(density(out[,4]),
col = 'green', lwd = 2)
abline(v = 0, lty = 2)
legend('topleft', legend = c('True model', 'GAM fit', 'FPL fit'),
lwd = 2, col = c('black', 'red', 'green'))
## finally evaluate
# for estimator
# -bias
# -MSE (variance)
# -distribution
# for CI
# -width (how wide are the CI)
# -coverage (do they include the true parameter)
# for curve
# -point-wise (only compare at specific locations)
# -integrate (AMSE: average MSE,
# IMSE: integrated MSE)
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