├── .gitignore ├── data ├── .m88-v2.csv.swp ├── maxKnownKs.csv ├── m88.csv ├── m88-v2.csv ├── m88-v3.csv └── K-3x3.csv ├── scripts ├── listEdges.R ├── compressionLength.R ├── relabelTables.R ├── loadGraph.R ├── loadGraph.R~ ├── BDM2D.R ├── BDM1D.R └── edgeAndVertexKnockout.R ├── report.Rmd ├── README.md ├── BDM2D.R ├── ui.R ├── server.R └── LICENSE.md /.gitignore: -------------------------------------------------------------------------------- 1 | .Rhistory 2 | ./scripts/.Rhistory 3 | -------------------------------------------------------------------------------- /data/.m88-v2.csv.swp: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/algorithmicnaturelab/OACC/HEAD/data/.m88-v2.csv.swp -------------------------------------------------------------------------------- /data/maxKnownKs.csv: -------------------------------------------------------------------------------- 1 | "","K.2","K.4","K.5","K.6","K.9" 2 | "1",37.0641988031379,40.5065171208468,42.2639233189173,43.6574062551325,52.8978703506378 3 | -------------------------------------------------------------------------------- /data/m88.csv: -------------------------------------------------------------------------------- 1 | 1,1,1,0,0,0,1,1 2 | 1,0,0,1,1,0,1,0 3 | 0,0,0,0,0,0,0,0 4 | 0,0,0,1,1,0,1,0 5 | 1,1,0,0,0,0,1,0 6 | 0,0,1,0,1,1,1,0 7 | 0,1,1,1,1,0,1,0 8 | 1,1,1,1,1,1,1,0 -------------------------------------------------------------------------------- /data/m88-v2.csv: -------------------------------------------------------------------------------- 1 | 0,1,0,0,0,0,1,0 2 | 1,0,0,1,1,0,1,0 3 | 0,0,0,0,0,0,0,0 4 | 0,0,0,1,0,0,0,0 5 | 1,1,0,0,0,0,0,0 6 | 0,0,1,0,0,1,1,0 7 | 0,1,1,1,0,0,0,0 8 | 1,1,1,1,0,1,1,0 9 | -------------------------------------------------------------------------------- /data/m88-v3.csv: -------------------------------------------------------------------------------- 1 | 1,1,0,0,0,0,1,1 2 | 1,1,1,1,1,0,1,1 3 | 1,1,1,0,1,0,0,1 4 | 0,0,0,1,0,0,0,1 5 | 1,1,0,0,0,0,0,1 6 | 1,1,1,0,0,1,1,1 7 | 1,1,1,1,0,0,0,0 8 | 1,1,1,1,0,1,1,1 9 | -------------------------------------------------------------------------------- /scripts/listEdges.R: -------------------------------------------------------------------------------- 1 | listEdges <- function(gra){ 2 | 3 | edgeList <- as_edgelist(gra, names = TRUE) 4 | 5 | printableEdgeList <- character(nrow(edgeList)) 6 | 7 | if (nrow(edgeList) > 0){ 8 | for (i in 1:nrow(edgeList)) { 9 | 10 | printableEdgeList[i] <- paste0(edgeList[i,1], 11 | "|", 12 | edgeList[i, 2]) 13 | 14 | } 15 | 16 | return(printableEdgeList) 17 | } 18 | return("no links") 19 | } -------------------------------------------------------------------------------- /scripts/compressionLength.R: -------------------------------------------------------------------------------- 1 | compressionLength <- function(string, compressionType){ 2 | len <- length(memCompress(string, compressionType)) 3 | return(len) 4 | } 5 | 6 | ## https://en.wikipedia.org/wiki/Gzip 7 | #g <- compressionLength("0101010101010101", "gzip") 8 | #g 9 | 10 | ## https://en.wikipedia.org/wiki/Bzip2 11 | # b <- compressionLength("0101010101010101", "bzip") 12 | # b 13 | # 14 | 15 | ## https://en.wikipedia.org/wiki/Xz 16 | # x <- compressionLength("0101010101010101", "xz") 17 | # x 18 | 19 | #testList <- c("00000000001", "0000000000", "010101010101") 20 | #test <- lapply(testList, compressionLength, compressionType = "gzip") 21 | #test 22 | 23 | # if (!is.matrix(test)) { 24 | # test <- as.matrix(test) 25 | # colnames(test) <- "Compression length by gzip" 26 | # } 27 | # test 28 | # 29 | # test2 <- "000000000000000000000000111111111000" 30 | # len <- compressionLength(test2, "gzip") 31 | # len 32 | # gz1 <- gzfile("test2.gz", "w") 33 | # write.csv(test2, gz1) 34 | # close(gz1) -------------------------------------------------------------------------------- /scripts/relabelTables.R: -------------------------------------------------------------------------------- 1 | relabelVertexTable <- function(oldVertexTable){ 2 | 3 | vertexTable <- oldVertexTable 4 | 5 | vertexTable$name <- NULL 6 | vertexTable$color <- NULL 7 | 8 | #print(vertexTable) 9 | 10 | vertexTable$bdmIncrease[vertexTable$bdmIncrease] <- "positive" 11 | vertexTable$bdmIncrease[vertexTable$bdmIncrease == FALSE] <- "negative" 12 | 13 | colnames(vertexTable) <- c("Node label", 14 | "BDM of graph after node knockout", 15 | "BDM diff", 16 | "Information value", 17 | "Perturbation rank") 18 | 19 | return(vertexTable) 20 | 21 | } 22 | 23 | relabelEdgeTable <- function(oldEdgeTable){ 24 | 25 | edgeTable <- oldEdgeTable 26 | 27 | edgeTable$color <- NULL 28 | 29 | edgeTable$bdmIncrease[edgeTable$bdmIncrease] <- "positive" 30 | edgeTable$bdmIncrease[edgeTable$bdmIncrease == FALSE] <- "negative" 31 | colnames(edgeTable) <- c("Link origin", 32 | "Link end", 33 | "BDM value after link knockout", 34 | "BDM diff", 35 | "Information value", 36 | "Perturbation rank") 37 | return (edgeTable) 38 | } -------------------------------------------------------------------------------- /scripts/loadGraph.R: -------------------------------------------------------------------------------- 1 | require("igraph") 2 | 3 | #(PA for Perturbation Analysis) 4 | loadGraphPA <- function(dataPath) 5 | { 6 | 7 | loadedDF <- read.csv(dataPath, 8 | header=FALSE, 9 | sep=',', #separate by comma 10 | quote="'", # quote by ' 11 | stringsAsFactors = FALSE, 12 | check.names = FALSE) 13 | 14 | #selects numeric values, drops the rest 15 | loadedDF <- loadedDF[sapply(loadedDF, is.numeric)] 16 | 17 | rownames(loadedDF) <- colnames(loadedDF) 18 | loadedMat <- as.matrix(loadedDF) 19 | 20 | g <- graph_from_adjacency_matrix(loadedMat) %>% 21 | set_vertex_attr("label", value = rownames(loadedDF) ) 22 | 23 | 24 | return(g) 25 | } 26 | 27 | 28 | loadGraph <- function(dataPath, 29 | separator, 30 | quoteSymbol) 31 | { 32 | 33 | loadedDf <- read.csv(dataPath, 34 | header=FALSE, 35 | sep=separator, 36 | quote=quoteSymbol, 37 | stringsAsFactors = FALSE, 38 | check.names = FALSE) 39 | 40 | #selects numeric values, drops the rest 41 | loadedDf <- loadedDf[sapply(loadedDf, is.numeric)] 42 | 43 | rownames(loadedDf) <- colnames(loadedDf) 44 | loadedMat <- as.matrix(loadedDf) 45 | loadedMat 46 | } 47 | 48 | 49 | #### remember to test path 50 | #test <- loadGraphPA("./data/m88.csv") 51 | #plot(test, edge.arrow.size = 0.2) 52 | -------------------------------------------------------------------------------- /scripts/loadGraph.R~: -------------------------------------------------------------------------------- 1 | require("igraph") 2 | 3 | #(PA for Perturbation Analysis) 4 | loadGraphPA <- function(dataPath) 5 | { 6 | 7 | loadedDF <- read.csv(dataPath, 8 | header=FALSE, 9 | sep=',', #separate by comma 10 | quote="'", # quote by ' 11 | stringsAsFactors = FALSE, 12 | check.names = FALSE) 13 | 14 | #selects numeric values, drops the rest 15 | loadedDF <- loadedDF[sapply(loadedDF, is.numeric)] 16 | 17 | rownames(loadedDF) <- colnames(loadedDF) 18 | loadedMat <- as.matrix(loadedDF) 19 | 20 | g <- graph_from_adjacency_matrix(loadedMat) %>% 21 | set_vertex_attr("label", value = rownames(loadedDF) ) 22 | 23 | 24 | return(g) 25 | } 26 | 27 | 28 | loadGraph <- function(dataPath, 29 | separator, 30 | quoteSymbol) 31 | { 32 | 33 | loadedDf <- read.csv(dataPath, 34 | header=FALSE, 35 | sep=separator, 36 | quote=quoteSymbol, 37 | stringsAsFactors = FALSE, 38 | check.names = FALSE) 39 | 40 | #selects numeric values, drops the rest 41 | loadedDf <- loadedDf[sapply(loadedDf, is.numeric)] 42 | 43 | rownames(loadedDf) <- colnames(loadedDf) 44 | loadedMat <- as.matrix(loadedDf) 45 | loadedMat 46 | } 47 | 48 | 49 | #### remember to test path 50 | #test <- loadGraphPA("./data/m88.csv") 51 | #plot(test, edge.arrow.size = 0.2) 52 | -------------------------------------------------------------------------------- /report.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Perturbation Analysis Report" 3 | output: html_document 4 | params: 5 | perturbationCounter: NA 6 | graphHistory: !r list() 7 | pvHistory: !r list() 8 | peHistory: !r list() 9 | delEventHistory: !r list() 10 | 11 | --- 12 | 13 | ```{r eval = FALSE, echo = FALSE} 14 | # For PDF output, change the header to have "output: pdf_document". 15 | # 16 | # Note that due to an issue in rmarkdown, the default value of a parameter in 17 | # the header cannot be `NULL`, so I used a default of `NA` for the default value 18 | # of `n`. 19 | require(igraph) 20 | require(knitr) 21 | require(xtable) 22 | ``` 23 | 24 | 25 | 26 | ```{r results= 'asis', echo = FALSE} 27 | 28 | # https://stackoverflow.com/questions/28313600/r-knitr-print-in-a-loop 29 | # https://github.com/yihui/knitr/issues/886 30 | 31 | 32 | #https://stackoverflow.com/questions/32418860/output-markdown-in-r-code-chunk 33 | for (i in 1:params$perturbationCounter){ 34 | 35 | cat('\n') 36 | 37 | cat(paste0("### ",params$delEventHistory[[i]])) 38 | 39 | cat('\n') 40 | 41 | coords <- layout_(params$graphHistory[[i]], as_star()) 42 | 43 | plot(params$graphHistory[[i]], layout = coords, edge.arrow.size = 0.4, 44 | vertex.size = 25, vertex.label.family = "Arial Black") 45 | 46 | cat('\n') 47 | 48 | print(knitr::kable(params$pvHistory[[i]], caption = "Possible node perturbations", 49 | row.names = FALSE)) 50 | cat('\n') 51 | 52 | print(knitr::kable(params$peHistory[[i]], caption = "Possible link perturbations", 53 | row.names = FALSE)) 54 | 55 | cat('\n') 56 | 57 | } 58 | ``` 59 | 60 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # The Online Algorithmic Complexity Calculator v3.0 2 | ## R Shiny App 3 | 4 | 5 | [R Shiny](http://shiny.rstudio.com/) code for the [Online Algorithmic Complexity Calculator](http://complexitycalculator.com/) version 3.0 by the [Algorithmic Nature Group](http://algorithmicnaturelab.org/) and the [Algorithmic Dynamics Lab](http://www.algorithmicdynamics.net/). 6 | 7 | To run this app locally, download [R Studio](https://www.rstudio.com/) and follow the instructions [here](http://shiny.rstudio.com/tutorial/lesson1/). 8 | 9 | 10 | Our Block Decomposition Method to estimate [Kolmogorov complexity](http://www.scholarpedia.org/article/Algorithmic_complexity) in strings and graphs of arbitrary size is described in **[A Divide-and-Conquer Method for Local Estimations of Algorithmic Complexity Lower Bounded by Shannon Entropy](https://arxiv.org/pdf/1609.00110.pdf)** by H. Zenil, F. Soler-Toscano, N.A. Kiani, S. Hernández-Orozco, and A. Rueda-Toicen. 11 | 12 | The Coding Theorem Method and the Block Decomposition Method are also described in **[Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096223)** by F. Soler-Toscano, H. Zenil, J.-P. Delahaye and N. Gauvrit. 13 | 14 | If you make use of results from this calculator, please make sure to visit [How To Cite](http://complexitycalculator.com/HowToCite.html). 15 | 16 | This app uses the [acss package](https://cran.r-project.org/web/packages/acss/index.html) available at CRAN and maintained by Henrik Singmann. 17 | 18 | ## License 19 | 20 | [GNU Affero General Public License v3.0](http://choosealicense.com/licenses/agpl-3.0) 21 | 22 | #### Maintainer 23 | 24 | [Antonio Rueda-Toicen](http://www.digital-spaceti.me/) 25 | 26 | antonio.rueda.toicen " at " algorithmicnaturelab (-dot-) org 27 | 28 | #### [www.complexitycalculator.com](www.complexitycalculator.com) 29 | 30 | #### [www.algorithmicnaturelab.org](www.algorithmicnaturelab.org) 31 | 32 | -------------------------------------------------------------------------------- /BDM2D.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | #load 4 x 4 CTM values 4 | fourByFourCTM <- read.csv("./data/K-4x4.csv", 5 | stringsAsFactors=FALSE, 6 | colClasses = 7 | c("character", "numeric"), 8 | header = FALSE) 9 | 10 | colnames(fourByFourCTM) <- c("square", "CTM") 11 | rownames(fourByFourCTM) <- fourByFourCTM$square 12 | fourByFourCTM$square <- NULL 13 | 14 | #load 3 x 3 CTM values 15 | threeByThreeCTM <- read.csv("./data/K-3x3.csv", 16 | stringsAsFactors=FALSE, 17 | colClasses = 18 | c("character", "numeric"), 19 | header = FALSE) 20 | 21 | colnames(threeByThreeCTM) <- c("square", "CTM") 22 | rownames(threeByThreeCTM) <- threeByThreeCTM$square 23 | threeByThreeCTM$square <- NULL 24 | 25 | 26 | ##split matrices in blocks 27 | require(purrr) 28 | ind <- function(matDim, blockSize, offset) { 29 | Map(`:`, seq(1, matDim-blockSize+1, by = offset), 30 | seq(blockSize, matDim, by = offset)) 31 | } 32 | 33 | # this is a helper function that generates subset indexing 34 | # according to dimension of the 35 | # matrix, the first sequence constructs the starting point of the subset index considering 36 | # the offset while the second sequence constructs 37 | # the ending point of the subset index 38 | myPartition <- function(mat, blockSize, offset) { 39 | lapply(cross2(ind(nrow(mat),blockSize,offset), 40 | ind(ncol(mat),blockSize,offset)), 41 | function(i) mat[i[[1]], i[[2]]]) 42 | } 43 | 44 | #used to lookup entries in fourByFourCTM and threeByThreeCTM 45 | stringify <- function(smallBlock){ 46 | paste0(c(t(smallBlock)), collapse ="") 47 | } 48 | 49 | blockEntropy <- function(mat, blockSize, offset){ 50 | 51 | parts <- myPartition(mat, blockSize, offset) 52 | 53 | flatSquares <- unlist(lapply(parts, stringify)) 54 | 55 | squaresTally <- as.data.frame(table(flatSquares)) 56 | 57 | rownames(squaresTally) <- squaresTally$flatSquare 58 | 59 | squaresTally$flatSquares <- NULL 60 | 61 | probs = squaresTally[, 1]/nrow(squaresTally) 62 | 63 | return(-sum(probs*log2(probs))) 64 | 65 | } 66 | 67 | bdm2D <- function(mat, blockSize, offset){ 68 | 69 | parts <- myPartition(mat, blockSize, offset) 70 | 71 | flatSquares <- unlist(lapply(parts, stringify)) 72 | 73 | squaresTally <- as.data.frame(table(flatSquares)) 74 | 75 | rownames(squaresTally) <- squaresTally$flatSquares 76 | 77 | squaresTally$flatSquares <- NULL 78 | 79 | if(blockSize == 4){ 80 | bdm <- (sum(fourByFourCTM[rownames(squaresTally),]) 81 | + sum(log2(squaresTally$Freq))) 82 | } else{ 83 | bdm <- (sum(threeByThreeCTM[rownames(squaresTally),]) 84 | + sum(log2(squaresTally$Freq))) 85 | } 86 | 87 | return(bdm) 88 | } 89 | 90 | ## tests 91 | # set.seed(42) 92 | # m99 <- apply(matrix(0, 9, 9), c(1,2), function(x) sample(c(0,1),1)) 93 | # m99 94 | 95 | # testResult1 <- bdm2D(m88, 4, 4) 96 | # testResult1 97 | # 98 | # 99 | # testResult2 <- bdm2D(m99, 3, 3) 100 | # testResult2 101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /scripts/BDM2D.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | #load 4 x 4 CTM values 4 | fourByFourCTM <- read.csv("data/K-4x4.csv", 5 | stringsAsFactors=FALSE, 6 | colClasses = 7 | c("character", "numeric"), 8 | header = FALSE) 9 | 10 | colnames(fourByFourCTM) <- c("square", "CTM") 11 | rownames(fourByFourCTM) <- fourByFourCTM$square 12 | fourByFourCTM$square <- NULL 13 | 14 | #load 3 x 3 CTM values 15 | threeByThreeCTM <- read.csv("data/K-3x3.csv", 16 | stringsAsFactors=FALSE, 17 | colClasses = 18 | c("character", "numeric"), 19 | header = FALSE) 20 | 21 | colnames(threeByThreeCTM) <- c("square", "CTM") 22 | rownames(threeByThreeCTM) <- threeByThreeCTM$square 23 | threeByThreeCTM$square <- NULL 24 | 25 | 26 | ##split matrices in blocks 27 | require(purrr) 28 | ind <- function(matDim, blockSize, offset) { 29 | Map(`:`, seq(1, matDim-blockSize+1, by = offset), 30 | seq(blockSize, matDim, by = offset)) 31 | } 32 | 33 | # this is a helper function that generates subset indexing 34 | # according to dimension of the 35 | # matrix, the first sequence constructs the starting point of the subset index considering 36 | # the offset while the second sequence constructs 37 | # the ending point of the subset index 38 | myPartition <- function(mat, blockSize, offset) { 39 | lapply(cross2(ind(nrow(mat),blockSize,offset), 40 | ind(ncol(mat),blockSize,offset)), 41 | function(i) mat[i[[1]], i[[2]]]) 42 | } 43 | 44 | #used to lookup entries in fourByFourCTM and threeByThreeCTM 45 | stringify <- function(smallBlock){ 46 | paste0(c(t(smallBlock)), collapse ="") 47 | } 48 | 49 | blockEntropy <- function(mat, blockSize, offset){ 50 | 51 | parts <- myPartition(mat, blockSize, offset) 52 | 53 | flatSquares <- unlist(lapply(parts, stringify)) 54 | 55 | squaresTally <- as.data.frame(table(flatSquares)) 56 | 57 | rownames(squaresTally) <- squaresTally$flatSquare 58 | 59 | squaresTally$flatSquares <- NULL 60 | 61 | probs = squaresTally[, 1]/nrow(squaresTally) 62 | 63 | return(-sum(probs*log2(probs))) 64 | 65 | } 66 | 67 | bdm2D <- function(mat, blockSize, offset){ 68 | 69 | parts <- myPartition(mat, blockSize, offset) 70 | 71 | flatSquares <- unlist(lapply(parts, stringify)) 72 | 73 | squaresTally <- as.data.frame(table(flatSquares)) 74 | 75 | rownames(squaresTally) <- squaresTally$flatSquares 76 | 77 | squaresTally$flatSquares <- NULL 78 | 79 | if(blockSize == 4){ 80 | bdm <- (sum(fourByFourCTM[rownames(squaresTally),]) 81 | + sum(log2(squaresTally$Freq))) 82 | } else{ 83 | bdm <- (sum(threeByThreeCTM[rownames(squaresTally),]) 84 | + sum(log2(squaresTally$Freq))) 85 | } 86 | 87 | return(bdm) 88 | } 89 | 90 | # tests 91 | # set.seed(42) 92 | # m99 <- apply(matrix(0, 9, 9), c(1,2), function(x) sample(c(0,1),1)) 93 | # m99 94 | # 95 | # testResult1 <- bdm2D(m88, 4, 4) 96 | # testResult1 97 | # 98 | # 99 | # testResult2 <- bdm2D(m99, 3, 3) 100 | # testResult2 101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /scripts/BDM1D.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | library(acss) 4 | 5 | maxKnownKs <- read.csv("data/maxKnownKs.csv") 6 | maxKnownKs$X <- NULL 7 | 8 | ldData <- read.csv("data/logicalDepthsBinaryStrings.csv", 9 | colClasses = c('character',"numeric")) 10 | 11 | colnames(ldData) <- c('string','ld') 12 | logicalDepths <- data.frame(ldData$ld) 13 | rownames(logicalDepths) <- ldData$string 14 | 15 | 16 | countSymbols <- function(string){ 17 | return(length(table(strsplit(string, NULL)))) 18 | } 19 | 20 | number2binary <- function(number, noBits) { 21 | 22 | binary_vector = rev(as.numeric(intToBits(number))) 23 | 24 | if(missing(noBits)) { 25 | 26 | return(binary_vector) 27 | } 28 | else { 29 | 30 | binary_vector[-(1:(length(binary_vector) - noBits))] 31 | } 32 | } 33 | 34 | getBinString <-function(string) { 35 | 36 | string <- utf8ToInt(string) 37 | 38 | bitList <- lapply(string, number2binary, noBits=8) 39 | 40 | bitList2 <- lapply(bitList, paste0, collapse="") 41 | 42 | binString <- paste0(bitList2, collapse="") 43 | 44 | return (binString) 45 | } 46 | 47 | splitString <- function(string, blockSize, offset){ 48 | 49 | if(blockSize > nchar(string)){ 50 | return (string) 51 | } 52 | 53 | if(offset > blockSize){ 54 | return ("ERROR: offset cannot be greater than blockSize.") 55 | } 56 | 57 | subs <- character() 58 | startIndices <- seq(1, nchar(string), offset) 59 | 60 | for(i in startIndices){ 61 | 62 | first <- i 63 | 64 | last <- -1 65 | 66 | if (last > nchar(string)){ 67 | last <- nchar(string) -1 68 | } else{ 69 | last <- i + blockSize -1 70 | } 71 | 72 | sub <- substr(string, first, last) 73 | subs <- append(subs, sub) 74 | 75 | lastStep = FALSE 76 | if (nchar(sub) == blockSize && last == nchar(string)){ 77 | lastStep = TRUE 78 | } 79 | if(lastStep)break 80 | } 81 | return (subs) 82 | } 83 | 84 | #receives the already splitted vector of input strings 85 | stringBDM <- function (stringsVector, base) { 86 | 87 | stringCounts <- as.data.frame(table(stringsVector)) 88 | 89 | #complexities 90 | 91 | stringCounts["ks"] <- acss(as.vector(stringCounts[["stringsVector"]]), base)[, 1] 92 | 93 | naIndices <- as.integer(which(is.na(stringCounts$ks))) 94 | 95 | naStrings <- as.vector(stringCounts$stringsVector[naIndices]) 96 | 97 | naLengths <- unlist(lapply(naStrings, nchar)) 98 | 99 | # more complex (+1) than the highest known values 100 | naKs <- maxKnownKs[, paste0("K.", toString(base))] + 1 101 | 102 | stringCounts[is.na(stringCounts)] <- naKs 103 | 104 | bdm <- sum(log2(stringCounts$Freq)) + sum(stringCounts$ks) 105 | 106 | return(bdm) 107 | 108 | } 109 | 110 | #receives the already splitted vector of input strings 111 | stringBDMLD <- function (stringsVector, base) { 112 | 113 | stringCounts <- as.data.frame(table(stringsVector)) 114 | 115 | #stringCounts 116 | sum(logicalDepths[as.character(stringCounts$stringsVector), ] * (log2(stringCounts$Freq)+1)) 117 | } 118 | 119 | # ## should print 80.12 bits 120 | #testBDM <- stringBDM(c("000110100111","111001011000"),2) 121 | #testBDM 122 | 123 | # ## should print 1002 steps 124 | # testLD <- stringBDMLD(c("000110100111","111001011000"),2) 125 | # testLD 126 | 127 | # ##should print 31 * (log2(3) + 1) = 80.13 steps 128 | #testLD2 <- stringBDMLD(c("010101010101", "010101010101", "010101010101"),2) 129 | #testLD2 130 | -------------------------------------------------------------------------------- /scripts/edgeAndVertexKnockout.R: -------------------------------------------------------------------------------- 1 | 2 | require("igraph") 3 | source("BDM2D.R") 4 | 5 | 6 | calculatePerturbationByVertexDeletion <- function(origGraph, blockSize, offset){ 7 | 8 | origMatrix <- as.matrix(as_adjacency_matrix(origGraph)) 9 | 10 | bdmOrig <- bdm2D(origMatrix, 11 | blockSize = blockSize, 12 | offset = offset) 13 | 14 | vertexPerturbationsDF <- as_data_frame(origGraph, 15 | what = "vertices") 16 | 17 | computedCols <- c("bdmValue", 18 | "bdmDifferenceAfterDeletion", 19 | "bdmIncrease") 20 | 21 | vertexPerturbationsDF[, computedCols] <- NA 22 | 23 | for(i in 1:nrow(vertexPerturbationsDF)){ 24 | 25 | delMat <- as.matrix(as_adjacency_matrix( 26 | delete_vertices(origGraph, 27 | V(origGraph)[i]))) 28 | 29 | bdmValueDel <- bdm2D(delMat, 30 | blockSize = blockSize, 31 | offset = offset) 32 | 33 | vertexPerturbationsDF[i, ]$bdmValue <- bdmValueDel 34 | 35 | bdmDiff <- bdmOrig - bdmValueDel 36 | vertexPerturbationsDF[i, ]$bdmDifferenceAfterDeletion <- bdmDiff 37 | 38 | bdmIncrease <- (bdmValueDel > bdmOrig) 39 | vertexPerturbationsDF[i, ]$bdmIncrease <- bdmIncrease 40 | } 41 | 42 | vertexPerturbationsDF$perturbationsRank <-rank( 43 | -as.numeric(vertexPerturbationsDF$bdmDifferenceAfterDeletion), 44 | ties.method="min") 45 | 46 | 47 | return (vertexPerturbationsDF) 48 | } 49 | 50 | calculatePerturbationByEdgeDeletion <- function(origGraph, blockSize, offset){ 51 | 52 | origMatrix <- as.matrix(as_adjacency_matrix(origGraph)) 53 | 54 | bdmOrig <- bdm2D(origMatrix, blockSize = blockSize, offset = offset) 55 | 56 | edgePerturbationsDF <- as_data_frame(origGraph, what = "edges") 57 | 58 | computedCols <- c("bdmValue", 59 | "bdmDifferenceAfterDeletion", 60 | "bdmIncrease") 61 | 62 | edgePerturbationsDF[, computedCols] <- NA 63 | 64 | for(i in 1:nrow(edgePerturbationsDF)){ 65 | 66 | deletedEdgeGraph <- delete_edges(origGraph, 67 | paste0(edgePerturbationsDF[i, ]$from, 68 | "|",edgePerturbationsDF[i, ]$to)) 69 | 70 | deletedEdgeAdjMatrix <- as.matrix(as_adjacency_matrix(deletedEdgeGraph)) 71 | 72 | #added $bdmValue 73 | deletedEdgeBDM <- bdm2D(deletedEdgeAdjMatrix, 74 | blockSize = blockSize, 75 | offset = offset) 76 | 77 | edgePerturbationsDF[i, ]$bdmValue <- deletedEdgeBDM 78 | 79 | edgePerturbationsDF[i, ]$bdmDifferenceAfterDeletion <- (bdmOrig - deletedEdgeBDM) 80 | 81 | edgePerturbationsDF[i, ]$bdmIncrease <- (deletedEdgeBDM > bdmOrig) 82 | 83 | } 84 | 85 | edgePerturbationsDF$perturbationsRank <- rank( 86 | -as.numeric(edgePerturbationsDF$bdmDiff), ties.method ="min" 87 | ) 88 | 89 | return(edgePerturbationsDF) 90 | } 91 | 92 | 93 | 94 | getColorRampPalette <- function(perturbationsDF){ 95 | if(min(perturbationsDF$bdmDifferenceAfterDeletion) >= 0 ){ 96 | pal <- colorRampPalette(c("light gray", "light blue")) 97 | } else if (max(perturbationsDF$bdmDifferenceAfterDeletion) < 0){ 98 | pal <- colorRampPalette(c("orangered", "red")) 99 | } else { 100 | pal <- colorRampPalette(rev(c("red", "orangered", "light gray", "light blue"))) 101 | } 102 | return(pal) 103 | 104 | } 105 | 106 | setGraphColors <- function(evaluatedGraph, vertexPerturbationsDF, edgePerturbationsDF){ 107 | 108 | vertexPal <- getColorRampPalette(vertexPerturbationsDF) 109 | edgePal <- getColorRampPalette(edgePerturbationsDF) 110 | 111 | E(evaluatedGraph)$color <- edgePal(ecount(evaluatedGraph))[edgePerturbationsDF$perturbationsRank] 112 | V(evaluatedGraph)$color <- vertexPal(vcount(evaluatedGraph))[vertexPerturbationsDF$perturbationsRank] 113 | 114 | return(evaluatedGraph) 115 | } 116 | 117 | ######################## 118 | 119 | 120 | ## test matrix 121 | # ro <- 5 122 | # co <- 5 123 | # 124 | # set.seed(3) 125 | # testMatrix <- apply(matrix(0, ro, co), c(1, 2), function(x) sample(c(0, 1), 1)) 126 | # 127 | # testGraph <- graph_from_adjacency_matrix(testMatrix) %>% 128 | # set_vertex_attr("label", value = LETTERS[1: 5]) 129 | 130 | # i <-1 131 | # td <- delete_vertices(testGraph, V(testGraph)[i]) 132 | # plot(td) 133 | ####################################### 134 | # 135 | #edgePerturbationsDF <- calculatePerturbationByEdgeDeletion(testGraph, 4, 1) 136 | # 137 | # vertexPal <- getColorRampPalette(vertexPerturbationsDF) 138 | # edgePal <- getColorRampPalette(edgePerturbationsDF) 139 | # 140 | # E(testGraph)$color <- edgePal(ecount(testGraph))[edgePerturbationsDF$perturbationsRank] 141 | # V(testGraph)$color <- vertexPal(vcount(testGraph))[vertexPerturbationsDF$perturbationsRank] 142 | # 143 | # plot(testGraph, vertex.label.family = "Arial Black", edge.arrow.size = .1, vertex.size = 25, 144 | # vertex.label.color="black") 145 | # 146 | 147 | 148 | ############### 149 | # edgePerturbationsDF <- calculatePerturbationByEdgeDeletion(testGraph, 4, 1) 150 | # print(edgePerturbationsDF) 151 | 152 | # vertexPerturbationsDF <- calculatePerturbationByVertexDeletion(testGraph, 4, 1) 153 | # print(vertexPerturbationsDF) 154 | 155 | 156 | 157 | -------------------------------------------------------------------------------- /ui.R: -------------------------------------------------------------------------------- 1 | library(shiny) 2 | 3 | shinyUI( 4 | fluidPage( 5 | 6 | #titlePanel("The Online Algorithmic Complexity Calculator"), 7 | 8 | sidebarLayout( 9 | 10 | column(6, 11 | tabsetPanel( 12 | tabPanel("For any string", 13 | 14 | value = 1, 15 | 16 | h3("Block Decomposition Method for Strings"), 17 | 18 | div(wellPanel( 19 | 20 | textInput(inputId = "bdmInputString", 21 | label = "Enter a string", 22 | value ="010101010101010101010101010101010101", 23 | width ="800px"), 24 | 25 | 26 | sliderInput(inputId = "blockSize", 27 | label = "Block size", 28 | min = 2, max = 12, value = 12, step = 1), 29 | 30 | #max becomes the current value of blockSize -1 31 | #dynamically 32 | sliderInput(inputId = "blockOverlap", 33 | label = "Block overlap", 34 | min = 0, max = 11, value = 0, step = 1), 35 | 36 | radioButtons(inputId = "bdmAlphabet", 37 | label = "Alphabet size", 38 | inline = TRUE, 39 | choices = list("2" = 2, 40 | "4" = 4, 41 | "5" = 5, 42 | "6" = 6, 43 | "9" = 9, 44 | "256 (utf-8)" = 256), 45 | selected = 2), 46 | 47 | br(), 48 | actionButton("goButtonBDM1D", "Evaluate") 49 | 50 | ), style="font-size:115%") 51 | 52 | ), 53 | 54 | tabPanel("For binary arrays", 55 | value = 2, 56 | h3( 57 | "Block Decomposition Method for Unweighted Adjacency Matrices"), 58 | div(wellPanel( 59 | fileInput(inputId = 'file1', 60 | label = "Choose a CSV file", 61 | accept = c('text/comma-separated-values', 62 | 'text/plain', 63 | 'text/csv', '.csv') 64 | ), 65 | 66 | radioButtons(inputId = 'bdm2DBlockSize', 67 | label = 'Block size', 68 | choices = c('4 x 4' = 4, 69 | '3 x 3' = 3), 70 | selected = 4), 71 | 72 | sliderInput(inputId = 'bdm2DOverlap', 73 | label = "Block overlap (rows and columns)", 74 | min = 0, 75 | max = 3, 76 | step = 1, 77 | value = 0), 78 | 79 | actionButton("goButtonBDM2D", "Evaluate") 80 | 81 | ) #end wellPanel BDM 2D 82 | , style="font-size: 115%") 83 | ), #end tabPanel BDM 2D 84 | 85 | tabPanel("For short strings", 86 | value = 3, 87 | h3("Algorithmic Complexity for Short Strings"), 88 | 89 | div(wellPanel( 90 | 91 | 92 | textInput(inputId = "ctmInputStrings", 93 | label = "Strings to evaluate", 94 | value ="AAAAAAAAAAAA ATATATATATAT ATTGCCGGCCTA", 95 | width = "800px") 96 | 97 | , 98 | 99 | p("Use space to separate strings."), 100 | p("The length of each string must be shorter than 13 characters."), 101 | 102 | 103 | radioButtons(inputId = "ctmAlphabet", 104 | label = "Alphabet size", 105 | inline = TRUE, 106 | choices = list("2" = 2, 107 | "4" = 4, 108 | "5" = 5, 109 | "6" = 6, 110 | "9" = 9), 111 | selected = 4), 112 | 113 | selectInput(#inputId="shortStringsEvalFunction", #"argument is not interpretable as logical" error 114 | inputId="funct", 115 | label = "Function used to evaluate the strings", 116 | choices = list("CTM Kolmogorov complexity estimated by algorithmic probability " = "acss", 117 | "Shannon entropy" = "entropy", 118 | "Second order entropy" = "entropy2", 119 | "Compression length by gzip"="compression-gzip", 120 | "Compression length by bzip2" = "compression-bzip2", 121 | "Compression length by xz" = "compression-xz", 122 | "Likelihood of production by Turing machines (deterministic process)" = "likelihood_d", 123 | "Likelihood of production by Turing machines (random process)" = "likelihood_ratio", 124 | "Conditional probability of random appearance" = "prob_random" 125 | ), 126 | selected = "acss"), 127 | 128 | 129 | actionButton("goButtonCTM", "Evaluate") 130 | 131 | )), # end wellPanel "For short strings", 132 | style="font-size:115%"), #end tabPanel "For short strings" 133 | 134 | tabPanel("Network perturbation", 135 | value = 4, 136 | h3("Perturbation Analysis of Unweighted Networks"), 137 | div(wellPanel( 138 | 139 | fileInput(inputId = "file2", 140 | label = "Choose a CSV file", 141 | accept = c('text/comma-separated-values', 142 | 'text/plain', 143 | 'text/csv', 144 | '.csv') 145 | ), 146 | 147 | selectInput(inputId = "vertexToDelete", 148 | label = "Node to delete", 149 | choices = ""), # choices filled in by server 150 | 151 | actionButton(inputId = "goButtonDeleteVertex", 152 | label = "Delete node"), 153 | 154 | span(textOutput(outputId = "cantDeleteVertex"), 155 | style = "color:red"), 156 | 157 | hr(), 158 | 159 | selectInput(inputId = "edgeToDelete", 160 | label = "Edge to delete", 161 | choices = ""), # choices filled in by server 162 | 163 | actionButton(inputId = "goButtonDeleteEdge", 164 | label = "Delete link"), 165 | 166 | 167 | span(textOutput(outputId = "cantDeleteLink"), 168 | style="color:red"), 169 | 170 | hr(), 171 | 172 | radioButtons(inputId = "printTable", 173 | label = h4("Perturbation Table"), 174 | choices = list("Nodes" = "vertices", 175 | "Links" = "edges"), 176 | selected = "vertices"), 177 | 178 | hr(), 179 | 180 | downloadButton('report', # name of downloadHandler in server 181 | 'Download report') 182 | 183 | )), 184 | style = "font-size:115%"), # end tabPanel "Network Perturbation" 185 | 186 | id = "conditionedPanels" 187 | ) 188 | ), 189 | 190 | mainPanel( 191 | withMathJax(), 192 | conditionalPanel(condition="input.conditionedPanels==1", 193 | 194 | br(), 195 | 196 | h3("Result of Evaluation"), 197 | 198 | br(), 199 | 200 | div(p(textOutput("evaluatedString")), 201 | style="font-size:120%", 202 | align="center"), 203 | 204 | br(), 205 | 206 | div(tableOutput("resultBDMTable"), 207 | style="font-size : 120%; 208 | font-family: Arial, Helvetica, sans-serif;", 209 | align="center"), 210 | 211 | hr(), 212 | 213 | div(p("$$\\textit{BDM} = 214 | \\sum_{i=1}^{n} \\textit{K}(\\textit{block}_{i}) 215 | +\\textit{log}_{2}(|\\textit{block}_{i}|)$$"), 216 | style="font-size: 120%", 217 | align="center"), 218 | 219 | hr(), 220 | 221 | div(p("Strings that don't appear in the 222 | \\(D(\\#\\textit{of states}, \\#\\textit{ of symbols})\\) 223 | distribution have their 224 | \\(\\textit{K}\\) value estimated as"), 225 | style="font-size:110%"), 226 | 227 | div(p("$$ \\textit{Max}(K(\\#\\textit{ of states}, \\#\\textit{ of symbols})) 228 | + 1 $$"), 229 | style="font-size:110%") 230 | ), ##end BDM 1D tab 231 | 232 | conditionalPanel(condition="input.conditionedPanels==2", 233 | br(), 234 | 235 | h3("Adjacency Matrix"), 236 | 237 | div(tableOutput("loadedGraph"), align="center", style="font-size: 110%"), 238 | 239 | br(), 240 | h3("Result of Evaluation"), 241 | div(tableOutput("resultBDM2DTable"), style="font-size: 120%", align="center"), 242 | 243 | hr(), 244 | 245 | div(p("$$BDM = 246 | \\sum_{i=1}^{n} K(block_{i})+log_{2}(|block_{i}|)$$"), 247 | style ="font-size: 120%") 248 | ), ##end BDM 2D tab 249 | 250 | conditionalPanel(condition ="input.conditionedPanels==3", 251 | br(), 252 | h3("Result of Evaluation"), 253 | br(), 254 | div(tableOutput("resultCTM"), 255 | style = "font-size: 120%", 256 | align = "center"), 257 | hr(), 258 | 259 | conditionalPanel(condition = "input.funct == 'acss'", 260 | 261 | div(p("$$K(\\#\\textit{ of states}, \\#\\textit{ of symbols}) = 262 | -log_{2}(D(\\#\\textit{of states}, \\textit{# of symbols})$$"), 263 | style = "font-size: 120%"), 264 | 265 | hr(), 266 | 267 | div(p("\\(\\textit{}~\\)\\(K(\\#\\textit{ of states}, \\#\\textit{ of symbols})\\) 268 | indicates the estimated Kolmogorov complexity of 269 | the string by the Coding Theorem Method."), 270 | style="font-size:110%"), 271 | 272 | hr(), 273 | 274 | div(p("\\(D(\\#\\textit{of states}, \\#\\textit{ of symbols})\\) indicates the 275 | estimated algorithmic probability, 276 | which is the output frequency of the string 277 | by Turing machines with the same alphabet."), 278 | style="font-size:110%"), 279 | 280 | hr(), 281 | 282 | div(p("Strings that don't appear in the 283 | \\(D(\\#\\textit{of states}, \\#\\textit{ of symbols})\\) 284 | distribution have their 285 | \\(\\textit{K}\\) value estimated as"), 286 | style="font-size:110%"), 287 | 288 | div(p("$$ \\textit{Max}(K(\\#\\textit{ of states}, \\#\\textit{ of symbols})) 289 | + 1 $$"), 290 | style="font-size:110%"), 291 | hr(), 292 | 293 | div(p("More information on the other complexity 294 | functions is available in the ", 295 | a(href="https://cran.r-project.org/web/packages/acss/acss.pdf", 296 | "documentation of the ACSS package @ CRAN.")), 297 | style="font-size:110%", align="center") 298 | ) 299 | 300 | ), ## #end conditionalPanel CTM chosen 301 | 302 | conditionalPanel(condition ="input.conditionedPanels==4", 303 | br(), 304 | plotOutput("graphPlot"), 305 | tableOutput("perturbationTable") 306 | ) 307 | 308 | ) ## end mainPanel 309 | 310 | ) 311 | )) -------------------------------------------------------------------------------- /data/K-3x3.csv: -------------------------------------------------------------------------------- 1 | 000000000,13.713356989265955 2 | 000000001,14.914491375110648 3 | 000000010,14.815062288419652 4 | 000000011,15.941579969636367 5 | 000000100,14.914491375110648 6 | 000000101,16.900030404099436 7 | 000000110,15.941579969636367 8 | 000000111,16.95562693976486 9 | 000001000,14.815062288419652 10 | 000001001,15.941579969636367 11 | 000001010,15.696612950809524 12 | 000001011,17.62373085163188 13 | 000001100,15.78735242844744 14 | 000001101,17.854607164922214 15 | 000001110,16.261880476627034 16 | 000001111,17.79099027006017 17 | 000010000,14.366394383270709 18 | 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011011100,16.85323886184152 222 | 011011101,16.261880476627034 223 | 011011110,16.27316424670126 224 | 011011111,15.941579969636367 225 | 011100000,16.261880476627034 226 | 011100001,18.980868502975127 227 | 011100010,17.39355468629877 228 | 011100011,18.59627095161859 229 | 011100100,16.85323886184152 230 | 011100101,17.165765197608998 231 | 011100110,16.36182798445243 232 | 011100111,15.970999378598538 233 | 011101000,18.45555102314149 234 | 011101001,18.92890357141047 235 | 011101010,18.13105140995317 236 | 011101011,18.3368731060255 237 | 011101100,16.36182798445243 238 | 011101101,15.970999378598538 239 | 011101110,15.601133845368501 240 | 011101111,15.4100416811352 241 | 011110000,16.476306662461006 242 | 011110001,18.980868502975127 243 | 011110010,17.231501378198235 244 | 011110011,18.38286372231791 245 | 011110100,16.81279391621911 246 | 011110101,17.212914516396264 247 | 011110110,16.27316424670126 248 | 011110111,15.78735242844744 249 | 011111000,17.491110914380187 250 | 011111001,17.854607164922214 251 | 011111010,17.316632891027243 252 | 011111011,16.900030404099436 253 | 011111100,16.27316424670126 254 | 011111101,15.78735242844744 255 | 011111110,15.49045504078404 256 | 011111111,14.914491375110648 257 | 100000000,14.914491375110648 258 | 100000001,15.49045504078404 259 | 100000010,15.78735242844744 260 | 100000011,16.27316424670126 261 | 100000100,16.900030404099436 262 | 100000101,17.316632891027243 263 | 100000110,17.854607164922214 264 | 100000111,17.491110914380187 265 | 100001000,15.78735242844744 266 | 100001001,16.27316424670126 267 | 100001010,17.212914516396264 268 | 100001011,16.81279391621911 269 | 100001100,18.38286372231791 270 | 100001101,17.231501378198235 271 | 100001110,18.980868502975127 272 | 100001111,16.476306662461006 273 | 100010000,15.4100416811352 274 | 100010001,15.601133845368501 275 | 100010010,15.970999378598538 276 | 100010011,16.36182798445243 277 | 100010100,18.3368731060255 278 | 100010101,18.13105140995317 279 | 100010110,18.92890357141047 280 | 100010111,18.45555102314149 281 | 100011000,15.970999378598538 282 | 100011001,16.36182798445243 283 | 100011010,17.165765197608998 284 | 100011011,16.85323886184152 285 | 100011100,18.59627095161859 286 | 100011101,17.39355468629877 287 | 100011110,18.980868502975127 288 | 100011111,16.261880476627034 289 | 100100000,15.941579969636367 290 | 100100001,16.27316424670126 291 | 100100010,16.261880476627034 292 | 100100011,16.85323886184152 293 | 100100100,16.95562693976486 294 | 100100101,17.491110914380187 295 | 100100110,17.79099027006017 296 | 100100111,17.616157737686187 297 | 100101000,17.104691927835958 298 | 100101001,16.21735754583781 299 | 100101010,17.39355468629877 300 | 100101011,16.36182798445243 301 | 100101100,18.680113095310837 302 | 100101101,17.018774218181772 303 | 100101110,18.92890357141047 304 | 100101111,16.49512736777431 305 | 100110000,16.49512736777431 306 | 100110001,16.36182798445243 307 | 100110010,16.476306662461006 308 | 100110011,16.81279391621911 309 | 100110100,18.327674281086303 310 | 100110101,18.45555102314149 311 | 100110110,17.79099027006017 312 | 100110111,17.62373085163188 313 | 100111000,17.018774218181772 314 | 100111001,16.21735754583781 315 | 100111010,17.231501378198235 316 | 100111011,16.27316424670126 317 | 100111100,18.580864438414107 318 | 100111101,17.104691927835958 319 | 100111110,17.854607164922214 320 | 100111111,15.941579969636367 321 | 101000000,16.900030404099436 322 | 101000001,17.316632891027243 323 | 101000010,18.38286372231791 324 | 101000011,17.231501378198235 325 | 101000100,17.316632891027243 326 | 101000101,17.355908321894567 327 | 101000110,17.231501378198235 328 | 101000111,16.719591645677742 329 | 101001000,17.854607164922214 330 | 101001001,17.491110914380187 331 | 101001010,18.980868502975127 332 | 101001011,16.476306662461006 333 | 101001100,17.231501378198235 334 | 101001101,16.719591645677742 335 | 101001110,17.165765197608998 336 | 101001111,16.149904887374973 337 | 101010000,18.3368731060255 338 | 101010001,18.13105140995317 339 | 101010010,18.59627095161859 340 | 101010011,17.39355468629877 341 | 101010100,18.13105140995317 342 | 101010101,17.082165127627736 343 | 101010110,17.39355468629877 344 | 101010111,16.73365644982906 345 | 101011000,18.92890357141047 346 | 101011001,18.45555102314149 347 | 101011010,18.980868502975127 348 | 101011011,16.261880476627034 349 | 101011100,17.39355468629877 350 | 101011101,16.73365644982906 351 | 101011110,17.212914516396264 352 | 101011111,15.696612950809524 353 | 101100000,17.854607164922214 354 | 101100001,17.231501378198235 355 | 101100010,18.980868502975127 356 | 101100011,17.165765197608998 357 | 101100100,17.491110914380187 358 | 101100101,16.719591645677742 359 | 101100110,16.476306662461006 360 | 101100111,16.149904887374973 361 | 101101000,18.580864438414107 362 | 101101001,17.018774218181772 363 | 101101010,18.59627095161859 364 | 101101011,15.970999378598538 365 | 101101100,17.018774218181772 366 | 101101101,16.16173099207288 367 | 101101110,15.970999378598538 368 | 101101111,15.32169282402135 369 | 101110000,18.92890357141047 370 | 101110001,17.39355468629877 371 | 101110010,18.980868502975127 372 | 101110011,17.212914516396264 373 | 101110100,18.45555102314149 374 | 101110101,16.73365644982906 375 | 101110110,16.261880476627034 376 | 101110111,15.696612950809524 377 | 101111000,18.680113095310837 378 | 101111001,17.104691927835958 379 | 101111010,18.38286372231791 380 | 101111011,15.78735242844744 381 | 101111100,17.104691927835958 382 | 101111101,16.255262060168807 383 | 101111110,15.78735242844744 384 | 101111111,14.815062288419652 385 | 110000000,15.941579969636367 386 | 110000001,16.27316424670126 387 | 110000010,17.104691927835958 388 | 110000011,16.21735754583781 389 | 110000100,17.854607164922214 390 | 110000101,17.231501378198235 391 | 110000110,18.580864438414107 392 | 110000111,17.018774218181772 393 | 110001000,16.261880476627034 394 | 110001001,16.85323886184152 395 | 110001010,17.39355468629877 396 | 110001011,16.36182798445243 397 | 110001100,18.980868502975127 398 | 110001101,17.165765197608998 399 | 110001110,18.59627095161859 400 | 110001111,15.970999378598538 401 | 110010000,16.49512736777431 402 | 110010001,16.36182798445243 403 | 110010010,17.018774218181772 404 | 110010011,16.21735754583781 405 | 110010100,18.92890357141047 406 | 110010101,17.39355468629877 407 | 110010110,18.680113095310837 408 | 110010111,17.104691927835958 409 | 110011000,16.476306662461006 410 | 110011001,16.81279391621911 411 | 110011010,17.231501378198235 412 | 110011011,16.27316424670126 413 | 110011100,18.980868502975127 414 | 110011101,17.212914516396264 415 | 110011110,18.38286372231791 416 | 110011111,15.78735242844744 417 | 110100000,17.62373085163188 418 | 110100001,16.81279391621911 419 | 110100010,18.45555102314149 420 | 110100011,16.36182798445243 421 | 110100100,17.79099027006017 422 | 110100101,16.476306662461006 423 | 110100110,18.327674281086303 424 | 110100111,16.49512736777431 425 | 110101000,18.45555102314149 426 | 110101001,16.36182798445243 427 | 110101010,18.13105140995317 428 | 110101011,15.601133845368501 429 | 110101100,18.92890357141047 430 | 110101101,15.970999378598538 431 | 110101110,18.3368731060255 432 | 110101111,15.4100416811352 433 | 110110000,17.616157737686187 434 | 110110001,16.85323886184152 435 | 110110010,17.491110914380187 436 | 110110011,16.27316424670126 437 | 110110100,17.79099027006017 438 | 110110101,16.261880476627034 439 | 110110110,16.95562693976486 440 | 110110111,15.941579969636367 441 | 110111000,17.491110914380187 442 | 110111001,16.27316424670126 443 | 110111010,17.316632891027243 444 | 110111011,15.49045504078404 445 | 110111100,17.854607164922214 446 | 110111101,15.78735242844744 447 | 110111110,16.900030404099436 448 | 110111111,14.914491375110648 449 | 111000000,16.95562693976486 450 | 111000001,17.491110914380187 451 | 111000010,18.680113095310837 452 | 111000011,17.018774218181772 453 | 111000100,17.491110914380187 454 | 111000101,16.719591645677742 455 | 111000110,17.018774218181772 456 | 111000111,16.16173099207288 457 | 111001000,17.79099027006017 458 | 111001001,17.616157737686187 459 | 111001010,18.92890357141047 460 | 111001011,16.49512736777431 461 | 111001100,16.476306662461006 462 | 111001101,16.149904887374973 463 | 111001110,15.970999378598538 464 | 111001111,15.32169282402135 465 | 111010000,18.327674281086303 466 | 111010001,18.45555102314149 467 | 111010010,18.580864438414107 468 | 111010011,17.104691927835958 469 | 111010100,18.45555102314149 470 | 111010101,16.73365644982906 471 | 111010110,17.104691927835958 472 | 111010111,16.255262060168807 473 | 111011000,17.79099027006017 474 | 111011001,17.62373085163188 475 | 111011010,17.854607164922214 476 | 111011011,15.941579969636367 477 | 111011100,16.261880476627034 478 | 111011101,15.696612950809524 479 | 111011110,15.78735242844744 480 | 111011111,14.815062288419652 481 | 111100000,17.79099027006017 482 | 111100001,16.476306662461006 483 | 111100010,18.92890357141047 484 | 111100011,15.970999378598538 485 | 111100100,17.616157737686187 486 | 111100101,16.149904887374973 487 | 111100110,16.49512736777431 488 | 111100111,15.32169282402135 489 | 111101000,18.327674281086303 490 | 111101001,16.49512736777431 491 | 111101010,18.3368731060255 492 | 111101011,15.4100416811352 493 | 111101100,16.49512736777431 494 | 111101101,15.32169282402135 495 | 111101110,15.4100416811352 496 | 111101111,14.366394383270709 497 | 111110000,17.79099027006017 498 | 111110001,16.261880476627034 499 | 111110010,17.854607164922214 500 | 111110011,15.78735242844744 501 | 111110100,17.62373085163188 502 | 111110101,15.696612950809524 503 | 111110110,15.941579969636367 504 | 111110111,14.815062288419652 505 | 111111000,16.95562693976486 506 | 111111001,15.941579969636367 507 | 111111010,16.900030404099436 508 | 111111011,14.914491375110648 509 | 111111100,15.941579969636367 510 | 111111101,14.815062288419652 511 | 111111110,14.914491375110648 512 | 111111111,13.713356989265955 -------------------------------------------------------------------------------- /server.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | require("igraph") 4 | 5 | source("scripts/BDM1D.R") 6 | source("scripts/BDM2D.R") 7 | source("scripts/compressionLength.R") 8 | source("scripts/loadGraph.R") 9 | source("scripts/edgeAndVertexKnockout.R") 10 | source("scripts/relabelTables.R") 11 | 12 | source("scripts/listEdges.R") 13 | 14 | 15 | 16 | shinyServer(function(input, output, session) { 17 | 18 | #updates the slider for overlap in BDM 1D dynamically 19 | observeEvent(input$blockSize, { 20 | updateSliderInput(session, 21 | "blockOverlap", 22 | max=input$blockSize-1) 23 | }) 24 | 25 | 26 | output$symbolCount <- renderText({ 27 | 28 | input$goButton 29 | 30 | y <- isolate( 31 | paste0( 32 | 'The string has length ', 33 | nchar(input$bdmInputString), 34 | ' and contains ', 35 | countSymbols(input$bdmInputString), 36 | ' different symbols. \n It has Shannon entropy = ', 37 | entropy(input$bdmInputString), 38 | ' bit(s), and compressed length in bytes = ', 39 | compressionLength(input$bdmInputString, "gzip"), 40 | ' (using gzip)')) 41 | }) 42 | 43 | ### CTM Tab 44 | output$resultCTM <- renderTable({ 45 | input$goButtonCTM 46 | isolate({ 47 | 48 | if(input$funct == "entropy"){ 49 | strings <- unlist(strsplit(input$ctmInputStrings, " ")) 50 | z <- lapply(strings, entropy) 51 | if (!is.matrix(z)) { 52 | z <- as.matrix(z) 53 | colnames(z) <- "Shannon entropy" 54 | rownames(z) <- strings 55 | } 56 | } 57 | 58 | else if(input$funct == "entropy2"){ 59 | 60 | strings <- unlist(strsplit(input$ctmInputStrings, " ")) 61 | z <- lapply(strings, entropy2) 62 | if (!is.matrix(z)) { 63 | z <- as.matrix(z) 64 | colnames(z) <- "Second order entropy" 65 | rownames(z) <- strings 66 | } 67 | } 68 | 69 | else if(input$funct == "compression-gzip"){ 70 | 71 | strings <- unlist(strsplit(input$ctmInputStrings, " ")) 72 | 73 | z <- lapply(strings, 74 | compressionLength, 75 | compressionType = "gzip") 76 | 77 | if (!is.matrix(z)) { 78 | z <- as.matrix(z) 79 | colnames(z) <- "Compression length (bytes)" 80 | rownames(z) <- strings 81 | } 82 | 83 | } 84 | 85 | else if(input$funct == "compression-bzip2"){ 86 | 87 | strings <- unlist(strsplit(input$ctmInputStrings, " ")) 88 | 89 | z <- lapply(strings, 90 | compressionLength, 91 | compressionType = "bzip2") 92 | 93 | if (!is.matrix(z)) { 94 | z <- as.matrix(z) 95 | colnames(z) <- "Compression length (bytes)" 96 | rownames(z) <- strings 97 | } 98 | 99 | } 100 | 101 | else if(input$funct == "compression-xz"){ 102 | 103 | strings <- unlist(strsplit(input$ctmInputStrings, " ")) 104 | 105 | z <- lapply(strings, 106 | compressionLength, 107 | compressionType = "xz") 108 | 109 | if (!is.matrix(z)) { 110 | z <- as.matrix(z) 111 | colnames(z) <- "Compression length (bytes)" 112 | rownames(z) <- strings 113 | } 114 | 115 | } 116 | 117 | #call acss's CTM 118 | else if(input$funct == "acss"){ 119 | 120 | z <- do.call(acss, 121 | args = list(string = unlist(strsplit(input$ctmInputStrings, " ")), 122 | alphabet = as.numeric(input$ctmAlphabet))) 123 | 124 | if (input$ctmAlphabet == "2"){ 125 | colnames(z) <- c("K(5, 2)", "D(5, 2)") 126 | } 127 | else if (input$ctmAlphabet == "4"){ 128 | colnames(z) <- c("K(4, 4)", "D(4, 4)") 129 | } 130 | else if (input$ctmAlphabet == "5"){ 131 | colnames(z) <- c("K(4, 5)", "D(4, 5)") 132 | } 133 | else if (input$ctmAlphabet == "6"){ 134 | colnames(z) <- c("K(4, 6)", "D(4, 6)") 135 | } 136 | else if (input$ctmAlphabet == "9"){ 137 | colnames(z) <- c("K(4, 9)", "D(4, 9)") 138 | } 139 | 140 | 141 | } 142 | 143 | #call likelihood of production 144 | else{ 145 | z <- do.call(input$funct, 146 | args = list(string = unlist(strsplit(input$ctmInputStrings, " ")), 147 | alphabet = as.numeric(input$ctmAlphabet))) 148 | if (!is.matrix(z)){ 149 | z <- as.matrix(z) 150 | 151 | 152 | colnames(z) <- paste0(input$funct, ": ", 153 | input$ctmAlphabet) 154 | } 155 | } 156 | }) 157 | z 158 | }, rownames = TRUE, digits = 16) 159 | 160 | #### BDM 1D Tab 161 | 162 | output$evaluatedString <- renderText({ 163 | 164 | input$goButtonBDM1D 165 | isolate({ 166 | x <- paste0("Evaluated string = \"", 167 | input$bdmInputString, "\"") 168 | }) 169 | x 170 | }) 171 | 172 | 173 | #BDM 1D table of results 174 | output$resultBDMTable <- renderTable({ 175 | 176 | input$goButtonBDM1D 177 | isolate({ 178 | 179 | values <- c () 180 | 181 | if (input$bdmAlphabet == 256){ 182 | 183 | # convert UTF-8 string to binary 184 | binString <- getBinString(input$bdmInputString) 185 | 186 | values[1] <- paste0( 187 | sprintf("%.4f",stringBDM( 188 | splitString(binString, 189 | blockSize = input$blockSize, 190 | offset = input$blockSize -input$blockOverlap), 191 | base = 2)), 192 | " bits") 193 | 194 | values[2] <- paste0( 195 | sprintf("%.4f",stringBDMLD( 196 | splitString(binString, 197 | blockSize = input$blockSize, 198 | offset = (input$blockSize -input$blockOverlap)), 199 | base = input$bdmAlphabet)), 200 | " steps") 201 | } 202 | else { 203 | values[1] <- paste0( 204 | sprintf("%.4f",stringBDM( 205 | splitString(input$bdmInputString, 206 | blockSize = input$blockSize, 207 | offset = input$blockSize -input$blockOverlap), 208 | base = input$bdmAlphabet)), 209 | " bits") 210 | } 211 | 212 | if (input$bdmAlphabet == 2){ 213 | values[2] <- paste0( 214 | sprintf("%.4f",stringBDMLD( 215 | splitString(input$bdmInputString, 216 | blockSize = input$blockSize, 217 | offset = input$blockSize -input$blockOverlap), 218 | base = input$bdmAlphabet)), 219 | " steps") 220 | } 221 | #entropy 222 | values[3] <- paste0(sprintf("%.4f", 223 | entropy(input$bdmInputString)), 224 | " bit(s)") 225 | 226 | #second order entropy 227 | values[4] <- paste0(sprintf("%.4f", 228 | entropy2(input$bdmInputString)), 229 | " bit(s)") 230 | 231 | #compression length 232 | values[5] <- paste0(compressionLength(input$bdmInputString, 233 | "gzip") * 8, 234 | " bits") 235 | 236 | values[6] <- nchar(input$bdmInputString) 237 | values[7] <- countSymbols(input$bdmInputString) 238 | values[8] <- input$bdmAlphabet 239 | values[9] <- input$blockSize 240 | values[10] <- input$blockOverlap 241 | 242 | 243 | 244 | resultRowNames <- c("BDM algorithmic complexity estimation", 245 | "BDM logical depth estimation", 246 | "Shannon entropy", 247 | "Second order entropy", 248 | "Compression length (using gzip)", 249 | "String length", 250 | "# of symbols in string", 251 | "# of symbols in CTM alphabet", 252 | "Block size", 253 | "Block overlap") 254 | 255 | if (!(input$bdmAlphabet == 2 || input$bdmAlphabet == 256)) 256 | { 257 | values <- values[-2] 258 | resultRowNames <- resultRowNames[-2] 259 | } 260 | 261 | result <- data.frame(values) 262 | rownames(result) <- resultRowNames 263 | 264 | 265 | }) 266 | result}, 267 | rownames = TRUE, colnames = FALSE) 268 | 269 | 270 | ##### BDM 2D Tab 271 | loadedGraph <- reactive({ 272 | 273 | inFile <- input$file1 274 | if(input$goButtonBDM2D == 0 || is.null(inFile$datapath)){ 275 | graph <- loadGraph("data/m88.csv", sep = ",", quote = '"' ) 276 | return (graph) 277 | } 278 | 279 | graph <- loadGraph(inFile$datapath, 280 | sep = ",", 281 | quote = '"') 282 | 283 | graph 284 | }) 285 | 286 | #render adjacency matrix as a dataframe 287 | output$loadedGraph <- renderTable({ 288 | 289 | loadedGraph() 290 | 291 | }) 292 | 293 | output$resultBDM2DTable <- renderTable({ 294 | 295 | input$goButtonBDM2D 296 | isolate({ 297 | 298 | #BDM2D 299 | values <- c () 300 | 301 | values[1] <- paste0( 302 | 303 | sprintf("%.4f", 304 | bdm2D(loadedGraph(), 305 | blockSize = as.numeric(input$bdm2DBlockSize), 306 | offset = (as.numeric(input$bdm2DBlockSize) - 307 | as.numeric(input$bdm2DOverlap)) )), 308 | " bits") 309 | 310 | # Shannon entropy 311 | values[2] <- paste0(sprintf("%.4f", 312 | entropy(toString(loadedGraph()))[[1]]), 313 | " bit(s)") 314 | 315 | # Block entropy 316 | values[3] <- paste0( 317 | 318 | sprintf("%.4f", blockEntropy(loadedGraph(), 319 | blockSize = as.numeric(input$bdm2DBlockSize), 320 | offset = (as.numeric(input$bdm2DBlockSize) - 321 | as.numeric(input$bdm2DOverlap)) )), 322 | " bits") 323 | 324 | # compression length 325 | values[4] <- paste0(compressionLength(toString(loadedGraph()), "gzip") * 8, 326 | " bits") 327 | 328 | # matrix dimensions 329 | values[5] <- paste0(nrow(loadedGraph()), " x ", ncol(loadedGraph())) 330 | 331 | 332 | #2D block size 333 | values[6] <- paste0(input$bdm2DBlockSize, " x ", input$bdm2DBlockSize) 334 | 335 | #2D block overlap 336 | values[7] <- input$bdm2DOverlap 337 | 338 | result <- data.frame(values) 339 | 340 | rownames(result) <- c("BDM algorithmic complexity estimation", 341 | "Shannon entropy", 342 | "Block entropy", 343 | "Compression length (using gzip)", 344 | "Matrix dimensions", 345 | "Block size", 346 | "Block overlap (both rows and columns)") 347 | 348 | 349 | }) 350 | result}, rownames = TRUE, colnames = FALSE) 351 | 352 | 353 | output$resultBDM2D <- renderText({ 354 | 355 | input$goButtonBDM2D 356 | isolate({ 357 | 358 | if(input$bdm2DOverlap >= input$bdm2DBlockSize){ 359 | x <- "The maximum possible overlap is block size - 1." 360 | } 361 | else{ 362 | x <- paste0('BDM of the evaluated adjacency matrix considering ', 363 | input$bdm2DBlockSize, ' x ', 364 | input$bdm2DBlockSize, 365 | " blocks, with overlap of ", 366 | input$bdm2DOverlap, 367 | ' rows and ', 368 | input$bdm2DOverlap, 369 | ' columns is \n', 370 | bdm2D(loadedGraph(), 371 | blockSize = as.numeric(input$bdm2DBlockSize), 372 | offset = (as.numeric(input$bdm2DBlockSize) - 373 | as.numeric(input$bdm2DOverlap)) ), 374 | 375 | ' bit(s).' 376 | ) 377 | } 378 | x 379 | }) 380 | }) 381 | 382 | output$result2DEntropyAndCompLength <- renderText({ 383 | 384 | input$goButtonBDM2D 385 | isolate({ 386 | 387 | 388 | 389 | x <- paste0("The Shannon entropy of the adjacency matrix is ", 390 | entropy(toString(loadedGraph()))[[1]], 391 | " bit(s), and its compressed length is ", 392 | compressionLength(toString(loadedGraph()), "gzip"), 393 | ' bytes (using gzip).' 394 | ) 395 | 396 | x 397 | }) 398 | 399 | }) 400 | 401 | ###### Perturbation Analysis Tab 402 | 403 | #runs once, when server starts 404 | g <- loadGraphPA("./data/m88.csv") 405 | pv <- calculatePerturbationByVertexDeletion(g, 4, 1) 406 | pe <- calculatePerturbationByEdgeDeletion(g, 4, 1) 407 | g <- setGraphColors(g, pv, pe) 408 | 409 | #all of these must be cleared and updated when a new graph is loaded 410 | 411 | sizeOfHistory <- vcount(g) + ecount(g) 412 | 413 | graphHistory <- list() 414 | 415 | pvHistory <- list() 416 | 417 | peHistory <- list() 418 | 419 | delEventHistory <- vector("list", sizeOfHistory) 420 | 421 | graphHistory[[1]] <- g 422 | pvHistory[[1]] <- relabelVertexTable(pv) 423 | peHistory[[1]] <- relabelEdgeTable(pe) 424 | delEventHistory[[1]] <- "Initial state of the network" 425 | 426 | #starts with 1 even though the network hasn't been changed at the start 427 | perturbationCounter <- as.integer(1) 428 | 429 | my <- reactiveValues(g = g, 430 | pv = pv, 431 | pe = pe, 432 | perturbationCounter = perturbationCounter, 433 | sizeOfHistory = sizeOfHistory, 434 | graphHistory = graphHistory, 435 | pvHistory = pvHistory, 436 | peHistory = peHistory, 437 | delEventHistory = delEventHistory) 438 | 439 | observe({ 440 | 441 | updateSelectInput(session, "vertexToDelete", 442 | choices = V(my$g)$label) 443 | 444 | updateSelectInput(session, "edgeToDelete", 445 | choices = listEdges(my$g)) 446 | 447 | }) 448 | 449 | observeEvent(input$file2, { 450 | 451 | inFile <- input$file2 452 | 453 | if (is.null(inFile$datapath)){ 454 | 455 | 456 | } else { 457 | 458 | g <- loadGraphPA(inFile$datapath) 459 | my$pv <- calculatePerturbationByVertexDeletion(g, 4, 1) 460 | my$pe <- calculatePerturbationByEdgeDeletion(g, 4, 1) 461 | my$g <- setGraphColors(g, my$pv, my$pe) 462 | 463 | my$sizeOfHistory <- vcount(my$g) + ecount(my$g) 464 | 465 | my$perturbationCounter <- as.integer(1) 466 | 467 | #todo: check if this vector("list", my$sizeOfHistory) works with list() in Report.Rmd 468 | graphHistory <- vector("list", my$sizeOfHistory) 469 | ptHistory <- vector("list", my$sizeOfHistory) 470 | pvHistory <- vector("list", my$sizeOfHistory) 471 | delEventHistory <- vector("list", my$sizeOfHistory) 472 | 473 | } 474 | 475 | }, ignoreInit = FALSE) 476 | # ignoreInit = FALSE is default behavior 477 | # these initializations run when the server is first set up 478 | 479 | observeEvent(input$goButtonDeleteVertex, { 480 | 481 | if(vcount(my$g) > 5){ 482 | 483 | my$perturbationCounter <- my$perturbationCounter + 1 484 | 485 | 486 | 487 | my$g <- delete_vertices(my$g, 488 | input$vertexToDelete) 489 | 490 | my$pv <- calculatePerturbationByVertexDeletion(my$g, 4, 1) 491 | my$pe <- calculatePerturbationByEdgeDeletion(my$g, 4, 1) 492 | 493 | my$g <- setGraphColors(my$g, my$pv, my$pe) 494 | 495 | my$graphHistory[[my$perturbationCounter]] <- my$g 496 | my$pvHistory[[my$perturbationCounter]] <- relabelVertexTable(my$pv) 497 | my$peHistory[[my$perturbationCounter]] <- relabelEdgeTable(my$pe) 498 | 499 | my$delEventHistory[[my$perturbationCounter]] <- paste0("Deletion of node ", 500 | input$vertexToDelete) 501 | 502 | 503 | } 504 | 505 | else { 506 | 507 | output$cantDeleteVertex <- renderText({ 508 | 509 | "can't delete more nodes" 510 | 511 | }) 512 | 513 | } 514 | 515 | }) 516 | 517 | observeEvent (input$goButtonDeleteEdge, { 518 | 519 | if(ecount(my$g) > 1){ 520 | 521 | my$g <- delete_edges(my$g, 522 | input$edgeToDelete) 523 | 524 | my$pv <- calculatePerturbationByVertexDeletion(my$g, 4, 1) 525 | my$pe <- calculatePerturbationByEdgeDeletion(my$g, 4, 1) 526 | 527 | my$g <- setGraphColors(my$g, my$pv, my$pe) 528 | 529 | my$perturbationCounter <- my$perturbationCounter + 1 530 | 531 | my$graphHistory[[my$perturbationCounter]] <- my$g 532 | my$pvHistory[[my$perturbationCounter]] <- relabelVertexTable(my$pv) 533 | my$peHistory[[my$perturbationCounter]] <- relabelEdgeTable(my$pe) 534 | 535 | 536 | my$delEventHistory[[my$perturbationCounter]] <- paste0("Deletion of link ", 537 | input$edgeToDelete) 538 | 539 | } else { 540 | 541 | output$cantDeleteLink <- renderText("can't delete more links") 542 | 543 | } 544 | 545 | }) 546 | 547 | output$graphPlot <- renderPlot({ 548 | 549 | coords <- layout_(my$g, as_star()) 550 | 551 | plot(my$g, 552 | layout = coords, 553 | edge.arrow.size = 0.4, 554 | vertex.size = 25, 555 | vertex.label.family = "Arial Black") 556 | 557 | }) 558 | 559 | output$perturbationTable <- renderTable ({ 560 | 561 | if(input$printTable == "edges"){ 562 | edgeTable <- relabelEdgeTable(my$pe) 563 | return(edgeTable) 564 | } 565 | else { 566 | 567 | vertexTable <- relabelVertexTable(my$pv) 568 | 569 | return(vertexTable) 570 | 571 | } 572 | 573 | }, digits = 3) 574 | 575 | 576 | ### downloadHandler for HTML report 577 | 578 | output$report <- downloadHandler ( 579 | 580 | filename = "report.html", 581 | 582 | content = function(file){ 583 | 584 | tempReport <- file.path(tempdir(), 585 | "report.Rmd") 586 | 587 | file.copy("report.Rmd", tempReport, 588 | overwrite = TRUE) 589 | 590 | printG <- my$g 591 | 592 | printVT <- relabelVertexTable(my$pv) 593 | 594 | printET <- relabelEdgeTable(my$pe) 595 | 596 | 597 | params <- list(graphHistory = my$graphHistory, 598 | pvHistory = my$pvHistory, 599 | peHistory = my$peHistory, 600 | perturbationCounter = my$perturbationCounter, 601 | delEventHistory = my$delEventHistory) 602 | 603 | rmarkdown::render(tempReport, 604 | output_file = file, 605 | params = params, 606 | envir = new.env(globalenv())) 607 | 608 | } 609 | 610 | ) 611 | 612 | })#end shinyServer function 613 | 614 | 615 | 616 | -------------------------------------------------------------------------------- /LICENSE.md: 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No Surrender of Others' Freedom. 529 | 530 | If conditions are imposed on you (whether by court order, agreement or 531 | otherwise) that contradict the conditions of this License, they do not 532 | excuse you from the conditions of this License. If you cannot convey a 533 | covered work so as to satisfy simultaneously your obligations under this 534 | License and any other pertinent obligations, then as a consequence you may 535 | not convey it at all. For example, if you agree to terms that obligate you 536 | to collect a royalty for further conveying from those to whom you convey 537 | the Program, the only way you could satisfy both those terms and this 538 | License would be to refrain entirely from conveying the Program. 539 | 540 | 13. Remote Network Interaction; Use with the GNU General Public License. 541 | 542 | Notwithstanding any other provision of this License, if you modify the 543 | Program, your modified version must prominently offer all users 544 | interacting with it remotely through a computer network (if your version 545 | supports such interaction) an opportunity to receive the Corresponding 546 | Source of your version by providing access to the Corresponding Source 547 | from a network server at no charge, through some standard or customary 548 | means of facilitating copying of software. This Corresponding Source 549 | shall include the Corresponding Source for any work covered by version 3 550 | of the GNU General Public License that is incorporated pursuant to the 551 | following paragraph. 552 | 553 | Notwithstanding any other provision of this License, you have 554 | permission to link or combine any covered work with a work licensed 555 | under version 3 of the GNU General Public License into a single 556 | combined work, and to convey the resulting work. The terms of this 557 | License will continue to apply to the part which is the covered work, 558 | but the work with which it is combined will remain governed by version 559 | 3 of the GNU General Public License. 560 | 561 | 14. Revised Versions of this License. 562 | 563 | The Free Software Foundation may publish revised and/or new versions of 564 | the GNU Affero General Public License from time to time. Such new versions 565 | will be similar in spirit to the present version, but may differ in detail to 566 | address new problems or concerns. 567 | 568 | Each version is given a distinguishing version number. If the 569 | Program specifies that a certain numbered version of the GNU Affero General 570 | Public License "or any later version" applies to it, you have the 571 | option of following the terms and conditions either of that numbered 572 | version or of any later version published by the Free Software 573 | Foundation. If the Program does not specify a version number of the 574 | GNU Affero General Public License, you may choose any version ever published 575 | by the Free Software Foundation. 576 | 577 | If the Program specifies that a proxy can decide which future 578 | versions of the GNU Affero General Public License can be used, that proxy's 579 | public statement of acceptance of a version permanently authorizes you 580 | to choose that version for the Program. 581 | 582 | Later license versions may give you additional or different 583 | permissions. However, no additional obligations are imposed on any 584 | author or copyright holder as a result of your choosing to follow a 585 | later version. 586 | 587 | 15. Disclaimer of Warranty. 588 | 589 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 590 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 591 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 592 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 593 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 594 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 595 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 596 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 597 | 598 | 16. Limitation of Liability. 599 | 600 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 601 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 602 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 603 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 604 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 605 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 606 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 607 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 608 | SUCH DAMAGES. 609 | 610 | 17. Interpretation of Sections 15 and 16. 611 | 612 | If the disclaimer of warranty and limitation of liability provided 613 | above cannot be given local legal effect according to their terms, 614 | reviewing courts shall apply local law that most closely approximates 615 | an absolute waiver of all civil liability in connection with the 616 | Program, unless a warranty or assumption of liability accompanies a 617 | copy of the Program in return for a fee. 618 | 619 | END OF TERMS AND CONDITIONS 620 | 621 | How to Apply These Terms to Your New Programs 622 | 623 | If you develop a new program, and you want it to be of the greatest 624 | possible use to the public, the best way to achieve this is to make it 625 | free software which everyone can redistribute and change under these terms. 626 | 627 | To do so, attach the following notices to the program. It is safest 628 | to attach them to the start of each source file to most effectively 629 | state the exclusion of warranty; and each file should have at least 630 | the "copyright" line and a pointer to where the full notice is found. 631 | 632 | 633 | Copyright (C) 634 | 635 | This program is free software: you can redistribute it and/or modify 636 | it under the terms of the GNU Affero General Public License as published 637 | by the Free Software Foundation, either version 3 of the License, or 638 | (at your option) any later version. 639 | 640 | This program is distributed in the hope that it will be useful, 641 | but WITHOUT ANY WARRANTY; without even the implied warranty of 642 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 643 | GNU Affero General Public License for more details. 644 | 645 | You should have received a copy of the GNU Affero General Public License 646 | along with this program. If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | --------------------------------------------------------------------------------