1){\n groups <- c(groups,lenGroups+1)\n names(groups)[length(groups)] = \"All sequences combined\"\n bayesPDF_groups_cdr[[lenGroups+1]] = groupPosteriors(bayesPDF_groups_cdr,length_sigma=4001)\n bayesPDF_groups_fwr[[lenGroups+1]] = groupPosteriors(bayesPDF_groups_fwr,length_sigma=4001)\n }\n \n #Bayesian Outputs\n bayes_cdr = t(sapply(bayesPDF_cdr,calcBayesOutputInfo))\n bayes_fwr = t(sapply(bayesPDF_fwr,calcBayesOutputInfo))\n bayes_germlines_cdr = t(sapply(bayesPDF_germlines_cdr,calcBayesOutputInfo))\n bayes_germlines_fwr = t(sapply(bayesPDF_germlines_fwr,calcBayesOutputInfo))\n bayes_groups_cdr = t(sapply(bayesPDF_groups_cdr,calcBayesOutputInfo))\n bayes_groups_fwr = t(sapply(bayesPDF_groups_fwr,calcBayesOutputInfo))\n \n #P-values\n simgaP_cdr = sapply(bayesPDF_cdr,computeSigmaP)\n simgaP_fwr = sapply(bayesPDF_fwr,computeSigmaP)\n \n simgaP_germlines_cdr = sapply(bayesPDF_germlines_cdr,computeSigmaP)\n simgaP_germlines_fwr = sapply(bayesPDF_germlines_fwr,computeSigmaP)\n \n simgaP_groups_cdr = sapply(bayesPDF_groups_cdr,computeSigmaP)\n simgaP_groups_fwr = sapply(bayesPDF_groups_fwr,computeSigmaP)\n \n \n #Format output\n \n # Round expected mutation frequencies to 3 decimal places\n matMutationInfo[germlinesOriginal[indelPos],] = NA\n if(nrow(matMutationInfo)==1){\n matMutationInfo[5:8] = round(matMutationInfo[,5:8]/sum(matMutationInfo[,5:8],na.rm=T),3)\n }else{\n matMutationInfo[,5:8] = t(round(apply(matMutationInfo[,5:8],1,function(x){ return(x/sum(x,na.rm=T)) }),3))\n }\n \n listPDFs = list()\n nRows = length(unique(groups)) + length(unique(germlines)) + length(groups)\n \n matOutput = matrix(NA,ncol=18,nrow=nRows)\n rowNumb = 1\n for(G in unique(groups)){\n #print(G)\n matOutput[rowNumb,c(1,2,11:18)] = c(\"Group\",names(groups)[groups==G][1],bayes_groups_cdr[G,],bayes_groups_fwr[G,],simgaP_groups_cdr[G],simgaP_groups_fwr[G])\n listPDFs[[rowNumb]] = list(\"CDR\"=bayesPDF_groups_cdr[[G]],\"FWR\"=bayesPDF_groups_fwr[[G]])\n names(listPDFs)[rowNumb] = names(groups[groups==paste(G)])[1]\n #if(names(groups)[which(groups==G)[1]]!=\"All sequences combined\"){\n gs = unique(germlines[groups==G])\n rowNumb = rowNumb+1\n if( !is.na(gs) ){\n for( g in gs ){\n matOutput[rowNumb,c(1,2,11:18)] = c(\"Germline\",names(germlines)[germlines==g][1],bayes_germlines_cdr[g,],bayes_germlines_fwr[g,],simgaP_germlines_cdr[g],simgaP_germlines_fwr[g])\n listPDFs[[rowNumb]] = list(\"CDR\"=bayesPDF_germlines_cdr[[g]],\"FWR\"=bayesPDF_germlines_fwr[[g]])\n names(listPDFs)[rowNumb] = names(germlines[germlines==paste(g)])[1]\n rowNumb = rowNumb+1\n indexesOfInterest = which(germlines==g)\n numbSeqsOfInterest = length(indexesOfInterest)\n rowNumb = seq(rowNumb,rowNumb+(numbSeqsOfInterest-1))\n matOutput[rowNumb,] = matrix( c( rep(\"Sequence\",numbSeqsOfInterest),\n rownames(matInput)[indexesOfInterest],\n c(matMutationInfo[indexesOfInterest,1:4]),\n c(matMutationInfo[indexesOfInterest,5:8]),\n c(bayes_cdr[indexesOfInterest,]),\n c(bayes_fwr[indexesOfInterest,]),\n c(simgaP_cdr[indexesOfInterest]),\n c(simgaP_fwr[indexesOfInterest]) \n ), ncol=18, nrow=numbSeqsOfInterest,byrow=F)\n increment=0\n for( ioi in indexesOfInterest){\n listPDFs[[min(rowNumb)+increment]] = list(\"CDR\"=bayesPDF_cdr[[ioi]] , \"FWR\"=bayesPDF_fwr[[ioi]])\n names(listPDFs)[min(rowNumb)+increment] = rownames(matInput)[ioi]\n increment = increment + 1\n }\n rowNumb=max(rowNumb)+1\n\n }\n }\n }\n colsToFormat = 11:18\n matOutput[,colsToFormat] = formatC( matrix(as.numeric(matOutput[,colsToFormat]), nrow=nrow(matOutput), ncol=length(colsToFormat)) , digits=3)\n matOutput[matOutput== \" NaN\"] = NA\n \n \n \n colnames(matOutput) = c(\"Type\", \"ID\", \"Observed_CDR_R\", \"Observed_CDR_S\", \"Observed_FWR_R\", \"Observed_FWR_S\",\n \"Expected_CDR_R\", \"Expected_CDR_S\", \"Expected_FWR_R\", \"Expected_FWR_S\",\n paste( rep(testName,6), rep(c(\"Sigma\",\"CIlower\",\"CIupper\"),2),rep(c(\"CDR\",\"FWR\"),each=3), sep=\"_\"),\n paste( rep(testName,2), rep(\"P\",2),c(\"CDR\",\"FWR\"), sep=\"_\")\n )\n fileName = paste(outputPath,outputID,\".txt\",sep=\"\")\n write.table(matOutput,file=fileName,quote=F,sep=\"\\t\",row.names=T,col.names=NA)\n fileName = paste(outputPath,outputID,\".RData\",sep=\"\")\n save(listPDFs,file=fileName)\n\nindelWarning = FALSE\nif(sum(indelPos)>0){\n indelWarning = \"Warning: The following sequences have either gaps and/or deletions, and have been ommited from the analysis.\";\n indelWarning = paste( indelWarning , \"
\", sep=\"\" )\n for(indels in names(indelPos)[indelPos]){\n indelWarning = paste( indelWarning , \"- \", indels, \"
\", sep=\"\" )\n }\n indelWarning = paste( indelWarning , \"
\", sep=\"\" )\n}\n\ncloneWarning = FALSE\nif(clonal==1){\n if(sum(matInputErrors)>0){\n cloneWarning = \"Warning: The following clones have sequences of unequal length.\";\n cloneWarning = paste( cloneWarning , \"
\", sep=\"\" )\n for(clone in names(matInputErrors)[matInputErrors]){\n cloneWarning = paste( cloneWarning , \"- \", names(germlines)[as.numeric(clone)], \"
\", sep=\"\" )\n }\n cloneWarning = paste( cloneWarning , \"
\", sep=\"\" )\n }\n}\ncat(paste(\"Success\",outputID,indelWarning,cloneWarning,sep=\"|\"))\n", "meta": {"hexsha": "abb8c5ca05eb1a8a04a7e2e2d039093137d79d80", "size": 17368, "ext": "r", "lang": "R", "max_stars_repo_path": "baseline/Baseline_Main.r", "max_stars_repo_name": "ESehlhoff/shm_csr", "max_stars_repo_head_hexsha": "78ace939ed7437b8b360588032449a99aad949eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-20T12:39:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-20T12:39:30.000Z", "max_issues_repo_path": "baseline/Baseline_Main.r", "max_issues_repo_name": "ESehlhoff/shm_csr", "max_issues_repo_head_hexsha": "78ace939ed7437b8b360588032449a99aad949eb", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1, "max_issues_repo_issues_event_min_datetime": "2021-09-10T14:28:41.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-10T14:28:41.000Z", "max_forks_repo_path": "baseline/Baseline_Main.r", "max_forks_repo_name": "ESehlhoff/shm_csr", "max_forks_repo_head_hexsha": "78ace939ed7437b8b360588032449a99aad949eb", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-09-28T11:16:11.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-28T11:16:11.000Z", "avg_line_length": 44.64781491, "max_line_length": 187, "alphanum_fraction": 0.6080723169, "num_tokens": 4944, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.476579651063676, "lm_q2_score": 0.03161877000619939, "lm_q1q2_score": 0.01506886237661713}}
{"text": "## Created on Friday, August 27, 2021 at 7:14pm EDT by Weekend Editor on WeekendEditorMachine.\n## Copyright (c) 2021, SomeWeekendReading.blog. All rights reserved. As if you care.\n\ntoolsDir <- \"../../tools\" # Various tools available from author\nsource(file.path(toolsDir, \"graphics-tools.r\")) # Various graphics hacks\n\n##\n## After accounting for age and vax status to evade Simpson's paradox, show vaccine efficacy\n## in Israel (which has much better medical records than the US!) vs age cohort.\n##\n\ndoit <- function(cohorts = c(\"12-15\", \"16-19\", \"20-29\", \"30-39\", \"40-49\", \"50-59\",\n \"60-69\", \"70-79\", \"80-89\", \"90+\"),\n efficacies = c(100.0, 100.0, 100.0, 96.8, 93.9, 92.8,\n 88.7, 89.6, 81.1, 92.4),\n plotFile = \"./2021-08-29-covid-simpson-ve-by-age.png\") {\n ## *** IWBNI we knew 95% cl bars on the efficacies, say by posterior Beta + Monte Carlo method\n withPNG(plotFile, 400, 400, FALSE, function() { # Capture graphics to file\n withPars(function() { # Save/restore graphics params\n plot(x = 1 : length(cohorts), y = efficacies, # Plot points & lines\n type = \"b\", pch = 21, col = \"black\", bg = \"blue\",\n xaxt = \"n\", xlab = NA, ylab = \"Vaccine Efficacy (%)\", ylim = c(0, 100),\n main = \"Vaccine efficacy by age cohort\") # Horizontal axis done manually\n axis(side = 1, at = 1 : length(cohorts), labels = cohorts)\n title(xlab = \"Age Cohort\", line = 3.5) # Horizontal axis title\n }, pty = \"m\", # Maximal plotting area\n bg = \"white\", # White background\n mar = c(5, 3, 2, 1), # Margins for axis labels, title\n mgp = c(1.7, 0.5, 0), # Axis title, labels, ticks\n ps = 16, # Larger type size for file capture\n las = 2) # Axis labels always perp to axis\n }) # Done!\n} #\n", "meta": {"hexsha": "832222347f6943acdd5e8dbc8180a8730c8c2b68", "size": 2257, "ext": "r", "lang": "R", "max_stars_repo_path": "assets/2021-08-29-covid-simpson-ve-by-age.r", "max_stars_repo_name": "SomeWeekendReading/SomeWeekendReading.github.io", "max_stars_repo_head_hexsha": "e9b63e1668a0c267dd053bbd0e3357e4c38142e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "assets/2021-08-29-covid-simpson-ve-by-age.r", "max_issues_repo_name": "SomeWeekendReading/SomeWeekendReading.github.io", "max_issues_repo_head_hexsha": "e9b63e1668a0c267dd053bbd0e3357e4c38142e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 17, "max_issues_repo_issues_event_min_datetime": "2020-10-14T16:09:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-18T21:01:06.000Z", "max_forks_repo_path": "assets/2021-08-29-covid-simpson-ve-by-age.r", "max_forks_repo_name": "SomeWeekendReading/SomeWeekendReading.github.io", "max_forks_repo_head_hexsha": "e9b63e1668a0c267dd053bbd0e3357e4c38142e0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 66.3823529412, "max_line_length": 96, "alphanum_fraction": 0.4847142224, "num_tokens": 600, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.47268347662043286, "lm_q2_score": 0.0316187652154802, "lm_q1q2_score": 0.014945667868498392}}
{"text": "#' Matching Estimators with Network Data\n#'\n#' \\strong{WARNING}: This function is still in development and has not been tested throughly.\n#' Following Aral et al. (2009), \\code{netmatch} computes matching\n#' estimators for network data. The function \\code{netmatch_prepare}, which\n#' prepares the data to be used with \\code{\\link[MatchIt:matchit]{matchit}} from\n#' the \\pkg{\\link[MatchIt:MatchIt]{MatchIt}} package, is called by \\code{netmatch}.\n#'\n#' @param dat \\code{data.frame} with dynamic data. Must be of\n#' \\code{nrow(dat)==nslices(graph)*nnodes(graph)}.\n#' @param graph List with sparse matrices.\n#' @param timevar Character scalar. Name of time variable\n#' @param depvar Character scalar. Name of the dependent variable\n#' @param covariates Character vector. Name(s) of the control variable(s).\n#' @param adopt_thr Either a numeric scalar or vector of length \\code{nslices(graph)}.\n#' Sets the threshold of \\code{depvar} at which it is considered that an observation\n#' has adopted a behavior.\n#' @param treat_thr Either a numeric scalar or vector of length \\code{nslices(graph)}.\n#' Sets the threshold of \\code{exposure} at which it is considered that an\n#' observation is treated.\n#' @param expo_pcent Logical scalar. When \\code{TRUE}, exposure is computed\n#' non-normalized (so it is a count rather than a percentage).\n#' @param expo_lag Integer scalar. Number of lags to consider when computing\n#' exposure. \\code{expo_lag=1} defines exposure in T considering behavior and\n#' network at T-1.\n#' @param ... Further arguments to be passed to \\code{\\link[MatchIt:matchit]{matchit}}.\n#' @return In the case of \\code{netmatch_prepare}\n#' \\item{dat}{A \\code{data.frame} with the original data (covariates), plus the\n#' following new variables: \\code{treat}, \\code{adopt}, \\code{exposure}.\n#' }\n#' \\item{match_model}{A formula to be passed to \\code{netmatch}}\n#'\n#' \\code{netmatch} returns the following:\n#' \\item{fATT}{A numeric vector of length \\eqn{N_1}{N1} (number of treated used\n#' in the matching process). Treatment effects on the treated at the individual\n#' level}\n#' \\item{match_obj}{The output from \\code{matchit}.}\n#' @name netmatch\n#' @author\n#' George G. Vega Yon\n#' @references\n#' Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing\n#' influence-based contagion from homophily-driven diffusion in dynamic networks.\n#' Proceedings of the National Academy of Sciences of the United States of America,\n#' 106(51), 21544–21549. \\doi{10.1073/pnas.0908800106}\n#'\n#' Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the\n#' Econometrics of Program Evaluation. Journal of Economic Literature, 47(1),\n#' 5–86. \\doi{10.1257/jel.47.1.5}\n#'\n#' King, G., & Nielsen, R. (2015). Why Propensity Scores Should Not Be Used for.\n#'\n#' Sekhon, J. S. (2008). The Neyman-Rubin Model of Causal Inference and Estimation\n#' Via Matching Methods. The Oxford Handbook of Political Methodology.\n#' \\doi{10.1093/oxfordhb/9780199286546.003.0011}\n#' @details\n#'\n#' In Aral et al. (2009), the matching estimator is used as a response to the\n#' fact that the observed network is homophilous. Essentially, using exposure\n#' as a treatment indicator, which is known to be endogenous, we can apply the\n#' same principle of matching estimators in which, after controlling for characteristics\n#' (covariates), individuals from the treated group (exposed to some behavior)\n#' can be compared to individuals from the control group (not exposed to that\n#' behavior), as the only difference between the two is the exposure.\n#'\n#' As pointed out in King & Nielsen (2015), it is suggested that, contrary to\n#' what Aral et al. (2009), the matching is not performed over propensity score\n#' since it is know that the later can increase imbalances in the data and thus\n#' obtaining exactly the opposed outcome that matching based estimators pursue.\n#'\n#' A couple of good references for matching estimators are Imbens and Wooldridge\n#' (2009), and Sekhon (2008).\nNULL\n\n#' @export\n#' @rdname netmatch\nnetmatch_prepare <- function (\n dat, graph, timevar, depvar, covariates,\n treat_thr = rep(1L, length(graph)),\n adopt_thr = rep(1L, length(graph)),\n expo_pcent = FALSE,\n expo_lag = 0L\n) {\n\n # Subset\n Y <- as.matrix(dat[, depvar, drop=FALSE])\n X <- as.matrix(dat[, covariates, drop=FALSE])\n Time <- as.matrix(dat[, timevar, drop=FALSE])\n\n # Checking thresholds\n if (length(treat_thr)==1)\n treat_thr <- rep(treat_thr, length(graph))\n if (length(adopt_thr)==1)\n adopt_thr <- rep(adopt_thr, length(graph))\n\n # Obtaining constants\n pers <- sort(unique(Time))\n npers <- length(pers)\n n <- length(Y)/npers\n\n if (expo_lag >= npers)\n stop(\"Not enought time points for such lag.\")\n\n # Generating exposures and treatment\n expo <- matrix(NA, ncol=npers, nrow=n)\n adopt <- matrix(NA, ncol=npers, nrow=n)\n treat <- matrix(NA, ncol=npers, nrow=n)\n ans <- vector(\"list\", npers)\n for (i in seq_along(pers)) {\n\n # Subset data\n per <- pers[i]\n y <- Y[Time == per,, drop=FALSE]\n x <- X[Time == per,, drop=FALSE]\n\n # Computing exposure and seting treatment\n adopt[,i] <- as.integer(y >= adopt_thr[i])\n\n # Skiping lags\n if (i <= expo_lag) next\n\n # Computing exposure (and normalizing if required)\n expo[,i] <- as.vector(graph[[i-expo_lag]] %*% adopt[,i-expo_lag,drop=FALSE])\n if (expo_pcent)\n expo[,i] <- expo[,i]/(Matrix::rowSums(graph[[i-expo_lag]]) + 1e-20)\n\n # Assigning regime\n treat[,i] <- as.integer(expo[,i] >= treat_thr[i])\n\n # Creating dataset\n ans[[i]] <- data.frame(Y=y, X=x, treat=treat[,i],\n adopt = adopt[,i-expo_lag],\n expo = expo[,i], check.names = FALSE)\n colnames(ans[[i]]) <-\n c(\"Y\", covariates, \"treat\", \"adopt\", \"expo\")\n\n }\n\n # Model\n match_model <- as.formula(paste(\"treat ~\", paste0(covariates, collapse=\" + \")))\n\n # Returning outcome\n return(list(dat=ans, match_model=match_model))\n}\n\n\n#' @rdname netmatch\n#' @export\nnetmatch <- function(\n dat, graph, timevar, depvar, covariates,\n treat_thr = rep(1L, length(graph)),\n adopt_thr = rep(1L, length(graph)),\n expo_pcent = FALSE,\n expo_lag = 0L, ...) {\n\n # No so good for now.\n if (nslices(graph) != 2)\n stop(\"-netmatch- is currently supported only for nslices(graph) == 2.\")\n\n # Preparing the data\n ans <- netmatch_prepare(dat=dat, graph = graph, timevar = timevar,\n depvar = depvar, covariates = covariates,\n treat_thr = treat_thr, adopt_thr = adopt_thr,\n expo_pcent = expo_pcent, expo_lag = expo_lag)\n\n # print(str(ans[[1]]))\n # Processing the data so we can use it with -matchit-\n dat <- ans$dat[[2]]\n rownames(dat) <- 1:nrow(dat)\n\n # Matching (with replacement)\n match_obj <- MatchIt::matchit(ans$match_model, data=dat, ...)\n\n mmethod <- match_obj$call$method\n if (length(mmethod) && (\"cem\" %in% mmethod)) {\n\n # Retrieving sub classes\n dat1 <- MatchIt::match.data(match_obj, \"treat\")\n dat0 <- MatchIt::match.data(match_obj, \"control\")\n\n # Computing effect. At the end CEM gives weights (very simple)\n # coef(lm(Y~treat, data=match.data(match_obj), weights=weights))[\"treat\"]\n fATT <- with(dat1, sum(Y*weights)/sum(weights)) -\n with(dat0, sum(Y*weights)/sum(weights))\n\n } else {\n match_index <- match_obj$match.matrix\n\n # Computing feasible Average Treatment Effect on the Treated\n fATT <- dat[,\"Y\"]\n\n matched_control <- lapply(1:nrow(match_index), function(x) {\n x <- match_index[x,,drop=TRUE]\n as.integer(x[!is.na(x)])\n })\n\n # Computing differences\n fATT <-Map(function(i1, i0) mean(fATT[i1] - fATT[i0]),\n i0=as.integer(rownames(match_index)),\n i1=matched_control\n )\n\n fATT <- mean(unlist(fATT, recursive = TRUE), na.rm=TRUE)\n }\n\n\n # Wrapping up\n return(list(\n fATT = fATT,\n match_obj = match_obj,\n exposures = dat$expo\n )\n )\n\n}\n", "meta": {"hexsha": "532fc37b6b7f855137914e3a96248626c2ad4bbd", "size": 7972, "ext": "r", "lang": "R", "max_stars_repo_path": "R/netmatch.r", "max_stars_repo_name": "USCCANA/netdiffuseR", "max_stars_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 69, "max_stars_repo_stars_event_min_datetime": "2015-12-15T02:49:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T02:48:37.000Z", "max_issues_repo_path": "R/netmatch.r", "max_issues_repo_name": "USCCANA/netdiffuseR", "max_issues_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 30, "max_issues_repo_issues_event_min_datetime": "2015-12-17T03:43:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-21T18:50:22.000Z", "max_forks_repo_path": "R/netmatch.r", "max_forks_repo_name": "USCCANA/netdiffuseR", "max_forks_repo_head_hexsha": "4cda66a4381c2df3ee2d738f6c97ac0b2916a5c4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19, "max_forks_repo_forks_event_min_datetime": "2015-12-28T21:47:05.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-22T19:48:08.000Z", "avg_line_length": 36.9074074074, "max_line_length": 93, "alphanum_fraction": 0.6714751631, "num_tokens": 2265, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.411110869232168, "lm_q2_score": 0.034618837220668094, "lm_q1q2_score": 0.01423218026159579}}
{"text": "#' @export\n#' @title coordinates.db\n#' @description this is an assortment of coordinates for commonly used objects\n#' @param \\code{item} the desired object. Valid values are 'gully','nens.ss.6-7','nens.ss.5-6','nens.ss.4-5','nens.ss.3-4','nens.ss.2-3','nens.ss.1-2','nens.ss.outside.1','nens.ss.outside.2','gulf-ss','nens-sens','cfa23.inshore-offshore','cfa23-cfa24','sens-cfa4x','shrimp.nw.sable','shrimp.louisburg','shrimp.bad.neighbours'\n#' @return \\code{} \n#' @examples\n#' coordinates.db('gully')\n#' @note It is tempting to mark this as deprecated, as it is not clear that these objects will be updated to reflect a more definitive source \n#' @author unknown, \\email{@@dfo-mpo.gc.ca}\n#' @export\n coordinates.db = function( item ) {\n \n if ( item==\"gully\" ) {\n a = as.data.frame( matrix( c( \n -59.10051192315181,44.21678274656149,\n -59.33516115171256,44.10057784448195,\n -59.13782200164582,43.91718619192973,\n -59.13430015596633,43.58357892651157,\n -58.58296879641245,43.58284385250472,\n -58.58345034613519,43.78360045129342,\n -59.10051192315181,44.21678274656149\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.6-7\" ) {\n a = as.data.frame( matrix( c( \n -59.8669459147477,46.61829003097517,\n -59.86714927555937,46.1903922915425\n ), ncol=2, byrow=T ) )\n \n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n\n if ( item==\"nens.ss.5-6\" ) {\n a = as.data.frame( matrix( c( \n -60.15013169910874,46.61686051036362,\n -60.15070296987705,46.25403077923779\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.4-5\" ) {\n a = as.data.frame( matrix( c( \n -58.57169296505639,46.61743715738334,\n -60.35101630484722,46.61743036291101\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.3-4\" ) {\n a = as.data.frame( matrix( c( \n -60.32934961857659,46.73377402095574,\n -58.70455622211595,46.73425821633046\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.2-3\" ) {\n a = as.data.frame( matrix( c( \n -60.29739776341232,46.8490837267233,\n -58.83935581691834,46.8522334225455\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.1-2\" ) {\n a = as.data.frame( matrix( c( \n -60.39965967598059,47.00047832780142,\n -59.01461782671211,47.00028076937365\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.outside.1\" ) {\n a = as.data.frame( matrix( c( \n -59.99891390682926,47.83103013198976,\n -58.56775382241514,46.61649379280151\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens.ss.outside.2\" ) {\n a = as.data.frame( matrix( c( \n -57.78591208190541,46.00109962205628,\n -58.56775382241514,46.61649379280151\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"gulf-ss\" ) {\n a = as.data.frame( matrix( c( \n -60.41568418984078,47.03853523297152,\n -60.00014897319883,47.83408958793515 \n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"nens-sens\" ) {\n a = as.data.frame( matrix( c( \n -59.85247480316924,46.00291960992756,\n -57.78560987011954,46.00106076110973\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"cfa23.inshore-offshore\" ) {\n a = as.data.frame( matrix( c( \n -59.92853837122961,44.69774735070251,\n -58.41005287016962,46.00038380222262\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"cfa23-cfa24\" ) {\n a = as.data.frame( matrix( c( \n -60.66040620990439,45.58083805201212,\n -59.11881785180857,43.67610276909335\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"sens-cfa4x\" ) {\n a = as.data.frame( matrix( c( \n -63.52502801857509,44.5005704612574,\n -63.33296001561555,44.33343763428088,\n -63.33161397633095,42.50186912534272\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"shrimp.nw.sable\" ) {\n a = as.data.frame( matrix( c( \n -60.53348522264675,44.16713857065227,\n -59.83402446317178,44.16674842805921,\n -59.8337724318864,44.35020211840867,\n -60.53437630895549,44.35056350439965,\n -60.53348522264675,44.16713857065227\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"shrimp.louisburg\" ) {\n a = as.data.frame( matrix( c( \n -59.16722567748156,45.50037490107675,\n -58.11756232854311,45.50029678441212,\n -58.11736586354162,45.91675134337378,\n -59.16724395727103,45.91694991968841,\n -59.16722567748156,45.50037490107675\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"shrimp.eastern\" ) {\n a = as.data.frame( matrix( c( \n -60.99983106518073,44.58360971012488,\n -58.0005005513048,44.58361575122798,\n -58.00112052095971,44.9165492050831,\n -58.66697123168657,44.91801611900925,\n -58.66586013724016,45.00054787436935,\n -61.00063058037657,45.00080688407766,\n -60.99983106518073,44.58360971012488\n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", \"lat\", \"elevation\" )\n return( a )\n }\n \n if ( item==\"shrimp.bad.neighbours\" ) {\n a = as.data.frame( matrix( c( \n -60.95065555250798,45.31550857725852,\n -60.08311721156429,45.46714417665778,\n -60.08371925586215,45.80040191216285 \n ), ncol=2, byrow=T ) )\n a$elevation = 0\n names( a ) = c(\"lon\", 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{"text": "###############################################################################\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n###############################################################################\n# Read GNU MathProg model \n# Copyright (C) 2012 Michael Kapler\n#\n# For more information please visit my blog at www.SystematicInvestor.wordpress.com\n# or drop me a line at TheSystematicInvestor at gmail\n###############################################################################\n\n\n###############################################################################\n# Read GNU MathProg model\n# based on Rglpk_read_file, modified to eliminate objective fn from constraint matrix\n#' @export \n############################################################################### \nRglpk.read.model <- function(file, type = c(\"MPS_fixed\", \"MPS_free\", \"CPLEX_LP\", \"MathProg\"), ignore_first_row = FALSE, verbose = FALSE){\n\tif(!file.exists(file)) stop(paste(\"There is no file called\", file, \"!\"))\n\t\n \ttype_db <- c(\"MPS_fixed\" = 1L, \"MPS_free\" = 2L, \"CPLEX_LP\" = 3L, \"MathProg\" = 4L)\n \tobj <- list(file = tools::file_path_as_absolute(file), type = type_db[match.arg(type)])\n \t\n\tmeta_data <- Rglpk:::glp_get_meta_data_from_file(obj, verbose)\n\tmilp_data <- Rglpk:::glp_retrieve_MP_from_file(meta_data, ignore_first_row, verbose)\n\tMP_data <- Rglpk:::glp_merge_MP_data(meta_data, milp_data)\n\t\n\tdir_db <- c(\"free\" = 1L, \">=\" = 2L, \"<=\" = 3L, \"DB\" = 4L, \"==\" = 5L)\n \tMP_data$direction_of_constraints <- names(dir_db[MP_data$direction_of_constraints])\n \t\n\ttypes <- rep(\"C\", length.out = MP_data$n_objective_vars)\n \tif(any(MP_data$objective_var_is_integer)) types[MP_data$objective_var_is_integer] <- \"I\" \t\n \tif(any(MP_data$objective_var_is_binary)) types[MP_data$objective_var_is_binary] <- \"B\" \t\n \tMP_data$types = types\n \t\n\t# remove objective fn from constraints\n\tindex = which(MP_data$direction_of_constraints == 'free')\n\tif( length(index) > 0 ) {\n\t\tMP_data$constraint_matrix = as.matrix(MP_data$constraint_matrix)[-index,]\n\t\tMP_data$direction_of_constraints = MP_data$direction_of_constraints[-index]\n\t\tMP_data$right_hand_side = MP_data$right_hand_side[-index]\t\n\t} \n\tMP_data\n}\n\n\n###############################################################################\n# Create constraints structure from model data (Rglpk.read.model)\n#' @export \n############################################################################### \nRglpk.create.constraints <- function( prob ) \n{ \n\t#--------------------------------------------------------------------------\n\t# Create constraints\n\t#--------------------------------------------------------------------------\n\tn = prob$n_objective_vars\n\t\n\tlb = rep(NA,n)\n\t\tlb[prob$bounds$lower$ind] = prob$bounds$lower$val\n\tub = rep(NA,n)\n\t\tub[prob$bounds$upper$ind] = prob$bounds$upper$val\n\tconstraints = new.constraints(n, lb = lb, ub = ub)\n\t\n\t# binary variables\n\tconstraints$binary.index = which(prob$objective_var_is_binary == 1)\t\n\tif(len(constraints$binary.index) == 0) constraints$binary.index = 0\n\t\t\t\t\t\n\t#constraints\n\tif(is.null(dim(prob$constraint_matrix))) {\n\t\tprob$constraint_matrix = matrix(prob$constraint_matrix)\n\t} else {\n\t\tprob$constraint_matrix = t(prob$constraint_matrix)\n\t}\n\t\n\tindex = which(prob$direction_of_constraints == '==') \n\tif(len(index)>0) constraints = add.constraints(prob$constraint_matrix[,index], type = '=', b = prob$right_hand_side[index], constraints)\n\t\n\tindex = which(prob$direction_of_constraints == '<=') \n\tif(len(index)>0) constraints = add.constraints(prob$constraint_matrix[,index], type = '<=', b = prob$right_hand_side[index], constraints)\n\n\tindex = which(prob$direction_of_constraints == '>=') \n\tif(len(index)>0) constraints = add.constraints(prob$constraint_matrix[,index], type = '>=', b = prob$right_hand_side[index], constraints)\n\t\n\t# objective function\n\tf.obj = prob$objective_coefficients\n\tdir = ifelse(prob$maximize, 'max', 'min')\n\n \tprob$names = prob$objective_vars_names\n \tprob$tickers = prob$objective_vars_names\n\t\n # find tickers wgt[AAPL]\n if(len(grep('\\\\[',prob$objective_vars_names)) > 0) {\n temp = matrix(spl(gsub(']','', prob$objective_vars_names),'\\\\['), nr=2)\n \tprob$names = temp[1,]\n \tprob$tickers = temp[2,]\n }\n\t\n\treturn(list(constraints=constraints, f.obj=f.obj, dir=dir, prob=prob))\n}\n \n\n\n\n###############################################################################\n# Parse Views / Constraints using GNU Mathprog specifications\n#' @export \n############################################################################### \nparse.views = function(symbolnames, views) {\n\tload.packages('Rglpk')\n\n\tviews = parse.expr(views)\n\tif (len(views)==0)\n\t\treturn(list(\n\t\t\tA = matrix(0, nr=0, nc=len(symbolnames)),\n\t\t\tb = c(),\n\t\t\tmeq = 0\n\t\t))\t\n\t\nmodel.file = tempfile('temp.model')\non.exit(unlink(model.file))\t\n\t\n\t# create GNU MathProg model\n\tcat(\"\t\n###############################################################################\t\n# Define Variables\n\", join(paste0('var ', symbolnames, '>=0;'),'\\n'), \"\n\n# Define Objective\nminimize objective : \", join(symbolnames, '+'), \";\n\n# Define Constraints\n\", join(paste0('V', 1:len(views), ':', views,';'),'\\n'), \"\n \n###############################################################################\n\t\", file = model.file, append = FALSE)\n\t\n\t#--------------------------------------------------------------------------\n\t# Read GNU MathProg model/Setup constraints/Solve QP problem\n\t#--------------------------------------------------------------------------\t\n\t# read model\n\tmodel = Rglpk.read.model(model.file,type = 'MathProg') \t\n\n\t# convert GNU MathProg model to constraint used in solve.QP\n\ttemp = Rglpk.create.constraints(model)$constraints\t\n\t\n\tA = t(as.matrix(temp$A))\n\t\tcolnames(A) = symbolnames\n\t\n\tlist(\n\t\tA = A,\n\t\tb = temp$b,\n\t\tmeq = temp$meq\n\t)\n}\t\n\t\n\n\n###############################################################################\n# Helper function to find Minimum Variance Portfolio\n#' @export \n############################################################################### \nmin.var.portfolio.gmpl <- function(ia, constraints)\n{\n\t#--------------------------------------------------------------------------\n\t# Adjust Covariance matrix\n\t#--------------------------------------------------------------------------\n\tload.packages('quadprog,corpcor')\n\t\n\tcov.temp = ia$cov\n\t\n\t# check if there are dummy variables\n\tn0 = ia$n\n\tn = nrow(constraints$A)\t\t\t\n\tif( n != nrow(cov.temp) ) {\n\t\ttemp = matrix(0, n, n)\n\t\ttemp[1:n0, 1:n0] = cov.temp[1:n0, 1:n0]\n\t\tcov.temp = temp\n\t}\t\n\t\n\tif(!is.positive.definite(cov.temp)) {\n\t\tcov.temp <- make.positive.definite(cov.temp, 0.000000001)\n\t}\t\n\t\n\t#--------------------------------------------------------------------------\n\t# Solve QP problem\n\t#--------------------------------------------------------------------------\n\tbinary.vec = 0\n\tif(!is.null(constraints$binary.index)) binary.vec = constraints$binary.index\n\t\n\tsol = solve.QP.bounds(Dmat = cov.temp, dvec = rep(0, nrow(cov.temp)) , \n\t\tAmat=constraints$A, bvec=constraints$b, constraints$meq,\n\t\tlb = constraints$lb, ub = constraints$ub, binary.vec = binary.vec)\n\n\tif(binary.vec[1] != 0) cat(sol$counter,'QP calls made to solve problem with', len(binary.vec), 'binary variables using Branch&Bound', '\\n')\t\t\n\t\n\tx = sol$solution[1:ia$n]\n\t\tnames(x) = ia$symbols\n\n\treturn(x)\n}\t\t\n\n\n###############################################################################\n# Portfolio Optimization: Specify constraints with GNU MathProg language\n#\n# Examples:\n# http://en.wikibooks.org/wiki/GLPK/GMPL_%28MathProg%29\n# http://en.wikibooks.org/wiki/GLPK/Literature#Official_GLPK_documentation\n#\n# The GNU Linear Programming Kit (GLPK) : Resources, Tutorials\n# http://spokutta.wordpress.com/tag/gnu-mathprog/\n###############################################################################\nportopt.mathprog.test <- function( ) \n{\n\t#*****************************************************************\n\t# Load packages\n\t#****************************************************************** \n\tload.packages('quantmod,quadprog,corpcor')\n\t\t\t\n\t#--------------------------------------------------------------------------\n\t# Create historical input assumptions\n\t#--------------------------------------------------------------------------\n\ttickers = dow.jones.components()\n\tia = aa.test.create.ia.custom(tickers, dates = '2000::2010')\n\t\n\t#--------------------------------------------------------------------------\n\t# Create Constraints & Solve QP problem\n\t#--------------------------------------------------------------------------\n\tn = ia$n\t\t\n\n\t# 0 <= x.i <= 1 \n\tconstraints = new.constraints(n, lb = 0, ub = 1)\n\n\t# SUM x.i = 1\n\tconstraints = add.constraints(rep(1, n), 1, type = '=', constraints)\t\t\n\t\n\t# Solve QP problem\n\tx = min.var.portfolio.gmpl(ia, constraints)\t\n\t\n\t# plot weights\npng(filename = 'plot1.png', width = 600, height = 400, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tbarplot(100*x, las=2, main='Minimum Variance Portfolio')\ndev.off()\t\n\t\n\n\t\n\t\n\t\n\t\n\t#*****************************************************************\n\t# Load packages\n\t#****************************************************************** \n\t# load Rglpk to read GNU MathProg files\n\tload.packages('Rglpk')\n\t\t\n\t#--------------------------------------------------------------------------\n\t# Create Constraints: GNU MathProg model\n\t#--------------------------------------------------------------------------\n\tmodel.file = 'model1.mod'\n\t\n\t# create GNU MathProg model\n\tcat('\t\n###############################################################################\t\nset SYMBOLS ;\t\n\t\n# set min/max weights for individual stocks\nvar weight{i in SYMBOLS} >= 0, <= 1 ;\n \n# objective function, NOT USED\nminimize alpha : sum{i in SYMBOLS} weight[i] ;\n \n# weights must sum to 1 (fully invested)\nsubject to fully_invested : sum{i in SYMBOLS} weight[i] = 1 ;\n\ndata;\n\nset SYMBOLS := ', ia$symbols, ';\n###############################################################################\n\t', file = model.file, append = FALSE)\n\n\t#--------------------------------------------------------------------------\n\t# Read GNU MathProg model/Setup constraints/Solve QP problem\n\t#--------------------------------------------------------------------------\t\n\t# read model\n\tmodel = Rglpk.read.model(model.file,type = 'MathProg') \t\n\n\t# convert GNU MathProg model to constraint used in solve.QP\n\tconstraints = Rglpk.create.constraints(model)$constraints\t\n\t\t\n\t# Solve QP problem\n\tx = min.var.portfolio.gmpl(ia, constraints)\t\n\t\n\t# plot weights\npng(filename = 'plot2.png', width = 600, height = 400, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tbarplot(100*x, las=2, main='Minimum Variance Portfolio using GNU MathProg model')\ndev.off()\t\n\n\t\n\t#--------------------------------------------------------------------------\n\t# Create Constraints: GNU MathProg model\n\t# Control Minimum Investment and Number of Assets: Portfolio Cardinality Constraints\n\t# http://systematicinvestor.wordpress.com/2011/10/20/minimum-investment-and-number-of-assets-portfolio-cardinality-constraints/\n\t#--------------------------------------------------------------------------\n\tmodel.file = 'model2.mod'\n\t\n\t# create GNU MathProg model\n\tcat('\t\n###############################################################################\t\nset SYMBOLS ;\t\n\t\n# set min/max weights for individual stocks\nvar weight{i in SYMBOLS} >= 0, <= 1 ;\n \n# add binary, 1 if held, 0 if not held\nvar held{SYMBOLS} binary;\n\n# objective function, NOT USED\nminimize alpha : sum{i in SYMBOLS} weight[i] ;\n \n# weights must sum to 1 (fully invested)\nsubject to fully_invested : sum{i in SYMBOLS} weight[i] = 1 ;\n\n# min weight constraint for individual asset\nsubject to MinWgt {i in SYMBOLS} : weight[i] >= 0.025 * held[i];\n \n# max weight constraint for individual asset\nsubject to MaxWgt {i in SYMBOLS} : weight[i] <= .20 * held[i] ;\n\n# number of stocks in portfolio\nsubject to MaxAssetsLB : 0 <= sum {i in SYMBOLS} held[i] ;\nsubject to MaxAssetsUB : sum {i in SYMBOLS} held[i] <= 6 ;\n \ndata;\n\nset SYMBOLS := ', ia$symbols, ';\n###############################################################################\n\t', file = model.file, append = FALSE)\n\t\n\t#--------------------------------------------------------------------------\n\t# Read GNU MathProg model/Setup constraints/Solve QP problem\n\t#--------------------------------------------------------------------------\t\n\t# read model\n\tmodel = Rglpk.read.model(model.file,type = 'MathProg') \t\n\n\t# convert GNU MathProg model to constraint used in solve.QP\n\tconstraints = Rglpk.create.constraints(model)$constraints\t\n\t\t\n\t# Solve QP problem\n\tx = min.var.portfolio.gmpl(ia, constraints)\t\n\t\n\t# plot weights\npng(filename = 'plot3.png', width = 600, height = 400, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tbarplot(100*x, las=2, main='Minimum Variance Portfolio using GNU MathProg model \\n with Minimum Investment and Number of Assets Constraints')\ndev.off()\t\n\n\t\n\t#--------------------------------------------------------------------------\n\t# Create Constraints: GNU MathProg model\n\t# Control Long and Short positions based on 130/30 Portfolio Construction\n\t# http://systematicinvestor.wordpress.com/2011/10/18/13030-porfolio-construction/\n\t#--------------------------------------------------------------------------\n\tmodel.file = 'model3.mod'\n\n\t# create GNU MathProg model\n\tcat('\t\n###############################################################################\t\nset SYMBOLS ;\t\n\t\n# set min/max weights for individual stocks\nvar long {i in SYMBOLS} >= 0, <= 0.8 ;\nvar short{i in SYMBOLS} >= 0, <= 0.5 ;\n \n# add binary, 1 if long, 0 if short\nvar islong{SYMBOLS} binary;\n\n# objective function, NOT USED\nminimize alpha : sum{i in SYMBOLS} long[i] ;\n \n# weights must sum to 1 (fully invested)\nsubject to fully_invested : sum{i in SYMBOLS} (long[i] - short[i]) = 1 ;\n\n# leverage is 1.6 = longs + shorts\nsubject to leverage : sum{i in SYMBOLS} (long[i] + short[i]) = 1.6 ;\n\n# force long and short to be mutually exclusive (only one of them is greater then 0 for each i)\nsubject to long_flag {i in SYMBOLS} : long[i] <= islong[i] ;\nsubject to short_flag {i in SYMBOLS} : short[i] <= (1 - islong[i]) ;\n \ndata;\n\nset SYMBOLS := ', ia$symbols, ';\n###############################################################################\n\t', file = model.file, append = FALSE)\n\t\n\t#--------------------------------------------------------------------------\n\t# Read GNU MathProg model/Setup constraints/Solve QP problem\n\t#--------------------------------------------------------------------------\t\n\t# read model\n\tmodel = Rglpk.read.model(model.file,type = 'MathProg') \t\n\n\t# convert GNU MathProg model to constraint used in solve.QP\n\tconstraints = Rglpk.create.constraints(model)$constraints\t\n\t\t\n\t# Solve QP problem, modify Input Assumptions to include short positions\n\tx = min.var.portfolio.gmpl(aa.test.ia.add.short(ia), constraints)\t\n\t\n\t# Compute total weight = longs - short\n\tx = x[1:ia$n] - x[-c(1:ia$n)]\n\t\n\t# plot weights\npng(filename = 'plot4.png', width = 600, height = 400, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tbarplot(100*x, las=2, main='Minimum Variance Portfolio using GNU MathProg model \\n with 130:30 Constraints')\ndev.off()\t\n\t\n\n\n\n\n\t# reduce problem size\n\tia = aa.test.create.ia.custom(tickers[1:15], dates = '2000::2010')\n\n\t#--------------------------------------------------------------------------\n\t# Create Constraints: GNU MathProg model\n\t# Turnover Constraints : Control Maximum Trade Size and Number of Trades\n\t#--------------------------------------------------------------------------\n\tmodel.file = 'model4.mod'\n\n\t# create parameters to hold Current Weight\n\tparam = ia$cov[,1,drop=F]\n\t\tcolnames(param) = 'CurWgt'\n\t\tparam[,'CurWgt'] = 1/ia$n\n\t\t\n\t\n\t# create GNU MathProg model\n\tcat('\t\n###############################################################################\t\nset SYMBOLS ;\t\n\t\nparam CurWgt{SYMBOLS} ;\n\n# set min/max weights for individual stocks\nvar weight{i in SYMBOLS} >= 0, <= 1 ;\n\n# TradePos[i] - TradeNeg[i] = CurWgt[i] - weight[i]\nvar TradePos{i in SYMBOLS} >= 0 ;\nvar TradeNeg{i in SYMBOLS} >= 0 ;\n\n# Only one of TradePos or TradeNeg is > 0\nvar TradeFlag{SYMBOLS} binary;\n\n# add binary, 1 if traded, 0 if not traded\nvar trade{SYMBOLS} binary;\n\n# objective function, NOT USED\nminimize alpha : sum{i in SYMBOLS} weight[i] ;\n \n# weights must sum to 1 (fully invested)\nsubject to fully_invested : sum{i in SYMBOLS} weight[i] = 1 ;\n\n# setup Trades for individual asset\nsubject to TradeRange {i in SYMBOLS} : (CurWgt[i] - weight[i]) = (TradePos[i] - TradeNeg[i]) ;\n\n# Only one of TradePos or TradeNeg is > 0\nsubject to TradeFlagPos {i in SYMBOLS} : TradePos[i] <= 100 * TradeFlag[i];\nsubject to TradeFlagNeg {i in SYMBOLS} : TradeNeg[i] <= 100 * (1 - TradeFlag[i]);\n\n# min trade size constraint for individual asset\nsubject to MinTradeSize {i in SYMBOLS} : (TradePos[i] + TradeNeg[i]) >= 0.01 * trade[i];\nsubject to MaxTradeSize {i in SYMBOLS} : (TradePos[i] + TradeNeg[i]) <= .90 * trade[i] ; \n\n# number of trades in portfolio\nsubject to MaxTrade : sum {i in SYMBOLS} trade[i] <= 48 ;\n \ndata;\n\nset SYMBOLS := ', ia$symbols, ';\n\nparam : CurWgt:=\n\t', file = model.file, append = FALSE)\n\t\nwrite.table(param, sep='\\t', quote = F, col.names = F, file = model.file, append = TRUE)\ncat('; \n###############################################################################\n\t', file = model.file, append = TRUE)\n\n\t\n\t#--------------------------------------------------------------------------\n\t# Read GNU MathProg model/Setup constraints/Solve QP problem\n\t#--------------------------------------------------------------------------\t\n\tmodel = Rglpk.read.model(model.file,type = 'MathProg') \t\n\tconstraints = Rglpk.create.constraints(model)$constraints\t\n\t\n\n\t# Solve QP problem\n\tx = min.var.portfolio.gmpl(ia, constraints)\t\n\t\tsqrt(x %*% ia$cov %*% x)\n\t\n\t\n\t# plot weights\npng(filename = 'plot5.png', width = 600, height = 400, units = 'px', pointsize = 12, bg = 'white')\t\t\t\t\t\t\t\t\t\t\n\tbarplot(100*x, las=2, main='Minimum Variance Portfolio using GNU MathProg model \\n with Turnover Constraints')\ndev.off()\t\t\n\n\n\n\n\n\n\n\n\n\n# reduce problem size\nia = aa.test.create.ia.custom(tickers[1:10], dates = '2000::2010')\n\n\n\t#--------------------------------------------------------------------------\n\t# Create Constraints: GNU MathProg model\n\t# Turnover Constraints : Control Maximum Trade Size and Number of Trades\n\t#--------------------------------------------------------------------------\n\tmodel.file = 'model4.mod'\n\n\t# create parameters to hold Current Weight\n\tparam = ia$cov[,1,drop=F]\n\t\tcolnames(param) = 'CurWgt'\n\t\tparam[,'CurWgt'] = 1/ia$n\n\t\t\n\t\n\t# create GNU MathProg model\n\tcat('\t\n###############################################################################\t\nset SYMBOLS ;\t\n\t\nparam CurWgt{SYMBOLS} ;\n\n# set min/max weights for individual stocks\nvar weight{i in SYMBOLS} >= 0, <= 1 ;\n\n# TradePos[i] - TradeNeg[i] = CurWgt[i] - weight[i]\nvar TradePos{i in SYMBOLS} >= 0 ;\nvar TradeNeg{i in SYMBOLS} >= 0 ;\n\n# Only one of TradePos or TradeNeg is > 0\nvar TradeFlag{SYMBOLS} binary;\n\n# add binary, 1 if traded, 0 if not traded\nvar trade{SYMBOLS} binary;\n\n# objective function, NOT USED\nminimize alpha : sum{i in SYMBOLS} weight[i] ;\n \n# weights must sum to 1 (fully invested)\nsubject to fully_invested : sum{i in SYMBOLS} weight[i] = 1 ;\n\n# setup Trades for individual asset\nsubject to TradeRange {i in SYMBOLS} : (CurWgt[i] - weight[i]) = (TradePos[i] - TradeNeg[i]) ;\n\n# Only one of TradePos or TradeNeg is > 0\nsubject to TradeFlagPos {i in SYMBOLS} : TradePos[i] <= 100 * TradeFlag[i];\nsubject to TradeFlagNeg {i in SYMBOLS} : TradeNeg[i] <= 100 * (1 - TradeFlag[i]);\n\n# min trade size constraint for individual asset\nsubject to MinTradeSize {i in SYMBOLS} : (TradePos[i] + TradeNeg[i]) >= 0.05 * trade[i];\nsubject to MaxTradeSize {i in SYMBOLS} : (TradePos[i] + TradeNeg[i]) <= .20 * trade[i] ; \n\n# number of trades in portfolio\nsubject to MaxTrade : sum {i in SYMBOLS} trade[i] <= 8 ;\n \ndata;\n\nset SYMBOLS := ', ia$symbols, ';\n\nparam : CurWgt:=\n\t', file = model.file, append = FALSE)\n\t\nwrite.table(param, sep='\\t', quote = F, col.names = F, file = model.file, append = TRUE)\ncat('; \n###############################################################################\n\t', file = model.file, append = TRUE)\n\n\t\n\t#--------------------------------------------------------------------------\n\t# Read GNU MathProg model/Setup constraints/Solve QP problem\n\t#--------------------------------------------------------------------------\t\n\tmodel = Rglpk.read.model(model.file,type = 'MathProg') \t\n\tconstraints = Rglpk.create.constraints(model)$constraints\t\n\t\n\n\t# Solve QP problem\n\tx = min.var.portfolio.gmpl(ia, constraints)\t\n\t\tsqrt(x %*% 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{"text": "MetaAnalysis.RVE <- function(\r\n data, \r\n measureType = \"r\",\r\n measureCol = \"Corr\", \r\n n = \"n\", \r\n grping1, grping2,\r\n published = \"Published\",\r\n PA = NULL,\r\n zTrans = F,\r\n user_beta = NULL)\r\n{\r\n notes <- c()\r\n \r\n # Remove empty samples\r\n data$n <- data[[n]]\r\n data <- subset(data, data$n > 0)\r\n \r\n # Define grouping, effect-size and published columns\r\n data$grp1 <- data[[grping1]]\r\n data$grp2 <- data[[grping2]]\r\n data$Meas <- data[[measureCol]]\r\n MeasInit <- data$Meas\r\n data$published <- as.logical(data[[published]])\r\n grping1_lvls <- levels(data$grp1)\r\n \r\n nName <- n\r\n \r\n # Get sample variances\r\n if (zTrans) {\r\n data <- escalc(\r\n ri = Meas,\r\n ni = n, \r\n measure = \"ZCOR\",\r\n data = data,\r\n var.names = c(\"Meas\", \"Var\")\r\n )\r\n } else if (measureType == \"r\") {\r\n data <- escalc(\r\n ri = Meas,\r\n ni = n, \r\n measure = \"COR\",\r\n data = data,\r\n var.names = c(\"Meas\", \"Var\")\r\n )\r\n } else if (measureType == \"k\") {\r\n pci <- (data[[PA]] - data$Meas) / (1 - data$Meas)\r\n data$Var <- data[[PA]] * (1 - data[[PA]]) / ((1 - pci) ^ 2 * data$n)\r\n } else if (measureType == \"p\") {\r\n data$Var <- data$Meas * (1 - data$Meas) / (data$n + 1e-4)\r\n } else {\r\n stop(\"Unknown 'measureType' specified.\")\r\n }\r\n \r\n # Remove any undefined data (and take notes)\r\n if (any(is.na(data$Meas))) {\r\n notes <- c(notes, paste0(\r\n \"\\tThe following rows were removed because the effect-size was undefined:\\n\\n\"))\r\n notes <- c(notes, paste0(\r\n \"\\t * Grp 1 - \", data$grp1[is.na(data$Meas)], \", Grp 2 - \", data$grp2[is.na(data$Meas)],\r\n \" (Original effect-size: \", MeasInit[is.na(data$Meas)], \")\\n\"))\r\n notes <- c(notes, \"\\n\")\r\n data <- data[!is.na(data$Meas),]\r\n }\r\n \r\n # Make sure none of variances are 0, otherwise analysis will fail\r\n data$Var[data$Var == 0] <- 1e-9\r\n \r\n # Build random-effects models using Robust Variance Estimation\r\n # Also run sensitivity analysis on rho and conduct Egger's regression test for bias\r\n REModels <- list()\r\n rhoSensitivity <- list()\r\n eggersRegTest <- list()\r\n dataList <- list()\r\n for (grping in grping1_lvls) {\r\n if (sum(data$grp1 == grping) > 1)\r\n {\r\n data.grp = data[data$grp1 == grping,]\r\n \r\n # Calculate random effects model\r\n argList <- list(\r\n formula = Meas ~ 1,\r\n data = data.grp,\r\n studynum = data.grp$grp2,\r\n var.eff.size = data.grp$Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\"\r\n )\r\n REModels[[grping]] <- do.call(robu, argList)\r\n dataList[[grping]] <- data.grp\r\n \r\n if (!is.null(user_beta))\r\n {\r\n REModels[[grping]]$reg_table$b.r <- user_beta[[grping]]$b.r\r\n REModels[[grping]]$reg_table$SE <- user_beta[[grping]]$SE\r\n REModels[[grping]]$reg_table$CI.L <- user_beta[[grping]]$CI.L\r\n REModels[[grping]]$reg_table$CI.U <- user_beta[[grping]]$CI.U\r\n REModels[[grping]]$data.full$r.weights <- user_beta[[grping]]$mdlWeights\r\n }\r\n \r\n dataList[[grping]]$mdlWeights <- REModels[[grping]]$data.full$r.weights\r\n dataList[[grping]]$mdlWeights2 <- REModels[[grping]]$data.full$weights\r\n \r\n # Perform sensitivity analysis\r\n tmp <- capture.output(rhoSensitivity[[grping]] <- sensitivity(REModels[[grping]]))\r\n \r\n if (sum(data.grp$published) > 2 && measureType != \"p\") {\r\n # Conduct regression test\r\n eggersRegTest[[grping]] <- robu(\r\n formula = Meas ~ 1 + sqrt(Var),\r\n data = data.grp[data.grp$published,],\r\n studynum = grp2,\r\n var.eff.size = Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\",\r\n userweights = REModels[[grping]]$data.full$r.weights[data.grp$published]\r\n )\r\n } else {\r\n eggersRegTest[[grping]] <- list(reg_table = list(prob = c(NaN,NaN)))\r\n }\r\n } else {\r\n data.grp = data[data$grp1 == grping,]\r\n dataList[[grping]] <- data.grp\r\n if (nrow(data.grp) > 0) dataList[[grping]]$mdlWeights <- 1\r\n notes <- c(notes, paste0(\"\\tSkipped '\", grping, \"' - \", sum(data$grp1 == grping), \" studies.\\n\"))\r\n }\r\n }\r\n \r\n crossTab <- table(data$grp1)\r\n data.grp <- data[data$grp1 %in% names(crossTab)[crossTab > 0], ]\r\n argList <- list(\r\n formula = Meas ~ 1,\r\n data = data.grp,\r\n studynum = data.grp$grp2,\r\n var.eff.size = data.grp$Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\"\r\n )\r\n REModels$overall <- do.call(robu, argList)\r\n dataList$overall <- data.grp\r\n \r\n if (!is.null(user_beta))\r\n {\r\n REModels$overall$reg_table$b.r <- user_beta$overall$b.r\r\n REModels$overall$reg_table$SE <- user_beta$overall$SE\r\n REModels$overall$reg_table$CI.L <- user_beta$overall$CI.L\r\n REModels$overall$reg_table$CI.U <- user_beta$overall$CI.U\r\n REModels$overall$data.full$r.weights <- user_beta$overall$mdlWeights\r\n }\r\n \r\n dataList$overall$mdlWeights <- REModels$overall$data.full$r.weights\r\n\r\n tmp <- capture.output(rhoSensitivity$overall <- sensitivity(REModels$overall))\r\n \r\n if (measureType != \"p\")\r\n eggersRegTest$overall <- robu(\r\n formula = Meas ~ 1 + sqrt(Var),\r\n data = data.grp[data.grp$published,],\r\n studynum = grp2,\r\n var.eff.size = Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\",\r\n userweights = REModels$overall$data.full$r.weights[data.grp$published]\r\n )\r\n \r\n return(\r\n list(\r\n REModels = REModels,\r\n rhoSensitivity = rhoSensitivity,\r\n eggersRegTest = eggersRegTest,\r\n data = data,\r\n dataList = dataList,\r\n notes = notes,\r\n measureType = measureType,\r\n zTrans = zTrans\r\n )\r\n )\r\n}\r\n\r\nSummary.RVE <- function(\r\n RVEModel,\r\n fileName,\r\n studyName = \"Authors\"\r\n)\r\n{\r\n output <- c()\r\n \r\n # Unpack\r\n REModels <- RVEModel$REModels\r\n rhoSensitivity <- RVEModel$rhoSensitivity\r\n eggersRegTest <- RVEModel$eggersRegTest\r\n notes <- RVEModel$notes\r\n zTrans <- RVEModel$zTrans\r\n measureType <- RVEModel$measureType\r\n dataList <- RVEModel$dataList\r\n \r\n output <- c(output, \"Notes\\n-----\\n\", paste(notes, collapse = \"\"), \"\\n\")\r\n \r\n # Get first level groupings\r\n grping1_lvls <- names(REModels)\r\n \r\n for (grping in grping1_lvls)\r\n {\r\n # Print group heading\r\n output <- c(output, \"\\n\\n========\", grping, \"========\\n\\n\")\r\n \r\n # Print model statistics and tests\r\n Q <- t(REModels[[grping]]$data.full$e) %*% \r\n diag(REModels[[grping]]$data.full$weights) %*% \r\n REModels[[grping]]$data.full$e\r\n \r\n if (zTrans) {\r\n output <- c(output, \"\\tEffect-size (z): \\t\", REModels[[grping]]$reg_table$b.r, \"\\n\")\r\n output <- c(output, \"\\tEffect-size (eff-size): \\t\", tanh(REModels[[grping]]$reg_table$b.r), \"\\n\")\r\n output <- c(output, \"\\tStandard error (z): \\t\", REModels[[grping]]$reg_table$SE, \"\\n\")\r\n } else if (measureType == \"p\") {\r\n output <- c(output, \"\\tEffect-size (log-odds): \\t\", REModels[[grping]]$reg_table$b.r, \"\\n\")\r\n output <- c(output, \"\\tEffect-size (eff-size): \\t\", 1/(1+exp(-REModels[[grping]]$reg_table$b.r)), \"\\n\")\r\n output <- c(output, \"\\tStandard error (log-odds):\\t\", REModels[[grping]]$reg_table$SE, \"\\n\")\r\n } else {\r\n output <- c(output, \"\\tEffect-size (eff-size): \\t\", REModels[[grping]]$reg_table$b.r, \"\\n\")\r\n output <- c(output, \"\\tStandard error (eff-size):\\t\", REModels[[grping]]$reg_table$SE, \"\\n\")\r\n }\r\n output <- c(output, \"\\tk (ind. samples): \\t\", length(unique(dataList[[grping]]$grp2)), \"\\n\")\r\n output <- c(output, \"\\tn (participants): \\t\", sum(aggregate(n ~ grp2, data = dataList[[grping]], max)$n), \"\\n\")\r\n output <- c(output, \"\\tp-value: \\t\", REModels[[grping]]$reg_table$prob, \"\\n\")\r\n output <- c(output, \"\\tdf: \\t\", REModels[[grping]]$reg_table$dfs, \"\\n\")\r\n output <- c(output, \"\\tI.sq: \\t\", REModels[[grping]]$mod_info$I.2, \"\\n\")\r\n output <- c(output, \"\\tQ-test: \\t\", Q, \"\\n\")\r\n output <- c(output, \"\\tTau.sq: \\t\", REModels[[grping]]$mod_info$tau.sq, \"\\n\\n\")\r\n \r\n output <- c(output, \"\\tEgger's Regression Test for Bias\\n\\n\")\r\n \r\n if (is.nan(eggersRegTest[[grping]]$reg_table$prob[2])) {\r\n output <- c(output, \"\\t\\tEgger's test not defined (probably not enough data)\\n\\n\")\r\n } else {\r\n output <- c(output, \"\\t\\tp-value: \\t\", eggersRegTest[[grping]]$reg_table$prob[2], \r\n if (eggersRegTest[[grping]]$reg_table$prob[2] < 0.05) \"*\" else \"\", \"\\n\")\r\n output <- c(output, \"\\t\\tdf: \\t\", eggersRegTest[[grping]]$reg_table$dfs[2], \"\\n\\n\")\r\n }\r\n \r\n # Print sensitivity analysis\r\n output <- c(output, \"\\tIntra-study clustering sensitivity analysis:\\n\")\r\n output <- c(output, paste0(\"\\t\\t\", capture.output(rhoSensitivity[[grping]]), sep = \"\\n\"))\r\n }\r\n \r\n cat(output)\r\n capture.output(cat(output), file = fileName)\r\n \r\n flush.console()\r\n invisible()\r\n}\r\n\r\n\r\nforest.RVE <- function(\r\n RVEModel,\r\n studyNames,\r\n sampleNames,\r\n effSizeName,\r\n fileName,\r\n extraCol = NULL,\r\n extraColName = NULL,\r\n fullData = NULL\r\n) \r\n{\r\n # Unpack\r\n REModels <- RVEModel$REModels\r\n data <- RVEModel$data\r\n zTrans <-RVEModel$zTrans\r\n data$study <- as.character(data[[studyNames]])\r\n data$sample <- data[[sampleNames]]\r\n grping1_lvls <- levels(data$grp1)\r\n \r\n measScale <- 1\r\n if (RVEModel$measureType == \"p\")\r\n {\r\n measScale <- 100\r\n }\r\n data$Meas <- data$Meas * measScale\r\n \r\n if (!is.null(extraCol)) {\r\n data$extra <- data[[extraCol]]\r\n }\r\n \r\n if (zTrans) {\r\n transf <- tanh\r\n } else {\r\n transf <- function(x) {x}\r\n }\r\n \r\n # Get weights; summary effects and standard errors; and n sums for plotting\r\n effSize <- c()\r\n standErr <- c()\r\n ci.lb <- c()\r\n ci.ub <- c()\r\n nSum <- c()\r\n data$weight <- NA\r\n for (grping in grping1_lvls)\r\n {\r\n indx <- data$grp1 == grping & !is.na(data$n)\r\n if (sum(indx) > 1) {\r\n data$weight[indx] <- REModels[[grping]]$data.full$r.weights / \r\n sum(REModels[[grping]]$data.full$r.weights) * 100\r\n \r\n effSize[grping] <- REModels[[grping]]$reg_table$b.r\r\n standErr[grping] <- REModels[[grping]]$reg_table$SE\r\n ci.lb[grping] <- REModels[[grping]]$reg_table$CI.L\r\n ci.ub[grping] <- REModels[[grping]]$reg_table$CI.U\r\n nSum[grping] <- sum(aggregate(n ~ grp2, data = data[indx,], max)$n)\r\n } else {\r\n data$weight[indx] <- 100\r\n effSize[grping] <- NA\r\n standErr[grping] <- NA\r\n ci.lb[grping] <- NA\r\n ci.ub[grping] <- NA\r\n nSum[grping] <- NA\r\n }\r\n }\r\n \r\n # Sort data by level 1 and level 2 grouping and add an id\r\n data <- data %>% arrange(as.integer(grp1), study)\r\n data$ID <- 1:nrow(data)\r\n \r\n # Define headings for studies with multiple endpoints\r\n for (grping1 in grping1_lvls)\r\n {\r\n data.grp <- data[data$grp1 == grping1,]\r\n for (grping2 in unique(data.grp$grp2))\r\n {\r\n data.grp2 <- data.grp[data.grp$grp2 == grping2,]\r\n if (nrow(data.grp2) > 1) {\r\n minID <- min(data.grp2$ID)\r\n study <- data.grp2$study[1]\r\n indx <- data$grp1 == grping1 & data$grp2 == grping2\r\n data$study[indx] <- paste0(\" \", data$sample[indx])\r\n data <- rbindlist(\r\n list(data, \r\n data.frame(grp1 = grping1, grp2 = grping2, study = study, ID = minID - 0.5, \r\n n = max(data.grp2$n), weight = sum(data.grp2$weight), stringsAsFactors = F)),\r\n fill = T)\r\n }\r\n }\r\n }\r\n # Re-sort data\r\n data <- data %>% arrange(ID)\r\n \r\n # Define forest plot row formatting\r\n headingLocations <- c()\r\n summaryLocations <- c()\r\n sampleLocations <- c()\r\n \r\n for (grping in grping1_lvls)\r\n {\r\n if (length(headingLocations) == 0)\r\n {\r\n headingLocations <- 1\r\n }\r\n else\r\n {\r\n headingLocations <- c(headingLocations, max(c(sampleLocations, summaryLocations)) + 2)\r\n }\r\n sampleLocations <- c(\r\n sampleLocations, \r\n max(headingLocations) + seq(1,sum(data$grp1 == grping),length = sum(data$grp1 == grping))\r\n )\r\n # if (sum(data$grp1 == grping) != 1)\r\n summaryLocations <- c(summaryLocations, max(headingLocations) + sum(data$grp1 == grping) + 1)\r\n }\r\n maxLocations <- max(summaryLocations, sampleLocations, headingLocations)\r\n headingLocations <- maxLocations - headingLocations + 1\r\n sampleLocations <- maxLocations - sampleLocations + 1\r\n summaryLocations <- maxLocations - summaryLocations + 1\r\n \r\n # Add indent to study names for formatting\r\n data$study <- paste0(\" \", data$study)\r\n\r\n # Split out study headings and samples\r\n studyHeadingLocations <- sampleLocations[is.na(data$Meas)]\r\n sampleLocations <- sampleLocations[!is.na(data$Meas)]\r\n studyHeadings <- data$study[is.na(data$Meas)]\r\n studyHeadingsN <- data$n[is.na(data$Meas)]\r\n studyHeadingsWeight <- round(data$weight[is.na(data$Meas)], 2)\r\n data <- data[!is.na(data$Meas),]\r\n \r\n # Reset zero variances to zero and unity correlations to unity\r\n if (!zTrans) {\r\n data$Meas[data$Meas == 1] <- 1-1e-8\r\n data$Var[data$Var < 1e-8] <- 0\r\n }\r\n \r\n # Initialise forest plot with sample effect-sizes and CIs\r\n cex = 1.5\r\n op <- par(mar=c(4,4,0,2))\r\n ylim <- c(-1, maxLocations+3)\r\n efac <- 0.4\r\n digits <- if (RVEModel$measureType == \"p\") 1 else 2\r\n slab.col <- rep(\"black\", nrow(data))\r\n slab.col[grep(\" \", data$study)] <- \"dimgray\"\r\n anno.pos <- 2.5 * measScale\r\n if (is.null(extraCol)) {\r\n ilab = cbind(data$n, format(round(data$weight,2), trim = F))\r\n ilab.xpos <- c(-1.9, -1.2) * measScale + if (RVEModel$measureType == \"p\") measScale else 0\r\n ilab.pos <- 2\r\n xlim <- c(if (RVEModel$measureType == \"p\") -4 else -5, 2.5) * measScale\r\n colLabels <- c(\"N\", \"Weight\", paste(effSizeName, \"[95% CI]\"))\r\n } else {\r\n ilab = cbind(data$n, format(round(data$weight,2), trim = F), data$extra)\r\n ilab.xpos <- c(c(-1.9, -1.2) * measScale + if (RVEModel$measureType == \"p\") measScale else 0, 3 * measScale)\r\n ilab.pos <- 2\r\n xlim <- c(if (RVEModel$measureType == \"p\") -4 else -5, 3) * measScale\r\n colLabels <- c(\"N\", \"Weight\", extraColName, paste(effSizeName, \"[95% CI]\"))\r\n }\r\n \r\n \r\n tiff(filename = fileName,\r\n width=18, height = 3+25*maxLocations/75, units = \"cm\", res = 600,\r\n compression = \"lzw\", pointsize = 6)\r\n forest.col(\r\n x = data$Meas,\r\n vi = data$Var * measScale^2,\r\n xlim = xlim,\r\n alim = c(if (RVEModel$measureType == \"p\") 0 else -1, 1) * measScale,\r\n ylim = ylim,\r\n digits = digits,\r\n xlab = effSizeName,\r\n slab = data$study,\r\n ilab = ilab,\r\n ilab.xpos = ilab.xpos,\r\n ilab.pos = ilab.pos,\r\n transf = transf,\r\n rows = sampleLocations,\r\n efac = efac,\r\n cex = cex,\r\n slab.col = slab.col,\r\n anno.pos = anno.pos,\r\n refline = if (RVEModel$measureType == \"p\") c(0, 0.25, 0.5, 0.75, 1)*measScale else 0\r\n )\r\n \r\n # Add study headings (level 2 groupings)\r\n if (length(studyHeadings) > 0) {\r\n text(\r\n x = xlim[1],\r\n y = studyHeadingLocations,\r\n labels = studyHeadings,\r\n cex = cex,\r\n pos = 4\r\n )\r\n text(\r\n x = ilab.xpos[1],\r\n y = studyHeadingLocations,\r\n labels = studyHeadingsN,\r\n cex = cex,\r\n pos = 2\r\n )\r\n text(\r\n x = ilab.xpos[2],\r\n y = studyHeadingLocations,\r\n labels = format(studyHeadingsWeight),\r\n cex = cex,\r\n pos = 2\r\n )\r\n }\r\n \r\n # Add level 1 grouping headings\r\n text(\r\n x = xlim[1],\r\n y = headingLocations,\r\n labels = grping1_lvls,\r\n cex = cex,\r\n pos = 4,\r\n font = 4\r\n )\r\n \r\n # Add level 1 group summaries and overall summary\r\n grpIncl <- grping1_lvls %in% names(REModels)\r\n text(\r\n x = c(rep(ilab.xpos[1], sum(grpIncl)), rep(ilab.xpos[2], sum(grpIncl))),\r\n y = rep(summaryLocations[grpIncl], 2),\r\n labels = c(nSum[grpIncl], rep(\"100.00\", sum(grpIncl))),\r\n cex = cex,\r\n pos = ilab.pos,\r\n font = 2\r\n )\r\n text(\r\n x = c(ilab.xpos[1], ilab.xpos[2]),\r\n y = ylim[1],\r\n labels = c(sum(aggregate(n ~ grp2, data = data, max)$n), \"100.00\"),\r\n cex = cex,\r\n pos = ilab.pos,\r\n font = 2\r\n )\r\n \r\n if(sum(!grpIncl) > 0)\r\n text(\r\n x = c(rep(ilab.xpos[1], sum(!grpIncl)), rep(ilab.xpos[2], sum(!grpIncl))),\r\n y = rep(summaryLocations[!grpIncl], 2),\r\n labels = rep(\"N/A\", sum(!grpIncl)*2),\r\n cex = cex,\r\n pos = ilab.pos,\r\n font = 2\r\n )\r\n \r\n addpoly.col(\r\n x = effSize[grpIncl],\r\n ci.lb = ci.lb[grpIncl],\r\n ci.ub = ci.ub[grpIncl],\r\n rows = summaryLocations[grpIncl],\r\n cex = cex,\r\n mlab = rep(\" RE Model\", sum(grpIncl)),\r\n transf = transf,\r\n efac = efac, \r\n digits = digits,\r\n font = 2,\r\n anno.pos = anno.pos\r\n )\r\n addpoly.col(\r\n x = REModels$overall$reg_table$b.r,\r\n sei = REModels$overall$reg_table$SE,\r\n rows = ylim[1],\r\n cex = cex,\r\n mlab = \"Overall RE Model\",\r\n transf = transf,\r\n efac = efac, \r\n digits = digits,\r\n font = 2,\r\n anno.pos = anno.pos\r\n )\r\n \r\n # Add column labels\r\n text(\r\n x = c(ilab.xpos, anno.pos),\r\n y = maxLocations + 2,\r\n labels = colLabels,\r\n cex = cex,\r\n pos = ilab.pos,\r\n font = 2\r\n )\r\n \r\n dev.off()\r\n \r\n invisible()\r\n}\r\n\r\nfunnel.RVE <- function(\r\n RVEModel,\r\n fileName,\r\n trimnfill = T,\r\n plotReg = T,\r\n effSizeName = \"Cohen's kappa\"\r\n)\r\n{\r\n # Unpack\r\n data <- RVEModel$data\r\n zTrans <- RVEModel$zTrans\r\n REModels <- RVEModel$REModels\r\n eggersRegTest <- RVEModel$eggersRegTest\r\n grping1_lvls <- names(REModels)\r\n grpIncl <- grping1_lvls %in% levels(data$grp1)\r\n overallB <- REModels$overall$reg_table$b.r\r\n data$standErr <- sqrt(data$Var)\r\n maxStandErr <- max(data$standErr, na.rm = T)\r\n \r\n if (plotReg)\r\n {\r\n # Get Egger's regression line\r\n xEgg <- seq(-1,1,0.001)\r\n mdl <- REModels$overall\r\n egg <- eggersRegTest$overall\r\n intercept <- -egg$reg_table$b.r[1]/egg$reg_table$b.r[2]\r\n gradient <- 1/egg$reg_table$b.r[2]\r\n regLine <- xEgg * gradient + intercept\r\n print(paste0(\"Egger's regression p-value: \", egg$reg_table$prob[2]))\r\n \r\n if (zTrans)\r\n regLine <- tanh(regLine)\r\n }\r\n \r\n if (trimnfill)\r\n {\r\n tnf <- meta::trimfill.default(\r\n data$Meas[data$published], \r\n data$standErr[data$published],\r\n left = T,\r\n sm = if (zTrans) \"ZCOR\" else \"COR\",\r\n ma.fixed = F,\r\n type = \"L\"\r\n )\r\n \r\n tnf_Meas <- tnf$TE[tnf$trimfill]\r\n tnf_standErr <- tnf$seTE[tnf$trimfill]\r\n tnf_study <- tnf$studlab[tnf$trimfill]\r\n \r\n if (zTrans)\r\n {\r\n tnf_standErr <- (tanh(tnf_Meas + tnf_standErr) - tanh(tnf_Meas - tnf_standErr)) / 2\r\n tnf_Meas <- tanh(tnf_Meas)\r\n }\r\n }\r\n \r\n if (zTrans)\r\n {\r\n data$standErr <- (tanh(data$Meas + sqrt(data$Var)) - tanh(data$Meas - sqrt(data$Var))) / 2\r\n data$Meas <- tanh(data$Meas)\r\n maxStandErr <- max(data$standErr)\r\n overallB <- tanh(overallB)\r\n }\r\n \r\n op <- par(mar = c(4,4,1,2))\r\n tiff(filename = fileName,\r\n width = 15, height = 12, units = \"cm\", res = 600,\r\n compression = \"lzw\", pointsize = 8)\r\n \r\n funnel(\r\n x = c(-99, -99),\r\n vi = c(99, 99),\r\n yaxis = \"sei\",\r\n xlim = c(-1, 1),\r\n ylim = c(0, maxStandErr),\r\n xlab = effSizeName,\r\n back = \"white\",\r\n shade = F,\r\n hlines = \"white\",\r\n pch = 1,\r\n cex = 1,\r\n refline = overallB\r\n )\r\n box()\r\n \r\n points(\r\n x = data$Meas[data$published],\r\n y = data$standErr[data$published],\r\n # pch = as.integer(data$grp1[data$published]),\r\n pch = 1,\r\n cex = 1\r\n )\r\n points(\r\n x = data$Meas[!data$published],\r\n y = data$standErr[!data$published],\r\n pch = 16,\r\n cex = 1\r\n )\r\n\r\n if (plotReg)\r\n {\r\n lines(xEgg[regLine >= 0], regLine[regLine >= 0], lty = 2)\r\n }\r\n \r\n if (trimnfill && length(tnf_Meas)>0)\r\n {\r\n points(\r\n x = tnf_Meas,\r\n y = tnf_standErr,\r\n pch = 17,\r\n cex = 1\r\n )\r\n \r\n crossTab <- table(data$grp1)\r\n data.grp <- data[data$grp1 %in% names(crossTab)[crossTab > 0], ]\r\n tnf.data.grp <- data.frame(\r\n grp2 = tnf_study,\r\n Meas = tnf_Meas,\r\n n = data$n[as.numeric(gsub(\"Filled: \", \"\", tnf_study))]\r\n )\r\n data.grp <- rbind.fill(data.grp, tnf.data.grp)\r\n \r\n data.grp <- escalc(\r\n ri = Meas,\r\n ni = n, \r\n measure = if (zTrans) \"ZCOR\" else \"COR\",\r\n data = data.grp,\r\n var.names = c(\"Meas\", \"Var\")\r\n )\r\n argList <- list(\r\n formula = Meas ~ 1,\r\n data = data.grp,\r\n studynum = data.grp$grp2,\r\n var.eff.size = data.grp$Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\"\r\n )\r\n adjMdl <- do.call(robu, argList)\r\n lines(c(adjMdl$reg_table$b.r, adjMdl$reg_table$b.r), c(0, 10), lty = 2)\r\n }\r\n \r\n legend(\r\n x = \"topleft\",\r\n legend = eval(parse(text = \r\n paste0(\"expression('Published', 'Unpublished', 'Original estimate','Eggers regression line ('*italic('p')*' value = '*\", as.character(round(egg$reg_table$prob[2], 3)), \"*')')\"))),\r\n pch = c(1, 16, NA, NA),\r\n lty = c(0, 0, 1, 2),\r\n col = \"black\",\r\n pt.cex = 1,\r\n bty = \"n\"\r\n )\r\n \r\n dev.off()\r\n \r\n invisible()\r\n}\r\n\r\nSensitivityAnalysis.RVE <- function(\r\n data,\r\n measureType = \"r\",\r\n measureCol = \"Corr\",\r\n sensitivityCol,\r\n n = \"n\",\r\n grping1, grping2,\r\n PA = NULL,\r\n zTrans = F,\r\n grpd = T)\r\n{\r\n notes <- c()\r\n \r\n # Remove empty samples\r\n data$n <- data[[n]]\r\n data <- data[data$n > 0,]\r\n \r\n data$grp1 <- data[[grping1]]\r\n data$grp2 <- data[[grping2]]\r\n data$Meas <- data[[measureCol]]\r\n data$Sens <- data[[sensitivityCol]]\r\n IsBinary <- length(na.omit(unique(data$Sens))) <= 2\r\n MeasInit <- data$Meas\r\n SensInit <- data$Sens\r\n grping1_lvls <- levels(data$grp1)\r\n \r\n nName <- n\r\n \r\n # Get sample variances\r\n if (zTrans) {\r\n data <- escalc(\r\n ri = Meas,\r\n ni = n, \r\n measure = \"ZCOR\",\r\n data = data,\r\n var.names = c(\"Meas\", \"Var\")\r\n )\r\n } else if (measureType == \"r\") {\r\n data <- escalc(\r\n ri = Meas,\r\n ni = n, \r\n measure = \"COR\",\r\n data = data,\r\n var.names = c(\"Meas\", \"Var\")\r\n )\r\n data$Meas[data$Meas == 1] <- 1 - 1e-6\r\n data$Meas[data$Meas == 0] <- 1e-6\r\n } else if (measureType == \"k\") {\r\n pci <- (data[[PA]] - data$Meas) / (1 - data$Meas)\r\n data$Var <- data[[PA]] * (1 - data[[PA]]) / ((1 - pci) ^ 2 * data$n)\r\n } else if (measureType == \"p\") {\r\n data <- escalc(\r\n xi = Meas,\r\n ni = n,\r\n measure = \"PLO\",\r\n data = data,\r\n var.names = c(\"Meas\", \"Var\")\r\n )\r\n } else {\r\n stop(\"Unknown 'measureType' specified.\")\r\n }\r\n \r\n # Remove any undefined data (and take notes)\r\n if (any(is.na(data$Meas))) {\r\n notes <- c(notes, paste0(\r\n \"\\tThe following rows were removed because the effect-size was undefined:\\n\\n\"))\r\n notes <- c(notes, paste0(\r\n \"\\t * \", data$grp1[is.na(data$Meas)], \" - \", data$grp2[is.na(data$Meas)],\r\n \" (\", data$Meas[is.na(data$Meas)], \")\\n\"))\r\n notes <- c(notes, \"\\n\")\r\n data <- data[!is.na(data$Meas),]\r\n }\r\n if (any(is.na(data$Sens))) {\r\n notes <- c(notes, paste0(\r\n \"\\tThe following rows were removed because the sensitivity variable was undefined:\\n\\n\"))\r\n notes <- c(notes, paste0(\r\n \"\\t * \", data$grp1[is.na(data$Sens)], \" - \", data$grp2[is.na(data$Sens)],\r\n \" (\", data$Sens[is.na(data$Sens)], \")\\n\"))\r\n notes <- c(notes, \"\\n\")\r\n data <- data[!is.na(data$Sens),]\r\n }\r\n \r\n # Make sure none of variances are 0, otherwise analysis will fail\r\n data$Var[data$Var == 0] <- 1e-6\r\n \r\n # Remove first level groups with only 1 study\r\n for (grping in grping1_lvls)\r\n if (sum(data$grp1 == grping) < 1)\r\n {\r\n notes <- c(notes, paste0(\"\\tRemoved '\", grping, \"' - \", sum(data$grp1 == grping), \" studies.\\n\"))\r\n data <- data[data$grp1 != grping,]\r\n grping1_lvls <- grping1_lvls[grping1_lvls != grping]\r\n }\r\n \r\n # Build random-effects models using Robust Variance Estimation\r\n REModels <- list()\r\n dataList <- list()\r\n for (grping in grping1_lvls) {\r\n data.grp = data[data$grp1 == grping,]\r\n \r\n if ((!IsBinary || all(table(data.grp$Sens) >= 2)) && length(unique(data.grp$Sens)) > 1 && nrow(data.grp) > 2) {\r\n # Calculate random effects model\r\n argList <- list(\r\n formula = Meas ~ 1 + Sens,\r\n data = data.grp,\r\n studynum = data.grp$grp2,\r\n var.eff.size = data.grp$Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\"\r\n )\r\n REModels[[grping]] <- do.call(robu, argList)\r\n dataList[[grping]] <- data.grp\r\n dataList[[grping]]$mdlWeights <- REModels[[grping]]$data.full$r.weights\r\n } else {\r\n notes <- c(notes, paste0(\"\\tSkipped '\", grping, \"' due to lack of data (we require at least 1 per group and 2 overall)\\n\"))\r\n }\r\n }\r\n\r\n if (grpd)\r\n {\r\n formula <- as.formula(Meas ~ 1 + Sens + grp1)\r\n }\r\n else\r\n {\r\n formula <- as.formula(Meas ~ 1 + Sens)\r\n }\r\n res <- try(\r\n {\r\n dataOA <- data\r\n if ((!IsBinary || all(table(dataOA$Sens) >= 2)) && length(unique(dataOA$Sens)) > 1 && nrow(dataOA) >= 2) {\r\n argList <- list(\r\n formula = formula,\r\n data = dataOA,\r\n studynum = dataOA$grp2,\r\n var.eff.size = dataOA$Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\"\r\n )\r\n REModels$overall <- do.call(robu, argList)\r\n dataList$overall <- dataOA\r\n dataList$overall$mdlWeights <- REModels$overall$data.full$r.weights\r\n }\r\n }\r\n )\r\n if (inherits(res, \"try-error\") || any(is.nan(REModels$overall$reg_table$prob))) {\r\n crossTab <- table(data$grp1)\r\n dataOA <- data[data$grp1 %in% names(crossTab)[crossTab > 1],]\r\n if ((!IsBinary || all(table(dataOA$Sens) >= 2)) && length(unique(dataOA$Sens)) > 1 && nrow(dataOA) >= 2 &&\r\n length(REModels) > 0) {\r\n argList <- list(\r\n formula = formula,\r\n data = dataOA,\r\n studynum = dataOA$grp2,\r\n var.eff.size = dataOA$Var,\r\n rho = 0.8,\r\n small = T,\r\n modelweights = \"CORR\"\r\n )\r\n REModels$overall <- do.call(robu, argList)\r\n dataList$overall <- dataOA\r\n dataList$overall$mdlWeights <- REModels$overall$data.full$r.weights\r\n }\r\n }\r\n \r\n \r\n return(\r\n list(\r\n REModels = REModels,\r\n data = data,\r\n dataList = dataList,\r\n notes = notes,\r\n measureType = measureType,\r\n sensitivityCol = sensitivityCol,\r\n zTrans = zTrans\r\n )\r\n )\r\n}\r\n\r\nSensSummary.RVE <- function(\r\n RVEModel,\r\n fileName,\r\n isBinary = T,\r\n overallOnly = F,\r\n studyName = \"Authors\"\r\n)\r\n{\r\n output <- c()\r\n \r\n # Unpack\r\n REModels <- RVEModel$REModels\r\n notes <- RVEModel$notes\r\n zTrans <- RVEModel$zTrans\r\n measureType <- RVEModel$measureType\r\n sensCol <- RVEModel$sensitivityCol\r\n data <- RVEModel$data\r\n dataList <- RVEModel$dataList\r\n \r\n if (length(REModels) == 0) {\r\n output <- c(output, \"Notes\\n-----\\n\", paste(notes, collapse = \"\"), \"\\n\\n\")\r\n output <- c(output, \"No models built - see notes\\n\")\r\n \r\n cat(output)\r\n capture.output(cat(output), file = fileName)\r\n \r\n return(invisible())\r\n } \r\n \r\n output <- c(output, \"\\n\\n========\", sensCol, \"========\")\r\n output <- c(output, \"\\nNotes\\n-----\\n\", paste(notes, collapse = \"\"), \"\\n\")\r\n \r\n # Determine relvant levels\r\n if (isBinary) {\r\n factors <- c(0, 1)\r\n } else {\r\n factors <- c(\r\n min(data$Sens), \r\n (min(data$Sens) + max(data$Sens))/2, \r\n max(data$Sens)\r\n )\r\n output <- c(output, \"\\tLine of best fit estimations\\n\")\r\n }\r\n \r\n # Get first level groupings\r\n grping1_lvls <- names(REModels)\r\n if (overallOnly) {\r\n grping1_lvls <- \"overall\"\r\n }\r\n \r\n probVec <- c()\r\n for (grping in grping1_lvls)\r\n {\r\n # Print group heading\r\n output <- c(output, \"\\n\", grping, \"\\n-------\\n\\n\")\r\n \r\n regTable <- REModels[[grping]]$reg_table\r\n data.grp <- dataList[[grping]]\r\n if (is.character(data.grp$Sens)) {\r\n data.grp$Sens <- as.integer(as.factor(data.grp$Sens))\r\n }\r\n if (isBinary) {\r\n posGrp <- data.grp$Sens == 1\r\n } else {\r\n posGrp <- rep(T, nrow(data.grp))\r\n }\r\n \r\n output <- c(output, \"\\tk_total: \\t\", length(unique(data.grp$grp2)), \"\\n\")\r\n output <- c(output, \"\\tk_posGrp: \\t\", length(unique(data.grp$grp2[posGrp])), \"\\n\")\r\n output <- c(output, \"\\tn_total: \\t\", sum(aggregate(n ~ grp2, data = data.grp, max)$n), \"\\n\")\r\n output <- c(output, \"\\tn_posGrp: \\t\", sum(aggregate(n ~ grp2, data = data.grp[posGrp,], max)$n), \"\\n\")\r\n output <- c(output, \"\\tb_0: \\t\", regTable$b.r[1], \"\\n\")\r\n output <- c(output, \"\\tb_1: \\t\", regTable$b.r[2], \"\\n\")\r\n output <- c(output, \"\\tCI(b_0): \\t(\", regTable$CI.L[1], \", \", regTable$CI.U[1], \")\\n\")\r\n output <- c(output, \"\\tCI(b_1): \\t(\", regTable$CI.L[2], \", \", regTable$CI.U[2], \")\\n\")\r\n output <- c(output, \"\\tSE(b_0): \\t\", regTable$SE[1], \"\\n\")\r\n output <- c(output, \"\\tSE(b_1): \\t\", regTable$SE[2], \"\\n\")\r\n output <- c(output, \"\\tdf_0: \\t\", regTable$dfs[1], \"\\n\")\r\n output <- c(output, \"\\tdf_1: \\t\", regTable$dfs[2], \"\\n\")\r\n output <- c(output, \"\\tt_0: \\t\", regTable$t[1], \"\\n\")\r\n output <- c(output, \"\\tt_1: \\t\", regTable$t[2], \"\\n\")\r\n output <- c(output, \"\\tp_0: \\t\", regTable$prob[1], \"\\n\")\r\n output <- c(output, \"\\tp_1: \\t\", regTable$prob[2], \"\\n\")\r\n output <- c(output, \"\\tTauSq: \\t\", REModels[[grping]]$mod_info$tau.sq, \"\\n\")\r\n output <- c(output, \"\\tISq: \\t\", REModels[[grping]]$mod_info$I.2, \"\\n\\n\")\r\n \r\n roundDig <- 3\r\n if (is.na(regTable$prob[1]))\r\n {\r\n probString0 <- probString1 <- \"NA\"\r\n }\r\n else\r\n {\r\n probString0 <- paste0(round(regTable$prob[1],roundDig), if (regTable$prob[1] < 0.01) \"***\" else if (regTable$prob[1] < 0.05) \"**\" else if (regTable$prob[1] < 0.1) \"*\" else \"\")\r\n probString1 <- paste0(round(regTable$prob[2],roundDig), if (regTable$prob[2] < 0.01) \"***\" else if (regTable$prob[2] < 0.05) \"**\" else if (regTable$prob[2] < 0.1) \"*\" else \"\")\r\n }\r\n if (grping == \"overall\")\r\n {\r\n desc <- c(\r\n sensCol,\r\n length(unique(data.grp$grp2)),\r\n length(unique(data.grp$grp2[posGrp])),\r\n sum(aggregate(n ~ grp2, data = data.grp, max)$n),\r\n sum(aggregate(n ~ grp2, data = data.grp[posGrp,], max)$n),\r\n paste0(round(regTable$b.r[1],roundDig), \" [\", round(regTable$CI.L[1],roundDig), \", \", round(regTable$CI.U[1],roundDig), \"]\"),\r\n paste0(round(regTable$b.r[2],roundDig), \" [\", round(regTable$CI.L[2],roundDig), \", \", round(regTable$CI.U[2],roundDig), \"]\"),\r\n # round(regTable$SE[1],roundDig),\r\n # round(regTable$SE[2],roundDig),\r\n round(regTable$dfs[1],roundDig),\r\n round(regTable$dfs[2],roundDig),\r\n round(regTable$t[1],roundDig),\r\n round(regTable$t[2],roundDig),\r\n probString0,\r\n probString1,\r\n round(REModels[[grping]]$mod_info$tau.sq,roundDig),\r\n round(REModels[[grping]]$mod_info$I.2,roundDig)\r\n )\r\n }\r\n probVec[[grping]] <- probString1\r\n }\r\n \r\n \r\n cat(output)\r\n capture.output(cat(output), file = fileName)\r\n \r\n flush.console()\r\n return(list(text = output, matrix = probVec, desc = desc))\r\n}\r\n\r\nlogit <- function(p)\r\n{\r\n log(p / (1 - p))\r\n}\r\n\r\nsigmoid <- function(x)\r\n{\r\n 1 / (1 + exp(-x))\r\n}\r\n\r\n# Wilson score method\r\n#\r\n# Wilson, E. B. ‘Probable inference, the law of succession, and statistical inference’,\r\n# Journal of the AmericanStatistical Association\r\n# \r\np_CI <- function(n, p, z)\r\n{\r\n list(\r\n (2 * n * p + z^2 + z * sqrt(z^2 + 4 * n * p * (1 - p))) / (2 * (n + z^2)),\r\n (2 * n * p + z^2 - z * sqrt(z^2 + 4 * n * p * (1 - p))) / (2 * (n + z^2))\r\n )\r\n}", "meta": {"hexsha": "da292f5138ec545138aab5980a7ad527c1e8a5ac", "size": 32214, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/Utilities/RVE.r", "max_stars_repo_name": "timesler/AttachmentStabilityMetaAnalysis_Opie2018", "max_stars_repo_head_hexsha": "b55a9a635e6a1669fce1a22e37ed0f39a026cd54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/Utilities/RVE.r", "max_issues_repo_name": "timesler/AttachmentStabilityMetaAnalysis_Opie2018", "max_issues_repo_head_hexsha": "b55a9a635e6a1669fce1a22e37ed0f39a026cd54", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/Utilities/RVE.r", "max_forks_repo_name": "timesler/AttachmentStabilityMetaAnalysis_Opie2018", "max_forks_repo_head_hexsha": "b55a9a635e6a1669fce1a22e37ed0f39a026cd54", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1246376812, "max_line_length": 186, "alphanum_fraction": 0.5411622276, "num_tokens": 10221, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "#!/usr/bin/Rscript\n\n## recombine.r -- carries out a simulated recombination of chromosomes\n\n## Author: David Eccles (gringer), 2008 \n\nusage <- function(){\n cat(\"usage: ./recombine.r\\n\");\n cat(\"\\nOther Options:\\n\");\n cat(\"-help : Only display this help message\\n\");\n cat(\"\\n\");\n}\n\nargLoc <- 1;\nwhile(!is.na(commandArgs(TRUE)[argLoc])){\n if(commandArgs(TRUE)[argLoc] == \"-help\"){\n usage();\n quit(save = \"no\", status=0);\n }\n argLoc <- argLoc + 1;\n}\n\nmakeUniformChromosome <- function(colour = \"NA\"){\n if(colour == \"NA\"){\n colour = sample(colors(),1);\n }\n return(data.frame(Block = colour, Points = 0));\n}\n\ndrawChromosome <- function(ChrX, xPos = 0.5, yPos = 0, scale = 1,\n yScale = 0.5, new = TRUE){\n xLeft = xPos;\n yBottom = yPos;\n xRight = xPos + 0.05 * scale;\n if(new){\n plot.new();\n }\n for(i in 1:(dim(ChrX)[1])){\n if(i == dim(ChrX)[1]){\n rect(xLeft,yBottom+ChrX[i,2]*scale*yScale,xRight,\n yBottom+1*scale*yScale,\n col=as.character(ChrX[i,1]));\n } else {\n rect(xLeft,yBottom+ChrX[i,2]*scale*yScale,xRight,\n yBottom+ChrX[i+1,2]*scale*yScale,\n col=as.character(ChrX[i,1]));\n }\n }\n}\n\nrecombine <- function(ChrX, ChrY, minPoints = 3, maxPoints = 4){\n result <- NULL;\n recombPoints <- sort(runif(sample(minPoints:maxPoints,1)));\n currentChr <- sample(c(0,1),1);\n startPos <- 0;\n newPoints <- NULL;\n for(endPos in c(recombPoints,1)){\n if(currentChr == 0){\n stopX <- c(ChrX$Points[-1],1);\n # list of recombination points on ChrX in the correct interval\n curPoints = which((ChrX$Points<=endPos) &\n (stopX >= startPos));\n newPoints <- ChrX[curPoints,];\n } else {\n stopY <- c(ChrY$Points[-1],1);\n # list of recombination points on ChrY in the correct interval\n curPoints = which((ChrY$Points<=endPos) &\n (stopY >= startPos));\n newPoints <- ChrY[curPoints,];\n }\n # set start recombination point to be where the recombination took place\n newPoints$Points[1] <- startPos;\n result <- rbind(result,newPoints);\n startPos <- endPos;\n currentChr <- (currentChr + 1) %% 2;\n }\n return(result);\n}\n\n# merges regions of the same colour\nchrClean <- function(ChrX){\n result <- ChrX[1,];\n oldColour <- ChrX$Block[1];\n for(pos in 2:(dim(ChrX)[1])){\n if(oldColour != ChrX$Block[pos]){\n## cat(oldColour, \"does not equal\", ChrX$Block[pos],\"\\n\");\n result <- rbind(result,ChrX[pos,]);\n oldColour <- ChrX$Block[pos];\n }\n }\n return(result);\n}\n\n# combines two chromosomes, so that regions of target colour common to\n# both input chromosomes are in the result chromosome\nchrUnion <- function(colourName, ChrX, ChrY, ChrOther = NULL, ...){\n if(is.null(ChrOther)){\n result <- rbind(ChrX, ChrY);\n result <- result[order(result$Points),];\n for(pos in 1:(dim(result)[1])){\n blockX <- ChrX$Block[max(which(ChrX$Points <= result$Points[pos]))];\n blockY <- ChrY$Block[max(which(ChrY$Points <= result$Points[pos]))];\n if(blockX == colourName){\n result$Block[pos] <- blockX;\n }\n if(blockY == colourName){\n result$Block[pos] <- blockY;\n }\n }\n result <- chrClean(result);\n return(result);\n } else {\n return(chrUnion(colourName, chrUnion(colourName, ChrX, ChrY), ChrOther, ...));\n }\n}\n\n## recombination descendancy, haplotype dilution\noc <- makeUniformChromosome(\"yellow\");\ng1 <- list(makeUniformChromosome(\"red\"));\ng2 <- list(\n chrClean(recombine(g1[[1]],oc)),\n chrClean(recombine(g1[[1]],oc))\n );\ng2u<- chrUnion(\"red\",g2[[1]],g2[[2]]);\ng3 <- list(\n chrClean(recombine(g2[[1]],oc)),\n chrClean(recombine(g2[[1]],oc)),\n chrClean(recombine(g2[[2]],oc)),\n chrClean(recombine(g2[[2]],oc))\n );\ng3u<- chrUnion(\"red\",g3[[1]],g3[[2]],g3[[3]],g3[[4]]);\ng4 <- list(\n chrClean(recombine(g3[[1]],oc)),\n chrClean(recombine(g3[[1]],oc)),\n chrClean(recombine(g3[[2]],oc)),\n chrClean(recombine(g3[[2]],oc)),\n chrClean(recombine(g3[[3]],oc)),\n chrClean(recombine(g3[[3]],oc)),\n chrClean(recombine(g3[[4]],oc)),\n chrClean(recombine(g3[[4]],oc))\n );\ng4u<- chrUnion(\"red\",g4[[1]],g4[[2]],g4[[3]],g4[[4]],\n g4[[5]],g4[[6]],g4[[7]],g4[[8]]);\ng5 <- list(\n chrClean(recombine(g4[[1]],oc)),\n chrClean(recombine(g4[[1]],oc)),\n chrClean(recombine(g4[[2]],oc)),\n chrClean(recombine(g4[[2]],oc)),\n chrClean(recombine(g4[[3]],oc)),\n chrClean(recombine(g4[[3]],oc)),\n chrClean(recombine(g4[[4]],oc)),\n chrClean(recombine(g4[[4]],oc)),\n chrClean(recombine(g4[[5]],oc)),\n chrClean(recombine(g4[[5]],oc)),\n chrClean(recombine(g4[[6]],oc)),\n chrClean(recombine(g4[[6]],oc)),\n chrClean(recombine(g4[[7]],oc)),\n chrClean(recombine(g4[[7]],oc)),\n chrClean(recombine(g4[[8]],oc)),\n chrClean(recombine(g4[[8]],oc))\n );\ng5u <- chrUnion(\"red\",g5[[1]],g5[[2]],g5[[3]],g5[[4]],\n g5[[5]],g5[[6]],g5[[7]],g5[[8]],\n g5[[9]],g5[[10]],g5[[11]],g5[[12]],\n g5[[13]],g5[[14]],g5[[15]],g5[[16]]);\n\npar(mar=c(0,0,0,0));\nplot.new();\nplot.window(xlim=c(0,1),ylim=c(0.2,1));\nfor(i in 1:8){\n drawChromosome(g5[[i*2-1]],xPos = (i-1)*0.125, yPos = 0.8,\n scale = 0.4, new = FALSE);\n drawChromosome(g5[[i*2]],xPos = (i-1)*0.125+0.05, yPos = 0.8,\n scale = 0.4, new = FALSE);\n}\ndrawChromosome(g5u,xPos = (7)*0.125+0.125, yPos = 0.8, scale = 0.4, new = FALSE);\nfor(i in 1:4){\n drawChromosome(g4[[i*2-1]],xPos = (i-1)*0.25+0.0625, yPos = 0.58,\n scale = 0.4, new = FALSE);\n drawChromosome(g4[[i*2]],xPos = (i-1)*0.25+0.0625+0.05, yPos = 0.58,\n scale = 0.4, new = FALSE);\n}\ndrawChromosome(g4u,xPos = (7)*0.125+0.095, yPos = 0.58, scale = 0.4, new = FALSE);\nfor(i in 1:2){\n drawChromosome(g3[[i*2-1]],xPos = (i-1)*0.5+0.1875, yPos = 0.4,\n scale = 0.4, new = FALSE);\n drawChromosome(g3[[i*2]],xPos = (i-1)*0.5+0.1875+0.05, yPos = 0.4,\n scale = 0.4, new = FALSE);\n}\ndrawChromosome(g3u,xPos = (7)*0.125+0.065, yPos = 0.4, scale = 0.4, new = FALSE);\ndrawChromosome(g2[[1]],xPos = 0.4375, yPos = 0.22,\n scale = 0.4, new = FALSE);\ndrawChromosome(g2[[2]],xPos = 0.4375+0.05, yPos = 0.22,\n scale = 0.4, new = FALSE);\ndrawChromosome(g2u,xPos = (7)*0.125+0.035, yPos = 0.22, scale = 0.4, new = FALSE);\ndrawChromosome(g1[[1]],xPos = 0.0875, yPos = 0.22, scale = 0.4, new = FALSE);\narrows(x1=seq(0.035,0.91,length.out=8),\n x0=seq(0.035,0.91,length.out=8) + rep(c(0.015,-0.015),4),\n y1=0.79, y0=0.68, length = 0.1);\narrows(x1=seq(0.0975,0.8475,length.out=4),\n x0=seq(0.0975,0.8475,length.out=4) + rep(c(0.08,-0.08),2),\n y1=0.57, y0=0.5, length = 0.1);\narrows(x1=seq(0.2225,0.7225,length.out=2),\n x0=seq(0.2225,0.7225,length.out=2) + rep(c(0.2,-0.2),2),\n y1=0.39, y0=0.32, length = 0.1);\narrows(x1 = 0.42, x0 = 0.1275, y1 = 0.30, y0 = 0.30, length = 0.1);\narrows(x1 = 0.975, x0 = 0.875, y1 = 1.01, y0 = 0.21, length = 0);\n\n## recombination ancestry, unbalanced haplotypes\ng1 <- list(\n makeUniformChromosome(\"black\"),\n makeUniformChromosome(\"red\"),\n makeUniformChromosome(\"magenta\"),\n makeUniformChromosome(\"blue\"),\n makeUniformChromosome(\"cyan\"),\n makeUniformChromosome(\"green\"),\n makeUniformChromosome(\"yellow\"),\n makeUniformChromosome(\"white\"));\ng2 <- list(\n recombine(g1[[1]],g1[[1]]),\n recombine(g1[[2]],g1[[2]]),\n recombine(g1[[3]],g1[[3]]),\n recombine(g1[[4]],g1[[4]]),\n recombine(g1[[5]],g1[[5]]),\n recombine(g1[[6]],g1[[6]]),\n recombine(g1[[7]],g1[[7]]),\n recombine(g1[[8]],g1[[8]]));\ng3 <- list(\n recombine(g2[[1]],g2[[2]]),\n recombine(g2[[3]],g2[[4]]),\n recombine(g2[[5]],g2[[6]]),\n recombine(g2[[7]],g2[[8]]));\ng4 <- list(\n recombine(g3[[1]],g3[[2]]),\n recombine(g3[[3]],g3[[4]]));\ng5 <- list(\n recombine(g4[[1]],g4[[2]]));\n\npdf(\"recombination_ancestry.pdf\",width=8,height=8);\npar(mar=c(0,0,0,0));\nplot.new();\nplot.window(xlim=c(0,1),ylim=c(0.2,1));\nfor(i in 1:8){\n drawChromosome(g1[[i]],xPos = (i-1)*0.125, yPos = 0.8,\n scale = 0.4, new = FALSE);\n drawChromosome(g1[[i]],xPos = (i-1)*0.125+0.05, yPos = 0.8,\n scale = 0.4, new = FALSE);\n}\nfor(i in 1:4){\n drawChromosome(g2[[i*2-1]],xPos = (i-1)*0.25+0.0625, yPos = 0.58,\n scale = 0.4, new = FALSE);\n drawChromosome(g2[[i*2]],xPos = (i-1)*0.25+0.0625+0.05, yPos = 0.58,\n scale = 0.4, new = FALSE);\n}\nfor(i in 1:2){\n drawChromosome(g3[[i*2-1]],xPos = (i-1)*0.5+0.1875, yPos = 0.4,\n scale = 0.4, new = FALSE);\n drawChromosome(g3[[i*2]],xPos = (i-1)*0.5+0.1875+0.05, yPos = 0.4,\n scale = 0.4, new = FALSE);\n}\ndrawChromosome(g4[[1]],xPos = 0.4375, yPos = 0.22,\n scale = 0.4, new = FALSE);\ndrawChromosome(g4[[2]],xPos = 0.4375+0.05, yPos = 0.22,\n scale = 0.4, new = FALSE);\ndrawChromosome(g5[[1]],xPos = 0.8375, yPos = 0.22, scale = 0.4, new = FALSE);\narrows(x0=seq(0.035,0.91,length.out=8),\n x1=seq(0.035,0.91,length.out=8) + rep(c(0.015,-0.015),4),\n y0=0.79, y1=0.68, length = 0.1);\narrows(x0=seq(0.0975,0.8475,length.out=4),\n x1=seq(0.0975,0.8475,length.out=4) + rep(c(0.08,-0.08),2),\n y0=0.57, y1=0.5, length = 0.1);\narrows(x0=seq(0.2225,0.7225,length.out=2),\n x1=seq(0.2225,0.7225,length.out=2) + rep(c(0.2,-0.2),2),\n y0=0.39, y1=0.32, length = 0.1);\narrows(x0 = 0.52, x1 = 0.82, y0 = 0.30, y1 = 0.30, length = 0.1);\ndummy <- dev.off();\n", "meta": {"hexsha": "1e9090d1a9446e71c0b3c008f208ce62d21a0513", "size": 9962, "ext": "r", "lang": "R", "max_stars_repo_path": "recombine.r", "max_stars_repo_name": "gringer/bootstrap-subsampling", "max_stars_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_stars_repo_licenses": ["ISC"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "recombine.r", "max_issues_repo_name": "gringer/bootstrap-subsampling", "max_issues_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_issues_repo_licenses": ["ISC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "recombine.r", "max_forks_repo_name": "gringer/bootstrap-subsampling", "max_forks_repo_head_hexsha": 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{"text": "# <-- encoding UTF-8 -->\n# Portal for the code of: Socioeconomic Status Inequality and Morbidity Rate Differences in China: Based on NHSS and CHARLS Data\n# Author: Y. Jiang, H. Zheng, T. Zhao\n# -------------------------------------------\n## DOC STRING\n# \n# The script is the portal of all programs of our submtted paper;\n# you may run this file, or follow the documentation, README, and comments to specific source files.\n# Please refer to README.md for more information.\n# and, all scripts & file I/O are encoded in UTF-8 (because Chinese charaters, in data and/or scripts, may be included & used)\n# \n# Tianhao Zhao (GitHub: Clpr)\n# Dec 2018\n# -------------------------------------------\n## DEPENDENCY\n#\n# 1. readxl: IO of xlsx files\n# 2. plm: panel data models\n# 3. nlme: GLS estimation of linear models\n# 4. car: VIFs, qqplots\n# 5. ggplot2, plyr, maptools, mapproj, Cairo: map drawing\n# 6. sqldf: api of firendly sql enqury\n# 7. nortest: normality tests\n# 8. purrr: std lib, to do function operations\n# 9. openxlsx: easy xlsx I/O, no dependency on rJava\n# 10. psych: easier APIs for PCA\n# -------------------------------------------\n\nrm(list=ls()) # clear\nsetwd(\"C:/Users/Lenovo/OneDrive/GitHubProj/HealthInequality2018Dec\") # working directory\n\n\n\n\n\n\n\n\n# -------------------------------------------\n# Section 1: NHSS data processing & variable selection\ncat(\"\\nScript 1: NHSS data processing & variable selection\")\n# MISSION:\n# 1. NHSS data I/O\n# 2. NHSS data processing, missing values\n# 3. collinearity between income & edu: resid(income ~ edu) --> income\n# 4. NHSS (control) variable selection I: through single-variable regressions\n# 5. NHSS (control) variable selection II: PCA\n# 6. VIFs of final specifications\nsource(\"./scripts/proc_1_NHSS.r\")\n# LEGACY IN WORKSPACE:\n# 1. df_NHSSraw: raw NHSS dataset, consisting of all potential vars & useless vars; as backup\n# 2. df_NHSS: processed NHSS dataset\n# 3. li_Dat_NHSS: datasets for every specification\n# 3. li_DatPlm_NHSS: panel-data for every specification\n# 4. li_PCAres_NHSS: PCA results\n# 5. li_PCAk_NHSS: numebr of components used in every specification\n# 5. li_VIF_NHSS: VIF results of final specifications\n# 6. li_Eq_NHSS: a list consisting of formula objects of final specifications\n# 7. li_Xnames_NHSS: a list of namelists of independents of every specification\n# 7. envNHSS: environment variables of NHSS dataset\n# NOTE: pls go to ./output/proc_1_NHSS/ for output results (if applicable)\n# ---------------------------------------------\n\n# ---------------------------------------------\n# Section 2: NHSS data, descriptive statistics (basic and advanced)\ncat(\"\\nScript 2: NHSS data descriptive statistics\")\n# DEPENDENCY: using legacy of proc_1_NHSS.r\n# MISSION:\n# 1. NHSS data, general descriptive stats (mean, sd etc)\n# 2. county-level (individual) Lorenz Curve & Gini coef of health outcomes\n# 3. county-level (individual) Theil-I, Theil-II\n# 4. county-level (individual) C.V., coefficient of variance\n# 5. county-level (individual) Variance\n# 6. NHSS data, the existence of the inequalities of health outcomes among areas in China (colored map)\n# 7. NHSS data, the difference of (income & edu) among areas in china (colored map)\nsource(\"./scripts/proc_2_NHSS.r\")\n# LEGACY IN WORKSPACE:\n# 1. li_Descript_NHSS: tables of the descriptive statistics of NHSS data (only income & edu, we do not do this on normalized+centered principle components!)\n# 2. df_InequalIdx_NHSS: a table of different kinds of inequality indices of NHSS data\n# 3. MapShapeCH: a dataset of Chinese GIS data; will be used later\n# 4. func_DescrStat: a function to do descriptive statistics\n# 5. func_MapProv: draws colored map based on Chinese GIS data; province level\n# 6. func_SaveMap2PDF: easy output method of the figures created by func_MapProv()\n# 7. LorenzCurve: easy function to compute Lorenz curve & Gini coef\n# 8. Theil: easy function to compute Theil-I/II index\n# 9. TrapeInt: easy function to compute trapezium integral\n# NOTE: pls go to ./output/proc_2_NHSS/ for output results (applicable)\n# ---------------------------------------------\n\n# ---------------------------------------------\n# Section 3: NHSS data, econometric analysis (pool, fix, random, Hausman)\ncat(\"\\nScript 3: NHSS data, econometric analysis (pool, fix, random, Hausman)\")\n# DEPENDENCY: using legacy of proc_1_NHSS.r\n# \n# IMPORTANT NOTE: please refer to PanelAnalysisLogic.md under the ./docs directory to help understand how we designed this section !!! :)\n# \n# MISSION:\n# 1. one-way fixed individual effect model & random individual effect model (FGLS estimators)\n# 2. Hausman test & robust Hausman test (Wooldridge, 2010)\n# 3. Residual QQplots & normality tests, one-way fixed individual effect model (only)\n# 4. ROBUST: two-ways fixed effect model & Haussman test (to see if one-way & two-ways are consistent)\n# 5. ROBUST: pooling, OLS\n# 6. ROBUST: pooling, FGLS\n# \n# 5. ROBUST: add possible independent: \nsource(\"./scripts/proc_3_NHSS.r\")\n# LEGACY IN WORKSPACE:\n# NOTE: pls go to ./output/proc_3_NHSS/ for output results (applicable)\n# -------------------------------------------\n\n\n\n\n\n\n\n\n\n\n\n# -------------------------------------------\n# Section 4: CHARLS data processing & variable selection\ncat(\"\\nScript 4: CHARLS data processing & variable selection\")\n# MISSION:\n# 1. CHARLS data I/O\n# 2. CHARLS data processing, missing values\n# 3. collinearity between income & edu: resid(income ~ edu) --> income\n# 4. CHARLS (control) variable selection I: through single-variable regressions\n# 5. CHARLS (control) variable selection II: AIC stepwise to FURTHER select control variables into final specifications\n# 6. VIFs of final specifications\nsource(\"./scripts/proc_1_CHARLS.r\")\n# LEGACY IN WORKSPACE:\n# 1. df_CHARLS_backup: raw CHARLS dataset, consisting of all potential vars & useless vars; as backup\n# 2. df_CHARLS: processed CHARLS dataset\n# 3. dfp_CHARLS: a panel-data-type dataframe (converted from df_NHSS, used in package plm)\n# 4. df_FinalSpecif_CHARLS: a dataframe marking which variables in the final specifications\n# 5. df_VIF_CHARLS: VIF results of final specifications\n# 6. li_Eq_CHARLS: a list consisting of formula objects of final specifications\n# 7. envCHARLS: environment variables of CHARLS dataset\n# NOTE: pls go to ./output/proc_1_CHARLS/ for output results (if applicable)\n# ---------------------------------------------\n\n# ---------------------------------------------\n# Section 5: CHARLS data, descriptive statistics (basic and advanced)\ncat(\"\\nScript 5: CHARLS data, descriptive statistics (basic and advanced)\")\n# DEPENDENCY: using legacy of proc_1_NHSS.r\n# MISSION:\n# 1. CHARLS data, general descriptive stats (mean, sd etc)\n# 2. county-level (individual) Lorenz Curve & Gini coef of health outcomes\n# 3. county-level (individual) Theil-I, Theil-II\n# 4. county-level (individual) C.V., coefficient of variance\n# 5. county-level (individual) Variance\n# 6. CHARLS data, the existence of the inequalities of health outcomes among areas in China (colored map)\n# 7. CHARLS data, the difference of (income & edu) among areas in china (colored map)\nsource(\"./scripts/proc_2_CHARLS.r\")\n# LEGACY IN WORKSPACE:\n# 1. df_Descript_CHARLS: a table of the descriptive statistics of NHSS data (final specification)\n# 2. df_InequalIdx_CHARLS: a table of different kinds of inequality indices of NHSS data\n# 3. MapShapeCH: a dataset of Chinese GIS data; will be used later\n# 4. func_DescrStat: a function to do descriptive statistics\n# 5. func_MapProv: draws colored map based on Chinese GIS data; province level\n# 6. func_SaveMap2PDF: easy output method of the figures created by func_MapProv()\n# 7. LorenzCurve: easy function to compute Lorenz curve & Gini coef\n# 8. Theil: easy function to compute Theil-I/II index\n# 9. TrapeInt: easy function to compute trapezium integral\n# NOTE: pls go to ./output/proc_2_CHARLS/ for output results (applicable)\n# ---------------------------------------------\n\n# ---------------------------------------------\n# Section 6: CHARLS data, econometric analysis (pool, fix, random, Hausman)\ncat(\"\\nScript 3: CHARLS data, econometric analysis (pool, fix, random, Hausman)\")\n# DEPENDENCY: using legacy of proc_1_CHARLS.r\n# \n# IMPORTANT NOTE: please refer to PanelAnalysisLogic.md under the ./docs directory to help understand how we designed this section !!! :)\n# \n# MISSION:\n# 1. one-way fixed individual effect model & random individual effect model (FGLS estimators)\n# 2. Hausman test & robust Hausman test (Wooldridge, 2010)\n# 3. Residual QQplots & normality tests, one-way fixed individual effect model (only)\n# 4. ROBUST: two-ways fixed effect model & Haussman test (to see if one-way & two-ways are consistent)\n# 5. ROBUST: pooling, OLS\n# 6. ROBUST: pooling, FGLS\n# \n# 5. ROBUST: add possible independent: \nsource(\"./scripts/proc_3_CHARLS.r\")\n# LEGACY IN WORKSPACE:\n# NOTE: pls go to ./output/proc_3_CHARLS/ for output results (applicable)\n# -------------------------------------------\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "meta": {"hexsha": "e7246c505aff0f7030832c1b5d39f2f172e89cfa", "size": 9919, "ext": "r", "lang": "R", "max_stars_repo_path": "main.r", "max_stars_repo_name": "Clpr/HealthInequality2018Dec", "max_stars_repo_head_hexsha": "d88d80c97e46f3e0b10c2c15e83eb0932957e69d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "main.r", "max_issues_repo_name": "Clpr/HealthInequality2018Dec", "max_issues_repo_head_hexsha": "d88d80c97e46f3e0b10c2c15e83eb0932957e69d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "main.r", "max_forks_repo_name": "Clpr/HealthInequality2018Dec", "max_forks_repo_head_hexsha": "d88d80c97e46f3e0b10c2c15e83eb0932957e69d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.2333333333, "max_line_length": 173, "alphanum_fraction": 0.6282891421, "num_tokens": 2566, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "###############################################################################\r\n# \"Infection Rate Models for COVID-19: \r\n# Model Risk and Public Health News Sentiment Exposure Adjustments\"\r\n# \r\n# Ioannis Chalkiadakis, Kevin Hongxuan Yan, Gareth W. Peters, Pavel V. Shevchenko\r\n#\r\n# Kevin Hongxuan Yan\r\n# March 2021\r\n###############################################################################\r\n\r\n\r\n####### read new data Y\r\nsetwd(\"./covid19modelrisk/data/covid19_infected/\")\r\n\r\n y_GM=read.csv(\"COVID-19_confirmed__Germany.csv\")[,2]\r\n y_IT=read.csv(\"COVID-19_confirmed__Italy.csv\")[,2]\r\n y_JP=read.csv(\"COVID-19_confirmed__Japan.csv\")[,2]\r\n y_SP=read.csv(\"COVID-19_confirmed__Spain.csv\")[,2]\r\n y_UK=read.csv(\"COVID-19_confirmed__United Kingdom.csv\")[,2]\r\n y_US=read.csv(\"COVID-19_confirmed__US.csv\")[,2]\r\n y_AU=read.csv(\"COVID-19_confirmed_Australia.csv\")[,2]\r\n\r\n\r\n\r\nname=c(\"Germany\",\"Italy\",\"Japan\",\"Spain\",\"U.K.\",\"U.S.\",\"Australia\")\r\n\r\npdf(\"UK_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_UK,main=name[5],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_UK)), labels=c(\"Jan/31\",\"May/09\",\"Aug/06\")) ### UK\r\ndev.off()\r\n\r\n\r\npdf(\"GM_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_GM,main=name[1],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_GM)), labels=c(\"Jan/27\",\"May/05\",\"Aug/06\")) ### GM\r\ndev.off()\r\n\r\n\r\npdf(\"IT_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_IT,main=name[2],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_IT)), labels=c(\"Jan/31\",\"May/09\",\"Aug/06\")) ### IT\r\ndev.off()\r\n\r\n\r\npdf(\"SP_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_SP,main=name[4],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_SP)), labels=c(\"Feb/01\",\"May/10\",\"Aug/06\")) ### SP\r\ndev.off()\r\n\r\n\r\npdf(\"US_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_US,main=name[6],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_US)), labels=c(\"Jan/22\",\"Apr/30\",\"Aug/06\")) ### US\r\ndev.off()\r\n\r\n\r\npdf(\"JP_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_JP,main=name[3],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_JP)), labels=c(\"Jan/22\",\"Apr/30\",\"Aug/06\")) ### JP\r\ndev.off()\r\n\r\n\r\npdf(\"AU_tplot.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_AU,main=name[7],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,length(y_AU)), labels=c(\"Jan/26\",\"May/04\",\"Aug/06\")) ### AU\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#################### Violin plot \r\n\r\nlibrary(methods)\r\nlibrary(rstan)\r\n\r\n\r\nname=c(\"Germany\",\"Italy\",\"Japan\",\"Spain\",\"U.K.\",\"U.S.\",\"Australia\")\r\nabn=c(\"GM\",\"IT\",\"JP\",\"SP\",\"UK\",\"US\",\"AU\")\r\n\r\n\r\n\r\nk=7\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M2 Rdata\")\r\nload(paste(\"M2_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(6+N):(5+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_2=(res-mean(res))/(sd(res))\r\nR_2=cbind(Res_2,rep(\"M2\",length(Res_2)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M4 Rdata\")\r\nload(paste(\"M4_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(7+N):(6+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_4=(res-mean(res))/(sd(res))\r\nR_4=cbind(Res_4,rep(\"M4\",length(Res_4)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M7 Rdata\")\r\nload(paste(\"M7_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(7+N):(6+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_7=(res-mean(res))/(sd(res))\r\nR_7=cbind(Res_7,rep(\"M7\",length(Res_7)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M8 Rdata\")\r\nload(paste(\"M8_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(8+N):(7+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_8=(res-mean(res))/(sd(res))\r\nR_8=cbind(Res_8,rep(\"M8\",length(Res_8)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M9 Rdata\")\r\nload(paste(\"M9_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(6+N):(5+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_9=(res-mean(res))/(sd(res))\r\nR_9=cbind(Res_9,rep(\"M9\",length(Res_9)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M10 Rdata\")\r\nload(paste(\"M10_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(8+N):(7+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_10=(res-mean(res))/(sd(res))\r\nR_10=cbind(Res_10,rep(\"M10\",length(Res_10)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M11 Rdata\")\r\nload(paste(\"M11_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(8+N):(7+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_11=(res-mean(res))/(sd(res))\r\nR_11=cbind(Res_11,rep(\"M11\",length(Res_11)))\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M12 Rdata\")\r\nload(paste(\"M12_\",abn[k],\".Rdata\", sep=\"\"))\r\nEpars_m <- apply( as.data.frame(fit@sim[[1]][[1]])[10001:100000,(7+N):(6+2*N)] , 2 , mean )\r\nres=Epars_m-y\r\nRes_12=(res-mean(res))/(sd(res))\r\nR_12=cbind(Res_12,rep(\"M12\",length(Res_12)))\r\n\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\final plot\")\r\n\r\npdf(paste(abn[k],\"_plot.pdf\",sep=\"\"), width = 16.5, height = 8.50) \r\n\r\nlibrary(\"vioplot\")\r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nvioplot(Res_2,Res_4,Res_7,Res_8,Res_9,Res_10,Res_11,Res_12,names=c(\"M2\",\"M4\",\"M7\",\"M8\",\"M9\",\"M10\",\"M11\",\"M12\"))\r\ntitle(paste(\"Residual plots for \",name[k],sep=\"\"))\r\n\r\n\r\ndev.off()\r\n\r\n\r\n\r\n############## boxplot ########################################################################################################################################\r\n\r\n\r\n\r\n\r\n\r\n\r\nlibrary(methods)\r\nlibrary(rstan)\r\n\r\n\r\nname=c(\"Germany\",\"Italy\",\"Japan\",\"Spain\",\"U.K.\",\"U.S.\",\"Australia\")\r\nabn=c(\"GM\",\"IT\",\"JP\",\"SP\",\"UK\",\"US\",\"AU\")\r\n\r\n\r\n\r\nk=3\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\M2 Rdata\") ##### change\r\nload(paste(\"M2_\",abn[k],\".Rdata\", sep=\"\")) ##### change\r\nEpars <- as.data.frame(fit@sim[[1]][[1]])[10001:100000,(6+N):(5+2*N)]\r\n\r\n\r\nres_all=matrix(,nrow=dim(Epars)[1],ncol=dim(Epars)[2])\r\nY_m=matrix(rep(y,nrow=dim(Epars)[1]),nrow=dim(Epars)[1],ncol=dim(Epars)[2],byrow=T)\r\nres_all=as.matrix(Epars-Y_m)\r\n\r\n\r\nres_st=matrix(,nrow=dim(Epars)[1],ncol=dim(Epars)[2])\r\nfor (i in 1:dim(Epars)[1]){\r\nres_st[i,]=(res_all[i,]-mean(res_all[i,]))/(sd(res_all[i,]))\r\n}\r\n\r\n\r\n\r\n\r\n\r\nsetwd(\"C:\\\\Users\\\\yhx19\\\\Desktop\\\\NEW RUN\\\\final plot\")\r\n\r\n\r\npdf(paste(abn[k],\"_boxplot.pdf\",sep=\"\"), width = 16.5, height = 8.50) \r\n\r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nboxplot(res_st,xlab=\"time steps\")\r\ntitle(paste(\"M2 residual boxplots for \",name[k],sep=\"\")) ##### change\r\n\r\n\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n############## NEW plot ########################################################################################################################################\r\n############## NEW plot ########################################################################################################################################\r\n\r\n\r\n\r\n####### read new data Y\r\nsetwd(\"G:\\\\Covid paper\\\\additionalcountdataofinfectedcases\\\\new data\")\r\n\r\n y_GM=read.csv(\"COVID-19_confirmed__Germany_latest_Feb.csv\")[,2]\r\n y_IT=read.csv(\"COVID-19_confirmed__Italy_latest_Feb.csv\")[,2]\r\n y_JP=read.csv(\"COVID-19_confirmed__Japan_latest_Feb.csv\")[,2]\r\n y_SP=read.csv(\"COVID-19_confirmed__Spain_latest_Feb.csv\")[,2]\r\n y_UK=read.csv(\"COVID-19_confirmed__United Kingdom_latest_Feb.csv\")[,2]\r\n y_US=read.csv(\"COVID-19_confirmed__US_latest_Feb.csv\")[,2]\r\n y_AU=read.csv(\"COVID-19_confirmed_Australia_latest_Feb.csv\")[,2]\r\n\r\n\r\nD_GM=c(\"Jan/27\",\"May/05\",\"Aug/13\",\"Nov/21\",\"Jan/10\")\r\nD_IT=c(\"Jan/31\",\"May/09\",\"Aug/17\",\"Nov/25\",\"Jan/14\")\r\nD_JP=c(\"Jan/22\",\"Apr/30\",\"Aug/08\",\"Nov/16\",\"Jan/5\")\r\nD_SP=c(\"Feb/01\",\"May/10\",\"Aug/18\",\"Nov/26\",\"Jan/15\")\r\nD_UK=c(\"Jan/31\",\"May/09\",\"Aug/17\",\"Nov/25\",\"Jan/14\")\r\nD_US=c(\"Jan/22\",\"Apr/30\",\"Aug/08\",\"Nov/16\",\"Jan/5\")\r\nD_AU=c(\"Jan/26\",\"May/04\",\"Aug/12\",\"Nov/20\",\"Jan/9\")\r\n\r\nDate=c(D_GM,D_IT,D_JP,D_SP,D_UK,D_US,D_AU)\r\n\r\n\r\nname=c(\"Germany\",\"Italy\",\"Japan\",\"Spain\",\"U.K.\",\"U.S.\",\"Australia\")\r\n\r\nk=5\r\npdf(\"UK_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_UK,main=name[5],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Jan/31\",\"May/09\",\"Aug/17\",\"Nov/25\",\"Jan/14\")) \r\n ### UK\r\ndev.off()\r\n\r\n\r\nk=1\r\npdf(\"GM_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_GM,main=name[1],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Jan/27\",\"May/05\",\"Aug/13\",\"Nov/21\",\"Jan/10\")) \r\n ### GM\r\ndev.off()\r\n\r\nk=2\r\npdf(\"IT_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_IT,main=name[2],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Jan/31\",\"May/09\",\"Aug/17\",\"Nov/25\",\"Jan/14\")) \r\n ### IT\r\ndev.off()\r\n\r\nk=4\r\npdf(\"SP_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_SP,main=name[4],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Feb/01\",\"May/10\",\"Aug/18\",\"Nov/26\",\"Jan/15\")) \r\n ### SP\r\ndev.off()\r\n\r\nk=6\r\npdf(\"US_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_US,main=name[6],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Jan/22\",\"Apr/30\",\"Aug/08\",\"Nov/16\",\"Jan/5\")) \r\n ### US\r\ndev.off()\r\n\r\nk=3\r\npdf(\"JP_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_JP,main=name[3],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Jan/22\",\"Apr/30\",\"Aug/08\",\"Nov/16\",\"Jan/5\")) \r\n ### JP\r\ndev.off()\r\n\r\nk=7\r\npdf(\"AU_tplot_LONG.pdf\", width = 16.5, height = 8.50) \r\npar(cex.axis=2.5,cex.lab=2.5,cex.main=2.5,mar=c(5, 5, 3, 0.5))\r\nplot(y_AU,main=name[7],xlab=\"time\",ylab=\"count\",xaxt = 'n')\r\naxis(1, at=c(0,100,200,300,350), labels=c(\"Jan/26\",\"May/04\",\"Aug/12\",\"Nov/20\",\"Jan/9\")) \r\n ### AU\r\ndev.off()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "meta": {"hexsha": "f90149b3ded43310f310e8d513cea10b9119a0c8", "size": 10603, "ext": "r", "lang": "R", "max_stars_repo_path": "R/final plot.r", "max_stars_repo_name": "ichalkiad/covid19modelrisk", "max_stars_repo_head_hexsha": "eaf14f373411c0122c73439f089e9626164c87d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/final plot.r", "max_issues_repo_name": "ichalkiad/covid19modelrisk", "max_issues_repo_head_hexsha": "eaf14f373411c0122c73439f089e9626164c87d1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/final plot.r", "max_forks_repo_name": "ichalkiad/covid19modelrisk", 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NO\n2. NO", "lm_q1_score": 0.3629691917376783, "lm_q2_score": 0.034618842698694585, "lm_q1q2_score": 0.012565573353238999}}
{"text": "##\n# layer_class.r\n# Copyright (C) Jonathan Bramble 2011\n# \n# FBF-Optics is free software: you can redistribute it and/or modify it\n# under the terms of the GNU General Public License as published by the\n# Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n# \n# FBF-Optics is distributed in the hope that it will be useful, but\n# WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n# See the GNU General Public License for more details.\n# \n# You should have received a copy of the GNU General Public License along\n# with this program. If not, see .\n#\n\n#' An S4 class to represent a thin dielectric or metallic layer.\n#'\n#' @slot d Thickness of the layer in m\n#' @slot eps The relative permittivity of the material as a complex number\n#' \n\nIsoLayer <- setClass(\"IsoLayer\", \n representation(\n fitd=\"logical\",\n dstart=\"numeric\",\n dend=\"numeric\",\n d=\"numeric\",\n eps=\"complex\"\n ),\n prototype(fitd=FALSE,dstart=0.0,dend=0.0,d=0.0,eps=0+0i)\n)\n\nsetGeneric(\"d<-\",function(x,value) standardGeneric(\"d<-\"))\nsetReplaceMethod(\"d\",\"IsoLayer\", function(x,value) {x@d <- value; validObject(x); x})\n\n#' An S4 class to represent anisotropic layer.\n#'\n#' @slot d Thickness of the layer in m\n#' @slot epsx The relative permittivity of the material in the x direction\n#' @slot epsy The relative permittivity of the material in the y direction\n#' @slot epsz The relative permittivity of the material in the z direction\n#' @slot theta angle of the molecular axis in relation to the prism axis - see diagram\n#' @slot phi angle of the molecular axis in relation to the prism axis - see diagram\n#' @slot S the order parameter for the layer\n#' \n\nAnisoLayer <- setClass(\"AnisoLayer\", \n representation(\n d=\"numeric\",\n epsx=\"numeric\",\n epsy=\"numeric\",\n epsz=\"numeric\",\n theta=\"numeric\",\n phi=\"numeric\",\n S=\"numeric\"\n )\n)\n\nsetMethod(\"show\", signature(object=\"IsoLayer\"),function(object){\n cat(\" Isotropic Layer data \\n\")\n})\n\nsetMethod(\"show\", signature(object=\"AnisoLayer\"),function(object){\n cat(\" Anisotropic Layer data \\n\")\n cat(\" Layer Thickness:\", object@d , \"\\n\")\n cat(\" eps along x:\", object@epsx , \"\\n\")\n cat(\" eps along y:\", object@epsy , \"\\n\")\n cat(\" eps along z:\", object@epsz , \"\\n\")\n cat(\" Theta angle:\", object@theta, \"\\n\")\n cat(\" Phi angle:\", object@phi , \"\\n\")\n cat(\" Order Parameter S:\", object@S , \"\\n\")\n})\n", "meta": {"hexsha": "f5ce08a3ab48e1262892b083f0a3439d0dcfe2e9", "size": 2713, "ext": "r", "lang": "R", "max_stars_repo_path": "R/layer_class.r", "max_stars_repo_name": "jonbramble/RFBFoptics", "max_stars_repo_head_hexsha": "db7768b0e2f0e7defe3cf572292bd4f0c16c08ce", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/layer_class.r", "max_issues_repo_name": "jonbramble/RFBFoptics", "max_issues_repo_head_hexsha": "db7768b0e2f0e7defe3cf572292bd4f0c16c08ce", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/layer_class.r", "max_forks_repo_name": "jonbramble/RFBFoptics", "max_forks_repo_head_hexsha": "db7768b0e2f0e7defe3cf572292bd4f0c16c08ce", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6973684211, "max_line_length": 86, "alphanum_fraction": 0.6343531146, "num_tokens": 666, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41489886026626094, "lm_q2_score": 0.030214589468852714, "lm_q1q2_score": 0.012535998734039962}}
{"text": "#functions used to calculate diversity statistics within populations\n# make sure the output file is already written in format (header):\n# population1 population2 statistic chromosome start end number_sites number_variable_sites calculated_stat\n# e.g., write(c(\"pop1\", \"pop2\", \"stat\", \"chr\", \"start\", \"end\", \"number_sites\", \"number_variable_sites\", \"calculated_stat\"), ncolumns=9, file=outname, sep=\"\\t\")\n\n# input is simplified vcf (entire vcf) and 3-column popmap (ordered the same as the vcf) and output file name\n# and the input simple vcf file name that contains the chr and start and end information\n# only calculate for invariant sites and biallelic snps\nheterozygosity <- function(xxx, popmap, outname, filename) {\n options(scipen=999)\n xxx <- xxx[nchar(xxx[,2]) == 1 & nchar(xxx[,3]) == 1, ]\n for(a in 1:nrow(popmap)) {\n output_rep <- c(popmap[a,1], \"none\", \"heterozygosity\", strsplit(filename, \":\")[[1]][1], \n as.numeric(strsplit(strsplit(filename, \":\")[[1]][2], \"-\")[[1]][1]),\n as.numeric(strsplit(strsplit(filename, \"-\")[[1]][2], \".simple\")[[1]][1]))\n # select this individual\n a_rep <- xxx[,a+3]\n # remove missing genotypes\n a_rep <- a_rep[a_rep != \"./.\"]\n # count number of sites\n a_total <- length(a_rep)\n # remove phasing information\n a_rep <- gsub(\"\\\\|\", \"/\", a_rep)\n # find number of heterozygous sites\n a_het <- length(a_rep[a_rep == \"0/1\"])\n # add to output\n output_rep <- c(output_rep, a_total, a_het, a_het/a_total)\n write(output_rep, file=outname, append=T, ncolumns=9, sep=\"\\t\")\n }\n}\n\n# helper function to identify transition variants from biallelic alleles in rows (used in titv function)\ntransitions <- function(x1) {\n\ta_rep <- sort(x1)\n\tif(a_rep[1] == \"A\" & a_rep[2] == \"G\") {\n\t\treturn(1)\n\t} else if(a_rep[1] == \"C\" & a_rep[2] == \"T\") {\n\t\treturn(1)\n\t} else {\n\t\treturn(0)\n\t}\n}\n\n\n\n# input is simplified vcf (entire vcf) and other files as in heterozygosity calcs, to calculate transition transversion ratio\ntitv <- function(xxx, popmap, outname, filename) {\n options(scipen=999)\n xxx <- xxx[nchar(xxx[,2]) == 1 & nchar(xxx[,3]) == 1, ]\n # filter to variable sites\n xxx <- xxx[xxx[,3] != \".\", ]\n \n # sites used for titv \n # total sites used same as het sites\n a_total <- nrow(xxx)\n a_het <- a_total\n \n # find number of transitions using helper function\n total_transitions <- sum(apply(xxx[,2:3], 1, transitions))\n # calculate transition / transversion ratio\n titv_ratio <- total_transitions / (a_total - total_transitions)\n \n # output info\n output_rep <- c(\"all_inds\", \"none\", \"titv\", strsplit(filename, \":\")[[1]][1], \n as.numeric(strsplit(strsplit(filename, \":\")[[1]][2], \"-\")[[1]][1]),\n as.numeric(strsplit(strsplit(filename, \"-\")[[1]][2], \".simple\")[[1]][1]))\n \n output_rep <- output_rep <- c(output_rep, a_total, a_het, titv_ratio)\n write(output_rep, file=outname, append=T, ncolumns=9, sep=\"\\t\")\n \n}\n\n\n\n\n# input is one simplified vcfs, \n# the popmap, and output file name\n# and the input simple vcf file name that contains the chr and start and end information\n# only for invariant sites and biallelic snps\n# lastly, input number of sites needed to write file\n\ncreate_fasta_from_vcf <- function(xxx, popmap, outname, filename, num_sites_needed) {\n options(scipen=999)\n # remove sites that are not either invariant or bi-allelic SNPs\n xxx <- xxx[nchar(xxx[,2]) == 1 & nchar(xxx[,3]) == 1, ]\n \n # keep going only if enough sites\n if(num_sites_needed <= nrow(xxx)) {\n # remove phasing information\n for(a in 4:ncol(xxx)) {\n xxx[,a] <- gsub(\"\\\\|\", \"/\", xxx[,a])\n }\n \n # define names of individuals in output fasta\n output_names_fasta <- paste(\">\", popmap[,1], sep=\"\")\n \n # subset the genotypes from the allele info\n allele_info <- xxx[,2:3]\n genotypes <- xxx[,4:ncol(xxx)]\n \n # convert all numbers in genotypes to actual bases and ambiguities\n for(a in 1:nrow(genotypes)) {\n if(allele_info[a,2] == \".\") { # if non-polymorphic\n genotypes[a,] <- gsub(\"0/0\", allele_info[a,1], genotypes[a,])\n genotypes[a,] <- gsub(\"\\\\./\\\\.\", \"?\", genotypes[a,])\n } else { # if polymorphic\n both_genotypes <- sort(as.character(allele_info[a,]))\n if(both_genotypes[1] == \"A\" & both_genotypes[2] == \"C\") { het = \"M\" }\n if(both_genotypes[1] == \"A\" & both_genotypes[2] == \"G\") { het = \"R\" }\n if(both_genotypes[1] == \"A\" & both_genotypes[2] == \"T\") { het = \"W\" }\n if(both_genotypes[1] == \"C\" & both_genotypes[2] == \"G\") { het = \"S\" }\n if(both_genotypes[1] == \"C\" & both_genotypes[2] == \"T\") { het = \"Y\" }\n if(both_genotypes[1] == \"G\" & both_genotypes[2] == \"T\") { het = \"K\" }\n genotypes[a,] <- gsub(\"0/0\", allele_info[a,1], genotypes[a,])\n genotypes[a,] <- gsub(\"\\\\./\\\\.\", \"?\", genotypes[a,])\n genotypes[a,] <- gsub(\"1/1\", allele_info[a,2], genotypes[a,])\n genotypes[a,] <- gsub(\"0/1\", het, genotypes[a,])\n }\n }\n \n # write output\n for(a in 1:ncol(genotypes)) {\n if(a == 1) {\n write(output_names_fasta[a], file=outname, ncolumns=1)\n write(paste(genotypes[,a], collapse=\"\"), file=outname, ncolumns=1, append=T)\n } else {\n write(output_names_fasta[a], file=outname, ncolumns=1, append=T)\n write(paste(genotypes[,a], collapse=\"\"), file=outname, ncolumns=1, append=T)\n }\n }\n }\n}\n", "meta": {"hexsha": "297de600e9475a51ffc220f7a947a72653c8107d", "size": 6572, "ext": "r", "lang": "R", "max_stars_repo_path": "03_reseq_camp/window_stat_calculations.r", "max_stars_repo_name": "jdmanthey/camponotus_genomes1", "max_stars_repo_head_hexsha": "5caf349e3f84548e24da239d6d3fabca4171dcbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "03_reseq_camp/window_stat_calculations.r", "max_issues_repo_name": "jdmanthey/camponotus_genomes1", "max_issues_repo_head_hexsha": "5caf349e3f84548e24da239d6d3fabca4171dcbe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "03_reseq_camp/window_stat_calculations.r", "max_forks_repo_name": "jdmanthey/camponotus_genomes1", "max_forks_repo_head_hexsha": "5caf349e3f84548e24da239d6d3fabca4171dcbe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-01-29T16:29:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-29T16:29:49.000Z", "avg_line_length": 49.4135338346, "max_line_length": 159, "alphanum_fraction": 0.5091296409, "num_tokens": 1670, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4455295497638851, "lm_q2_score": 0.027585283425931213, "lm_q1q2_score": 0.012290058904864294}}
{"text": "\r\ncalc_fct <- function(res, ncores = NULL, SourceSplit = NULL, \r\n dxy = 1, # in m\r\n tolerance = 1e-5, # digits for rounding\r\n checkArea = TRUE\r\n ){\r\n \r\n if(!is.null(ncores)) setDTthreads(ncores)\r\n\r\n # convert old versions \r\n sres <- as.character(substitute(res))\r\n res <- copy(res)\r\n if(is.null(attr(res, \"Version\"))){\r\n warning(paste0(\"Object '\", sres[min(length(sres), 2)], \"' has not yet been converted to version 4.2+\"))\r\n convert(res)\r\n }\r\n\r\n for(g in 1:20) gc()\r\n\r\n # get relevant sources\r\n uSous <- res[, unique(Source)]\r\n Sources <- attr(res, \"ModelInput\")$Sources\r\n SourcesList <- lapply(uSous, function(x){\r\n out <- Sources[Sources[,1] %in% x,]\r\n attr(out, \"area\") <- getArea(out)\r\n out\r\n })\r\n names(SourcesList) <- uSous\r\n\r\n # get relevant sensors\r\n uSens <- res[, unique(Sensor)]\r\n Sensors <- attr(res, \"ModelInput\")$Sensors\r\n C_sens_all <- procSensors(Sensors)$Calc.Sensors\r\n C_sens <- C_sens_all[C_sens_all[, \"Sensor Name\"] %in% uSens, ]\r\n\r\n # get relevant catalogs\r\n uRn <- res[, unique(rn)]\r\n All_cats <- Catalogs(res)[Sensor %in% uSens & rn %in% uRn]\r\n # prepare catalogs\r\n Cat_Path <- CatPath(res)\r\n All_cats[, CatNum := as.integer(as.factor(Cat.Name))]\r\n C_cats <- All_cats[,{\r\n .(\r\n CatNameList = list(setNames(Cat.Name, PointSensor)),\r\n CatNums = paste(sort(unique(CatNum)), collapse = \",\"),\r\n Subset_seed = seed[1]\r\n )\r\n }, by = .(rn, Sensor)]\r\n\r\n # split sources into sub areas\r\n if(is.null(SourceSplit)) {\r\n # split\r\n SourceSplit <- splitSource(Sources[Sources[, 1] %in% uSous, ],\r\n dxy = dxy, tolerance = tolerance, checkArea = checkArea)\r\n } else {\r\n for(i in seq_along(SourceSplit)){\r\n for(j in seq_along(SourceSplit[[i]])){\r\n # allocate memory\r\n SourceSplit[[i]][[j]]$polygons <- alloc.col(SourceSplit[[i]][[j]]$polygons)\r\n }\r\n }\r\n }\r\n \r\n # copy original data.table & add columns\r\n Calc <- merge(res[,.(\r\n rn,\r\n Sensor,\r\n Source,\r\n # Subset_seed,\r\n WD,\r\n SensorHeight,\r\n Ustar,\r\n L,\r\n Zo,\r\n sUu,\r\n sVu,\r\n bw,\r\n C0,\r\n kv,\r\n A,\r\n alpha,\r\n MaxFetch,\r\n N0,\r\n fct = NA_real_\r\n )], C_cats, by = c(\"rn\", \"Sensor\"))\r\n\r\n if(!is.null(ncores) && ncores > 1){\r\n # showConnections(T)\r\n # getConnection(3)\r\n cl <- parallel::makePSOCKcluster(ncores)\r\n on.exit(parallel::stopCluster(cl))\r\n parallel::clusterEvalQ(cl, {\r\n # library(data.table)\r\n # library(bLSmodelR)\r\n data.table::setDTthreads(1)\r\n })\r\n # call parallel\r\n InList <- lapply(seq_len(nrow(Calc)), function(x, y) y[x, ], y = as.data.frame(Calc))\r\n parallel::clusterExport(cl, c(\"SourceSplit\", \"SourcesList\", \"C_sens\"), envir = environment())\r\n fct <- unlist(parallel::clusterApplyLB(cl, InList, .fct, \r\n C_Path = Cat_Path, parallel = TRUE))\r\n # fct <- unlist(parallel::clusterApplyLB(cl, InList, .fct, \r\n # SouSp = SourceSplit, SouLi = SourcesList, c_sens = C_sens, \r\n # C_Path = Cat_Path))\r\n parallel::stopCluster(cl)\r\n on.exit()\r\n } else {\r\n\r\n for(g in 1:20) gc()\r\n\r\n # loop over rows\r\n fct <- .fct(Calc, SouSp = SourceSplit, SouLi = SourcesList, \r\n c_sens = C_sens, C_Path = Cat_Path)\r\n }\r\n\r\n cbind(res, fct = fct)\r\n}\r\n\r\n# loop over rows\r\n.fct <- function(DT, SouSp = SourceSplit, SouLi = SourcesList, c_sens = C_sens, \r\n C_Path = Cat_Path, parallel = FALSE){\r\n if(parallel){\r\n setDT(DT)\r\n for(i in seq_along(SouSp)){\r\n for(j in seq_along(SouSp[[i]])){\r\n # allocate memory\r\n SouSp[[i]][[j]]$polygons <- alloc.col(SouSp[[i]][[j]]$polygons)\r\n }\r\n }\r\n }\r\n\r\n DT[,{\r\n if(!parallel){\r\n cat(\"*** row\", .BY[[1]], \"\\n\")\r\n }\r\n # get source\r\n iSource <- SouSp[[Source]]\r\n # add inside tag\r\n for(j in seq_along(iSource)){\r\n iSource[[j]]$polygons[, isInside := FALSE]\r\n }\r\n \r\n # list with point sensor names\r\n Point_Sensor <- split(names(CatNameList[[1]]), CatNameList[[1]])\r\n\r\n # loop over Catalogs\r\n for(Cat_Name in unique(CatNameList[[1]])){\r\n # get Catalog\r\n Cat0 <- readCatalog(file.path(C_Path, Cat_Name))\r\n # initialize\r\n initializeCatalog(.SD, Catalog = Cat0)\r\n # get cat N0\r\n Cat_N0 <- attr(Cat0, \"N0\")\r\n # take subset\r\n if (Cat_N0 > N0) {\r\n env <- globalenv()\r\n oseed <- env$.Random.seed\r\n set.seed(Subset_seed, kind = \"L'Ecuyer-CMRG\") \r\n takeSub <- sample.int(Cat_N0, N0)\r\n if (is.null(oseed)) {\r\n rm(list = \".Random.seed\", envir = env)\r\n } else {\r\n assign(\".Random.seed\", value = oseed, envir = env)\r\n }\r\n indexNew <- 1:N0\r\n names(indexNew) <- takeSub\r\n Cat0 <- Cat0[Traj_ID %in% takeSub, ]\r\n Cat0[, Traj_ID := indexNew[as.character(Traj_ID)]]\r\n }\r\n # rotate catalog\r\n rotateCatalog(Cat0, WD)\r\n \r\n # loop over point sensors\r\n for(ps in Point_Sensor[[Cat_Name]]){\r\n cat(\"|\\t\", ps, \"\\n\")\r\n Sens_xy <- as.numeric(c_sens[c_sens[, \"Point Sensor Name\"] %in% ps, \r\n c(\"x-Coord (m)\", \"y-Coord (m)\")])\r\n # copy catalog\r\n Cat <- Cat0[, .(x, y, inside = TRUE, inside0 = FALSE)]\r\n # add Sensor x & y\r\n Cat[, \":=\"(\r\n x = x + Sens_xy[1],\r\n y = y + Sens_xy[2]\r\n )]\r\n # get IDs inside, initial inside has to be TRUE\r\n tagNear(Cat, apply(SouLi[[Source[1]]][,2:3], 2, range))\r\n Cat[, inside0 := inside]\r\n # initialize all_check\r\n all_check <- 0\r\n # loop over sources\r\n for(jSrc in seq_along(iSource)){\r\n # jSrc <- 1\r\n iSource[[jSrc]]$polygons[(!isInside), isInside := {\r\n # copy from original inside\r\n Cat[, inside := inside0]\r\n # tag near\r\n tagNear(Cat[(inside)], .(x, y))\r\n C_sub <- Cat[(inside), .(x, y)]\r\n any(as.logical(.Call(\"pip\", C_sub[, x], C_sub[, y], C_sub[, .N], x, y, length(x), PACKAGE = \"bLSmodelR\")))\r\n }, by = .(tile, polygon)]\r\n all_check <- all_check + iSource[[jSrc]]$polygons[, as.integer(all(isInside))]\r\n }\r\n # stop if all tiles covered\r\n if(all_check == length(iSource)) break\r\n }\r\n # stop if all tiles covered\r\n if(all_check == length(iSource)) break\r\n }\r\n # calculate & return fct\r\n .(\r\n fct = sum(\r\n unlist(\r\n lapply(iSource, function(X) \r\n X$polygons[(isInside), area[1], by = .(tile, polygon)][, sum(V1)]\r\n )\r\n )\r\n ) / attr(SouLi[[Source[1]]], \"area\")\r\n )\r\n }, by = .(.Row__ = seq_len(nrow(DT)))][order(.Row__), fct]\r\n}\r\n\r\n\r\nsplitSource <- function(\r\n SourceIn,\r\n dxy = 1, # in m\r\n tolerance = 1e-5, # digits for rounding\r\n checkArea = TRUE\r\n ) {\r\n digits <- -floor(log10(tolerance))\r\n tol <- 10 ^ -digits\r\n\r\n setNumericRounding(2)\r\n\r\n # get source names\r\n uSourceNames <- unique(SourceIn[, 1])\r\n\r\n out <- lapply(uSourceNames, function(SourceName){\r\n # SourceName <- Source[1,1]\r\n Source <- SourceIn[SourceIn[, 1] %in% SourceName, ]\r\n # start lapply over polyID\r\n lapply(unique(Source[,4]), function(polyID){\r\n SubSource <- Source[Source[,4] %in% polyID,]\r\n xyMeans <- colMeans(SubSource[,2:3])\r\n SubSource[,2:3] <- round(sweep(SubSource[, 2:3], 2, xyMeans), digits - 1)\r\n Poly <- setNames(SubSource[,2:3], c(\"x\", \"y\"))\r\n Poly_extr <- attr(procSources(SubSource), \"SourceList\")[[SourceName]][, 1:2]\r\n Poly_lines <- lapply(seq_len(nrow(Poly_extr) - 1), function(x) {\r\n list(\r\n p1 = as.numeric(Poly_extr[x, ]),\r\n p2 = as.numeric(Poly_extr[x + 1, ])\r\n )\r\n })\r\n # # possibly problems with values close to 0?\r\n # Poly_lines_dir <- lapply(Poly_lines, function(x){\r\n # list(x = sign(x$p2[1] - x$p1[1]), y = sign(x$p2[2] - x$p1[2]))\r\n # })\r\n\r\n ##### Ablauf:\r\n # browser()\r\n # get BB (extended)\r\n BBoxOrig <- list(\r\n x = trunc(range(Poly$x)) + c(-1, 1), \r\n y = trunc(range(Poly$y)) + c(-1, 1)\r\n # x = range(Poly$x), \r\n # y = range(Poly$y)\r\n )\r\n BBdim <- lapply(BBoxOrig, diff)\r\n BBdim_ext <- lapply(BBdim, function(x) ceiling(x / dxy) * dxy)\r\n BBadd <- mapply(\"-\", BBdim_ext, BBdim) / 2\r\n BBox <- mapply(function(x, y) x + c(-1, 1) * y, x = BBoxOrig, y = BBadd, SIMPLIFY = FALSE)\r\n\r\n # liste mit matrix mit dxy -> grid\r\n # x & y vectors\r\n x_seq <- seq(BBox$x[1], BBox$x[2], by = dxy)\r\n y_seq <- seq(BBox$y[1], BBox$y[2], by = dxy)\r\n\r\n # create matrix M\r\n dimx <- length(x_seq) - 1\r\n dimy <- length(y_seq) - 1\r\n M <- matrix(seq_len(dimx * dimy), ncol = dimy, nrow = dimx)\r\n\r\n ####### inside\r\n # create grids Gxy & G\r\n Gxy <- as.matrix(expand.grid(row = seq_along(x_seq), col = seq_along(y_seq), KEEP.OUT.ATTRS = FALSE))\r\n G <- as.matrix(expand.grid(x = x_seq, y = y_seq, KEEP.OUT.ATTRS = FALSE))\r\n\r\n # - check Eckpunkte inside\r\n corner_inside <- as.logical(.Call(\"pip\", G[, 1], G[, 2], nrow(G), Poly[, 1], \r\n Poly[, 2], nrow(Poly), PACKAGE = \"bLSmodelR\"))\r\n corner_inside_index <- which(corner_inside)\r\n Gxy_inside <- Gxy[corner_inside,, drop = FALSE]\r\n\r\n # get tiles in M\r\n M_xy <- apply(Gxy_inside, 1, function(x){\r\n c(x[1] + c(-1, 0), x[2] + c(-1, 0))\r\n })\r\n M_xy[M_xy == 0] <- NA\r\n M_tiles <- apply(M_xy, 2, function(x){\r\n c(\r\n (x[3] - 1) * dimx + x[1],\r\n (x[3] - 1) * dimx + x[2],\r\n (x[4] - 1) * dimx + x[1],\r\n (x[4] - 1) * dimx + x[2]\r\n )\r\n })\r\n # count corners inside per tile\r\n Tiles_count <- table(M_tiles)\r\n\r\n ####### intersecting\r\n # get all points with x_i | y_j intersect mit source_poly\r\n vert <- lapply(x_seq, function(px) list(p1 = c(px, BBox$y[1]), p2 = c(px, BBox$y[2])))\r\n horiz <- lapply(y_seq, function(py) list(p1 = c(BBox$x[1], py), p2 = c(BBox$x[2], py)))\r\n \r\n # check vertical grid\r\n Vcheck <- do.call(\"rbind\", \r\n lapply(seq_along(vert), function(v) do.call(\"rbind\", \r\n lapply(seq_along(Poly_lines), function(i){\r\n cbind(inters(Poly_lines[[i]]$p1, Poly_lines[[i]]$p2, vert[[v]]$p1, vert[[v]]$p2, tol), \r\n Poly_lines_entry = i, Line_row = v, Line_col = NA)\r\n }))))\r\n # check horizontal grid\r\n Hcheck <- do.call(\"rbind\", \r\n lapply(seq_along(horiz), function(v) do.call(\"rbind\",\r\n lapply(seq_along(Poly_lines), function(i){\r\n cbind(inters(Poly_lines[[i]]$p1, Poly_lines[[i]]$p2, horiz[[v]]$p1, horiz[[v]]$p2, tol), \r\n Poly_lines_entry = i, Line_row = NA, Line_col = v)\r\n }))))\r\n\r\n # bind together\r\n All_points <- rbind(\r\n Vcheck[Vcheck[,3] == 1, ], \r\n Hcheck[Hcheck[,3] == 1, ]\r\n )\r\n\r\n # inters(Poly_lines[[1]]$p1, Poly_lines[[1]]$p2, horiz[[18]]$p1, horiz[[18]]$p2, tol)\r\n\r\n # find segment lines by x & y -> row, col\r\n Line_rows <- All_points[,\"Line_row\"]\r\n isna_row <- is.na(Line_rows)\r\n Line_rows[isna_row] <- findInterval(All_points[isna_row, 1], x_seq, rightmost.closed = TRUE)\r\n Line_cols <- All_points[,\"Line_col\"]\r\n isna_col <- is.na(Line_cols)\r\n Line_cols[isna_col] <- findInterval(All_points[isna_col, 2], y_seq, rightmost.closed = TRUE)\r\n\r\n # check exact matching\r\n Outer_rows <- abs(outer(All_points[, 1], x_seq, \"-\")) < tol\r\n Exact_rows <- as.logical(rowSums(Outer_rows) > 0)\r\n Outer_cols <- abs(outer(All_points[, 2], y_seq, \"-\")) < tol\r\n Exact_cols <- as.logical(rowSums(Outer_cols) > 0)\r\n # check for exact matches in foundIntervals\r\n if(any(Exact_rows[isna_row])){\r\n Line_rows[Exact_rows & isna_row] <- apply(Outer_rows[Exact_rows & isna_row, , drop = FALSE], 1, which)\r\n }\r\n if(any(Exact_cols[isna_col])){\r\n Line_cols[Exact_cols & isna_col] <- apply(Outer_cols[Exact_cols & isna_col, , drop = FALSE], 1, which)\r\n }\r\n\r\n # check exact corner\r\n Exact_corners <- Exact_rows & Exact_cols\r\n \r\n # round with digits\r\n All_points[, \"x\"] <- round(All_points[, \"x\"], digits - 1)\r\n All_points[, \"y\"] <- round(All_points[, \"y\"], digits - 1)\r\n\r\n # find Tiles to check: lines -> 2 Tiles\r\n Border_x <- rbind(\r\n data.table(\r\n row = Line_rows[Exact_rows & !Exact_corners],\r\n col = Line_cols[Exact_rows & !Exact_corners],\r\n Px = All_points[Exact_rows & !Exact_corners, 1],\r\n Py = All_points[Exact_rows & !Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_rows & !Exact_corners, 4]\r\n ),\r\n data.table(\r\n row = Line_rows[Exact_rows & !Exact_corners] - 1,\r\n col = Line_cols[Exact_rows & !Exact_corners],\r\n Px = All_points[Exact_rows & !Exact_corners, 1],\r\n Py = All_points[Exact_rows & !Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_rows & !Exact_corners, 4]\r\n ))\r\n Border_y <- rbind(\r\n data.table(\r\n row = Line_rows[Exact_cols & !Exact_corners],\r\n col = Line_cols[Exact_cols & !Exact_corners],\r\n Px = All_points[Exact_cols & !Exact_corners, 1],\r\n Py = All_points[Exact_cols & !Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_cols & !Exact_corners, 4]\r\n ),\r\n data.table(\r\n row = Line_rows[Exact_cols & !Exact_corners],\r\n col = Line_cols[Exact_cols & !Exact_corners] - 1,\r\n Px = All_points[Exact_cols & !Exact_corners, 1],\r\n Py = All_points[Exact_cols & !Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_cols & !Exact_corners, 4]\r\n ))\r\n\r\n # find Tiles to check: corners -> 4 Tiles\r\n Border_xy <- rbind(\r\n data.table(\r\n row = Line_rows[Exact_corners],\r\n col = Line_cols[Exact_corners],\r\n Px = All_points[Exact_corners, 1],\r\n Py = All_points[Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_corners, 4]\r\n ),\r\n data.table(\r\n row = Line_rows[Exact_corners] - 1,\r\n col = Line_cols[Exact_corners],\r\n Px = All_points[Exact_corners, 1],\r\n Py = All_points[Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_corners, 4]\r\n ),\r\n data.table(\r\n row = Line_rows[Exact_corners],\r\n col = Line_cols[Exact_corners] - 1,\r\n Px = All_points[Exact_corners, 1],\r\n Py = All_points[Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_corners, 4]\r\n ),\r\n data.table(\r\n row = Line_rows[Exact_corners] - 1,\r\n col = Line_cols[Exact_corners] - 1,\r\n Px = All_points[Exact_corners, 1],\r\n Py = All_points[Exact_corners, 2],\r\n Poly_lines_entry = All_points[Exact_corners, 4]\r\n )\r\n )\r\n\r\n # gather all tiles\r\n Border_all <- rbind(Border_x,Border_y,Border_xy)\r\n\r\n # remove borders\r\n Border <- Border_all[\r\n row > 0 &\r\n col > 0 &\r\n row <= dimx &\r\n col <= dimy,]\r\n # add Tile number\r\n Border[, tile := (Border$col - 1) * dimx + Border$row]\r\n\r\n # get number of corners inside source\r\n Border[, nc := 0L]\r\n Border[as.character(tile) %chin% names(Tiles_count), nc := {\r\n t_c <- as.character(tile)\r\n Tiles_count[t_c[t_c %in% names(Tiles_count)]]\r\n }]\r\n\r\n\r\n # # -> was falls mehrere polygons? (eher unwahrsch.)\r\n # # einbauen: if(count(Poly_lines_entry) > 2) -> mehrere Polygons\r\n # if(polyID == 2)browser()\r\n\r\n # check & get polygons\r\n All <- Border[, {\r\n\r\n # borders\r\n x_le <- x_seq[row[1]]\r\n x_ri <- x_seq[row[1] + 1]\r\n y_lo <- y_seq[col[1]]\r\n y_hi <- y_seq[col[1] + 1]\r\n\r\n # any points inside?\r\n pts_inside <- suppressWarnings(do.call(rbind, lapply(seq_len(nrow(Poly)), function(i){\r\n if(Poly[i, 1] >= x_le & Poly[i, 1] <= x_ri & \r\n Poly[i, 2] >= y_lo & Poly[i, 2] <= y_hi ){\r\n data.table(Poly[i, ], label = paste0(\"L\",c((i - 2) %% nrow(Poly) + 1, i)))\r\n } else {\r\n NULL\r\n }\r\n })))\r\n\r\n # get corners inside\r\n Corner <- as.data.table(G[corner_inside_index[which(colSums(M_tiles == .BY[[1]]) > 0)], , drop = FALSE])\r\n\r\n # get labels of corners\r\n Corner[, label := {\r\n LR <- c(\"L\", \"R\")[(abs(x - x_ri) < tol) + 1]\r\n BT <- c(\"B\", \"T\")[(abs(y - y_hi) < tol) + 1]\r\n paste0(\"C\", sapply(paste0(LR,BT), switch,\r\n \"LB\" = 1,\r\n \"RB\" = 2,\r\n \"RT\" = 3,\r\n \"LT\" = 4\r\n ))\r\n }]\r\n\r\n # browser()\r\n\r\n # cat(.BY$tile,\"\\n\")\r\n # if(.BY$tile == 14) browser() # -> Corner of Source Area!\r\n # if(.BY$tile == 18) browser() # -> sprintf of 0 results in -0.00 ???!!!\r\n\r\n # check if line corner is exactly on corner (klappt das so, falls beide lines entlang dem edge gehen???)\r\n fmt <- paste0(\"%1.\", digits,\"f\")\r\n fmt_xy <- paste(fmt, fmt, sep = \"::\")\r\n crn_xy <- Corner[, sprintf(fmt_xy, abs(x) * sign(x), abs(y) * sign(y))]\r\n sd_xy <- sprintf(fmt_xy, abs(Px) * sign(Px), abs(Py) * sign(Py))\r\n if(any(sd_dup <- duplicated(sd_xy))){\r\n sd_check <- sd_xy[sd_dup]\r\n sd_remove <- sd_check[sd_check %in% crn_xy]\r\n if(length(sd_remove) > 0){\r\n # remove lines on corner\r\n SD0 <- .SD[!(sd_xy %in% sd_remove), \r\n .(x = Px, y = Py, label = paste0(\"L\", Poly_lines_entry))]\r\n sd_xy <- sd_xy[!(sd_xy %in% sd_remove)]\r\n } else {\r\n # remove corners on line\r\n SD0 <- .SD[, .(x = Px, y = Py, label = paste0(\"L\", Poly_lines_entry))]\r\n }\r\n } else if(.N > 1) {\r\n # remove corners on line\r\n SD0 <- .SD[, .(x = Px, y = Py, label = paste0(\"L\", Poly_lines_entry))]\r\n } else {\r\n # corner lies ecaxtly on line corner\r\n SD0 <- NULL\r\n sd_xy <- NULL\r\n }\r\n\r\n # check if corner falls on line or on pnts inside\r\n if(is.null(pts_inside)){\r\n if(any(crn_xy %in% sd_xy)) {\r\n Corner0 <- Corner[!(crn_xy %in% sd_xy)]\r\n } else {\r\n Corner0 <- Corner\r\n }\r\n } else {\r\n inside_xy <- pts_inside[, sprintf(fmt_xy, abs(x) * sign(x), abs(y) * sign(y))]\r\n if(any(crn_xy %in% sd_xy, crn_xy %in% inside_xy)) {\r\n Corner0 <- Corner[!(crn_xy %in% sd_xy) & !(crn_xy %in% inside_xy)]\r\n } else {\r\n Corner0 <- Corner\r\n } \r\n }\r\n\r\n # rbind (unique nicht mehr noetig?)\r\n DT <- unique(rbind(\r\n Corner0,\r\n # Corner,\r\n # if(!is.null(SD0)) SD0[.N:1, ],\r\n SD0,\r\n pts_inside\r\n ))\r\n\r\n # plot(c(x_le, x_ri), c(y_lo, y_hi), type = \"n\")\r\n # rect(x_le, y_lo, x_ri, y_hi, lwd = 2, border = \"darkgrey\")\r\n # if(!is.null(pts_inside)) pts_inside[, points(x, y, col = \"darkgreen\")]\r\n # Corner[, points(x, y, pch = 20, cex = 1.5)]\r\n # PLE <- unique(Poly_lines_entry)\r\n # for(ple in seq_along(PLE)){\r\n # points(Px[Poly_lines_entry == PLE[ple]], Py[Poly_lines_entry == PLE[ple]], type = \"b\", col = \"red\")\r\n # }\r\n\r\n # if(.BY$tile == 9721) browser()\r\n # if(polyID == 2 && .BY$tile == 187) browser()\r\n\r\n # go around corners\r\n if(DT[, .N > 0]){\r\n DT[, \":=\"(\r\n included = FALSE,\r\n rn = 1:.N,\r\n polygon = NA_character_,\r\n polygon_order = NA_integer_,\r\n edge = checkEdge(x, y, y_lo, x_ri, y_hi, x_le, tol),\r\n corners = nc[1]\r\n ) ]\r\n poly_order <- poly_number <- 1L\r\n # get first point\r\n pnt <- DT[1,]\r\n # set polygon values in DT\r\n DT[1, \":=\"(\r\n included = TRUE,\r\n polygon = letters[poly_number],\r\n polygon_order = poly_order\r\n )]\r\n while(DT[,any(!included)]){\r\n # get point label\r\n pnt_lbl <- pnt[, label]\r\n # check if corner or line\r\n if(grepl(\"C\", pnt_lbl)){\r\n # corner\r\n if(pnt_lbl %in% c(\"C4\", \"C2\")){\r\n # check matching x & best y\r\n pnt <- DT[!included & abs(x - pnt[, x]) < tol][\r\n which.min(abs(y - pnt[, y]))]\r\n } else {\r\n # check matching y & best x\r\n pnt <- DT[!included & abs(y - pnt[, y]) < tol][\r\n which.min(abs(x - pnt[, x]))]\r\n }\r\n } else {\r\n # line\r\n if(DT[!(included), pnt_lbl %chin% label]){\r\n # take next point with label\r\n pnt <- DT[!included & label %in% pnt_lbl]\r\n } else {\r\n # search for next point on corresponding edge\r\n if(pnt[, edge == 5]){\r\n # point inside -> check for point repetition\r\n pnt <- DT[!included & abs(x - pnt[,x]) < tol & abs(y - pnt[,y]) < tol]\r\n } else {\r\n # check side for next point\r\n Side <- pnt[, edge]\r\n if(Side %in% c(2, 4)){\r\n # check matching x & best y\r\n pnt <- DT[!included & abs(x - pnt[, x]) < tol][\r\n which.min(abs(y - pnt[, y]))]\r\n } else {\r\n # check matching y & best x\r\n pnt <- DT[!included & abs(y - pnt[, y]) < tol][\r\n which.min(abs(x - pnt[, x]))]\r\n }\r\n }\r\n }\r\n }\r\n if(nrow(pnt)){\r\n # update poly_order\r\n poly_order <- poly_order + 1\r\n # set polygon values in DT\r\n DT[rn == pnt[, rn], \":=\"(\r\n included = TRUE,\r\n polygon = letters[poly_number],\r\n polygon_order = poly_order\r\n )]\r\n } else {\r\n # update poly_* for next polygon\r\n poly_order <- 1\r\n poly_number <- poly_number + 1\r\n pnt <- DT[!(included)][1,] \r\n DT[rn == pnt[, rn], \":=\"(\r\n included = TRUE,\r\n polygon = letters[poly_number],\r\n polygon_order = poly_order\r\n )]\r\n }\r\n }\r\n\r\n\r\n # DT[order(polygon_order)][,{\r\n # polygon(x, y, \r\n # col = c(\"#D63636\", \"#2E64EE\", \"green\", \"orange\", \"purple\")[.BY$corners + 1])\r\n # }, by = .(corners, polygon)]\r\n\r\n # DT[order(polygon_order), plot(x, y, type = \"b\")]\r\n DT[order(polygon_order), ][, area := {\r\n ind <- c(.N, seq(.N - 1))\r\n abs(sum(x * y[ind] - x[ind] * y)) / 2 \r\n }, by = polygon]\r\n } else {\r\n NULL\r\n }\r\n }, by = tile]\r\n\r\n # correct for xyMeans\r\n x_seq <- x_seq + xyMeans[1]\r\n y_seq <- y_seq + xyMeans[2]\r\n Poly$x <- Poly$x + xyMeans[1]\r\n Poly$y <- Poly$y + xyMeans[2]\r\n\r\n # output\r\n out <- list(\r\n x_seq = x_seq,\r\n y_seq = y_seq,\r\n polygons = rbind(\r\n All[, .(tile, polygon,\r\n x = x + xyMeans[1], \r\n y = y + xyMeans[2], \r\n corners, area)],\r\n data.table(tile = as.integer(names(Tiles_count[Tiles_count == 4])) %w/o% All[,unique(tile)])[, {\r\n col <- ceiling(tile / dimx)\r\n row <- tile - (col - 1) * dimx\r\n .(\r\n polygon = \"a\",\r\n x = x_seq[row + c(0, 1, 1, 0)],\r\n y = y_seq[col + c(0, 0, 1, 1)],\r\n corners = -1L,\r\n area = dxy * dxy\r\n )}, by = tile]),\r\n dxy = dxy,\r\n tolerance = tolerance,\r\n digits = digits,\r\n tol = tol,\r\n SourcePoly = Poly\r\n )\r\n\r\n if(checkArea && abs(getArea(SubSource) - out$polygons[,area[1], by = .(tile, polygon)][,sum(V1)]) > sqrt(tol)){\r\n x11()\r\n # plotM(out)\r\n plot(Poly$x[c(1:nrow(Poly),1)], Poly$y[c(1:nrow(Poly),1)], type = \"l\", lwd = 2)\r\n # ,xlim = c(-1, 1) + 711620, ylim = c(-1, 1) + 260850)\r\n plotPolys(out)\r\n stop(\"polygons area and total area differ by more than sqrt(tol)! try to set a better dxy..\")\r\n } else {\r\n out\r\n }\r\n\r\n })\r\n # end lapply over polygon ID\r\n })\r\n # end lapply over sources\r\n setNames(out, uSourceNames)\r\n}\r\n\r\ninters <- function(l1_p1, l1_p2, l2_p1, l2_p2, tol = 1e-5){\r\n # rewrite as vectors\r\n # -> https://demonstrations.wolfram.com/IntersectionOfTwoLinesUsingVectors/\r\n # browser()\r\n VZ <- l1_p2 - l1_p1\r\n UW <- l2_p2 - l2_p1\r\n mvz <- sqrt(sum(VZ ^ 2))\r\n muw <- sqrt(sum(UW ^ 2))\r\n beta <- acos(sum(VZ * UW) / (mvz * muw))\r\n UV <- l2_p1 - l1_p1\r\n muv <- sqrt(sum(UV ^ 2))\r\n alpha <- acos(sum(UV * UW) / (muv * muw))\r\n\r\n out <- l1_p1 + muv * sin(alpha) / sin(beta) * VZ / mvz\r\n \r\n return(cbind(\r\n x = out[1],\r\n y = out[2],\r\n inside =\r\n (\r\n (\r\n (out[1] - l1_p1[1]) >= -tol &&\r\n (out[1] - l1_p2[1]) <= tol\r\n ) || (\r\n (out[1] - l1_p1[1]) <= tol &&\r\n (out[1] - l1_p2[1]) >= -tol\r\n )\r\n ) && (\r\n (\r\n (out[1] - l2_p1[1]) >= -tol &&\r\n (out[1] - l2_p2[1]) <= tol\r\n ) || (\r\n (out[1] - l2_p1[1]) <= tol &&\r\n (out[1] - l2_p2[1]) >= -tol\r\n )\r\n ) && (\r\n (\r\n (out[2] - l1_p1[2]) >= -tol &&\r\n (out[2] - l1_p2[2]) <= tol\r\n ) || (\r\n (out[2] - l1_p1[2]) <= tol &&\r\n (out[2] - l1_p2[2]) >= -tol\r\n )\r\n ) && (\r\n (\r\n (out[2] - l2_p1[2]) >= -tol &&\r\n (out[2] - l2_p2[2]) <= tol\r\n ) || (\r\n (out[2] - l2_p1[2]) <= tol &&\r\n (out[2] - l2_p2[2]) >= -tol\r\n )\r\n ) \r\n ))\r\n}\r\n\r\ncheckEdge <- function(x, y, y_lo, x_ri, y_hi, x_le, tol = 1e-5){\r\n out <- rep(1, length(x))\r\n out[abs(x - x_le) < tol] <- out[abs(x - x_le) < tol] * 11\r\n out[abs(y - y_hi) < tol] <- out[abs(y - y_hi) < tol] * 7\r\n out[abs(x - x_ri) < tol] <- out[abs(x - x_ri) < tol] * 5\r\n out[abs(y - y_lo) < tol] <- out[abs(y - y_lo) < tol] * 3\r\n sapply(as.character(out), switch,\r\n \"3\" = 1, \"33\" = 1,\r\n \"5\" = 2, \"15\" = 2,\r\n \"7\" = 3, \"35\" = 3,\r\n \"11\" = 4, \"77\" = 4, 5)\r\n}\r\n\r\n# plotM <- function(X, xlim = NULL, ylim = NULL, cex.text = 0.35){\r\n# plot(range(X$x_seq), range(X$y_seq),type = \"n\", xlim = xlim, ylim = ylim)\r\n# lines(X$SourcePoly$x[c(1:nrow(X$SourcePoly),1)],X$SourcePoly$y[c(1:nrow(X$SourcePoly),1)], lwd = 2)\r\n# abline(h = X$y_seq)\r\n# abline(h = X$y_seq[c(1, length(X$y_seq))], lwd = 2)\r\n# abline(v = X$x_seq)\r\n# abline(v = X$x_seq[c(1, length(X$x_seq))], lwd = 2)\r\n# X$polygons[,{\r\n# polygon(x, y, \r\n# col = c(\"lightgrey\", \"#D63636\", \"#2E64EE\", \"green\", \"orange\", \"purple\")[.BY$corners + 2])\r\n# }, by = .(corners, tile, polygon)]\r\n# Tgrid <- expand.grid(X$x_seq[-1] - X$dxy/2, X$y_seq[-1] - X$dxy/2)\r\n# text(Tgrid[,1], Tgrid[,2], 1:nrow(Tgrid), cex = cex.text)\r\n# }\r\n\r\nplotPolys <- function(X, xlim = NULL, ylim = NULL, cex.text = 0.35,\r\n col = c(\"lightgrey\", \"#D63636\", \"#2E64EE\", \"green\", \"orange\", \"purple\"),\r\n tileNumbers = TRUE){\r\n if(length(col) == 1) col <- rep(col , 6)\r\n X$polygons[,{\r\n polygon(x, y, \r\n col = col[.BY$corners + 2])\r\n }, by = .(corners, tile, polygon)]\r\n if(tileNumbers){\r\n Tgrid <- expand.grid(X$x_seq[-1] - X$dxy/2, X$y_seq[-1] - X$dxy/2)\r\n text(Tgrid[,1], Tgrid[,2], 1:nrow(Tgrid), cex = cex.text)\r\n }\r\n}\r\n", "meta": {"hexsha": "36f34a6740078c2f3339088c10a8d7a8d1018ca3", "size": 27801, "ext": "r", "lang": "R", "max_stars_repo_path": "R/fraction_function.r", "max_stars_repo_name": "kwheelan/bLSmodelR", "max_stars_repo_head_hexsha": "a89d710c03786a12716ef40acfaf6071230b38d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_repo_stars_event_min_datetime": "2021-03-04T17:32:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-31T10:56:20.000Z", "max_issues_repo_path": "R/fraction_function.r", "max_issues_repo_name": "kwheelan/bLSmodelR", "max_issues_repo_head_hexsha": "a89d710c03786a12716ef40acfaf6071230b38d7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2021-09-10T07:34:26.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-22T12:41:34.000Z", "max_forks_repo_path": "R/fraction_function.r", "max_forks_repo_name": "kwheelan/bLSmodelR", "max_forks_repo_head_hexsha": "a89d710c03786a12716ef40acfaf6071230b38d7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2, "max_forks_repo_forks_event_min_datetime": "2021-11-12T10:56:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-21T23:22:52.000Z", "avg_line_length": 34.7947434293, "max_line_length": 119, "alphanum_fraction": 0.4932916082, "num_tokens": 8353, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.44552952031526044, "lm_q2_score": 0.02716923340648548, "lm_q1q2_score": 0.012104695526924825}}
{"text": "co2_data <- read.csv(\"https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv\")\nlibrary(shiny)\nlibrary(dplyr)\nlibrary(plotly)\nui <- fluidPage(\n tabsetPanel(\n tabPanel(\"Introduction\",\n h2(\"Overview\"),\n br(),\n p(\"This is a paragraph. In this assignment I have chosen to look at the change in CO2 over time in comparison to country. I chose to use CO2 in millions of tons and CO2 per capita based on the country. \n The line graph shows a trend from when the data was first collected until 2020. The dataset consists of many features regarding the different ways CO2 is encountered in our world. The data is collected\n and maintined by Our World in Data, which is a publication that is tasked with finding the largest global problems\n (in this case CO2 emission) and how pwerful changes can reshape the world. As per the About page on their website, Our World In Data \n uses both a small team and the works and research from a community of scholars around the world.\n They do this so that they can get multiple persepctives and post the best available research and data in an understandable way.\n Further, the publication collects the data so that there is available information about CO2 emissions for the public to educate themselves with. \n They design their work in an effort to create an impact that goes beyond what they themselves as a company can fulfill directly.Because the dataset is so large and contains\n data from countries all over the world, one limitation could be that the data is skewed in countries that are not super open about their CO2 emissions. \n In addition, another problem with the data could be that it has periods of time where there is no data present, making a variable's trend seem steeper in either way.\"),\n br(),\n p(\"Some relevant values in the data are: 2005 is the largest amount of CO2 emitted by the United states in history at a staggering 6,134 million tons. This is in comparison\n to when it was at its lowest in 1800 at .253 million tons. Over the past 10 years, CO2 emissions in America has dropped by 853 million tons. Currently, the United States\n is putting out about 4,712 million tons of CO2 (in 2020). Lastly, since the start of when the data was recorded for America, the median amount is 2,356.51 million tons.\"),\n \n ),\n \n tabPanel(\"Visualization\",\n sidebarLayout(\n sidebarPanel(\n selectInput(\"Variable\", \"Select the Variable\", c(\"co2\", \"co2_per_capita\")),\n selectInput(\"Country\", \"Select Country\", sort(unique(co2_data$country)))\n \n ),\n mainPanel(\n plotlyOutput(\"Plot\")\n )\n )),\n tabPanel(\"Value Sensitive Design\",\n h2(\"Value Sensitive Design\"),\n br(),\n p(\"Envision your system in use by a single stakeholder. Now imagine 100 such individuals interacting with the system. \n Then 1,000 individuals. Then 100,00. What new interactions might emerge from widespread use? Find three synergistic benefits of widespread \n use and three potential breakdowns.\"),\n br(),\n p(\"Three benefits of widespread use can include the following: More people are educated on why a certain topic it matters.\n Another is that, as I stated before, people can act upon something that is widespread that has come across their path in ways that others\n before could not have. An example of this is that an issue like this comes to the attention of a large company and they get involved to \n improve the sitaution. Lastly, this eliminates the questions about certain phenomenons such as CO2 emissions by widespreading the data\n thereby making it a fact knwon by all.\"),\n br(),\n p(\"Three potential breakdowns of widespread use are as follows: Creation of tensions between those who collect data such as researchers and companies due \n to different findings. In addition, widespread use of real-world data can also get into the hands of those who intend to intentionally misuse it to \n push an agenda. This was seen a lot during the COVID-19 pandemic. Lastly, when data is released widespread it is the raw form of the data, so it\n is hard for an everyday person to interpret the data unles they know how to.\"),\n \n ))\n \n )\n \nserver <- function(input, output) {\n output$Plot <- renderPlotly({\n ylab <- setNames(c(\"CO2 in Millions of Tons\", \"CO2 in Tons Per Capita\"),\n c(\"co2\", \"co2_per_capita\"))\n df <- co2_data %>%\n filter(country == input$Country)\n plot_ly() %>%\n add_trace(\n type = \"scatter\", mode = \"markers+lines+text\", \n x = df$year, y = df[,input$Variable]\n \n ) %>%\n layout(title = \"CO2 Change Over Time\", xaxis = list(title = \"Year\"), yaxis = list(title = ylab[input$Variable]))\n })\n \n}\nshinyApp(ui, server)\n", "meta": {"hexsha": "fda4c7c9d0e001ec550968bbd297ae092a590053", "size": 5256, "ext": "r", "lang": "R", "max_stars_repo_path": "app_server.r", "max_stars_repo_name": "info-201b-wi22/a4-climate-change-wsdmdsw1-1764374", "max_stars_repo_head_hexsha": "6695be91863c0772386aa86b0a4039bfbedb5ad8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "app_server.r", "max_issues_repo_name": "info-201b-wi22/a4-climate-change-wsdmdsw1-1764374", "max_issues_repo_head_hexsha": "6695be91863c0772386aa86b0a4039bfbedb5ad8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "app_server.r", "max_forks_repo_name": "info-201b-wi22/a4-climate-change-wsdmdsw1-1764374", "max_forks_repo_head_hexsha": "6695be91863c0772386aa86b0a4039bfbedb5ad8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 68.2597402597, "max_line_length": 216, "alphanum_fraction": 0.6586757991, "num_tokens": 1103, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2689414330889797, "lm_q2_score": 0.044680867569444296, "lm_q1q2_score": 0.012016536555785266}}
{"text": "# The MIT License (MIT)\n# Copyright (c) 2017 Louise AC Millard, MRC Integrative Epidemiology Unit, University of Bristol\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without\n# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\n# the Software, and to permit persons to whom the Software is furnished to do so, subject to the following\n# conditions:\n#\n# The above copyright notice and this permission notice shall be included in all copies or substantial portions\n# of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\n# TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF\n# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n\n\n# Performs ordered logistic regression test and saves results in ordered logistic results file\ntestCategoricalOrdered <- function(varName, currentVar, varType, thisdata, orderStr=\"\") {\n\n\n\tpheno = thisdata[,phenoStartIdx:ncol(thisdata)]\n\tgeno = thisdata[,\"geno\"]\n\n\tcat(\"CAT-ORD || \");\n\tincrementCounter(\"ordCat\")\n\n\tdoCatOrdAssertions(pheno)\n\n\tuniqVar = unique(na.omit(pheno));\n\n\t# log the ordering of categories used\n\torderStr = setOrderString(orderStr, uniqVar);\n\tcat(\"order: \", orderStr, \" || \", sep=\"\");\n\n\t# check sample size\n\tnumNotNA = length(which(!is.na(pheno)))\n\tif (numNotNA<500) {\n\t\tcat(\"CATORD-SKIP-500 (\", numNotNA, \") || \",sep=\"\");\n\t\tincrementCounter(\"ordCat.500\")\n\t}\n\telse {\n\t\t# test this cat ordered variable with ordered logistic regression\t\n\n\t\tphenoFactor = factor(pheno)\n\n\t\tcat(\"num categories: \", length(unique(na.omit(phenoFactor))), \" || \", sep=\"\");\n\n\t\tif (opt$save == TRUE) {\n\t\t\t# add pheno to dataframe\n\t\t\t#storeNewVar(thisdata[,\"userID\"], phenoFactor, varName, 'catOrd')\n\t\t\tstoreNewVar(thisdata[,\"userID\"], phenoFactor, currentVar, 'catOrd')\n\t\t\tcat(\"SUCCESS results-ordered-logistic\");\n\t\t\tincrementCounter(\"success.ordCat\")\n\t\t}\n\n\t\t# ordinal logistic regression\n\t\tsink()\n\t\tsink(modelFitLogFile, append=TRUE)\n\t\tprint(\"--------------\")\n\t\tprint(varName)\n\n\t\trequire(MASS)\n\t\trequire(lmtest)\n\n\t\t### BEGIN TRYCATCH\n\t\ttryCatch({\n\t\t\tconfounders=thisdata[,3:numPreceedingCols, drop = FALSE]\n\n\t\t\tif (opt$standardise==TRUE) {\n\t\t\t\tgeno = scale(geno)\n\t\t\t}\n\n\t\t\tfit <- polr(phenoFactor ~ geno + ., data=confounders, Hess=TRUE)\n\n\t\t\tctable <- coef(summary(fit))\n\t\t\tsink()\n\t\t\tsink(resLogFile, append=TRUE)\n\n\t\t\tct = coeftest(fit)\n\t\t\tpvalue = ct[\"geno\",\"Pr(>|t|)\"]\n\t\t\tbeta = ctable[\"geno\", \"Value\"];\n\n\t\t\tif (opt$confidenceintervals == TRUE) {\n\t\t\t\tse = ctable[\"geno\", \"Std. Error\"]\n\t\t\t\tlower = beta - 1.96*se;\n\t\t\t\tupper = beta + 1.96*se;\n\t\t\t}\n\t\t\telse {\n\t\t\t\tlower = NA\n\t\t\t\tupper = NA\n\t\t\t}\n\n\t\t\twrite(paste(paste0(\"\\\"\", varName, \"\\\"\"), paste0(\"\\\"\", currentVar, \"\\\"\"), varType, numNotNA, beta, lower, upper, pvalue, sep=\",\"), file=paste(opt$resDir,\"results-ordered-logistic-\",opt$varTypeArg,\".txt\",sep=\"\"), append=\"TRUE\");\n\t\t\tcat(\"SUCCESS results-ordered-logistic\");\n\t\t\tincrementCounter(\"success.ordCat\")\n\n\t\t\tisExposure = getIsExposure(varName)\n\t\t\tif (isExposure == TRUE) {\n\t\t\t\tincrementCounter(\"success.exposure.ordCat\")\n\t\t\t}\n\n\t\t\t### END TRYCATCH\n\t\t}, error = function(e) {\n\t\t\tsink()\n\t\t\tsink(resLogFile, append=TRUE)\n\t\t\tcat(paste(\"ERROR:\", varName,gsub(\"[\\r\\n]\", \"\", e), sep=\" \"))\n\t\t\tincrementCounter(\"ordCat.error\")\n\t\t})\n\t}\n}\n\n# check that the phenotype is valid - that there are more than two categories\n# and that these all have at least 10 cases\n# something has gone wrong if this is the case\ndoCatOrdAssertions <- function(pheno) {\n\n\t# assert variable has only one column \n\tif (!is.null(dim(pheno))) stop(\"More than one column for categorical ordered\")\n\n\tuniqVar = unique(na.omit(pheno));\n\n\t# assert more than 2 categories\n\tif (length(uniqVar)<=1) stop(\"1 or zero values\")\n\tif (length(uniqVar)==2) stop(\"this variable is binary\")\n\n\t# assert each value has >= 10 examples\n\tfor (u in uniqVar) {\n\t\twithValIdx = which(pheno==u)\n\t\tnumWithVal = length(withValIdx);\n\n\t\tif (numWithVal=0) # ignore missing values\n\t\t\t\torderStr = paste(orderStr, i, sep=\"\");\n\t\t\tfirst=0;\n\t\t\tend\n\t\t}\n\t}\n\treturn(orderStr);\n}\n", "meta": {"hexsha": "7e78c40faf404a560246251448bc3efc680befc4", "size": 5038, "ext": "r", "lang": "R", "max_stars_repo_path": "WAS/testCategoricalOrdered.r", "max_stars_repo_name": "kevmanderson/PHESANT", "max_stars_repo_head_hexsha": "cddb747de7b7ce91a687382bb9684ab43d4008a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "WAS/testCategoricalOrdered.r", "max_issues_repo_name": "kevmanderson/PHESANT", "max_issues_repo_head_hexsha": "cddb747de7b7ce91a687382bb9684ab43d4008a9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "WAS/testCategoricalOrdered.r", "max_forks_repo_name": "kevmanderson/PHESANT", "max_forks_repo_head_hexsha": "cddb747de7b7ce91a687382bb9684ab43d4008a9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2919254658, "max_line_length": 229, "alphanum_fraction": 0.6899563319, "num_tokens": 1404, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.41869689484852374, "lm_q2_score": 0.02800752334416486, "lm_q1q2_score": 0.011726663056599368}}
{"text": "whack <- function(s) {\n paste( rev( unlist(strsplit(s, \" \"))), collapse=' ' ) }\n\npoem <- unlist( strsplit(\n'------------ Eldorado ----------\n\n... here omitted lines ...\n\nMountains the \"Over\nMoon, the Of\nShadow, the of Valley the Down\nride,\" boldly Ride,\nreplied,--- shade The\nEldorado!\" for seek you \"If\n\nPoe Edgar -----------------------', \"\\n\"))\n\nfor (line in poem) cat( whack(line), \"\\n\" )\n", "meta": {"hexsha": "a3d23e91897d2c71a8a3ddc1c4d7ce26313b15e4", "size": 394, "ext": "r", "lang": "R", "max_stars_repo_path": "Task/Reverse-words-in-a-string/R/reverse-words-in-a-string-1.r", "max_stars_repo_name": "LaudateCorpus1/RosettaCodeData", "max_stars_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_stars_repo_licenses": ["Info-ZIP"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-05-05T13:42:20.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-05T13:42:20.000Z", "max_issues_repo_path": "Task/Reverse-words-in-a-string/R/reverse-words-in-a-string-1.r", "max_issues_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_issues_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_issues_repo_licenses": ["Info-ZIP"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Task/Reverse-words-in-a-string/R/reverse-words-in-a-string-1.r", "max_forks_repo_name": "seanwallawalla-forks/RosettaCodeData", "max_forks_repo_head_hexsha": "9ad63ea473a958506c041077f1d810c0c7c8c18d", "max_forks_repo_licenses": ["Info-ZIP"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.7368421053, "max_line_length": 57, "alphanum_fraction": 0.5583756345, "num_tokens": 112, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.2974699426047947, "lm_q2_score": 0.03904829174772427, "lm_q1q2_score": 0.011615693105010818}}
{"text": "# Climate Indecies Calculation software\n# R language with TCL/TK package\n# Programmed by Yujun Ouyang,Mar,2004\n# rewritten by Yang Feng, July 2004\n# modified by Viacheslav Shalisko 2015\n\n# version 1.0, 2004-10-14\n# modified, 2006-01-24, \n# change .Internal(cbind) to cbind\n# change .Internal(rbind) to rbind\n# modified 2007-03-23\n# change .Internal(rep(0,n)) to rep(0,n)\n# modified, 2007-11-26,\n# get rid of .Internal on some functions: min(...), sort(...), round(...)\n# max(...), also get rid of dig=.. part from sort(...) function\n# change decrease=... part to decreasing=... at sort(...)\n# modified, 2008-05-05,\n# output TMAX mean value and TMIN mean value in nastat() function\n# modified, 2008-05-06,\n# add a random series on TMAX and TMIN in exceedance rate function\n# modified thresholds in TN10p calculation, add an 1e-5 item to avoid \n# computational error like 3.60000 > 3.6000, two functions involved:\n# nordaytem1() and exceedance()\n# modified, 2008-06-16\n# change all sort() to mysort(), deal with different version, also combined\n# different levels threshold()\n\n\n# Part I\n# General functions & TCL/TK functions\n\nlibrary(cluster)\nrequire(tcltk)\nmysort<- if(getRversion()<='2.4.1') function(x,decreasing){.Internal(sort(x,decreasing=decreasing))} else function(x,decreasing){sort.int(x,decreasing=decreasing)}\n\nfontHeading <- tkfont.create(family=\"times\",size=20,weight=\"bold\",slant=\"italic\")\nfontHeading1<-tkfont.create(family=\"times\",size=16,weight=\"bold\")\nfontHeading2<-tkfont.create(family=\"times\",size=14,weight=\"bold\")\nfontTextLabel <- tkfont.create(family=\"times\",size=12)\nfontFixedWidth <- tkfont.create(family=\"courier\",size=12)\n# initial value for check box\ncbvalue1<-tclVar(\"1\");cbvalue2<-tclVar(\"1\");cbvalue3<-tclVar(\"1\")\ncbvalue4<-tclVar(\"1\");cbvalue5<-tclVar(\"1\");cbvalue6<-tclVar(\"1\")\ncbvalue7<-tclVar(\"1\");cbvalue8<-tclVar(\"1\");cbvalue9<-tclVar(\"1\")\ncbvalue10<-tclVar(\"1\");cbvalue11<-tclVar(\"1\");cbvalue12<-tclVar(\"1\")\ncbvalue13<-tclVar(\"1\");cbvalue14<-tclVar(\"1\");cbvalue15<-tclVar(\"1\")\ncbvalue16<-tclVar(\"0\");cbvalue17<-tclVar(\"0\");cbvalue18<-tclVar(\"0\")\ncbvalue19<-tclVar(\"0\");cbvalue21<-tclVar(\"1\")\n# initial value for parameters\nstations<-tclVar(paste(\"\"));stdt<-tclVar(paste(\"3\"))\nEntry1<-tclVar(paste(\"1961\"));Entry2<-tclVar(paste(\"1990\"))\n#Entry3<-tclVar(paste(\"5\"))\nEntry4<-tclVar(paste(\"0\"))\nEntry5<-tclVar(paste(\"0\"))\nEntry6<-tclVar(paste(\"25\"));Entry7<-tclVar(paste(\"0\"))\nEntry8<-tclVar(paste(\"20\"));Entry9<-tclVar(paste(\"0\"))\n#Entry10<-tclVar(paste(\"10\"));Entry11<-tclVar(paste(\"5\"))\nEntry12<-tclVar(paste(\"25\"))\ndayim<-as.integer(c(31,28,31,30,31,30,31,31,30,31,30,31))\ncrt<-3;flag=F\ntreshold=5;winsize=5\nuu<-25;lu<-20\nul<-0;ll<-0\n\ntitle1<-\"Grafica del Ind143\";title2<-\"Ind143\";title3<-\"Años\"\nfrc<-function(dd,year,month,item){\n\n a<-dd[dd$year==year & dd$month==month,item]\n a<-a[a>-99]\n frc<-length(a)/rdim(year,month)\n }#end\n\ndone<-function(){tkdestroy(start1)}\n\npercentile<-function(n,x,pctile){\n\t x1<-x[is.na(x)==F]\n\t n1<-length(x1)\n a<-mysort(x1,decreasing=F)\n b<-n1*pctile+0.3333*pctile+0.3333\n bb<-trunc(b)\n percentile<-a[bb]+(b-bb)*(a[bb+1]-a[bb]) }#end\n\npplotts<-function(var=\"prcp\",type=\"h\",tit=NULL){\n if(var==\"dtr\"){\n ymax<-max(dd[,\"tmax\"]-dd[,\"tmin\"],na.rm=T)\n ymin<-0\n }\n else if(var==\"prcp\"){\n ymax<-max(dd[,var],na.rm=T)\n ymin<-0\n }\n else{\n ymax<-max(dd[,var],na.rm=T)+1\n ymin<-min(dd[,var],na.rm=T)-1\n }\n if(ymin>0){\n ymin<-0\n }\n if(is.na(ymax)|is.na(ymin)|(ymax==-Inf)|(ymin==-Inf)){\n ymax<-100\n ymin<-(-100)\n }\n par(mfrow=c(4,1))\n par(mar=c(3.1,2.1,2.1,2.1))\n for(i in seq(years,yeare,10)){\n at<-rep(1,10)\n# if(i>yeare)\n for(j in (i+1):min(i+9,yeare+1)){\n if(leapyear(j)) at[j-i+1]<-at[j-i]+366\n else at[j-i+1]<-at[j-i]+365\n }\n if(var==\"dtr\")\n ttmp<-dd[dd$year>=i&dd$year<=min(i+9,yeare),\"tmax\"]-dd[dd$year>=i&dd$year<=min(i+9,yeare),\"tmin\"]\n else ttmp<-dd[dd$year>=i&dd$year<=min(i+9,yeare),var]\n plot(1:length(ttmp),ttmp,type=type,col=\"blue\",xlab=\"\",ylab=\"\",xaxt=\"n\",xlim=c(1,3660),ylim=c(ymin,ymax))\n abline(h=0)\n tt<-seq(1,length(ttmp))\n tt<-tt[is.na(ttmp)==T]\n axis(side=1,at=at,labels=c(i:(i+9)))\n for(k in 1:10) abline(v=at[k],col=\"lightgreen\")\n lines(tt,rep(0,length(tt)),type=\"p\",col=\"red\")\n title(paste(\"Estación: \",tit,\", \",i,\"~\",min(i+9,yeare),\", \",var,sep=\"\"))\n }\n}\n\nind143gsl<-function(){\n if (latitude<0) south=T else south=F\n if (latitude<0) eyear=yeare-1 else eyear=yeare\n threshold<-5\n a<-eyear-years+1\n b<-rep(0,a)\n b<-cbind(b,b)\n colnames(b)<-c(\"year\",\"gsl\")\n i=1\n for (year in years:eyear) {\n b[i,\"year\"]<-year\n if(south){\n gslstart<-dd[dd$year==year&dd$month>6,]\n gslstart<-(gslstart[,\"tmax\"]+gslstart[,\"tmin\"])/2\n gslend<-dd[dd$year==(year+1)&dd$month<7,]\n gslend<-(gslend[,\"tmax\"]+gslend[,\"tmin\"])/2\n }\n else{\n gslstart<-dd[dd$year==year&dd$month<7,]\n gslstart<-(gslstart[,\"tmax\"]+gslstart[,\"tmin\"])/2\n gslend<-dd[dd$year==year&dd$month>6,]\n gslend<-(gslend[,\"tmax\"]+gslend[,\"tmin\"])/2\n }\n beginday<-0\n count=0\n for(step in 1:length(gslstart)){\n if(is.na(gslstart[step])==F){\n if(gslstart[step]>threshold) count<-count+1\n else count<-0\n }\n else count<-0\n if(count>5){\n beginday<-step-5\n break\n }\n }\n\n# if(beginday==0){\n# b[i,\"gsl\"]<-0\n# break\n# }\n\n endday<-0\n count<-0\n for(step in 1:length(gslend)){\n if(is.na(gslend[step])==F){\n if(gslend[step]5){\n endday<-step-5\n break\n }\n }\n \n if(sum(is.na(gslstart))+sum(is.na(gslend))>15) b[i,\"gsl\"]<-NA\n else{\n if(beginday==0)\n b[i,\"gsl\"]<-0\n else {\n if(endday==0) b[i,\"gsl\"]<-length(gslend)+length(gslstart)-beginday\n else b[i,\"gsl\"]<-endday+length(gslstart)-beginday\n }\n }\n \n i=i+1\n } \n b<-as.data.frame(b)\n nam1<-paste(outinddir,paste(ofilename,\"_GSL.csv\",sep=\"\"),sep=\"/\")\n write.table(b,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n \n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(b[,\"gsl\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(b[,1],b[,\"gsl\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"gsl\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n \n nam2<-paste(outjpgdir,paste(ofilename,\"_GSL.png\",sep=\"\"),sep=\"/\")\n png(file=nam2,width=1024,height=768,bg=\"white\")\n plotx(b[,1],b[,\"gsl\"],main=paste(\"GSL\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"GSL\")\n dev.off()\n nam3<-paste(outjpgdir,paste(ofilename,\"_GSL.pdf\",sep=\"\"),sep=\"/\")\n pdf(file=nam3)\n plotx(b[,1],b[,\"gsl\"],main=paste(\"GSL\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"GSL\")\n dev.off()\n}\n\ndataext<-function(dd,year,month,day,item){\ndataext<-dd[dd$year==year & dd$month==month & dd$day==day,item]\n}\n\nleapyear<-function(year){\n remainder400 <-trunc(year-400*trunc(year/400))\n remainder100 <-trunc(year-100*trunc(year/100))\n remainder4 <-trunc(year-4*trunc(year/4))\n if (remainder400 == 0) leapyear = T\n else{\n if(remainder100 == 0) leapyear = F\n else{\n if(remainder4 == 0) leapyear = T\n\t else leapyear = F\n\t }\n }\n}\n\nrdim<-function(year,month) {\n a<-leapyear(year) \n if (month==1) rdim<-31\n else if (month==3) rdim<-31\n else if (month==4) rdim<-30\n else if (month==5) rdim<-31\n else if (month==6) rdim<-30\n else if (month==7) rdim<-31\n else if (month==8) rdim<-31\n else if (month==9) rdim<-30\n else if (month==10) rdim<-31\n else if (month==11) rdim<-30\n else if (month==12) rdim<-31\n else if (a==T & month==2) rdim<-29\n else rdim<-28 \n }\n\n\nqcontrol<-function(){\n tkmessageBox(message=paste(\"Data QC(\",ofilename,\") may take a few minutes, click OK to continue.\",sep=\"\"))\n# output records of problematic like prcp <0 and NA\nddu<-duplicated(dd[,c(\"year\",\"month\",\"day\")])\nif(sum(ddu)>0){\n nam1<-paste(outlogdir,paste(ofilename,\"dupliQC.csv\",sep=\"\"),sep=\"/\")\n msg=paste(\"Date duplicated found in original data file, please check:\",nam1,sep=\" \")\n tkmessageBox(message=msg)\n ddu2<-dd[duplicated(dd[,c(\"year\",\"month\",\"day\")])==T,c(\"year\", \"month\", \"day\")]\n nam1<-paste(outlogdir,paste(ofilename,\"dupliQC.csv\",sep=\"\"),sep=\"/\")\n write.table(ddu2,file=nam1,append=F,quote=F,sep=\", \",row.names=F)\n tkdestroy(start1)\n stop(paste(\"QC stopped due to duplicated date, please check \",nam1,sep=\"\"))\n}\n \nmid<-dd[is.na(dd$prcp)==F,]\nmid<-mid[mid$prcp<0,]\n#dd[is.na(dd$prcp)==F & dd$prcp<0,\"prcp\"]<-NA\nnam1<-paste(outlogdir,paste(ofilename,\"_prcpQC.csv\",sep=\"\"),sep=\"/\")\nwrite.table(mid,file=nam1,append=F,quote=F,sep=\", \",row.names=F)\nif (dim(mid)[1]>0) tkmessageBox(message=paste(\"Errors in prcp, please check the log file\",nam1,sep=\" \"))\n# output plots for PRCP\nnam1<-paste(outlogdir,paste(ofilename,\"_prcpPLOT.pdf\",sep=\"\"),sep=\"/\")\npdf(file=nam1)\nttmp<-dd[dd$prcp>=1,\"prcp\"]\nttmp<-ttmp[is.na(ttmp)==F]\nif(length(ttmp)>30){\n hist(ttmp,main=paste(\"Histograma para estación: \",ofilename,\", PRCP >= 1mm\",sep=\"\"),breaks=c(0,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,125,150,175,max(200,ttmp)),xlab=\"\",ylab=\"Densidad\",col=\"gray\",freq=F)\n lines(density(ttmp,bw=0.2,from=1),col=\"red\")\n}\npplotts(var=\"prcp\",tit=ofilename)\ndev.off()\nnam1<-paste(outlogdir,paste(ofilename,\"_tmaxPLOT.pdf\",sep=\"\"),sep=\"/\")\npdf(file=nam1)\npplotts(var=\"tmax\",type=\"l\",tit=ofilename)\ndev.off()\nnam1<-paste(outlogdir,paste(ofilename,\"_tminPLOT.pdf\",sep=\"\"),sep=\"/\")\npdf(file=nam1)\npplotts(var=\"tmin\",type=\"l\",tit=ofilename)\ndev.off()\nnam1<-paste(outlogdir,paste(ofilename,\"_dtrPLOT.pdf\",sep=\"\"),sep=\"/\")\npdf(file=nam1)\npplotts(var=\"dtr\",type=\"l\",tit=ofilename)\ndev.off()\n\n#par(mfrow=c(1,1))\n# output problematic temperature like tmax < tmin\nmm<-dd[,\"tmax\"]-dd[,\"tmin\"]\ndd<-cbind(dd,mm)\ndimnames(dd)[[2]][7]<-\"dtr\"\n #output \"log\" file review\n temiss<-dd\n temiss<-temiss[is.na(temiss[,\"tmax\"])==F&is.na(temiss[,\"tmin\"])==F,]\n# temiss<-temiss[is.na(temiss[,6])==F,]\n temiss<-temiss[temiss[,7]<=0|temiss[,5]<=(-70)|temiss[,5]>=70|temiss[,6]<=(-70)|temiss[,6]>=70,]\n dimnames(temiss)[[2]][7]<-\"tmax-tmin\"\n nam1<-paste(outlogdir,paste(ofilename,\"_tempQC.csv\",sep=\"\"),sep=\"/\")\n write.table(temiss,file=nam1,append=F,quote=F,sep=\", \",row.names=F)\nif (dim(temiss)[1]>0) {\n tkmessageBox(message=paste(\"Errors in temperature, please check the log file\",nam1,sep=\" \"))\n # records with abs(tmax)>=70, abs(tmin)>=70 set to NA\n dd[is.na(dd[,5])==F & abs(dd[,5])>=70,5]<-NA\n dd[is.na(dd[,6])==F & abs(dd[,6])>=70,6]<-NA\n # records with tmax < tmin are set to NA\n dd[is.na(dd[,5])==F & is.na(dd[,6])==F & dd[,\"dtr\"]<0,c(\"tmax\",\"tmin\")]<-NA\n# dd[is.na(dd[,5])==F & dd[,\"mm\"]<0,\"tmin\"]<-NA\n}\n# dd<-dd[,-7]\n\n# output problematic temperature which is out of 3 standard diviation (temp only)\nys<-yeare-years+1\n\ntmaxm<-matrix(0,ys,365)\ntminm<-matrix(0,ys,365)\ntdtrm<-matrix(0,ys,365)\n\ntmaxstd<-rep(0,365)\ntminstd<-rep(0,365)\ntdtrstd<-rep(0,365)\n\ntmaxmean<-rep(0,365)\ntminmean<-rep(0,365)\ntdtrmean<-rep(0,365)\n\nfor(i in 1:ys)\n tmaxm[i,]<-dd[dd[,\"year\"]==(i+years-1)&(dd[,\"month\"]*100+dd[,\"day\"]!=229),\"tmax\"]\nfor(i in 1:365){\n tmaxstd[i]<-sqrt(var(tmaxm[,i],na.rm=T))\n tmaxmean[i]<-mean(tmaxm[,i],na.rm=T)\n}\n\nfor(i in 1:ys)\n tminm[i,]<-dd[dd[,\"year\"]==(i+years-1)&(dd[,\"month\"]*100+dd[,\"day\"]!=229),\"tmin\"]\nfor(i in 1:365){\n tminstd[i]<-sqrt(var(tminm[,i],na.rm=T))\n tminmean[i]<-mean(tminm[,i],na.rm=T)\n}\n\nfor(i in 1:ys)\n tdtrm[i,]<-dd[dd[,\"year\"]==(i+years-1)&(dd[,\"month\"]*100+dd[,\"day\"]!=229),\"dtr\"]\nfor(i in 1:365){\n tdtrstd[i]<-sqrt(var(tdtrm[,i],na.rm=T))\n tdtrmean[i]<-mean(tdtrm[,i],na.rm=T)\n}\n\ntmaxstdleap<-rep(0,366)\ntmaxstdleap[1:59]<-tmaxstd[1:59]\ntmaxstdleap[60]<-tmaxstd[59]\ntmaxstdleap[61:366]<-tmaxstd[60:365]\n\ntmaxmeanleap<-rep(0,366)\ntmaxmeanleap[1:59]<-tmaxmean[1:59]\ntmaxmeanleap[60]<-tmaxmean[59]\ntmaxmeanleap[61:366]<-tmaxmean[60:365]\n\ntminstdleap<-rep(0,366)\ntminstdleap[1:59]<-tminstd[1:59]\ntminstdleap[60]<-tminstd[59]\ntminstdleap[61:366]<-tminstd[60:365]\n\ntminmeanleap<-rep(0,366)\ntminmeanleap[1:59]<-tminmean[1:59]\ntminmeanleap[60]<-tminmean[59]\ntminmeanleap[61:366]<-tminmean[60:365]\n\ntdtrstdleap<-rep(0,366)\ntdtrstdleap[1:59]<-tdtrstd[1:59]\ntdtrstdleap[60]<-tdtrstd[59]\ntdtrstdleap[61:366]<-tdtrstd[60:365]\n\ntdtrmeanleap<-rep(0,366)\ntdtrmeanleap[1:59]<-tdtrmean[1:59]\ntdtrmeanleap[60]<-tdtrmean[59]\ntdtrmeanleap[61:366]<-tdtrmean[60:365]\n\ntmp<-matrix(0,dim(dd)[1],6)\ndimnames(tmp)<-list(NULL,c(\"tmaxlow\",\"tmaxup\",\"tminlow\",\"tminup\",\"dtrlow\",\"dtrup\"))\n\nidx<-0\nfor(i in years:yeare){\n if(leapyear(i)==T){\n tmp[(idx+1):(idx+366),1]<-tmaxmeanleap-crt*tmaxstdleap\n tmp[(idx+1):(idx+366),2]<-tmaxmeanleap+crt*tmaxstdleap\n tmp[(idx+1):(idx+366),3]<-tminmeanleap-crt*tminstdleap\n tmp[(idx+1):(idx+366),4]<-tminmeanleap+crt*tminstdleap\n tmp[(idx+1):(idx+366),5]<-tdtrmeanleap-crt*tdtrstdleap\n tmp[(idx+1):(idx+366),6]<-tdtrmeanleap+crt*tdtrstdleap\n idx<-idx+366\n }\n else{\n tmp[(idx+1):(idx+365),1]<-tmaxmean-crt*tmaxstd\n tmp[(idx+1):(idx+365),2]<-tmaxmean+crt*tmaxstd\n tmp[(idx+1):(idx+365),3]<-tminmean-crt*tminstd\n tmp[(idx+1):(idx+365),4]<-tminmean+crt*tminstd\n tmp[(idx+1):(idx+365),5]<-tdtrmean-crt*tdtrstd\n tmp[(idx+1):(idx+365),6]<-tdtrmean+crt*tdtrstd\n idx<-idx+365\n }\n}\n\nodata<-cbind(dd,tmp)\n\nodata<-odata[is.na(odata[,\"tmax\"])==F,]\nodata<-odata[is.na(odata[,\"tmin\"])==F,]\nodata<-odata[is.na(odata[,\"dtr\"])==F,]\no1data<-odata[odata[,5]odata[,9]|odata[,6]odata[,11]|odata[,7]odata[,13],]\n#o2data<-odata[odata[,5]>odata[,8],]\n#o3data<-odata[odata[,6]odata[,10],]\n\n#write.table(errstdo,file=nam1,append=F,quote=F,sep=\",\",row.names=F)\nif (dim(o1data)[1] > 0){\n nam1<-paste(outlogdir,paste(ofilename,\"_tepstdQC.csv\",sep=\"\"),sep=\"/\")\n tkmessageBox(message=paste(\"Outliers found, please check the log file: \",nam1,sep=\"\"))\n ofile<-cbind(o1data[,c(1,2,3,8,5,9,10,6,11,12,7,13)])\n write.table(round(ofile,digit=2),file=nam1,append=F,quote=F,sep=\",\",row.names=F)\n}\n\ndd<-dd[,c(\"year\",\"month\",\"day\",\"prcp\",\"tmax\",\"tmin\")];assign(\"dd\",dd,envir=.GlobalEnv)\n\nnamcal<-paste(nama,\"indcal.csv\",sep=\"\")\nassign(\"namcal\",namcal,envir=.GlobalEnv)\nwrite.table(dd,file=namcal,append=F,quote=F,sep=\",\",row.names=F,na=\"-99.9\")\n\ntkmessageBox(message=paste(\"If you have checked data(\", namcal,\"), click OK to continue.\",sep=\"\"))\n}# end of qcontrol()\n\nnastat<-function(){\n dd <- read.table(namcal,header=T,sep=\",\",na.strings=\"-99.9\",colClasses=rep(\"real\",6))\n assign(\"dd\",dd,envir=.GlobalEnv)\n# NA statistics\nnast<-rep(0,12)\nnast<-array(nast,c(1,12,12,(yeare-years+1)))\ndimnames(nast)<-list(NULL,c(\"ynapr\",\"ynatma\",\"ynatmi\",\"napr\",\"natma\",\"natmi\",\"mnapr>3\",\"mnatma>3\",\"mnatmi>3\",\"ynapr>15\",\"ynatma>15\",\"ynatmi>15\"),NULL,NULL)\nys<-yeare-years+1 \nyear=years\naa1<-matrix(NA,12*ys,4)\ndimnames(aa1)<-list(NULL,c(\"year\",\"month\",\"tmaxm\",\"tminm\"))\naa1[,\"year\"]<-years:yeare\naa1[,\"year\"]<-mysort(aa1[,\"year\"],decreasing=F)\naa1[,\"month\"]<-1:12\naa2<-matrix(NA,ys,3)\ndimnames(aa2)<-list(NULL,c(\"year\",\"tmaxm\",\"tminm\"))\naa2[,\"year\"]<-years:yeare\nfor (i in 1:(yeare-years+1)){\n month<-1;midvalue1<-dd[dd$year==year,]\n aa2[i,\"tmaxm\"]<-mean(midvalue1[,\"tmax\"],na.rm=T)\n aa2[i,\"tminm\"]<-mean(midvalue1[,\"tmin\"],na.rm=T)\n for (j in 1:12){\n midvalue2<-midvalue1[midvalue1$month==month,]\n aa1[(i-1)*12+j,\"tmaxm\"]<-mean(midvalue2[,\"tmax\"],na.rm=T)\n aa1[(i-1)*12+j,\"tminm\"]<-mean(midvalue2[,\"tmin\"],na.rm=T)\n nast[1,\"ynapr\",j,i]<-dim(midvalue1[is.na(midvalue1$prcp),])[1]\n if (nast[1,\"ynapr\",j,i]>15) nast[1,\"ynapr>15\",j,i]<-NA\n nast[1,\"ynatma\",j,i]<-dim(midvalue1[is.na(midvalue1$tmax),])[1]\n if (nast[1,\"ynatma\",j,i]>15) nast[1,\"ynatma>15\",j,i]<-NA\n nast[1,\"ynatmi\",j,i]<-dim(midvalue1[is.na(midvalue1$tmin),])[1]\n if (nast[1,\"ynatmi\",j,i]>15) nast[1,\"ynatmi>15\",j,i]<-NA\n nast[1,\"napr\",j,i]<-dim(midvalue2[is.na(midvalue2$prcp),])[1]\n if (nast[1,\"napr\",j,i]>3) nast[1,\"mnapr>3\",j,i]<-NA\n nast[1,\"natma\",j,i]<-dim(midvalue2[is.na(midvalue2$tmax),])[1]\n if (nast[1,\"natma\",j,i]>3) nast[1,\"mnatma>3\",j,i]<-NA\n nast[1,\"natmi\",j,i]<-dim(midvalue2[is.na(midvalue2$tmin),])[1]\n if (nast[1,\"natmi\",j,i]>3) nast[1,\"mnatmi>3\",j,i]<-NA\n month=month+1 }\n year=year+1 }\n nasto<-t(nast[,,,1])\n for ( i in 2:(yeare-years+1)){\n nasto<-rbind(nasto,t(nast[,,,i])) }\n nastout<-matrix(0,(yeare-years+1)*12,2)\n dimnames(nastout)<-list(NULL,c(\"year\",\"month\")) \n nastout<-as.data.frame(nastout)\n nastout[,\"year\"]<-years:yeare\n nastout[,\"year\"]<-mysort(nastout[,\"year\"],decreasing=F)\n nastout[,\"month\"]<-1:12\n \n nastout<-cbind(nastout,nasto);assign(\"nastout\",nastout,envir=.GlobalEnv)\n nastatistic<-nastout[,1:8]\n \n nacor<-nastout[,-(3:8)]\n ynacor<-matrix(0,ys,4)\n dimnames(ynacor)<-list(NULL,c(\"year\",\"ynapr>15\",\"ynatma>15\",\"ynatmi>15\"))\n ynacor[,\"year\"]<-years:yeare\n ynacor<-as.data.frame(ynacor)\n for (year in years:yeare){\n ynacor[ynacor$year==year,\"ynapr>15\"]<-nacor[nacor$year==year & nacor$month==1,\"ynapr>15\"]\n ynacor[ynacor$year==year,\"ynatma>15\"]<-nacor[nacor$year==year & nacor$month==1,\"ynatma>15\"]\n ynacor[ynacor$year==year,\"ynatmi>15\"]<-nacor[nacor$year==year & nacor$month==1,\"ynatmi>15\"]}\n nacor<-nacor[,1:5]\n assign(\"nacor\",nacor,envir=.GlobalEnv)\n assign(\"ynacor\",ynacor,envir=.GlobalEnv)\n if(sum(is.na(ynacor[,\"ynapr>15\"])==F)==0) prallna<-1\n else prallna<-0\n if(sum(is.na(ynacor[,\"ynatma>15\"])==F)==0) txallna<-1\n else txallna<-0\n if(sum(is.na(ynacor[,\"ynatmi>15\"])==F)==0) tnallna<-1\n else tnallna<-0\n assign(\"prallna\",prallna,envir=.GlobalEnv)\n assign(\"txallna\",txallna,envir=.GlobalEnv)\n assign(\"tnallna\",tnallna,envir=.GlobalEnv)\n assign(\"nastatistic\",nastatistic,envir=.GlobalEnv)\n nam1<-paste(outlogdir,paste(ofilename,\"_nastatistic.csv\",sep=\"\"),sep=\"/\")\n cat(file=nam1,\"TITLE,YEAR,JAN,FEB,MAR,APR,MAY,JUN,JUL,AUG,SEP,OCT,NOV,DEC,ANN\\n\")\n for(year in years:yeare)\n for(i in 1:3) {\n if(i==1) tit<-\"PRCP\"\n\tif(i==2) tit<-\"TMAX\"\n\tif(i==3) tit<-\"TMIN\"\n\tline<-paste(tit,year,sep=\",\")\n\tfor(mon in 1:12)\n\tline<-paste(line,nastatistic[nastatistic$year==year&nastatistic$month==mon,i+5],sep=\",\")\n\tline<-paste(line,nastatistic[nastatistic$year==year&nastatistic$month==1,i+2],sep=\",\")\n\tcat(file=nam1,line,fill=100,append=T)\n }\n# write.table(nastatistic,file=nam1,append=F,quote=F,sep=\", \",row.names=F)\n aa1[,\"tmaxm\"]<-aa1[,\"tmaxm\"]+nacor[,\"mnatma>3\"]\n aa1[,\"tminm\"]<-aa1[,\"tminm\"]+nacor[,\"mnatmi>3\"]\n aa2[,\"tmaxm\"]<-aa2[,\"tmaxm\"]+ynacor[,\"ynatma>15\"]\n aa2[,\"tminm\"]<-aa2[,\"tminm\"]+ynacor[,\"ynatmi>15\"]\n ofile1<-paste(outinddir,paste(ofilename,\"_TMAXmean.csv\",sep=\"\"),sep=\"/\")\n odata<-matrix(0,ys,14)\n dimnames(odata)<-list(NULL,c(\"year\",\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\",\"annual\"))\n odata[,1]<-years:yeare\n odata[,14]<-aa2[,\"tmaxm\"]\n for(i in 1:ys) odata[i,2:13]<-aa1[((i-1)*12+1):(i*12),\"tmaxm\"]\n write.table(round(odata,2),file=ofile1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n odata[,14]<-aa2[,\"tminm\"]\n for(i in 1:ys) odata[i,2:13]<-aa1[((i-1)*12+1):(i*12),\"tminm\"]\n ofile1<-paste(outinddir,paste(ofilename,\"_TMINmean.csv\",sep=\"\"),sep=\"/\")\n write.table(round(odata,2),file=ofile1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n parameter()\n }#end of nastat()\n\n\ngetfile<-function() {\n name <- tclvalue(tkgetOpenFile(filetypes=\"{{TEXT Files} {.txt}} {{All files} *}\"))\n if (name==\"\") return();\n dd <- read.table(name,header=F,col.names=c(\"year\",\"month\",\"day\",\"prcp\",\"tmax\",\"tmin\"),colClasses=rep(\"real\",6))\n nama<-substr(name,start=1,stop=(nchar(name)-4))\n outdirtmp<-strsplit(name,\"/\")[[1]]\n if(length(outdirtmp)<=2){\n outinddir<-paste(strsplit(name,\":\")[[1]][1],\"indices\",sep=\":/\")\n outlogdir<-paste(strsplit(name,\":\")[[1]][1],\"log\",sep=\":/\")\n outjpgdir<-paste(strsplit(name,\":\")[[1]][1],\"plots\",sep=\":/\")\n outtrddir<-paste(strsplit(name,\":\")[[1]][1],\"trend\",sep=\":/\")\n }\n else{\n outdir<-outdirtmp[1]\n for(i in 2:(length(outdirtmp)-1))\n outdir<-paste(outdir,outdirtmp[i],sep=\"/\")\n outinddir<-paste(outdir,\"indices\",sep=\"/\")\n outlogdir<-paste(outdir,\"log\",sep=\"/\")\n outjpgdir<-paste(outdir,\"plots\",sep=\"/\")\n outtrddir<-paste(outdir,\"trend\",sep=\"/\")\n }\n ofilename<-substr(outdirtmp[length(outdirtmp)],start=1,stop=(nchar(outdirtmp[length(outdirtmp)])-4))\n if(!file.exists(outinddir)) dir.create(outinddir)\n if(!file.exists(outlogdir)) dir.create(outlogdir)\n if(!file.exists(outjpgdir)) dir.create(outjpgdir)\n if(!file.exists(outtrddir)) dir.create(outtrddir)\n \n# dimnames(dd)<-list(NULL,c(\"year\",\"month\",\"day\",\"prcp\",\"tmax\",\"tmin\"))\n assign(\"nama\",nama,envir=.GlobalEnv)\n assign(\"outinddir\",outinddir,envir=.GlobalEnv)\n assign(\"outlogdir\",outlogdir,envir=.GlobalEnv)\n assign(\"outjpgdir\",outjpgdir,envir=.GlobalEnv)\n assign(\"outtrddir\",outtrddir,envir=.GlobalEnv)\n assign(\"ofilename\",ofilename,envir=.GlobalEnv)\n # dd<-dd[dd$tmax!=-99.9,]\n # dd$year<-dd$year+40 # just for the test data\n # replace missing value with NA\n dd[dd$prcp<=(-99.),\"prcp\"]<-NA\n dd[dd$tmax<=(-99.),\"tmax\"]<-NA\n dd[dd$tmin<=(-99.),\"tmin\"]<-NA\n # replace missing records\n ddd<-matrix(NA,365,6)\n dddl<-matrix(NA,366,6)\n dimnames(ddd)<-list(NULL,c(\"year\",\"month\",\"day\",\"prcp\",\"tmax\",\"tmin\"))\n dimnames(dddl)<-list(NULL,c(\"year\",\"month\",\"day\",\"prcp\",\"tmax\",\"tmin\"))\n ddd[1:31,\"month\"]<-1;ddd[1:31,\"day\"]<-c(1:31);ddd[32:59,\"month\"]<-2;ddd[32:59,\"day\"]<-c(1:28)\n ddd[60:90,\"month\"]<-3;ddd[60:90,\"day\"]<-c(1:31);ddd[91:120,\"month\"]<-4;ddd[91:120,\"day\"]<-c(1:30)\n ddd[121:151,\"month\"]<-5;ddd[121:151,\"day\"]<-c(1:31);ddd[152:181,\"month\"]<-6;ddd[152:181,\"day\"]<-c(1:30)\n ddd[182:212,\"month\"]<-7;ddd[182:212,\"day\"]<-c(1:31);ddd[213:243,\"month\"]<-8;ddd[213:243,\"day\"]<-c(1:31)\n ddd[244:273,\"month\"]<-9;ddd[244:273,\"day\"]<-c(1:30);ddd[274:304,\"month\"]<-10;ddd[274:304,\"day\"]<-c(1:31)\n ddd[305:334,\"month\"]<-11;ddd[305:334,\"day\"]<-c(1:30);ddd[335:365,\"month\"]<-12;ddd[335:365,\"day\"]<-c(1:31)\n\n dddl[1:31,\"month\"]<-1;dddl[1:31,\"day\"]<-c(1:31);dddl[32:60,\"month\"]<-2;dddl[32:60,\"day\"]<-c(1:29)\n dddl[61:91,\"month\"]<-3;dddl[61:91,\"day\"]<-c(1:31);dddl[92:121,\"month\"]<-4;dddl[92:121,\"day\"]<-c(1:30)\n dddl[122:152,\"month\"]<-5;dddl[122:152,\"day\"]<-c(1:31);dddl[153:182,\"month\"]<-6;dddl[153:182,\"day\"]<-c(1:30)\n dddl[183:213,\"month\"]<-7;dddl[183:213,\"day\"]<-c(1:31);dddl[214:244,\"month\"]<-8;dddl[214:244,\"day\"]<-c(1:31)\n dddl[245:274,\"month\"]<-9;dddl[245:274,\"day\"]<-c(1:30);dddl[275:305,\"month\"]<-10;dddl[275:305,\"day\"]<-c(1:31)\n dddl[306:335,\"month\"]<-11;dddl[306:335,\"day\"]<-c(1:30);dddl[336:366,\"month\"]<-12;dddl[336:366,\"day\"]<-c(1:31)\n\n years<-dd[1,1];yeare<-dd[dim(dd)[1],1]\n if (leapyear(years)) dddd<-dddl else dddd<-ddd\n dddd[,\"year\"]<-years\n for (year in years:yeare){ # year loop start\n if (leapyear(year)) dddd1<-dddl else dddd1<-ddd\n dddd1[,\"year\"]<-year\n if (year!=years) dddd<-rbind(dddd,dddd1) }# year loop end\n\n dddd<-as.data.frame(dddd)\n dddd2<-merge(dddd,dd,by=c(\"year\",\"month\",\"day\"),all.x=T)\n dddd2<-dddd2[,-(4:6)]\n dimnames(dddd2)[[2]]<-c(\"year\",\"month\",\"day\",\"prcp\",\"tmax\",\"tmin\")\n tmporder<-dddd2[,\"year\"]*10000+dddd2[,\"month\"]*100+dddd2[,\"day\"]\n dd<-dddd2[order(tmporder),]\n \n assign(\"years\",years,envir=.GlobalEnv)\n assign(\"yeare\",yeare,envir=.GlobalEnv)\n assign(\"dd\",dd,envir=.GlobalEnv)\n\n tkmessageBox(message=paste(\"Data(\",ofilename,\") loaded, click OK to continue.\",sep=\"\"))\n \n# enter station name and the times of stadard deviation\n infor1<-tktoplevel()\n tkfocus(infor1)\n tkgrab.set(infor1)\n tkwm.title(infor1,\"Set Parameters for Data QC\")\n\n textEntry1<-stations;textEntry2<-stdt\n \n textEntryWidget1<-tkentry(infor1,width=30,textvariable=textEntry1)\n textEntryWidget2<-tkentry(infor1,width=30,textvariable=textEntry2)\n \n# tkgrid(tklabel(infor1,text=\"!!Enter parameters please\",font=fontHeading1))\n tkgrid(tklabel(infor1,text=\" Station name or code:\"),textEntryWidget1)\n tkgrid(tklabel(infor1,text=\"Criteria(number of Standard Deviation):\"),textEntryWidget2)\n \n ok1<-function(){\n station<-as.character(tclvalue(textEntry1));assign(\"station\",station,envir=.GlobalEnv)\n crt<-as.numeric(tclvalue(textEntry2));assign(\"crt\",crt,envir=.GlobalEnv)\n tkgrab.release(infor1)\n tkdestroy(infor1)\n stations<-textEntry1;assign(\"stations\",stations,envir=.GlobalEnv)\n stdt<-textEntry2;assign(\"stdt\",stdt,envir=.GlobalEnv)\n qcontrol();tkfocus(start1)\n }# end of ok\n\n cancel1<-function(){\n tkmessageBox(message=\"You have to enter these parameters before you can move on.\")\n tkfocus(infor1)}# end of cancel1\n \n ok1.but<- tkbutton(infor1,text=\" OK \",command=ok1)\n cancel1.but<-tkbutton(infor1,text=\" CANCEL \",command=cancel1)\n tkgrid(ok1.but,cancel1.but)\n }# end of getfile\n\nparameter<-function(){\n infor<-tktoplevel()\n tkfocus(infor)\n tkgrab.set(infor)\n tkwm.title(infor,\"Set Parameter Values\")\n\n textEntry1<-Entry1\n textEntry2<-Entry2\n# textEntry3<-Entry3\n textEntry4<-Entry4\n textEntry5<-Entry5\n textEntry6<-Entry6;textEntry7<-Entry7\n textEntry8<-Entry8;textEntry9<-Entry9\n# textEntry10<-Entry10;textEntry11<-Entry11\n textEntry12<-Entry12\n \n textEntryWidget1<-tkentry(infor,width=20,textvariable=textEntry1)\n textEntryWidget2<-tkentry(infor,width=20,textvariable=textEntry2)\n# textEntryWidget3<-tkentry(infor,width=20,textvariable=textEntry3)\n textEntryWidget4<-tkentry(infor,width=20,textvariable=textEntry4)\n textEntryWidget5<-tkentry(infor,width=20,textvariable=textEntry5)\n textEntryWidget6<-tkentry(infor,width=20,textvariable=textEntry6)\n textEntryWidget7<-tkentry(infor,width=20,textvariable=textEntry7)\n textEntryWidget8<-tkentry(infor,width=20,textvariable=textEntry8)\n textEntryWidget9<-tkentry(infor,width=20,textvariable=textEntry9)\n# textEntryWidget10<-tkentry(infor,width=20,textvariable=textEntry10)\n# textEntryWidget11<-tkentry(infor,width=20,textvariable=textEntry11)\n textEntryWidget12<-tkentry(infor,width=20,textvariable=textEntry12)\n \n tkgrid(tklabel(infor,text=\"User defined parameters for Indices Calculation\",font=fontHeading1))\n tkgrid(tklabel(infor,text=\"First year of base period\"),textEntryWidget1)\n tkgrid(tklabel(infor,text=\"Last year of base period\"),textEntryWidget2)\n tkgrid(tklabel(infor,text=\"Latitude of this station location\"),textEntryWidget4)\n tkgrid(tklabel(infor,text=\"Longitude of this station location\"),textEntryWidget5)\n tkgrid(tklabel(infor,text=\"User defined upper threshold of daily maximum temperature\"),textEntryWidget6)\n tkgrid(tklabel(infor,text=\"User defined lower threshold of daily maximum temperature\"),textEntryWidget7)\n tkgrid(tklabel(infor,text=\"User defined upper threshold of daily minimum temperature\"),textEntryWidget8)\n tkgrid(tklabel(infor,text=\"User defined lower threshold of daily minimum temperature\"),textEntryWidget9)\n tkgrid(tklabel(infor,text=\"User defined daily precipitation threshold\"),textEntryWidget12)\n \n ok1<-function(){\n# tkmessageBox(message=\"This process may take 2 mins to initialize the data. Please wait until the window disapear!\")\n startyear<-as.numeric(tclvalue(textEntry1));assign(\"startyear\",startyear,envir=.GlobalEnv)\n endyear<-as.numeric(tclvalue(textEntry2));assign(\"endyear\",endyear,envir=.GlobalEnv)\n if(startyearyeare){\n if(startyear0)\n icnt<-icnt+1\n else{\n if(icnt>=6) ycnt<-ycnt+icnt\n\ticnt<-0\n\t}\n if(i==ylen&icnt>=6) ycnt<-ycnt+icnt\n }\n hwfi[year-years+1,2]<-ycnt\n }\n hwfi<-as.data.frame(hwfi)\n hwfi[,\"wsdi\"]<-hwfi[,\"wsdi\"]+ynacor[,\"ynatma>15\"] \n nam1<-paste(outinddir,paste(ofilename,\"_WSDI.csv\",sep=\"\"),sep=\"/\")\n write.table(hwfi,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n \n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(hwfi[,\"wsdi\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(hwfi[,\"year\"],hwfi[,\"wsdi\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"wsdi\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_WSDI.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(hwfi[,1],hwfi[,2], main=paste(\"WSDI\",ofilename,sep=\" \"),ylab=\"WSDI\",xlab=\"Año\")\n dev.off()\n nam3<-paste(outjpgdir,paste(ofilename,\"_WSDI.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam3)\n plotx(hwfi[,1],hwfi[,2], main=paste(\"WSDI\",ofilename,sep=\" \"),ylab=\"WSDI\",xlab=\"Año\")\n dev.off()\n} # end of hwfi function\n\ncwdi<-function(){\n if (flag==T) return()\n cwdi<-matrix(0,(yeare-years+1),2)\n dimnames(cwdi)[[2]]<-c(\"year\",\"csdi\")\n cwdi[,\"year\"]<-years:yeare\n for (year in years:yeare) {\n if(leapyear(year)){\n aa<-rep(0,366)\n aa[1:59]<-aas[,\"pcmin10\"][1:59]\n aa[60]<-aa[59]\n aa[61:366]<-aas[,\"pcmin10\"][60:365]\n }\n else aa<-aas[,\"pcmin10\"]\n bb<-dd[dd$year==year,\"tmin\"]\n if(length(aa)!=length(bb)) stop(\"ERROR in CWDI, check data!\")\n midval<-aa-bb\n ylen<-length(aa)\n ycnt<-0\n icnt<-0\n for(i in 1:ylen){\n if(is.na(midval[i])==F&midval[i]>0)\n icnt<-icnt+1\n else{\n if(icnt>=6) ycnt<-ycnt+icnt\n\ticnt<-0\n }\n if(i==ylen&icnt>=6) ycnt<-ycnt+icnt\n }\n cwdi[year-years+1,2]<-ycnt\n}\n cwdi<-as.data.frame(cwdi)\n cwdi[,\"csdi\"]<-cwdi[,\"csdi\"]+ynacor[,\"ynatmi>15\"] \n nam1<-paste(outinddir,paste(ofilename,\"_CSDI.csv\",sep=\"\"),sep=\"/\")\n write.table(cwdi,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(cwdi[,\"csdi\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(cwdi[,\"year\"],cwdi[,\"csdi\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"csdi\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_CSDI.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(cwdi[,1],cwdi[,2],main=paste(\"CSDI\",ofilename,sep=\" \"),ylab=\"CSDI\",xlab=\"Año\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_CSDI.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(cwdi[,1],cwdi[,2],main=paste(\"CSDI\",ofilename,sep=\" \"),ylab=\"CSDI\",xlab=\"Año\")\n dev.off()\n} # end of cwdi function\n\nr95ptot<-function(){\n prcptmp<-dd[dd$year>=startyear&dd$year<=endyear&dd$prcp>=1,\"prcp\"]\n prcptmp<-prcptmp[is.na(prcptmp)==F]\n len<-length(prcptmp)\n prcp95<-percentile(len,prcptmp,0.95)\n prcp99<-percentile(len,prcptmp,0.99)\n\n ys<-yeare-years+1\n \n dp<-matrix(0,ys,4)\n dimnames(dp)<-list(NULL,c(\"year\",\"r95p\",\"r99p\",\"prcptot\"))\n dp[,\"year\"]<-years:yeare\n for(i in years:yeare){\n dp[(i-years+1),\"r95p\"]<-sum(dd[dd$year==i&dd$prcp>prcp95,\"prcp\"],na.rm=T)\n dp[(i-years+1),\"r99p\"]<-sum(dd[dd$year==i&dd$prcp>prcp99,\"prcp\"],na.rm=T)\n dp[(i-years+1),\"prcptot\"]<-sum(dd[dd$year==i&dd$prcp>=1,\"prcp\"],na.rm=T)\n }\n dp[,\"r95p\"]<-round(dp[,\"r95p\"],1)+ynacor[,\"ynapr>15\"]\n dp[,\"r99p\"]<-round(dp[,\"r99p\"],1)+ynacor[,\"ynapr>15\"]\n dp[,\"prcptot\"]<-round(dp[,\"prcptot\"],1)+ynacor[,\"ynapr>15\"]\n dp<-as.data.frame(dp)\n nam1<-paste(outinddir,paste(ofilename,\"_R95p.csv\",sep=\"\"),sep=\"/\")\n nam2<-paste(outinddir,paste(ofilename,\"_R99p.csv\",sep=\"\"),sep=\"/\")\n nam3<-paste(outinddir,paste(ofilename,\"_PRCPTOT.csv\",sep=\"\"),sep=\"/\")\n write.table(dp[,c(\"year\",\"r95p\")],file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n write.table(dp[,c(\"year\",\"r99p\")],file=nam2,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n write.table(dp[,c(\"year\",\"prcptot\")],file=nam3,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n for(i in c(\"r95p\",\"r99p\",\"prcptot\")){\n if(sum(is.na(dp[,i]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(dp[,\"year\"],dp[,i])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n}\n\n nam4<-paste(outjpgdir,paste(ofilename,\"_R95p.png\",sep=\"\"),sep=\"/\")\n png(nam4,width=1024,height=768,bg=\"white\")\n plotx(dp[,1],dp[,\"r95p\"],main=paste(\"R95p\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R95p\")\n dev.off()\n nam4a<-paste(outjpgdir,paste(ofilename,\"_R95p.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam4a)\n plotx(dp[,1],dp[,\"r95p\"],main=paste(\"R95p\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R95p\")\n dev.off()\n nam5<-paste(outjpgdir,paste(ofilename,\"_R99p.png\",sep=\"\"),sep=\"/\")\n png(nam5,width=1024,height=768,bg=\"white\")\n plotx(dp[,1],dp[,\"r99p\"],main=paste(\"R99p\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R99p\")\n dev.off()\n nam5a<-paste(outjpgdir,paste(ofilename,\"_R99p.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam5a)\n plotx(dp[,1],dp[,\"r99p\"],main=paste(\"R99p\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R99p\")\n dev.off()\n nam6<-paste(outjpgdir,paste(ofilename,\"_PRCPTOT.png\",sep=\"\"),sep=\"/\")\n png(nam6,width=1024,height=768,bg=\"white\")\n plotx(dp[,1],dp[,\"prcptot\"],main=paste(\"PRCPTOT\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"PRCPTOT\")\n dev.off()\n nam6a<-paste(outjpgdir,paste(ofilename,\"_PRCPTOT.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam6a)\n plotx(dp[,1],dp[,\"prcptot\"],main=paste(\"PRCPTOT\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"PRCPTOT\")\n dev.off()\n} # end of function r95ptot\n\ndaysprcp20<-function(){\n ys<-yeare-years+1\nR20<-rep(0,ys)\nyearss<-c(years:yeare)\ntarget<-as.data.frame(cbind(yearss,R20))\nfor (year in years:yeare){\n mid<-dd[dd$year==year,\"prcp\"]\n mid<-mid[is.na(mid)==F]\ntarget[target$yearss==year,\"R20\"]<-length(mid[mid>=20])}# end for\ndimnames(target)[[2]][1]<-\"year\"\n target[,\"R20\"]<-target[,\"R20\"]+ynacor[,\"ynapr>15\"]\nnam1<-paste(outinddir,paste(ofilename,\"_R20mm.csv\",sep=\"\"),sep=\"/\")\nwrite.table(target,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(target[,\"R20\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(target[,\"year\"],target[,\"R20\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"r20mm\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\nnam2<-paste(outjpgdir,paste(ofilename,\"_R20mm.png\",sep=\"\"),sep=\"/\")\npng(nam2,width=1024,height=768,bg=\"white\")\nplotx(target[,1],target[,2],main=paste(\"R20mm\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R20mm\")\ndev.off()\nnam2a<-paste(outjpgdir,paste(ofilename,\"_R20mm.pdf\",sep=\"\"),sep=\"/\")\npdf(nam2a)\nplotx(target[,1],target[,2],main=paste(\"R20mm\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R20mm\")\ndev.off()\n}\n\ndaysprcpn<-function(){\n ys<-yeare-years+1\nRnn<-rep(0,ys)\nyearss<-c(years:yeare)\ntarget<-as.data.frame(cbind(yearss,Rnn))\nfor (year in years:yeare){\n mid<-dd[dd$year==year,\"prcp\"]\n mid<-mid[is.na(mid)==F]\n target[target$yearss==year,\"Rnn\"]<-length(mid[mid>=nn])\n}\n dimnames(target)[[2]][1]<-\"year\"\n target[,\"Rnn\"]<-target[,\"Rnn\"]+ynacor[,\"ynapr>15\"]\n\n nam1<-paste(outinddir,paste(ofilename,\"_R\",as.character(nn),\"mm.csv\",sep=\"\"),sep=\"/\")\n write.table(target,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(target[,\"Rnn\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(target[,1],target[,\"Rnn\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,paste(\"R\",as.character(nn),\"mm\",sep=\"\"),years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_R\",as.character(nn),\"mm.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(target[,1],target[,2],main=paste(\"R\",as.character(nn),\"mm\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"Rnnmm\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_R\",as.character(nn),\"mm.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(target[,1],target[,2],main=paste(\"R\",as.character(nn),\"mm\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"Rnnmm\")\n dev.off()\n }\n\nnordaytem1<-function(){ # initialize data\n# normal temp\n\ndaynorm<-dd[dd$year>=startyear,]\ndaynorm<-daynorm[daynorm$year<=endyear,] # initialize daynorm matrix\ndaynor<-daynorm # create target matrix\nnn<-dd[dd$year==startpoint,]\nnn<-nn[nn$month==12,]\nnn<-nn[nn$day>(31-round(winsize/2)),]\ndaynorm<-rbind(nn,daynorm)\nnn<-dd[dd$year==endpoint,]\nnn<-nn[nn$month==1,]\nnn<-nn[nn$day<=round(winsize/2),]\ndaynorm<-rbind(daynorm,nn)\n\ndaynorm1<-daynorm[,-4]\ndaynorm1[daynorm1$month==2 & daynorm1$day==29,]<--99\ndaynorm1<-daynorm1[daynorm1$year!=-99,]\ndayt<-daynorm1[,c(\"tmax\",\"tmin\")]\n\nddtem<-dd[,-4]\nddtem[ddtem$month==2 & ddtem$day==29,]<--99\nddtem<-ddtem[ddtem$year!=-99,]\nassign(\"ddtem\",ddtem,envir=.GlobalEnv)\n\na<-matrix(-99,5,5)\ndimnames(a)[[2]]<-c(\"year\",\"month\",\"day\",\"tmax\",\"tmin\")\nddtemt<-rbind(a,ddtem);assign(\"ddtemt\",ddtemt,envir=.GlobalEnv)\n\nys<-endyear-startyear+1\nwindow<-matrix(0,winsize,2)\nwindows<-array(window,c(winsize,2,366,ys))\ndimnames(windows)<-list(NULL,c(\"tmax\",\"tmin\"),NULL,NULL)\n\ni=winsize-round(winsize/2,digits=0)\ni1=round(winsize/2,digits=0)\ndaynormm<-daynorm[,c(\"tmax\",\"tmin\")]\ndaynormm<-as.matrix(daynormm)\nyear<-startyear\n\nfor (k in 1:ys){\n if (leapyear(year)==T) jj<-366 else {jj<-365; windows[,,366,k]<--99 }\n year<-year+1\nfor (j in 1:jj){\n windows[,,j,k]<-daynormm[(i-i1):(i+i1),]\n i=i+1 }}\n\nmwindows<-colMeans(windows,na.rm=T)\ntmax<-mwindows[\"tmax\",,];tmax<-tmax[tmax!=-99]\ntmin<-mwindows[\"tmin\",,];tmin<-tmin[tmin!=-99]\ndaynor[,\"tmax\"]<-tmax;daynor[,\"tmin\"]<-tmin\n\na<-rep(0,nrow(daynor))\na<-(daynor[,\"tmax\"]+daynor[,\"tmin\"])/2\ndaytemave<-a \ndaynor<-cbind(daynor,daytemave)\n\n# output the result to globe enviroment\nassign(\"daynor\",daynor,envir=.GlobalEnv)\nassign(\"daynorm\",daynorm,envir=.GlobalEnv)\nassign(\"daynorm1\",daynorm1,envir=.GlobalEnv)\nassign(\"dayt\",dayt,envir=.GlobalEnv)\n\n# prcp percentile 95% and 99%\n prcpnorm<-dd[dd$year>=startyear,]\n prcpnorm<-prcpnorm[prcpnorm$year<=endyear,] # initialize prcpnorm matrix\n nnp<-dd[dd$year==startpoint,]\n nnp<-nnp[nnp$month==12,]\n nnp<-nnp[nnp$day>29,]\n prcpnorm<-rbind(nnp,prcpnorm)\n nnp<-dd[dd$year==endpoint,]\n nnp<-nnp[nnp$month==1,]\n nnp<-nnp[nnp$day<=2,]\n prcpnorm<-rbind(prcpnorm,nnp)\n prcpnorm<-prcpnorm[,1:4]\n# remove Feb 29\n prcpnorm[prcpnorm$month==2 & prcpnorm$day==29,]<--99\n prcpnorm<-prcpnorm[prcpnorm$year!=-99,]\n assign(\"prcpnorm\",prcpnorm,envir=.GlobalEnv)\n\n ys<-endyear-startyear+1 \n \n aasp<-matrix(NA,365,3)\n dimnames(aasp)<-list(NULL,c(\"day\",\"prcp95\",\"prcp99\"))\n aasp[,\"day\"]<-1:365\n\n msp<-5*ys\n prcpnorm<-as.matrix(prcpnorm)\n pwindow<-matrix(0,5,1)\n pwindows<-array(pwindow,c(5,1,365,ys)) #array used to store all windows\n ip=3\n ip1=2\n for (k in 1:ys){\n for (j in 1:365){\n \n pwindows[,,j,k]<-prcpnorm[(ip-ip1):(ip+ip1),\"prcp\"]\n ip=ip+1}}\n\n prcpwin<-matrix(0,msp,2)\n prcpwin[,2]<-1:ys\n prcpwin[,2]<-mysort(prcpwin[,2],decreasing=F)\n\n prcpwins<-array(prcpwin,c(msp,2,365)) \n\nfor (j in 1:365){\nfor (i in 1:ys){\n prcpwins[prcpwins[,2,j]==i,1,j]<-pwindows[,,j,i]}}\n#assign(\"exwins\",exwins,envir=.GlobalEnv)\n\n for (i in 1:365){\n assp<-prcpwins[,,i]\n if(sum(is.na(assp[,1])==F)>=1)\n aasp[i,\"prcp95\"]<-percentile(msp,assp[,1],0.95)\n else aasp[i,\"prcp95\"]<-NA\n if(sum(is.na(assp[,1])==F)>=1)\n aasp[i,\"prcp99\"]<-percentile(msp,assp[,1],0.99)\n else aasp[i,\"prcp99\"]<-NA\n }\nassign(\"aasp\",aasp,envir=.GlobalEnv)\n\naas<-matrix(NA,365,5)\ndimnames(aas)<-list(NULL,c(\"day\",\"pcmax10\",\"pcmax90\",\"pcmin10\",\"pcmin90\"))\naas[,\"day\"]<-1:365\n\nms<-winsize*ys\ndayt<-as.matrix(dayt)\nwindow<-matrix(0,winsize,2)\nwindows<-array(window,c(winsize,2,365,ys)) #array used to store all windows\ni=winsize-round(winsize/2,digits=0)\ni1=round(winsize/2,digits=0)\nfor (k in 1:ys){\nfor (j in 1:365){\n \n windows[,,j,k]<-dayt[(i-i1):(i+i1),]\n i=i+1}}\n\nexwin<-matrix(0,ms,3)\nexwin[,3]<-1:ys\nexwin[,3]<-mysort(exwin[,3],decreasing=F)\nassign(\"exwin\",exwin,envir=.GlobalEnv)\n#indd<-exwin[exwin[,3]!=ys,3]\nexwins<-array(exwin,c(ms,3,365)) # array for bootstrap\n\nfor (j in 1:365){\nfor (i in 1:ys){\n exwins[exwins[,3,j]==i,1:2,j]<-windows[,,j,i]}}\nassign(\"exwins\",exwins,envir=.GlobalEnv)\n\nfor ( i in 1:365){\n ass<-exwins[,,i]\n ass1<-ass[,1]\n ass2<-ass[,2]\n kgb1<-length(ass1[is.na(ass1)])\n kgb2<-length(ass2[is.na(ass2)])\n if (kgb1>37.5 | kgb2>37.5) {flag=T;break}}#150*0.25=37.5\n\nassign(\"flag\",flag,envir=.GlobalEnv)\nif (flag==T) tkmessageBox(message=\"More than 25% data missing, Exceedance rate, HWDI,CWDI will not be calculated!!\")\nif (flag==T) return()\nfor (i in 1:365){\n ass<-exwins[,,i]\n# if(i == 363) {\n# ttmp<-ms\n# assign(\"ttmp\",ttmp,envir=.GlobalEnv)\n# }\n itmp<-percentile(ms,ass[,1],c(0.1,0.9))\n aas[i,\"pcmax10\"]<-itmp[1]-1e-5\n aas[i,\"pcmax90\"]<-itmp[2]+1e-5\n itmp<-percentile(ms,ass[,2],c(0.1,0.9))\n aas[i,\"pcmin10\"]<-itmp[1]-1e-5\n aas[i,\"pcmin90\"]<-itmp[2]+1e-5 }\n\nassign(\"aas\",aas,envir=.GlobalEnv)# matrix to store 10 and 90 percentile\n# exceedance rate before 1961 and after 2000\nbefore<-dd[dd$yearendyear,]\n\n# dataframe store the before monthly exceedance rate\nys1<-startyear-years;ys2<-yeare-endyear\nbmonex<-matrix(NA,ys1*12,6)\ndimnames(bmonex)<-list(NULL,c(\"year\",\"month\",\"tx10p\",\"tx90p\",\"tn10p\",\"tn90p\"))\nbmonex[,\"month\"]<-rep(1:12,ys1)\nbmonex[,\"year\"]<-years:(startyear-1)\nbmonex[,\"year\"]<-mysort(bmonex[,\"year\"],decreasing=F)\nbmonex<-as.data.frame(bmonex)\n\n# dataframe store the after monthly exceedance rate\namonex<-matrix(NA,ys2*12,6)\ndimnames(amonex)<-list(NULL,c(\"year\",\"month\",\"tx10p\",\"tx90p\",\"tn10p\",\"tn90p\"))\namonex[,\"month\"]<-rep(1:12,ys2)\namonex[,\"year\"]<-(endyear+1):yeare\namonex[,\"year\"]<-mysort(amonex[,\"year\"],decreasing=F)\namonex<-as.data.frame(amonex)\n\n# dataframe store yearly exceedance rate (before and after)\nyearex<-c(years:(startyear-1));txg10p<-rep(0,length(yearex))\ntxg90p<-rep(0,length(yearex));tng10p<-rep(0,length(yearex))\ntng90p<-rep(0,length(yearex))\nbd<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))\ncolnames(bd)[1]<-\"year\"\n\nyearex<-c((endyear+1):yeare);txg10p<-rep(0,length(yearex))\ntxg90p<-rep(0,length(yearex));tng10p<-rep(0,length(yearex))\ntng90p<-rep(0,length(yearex))\nad<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))\ncolnames(ad)[1]<-\"year\"\n\nyear=years;jjj6=1\nfor (i in 1:ys1){\n midvalue<-ddtem[ddtem$year==year,]\n exmax10<-midvalue[,4]-aas[,2]\n exmax10m1<-exmax10[1:31];exmax10m2<-exmax10[32:59];exmax10m3<-exmax10[60:90]\n exmax10m4<-exmax10[91:120];exmax10m5<-exmax10[121:151];exmax10m6<-exmax10[152:181]\n exmax10m7<-exmax10[182:212];exmax10m8<-exmax10[213:243];exmax10m9<-exmax10[244:273]\n exmax10m10<-exmax10[274:304];exmax10m11<-exmax10[305:334];exmax10m12<-exmax10[335:365]\n \n exmax90<-midvalue[,4]-aas[,3]\n exmax90m1<-exmax90[1:31];exmax90m2<-exmax90[32:59];exmax90m3<-exmax90[60:90]\n exmax90m4<-exmax90[91:120];exmax90m5<-exmax90[121:151];exmax90m6<-exmax90[152:181]\n exmax90m7<-exmax90[182:212];exmax90m8<-exmax90[213:243];exmax90m9<-exmax90[244:273]\n exmax90m10<-exmax90[274:304];exmax90m11<-exmax90[305:334];exmax90m12<-exmax90[335:365]\n\n exmin10<-midvalue[,5]-aas[,4]\n exmin10m1<-exmin10[1:31];exmin10m2<-exmin10[32:59];exmin10m3<-exmin10[60:90]\n exmin10m4<-exmin10[91:120];exmin10m5<-exmin10[121:151];exmin10m6<-exmin10[152:181]\n exmin10m7<-exmin10[182:212];exmin10m8<-exmin10[213:243];exmin10m9<-exmin10[244:273]\n exmin10m10<-exmin10[274:304];exmin10m11<-exmin10[305:334];exmin10m12<-exmin10[335:365]\n\n exmin90<-midvalue[,5]-aas[,5]\n exmin90m1<-exmin90[1:31];exmin90m2<-exmin90[32:59];exmin90m3<-exmin90[60:90]\n exmin90m4<-exmin90[91:120];exmin90m5<-exmin90[121:151];exmin90m6<-exmin90[152:181]\n exmin90m7<-exmin90[182:212];exmin90m8<-exmin90[213:243];exmin90m9<-exmin90[244:273]\n exmin90m10<-exmin90[274:304];exmin90m11<-exmin90[305:334];exmin90m12<-exmin90[335:365]\n\n bd[i,\"txg10p\"]<-length(exmax10[exmax10<0&is.na(exmax10)==F])\n bd[i,\"txg90p\"]<-length(exmax90[exmax90>0&is.na(exmax90)==F])\n bd[i,\"tng10p\"]<-length(exmin10[exmin10<0&is.na(exmin10)==F])\n bd[i,\"tng90p\"]<-length(exmin90[exmin90>0&is.na(exmin90)==F])\n \n bmonex[jjj6,\"tx10p\"]<-length(exmax10m1[exmax10m1<0&is.na(exmax10m1)==F])\n bmonex[jjj6,\"tx90p\"]<-length(exmax90m1[exmax90m1>0&is.na(exmax90m1)==F])\n bmonex[jjj6,\"tn10p\"]<-length(exmin10m1[exmin10m1<0&is.na(exmin10m1)==F])\n bmonex[jjj6,\"tn90p\"]<-length(exmin90m1[exmin90m1>0&is.na(exmin90m1)==F])\n\n bmonex[jjj6+1,\"tx10p\"]<-length(exmax10m2[exmax10m2<0&is.na(exmax10m2)==F])\n bmonex[jjj6+1,\"tx90p\"]<-length(exmax90m2[exmax90m2>0&is.na(exmax90m2)==F])\n bmonex[jjj6+1,\"tn10p\"]<-length(exmin10m2[exmin10m2<0&is.na(exmin10m2)==F])\n bmonex[jjj6+1,\"tn90p\"]<-length(exmin90m2[exmin90m2>0&is.na(exmin90m2)==F]) \n\n bmonex[jjj6+2,\"tx10p\"]<-length(exmax10m3[exmax10m3<0&is.na(exmax10m3)==F])\n bmonex[jjj6+2,\"tx90p\"]<-length(exmax90m3[exmax90m3>0&is.na(exmax90m3)==F])\n bmonex[jjj6+2,\"tn10p\"]<-length(exmin10m3[exmin10m3<0&is.na(exmin10m3)==F])\n bmonex[jjj6+2,\"tn90p\"]<-length(exmin90m3[exmin90m3>0&is.na(exmin90m3)==F])\n\n bmonex[jjj6+3,\"tx10p\"]<-length(exmax10m4[exmax10m4<0&is.na(exmax10m4)==F])\n bmonex[jjj6+3,\"tx90p\"]<-length(exmax90m4[exmax90m4>0&is.na(exmax90m4)==F])\n bmonex[jjj6+3,\"tn10p\"]<-length(exmin10m4[exmin10m4<0&is.na(exmin10m4)==F])\n bmonex[jjj6+3,\"tn90p\"]<-length(exmin90m4[exmin90m4>0&is.na(exmin90m4)==F])\n\n bmonex[jjj6+4,\"tx10p\"]<-length(exmax10m5[exmax10m5<0&is.na(exmax10m5)==F])\n bmonex[jjj6+4,\"tx90p\"]<-length(exmax90m5[exmax90m5>0&is.na(exmax90m5)==F])\n bmonex[jjj6+4,\"tn10p\"]<-length(exmin10m5[exmin10m5<0&is.na(exmin10m5)==F])\n bmonex[jjj6+4,\"tn90p\"]<-length(exmin90m5[exmin90m5>0&is.na(exmin90m5)==F])\n\n bmonex[jjj6+5,\"tx10p\"]<-length(exmax10m6[exmax10m6<0&is.na(exmax10m6)==F])\n bmonex[jjj6+5,\"tx90p\"]<-length(exmax90m6[exmax90m6>0&is.na(exmax90m6)==F])\n bmonex[jjj6+5,\"tn10p\"]<-length(exmin10m6[exmin10m6<0&is.na(exmin10m6)==F])\n bmonex[jjj6+5,\"tn90p\"]<-length(exmin90m6[exmin90m6>0&is.na(exmin90m6)==F])\n\n bmonex[jjj6+6,\"tx10p\"]<-length(exmax10m7[exmax10m7<0&is.na(exmax10m7)==F])\n bmonex[jjj6+6,\"tx90p\"]<-length(exmax90m7[exmax90m7>0&is.na(exmax90m7)==F])\n bmonex[jjj6+6,\"tn10p\"]<-length(exmin10m7[exmin10m7<0&is.na(exmin10m7)==F])\n bmonex[jjj6+6,\"tn90p\"]<-length(exmin90m7[exmin90m7>0&is.na(exmin90m7)==F])\n\n bmonex[jjj6+7,\"tx10p\"]<-length(exmax10m8[exmax10m8<0&is.na(exmax10m8)==F])\n bmonex[jjj6+7,\"tx90p\"]<-length(exmax90m8[exmax90m8>0&is.na(exmax90m8)==F])\n bmonex[jjj6+7,\"tn10p\"]<-length(exmin10m8[exmin10m8<0&is.na(exmin10m8)==F])\n bmonex[jjj6+7,\"tn90p\"]<-length(exmin90m8[exmin90m8>0&is.na(exmin90m8)==F])\n\n bmonex[jjj6+8,\"tx10p\"]<-length(exmax10m9[exmax10m9<0&is.na(exmax10m9)==F])\n bmonex[jjj6+8,\"tx90p\"]<-length(exmax90m9[exmax90m9>0&is.na(exmax90m9)==F])\n bmonex[jjj6+8,\"tn10p\"]<-length(exmin10m9[exmin10m9<0&is.na(exmin10m9)==F])\n bmonex[jjj6+8,\"tn90p\"]<-length(exmin90m9[exmin90m9>0&is.na(exmin90m9)==F])\n\n bmonex[jjj6+9,\"tx10p\"]<-length(exmax10m10[exmax10m10<0&is.na(exmax10m10)==F])\n bmonex[jjj6+9,\"tx90p\"]<-length(exmax90m10[exmax90m10>0&is.na(exmax90m10)==F])\n bmonex[jjj6+9,\"tn10p\"]<-length(exmin10m10[exmin10m10<0&is.na(exmin10m10)==F])\n bmonex[jjj6+9,\"tn90p\"]<-length(exmin90m10[exmin90m10>0&is.na(exmin90m10)==F])\n\n bmonex[jjj6+10,\"tx10p\"]<-length(exmax10m11[exmax10m11<0&is.na(exmax10m11)==F])\n bmonex[jjj6+10,\"tx90p\"]<-length(exmax90m11[exmax90m11>0&is.na(exmax90m11)==F])\n bmonex[jjj6+10,\"tn10p\"]<-length(exmin10m11[exmin10m11<0&is.na(exmin10m11)==F])\n bmonex[jjj6+10,\"tn90p\"]<-length(exmin90m11[exmin90m11>0&is.na(exmin90m11)==F])\n\n bmonex[jjj6+11,\"tx10p\"]<-length(exmax10m12[exmax10m12<0&is.na(exmax10m12)==F])\n bmonex[jjj6+11,\"tx90p\"]<-length(exmax90m12[exmax90m12>0&is.na(exmax90m12)==F])\n bmonex[jjj6+11,\"tn10p\"]<-length(exmin10m12[exmin10m12<0&is.na(exmin10m12)==F])\n bmonex[jjj6+11,\"tn90p\"]<-length(exmin90m12[exmin90m12>0&is.na(exmin90m12)==F])\n\n if(leapyear(year)){\n if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"]>aas[59,\"pcmax90\"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"])==F&is.na(aas[59,\"pcmax90\"])==F)\n bmonex[jjj6+1,\"tx90p\"]<-bmonex[jjj6+1,\"tx90p\"]+1\n if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"]aas[59,\"pcmin90\"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,\"tmin\"])==F&is.na(aas[59,\"pcmin90\"])==F)\n bmonex[jjj6+1,\"tn90p\"]<-bmonex[jjj6+1,\"tn90p\"]+1\n if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmin\"]0&is.na(exmax90)==F])\n ad[i,\"tng10p\"]<-length(exmin10[exmin10<0&is.na(exmin10)==F])\n ad[i,\"tng90p\"]<-length(exmin90[exmin90>0&is.na(exmin90)==F])\n \n amonex[jjj6,\"tx10p\"]<-length(exmax10m1[exmax10m1<0&is.na(exmax10m1)==F])\n amonex[jjj6,\"tx90p\"]<-length(exmax90m1[exmax90m1>0&is.na(exmax90m1)==F])\n amonex[jjj6,\"tn10p\"]<-length(exmin10m1[exmin10m1<0&is.na(exmin10m1)==F])\n amonex[jjj6,\"tn90p\"]<-length(exmin90m1[exmin90m1>0&is.na(exmin90m1)==F])\n\n amonex[jjj6+1,\"tx10p\"]<-length(exmax10m2[exmax10m2<0&is.na(exmax10m2)==F])\n amonex[jjj6+1,\"tx90p\"]<-length(exmax90m2[exmax90m2>0&is.na(exmax90m2)==F])\n amonex[jjj6+1,\"tn10p\"]<-length(exmin10m2[exmin10m2<0&is.na(exmin10m2)==F])\n amonex[jjj6+1,\"tn90p\"]<-length(exmin90m2[exmin90m2>0&is.na(exmin90m2)==F]) \n\n amonex[jjj6+2,\"tx10p\"]<-length(exmax10m3[exmax10m3<0&is.na(exmax10m3)==F])\n amonex[jjj6+2,\"tx90p\"]<-length(exmax90m3[exmax90m3>0&is.na(exmax90m3)==F])\n amonex[jjj6+2,\"tn10p\"]<-length(exmin10m3[exmin10m3<0&is.na(exmin10m3)==F])\n amonex[jjj6+2,\"tn90p\"]<-length(exmin90m3[exmin90m3>0&is.na(exmin90m3)==F])\n\n amonex[jjj6+3,\"tx10p\"]<-length(exmax10m4[exmax10m4<0&is.na(exmax10m4)==F])\n amonex[jjj6+3,\"tx90p\"]<-length(exmax90m4[exmax90m4>0&is.na(exmax90m4)==F])\n amonex[jjj6+3,\"tn10p\"]<-length(exmin10m4[exmin10m4<0&is.na(exmin10m4)==F])\n amonex[jjj6+3,\"tn90p\"]<-length(exmin90m4[exmin90m4>0&is.na(exmin90m4)==F])\n\n amonex[jjj6+4,\"tx10p\"]<-length(exmax10m5[exmax10m5<0&is.na(exmax10m5)==F])\n amonex[jjj6+4,\"tx90p\"]<-length(exmax90m5[exmax90m5>0&is.na(exmax90m5)==F])\n amonex[jjj6+4,\"tn10p\"]<-length(exmin10m5[exmin10m5<0&is.na(exmin10m5)==F])\n amonex[jjj6+4,\"tn90p\"]<-length(exmin90m5[exmin90m5>0&is.na(exmin90m5)==F])\n\n amonex[jjj6+5,\"tx10p\"]<-length(exmax10m6[exmax10m6<0&is.na(exmax10m6)==F])\n amonex[jjj6+5,\"tx90p\"]<-length(exmax90m6[exmax90m6>0&is.na(exmax90m6)==F])\n amonex[jjj6+5,\"tn10p\"]<-length(exmin10m6[exmin10m6<0&is.na(exmin10m6)==F])\n amonex[jjj6+5,\"tn90p\"]<-length(exmin90m6[exmin90m6>0&is.na(exmin90m6)==F])\n\n amonex[jjj6+6,\"tx10p\"]<-length(exmax10m7[exmax10m7<0&is.na(exmax10m7)==F])\n amonex[jjj6+6,\"tx90p\"]<-length(exmax90m7[exmax90m7>0&is.na(exmax90m7)==F])\n amonex[jjj6+6,\"tn10p\"]<-length(exmin10m7[exmin10m7<0&is.na(exmin10m7)==F])\n amonex[jjj6+6,\"tn90p\"]<-length(exmin90m7[exmin90m7>0&is.na(exmin90m7)==F])\n\n amonex[jjj6+7,\"tx10p\"]<-length(exmax10m8[exmax10m8<0&is.na(exmax10m8)==F])\n amonex[jjj6+7,\"tx90p\"]<-length(exmax90m8[exmax90m8>0&is.na(exmax90m8)==F])\n amonex[jjj6+7,\"tn10p\"]<-length(exmin10m8[exmin10m8<0&is.na(exmin10m8)==F])\n amonex[jjj6+7,\"tn90p\"]<-length(exmin90m8[exmin90m8>0&is.na(exmin90m8)==F])\n\n amonex[jjj6+8,\"tx10p\"]<-length(exmax10m9[exmax10m9<0&is.na(exmax10m9)==F])\n amonex[jjj6+8,\"tx90p\"]<-length(exmax90m9[exmax90m9>0&is.na(exmax90m9)==F])\n amonex[jjj6+8,\"tn10p\"]<-length(exmin10m9[exmin10m9<0&is.na(exmin10m9)==F])\n amonex[jjj6+8,\"tn90p\"]<-length(exmin90m9[exmin90m9>0&is.na(exmin90m9)==F])\n\n amonex[jjj6+9,\"tx10p\"]<-length(exmax10m10[exmax10m10<0&is.na(exmax10m10)==F])\n amonex[jjj6+9,\"tx90p\"]<-length(exmax90m10[exmax90m10>0&is.na(exmax90m10)==F])\n amonex[jjj6+9,\"tn10p\"]<-length(exmin10m10[exmin10m10<0&is.na(exmin10m10)==F])\n amonex[jjj6+9,\"tn90p\"]<-length(exmin90m10[exmin90m10>0&is.na(exmin90m10)==F])\n\n amonex[jjj6+10,\"tx10p\"]<-length(exmax10m11[exmax10m11<0&is.na(exmax10m11)==F])\n amonex[jjj6+10,\"tx90p\"]<-length(exmax90m11[exmax90m11>0&is.na(exmax90m11)==F])\n amonex[jjj6+10,\"tn10p\"]<-length(exmin10m11[exmin10m11<0&is.na(exmin10m11)==F])\n amonex[jjj6+10,\"tn90p\"]<-length(exmin90m11[exmin90m11>0&is.na(exmin90m11)==F])\n\n amonex[jjj6+11,\"tx10p\"]<-length(exmax10m12[exmax10m12<0&is.na(exmax10m12)==F])\n amonex[jjj6+11,\"tx90p\"]<-length(exmax90m12[exmax90m12>0&is.na(exmax90m12)==F])\n amonex[jjj6+11,\"tn10p\"]<-length(exmin10m12[exmin10m12<0&is.na(exmin10m12)==F])\n amonex[jjj6+11,\"tn90p\"]<-length(exmin90m12[exmin90m12>0&is.na(exmin90m12)==F])\n\n if(leapyear(year)){\n if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"]>aas[59,\"pcmax90\"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"])==F&is.na(aas[59,\"pcmax90\"])==F)\n amonex[jjj6+1,\"tx90p\"]<-amonex[jjj6+1,\"tx90p\"]+1\n if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"]aas[59,\"pcmin90\"]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,\"tmin\"])==F&is.na(aas[59,\"pcmin90\"])==F)\n amonex[jjj6+1,\"tn90p\"]<-amonex[jjj6+1,\"tn90p\"]+1\n if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmin\"]3\"]+nacor[,\"mnatmi>3\"]\n ofile<-matrix(0,len,14)\n dimnames(ofile)<-list(NULL,c(\"year\",\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\",\"annual\"))\n ofile<-as.data.frame(ofile)\n for(j in years:yeare){\n k<-j-years+1\n ofile[k,1]<-j\n ofile[k,2:13]<-round(aa1[aa1[,\"year\"]==j,\"dtr\"],digit=2)\n ofile[k,14]<-round(mean(t(ofile[k,2:13]),na.rm=T),digit=2)\n }\n ofile[,14]<-ofile[,14]+ynacor[,\"ynatma>15\"]+ynacor[,\"ynatmi>15\"]\n nam1<-paste(outinddir,paste(ofilename,\"_DTR.csv\",sep=\"\"),sep=\"/\")\n write.table(ofile,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(ofile[,1],ofile[,14])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"dtr\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_DTR.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(ofile[,1],ofile[,14],main=paste(\"DTR\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"DTR\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_DTR.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(ofile[,1],ofile[,14],main=paste(\"DTR\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"DTR\")\n dev.off()\n } # end of dtr\n\ndaysprcp10<-function(){\n ys<-yeare-years+1\nR10<-rep(0,ys)\nyearss<-c(years:yeare)\ntarget<-as.data.frame(cbind(yearss,R10))\nfor (year in years:yeare){\n mid<-dd[dd$year==year,\"prcp\"]\n mid<-mid[is.na(mid)==F]\ntarget[target$yearss==year,\"R10\"]<-length(mid[mid>=10])}\ndimnames(target)[[2]][1]<-\"year\"\n target[,\"R10\"]<-target[,\"R10\"]+ynacor[,\"ynapr>15\"]\nnam1<-paste(outinddir,paste(ofilename,\"_R10mm.csv\",sep=\"\"),sep=\"/\")\nwrite.table(target,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(target[,\"R10\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(target[,1],target[,\"R10\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"r10mm\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\nnam2<-paste(outjpgdir,paste(ofilename,\"_R10mm.png\",sep=\"\"),sep=\"/\")\npng(nam2,width=1024,height=768)\nplotx(target[,1],target[,2],main=paste(\"R10mm\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R10mm\")\ndev.off()\nnam2a<-paste(outjpgdir,paste(ofilename,\"_R10mm.pdf\",sep=\"\"),sep=\"/\")\npdf(nam2a)\nplotx(target[,1],target[,2],main=paste(\"R10mm\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"R10mm\")\ndev.off()\n}\n\nextremedays<-function(opt=0){\n if(opt==0){\n euu=uu\n eul=ul\n elu=lu\n ell=ll\n }\n else{\n euu=uuu\n eul=uul\n elu=ulu\n ell=ull\n }\n ys<-yeare-years+1\n# beginyear<-dd[1,1]\n# endyear<-dd[dim(dd)[1],1]\n tclext<-c(years:yeare)\n su<-rep(0,ys)\n id<-su\n tr<-su\n fd<-su\n tclext<-cbind(tclext,su,id,tr,fd)\n dimnames(tclext)[[2]][1]<-\"year\"\n i=1\n for (year in years:yeare) {\n mid1<-dd[dd$year==year,\"tmax\"];mid1<-mid1[is.na(mid1)==F]\n mid2<-dd[dd$year==year,\"tmin\"];mid2<-mid2[is.na(mid2)==F]\n tclext[i,\"su\"]<-length(mid1[mid1>euu])\n tclext[i,\"id\"]<-length(mid1[mid1elu])\n tclext[i,\"fd\"]<-length(mid2[mid215\"]\n tclext[,\"id\"]<-tclext[,\"id\"]+ynacor[,\"ynatma>15\"]\n tclext[,\"tr\"]<-tclext[,\"tr\"]+ynacor[,\"ynatmi>15\"]\n tclext[,\"fd\"]<-tclext[,\"fd\"]+ynacor[,\"ynatmi>15\"]\n # assign(\"extdays\",tclext,envir=.GlobalEnv)\n if(opt==0){\n nam1<-paste(outinddir,paste(ofilename,\"_SU25.csv\",sep=\"\"),sep=\"/\")\n nam2<-paste(outinddir,paste(ofilename,\"_ID0.csv\",sep=\"\"),sep=\"/\")\n nam3<-paste(outinddir,paste(ofilename,\"_TR20.csv\",sep=\"\"),sep=\"/\")\n nam4<-paste(outinddir,paste(ofilename,\"_FD0.csv\",sep=\"\"),sep=\"/\")\n }\n else{\n nam1<-paste(outinddir,paste(ofilename,\"_SU\",as.character(euu),\".csv\",sep=\"\"),sep=\"/\")\n nam2<-paste(outinddir,paste(ofilename,\"_ID\",as.character(eul),\".csv\",sep=\"\"),sep=\"/\")\n nam3<-paste(outinddir,paste(ofilename,\"_TR\",as.character(elu),\".csv\",sep=\"\"),sep=\"/\")\n nam4<-paste(outinddir,paste(ofilename,\"_FD\",as.character(ell),\".csv\",sep=\"\"),sep=\"/\")\n }\n\n write.table(tclext[,c(\"year\",\"su\")],file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n write.table(tclext[,c(\"year\",\"id\")],file=nam2,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n write.table(tclext[,c(\"year\",\"tr\")],file=nam3,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n write.table(tclext[,c(\"year\",\"fd\")],file=nam4,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n# output trend base on annual indicies data\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n for( i in c(\"su\",\"id\",\"tr\",\"fd\")){\n if(sum(is.na(tclext[,i]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(tclext[,\"year\"],tclext[,i])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n if(opt==0){\n if(i==\"su\") ii<-\"su25\"\n if(i==\"id\") ii<-\"id0\"\n if(i==\"fd\") ii<-\"fd0\"\n if(i==\"tr\") ii<-\"tr20\"\n }\n else{\n if(i==\"su\") ii<-paste(\"su\",as.character(euu),sep=\"\")\n if(i==\"id\") ii<-paste(\"id\",as.character(eul),sep=\"\")\n if(i==\"tr\") ii<-paste(\"tr\",as.character(elu),sep=\"\")\n if(i==\"fd\") ii<-paste(\"fd\",as.character(ell),sep=\"\")\n }\n cat(file=namt,paste(latitude,longitude,ii,years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n }\n \n namp<-c(\"\",\"\",\"\",\"\")\n namppdf<-c(\"\",\"\",\"\",\"\")\n if(opt==0){\n namp[1]<-paste(outjpgdir,paste(ofilename,\"_SU25.png\",sep=\"\"),sep=\"/\")\n namp[2]<-paste(outjpgdir,paste(ofilename,\"_ID0.png\",sep=\"\"),sep=\"/\")\n namp[3]<-paste(outjpgdir,paste(ofilename,\"_TR20.png\",sep=\"\"),sep=\"/\")\n namp[4]<-paste(outjpgdir,paste(ofilename,\"_FD0.png\",sep=\"\"),sep=\"/\")\n namppdf[1]<-paste(outjpgdir,paste(ofilename,\"_SU25.pdf\",sep=\"\"),sep=\"/\")\n namppdf[2]<-paste(outjpgdir,paste(ofilename,\"_ID0.pdf\",sep=\"\"),sep=\"/\")\n namppdf[3]<-paste(outjpgdir,paste(ofilename,\"_TR20.pdf\",sep=\"\"),sep=\"/\")\n namppdf[4]<-paste(outjpgdir,paste(ofilename,\"_FD0.pdf\",sep=\"\"),sep=\"/\")\n }\n else{\n namp[1]<-paste(outjpgdir,paste(ofilename,\"_SU\",as.character(euu),\".png\",sep=\"\"),sep=\"/\")\n namp[2]<-paste(outjpgdir,paste(ofilename,\"_ID\",as.character(eul),\".png\",sep=\"\"),sep=\"/\")\n namp[3]<-paste(outjpgdir,paste(ofilename,\"_TR\",as.character(elu),\".png\",sep=\"\"),sep=\"/\")\n namp[4]<-paste(outjpgdir,paste(ofilename,\"_FD\",as.character(ell),\".png\",sep=\"\"),sep=\"/\")\n namppdf[1]<-paste(outjpgdir,paste(ofilename,\"_SU\",as.character(euu),\".pdf\",sep=\"\"),sep=\"/\")\n namppdf[2]<-paste(outjpgdir,paste(ofilename,\"_ID\",as.character(eul),\".pdf\",sep=\"\"),sep=\"/\")\n namppdf[3]<-paste(outjpgdir,paste(ofilename,\"_TR\",as.character(elu),\".pdf\",sep=\"\"),sep=\"/\")\n namppdf[4]<-paste(outjpgdir,paste(ofilename,\"_FD\",as.character(ell),\".pdf\",sep=\"\"),sep=\"/\")\n \n }\n if(opt==0) ylab<-c(\"SU25\",\"ID0\",\"TR20\",\"FD0\")\n else ylab<-c(paste(\"SU\",as.character(euu),sep=\"\"), \n paste(\"ID\",as.character(eul),sep=\"\"), \n paste(\"TR\",as.character(elu),sep=\"\"), \n paste(\"FD\",as.character(ell),sep=\"\"))\n\n xlab<-rep(\"year\",4)\n for(i in 1:4){\n title1[i]<-paste(ylab[i],ofilename,sep=\" \")\n png(file=namp[i],width=1024,height=768,bg=\"white\")\n plotx(tclext[,1],tclext[,i+1],main=title1[i],ylab=ylab[i],xlab=\"Año\")\n dev.off()\n pdf(file=namppdf[i])\n plotx(tclext[,1],tclext[,i+1],main=title1[i],ylab=ylab[i],xlab=\"Año\")\n dev.off()\n }\n}\n\nexceedance<-function(){\n if (flag==T) return()\n a<-1:365\n ys<-endyear-startyear+1;yss<-ys-1\n mondays<-c(31,28,31,30,31,30,31,31,30,31,30,31)\n mone<-rep(0,12);mons<-mone\n for(i in 1:12) mone[i]<-sum(mondays[1:i])\n mons[1]<-1\n for(i in 2:12) mons[i]<-mone[i-1]+1\n\n monex<-matrix(NA,ys*12,6)\n dimnames(monex)<-list(NULL,c(\"year\",\"month\",\"tx10p\",\"tx90p\",\"tn10p\",\"tn90p\"))\n monex[,\"month\"]<-rep(1:12,ys)\n monex[,\"year\"]<-startyear:endyear\n monex[,\"year\"]<-mysort(monex[,\"year\"],decreasing=F)\n monex<-as.data.frame(monex)\n\n\n b<-matrix(0,365,4)\n a<-cbind(a,b)\n aa<-array(a,c(365,5,ys))\n dimnames(aa)<-list(NULL,c(\"day\",\"pcmax10\",\"pcmax90\",\"pcmin10\",\"pcmin90\"),NULL)\n ms<-winsize*ys\n i=winsize-round(winsize/2,digits=0)\n i1=round(winsize/2,digits=0)\n \n# daynorm2<-daynorm1[-(1:i1),] # daynorm2 is total base period normalized data\n daynorm2<-dd[dd$year>=startyear,]\n daynorm2<-daynorm2[daynorm2$year<=endyear,]\n daynorm2<-daynorm2[daynorm2$month!=2|daynorm2$day!=29,]\n daynorm2<-daynorm2[,-4]\n# i2<-dim(daynorm2)[1]\n# i3<-i2-i1+1\n# daynorm2<-daynorm2[-(i3:i2),]\n \n yearex<-c(startyear:endyear);txg10p<-rep(0,length(yearex))\n txg90p<-rep(0,length(yearex));tng10p<-rep(0,length(yearex))\n tng90p<-rep(0,length(yearex))\n d<-as.data.frame(cbind(yearex,txg10p,txg90p,tng10p,tng90p))\n colnames(d)[1]<-\"year\"\n\n monex<-matrix(0,ys*12,6)\n dimnames(monex)<-list(NULL,c(\"year\",\"month\",\"tx10p\",\"tx90p\",\"tn10p\",\"tn90p\"))\n monex[,\"month\"]<-rep(1:12,ys)\n monex[,\"year\"]<-startyear:endyear\n monex[,\"year\"]<-mysort(monex[,\"year\"],decreasing=F)\n monex<-as.data.frame(monex)\n\n ratecount<-matrix(0,365,4)\n dimnames(ratecount)<-list(NULL,c(\"pcmax10\",\"pcmax90\",\"pcmin10\",\"pcmin90\"))\n\n for (year in startyear:endyear){ # year loop start\n\n midvalue<-daynorm2[daynorm2$year==year,]\n zz=year-startpoint #index in base period, say, zzth year\n\n indd<-exwin[exwin[,3]!=ys,3]\n \n for (k in 1:(ys-1)){ # for k (boot strap) start\n\n for (i in 1:365){ # day loop start\n ppc<-exwins[,,i]\n ppc<-ppc[ppc[,3]!=zz,]\n ppc<-ppc[,-3]\n \n ppc<-cbind(ppc,indd)\n \n ppcc<-rbind(ppc[ppc[,\"indd\"]==k,],ppc)\n\titmp<-percentile(ms,ppcc[,1],c(0.1,0.9))\n aa[i,\"pcmax10\",zz]<-itmp[1]-1e-5\n aa[i,\"pcmax90\",zz]<-itmp[2]+1e-5\n\titmp<-percentile(ms,ppcc[,2],c(0.1,0.9))\n aa[i,\"pcmin10\",zz]<-itmp[1]-1e-5\n aa[i,\"pcmin90\",zz]<-itmp[2]+1e-5\n }\n ratecount[,\"pcmax10\"]<-midvalue[,\"tmax\"]-aa[,\"pcmax10\",zz]\n ratecount[,\"pcmax90\"]<-midvalue[,\"tmax\"]-aa[,\"pcmax90\",zz]\n ratecount[,\"pcmin10\"]<-midvalue[,\"tmin\"]-aa[,\"pcmin10\",zz]\n ratecount[,\"pcmin90\"]<-midvalue[,\"tmin\"]-aa[,\"pcmin90\",zz]\n for(mon in 1:12){\n tmptx10p<-ratecount[mons[mon]:mone[mon],\"pcmax10\"]\n tmptx90p<-ratecount[mons[mon]:mone[mon],\"pcmax90\"]\n tmptn10p<-ratecount[mons[mon]:mone[mon],\"pcmin10\"]\n tmptn90p<-ratecount[mons[mon]:mone[mon],\"pcmin90\"]\n monex[(zz-1)*12+mon,\"tx10p\"]<- monex[(zz-1)*12+mon,\"tx10p\"]+length(tmptx10p[tmptx10p<0&is.na(tmptx10p)==F])\n monex[(zz-1)*12+mon,\"tx90p\"]<- monex[(zz-1)*12+mon,\"tx90p\"]+length(tmptx90p[tmptx90p>0&is.na(tmptx90p)==F])\n monex[(zz-1)*12+mon,\"tn10p\"]<- monex[(zz-1)*12+mon,\"tn10p\"]+length(tmptn10p[tmptn10p<0&is.na(tmptn10p)==F])\n monex[(zz-1)*12+mon,\"tn90p\"]<- monex[(zz-1)*12+mon,\"tn90p\"]+length(tmptn90p[tmptn90p>0&is.na(tmptn90p)==F])\n\tif(leapyear(year)&mon==2){\n\t if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"]>aa[59,\"pcmax90\",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"])==F&is.na(aa[58,\"pcmax90\",zz])==F)\n\t monex[(zz-1)*12+mon,\"tx90p\"]<-monex[(zz-1)*12+mon,\"tx90p\"]+1\n\t if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmax\"]aa[59,\"pcmin90\",zz]&is.na(dd[dd$year==year&dd$month==2&dd$day==29,\"tmin\"])==F&is.na(aa[58,\"pcmin90\",zz])==F)\n\t monex[(zz-1)*12+mon,\"tn90p\"]<-monex[(zz-1)*12+mon,\"tn90p\"]+1\n\t if(dd[dd$year==year&dd$month==2&dd$day==29,\"tmin\"]10) ofile[k,(mon+1)]<-NA\n else ofile[k,(mon+1)]<-dm[(k-1)*12+mon,kk]*fulldays[mon]/(fulldays[mon]-nastatistic[(k-1)*12+mon,nastat])\n }\n ofile[k,14]<-sum(ofile[k,2:13],na.rm=T)\n }\n ofile[,14]<-ofile[,14]+ynacor[,nastat-4]\n for(j in years:yeare){\n k<-j-years+1\n if(leapyear(j)) fulldays<-c(31,29,31,30,31,30,31,31,30,31,30,31)\n else fulldays<-c(31,28,31,30,31,30,31,31,30,31,30,31)\n for(mon in 1:12) ofile[k,mon+1]<-ofile[k,mon+1]*100/fulldays[mon] # change output from counting days to %\n }\n ofile[,14]<-ofile[,14]*100/365 # change output from counting days to %\n write.table(round(ofile,2),file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n \n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(ofile[,1],ofile[,14])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_\",toupper(i),\".png\",sep=\"\"),sep=\"/\")\n png(file=nam2,width=1024,height=768,bg=\"white\")\n plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep=\" \"),ylab=toupper(i),xlab=\"Año\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_\",toupper(i),\".pdf\",sep=\"\"),sep=\"/\")\n pdf(file=nam2a)\n plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep=\" \"),ylab=toupper(i),xlab=\"Año\")\n dev.off()\n }\n}\n\nindex641cdd<-function(){\nys<-yeare-years+1\ncdd<-rep(0,ys)\nyear<-c(years:yeare)\ntarget<-as.data.frame(cbind(year,cdd))\nyear=years\nfor (i in 1:ys){\n mid<-dd[dd$year==year,\"prcp\"]\n# mid<-mid[is.na(mid)==F]\n if(i==1) kk<-0\n mm<-0\n for(j in 1:length(mid)){\n if(mid[j]<1&is.na(mid[j])==F) kk<-kk+1\n else {\n if(mm=1|is.na(dd[dd$year==year+1&dd$month==1&dd$day==1,\"prcp\"])==T) mm<-kk\n# in case whole year dry, the next year will have a CDD bigger than 365\n# then the CDD indice for current year should not be 0 but NA\n if(mm==0) mm<-NA\n }\n target[i,\"cdd\"]<-mm\n year=year+1\n}\n\n#for(i in 1:(ys-1))\n# if(target[i,\"cdd\"]==0&target[i+1,\"cdd\"]>=365) target[i,\"cdd\"]<-NA\n\ntarget[,\"cdd\"]<-target[,\"cdd\"]+ynacor[,\"ynapr>15\"]\nnam1<-paste(outinddir,paste(ofilename,\"_CDD.csv\",sep=\"\"),sep=\"/\")\nwrite.table(target,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(target[,\"cdd\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(target[,1],target[,\"cdd\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"cdd\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\nnam2<-paste(outjpgdir,paste(ofilename,\"_CDD.png\",sep=\"\"),sep=\"/\")\npng(nam2,width=1024,height=768,bg=\"white\")\nplotx(target[,1],target[,2],main=paste(\"CDD\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"CDD\")\ndev.off()\nnam2a<-paste(outjpgdir,paste(ofilename,\"_CDD.pdf\",sep=\"\"),sep=\"/\")\npdf(nam2a)\nplotx(target[,1],target[,2],main=paste(\"CDD\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"CDD\")\ndev.off()\n}\n\nindex641cwd<-function(){\nys<-yeare-years+1\ncwd<-rep(0,ys)\nyear<-years:yeare\ntarget<-as.data.frame(cbind(year,cwd))\nyear=years\nfor (i in 1:ys){\n mid<-dd[dd$year==year,\"prcp\"]\n# mid<-mid[is.na(mid)==F]\n if(i==1) kk<-0\n mm<-0\n for(j in 1:length(mid)){\n if(mid[j]>=1&is.na(mid[j])==F) kk<-kk+1\n else {\n if(mm15\"]\nnam1<-paste(outinddir,paste(ofilename,\"_CWD.csv\",sep=\"\"),sep=\"/\")\nwrite.table(target,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(target[,\"cwd\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(target[,1],target[,\"cwd\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"cwd\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\nnam2<-paste(outjpgdir,paste(ofilename,\"_CWD.png\",sep=\"\"),sep=\"/\")\npng(nam2,width=1024,height=768,bg=\"white\")\nplotx(target[,1],target[,2],main=paste(\"CWD\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"CWD\")\ndev.off()\nnam2a<-paste(outjpgdir,paste(ofilename,\"_CWD.pdf\",sep=\"\"),sep=\"/\")\npdf(nam2a)\nplotx(target[,1],target[,2],main=paste(\"CWD\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"CWD\")\ndev.off()\n}\n\nrx1d<-function(){\n len<-yeare-years+1\n aa1<-matrix(NA,12*len,3)\n dimnames(aa1)<-list(NULL,c(\"year\",\"month\",\"rx1d\"))\n aa1[,\"year\"]<-years:yeare\n aa1[,\"year\"]<-mysort(aa1[,\"year\"],decreasing=F)\n aa1[,\"month\"]<-1:12\n jjj3=1\n mid<-dd[,1:4]\n for (year in years:yeare){\n aaaa<-mid[mid$year==year,]\n aaaam1<-aaaa[aaaa$month==1,\"prcp\"];aaaam2<-aaaa[aaaa$month==2,\"prcp\"]\n aaaam3<-aaaa[aaaa$month==3,\"prcp\"];aaaam4<-aaaa[aaaa$month==4,\"prcp\"]\n aaaam5<-aaaa[aaaa$month==5,\"prcp\"];aaaam6<-aaaa[aaaa$month==6,\"prcp\"]\n aaaam7<-aaaa[aaaa$month==7,\"prcp\"];aaaam8<-aaaa[aaaa$month==8,\"prcp\"]\n aaaam9<-aaaa[aaaa$month==9,\"prcp\"];aaaam10<-aaaa[aaaa$month==10,\"prcp\"]\n aaaam11<-aaaa[aaaa$month==11,\"prcp\"];aaaam12<-aaaa[aaaa$month==12,\"prcp\"]\n aa1[jjj3,\"rx1d\"]<-max(aaaam1,na.rm=T);aa1[jjj3+1,\"rx1d\"]<-max(aaaam2,na.rm=T)\n aa1[jjj3+2,\"rx1d\"]<-max(aaaam3,na.rm=T);aa1[jjj3+3,\"rx1d\"]<-max(aaaam4,na.rm=T)\n aa1[jjj3+4,\"rx1d\"]<-max(aaaam5,na.rm=T);aa1[jjj3+5,\"rx1d\"]<-max(aaaam6,na.rm=T)\n aa1[jjj3+6,\"rx1d\"]<-max(aaaam7,na.rm=T);aa1[jjj3+7,\"rx1d\"]<-max(aaaam8,na.rm=T)\n aa1[jjj3+8,\"rx1d\"]<-max(aaaam9,na.rm=T);aa1[jjj3+9,\"rx1d\"]<-max(aaaam10,na.rm=T)\n aa1[jjj3+10,\"rx1d\"]<-max(aaaam11,na.rm=T);aa1[jjj3+11,\"rx1d\"]<-max(aaaam12,na.rm=T)\n jjj3=jjj3+12}\n aa1[,\"rx1d\"]<-aa1[,\"rx1d\"]+nacor[,\"mnapr>3\"]\n ofile<-matrix(0,len,14)\n# aa1<-as.data.frame(aa1)\n dimnames(ofile)<-list(NULL,c(\"year\",\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\",\"annual\"))\n ofile<-as.data.frame(ofile)\n for(j in years:yeare){\n k<-j-years+1\n ofile[k,1]<-j\n ofile[k,2:13]<-aa1[aa1[,1]==j,3]\n ofile[k,14]<-max(ofile[k,2:13],na.rm=F)\n }\n nam1<-paste(outinddir,paste(ofilename,\"_RX1day.csv\",sep=\"\"),sep=\"/\")\n write.table(ofile,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(ofile[,1],ofile[,14])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"rx1day\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_RX1day.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(ofile[,1],ofile[,14],main=paste(\"RX1day\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"RX1day\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_RX1day.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(ofile[,1],ofile[,14],main=paste(\"RX1day\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"RX1day\")\n dev.off()\n}# end of rx1d()\n\nrx5d<-function(){\n a2<-c(0,0,0,0)\n a1<-dd[,\"prcp\"]\n a1<-append(a2,a1)\n n<-length(a1)\n a<-rep(0,n)\n for (i in 5:n){\n a[i]<-sum(a1[(i-4):i],na.rm=T)}\n a<-a[-(1:4)]\n a<-cbind(dd[,1:2],a)\n \n len<-yeare-years+1\n aa1<-matrix(NA,12*len,3)\n dimnames(aa1)<-list(NULL,c(\"year\",\"month\",\"rx5d\"))\n aa1[,\"year\"]<-years:yeare\n aa1[,\"year\"]<-mysort(aa1[,\"year\"],decreasing=F)\n aa1[,\"month\"]<-1:12\n jjj2=1\n for (year in years:yeare){\n aaaa<-a[a$year==year,]\n aaaam1<-aaaa[aaaa$month==1,\"a\"];aaaam2<-aaaa[aaaa$month==2,\"a\"]\n aaaam3<-aaaa[aaaa$month==3,\"a\"];aaaam4<-aaaa[aaaa$month==4,\"a\"]\n aaaam5<-aaaa[aaaa$month==5,\"a\"];aaaam6<-aaaa[aaaa$month==6,\"a\"]\n aaaam7<-aaaa[aaaa$month==7,\"a\"];aaaam8<-aaaa[aaaa$month==8,\"a\"]\n aaaam9<-aaaa[aaaa$month==9,\"a\"];aaaam10<-aaaa[aaaa$month==10,\"a\"]\n aaaam11<-aaaa[aaaa$month==11,\"a\"];aaaam12<-aaaa[aaaa$month==12,\"a\"]\n aa1[jjj2,\"rx5d\"]<-max(aaaam1,na.rm=T);aa1[jjj2+1,\"rx5d\"]<-max(aaaam2,na.rm=T)\n aa1[jjj2+2,\"rx5d\"]<-max(aaaam3,na.rm=T);aa1[jjj2+3,\"rx5d\"]<-max(aaaam4,na.rm=T)\n aa1[jjj2+4,\"rx5d\"]<-max(aaaam5,na.rm=T);aa1[jjj2+5,\"rx5d\"]<-max(aaaam6,na.rm=T)\n aa1[jjj2+6,\"rx5d\"]<-max(aaaam7,na.rm=T);aa1[jjj2+7,\"rx5d\"]<-max(aaaam8,na.rm=T)\n aa1[jjj2+8,\"rx5d\"]<-max(aaaam9,na.rm=T);aa1[jjj2+9,\"rx5d\"]<-max(aaaam10,na.rm=T)\n aa1[jjj2+10,\"rx5d\"]<-max(aaaam11,na.rm=T);aa1[jjj2+11,\"rx5d\"]<-max(aaaam12,na.rm=T)\n jjj2=jjj2+12}\n\n aa1[,\"rx5d\"]<-aa1[,\"rx5d\"]+nacor[,\"mnapr>3\"]\n \n ofile<-matrix(0,len,14)\n# aa1<-as.data.frame(aa1)\n dimnames(ofile)<-list(NULL,c(\"year\",\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\",\"annual\"))\n ofile<-as.data.frame(ofile)\n for(j in years:yeare){\n k<-j-years+1\n ofile[k,1]<-j\n ofile[k,2:13]<-aa1[aa1[,1]==j,\"rx5d\"]\n ofile[k,14]<-max(ofile[k,2:13],na.rm=F)\n }\n# print(dim(rx5d))\n nam1<-paste(outinddir,paste(ofilename,\"_RX5day.csv\",sep=\"\"),sep=\"/\")\n write.table(ofile,file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(ofile[,1],ofile[,14])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"rx5day\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_RX5day.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(ofile[,1],ofile[,14],main=paste(\"RX5day\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"RX5day\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_RX5day.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(ofile[,1],ofile[,14],main=paste(\"RX5day\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"RX5day\")\n dev.off()\n }# end of rx5d()\n\nextremedaytem<-function(){\n len<-yeare-years+1\n aa1<-matrix(NA,12*len,6)\n dimnames(aa1)<-list(NULL,c(\"year\",\"month\",\"txx\",\"txn\",\"tnx\",\"tnn\"))\n aa1[,\"year\"]<-years:yeare\n aa1[,\"year\"]<-mysort(aa1[,\"year\"],decreasing=F)\n aa1[,\"month\"]<-1:12\n jjj4=1\n for (year in years:yeare){\n aaaa<-dd[dd$year==year,]\n aaaama1<-aaaa[aaaa$month==1,\"tmax\"];aaaama2<-aaaa[aaaa$month==2,\"tmax\"]\n aaaama3<-aaaa[aaaa$month==3,\"tmax\"];aaaama4<-aaaa[aaaa$month==4,\"tmax\"]\n aaaama5<-aaaa[aaaa$month==5,\"tmax\"];aaaama6<-aaaa[aaaa$month==6,\"tmax\"]\n aaaama7<-aaaa[aaaa$month==7,\"tmax\"];aaaama8<-aaaa[aaaa$month==8,\"tmax\"]\n aaaama9<-aaaa[aaaa$month==9,\"tmax\"];aaaama10<-aaaa[aaaa$month==10,\"tmax\"]\n aaaama11<-aaaa[aaaa$month==11,\"tmax\"];aaaama12<-aaaa[aaaa$month==12,\"tmax\"]\n \n aaaami1<-aaaa[aaaa$month==1,\"tmin\"];aaaami2<-aaaa[aaaa$month==2,\"tmin\"]\n aaaami3<-aaaa[aaaa$month==3,\"tmin\"];aaaami4<-aaaa[aaaa$month==4,\"tmin\"]\n aaaami5<-aaaa[aaaa$month==5,\"tmin\"];aaaami6<-aaaa[aaaa$month==6,\"tmin\"]\n aaaami7<-aaaa[aaaa$month==7,\"tmin\"];aaaami8<-aaaa[aaaa$month==8,\"tmin\"]\n aaaami9<-aaaa[aaaa$month==9,\"tmin\"];aaaami10<-aaaa[aaaa$month==10,\"tmin\"]\n aaaami11<-aaaa[aaaa$month==11,\"tmin\"];aaaami12<-aaaa[aaaa$month==12,\"tmin\"]\n \n aa1[jjj4,\"txx\"]<-max(aaaama1,na.rm=T);aa1[jjj4+1,\"txx\"]<-max(aaaama2,na.rm=T)\n aa1[jjj4+2,\"txx\"]<-max(aaaama3,na.rm=T);aa1[jjj4+3,\"txx\"]<-max(aaaama4,na.rm=T)\n aa1[jjj4+4,\"txx\"]<-max(aaaama5,na.rm=T);aa1[jjj4+5,\"txx\"]<-max(aaaama6,na.rm=T)\n aa1[jjj4+6,\"txx\"]<-max(aaaama7,na.rm=T);aa1[jjj4+7,\"txx\"]<-max(aaaama8,na.rm=T)\n aa1[jjj4+8,\"txx\"]<-max(aaaama9,na.rm=T);aa1[jjj4+9,\"txx\"]<-max(aaaama10,na.rm=T)\n aa1[jjj4+10,\"txx\"]<-max(aaaama11,na.rm=T);aa1[jjj4+11,\"txx\"]<-max(aaaama12,na.rm=T)\n \n aa1[jjj4,\"txn\"]<-min(aaaama1,na.rm=T);aa1[jjj4+1,\"txn\"]<-min(aaaama2,na.rm=T)\n aa1[jjj4+2,\"txn\"]<-min(aaaama3,na.rm=T);aa1[jjj4+3,\"txn\"]<-min(aaaama4,na.rm=T)\n aa1[jjj4+4,\"txn\"]<-min(aaaama5,na.rm=T);aa1[jjj4+5,\"txn\"]<-min(aaaama6,na.rm=T)\n aa1[jjj4+6,\"txn\"]<-min(aaaama7,na.rm=T);aa1[jjj4+7,\"txn\"]<-min(aaaama8,na.rm=T)\n aa1[jjj4+8,\"txn\"]<-min(aaaama9,na.rm=T);aa1[jjj4+9,\"txn\"]<-min(aaaama10,na.rm=T)\n aa1[jjj4+10,\"txn\"]<-min(aaaama11,na.rm=T);aa1[jjj4+11,\"txn\"]<-min(aaaama12,na.rm=T)\n\n aa1[jjj4,\"tnx\"]<-max(aaaami1,na.rm=T);aa1[jjj4+1,\"tnx\"]<-max(aaaami2,na.rm=T)\n aa1[jjj4+2,\"tnx\"]<-max(aaaami3,na.rm=T);aa1[jjj4+3,\"tnx\"]<-max(aaaami4,na.rm=T)\n aa1[jjj4+4,\"tnx\"]<-max(aaaami5,na.rm=T);aa1[jjj4+5,\"tnx\"]<-max(aaaami6,na.rm=T)\n aa1[jjj4+6,\"tnx\"]<-max(aaaami7,na.rm=T);aa1[jjj4+7,\"tnx\"]<-max(aaaami8,na.rm=T)\n aa1[jjj4+8,\"tnx\"]<-max(aaaami9,na.rm=T);aa1[jjj4+9,\"tnx\"]<-max(aaaami10,na.rm=T)\n aa1[jjj4+10,\"tnx\"]<-max(aaaami11,na.rm=T);aa1[jjj4+11,\"tnx\"]<-max(aaaami12,na.rm=T)\n\n aa1[jjj4,\"tnn\"]<-min(aaaami1);aa1[jjj4+1,\"tnn\"]<-min(aaaami2,na.rm=T)\n aa1[jjj4+2,\"tnn\"]<-min(aaaami3,na.rm=T);aa1[jjj4+3,\"tnn\"]<-min(aaaami4,na.rm=T)\n aa1[jjj4+4,\"tnn\"]<-min(aaaami5,na.rm=T);aa1[jjj4+5,\"tnn\"]<-min(aaaami6,na.rm=T)\n aa1[jjj4+6,\"tnn\"]<-min(aaaami7,na.rm=T);aa1[jjj4+7,\"tnn\"]<-min(aaaami8,na.rm=T)\n aa1[jjj4+8,\"tnn\"]<-min(aaaami9,na.rm=T);aa1[jjj4+9,\"tnn\"]<-min(aaaami10,na.rm=T)\n aa1[jjj4+10,\"tnn\"]<-min(aaaami11,na.rm=T);aa1[jjj4+11,\"tnn\"]<-min(aaaami12,na.rm=T)\n \n jjj4=jjj4+12}\n exdaytem<-as.data.frame(aa1)\n# midnacor<-nacor[nacor$year>=startyear,]\n# midnacor<-midnacor[midnacor$year<=endyear,]\n exdaytem[,\"txx\"]<-exdaytem[,\"txx\"]+nacor[,\"mnatma>3\"]\n exdaytem[,\"txn\"]<-exdaytem[,\"txn\"]+nacor[,\"mnatma>3\"]\n exdaytem[,\"tnx\"]<-exdaytem[,\"tnx\"]+nacor[,\"mnatmi>3\"]\n exdaytem[,\"tnn\"]<-exdaytem[,\"tnn\"]+nacor[,\"mnatmi>3\"]\n\n for(i in c(\"txx\",\"txn\",\"tnx\",\"tnn\")){\n if (i==\"txx\") {ii<-\"_TXx.csv\";ij<-\"_TXx.png\";ijpdf<-\"_TXx.pdf\";kk<-3;ik<-1;ki<-3}# ik=1, take max as yearly record\n if (i==\"txn\") {ii<-\"_TXn.csv\";ij<-\"_TXn.png\";ijpdf<-\"_TXn.pdf\";kk<-4;ik<-0;ki<-3}# ik=0, take min as yearly record\n if (i==\"tnx\") {ii<-\"_TNx.csv\";ij<-\"_TNx.png\";ijpdf<-\"_TNx.pdf\";kk<-5;ik<-1;ki<-4}# ki=3, take TMAX annual missing values\n if (i==\"tnn\") {ii<-\"_TNn.csv\";ij<-\"_TNn.png\";ijpdf<-\"_TNn.pdf\";kk<-6;ik<-0;ki<-4}# ki=4, take TMIN annual missing values\n nam<-paste(outinddir,paste(ofilename,ii,sep=\"\"),sep=\"/\")\n ofile<-matrix(0,len,14)\n# ojpg<-rep(0,len)\n dimnames(ofile)<-list(NULL,c(\"year\",\"jan\",\"feb\",\"mar\",\"apr\",\"may\",\"jun\",\"jul\",\"aug\",\"sep\",\"oct\",\"nov\",\"dec\",\"annual\"))\n ofile<-as.data.frame(ofile)\n for(j in years:yeare){\n k<-j-years+1\n ofile[k,1]<-j\n ofile[k,2:13]<-exdaytem[exdaytem$year==j,kk]\n if(ik==1) ofile[k,14]<-max(ofile[k,2:13],na.rm=T)\n else ofile[k,14]<-min(ofile[k,2:13],na.rm=T)\n }\n ofile[,14]<-ofile[,14]+ynacor[,ki]\n write.table(ofile,file=nam,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n \n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(ofile[,14]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(ofile[,1],ofile[,14])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,i,years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,ij,sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep=\" \"),xlab=\"Año\",ylab=(paste(toupper(substr(i,1,2)),substr(i,3,3),sep=\"\")))\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,ijpdf,sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(ofile[,1],ofile[,14],main=paste(toupper(i),ofilename,sep=\" \"),xlab=\"Año\",ylab=(paste(toupper(substr(i,1,2)),substr(i,3,3),sep=\"\")))\n dev.off()\n }\n}\n\nindex646<-function(){\n ys=yeare-years+1\n b<-matrix(0,ncol=2,nrow=ys)\n dimnames(b)<-list(NULL,c(\"year\",\"sdii\"))\n b[,\"year\"]<-c(years:yeare)\n b<-as.data.frame(b)\n year=years\n for (i in 1:ys){\n mid<-dd[dd$year==year,\"prcp\"]\n mid<-mid[mid>=1]\n b[i,\"sdii\"]<-mean(mid,na.rm=T)\n year=year+1 }\n b[,\"sdii\"]<-b[,\"sdii\"]+ynacor[,\"ynapr>15\"]\n nam1<-paste(outinddir,paste(ofilename,\"_SDII.csv\",sep=\"\"),sep=\"/\")\n write.table(round(b,digit=1),file=nam1,append=F,quote=F,sep=\", \",na=\"-99.9\",row.names=F)\n\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n if(sum(is.na(b[,\"sdii\"]))>=(yeare-years+1-10)){\n betahat<-NA\n betastd<-NA\n pvalue<-NA\n }\n else{\n fit1<-lsfit(b[,\"year\"],b[,\"sdii\"])\n out1<-ls.print(fit1,print.it=F)\n pvalue<-round(as.numeric(out1$summary[1,6]),3)\n betahat<-round(as.numeric(out1$coef.table[[1]][2,1]),3)\n betastd<-round(as.numeric(out1$coef.table[[1]][2,2]),3)\n }\n cat(file=namt,paste(latitude,longitude,\"sdii\",years,yeare,betahat,betastd,pvalue,sep=\",\"),fill=180,append=T)\n\n nam2<-paste(outjpgdir,paste(ofilename,\"_SDII.png\",sep=\"\"),sep=\"/\")\n png(nam2,width=1024,height=768,bg=\"white\")\n plotx(b[,1],b[,2],main=paste(\"SDII\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"SDII\")\n dev.off()\n nam2a<-paste(outjpgdir,paste(ofilename,\"_SDII.pdf\",sep=\"\"),sep=\"/\")\n pdf(nam2a)\n plotx(b[,1],b[,2],main=paste(\"SDII\",ofilename,sep=\" \"),xlab=\"Año\",ylab=\"SDII\")\n dev.off()\n}\n\nmain1<-function(){\n main<-tktoplevel()\n tkfocus(main)\n tkwm.title(main,\"Calculating Climate Indices\")\n tkgrid(tklabel(main,text=\"Check desired indices\",font=fontHeading1))\n\ttxt=\"It may take 5 minutes to compute all the indices, \"\n#\tif(prallna==1) tkgrid(tklabel(main, text=\"PRCP all missing, indices related to PRCP may not be calculated\",font=fontHeading2))\n#\tif(txallna==1|tnallna==1) tkgrid(tklabel(main,text=\"TMAX or TMIN all missing, indices related to TMAX and TMIN may not be calculated\",font=fontHeading2))\n\n#cb0 <- tkcheckbutton(main);cb0Val <- cbvalue0\n\ncb1 <- tkcheckbutton(main);cb1Val <- cbvalue1\ncb2 <- tkcheckbutton(main);cb2Val <- cbvalue2\ncb3 <- tkcheckbutton(main);cb3Val <- cbvalue3\ncb4 <- tkcheckbutton(main);cb4Val <- cbvalue4\ncb5 <- tkcheckbutton(main);cb5Val <- cbvalue5\ncb6 <- tkcheckbutton(main);cb6Val <- cbvalue6\ncb7 <- tkcheckbutton(main);cb7Val <- cbvalue7\ncb8 <- tkcheckbutton(main);cb8Val <- cbvalue8\ncb9 <- tkcheckbutton(main);cb9Val <- cbvalue9\ncb10 <- tkcheckbutton(main);cb10Val <- cbvalue10\ncb11 <- tkcheckbutton(main);cb11Val <- cbvalue11\ncb12 <- tkcheckbutton(main);cb12Val <- cbvalue12\ncb13 <- tkcheckbutton(main);cb13Val <- cbvalue13\ncb14 <- tkcheckbutton(main);cb14Val <- cbvalue14\ncb15 <- tkcheckbutton(main);cb15Val <- cbvalue15\ncb21 <- tkcheckbutton(main);cb21Val <- cbvalue21\n \n#tkconfigure(cb0,variable=cb0Val)#,value=cb1Val)\ntkconfigure(cb1,variable=cb1Val)#,value=cb1Val)\ntkconfigure(cb2,variable=cb2Val)#,value=cb2Val)\ntkconfigure(cb3,variable=cb3Val)#,value=cb3Val)#\"dtr\")\ntkconfigure(cb4,variable=cb4Val)#,value=cb4Val)#\"daysprcp10\")\ntkconfigure(cb5,variable=cb5Val)#,value=cb5Val)#\"nordaytem1\")\ntkconfigure(cb6,variable=cb6Val)#,value=cb6Val)#\"extremedays\")\ntkconfigure(cb7,variable=cb7Val)#,value=cb7Val)#\"exceedance\")\ntkconfigure(cb8,variable=cb8Val)#,value=cb8Val)#\"ind144hwd\")\ntkconfigure(cb9,variable=cb9Val)#,value=cb9Val)#\"ind641cdd\")\ntkconfigure(cb10,variable=cb10Val)#,value=cb10Val)#\"rx5d\")\ntkconfigure(cb11,variable=cb11Val)#,value=cb11Val)#\"ind646\")\ntkconfigure(cb12,variable=cb12Val)#,value=cb12Val)#\"ind695\")\ntkconfigure(cb13,variable=cb13Val)#,value=cb13Val)#\"rx1d\"\ntkconfigure(cb14,variable=cb14Val)#,value=cb14Val)#\"extreme day tem\"\ntkconfigure(cb15,variable=cb15Val)\ntkconfigure(cb21,variable=cb21Val)\n \n#tkgrid(tklabel(main,text=\"Select the indices you want to calculate:\"))\n\n#tkgrid(tklabel(main,text=\"ALL 26 indices!!\"),cb0)\ntkgrid(tklabel(main,text=\"SU25, FD0, TR20, ID0\"),cb1)\ntkgrid(tklabel(main,text=\"User Defined SU, FD, TR, ID\"),cb21)\n\ntkgrid(tklabel(main,text=\"GSL, growing season length\"),cb2)#143\n\ntkgrid(tklabel(main,text=\"TXx, TXn, TNx, TNn\"),cb3)\n#tkgrid(tklabel(main,text=\"TXn, TNx, TNn, following by same choice\"),cb3)#extremedaytem\n\ntkgrid(tklabel(main,text=\"TX10p, TX90p, TN10p, TN90p\"),cb4)\n#tkgrid(tklabel(main,text=\"TX90p, TN10p, TN90p, following by same choice\"),cb4)#exceedance\n\ntkgrid(tklabel(main,text=\"WSDI\"),cb5)#hwfi\ntkgrid(tklabel(main,text=\"CSDI\"),cb6)#cwdi\n \n#tkgrid(tklabel(main,text=\"Normal day temperature with user defined window size\"),cb2)\ntkgrid(tklabel(main,text=\"DTR\"),cb7)#dtr\n \ntkgrid(tklabel(main,text=\"Rx1day\"),cb8)#rx1d\ntkgrid(tklabel(main,text=\"Rx5day\"),cb9)#rx5d\n \ntkgrid(tklabel(main,text=\"SDII\"),cb10)#index646\n\ntkgrid(tklabel(main,text=\"R10mm\"),cb11)#daysprcp10()\ntkgrid(tklabel(main,text=\"R20mm\"),cb12)#daysprcp20()\ntkgrid(tklabel(main,text=\"Rnnmm\"),cb13)#daysprcpn()\n\ntkgrid(tklabel(main,text=\"CDD, CWD\"),cb14)\n#tkgrid(tklabel(main,text=\"CWD\"),cb14)#641\n\n#tkgrid(tklabel(main,text=\"daynortem \"),cb5)\n#tkgrid(tklabel(main,text=\"ind144hwd \"),cb8)#144\n \ntkgrid(tklabel(main,text=\"R95p, R99p, PRCPTOT\"),cb15)#695\n#tkgrid(tklabel(main,text=\"R99pTOT\"),cb15)\n \n#tkgrid(tklabel(main,text=\"Annual Days with PRCP>=95 percentile\"),cb17)#r95ptot\n#tkgrid(tklabel(main,text=\"Annual Days with PRCP>=99 percentile\"),cb17)\n\n OnOK <- function(){\n#filename<-tclvalue(tkgetSaveFile(filetypes=\"{{EXCEL Files} {.csv}} {{All files} *}\"))\n#if (!nchar(filename))\n#tkmessageBox(message=\"No file was selected!\")\n#else tkmessageBox(message=paste(\"The results will be saved under\",filename))\n#nam<-substr(filename,start=1,stop=(nchar(filename)-4))\n#assign(\"nam\",nam,envir=.GlobalEnv)\n\n# cbv0 <- as.character(tclvalue(cb0Val))\n cbv1 <- as.character(tclvalue(cb1Val))\n cbv21 <- as.character(tclvalue(cb21Val))\n cbv2 <- as.character(tclvalue(cb2Val))\n cbv3 <- as.character(tclvalue(cb3Val))\n cbv4 <- as.character(tclvalue(cb4Val))\n cbv5 <- as.character(tclvalue(cb5Val))\n cbv6 <- as.character(tclvalue(cb6Val))\n cbv7 <- as.character(tclvalue(cb7Val))\n cbv8 <- as.character(tclvalue(cb8Val))\n cbv9 <- as.character(tclvalue(cb9Val))\n cbv10 <- as.character(tclvalue(cb10Val))\n cbv11 <- as.character(tclvalue(cb11Val))\n cbv12 <- as.character(tclvalue(cb12Val))\n cbv13 <- as.character(tclvalue(cb13Val))\n cbv14 <- as.character(tclvalue(cb14Val))\n cbv15 <- as.character(tclvalue(cb15Val))\n tkdestroy(main)\n# if (cbv0==1) {cbv1<-1;cbv2<-1;cbv3<-1;cbv4<-1;cbv5<-1;\n namt<-paste(outtrddir,paste(ofilename,\"_trend.csv\",sep=\"\"),sep=\"/\")\n cat(file=namt,paste(\"Lon\",\"Lat\",\"Indices\",\"SYear\",\"EYear\",\"Slope\",\"STD_of_Slope\",\"P_Value\",sep=\",\"),fill=180,append=F)\n if (cbv1==1) if(txallna==0&tnallna==0) extremedays()\n if (cbv21==1) if(txallna==0&tnallna==0) extremedays(opt=1)\n if (cbv2==1) if(txallna==0&tnallna==0) ind143gsl()\n if (cbv3==1) if(txallna==0&tnallna==0) extremedaytem()\n if (cbv4==1) if(txallna==0&tnallna==0) exceedance()\n if (cbv5==1) if(txallna==0) hwfi()\n if (cbv6==1) if(tnallna==0) cwdi()\n if (cbv7==1) if(txallna==0&tnallna==0) dtr()\n if (cbv8==1) if(prallna==0) rx1d()\n if (cbv9==1) if(prallna==0) rx5d()\n if (cbv10==1) if(prallna==0) index646()\n if (cbv11==1) if(prallna==0) daysprcp10()\n if (cbv12==1) if(prallna==0) daysprcp20()\n if (cbv13==1) if(prallna==0) daysprcpn()\n if (cbv14==1) if(prallna==0) {index641cdd();index641cwd()}\n if (cbv15==1) if(prallna==0) r95ptot()\n cbvalue1<-cb1Val;assign(\"cbvalue1\",cbvalue1,envir=.GlobalEnv)\n cbvalue2<-cb2Val;assign(\"cbvalue2\",cbvalue2,envir=.GlobalEnv)\n cbvalue3<-cb3Val;assign(\"cbvalue3\",cbvalue3,envir=.GlobalEnv)\n cbvalue4<-cb4Val;assign(\"cbvalue4\",cbvalue4,envir=.GlobalEnv)\n cbvalue5<-cb5Val;assign(\"cbvalue5\",cbvalue5,envir=.GlobalEnv)\n cbvalue6<-cb6Val;assign(\"cbvalue6\",cbvalue6,envir=.GlobalEnv)\n cbvalue7<-cb7Val;assign(\"cbvalue7\",cbvalue7,envir=.GlobalEnv)\n cbvalue8<-cb8Val;assign(\"cbvalue8\",cbvalue8,envir=.GlobalEnv)\n cbvalue9<-cb9Val;assign(\"cbvalue9\",cbvalue9,envir=.GlobalEnv)\n cbvalue10<-cb10Val;assign(\"cbvalue10\",cbvalue10,envir=.GlobalEnv)\n cbvalue11<-cb11Val;assign(\"cbvalue11\",cbvalue11,envir=.GlobalEnv)\n cbvalue12<-cb12Val;assign(\"cbvalue12\",cbvalue12,envir=.GlobalEnv)\n cbvalue13<-cb13Val;assign(\"cbvalue13\",cbvalue13,envir=.GlobalEnv)\n cbvalue14<-cb14Val;assign(\"cbvalue14\",cbvalue14,envir=.GlobalEnv)\n cbvalue15<-cb15Val;assign(\"cbvalue15\",cbvalue15,envir=.GlobalEnv)\n cbvalue21<-cb21Val;assign(\"cbvalue21\",cbvalue21,envir=.GlobalEnv)\n nstation<-tktoplevel()\n tkwm.title(nstation,\"Calculation Done\")\n tkfocus(nstation)\n okk<-function(){tkdestroy(nstation);tkfocus(start1)}\n textlabel0<-tklabel(nstation,text=\" \")\n textlabel1<-tklabel(nstation,text=\"Indices calculation completed\",font=fontHeading1)\n textlabel2<-tklabel(nstation,text=paste(\"Plots are in: \",outjpgdir,sep=\" \"),font=fontHeading1)\n okk.but<-tkbutton(nstation,text=\" OK \",command=okk,width=20)\n tkgrid(textlabel0)\n tkgrid(textlabel1)\n tkgrid(textlabel2)\n tkgrid.configure(textlabel1,sticky=\"w\")\n tkgrid.configure(textlabel2,sticky=\"w\")\n tkgrid.configure(textlabel0,sticky=\"e\")\n# cancell.but<-tkbutton(nstation,text=\" NO \",command=cancell,width=15)\n# tkgrid(textlabel2,okk.but,cancell.but,textlabel0)\n tkgrid(okk.but,textlabel0)\n# tkgrid.configure(cancell.but,sticky=\"w\")\n tkgrid(textlabel0)\n }\n done2<-function(){\n tkdestroy(main)\n tkfocus(start1)\n# return()\n }\n\n ok.but <-tkbutton(main,text=\" OK \",command=OnOK,width=30)\n cancel.but<-tkbutton(main,text=\"CANCELAR\",command=done2,width=30)\n tkgrid(ok.but)\n tkgrid(cancel.but)\n tkgrid(tklabel(main,text=\"It may take more than 5 minutes to compute all the indices\",font=fontHeading2))\n tkgrid(tklabel(main,text=\"Please be patient, you will be informed once computations are done\",font=fontHeading2))\n tkgrid(tklabel(main,text=\"\",font=fontHeading))#empty line\n \n }\n\n\n\n\n# End of Part II ( Functions of Calculating climate indecies )\n\n# Part III\n# Main program\n# call getfile, read data file and store in dd.\nstart1<-tktoplevel()\n\nplotx<-\nfunction (x,y,main=\"\",xlab=\"\",ylab=\"\")\n{\n par(oma=c(3,0,0,0))\n plot(x,y,xlab=xlab,ylab=ylab,type=\"b\",col=\"gray40\")\n\n fit<-lsfit(x,y) # least squares linear fit\n out<-ls.print(fit,print.it=F)\n r2<-round(100*as.numeric(out$summary[1,2]),1)\n pval<-round(as.numeric(out$summary[1,6]),3)\n beta<-round(as.numeric(out$coef.table[[1]][2,1]),3)\n betaerr<-round(as.numeric(out$coef.table[[1]][2,2]),3)\n #abline(fit,lwd=3,col=\"blue\") \n\n # linear fit & confidence interval lines--------------\n fit2<-lm(y ~ x)\n\n newx <- seq(min(x), max(x), length.out=100)\n preds <- predict(fit2, newdata = data.frame(x=newx), \n interval = 'confidence')\n polygon(c(rev(newx), newx), c(rev(preds[ ,3]), preds[ ,2]),col = 'lightgray', border = NA, density = 20)\n lines(newx,preds[,2],lty = 'dashed',col=\"blue\")\n lines(newx,preds[,3],lty = 'dashed',col=\"blue\")\n abline(fit2,lwd=3,col=\"blue\")\n \n #beta_alt <- summary(fit2)$coefficients[2, 1]\n #beta_alt <- round(as.numeric(beta_alt,3))\n #beta_error_alt <- summary(fit2)$coefficients[2, 2]\n #beta_error_alt <- round(as.numeric(beta_error_alt,3))\n\n # lowess line\n xy<-cbind(x,y)\n xy<-na.omit(xy)\n lines(lowess(xy[,1],xy[,2]),lwd=3,lty=2,col=\"red\")\n\n prefiction_sequence <- data.frame(x = c(1970,1980,1990,2000,2010,2020,2030,2040,2050,2060,2070))\n prediction <- predict(fit2, prefiction_sequence, se.fit=TRUE, interval=\"confidence\",level=0.95) \n #prediction <- round(as.numeric(prediction),1)\n prediction_text <- paste(\n \" 1970:\",round(as.numeric(prediction$fit[1]),2), \n \" 1980:\",round(as.numeric(prediction$fit[2]),2),\n \" 1990:\",round(as.numeric(prediction$fit[3]),2),\n \" 2000:\",round(as.numeric(prediction$fit[4]),2),\n \" 2010:\",round(as.numeric(prediction$fit[5]),2),'\\n',\n \" 2020:\",round(as.numeric(prediction$fit[6]),2),\n \" 2030:\",round(as.numeric(prediction$fit[7]),2),\n \" 2040:\",round(as.numeric(prediction$fit[8]),2), \n \" 2050:\",round(as.numeric(prediction$fit[9]),2),\n \" 2060:\",round(as.numeric(prediction$fit[10]),2)\n )\n \n subtit=paste(\"R² = \",r2,\" Valor P = \",pval,\" Pendiente = \",beta,\" Error est. de pendiente = \",betaerr)\n #subtit=paste(\"R² = \",r2,\" Valor P = \",pval,\" Pendiente = \",beta_alt,\" Error de pendiente = \",beta_error_alt)\n title(main=main)\n title(sub=subtit,cex=0.5)\n mtext(\" Estimación:\", SOUTH<-1, line=0.4, adj=0, cex=0.8, outer=TRUE)\n mtext(prediction_text, SOUTH<-1, line=1.4, adj=0.5, cex=0.8, outer=TRUE)\n}\n\n\nts<-function(ys=x[1,1],ye=x[nrow(x),1],x=dd) \n{\n#\n# EDA function\n#\npar(mfrow=c(3,1))\nxs<-x[(x[,1]>=ys)&(x[,1]<=ye),]\nts.plot(xs[,4])\ntitle(\"Precipitación diaria\",xlab=\"dia\",ylab=\"PRECIP\")\nts.plot(xs[,5])\ntitle(\"Máximo diario de temperatura\",xlab=\"dia\",ylab=\"Tmax\")\nts.plot(xs[,6])\ntitle(\"Minimo diario de temperatura\",xlab=\"dia\",ylab=\"Tmin\")\npar(mfrow=c(1,1))\n\ncat(paste(\"Estaciones con datos de \",x[1,1],\" a \",x[nrow(x),1],\"\\n\"))\ncat(paste(\"Estadistica sumatoria para ventana de \",ys,\" a \", ye,\"\\n\"))\n}\n\nfunction (y1=x[1,1],y2=dd[nrow(x),1],m=1,v=4,x=dd) \n{\n#\n# Little function to see how many NAs there are in month m\n# in period y1 to y2 for variable v:\n# \n# Usage: nas(1960,1990,1,4)\n#\n\nxs<-x[(x[,1]>=y1)&(x[,1]<=y2)&(x[,2]==m),]\ncat(paste(\"Años \",y1,\"-\",y2,\" Mes =\",m,\" Variable =\",v,\"\\n\"))\ncat(paste(\"Total número de dias =\",length(xs[,v]),\" Total dias sin datos =\",sum(is.na(xs[,v])),\"\\n\"))\n}\n\nstartss<-function(){\ntkwm.title(start1,\"RclimDex\")\ntkgrid(tklabel(start1,text=\" RClimDex 1.0 \",font=fontHeading))\ntkgrid(tklabel(start1,text=\"\",font=fontHeading))#empty line\ntkgrid(tklabel(start1,text=\"\",font=fontHeading))#empty line\n#tkgrid(tklabel(start1,text=\"\",font=fontHeading))#empty line\nstart.but<-tkbutton(start1,text=\"Cargar datos y realizar QC\",command=getfile,width=30)\ncal.but<-tkbutton(start1,text=\"Cálculo de indicadores\",command=nastat,width=30)\ncancel.but<-tkbutton(start1,text=\"Salir\",command=done,width=30)\ntkgrid(start.but)\ntkgrid(cal.but)\ntkgrid(cancel.but)\ntkgrid(tklabel(start1,text=\"\",font=fontHeading)) }#end of startss function\n\nstartss()\n", "meta": {"hexsha": 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{"text": "###\n### file: heatmap.R\n### author: Ali Tofigh\n###\n### Functions for displaying heatmaps. Instead of using one giant heatmap\n### function that tries to do everything, the common tasks have been divided\n### into several functions. It is assumed that the user will use 'layout()' to\n### divide the graphic output into segments and will provide space for column-\n### and rownames himself. A good example of how to use these functions is\n### provided in experiment 01 in project strepi.\n###\n### The function heatmap.simple() is also provided to support quick full\n### featured figures.\n\n\n\nlibrary(colorspace)\nlibrary(grid)\nlibrary(gridBase)\n\n\ngrid.newpage.loop <- function(...) {\n plot.new()\n require(grid)\n grid.clip(...)\n} \n\n### heatmap.color.scheme()\n###\n### Computes break points and color vector for heatmaps. Can also plot\n### the HCL space for easier customization of colors.\n###\n### Arguments:\n### col.palette either a palette name or index, or a full\n### specification of an hcl palette. See the\n### 'supported.palettes' definition in the code for\n### examples.\n### low.breaks\n### high.breaks breakpoints for the low and high colors. If\n### there is a gap between these vectors,\n### 'masked.color' will be used for the gap.\n### masked.color the color to be used for the gap between the\n### low and high colors.\n### plot if TRUE, will plot the HCL color space for the\n### chosen color palette.\n### gp graphical parameters passed to grid.points and\n### grid.lines when 'plot' is TRUE.\n###\n### Returns: a list with three fields:\n### breaks vector with break points, size is one more that 'col'\n### col vector with colors, size one less than 'breaks'\n### masked.idx idx into 'col' and 'breaks' pointing to the color and\n### the left break point of the masked region (if such a\n### region exists).\nheatmap.color.scheme <- function(low.breaks=seq(-3, 0, length.out=21),\n high.breaks=seq(0, 3, length.out=21),\n masked.color=\"black\",\n col.palette=c(\"blue.red\"),\n plot=FALSE, gp=gpar())\n{\n library(colorspace)\n\n stopifnot(length(low.breaks) > 0 || length(high.breaks) > 0)\n stopifnot(length(low.breaks) == 0 || is.numeric(low.breaks) && length(low.breaks) >= 2)\n stopifnot(length(high.breaks) == 0 || is.numeric(high.breaks) && length(high.breaks) >= 2)\n stopifnot(length(low.breaks) == 0 || all(diff(low.breaks) > 0))\n stopifnot(length(high.breaks) == 0 || all(diff(high.breaks) > 0))\n stopifnot(length(low.breaks) == 0 || length(high.breaks) == 0 || tail(low.breaks, 1) <= head(high.breaks, 1))\n\n supported.palettes <- list(\"blue.red\"=list(h=c(260, 0), c=c(0, 80), l=c(85, 30), power=1.5),\n \"blue.red.2\"=list(h=c(260, 0), c=c(0, 100), l=c(90, 50), power=1.5),\n \"blue.orange.1\"=list(h=c(240, 60), c=c(0, 77), l=c(98, 72), power=1.5))\n\n if (is.character(col.palette) || is.numeric(col.palette))\n pal <- supported.palettes[[col.palette]]\n else\n pal <- col.palette\n\n if (!c(\"h\", \"c\", \"l\", \"power\") %in% names(pal) ||\n !is.numeric(pal$h) || !is.numeric(pal$c) || !is.numeric(pal$l) || !is.numeric(pal$power) ||\n length(pal$h) != 2 || length(pal$c) != 2 || length(pal$l) != 2 || length(pal$power) != 1)\n stop(\"invalid 'col.palette' specification\")\n\n color.points <- function(b) {\n if (length(b) == 2)\n {\n ret <- 1\n }\n else if (length(b) == 3)\n {\n ret <- c(0, 1)\n }\n else\n {\n mid.b <- b[-c(1, length(b))]\n ret <- c(b[1], mid.b[-length(mid.b)] + diff(mid.b)/2, b[length(b)])\n ret <- ret - ret[1]\n ret <- ret / tail(ret, 1)\n }\n ret\n }\n\n if (length(low.breaks) > 0)\n {\n low.color.points <- color.points(low.breaks)^pal$power\n low.L <- pal$l[1] + diff(pal$l) * low.color.points\n low.C <- pal$c[1] + diff(pal$c) * low.color.points\n low.colors <- rev(hex(polarLUV(L=low.L, C=low.C, H=pal$h[1]), fixup=TRUE))\n }\n\n if (length(high.breaks) > 0)\n {\n high.color.points <- color.points(high.breaks)^pal$power\n high.L <- pal$l[1] + diff(pal$l) * high.color.points\n high.C <- pal$c[1] + diff(pal$c) * high.color.points\n high.colors <- hex(polarLUV(L=high.L, C=high.C, H=pal$h[2]), fixup=TRUE)\n }\n\n\n scheme <- list()\n if (length(low.breaks) == 0)\n {\n scheme$breaks <- high.breaks\n scheme$col <- c(high.colors)\n }\n else if (length(high.breaks) == 0)\n {\n scheme$breaks <- low.breaks\n scheme$col <- c(low.colors)\n\n }\n else if (tail(low.breaks, 1) == head(high.breaks, 1))\n {\n scheme$breaks <- c(low.breaks, high.breaks[-1])\n scheme$col <- c(low.colors, high.colors)\n }\n else\n {\n scheme$breaks <- c(low.breaks, high.breaks)\n scheme$col <- c(low.colors, masked.color, high.colors)\n scheme$masked.idx <- length(low.colors) + 1\n }\n\n if (isTRUE(plot))\n {\n plot.h <- function(h, pixels)\n {\n c <- seq(0,100,length=pixels)[-1];\n l <- seq(0,100,length=pixels)[-1];\n xy <- expand.grid(c=c, l=l);\n rect.col <- hcl(h, xy$c, xy$l, fixup=FALSE)\n grid.rect(xy$c/100, xy$l/100, width=1/length(c), height=1/length(l),\n gp=gpar(fill=rect.col, col=rect.col, linejoin=\"mitre\", lwd=0.01), just=c(\"right\", \"top\"),\n default.unit=\"native\")\n grid.text(h, 0.8, 0.1, default.units=\"native\")\n }\n\n pushViewport(viewport(layout=grid.layout(3, 2, heights=c(4, 1, 4))))\n\n pushViewport(viewport(layout.pos.row=1, layout.pos.col=1, xscale=c(1, 0), clip=\"off\"))\n plot.h(pal$h[1], 100)\n grid.lines(pal$c/100, pal$l/100, default.unit=\"native\", gp=gp)\n grid.points(low.C / 100, low.L / 100, default.units=\"native\", gp=gp)\n popViewport()\n\n pushViewport(viewport(layout.pos.row=1, layout.pos.col=2))\n plot.h(pal$h[2], 100)\n grid.lines(pal$c/100, pal$l/100, default.unit=\"native\", gp=gp)\n grid.points(high.C / 100, high.L / 100, default.units=\"native\", gp=gp)\n popViewport()\n\n pushViewport(viewport(layout.pos.row=2, layout.pos.col=1:2, xscale=range(scheme$breaks)))\n grid.rect()\n grid.rect(scheme$breaks[-1], 0, width=diff(scheme$breaks), height=1,\n gp=gpar(fill=scheme$col, col=scheme$col, linejoin=\"mitre\", lwd=0.01),\n just=c(\"right\", \"bottom\"),\n default.units=\"native\")\n popViewport()\n\n pushViewport(viewport(layout.pos.row=3, layout.pos.col=1:2, xscale=range(scheme$breaks)))\n pushViewport(viewport(layout=grid.layout(4, 9)))\n\n rc <- expand.grid(c=1:9, r=1:4)\n h <- seq(0, 359, 10)\n for (i in 1:length(h))\n {\n pushViewport(viewport(layout.pos.row=rc[i, \"r\"], layout.pos.col=rc[i, \"c\"]))\n plot.h(h[i], 80)\n popViewport()\n }\n\n popViewport()\n popViewport()\n\n popViewport()\n }\n\n return(scheme)\n}\n\n\n### heatmap.is.valid.color.scheme()\n###\n### internal function for checking color schemes.\nheatmap.is.valid.color.scheme <- function(color.scheme)\n{\n is.list(color.scheme) &&\n (c(\"breaks\", \"col\") %in% names(color.scheme)) &&\n is.numeric(color.scheme$breaks) &&\n (length(color.scheme$breaks) == length(color.scheme$col) + 1) &&\n (is.null(color.scheme$masked.idx) || color.scheme$masked.idx %in% 1:length(color.scheme$col))\n}\n\n\n### heatmap.map()\n###\n### plots a heatmap of 'x' in the current viewport. Returns the color\n### scheme used.\n###\n### Arguments:\n### x a numeric matrix with the heatmap values\n### color.scheme see the function heatmap.color.scheme() for details\n### na.color color used for NA values in 'x'\n###\n### Returns:\n###\n### The color scheme invisibly.\nheatmap.map <- function(x, color.scheme=heatmap.color.scheme(), na.color = \"grey\")\n{\n library(\"colorspace\")\n\n if (length(dim(x)) != 2 || !is.numeric(x))\n stop(\"'x' must be a numeric matrix\")\n\n bc <- color.scheme\n stopifnot(heatmap.is.valid.color.scheme(bc))\n\n ## make sure no values are outside the range ['min.val', 'max.val']\n min.val <- head(bc$breaks, 1)\n max.val <- tail(bc$breaks, 1)\n x[x < min.val] <- min.val\n x[x > max.val] <- max.val\n\n ## convert numbers to colors according to our breaks\n x.colors <- findInterval(x, bc$breaks, all.inside=TRUE)\n\n ## add na.color at the end of the color scheme\n bc$col <- c(bc$col, na.color)\n x.colors[is.na(x.colors)] <- length(bc$col)\n\n ## this viewport is set up so that origo is on the top left and the\n ## ranges of the \"native\" units are number of columns and rows of x.\n pushViewport(viewport(xscale=c(0, ncol(x)), yscale=c(nrow(x),0)))\n\n ## now draw the heatmap\n grid.rect(col(x), row(x), gp=gpar(col=bc$col[x.colors], fill=bc$col[x.colors], linejoin=\"mitre\", lwd=0.01),\n width=1, height=1, just=c(\"right\", \"top\"), default.units=\"native\")\n\n popViewport()\n\n return(invisible(color.scheme))\n}\n\n\n### heatmap.key()\n###\n### plots a heatmap key in the current viewport.\n###\n### Arguments:\n### x numeric matrix that was used to plot a heatmap\n### color.scheme the color scheme used to plot the heatmap\n### key.min should be less than the least break point of the\n### color scheme\n### key.max should be greater than the greates break point of the\n### color scheme\n### key.line.color the color used to draw the histogram\n###\n### Returns:\n### invisible(NULL)\nheatmap.key <- function(x, color.scheme=heatmap.color.scheme(),\n key.min = head(color.scheme$breaks, 1) - 1,\n key.max = tail(color.scheme$breaks, 1) + 1,\n key.line.color = \"cyan\")\n{\n if (!is.numeric(x))\n stop(\"'x' must be a numeric matrix\")\n stopifnot(heatmap.is.valid.color.scheme(color.scheme))\n\n bc <- color.scheme\n\n min.val <- head(bc$breaks, 1)\n max.val <- tail(bc$breaks, 1)\n key.min <- min(key.min, min.val)\n key.max <- max(key.max, max.val)\n\n ## add breakpoints and colors for the masked region, from min.val to\n ## key.min, and from max.val to key.max. This is so that we can get\n ## a good histogram showing in the key.\n break.width <- min(diff(bc$breaks))\n\n if (!is.null(bc$masked.idx)) # extend masked region\n {\n masked.color <- bc$col[bc$masked.idx]\n a <- bc$breaks[bc$masked.idx]\n b <- bc$breaks[bc$masked.idx + 1]\n n <- round((b - a) / break.width)\n n <- max(1, n)\n new.breaks <- seq(a, b, length.out = n + 1)[-c(1, n+1)]\n bc$breaks <- c(bc$breaks[1:bc$masked.idx],\n new.breaks,\n bc$breaks[(bc$masked.idx + 1):length(bc$breaks)])\n bc$col <- c(bc$col[1:bc$masked.idx],\n rep(masked.color, n - 1),\n bc$col[(bc$masked.idx + 1):length(bc$col)])\n }\n if (key.min < min.val) # extend region between min.val and key.min\n {\n n <- round((min.val - key.min) / break.width)\n n <- max(1, n)\n new.breaks <- seq(key.min, min.val, length.out = n + 1)[-(n+1)]\n bc$breaks <- c(new.breaks, bc$breaks)\n bc$col <- c(rep(bc$col[1], n), bc$col)\n }\n if (key.max > max.val) # extend region between max.val and key.max\n {\n n <- round((key.max - max.val) / break.width)\n n <- max(1, n)\n new.breaks <- seq(max.val, key.max, length.out = n + 1)[-1]\n bc$breaks <- c(bc$breaks, new.breaks)\n bc$col <- c(bc$col, rep(bc$col[length(bc$col)], n))\n }\n\n ## draw the key\n\n ## we will leave 30% of the area below the key histogram for drawing\n ## an axis, which needs about 2 lines of text.\n axis.text <- do.call(paste, as.list(grid.pretty(c(key.min, key.max))))\n height <- convertHeight(unit(2, \"lines\"), \"points\", valueOnly=T)/convertHeight(unit(1, \"npc\"), \"points\", valueOnly=T)\n axis.cex <- min((as.double(convertHeight(unit(height, \"native\"), \"lines\")) / 2),\n (1/as.double(convertWidth(unit(1, \"strwidth\", axis.text), \"npc\"))))*0.8\n height <- height * axis.cex\n\n pushViewport(viewport(0.5, 0.9, width=0.8, height=1-height, xscale=range(bc$breaks), yscale=c(1, 0),\n just=c(\"centre\", \"top\")))\n grid.rect(bc$breaks[-1], 0,\n width=diff(bc$breaks), height=1, gp=gpar(col=NA, fill=bc$col, lty=0),\n default.units=\"native\", just=c(\"right\", \"bottom\"))\n\n\n if (!all(is.na(x)))\n {\n h <- hist(x, plot = FALSE,\n breaks = c(min(x, na.rm=TRUE), bc$breaks, max(x, na.rm = TRUE)))\n pushViewport(viewport(xscale=c(key.min, key.max), yscale=c(0, max(h$counts)*1.05)))\n breaks <- h$breaks[-c(1, length(h$breaks))]\n counts <- h$counts[-c(1, length(h$counts))]\n grid.lines(rep(breaks, each=2), c(0, rep(counts, each=2), 0),\n gp=gpar(col=key.line.color), default.units=\"native\")\n grid.xaxis(gp=gpar(cex=axis.cex, lex=axis.cex))\n popViewport()\n }\n else\n {\n pushViewport(viewport(xscale=c(key.min, key.max)))\n grid.xaxis(gp=gpar(cex=axis.cex, lex=axis.cex))\n popViewport()\n }\n popViewport()\n\n return(invisible(NULL))\n}\n\n\n### heatmap.clinical()\n###\n### plots clinical variables as boxes in the current frame. Assumes 'x'\n### is a matrix with color information in each cell. Colors are usually\n### given using character strings such as \"white\", \"red\", etc. See\n### 'colors()' for a list of color names. NAs are plotted with color as\n### given by 'na.color'.\n###\n### Arguments:\n### x matrix with colors in each cell, or NAs. Will also\n### accept data frames (with rows representing patients)\n### na.color color to be used for missing values, i.e., NAs\n###\n### Returns:\n### NULL\nheatmap.clinical <- function(x, na.color = \"grey\")\n{\n if (length(dim(x)) != 2)\n stop(\"'x' must be a color matrix\")\n\n if (is.data.frame(x))\n x <- t(as.matrix(x))\n\n x[is.na(x)] <- na.color\n\n pushViewport(viewport(xscale=c(0, ncol(x)), yscale=c(nrow(x), 0)))\n\n grid.rect(col(x), row(x), width=1, height=1, gp=gpar(col=x, fill=x, linejoin=\"mitre\", lwd=0.01),\n just=c(\"right\", \"top\"), default.units=\"native\")\n grid.rect(ncol(x), 1:nrow(x), width=ncol(x), height=1, gp=gpar(col=\"black\", fill=NA),\n just=c(\"right\", \"top\"), default.units=\"native\")\n\n popViewport()\n\n return(invisible(NULL))\n}\n\n\n### heatmap.ddr()\n###\n### A very flexible function for clustering. It allows multiple ways of\n### specifying how the dendrogram is to be computed and it knows about\n### the cor() and hclust() functions.\n###\n### Arguments:\n###\n### exprs a matrix-like object whose columns are to be\n### clustered. May be omitted if a dendrogram can be\n### obtained using only the other arguments.\n### dist.elem specifies how to compute the distance matrix. May be\n### omitted if a dendrogram can be obtained from\n### 'clust.elem' alone. Possible specifications are: An\n### object that can be coerced to a distance matrix\n### (using as.dist()), a function that takes 'exprs' and\n### produces a distance matrix, or a character string\n### specifying how a distance matrix is to be computed\n### from 'exprs'. The acceptable character strings are\n### \"cor\" and any method specification that can be\n### passed on to the dist() function. If 'dist.elem' is\n### \"cor\", then 1-correlation (between the columns of\n### 'exprs') is used as distance.\n### clust.elem specifies how to compute the dendrogram. Possible\n### specifications are: An object that can be coerced to\n### \"dendrogram\" (using as.dendrogram()), a function\n### that takes a distance matrix and produces an object\n### that can be coerced to a dendrogram, a character\n### string specifying how to compute, from a distance\n### matrix, an object that can be coerced to a\n### dendrogram, or a character string specifying how to\n### compute a dendrogram from a distance matrix.\n### p passed to the dist() function and used only when\n### 'dist.elem' is \"minkowski\" (see help on the dist()\n### function for details).\nheatmap.ddr <- function(exprs=NULL, dist.elem=\"cor\", clust.elem=\"ward\", p=2)\n{\n if (class(clust.elem) != \"dendrogram\")\n {\n ret <- try(as.dendrogram(clust.elem), silent=TRUE)\n if (class(ret) == \"try-error\")\n {\n if (mode(dist.elem) == \"numeric\" && class(dist.elem) == \"matrix\")\n {\n dist.mat <- as.dist(dist.elem)\n }\n else if (is.null(exprs))\n {\n stop(\"heatmap.ddr: 'exprs' missing\")\n }\n else if (mode(dist.elem) == \"function\")\n {\n dist.mat <- as.dist(dist.elem(exprs))\n }\n else if (mode(dist.elem) == \"character\" && length(dist.elem) == 1 && dist.elem == \"cor\")\n {\n cors <- cor(exprs, use=\"pairwise.complete.obs\")\n cors[is.na(cors)] <- 0\n dist.mat <- as.dist(1 - cors)\n }\n else if (mode(dist.elem) == \"character\" && length(dist.elem) == 1)\n {\n dist.mat <- dist(t(exprs), method=dist.elem, p=p)\n }\n else\n {\n stop(\"heatmap.ddr: bad 'dist.elem'\")\n }\n\n if (mode(clust.elem) == \"function\")\n {\n ret <- as.dendrogram(clust.elem(dist.mat))\n }\n else if (mode(clust.elem) == \"character\" && length(clust.elem) == 1)\n {\n ret <- as.dendrogram(hclust(dist.mat, method=clust.elem))\n }\n else\n {\n stop(\"heatmap.ddr: bad 'dist.elem'\")\n }\n }\n }\n else\n {\n ret <- clust.elem\n }\n\n return(ret)\n}\n\n\n### heatmap.labels.cex()\n###\n### computes a cex value that can be used when plotting text in the\n### current viewport. The assumption is that 'labels' is a character\n### vector containing text strings that are meant to be drawn in the\n### current viewport (either horizontally or vertically). This function\n### will compute and return a cex value that can be passed to\n### grid.text() such that the strings in 'labels' will fit in the\n### current viewport when drawn on separate lines.\n###\n### Arguments:\n###\n### labels\n###\n### character vector\n###\n### type\n###\n### either \"row.labels\" or \"col.labels\" indicating whether the\n### labels are intended to be drawn horizontally or vertically.\n###\n### Returns:\n###\n### a cex value to be used when drawing the strings in 'labels' in\n### the current viewport\nheatmap.labels.cex <- function(labels, type=c(\"row.labels\", \"col.labels\"))\n{\n type <- match.arg(type)\n\n rw <- convertWidth(max(do.call(unit.c, lapply(labels, function(label) {unit(1, \"strwidth\", label)}))), \"npc\")\n rw <- convertWidth((rw + unit(2, \"strwidth\", \"X\")), \"npc\")\n rh <- convertHeight(unit(1, \"strheight\", \"X\"), \"npc\") * 1.8 * length(labels)\n if (type == \"row.labels\")\n {\n cex <- min(1/as.double(convertWidth(rh, \"npc\")), 1/as.double(rw))\n }\n else\n {\n rw <- convertUnit(rw, \"npc\", \"x\", \"dimension\", \"y\", \"dimension\")\n rh <- convertUnit(rh, \"npc\", \"y\", \"dimension\", \"x\", \"dimension\")\n cex <- min(1/as.double(convertWidth(rh, \"npc\")), 1/as.double(rw))\n }\n return(cex)\n}\n\n\n### heatmap.labels()\n###\n### plots each string in 'labels' on a separate line in the current\n### viewport. The size of the text and spacing between strings are\n### adjusted so that the text will fill the viewport as much as\n### possible.\n###\n### Arguments:\n###\n### labels\n###\n### character vector with strings to be drawn in the current\n### viewport\n###\n### type\n###\n### either \"row.labels\" or \"col.lables\", determining if the text\n### is to be drawn horizontally or vertically.\n###\n### just\n###\n### the justification of the text.\n###\n### rot\n###\n### rotation of the text in angles.\n###\n### Returns:\n###\n### invisible(NULL)\nheatmap.labels <- function(labels, type=c(\"row.labels\", \"col.labels\"), just=c(\"left\", \"right\", \"centre\"), rot=0)\n{\n type <- match.arg(type)\n just <- match.arg(just)\n\n if (type == \"row.labels\")\n {\n pushViewport(viewport(xscale=c(0, 1), yscale=c(1, 0)))\n if (just == \"left\")\n x <- unit(1, \"strwidth\", \"X\")\n else if (just == \"right\")\n x <- unit(1, \"npc\") - unit(1, \"strwidth\", \"X\")\n else\n x <- 0.5\n y <- ((1:length(labels)) - 0.5) /length(labels)\n grid.text(labels, x, y, default.units=\"native\", just=just, rot = rot,\n gp=gpar(cex=heatmap.labels.cex(labels, type=type)))\n popViewport()\n }\n else\n {\n pushViewport(viewport(xscale=c(0, 1), yscale=c(0, 1)))\n x <- ((1:length(labels)) - 0.5) /length(labels)\n if (just == \"left\")\n y <- unit(1, \"strwidth\", \"X\")\n else if (just == \"right\")\n y <- unit(1, \"npc\") - unit(1, \"strwidth\", \"X\")\n else\n y <- 0.5\n grid.text(labels, x, y, default.units=\"native\", just=just, rot=90 + rot,\n gp=gpar(cex=heatmap.labels.cex(labels, type=type)))\n popViewport()\n }\n invisible(NULL)\n}\n\n\n### heatmap.simple()\n###\n### Provides an easy way to produce full heatmap figures with the most\n### common features such as dendrograms from clustering, row and column\n### labels and a matrix of clinical variables. Many of the arguments\n### will take on sensible defaults if omitted (as described below).\n###\n### this function makes extensive use of the grid package and mixes it\n### with standard R graphics plots using gridBase. The figure is divided\n### into regions (with grid.layout which is similar to base R's layout\n### function) as directed by the layout.mat character matrix. The\n### strings in layout.mat will determine where each component of the\n### figure will be drawn. Other arguments are as described for the\n### heatmap functions above.\n###\n### Arguments:\n### x matrix of values for the main heatmap.\n### layout.mat a character matrix describing the layout of\n### the figure and its parts. The figure will be\n### divided into rows and columns corresponding\n### to the rows and columns of layout.mat. Each\n### entry should be a string describing what\n### should be plotted in that region. The list\n### of recognized entries are:\n###\n### heatmap\n### key\n### title\n### row.labels.ljust\n### row.labels.rjust\n### col.labels.ljust\n### col.labels.rjust\n### row.ddr.left\n### row.ddr.right\n### col.ddr.up\n### col.ddr.down\n### clinical\n### clinical.labels.ljust\n### clinical.labels.rjust\n###\n### If layout.mat is omitted, the function will\n### try to provide a sensible one depending on\n### what other options have been supplied.\n### widths\n### heights relative widths and heights for the\n### layout. If 'layout.mat' is omitted, care should be taken\n### when supplying these since they have to match\n### the size of the default layout.mat, which can vary\n### depending on other arguments.\n### color.scheme a list defining the color scheme to be used for the\n### heatmap. See the heatmap.color.scheme() function\n### for details on the requirements for this variable\n### scale for now only supports \"row\", \"none\", or a\n### user-supplied function that will be applied\n### to each row.\n### row.labels If omitted, will default to rownames of x.\n### col.labels If omitted, will default to colnames of x.\n### row.clust\n### row.clust.dist.fun\n### row.clust.fun\n### col.clust\n### col.clust.dist.fun\n### col.clust.fun If row.clust is TRUE, then the rows will be\n### clustered by the heatmap.ddr() function, to\n### which row.clust.dist.fun and row.clust.fun\n### are passed. See that function for details on\n### these arguments. An analogous description\n### applies to col.clust.*. Note that the expression\n### matrix will still be reordered if you pass\n### a dendrogram of your own (using order.dendrogram()).\n### So make sure the dendrogram was derived from\n### 'exprs' and that 'exprs' has not been reordered.\n### clinical a matrix of colors or a data frame to be\n### passed to heatmap.clinical.\n### clinical.labels If omitted, will default to rownames of\n### 'clinical'.\n### title text string to be printed as a title\nheatmap.simple <- function(exprs,\n layout.mat=NULL,\n widths=NULL,\n heights=NULL,\n color.scheme=heatmap.color.scheme(),\n key.min=head(color.scheme$breaks, 1) - 1,\n key.max=tail(color.scheme$breaks, 1) + 1,\n na.color=\"grey\",\n scale=\"row\",\n key.line.color=\"cyan\",\n row.labels=NULL,\n col.labels=NULL,\n row.clust=TRUE,\n row.clust.dist.fun=\"cor\",\n row.clust.fun=\"ward\",\n col.clust=TRUE,\n col.clust.dist.fun=\"cor\",\n col.clust.fun=\"ward\",\n clinical=NULL,\n clinical.labels=NULL,\n clinical.na.color=na.color,\n title=NULL)\n{\n library(grid)\n library(gridBase)\n\n ## save all standard R graphical parameters\n old.par <- par(no.readonly=TRUE)\n\n ## if clinical is a vector or data.frame, then convert it to a matrix\n if (!is.null(clinical))\n {\n if (is.vector(clinical))\n clinical <- matrix(clinical, nrow=1)\n else if (is.data.frame(clinical))\n clinical <- t(as.matrix(clinical))\n }\n\n ## get default values for arguments\n if (is.null(row.labels))\n {\n row.labels <- rownames(exprs)\n if (is.null(row.labels))\n row.labels <- rep(\"\", nrow(exprs))\n }\n if (is.null(col.labels))\n {\n col.labels <- colnames(exprs)\n if (is.null(col.labels))\n col.labels <- rep(\"\", ncol(exprs))\n }\n if (is.null(clinical.labels) && !is.null(clinical))\n clinical.labels <- rownames(clinical)\n\n ## if layout.mat is omitted, try to come up with a good default layout\n if (is.null(layout.mat))\n {\n layout.mat <- matrix(c(\"key\", \"col.ddr.up\", \"\",\n \"row.ddr.left\", \"heatmap\", \"row.labels.ljust\",\n \"\", \"col.labels.rjust\", \"\"), ncol=3, byrow=TRUE)\n if (!is.null(clinical))\n {\n layout.mat <- rbind(layout.mat, c(\"clinical.labels.rjust\", \"clinical\", \"\"))\n layout.mat <- rbind(layout.mat, c(\"\", \"\", \"\"))\n }\n if (!is.null(title))\n {\n layout.mat <- rbind(c(\"\", \"title\", \"\"), layout.mat)\n }\n if (is.null(widths))\n {\n widths <- c(0.3, 1, 0.3)\n }\n if (is.null(heights))\n {\n heights <- rep(0.3, nrow(layout.mat))\n heights[which(layout.mat == \"heatmap\", arr.ind=TRUE)[1]] <- 1\n if (!is.null(clinical))\n {\n heights[length(heights) - 1] <- 0.03*nrow(clinical)\n heights[length(heights)] <- 0.03\n }\n if (!is.null(title))\n {\n heights[1] <- 0.15\n }\n }\n if (!isTRUE(row.clust))\n {\n layout.mat[grep(\"row.ddr\", layout.mat)] <- \"\"\n }\n if (!isTRUE(col.clust))\n {\n layout.mat[grep(\"col.ddr\", layout.mat)] <- \"\"\n }\n\n stopifnot(length(widths) == ncol(layout.mat))\n stopifnot(length(heights) == nrow(layout.mat))\n }\n\n ## make sure layout is character matrix\n mode(layout.mat) <- \"character\"\n layout.mat <- as.matrix(layout.mat)\n\n ## scale the rows (if indicated by caller). When using correlation\n ## as distance, we definitely need to scale the rows *before*\n ## clustering since the mean of the columns don't really mean\n ## anything (expressions of different genes are not comparable in\n ## microarray experiments). Are there situations where we might want\n ## to cluster before scaling?\n if (is.function(scale))\n {\n exprs <- t(apply(exprs, 1, scale))\n }\n else\n {\n stopifnot(length(scale) == 1 && scale %in% c(\"row\", \"none\"))\n ## scale the matrix if given\n if (scale == \"row\")\n exprs <- t(scale(t(exprs)))\n }\n\n ## get dendrograms and reorder the data matrix\n row.ddr <- NULL\n if (isTRUE(row.clust))\n {\n row.ddr <- rev(heatmap.ddr(t(exprs), row.clust.dist.fun, row.clust.fun))\n exprs <- exprs[order.dendrogram(rev(row.ddr)), ]\n row.labels <- row.labels[order.dendrogram(rev(row.ddr))]\n }\n\n col.ddr <- NULL\n if (isTRUE(col.clust))\n {\n col.ddr <- heatmap.ddr(exprs, col.clust.dist.fun, col.clust.fun)\n exprs <- exprs[ , order.dendrogram(col.ddr)]\n col.labels <- col.labels[order.dendrogram(col.ddr)]\n if (!is.null(clinical))\n clinical <- clinical[, order.dendrogram(col.ddr), drop=FALSE]\n }\n\n ## create the top viewport\n the.layout <- grid.layout(nrow(layout.mat), ncol(layout.mat), widths=widths, heights=heights)\n top.vp <- viewport(layout=the.layout, name=\"heatmap.top.vp\")\n pushViewport(top.vp)\n\n ## get all unique strings in 'layout.mat'\n elements <- unique(as.vector(layout.mat))\n elements <- elements[elements != \"\" & !is.na(elements)]\n\n ## for the known strings in layout.mat, seek the named viewport and\n ## draw accordingly\n elem <- \"heatmap\"\n if (elem %in% elements)\n {\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n heatmap.map(exprs, color.scheme=color.scheme, na.color=na.color)\n upViewport()\n }\n elem <- \"key\"\n if (elem %in% elements)\n {\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n heatmap.key(exprs, color.scheme=color.scheme, key.min=key.min, key.max=key.max, key.line.color=key.line.color)\n upViewport()\n }\n elem <- \"row.labels.ljust\"\n if (elem %in% elements)\n {\n if (length(row.labels) != nrow(exprs))\n warning(\"the number of row labels is not equal to the number of rows of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n row.labels[is.na(row.labels)] <- \"\"\n heatmap.labels(row.labels, type=\"row.labels\", just=\"left\")\n upViewport()\n }\n elem <- \"row.labels.rjust\"\n if (elem %in% elements)\n {\n if (length(row.labels) != nrow(exprs))\n warning(\"the number of row labels is not equal to the number of rows of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n row.labels[is.na(row.labels)] <- \"\"\n heatmap.labels(row.labels, type=\"row.labels\", just=\"right\")\n upViewport()\n }\n elem <- \"col.labels.ljust\"\n if (elem %in% elements)\n {\n if (length(col.labels) != ncol(exprs))\n warning(\"the number of column labels is not equal to the number of columns of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n col.labels[is.na(col.labels)] <- \"\"\n heatmap.labels(col.labels, type=\"col.labels\", just=\"left\")\n upViewport()\n }\n elem <- \"col.labels.rjust\"\n if (elem %in% elements)\n {\n if (length(col.labels) != ncol(exprs))\n warning(\"the number of column labels is not equal to the number of columns of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n col.labels[is.na(col.labels)] <- \"\"\n heatmap.labels(col.labels, type=\"col.labels\", just=\"right\")\n upViewport()\n }\n elem <- \"row.ddr.left\"\n if (elem %in% elements && !is.null(row.ddr))\n {\n if (attr(row.ddr, \"members\") != nrow(exprs))\n warning(\"the number of row dendrogram leaves is not equal to the number of rows of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2]), clip=\"off\"))\n par(new=TRUE, fig=gridFIG(), mar=c(0,0,0,0))\n plot(row.ddr, horiz=TRUE, leaflab=\"none\", xaxt=\"n\", yaxt=\"n\", xaxs=\"r\", yaxs=\"i\")\n upViewport()\n }\n elem <- \"row.ddr.right\"\n if (elem %in% elements && !is.null(row.ddr))\n {\n if (attr(row.ddr, \"members\") != nrow(exprs))\n warning(\"the number of row dendrogram leaves is not equal to the number of rows of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n par(new=TRUE, fig=gridFIG(), mar=c(0,0,0,0))\n plot(row.ddr, horiz=TRUE, leaflab=\"none\", xaxt=\"n\", yaxt=\"n\", xaxs=\"r\", yaxs=\"i\",\n xlim=c(0, attr(row.ddr, \"height\")))\n upViewport()\n }\n elem <- \"col.ddr.up\"\n if (elem %in% elements && !is.null(col.ddr))\n {\n if (attr(col.ddr, \"members\") != ncol(exprs))\n warning(\"the number of column dendrogram leaves is not equal to the number of columns of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n par(new=TRUE, fig=gridFIG(), mar=c(0,0,0,0))\n plot(col.ddr, horiz=FALSE, leaflab=\"none\", xaxt=\"n\", yaxt=\"n\", xaxs=\"i\", yaxs=\"r\")\n upViewport()\n }\n elem <- \"col.ddr.down\"\n if (elem %in% elements && !is.null(col.ddr))\n {\n if (attr(col.ddr, \"members\") != ncol(exprs))\n warning(\"the number of column dendrogram leaves is not equal to the number of columns of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n par(new=TRUE, fig=gridFIG(), mar=c(0,0,0,0))\n plot(col.ddr, horiz=FALSE, leaflab=\"none\", xaxt=\"n\", yaxt=\"n\", xaxs=\"i\", yaxs=\"r\",\n ylim=c(attr(col.ddr, \"height\"), 0))\n upViewport()\n }\n elem <- \"clinical\"\n if (elem %in% elements && !is.null(clinical))\n {\n if (ncol(clinical) != ncol(exprs))\n warning(\"the size of clinical does not correspond to the number of columns of exprs\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n heatmap.clinical(clinical, clinical.na.color)\n upViewport()\n }\n elem <- \"clinical.labels.ljust\"\n if (elem %in% elements && !is.null(clinical.labels))\n {\n if (length(clinical.labels) != nrow(clinical))\n warning(\"the number of clinical labels is not equal to the number of variables in clinical\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n clinical.labels[is.na(clinical.labels)] <- \"\"\n heatmap.labels(labels=clinical.labels, type=\"row.labels\", just=\"left\")\n upViewport()\n }\n elem <- \"clinical.labels.rjust\"\n if (elem %in% elements && !is.null(clinical.labels))\n {\n if (length(clinical.labels) != nrow(clinical))\n warning(\"the number of clinical labels is not equal to the number of variables in clinical\")\n\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n clinical.labels[is.na(clinical.labels)] <- \"\"\n heatmap.labels(labels=clinical.labels, type=\"row.labels\", just=\"right\")\n upViewport()\n }\n elem <- \"title\"\n if (elem %in% elements && !is.null(title))\n {\n idx <- which(layout.mat == elem, arr.ind=TRUE)\n pushViewport(viewport(name=elem,\n layout.pos.row=unique(idx[,1]),\n layout.pos.col=unique(idx[,2])))\n\n grid.text(title, gp=gpar(cex=0.9 * heatmap.labels.cex(title)))\n upViewport()\n }\n\n ## restore all standard R graphical parameters\n par(old.par)\n upViewport()\n\n\n row.ind <- 1:nrow(exprs)\n col.ind <- 1:ncol(exprs)\n if (!is.null(row.ddr)) row.ind <- order.dendrogram(rev(row.ddr))\n if (!is.null(col.ddr)) col.ind <- order.dendrogram(col.ddr)\n\n return(invisible(list(layout.mat=layout.mat,\n widths=widths,\n heights=heights,\n color.scheme=color.scheme,\n key.min=key.min,\n key.max=key.max,\n na.color=na.color,\n scale=scale,\n key.line.color=key.line.color,\n row.labels=row.labels,\n col.labels=col.labels,\n row.clust=row.clust,\n row.clust.dist.fun=row.clust.dist.fun,\n row.clust.fun=row.clust.fun,\n col.clust=col.clust,\n col.clust.dist.fun=col.clust.dist.fun,\n col.clust.fun=col.clust.fun,\n clinical=clinical,\n clinical.labels=clinical.labels,\n clinical.na.color=clinical.na.color,\n title=title,\n row.ind=row.ind, col.ind=col.ind,\n row.ddr=row.ddr, col.ddr=col.ddr)))\n}\n\n\n## creates html making a heatmap image created by heatmap.simple clickable.\n## e.g.\n## png(\"heatmap.png\", width=800, height=1200)\n## heatmap.out <- heatmap.simple(...)\n## dev.off()\n## writeLines(heatmap.simple.html.map(heatmap.out, \"clickable\", 800, 1200, \"heatmap.png\")$html, con=\"heatmap.html\")\nheatmap.simple.html.map <- function(heatmap.out, heatmap.name, image.width, image.height, image.filename,\n clickable=c(\"heatmap\",\"col.labels\",\"row.labels\",\"key\",\n \"title\",\"clinical\", \"clinical.labels\")) {\n col.labels <- make.names(heatmap.out$col.labels, unique=T)\n row.labels <- make.names(heatmap.out$row.labels, unique=T)\n clinical.labels <- make.names(heatmap.out$clinical.labels, unique=T)\n\n names(col.labels) <- heatmap.out$col.labels\n names(row.labels) <- heatmap.out$row.labels\n names(clinical.labels) <- heatmap.out$clinical.labels\n\n clickable <- intersect(c(clickable, paste0(clickable, \".rjust\"), paste0(clickable, \".ljust\")), heatmap.out$layout.mat)\n\n href <- sapply(clickable, function(item) {\n switch(gsub(\".[rl]{1}just$\", \"\", item),\n heatmap=sapply(col.labels, function(col)\n sapply(row.labels, function(row)\n paste(heatmap.name, \"-row-\", row, \"-col-\", col, \".html\", sep=\"\"))),\n\n col.labels=matrix(sapply(col.labels, function(col)\n paste(heatmap.name, \"-col-\", col, \".html\", sep=\"\")), nrow=1),\n\n row.labels=matrix(sapply(row.labels, function(row)\n paste(heatmap.name, \"-row-\", row, \".html\", sep=\"\")), ncol=1),\n\n key=matrix(paste(heatmap.name, \"-key.html\", sep=\"\"), nrow=1),\n\n title=matrix(paste(heatmap.name, \"-title.html\", sep=\"\"), nrow=1),\n\n clinical=sapply(col.labels, function(col)\n sapply(clinical.labels, function(clinical)\n paste(heatmap.name, \"-clinical-\", clinical, \"-col-\", col, \".html\", sep=\"\"))),\n\n clinical.labels=matrix(sapply(clinical.labels, function(label)\n paste(heatmap.name, \"-clinical-\", label, \".html\", sep=\"\")), ncol=1))\n }, simplify=F)\n\n list(html=do.call(\"layout.mat.html.map\",\n c(list(layout.mat=heatmap.out$layout.mat,\n layout.widths=heatmap.out$widths,\n layout.heights=heatmap.out$heights,\n name=heatmap.name,\n image.width=image.width,\n image.height=image.height,\n image.filename=image.filename),\n href)),\n row.labels=row.labels,\n col.labels=col.labels,\n clinical.labels=clinical.labels,\n href=href)\n}\n\n## creates an html image map according to the items in the layout.mat\n## \"...\" argument are matrices whose names match items (regions) in layout.mat\n## each corresponding region is divided into equal-sized cells corresponding\n## to the dimensions of the matrix\n## the matrix contains the link targets\n## returns image map code\nlayout.mat.html.map <- function(layout.mat, layout.widths, layout.heights,\n name,\n image.width, image.height,\n image.filename, ...) {\n stopifnot(nrow(layout.mat) == length(layout.heights))\n stopifnot(ncol(layout.mat) == length(layout.widths))\n stopifnot(all(names(list(...)) %in% layout.mat))\n\n bounding.box <- function(name) {\n mat.col <- 1:ncol(layout.mat)\n mat.row <- 1:nrow(layout.mat)\n i <- which(name == layout.mat)\n if (length(i) == 0) return(NULL)\n if (length(i) > 1) {\n warning(paste(\"layout.mat.html:bounding.box: '\", name,\n \"' matches multiple items in layout.mat, \",\n \"making only the first one clickable.\", sep=\"\"))\n i <- i[1]\n }\n col.ind <- ceiling(i/nrow(layout.mat))\n row.ind <- i - (col.ind-1)*nrow(layout.mat)\n list(x1=sum(layout.widths[which(mat.col < col.ind)])/sum(layout.widths) * image.width,\n x2=sum(layout.widths[which(mat.col <= col.ind)])/sum(layout.widths) * image.width,\n y1=sum(layout.heights[which(mat.row < row.ind)])/sum(layout.heights) * image.height,\n y2=sum(layout.heights[which(mat.row <= row.ind)])/sum(layout.heights) * image.height)\n }\n html.rect <- function(row,col,nrow,ncol,bb,link) {\n cell.width <- (bb$x2 - bb$x1)/ncol\n cell.height <- (bb$y2 - bb$y1)/nrow\n x1 <- cell.width*(col-1) + bb$x1\n x2 <- x1 + cell.width\n y1 <- cell.height*(row-1) + bb$y1\n y2 <- y1 + cell.height\n coords <- paste(floor(x1), ceiling(y1), floor(x2), ceiling(y2), sep=\",\")\n paste(\"\", sep=\"\")\n }\n\n out <- paste(\"![](\\\"\",image.filename,\"\\\")
\",sep=\"\")\n out <- c(out,paste(\"\")\n}\n\n\n## add 1-3 marks to each cell of a simple heatmap\n## e.g.\n## heatmap.out <- heatmap.simple(exprs, ...)\n## marks <- matrix(0, ncol=ncol(exprs), nrow=nrow(exprs))\n## marks[pvals < 0.01] <- 1\n## marks[pvals < 0.001] <- 2\n## heatmap.mark(heatmap.out, marks)\nheatmap.mark <- function(heatmap.out, marks, mark=\"star\") {\n stopifnot(ncol(marks) == length(heatmap.out$col.ind))\n stopifnot(nrow(marks) == length(heatmap.out$row.ind))\n stopifnot(all(marks %in% 0:3))\n\n plot.marks <- function(...) {\n if (mark == \"star\")\n plot.stars(...)\n else\n plot.box(...)\n }\n \n plot.stars <- function(x, y, pos) {\n if (length(x) == 0)\n return()\n\n id <- rep(1:length(x), 3)\n x <- x - (pos - 1)/8\n grid.polygon(c(x, x, x-(1/8)), c(y-1, y-1+(1/4), y-1),\n gp=gpar(fill=\"black\", lty=0), id = id, default.units = \"native\")\n }\n\n plot.box <- function(x,y,pos) {\n if (length(x) == 0) return()\n\n id <- rep(1:length(x), 4)\n grid.polygon(c(x,x,x-1,x-1), c(y-1,y,y,y-1),\n gp=gpar(lty=1, col=\"black\"), id=id, \n default.units=\"native\")\n }\n\n marks <- marks[heatmap.out$row.ind,]\n marks <- marks[,heatmap.out$col.ind]\n\n n <- downViewport(\"heatmap\")\n pushViewport(viewport(xscale=c(0, ncol(marks)), yscale=c(nrow(marks), 0)))\n for (i in setdiff(unique(marks), 0)) {\n idx <- which(marks>=i, arr.ind=TRUE)\n plot.marks(idx[, \"col\"], idx[, \"row\"], i)\n }\n popViewport()\n upViewport(n)\n}\n\n\n## draw a line under the row given by row.label\n## e.g.\n## heatmap.out <- heatmap.simple(exprs, ...)\n## heatmap.underline.row(heatmap.out, row.label=\"NR3C1\")\nheatmap.underline.row <- function(heatmap.out, row.label, below=TRUE, gp=gpar(col=\"black\", lty=1)) {\n y <- 1-(which(heatmap.out$row.labels == row.label) - sign(!below))/length(heatmap.out$row.labels)\n for (element in c(\"heatmap\", \"row.labels.rjust\", \"row.labels.ljust\")) {\n if (element %in% heatmap.out$layout.mat) {\n depth <- downViewport(element)\n grid.polyline(c(0,1), c(y,y), gp=gp)\n upViewport(depth)\n }\n }\n}\n\n## draw a line next to the column given by col.label\n## e.g.\n## heatmap.out <- heatmap.simple(exprs, ...)\n## heatmap.underline.column(heatmap.out, col.label=\"curtis.323\")\nheatmap.underline.column <- function(heatmap.out, col.label, left=TRUE, gp=gpar(col=\"black\", lty=1)) {\n y <- (which(heatmap.out$col.labels == col.label) - sign(left))/length(heatmap.out$col.labels)\n depth <- downViewport(\"heatmap\")\n grid.polyline(c(y,y),c(0,1), gp=gp)\n upViewport(depth)\n}\n", "meta": {"hexsha": "af2dc951479e6578fa129316ed0e3393d83a370a", "size": 49236, "ext": "r", "lang": "R", "max_stars_repo_path": "paper/heatmap-function.r", "max_stars_repo_name": "MRCIEU/ewascatalog", "max_stars_repo_head_hexsha": "a37dfeb207537831b4c5e313e0edecbad8a7c1a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-08-05T09:39:48.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-05T09:39:48.000Z", "max_issues_repo_path": "paper/heatmap-function.r", "max_issues_repo_name": "MRCIEU/ewascatalog", "max_issues_repo_head_hexsha": "a37dfeb207537831b4c5e313e0edecbad8a7c1a2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "paper/heatmap-function.r", "max_forks_repo_name": "MRCIEU/ewascatalog", "max_forks_repo_head_hexsha": "a37dfeb207537831b4c5e313e0edecbad8a7c1a2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.6467817896, "max_line_length": 121, "alphanum_fraction": 0.5433422699, "num_tokens": 12311, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.4035668680822513, "lm_q2_score": 0.028436033559453556, "lm_q1q2_score": 0.011475841004270463}}
{"text": "#=================================================================================================#\n# harvard_forest_datasets.r\n# Datasets and parsing functions for Harvard Forest EMS flux tower (Little Prospect Hill)\n# Note: site class is ems (EMS tower)\n# Adam Erickson, PhD, Washington State University\n# Contact: adam.michael.erickson@gmail.com\n# March 14, 2019\n# License: Apache 2.0\n#\n# Copyright 2019 Washington State University\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#=================================================================================================#\n\n#-------------------------------------------------------------------------------------------------#\n# Datasets specific to EMS tower:\n#-------------------------------------------------------------------------------------------------#\n# HF004: Canopy-Atmosphere Exchange of Carbon, Water and Energy at Harvard Forest EMS Tower since 1991\n# HF006: Soil Respiration, Temperature and Moisture at Harvard Forest EMS Tower since 1995\n# HF060: EMS - Automated High-Frequency Methane Data\n# HF066: Concentrations and Surface Exchange of Air Pollutants at Harvard Forest EMS Tower since 1990\n# HF067: CFCs and Radiatively Important Trace Species at Harvard Forest EMS Tower 1996-2005\n# HF068: Soil Respiration Along a Hydrological Gradient at Harvard Forest EMS Tower 2003-2006\n# HF069: Biomass Inventories at Harvard Forest EMS Tower since 1993\n# HF102: Radiation Measurements at Harvard Forest EMS Tower 1991-2007\n# HF145: Hydrocarbon Concentrations at Harvard Forest EMS Tower 1992-2001\n# HF206: Microclimate at Harvard Forest HEM, LPH and EMS Towers since 2005\n# HF209: Isotopic Composition of Net Ecosystem CO2 Exchange at Harvard Forest EMS Tower since 2011\n# HF214: Fluxes of Carbonyl Sulfide at Harvard Forest EMS Tower since 2010\n# HF288: Fluxes of Molecular Hydrogen (H2) at Harvard Forest EMS Tower 2010-2012\n# HF306: Atmospheric Oxygen and Carbon Dioxide at Harvard Forest EMS Tower since 2006\n\n#-------------------------------------------------------------------------------------------------#\n# General data:\n#-------------------------------------------------------------------------------------------------#\n# \"hf069-13-annual-summ.csv\" biomass_ag (woody), ANPP (woody), recruitment, mortality, 1993-2017\n# \"hf069-15-annual-summ.csv\" biomass_ag (woody), ANPP (woody), recruitment, mortality, 1994-2013 (old)\n# \"hf069-14-understory-summ.csv\" understory regenertion, biomass, 2004-2006\n# \"hf001-04-monthly-m.csv\" meteorology, 2001-2014\n# \"hf004-02-filled.csv\" NEE/GEE, respiration (ecosystem), 1991-2015\n# \"hf069-09-trees.csv\" species, DBH, 1993-2017\n# \"hf069-11-trees.csv\" species, DBH, 1998-2013\n# \"hf008-04-tree.csv\" species, dbh, 1998-2008\n# \"hf213-01-hf-inventory.csv\" species, dbh, coordinates, 2009-2011\n# \"hf154-03-lph-cores.csv\" ANPP, 2005\n# \"hf133-03-productivity.csv\" biomass, 1996-1997\n# \"hf137-01-tree.csv\" biomass, ANPP, species, 1996 & 2006\n# \"hf008-01-foliage.csv\" canopy chemistry, 1988-2011\n# \"hf008-02-litter.csv\" canopy chemistry, 1988-2002\n# \"hf062-01-plot.csv\" canopy chemistry, 1992\n# \"hf062-02-tree.csv\" canopy chemistry, 1992\n# \"hf069-06-canopy-chem.csv\" canopy chemistry, 1998-2008\n# \"hf008-07-decomp.csv\" litter chemistry, 1998-2008\n# \"hf069-05-litter-chem.csv\" litter chemistry, 1998-2000\n# \"hf047-01-regen.csv\" regeneration, 1990-2000\n# \"hf005-05-soil-respiration.csv\" r_soil, 1991-2002, C = control\n# \"hf006-01-soil-respiration.csv\" r_soil, 1995-2010\n# \"hf006-03-autochamber.csv\" r_soil, 2003\n# \"hf068-01-soil-resp.csv\" r_soil, 2003-2006\n# \"hf045-01-soil-resp.csv\" r_soil, 2006-2012\n# \"hf148-02-lph-soil-respiration.csv\" r_soil, 2002-2007\n# \"hf194-01-soil-resp.csv\" r_soil, 1993-2006\n# \"hf194-02-synthesis-data\" r_soil, 1991-2008\n# \"hf243-01-soil-respiration.csv\" r_soil (Finzi), 2009-2010\n# \"hf040-02-species.csv\" species abundance, 1937-1991\n# \"hf015-06-species.csv\" species codes\n# \"hf040-01-species.csv\" species codes\n# \"hf222-01-vegetation.csv\" species codes\n# \"hf039-02-tree.csv\" species DBH-height, 1937\n# \"hf005-01-trace-gas.csv\" co2, ch4, n20, 1991-2002\n# \"hf003-01-plant.csv\" tree canopy dimensions, 1991\n# \"hf069-16-allometries.txt\" allometry equations\n#-------------------------------------------------------------------------------------------------#\n\n#-------------------------------------------------------------------------------------------------#\n# Metrics and units\n#-------------------------------------------------------------------------------------------------#\n# Note: kg m-2 (10 Mg ha-1); µmol m-2 sec-1 (1e-6 * 60 * 60 * 24 * 365.24 mol m-2 year-1)\n# nee ................. kg C m-2 year-1\n# biomass_ag .......... kg m-2\n# c_ag ................ kg C m-2\n# n_ag ................ kg N m-2\n# biomass_bg .......... kg m-2\n# c_bg ................ kg C m-2\n# n_bg ................ kg N m-2\n# c_so ................ kg C m-2\n# n_so ................ kg N m-2\n# r_soil .............. kg C m-2 year-1\n# anpp ................ kg m-2 year-1\n# biomass_sp .......... kg m-2\n# abundance_sp ........ % of total n-trees\n\n#-------------------------------------------------------------------------------------------------#\n# Selected data:\n#-------------------------------------------------------------------------------------------------#\n# \"hf069-13-annual-summ.csv\" (annual woody) or...\n# \"hf069-09-trees.csv\"\n# timescale: 1993-2017\n# variables: biomass_ag and modeled c_ag, n_ag, biomass_bg, c_bg, n_bg, anpp, biomass_sp, abundance_sp\n# \"hf006-01-soil-respiration.csv\"\n# timescale: 1995-2010\n# variables: r_soil,\n# \"hf015-05-soils.csv\"\n# timescale: 1937-2013\n# variables: c_so, n_so\n# \"hf004-02-filled.csv\"\n# timescale: 1991-2015\n# variables: nee\n\n#-------------------------------------------------------------------------------------------------#\n# Time period:\n#-------------------------------------------------------------------------------------------------#\n# Intersection: 1996-2009 (14 years)\n# Union: 1937-2017 (81 years)\n# Climate: 2002-2012 (11 years)\n\n# Example: download observation data\n#url <- \"http://harvardforest.fas.harvard.edu/data/p06/hf069/hf069-09-trees.csv\"\n#dest <- file.path(base, \"observations\",\"hf_ems\",\"hf069-09-trees.csv\")\n#download_file(url=url, destination=dest)\n\n#-------------------------------------------------------------------------------------------------#\n# Base directory\n#-------------------------------------------------------------------------------------------------#\nbase_hf_data <- file.path(base, \"observations\", \"hf_ems\")\n\n#-------------------------------------------------------------------------------------------------#\n# Species look-up table\n#-------------------------------------------------------------------------------------------------#\n# http://harvardforest.fas.harvard.edu/sites/harvardforest.fas.harvard.edu/files/data/p06/hf069/hf069-17-allometries.txt\n# http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf003\n# Using USDA PLANTS database codes: https://plants.usda.gov\nspecies_lut <- data.frame(\n abbr = c(\"AB\",\"AC\",\"ash\",\"bb\",\n \"BB\",\"BC\",\"beech\",\"bo\",\n \"cherry\",\"chestnut\",\"EH\",\"FPC\",\n \"gb\",\"haw\",\"hbb\",\"HBB\",\n \"hem\",\"HT\",\"nwr\",\"NWR\",\n \"PB\",\"rm\",\"RM\",\"ro\",\n \"RO\",\"rp\",\"sm\",\"SM\",\n \"SPI\",\"SS\",\"wb\",\"wh\",\n \"WH\",\"wo\",\"wp\",\"WP\",\n \"ws\",\"WS\",\"yb\",\"YB\"),\n name = c(\"Fagus grandifolia\",\"Castenea dentate\",\"Fraxinus americana\",\"Betula lenta\",\n \"Betula lenta\",\"Prunus serotina\",\"Fagus grandifolia\",\"Quercus velutina\",\n \"Prunus serotina\",\"Castenea dentate\",\"Tsuga canadensis\",\"?\",\n \"Betula populifolia\",\"Crataegus\",\"Vaccinium\",\"Vaccinium\",\n \"Tsuga canadensis\",\"Crataegus\",\"Viburnum\",\"Viburnum\",\n \"Betula papyrifera\",\"Acer rubrum\",\"Acer rubrum\",\"Quercus rubra\",\n \"Quercus rubra\",\"Pinus resinosa\",\"Acer pennsylvanicum\",\"Acer pennsylvanicum\",\n \"Spiraea\",\"?\",\"Ilex verticillata\",\"Hamamelis virginiania\",\n \"Hamamelis virginiania\",\"Quercus alba\",\"Pinus strobus\",\"Pinus strobus\",\n \"Picea glauca\",\"Picea glauca\",\"Betula alleghaniensis\",\"Betula alleghaniensis\"),\n genus = c(\"Fagus\",\"Castenea\",\"Fraxinus\",\"Betula\",\"Betula\",\"Prunus\",\"Fagus\",\"Quercus\",\"Prunus\",\n \"Castenea\",\"Tsuga\",\"\",\"Betula\",\"Crataegus\",\"Vaccinium\",\"Vaccinium\",\"Tsuga\",\"Crataegus\",\n \"Viburnum\",\"Viburnum\",\"Betula\",\"Acer\",\"Acer\",\"Quercus\",\"Quercus\",\"Pinus\",\"Acer\",\n \"Acer\",\"Spiraea\",\"\",\"Ilex\",\"Hamamelis\",\"Hamamelis\",\"Quercus\",\"Pinus\",\"Pinus\",\"Picea\",\n \"Picea\",\"Betula\",\"Betula\"),\n code = c(\"FAGR\",\"CADE\",\"FRAM\",\"BELE\",\n \"BELE\",\"PRSE\",\"FAGR\",\"QUVE\",\n \"PRSE\",\"CADE\",\"TSCA\",\"\",\n \"BEPO\",\"CRSP\",\"VACO\",\"VACO\",\n \"TSCA\",\"CRSP\",\"VICA\",\"VICA\",\n \"BEPA\",\"ACRU\",\"ACRU\",\"QURU\",\n \"QURU\",\"PIRE\",\"ACPE\",\"ACPE\",\n \"\",\"\",\"ILVE\",\"HAVI\",\n \"HAVI\",\"QUAL\",\"PIST\",\"PIST\",\n \"PIGL\",\"PIGL\",\"BEAL\",\"BEAL\")\n)\n#message(paste(\"Species LUT:\", jsonlite::toJSON(species_lut, pretty=TRUE)))\n\n#-------------------------------------------------------------------------------------------------#\n# Observation file list\n#-------------------------------------------------------------------------------------------------#\nobservation_list <- list(\n climate = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf001-04-monthly-m.csv\"),\n fluxes = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf004-02-filled.csv\"),\n inventory = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf069-09-trees.csv\"),\n root_cn = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf278-04-root-carbon-nitrogen.csv\"),\n litter_cn = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf069-06-canopy-chem.csv\"),\n soils = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf015-05-soils-cn.csv\"),\n r_soil = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf006-01-soil-respiration.csv\"),\n anpp = file.path(base_hf_data, \"observations\", \"hf_ems\", \"hf069-13-annual-summ.csv\")\n)\n\n#-------------------------------------------------------------------------------------------------#\n# Functions\n#-------------------------------------------------------------------------------------------------#\n\n# Tree species aboveground biomass equations; kg; DBH cm; Chojnacky, Heath, Jenkins (2014)\nget_tree_biomass_ag <- function(code, dbh) {\n switch(code, # WSG\n ACPE = { exp(-2.0470 + 2.3852 * log(dbh)) }, # 0.44\n ACRU = { exp(-2.0470 + 2.3852 * log(dbh)) }, # 0.49\n BEAL = { exp(-1.8096 + 2.3480 * log(dbh)) }, # 0.55\n BELE = { exp(-1.8096 + 2.3480 * log(dbh)) }, # 0.60\n BEPA = { exp(-2.2271 + 2.4513 * log(dbh)) }, # 0.48\n BEPO = { exp(-2.2271 + 2.4513 * log(dbh)) }, # 0.45\n CADE = { exp(-2.0705 + 2.4410 * log(dbh)) }, # 0.40\n FAGR = { exp(-2.0705 + 2.4410 * log(dbh)) }, # 0.56\n FRAM = { exp(-1.8384 + 2.3524 * log(dbh)) }, # 0.55\n PIGL = { exp(-2.1364 + 2.3233 * log(dbh)) }, # 0.37\n PIRE = { exp(-2.6177 + 2.4638 * log(dbh)) }, # 0.41\n PIST = { exp(-2.6177 + 2.4638 * log(dbh)) }, # 0.34\n PRSE = { exp(-2.2118 + 2.4133 * log(dbh)) }, # 0.47\n QUAL = { exp(-2.0705 + 2.4410 * log(dbh)) }, # 0.60\n QURU = { exp(-2.0705 + 2.4410 * log(dbh)) }, # 0.56\n QUVE = { exp(-2.0705 + 2.4410 * log(dbh)) }, # 0.56\n TSCA = { exp(-2.3480 + 2.3876 * log(dbh)) }, # 0.38\n CRSP = { NA }, # shrub\n HAVI = { NA }, # shrub\n ILVE = { NA }, # shrub\n VACO = { NA }, # shrub\n VICA = { NA }, # shrub\n NA # else\n )\n}\n\n# Tree species belowground biomass equations; kg; DBH cm; Chojnacky, Heath, Jenkins (2014)\nget_tree_biomass_bg <- function(code, dbh) {\n biomass_ag = get_tree_biomass_ag(code, dbh)\n root_coarse_frac = exp(-1.4485 - 0.03476 * log(dbh))\n root_fine_frac = exp(-1.8629 - 0.77534 * log(dbh))\n soil_frac = 0.68\n biomass_bg = (biomass_ag * root_coarse_frac) + (biomass_ag * root_fine_frac) + (biomass_ag * soil_frac)\n return(biomass_bg)\n}\n\n# Root biomass fraction by DBH class; treated as constants per Chojnacky, Heath, Jenkins (2014)\n#plot(1:100, sapply(1:100, function(dbh) exp(-1.4485 - 0.03476 * log(dbh))), type=\"l\")\n#plot(1:100, sapply(1:100, function(dbh) exp(-1.8629 - 0.77534 * log(dbh))), type=\"l\")\n\n# Main\n\n# Parent function containing child functions\nget_observations <- function(metric=\"all\") {\n funs = list(get_nee(), get_biomass_ag(), get_c_ag(), get_n_ag(), get_biomass_bg(),\n get_c_bg(), get_n_bg(), get_c_so(), get_n_so(), get_r_soil(), get_anpp(),\n get_biomass_sp(), get_abundance_sp())\n switch(metric,\n nee = { get_nee() },\n biomass_ag = { get_biomass_ag() },\n c_ag = { get_c_ag() },\n n_ag = { get_n_ag() },\n biomass_bg = { get_biomass_bg() },\n c_bg = { get_c_bg() },\n n_bg = { get_n_bg() },\n c_so = { get_c_so() },\n n_so = { get_n_so() },\n r_soil = { get_r_soil() },\n anpp = { get_anpp() },\n biomass_sp = { get_biomass_sp() },\n abundance_sp = { get_abundance_sp() },\n biomass_total = { cbind(get_biomass_ag()[,1], get_biomass_ag()[,2] + get_biomass_bg()[,2]) },\n c_total = { cbind(get_c_ag()[,1], get_c_ag()[,2] + get_c_bg()[,2]) },\n n_total = { cbind(get_n_ag()[,1], get_n_ag()[,2] + get_n_bg()[,2]) },\n all = { setNames(lapply(funs, eval),\n c(\"nee\",\"biomass_ag\",\"c_ag\",\"n_ag\",\"biomass_bg\",\n \"c_bg\",\"n_bg\",\"c_so\",\"n_so\",\"r_soil\",\"anpp\",\n \"biomass_sp\",\"abundance_sp\")) },\n { stop(paste(\"Variable\", metric, \"not found\")) }\n )\n}\n\n# NEE; kg C m-2 year-1; Converted from µmol CO2 m-2 sec-1 (10^-6 moles)\nget_nee <- function() {\n #df = read.csv(self$files$observations$flux, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf004-02-filled.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df$c_kg = df$nee * 1e-6 * 12 / 1000 # convert µmol CO2 to mol CO2 to g C to kg C\n df$c_kg = df$c_kg * 60 * 60 * 24 * 365.24 # convert kg C m-2 second-1 to kg C m-2 year-1\n return(setNames(aggregate(df$c_kg, by=list(df$year), FUN=mean), c(\"year\",\"nee\")))\n}\n\n# Aboveground biomass; kg m-2; 34 EMS tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\nget_biomass_ag <- function() {\n #df = read.csv(self$files$observations$inventory, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf069-09-trees.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n #df = df[df$site ==\"ems\" & df$tree.type == \"live\",]\n df = df[df$site ==\"ems\" & df$tree.type == \"live\" & !is.na(df$year) & df$year > 1993,]\n df$code = species_lut$code[match(df$species, species_lut$abbr)]\n df = df[df$code %in% c(\"ACPE\",\"ACRU\",\"BEAL\",\"BELE\",\"BEPO\",\"FAGR\",\"FRAM\",\"PIGL\",\n \"PIRE\",\"PIST\",\"PRSE\",\"QURU\",\"QUVE\",\"TSCA\"),] # modeled species\n # Mean annual individual tree DBH\n df = setNames(aggregate(df$dbh, by=list(df$year, df$code, df$plot, df$tag), FUN=mean, na.rm=TRUE),\n c(\"year\",\"code\",\"plot\",\"tag\",\"dbh\"))\n df = df[,c(\"year\",\"code\",\"dbh\")]\n df$biomass_ag = apply(df, 1, function(x) get_tree_biomass_ag(x[2], as.numeric(x[3])))\n agb = setNames(aggregate(df$biomass_ag, by=list(df$year), FUN=sum, na.rm=TRUE), c(\"year\",\"biomass_ag\"))\n agb$biomass_ag = agb$biomass_ag / 10681.42 # convert kg to kg m-2\n return(agb)\n}\n\n# Aboveground carbon; kg C m-2; 34 tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\nget_c_ag <- function() {\n df = get_biomass_ag()\n df$c_ag = df$biomass_ag * 0.5\n return(df[,c(\"year\",\"c_ag\")])\n}\n\n# Aboveground carbon; kg N m-2; 34 tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\nget_n_ag <- function() {\n agc = get_c_ag()\n #df = read.csv(self$files$observations$root_cn, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf278-04-root-carbon-nitrogen.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df$cn = df$c.n / 100\n cn_stem = mean(df$cn, na.rm=TRUE)\n #df = read.csv(self$files$observations$leaf_cn, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf069-06-canopy-chem.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df$cn_leaf = df$c.percent / df$n.percent\n cn_leaf = mean(df$cn_leaf, na.rm=TRUE)\n agc$n_ag = agc$c_ag / ((cn_stem * 0.80) + (cn_leaf * 0.20)) # assumes general 80/20 stem/leaf fraction\n return(agc[,c(\"year\",\"n_ag\")])\n}\n\n# Belowground biomass; kg m-2; 34 tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\n# Uses general AGB-to-BGB model of Chojnacky, Heath, Jenkins (2014)\nget_biomass_bg <- function() {\n df = read.csv(file.path(base_hf_data, \"hf069-09-trees.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df = df[df$site ==\"ems\" & df$tree.type == \"live\",]\n df$code = species_lut$code[match(df$species, species_lut$abbr)]\n df = df[df$code %in% c(\"ACPE\",\"ACRU\",\"BEAL\",\"BELE\",\"BEPO\",\"FAGR\",\"FRAM\",\"PIGL\",\n \"PIRE\",\"PIST\",\"PRSE\",\"QURU\",\"QUVE\",\"TSCA\"),] # modeled species\n # Mean annual individual tree DBH\n df = setNames(aggregate(df$dbh, by=list(df$year, df$code, df$plot, df$tag), FUN=mean, na.rm=TRUE),\n c(\"year\",\"code\",\"plot\",\"tag\",\"dbh\"))\n df = df[,c(\"year\",\"code\",\"dbh\")]\n df$biomass_ag = apply(df, 1, function(x) get_tree_biomass_ag(x[2], as.numeric(x[3])))\n #df$root_coarse = df$biomass_ag * 0.22\n #df$root_fine = df$biomass_ag * 0.01\n df$root_coarse = df$biomass_ag * exp(-1.4485 - 0.03476 * log(df$dbh))\n df$root_fine = df$biomass_ag * exp(-1.8629 - 0.77534 * log(df$dbh))\n df$soil = df$biomass_ag * 0.68\n df$biomass_bg = df$root_coarse + df$root_fine + df$soil\n bgb = setNames(aggregate(df$biomass_bg, by=list(df$year), FUN=sum, na.rm=TRUE), c(\"year\",\"biomass_bg\"))\n bgb$biomass_bg = bgb$biomass_bg / 10681.42 # convert kg to kg m-2\n return(bgb[,c(\"year\",\"biomass_bg\")])\n}\n\n# Belowground C; kg C m-2; 34 tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\nget_c_bg <- function () {\n bgb = get_biomass_bg()\n #df = read.csv(self$files$observations$root_cn, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf278-04-root-carbon-nitrogen.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n bgb$c_bg = bgb$biomass_bg * (mean(df$c.per, na.rm=TRUE) / 100)\n return(bgb[,c(\"year\",\"c_bg\")])\n}\n\n# Belowground N; kg N m-2; 34 tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\nget_n_bg <- function () {\n bgb = get_biomass_bg()\n #df = read.csv(self$files$observations$root_cn, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf278-04-root-carbon-nitrogen.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n bgb$n_bg = bgb$biomass_bg * (mean(df$n.per, na.rm=TRUE) / 100)\n return(bgb[,c(\"year\",\"n_bg\")])\n}\n\n# Soil organic C; g C cm-3; 240 (22.5 x 22.5m) 506.25 m2 plots (121500 m2 total) of 269 original plots\nget_c_so <- function() {\n #df = read.csv(self$files$observations$soils, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf015-05-soils-cn.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n bd = df$bdgcc.corr # bulk density or g soil per cm3\n c_gg = df$gcgsoil # g C per g soil\n c_so = bd * c_gg / 1000 * 1e+6 # g C per cm3 soil converted to kg per m3\n c_so = mean(c_so, na.rm=TRUE)\n return(data.frame(year=NA, c_so=c_so))\n}\n\n# Soil organic N; g N cm-3; 240 (22.5 x 22.5m) 506.25 m2 plots (121500 m2 total) of 269 original plots\nget_n_so <- function() {\n #df = read.csv(self$files$observations$soils, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf015-05-soils-cn.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n bd = df$bdgcc.corr # bulk density or g soil per cm3\n n_gg = df$gngsoil # g N per g soil\n n_so = bd * n_gg / 1000 * 1e+6 # g C per cm3 soil converted to kg per m3\n n_so = mean(n_so, na.rm=TRUE)\n return(data.frame(year=NA, n_so=n_so))\n}\n\n# Soil respiration; mg C m-2 hour-1; kg C m-2 year-1; 6 sites near EMS tower\nget_r_soil <- function() {\n #df = read.csv(self$files$observations$r_soil, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf006-01-soil-respiration.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df$date = as.POSIXlt(df$date, format=\"%m/%d/%Y\", tz=\"EST\")\n df$year = as.numeric(format(df$date, \"%Y\"))\n df$flux = df$flux / 1e+6 * 24 * 365.24 # convert mg C m-2 hour-1 to kg C m-2 year-1\n return(setNames(aggregate(df$flux, by=list(df$year), FUN=mean, na.rm=TRUE), c(\"year\",\"r_soil\")))\n}\n\n# Annual net primary productivity (ANPP); Mg year-1; kg C m-2 year-1\nget_anpp <- function() {\n #df = read.csv(self$files$observations$anpp, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf069-13-annual-summ.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df = df[df$site ==\"ems\",]\n df = df[!is.na(df$anpp),]\n df$anpp = df$anpp * 1000 / 10681.42 * 0.5 # Mg mass year-1 to kg C m-2 year-1\n return(df[,c(\"year\",\"anpp\")])\n}\n\n# Total species aboveground biomass; kg m-2; 34 tower plots (10m radius; (pi*10^2)*34 = 10681.42 m2)\nget_biomass_sp <- function() {\n #df = read.csv(self$files$observations$inventory, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf069-09-trees.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df = df[df$site ==\"ems\" & df$tree.type == \"live\",]\n df$code = species_lut$code[match(df$species, species_lut$abbr)]\n df = df[df$code %in% c(\"ACPE\",\"ACRU\",\"BEAL\",\"BELE\",\"BEPO\",\"FAGR\",\"FRAM\",\"PIGL\",\n \"PIRE\",\"PIST\",\"PRSE\",\"QURU\",\"QUVE\",\"TSCA\"),] # modeled species\n # Mean annual individual tree DBH\n df = setNames(aggregate(df$dbh, by=list(df$year, df$code, df$plot, df$tag), FUN=mean, na.rm=TRUE),\n c(\"year\",\"code\",\"plot\",\"tag\",\"dbh\"))\n df = df[,c(\"year\",\"code\",\"dbh\")]\n df$biomass_ag = apply(df, 1, function(x) get_tree_biomass_ag(x[2], as.numeric(x[3])))\n biomass_sp = aggregate(df$biomass_ag, by=list(df$year, df$code), FUN=sum, na.rm=TRUE)\n colnames(biomass_sp) = c(\"year\",\"species\",\"biomass_ag\")\n biomass_sp$biomass_ag = biomass_sp$biomass_ag / 10681.42\n return(biomass_sp)\n}\n\n# Species relative abundance; percent of n-trees\nget_abundance_sp <- function() {\n #df = read.csv(self$files$observations$inventory, header=TRUE, stringsAsFactors=FALSE)\n df = read.csv(file.path(base_hf_data, \"hf069-09-trees.csv\"), header=TRUE, stringsAsFactors=FALSE)\n colnames(df) = tolower(colnames(df))\n df = df[df$site ==\"ems\" & df$tree.type == \"live\",]\n df$code = species_lut$code[match(df$species, species_lut$abbr)]\n df = df[df$code %in% c(\"ACPE\",\"ACRU\",\"BEAL\",\"BELE\",\"BEPO\",\"FAGR\",\"FRAM\",\"PIGL\",\n \"PIRE\",\"PIST\",\"PRSE\",\"QURU\",\"QUVE\",\"TSCA\"),] # modeled species\n ntrees_yr = setNames(aggregate(df$code, by=list(df$year), FUN=length), c(\"year\",\"ntrees\"))\n df$ntrees_yr = ntrees_yr$ntrees[match(df$year, ntrees_yr$year)]\n df$fraction_yr = 1 / df$ntrees_yr # relative abundance of each tree\n return(setNames(aggregate(df$fraction_yr, by=list(df$year, df$code), FUN=sum), c(\"year\",\"species\",\"abundance\")))\n}\n", "meta": {"hexsha": "28487dd0c50cc6c3e2882b1ed3a40e5507a093d0", "size": 24791, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/harvard_forest_datasets.r", "max_stars_repo_name": "adam-erickson/ecosystem-model-comparison", "max_stars_repo_head_hexsha": "4eb34532a0cef99e5a556c0fc1157096d44d23e5", 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{"text": "# ************************************************** #\n# Read exposure and outcome #\n# ************************************************** #\nread_gsmr_trait = function(file_con) {\n expo_str = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n outcome_str = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n strbuf = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n return(list(expo_str=expo_str, outcome_str=outcome_str))\n}\n\n# ************************************************** #\n# Read GSMR result #\n# ************************************************** #\nread_gsmr_result = function(file_con) {\n expo_str = outcome_str = bxy = bxy_se = bxy_pval = bxy_m = c()\n while(1) {\n strbuf = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n if(strbuf[1] == \"#gsmr_end\") break;\n if(strbuf[1] == \"Exposure\") next;\n expo_str = c(expo_str, strbuf[1]);\n outcome_str = c(outcome_str, strbuf[2]);\n bxy = c(bxy, as.numeric(strbuf[3]));\n bxy_se = c(bxy_se, as.numeric(strbuf[4]));\n bxy_pval = c(bxy_pval, as.numeric(strbuf[5]));\n bxy_m = c(bxy_m, as.numeric(strbuf[6]));\n }\n return(cbind(expo_str, outcome_str, bxy, bxy_se, bxy_pval, bxy_m))\n}\n\n# ************************************************** #\n# Read SNP effects #\n# ************************************************** #\nread_snp_effect = function(file_con) {\n snp_effect = c()\n while(1) {\n strbuf = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n if(strbuf[1] == \"#effect_end\") break;\n snp_effect = rbind(snp_effect, strbuf);\n print(length(strbuf))\n if(length(strbuf)<14) print(strbuf)\n }\n return(snp_effect)\n}\n\n# ************************************************** #\n# Read SNP instruments #\n# ************************************************** #\nread_snp_instru = function(file_con, snplist, nexpo, noutcome) {\n nrow = length(snplist); ncol = nexpo+noutcome\n snp_instru = matrix(NA, nrow, ncol)\n while(1) {\n strbuf = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n if(strbuf[1] == \"#marker_end\") break;\n expo_indx = as.numeric(strbuf[1]); outcome_indx = as.numeric(strbuf[2]);\n forward_flag = TRUE;\n if(expo_indx < outcome_indx) {\n outcome_indx = outcome_indx - nexpo\n } else {\n expo_indx = expo_indx - nexpo\n forward_flag = FALSE;\n }\n snpbuf = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n snp_indx = match(snpbuf, snplist)\n posbuf = rep(0, nrow); posbuf[snp_indx] = 1;\n indxbuf = expo_indx\n if(!forward_flag) indxbuf = indxbuf + nexpo\n if(length(which(!is.na(snp_instru[,indxbuf])))==0) {\n snp_instru[,indxbuf] = posbuf;\n } else {\n snp_instru[,indxbuf] = paste(snp_instru[,indxbuf], posbuf, sep=\"\")\n }\n }\n return(snp_instru)\n}\n\n# ************************************************** #\n# Read output by GCTA-GSMR for plot #\n# ************************************************** #\nread_gsmr_data = function(gsmr_effect_file) {\n trait_flag = gsmr_flag = marker_flag = effect_flag = FALSE;\n file_con = file(gsmr_effect_file, \"r\")\n while(1) {\n strbuf = scan(file_con, nlines=1, quiet=TRUE, what=\"\");\n if(strbuf == \"#trait_begin\") {\n # Read the exposures and outcomes\n resbuf = read_gsmr_trait(file_con);\n expo_str = resbuf$expo_str; \n outcome_str = resbuf$outcome_str;\n pheno_str = c(expo_str, outcome_str);\n nexpo = length(expo_str); noutcome = length(outcome_str)\n trait_flag = TRUE;\n } else if(strbuf == \"#gsmr_begin\") {\n # Read the GSMR result\n bxy_result = read_gsmr_result(file_con);\n colnames(bxy_result) = c(\"Exposure\", \"Outcome\", \"bxy\", \"se\", \"p\", \"n_snps\")\n gsmr_flag = TRUE;\n } else if(strbuf == \"#effect_begin\") {\n # Read the summary statistics\n snp_effect = read_snp_effect(file_con);\n snplist = as.character(snp_effect[,1])\n effect_flag = TRUE;\n } else if(strbuf == \"#marker_begin\") {\n # Read the SNPs\n snp_instru = read_snp_instru(file_con, snplist, nexpo, noutcome);\n snp_effect = cbind(snp_effect[,1], snp_instru, snp_effect[,-1])\n marker_flag = TRUE;\n }\n if(trait_flag==T & gsmr_flag==T & marker_flag==T & effect_flag==T) break;\n }\n return(list(pheno=c(nexpo, noutcome, pheno_str), bxy_result=bxy_result, snp_effect = snp_effect))\n}\n\n# ************************************************** #\n# Display summary of the gsmr data #\n# ************************************************** #\ngsmr_summary = function(gsmr_data) {\n message(\"\\n## Exposure and outcome\")\n pheno_str = gsmr_data$pheno[c(-1,-2)]\n # exposure\n nexpo = as.numeric(gsmr_data$pheno[1]);\n noutcome = as.numeric(gsmr_data$pheno[2]);\n logger_m = paste(nexpo, \"exposure(s):\");\n logger_m = paste(logger_m, gsmr_data$pheno[3])\n if(nexpo > 1) {\n for(i in 2 : nexpo) \n logger_m = paste(logger_m, gsmr_data$pheno[i+2], sep=\", \")\n } \n message(logger_m)\n # outcome\n logger_m = paste(noutcome, \"outcome(s):\");\n logger_m = paste(logger_m, gsmr_data$pheno[3+nexpo])\n if(noutcome > 1) {\n for(i in 2 : noutcome) \n logger_m = paste(logger_m, gsmr_data$pheno[i+2+nexpo], sep=\", \")\n } \n message(logger_m)\n\n message(\"\\n## GSMR result\")\n m_bxy_rst = data.frame(gsmr_data$bxy_result)\n print(m_bxy_rst)\n}\n\n\n# ************************************************** #\n# Retrieve SNP effects #\n# ************************************************** #\ngsmr_snp_effect = function(gsmr_data, expo_str, outcome_str) {\n # index of SNP instruments\n pheno_str = as.character(gsmr_data$pheno[c(-1,-2)])\n nexpo = as.numeric(gsmr_data$pheno[1])\n noutcome = as.numeric(gsmr_data$pheno[2])\n expo_indx = match(expo_str, pheno_str)\n if(is.na(expo_indx)) stop(\"\\\"\", expo_str, \"\\\" is not found.\")\n outcome_indx = match(outcome_str, pheno_str)\n if(is.na(outcome_indx)) stop(\"\\\"\", outcome_str, \"\\\" is not found.\")\n forward_flag = TRUE;\n if(expo_indx > outcome_indx) forward_flag = FALSE;\n if(forward_flag) {\n outcome_indx = outcome_indx - nexpo;\n } else {\n expo_indx = expo_indx - nexpo;\n }\n indxbuf = expo_indx + 1\n if(!forward_flag) indxbuf = indxbuf + nexpo\n strbuf = as.character(substr(gsmr_data$snp_effect[,indxbuf], outcome_indx, outcome_indx))\n snpindx = which(strbuf==\"1\")\n if(length(snpindx) < 3) stop(\"Not enough SNPs retained.\")\n # bxy\n indxbuf = which(gsmr_data$bxy_result[,1]==expo_str & gsmr_data$bxy_result[,2]==outcome_str)\n bxy = as.numeric(gsmr_data$bxy_result[indxbuf, 3])\n # SNP effects\n if(forward_flag) {\n indxbuf1 = 1 + nexpo + noutcome + 3 + (expo_indx-1)*2 + 1\n indxbuf2 = 1 + nexpo + noutcome + 3 + nexpo*2 + (outcome_indx-1)*2 + 1\n } else {\n indxbuf1 = 1 + nexpo + noutcome + 3 + nexpo*2 + (expo_indx-1)*2 + 1\n indxbuf2 = 1 + nexpo + noutcome + 3 + (outcome_indx-1)*2 + 1\n }\n snpid = as.character(gsmr_data$snp_effect[snpindx,1])\n bzx = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf1]); indxbuf1 = indxbuf1 + 1;\n bzx_se = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf1]);\n bzx_pval = pchisq((bzx/bzx_se)^2, 1, lower.tail=F);\n bzy = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf2]); indxbuf2 = indxbuf2 + 1;\n bzy_se = as.numeric(gsmr_data$snp_effect[snpindx,indxbuf2]);\n bzy_pval = pchisq((bzy/bzy_se)^2, 1, lower.tail=F);\n return(list(snp=snpid, bxy=bxy, bzx=bzx, bzx_se=bzx_se, bzx_pval=bzx_pval, bzy=bzy, bzy_se=bzy_se, bzy_pval=bzy_pval))\n}\n\n# ************************************************** #\n# Plot bzy vs bzx #\n# ************************************************** #\nplot_snp_effect = function(expo_str, outcome_str, bxy, bzx, bzx_se, bzy, bzy_se, effect_col=colors()[75]) {\n vals = c(bzx-bzx_se, bzx+bzx_se)\n xmin = min(vals); xmax = max(vals)\n vals = c(bzy-bzy_se, bzy+bzy_se)\n ymin = min(vals); ymax = max(vals)\n plot(bzx, bzy, pch=20, cex=0.8, bty=\"n\", cex.axis=1.1, cex.lab=1.2,\n col=effect_col, xlim=c(xmin, xmax), ylim=c(ymin, ymax),\n xlab=substitute(paste(trait, \" (\", italic(b[zx]), \")\", sep=\"\"), list(trait=expo_str)),\n ylab=substitute(paste(trait, \" (\", italic(b[zy]), \")\", sep=\"\"), list(trait=outcome_str)))\n if(!is.na(bxy)) abline(0, bxy, lwd=1.5, lty=2, col=\"dim grey\")\n ## Standard errors\n nsnps = length(bzx)\n for( i in 1:nsnps ) {\n # x axis\n xstart = bzx[i] - bzx_se[i]; xend = bzx[i] + bzx_se[i]\n ystart = bzy[i]; yend = bzy[i]\n segments(xstart, ystart, xend, yend, lwd=1.5, col=effect_col)\n # y axis\n xstart = bzx[i]; xend = bzx[i] \n ystart = bzy[i] - bzy_se[i]; yend = bzy[i] + bzy_se[i]\n segments(xstart, ystart, xend, yend, lwd=1.5, col=effect_col)\n }\n}\n\n# ************************************************** #\n# Plot bzy_pval vs bzx_pval #\n# ************************************************** #\nplot_snp_pval = function(expo_str, outcome_str, bzx_pval, bzy_pval, gwas_thresh, truncation, effect_col) {\n eps = 1e-300; truncation = -log10(truncation);\n if(truncation > 300) {\n warning(\"The minimal truncated p-value would be 1e-300.\")\n truncation = 300\n }\n bzx_pval = -log10(bzx_pval + eps);\n bzy_pval = -log10(bzy_pval + eps);\n pval = c(bzx_pval, bzy_pval)\n min_val = 0; max_val = max(pval);\n max_val = ifelse(max_val > truncation, truncation, max_val)\n gwas_thresh = -log10(gwas_thresh);\n plot(bzx_pval, bzy_pval, pch=20, cex=0.8, bty=\"n\", cex.axis=1.1, cex.lab=1.2,\n col=effect_col, xlim=c(min_val, max_val), ylim=c(min_val, max_val),\n xlab=substitute(paste(trait, \" (\", -log[10], italic(P)[zx], \")\", sep=\"\"), list(trait=expo_str)),\n ylab=substitute(paste(trait, \" (\", -log[10], italic(P[zy]), \")\", sep=\"\"), list(trait=outcome_str)))\n abline(h=gwas_thresh, lty=2, lwd=1.5, col=\"maroon\")\n}\n\n# ************************************************** #\n# Plot bxy vs bzx_pval #\n# ************************************************** #\nplot_snp_bxy = function(expo_str, outcome_str, bxy, bzx_pval, effect_col) {\n eps = 1e-300;\n bzx_pval = -log10(bzx_pval + eps);\n xmin = min(bxy, na.rm=T); xmax = max(bxy, na.rm=T)\n ymin = min(bzx_pval); ymax = max(bzx_pval);\n plot(bxy, bzx_pval, pch=20, cex=0.8, bty=\"n\", cex.axis=1.1, cex.lab=1.2,\n col=effect_col, xlim=c(xmin, xmax), ylim=c(ymin, ymax),\n xlab=substitute(paste(italic(hat(b)[xy]), \" (\", trait1, \" -> \", trait2, \")\", sep=\"\"), list(trait1=expo_str, trait2=outcome_str)),\n ylab=substitute(paste(trait, \" (\", -log[10], italic(P[zx]), \")\", sep=\"\"), list(trait=expo_str)))\n}\n\n# ************************************************** #\n# Effect size plot #\n# ************************************************** #\n# expo_str, exposure\n# outcome_str, outcome\n# effect_col, plotting colour\nplot_gsmr_effect = function(gsmr_data, expo_str, outcome_str, effect_col=colors()[75]) {\n resbuf = gsmr_snp_effect(gsmr_data, expo_str, outcome_str);\n bxy = resbuf$bxy\n bzx = resbuf$bzx; bzx_se = resbuf$bzx_se;\n bzy = resbuf$bzy; bzy_se = resbuf$bzy_se;\n # plot\n plot_snp_effect(expo_str, outcome_str, bxy, bzx, bzx_se, bzy, bzy_se, effect_col)\n}\n\n# ************************************************** #\n# P-value plot #\n# ************************************************** #\n# expo_str, exposure\n# outcome_str, outcome\n# effect_col, plotting colour\nplot_gsmr_pvalue = function(gsmr_data, expo_str, outcome_str, gwas_thresh=5e-8, truncation=1e-50, effect_col=colors()[75]) {\n resbuf = gsmr_snp_effect(gsmr_data, expo_str, outcome_str);\n bzx_pval = resbuf$bzx_pval; bzy_pval = resbuf$bzy_pval;\n # plot\n plot_snp_pval(expo_str, outcome_str, bzx_pval, bzy_pval, gwas_thresh, truncation, effect_col)\n}\n\n# ************************************************** #\n# bxy distribution plot #\n# ************************************************** #\n\n# expo_str, exposure\n# outcome_str, outcome\n# effect_col, plotting colour\nplot_bxy_distribution = function(gsmr_data, expo_str, outcome_str, effect_col=colors()[75]) {\n resbuf = gsmr_snp_effect(gsmr_data, expo_str, outcome_str);\n bzx = resbuf$bzx; bzx_pval = resbuf$bzx_pval;\n bzy = resbuf$bzy; \n bxy = bzy/bzx\n # plot\n plot_snp_bxy(expo_str, outcome_str, bxy, bzx_pval, effect_col)\n}\n", "meta": {"hexsha": "84ead1d499511790ac4e220473f292c461837b66", "size": 12934, "ext": "r", "lang": "R", "max_stars_repo_path": "vendor/gsmr_plot.r", "max_stars_repo_name": "AtheroExpress/gsmr-bed-pipeline", "max_stars_repo_head_hexsha": "5582eae97b2bbe59593d70ffb7572378db8ec1ed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vendor/gsmr_plot.r", "max_issues_repo_name": "AtheroExpress/gsmr-bed-pipeline", "max_issues_repo_head_hexsha": "5582eae97b2bbe59593d70ffb7572378db8ec1ed", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vendor/gsmr_plot.r", "max_forks_repo_name": "AtheroExpress/gsmr-bed-pipeline", "max_forks_repo_head_hexsha": "5582eae97b2bbe59593d70ffb7572378db8ec1ed", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-06-08T12:59:11.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-08T12:59:11.000Z", "avg_line_length": 43.8440677966, "max_line_length": 138, "alphanum_fraction": 0.539662904, "num_tokens": 3795, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "## Author: Alan Ruttenberg\n## Project: OHD\n## Date: 2015-04-24\n##\n## Survival Analysis: Do it all: resin_restoration_survival_analysis()\n\nlibrary(survival)\nlibrary(\"simPH\")\n\n# Create an rsurv \"object\", mostly so that we can control printing\n# Sigh, have to be careful if we want to call by reference - i.e. slot changes persist\n# http://www.stat.berkeley.edu/~paciorek/computingTips/Pointers_passing_reference_.html\n\nrsurv <- function()\n { object=new.env(parent=globalenv()) \n class(object)=\"rsurv\"\n lastrsurv <<- object;\n return(object)\n }\n\n# We have a lot of state - don't print it all\nprint.rsurv <- function(object) { cat(\"\\n\") }\n\n# Main entry point. Query the database twice - once to fetch all\n# restorations, once to fetch all the restorations with a known\n# failure.\n# Returns an rsurv object. Important methods: plot, summary.\n\nresin_restoration_survival_analysis <- function ()\n { s <- rsurv()\n s$all <- collect_all_restorations_and_latest_encounter_after()\n s$fail <- collect_restoration_failures()\n create_rsurv(s)\n }\n\n# Summary, for now, is the correlates table\nsummary.rsurv <- function (it)\n { summary(it$correlates);\n }\n\n# Take the set of all resin restorations and subtract out those which\n# we know failed. The rest will be censored\n# Could do this in Sparql, but hadn't learned enough\n# SPARQL 1.1 when this was first written.\nsubset_all_minus_fail <- function(all, fail)\n{\n rownames(all)=all[,\"proci1\"]\n rownames(fail)=fail[,\"proci1\"]\n keep <- setdiff(rownames(all),rownames(fail));;\n res <- as.matrix(all[keep,]);\n}\n\n# Helper functions.\n\n# Takes two date strings(yyyy-mm-yy) and uses seq.date to return the\n# time difference in months. Rounds down.\n\ndifference_in_months <- function(start,end)\n { length(seq(as.Date(start),as.Date(end),by=\"month\"))-1 }\n\n# Takes two date strings(yyyy-mm-yy) and uses seq.date to return the\n# time difference in years. Rounds down.\n\ndifference_in_years <- function(start,end)\n { length(seq(as.Date(start),as.Date(end),by=\"year\"))-1 }\n\n# create arrays of correlates aligned with survival data - first the\n# the ones we know fail, then the ones that will be censored. Takes\n# as input the rsurv object, where we have already prepared the\n# matrices \"fail\" and \"have_last_visit\", each having one row per\n# \"event\".\n# Sets the \"correlates\" attribute to\n# the created data frame.\n\nprepare_survival_correlates <- function(s)\n { s$correlates <- rbind(data.frame(location=as.factor(s$fail[,\"tooth_type\"]),\n gender=as.factor(s$fail[,\"gender\"]),\n age=s$age_at_fail,\n patient=s$fail[,\"patienti\"],\n fail=TRUE),\n data.frame(location=as.factor(s$have_last_visit[,\"tooth_type\"]),\n gender=as.factor(s$have_last_visit[,\"gender\"]),\n age=s$age_at_last_visit,\n patient=s$have_last_visit[,\"patienti\"],\n fail=FALSE))\n # divide the ages into bins, [0,18) , [18-30), [30,60), over 60\n s$correlates[,\"age_group\"] <- as.factor(cut(s$correlates[,\"age\"],breaks=c(0,18,30,60,100)));\n }\n\n\n# having the failures (s$fail) and all events (s$all), create\n# s$have_last_visit as the events where we have a restoration and a\n# subsequent visit on record. These will be treated as right censored\n\nprepare_censored_events <- function(s)\n {\n # Compute all restorations without a subsequent failure on record\n no_record_of_failure <- NULL;\n no_record_of_failure <- subset_all_minus_fail(s$all,s$fail);\n\n # Filter out ones without a subsequent visit - we can't use those at all. Call those $have_last_visit.\n s$have_last_visit <- no_record_of_failure[!is.na(no_record_of_failure[,\"latest_date2\"]),];\n\n # Calculate the time in months between the restoration and the last visit date on record\n s$no_record_of_failure_in_months <-\n mapply(difference_in_months,s$have_last_visit[,\"date1\"],s$have_last_visit[,\"latest_date2\"])\n\n # Calculate the age at last visit\n s$age_at_last_visit <- mapply(difference_in_years,s$have_last_visit[,\"birthdate\"],s$have_last_visit[,\"date1\"])\n\n s$ncensor = length(s$no_record_of_failure_in_months);\n }\n\n# Calculate time until failure and the age at failure for those events\n# where there was a failure. We consider the failure event, for the\n# purpose of survival analysis, to be the date the failure is noticed\n# or replaced. For future improvement - treat the failure as interval\n# censored.\n\nprepare_failure_events <- function(s)\n{\n s$fail_difference_in_months <-\n mapply(difference_in_months,s$fail[,\"date1\"],s$fail[,\"soonest_date2\"])\n s$age_at_fail <- mapply(difference_in_years,s$fail[,\"birthdate\"],s$fail[,\"date1\"])\n s$nfail <- length(s$fail_difference_in_months);\n}\n\n# Main analysis\ncreate_rsurv <- function (s)\n {\n prepare_censored_events(s)\n\n prepare_failure_events(s)\n\n # Now compute the main inputs to the survival analysis. \n # s$status is 1 if the event was a failure, 0 if right censored.\n # combine the fail time and last visit times to make a vector\n # of time in months, and divide by 12 to get years.\n s$status <- c(rep(1,s$nfail),rep(0,s$ncensor));\n s$months <- c(s$fail_difference_in_months,s$no_record_of_failure_in_months)\n s$years <- c(s$fail_difference_in_months,s$no_record_of_failure_in_months)/12;\n prepare_survival_correlates(s)\n\n # Do the survival analysis, two argument version.\n surv <- Surv(s$years,s$status) ;\n s$surv <- surv;\n \n # Now do the univariate fits \n s$fit <- survfit(surv~1,conf.type=\"none\",data=s$correlates);\n s$location_fit <- survfit(surv~location,conf.type=\"none\",data=s$correlates);\n s$gender_fit <- survfit(surv~gender,conf.type=\"none\",data=s$correlates);\n s$age_fit <- survfit(surv~age_group,conf.type=\"none\",data=s$correlates);\n # Make the age groups prettier\n names(s$age_fit$strata)=\n c(\"age=18 and under\", \"age=18+ to 30\", \"age=30+ to 60\", \"age=60 and over\")\n \n # Compute the proportional hazards\n s$cox<-coxph(surv~location+gender+age, method=\"breslow\",data=s$correlates)\n\n s\n }\n\n# Draw one of the Kaplan-Meier plots\n# We use ggplot, and hunt the web for recipes to show the bits we want\n# to show.\n\nplot_rsurv_fit <- function (s,fit=s$location_fit,\n title=\"All\",\n xlab=\"Time in years\",\n ylab=\"Fraction surviving\",\n ci=F,col=c(\"dark green\",\"dark blue\")\n )\n { print(\n ggsurv(fit,cens.col='gray',plot.cens=F,back.white=T,\n main=title,xlab=xlab,ylab=ylab,CI=ci,surv.col=col)\n # set up the x scale\n + scale_x_continuous(breaks=1:13,expand=c(0,0),limits=c(0,12.5))\n # set up the y scale\n + scale_y_continuous(expand=c(0,.01))\n # axes font bold (tweak position to look better too)\n + theme(axis.title.y=element_text(vjust=1.5,face=\"bold\"))\n + theme(axis.title.x=element_text(vjust=-.7,face=\"bold\"))\n # So is title\n + theme(title=element_text(vjust=2,face=\"bold\"))\n # Place the legend inside the plot \n + theme(legend.position=c(.9,.5),\n legend.text=element_text(size=12,face=\"bold\"),\n legend.title=element_text(size=14))\n #add guides\n + guides(colour = guide_legend(override.aes = list(size=3))));\n\n }\n\n# Draw all of the plots. plotWrap prepares the device. bplotf devault\n# uses svg device and sends to browser.\n\nplot.rsurv <- function (sa,plotWrap=bplotf)\n{ plotWrap(function ()\n {plot_rsurv_fit(sa,title=\"Resin restoration longevity\",\n fit=sa$fit,\n col=\"dark blue\")},\n index=\"\",filebase=\"overall-longevity\")\n plotWrap(function ()\n {plot_rsurv_fit(sa,title=\"Resin restoration longevity by age at failure\",\n fit=sa$age_fit,\n col=c(\"dark gray\",\"dark green\",\"dark blue\",\"dark red\"))},\n index=\"\",filebase=\"longevity-by-age\")\n plotWrap(function () \n {plot_rsurv_fit(sa,title=\"Resin restoration longevity by gender\",\n fit=sa$gender_fit,\n col=c(\"dark blue\",\"dark red\"))},\n index=\"\",filebase=\"longevity-by-gender\")\n plotWrap(function ()\n {plot_rsurv_fit(sa,title=\"Resin restoration longevity by location\",\n fit=sa$location_fit,\n col=c(\"dark blue\",\"dark red\"))},\n index=\"\",filebase=\"longevity-by-location\")\n}\n\n\n# if factors, number of events for each factor\nsummary_text <- function(surv,correlate)\n { levels <- levels(as.factor(lastrsurv$correlates[,correlate]))\n c <- surv$correlates;\n factor_summary <- function(value,factor=correlate)\n { patient_count <- length(unique(c[(c[,factor]==value),\"patient\"]));\n event_count <- nrow(c[(c[,factor]==value),])\n failures_count <- nrow(c[(c[,factor]==value & c[,\"fail\"]),]);\n paste0(value,\": \",patient_count, \" patients\", \", \",event_count,\" events of which \",failures_count,\" were failures\")\n }\n do.call(paste,args=c(lapply(as.list(levels),factor_summary),sep=\"\\n\"))\n }\n\n\n\n\n# levels(as.factor(lastrsurv$correlates[,\"age_group\"]))\n\n## sa$cox$coef[\"locationposterior\"]\n## sa$cox$coef[\"gendermale\"]\n\n## bplotf(function(){\n## + baseline<-basehaz(coxph(x$gender_fit))\n## + plot(baseline$time, baseline$hazard, type='l',main=\"Hazard rates\") \n## + lines(baseline$time, exp(0.8245)*baseline$hazard, col=\"blue\") })\n\n## sa$correlates[,\"AgeMed\"]<-sa$correlates[,\"age\"]-40\n## Sim1 <- coxsimLinear(M1, b = \"AgeMed\", nsim = 100,Xj = seq(-30, 30, by=1))\n## bplotf(function() {print(simGG(Sim1))})\n\n## baseline<-basehaz(reg_fit)\n\n## #http://www.unc.edu/courses/2010spring/ecol/562/001/docs/lectures/lecture24.htm#coxzph\n## #http://www.uni-kiel.de/psychologie/rexrepos/posts/survivalCoxPH.html\n## #http://www.statisticsmentor.com/category/r-survival-analysis/\n\n", "meta": {"hexsha": "ae7157a9d46b22ec3aef42d2ef64feb302f444ee", "size": 10323, "ext": "r", "lang": "R", "max_stars_repo_path": "src/analysis/survival.r", "max_stars_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_stars_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2022-02-08T16:11:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-08T16:11:01.000Z", "max_issues_repo_path": "src/analysis/survival.r", "max_issues_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_issues_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": 9, "max_issues_repo_issues_event_min_datetime": "2019-05-13T19:04:16.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-31T18:34:56.000Z", "max_forks_repo_path": "src/analysis/survival.r", "max_forks_repo_name": "oral-health-and-disease-ontologies/OHD-ontology", "max_forks_repo_head_hexsha": "e22530f45f0bfc31ccd8e1e69aa00791328e08b7", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.1673151751, "max_line_length": 127, "alphanum_fraction": 0.6420614163, "num_tokens": 2678, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "#!/usr/bin/Rscript\n\n## bootstrap.r -- runs a bootstrap process across a genotype file\n\n## Author: David Eccles (gringer), 2009 \n\n## simplegt-formatted input file\ngenotypes.inFile = \"\";\n## columns of genotype file to consider cases/controls\n## (first value is ignored, so can be used as a group identifier)\ncasecontrolColumns.inFile = \"cases_controls.txt\";\n\n## number of subsamples to use for bootstrapping\nreplicates.proportion = 0.50;\nreplicates.cases = NULL; # if NULL, will be replicates.proportion or 5 less, whichever is smaller\nreplicates.controls = replicates.cases;\nrep.case.outFile = \"\";\nrep.control.outFile = \"\";\ncreateReplicates = TRUE; # if false, use existing replicate data\n## number of bootstraps to carry out\nbootstrap.count = 100;\n\n## file to place bootstrapped results into\nbootstraps.outFile = \"bootstrap_results.csv\";\n\n## should the output values be sorted within each marker?\nsortValues <- FALSE;\n\n## controls are on the first line of this file (normally expect cases first)\ncontrolsFirst <- FALSE;\n\n## method to use for calculating results\n## current options are Adelta, GTdelta, Chisqmax\nprocessMethod <- \"Adelta\";\n\n## consider complementary alleles to be the same (allows different\n## genotyping methods for the same mutation to be combined)\ncombineComplementary <- TRUE;\n\n## keep zero counts separate when calculating chi^2 (produces invalid\n## results when zero counts are observed for any genotype)\nstrictChi <- FALSE;\n\nusage <- function(){\n cat(\"usage: ./bootstrap.r\",\n \" [options]\\n\");\n cat(\"\\nOther Options:\\n\");\n cat(\"-help : Only display this help message\\n\");\n cat(\"-input : File containing genotype data\\n\");\n cat(\"-controlfile : File containing column data for cases/controls\\n\");\n cat(\"-repfiles : Files containing replicate columns (for repeat experiments)\\n\");\n cat(\"-controlsfirst : case/control file has controls as first line\\n\");\n cat(\"-count : Number of bootstrapts to carry out\\n\");\n cat(\"-casereps : case subpopulation size for boostraps (overrides proportion)\\n\");\n cat(\"-controlreps : control subpopulation size for boostraps (overrides proportion)\\n\");\n cat(\"-proportion : proportion of individuals for boostraps (currently \", replicates.proportion,\")\\n\", sep=\"\");\n cat(\"-sort : sort bootstrap results by value\\n\");\n cat(\"-strictGT : Keep complementary alleles separate (don't combine)\\n\");\n cat(\"-strictChi : Respect zero counts in chi^2 table, creates null results\\n\");\n cat(\"-output : output file for results\\n\");\n cat(\"-method : method to use for calculating results\\n\");\n cat(\" (Adelta, GTdelta, gChisq, Chisqmax, ChisqmaxAll, ShowValues)\\n\");\n cat(\"\\n\");\n}\n\nargLoc <- 1;\n\nargLoc <- grep(\"--args\",commandArgs()) + 1; # hack to get around R v2.4\n # issue stopping\n # commandArgs(TRUE) from\n # working\nwhile(!is.na(commandArgs()[argLoc])){\n if(file.exists(commandArgs()[argLoc])){ # file existence check\n casecontrolColumns.inFile <- commandArgs()[argLoc];\n } else {\n if(commandArgs()[argLoc] == \"-help\"){\n usage();\n quit(save = \"no\", status=0);\n }\n else if(commandArgs()[argLoc] == \"-input\"){\n genotypes.inFile <- commandArgs()[argLoc+1];\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-controlfile\"){\n casecontrolColumns.inFile <- commandArgs()[argLoc+1];\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-repfiles\"){\n rep.case.outFile <- commandArgs()[argLoc+1];\n rep.control.outFile <- commandArgs()[argLoc+2];\n createReplicates <- FALSE;\n argLoc <- argLoc + 2;\n }\n else if(commandArgs()[argLoc] == \"-controlsfirst\"){\n controlsFirst <- TRUE;\n }\n else if(commandArgs()[argLoc] == \"-sort\"){\n sortValues <- TRUE;\n }\n else if(commandArgs()[argLoc] == \"-strictGT\"){\n combineComplementary <- FALSE;\n }\n else if(commandArgs()[argLoc] == \"-strictChi\"){\n strictChi <- TRUE;\n }\n else if(commandArgs()[argLoc] == \"-proportion\"){\n replicates.proportion <- as.numeric(commandArgs()[argLoc+1]);\n cat(file=stderr(), \"setting proportion to\", replicates.proportion,\n \"(\",replicates.proportion*100,\"% )\\n\");\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-casereps\"){\n replicates.cases <- as.numeric(commandArgs()[argLoc+1]);\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-count\"){\n bootstrap.count <- as.numeric(commandArgs()[argLoc+1]);\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-controlreps\"){\n replicates.controls <- as.numeric(commandArgs()[argLoc+1]);\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-output\"){\n bootstraps.outFile <- commandArgs()[argLoc+1];\n argLoc <- argLoc + 1;\n }\n else if(commandArgs()[argLoc] == \"-method\"){\n processMethod <- commandArgs()[argLoc+1];\n argLoc <- argLoc + 1;\n }\n else {\n cat(\"Error: Argument '\",commandArgs()[argLoc],\n \"' is not understood by this program\\n\\n\", sep=\"\");\n usage();\n quit(save = \"no\", status=0);\n }\n }\n argLoc <- argLoc + 1;\n}\n\nif(!file.exists(casecontrolColumns.inFile)){\n cat(\"Error: No valid case/control column file given\\n\\n\");\n usage();\n quit(save = \"no\", status=1);\n}\n\nif((bootstraps.outFile != \"\") && (file.exists(bootstraps.outFile))){\n cat(\"Error: Output file (\", bootstraps.outFile,\n \") exists, please delete it before running this program\\n\\n\",sep=\"\");\n usage();\n quit(save = \"no\", status=1);\n}\n\nif(!createReplicates && (!file.exists(rep.case.outFile) || !file.exists(rep.control.outFile))){\n cat(\"Error: Replicate files do not exist\\n\\n\");\n usage();\n quit(save = \"no\", status=1);\n}\n\n## carries out a chisquare test of a vector, assuming entries 1..n/2\n## are observed, n/2+1..n are expected counts.\nvector.chisq <- function(in.vector){\n if(!is.vector(in.vector)){\n stop(\"Can only be called on a vector\");\n }\n if(length(in.vector) %% 2 == 1){\n stop(\"observed, expected must be same length\");\n }\n obs <- in.vector[seq(1,length(in.vector)/2)];\n exp <- in.vector[seq(length(in.vector)/2+1,length(in.vector))];\n ## last section taken (and simplified) from chisq.test code\n ## rescale expected values to same totals as observed values\n E = sum(obs) * exp / sum(exp);\n chisq.values <- (obs-E)^2/E;\n ## !is.nan removes NaN values from result, so cells with zero\n ## !expected counts are ignored\n if(!strictChi){\n return(sum(chisq.values[!is.nan(chisq.values)]));\n } else {\n return(sum((obs-E)^2/E));\n }\n ## don't warn about counts < 5 -- it will flood the output with\n ## warnings for markers in a whole genome\n}\n\nGTCalc <- function(in.genotypes, columns.pop1, columns.pop2, method = \"Adelta\"){\n num.reps <- dim(columns.pop1)[2];\n ## make everything upper case (simplifies search expressions)\n in.genotypes <- toupper(in.genotypes);\n if(combineComplementary){\n ## substitute complementary alleles\n in.genotypes <- gsub(\"T\",\"A\",in.genotypes);\n in.genotypes <- gsub(\"G\",\"C\",in.genotypes);\n in.genotypes <- gsub(\"CA\",\"AC\",in.genotypes);\n }\n ## calculate major/minor alleles\n gt.counts <- table(in.genotypes);\n if(sum(gt.counts) == 0){ # no individuals\n return(rep(NA, num.reps));\n }\n allele.names <- unique(sub(\"(A|C|G|T)\",\"\",names(gt.counts)));\n homozygous.counts <- sort(gt.counts[paste(allele.names, allele.names, sep=\"\")]);\n if(sum(homozygous.counts) == 0){ # no homozygous individuals\n return(rep(NA, num.reps));\n }\n minor.allele <- substr(names(homozygous.counts[1]),1,1);\n major.allele <- substr(names(homozygous.counts[length(homozygous.counts)]),1,1);\n if(major.allele == minor.allele){ # no minor allele (from homozygous counts)\n het.potentials <- names(gt.counts[grep(major.allele,names(gt.counts))]);\n if(length(het.potentials) == 1){ # no heterozygotes found, so no minor allele\n minor.allele <- \"\";\n# return(rep(NA, num.reps));\n } else {\n het.name <- het.potentials[het.potentials !=\n paste(major.allele, major.allele, sep=\"\")];\n minor.allele <- sub(major.allele, \"\", het.name);\n }\n }\n ## recode tables as Major/minor (as plink says it *should* be doing)\n ## by substituting alleles for M/m\n ## Note: everything that isn't M/m is considered an invalid\n ## genotype, so if mutation is trimorphic or tetramorphic, then only\n ## the most frequent homozygotes and least frequent homozygotes will\n ## be counted\n if(minor.allele != \"\"){\n in.genotypes <- gsub(major.allele,\"M\",gsub(minor.allele,\"m\",in.genotypes));\n } else {\n in.genotypes <- gsub(major.allele,\"M\",in.genotypes);\n }\n in.genotypes <- sub(\"Mm\",\"mM\",in.genotypes); # make heterozygotes consistently mM\n in.genotypes <- factor(in.genotypes, levels = c(\"MM\",\"mM\",\"mm\", NA));\n ## generate table based on genotype frequencies\n table1 <- apply(columns.pop1, 2, function(x){table(in.genotypes[x], exclude = NULL)})\n table2 <- apply(columns.pop2, 2, function(x){table(in.genotypes[x], exclude = NULL)})\n ## replace row name with \"XX\"\n rownames(table1)[is.na(rownames(table1))] <- \"XX\";\n rownames(table2)[is.na(rownames(table2))] <- \"XX\";\n ## return NA if there are no [non-null] genotypes for either population\n if((sum(table1[c(\"MM\",\"mM\",\"mm\"),]) == 0) ||\n (sum(table2[c(\"MM\",\"mM\",\"mm\"),]) == 0)){\n return(rep(NA, num.reps));\n }\n ## at this point, table1/2 will look like the following:\n ## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]\n ## MM 12 11 10 10 11 12 12 12 11 11\n ## mM 41 42 44 43 42 43 42 43 44 43\n ## mm 32 32 31 32 32 30 31 30 30 31\n ## XX 0 0 0 0 0 0 0 0 0 0\n ## table1: cases, table2: controls\n ## Rows are genotypes, columns are bootstrap number. MM,mM,mm are\n ## the raw genotype counts, XX is a null/bad genotype.\n ## Next step, calculate appropriate statistic\n if(method == \"GTdelta\") { # genotype freq based delta\n ## remove null values, scale for number of individuals\n table1a <- table1[c(\"MM\",\"mM\",\"mm\"),]/\n colSums(as.matrix(table1[c(\"MM\",\"mM\",\"mm\"),]));\n table2a <- table2[c(\"MM\",\"mM\",\"mm\"),]/\n colSums(as.matrix(table2[c(\"MM\",\"mM\",\"mm\"),]));\n retVal <-\n colSums(abs(table1a - table2a))/2;\n } else if(method == \"Adelta\") { # allele freq based delta\n ## convert to allele frequencies, scale for number of [non-null]\n ## individuals\n table1a <- (table1[\"MM\",]+table1[\"mM\",]/2) /\n colSums(as.matrix(table1[c(\"MM\",\"mM\",\"mm\"),]));\n table2a <- (table2[\"MM\",]+table2[\"mM\",]/2) /\n colSums(as.matrix(table2[c(\"MM\",\"mM\",\"mm\"),]));\n ## note: if only two alleles, then comparing one allele is the\n ## same as comparing both and dividing by two\n retVal <-\n abs(table1a - table2a);\n } else if((method == \"Chisqmax\")|| # Max chi square\n (method == \"ChisqmaxAll\")||\n (method == \"gChisq\")||\n (method == \"ShowValues\")) {\n\n ## calculations for observed, expected derived from plink code\n obs.1 <- colSums(as.matrix(table1[c(\"mm\",\"mM\",\"MM\"),]));\n obs.2 <- colSums(as.matrix(table2[c(\"mm\",\"mM\",\"MM\"),]));\n obs.all <- obs.1+obs.2;\n obs.mm <- (table1[\"mm\",] + table2[\"mm\",]);\n obs.mM <- (table1[\"mM\",] + table2[\"mM\",]);\n obs.MM <- (table1[\"MM\",] + table2[\"MM\",]);\n obs.m <- 2*obs.mm + obs.mM;\n obs.M <- 2*obs.MM + obs.mM;\n exp.1.m <- (obs.1 * obs.m) / obs.all;\n exp.1.M <- (obs.1 * obs.M) / obs.all;\n exp.2.m <- (obs.2 * obs.m) / obs.all;\n exp.2.M <- (obs.2 * obs.M) / obs.all;\n exp.1.mm <- (obs.1 * obs.mm) / obs.all;\n exp.1.mM <- (obs.1 * obs.mM) / obs.all;\n exp.1.MM <- (obs.1 * obs.MM) / obs.all;\n exp.2.mm <- (obs.2 * obs.mm) / obs.all;\n exp.2.mM <- (obs.2 * obs.mM) / obs.all;\n exp.2.MM <- (obs.2 * obs.MM) / obs.all;\n\n ## calculate genotype statistics for each homozygote vs others\n\n ## Dominant -DOM.CHISQ (testing major allele genotypes vs other)\n ## i.e. minor allele is dominant\n dom.test <- rbind(table1[\"mm\",]+table1[\"mM\",],\n table1[\"MM\",],\n table2[\"mm\",]+table2[\"mM\",],\n table2[\"MM\",],\n exp.1.mm+exp.1.mM,\n exp.1.MM,\n exp.2.mm+exp.2.mM,\n exp.2.MM);\n # cat(\"dom.chisq =\",mean(apply(dom.test,2,vector.chisq)));\n\n ## Recessive -REC.CHISQ (testing minor allele genotypes vs other)\n ## i.e. minor allele is recessive\n rec.test <- rbind(table1[\"mm\",],\n table1[\"MM\",]+table1[\"mM\",],\n table2[\"mm\",],\n table2[\"MM\",]+table2[\"mM\",],\n exp.1.mm,\n exp.1.MM+exp.1.mM,\n exp.2.mm,\n exp.2.MM+exp.2.mM);\n # cat(\", rec.chisq =\",mean(apply(rec.test,2,vector.chisq)));\n\n ## CA trend test -TREND.CHISQ\n ## Note: this assumes allele 1: minor, allele 2: Major\n CA <- ((obs.2/obs.all * table1[\"mM\",]) - (obs.1/obs.all * table2[\"mM\",])) +\n 2 * ((obs.2/obs.all * table1[\"MM\",]) - (obs.1/obs.all * table2[\"MM\",]));\n var.CA <- obs.1 * obs.2 *\n (( obs.all * ( obs.mM + 4 * obs.MM )\n - ( obs.mM + 2 * obs.MM )^2 )\n / (obs.all^3));\n CA.chisq <- (CA^2) / var.CA;\n # cat(\", CA.chisq =\",mean(CA.chisq));\n if ((method == \"ChisqmaxAll\") || (method == \"ShowValues\") || (method == \"gChisq\")) {\n geno.test <- rbind(table1[\"mm\",],\n table1[\"mM\",],\n table1[\"MM\",],\n table2[\"mm\",],\n table2[\"mM\",],\n table2[\"MM\",],\n exp.1.mm,\n exp.1.mM,\n exp.1.MM,\n exp.2.mm,\n exp.2.mM,\n exp.2.MM);\n # cat(\", geno.chisq =\",mean(apply(geno.test,2,vector.chisq)));\n\n ## Multiplicative / Allelic (actually testing allele quantities of m vs M)\n mult.test <- rbind(2*table1[\"mm\",]+table1[\"mM\",],\n 2*table1[\"MM\",]+table1[\"mM\",],\n 2*table2[\"mm\",]+table2[\"mM\",],\n 2*table2[\"MM\",]+table2[\"mM\",],\n exp.1.m,\n exp.1.M,\n exp.2.m,\n exp.2.M);\n # cat(\", mult.chisq =\",mean(apply(mult.test,2,vector.chisq)),\"\\n\");\n }\n ## -1 needed in apply to avoid warnings\n if(method == \"Chisqmax\"){\n retVal <-\n apply(rbind(\n CA.chisq,\n apply(dom.test,2,vector.chisq),\n apply(rec.test,2,vector.chisq),\n -Inf),2,max, na.rm = TRUE);\n } else if (method == \"gChisq\") {\n retVal <-\n apply(geno.test,2,vector.chisq);\n } else if (method == \"ChisqmaxAll\") {\n retVal <-\n apply(rbind(\n CA.chisq,\n apply(geno.test,2,vector.chisq),\n apply(mult.test,2,vector.chisq),\n apply(dom.test,2,vector.chisq),\n apply(rec.test,2,vector.chisq),\n -Inf),2,max, na.rm = TRUE);\n } else if (method == \"ShowValues\") {\n retVal <-\n apply(rbind(\n CA.chisq,\n apply(geno.test,2,vector.chisq),\n apply(mult.test,2,vector.chisq),\n apply(dom.test,2,vector.chisq),\n apply(rec.test,2,vector.chisq)),2,paste, collapse = \",\");\n }\n } else {\n retVal <- NULL;\n }\n ## retVal should be a vector of values, with length equal to the\n ## number of bootstraps\n return(retVal);\n}\n\n## matmap -- maps a vector onto a matrix of indexes to the vector\nmatmap <- function(vector.in, matrix.indices){\n if(max(matrix.indices) > length(vector.in)){\n cat(\"Error: maximum index greater than vector length\\n\", file = stderr());\n }\n res <- vector.in[matrix.indices];\n if(is.null(dim(matrix.indices))){\n dim(res) <- c(length(matrix.indices),1);\n } else {\n dim(res) <- dim(matrix.indices);\n }\n return(res);\n}\n\n\n## carries out allele frequency based delta on a single line of simplegt input\nprocessLine <-\n function(marker.name, genotypes, popSamples1, popSamples2, output.file = \"\",\n append = TRUE, GTmethod = \"Adelta\", sortbyValue = FALSE){\n# cat(dim(popSamples1), \"\\n\", file=stderr());\n if(length(marker.name) > 1){\n stop(\"Cannot process more than one line at a time\");\n }\n if((length(marker.name) == 0) || (marker.name == \"\")){\n stop(\"Empty input: input is not valid\");\n }\n bs.results <-\n GTCalc(genotypes,popSamples1,popSamples2,GTmethod);\n ## output 1 line per result\n if(sortValues){\n bs.order <- rev(order(bs.results));\n } else {\n bs.order <- 1:length(bs.results);\n }\n if((GTmethod == \"ShowValues\") && (append == FALSE)){\n cat(\"marker,bs.run,CA.chisq,geno.test,mult.test,dom.test,rec.test\\n\",\n file = output.file, append = FALSE);\n write.table(data.frame(marker = marker.name, bs.run = bs.order,\n bs.value = bs.results[bs.order]),\n output.file, sep = \",\", quote = FALSE, append = TRUE,\n col.names = FALSE, row.names = FALSE);\n } else {\n write.table(data.frame(marker = marker.name, bs.run = bs.order,\n bs.value = bs.results[bs.order]),\n output.file, sep = \",\", quote = FALSE, append = append,\n col.names = !append, row.names = FALSE);\n }\n}\n\ncasecontrolColumns.con <- file(casecontrolColumns.inFile);\nopen(casecontrolColumns.con);\n\nif(controlsFirst){\n controls.columns <-\n as.numeric((strsplit(readLines(casecontrolColumns.con, n = 1),\" \")[[1]])[-1]);\n cases.columns <-\n as.numeric((strsplit(readLines(casecontrolColumns.con, n = 1),\" \")[[1]])[-1]);\n} else {\n cases.columns <-\n as.numeric((strsplit(readLines(casecontrolColumns.con, n = 1),\" \")[[1]])[-1]);\n controls.columns <-\n as.numeric((strsplit(readLines(casecontrolColumns.con, n = 1),\" \")[[1]])[-1]);\n}\nclose(casecontrolColumns.con);\n\nif(is.null(replicates.cases)){\n replicates.cases <- min(length(cases.columns) - 5,\n trunc(length(cases.columns)*replicates.proportion));\n}\n\nif(is.null(replicates.controls)){\n replicates.controls <- min(length(controls.columns) - 5,\n trunc(length(controls.columns)*replicates.proportion));\n}\n\n## generate bootstrap replicates first. This improves speed and\n## maintains a consistent population subsample across multiple SNPs.\n## note: this is sampling with replacement\n## file to place subsample columns into\ncases.samples <- NULL;\ncontrols.samples <- NULL;\nif(createReplicates){\n rep.case.outFile = paste(\"caseReplicates\",bootstraps.outFile,sep=\"_\");\n rep.control.outFile = paste(\"controlReplicates\",bootstraps.outFile,sep=\"_\");\n cases.samples <- replicate(bootstrap.count,\n sample(cases.columns,replicates.cases));\n controls.samples <- replicate(bootstrap.count,\n sample(controls.columns,replicates.controls));\n ## write out replicates to a file, in case validation is needed\n write.table(t(cases.samples),rep.case.outFile, quote = FALSE,\n col.names = FALSE);\n write.table(t(controls.samples),rep.control.outFile, quote = FALSE,\n col.names = FALSE);\n} else {\n cases.samples <- t(read.table(rep.case.outFile, row.names = 1));\n controls.samples <- t(read.table(rep.control.outFile, row.names = 1));\n dcc <- c(dim(cases.samples), dim(controls.samples));\n cat(sprintf(\"Retrieved data for %d bootstraps (cases %d x %d, controls %d x %d)\\n\",\n dcc[2], dcc[1], dcc[2], dcc[3], dcc[4]), file = stderr());\n}\n\nif(genotypes.inFile != \"\"){\n genotypes.con <- gzfile(genotypes.inFile);\n open(genotypes.con)\n} else {\n genotypes.con <- file(\"stdin\");\n open(genotypes.con)\n}\n\ninput.line <- scan(genotypes.con, what = character(), nlines = 1, quiet = TRUE);\nwhile((length(input.line)>0) && (substr(input.line[1],1,1) == \"#\")){\n input.line <- scan(genotypes.con, what = character(), nlines = 1, quiet = TRUE);\n}\n\nnum.indivs <- length(input.line[-1]);\nif(num.indivs != (length(cases.columns) + length(controls.columns))){\n cat(sprintf(\"Warning: number of individuals detected on first line (%d) is not the same as number of cases (%d) + number of controls (%d)\\n\", num.indivs, length(cases.columns), length(controls.columns)), file = stderr());\n}\n\n## process line *without* append (creates header)\nprocessLine(input.line[1], input.line[-1], cases.samples, controls.samples,\n append = FALSE, output.file = bootstraps.outFile,\n GTmethod = processMethod, sortbyValue = sortValues);\ninput.line <- scan(genotypes.con, what = character(), nlines = 1, quiet = TRUE);\nwhile((length(input.line)>0) && (substr(input.line[1],1,1) == \"#\")){\n input.line <- scan(genotypes.con, what = character(), nlines = 1, quiet = TRUE);\n}\nline.number <- 1;\nwhile(length(input.line) > 0){\n ## process line *with* append (no header)\n processLine(input.line[1], input.line[-1], cases.samples, controls.samples,\n append = TRUE, output.file = bootstraps.outFile,\n GTmethod = processMethod, sortbyValue = sortValues);\n line.number <- line.number+1;\n if(line.number %% 1000 == 0){\n cat(\".\", file = stderr());\n }\n input.line <- scan(genotypes.con, what = character(), nlines = 1, quiet = TRUE);\n while((length(input.line)>0) && (substr(input.line[1],1,1) == \"#\")){\n input.line <- scan(genotypes.con, what = character(), nlines = 1, quiet = TRUE);\n }\n}\nif(genotypes.inFile != \"\"){\n close(genotypes.con);\n}\n\nif(line.number > 1){\n cat(\"(\",line.number,\" lines processed)\\n\", file = stderr(), sep = \"\");\n} else {\n cat(\"(\",line.number,\" line processed)\\n\", file = stderr(), sep = \"\");\n}\n", "meta": {"hexsha": "8212f7323658ce9d2ebb8ed2b2f0da6c9a1b259b", "size": 22421, "ext": "r", "lang": "R", "max_stars_repo_path": "bootstrap.r", "max_stars_repo_name": "gringer/bootstrap-subsampling", "max_stars_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_stars_repo_licenses": ["ISC"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "bootstrap.r", "max_issues_repo_name": "gringer/bootstrap-subsampling", "max_issues_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_issues_repo_licenses": ["ISC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bootstrap.r", "max_forks_repo_name": "gringer/bootstrap-subsampling", "max_forks_repo_head_hexsha": "9b794dbcd05e983dfd37bf46e39c5e873ed2ae37", "max_forks_repo_licenses": ["ISC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-11-02T11:22:10.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-02T11:22:10.000Z", "avg_line_length": 40.9142335766, "max_line_length": 223, "alphanum_fraction": 0.5821328219, "num_tokens": 6137, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "##################################################################################\n# Dynamic + vectorized \n# This code runs a Bonica-like algorithm to provide a longitudinal view of IFE's\n# Council General. \n# 27 Mar 2021 \n##################################################################################\n\nlibrary(arm)\nlibrary (MCMCpack)\nlibrary (foreign)\nlibrary (car)\nlibrary (gtools)\n#library (multicore)\nlibrary (R2jags)\nlibrary (mcmcplots)\nlibrary (sm)\nlibrary(lubridate)\n#\nlibrary (runjags)\nlibrary (coda)\n\n# un cambio minimo \nrm(list = ls())\nworkdir <- c(\"/home/eric/Dropbox/data/rollcall/ife_cg/ife-update/data/\")\nsetwd(workdir)\n\n# Define colors and plotting names\n# OJO: en tenure term==10 es a, term==11 es b etc. \n\nids <- matrix(c(\n ## \"Woldenberg\", \"woldenberg\", \"PRI\", \"23\",\n ## \"Barrag?n\", \"barragan\", \"PRD\", \"23\",\n ## \"Cant?\", \"cantu\", \"PRD\", \"23\",\n ## \"C?rdenas\", \"cardenas\", \"PRD\", \"23\",\n ## \"Lujambio\", \"lujambio\", \"PAN\", \"23\",\n ## \"Merino\", \"merino\", \"PRI\", \"23\",\n ## \"Molinar\", \"molinar\", \"PAN\", \"2\" ,\n ## \"Peschard\", \"peschard\", \"PRI\", \"23\",\n ## \"Zebad?a\", \"zebadua\", \"PRD\", \"2\" ,\n ## \"Rivera\", \"rivera\", \"PRI\", \"3\",\n ## \"Luken\", \"luken\", \"PAN\", \"3\",\n #\n \"Ugalde\", \"ugalde\", \"PRI\", \"4\",\n \"Albo\", \"albo\", \"PAN\", \"456\",\n \"Andrade\", \"andrade\", \"PRI\", \"4567\", \n \"Gmz. Alc?ntar\", \"alcantar\", \"PVEM\", \"4567\",\n \"Glez. Luna\", \"glezluna\", \"PAN\", \"456\",\n \"Latap?\", \"latapi\", \"PRI\", \"45\",\n \"L?pez Flores\", \"lopezflores\", \"PRI\", \"456\",\n \"Morales\", \"morales\", \"PAN\", \"45\",\n \"S?nchez\", \"sanchez\", \"PAN\", \"4567c\",\n \"Vald?s\", \"valdes\", \"PRD\", \"6789a\",\n \"Ba?os\", \"banos\", \"PRI\", \"6789abcde\",\n \"Nacif\", \"nacif\", \"PAN\", \"6789abcde\",\n \"Elizondo\", \"elizondo\", \"PAN\", \"789a\",\n \"Figueroa\", \"figueroa\", \"PRD\", \"789a\",\n \"Guerrero\", \"guerrero\", \"PRI\", \"789a\",\n \"C?rdova\", \"cordova\", \"PRD\", \"9abcdef\",\n \"Garc?a Rmz.\", \"garcia\", \"PRI\", \"9\" ,\n \"Marv?n\", \"marvan\", \"PAN\", \"9ab\",\n \"E. Andrade\", \"andrade2\", \"\", \"cde\",\n \"Favela\", \"favela\", \"\", \"cdef\",\n \"Santiago\", \"santiago\", \"\", \"c\",\n \"Galindo\", \"galindo\", \"\", \"c\",\n \"Murayama\", \"murayama\", \"\", \"cdef\",\n \"Ruiz Salda?a\", \"ruiz\", \"\", \"cdef\",\n \"San Mart?n\", \"snmartin\", \"\", \"cde\",\n \"Santiago\", \"santiago\", \"\", \"c\",\n \"Ravel\", \"ravel\", \"\", \"def\",\n \"J. Rivera\", \"rivera2\", \"\", \"def\",\n \"Zavala\", \"zavala\", \"\", \"def\",\n \"De la Cruz\", \"magana\", \"\", \"f\",\n \"Faz\", \"faz\", \"\", \"f\",\n \"Humphrey\", \"humphrey\", \"\", \"f\",\n \"Kib Espadas\", \"kib\", \"\", \"f\"),\n ncol = 4,\n byrow = TRUE)\n#\nids <- as.data.frame(ids, stringsAsFactors = FALSE)\ncolnames(ids) <- c(\"name\", \"column\", \"pty\", \"tenure\") \n#ids$tenure <- as.numeric(ids$tenure)\nids <- within(ids, party <- ifelse (pty==\"PRI\", 1,\n ifelse (pty==\"PAN\", 2,\n ifelse (pty==\"PRD\", 3, \n ifelse(pty==\"PVEM\", 4, 5)))))\nids <- within(ids, color <- ifelse (pty==\"PRI\", \"red\",\n ifelse (pty==\"PAN\", \"blue\",\n ifelse (pty==\"PRD\", \"gold\",\n ifelse(pty==\"PVEM\", \"green\", \"orangered4\")))))\nids # inspect\n\n\n####################################################################\n## select terms for analysis, e.g. for 4-11(b) \"[456789ab]\" works ##\n####################################################################\ntees <- c(\"6\",\"7\",\"8\",\"9\",\"a\")\ntees.grep <- \"[6789a]\"\nT <- length(tees)\n#\nsel <- grep(pattern = tees.grep, ids$tenure)\nname <- ids$name[sel]\nparty <- ids$party[sel]\ncolor <- ids$color[sel]\ncolumn <- ids$column[sel]\n\n## rgb.23 <- c(length=11)\n## rgb.23[c(1,6,8,10)] <- rgb(1, 0, 0, 0.6) #red\n## rgb.23[c(2:4,9)] <- rgb(1, 215/255, 0, 0.6) #gold\n## rgb.23[c(5,7,11)] <- rgb(0, 0, 1, 0.6) #blue\n\n############################################\n## Read votes ##\n## exported to disk by code/data-prep.r ##\n############################################\n#vot23 <- read.csv(\"v23.csv\" , header=TRUE)\nvot4on <- read.csv( \"v456789ab.csv\", header=TRUE)\n# choose what votes will be analyzed\n#vot <- vot23 # Woldenberg\nvot <- vot4on # Ugalde-Vald?s-C?rdova\n#colnames(vot)\n# subset to votes in chosen periods\nsel.r <- which(vot$term %in% tees)\ndrop.c <- ids$column[grep(pattern = tees.grep, ids$tenure)] # column names not in terms selected\ndrop.c <- setdiff(ids$column, drop.c)\ndrop.c <- which(colnames(vot) %in% drop.c)\nif (length(drop.c)>0) vot <- vot[sel.r, -drop.c]\ncolnames(vot)\n# total members\nJ <- length(name); J\n\n########################\n## recode vote values ##\n########################\nvs <- vot[,1:J]\n#table(v$albo, useNA = \"always\")\nvs[vs==0] <- NA ## Version probit requiere 0s y 1s\nvs[vs>2] <- NA\nvs[vs==2] <- 0\n\n# format dates\nvot$date <- ymd(vot$date)\n# summarize then drop uncontested votes\ntable(factor(vot$dunan, labels = c(\"contested\",\"not\")), vot$term, useNA = \"ifany\")\ntable(factor(vot$dunan, labels = c(\"contested\",\"not\")), useNA = \"ifany\")\nsel <- which(vot$dunan==1)\nif (length(sel)>0){\n vot <- vot[-sel,] # drop uncontested votes\n vs <- vs [-sel,] # drop uncontested votes\n}\n\n\n\n## ###########\n## ## MODEL ##\n## ###########\n## model1Dj.irt <- function() {\n## for (j in 1:J){ ## loop over respondents\n## for (i in 1:I){## loop over items\n## v[j,i] ~ dbern(p[j,i]); ## voting rule\n## probit(p[j,i]) <- mu[j,i]; ## sets 0I] <- I\nitem.date <- vot$date #ymd(vot$yr*10000+vot$mo*100+vot$dy)\nS <- length(inicio)\n\n\n# We need a matrix showing whether each councilor is actually in IFE the moment the vote takes place --- will be subset to J below\nIsCouncilor <- matrix (1, ncol=18, nrow=S)\nIsCouncilor[ vot$term < 4 | vot$term > 4,1 ] <- NA # ugalde 1\nIsCouncilor[ vot$term < 4 | vot$term > 6,2 ] <- NA # albo 2\nIsCouncilor[ vot$term < 4 | vot$term > 7,3 ] <- NA # andrade 3\nIsCouncilor[ vot$term < 4 | vot$term > 7,4 ] <- NA # alcantar 4\nIsCouncilor[ vot$term < 4 | vot$term > 6,5 ] <- NA # glezluna 5\nIsCouncilor[ vot$term < 4 | vot$term > 5,6 ] <- NA # latapi 6\nIsCouncilor[ vot$term < 4 | vot$term > 6,7 ] <- NA # lopezflores 7\nIsCouncilor[ vot$term < 4 | vot$term > 5,8 ] <- NA # morales 8\nIsCouncilor[ vot$term < 4 |(vot$term > 7 & vot$term!=12),9 ] <- NA # sanchez 9\nIsCouncilor[ vot$term < 6 | vot$term > 10,10] <- NA # valdes 10\nIsCouncilor[ vot$term < 6 | vot$term > 14,11] <- NA # banos 11\nIsCouncilor[ vot$term < 6 | vot$term > 14,12] <- NA # nacif 12\nIsCouncilor[ vot$term < 7 | vot$term > 10,13] <- NA # elizondo 13\nIsCouncilor[ vot$term < 7 | vot$term > 10,14] <- NA # figueroa 14\nIsCouncilor[ vot$term < 7 | vot$term > 10,15] <- NA # guerrero 15\nIsCouncilor[ vot$term < 9 | vot$term > 15,16] <- NA # cordova 16\nIsCouncilor[ vot$term < 9 | vot$term > 9,17] <- NA # garcia rmz 17\nIsCouncilor[ vot$term < 9 | vot$term > 11,18] <- NA # marvan 18\n####################################################\n## vector to select members present in estimation ##\n####################################################\nsel.members <- which(ids$name %in% name)\nIsCouncilor <- IsCouncilor[, sel.members]\n\n\n# Round 1 ideal points to anchor ideological space\n# (later entrants at party mean)\n# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25\n# u a a a g l l m s v b n e f g c g m a f g m r s s \n# g l n l l a p o a a a a l i u o a a n a a u u n a\n# a b d c z t z r n l ? c i g e r r r d v l r i m n\n# l o r a l a f a c d o i z u r d c v r e i a z a t\nx.location <- c(0, 0, 0, 2, 0, 0, 0, 0,-2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)[sel.members] # sel = members in estimation\nx.precision <- c(1, 1, 1, 4, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)[sel.members]\n#x.location <- c(1, 0, 0, 2,-2, 0, 0, 2,-2, 0, 2,-1,-2, 0, 2,-2, 2,-2, 0, 0, 0, 2, 0, 0, 0)[sel.members] # sel = members in estimation\n#x.precision <- c(4, 1, 1, 4, 4, 4, 1, 4, 4, 1, 4, 4, 4, 1, 4, 4, 4, 4, 1, 1, 1, 4, 1, 1, 1)[sel.members]\n# x.location and x.precision are manipulated by loop, keep a version to use as prior for new entrants instead of party mean\nx.prior.location <- x.location\nx.prior.precision <- x.precision\n#\nwindow.results <- list () ## WILL ADD SESSION'S RESULTS TO OBJECT HOLDING ALL RESULTS\npartyPlacement <- rep (NA,J)\nx.mean <- numeric ()\nx.tau <- numeric ()\n\n\nfor (s in 1:S){ # <= loop over S windows\n #\n #s <- 1 # debug\n councilor.in <- apply (IsCouncilor[inicio[s]:final[s],], 2, invalid)\n councilors <- name [councilor.in==FALSE] # window's info\n abbrev <- column [councilor.in==FALSE] # window's info\n sponsors <- party[councilor.in==FALSE] # window's info\n #\n for (c in 1:J){ # does it for all councilors whther or not present in window (hence J)\n# 5mar21: this uses partyPlacement for new entrants\n# x.mean[c] <- ifelse (!is.na(x.location[c]), x.location[c], partyPlacement[sponsors[c]])\n# x.tau[c] <- ifelse (!is.na(x.precision[c]), x.precision[c], 4)\n# 5mar21: this uses fixed x0 prior for new entrants\n x.mean[c] <- ifelse (!is.na(x.location [c]), x.location [c], x.prior.location [c])\n x.tau[c] <- ifelse (!is.na(x.precision[c]), x.precision[c], x.prior.precision[c])\n }\n #\n v <- vs[inicio[s]:final[s], 1:J][, councilor.in==FALSE]; ## EXTRACT 30 VOTES EACH TIME\n v <- as.data.frame(t(v)) ## ROLL CALLS NEED ITEMS IN COLUMNS, LEGISLATORS IN ROWS\n #\n ###################\n ## Vectorization ##\n ###################\n #J <- nrow(v); I <- ncol(v) ## SESSION TOTALS\n member.index <- 1:nrow(v)\n vote.index <- 1:ncol(v)\n \n ## Melt RC\n rc <- as.data.frame (v)\n colnames (rc) <- vote.index\n rc$member <- member.index\n #\n # determine north/south indexes in vote window\n north <- which(rownames(rc)==\"alcantar\")\n north <- ifelse (length(north)==0, NA, north) # NA if member absent, else her index \n south <- which(rownames(rc)==\"sanchez\")\n south <- ifelse (length(south)==0, NA, south) # NA if member absent, else her index \n #\n molten.rc <- reshape2::melt(rc, id.vars=\"member\", variable.name=\"vote\", value.name=\"rc\")\n #molten.rc$rc <- car::recode (molten.rc$rc, \"0=NA\")\n molten.rc <- na.omit (molten.rc)\n #molten.rc$rc <- car::recode (molten.rc$rc, \"2=0; c(3,4,5)=NA\")\n #\n ife.data.vector <- dump.format(list(\n y = molten.rc$rc\n , n.members = max(member.index)\n , n.item = max(vote.index)\n , n.obs = nrow(molten.rc)\n , vote = molten.rc$vote\n , member = molten.rc$member\n , north = north\n , south = south\n , x.mean = x.mean\n , x.tau = x.tau\n , sponsors = sponsors\n ))\n #\n ife.parameters = c(\"theta\", \"alpha\", \"beta\", \"partyPos\", \"deviance\")\n #\n ife.inits <- function() {\n dump.format(\n list(\n theta = rnorm(max(member.index))\n #theta = c(rnorm(3), NA, rnorm(4), NA, rnorm(max(member.index)-9)) # NAs s?lo en caso de spike priors, correcto?\n , alpha = rnorm(max(vote.index))\n , beta = rnorm(max(vote.index))\n , '.RNG.name'=\"base::Wichmann-Hill\"\n , '.RNG.seed'= 1971) #randomNumbers(n = 1, min = 1, max = 1e+04,col=1))\n )\n }\n #\n ife.model.v <- run.jags(\n model = ife.vector,\n monitor = ife.parameters,\n method = \"parallel\",\n data = ife.data.vector,\n inits = list (ife.inits(), ife.inits()),\n n.chains=2, thin=50, burnin=10000, sample=200,\n #n.chains=1, thin=5, burnin=200, sample=3,\n check.conv=FALSE, plots=FALSE\n )\n #\n chainsIFE.v <- mcmc.list(list (ife.model.v$mcmc[[1]], ife.model.v$mcmc[[2]])) \n gelman.diag (chainsIFE.v, multivariate=F)\n #\n Alpha.v <- rbind ( chainsIFE.v[[1]][,grep(\"alpha\", colnames(chainsIFE.v[[1]]))]\n , chainsIFE.v[[2]][,grep(\"alpha\", colnames(chainsIFE.v[[2]]))])\n Beta.v <- rbind ( chainsIFE.v[[1]][,grep(\"beta\", colnames(chainsIFE.v[[1]]))]\n , chainsIFE.v[[2]][,grep(\"beta\", colnames(chainsIFE.v[[2]]))])\n Theta.v <- rbind ( chainsIFE.v[[1]][,grep(\"theta\", colnames(chainsIFE.v[[1]]))]\n , chainsIFE.v[[2]][,grep(\"theta\", colnames(chainsIFE.v[[2]]))])\n #\n plot (colMeans (Alpha.v)) # difficulties\n plot (colMeans (Beta.v)) # signal\n #\n par (las=2, mar=c(7,3,2,2))\n plot (1:length(vot$date), colMeans (Beta.v)\n , type=\"n\"\n , axes=F, ylim=c(-1,1)\n , xlab=\"\", ylab=\"Ideal point\")\n axis (1, at=seq(1,length(vot$date),8)\n , labels=vot$date[seq(1,length(vot$date),8)], cex=0.8)\n par (las=0)\n mtext (side=1, line=6, text=\"Dates\")\n axis (2)\n for (i in 1:ncol(Theta.v)){\n segments (x0=min (c(1:length(vot$date))[vot[,i] != 0])\n , x1=max (c(1:length(vot$date))[vot[,i] != 0])\n , y0=colMeans (Theta.v)[i]\n , y1=colMeans (Theta.v)[i]\n , col=ids$color[i], lwd=3)\n }\n\t# ADD COUNCILOR NAMES AND VOTE INFO TO RESULTS OBJECT\n results <- c(results, councilors=list(councilors), abbrev=list(abbrev));\n results <- c(results, folio.date=list(vot[s,c(\"folio\",\"dy\",\"mo\",\"yr\")])); # add vote on which window is centered\n window.results <- c(window.results, list(results)); ## ADD SESSION'S RESULTS TO OBJECT HOLDING ALL RESULTS\n# results[[4]] <- GHconv; rm (GHconv)\n#\n\t# Update location of ideal point at time s, to be used as location prior at time s+1\n\tx.location <- rep (NA, J)\n\tx.precision <- rep (100, J)\n#\tlocs <- apply( rbind (results[[1]]$BUGSoutput$sims.list$x, results[[2]]$BUGSoutput$sims.list$x), 2, median)\n#\tpartyPlacement <- apply( rbind (results[[1]]$BUGSoutput$sims.list$partyPos, results[[2]]$BUGSoutput$sims.list$partyPos), 2, median)\n\tlocs <- apply( results$BUGSoutput$sims.list$x, 2, median)\n\tpartyPlacement <- apply( results$BUGSoutput$sims.list$partyPos, 2, median)\n\tfor (n in 1:J){\n\t\tif (length( which(councilors==name[n]) )==0) { # if councilor not member current round\n\t\t\tx.location[n] <- NA # then prior for next round set to NA\n\t\t\tx.precision[n] <- NA # (and line above sets it to party placement)\n\t\t}\n\t\telse { x.location[n] <- locs[which (councilors==name[n])] } # councilor's prior for next round is current x \n\t}\n\t# Precision prior is always constant at 100, implying standard deviation = sqrt (1/100) = 0.1\n} # <--- END OF LOOP OVER WINDOWS\n\n\n# plot results\ntit <- \"Terms 67 (Vald?s 2007-2010), Bonica method\"\nsource(\"../code/plot-posteriors.r\")\nx\n\n# rename object with posterior sims\nsummary(window.results[[190]])\nwindow.results.67 <- window.results\nrm(window.results)\n\n# clean\nls()\nrm(c, s, n, v, sel, ife.inits, ife.parameters, ife.data)\nrm(councilors, sponsors, inicio, final, councilor.in)\nrm(x.location, x.mean, x.precision, x.tau, item, results, item.date)\nrm(color, column, locs, name, party, partyPlacement)\n\n# save\nsummary(window.results.67)\n#summary(window.results[[232]]) # 11 members, overlap\nsave.image(file = \"posterior-samples/vald67-window-results-compress.RData\", compress = \"xz\")\n#save(window.results.23, file = \"posterior-samples/wold23-window-results-compress.RData\")\nx\n\n\n\n# Save window.results, containing all chains from all runs\n# save (window.results, file=\"DynUgaldeBonicaMarch21.RData\") # With more anchors\n# save (window.results, file=\"DynUgaldeBonicaMarch19.RData\")\n# save (window.results, file=\"DynUgaldeBonica.RData\")\n\nsource(\"http://rtm.wustl.edu/code/sendEmail.R\")\n#sendEmail (subject=\"Ugalde et al esta listo\", text=\"\", address=\"rosas.guillermo@gmail.com\")\nsendEmail (subject=\"Ugalde et al esta listo\", text=\"\", address=\"emagar@gmail.com\")\n\n# RData file with runs carried out in Mexico, early March\n# load (\"DynUgaldeBonica.RData\")\n\n# RData file with runs carried out in Wash U, March 19\n# These runs omit non-sitting Councilors and party precisions for new Councilors\nload (\"DynUgaldeBonicaMarch21.RData\")\n\n\n\n\nS <- length (semester.results)\nmultiGelman.hat <- numeric ()\nfor (i in 1:S){\n\tchainsConv <- mcmc.list(list (as.mcmc (semester.results[[i]][[2]]$BUGSoutput$sims.list$x), as.mcmc (semester.results[[i]][[1]]$BUGSoutput$sims.list$x)))\n\ttmp <- gelman.diag (chainsConv)[[2]]\n\tmultiGelman.hat <- c(multiGelman.hat, tmp)\n}\nrm (tmp, chainsConv)\n\nnonConverged <- ifelse (multiGelman.hat > 2, 1, 0) # Should be 0\n\n\nCouncilorIn <- matrix (1, nrow=J, ncol=S)\nCouncilorIn[1, all45678901$date > 20071217] <- NA\nCouncilorIn[2, all45678901$date > 20080814] <- NA\nCouncilorIn[3, all45678901$date > 20101027] <- NA\nCouncilorIn[4, all45678901$date > 20101027] <- NA\nCouncilorIn[5, all45678901$date > 20080814] <- NA\nCouncilorIn[6, all45678901$date > 20071217] <- NA\nCouncilorIn[7, all45678901$date > 20080814] <- NA\nCouncilorIn[8, all45678901$date > 20071217] <- NA\nCouncilorIn[9, all45678901$date > 20101027] <- NA\nCouncilorIn[10, all45678901$date < 20080215] <- NA\nCouncilorIn[11, all45678901$date < 20080215] <- NA\nCouncilorIn[12, all45678901$date < 20080215] <- NA\nCouncilorIn[13, all45678901$date < 20080829] <- NA\nCouncilorIn[14, all45678901$date < 20080829] <- NA\nCouncilorIn[15, all45678901$date < 20080829] <- NA\n#CouncilorIn[16, all45678901$date < 20111215] <- NA\n#CouncilorIn[17, all45678901$date < 20111215] <- NA\n#CouncilorIn[18, all45678901$date < 20111215] <- NA\n\n\n\n# If using DynUgaldeBonica.RData, use the following code to extract ideal points\nideal.points <- matrix (NA, nrow=S, ncol=J)\nfor (i in 1:S){\n\tideal.points[i,] <- apply (rbind (semester.results[[i]][[1]]$BUGSoutput$sims.list$x, semester.results[[i]][[2]]$BUGSoutput$sims.list$x), 2, median)\n}\n\n# If using DynUgaldeBonicaMarch19.RData or DynUgaldeBonicaMarch21.RData, use the following code to extract ideal points\nideal.points <- matrix (NA, nrow=S, ncol=J)\nideal.points.var <- matrix (NA, nrow=S, ncol=J)\nfor (i in 1:S){\n\tfor (j in 1:J){\n\t\tcouncilor <- names45678901[j]\n\t\tnum <- which (semester.results[[i]][[3]]==councilor)\n\t\tif ( length (num)==0 ) {\n\t\t\tideal.points[i,j] <- 1\n\t\t\tideal.points.var[i,j] <- 0\n\t\t} else {\n\t\t\t\tideal.points[i,j] <- median (c (semester.results[[i]][[1]]$BUGSoutput$sims.list$x[,num], semester.results[[i]][[2]]$BUGSoutput$sims.list$x[,num]))\n\t\t\t\tideal.points.var[i,j] <- var (c (semester.results[[i]][[1]]$BUGSoutput$sims.list$x[,num], semester.results[[i]][[2]]$BUGSoutput$sims.list$x[,num]))\n\t\t}\n\t}\n}\n\n\n# Plot different individual councilor paths:\n# There is definitely a problem at different spots of the estimation:\n# Between i=200 and i=500 and after i=600\n# The problem really obtains when we get new guys in\n# Valdes, Banos, Nacif, Elizondo, Figueroa, Guerrero come in in quick succession and destroy everything\n# Maybe the best solution is to break down estimation of this very long period in two or three chunks\n# Or maybe we need to combine priors on ideal points with priors on item parameters\nk <- 12\nplot (ideal.points[ideal.points.var[,k] != 0, k], type=\"l\")\n\n\n# Get SDs of estimates, the width should be useful to plot thickness of data points\nideal.points.var <- sqrt (ideal.points.var)\n\n# Non-smoothed ideal point time-paths\nsetwd (\"/Users/grosas/Dropbox/ifesharedge/graphs\")\npdf (\"UgaldeEtAlNonSmooth.pdf\", h=7, w=9)\nplot(c(1:S), ideal.points[1:S,1], main=\"\", ylim=c(-3,3), type=\"n\", xlab=\"\", ylab=\"Ideal points\")\nfor (j in 1:J){\n# \tlines(ideal.points[1:S,j], lwd=3, col=color45678901[j])\n\tlines(CouncilorIn[j,1:S] * ideal.points[1:S,j], lwd=3, col=rgb.45678901[j])\n}\ndev.off()\n\n\n\n# Smoothed ideal point time-paths\n# pdf (\"UgaldeEtAlSmooth.pdf\", h=7, w=9)\npdf (\"UgaldeEtAlSmoothMarch21.pdf\", h=7, w=9)\nplot(c(1:S), ideal.points[1:S,1], main=\"\", ylim=c(-3,3), type=\"n\", xlab=\"\", ylab=\"Ideal points\")\nfor (j in 1:J){\n\tlines(smooth.spline(c(1:S)[!is.na(CouncilorIn[j,1:S])], ideal.points[!is.na(CouncilorIn[j,1:S]),j], df=10), lwd=3, col=rgb.45678901[j])\n# \tlines(smooth.spline(c(1:S), ideal.points[,j], df=10), lwd=3, col=color45678901[j])\n}\ndev.off()\n\n\n\nsetwd (\"/Users/grosas/Dropbox/ifesharedge/graphs/animBits/ugalde/\")\n\n# Smoothed ideal point time-paths, daily change movie\nSmooth <- list ()\nfor (j in 1:J){\n\tSmooth[[j]] <- smooth.spline(c(1:S), ideal.points[,j], df=10)\n}\n\nfor (j in 1:J){\n\tSmooth[[j]]$y[is.na(CouncilorIn[j,1:S])] <- NA\n}\n\nsnapshot <- seq (1, 1091, by=10)\nfor (s in snapshot){\n\twhich.s <- which (snapshot==s)\n\tif (which.s < 10) {name = paste('Ugalde00', which.s,'plot.png',sep='')}\n\tif (which.s >= 10 && which.s < 100) {name = paste('Ugalde0', which.s,'plot.png', sep='')}\n\tif (which.s >= 100) {name = paste('Ugalde', which.s,'plot.png', sep='')}\n\n\tjpeg (name, quality=100, height=500, width=500)\n\n\tplot(c(1:S), ideal.points[1:S,1], main=\"\", ylim=c(-3,3), type=\"n\", xlab=\"\", ylab=\"Ideal points\")\n\tfor (j in 1:J){\n\t\tlines ( Smooth[[j]]$x[1:s], Smooth[[j]]$y[1:s], lwd=6, col=rgb.45678901[j])\n\t}\n\t# Eric: Creo que t? tienes m?s informaci?n para hacer los cambios en las siguientes tres l?neas\n\tlegend (\"topright\", bty=\"n\", legend=paste (\"vote\", s, sep=\" \")) #Change the legend for the date of the vote\n\t# Add vertical lines for elections here, but only for some realizations of S\n\t# Add names of councilors here as well, but only for some realizations of S\n\tdev.off()\n}\n\n# Make a short film\n# system(\"convert -loop 1 -delay 40 *.png IFEwolden.gif\")\nsystem(\"convert -loop 1 -delay 20 *.png IFEugaldeSofis.gif\")\n# The number after loop controls the number of automatic replays (0 stands for infinite loop)\n# The number after delay determines length of transition between 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{"text": "#' svaPrep\n#'\n#' prepare data for sva and/or isva analysis\n#' \n#' @param variable Independent variable vector.\n#' @param covariates Covariates data frame to include in regression model,\n#' one row per sample, one column per covariate (Default: NULL).\n#' @param beta.sva Methylation levels matrix, one row per CpG site, one column per sample.\n#' @param featureset Name from \\code{\\link{meffil.list.featuresets}()} (Default: NA).\n#' one row per sample, one column per covariate (Default: NULL).\n#' @param most.variable Apply (Independent) Surrogate Variable Analysis to the \n#' given most variable CpG sites (Default: 50000).\nsvaPrep <- function(variable,covariates,beta.sva,featureset,most.variable){\n\tautosomal.sites <- meffil.get.autosomal.sites(featureset)\n\tautosomal.sites <- intersect(autosomal.sites, rownames(beta.sva))\n\tif (length(autosomal.sites) < most.variable) {\n\t\twarning(\"Probes from the sex chromosomes will be used to calculate surrogate variables.\")\n\t} else {\n\t\tbeta.sva <- beta.sva[autosomal.sites,]\n\t}\n\tvar.idx <- order(rowVars(beta.sva, na.rm=T), decreasing=T)[1:most.variable]\n\tbeta.sva <- impute.matrix(beta.sva[var.idx,,drop=F])\n\t\n\tif (!is.null(covariates)) {\n\t\tcov.frame <- model.frame(~., data.frame(covariates, stringsAsFactors=F), na.action=na.pass)\n\t\tmod0 <- model.matrix(~., cov.frame)\n\t}\n\telse\n\t\tmod0 <- matrix(1, ncol=1, nrow=length(variable))\n\tmod <- cbind(mod0, variable)\n\tres <- list(\"mod\" = mod, \"beta.sva\"=beta.sva)\n\treturn(res)\n}\n\n#' svaIsva \n#' \n#' Constructs function calls to surrogate variable analysis or independent surrogate variable analysis\n#' \n#' @param beta.sva Methylation levels matrix, one row per CpG site, one column per sample.\n#' @param covariates Covariates data frame to include in regression model,\n#' one row per sample, one column per covariate (Default: NULL).\n#' @param random.seed Value with which to seed the pseudo random number generator for reproducible results\n#' @param mod model matrix form \\code{\\link{svaPrep}}\n#' @param n.sv Number of surrogate variables \n#' @param mode Do sva or isva analysis, accepts values \"sv\" or \"isv\"\n#' @param verbose Set verbosity of isva function\nsvaIsva <- function(beta.sva,covariates,random.seed,mod,n.sv,mode=\"sv\",verbose){\n\tset.seed(random.seed)\n\tif(mode==\"sv\"){\n\t\tsva.ret <- sva(beta.sva, mod=mod, mod0=mod[,1,drop=F], n.sv=n.sv)\n\t}\n\telse if (mode==\"isv\"){\n\t\tsva.ret <- isva(beta.sva, mod, ncomp=n.sv, verbose=verbose)\n\t}\n\tif (!is.null(covariates))\n\t\tsvaRes <- data.frame(covariates, sva.ret[[mode]], stringsAsFactors=F)\n\telse\n\t\tsvaRes <- as.data.frame(sva.ret[[mode]])\n\treturn(svaRes)\n}\n\n#' outliers\n#' \n#' selects outliers based on the value of \"outlier.iqr.factor\"\n#' @param beta Methylation levels matrix, one row per CpG site, one column per sample.\n#' @param outlier.iqr.factor For each CpG site, prior to fitting regression models,\n#' set methylation levels less than\n#' \\code{Q1 - outlier.iqr.factor * IQR} or more than\n#' \\code{Q3 + outlier.iqr.factor * IQR} to NA. Here IQR is the inter-quartile\n#' range of the methylation levels at the CpG site, i.e. Q3-Q1.\n#' Set to NA to skip this step (Default: NA)\noutliers <- function(beta,outlier.iqr.factor) {\n\tq <- rowQuantiles(beta, probs = c(0.25, 0.75), na.rm = T)\n\tiqr <- q[,2] - q[,1]\n\ttoo.hi <- which(beta > q[,2] + outlier.iqr.factor * iqr, arr.ind=T)\n\ttoo.lo <- which(beta < q[,1] - outlier.iqr.factor * iqr, arr.ind=T)\n\tif (nrow(too.hi) > 0) beta[too.hi] <- NA\n\tif (nrow(too.lo) > 0) beta[too.lo] <- NA\n\tres <- list(\"too.lo\"=too.lo,\"too.hi\"=too.hi)\n\treturn(res)\n}\n\n\n#' Epigenome-wide association study\n#'\n#' Test association with each CpG site.\n#'\n#' @param beta Methylation levels matrix, one row per CpG site, one column per sample.\n#' @param variable Independent variable vector. - NB only handles discrete 2 catagory variables\n#' @param covariates Covariates data frame to include in regression model,\n#' one row per sample, one column per covariate (Default: NULL).\n#' @param batch Batch vector to be included as a random effect (Default: NULL).\n#' @param weights Non-negative observation weights.\n#' Can be a numeric matrix of individual weights of same dimension as \\code{beta},\n#' or a numeric vector of weights with length \\code{ncol(beta)},\n#' or a numeric vector of weights with length \\code{nrow(beta)}. \n#' @param cell.counts Proportion of cell counts for one cell type in cases\n#' where the samples are mainly composed of two cell types (e.g. saliva) (Default: NULL).\n#' @param isva Apply Independent Surrogate Variable Analysis (ISVA) to the\n#' methylation levels and include the resulting variables as covariates in a\n#' regression model (Default: TRUE). \n#' @param sva Apply Surrogate Variable Analysis (SVA) to the\n#' methylation levels and covariates and include\n#' the resulting variables as covariates in a regression model (Default: TRUE).\n#' @param n.sv Number of surrogate variables to calculate (Default: NULL).\n#' @param winsorize.pct Apply all regression models to methylation levels\n#' winsorized to the given level. Set to NA to avoid winsorizing (Default: 0.05).\n#' @param robust Test associations with the 'robust' option when \\code{\\link{limma::eBayes}}\n#' is called (Default: TRUE).\n#' @param rlm Test assocaitions with the 'robust' option when \\code{\\link{limma:lmFit}}\n#' is called (Default: FALSE).\n#' @param outlier.iqr.factor For each CpG site, prior to fitting regression models,\n#' set methylation levels less than\n#' \\code{Q1 - outlier.iqr.factor * IQR} or more than\n#' \\code{Q3 + outlier.iqr.factor * IQR} to NA. Here IQR is the inter-quartile\n#' range of the methylation levels at the CpG site, i.e. Q3-Q1.\n#' Set to NA to skip this step (Default: NA).\n#' @param most.variable Apply (Independent) Surrogate Variable Analysis to the \n#' given most variable CpG sites (Default: 50000).\n#' @param featureset Name from \\code{\\link{meffil.list.featuresets}()} (Default: NA).\n#' @param random.seed Value with which to seed the pseudo random number generator for reproducible results\n#' @param verbose Set to TRUE if status updates to be printed (Default: FALSE).\n#'\n#' @export\nmeffil.ewas <- function(beta, variable,\n\t\t\t\t\t\tcovariates=NULL, batch=NULL, weights=NULL,\n\t\t\t\t\t\tcell.counts=NULL,\n\t\t\t\t\t\tisva=T, sva=T, ## cate?\n\t\t\t\t\t\tn.sv=NULL,\n\t\t\t\t\t\tisva0=F,isva1=F, ## deprecated\n\t\t\t\t\t\twinsorize.pct=0.05,\n\t\t\t\t\t\trobust=TRUE,\n\t\t\t\t\t\trlm=FALSE,\n\t\t\t\t\t\toutlier.iqr.factor=NA, ## typical value = 3\n\t\t\t\t\t\tmost.variable=min(nrow(beta), 50000),\n\t\t\t\t\t\tfeatureset=NA,\n\t\t\t\t\t\trandom.seed=20161123,\n\t\t\t\t\t\tverbose=F) {\n\t## depreciated Args\n\tif (isva0 || isva1)\n\t\tstop(\"isva0 and isva1 are deprecated and superceded by isva and sva\")\n\t# warn and set isva, sva values rather than kill?\n\t\n\tif (is.na(featureset))\n\t\tfeatureset <- guess.featureset(rownames(beta))\n\tfeatures <- meffil.get.features(featureset)\n\t\n\t## data Validation / type checks\n\tstopifnot(length(rownames(beta)) > 0 && all(rownames(beta) %in% features$name))\n\tstopifnot(ncol(beta) == length(variable))\n\tstopifnot(is.null(covariates) || is.data.frame(covariates) && nrow(covariates) == ncol(beta))\n\tstopifnot(is.null(batch) || length(batch) == ncol(beta))\n\tstopifnot(is.null(weights)\n\t\t\t|| is.numeric(weights) && (is.matrix(weights) && nrow(weights) == nrow(beta) && ncol(weights) == ncol(beta)\n\t\t\t\t\t\t\t\t\t\t|| is.vector(weights) && length(weights) == nrow(beta)\n\t\t\t\t\t\t\t\t\t\t|| is.vector(weights) && length(weights) == ncol(beta)))\n\tstopifnot(most.variable > 1 && most.variable <= nrow(beta))\n\tstopifnot(!is.numeric(winsorize.pct) || winsorize.pct > 0 && winsorize.pct < 0.5)\n\t\n\t## cache var and covars\n\toriginal.variable <- variable\n\toriginal.covariates <- covariates\n\tif (is.character(variable))\n\t\tvariable <- as.factor(variable)\n\t\n\tstopifnot(!is.factor(variable) || is.ordered(variable) || length(levels(variable)) == 2)\n\t\n\tmsg(\"Simplifying any categorical variables.\", verbose=verbose)\n\tvariable <- simplify.variable(variable)\n\tif (!is.null(covariates))\n\t\tcovariates <- do.call(cbind, lapply(covariates, simplify.variable))\n\t\n\t## Getting samples without missing values\n\tsample.idx <- which(!is.na(variable))\n\tif (!is.null(covariates))\n\t\tsample.idx <- intersect(sample.idx, which(apply(!is.na(covariates), 1, all)))\n\t\n\tmsg(\"Removing\", ncol(beta) - length(sample.idx), \"missing case(s).\", verbose=verbose)\n\t\n\tif (is.matrix(weights))\n\t\tweights <- weights[,sample.idx]\n\tif (is.vector(weights) && length(weights) == ncol(beta))\n\t\tweights <- weights[sample.idx]\n\t\n\tbeta <- beta[,sample.idx]\n\tvariable <- variable[sample.idx]\n\t\n\tif (!is.null(covariates))\n\t\tcovariates <- covariates[sample.idx,,drop=F]\n\t\n\tif (!is.null(batch))\n\t\tbatch <- batch[sample.idx]\n\t\n\tif (!is.null(cell.counts))\n\t\tcell.counts <- cell.counts[sample.idx]\n\t\n\tif (!is.null(covariates)) {\n\t\tpos.var.idx <- which(apply(covariates, 2, var, na.rm=T) > 0)\n\t\tmsg(\"Removing\", ncol(covariates) - length(pos.var.idx), \"covariates with no variance.\",\n\t\t\tverbose=verbose)\n\t\tcovariates <- covariates[,pos.var.idx, drop=F]\n\t}\n\t\n\t## ewas setup\n\t### Declace list to hold different covariate sets ewas results\n\tcovariate.sets <- list(none=NULL) \n\tif (!is.null(covariates))\n\t\tcovariate.sets$all <- covariates\n\t\n\t### winsorisation\n\tif (is.numeric(winsorize.pct)) {\n\t\tmsg(winsorize.pct, \"- winsorizing the beta matrix.\", verbose=verbose)\n\t\tbeta <- winsorize(beta, pct=winsorize.pct)\n\t}\n\t\n\t### outliers\n\ttoo.hi <- too.lo <- NULL\n\tif (is.numeric(outlier.iqr.factor)) {\n\t\toutliersRes <- outliers(beta,outlier.iqr.factor)\n\t\ttoo.hi <- outliersRes$too.hi\n\t\ttoo.lo <- outliersRes$too.lo\n\t}\n\t\n\t## sva/isva\n\tif (isva || sva) {\n\t\tbeta.sva <- beta\n\t\tres <- svaPrep(variable,covariates,beta.sva,featureset,most.variable)\n\t\tmod <- res$mod\n\t\tbeta.sva <- res$beta.sva\n\t\t\n\t\tif (isva) {\n\t\t\tmsg(\"ISVA.\", verbose=verbose)\n\t\t\tcovariate.sets$isva <- svaIsva(beta.sva,covariates,random.seed,mod,n.sv,mode=\"isv\",verbose)\n\t\t\tcat(\"\\n\")\n\t\t}\n\t\t\n\t\tif (sva) {\n\t\t\tmsg(\"SVA.\", verbose=verbose)\n\t\t\tcovariate.sets$sva <- svaIsva(beta.sva,covariates,random.seed,mod,n.sv,mode=\"sv\",verbose)\n\t\t\tcat(\"\\n\")\n\t\t}\n\t}\n\t\n\t## run EWASs\n\tanalyses <- sapply(names(covariate.sets), function(name) {\n\t\tmsg(\"EWAS for covariate set\", name, verbose=verbose)\n\t\tcovariates <- covariate.sets[[name]]\n\t\tewas(variable,\n\t\t\t beta=beta,\n\t\t\t covariates=covariates,\n\t\t\t batch=batch,\n\t\t\t weights=weights,\n\t\t\t cell.counts=cell.counts,\n\t\t\t winsorize.pct=winsorize.pct, \n\t\t\t robust=robust, \n\t\t\t rlm=rlm)\n\t}, simplify=F)\n\t\n\t## extract results\n\tp.values <- sapply(analyses, function(analysis) analysis$table$p.value)\n\tcoefficients <- sapply(analyses, function(analysis) analysis$table$coefficient)\n\trownames(p.values) <- rownames(coefficients) <- rownames(analyses[[1]]$table)\n\t\n\tfor (name in names(analyses)) {\n\t\tidx <- match(rownames(analyses[[name]]$table), features$name)\n\t\tanalyses[[name]]$table$chromosome <- features$chromosome[idx]\n\t\tanalyses[[name]]$table$position <- features$position[idx]\n\t}\n\t\n\t## Return results\n\tlist(class=\"ewas\",\n\t\t version=packageVersion(\"meffil\"),\n\t\t samples=sample.idx,\n\t\t variable=original.variable[sample.idx],\n\t\t covariates=original.covariates[sample.idx,,drop=F],\n\t\t winsorize.pct=winsorize.pct,\n\t\t robust=robust,\n\t\t rlm=rlm,\n\t\t outlier.iqr.factor=outlier.iqr.factor,\n\t\t most.variable=most.variable,\n\t\t p.value=p.values,\n\t\t coefficient=coefficients,\n\t\t analyses=analyses,\n\t\t random.seed=random.seed,\n\t\t too.hi=too.hi,\n\t\t too.lo=too.lo)\n}\n\n#' is.ewas.object\n#' \n#' @param object a 'meffil' object (not an actual S3/4 R object)\nis.ewas.object <- function(object)\n\tis.list(object) && \"class\" %in% names(object) && object$class == \"ewas\"\n\n#' ewas\n#' \n#' Test associations between \\code{variable} and each row of \\code{beta}\n#' while adjusting for \\code{covariates} (fixed effects) and \\code{batch} (random effect).\n#' NB only handles discrete 2 catagory variables\n#' If \\code{cell.counts} is not \\code{NULL}, then it is assumed that\n#' the methylation data is derived from samples with two cell types.\n#' \\code{cell.counts} should then be a vector of numbers\n#' between 0 and 1 of length equal to \\code{variable} corresponding\n#' to the proportions of cell of a selected cell type in each sample.\n#' The regression model is then modified in order to identify\n#' associations specifically in the selected cell type (PMID: 24000956).\newas <- function(variable, beta, covariates=NULL, batch=NULL, weights=NULL, cell.counts=NULL, winsorize.pct=0.05,\n\t\t\t\t robust=TRUE, rlm=FALSE, verbose=F) {\n\tstopifnot(all(!is.na(variable)))\n\tstopifnot(length(variable) == ncol(beta))\n\tstopifnot(is.null(covariates) || nrow(covariates) == ncol(beta))\n\tstopifnot(is.null(batch) || length(batch) == ncol(beta))\n\tstopifnot(is.null(cell.counts)\n\t\t\t || length(cell.counts) == ncol(beta)\n\t\t\t && all(cell.counts >= 0 & cell.counts <= 1))\n \n\tmethod <- \"ls\"\n\tif (rlm) method <- \"robust\"\n \n\tif (is.null(covariates))\n\t\tdesign <- data.frame(intercept=1, variable=variable)\n\telse\n\t\tdesign <- data.frame(intercept=1, variable=variable, covariates)\n\trownames(design) <- colnames(beta)\n\n\tif (!is.null(cell.counts)) {\n\t\t## Irizarray method: Measuring cell-type specific differential methylation\n\t\t##\t in human brain tissue\n\t\t## Mi is methylation level for sample i\n\t\t## Xi is the value of the variable of interest for sample i\n\t\t## pi is the proportion of the target cell type for sample i\n\t\t## thus: Mi ~ target cell methylation * pi + other cell methylation * (1-pi)\n\t\t## and target cell methylation = base target methylation + effect of Xi value\n\t\t## and other cell methylation = base other methylation + effect of Xi value\n\t\t## thus:\n\t\t## Mi = (T + U Xi)pi + (O + P Xi)(1-pi) + e\n\t\t##\t= C + A Xi pi + B Xi (1-pi) + e\t\t\n\t\tdesign <- design[,-which(colnames(design) == \"intercept\")]\n\t\t\n\t\tdesign <- cbind(design * cell.counts,\n\t\t\t\t\t\ttypeB=design * (1-cell.counts))\n\t}\n\n\tmsg(\"Linear regression with limma::lmFit\", verbose=verbose)\n\tfit <- NULL\n\tbatch.cor <- NULL\n\tif (!is.null(batch)) {\n\t\tmsg(\"Adjusting for batch effect\", verbose=verbose)\n\t\tcorfit <- duplicateCorrelation(beta, design, block=batch, ndups=1)\n\t\tbatch.cor <- corfit$consensus\n\t\tmsg(\"Linear regression with batch as random effect\", verbose=verbose)\n\t\ttryCatch(fit <- lmFit(beta, design, method=method, block=batch, cor=batch.cor, weights=weights),\n\t\t\t\t error=function(e) {\n\t\t\t\t\t print(e)\n\t\t\t\t\t msg(\"lmFit failed with random effect batch variable, omitting\", verbose=verbose)\n\t\t\t\t })\n\t}\n\tif (is.null(fit)) {\n\t\tmsg(\"Linear regression with only fixed effects\", verbose=verbose)\n\t\tbatch <- NULL\n\t\tfit <- lmFit(beta, design, method=method, weights=weights)\n\t}\n\t\n\tmsg(\"Empirical Bayes\", verbose=verbose)\n\tif (is.numeric(winsorize.pct) && robust) {\n\t fit.ebayes <- eBayes(fit, robust=T, winsor.tail.p=c(winsorize.pct, winsorize.pct))\n\t} else {\n\t fit.ebayes <- eBayes(fit, robust=robust)\n\t}\n\n\talpha <- 0.975\n\tstd.error <- (sqrt(fit.ebayes$s2.post) * fit.ebayes$stdev.unscaled[,\"variable\"])\n\tmargin.error <- (std.error * qt(alpha, df=fit.ebayes$df.total))\n\tn <- rowSums(!is.na(beta))\n\n\tlist(design=design,\n\t\t batch=batch,\n\t\t batch.cor=batch.cor,\n\t\t cell.counts=cell.counts,\n\t\t table=data.frame(p.value=fit.ebayes$p.value[,\"variable\"],\n\t\t\t fdr=p.adjust(fit.ebayes$p.value[,\"variable\"], \"fdr\"),\n\t\t\t p.holm=p.adjust(fit.ebayes$p.value[,\"variable\"], \"holm\"),\n\t\t\t t.statistic=fit.ebayes$t[,\"variable\"],\n\t\t\t coefficient=fit.ebayes$coefficient[,\"variable\"],\n\t\t\t coefficient.ci.high=fit.ebayes$coefficient[,\"variable\"] + margin.error,\n\t\t\t coefficient.ci.low=fit.ebayes$coefficient[,\"variable\"] - margin.error,\n\t\t\t coefficient.se=std.error,\n\t\t\t n=n))\n}\n\n\n#' Epigenome-wide association study (lm)\n#'\n#' Test association with each CpG site.\n#'\n#' @param beta Methylation levels matrix, one row per CpG site, one column per sample.\n#' @param variable Independent variable vector.\n#' @param covariates Covariates data frame to include in regression model,\n#' one row per sample, one column per covariate (Default: NULL).\n#' @param weights numeric vector of weights to provide to the lm()/glm() function\n#' @param family to perform generalised linear modeling with r's glm() function\n#' @param isva Apply Independent Surrogate Variable Analysis (ISVA) to the\n#' methylation levels and include the resulting variables as covariates in a\n#' regression model (Default: TRUE). \n#' @param sva Apply Surrogate Variable Analysis (SVA) to the\n#' methylation levels and covariates and include\n#' the resulting variables as covariates in a regression model (Default: TRUE).\n#' @param n.sv Number of surrogate variables to calculate (Default: NULL).\n#' @param winsorize.pct Apply all regression models to methylation levels\n#' winsorized to the given level. Set to NA to avoid winsorizing (Default: 0.05).\n#' @param outlier.iqr.factor For each CpG site, prior to fitting regression models,\n#' set methylation levels less than\n#' \\code{Q1 - outlier.iqr.factor * IQR} or more than\n#' \\code{Q3 + outlier.iqr.factor * IQR} to NA. Here IQR is the inter-quartile\n#' range of the methylation levels at the CpG site, i.e. Q3-Q1.\n#' Set to NA to skip this step (Default: NA).\n#' @param most.variable Apply (Independent) Surrogate Variable Analysis to the \n#' given most variable CpG sites (Default: 50000).\n#' @param featureset Name from \\code{\\link{meffil.list.featuresets}()} (Default: NA).\n#' @param random.seed Value with which to seed the pseudo random number generator for reproducible results\n#' @param verbose Set to TRUE if status updates to be printed (Default: FALSE).\n#'\n#' @export\nmeffil.ewas.lm <- function(beta, variable,\n\t\t\t\t\t\t\tcovariates=NULL, \n\t\t\t\t\t\t\t#batch=NULL, \n\t\t\t\t\t\t\tweights=NULL,\n\t\t\t\t\t\t\tfamily=NULL,\n\t\t\t\t\t\t\t#cell.counts=NULL,\n\t\t\t\t\t\t\tisva=T, sva=T, ## cate?\n\t\t\t\t\t\t\tn.sv=NULL,\n\t\t\t\t\t\t\tisva0=F,isva1=F, ## deprecated\n\t\t\t\t\t\t\twinsorize.pct=0.05,\n\t\t\t\t\t\t\toutlier.iqr.factor=NA, ## typical value = 3\n\t\t\t\t\t\t\tmost.variable=min(nrow(beta), 50000),\n\t\t\t\t\t\t\tfeatureset=NA,\n\t\t\t\t\t\t\trandom.seed=20161123,\n\t\t\t\t\t\t\tverbose=F) {\n\t## depreciated Args\n\tif (isva0 || isva1)\n\t\tstop(\"isva0 and isva1 are deprecated and superceded by isva and sva\")\n\t# warn and set isva, sva values rather than kill?\n\t\n\tif (is.na(featureset))\n\t\tfeatureset <- guess.featureset(rownames(beta))\n\tfeatures <- meffil.get.features(featureset)\n\t\n\tphenotype.data <- cbind(variable,covariates)\n\t\n\tvarName <- colnames(variable)\n\tvariable <- unlist(variable,use.names = F)\n\t\n\t## data Validation / type checks\n\tstopifnot(length(rownames(beta)) > 0 && all(rownames(beta) %in% features$name))\n\tstopifnot(ncol(beta) == length(variable))\n\tstopifnot(is.null(covariates) || is.data.frame(covariates) && nrow(covariates) == ncol(beta))\n\t#stopifnot(is.null(batch) || length(batch) == ncol(beta))\n\tstopifnot(is.null(weights)|| is.numeric(weights))\n\tstopifnot(most.variable > 1 && most.variable <= nrow(beta))\n\tstopifnot(!is.numeric(winsorize.pct) || winsorize.pct > 0 && winsorize.pct < 0.5)\n\t\n\t## cache var and covars\n\toriginal.variable <- variable\n\toriginal.covariates <- covariates\n\n\tmsg(\"Simplifying any categorical variables.\", verbose=verbose)\n\tvariable <- simplify.variable(variable)\n\tif (!is.null(covariates))\n\t\tcovariates <- do.call(cbind, lapply(covariates, simplify.variable))\n\t\n\t## Getting samples without missing values\n\t# sample.idx = all samples without missing value?s\n\tsample.idx <- which(!is.na(variable))\n\t\n\t##!! removing missing values from NB phenotype.data !!##\n\t##!! get rid ot phenotype.data and merge var/covar to data frame and pass to ewas.lm !!##\n\tphenotype.data[sample.idx,,drop=F]\n\t\n\tif (!is.null(covariates))\n\t\tsample.idx <- intersect(sample.idx, which(apply(!is.na(covariates), 1, all)))\n\t\n\tmsg(\"Removing\", ncol(beta) - length(sample.idx), \"missing case(s).\", verbose=verbose)\n\t\n\tif (is.vector(weights) && length(weights) == ncol(beta))\n\t\tweights <- weights[sample.idx]\n\t\n\tbeta <- beta[,sample.idx]\n\tvariable <- variable[sample.idx]\n\t\n\tif (!is.null(covariates))\n\t\tcovariates <- covariates[sample.idx,,drop=F]\n\t\n\t# if (!is.null(batch))\n\t# \tbatch <- batch[sample.idx]\n\t# \n\t# if (!is.null(cell.counts))\n\t# \tcell.counts <- cell.counts[sample.idx]\n\t\n\tif (!is.null(covariates)) {\n\t\tpos.var.idx <- which(apply(covariates, 2, var, na.rm=T) > 0)\n\t\tmsg(\"Removing\", ncol(covariates) - length(pos.var.idx), \"covariates with no variance.\",\n\t\t\tverbose=verbose)\n\t\tcovariates <- covariates[,pos.var.idx, drop=F]\n\t}\n\t\n\t## ewas setup\n\t### Declace list to hold different covariate sets ewas results\n\tcovariate.sets <- list(none=NULL) \n\tif (!is.null(covariates))\n\t\tcovariate.sets$all <- covariates\n\t\n\t### winsorisation\n\tif (is.numeric(winsorize.pct)) {\n\t\tmsg(winsorize.pct, \"- winsorizing the beta matrix.\", verbose=verbose)\n\t\tbeta <- winsorize(beta, pct=winsorize.pct)\n\t}\n\t\n\t### outliers\n\ttoo.hi <- too.lo <- NULL\n\tif (is.numeric(outlier.iqr.factor)) {\n\t\toutliersRes <- outliers(beta,outlier.iqr.factor)\n\t\ttoo.hi <- outliersRes$too.hi\n\t\ttoo.lo <- outliersRes$too.lo\n\t}\n\t\n\t## sva/isva\n\tif (isva || sva) {\n\t\tbeta.sva <- beta\n\t\tres <- svaPrep(variable,covariates,beta.sva,featureset,most.variable)\n\t\tmod <- res$mod\n\t\tbeta.sva <- res$beta.sva\n\t\t\n\t\tif (isva) {\n\t\t\tmsg(\"ISVA.\", verbose=verbose)\n\t\t\tcovariate.sets$isva <- svaIsva(beta.sva,covariates,random.seed,mod,n.sv,mode=\"isv\",verbose)\n\t\t\tcat(\"\\n\")\n\t\t}\n\t\t\n\t\tif (sva) {\n\t\t\tmsg(\"SVA.\", verbose=verbose)\n\t\t\tcovariate.sets$sva <- svaIsva(beta.sva,covariates,random.seed,mod,n.sv,mode=\"sv\",verbose)\n\t\t\tcat(\"\\n\")\n\t\t}\n\t}\n\t\n\t## run EWASs\n\tanalyses <- sapply(names(covariate.sets), function(name) {\n\t\tmsg(\"EWAS for covariate set\", name, verbose=verbose)\n\t\tcovariates <- covariate.sets[[name]]\n\t\tif(!is.null(covariate.sets[[name]])){\n\t\t\tphenotype.data <- cbind(phenotype.data,covariate.sets[[name]]) ##!!\n\t\t\t#phenotype.data <- cbind(variable,covariate.sets[[name]]) ##!! ??\n\t\t\t#colnames(phenotype.data) <- c(varName,colnames(covariates))\n\t\t}\n\t\tewas.lm(variable=varName,\n\t\t\t\tbeta=beta,\n\t\t\t\tcovariates=colnames(covariates),\n\t\t\t\t#batch=batch,\n\t\t\t\tweights=weights,\n\t\t\t\t#cell.counts=cell.counts,\n\t\t\t\twinsorize.pct=winsorize.pct,\n\t\t\t\tphenotype.data = phenotype.data,\n\t\t\t\tfamily=family\n\t\t)\n\t}, simplify=F)\n\t\n\t## extract results\n\tp.values <- sapply(analyses, function(analysis) analysis$table$p.value)\n\tcoefficients <- sapply(analyses, function(analysis) analysis$table$coefficient)\n\trownames(p.values) <- rownames(coefficients) <- rownames(analyses[[1]]$table)\n\t\n\tfor (name in names(analyses)) {\n\t\tidx <- match(rownames(analyses[[name]]$table), features$name)\n\t\tanalyses[[name]]$table$chromosome <- features$chromosome[idx]\n\t\tanalyses[[name]]$table$position <- features$position[idx]\n\t}\n\t\n\tlist(class=\"ewas\",\n\t\tversion=packageVersion(\"meffil\"),\n\t\tsamples=sample.idx,\n\t\tvariable=original.variable[sample.idx],\n\t\tcovariates=original.covariates[sample.idx,,drop=F],\n\t\twinsorize.pct=winsorize.pct,\n\t\t#robust=robust,\n\t\t#rlm=rlm,\n\t\toutlier.iqr.factor=outlier.iqr.factor,\n\t\tmost.variable=most.variable,\n\t\tp.value=p.values,\n\t\tcoefficient=coefficients,\n\t\tanalyses=analyses,\n\t\trandom.seed=random.seed,\n\t\ttoo.hi=too.hi,\n\t\ttoo.lo=too.lo)\n}\n\n#' ewas.lm\n#' \n#' performs EWAS using r's lm()/glm() functions\n#' \n#' Test associations between \\code{variable} and each row of \\code{beta}\n#' while adjusting for \\code{covariates} \newas.lm <- function(variable, beta, covariates=NULL,\n\t\t\t\t\tweights=NULL, winsorize.pct=0.05,\n\t\t\t\t\tverbose=F,phenotype.data=NULL,family=NULL) {\n\t## Data Validation\n\tstopifnot(is.character(variable))\n\t#stopifnot(is.character(covariates))\n\tstopifnot(is.null(phenotype.data) || nrow(phenotype.data) == ncol(beta))\n\tn <- rowSums(!is.na(beta))\n\t\n\t## Set design\n\t#(different if no covars)\n\tdesign <- NULL\n\tdesignFx <- NULL\n\tif(is.null(covariates)) {\n\t\tdesign <- data.frame(intercept=1, variable=phenotype.data[,variable])\n\t\tdesignFx <- function(variable,covariates) {\n\t\t\tpaste0(variable,\"~meth.matrix[cpg,]\")\n\t\t}\n\t}\n\telse {\n\t\tdesign <- data.frame(intercept=1, variable=phenotype.data[,variable], covariates=phenotype.data[,covariates])\n\t\tdesignFx <- function(variable,covariates) {\n\t\tpaste0(variable,\"~meth.matrix[cpg,]+\",paste0(covariates,collapse=\"+\"))\n\t\t}\n\t}\n\trownames(design) <- colnames(beta)\n\t\n\t## Set modeling type - fit glm if family provided\n\tmodFx <- NULL\n\tif(is.null(family)) {\n\t\tmodFx <- function(FORM,data,family,weights){\n\t\t\tlm(FORM,data,weights)\n\t\t}\n\t}\n\telse {\n\t\tmodFx <- function(FORM,data,family,weights){\n\t\tglm(FORM,data,family = family,weights=weights)\n\t\t}\n\t}\n\t\n\t## Model building and parsing function\n\tmodel <- function(cpg, meth.matrix, variable, covariates, phenotype.data,family,weights) {\n\t\tFORM=formula(designFx(variable,covariates))\n\t\tmod= modFx(FORM,data=phenotype.data,family = family,weights=weights)\n\t\tres = summary(mod)$coef[2,c(\"Estimate\",\"Std. Error\",\"Pr(>|t|)\")]\n\t}\n\t\n\t## Run models (in parallel)\n\tewas_res <- mclapply(setNames(seq_len(nrow(beta)),dimnames(beta)[[1]]),\n\t\t\t\t\t\tmodel,\n\t\t\t\t\t\tmeth.matrix=beta,\n\t\t\t\t\t\tvariable=variable,\n\t\t\t\t\t\tcovariates=covariates,\n\t\t\t\t\t\tphenotype.data=phenotype.data,\n\t\t\t\t\t\tfamily=family,\n\t\t\t\t\t\tweights=weights)\n\t\n\tewas_res <- data.frame(t(sapply(ewas_res,c)))\n\t\n\tcolnames(ewas_res)<-c(\"effect.size\",\"std.err\",\"P.value\")\n\t\n\t## Return results\n\tlist(design=design,\n\t\t#batch=batch,\n\t\t#batch.cor=batch.cor,\n\t\t#cell.counts=cell.counts,\n\t\ttable=data.frame(p.value=ewas_res$P.value,\n\t\t\t\t\t\tcoefficient=ewas_res$effect.size,\n\t\t\t\t\t\tcoefficient.se=ewas_res$std.err,\n\t\t\t\t\t\tn=n))\n}\n\n", "meta": {"hexsha": "a7895879da8386b11bfef2e47d5370bcc377b0d2", "size": 25090, "ext": "r", "lang": "R", "max_stars_repo_path": "R/ewas.r", "max_stars_repo_name": "RichardJActon/meffil_duplicate", "max_stars_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_stars_repo_licenses": ["Artistic-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/ewas.r", "max_issues_repo_name": "RichardJActon/meffil_duplicate", "max_issues_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_issues_repo_licenses": ["Artistic-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/ewas.r", "max_forks_repo_name": "RichardJActon/meffil_duplicate", "max_forks_repo_head_hexsha": "22fd6b59caef488adcee59619e9f9471e22cb645", "max_forks_repo_licenses": ["Artistic-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.2255192878, "max_line_length": 113, "alphanum_fraction": 0.6971701873, "num_tokens": 7422, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.4765796510636759, "lm_q2_score": 0.02228618643212032, "lm_q1q2_score": 0.010621142953359931}}
{"text": "# Copyright 2018 Dominic Davis-Foster, except where noted below\r\n#' v1.1'\r\n\r\n#' Clear Terminal Function\r\n#'\r\n#' This function clears the terminal on Unix systems.\r\n#' Does not work on Windows.\r\n#' Run makeActiveBinding(\"clear\", clear, environment()) \r\n#'\tto be able to type \"clear\"\r\n#' @keywords clear\r\n#' @export\r\n#' @examples\r\n#' clear()\r\nclear <- function() cat(c(\"\\033[2J\",\"\\033[0;0H\"));\r\n\r\n\r\n#' Newline Function\r\n#'\r\n#' This function starts a new line on the terminal.\r\n#' Can also be used to print a blank line.\r\n#' Run makeActiveBinding(\"newline\", newline, environment())\r\n#'\tto be able to type \"newline\r\n#' @keywords newline\r\n#' @export\r\n#' @examples\r\n#' newline()\r\nnewline <- function() cat(\"\\n\");\r\n\r\n#' Input Prompt Function\r\n#'\r\n#' This function creates an input prompt with customisable text.\r\n#' Multiple inputs are possible with \"lines\" option\".\r\n#'\r\n#' @keywords input\r\n#' @export\r\n#' @examples\r\n#' input(\"Press [Enter] to Continue\")\r\n#' name = input(\"Enter your name: \", lines=2)\r\ninput <- function(text='', lines=1) {\r\n\tcat(text);\r\n\ta <- readLines(\"stdin\",n=lines);\r\n\treturn(a)\r\n}\r\n\r\n#' Type Function\r\n#'\r\n#' Synonym for builtin \"mode\" function.\r\n#'\r\n#' @keywords type mode\r\n#' @export\r\n#' @examples\r\n#' type(LETTERS)\r\n#' \r\ntype <- function(variable) {\r\n\ta <- mode(variable);\r\n\treturn(a)\r\n}\r\n\r\n#' Mode Average Function\r\n#'\r\n#' Function to calculate the modal average.\r\n#'\r\n#' @keywords mode average\r\n#' @export\r\n#' @examples\r\n#' modes(sample(1:10, 10, replace=TRUE))\r\n#'\t\r\nmodes <- function(x) {\r\n ux <- unique(x)\r\n tab <- tabulate(match(x, ux))\r\n ux[tab == max(tab)]\r\n}\r\n\r\n#' Capitalise First Letter Function\r\n#'\r\n#' Function to capitalise the first letter of a character string.\r\n#' From https://stackoverflow.com/questions/18509527/first-letter-to-upper-case/18509816\r\n#'\r\n#' @keywords capitalise letter character string\r\n#' @export\r\n#' @examples\r\n#' firstup(\"rStudio\")\r\n#' \r\nfirstup <- function(x) {\r\n substr(x, 1, 1) <- toupper(substr(x, 1, 1))\r\nx\r\n}\r\n\r\n#' Shapiro-Wilk Test for Data Frames\r\n#'\r\n#' Function to perform Shapiro-Wilk test for each column of a Data Frame.\r\n#' Can provide interpretation for p-value with \"interpret=TRUE\".\r\n#' Modified from https://stackoverflow.com/questions/45226457/r-loop-performing-ks-tests-across-data-frame-stored-in-matrix\r\n#'\r\n#' @keywords Shapiro Shapiro-Wilk data.frame normality\r\n#' @export\r\n#' @examples\r\n#' colShapiro(dataframe, interpret=TRUE)\r\n#' \r\ncolShapiro <- function(x, print=FALSE, interpret=FALSE) {\t\r\n\tres <- sapply(x, function(y) {\r\n\t shapiro <- shapiro.test(y)\r\n\tif (interpret) {\r\n\t\tc(statistic=shapiro$statistic, p.value=shapiro$p.value, normal=shapiro$p.value>0.05)\r\n\t\tsetNames(c(shapiro$statistic, shapiro$p.value, normal=shapiro$p.value>0.05), c(\"statistic\", \"p.value\", \"normal\"))\r\n\t}\r\n\telse {\r\n\t\tc(statistic=shapiro$statistic, p.value=shapiro$p.value)\r\n\t\tsetNames(c(shapiro$statistic, shapiro$p.value), c(\"statistic\", \"p.value\"))\r\n\t}\r\n\t})\r\n\t\r\n\tif (print) {\r\n\t\tprint(\"Shapiro-Wilk Test\")\r\n\t}\r\n\treturn(res)\r\n}\r\n\r\n#' Kolmogorov-Smirnov Normal Distribution Test for Data Frames\r\n#'\r\n#' Function to perform Kolmogorov-Smirnov test for normal distribution \r\n#' on each column of a Data Frame.\r\n#' Can provide interpretation for p-value with \"interpret=TRUE\".\r\n#' Modified from https://stackoverflow.com/questions/45226457/r-loop-performing-ks-tests-across-data-frame-stored-in-matrix\r\n#'\r\n#' @keywords Kolmogorov-Smirnov data.frame normalty KS\r\n#' @export\r\n#' @examples\r\n#' colKS(dataframe, interpret=TRUE)\r\n#' \r\ncolKS <- function(x, print = FALSE, interpret=FALSE) {\r\n\tres <- sapply(x, function(y) {\r\n\tks <- ks.test(y, pnorm, mean(y), sd(y))\r\n\tif (interpret) {\r\n\t\tc(statistic=ks$statistic, p.value=ks$p.value, normal=ks$p.value>0.05)\r\n\t\tsetNames(c(ks$statistic, ks$p.value, normal=ks$p.value>0.05), c(\"statistic\", \"p.value\", \"normal\"))\r\n\t}\r\n\telse {\r\n\t\tc(statistic=ks$statistic, p.value=ks$p.value)\r\n\t\tsetNames(c(ks$statistic, ks$p.value), c(\"statistic\", \"p.value\"))\r\n\t}\r\n\t})\r\n\r\n\tif (print) {\r\n\t\tprint(\"Kolmogorov-Smirnov Test\")\r\n\t}\r\n\treturn(res)\r\n}\r\n\r\n#' Standard Deviation for Data Frames\r\n#'\r\n#' Function to calculate Standard Deviation for each column of a Data Frame.\r\n#' Modified from https://stackoverflow.com/questions/37806387/r-calculate-standard-deviation-in-cols-in-a-data-frame-despite-of-na-values\r\n#'\r\n#' @keywords Standard Deviation std sd stdev data.frame\r\n#' @export\r\n#' @examples\r\n#' colSD(dataframe)\r\n#' \r\n# data frame sd\r\ncolSD <- function(x, print=FALSE) {\r\n\tif (print) {\r\n\t\tprint(\"Standard Deviation\")\r\n\t}\r\n\tsapply(x, sd, na.rm = TRUE)\r\n}\r\n\r\n#' Median for Data Frames\r\n#'\r\n#' Function to calculate Median for each column of a Data Frame.\r\n#' Modified from https://stackoverflow.com/questions/37806387/r-calculate-standard-deviation-in-cols-in-a-data-frame-despite-of-na-values\r\n#'\r\n#' @keywords Median data.frame\r\n#' @export\r\n#' @examples\r\n#' colMedian(dataframe)\r\n#' \r\n# data frame sd\r\ncolMedian <- function(x, print=FALSE) {\r\n\tif (print) {\r\n\t\tprint(\"Median\")\r\n\t}\r\n\tsapply(x, median, na.rm = TRUE)\r\n}\r\n", "meta": {"hexsha": "cde54112cdd23799296c42fa1c6988fba10153ae", "size": 5009, "ext": "r", "lang": "R", "max_stars_repo_path": "R/domtools.r", "max_stars_repo_name": "domdfcoding/domtools", "max_stars_repo_head_hexsha": "f37181a7793a8be7a15aa615c6babf60dc1ddf34", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/domtools.r", "max_issues_repo_name": "domdfcoding/domtools", "max_issues_repo_head_hexsha": "f37181a7793a8be7a15aa615c6babf60dc1ddf34", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/domtools.r", "max_forks_repo_name": "domdfcoding/domtools", "max_forks_repo_head_hexsha": "f37181a7793a8be7a15aa615c6babf60dc1ddf34", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0756756757, "max_line_length": 138, "alphanum_fraction": 0.6673986824, "num_tokens": 1351, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2538610069692489, "lm_q2_score": 0.040845712261275036, "lm_q1q2_score": 0.010369133645023476}}
{"text": "# Copyright © 2013-2015 Université catholique de Louvain, Belgium - UCL\n# All rights reserved.\n#\n# This file is part of the precall package.\n#\n# The precall package has been developed by Adrien Dessy [Machine Learning\n# Group (MLG) - Institute of Information and Communication Technologies,\n# Electronics and Applied Mathematics (ICTEAM)] for the Université catholique de\n# Louvain (UCL). The precall package enables to plot precision-recall curves and\n# to compute the Area Under Precision-Recall curves.\n#\n# The precall package is distributed under the terms of the MIT License (MIT).\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is furnished\n# to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n\n#' @importFrom magrittr \"%>%\"\nNULL\n\n\n#' Plot precision-recall curves\n#'\n#' \\code{plot_precall} creates a ggplot object that represents precision-recall\n#' curves corresponding to several rankings.\n#'\n#' @param scores A matrix or vector of numeric values that specifies the rankings\n#' @param labels A vector of logical values that encodes the POSITIVE/NEGATIVE status of data\n#' @param print_friendly A boolean\n#' @param use_color A boolean\n#' @param use_linetype A boolean\n#' @param axis_label_size A numeric\n#' @param legend_title_size A numeric\n#' @param legend_text_size A numeric\n#' @param linewidth A numeric\n#'\n#' @details\n#'\n#' The \\code{labels} encode the POSITIVE/NEGATIVE (\\code{TRUE}/\\code{FALSE}) status of data\n#' while each column of \\code{scores} provides scores corresponding to a ranking of those data:\n#' a smaller score means a smaller rank.\n#'\n#' A precision-recall curve is plotted for each column of the \\code{scores}. The interpolation\n#' between points is done according to the principle of partial acceptance.\n#'\n#' \\code{print_friendly} enables to produce print-friendly plots.\n#' If set to \\code{TRUE}, the function yields a black and white plot (background and lines)\n#' with a different line type for each curve.\n#' \\code{use_color} and \\code{use_linetype}, if present, specifies if the curved should be colored or\n#' drawn with different line types.\n#' These parameters override the behavior specified by \\code{print_friendly}.\n#'\n#' \\code{axis_label_size}, \\code{legend_title_size}, \\code{legend_text_size} and \\code{linewidth} are\n#' self-explanatory parameters to tune a few aesthetics.\n#' Besides, the function returns a ggplot object which can be modified a posteriori using the ggplot\n#' interface.\n#'\n#' @return A \\code{ggplot} object representing precision-recall curves.\n#' @examples\n#'\n#' labels = c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE) # 8 pieces of data\n#'\n#' ranking1 = c(1,1,8,3,7,6,4,5)\n#' ranking2 = c(4,6,3,3,2,5,6,6)\n#' ranking3 = c(6,-1,3,3,2,4,1,5)\n#'\n#' scores = cbind(ranking1, ranking2, ranking3)\n#' colnames(scores) = c(\"method a\", \"method b\", \"method c\")\n#'\n#' # Default plot\n#' plot_precall(labels, scores)\n#'\n#' # Black and white plot\n#' plot_precall(labels, scores, print_friendly=TRUE)\n#'\n#' # Print-friendly background with colored curves\n#' plot_precall(labels, scores, print_friendly=TRUE, use_color=TRUE, use_linetype=FALSE)\n#'\n#' @export\nplot_precall <- function(labels, scores, print_friendly = FALSE, use_color, use_linetype,\n axis_label_size=15, legend_title_size=13, legend_text_size=10,\n linewidth=0.7)\n{\n\n if(missing(scores)) scores = matrix(seq_along(labels))\n if(missing(use_color)) use_color = !print_friendly\n if(missing(use_linetype)) use_linetype = print_friendly\n\n if(scores %>% is.vector) scores = matrix(scores)\n check_arguments(labels, scores)\n\n # Each column corresponds to the ranking of a method.\n meth_names = paste(\"ranking\", seq_len(ncol(scores)))\n col_names = colnames(scores)\n if(!is.null(col_names)){\n meth_names[col_names != \"\"] = col_names[col_names != \"\"]\n }\n\n coord_matrices = plyr::alply(.data=scores, .margins=2,\n .fun=precall_interpolation,\n labels=labels)\n names(coord_matrices) = meth_names\n coord_matrices = lapply(meth_names, FUN=function(n) cbind(coord_matrices[[n]],n))\n coord_matrix = do.call(rbind, coord_matrices)\n coord_matrix = data.frame(coord_matrix)\n colnames(coord_matrix) = c(\"precision\", \"recall\", \"Method\")\n\n # Creating the PR-curve\n aesthetics = ggplot2::aes(x=recall, y=precision, color=Method, linetype=Method)\n if(!use_color) aesthetics$colour = NULL\n if(!use_linetype) aesthetics$linetype = NULL\n\n plot = ggplot2::ggplot(coord_matrix, aesthetics)\n plot = plot + ggplot2::geom_line(size=linewidth)\n\n plot = plot + ggplot2::scale_x_continuous(limits = c(0,1), name=\"Recall\")\n plot = plot + ggplot2::scale_y_continuous(limits = c(0,1), name=\"Precision\")\n if(print_friendly) plot = plot + ggplot2::theme_bw()\n plot = plot + ggplot2::theme(axis.title = ggplot2::element_text(size=axis_label_size, face=\"bold\"))\n plot = plot + ggplot2::theme(legend.title = ggplot2::element_text(size=legend_title_size, face=\"bold\"))\n plot = plot + ggplot2::theme(legend.text = ggplot2::element_text(size=legend_text_size))\n\n return(plot)\n}\n\n\n#' Computes the point coordinates of a precision-recall curve\n#'\n#' @param scores A vector of numeric values\n#' @param labels A vector of logical values\n#' @param nb_interpolation_points A numeric\nprecall_interpolation <- function(labels, scores, nb_interpolation_points=30)\n{\n unique_scores <- sort(unique(scores), decreasing = FALSE)\n\n if(length(unique_scores)==1) {\n precision = sum(labels)/length(labels)\n precisions = c(precision, precision)\n recalls = c(0,1)\n return(data.frame(precision = precisions, recall = recalls))\n }\n\n cumsum_num = sapply(unique_scores, function(s) sum(scores[ labels] <= s))\n cumsum_denom = sapply(unique_scores, function(s) sum(scores <= s))\n\n\n interp_num = sapply(seq_len(length(cumsum_num) - 1), function(i) {\n seq(cumsum_num[i], cumsum_num[i + 1], length = nb_interpolation_points)\n })\n interp_denom = sapply(seq_len(length(cumsum_num) - 1), function(i) {\n seq(cumsum_denom[i], cumsum_denom[i + 1], length = nb_interpolation_points)\n })\n\n dim(interp_num) = NULL\n dim(interp_denom) = NULL\n\n precisions = interp_num/interp_denom\n recalls = interp_num/sum(labels)\n precisions = c(precisions[1], precisions)\n recalls = c(0, recalls)\n\n drop = sapply(2:(length(recalls)-1), function(i) recalls[i-1]==recalls[i+1])\n drop = c(FALSE, drop, FALSE)\n\n recalls = recalls[!drop]\n precisions = precisions[!drop]\n\n return(data.frame(precision = precisions, recall = recalls))\n}\n\n\n#' Compute the AUPR for precision-recall curves.\n#'\n#' Compute the Area Under the Precision-Recall curve (AUPR) for one or multiple precision-recall curves.\n#'\n#' @param scores A matrix or vector of numeric values that specifies the rankings\n#' @param labels A vector of logical values that encodes the POSITIVE/NEGATIVE status of data\n#'\n#' @return The vector of AUPR's.\n#'\n#' @details\n#'\n#' The \\code{labels} encode the POSITIVE/NEGATIVE (\\code{TRUE}/\\code{FALSE}) status of data\n#' while each column of \\code{scores} provides scores corresponding to a ranking of those data:\n#' a smaller score means a smaller rank.\n#' A vector can be used as \\code{scores} instead of a 1-column matrix to compute the AUPR of\n#' only one ranking. If no \\code{scores} are provided, it is assumed that the labels are already\n#' sorted and the function computes the corresponding AUPR.\n#'\n#' If \\code{scores} is a k-column matrix, the function returns a vector of length k, otherwise it\n#' returns a scalar.\n#'\n#' The area is computed from precision-recall curves interpolated using the principle of partial\n#' acceptance (see vignette \\code{precall} for more details).\n#'\n#' @examples\n#'\n#' labels = c(TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE) # 8 pieces of data\n#'\n#' ranking1 = c(1,1,8,3,7,6,4,5)\n#' ranking2 = c(4,6,3,3,2,5,6,6)\n#' ranking3 = c(6,-1,3,3,2,4,1,5)\n#'\n#' scores = cbind(ranking1, ranking2, ranking3)\n#' colnames(scores) = c(\"method a\", \"method b\", \"method c\")\n#'\n#' # Implicit ranking\n#' aupr(labels)\n#' aupr(labels) == aupr(labels, seq_along(labels)) # is TRUE\n#'\n#' # Explicit single ranking\n#' aupr(labels, ranking1)\n#'\n#' # Explicit multiple rankings\n#' aupr(labels, scores)\n#'\n#' @export\naupr <- function(labels, scores){\n\n if(missing(scores)) scores = matrix(seq_along(labels))\n if(scores %>% is.vector) scores = matrix(scores)\n check_arguments(labels, scores)\n\n apply(scores, 2, function(s) {compute_aupr(labels,s)})\n}\n\n\ncompute_aupr <- function(labels, scores){\n\n unique_scores <- sort(unique(scores), decreasing = FALSE)\n total_positives <- sum(labels)\n\n true_positives <- sapply(unique_scores, function(s) sum(scores[labels] <= s))\n positives <- sapply(unique_scores, function(s) sum(scores <= s))\n\n\n if(true_positives[1] != 0){\n area = true_positives[1] / total_positives\n area = area * true_positives[1] / positives[1]\n } else {\n area = 0\n }\n\n len = length(unique_scores) - 1\n for(i in seq_len(len)){\n\n tp1 = true_positives[i]\n tp2 = true_positives[i+1]\n\n if(tp1 == tp2) next\n\n p1 = positives[i]\n p2 = positives[i+1]\n\n area = area + .aupr_piece(tp1, tp2, p1, p2, total_positives)\n }\n\n return(area)\n}\n\n\n#' Computes a partial AUPR\n#'\n#' Computes a partial AUPR (area under precision-recall curve)\n#'\n#' @param p Total number of positives\n#' @param p1 Number of positives at position 1\n#' @param p2 Number of positives at position 2\n#' @param tp1 Number of true positives at position 1\n#' @param tp2 Number of true positives at position 2\n#'\n#' @return Return the aupr between position 1 and position 2.\n.aupr_piece <- function(tp1, tp2, p1, p2, p){\n area <- log(p2/p1) * (tp1 * p2 - tp2 * p1) / (p1 - p2)^2\n area <- area + (tp1 - tp2)/(p1 - p2)\n area <- area * (tp2-tp1)/p\n\n return(area)\n}\n\n\n# Check the validity of scores and labels arguments.\ncheck_arguments <- function(labels, scores) {\n assertthat::assert_that(scores %>% is.numeric, msg=\"scores must be a numeric matrix or vector\")\n assertthat::assert_that(scores %>% is.matrix, msg=\"scores must be a numeric matrix or vector\")\n assertthat::assert_that(labels %>% is.logical, msg=\"labels must be a vector of logical values\")\n assertthat::assert_that(labels %>% is.vector, msg=\"labels must be a vector of logical values\")\n\n assertthat::assert_that(length(labels) == nrow(scores),\n msg=\"the number of rows of scores must be equal to the length of labels.\")\n}\n", "meta": {"hexsha": "d91fca3d007cb63466b205a43abd67acc5043992", "size": 11448, "ext": "r", "lang": "R", "max_stars_repo_path": "R/precall.r", "max_stars_repo_name": "adessy/precall", "max_stars_repo_head_hexsha": "09f0053ab54e01032816ce5c088132264430bed6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2015-06-10T11:29:28.000Z", "max_stars_repo_stars_event_max_datetime": "2015-06-10T11:29:28.000Z", "max_issues_repo_path": "R/precall.r", "max_issues_repo_name": "adessy/precall", "max_issues_repo_head_hexsha": "09f0053ab54e01032816ce5c088132264430bed6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/precall.r", "max_forks_repo_name": "adessy/precall", "max_forks_repo_head_hexsha": "09f0053ab54e01032816ce5c088132264430bed6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.2899022801, "max_line_length": 105, "alphanum_fraction": 0.7110412299, "num_tokens": 3061, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "\n\n\n\n\n\n\n C\bCH\bHA\bAP\bPT\bTE\bER\bR 2\b2\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs\n\n\n\n\n The following functions allow one to create and manipu-\n late the various types of lisp data structures. Refer to\n S1.2 for details of the data structures known to FRANZ LISP.\n\n\n\n 2\b2.\b.1\b1.\b. L\bLi\bis\bst\bts\bs\n\n The following functions exist for the creation\n and manipulating of lists. Lists are composed of a\n linked list of objects called either 'list cells',\n 'cons cells' or 'dtpr cells'. Lists are normally ter-\n minated with the special symbol n\bni\bil\bl. n\bni\bil\bl is both a\n symbol and a representation for the empty list ().\n\n\n\n 2\b2.\b.1\b1.\b.1\b1.\b. l\bli\bis\bst\bt c\bcr\bre\bea\bat\bti\bio\bon\bn\n\n (\b(c\bco\bon\bns\bs 'g_arg1 'g_arg2)\b)\n\n RETURNS: a new list cell whose car is g_arg1 and whose\n cdr is g_arg2.\n\n (\b(x\bxc\bco\bon\bns\bs 'g_arg1 'g_arg2)\b)\n\n EQUIVALENT TO: _\b(_\bc_\bo_\bn_\bs _\b'_\bg_\b__\ba_\br_\bg_\b2 _\b'_\bg_\b__\ba_\br_\bg_\b1_\b)\n\n (\b(n\bnc\bco\bon\bns\bs 'g_arg)\b)\n\n EQUIVALENT TO: _\b(_\bc_\bo_\bn_\bs _\b'_\bg_\b__\ba_\br_\bg _\bn_\bi_\bl_\b)\n\n (\b(l\bli\bis\bst\bt ['g_arg1 ... ])\b)\n\n RETURNS: a list whose elements are the g_arg_\bi.\n\n\n\n\n\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b1\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b2\n\n\n (\b(a\bap\bpp\bpe\ben\bnd\bd 'l_arg1 'l_arg2)\b)\n\n RETURNS: a list containing the elements of l_arg1 fol-\n lowed by l_arg2.\n\n NOTE: To generate the result, the top level list cells\n of l_arg1 are duplicated and the cdr of the last\n list cell is set to point to l_arg2. Thus this\n is an expensive operation if l_arg1 is large.\n See the descriptions of _\bn_\bc_\bo_\bn_\bc and _\bt_\bc_\bo_\bn_\bc for\n cheaper ways of doing the _\ba_\bp_\bp_\be_\bn_\bd if the list\n l_arg1 can be altered.\n\n (\b(a\bap\bpp\bpe\ben\bnd\bd1\b1 'l_arg1 'g_arg2)\b)\n\n RETURNS: a list like l_arg1 with g_arg2 as the last\n element.\n\n NOTE: this is equivalent to (append 'l_arg1 (list\n 'g_arg2)).\n\n\n ____________________________________________________\n\n ; A common mistake is using append to add one element to the end of a list\n -> _\b(_\ba_\bp_\bp_\be_\bn_\bd _\b'_\b(_\ba _\bb _\bc _\bd_\b) _\b'_\be_\b)\n (a b c d . e)\n ; The user intended to say:\n -> _\b(_\ba_\bp_\bp_\be_\bn_\bd _\b'_\b(_\ba _\bb _\bc _\bd_\b) _\b'_\b(_\be_\b)_\b)\n _\b(_\ba _\bb _\bc _\bd _\be_\b)\n _\b; _\bb_\be_\bt_\bt_\be_\br _\bi_\bs _\ba_\bp_\bp_\be_\bn_\bd_\b1\n _\b-_\b> _\b(_\ba_\bp_\bp_\be_\bn_\bd_\b1 _\b'_\b(_\ba _\bb _\bc _\bd_\b) _\b'_\be_\b)\n _\b(_\ba _\bb _\bc _\bd _\be_\b)\n _\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b_\n\n\n\n\n (\b(q\bqu\buo\bot\bte\be!\b! [g_qform_\bi] ...[! 'g_eform_\bi] ... [!! 'l_form_\bi] ...)\b)\n\n RETURNS: The list resulting from the splicing and\n insertion process described below.\n\n NOTE: _\bq_\bu_\bo_\bt_\be_\b! is the complement of the _\bl_\bi_\bs_\bt function.\n _\bl_\bi_\bs_\bt forms a list by evaluating each for in the\n argument list; evaluation is suppressed if the\n form is _\bq_\bu_\bo_\bt_\beed. In _\bq_\bu_\bo_\bt_\be_\b!_\b, each form is implic-\n itly _\bq_\bu_\bo_\bt_\beed. To be evaluated, a form must be\n preceded by one of the evaluate operations ! and\n !!. ! g_eform evaluates g_form and the value is\n inserted in the place of the call; !! l_form\n evaluates l_form and the value is spliced into\n the place of the call.\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b3\n\n\n `Splicing in' means that the parentheses sur-\n rounding the list are removed as the example\n below shows. Use of the evaluate operators can\n occur at any level in a form argument.\n\n Another way to get the effect of the _\bq_\bu_\bo_\bt_\be_\b! func-\n tion is to use the backquote character macro (see\n S 8.3.3).\n\n\n ____________________________________________________\n\n _\b(_\bq_\bu_\bo_\bt_\be_\b! _\bc_\bo_\bn_\bs _\b! _\b(_\bc_\bo_\bn_\bs _\b1 _\b2_\b) _\b3_\b) _\b= _\b(_\bc_\bo_\bn_\bs _\b(_\b1 _\b. _\b2_\b) _\b3_\b)\n _\b(_\bq_\bu_\bo_\bt_\be_\b! _\b1 _\b!_\b! _\b(_\bl_\bi_\bs_\bt _\b2 _\b3 _\b4_\b) _\b5_\b) _\b= _\b(_\b1 _\b2 _\b3 _\b4 _\b5_\b)\n _\b(_\bs_\be_\bt_\bq _\bq_\bu_\bo_\bt_\be_\bd _\b'_\be_\bv_\ba_\bl_\be_\bd_\b)_\b(_\bq_\bu_\bo_\bt_\be_\b! _\b! _\b(_\b(_\bI _\ba_\bm _\b! _\bq_\bu_\bo_\bt_\be_\bd_\b)_\b)_\b) _\b= _\b(_\b(_\bI _\ba_\bm _\be_\bv_\ba_\bl_\be_\bd_\b)_\b)\n _\b(_\bq_\bu_\bo_\bt_\be_\b! _\bt_\br_\by _\b! _\b'_\b(_\bt_\bh_\bi_\bs _\b! _\bo_\bn_\be_\b)_\b) _\b= _\b(_\bt_\br_\by _\b(_\bt_\bh_\bi_\bs _\b! _\bo_\bn_\be_\b)_\b)\n ____________________________________________________\n\n\n\n\n\n (\b(b\bbi\big\bgn\bnu\bum\bm-\b-t\bto\bo-\b-l\bli\bis\bst\bt 'b_arg)\b)\n\n RETURNS: A list of the fixnums which are used to repre-\n sent the bignum.\n\n NOTE: the inverse of this function is _\bl_\bi_\bs_\bt_\b-_\bt_\bo_\b-_\bb_\bi_\bg_\bn_\bu_\bm_\b.\n\n (\b(l\bli\bis\bst\bt-\b-t\bto\bo-\b-b\bbi\big\bgn\bnu\bum\bm 'l_ints)\b)\n\n WHERE: l_ints is a list of fixnums.\n\n RETURNS: a bignum constructed of the given fixnums.\n\n NOTE: the inverse of this function is _\bb_\bi_\bg_\bn_\bu_\bm_\b-_\bt_\bo_\b-_\bl_\bi_\bs_\bt_\b.\n\n\n\n\n 2\b2.\b.1\b1.\b.2\b2.\b. l\bli\bis\bst\bt p\bpr\bre\bed\bdi\bic\bca\bat\bte\bes\bs\n\n (\b(d\bdt\btp\bpr\br 'g_arg)\b)\n\n RETURNS: t iff g_arg is a list cell.\n\n NOTE: that (dtpr '()) is nil. The name d\bdt\btp\bpr\br is a con-\n traction for ``dotted pair''.\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-4\b4\n\n\n (\b(l\bli\bis\bst\btp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a list object or nil.\n\n (\b(t\bta\bai\bil\blp\bp 'l_x 'l_y)\b)\n\n RETURNS: l_x, if a list cell _\be_\bq to l_x is found by\n _\bc_\bd_\bring down l_y zero or more times, nil other-\n wise.\n\n\n ____________________________________________________\n\n -> _\b(_\bs_\be_\bt_\bq _\bx _\b'_\b(_\ba _\bb _\bc _\bd_\b) _\by _\b(_\bc_\bd_\bd_\br _\bx_\b)_\b)\n (c d)\n -> _\b(_\ba_\bn_\bd _\b(_\bd_\bt_\bp_\br _\bx_\b) _\b(_\bl_\bi_\bs_\bt_\bp _\bx_\b)_\b) ; x and y are dtprs and lists\n t\n -> _\b(_\bd_\bt_\bp_\br _\b'_\b(_\b)_\b) ; () is the same as nil and is not a dtpr\n nil\n -> _\b(_\bl_\bi_\bs_\bt_\bp _\b'_\b(_\b)_\b) ; however it is a list\n t\n -> _\b(_\bt_\ba_\bi_\bl_\bp _\by _\bx_\b)\n (c d)\n ____________________________________________________\n\n\n\n\n (\b(l\ble\ben\bng\bgt\bth\bh 'l_arg)\b)\n\n RETURNS: the number of elements in the top level of\n list l_arg.\n\n\n\n 2\b2.\b.1\b1.\b.3\b3.\b. l\bli\bis\bst\bt a\bac\bcc\bce\bes\bss\bsi\bin\bng\bg\n\n (\b(c\bca\bar\br 'l_arg)\b)\n (\b(c\bcd\bdr\br 'l_arg)\b)\n\n RETURNS: _\bc_\bo_\bn_\bs cell. (_\bc_\ba_\br (_\bc_\bo_\bn_\bs x y)) is always x, (_\bc_\bd_\br\n (_\bc_\bo_\bn_\bs x y)) is always y. In FRANZ LISP, the\n cdr portion is located first in memory. This\n is hardly noticeable, and we mention it pri-\n marily as a curiosity.\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-5\b5\n\n\n (\b(c\bc.\b..\b.r\br 'lh_arg)\b)\n\n WHERE: the .. represents any positive number of a\ba's\n and d\bd's.\n\n RETURNS: the result of accessing the list structure in\n the way determined by the function name. The\n a\ba's and d\bd's are read from right to left, a d\bd\n directing the access down the cdr part of the\n list cell and an a\ba down the car part.\n\n NOTE: lh_arg may also be nil, and it is guaranteed that\n the car and cdr of nil is nil. If lh_arg is a\n hunk, then _\b(_\bc_\ba_\br _\b'_\bl_\bh_\b__\ba_\br_\bg_\b) is the same as\n _\b(_\bc_\bx_\br _\b1 _\b'_\bl_\bh_\b__\ba_\br_\bg_\b) and _\b(_\bc_\bd_\br _\b'_\bl_\bh_\b__\ba_\br_\bg_\b) is the same as\n _\b(_\bc_\bx_\br _\b0 _\b'_\bl_\bh_\b__\ba_\br_\bg_\b).\n It is generally hard to read and understand the\n context of functions with large strings of a\ba's\n and d\bd's, but these functions are supported by\n rapid accessing and open-compiling (see Chapter\n 12).\n\n (\b(n\bnt\bth\bh 'x_index 'l_list)\b)\n\n RETURNS: the nth element of l_list, assuming zero-based\n index. Thus (nth 0 l_list) is the same as\n (car l_list). _\bn_\bt_\bh is both a function, and a\n compiler macro, so that more efficient code\n might be generated than for _\bn_\bt_\bh_\be_\bl_\be_\bm (described\n below).\n\n NOTE: If x_arg1 is non-positive or greater than the\n length of the list, nil is returned.\n\n (\b(n\bnt\bth\bhc\bcd\bdr\br 'x_index 'l_list)\b)\n\n RETURNS: the result of _\bc_\bd_\bring down the list l_list\n x_index times.\n\n NOTE: If x_index is less than 0, then\n _\b(_\bc_\bo_\bn_\bs _\bn_\bi_\bl _\b'_\bl_\b__\bl_\bi_\bs_\bt_\b) is returned.\n\n (\b(n\bnt\bth\bhe\bel\ble\bem\bm 'x_arg1 'l_arg2)\b)\n\n RETURNS: The x_arg1'_\bs_\bt element of the list l_arg2.\n\n NOTE: This function comes from the PDP-11 Lisp system.\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-6\b6\n\n\n (\b(l\bla\bas\bst\bt 'l_arg)\b)\n\n RETURNS: the last list cell in the list l_arg.\n\n EXAMPLE: _\bl_\ba_\bs_\bt does NOT return the last element of a\n list!\n _\b(_\bl_\ba_\bs_\bt _\b'_\b(_\ba _\bb_\b)_\b) = (b)\n\n (\b(l\bld\bdi\bif\bff\bf 'l_x 'l_y)\b)\n\n RETURNS: a list of all elements in l_x but not in l_y\n , i.e., the list difference of l_x and l_y.\n\n NOTE: l_y must be a tail of l_x, i.e., _\be_\bq to the result\n of applying some number of _\bc_\bd_\br's to l_x. Note\n that the value of _\bl_\bd_\bi_\bf_\bf is always new\n list structure unless l_y is nil, in which case\n _\b(_\bl_\bd_\bi_\bf_\bf _\bl_\b__\bx _\bn_\bi_\bl_\b) is l_x itself. If l_y is not\n a tail of l_x, _\bl_\bd_\bi_\bf_\bf generates an error.\n\n EXAMPLE: _\b(_\bl_\bd_\bi_\bf_\bf _\b'_\bl_\b__\bx _\b(_\bm_\be_\bm_\bb_\be_\br _\b'_\bg_\b__\bf_\bo_\bo _\b'_\bl_\b__\bx_\b)_\b) gives all\n elements in l_x up to the first g_foo.\n\n\n\n 2\b2.\b.1\b1.\b.4\b4.\b. l\bli\bis\bst\bt m\bma\ban\bni\bip\bpu\bul\bla\bat\bti\bio\bon\bn\n\n (\b(r\brp\bpl\bla\bac\bca\ba 'lh_arg1 'g_arg2)\b)\n\n RETURNS: the modified lh_arg1.\n\n SIDE EFFECT: the car of lh_arg1 is set to g_arg2. If\n lh_arg1 is a hunk then the second element\n of the hunk is set to g_arg2.\n\n (\b(r\brp\bpl\bla\bac\bcd\bd 'lh_arg1 'g_arg2)\b)\n\n RETURNS: the modified lh_arg1.\n\n SIDE EFFECT: the cdr of lh_arg2 is set to g_arg2. If\n lh_arg1 is a hunk then the first element\n of the hunk is set to g_arg2.\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-7\b7\n\n\n (\b(a\bat\btt\bta\bac\bch\bh 'g_x 'l_l)\b)\n\n RETURNS: l_l whose _\bc_\ba_\br is now g_x, whose _\bc_\ba_\bd_\br is the\n original _\b(_\bc_\ba_\br _\bl_\b__\bl_\b), and whose _\bc_\bd_\bd_\br is the\n original _\b(_\bc_\bd_\br _\bl_\b__\bl_\b).\n\n NOTE: what happens is that g_x is added to the begin-\n ning of list l_l yet maintaining the same list\n cell at the beginning of the list.\n\n (\b(d\bde\bel\ble\bet\bte\be 'g_val 'l_list ['x_count])\b)\n\n RETURNS: the result of splicing g_val from the top\n level of l_list no more than x_count times.\n\n NOTE: x_count defaults to a very large number, thus if\n x_count is not given, all occurrences of g_val\n are removed from the top level of l_list. g_val\n is compared with successive _\bc_\ba_\br's of l_list using\n the function _\be_\bq_\bu_\ba_\bl.\n\n SIDE EFFECT: l_list is modified using rplacd, no new\n list cells are used.\n\n (\b(d\bde\bel\blq\bq 'g_val 'l_list ['x_count])\b)\n (\b(d\bdr\bre\bem\bmo\bov\bve\be 'g_val 'l_list ['x_count])\b)\n\n RETURNS: the result of splicing g_val from the top\n level of l_list no more than x_count times.\n\n NOTE: _\bd_\be_\bl_\bq (and _\bd_\br_\be_\bm_\bo_\bv_\be) are the same as _\bd_\be_\bl_\be_\bt_\be except\n that _\be_\bq is used for comparison instead of _\be_\bq_\bu_\ba_\bl.\n\n\n ____________________________________________________\n\n ; note that you should use the value returned by _\bd_\be_\bl_\be_\bt_\be or _\bd_\be_\bl_\bq\n ; and not assume that g_val will always show the deletions.\n ; For example\n\n -> _\b(_\bs_\be_\bt_\bq _\bt_\be_\bs_\bt _\b'_\b(_\ba _\bb _\bc _\ba _\bd _\be_\b)_\b)\n (a b c a d e)\n -> _\b(_\bd_\be_\bl_\be_\bt_\be _\b'_\ba _\bt_\be_\bs_\bt_\b)\n (b c d e) ; the value returned is what we would expect\n -> _\bt_\be_\bs_\bt\n (a b c d e) ; but test still has the first a in the list!\n ____________________________________________________\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-8\b8\n\n\n (\b(r\bre\bem\bmq\bq 'g_x 'l_l ['x_count])\b)\n (\b(r\bre\bem\bmo\bov\bve\be 'g_x 'l_l)\b)\n\n RETURNS: a _\bc_\bo_\bp_\by of l_l with all top level elements\n _\be_\bq_\bu_\ba_\bl to g_x removed. _\br_\be_\bm_\bq uses _\be_\bq instead of\n _\be_\bq_\bu_\ba_\bl for comparisons.\n\n NOTE: remove does not modify its arguments like _\bd_\be_\bl_\be_\bt_\be,\n and _\bd_\be_\bl_\bq do.\n\n (\b(i\bin\bns\bse\ber\brt\bt 'g_object 'l_list 'u_comparefn 'g_nodups)\b)\n\n RETURNS: a list consisting of l_list with g_object\n destructively inserted in a place determined\n by the ordering function u_comparefn.\n\n NOTE: _\b(_\bc_\bo_\bm_\bp_\ba_\br_\be_\bf_\bn _\b'_\bg_\b__\bx _\b'_\bg_\b__\by_\b) should return something\n non-nil if g_x can precede g_y in sorted order,\n nil if g_y must precede g_x. If u_comparefn is\n nil, alphabetical order will be used. If\n g_nodups is non-nil, an element will not be\n inserted if an equal element is already in the\n list. _\bi_\bn_\bs_\be_\br_\bt does binary search to determine\n where to insert the new element.\n\n (\b(m\bme\ber\brg\bge\be 'l_data1 'l_data2 'u_comparefn)\b)\n\n RETURNS: the merged list of the two input sorted lists\n l_data1 and l_data1 using binary comparison\n function u_comparefn.\n\n NOTE: _\b(_\bc_\bo_\bm_\bp_\ba_\br_\be_\bf_\bn _\b'_\bg_\b__\bx _\b'_\bg_\b__\by_\b) should return something\n non-nil if g_x can precede g_y in sorted order,\n nil if g_y must precede g_x. If u_comparefn is\n nil, alphabetical order will be used.\n u_comparefn should be thought of as \"less than or\n equal\". _\bm_\be_\br_\bg_\be changes both of its data argu-\n ments.\n\n (\b(s\bsu\bub\bbs\bst\bt 'g_x 'g_y 'l_s)\b)\n (\b(d\bds\bsu\bub\bbs\bst\bt 'g_x 'g_y 'l_s)\b)\n\n RETURNS: the result of substituting g_x for all _\be_\bq_\bu_\ba_\bl\n occurrences of g_y at all levels in l_s.\n\n NOTE: If g_y is a symbol, _\be_\bq will be used for compar-\n isons. The function _\bs_\bu_\bb_\bs_\bt does not modify l_s\n but the function _\bd_\bs_\bu_\bb_\bs_\bt (destructive substitu-\n tion) does.\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-9\b9\n\n\n (\b(l\bls\bsu\bub\bbs\bst\bt 'l_x 'g_y 'l_s)\b)\n\n RETURNS: a copy of l_s with l_x spliced in for every\n occurrence of of g_y at all levels. Splicing\n in means that the parentheses surrounding the\n list l_x are removed as the example below\n shows.\n\n\n ____________________________________________________\n\n -> _\b(_\bs_\bu_\bb_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b) _\b'_\bx _\b'_\b(_\bx _\by _\bz _\b(_\bx _\by _\bz_\b) _\b(_\bx _\by _\bz_\b)_\b)_\b)\n ((a b c) y z ((a b c) y z) ((a b c) y z))\n -> _\b(_\bl_\bs_\bu_\bb_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b) _\b'_\bx _\b'_\b(_\bx _\by _\bz _\b(_\bx _\by _\bz_\b) _\b(_\bx _\by _\bz_\b)_\b)_\b)\n (a b c y z (a b c y z) (a b c y z))\n ____________________________________________________\n\n\n\n\n (\b(s\bsu\bub\bbp\bpa\bai\bir\br 'l_old 'l_new 'l_expr)\b)\n\n WHERE: there are the same number of elements in\n l_old as l_new.\n\n RETURNS: the list l_expr with all occurrences of a\n object in l_old replaced by the corresponding\n one in l_new. When a substitution is made, a\n copy of the value to substitute in is not\n made.\n\n EXAMPLE: _\b(_\bs_\bu_\bb_\bp_\ba_\bi_\br _\b'_\b(_\ba _\bc_\b)_\b' _\b(_\bx _\by_\b) _\b'_\b(_\ba _\bb _\bc _\bd_\b)_\b) _\b= _\b(_\bx _\bb _\by _\bd_\b)\n\n\n (\b(n\bnc\bco\bon\bnc\bc 'l_arg1 'l_arg2 ['l_arg3 ...])\b)\n\n RETURNS: A list consisting of the elements of l_arg1\n followed by the elements of l_arg2 followed by\n l_arg3 and so on.\n\n NOTE: The _\bc_\bd_\br of the last list cell of l_arg_\bi is\n changed to point to l_arg_\bi_\b+_\b1.\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b10\b0\n\n\n\n ____________________________________________________\n\n ; _\bn_\bc_\bo_\bn_\bc is faster than _\ba_\bp_\bp_\be_\bn_\bd because it doesn't allocate new list cells.\n -> _\b(_\bs_\be_\bt_\bq _\bl_\bi_\bs_\b1 _\b'_\b(_\ba _\bb _\bc_\b)_\b)\n (a b c)\n -> _\b(_\bs_\be_\bt_\bq _\bl_\bi_\bs_\b2 _\b'_\b(_\bd _\be _\bf_\b)_\b)\n (d e f)\n -> _\b(_\ba_\bp_\bp_\be_\bn_\bd _\bl_\bi_\bs_\b1 _\bl_\bi_\bs_\b2_\b)\n (a b c d e f)\n -> _\bl_\bi_\bs_\b1\n (a b c) ; note that lis1 has not been changed by _\ba_\bp_\bp_\be_\bn_\bd\n -> _\b(_\bn_\bc_\bo_\bn_\bc _\bl_\bi_\bs_\b1 _\bl_\bi_\bs_\b2_\b)\n (a b c d e f) ; _\bn_\bc_\bo_\bn_\bc returns the same value as _\ba_\bp_\bp_\be_\bn_\bd\n -> _\bl_\bi_\bs_\b1\n (a b c d e f) ; but in doing so alters lis1\n ____________________________________________________\n\n\n\n\n\n (\b(r\bre\bev\bve\ber\brs\bse\be 'l_arg)\b)\n (\b(n\bnr\bre\bev\bve\ber\brs\bse\be 'l_arg)\b)\n\n RETURNS: the list l_arg with the elements at the top\n level in reverse order.\n\n NOTE: The function _\bn_\br_\be_\bv_\be_\br_\bs_\be does the reversal in place,\n that is the list structure is modified.\n\n (\b(n\bnr\bre\bec\bco\bon\bnc\bc 'l_arg 'g_arg)\b)\n\n EQUIVALENT TO: _\b(_\bn_\bc_\bo_\bn_\bc _\b(_\bn_\br_\be_\bv_\be_\br_\bs_\be _\b'_\bl_\b__\ba_\br_\bg_\b) _\b'_\bg_\b__\ba_\br_\bg_\b)\n\n\n\n\n 2\b2.\b.2\b2.\b. P\bPr\bre\bed\bdi\bic\bca\bat\bte\bes\bs\n\n The following functions test for properties of\n data objects. When the result of the test is either\n 'false' or 'true', then n\bni\bil\bl will be returned for\n 'false' and something other than n\bni\bil\bl (often t\bt) will be\n returned for 'true'.\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b11\b1\n\n\n (\b(a\bar\brr\bra\bay\byp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is of type array.\n\n (\b(a\bat\bto\bom\bm 'g_arg)\b)\n\n RETURNS: t iff g_arg is not a list or hunk object.\n\n NOTE: _\b(_\ba_\bt_\bo_\bm _\b'_\b(_\b)_\b) returns t.\n\n (\b(b\bbc\bcd\bdp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a data object of type binary.\n\n NOTE: This function is a throwback to the PDP-11 Lisp\n system. The name stands for binary code predi-\n cate.\n\n (\b(b\bbi\big\bgp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a bignum.\n\n (\b(d\bdt\btp\bpr\br 'g_arg)\b)\n\n RETURNS: t iff g_arg is a list cell.\n\n NOTE: that (dtpr '()) is nil.\n\n (\b(h\bhu\bun\bnk\bkp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a hunk.\n\n (\b(l\bli\bis\bst\btp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a list object or nil.\n\n (\b(s\bst\btr\bri\bin\bng\bgp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a string.\n\n (\b(s\bsy\bym\bmb\bbo\bol\blp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a symbol.\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b12\b2\n\n\n (\b(v\bva\bal\blu\bue\bep\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a value cell\n\n (\b(v\bve\bec\bct\bto\bor\brp\bp 'v_vector)\b)\n\n RETURNS: t\bt iff the argument is a vector.\n\n (\b(v\bve\bec\bct\bto\bor\bri\bip\bp 'v_vector)\b)\n\n RETURNS: t\bt iff the argument is an immediate-vector.\n\n (\b(t\bty\byp\bpe\be 'g_arg)\b)\n (\b(t\bty\byp\bpe\bep\bp 'g_arg)\b)\n\n RETURNS: a symbol whose pname describes the type of\n g_arg.\n\n (\b(s\bsi\big\bgn\bnp\bp s_test 'g_val)\b)\n\n RETURNS: t iff g_val is a number and the given test\n s_test on g_val returns true.\n\n NOTE: The fact that _\bs_\bi_\bg_\bn_\bp simply returns nil if g_val\n is not a number is probably the most important\n reason that _\bs_\bi_\bg_\bn_\bp is used. The permitted values\n for s_test and what they mean are given in this\n table.\n\n +--------------------+\n |s_test tested |\n | |\n +--------------------+\n |l g_val < 0 |\n |le g_val <= 0 |\n |e g_val = 0 |\n |n g_val != 0 |\n |ge g_val >= 0 |\n |g g_val > 0 |\n +--------------------+\n\n (\b(e\beq\bq 'g_arg1 'g_arg2)\b)\n\n RETURNS: t if g_arg1 and g_arg2 are the exact same lisp\n object.\n\n NOTE: _\bE_\bq simply tests if g_arg1 and g_arg2 are located\n in the exact same place in memory. Lisp objects\n which print the same are not necessarily _\be_\bq. The\n only objects guaranteed to be _\be_\bq are interned\n symbols with the same print name. [Unless a sym-\n bol is created in a special way (such as with\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b13\b3\n\n\n _\bu_\bc_\bo_\bn_\bc_\ba_\bt or _\bm_\ba_\bk_\bn_\ba_\bm) it will be interned.]\n\n (\b(n\bne\beq\bq 'g_x 'g_y)\b)\n\n RETURNS: t if g_x is not _\be_\bq to g_y, otherwise nil.\n\n (\b(e\beq\bqu\bua\bal\bl 'g_arg1 'g_arg2)\b)\n (\b(e\beq\bqs\bst\btr\br 'g_arg1 'g_arg2)\b)\n\n RETURNS: t iff g_arg1 and g_arg2 have the same struc-\n ture as described below.\n\n NOTE: g_arg and g_arg2 are _\be_\bq_\bu_\ba_\bl if\n\n (1) they are _\be_\bq.\n\n (2) they are both fixnums with the same value\n\n (3) they are both flonums with the same value\n\n (4) they are both bignums with the same value\n\n (5) they are both strings and are identical.\n\n (6) they are both lists and their cars and cdrs are\n _\be_\bq_\bu_\ba_\bl.\n\n\n ____________________________________________________\n\n ; _\be_\bq is much faster than _\be_\bq_\bu_\ba_\bl, especially in compiled code,\n ; however you cannot use _\be_\bq to test for equality of numbers outside\n ; of the range -1024 to 1023. _\be_\bq_\bu_\ba_\bl will always work.\n -> _\b(_\be_\bq _\b1_\b0_\b2_\b3 _\b1_\b0_\b2_\b3_\b)\n t\n -> _\b(_\be_\bq _\b1_\b0_\b2_\b4 _\b1_\b0_\b2_\b4_\b)\n nil\n -> _\b(_\be_\bq_\bu_\ba_\bl _\b1_\b0_\b2_\b4 _\b1_\b0_\b2_\b4_\b)\n t\n ____________________________________________________\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b14\b4\n\n\n (\b(n\bno\bot\bt 'g_arg)\b)\n (\b(n\bnu\bul\bll\bl 'g_arg)\b)\n\n RETURNS: t iff g_arg is nil.\n\n\n (\b(m\bme\bem\bmb\bbe\ber\br 'g_arg1 'l_arg2)\b)\n (\b(m\bme\bem\bmq\bq 'g_arg1 'l_arg2)\b)\n\n RETURNS: that part of the l_arg2 beginning with the\n first occurrence of g_arg1. If g_arg1 is not\n in the top level of l_arg2, nil is returned.\n\n NOTE: _\bm_\be_\bm_\bb_\be_\br tests for equality with _\be_\bq_\bu_\ba_\bl, _\bm_\be_\bm_\bq tests\n for equality with _\be_\bq.\n\n\n\n\n 2\b2.\b.3\b3.\b. S\bSy\bym\bmb\bbo\bol\bls\bs a\ban\bnd\bd S\bSt\btr\bri\bin\bng\bgs\bs\n\n In many of the following functions the distinc-\n tion between symbols and strings is somewhat blurred.\n To remind ourselves of the difference, a string is a\n null terminated sequence of characters, stored as com-\n pactly as possible. Strings are used as constants in\n FRANZ LISP. They _\be_\bv_\ba_\bl to themselves. A symbol has\n additional structure: a value, property list, function\n binding, as well as its external representation (or\n print-name). If a symbol is given to one of the\n string manipulation functions below, its print name\n will be used as the string.\n\n Another popular way to represent strings in Lisp\n is as a list of fixnums which represent characters.\n The suffix 'n' to a string manipulation function indi-\n cates that it returns a string in this form.\n\n\n\n 2\b2.\b.3\b3.\b.1\b1.\b. s\bsy\bym\bmb\bbo\bol\bl a\ban\bnd\bd s\bst\btr\bri\bin\bng\bg c\bcr\bre\bea\bat\bti\bio\bon\bn\n\n (\b(c\bco\bon\bnc\bca\bat\bt ['stn_arg1 ... ])\b)\n (\b(u\buc\bco\bon\bnc\bca\bat\bt ['stn_arg1 ... ])\b)\n\n RETURNS: a symbol whose print name is the result of\n concatenating the print names, string charac-\n ters or numerical representations of the\n sn_arg_\bi.\n\n NOTE: If no arguments are given, a symbol with a null\n pname is returned. _\bc_\bo_\bn_\bc_\ba_\bt places the symbol cre-\n ated on the oblist, the function _\bu_\bc_\bo_\bn_\bc_\ba_\bt does the\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b15\b5\n\n\n same thing but does not place the new symbol on\n the oblist.\n\n EXAMPLE: _\b(_\bc_\bo_\bn_\bc_\ba_\bt _\b'_\ba_\bb_\bc _\b(_\ba_\bd_\bd _\b3 _\b4_\b) _\b\"_\bd_\be_\bf_\b\"_\b) = abc7def\n\n (\b(c\bco\bon\bnc\bca\bat\btl\bl 'l_arg)\b)\n\n EQUIVALENT TO: _\b(_\ba_\bp_\bp_\bl_\by _\b'_\bc_\bo_\bn_\bc_\ba_\bt _\b'_\bl_\b__\ba_\br_\bg_\b)\n\n\n (\b(i\bim\bmp\bpl\blo\bod\bde\be 'l_arg)\b)\n (\b(m\bma\bak\bkn\bna\bam\bm 'l_arg)\b)\n\n WHERE: l_arg is a list of symbols, strings and small\n fixnums.\n\n RETURNS: The symbol whose print name is the result of\n concatenating the first characters of the\n print names of the symbols and strings in the\n list. Any fixnums are converted to the equiv-\n alent ascii character. In order to concate-\n nate entire strings or print names, use the\n function _\bc_\bo_\bn_\bc_\ba_\bt.\n\n NOTE: _\bi_\bm_\bp_\bl_\bo_\bd_\be interns the symbol it creates, _\bm_\ba_\bk_\bn_\ba_\bm\n does not.\n\n (\b(g\bge\ben\bns\bsy\bym\bm ['s_leader])\b)\n\n RETURNS: a new uninterned atom beginning with the first\n character of s_leader's pname, or beginning\n with g if s_leader is not given.\n\n NOTE: The symbol looks like x0nnnnn where x is\n s_leader's first character and nnnnn is the num-\n ber of times you have called gensym.\n\n (\b(c\bco\bop\bpy\bys\bsy\bym\bmb\bbo\bol\bl 's_arg 'g_pred)\b)\n\n RETURNS: an uninterned symbol with the same print name\n as s_arg. If g_pred is non nil, then the\n value, function binding and property list of\n the new symbol are made _\be_\bq to those of s_arg.\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b16\b6\n\n\n (\b(a\bas\bsc\bci\bii\bi 'x_charnum)\b)\n\n WHERE: x_charnum is between 0 and 255.\n\n RETURNS: a symbol whose print name is the single char-\n acter whose fixnum representation is\n x_charnum.\n\n\n (\b(i\bin\bnt\bte\ber\brn\bn 's_arg)\b)\n\n RETURNS: s_arg\n\n SIDE EFFECT: s_arg is put on the oblist if it is not\n already there.\n\n (\b(r\bre\bem\bmo\bob\bb 's_symbol)\b)\n\n RETURNS: s_symbol\n\n SIDE EFFECT: s_symbol is removed from the oblist.\n\n (\b(r\bre\bem\bma\bat\bto\bom\bm 's_arg)\b)\n\n RETURNS: t if s_arg is indeed an atom.\n\n SIDE EFFECT: s_arg is put on the free atoms list,\n effectively reclaiming an atom cell.\n\n NOTE: This function does _\bn_\bo_\bt check to see if s_arg is\n on the oblist or is referenced anywhere. Thus\n calling _\br_\be_\bm_\ba_\bt_\bo_\bm on an atom in the oblist may\n result in disaster when that atom cell is reused!\n\n\n\n 2\b2.\b.3\b3.\b.2\b2.\b. s\bst\btr\bri\bin\bng\bg a\ban\bnd\bd s\bsy\bym\bmb\bbo\bol\bl p\bpr\bre\bed\bdi\bic\bca\bat\bte\bes\bs\n\n (\b(b\bbo\bou\bun\bnd\bdp\bp 's_name)\b)\n\n RETURNS: nil if s_name is unbound: that is, it has\n never been given a value. If x_name has the\n value g_val, then (nil . g_val) is returned.\n See also _\bm_\ba_\bk_\bu_\bn_\bb_\bo_\bu_\bn_\bd.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b17\b7\n\n\n (\b(a\bal\blp\bph\bha\bal\ble\bes\bss\bsp\bp 'st_arg1 'st_arg2)\b)\n\n RETURNS: t iff the `name' of st_arg1 is alphabetically\n less than the name of st_arg2. If st_arg is a\n symbol then its `name' is its print name. If\n st_arg is a string, then its `name' is the\n string itself.\n\n\n\n 2\b2.\b.3\b3.\b.3\b3.\b. s\bsy\bym\bmb\bbo\bol\bl a\ban\bnd\bd s\bst\btr\bri\bin\bng\bg a\bac\bcc\bce\bes\bss\bsi\bin\bng\bg\n\n (\b(s\bsy\bym\bme\bev\bva\bal\bl 's_arg)\b)\n\n RETURNS: the value of symbol s_arg.\n\n NOTE: It is illegal to ask for the value of an unbound\n symbol. This function has the same effect as\n _\be_\bv_\ba_\bl, but compiles into much more efficient code.\n\n (\b(g\bge\bet\bt_\b_p\bpn\bna\bam\bme\be 's_arg)\b)\n\n RETURNS: the string which is the print name of s_arg.\n\n (\b(p\bpl\bli\bis\bst\bt 's_arg)\b)\n\n RETURNS: the property list of s_arg.\n\n (\b(g\bge\bet\btd\bd 's_arg)\b)\n\n RETURNS: the function definition of s_arg or nil if\n there is no function definition.\n\n NOTE: the function definition may turn out to be an\n array header.\n\n (\b(g\bge\bet\btc\bch\bha\bar\br 's_arg 'x_index)\b)\n (\b(n\bnt\bth\bhc\bch\bha\bar\br 's_arg 'x_index)\b)\n (\b(g\bge\bet\btc\bch\bha\bar\brn\bn 's_arg 'x_index)\b)\n\n RETURNS: the x_index_\bt_\bh character of the print name of\n s_arg or nil if x_index is less than 1 or\n greater than the length of s_arg's print name.\n\n NOTE: _\bg_\be_\bt_\bc_\bh_\ba_\br and _\bn_\bt_\bh_\bc_\bh_\ba_\br return a symbol with a single\n character print name, _\bg_\be_\bt_\bc_\bh_\ba_\br_\bn returns the fixnum\n representation of the character.\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b18\b8\n\n\n (\b(s\bsu\bub\bbs\bst\btr\bri\bin\bng\bg 'st_string 'x_index ['x_length])\b)\n (\b(s\bsu\bub\bbs\bst\btr\bri\bin\bng\bgn\bn 'st_string 'x_index ['x_length])\b)\n\n RETURNS: a string of length at most x_length starting\n at x_index_\bt_\bh character in the string.\n\n NOTE: If x_length is not given, all of the characters\n for x_index to the end of the string are\n returned. If x_index is negative the string\n begins at the x_index_\bt_\bh character from the end.\n If x_index is out of bounds, nil is returned.\n\n NOTE: _\bs_\bu_\bb_\bs_\bt_\br_\bi_\bn_\bg returns a list of symbols, _\bs_\bu_\bb_\bs_\bt_\br_\bi_\bn_\bg_\bn\n returns a list of fixnums. If _\bs_\bu_\bb_\bs_\bt_\br_\bi_\bn_\bg_\bn is\n given a 0 x_length argument then a single fixnum\n which is the x_index_\bt_\bh character is returned.\n\n\n\n 2\b2.\b.3\b3.\b.4\b4.\b. s\bsy\bym\bmb\bbo\bol\bl a\ban\bnd\bd s\bst\btr\bri\bin\bng\bg m\bma\ban\bni\bip\bpu\bul\bla\bat\bti\bio\bon\bn\n\n (\b(s\bse\bet\bt 's_arg1 'g_arg2)\b)\n\n RETURNS: g_arg2.\n\n SIDE EFFECT: the value of s_arg1 is set to g_arg2.\n\n (\b(s\bse\bet\btq\bq s_atm1 'g_val1 [ s_atm2 'g_val2 ... ... ])\b)\n\n WHERE: the arguments are pairs of atom names and\n expressions.\n\n RETURNS: the last g_val_\bi.\n\n SIDE EFFECT: each s_atm_\bi is set to have the value\n g_val_\bi.\n\n NOTE: _\bs_\be_\bt evaluates all of its arguments, _\bs_\be_\bt_\bq does not\n evaluate the s_atm_\bi.\n\n (\b(d\bde\bes\bse\bet\btq\bq sl_pattern1 'g_exp1 [... ...])\b)\n\n RETURNS: g_expn\n\n SIDE EFFECT: This acts just like _\bs_\be_\bt_\bq if all the\n sl_pattern_\bi are symbols. If sl_pattern_\bi\n is a list then it is a template which\n should have the same structure as g_exp_\bi\n The symbols in sl_pattern are assigned to\n the corresponding parts of g_exp. (See\n also _\bs_\be_\bt_\bf )\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-1\b19\b9\n\n\n EXAMPLE: _\b(_\bd_\be_\bs_\be_\bt_\bq _\b(_\ba _\bb _\b(_\bc _\b. _\bd_\b)_\b) _\b'_\b(_\b1 _\b2 _\b(_\b3 _\b4 _\b5_\b)_\b)_\b)\n sets a to 1, b to 2, c to 3, and d to (4 5).\n\n\n (\b(s\bse\bet\btp\bpl\bli\bis\bst\bt 's_atm 'l_plist)\b)\n\n RETURNS: l_plist.\n\n SIDE EFFECT: the property list of s_atm is set to\n l_plist.\n\n (\b(m\bma\bak\bku\bun\bnb\bbo\bou\bun\bnd\bd 's_arg)\b)\n\n RETURNS: s_arg\n\n SIDE EFFECT: the value of s_arg is made `unbound'. If\n the interpreter attempts to evaluate s_arg\n before it is again given a value, an\n unbound variable error will occur.\n\n (\b(a\bae\bex\bxp\bpl\blo\bod\bde\be 's_arg)\b)\n (\b(e\bex\bxp\bpl\blo\bod\bde\be 'g_arg)\b)\n (\b(a\bae\bex\bxp\bpl\blo\bod\bde\bec\bc 's_arg)\b)\n (\b(e\bex\bxp\bpl\blo\bod\bde\bec\bc 'g_arg)\b)\n (\b(a\bae\bex\bxp\bpl\blo\bod\bde\ben\bn 's_arg)\b)\n (\b(e\bex\bxp\bpl\blo\bod\bde\ben\bn 'g_arg)\b)\n\n RETURNS: a list of the characters used to print out\n s_arg or g_arg.\n\n NOTE: The functions beginning with 'a' are internal\n functions which are limited to symbol arguments.\n The functions _\ba_\be_\bx_\bp_\bl_\bo_\bd_\be and _\be_\bx_\bp_\bl_\bo_\bd_\be return a list\n of characters which _\bp_\br_\bi_\bn_\bt would use to print the\n argument. These characters include all necessary\n escape characters. Functions _\ba_\be_\bx_\bp_\bl_\bo_\bd_\be_\bc and\n _\be_\bx_\bp_\bl_\bo_\bd_\be_\bc return a list of characters which _\bp_\ba_\bt_\bo_\bm\n would use to print the argument (i.e. no escape\n characters). Functions _\ba_\be_\bx_\bp_\bl_\bo_\bd_\be_\bn and _\be_\bx_\bp_\bl_\bo_\bd_\be_\bn\n are similar to _\ba_\be_\bx_\bp_\bl_\bo_\bd_\be_\bc and _\be_\bx_\bp_\bl_\bo_\bd_\be_\bc except that\n a list of fixnum equivalents of characters are\n returned.\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b20\b0\n\n\n\n ____________________________________________________\n\n -> _\b(_\bs_\be_\bt_\bq _\bx _\b'_\b|_\bq_\bu_\bo_\bt_\be _\bt_\bh_\bi_\bs _\b\\_\b| _\bo_\bk_\b?_\b|_\b)\n |quote this \\| ok?|\n -> _\b(_\be_\bx_\bp_\bl_\bo_\bd_\be _\bx_\b)\n (q u o t e |\\\\| | | t h i s |\\\\| | | |\\\\| |\\|| |\\\\| | | o k ?)\n ; note that |\\\\| just means the single character: backslash.\n ; and |\\|| just means the single character: vertical bar\n ; and | | means the single character: space\n\n -> _\b(_\be_\bx_\bp_\bl_\bo_\bd_\be_\bc _\bx_\b)\n (q u o t e | | t h i s | | |\\|| | | o k ?)\n -> _\b(_\be_\bx_\bp_\bl_\bo_\bd_\be_\bn _\bx_\b)\n (113 117 111 116 101 32 116 104 105 115 32 124 32 111 107 63)\n ____________________________________________________\n\n\n\n\n\n\n 2\b2.\b.4\b4.\b. V\bVe\bec\bct\bto\bor\brs\bs\n\n See Chapter 9 for a discussion of vectors. They\n are less efficient that hunks but more efficient than\n arrays.\n\n\n\n 2\b2.\b.4\b4.\b.1\b1.\b. v\bve\bec\bct\bto\bor\br c\bcr\bre\bea\bat\bti\bio\bon\bn\n\n (\b(n\bne\bew\bw-\b-v\bve\bec\bct\bto\bor\br 'x_size ['g_fill ['g_prop]])\b)\n\n RETURNS: A v\bve\bec\bct\bto\bor\br of length x_size. Each data entry is\n initialized to g_fill, or to nil, if the argu-\n ment g_fill is not present. The vector's\n property is set to g_prop, or to nil, by\n default.\n\n (\b(n\bne\bew\bw-\b-v\bve\bec\bct\bto\bor\bri\bi-\b-b\bby\byt\bte\be 'x_size ['g_fill ['g_prop]])\b)\n (\b(n\bne\bew\bw-\b-v\bve\bec\bct\bto\bor\bri\bi-\b-w\bwo\bor\brd\bd 'x_size ['g_fill ['g_prop]])\b)\n (\b(n\bne\bew\bw-\b-v\bve\bec\bct\bto\bor\bri\bi-\b-l\blo\bon\bng\bg 'x_size ['g_fill ['g_prop]])\b)\n\n RETURNS: A v\bve\bec\bct\bto\bor\bri\bi with x_size elements in it. The\n actual memory requirement is two long words +\n x_size*(n bytes), where n is 1 for new-vector-\n byte, 2 for new-vector-word, or 4 for new-\n vectori-long. Each data entry is initialized\n to g_fill, or to zero, if the argument g_fill\n is not present. The vector's property is set\n to g_prop, or nil, by default.\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b21\b1\n\n\n Vectors may be created by specifying multiple initial\n values:\n\n (\b(v\bve\bec\bct\bto\bor\br ['g_val0 'g_val1 ...])\b)\n\n RETURNS: a v\bve\bec\bct\bto\bor\br, with as many data elements as there\n are arguments. It is quite possible to have a\n vector with no data elements. The vector's\n property will be a null list.\n\n (\b(v\bve\bec\bct\bto\bor\bri\bi-\b-b\bby\byt\bte\be ['x_val0 'x_val2 ...])\b)\n (\b(v\bve\bec\bct\bto\bor\bri\bi-\b-w\bwo\bor\brd\bd ['x_val0 'x_val2 ...])\b)\n (\b(v\bve\bec\bct\bto\bor\bri\bi-\b-l\blo\bon\bng\bg ['x_val0 'x_val2 ...])\b)\n\n RETURNS: a v\bve\bec\bct\bto\bor\bri\bi, with as many data elements as there\n are arguments. The arguments are required to\n be fixnums. Only the low order byte or word\n is used in the case of vectori-byte and vec-\n tori-word. The vector's property will be\n null.\n\n\n\n 2\b2.\b.4\b4.\b.2\b2.\b. v\bve\bec\bct\bto\bor\br r\bre\bef\bfe\ber\bre\ben\bnc\bce\be\n\n (\b(v\bvr\bre\bef\bf 'v_vect 'x_index)\b)\n (\b(v\bvr\bre\bef\bfi\bi-\b-b\bby\byt\bte\be 'V_vect 'x_bindex)\b)\n (\b(v\bvr\bre\bef\bfi\bi-\b-w\bwo\bor\brd\bd 'V_vect 'x_windex)\b)\n (\b(v\bvr\bre\bef\bfi\bi-\b-l\blo\bon\bng\bg 'V_vect 'x_lindex)\b)\n\n RETURNS: the desired data element from a vector. The\n indices must be fixnums. Indexing is zero-\n based. The vrefi functions sign extend the\n data.\n\n (\b(v\bvp\bpr\bro\bop\bp 'Vv_vect)\b)\n\n RETURNS: The Lisp property associated with a vector.\n\n (\b(v\bvg\bge\bet\bt 'Vv_vect 'g_ind)\b)\n\n RETURNS: The value stored under g_ind if the Lisp prop-\n erty associated with 'Vv_vect is a disembodied\n property list.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b22\b2\n\n\n (\b(v\bvs\bsi\biz\bze\be 'Vv_vect)\b)\n (\b(v\bvs\bsi\biz\bze\be-\b-b\bby\byt\bte\be 'V_vect)\b)\n (\b(v\bvs\bsi\biz\bze\be-\b-w\bwo\bor\brd\bd 'V_vect)\b)\n\n RETURNS: the number of data elements in the vector.\n For immediate-vectors, the functions vsize-\n byte and vsize-word return the number of data\n elements, if one thinks of the binary data as\n being comprised of bytes or words.\n\n\n\n 2\b2.\b.4\b4.\b.3\b3.\b. v\bve\bec\bct\bto\bor\br m\bmo\bod\bdf\bfi\bic\bca\bat\bti\bio\bon\bn\n\n (\b(v\bvs\bse\bet\bt 'v_vect 'x_index 'g_val)\b)\n (\b(v\bvs\bse\bet\bti\bi-\b-b\bby\byt\bte\be 'V_vect 'x_bindex 'x_val)\b)\n (\b(v\bvs\bse\bet\bti\bi-\b-w\bwo\bor\brd\bd 'V_vect 'x_windex 'x_val)\b)\n (\b(v\bvs\bse\bet\bti\bi-\b-l\blo\bon\bng\bg 'V_vect 'x_lindex 'x_val)\b)\n\n RETURNS: the datum.\n\n SIDE EFFECT: The indexed element of the vector is set\n to the value. As noted above, for vseti-\n word and vseti-byte, the index is con-\n strued as the number of the data element\n within the vector. It is not a byte\n address. Also, for those two functions,\n the low order byte or word of x_val is\n what is stored.\n\n (\b(v\bvs\bse\bet\btp\bpr\bro\bop\bp 'Vv_vect 'g_value)\b)\n\n RETURNS: g_value. This should be either a symbol or a\n disembodied property list whose _\bc_\ba_\br is a sym-\n bol identifying the type of the vector.\n\n SIDE EFFECT: the property list of Vv_vect is set to\n g_value.\n\n (\b(v\bvp\bpu\but\btp\bpr\bro\bop\bp 'Vv_vect 'g_value 'g_ind)\b)\n\n RETURNS: g_value.\n\n SIDE EFFECT: If the vector property of Vv_vect is a\n disembodied property list, then vputprop\n adds the value g_value under the indicator\n g_ind. Otherwise, the old vector property\n is made the first element of the list.\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b23\b3\n\n\n 2\b2.\b.5\b5.\b. A\bAr\brr\bra\bay\bys\bs\n\n See Chapter 9 for a complete description of\n arrays. Some of these functions are part of a Maclisp\n array compatibility package representing only one sim-\n ple way of using the array structure of FRANZ LISP.\n\n\n\n 2\b2.\b.5\b5.\b.1\b1.\b. a\bar\brr\bra\bay\by c\bcr\bre\bea\bat\bti\bio\bon\bn\n\n (\b(m\bma\bar\brr\bra\bay\by 'g_data 's_access 'g_aux 'x_length 'x_delta)\b)\n\n RETURNS: an array type with the fields set up from the\n above arguments in the obvious way (see S\n 1.2.10).\n\n (\b(*\b*a\bar\brr\bra\bay\by 's_name 's_type 'x_dim1 ... 'x_dim_\bn)\b)\n (\b(a\bar\brr\bra\bay\by s_name s_type x_dim1 ... x_dim_\bn)\b)\n\n WHERE: s_type may be one of t, nil, fixnum, flonum,\n fixnum-block and flonum-block.\n\n RETURNS: an array of type s_type with n dimensions of\n extents given by the x_dim_\bi.\n\n SIDE EFFECT: If s_name is non nil, the function defini-\n tion of s_name is set to the array struc-\n ture returned.\n\n NOTE: These functions create a Maclisp compatible\n array. In FRANZ LISP arrays of type t, nil,\n fixnum and flonum are equivalent and the elements\n of these arrays can be any type of lisp object.\n Fixnum-block and flonum-block arrays are\n restricted to fixnums and flonums respectively\n and are used mainly to communicate with foreign\n functions (see S8.5).\n\n NOTE: _\b*_\ba_\br_\br_\ba_\by evaluates its arguments, _\ba_\br_\br_\ba_\by does not.\n\n\n\n 2\b2.\b.5\b5.\b.2\b2.\b. a\bar\brr\bra\bay\by p\bpr\bre\bed\bdi\bic\bca\bat\bte\be\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b24\b4\n\n\n (\b(a\bar\brr\bra\bay\byp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is of type array.\n\n\n\n 2\b2.\b.5\b5.\b.3\b3.\b. a\bar\brr\bra\bay\by a\bac\bcc\bce\bes\bss\bso\bor\brs\bs\n\n\n (\b(g\bge\bet\bta\bac\bcc\bce\bes\bss\bs 'a_array)\b)\n (\b(g\bge\bet\bta\bau\bux\bx 'a_array)\b)\n (\b(g\bge\bet\btd\bde\bel\blt\bta\ba 'a_array)\b)\n (\b(g\bge\bet\btd\bda\bat\bta\ba 'a_array)\b)\n (\b(g\bge\bet\btl\ble\ben\bng\bgt\bth\bh 'a_array)\b)\n\n RETURNS: the field of the array object a_array given by\n the function name.\n\n (\b(a\bar\brr\bra\bay\byr\bre\bef\bf 'a_name 'x_ind)\b)\n\n RETURNS: the x_ind_\bt_\bh element of the array object\n a_name. x_ind of zero accesses the first ele-\n ment.\n\n NOTE: _\ba_\br_\br_\ba_\by_\br_\be_\bf uses the data, length and delta fields\n of a_name to determine which object to return.\n\n (\b(a\bar\brr\bra\bay\byc\bca\bal\bll\bl s_type 'as_array 'x_ind1 ... )\b)\n\n RETURNS: the element selected by the indices from the\n array a_array of type s_type.\n\n NOTE: If as_array is a symbol then the function binding\n of this symbol should contain an array object.\n s_type is ignored by _\ba_\br_\br_\ba_\by_\bc_\ba_\bl_\bl but is included\n for compatibility with Maclisp.\n\n (\b(a\bar\brr\bra\bay\byd\bdi\bim\bms\bs 's_name)\b)\n\n RETURNS: a list of the type and bounds of the array\n s_name.\n\n (\b(l\bli\bis\bst\bta\bar\brr\bra\bay\by 'sa_array ['x_elements])\b)\n\n RETURNS: a list of all of the elements in array\n sa_array. If x_elements is given, then only\n the first x_elements are returned.\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b25\b5\n\n\n\n ____________________________________________________\n\n ; We will create a 3 by 4 array of general lisp objects\n -> _\b(_\ba_\br_\br_\ba_\by _\be_\br_\bn_\bi_\be _\bt _\b3 _\b4_\b)\n array[12]\n\n ; the array header is stored in the function definition slot of the\n ; symbol ernie\n -> _\b(_\ba_\br_\br_\ba_\by_\bp _\b(_\bg_\be_\bt_\bd _\b'_\be_\br_\bn_\bi_\be_\b)_\b)\n t\n -> _\b(_\ba_\br_\br_\ba_\by_\bd_\bi_\bm_\bs _\b(_\bg_\be_\bt_\bd _\b'_\be_\br_\bn_\bi_\be_\b)_\b)\n (t 3 4)\n\n ; store in ernie[2][2] the list (test list)\n -> _\b(_\bs_\bt_\bo_\br_\be _\b(_\be_\br_\bn_\bi_\be _\b2 _\b2_\b) _\b'_\b(_\bt_\be_\bs_\bt _\bl_\bi_\bs_\bt_\b)_\b)\n (test list)\n\n ; check to see if it is there\n -> _\b(_\be_\br_\bn_\bi_\be _\b2 _\b2_\b)\n (test list)\n\n ; now use the low level function _\ba_\br_\br_\ba_\by_\br_\be_\bf to find the same element\n ; arrays are 0 based and row-major (the last subscript varies the fastest)\n ; thus element [2][2] is the 10th element , (starting at 0).\n -> _\b(_\ba_\br_\br_\ba_\by_\br_\be_\bf _\b(_\bg_\be_\bt_\bd _\b'_\be_\br_\bn_\bi_\be_\b) _\b1_\b0_\b)\n (ptr to)(test list) ; the result is a value cell (thus the (ptr to))\n ____________________________________________________\n\n\n\n\n\n\n 2\b2.\b.5\b5.\b.4\b4.\b. a\bar\brr\bra\bay\by m\bma\ban\bni\bip\bpu\bul\bla\bat\bti\bio\bon\bn\n\n (\b(p\bpu\but\bta\bac\bcc\bce\bes\bss\bs 'a_array 'su_func)\b)\n (\b(p\bpu\but\bta\bau\bux\bx 'a_array 'g_aux)\b)\n (\b(p\bpu\but\btd\bda\bat\bta\ba 'a_array 'g_arg)\b)\n (\b(p\bpu\but\btd\bde\bel\blt\bta\ba 'a_array 'x_delta)\b)\n (\b(p\bpu\but\btl\ble\ben\bng\bgt\bth\bh 'a_array 'x_length)\b)\n\n RETURNS: the second argument to the function.\n\n SIDE EFFECT: The field of the array object given by the\n function name is replaced by the second\n argument to the function.\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b26\b6\n\n\n (\b(s\bst\bto\bor\bre\be 'l_arexp 'g_val)\b)\n\n WHERE: l_arexp is an expression which references an\n array element.\n\n RETURNS: g_val\n\n SIDE EFFECT: the array location which contains the ele-\n ment which l_arexp references is changed\n to contain g_val.\n\n (\b(f\bfi\bil\bll\bla\bar\brr\bra\bay\by 's_array 'l_itms)\b)\n\n RETURNS: s_array\n\n SIDE EFFECT: the array s_array is filled with elements\n from l_itms. If there are not enough ele-\n ments in l_itms to fill the entire array,\n then the last element of l_itms is used to\n fill the remaining parts of the array.\n\n\n\n 2\b2.\b.6\b6.\b. H\bHu\bun\bnk\bks\bs\n\n Hunks are vector-like objects whose size can\n range from 1 to 128 elements. Internally, hunks are\n allocated in sizes which are powers of 2. In order to\n create hunks of a given size, a hunk with at least\n that many elements is allocated and a distinguished\n symbol EMPTY is placed in those elements not\n requested. Most hunk functions respect those distin-\n guished symbols, but there are two _\b(_\b*_\bm_\ba_\bk_\bh_\bu_\bn_\bk and\n _\b*_\br_\bp_\bl_\ba_\bc_\bx) which will overwrite the distinguished sym-\n bol.\n\n\n\n 2\b2.\b.6\b6.\b.1\b1.\b. h\bhu\bun\bnk\bk c\bcr\bre\bea\bat\bti\bio\bon\bn\n\n (\b(h\bhu\bun\bnk\bk 'g_val1 ['g_val2 ... 'g_val_\bn])\b)\n\n RETURNS: a hunk of length n whose elements are initial-\n ized to the g_val_\bi.\n\n NOTE: the maximum size of a hunk is 128.\n\n EXAMPLE: _\b(_\bh_\bu_\bn_\bk _\b4 _\b'_\bs_\bh_\ba_\br_\bp _\b'_\bk_\be_\by_\bs_\b) = {4 sharp keys}\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b27\b7\n\n\n (\b(m\bma\bak\bkh\bhu\bun\bnk\bk 'xl_arg)\b)\n\n RETURNS: a hunk of length xl_arg initialized to all\n nils if xl_arg is a fixnum. If xl_arg is a\n list, then we return a hunk of size\n _\b(_\bl_\be_\bn_\bg_\bt_\bh _\b'_\bx_\bl_\b__\ba_\br_\bg_\b) initialized to the elements\n in xl_arg.\n\n NOTE: _\b(_\bm_\ba_\bk_\bh_\bu_\bn_\bk _\b'_\b(_\ba _\bb _\bc_\b)_\b) is equivalent to\n _\b(_\bh_\bu_\bn_\bk _\b'_\ba _\b'_\bb _\b'_\bc_\b).\n\n EXAMPLE: _\b(_\bm_\ba_\bk_\bh_\bu_\bn_\bk _\b4_\b) = _\b{_\bn_\bi_\bl _\bn_\bi_\bl _\bn_\bi_\bl _\bn_\bi_\bl_\b}\n\n (\b(*\b*m\bma\bak\bkh\bhu\bun\bnk\bk 'x_arg)\b)\n\n RETURNS: a hunk of size 2initialized to EMPTY.\n\n NOTE: This is only to be used by such functions as _\bh_\bu_\bn_\bk\n and _\bm_\ba_\bk_\bh_\bu_\bn_\bk which create and initialize hunks for\n users.\n\n\n\n 2\b2.\b.6\b6.\b.2\b2.\b. h\bhu\bun\bnk\bk a\bac\bcc\bce\bes\bss\bso\bor\br\n\n (\b(c\bcx\bxr\br 'x_ind 'h_hunk)\b)\n\n RETURNS: element x_ind (starting at 0) of hunk h_hunk.\n\n (\b(h\bhu\bun\bnk\bk-\b-t\bto\bo-\b-l\bli\bis\bst\bt 'h_hunk)\b)\n\n RETURNS: a list consisting of the elements of h_hunk.\n\n\n\n 2\b2.\b.6\b6.\b.3\b3.\b. h\bhu\bun\bnk\bk m\bma\ban\bni\bip\bpu\bul\bla\bat\bto\bor\brs\bs\n\n (\b(r\brp\bpl\bla\bac\bcx\bx 'x_ind 'h_hunk 'g_val)\b)\n (\b(*\b*r\brp\bpl\bla\bac\bcx\bx 'x_ind 'h_hunk 'g_val)\b)\n\n RETURNS: h_hunk\n\n SIDE EFFECT: Element x_ind (starting at 0) of h_hunk is\n set to g_val.\n\n NOTE: _\br_\bp_\bl_\ba_\bc_\bx will not modify one of the distinguished\n (EMPTY) elements whereas _\b*_\br_\bp_\bl_\ba_\bc_\bx will.\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b28\b8\n\n\n (\b(h\bhu\bun\bnk\bks\bsi\biz\bze\be 'h_arg)\b)\n\n RETURNS: the size of the hunk h_arg.\n\n EXAMPLE: _\b(_\bh_\bu_\bn_\bk_\bs_\bi_\bz_\be _\b(_\bh_\bu_\bn_\bk _\b1 _\b2 _\b3_\b)_\b) = 3\n\n\n\n 2\b2.\b.7\b7.\b. B\bBc\bcd\bds\bs\n\n A bcd object contains a pointer to compiled code\n and to the type of function object the compiled code\n represents.\n\n (\b(g\bge\bet\btd\bdi\bis\bsc\bc 'y_bcd)\b)\n (\b(g\bge\bet\bte\ben\bnt\btr\bry\by 'y_bcd)\b)\n\n RETURNS: the field of the bcd object given by the func-\n tion name.\n\n (\b(p\bpu\but\btd\bdi\bis\bsc\bc 'y_func 's_discipline)\b)\n\n RETURNS: s_discipline\n\n SIDE EFFECT: Sets the discipline field of y_func to\n s_discipline.\n\n\n\n 2\b2.\b.8\b8.\b. S\bSt\btr\bru\buc\bct\btu\bur\bre\bes\bs\n\n There are three common structures constructed out\n of list cells: the assoc list, the property list and\n the tconc list. The functions below manipulate these\n structures.\n\n\n\n 2\b2.\b.8\b8.\b.1\b1.\b. a\bas\bss\bso\boc\bc l\bli\bis\bst\bt\n\n An `assoc list' (or alist) is a common lisp\n data structure. It has the form\n ((key1 . value1) (key2 . value2) (key3 . value3)\n ... (keyn . valuen))\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-2\b29\b9\n\n\n (\b(a\bas\bss\bso\boc\bc 'g_arg1 'l_arg2)\b)\n (\b(a\bas\bss\bsq\bq 'g_arg1 'l_arg2)\b)\n\n RETURNS: the first top level element of l_arg2 whose\n _\bc_\ba_\br is _\be_\bq_\bu_\ba_\bl (with _\ba_\bs_\bs_\bo_\bc) or _\be_\bq (with _\ba_\bs_\bs_\bq) to\n g_arg1.\n\n NOTE: Usually l_arg2 has an _\ba_\b-_\bl_\bi_\bs_\bt structure and g_arg1\n acts as key.\n\n (\b(s\bsa\bas\bss\bso\boc\bc 'g_arg1 'l_arg2 'sl_func)\b)\n\n RETURNS: the result of\n _\b(_\bc_\bo_\bn_\bd _\b(_\b(_\ba_\bs_\bs_\bo_\bc _\b'_\bg_\b__\ba_\br_\bg _\b'_\bl_\b__\ba_\br_\bg_\b2_\b) _\b(_\ba_\bp_\bp_\bl_\by _\b'_\bs_\bl_\b__\bf_\bu_\bn_\bc _\bn_\bi_\bl_\b)_\b)_\b)\n\n NOTE: sassoc is written as a macro.\n\n (\b(s\bsa\bas\bss\bsq\bq 'g_arg1 'l_arg2 'sl_func)\b)\n\n RETURNS: the result of\n _\b(_\bc_\bo_\bn_\bd _\b(_\b(_\ba_\bs_\bs_\bq _\b'_\bg_\b__\ba_\br_\bg _\b'_\bl_\b__\ba_\br_\bg_\b2_\b) _\b(_\ba_\bp_\bp_\bl_\by _\b'_\bs_\bl_\b__\bf_\bu_\bn_\bc _\bn_\bi_\bl_\b)_\b)_\b)\n\n NOTE: sassq is written as a macro.\n\n\n\n ____________________________________________________\n\n ; _\ba_\bs_\bs_\bo_\bc or _\ba_\bs_\bs_\bq is given a key and an assoc list and returns\n ; the key and value item if it exists, they differ only in how they test\n ; for equality of the keys.\n\n -> _\b(_\bs_\be_\bt_\bq _\ba_\bl_\bi_\bs_\bt _\b'_\b(_\b(_\ba_\bl_\bp_\bh_\ba _\b. _\ba_\b) _\b( _\b(_\bc_\bo_\bm_\bp_\bl_\be_\bx _\bk_\be_\by_\b) _\b. _\bb_\b) _\b(_\bj_\bu_\bn_\bk _\b. _\bx_\b)_\b)_\b)\n ((alpha . a) ((complex key) . b) (junk . x))\n\n ; we should use _\ba_\bs_\bs_\bq when the key is an atom\n -> _\b(_\ba_\bs_\bs_\bq _\b'_\ba_\bl_\bp_\bh_\ba _\ba_\bl_\bi_\bs_\bt_\b)\n (alpha . a)\n\n ; but it may not work when the key is a list\n -> _\b(_\ba_\bs_\bs_\bq _\b'_\b(_\bc_\bo_\bm_\bp_\bl_\be_\bx _\bk_\be_\by_\b) _\ba_\bl_\bi_\bs_\bt_\b)\n nil\n\n ; however _\ba_\bs_\bs_\bo_\bc will always work\n -> _\b(_\ba_\bs_\bs_\bo_\bc _\b'_\b(_\bc_\bo_\bm_\bp_\bl_\be_\bx _\bk_\be_\by_\b) _\ba_\bl_\bi_\bs_\bt_\b)\n ((complex key) . b)\n ____________________________________________________\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b30\b0\n\n\n (\b(s\bsu\bub\bbl\bli\bis\bs 'l_alst 'l_exp)\b)\n\n WHERE: l_alst is an _\ba_\b-_\bl_\bi_\bs_\bt.\n\n RETURNS: the list l_exp with every occurrence of key_\bi\n replaced by val_\bi.\n\n NOTE: new list structure is returned to prevent modifi-\n cation of l_exp. When a substitution is made, a\n copy of the value to substitute in is not made.\n\n\n\n 2\b2.\b.8\b8.\b.2\b2.\b. p\bpr\bro\bop\bpe\ber\brt\bty\by l\bli\bis\bst\bt\n\n A property list consists of an alternating\n sequence of keys and values. Normally a property\n list is stored on a symbol. A list is a 'disembod-\n ied' property list if it contains an odd number of\n elements, the first of which is ignored.\n\n (\b(p\bpl\bli\bis\bst\bt 's_name)\b)\n\n RETURNS: the property list of s_name.\n\n (\b(s\bse\bet\btp\bpl\bli\bis\bst\bt 's_atm 'l_plist)\b)\n\n RETURNS: l_plist.\n\n SIDE EFFECT: the property list of s_atm is set to\n l_plist.\n\n\n (\b(g\bge\bet\bt 'ls_name 'g_ind)\b)\n\n RETURNS: the value under indicator g_ind in ls_name's\n property list if ls_name is a symbol.\n\n NOTE: If there is no indicator g_ind in ls_name's prop-\n erty list nil is returned. If ls_name is a list\n of an odd number of elements then it is a disem-\n bodied property list. _\bg_\be_\bt searches a disembodied\n property list by starting at its _\bc_\bd_\br, and compar-\n ing every other element with g_ind, using _\be_\bq.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b31\b1\n\n\n (\b(g\bge\bet\btl\bl 'ls_name 'l_indicators)\b)\n\n RETURNS: the property list ls_name beginning at the\n first indicator which is a member of the list\n l_indicators, or nil if none of the indicators\n in l_indicators are on ls_name's property\n list.\n\n NOTE: If ls_name is a list, then it is assumed to be a\n disembodied property list.\n\n\n (\b(p\bpu\but\btp\bpr\bro\bop\bp 'ls_name 'g_val 'g_ind)\b)\n (\b(d\bde\bef\bfp\bpr\bro\bop\bp ls_name g_val g_ind)\b)\n\n RETURNS: g_val.\n\n SIDE EFFECT: Adds to the property list of ls_name the\n value g_val under the indicator g_ind.\n\n NOTE: _\bp_\bu_\bt_\bp_\br_\bo_\bp evaluates it arguments, _\bd_\be_\bf_\bp_\br_\bo_\bp does not.\n ls_name may be a disembodied property list, see\n _\bg_\be_\bt.\n\n (\b(r\bre\bem\bmp\bpr\bro\bop\bp 'ls_name 'g_ind)\b)\n\n RETURNS: the portion of ls_name's property list begin-\n ning with the property under the indicator\n g_ind. If there is no g_ind indicator in\n ls_name's plist, nil is returned.\n\n SIDE EFFECT: the value under indicator g_ind and g_ind\n itself is removed from the property list\n of ls_name.\n\n NOTE: ls_name may be a disembodied property list, see\n _\bg_\be_\bt.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b32\b2\n\n\n\n ____________________________________________________\n\n -> _\b(_\bp_\bu_\bt_\bp_\br_\bo_\bp _\b'_\bx_\bl_\ba_\bt_\be _\b'_\ba _\b'_\ba_\bl_\bp_\bh_\ba_\b)\n a\n -> _\b(_\bp_\bu_\bt_\bp_\br_\bo_\bp _\b'_\bx_\bl_\ba_\bt_\be _\b'_\bb _\b'_\bb_\be_\bt_\ba_\b)\n b\n -> _\b(_\bp_\bl_\bi_\bs_\bt _\b'_\bx_\bl_\ba_\bt_\be_\b)\n (alpha a beta b)\n -> _\b(_\bg_\be_\bt _\b'_\bx_\bl_\ba_\bt_\be _\b'_\ba_\bl_\bp_\bh_\ba_\b)\n a\n ; use of a disembodied property list:\n -> _\b(_\bg_\be_\bt _\b'_\b(_\bn_\bi_\bl _\bf_\ba_\bt_\be_\bm_\ba_\bn _\br_\bj_\bf _\bs_\bk_\bl_\bo_\bw_\be_\br _\bk_\bl_\bs _\bf_\bo_\bd_\be_\br_\ba_\br_\bo _\bj_\bk_\bf_\b) _\b'_\bs_\bk_\bl_\bo_\bw_\be_\br_\b)\n kls\n ____________________________________________________\n\n\n\n\n\n\n 2\b2.\b.8\b8.\b.3\b3.\b. t\btc\bco\bon\bnc\bc s\bst\btr\bru\buc\bct\btu\bur\bre\be\n\n A tconc structure is a special type of list\n designed to make it easy to add objects to the end.\n It consists of a list cell whose _\bc_\ba_\br points to a\n list of the elements added with _\bt_\bc_\bo_\bn_\bc or _\bl_\bc_\bo_\bn_\bc and\n whose _\bc_\bd_\br points to the last list cell of the list\n pointed to by the _\bc_\ba_\br_\b.\n\n (\b(t\btc\bco\bon\bnc\bc 'l_ptr 'g_x)\b)\n\n WHERE: l_ptr is a tconc structure.\n\n RETURNS: l_ptr with g_x added to the end.\n\n (\b(l\blc\bco\bon\bnc\bc 'l_ptr 'l_x)\b)\n\n WHERE: l_ptr is a tconc structure.\n\n RETURNS: l_ptr with the list l_x spliced in at the end.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b33\b3\n\n\n\n ____________________________________________________\n\n ; A _\bt_\bc_\bo_\bn_\bc structure can be initialized in two ways.\n ; nil can be given to _\bt_\bc_\bo_\bn_\bc in which case _\bt_\bc_\bo_\bn_\bc will generate\n ; a _\bt_\bc_\bo_\bn_\bc structure.\n\n ->_\b(_\bs_\be_\bt_\bq _\bf_\bo_\bo _\b(_\bt_\bc_\bo_\bn_\bc _\bn_\bi_\bl _\b1_\b)_\b)\n ((1) 1)\n\n ; Since _\bt_\bc_\bo_\bn_\bc destructively adds to\n ; the list, you can now add to foo without using _\bs_\be_\bt_\bq again.\n\n ->_\b(_\bt_\bc_\bo_\bn_\bc _\bf_\bo_\bo _\b2_\b)\n ((1 2) 2)\n ->_\bf_\bo_\bo\n ((1 2) 2)\n\n ; Another way to create a null _\bt_\bc_\bo_\bn_\bc structure\n ; is to use _\b(_\bn_\bc_\bo_\bn_\bs _\bn_\bi_\bl_\b).\n\n ->_\b(_\bs_\be_\bt_\bq _\bf_\bo_\bo _\b(_\bn_\bc_\bo_\bn_\bs _\bn_\bi_\bl_\b)_\b)\n (nil)\n ->_\b(_\bt_\bc_\bo_\bn_\bc _\bf_\bo_\bo _\b1_\b)\n ((1) 1)\n\n ; now see what _\bl_\bc_\bo_\bn_\bc can do\n -> _\b(_\bl_\bc_\bo_\bn_\bc _\bf_\bo_\bo _\bn_\bi_\bl_\b)\n ((1) 1) ; no change\n -> _\b(_\bl_\bc_\bo_\bn_\bc _\bf_\bo_\bo _\b'_\b(_\b2 _\b3 _\b4_\b)_\b)\n ((1 2 3 4) 4)\n ____________________________________________________\n\n\n\n\n\n\n 2\b2.\b.8\b8.\b.4\b4.\b. f\bfc\bcl\blo\bos\bsu\bur\bre\bes\bs\n\n An fclosure is a functional object which\n admits some data manipulations. They are discussed\n in S8.4. Internally, they are constructed from\n vectors.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b34\b4\n\n\n (\b(f\bfc\bcl\blo\bos\bsu\bur\bre\be 'l_vars 'g_funobj)\b)\n\n WHERE: l_vars is a list of variables, g_funobj is any\n object that can be funcalled (including, fclo-\n sures).\n\n RETURNS: A vector which is the fclosure.\n\n (\b(f\bfc\bcl\blo\bos\bsu\bur\bre\be-\b-a\bal\bli\bis\bst\bt 'v_fclosure)\b)\n\n RETURNS: An association list representing the variables\n in the fclosure. This is a snapshot of the\n current state of the fclosure. If the bind-\n ings in the fclosure are changed, any previ-\n ously calculated results of _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\b-_\ba_\bl_\bi_\bs_\bt\n will not change.\n\n (\b(f\bfc\bcl\blo\bos\bsu\bur\bre\be-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn 'v_fclosure)\b)\n\n RETURNS: the functional object part of the fclosure.\n\n (\b(f\bfc\bcl\blo\bos\bsu\bur\bre\bep\bp 'v_fclosure)\b)\n\n RETURNS: t iff the argument is an fclosure.\n\n (\b(s\bsy\bym\bme\bev\bva\bal\bl-\b-i\bin\bn-\b-f\bfc\bcl\blo\bos\bsu\bur\bre\be 'v_fclosure 's_symbol)\b)\n\n RETURNS: the current binding of a particular symbol in\n an fclosure.\n\n (\b(s\bse\bet\bt-\b-i\bin\bn-\b-f\bfc\bcl\blo\bos\bsu\bur\bre\be 'v_fclosure 's_symbol 'g_newvalue)\b)\n\n RETURNS: g_newvalue.\n\n SIDE EFFECT: The variable s_symbol is bound in the\n fclosure to g_newvalue.\n\n\n\n 2\b2.\b.9\b9.\b. R\bRa\ban\bnd\bdo\bom\bm f\bfu\bun\bnc\bct\bti\bio\bon\bns\bs\n\n The following functions don't fall into any of\n the classifications above.\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b35\b5\n\n\n (\b(b\bbc\bcd\bda\bad\bd 's_funcname)\b)\n\n RETURNS: a fixnum which is the address in memory where\n the function s_funcname begins. If s_funcname\n is not a machine coded function (binary) then\n _\bb_\bc_\bd_\ba_\bd returns nil.\n\n (\b(c\bco\bop\bpy\by 'g_arg)\b)\n\n RETURNS: A structure _\be_\bq_\bu_\ba_\bl to g_arg but with new list\n cells.\n\n (\b(c\bco\bop\bpy\byi\bin\bnt\bt*\b* 'x_arg)\b)\n\n RETURNS: a fixnum with the same value as x_arg but in a\n freshly allocated cell.\n\n (\b(c\bcp\bpy\by1\b1 'xvt_arg)\b)\n\n RETURNS: a new cell of the same type as xvt_arg with\n the same value as xvt_arg.\n\n (\b(g\bge\bet\bta\bad\bdd\bdr\bre\bes\bss\bs 's_entry1 's_binder1 'st_discipline1 [... ...\n ...])\b)\n\n RETURNS: the binary object which s_binder1's function\n field is set to.\n\n NOTE: This looks in the running lisp's symbol table for\n a symbol with the same name as s_entry_\bi. It then\n creates a binary object whose entry field points\n to s_entry_\bi and whose discipline is\n st_discipline_\bi. This binary object is stored in\n the function field of s_binder_\bi. If\n st_discipline_\bi is nil, then \"subroutine\" is used\n by default. This is especially useful for _\bc_\bf_\ba_\bs_\bl\n users.\n\n (\b(m\bma\bac\bcr\bro\boe\bex\bxp\bpa\ban\bnd\bd 'g_form)\b)\n\n RETURNS: g_form after all macros in it are expanded.\n\n NOTE: This function will only macroexpand expressions\n which could be evaluated and it does not know\n about the special nlambdas such as _\bc_\bo_\bn_\bd and _\bd_\bo,\n thus it misses many macro expansions.\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b36\b6\n\n\n (\b(p\bpt\btr\br 'g_arg)\b)\n\n RETURNS: a value cell initialized to point to g_arg.\n\n (\b(q\bqu\buo\bot\bte\be g_arg)\b)\n\n RETURNS: g_arg.\n\n NOTE: the reader allows you to abbreviate (quote foo)\n as 'foo.\n\n (\b(k\bkw\bwo\bot\bte\be 'g_arg)\b)\n\n RETURNS: _\b(_\bl_\bi_\bs_\bt _\b(_\bq_\bu_\bo_\bt_\be _\bq_\bu_\bo_\bt_\be_\b) _\bg_\b__\ba_\br_\bg_\b).\n\n (\b(r\bre\bep\bpl\bla\bac\bce\be 'g_arg1 'g_arg2)\b)\n\n WHERE: g_arg1 and g_arg2 must be the same type of\n lispval and not symbols or hunks.\n\n RETURNS: g_arg2.\n\n SIDE EFFECT: The effect of _\br_\be_\bp_\bl_\ba_\bc_\be is dependent on the\n type of the g_arg_\bi although one will\n notice a similarity in the effects. To\n understand what _\br_\be_\bp_\bl_\ba_\bc_\be does to fixnum and\n flonum arguments, you must first under-\n stand that such numbers are `boxed' in\n FRANZ LISP. What this means is that if\n the symbol x has a value 32412, then in\n memory the value element of x's symbol\n structure contains the address of another\n word of memory (called a box) with 32412\n in it.\n\n Thus, there are two ways of changing the\n value of x: the first is to change the\n value element of x's symbol structure to\n point to a word of memory with a different\n value. The second way is to change the\n value in the box which x points to. The\n former method is used almost all of the\n time, the latter is used very rarely and\n has the potential to cause great confu-\n sion. The function _\br_\be_\bp_\bl_\ba_\bc_\be allows you to\n do the latter, i.e., to actually change\n the value in the box.\n\n You should watch out for these situations.\n If you do _\b(_\bs_\be_\bt_\bq _\by _\bx_\b), then both x and y\n will point to the same box. If you now\n _\b(_\br_\be_\bp_\bl_\ba_\bc_\be _\bx _\b1_\b2_\b3_\b4_\b5_\b), then y will also have\n the value 12345. And, in fact, there may\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b37\b7\n\n\n be many other pointers to that box.\n\n Another problem with replacing fixnums is\n that some boxes are read-only. The\n fixnums between -1024 and 1023 are stored\n in a read-only area and attempts to\n replace them will result in an \"Illegal\n memory reference\" error (see the descrip-\n tion of _\bc_\bo_\bp_\by_\bi_\bn_\bt_\b* for a way around this\n problem).\n\n For the other valid types, the effect of\n _\br_\be_\bp_\bl_\ba_\bc_\be is easy to understand. The fields\n of g_val1's structure are made eq to the\n corresponding fields of g_val2's struc-\n ture. For example, if x and y have\n lists as values then the effect of\n _\b(_\br_\be_\bp_\bl_\ba_\bc_\be _\bx _\by_\b) is the same as\n _\b(_\br_\bp_\bl_\ba_\bc_\ba _\bx _\b(_\bc_\ba_\br _\by_\b)_\b) and _\b(_\br_\bp_\bl_\ba_\bc_\bd _\bx _\b(_\bc_\bd_\br _\by_\b)_\b).\n\n (\b(s\bsc\bco\bon\bns\bs 'x_arg 'bs_rest)\b)\n\n WHERE: bs_rest is a bignum or nil.\n\n RETURNS: a bignum whose first bigit is x_arg and whose\n higher order bigits are bs_rest.\n\n (\b(s\bse\bet\btf\bf g_refexpr 'g_value)\b)\n\n NOTE: _\bs_\be_\bt_\bf is a generalization of setq. Information\n may be stored by binding variables, replacing\n entries of arrays, and vectors, or being put on\n property lists, among others. Setf will allow\n the user to store data into some location, by\n mentioning the operation used to refer to the\n location. Thus, the first argument may be par-\n tially evaluated, but only to the extent needed\n to calculate a reference. _\bs_\be_\bt_\bf returns g_value.\n (Compare to _\bd_\be_\bs_\be_\bt_\bq )\n\n\n ____________________________________________________\n\n (setf x 3) = (setq x 3)\n (setf (car x) 3) = (rplaca x 3)\n (setf (get foo 'bar) 3) = (putprop foo 3 'bar)\n (setf (vref vector index) value) = (vset vector index value)\n ____________________________________________________\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n D\bDa\bat\bta\ba S\bSt\btr\bru\buc\bct\btu\bur\bre\be A\bAc\bcc\bce\bes\bss\bs 2\b2-\b-3\b38\b8\n\n\n (\b(s\bso\bor\brt\bt 'l_data 'u_comparefn)\b)\n\n RETURNS: a list of the elements of l_data ordered by\n the comparison function u_comparefn.\n\n SIDE EFFECT: the list l_data is modified rather than\n allocated in new storage.\n\n NOTE: _\b(_\bc_\bo_\bm_\bp_\ba_\br_\be_\bf_\bn _\b'_\bg_\b__\bx _\b'_\bg_\b__\by_\b) should return something\n non-nil if g_x can precede g_y in sorted order;\n nil if g_y must precede g_x. If u_comparefn is\n nil, alphabetical order will be used.\n\n (\b(s\bso\bor\brt\btc\bca\bar\br 'l_list 'u_comparefn)\b)\n\n RETURNS: a list of the elements of l_list with the\n _\bc_\ba_\br's ordered by the sort function\n u_comparefn.\n\n SIDE EFFECT: the list l_list is modified rather than\n copied.\n\n NOTE: Like _\bs_\bo_\br_\bt, if u_comparefn is nil, alphabetical\n order will be used.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n", "meta": {"hexsha": "0083d1537168bd3871afcc7ce82fa6eda3cda76c", "size": 82324, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch2.r", "max_stars_repo_name": "krytarowski/franz-lisp-christos", "max_stars_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-17T08:05:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T08:07:14.000Z", "max_issues_repo_path": "lisplib/manual/ch2.r", "max_issues_repo_name": "krytarowski/franz-lisp-christos", "max_issues_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lisplib/manual/ch2.r", "max_forks_repo_name": "krytarowski/franz-lisp-christos", "max_forks_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-15T16:38:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-15T16:38:53.000Z", "avg_line_length": 32.8114786768, "max_line_length": 194, "alphanum_fraction": 0.4713570769, "num_tokens": 27745, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.15002882814282253, "lm_q2_score": 0.06560484202633814, "lm_q1q2_score": 0.009842617569706506}}
{"text": "# HSR/NHANES/HSA7708_replication\nscript = 'HSA 7708 Replication Project\\nhttps://doi.org/10.3945/jn.109.112573'\n\n## Pull NHANES Data\nFiles = c('DEMO', 'DIQ', 'BPQ', 'BPX', 'FSQ', 'LAB13', 'LAB13AM', 'LAB10AM')\nCycles = c('1999-2000')\nDescriptions = c('Demographic Variables and Sample Weights', 'Diabetes', 'Blood Pressure & Cholesterol', 'Blood Pressure', 'Food Security', 'Cholesterol - Total & HDL', 'Cholesterol - LDL & Triglycerides', 'Plasma Fasting Glucose, Serum C-peptide & Insulin')\ndf_NH1 = pull_stack(Files, Cycles, Descriptions)\ndf_NH1$FSDAD <- df_NH1$ADFDSEC\nhead(df_NH1)\n\n## Pull NHANES Data\nFiles = c('DEMO', 'DIQ', 'BPQ', 'BPX', 'FSQ', 'L13', 'L13AM', 'L10AM')\nCycles = c('2001-2002')\nDescriptions = c('Demographic Variables and Sample Weights', 'Diabetes', 'Blood Pressure & Cholesterol', 'Blood Pressure', 'Food Security', 'Cholesterol - Total & HDL', 'Cholesterol - LDL & Triglycerides', 'Plasma Fasting Glucose, Serum C-peptide & Insulin')\ndf_NH2 = pull_stack(Files, Cycles, Descriptions)\ndf_NH2$FSDAD <- df_NH2$ADFDSEC\nhead(df_NH2)\n\n## Pull NHANES Data\nFiles = c('DEMO', 'DIQ', 'BPQ', 'BPX', 'FSQ', 'L13', 'L13AM', 'L10AM')\nCycles = c('2003-2004')\nDescriptions = c('Demographic Variables and Sample Weights', 'Diabetes', 'Blood Pressure & Cholesterol', 'Blood Pressure', 'Food Security', 'Cholesterol - Total & HDL', 'Cholesterol - LDL & Triglycerides', 'Plasma Fasting Glucose, Serum C-peptide & Insulin')\ndf_NH3 = pull_stack(Files, Cycles, Descriptions)\nhead(df_NH3)\n\n## Combine cycles\nV = colnames(df_NH1)\ndf_V1 = as.data.frame(V)\ndf_V1$Cycle <- '1999-2000'\nV = colnames(df_NH2)\ndf_V2 = as.data.frame(V)\ndf_V2$Cycle <- '2001-2002'\nV = colnames(df_NH3)\ndf_V3 = as.data.frame(V)\ndf_V3$Cycle <- '2003-2004'\ndf_V = merge(df_V1, df_V2, by = 'V', how = 'inner')\ndf_V = merge(df_V, df_V3, by = 'V', how = 'inner')\nvars = c(as.character(df_V$V))\ndf_NH = rbind(df_NH1[vars], df_NH2[vars], df_NH3[vars])\nhead(df_NH)\n\n## Select variables\nNHANES_Variables <- c('RIAGENDR',\n 'RIDAGEYR',\n 'RIDRETH1',\n 'DMDEDUC2',\n 'INDFMPIR',\n 'BPQ020',\n 'BPXSY1',\n 'BPXDI1', \n 'BPQ080',\n 'LBXTC', \n 'DIQ010',\n 'LBXGLU',\n 'FSDAD')\nNHANES_Description <- c('Gender',\n 'Age at Screening',\n 'Race/Ethnicity',\n 'Education Level - Adults 20+',\n 'Family income to Poverty Ratio',\n 'Doctor told you that you have high blood pressure',\n 'Systolic Blood Pressure',\n 'Diastolic Blood Pressure',\n 'Doctor told you that you have high cholesterol',\n 'Total Cholesterol (mg/dL)',\n 'Doctor told you that you have diabetes',\n 'Glucose, plasma (mg/dL)',\n 'Adult food security category')\ndf_NHANES = data.frame(NHANES_Variables, NHANES_Description)\n\nStudy_Variables <- c('Gender', \n 'Age', \n 'Race', \n 'Education', \n 'Income', \n 'Hypertension', \n 'Hyperlipidemia', \n 'Diabetes', \n 'Food')\nStudy_Description <- c('Female Identifying (RIAGENDR == 2)', \n 'Age in years (18 to 65) (RIAGENDR = RIAGENDR)', \n 'Non-white or Hispanic (RIDRETH1 != 3)', \n 'High School Education or less (DMDEDUC2 < 3)', \n 'Income Relative to Poverty Line (above 200%)', \n 'Hypertension Diagnosed (BPQ020 = 1), Clinical (BPXDI1 > 140, BPXSY1 > 90)', \n 'Hyperlipidemia Diagnosed (BPQ080 = 1), Clinical (LBXTC > 200, LBDLDL > 160)', \n 'Diabetes Diagnosed (DQI010 = 1), Clinical (LBXGLU > 126)', \n 'Food Security Low or Very Low, (FSDAD >= 3)')\ndf_Study = data.frame(Study_Variables, Study_Description)\n\n### Modify Variables\nNew = 'Gender'\nOld = 'RIAGENDR'\nLevel = 2\ndf_NH[New] <- 0\ndf_NH[New][df_NH[Old] == Level] <- 1\nsummary(df_NH[New])\nNew = 'Age'\nOld = 'RIDAGEYR'\ndf_NH[New] <- df_NH[Old]\nsummary(df_NH[New])\nNew = 'Race'\nOld = 'RIDRETH1'\nLevel = 1\ndf_NH[New] <- 3\ndf_NH[New][df_NH[Old] != Level] <- 1\nsummary(df_NH[New])\nNew = 'Education'\nOld = 'DMDEDUC2'\nLevel = 3\ndf_NH[New] <- 0\ndf_NH[New][df_NH[Old] < Level] <- 1\nsummary(df_NH[New]) \nNew = 'Income'\nOld = 'INDHHINC'\ndf_NH[New] <- df_NH[Old]\ndf_NH[New][df_NH[New] > 5] <- 5\nsummary(df_NH[New]) \nNew = 'Hypertension'\nOld = 'BPQ020'\nOld2 = 'BPXDI1'\nOld3 = 'BPXSY1'\nLevel = 1\nLevel2 = 140\nLevel3 = 90\ndf_NH[New] <- 0\ndf_NH[New][df_NH[Old] == Level] <- 1\ndf_NH[New][df_NH[Old2] > Level2] <- 1\ndf_NH[New][df_NH[Old3] > Level3] <- 1\nsummary(df_NH[New])\nNew = 'Hyperlipidemia'\nOld = 'BPQ080'\nOld2 = 'LBXTC'\nOld3 = 'LBDLDL'\nLevel = 1\nLevel2 = 200\nLevel3 = 160\ndf_NH[New] <- 0\ndf_NH[New][df_NH[Old] == Level] <- 1\ndf_NH[New][df_NH[Old2] > Level2] <- 1\ndf_NH[New][df_NH[Old3] > Level3] <- 1\nsummary(df_NH[New])\nNew = 'Diabetes'\nOld = 'DIQ010'\nOld2 = 'LBXGLU'\nLevel = 1\nLevel2 = 126\ndf_NH[New] <- 0\ndf_NH[New][df_NH[Old] == Level] <- 1\ndf_NH[New][df_NH[Old2] > Level2] <- 1\nsummary(df_NH[New])\nNew = 'Food'\nOld = 'FSDAD'\nLevel = 3\ndf_NH[New] <- 0\ndf_NH[New][df_NH[Old] >= Level] <- 1\nsummary(df_NH[New]) \n\n### Export to Summary File\noptions(width = 250)\nsink(file = 'summary.txt', append = TRUE, type = 'output')\ncat(script, '\\n\\n', file = 'summary.txt', append = TRUE)\ncat('NHANES Cycles: ', Cycles, '\\n')\ncat('NHANES Files: ', Files, '\\n')\ncat('NHANES Variables:', '\\n', file = 'summary.txt', append = TRUE)\nsummary(df_NH[NHANES_Variables])\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat('NHANES Descriptions:', '\\n', file = 'summary.txt', append = TRUE)\ndf_NHANES\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat('Study Variables:', '\\n', file = 'summary.txt', append = TRUE)\nsummary(df_NH[Study_Variables])\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat('Study Descriptions:', '\\n', file = 'summary.txt', append = TRUE)\ndf_Study\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat(c('####################', '\\n\\n'), file = 'summary.txt', append = TRUE)\nsink()\n\n### Survey Weighted Generalizaed Reghression Models\nD = df_NH[which(df_NH$INDFMPIR < 2 & df_NH$RIDAGEYR > 18 & df_NH$RIDAGEYR < 65), ]\nS = nhanes_survey_design(D, weights_column = 'WTINT2YR')\nX = 'Food'\nA = c('Gender', 'Age', 'Race', 'Education', 'Income')\nY1 = 'Hypertension'\nF1 = as.formula(paste(Y1, ' ~ ', paste(X, '+', paste(A, collapse = ' + ')), sep = ''))\nM1 = svyglm(F1, design = S, data = D, family = poisson()) # Log link [Y = ln(DV)] (aka: Log-Linear) with poisson error (aka: Poisson regression)\nY2 = 'Hyperlipidemia'\nF2 = as.formula(paste(Y2, ' ~ ', paste(X, '+', paste(A, collapse = ' + ')), sep = ''))\nM2 = svyglm(F2, design = S, data = D, family = poisson()) # Log link [Y = ln(DV)] (aka: Log-Linear) with poisson error (aka: Poisson regression)\nY3 = 'Diabetes'\nF3 = as.formula(paste(Y3, ' ~ ', paste(X, '+', paste(A, collapse = ' + ')), sep = ''))\nM3 = svyglm(F3, design = S, data = D, family = poisson()) # Log link [Y = ln(DV)] (aka: Log-Linear) with poisson error (aka: Poisson regression)\n\n### Export to Summary File\nsink(file = 'summary.txt', append = TRUE, type = 'output')\ncat('Generalized Regression Models using Survey Weights', '\\n\\n', file = 'summary.txt', append = TRUE)\ncat('Title:\\n', Y1, 'and', X, 'adjusted by', A, '\\n', file = 'summary.txt', append = TRUE)\nsummary(M1)\ncat('Odds Ratios: ', '\\n', 'Intercept', X, A, '\\n', exp(coef(M1)), '\\n', file = 'summary.txt', append = TRUE)\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat('Title:\\n', Y2, 'and', X, 'adjusted by', A, '\\n', file = 'summary.txt', append = TRUE)\nsummary(M2)\ncat('Odds Ratios: ', '\\n', 'Intercept', X, A, '\\n', exp(coef(M2)), '\\n', file = 'summary.txt', append = TRUE)\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat('Title:\\n', Y3, 'and', X, 'adjusted by', A, '\\n', file = 'summary.txt', append = TRUE)\nsummary(M3)\ncat('Odds Ratios: ', '\\n', 'Intercept', X, A, '\\n', exp(coef(M3)), '\\n', file = 'summary.txt', append = TRUE)\ncat('\\n', file = 'summary.txt', append = TRUE)\ncat(c('####################', '\\n\\n'), file = 'summary.txt', append = TRUE)\nsink()", "meta": {"hexsha": "e89fe2d244e9796e1b1d6df57b75e79ca57788b1", 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NO\n2. NO", "lm_q1_score": 0.37022539259558657, "lm_q2_score": 0.025178843620773834, "lm_q1q2_score": 0.009321847264603873}}
{"text": "#' @title Rarefy ampvis2 object\n#' @description This is just a wrapper of \\code{\\link[vegan]{rrarefy}} with convenient error messages and adjusted to work with ampvis2 objects.\n#'\n#' @param data (\\emph{required}) Data list as loaded with \\code{\\link{amp_load}}.\n#' @param rarefy (\\emph{required}) Passed directly to \\code{\\link[vegan]{rrarefy}}.\n#'\n#' @return An ampvis2 object with rarefied OTU abundances.\n#' @importFrom vegan rrarefy\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\namp_rarefy <- function(data, rarefy) {\n ### Data must be in ampvis2 format\n if (class(data) != \"ampvis2\") {\n stop(\"The provided data is not in ampvis2 format. Use amp_load() to load your data before using ampvis2 functions. (Or class(data) <- \\\"ampvis2\\\", if you know what you are doing.)\", call. = FALSE)\n }\n \n set.seed(0) # for reproducibility\n \n reads <- colSums(data$abund)\n maxreads <- max(reads)\n minreads <- min(reads)\n if (is.numeric(rarefy)) {\n if (rarefy > maxreads) {\n stop(\"The chosen rarefy size is larger than the largest amount of reads in any sample (\", as.character(maxreads), \").\", call. = FALSE)\n } else if (rarefy < minreads) {\n data$abund <- suppressWarnings(vegan::rrarefy(t(data$abund), sample = rarefy)) %>%\n t() %>%\n as.data.frame()\n warning(\"The chosen rarefy size (\", as.character(rarefy), \") is smaller than the smallest amount of reads in any sample (\", as.character(minreads), \").\", call. = FALSE)\n } else {\n data$abund <- suppressWarnings(vegan::rrarefy(t(data$abund), sample = rarefy)) %>%\n t() %>%\n as.data.frame()\n if (minreads < rarefy) {\n message(\"The following sample(s) have not been rarefied (less than \", as.character(rarefy), \" reads):\\n\", paste(rownames(data$metadata[which(reads < rarefy), ]), collapse = \", \"))\n }\n }\n } else if (!is.numeric(rarefy)) {\n stop(\"Argument rarefy must be numerical.\", call. = FALSE)\n }\n return(data)\n}\n\n#' @title Tidy up taxonomy\n#' @description Used internally in other ampvis functions.\n#'\n#' @param data (\\emph{required}) Data list as loaded with \\code{\\link{amp_load}}.\n#' @param tax_empty How to show OTUs without taxonomic information. One of the following:\n#' \\itemize{\n#' \\item \\code{\"remove\"}: Remove OTUs without taxonomic information.\n#' \\item \\code{\"best\"}: (\\emph{default}) Use the best classification possible.\n#' \\item \\code{\"OTU\"}: Display the OTU name.\n#' }\n#' @param tax_class Converts a specific phylum to class level instead, e.g. \\code{\"p__Proteobacteria\"}.\n#' @param tax_level The taxonomic level to remove OTUs with no assigned taxonomy, only used when \\code{tax_empty = \"remove\"}. (\\emph{default:} \\code{\"Genus\"})\n#'\n#' @return A list with 3 dataframes (4 if reference sequences are provided).\n#' @importFrom dplyr mutate\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\n#' @author Mads Albertsen \\email{MadsAlbertsen85@@gmail.com}\namp_rename <- function(data, tax_class = NULL, tax_empty = \"best\", tax_level = \"Genus\") {\n tax <- data[[\"tax\"]]\n \n ## First make sure that all entries are strings\n tax[] <- lapply(tax, as.character)\n \n ## Change a specific phylum to class level\n if (!is.null(tax_class)) {\n for (i in 1:nrow(tax)) {\n if (!is.na(tax$Phylum[i]) & tax$Phylum[i] %in% tax_class) {\n tax$Phylum[i] <- tax$Class[i]\n }\n }\n }\n \n ## Remove QIIME taxonomy format prefixes across all levels\n tax[] <- lapply(tax, gsub, pattern = \"^[dkpcofgs]_+\", replacement = \"\")\n \n ## Check if there is a species level otherwise add it for consistency\n if (is.null(tax$Species)) {\n tax$Species <- \"\"\n }\n \n ## NA's as empty strings\n tax[is.na(tax)] <- \"\"\n \n ## How to handle empty taxonomic assignments\n if (tax_empty == \"OTU\") {\n for (i in 1:nrow(tax)) {\n if (tax[i, \"Species\"] == \"\") {\n tax[i, \"Species\"] <- rownames(tax)[i]\n }\n if (tax[i, \"Genus\"] == \"\") {\n tax[i, \"Genus\"] <- rownames(tax)[i]\n }\n if (tax[i, \"Family\"] == \"\") {\n tax[i, \"Family\"] <- rownames(tax)[i]\n }\n if (tax[i, \"Order\"] == \"\") {\n tax[i, \"Order\"] <- rownames(tax)[i]\n }\n if (tax[i, \"Class\"] == \"\") {\n tax[i, \"Class\"] <- rownames(tax)[i]\n }\n if (tax[i, \"Phylum\"] == \"\") {\n tax[i, \"Phylum\"] <- rownames(tax)[i]\n }\n }\n }\n \n ## Handle empty taxonomic strings\n rn <- rownames(tax) # damn rownames are silently dropped by mutate()\n if (tax_empty == \"best\") {\n tax <- mutate(tax, Kingdom, Kingdom = ifelse(Kingdom == \"\", \"Unclassified\", Kingdom)) %>%\n mutate(Phylum, Phylum = ifelse(Phylum == \"\", paste(\"k__\", Kingdom, \"_\", rownames(tax), sep = \"\"), Phylum)) %>%\n mutate(Class, Class = ifelse(Class == \"\", ifelse(grepl(\"__\", Phylum), Phylum, paste(\"p__\", Phylum, \"_\", rownames(tax), sep = \"\")), Class)) %>%\n mutate(Order, Order = ifelse(Order == \"\", ifelse(grepl(\"__\", Class), Class, paste(\"c__\", Class, \"_\", rownames(tax), sep = \"\")), Order)) %>%\n mutate(Family, Family = ifelse(Family == \"\", ifelse(grepl(\"__\", Order), Order, paste(\"o__\", Order, \"_\", rownames(tax), sep = \"\")), Family)) %>%\n mutate(Genus, Genus = ifelse(Genus == \"\", ifelse(grepl(\"__\", Family), Family, paste(\"f__\", Family, \"_\", rownames(tax), sep = \"\")), Genus)) %>%\n mutate(Species, Species = ifelse(Species == \"\", ifelse(grepl(\"__\", Genus), Genus, paste(\"g__\", Genus, \"_\", rownames(tax), sep = \"\")), Species))\n }\n rownames(tax) <- rn\n \n if (tax_empty == \"remove\") {\n abund <- data[[\"abund\"]]\n tax <- subset(tax, tax[, tax_level] != \"\")\n abund <- subset(abund, rownames(abund) %in% rownames(tax))\n data[[\"abund\"]] <- abund\n }\n data[[\"tax\"]] <- tax\n rownames(data[[\"tax\"]]) <- rownames(tax)\n \n return(data)\n}\n\n#' @title Get data from the MiDAS field guide API\n#' @description Gets all fields from the MiDAS field guide (\\url{https://midasfieldguide.org}) returned in a list.\n#'\n#' @return A list with all fields.\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\ngetMiDASFGData <- function() {\n checkReqPkg(\"jsonlite\")\n jsonlite::read_json(\"http://midasfieldguide.org/api/microbes/fieldguide\")\n}\n\n#' @title Extract functional information about Genera from the MiDAS field guide\n#' @description Extract field related to properties and metabolism of all Genera from a list obtained by \\code{\\link{getMiDASFGData}} and return in a data frame.\n#'\n#' @param FGList Data list obtained by \\code{\\link{getMiDASFGData}}.\n#'\n#' @importFrom data.table rbindlist\n#'\n#' @return A data frame where each row is a Genus and each column is a \"function\".\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\nextractFunctions <- function(FGList) {\n functions <- lapply(FGList, function(x) {\n outList <- lapply(c(x[[\"properties\"]], x[[\"metabolism\"]]), function(y) {\n if (y[[\"In situ\"]] == \"Positive\" |\n (any(y[[\"In situ\"]] %in% c(\"Variable\", \"Not Assessed\")) &\n y[[\"Other\"]] == \"Positive\")) {\n return(\"POS\")\n }\n if (y[[\"In situ\"]] == \"Negative\" |\n (y[[\"In situ\"]] == \"Not Assessed\" & y[[\"Other\"]] == \"Negative\")) {\n return(\"NEG\")\n }\n if (y[[\"In situ\"]] == \"Variable\" |\n (y[[\"In situ\"]] == \"Not Assessed\" & y[[\"Other\"]] == \"Variable\")) {\n return(\"VAR\")\n }\n if (all(c(y[[\"In situ\"]], y[[\"Other\"]]) %in% \"Not Assessed\")) {\n return(\"NT\")\n }\n })\n c(\n \"Genus\" = gsub(\"^Ca \", \"Ca_\", x[[\"name\"]]),\n \"MiDAS\" = \"POS\",\n outList\n )\n })\n outDF <- as.data.frame(data.table::rbindlist(functions, fill = TRUE))\n outDF[is.na(outDF)] <- \"NT\"\n return(outDF)\n}\n\n#' @title Calculate weighted or unweighted UniFrac distances. Adopted from fastUniFrac() from phyloseq\n#'\n#' @param abund Abundance table with OTU counts, in \\code{ampvis2} objects it is available with simply data$abund\n#' @param tree Phylogenetic tree (rooted and with branch lengths) as loaded with \\code{\\link[ape]{read.tree}}.\n#' @param weighted Calculate weighted or unweighted UniFrac distances.\n#' @param normalise Should the output be normalised such that values range from 0 to 1 independent of branch length values? Note that unweighted UniFrac is always normalised. (\\emph{default:} \\code{TRUE})\n#' @param num_threads The number of threads to be used for calculating UniFrac distances. If set to more than \\code{1} then this is set by using \\code{\\link[doParallel]{registerDoParallel}}. (\\emph{default:} \\code{1})\n#'\n#' @importFrom ape is.rooted node.depth node.depth.edgelength reorder.phylo\n#' @return A distance matrix of class \\code{dist}.\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\nunifrac <- function(abund,\n tree,\n weighted = FALSE,\n normalise = TRUE,\n num_threads = 1L) {\n checkReqPkg(\"doParallel\")\n # foreach is installed with doParallel, but its namespace needs to be loaded\n checkReqPkg(\"foreach\")\n \n # check tree\n if (!class(tree) == \"phylo\") {\n stop(\"The provided phylogenetic tree must be of class \\\"phylo\\\" as loaded with the ape::read.tree() function.\", call. = FALSE)\n }\n if (!ape::is.rooted(tree)) {\n stop(\"Tree is not rooted!\", call. = FALSE)\n # message(\"Tree is not rooted, performing a midpoint root\")\n # tree <- phytools::midpoint.root(tree)\n }\n if (is.null(tree$edge.length)) {\n stop(\"Tree has no branch lengths, cannot compute UniFrac\", call. = FALSE)\n }\n OTU <- as.matrix(abund)\n ntip <- length(tree$tip.label)\n if (ntip != nrow(OTU)) {\n stop(\"OTU table and phylogenetic tree do not match\", call. = FALSE)\n }\n # if(!all(rownames(OTU) == tree$tip.label))\n # OTU <- OTU[tree$tip.label, , drop = FALSE]\n node.desc <- matrix(tree$edge[order(tree$edge[, 1]), ][, 2], byrow = TRUE, ncol = 2)\n edge_array <- matrix(0,\n nrow = ntip + tree$Nnode,\n ncol = ncol(OTU),\n dimnames = list(NULL, sample_names = colnames(OTU))\n )\n edge_array[1:ntip, ] <- OTU\n ord.node <- order(ape::node.depth(tree))[(ntip + 1):(ntip + tree$Nnode)]\n for (i in ord.node) {\n edge_array[i, ] <- colSums(edge_array[node.desc[i - ntip, ], , drop = FALSE], na.rm = TRUE)\n }\n edge_array <- edge_array[tree$edge[, 2], ]\n rm(node.desc)\n if (isFALSE(weighted)) {\n edge_occ <- (edge_array > 0) - 0\n }\n if (isTRUE(weighted) & isTRUE(normalise)) {\n z <- ape::reorder.phylo(tree, order = \"postorder\")\n tipAges <- ape::node.depth.edgelength(tree)\n tipAges <- tipAges[1:length(tree$tip.label)]\n names(tipAges) <- z$tip.label\n tipAges <- tipAges[rownames(OTU)]\n }\n samplesums <- colSums(OTU)\n if (num_threads == 1L) {\n foreach::registerDoSEQ()\n } else if (num_threads > 1) {\n doParallel::registerDoParallel(num_threads)\n }\n spn <- combn(colnames(OTU), 2, simplify = FALSE)\n distlist <- foreach::foreach(i = spn) %dopar% {\n A <- i[1]\n B <- i[2]\n AT <- samplesums[A]\n BT <- samplesums[B]\n if (isTRUE(weighted)) {\n wUF_branchweight <- abs(edge_array[, A] / AT - edge_array[, B] / BT)\n numerator <- sum(\n {\n tree$edge.length * wUF_branchweight\n },\n na.rm = TRUE\n )\n if (isFALSE(normalise)) {\n return(numerator)\n } else {\n denominator <- sum(\n {\n tipAges * (OTU[, A] / AT + OTU[, B] / BT)\n },\n na.rm = TRUE\n )\n return(numerator / denominator)\n }\n } else {\n edge_occ_AB <- edge_occ[, c(A, B)]\n edge_uni_AB_sum <- sum((tree$edge.length * edge_occ_AB)[rowSums(edge_occ_AB, na.rm = TRUE) < 2, ], na.rm = TRUE)\n uwUFpairdist <- edge_uni_AB_sum / sum(tree$edge.length[rowSums(edge_occ_AB, na.rm = TRUE) > 0])\n return(uwUFpairdist)\n }\n }\n UniFracMat <- matrix(NA_real_, ncol(OTU), ncol(OTU))\n rownames(UniFracMat) <- colnames(UniFracMat) <- colnames(OTU)\n matIndices <- do.call(rbind, spn)[, 2:1]\n if (!is.matrix(matIndices)) matIndices <- matrix(matIndices, ncol = 2)\n UniFracMat[matIndices] <- unlist(distlist)\n return(as.dist(UniFracMat))\n}\n\n#' Calculate Jensen-Shannon Divergence distances\n#'\n#' @param abund Abundance table with OTU counts, in \\code{ampvis2} objects it is available with simply data$abund.\n#' @param pseudocount Pseudocount to use instead of 0-divisions. (\\emph{default:} \\code{0.000001})\n#'\n#' @return A distance matrix of class \\code{dist}.\n# This is based on http://enterotype.embl.de/enterotypes.html\n# Abundances of 0 will be set to the pseudocount value to avoid 0-value denominators\n# Unfortunately this code is SLOOOOOOOOW\ndist.JSD <- function(abund, pseudocount = 0.000001) {\n inMatrix <- t(abund)\n KLD <- function(x, y) sum(x * log(x / y))\n JSD <- function(x, y) sqrt(0.5 * KLD(x, (x + y) / 2) + 0.5 * KLD(y, (x + y) / 2))\n matrixColSize <- length(colnames(inMatrix))\n matrixRowSize <- length(rownames(inMatrix))\n colnames <- colnames(inMatrix)\n resultsMatrix <- matrix(0, matrixColSize, matrixColSize)\n \n inMatrix <- apply(inMatrix, 1:2, function(x) ifelse(x == 0, pseudocount, x))\n \n for (i in 1:matrixColSize) {\n for (j in 1:matrixColSize) {\n resultsMatrix[i, j] <- JSD(\n as.vector(inMatrix[, i]),\n as.vector(inMatrix[, j])\n )\n }\n }\n colnames -> colnames(resultsMatrix) -> rownames(resultsMatrix)\n as.dist(resultsMatrix) -> resultsMatrix\n attr(resultsMatrix, \"method\") <- \"dist\"\n return(resultsMatrix)\n}\n\n#' @title Find lowest taxonomic level\n#' @description Finds the lowest taxonomic level of two given levels in \\code{tax}. The hierarchic order of taxonomic levels is simply taken from the order of column names in \\code{tax}\n#'\n#' @param tax_aggregate A character vector of one or more taxonomic levels (exactly as in the column names of \\code{ampvis2obj$tax}), fx Genus or Species. (\\emph{default:} \\code{NULL})\n#' @param tax_add A second character vector similar to \\code{tax_aggregate}. (\\emph{default:} \\code{NULL})\n#' @param tax The taxonomy table from an ampvis2 object (\\code{ampvis2obj$tax})\n#'\n#' @return A length one character vector with the lowest taxonomic level.\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\ngetLowestTaxLvl <- function(tax, tax_aggregate = NULL, tax_add = NULL) {\n if (is.null(tax_aggregate) & is.null(tax_add)) {\n tax_aggregate <- colnames(tax)[ncol(tax)]\n }\n # find the lowest taxonomic level of tax_aggregate and tax_add\n taxlevels <- factor(\n x = colnames(tax),\n levels = colnames(tax)\n )\n lowestlevel <- as.character(taxlevels[max(as.numeric(c(\n taxlevels[which(taxlevels %in% tax_aggregate)],\n taxlevels[which(taxlevels %in% tax_add)]\n )))])\n return(lowestlevel)\n}\n\n#' @title Aggregate OTUs to a specific taxonomic level\n#' @description Calculates the sum of OTUs per taxonomic level\n#'\n#' @param abund The OTU abundance table from an ampvis2 object (\\code{ampvis2obj$abund})\n#' @param tax The OTU abundance table from an ampvis2 object (\\code{ampvis2obj$tax})\n#' @param tax_aggregate Aggregate (sum) OTU's to a specific taxonomic level. (\\emph{default:} \\code{\"OTU\"})\n#' @param tax_add Add additional (higher) taxonomic levels to the taxonomy string. The OTU's will be aggregated to whichever level of the \\code{tax_aggregate} and \\code{tax_add} vectors is the lowest. (\\emph{default:} \\code{NULL})\n#' @param calcSums Whether to include the sums of read counts for each sample and taxonomic group. (\\emph{default:} \\code{TRUE})\n#' @param format Output format, \\code{\"long\"} or \\code{\"abund\"}. \\code{\"abund\"} corresponds to that of a read counts table with samples as columns and the aggregated taxa as rows.\n#'\n#' @importFrom data.table data.table melt\n#' @return A data.table.\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\naggregate_abund <- function(abund,\n tax,\n tax_aggregate = \"OTU\",\n tax_add = NULL,\n calcSums = TRUE,\n format = \"long\") {\n if (any(colnames(abund) %in% \"Display\")) {\n stop(\"A column is named \\\"Display\\\" in the OTU abundance table, please change it to continue\", call. = FALSE)\n }\n \n # make sure all tax columns are of type character (nchar() does not allow factors)\n tax[] <- lapply(tax, as.character)\n \n # find the lowest taxonomic level of tax_aggregate and tax_add\n lowestTaxLevel <- getLowestTaxLvl(\n tax = tax,\n tax_aggregate = tax_aggregate,\n tax_add = tax_add\n )\n \n # Remove all OTUs that are not assigned at the chosen taxonomic level\n # and print a status message with the number of removed OTUs\n newtax <- tax[which(nchar(tax[[lowestTaxLevel]]) > 1 &\n !is.na(tax[[lowestTaxLevel]]) &\n !grepl(\"^\\\\b[dkpcofgs]*[_:;]*\\\\b$\", tax[[lowestTaxLevel]])), ]\n newabund <- abund[rownames(newtax), , drop = FALSE]\n if (nrow(newtax) != nrow(tax)) {\n message(paste0(\n nrow(tax) - nrow(newtax),\n \" OTUs (out of \",\n nrow(tax),\n \") with no assigned taxonomy at \",\n lowestTaxLevel,\n \" level were removed before aggregating OTUs\"\n ))\n }\n \n abundTax <- data.table::data.table(\n newabund,\n Display = apply(\n newtax[, c(tax_add, tax_aggregate), drop = FALSE],\n 1,\n paste,\n collapse = \"; \"\n )\n )\n abundAggr <- data.table::melt(\n abundTax,\n id.vars = \"Display\",\n variable.name = \"Sample\",\n value.name = \"Abundance\",\n variable.factor = FALSE\n )\n if (isTRUE(calcSums)) {\n abundAggr[,\n Sum := sum(Abundance),\n keyby = .(Display, Sample)\n ]\n }\n if (format == \"long\") {\n out <- abundAggr\n } else if (format == \"abund\") {\n out <- as.data.frame(\n data.table::dcast(abundAggr,\n Display ~ Sample,\n value.var = \"Abundance\",\n fun.aggregate = sum\n )\n )\n rownames(out) <- as.character(out[[1]])\n out <- out[, -1, drop = FALSE]\n } else {\n stop(\"format must be either \\\"long\\\" or \\\"abund\\\"\")\n }\n return(out)\n}\n\nabundAreCounts <- function(data) {\n ### Data must be in ampvis2 format\n is_ampvis2(data)\n \n # check if $abund contains read counts and not normalised counts or any decimals\n all(\n !isTRUE(attributes(data)$normalised),\n all(data$abund %% 1L == 0),\n !all(colSums(data$abund) == 100)\n )\n}\n\n#' @title Normalise read counts to 100, i.e. in percent relative abundance per sample\n#'\n#' @param data (\\emph{required}) Data list as loaded with \\code{\\link{amp_load}}.\n#'\n#' @return A modifed ampvis2 object\nnormaliseTo100 <- function(data) {\n ### Data must be in ampvis2 format\n is_ampvis2(data)\n \n if (!abundAreCounts(data)) {\n warning(\"The data has already been normalised. Setting normalise = TRUE (the default) will normalise the data again and the relative abundance information about the original data of which the provided data is a subset will be lost.\", call. = FALSE)\n }\n # normalise each sample to sample totals, skip samples with 0 sum to avoid NaN's\n tmp <- data$abund[, which(colSums(data$abund) != 0), drop = FALSE]\n if (nrow(tmp) == 1L) {\n # apply returns a vector and drops rownames if only 1 row, therefore set to 100 instead\n tmp[1L, ] <- 100L\n } else if (nrow(tmp) > 1L) {\n tmp <- as.data.frame(apply(tmp, 2, function(x) {\n x / sum(x) * 100\n }))\n }\n data$abund[, which(colSums(data$abund) != 0)] <- tmp\n attributes(data)$normalised <- TRUE\n return(data)\n}\n\n#' @title Filter species by a threshold in percent\n#'\n#' @param data (\\emph{required}) Data list as loaded with \\code{\\link{amp_load}}.\n#' @param filter_species Remove low abundant OTU's across all samples below this threshold in percent. (\\emph{default}: \\code{0})\n#'\n#' @importFrom ape drop.tip\n#' @return An ampvis2 object\nfilter_species <- function(data, filter_species = 0) {\n ### Data must be in ampvis2 format\n is_ampvis2(data)\n \n if (is.numeric(filter_species)) {\n if (filter_species > 0) {\n # First transform to percentages\n abund_pct <- data$abund\n abund_pct[, which(colSums(abund_pct) != 0)] <- as.data.frame(apply(abund_pct[, which(colSums(abund_pct) != 0), drop = FALSE], 2, function(x) x / sum(x) * 100))\n rownames(abund_pct) <- rownames(data$abund) # keep rownames\n \n # Then filter low abundant OTU's where ALL samples have below the threshold set with filter_species in percent\n abund_subset <- abund_pct[!apply(abund_pct, 1, function(row) all(row <= filter_species)), , drop = FALSE] # remove low abundant OTU's\n data$abund <- data$abund[which(rownames(data$abund) %in% rownames(abund_subset)), , drop = FALSE]\n rownames(data$tax) <- data$tax$OTU\n \n # also filter taxonomy, tree, and sequences\n data$tax <- data$tax[which(rownames(data$tax) %in% rownames(abund_subset)), , drop = FALSE]\n \n if (!is.null(data$tree)) {\n data$tree <- ape::drop.tip(\n phy = data$tree,\n tip = data$tree$tip.label[!data$tree$tip.label %in% data$tax$OTU]\n )\n }\n \n if (!is.null(data$refseq)) {\n if (!is.null(names(data$refseq))) {\n # sometimes there is taxonomy alongside the OTU ID's. Anything after a \";\" will be ignored\n names_stripped <- stringr::str_split(names(data$refseq), \";\", simplify = TRUE)[, 1]\n data$refseq <- data$refseq[names_stripped %in% rownames(data$abund)]\n } else if (is.null(names(data$refseq))) {\n warning(\"DNA sequences have not been subsetted, could not find the names of the sequences in data$refseq.\", call. = FALSE)\n }\n }\n }\n }\n return(data)\n}\n\n#' @title Check if data has class \"ampvis2\"\n#' @description Checks if the object is of class \"ampvis2\".\n#'\n#' @param data Object to check\n#'\n#' @return Returns error if \\code{!inherits(data, \"ampvis2\")}, otherwise \\code{invisible(TRUE)}.\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\nis_ampvis2 <- function(data) {\n if (!inherits(data, \"ampvis2\")) {\n stop(\"The provided data is not in ampvis2 format. Use amp_load() to load your data before using ampvis2 functions. (Or class(data) <- \\\"ampvis2\\\", if you know what you are doing.)\", call. = FALSE)\n }\n invisible(TRUE)\n}\n\n#' @title Check for installed package\n#' @description Returns error if a required package is not installed. Mostly used for checking whether packages listed under the Suggests field in the DESCRIPTION file is installed.\n#'\n#' @param pkg The package to check for, a character of length 1.\n#' @param msg Optionally additional text appended (with \\code{paste0}) to the default error message.\n#'\n#' @return Returns error and message if not installed, otherwise \\code{invisible(TRUE)}\n#' @author Kasper Skytte Andersen \\email{ksa@@bio.aau.dk}\ncheckReqPkg <- function(pkg, msg = \"\") {\n stopifnot(is.character(pkg), length(pkg) == 1L, nchar(pkg) > 0)\n if (!requireNamespace(pkg, quietly = TRUE)) {\n stopifnot(is.character(msg))\n stop(\n paste0(\"Package '\", pkg, \"' is required but not installed.\", msg),\n call. = FALSE\n )\n }\n require(pkg, quietly = TRUE, character.only = TRUE)\n}\n\n#' Replacement for \":::\" to suppress R CMD CHECK warnings\n# `%:::%` <- function(pkg, fun)\n# get(fun, envir = asNamespace(pkg), inherits = FALSE)", "meta": {"hexsha": "d5ba5fafdee813035f3d4a18f96b67c7922ef8fc", "size": 23211, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/ampvis2_internals.r", "max_stars_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_stars_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "scripts/ampvis2_internals.r", "max_issues_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_issues_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "scripts/ampvis2_internals.r", "max_forks_repo_name": "pietervanveelen/biogeography_lark_microbiota", "max_forks_repo_head_hexsha": "da619565b9e55ebca4cf89462431eac2a8f721e1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.1542553191, "max_line_length": 252, "alphanum_fraction": 0.6334496575, "num_tokens": 6725, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.3040416749665475, "lm_q2_score": 0.029760095236751886, "lm_q1q2_score": 0.009048309202946015}}
{"text": "\n= La població d'Espanya i d'Europa\n\n:author\n Alfons Rovira\n:theme\n debian\n\n= Població\n\n((*Població absoluta*)): d'un territori és el nombre de persones que hi viuen.\n\n= Factors\n\n * (('wait'))Relleu\n * (('wait'))Clima\n\n= Raons\n\n * (('wait')) A on el relleu i el clima és més favorable major indústria i serveis.\n * (('wait')) Àrees de muntanya i d'interior. Clima més desfavorable. Poc poblades i activitats agràries.\n\n= Densitat\n\n * ((*Densitat de població*)): nombre d'habitants que viuen en un kilòmetre quadrat de superfície.\n * Espanya la densitat de població: 93,4 h/km (('sup:2'))\n\n= Definicions\n\n((*Demografia*)): Ciència que estudia la població, com es distribueix, com evoluciona la població en un lloc determinat.\n\n= Definicions\n\n((*Població urbana*)): Població que viu a municipis de més de ((*10.000*)) habitants.\n\n= Definicions\n\n((*Població absoluta*)): d'un territori és el nombre de persones que hi viuen.\n\n\n= Definicions\n\n((*Natalitat*)): nombre d'infants que naixen en un lloc durant un any.\n\n= Definicions\n\n((*Mortalitat*)): nombre d'persones que moren en un lloc durant un any.\n\n= Definicions\n\n((*Esperança de vida*)): mitjana d'anys que viu una població.\n\n= Definicions\n\n((*Creixement natural*)): diferència entre el total de naixements o natalitat i el total de defuncions o mortalitat.\n\n= Definicions\n\n((*Creixement real*)): creixement natural + saldo migratori\n\n= Definicions\n\n((*Saldo migratori*)): immigrants - emigrants\n\n= Definicions\n\n((*Població activa*)): població ocupada + població en atur\n\n\n= Definicions\n\n((*Població no activa*)): infants, estudinats, mestresses de casa i jubitalts.\n\n= Definicions\n\n((*Pirámide de població*)): és un gràfic que ordena els habitants d'un lloc per grups d'edat i sexe en un moment concret.\n\nParts:\n\n * Base: població jove\n * Tronc: població adulta\n * Cim: població anciana\n\n= Causes de les migracions\n\n * (('wait'))((*Socials i econòmiques*)): pobresa, cerca de millors condicions de vida\n * (('wait'))((*Polítiques*)): guerres, conflictes racials, conflictes religiosos\n * (('wait'))((*Naturals*)): huracans, tsunameis, terratremols sequeres\n * (('wait'))((*Familiars*)): reagrupament familiar\n\n\n= Definicions\n\n((*Migració exterior*)): abandonament del país d'origen.\n\n= Definicions\n\n((*Migració interior*)): moviment de la població dins d'un país.\n\n= Conseqüències de les migracions\n\n * ((*Dinàmica de població*)): aportació de població i augment de la natalitat\n * ((*Economia*)): creixement econòmic\n * ((*Diversistat cultural*)): contacte amb noves cultures i costums\n\n= Població europea\n\n * (('wait')) És irregular\n * (('wait')) Creixement dèbil\n * (('wait')) Envellida\n * (('wait')) Urbana\n * (('wait')) Amb molta immigració\n", "meta": {"hexsha": "af17244a7f58ec6229ee924631c1c3d8eccf1d32", "size": 2723, "ext": "rd", "lang": "R", "max_stars_repo_path": "01_socials/04/04_ud4.rd", "max_stars_repo_name": "inclusa/5pri", "max_stars_repo_head_hexsha": "d148abd29118857e5643efac8d4d65fbbf56b312", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "01_socials/04/04_ud4.rd", "max_issues_repo_name": "inclusa/5pri", "max_issues_repo_head_hexsha": "d148abd29118857e5643efac8d4d65fbbf56b312", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "01_socials/04/04_ud4.rd", "max_forks_repo_name": "inclusa/5pri", "max_forks_repo_head_hexsha": "d148abd29118857e5643efac8d4d65fbbf56b312", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.0973451327, "max_line_length": 121, "alphanum_fraction": 0.7010650018, "num_tokens": 911, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.24508500210441891, "lm_q2_score": 0.03676946383261257, "lm_q1q2_score": 0.009011644120794207}}
{"text": "/*\tNeutron Scattering.r -- resources for \"Neutron Scattering\" XOP */\n\n// Igor resources\n#include \"XOPStandardHeaders.r\"\n\nresource 'vers' (1) { /* XOP version info */\n 0x01, 0x16, final, 0x00, 0, /* version bytes and country integer */\n \"1.22\",\n \"1.22, © 2000-2021 ILL/SANE, all rights reserved.\"\n};\n\nresource 'vers' (2) { /* Igor version info */\n 0x08, 0x04, release, 0x00, 0, /* version bytes and country integer */\n \"8.04\",\n \"(for Igor Pro 8.04 or later)\"\n};\n\nresource 'STR#' (1100) {\t\t\t\t\t/* custom error messages */\n\t{\n\t\t/* [1] */\n\t\t\"Neutron Scattering XOP requires Igor 8.04 or later.\",\n\t\t/* [2] */\n\t\t\"Null Counting Time.\",\n\t\t/* [3] */\n\t\t\"Null Count Rate.\",\n\t\t/* [4] */\n\t\t\"Null Monitor Value: Beam may not be opened !\",\n\t\t/* [5] */\n\t\t\"Infinite Flipping Ratio.\",\n\t\t/* [6] */\n\t\t\"Infinite Flipping Ratio Standard Deviation.\",\n\t\t/* [7] */\n\t\t\"Infinite Counting Time.\",\n\t\t/* [8] */\n\t\t\"Infinite Counting Time Standard Deviation.\",\n\t\t/* [9] */\n\t\t\"Undefined Detector Position.\",\n\t\t/* [10] */\n\t\t\"Infinite Asymmetry.\",\n\t\t/* [11] */\n\t\t\"Infinite Asymmetry Standard Deviation.\",\n\t\t/* [12] */\n\t\t\"Undefined Count Rate.\",\n\t\t/* [13] */\n\t\t\"Wrong lattice parameters - Det[G] = 0.\",\n\t}\n};\n\n/* no menu item */\n\nresource 'XOPI' (1100) {\n XOP_VERSION, // XOP protocol version.\n DEV_SYS_CODE, // Code for development system used to make XOP\n XOP_FEATURE_FLAGS, // Tells Igor about XOP features\n XOPI_RESERVED, // Reserved - must be zero.\n XOP_TOOLKIT_VERSION, // XOP Toolkit version.\n};\n\nresource 'XOPF' (1100) {\n\t{\n\t\t\"CountRate\",\t\t\t\t\t\t/* function name */\n\t\tF_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\t\t\t\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"CountRateSDev\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"CountRateDDT\",\t\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"CountRateDDTSDev\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"CountRateMonitor\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"CountRateMonitorSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"FlippingRatio\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"FlippingRatioSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"FlippingRatioOpt\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"FlippingRatioOptSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"FlippingRatioOptDDT\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"FlippingRatioOptDDTSDev\",\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTpp\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTbp\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTpm\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTbm\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTppSDev\",\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTbpSDev\",\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTpmSDev\",\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"OptCountingTimeTbmSDev\",\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"PeakCentringLin\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"PeakCentringLinSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"PeakCentringGauss\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"PeakCentringGaussSDev\",\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLcubic\",\t\t\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLhexagonal\",\t\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLrhombohedral\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLtetragonal\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLorthorhombic\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLmonoclinicB\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLmonoclinicC\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"STLtriclinic\",\t\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"Asymmetry\",\t\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"AsymmetrySDev\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"AsymmetryOpt\",\t\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"AsymmetryOptSDev\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"AsymmetryOptDDT\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"AsymmetryOptDDTSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3Efficiency\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3EfficiencySDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3Efficiency2\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3Efficiency2SDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3Transmission\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3TransmissionSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n NT_FP64,\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3Polarisation\",\t\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n \n\t\t\"He3PolarisationSDev\",\t\t\t\t/* function name */\n F_UTIL | F_EXTERNAL, /* function category */\n\t\tNT_FP64,\t\t\t\t\t\t\t/* return value type */\t\t\t\n\t\t{\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\t\t\t\t\t\t/* parameter types */\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n NT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t\tNT_FP64,\n\t\t},\n\t}\n};\n", "meta": {"hexsha": "4685ad1111412bd3abffff16aa79adc815b6a1e7", "size": 14945, "ext": "r", "lang": "R", "max_stars_repo_path": "Xcode 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{"text": "#' @exportClass Node\n#' @name Node \n#' @rdname Node\n#' @aliases Node-class \n#' @title The Node class\n#'\n#' @description \n#' This class is part of the \\pkg{HNUORTools}. It represents \n#' the base class for every locateable class in an\n#' \\dfn{Operations-Research (OR)}-context.\n#' \n#' @details Find here the defined slots for this class.\n#' @section Slots: \n#' \\describe{\n#' \\item{\\code{id}:}{\n#' Object of class \\code{\"character\"}, containing data from id.\n#' \\strong{Should be unique}.\n#' The default value will be caluclated randomly.\n#' }\n#' \\item{\\code{label}:}{\n#' Object of class \\code{\"character\"}, containing the label of \n#' the \\code{\\link{Node}}.\n#' The default value will be caluclated randomly.\n#' }\n#' \\item{\\code{x}:}{\n#' Object of class \\code{\"numeric\"}, containing the x-coordinate \n#' of the \\code{\\link{Node}}.\n#' The default value will be caluclated randomly.\n#' }\n#' \\item{\\code{y}:}{\n#' Object of class \\code{\"numeric\"}, containing the y-coordinate \n#' of the \\code{\\link{Node}}.\n#' The default value will be caluclated randomly.\n#' }\n#' }\n#'\n#' @seealso The classes are derived from this class and the following Methods can be used with this class.:\n#' @section Creating objects of type \\code{\\link{Node}}:\n#' \\describe{\n#' \\item{Creating an \\code{S4-Object}}{ \n#' \\code{new(\"Node\", ...)}\n#' } \n#' \\item{Converting from a \\code{data.frame}}{ \n#' \\code{as.Node{}}\n#' See also below in the Methods-Section.\n#' }\n#' \\item{Converting from a \\code{list}}{ \n#' \\code{as.Node{}}\n#' See also below in the Methods-Section.\n#' }\n#' }\n#' @section Methods:\n#' \\describe{\n#' \\item{\\code{as.list(node, ...)}}{\n#' Converts a \\code{\\link{Node}} into a \\code{\\link{list}}.\n#' \\code{...} are user-defined (not used) parameters.\n#' }\n#' \\item{\\code{as.data.frame(node, ...)}}{\n#' Converts a \\code{\\link{Node}} into a \\code{\\link{data.frame}}.\n#' \\code{as.data.frame} accepts the optional parameter \\code{withrownames} of class \\code{\"logical\"} (default is \\code{TRUE}). \n#' If \\code{withrownames == TRUE} the returned \\code{\\link{data.frame}} will recieve the \\code{id} as rowname.\n#' \\code{...} are user-defined (not used) parameters.\n#'\n#' }\n#' \\item{\\code{as.Node(obj)}}{\n#' Converts an object of class \\code{\\link{data.frame}} or of class \\code{\\link{list}} into a \\code{\\link{Node}}.\n#' } \n#' \\item{\\code{is.Node(obj)}}{\n#' Checks if the object \\code{obj} is of type \\code{\\link{Node}}.\n#' } \n#' \\item{\\code{\\link{getDistance}}}{\n#' Calculating the distance between two \\code{\\link{Node}s} \n#' (As the classes \\code{\\link{Customer}} and \\code{\\link{Warehouse}} \n#' depend on \\code{\\link{Node}}, distances can be calculated between any of these objects).\n#' }\n#' \\item{\\code{\\link{getpolar}}}{\n#' Calculating the polar-angle to the x-Axis of a link, \n#' connecting two \\code{\\link{Node}s} \n#' (As the classes \\code{\\link{Customer}} and \\code{\\link{Warehouse}} \n#' depend on \\code{\\link{Node}}, polar-angles can be calculated between any of these objects).\n#' }\n#' } \n#' \n#' @section Derived Classes:\n#' \\describe{\n#' \\item{\\code{\\link{Customer}}}{\n#' This class extends the \\code{\\link{Node}}-Class \n#' with attributes for according to customers for OR-Problems.\n#' }\n#' \\item{\\code{\\link{Warehouse}}}{\n#' This class extends the \\code{\\link{Node}}-Class \n#' with attributes for according to warehouses for OR-Problems.\n#' }\n#' \\item{\\code{\\link{Link}}}{\n#' This class is constructed by two entities of the \\code{\\link{Node}}-Class \n#' representing the origin and destination of \\code{\\link{Link}} for OR-Problems.\n#' }\n#' }\n#' @section To be used for:\n#' \\describe{ \n#' \\item{SPP}{\n#' Shortest-Path-Problem\n#' }\n#' \\item{TSP}{\n#' Travelling Salesman Problem\n#' }\n#' } \n#' \n#' @note \n#' for citing use: Felix Lindemann (2014). HNUORTools: Operations Research Tools. R package version 1.1-0. \\url{http://felixlindemann.github.io/HNUORTools/}.\n#' \n#' @author Dipl. Kfm. Felix Lindemann \\email{felix.lindemann@@hs-neu-ulm.de} \n#' \n#' Wissenschaftlicher Mitarbeiter\n#' Kompetenzzentrum Logistik\n#' Buro ZWEI, 17\n#'\n#' Hochschule fur angewandte Wissenschaften \n#' Fachhochschule Neu-Ulm | Neu-Ulm University \n#' Wileystr. 1 \n#' \n#' D-89231 Neu-Ulm \n#' \n#' \n#' Phone +49(0)731-9762-1437 \n#' Web \\url{www.hs-neu-ulm.de/felix-lindemann/} \n#' \\url{http://felixlindemann.blogspot.de}\n#' @examples \n#' # create a new Node with specific values\n#' x<- new(\"Node\", x= 10, y=20, id=\"myid\", label = \"mylabel\")\n#' x\n#' # create from data.frame\n#' df<- data.frame(x=10,y=20)\n#' new(\"Node\", df)\n#' as(df, \"Node\")\n#' as.Node(df)\n#' #create some nodes\n#' n1 <- new(\"Node\",x=10,y=20, id =\"n1\")\n#' n2 <- new(\"Node\",x=13,y=24, id =\"n1\")\n#' # calculate Beeline distance\n#' #getDistance(n1,n2) # should result 5\n#' #getDistance(n1,n2, costs = 2) # should result 10\nsetClass(\n\tClass = \"Node\",\n representation=representation(\n \tid = \"character\",\n label = \"character\",\n \tx = \"numeric\",\n \ty = \"numeric\"\n ), \n validity = function(object){\n \n N <- length(object@id) \n if(length(object@label) != N) \n return(\"Invalid Object of Type 'Node': the length of the attributes 'id' and 'label' differ!\")\n if(length(object@x) != N) \n return(\"Invalid Object of Type 'Node': the length of the attributes 'id' and 'x' differ!\")\n if(length(object@y) != N) \n return(\"Invalid Object of Type 'Node': the length of the attributes 'id' and 'y' differ!\") \n\n if( sum(is.null(object@x)) + sum( is.na(object@x)) > 0 ) \n return(paste(\"Invalid Object of Type 'Node': Error with value x: Value is not initialized\", class(object@x)))\n \n if( sum(is.null(object@y)) + sum( is.na(object@y)) > 0) \n return(paste(\"Invalid Object of Type 'Node': Error with value y: Value is not initialized\", class(object@y)))\n \n if( class(object@x)!=\"numeric\" ) \n return(paste(\"Invalid Object of Type 'Node': Error with value x: expected numeric datatype, but obtained\", class(object@x)))\n \n if( class(object@y)!=\"numeric\" ) \n return(paste(\"Invalid Object of Type 'Node': Error with value y: expected numeric datatype, but obtained\", class(object@y))) \n\n return(TRUE) \n } \n)\n################################### initialize - Method ###################################################\n#' @title initialize Method\n#' @name initialize\n#' @aliases initialize,Node-method \n#' @rdname initialize-methods \n#' @param data can of type \\code{\\link{data.frame}} or \\code{\\link{list}}\nsetMethod(\"initialize\", signature=\"Node\", function(.Object, data=NULL, ...) {\n \n li <- list(...)\n N<-1\n if(is.null(li$showwarnings)) li$showwarnings <- FALSE\n if(!is.null(data)){ \n \n if(class(data) ==\"list\") {\n data <- as.data.frame(data)\n }\n if(class(data) ==\"data.frame\") {\n N<-nrow(data)\n li$id <- data$id\n li$label <- data$label \n li$x <- data$x\n li$y <- data$y\n\n } else{ \n stop(\"Error: argument data should be of type 'data.frame'!\")\n }\n } \n if(is.null(li$x)) {\n li$x <- as.numeric(runif(N,0,100)) \n w <- paste(\"x-Coordinate simulated (x= \",li$x,\").\")\n if(li$showwarnings) warning(w) \n }\n if(is.null(li$y)) {\n li$y <- as.numeric(runif(N,0,100)) \n w <- paste(\"y-Coordinate simulated (y= \",li$y,\").\")\n if(li$showwarnings) warning(w) \n }\n if(is.null(li$id)) {\n tmp.id <-NULL\n for(i in 1:N){\n tmp.id <- c(tmp.id, UUIDgenerate()) \n }\n li$id <- tmp.id\n w <- paste(\"Random ID (\",li$id,\") provided. Uniqueness should be given.\")\n if(li$showwarnings) warning(w) \n }\n if(is.null(li$label)) {\n li$label <- li$id \n }\n .Object@id <- as.character(li$id)\n .Object@label <- as.character(li$label)\n\n .Object@x <- as.numeric( li$x)\n .Object@y <- as.numeric( li$y)\n \n validObject(.Object)\n \n return(.Object ) \n})\n################################### extract - Method ###################################################\n\n#' @title Extract Methods\n#' @name $\n#' @aliases $,Node-method \n#' @rdname Extract-methods-1\n#' @param name Name of Attribute.\nsetMethod(\"$\",\"Node\",function(x,name) {return(slot(x,name))})\n#' @title Extract Methods\n#' @name [\n#' @aliases [,Node-method \n#' @rdname Extract-methods-2\n#' @param i Row Index\n#' @param j Name or Column index\n#' @param drop Optional value for Drop-Levels.\nsetMethod(\"[\", \"Node\",\n function(x, i, j, drop){\n N <- length(x)\n if(min(i) <=0) stop(\"Index i out of bound. it must be positive non zero.\")\n if(max(i) >N) stop(\"Index i out of bound. it must not be larger than the total length.\")\n \n\n if(!missing(j)){\n if(class(j) == \"character\"){\n return(slot(x,j)[i])\n }\n if(class(j) == \"integer\"){\n return(slot(x,j)[i]) \n }\n }\n df <- as.data.frame(x)[i,]\n return (new(\"Node\", df)) \n } \n) \n################################### Set - Method ###################################################\n \n#' @title Set Methods\n#' @name $<- \n#' @aliases $<-,Node-method \n#' @rdname Set-methods-1\n#' @param name Name of parameter to change\n#' @param value new Value.\nsetMethod(\"$<-\",\"Node\",function(x,name,value) {\n slot(x,name,check=TRUE) <- value\n valid<-validObject(x)\n return(x)\n})\n\n#' @title Set Methods\n#' @name [<-\n#' @aliases [<-,Node-method \n#' @rdname Set-methods-2\n#' @param x The Node\n#' @param i row-Index\n#' @param j Name or Column index\nsetReplaceMethod(\"[\",signature(x=\"Node\"),\n function(x,i,j,value){\n N <- length(x)\n if(min(i) <=0) stop(\"Index i out of bound. It must be positive non zero.\")\n if(max(i) >N) stop(\"Index i out of bound. It must not be larger than the total length.\")\n if(length(value)!=length(i)) stop(\"The replacement length is not equal to the number of elements to be replaced.\") \n \n if(is(value, \"Node\")){\n \n # copy original values to tmp vectors\n tmp <- as.data.frame(x)\n \n\n #replace the item(s) regarding to i\n tmp$id[i] <- value@id \n tmp$label[i] <- value@label \n tmp$x[i] <- value@x \n tmp$y[i] <- value@y\n \n # return new item\n return (new(\"Node\", tmp)) \n } else {\n if(missing(j)) {\n # call the function recursivly\n # might throw an error if the conversion is not supported.\n x[i] <- as.Node(value)\n }else{ \n if(class(j) == \"character\"){\n slot(x,j)[i] <- value\n }else if(class(j) == \"integer\" | class(j) == \"numeric\"){ \n slot(x,slotNames(x)[j])[i] <- value \n }else{\n warning(paste(\"no changes have been made. the provided value of j (class:\",class(j), \"value:\",j,\") was not recocnized.\"))\n }\n }\n valid<-validObject(x)\n return(x) \n }\n } \n)\n\n################################### as... - Method ###################################################\n#local Methods\nas.Node.list = function(x, ...){return(new(\"Node\", data=x))}\nas.Node.data.frame = function(x, ...){return(new(\"Node\", data=x))}\n#local convert message\nas.data.frame.Node = function(x, ...){\n li<-list(...)\n if(is.null(li$withrownames)) li$withrownames <- FALSE\n df<-data.frame(id=x@id, label = x@label, x= x@x, y= x@y)\n if(li$withrownames) rownames(df)<-x@id\n return (df)\n}\nas.list.Node = function(x, ...){list(id=x@id, label = x@label, x= x@x, y= x@y)}\n\n\n#' @export\nsetGeneric(\"as.Node\", function(x, ...) standardGeneric( \"as.Node\")) \n\n#' @title Convert Objects to Nodes\n#' @name as.Node \n#' @param x an Object to convert.\n#' @param ... optional parameters\n#' @aliases as.Node,list-method \n#' @rdname as.Node-methods \nsetMethod(\"as.Node\", signature(x = \"list\"), as.Node.list) \n\n#' @title Convert Objects to Nodes\n#' @name as.Node\n#' @aliases as.Node,data.frame-method \n#' @rdname as.Node-methods \nsetMethod(\"as.Node\", signature(x = \"data.frame\"), as.Node.data.frame) \n\n#' @title Convert Objects to lists\n#' @name as.list\n#' @param x the Node \n#' @aliases as.list,Node-method \n#' @rdname as.list-methods \nsetMethod(\"as.list\", signature(x = \"Node\"), as.list.Node) \n\n#' @title Convert Objects to data.frames\n#' @name as.data.frame\n#' @param x the Node \n#' @aliases as.data.frame,Node-method \n#' @rdname as.data.frame-methods \nsetMethod(\"as.data.frame\", signature(x = \"Node\"), as.data.frame.Node) \n \nsetAs(\"data.frame\", \"Node\", def=function(from){return(as.Node.data.frame(from))})\nsetAs(\"list\", \"Node\", def=function(from){return(as.Node.list(from))})\n################################### is... - Method ###################################################\n\n#' @title Checks if an Object is as Node\n#' @export\n#' @name is.Node\n#' @param x the Node \n#' @param ... optional Parameters (not supported)\nsetGeneric(\"is.Node\", function(x, ...) standardGeneric( \"is.Node\")) \n\n#' @aliases is.Node,Node-method \n#' @rdname is.Node\nsetMethod( \"is.Node\", \"Node\", function(x, ...){ \n return(is(x ,\"Node\")) \n})\n \n \n#' @title How many Nodes are included?\n#' @name length\n#' @param x the Nodes\n#' @aliases length,Node-method \n#' @rdname length \nsetMethod(\"length\", \"Node\",\n function(x){\n N <- length(x@id)\n return(N)\n } \n)\n \n#' @aliases getDistance,Node,Node-method \n#' @rdname getDistance \n#' @examples \n#' # w1 <- new(\"Node\", x=0, y=0)\n#' # c1 <- new(\"Node\", x=3, y=4)\n#' # getDistance(w1,c1) # should result 5. \n#' # getDistance(w1,c1,costfactor=3) # should result 15. \nsetMethod( \"getDistance\", signature = c(\"Node\", \"Node\"),\n function(n0,n1, ...) {\n li<-list(...)\n \n p0<-c(n0$x,n0$y)\n p1<-c(n1$x,n1$y)\n \n value <- getDistance(p0,p1, ...)\n \n return(value)\n }\n)\n \n#' @aliases getpolar,Node,Node-method \n#' @rdname getpolar \n#' @examples\n#' # w1 <- new(\"Node\", x=0, y=0)\n#' # c1 <- new(\"Node\", x=1, y=1)\n#' # getpolar(w1,c1,deg=TRUE) # should result 45. \nsetMethod(\"getpolar\", signature=c(\"Node\", \"Node\"),\n function(n0,n1, ...) {\n li<-list(...) \n validObject(n0)\n validObject(n1)\n \n p0<-c(n0$x,n0$y)\n p1<-c(n1$x,n1$y)\n\n value <- getpolar(p0,p1, ...)\n \n return(value)\n }\n)\n ", "meta": {"hexsha": "cd8ce4595878f57c73a2bb9d1f8e89e006a92e94", "size": 15936, "ext": "r", "lang": "R", "max_stars_repo_path": "R/01Node.r", "max_stars_repo_name": "felixlindemann/HNUORTools", "max_stars_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/01Node.r", "max_issues_repo_name": "felixlindemann/HNUORTools", "max_issues_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/01Node.r", "max_forks_repo_name": "felixlindemann/HNUORTools", "max_forks_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6510067114, "max_line_length": 162, "alphanum_fraction": 0.5181350402, "num_tokens": 4159, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "##\n# Copyright (c) 2015, Intel Corporation\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# \n# * Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of Intel Corporation nor the names of its contributors\n# may be used to endorse or promote products derived from this software\n# without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE\n# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#\n##\n# @file\n# This file is part of SeisSol.\n#\n# @author Alex Breuer (breuer AT mytum.de, http://www5.in.tum.de/wiki/index.php/Dipl.-Math._Alexander_Breuer)\n#\n# @section LICENSE\n# Copyright (c) 2014, SeisSol Group\n# All rights reserved.\n# \n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# \n# 1. Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n# \n# 2. Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n# \n# 3. Neither the name of the copyright holder nor the names of its\n# contributors may be used to endorse or promote products derived from this\n# software without specific prior written permission.\n# \n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n#\n# @section DESCRIPTION\n# Performance model for SeisSol setups.\n\ncomputeMeshStatistics <- function( i_meshName,\n i_pathToCsvFile,\n i_performanceParameters,\n i_outputFileTable,\n i_outputFilePlot ) {\n message( 'reading mesh', i_pathToCsvFile )\n l_meshStatistics <- read.csv( file = i_pathToCsvFile,\n header = TRUE )\n\n #\n # compute data to send and receive in MPI-communication\n #\n # mpi-information per time step in GB, regular mpi faces\n l_meshStatistics$mpi_send <- (l_meshStatistics$mpi_copy_elements*i_performanceParameters$dofs_size)/1024^3\n # mpi-information per time step in GB, dr mpi face come on top\n #TODO here need a more accurate number, DR is really complex depending on the neighour realitions: \n #TODO DR-DR coupling, DR-WP coupling, for now let's overestimate (2* for time derivatives instead of time integrated)\n l_meshStatistics$mpi_send <- l_meshStatistics$mpi_send + ((2*l_meshStatistics$mpi_dr_faces*i_performanceParameters$dofs_size)/1024^3)\n # now get time\n l_meshStatistics$mpi_send <- l_meshStatistics$mpi_send/i_performanceParameters$network_bandwidth\n\n # receiving is asymmetric to sending :-/\n l_meshStatistics$mpi_recv <- (l_meshStatistics$mpi_ghost_elements*i_performanceParameters$dofs_size)/1024^3\n l_meshStatistics$mpi_recv <- l_meshStatistics$mpi_recv + ((2*l_meshStatistics$mpi_dr_faces*i_performanceParameters$dofs_size)/1024^3)\n l_meshStatistics$mpi_recv <- l_meshStatistics$mpi_recv/i_performanceParameters$network_bandwidth\n\n # compute timings for\n # * local integration copy layer\n # * local integration interior\n # * neigh integration copy layer\n # * neigh integration interior\n # * additional flux computation for dunamic rupture faces\n # TODO: we might want to substract outflow boundary conditions\n # Please note we incease the time by 10% for the copy layers since they are very small \n # and therefore run at lower performance\n l_meshStatistics$local_integration_copy <- l_meshStatistics$mpi_copy_elements*i_performanceParameters$local_integration*1.1\n l_meshStatistics$local_integration_interior <- l_meshStatistics$interior_elements*i_performanceParameters$local_integration\n l_meshStatistics$neigh_integration_copy <- l_meshStatistics$mpi_copy_elements*i_performanceParameters$neigh_integration*1.1\n l_meshStatistics$neigh_integration_interior <- l_meshStatistics$interior_elements*i_performanceParameters$neigh_integration\n l_meshStatistics$dynamic_rupture_fluxes <- l_meshStatistics$dynamic_rupture_faces*i_performanceParameters$dynamic_rupture \n \n # compute MPI wait time (recv)\n l_meshStatistics$wait_recv <- pmax(l_meshStatistics$mpi_recv -\n l_meshStatistics$local_integration_copy -\n l_meshStatistics$local_integration_interior -\n l_meshStatistics$neigh_integration_interior , 0)\n\n # compute MPI wait time (send)\n # this assumes that waitforCopyLayerSends() is posted before receiveGhostLayer()\n # this is not true, but the error intorudced by this is very minor!\n l_meshStatistics$wait_send <- pmax(l_meshStatistics$mpi_send -\n l_meshStatistics$neigh_integration_copy -\n l_meshStatistics$local_integration_interior -\n l_meshStatistics$neigh_integration_interior -\n l_meshStatistics$dynamic_rupture_fluxes -\n l_meshStatistics$wait_recv , 0)\n\n # let's sum the waits up\n l_meshStatistics$time_communication_wait <- l_meshStatistics$wait_recv +\n l_meshStatistics$wait_send\n\n #let's sum everyting up, and we are done\n l_meshStatistics$time_total <- l_meshStatistics$local_integration_copy +\n l_meshStatistics$local_integration_interior +\n l_meshStatistics$neigh_integration_copy +\n l_meshStatistics$neigh_integration_interior +\n l_meshStatistics$dynamic_rupture_fluxes +\n l_meshStatistics$wait_recv +\n l_meshStatistics$wait_send\n \n #message( 'summary mesh statistics')\n #print( summary( l_meshStatistics ) )\n\n message( 'writing csv' )\n write.csv( x=l_meshStatistics, file=i_outputFileTable)\n \n message( 'plotting information' )\n pdf( file = i_outputFilePlot,\n paper = 'a4r',\n width=20,\n height=10)\n\n # overview boxplots\n par( mfrow=c(1,9) )\n boxplot(l_meshStatistics$local_integration_copy, main='local copy' )\n boxplot(l_meshStatistics$local_integration_interior, main='local interior' )\n boxplot(l_meshStatistics$neigh_integration_interior, main='neigh interior' )\n boxplot(l_meshStatistics$neigh_integration_copy, main='neigh copy' )\n boxplot(l_meshStatistics$dynamic_rupture_fluxes, main='dyn. rupt.' )\n boxplot(l_meshStatistics$mpi_recv, main='recv time' )\n boxplot(l_meshStatistics$mpi_recv, main='send time' )\n boxplot(l_meshStatistics$time_communication_wait, main='wait time' )\n boxplot(l_meshStatistics$time_total, main='total time' )\n \n # detailed plot of every characteristic value\n #for( l_name in names(l_meshStatistics[-1]) ) {\n # layout( matrix(c(1,2), 2, 2, byrow = TRUE), \n # widths=c(4,1) )\n # plot( x=l_meshStatistics$partition,\n # y=l_meshStatistics[,l_name],\n # xlab=\"partition\",\n # ylab=l_name )\n # boxplot(l_meshStatistics[l_name])\n #}\n dev.off()\n \n return( max(l_meshStatistics$time_total) )\n}\n\nprintWeakScaling <- function( i_machineList,\n i_nodeCounts,\n i_meshID ) {\n for( l_machine in i_machineList ) {\n l_runtime = list()\n for( l_nodes in i_nodeCounts ) {\n l_runtime = c( l_runtime, l_expectedSimulationTime[[paste(i_meshID, '_', l_nodes, '_', l_machine, sep='')]] )\n }\n l_runtime <- l_runtime[[1]] / unlist(l_runtime)\n \n print(l_machine)\n print(unlist(i_nodeCounts))\n print(l_runtime)\n \n pdf(paste('scaling_', i_meshID, '_', l_machine,'.pdf', sep=''))\n plot( x=i_nodeCounts, l_runtime, ylab='parallel efficiency (weak-scaling)', main=l_machine )\n dev.off()\n }\n}\n\nprintStrongScaling <- function( i_machineList,\n i_nodeCounts,\n i_meshID ) {\n for( l_machine in i_machineList ) {\n l_runtime = list()\n for( l_nodes in i_nodeCounts ) {\n l_runtime = c( l_runtime, l_expectedSimulationTime[[paste(i_meshID, '_', l_nodes, '_', l_machine, sep='')]] * ( l_nodes / i_nodeCounts[[1]]) )\n }\n l_runtime <- l_runtime[[1]] / unlist(l_runtime)\n \n print(l_machine)\n print(unlist(i_nodeCounts))\n print(l_runtime)\n \n pdf(paste('scaling_', i_meshID, '_', l_machine,'.pdf', sep=''))\n plot( x=i_nodeCounts, l_runtime, ylab='parallel efficiency (strong-scaling)', main=l_machine )\n dev.off()\n }\n}\n\nl_config = list ( mesh_names = list( #'statistics_cubes_1024',\n #'statistics_cubes_2048',\n #'statistics_cubes_4096',\n #'statistics_cubes_9216',\n #'statistics_Landers191M_02_05_02_512',\n #'statistics_Landers191M_02_05_02_768',\n #'statistics_Landers191M_02_05_02_1024',\n #'statistics_Landers191M_02_05_02_1536',\n #'statistics_Landers191M_02_05_02_3072',\n #'statistics_Landers191M_02_05_02_2048',\n #'statistics_Landers191M_02_05_02_4096',\n #'statistics_Landers191M_02_05_02_6144',\n #'statistics_Landers191M_02_05_02_9216',\n #'statistics_Landers191M_02_05_02_12288',\n #'statistics_Landers191M_02_05_02_24756',\n 'statistics_LOH1_small_1',\n 'statistics_LOH1_small_2',\n 'statistics_LOH1_small_4',\n 'statistics_LOH1_small_8',\n 'statistics_LOH1_small_16',\n 'statistics_LOH1_small_32',\n 'statistics_LOH1_small_64',\n 'statistics_LOH1_small_128',\n 'statistics_LOH1_small_256',\n 'statistics_LOH1_small_512',\n 'statistics_LOH1_small_1024'),\n \n statistics_directory = 'mesh_statistics',\n \n performance_parameters = list( supermuc = list( basisFunctions = 56,\n quantities = 9,\n dofs_size = 56*9*8,\n network_bandwidth = 0.4, # unidirectionl, TODO: missing reference point\n neigh_integration = 0.00000131, # 1250 elements per core, seissol proxy, random\n local_integration = 0.00000244, # 1250 elements per core, seissol proxy, random\n dynamic_rupture = 109.33 / 22798 / 1000 ) # TODO: missing reference point\n )\n )\n\nl_expectedSimulationTime = list()\n\nmessage( 'lets go' )\nfor( l_machine in list('supermuc') ) {\n for( l_meshName in l_config$mesh_names ) {\n message( 'analyzing: ', l_meshName )\n l_expectedSimulationTime[[paste(l_meshName,'_',l_machine, sep='')]] =\n computeMeshStatistics( i_meshName = l_meshName,\n i_pathToCsvFile = paste(l_config$statistics_directory,'/',l_meshName,'.csv', sep=''),\n i_performanceParameters = (l_config$performance_parameters)[[l_machine]],\n i_outputFileTable = paste(l_meshName,'_',l_machine,'.csv', sep=''),\n i_outputFilePlot = paste(l_meshName,'_',l_machine,'.pdf', sep='') )\n }\n}\n\n#message( 'generating weak-scaling plot for cubes' )\n#printWeakScaling( list('supermuc'), list( 1024, 2048, 4096, 9216 ), 'statistics_cubes' )\n\n#message( 'generating strong-scaling plot for Landers' )\n#printStrongScaling( list('supermuc'), list( 768, 1024, 1536, 2048, 3072, 4096, 6144, 9216, 12288, 24756 ), 'statistics_Landers191M_02_05_02' )\n\nmessage( 'generating strong-scaling plot for LOH1' )\nprintStrongScaling( list('supermuc'), list( 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024 ), 'statistics_LOH1_small' )\n\n\n", "meta": {"hexsha": "c7886ac6965e649e4bad78a3d0dd4131eaf5cb0f", "size": 15283, "ext": "r", "lang": "R", "max_stars_repo_path": "postprocessing/performance/model/mesh_based_model.r", "max_stars_repo_name": "fabian-kutschera/SeisSol", "max_stars_repo_head_hexsha": "d5656cd38e9eb1d91c05ebcbf173acbc3083da57", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 165, "max_stars_repo_stars_event_min_datetime": "2015-01-30T18:19:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T17:22:14.000Z", "max_issues_repo_path": "postprocessing/performance/model/mesh_based_model.r", "max_issues_repo_name": "fabian-kutschera/SeisSol", "max_issues_repo_head_hexsha": "d5656cd38e9eb1d91c05ebcbf173acbc3083da57", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 351, "max_issues_repo_issues_event_min_datetime": "2015-10-06T15:06:48.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T11:23:13.000Z", "max_forks_repo_path": "postprocessing/performance/model/mesh_based_model.r", "max_forks_repo_name": "fabian-kutschera/SeisSol", "max_forks_repo_head_hexsha": "d5656cd38e9eb1d91c05ebcbf173acbc3083da57", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 96, "max_forks_repo_forks_event_min_datetime": "2015-07-27T15:13:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T19:19:32.000Z", "avg_line_length": 54.7777777778, "max_line_length": 159, "alphanum_fraction": 0.6091735916, "num_tokens": 3234, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3380771241500058, "lm_q2_score": 0.025957354607609142, "lm_q1q2_score": 0.0087755877962824}}
{"text": "#' @exportClass Customer\n#' @name Customer \n#' @rdname Customer\n#' @aliases Customer-class \n#' @title The Customer class\n#'\n#' @description \n#' This class is part of the \\pkg{HNUORTools}. It represents \n#' the base class for every locateable class in an\n#' \\dfn{Operations-Research (OR)}-context.\n#' \n#' @details Find here the defined slots for this class.\n#' @section Slots: \n#' \\describe{\n#' \\item{\\code{demand}:}{\n#' Object of class \\code{\"numeric\"}, containing the amount for the demand.\n#' The default value cannot be negative.\n#' }\n#' \\item{\\code{isDummy}:}{\n#' Object of class \\code{\"logical\"}, indicating if a customer was added \n#' in an algorithm (e.g. TPP-Column-Minimum-Method) in order to avoid\n#' degenerated soulutions.\n#' }\n#' }\n#' @section Slots (from \\code{\\link{Node}}): \n#' \\describe{\n#' \\item{\\code{id}:}{\n#' Object of class \\code{\"character\"}, containing data from id.\n#' \\strong{Should be unique}.\n#' The default value will be caluclated randomly.\n#' }\n#' \\item{\\code{label}:}{\n#' Object of class \\code{\"character\"}, containing the label of \n#' the \\code{\\link{Customer}}.\n#' The default value will be caluclated randomly.\n#' }\n#' \\item{\\code{x}:}{\n#' Object of class \\code{\"numeric\"}, containing the x-coordinate \n#' of the \\code{\\link{Customer}}.\n#' The default value will be caluclated randomly.\n#' }\n#' \\item{\\code{y}:}{\n#' Object of class \\code{\"numeric\"}, containing the y-coordinate \n#' of the \\code{\\link{Customer}}.\n#' The default value will be caluclated randomly.\n#' }\n#' }\n#'\n#' @seealso The classes are derived from this class and the following Methods can be used with this class.:\n#' @section Creating objects of type \\code{\\link{Customer}}:\n#' \\describe{\n#' \\item{Creating an \\code{S4-Object}}{ \n#' \\code{new(\"Customer\", ...)}\n#' } \n#' \\item{Converting from a \\code{data.frame}}{ \n#' \\code{as.Customer{}}\n#' See also below in the Methods-Section.\n#' }\n#' \\item{Converting from a \\code{list}}{ \n#' \\code{as.Customer{}}\n#' See also below in the Methods-Section.\n#' }\n#' }\n#' @section Methods:\n#' \\describe{\n#' \\item{\\code{as.list(Customer, ...)}}{\n#' Converts a \\code{\\link{Customer}} into a \\code{\\link{list}}.\n#' \\code{...} are user-defined (not used) parameters.\n#' }\n#' \\item{\\code{as.data.frame(Customer, ...)}}{\n#' Converts a \\code{\\link{Customer}} into a \\code{\\link{data.frame}}.\n#' \\code{as.data.frame} accepts the optional parameter \\code{withrownames} of class \\code{\"logical\"} (default is \\code{TRUE}). \n#' If \\code{withrownames == TRUE} the returned \\code{\\link{data.frame}} will recieve the \\code{id} as rowname.\n#' \\code{...} are user-defined (not used) parameters.\n#'\n#' }\n#' \\item{\\code{as.Customer(obj)}}{\n#' Converts an object of class \\code{\\link{data.frame}} or of class \\code{\\link{list}} into a \\code{\\link{Customer}}.\n#' } \n#' \\item{\\code{is.Customer(obj)}}{\n#' Checks if the object \\code{obj} is of type \\code{\\link{Customer}}.\n#' } \n#' \\item{\\code{\\link{getDistance}}}{\n#' Calculating the distance between two \\code{\\link{Node}s} \n#' (As the classes \\code{\\link{Customer}} and \\code{\\link{Warehouse}} \n#' depend on \\code{\\link{Node}}, distances can be calculated between any of these objects).\n#' }\n#' \\item{\\code{\\link{getpolar}}}{\n#' Calculating the polar-angle to the x-Axis of a link, \n#' connecting two \\code{\\link{Node}s} \n#' (As the classes \\code{\\link{Customer}} and \\code{\\link{Warehouse}} \n#' depend on \\code{\\link{Node}}, polar-angles can be calculated between any of these objects).\n#' }\n#' } \n#' \n#' @section Derived Classes:\n#' None.\n#' @section To be used for:\n#' \\describe{\n#' \\item{WLP}{\n#' Warehouse-Location-Problem\n#' }\n#' \\item{TPP}{\n#' Transportation Problem\n#' }\n#' \\item{VRP}{\n#' Vehicle Routing Problem\n#' }\n#' } \n#' \n#' @note \n#' for citing use: Felix Lindemann (2014). HNUORTools: Operations Research Tools. R package version 1.1-0. \\url{http://felixlindemann.github.io/HNUORTools/}.\n#' \n#' @author Dipl. Kfm. Felix Lindemann \\email{felix.lindemann@@hs-neu-ulm.de} \n#' \n#' Wissenschaftlicher Mitarbeiter\n#' Kompetenzzentrum Logistik\n#' Buro ZWEI, 17\n#'\n#' Hochschule fur angewandte Wissenschaften \n#' Fachhochschule Neu-Ulm | Neu-Ulm University \n#' Wileystr. 1 \n#' \n#' D-89231 Neu-Ulm \n#' \n#' \n#' Phone +49(0)731-9762-1437 \n#' Web \\url{www.hs-neu-ulm.de/felix-lindemann/} \n#' \\url{http://felixlindemann.blogspot.de}\n#' @examples \n#' # create a new Customer with specific values\n#' x<- new(\"Customer\", x= 10, y=20, id=\"myid\", label = \"mylabel\", demand = 20)\n#' x\n#' # create from data.frame\n#' df<- data.frame(x=10,y=20, demand = 30)\n#' new(\"Customer\", df)\n#' as(df, \"Customer\")\n#' as.Customer(df)\n#' #create some Customers\n#' n1 <- new(\"Customer\",x=10,y=20, demand = 30, id =\"n1\")\n#' n2 <- new(\"Customer\",x=13,y=24, demand = 40, id =\"n2\")\n#' # calculate Beeline distance\n#' #getDistance(n1,n2) # should result 5\n#' #getDistance(n1,n2, costs = 2) # should result 10\nsetClass(\n Class = \"Customer\",\n representation=representation(\n demand=\"numeric\",\n isDummy = \"logical\"\n ), \n contains = \"Node\",\n validity = function(object){\n \n \n df <- as.data.frame(object)\n node <- as.Node(df)\n if(validObject(node)){ \n N <- length(object@id) \n if(length(object@demand) != N) \n return(\"Invalid Object of Type 'Customer': the length of the attributes 'id' and 'demand' differ!\")\n if(length(object@isDummy) != N) \n return(\"Invalid Object of Type 'Customer': the length of the attributes 'id' and 'isDummy' differ!\")\n \n if( sum(is.null(object@demand)) + sum( is.na(object@demand)) > 0 ) \n return(paste(\"Invalid Object of Type 'Customer': Error with value demand: Value is not initialized\", class(object@demand)))\n \n if( sum(is.null(object@isDummy)) + sum( is.na(object@isDummy)) > 0) \n return(paste(\"Invalid Object of Type 'Customer': Error with value isDummy: Value is not initialized\", class(object@isDummy)))\n \n if( class(object@demand)!=\"numeric\" ) \n return(paste(\"Invalid Object of Type 'Customer': Error with value demand: expected numeric datatype, but obtained\", class(object@demand)))\n \n if( class(object@isDummy)!=\"logical\" ) \n return(paste(\"Invalid Object of Type 'Customer': Error with value isDummy: expected logical datatype, but obtained\", class(object@isDummy))) \n \n if(min(object@demand)<0)\n return(paste(\"Invalid Object of Type 'Customer': Error with value demand: at least one value is negative. Only positive Values are allowed.\"))\n \n return(TRUE) \n \n }else{\n return (FALSE)\n }\n } \n)\n################################### initialize - Method ###################################################\n#' @title initialize Method\n#' @name initialize\n#' @aliases initialize,Customer-method \n#' @rdname initialize-methods \nsetMethod(\"initialize\", signature=\"Customer\", function(.Object, data=NULL, ...) {\n \n li <- list(...)\n if(is.null(li$showwarnings)) li$showwarnings <- FALSE\n \n #init from parent Node-Object.\n N<-1\n \n if(!is.null(data)){ \n \n if(class(data) ==\"list\") {\n data <- as.data.frame(data)\n }\n if(class(data) ==\"data.frame\") {\n N<-nrow(data)\n li$isDummy <- data$isDummy\n li$demand <- data$demand\n \n } else{ \n stop(\"Error: argument data should be of type 'data.frame'!\")\n }\n } \n if(is.null(li$demand) | length(li$demand) ==0) {\n li$demand <- as.numeric(sample(30:100,N,replace=TRUE)) \n w <- paste(\"y-Coordinate simulated (y= \",li$demand,\").\")\n if(li$showwarnings) warning(w) \n } \n if(is.null(li$isDummy)) {\n li$isDummy <- rep(FALSE,N)\n }\n \n .Object@demand <- as.numeric( li$demand)\n .Object@isDummy <- as.logical( li$isDummy) \n \n .Object<-callNextMethod(.Object,data,...) \n validObject(.Object)\n \n return(.Object ) \n})\n################################### extract - Method ###################################################\n#' @title Extract Methods\n#' @name [\n#' @aliases [,Customer-method \n#' @rdname Extract-methods-2 \nsetMethod(\"[\", \"Customer\",\n function(x, i, j, drop){\n N <- length(x)\n if(min(i) <=0) stop(\"Index i out of bound. it must be positive non zero.\")\n if(max(i) >N) stop(\"Index i out of bound. it must not be larger than the total length.\")\n \n \n if(!missing(j)){\n if(class(j) == \"character\"){\n return(slot(x,j)[i])\n }\n if(class(j) == \"integer\"){\n return(slot(x,j)[i]) \n }\n }\n df <- as.data.frame(x)[i,]\n return (new(\"Customer\", df)) \n } \n) \n################################### Set - Method ###################################################\n\n#' @title Set Methods\n#' @name [<-\n#' @aliases [<-,Customer-method \n#' @rdname Set-methods-2\n#' @param x The Customer\n#' @param i row-Index\n#' @param j Name or Column index\nsetReplaceMethod(\"[\",signature(x=\"Customer\"),\n function(x,i,j,value){\n N <- length(x)\n if(min(i) <=0) stop(\"Index i out of bound. It must be positive non zero.\")\n if(max(i) >N) stop(\"Index i out of bound. It must not be larger than the total length.\")\n if(length(value)!=length(i)) stop(\"The replacement length is not equal to the number of elements to be replaced.\") \n \n if(is(value, \"Customer\")){\n \n \n #replace the item(s) regarding to i\n x$id[i] <- value@id \n x$label[i] <- value@label \n x$x[i] <- value@x \n x$y[i] <- value@y\n x$demand[i] <- value@demand\n x$isDummy[i] <- value@isDummy\n \n # return new item\n return (x) \n } else {\n if(missing(j)) {\n # call the function recursivly\n # might throw an error if the conversion is not supported.\n x[i] <- as.Customer(value)\n }else{ \n if(class(j) == \"character\"){\n slot(x,j)[i] <- value\n }else if(class(j) == \"integer\" | class(j) == \"numeric\"){ \n slot(x,slotNames(x)[j])[i] <- value \n }else{\n warning(paste(\"no changes have been made. the provided value of j (class:\",class(j), \"value:\",j,\") was not recocnized.\"))\n }\n }\n valid<-validObject(x)\n return(x) \n }\n } \n)\n\n################################### as... - Method ###################################################\n#local Methods\nas.Customer.list = function(x, ...){return(new(\"Customer\", data=x))}\nas.Customer.data.frame = function(x, ...){return(new(\"Customer\", data=x))}\n#local convert message\nas.data.frame.Customer = function(x, ...){\n li<-list(...)\n if(is.null(li$withrownames)) li$withrownames <- FALSE\n df<-data.frame(id=x@id, label = x@label, x= x@x, y= x@y, demand=x@demand, isDummy = x@isDummy)\n if(li$withrownames) rownames(df)<-x@id\n return (df)\n}\nas.list.Customer = function(x, ...){list(id=x@id, label = x@label, x= x@x, y= x@y, demand=x@demand, isDummy = x@isDummy)}\n\n\n#' @export\nsetGeneric(\"as.Customer\", function(x, ...) standardGeneric( \"as.Customer\")) \n\n#' @title Convert Objects to Customers\n#' @name as.Customer \n#' @param x an Object to convert.\n#' @param ... optional parameters\n#' @aliases as.Customer,list-method \n#' @rdname as.Customer-methods \nsetMethod(\"as.Customer\", signature(x = \"list\"), as.Customer.list) \n\n#' @title Convert Objects to Customers\n#' @name as.Customer\n#' @aliases as.Customer,data.frame-method \n#' @rdname as.Customer-methods \nsetMethod(\"as.Customer\", signature(x = \"data.frame\"), as.Customer.data.frame) \n\n#' @title Convert Objects to lists\n#' @name as.list \n#' @aliases as.list,Customer-method \n#' @rdname as.list-methods \nsetMethod(\"as.list\", signature(x = \"Customer\"), as.list.Customer) \n\n#' @title Convert Objects to data.frames\n#' @name as.data.frame \n#' @aliases as.data.frame,Customer-method \n#' @rdname as.data.frame-methods \nsetMethod(\"as.data.frame\", signature(x = \"Customer\"), as.data.frame.Customer) \n\nsetAs(\"data.frame\", \"Customer\", def=function(from){return(as.Customer.data.frame(from))})\nsetAs(\"list\", \"Customer\", def=function(from){return(as.Customer.list(from))})\n################################### is... - Method ###################################################\n\n#' @title Checks if an Object is as Customer\n#' @export\n#' @name is.Customer\n#' @aliases is.Customer\n#' @param x the object to be checked for class \\code{\\link{Customer}}\n#' @param ... optional Parameters (not supported)\nsetGeneric(\"is.Customer\", function(x, ...) standardGeneric( \"is.Customer\")) \n\n#' @aliases is.Customer,Customer-method \n#' @rdname is.Customer\nsetMethod( \"is.Customer\", \"Customer\", function(x, ...){return(is(x ,\"Customer\"))})\n\n#' @title How many Customers are included?\n#' @name length \n#' @aliases length,Customer-method \n#' @rdname length \nsetMethod(\"length\", \"Customer\",\n function(x){\n N <- length(x@id)\n return(N)\n } \n) \n\n\n\n", "meta": {"hexsha": "49aa93c0216bc731abea8108c92ecfc6cdd24127", "size": 14775, "ext": "r", "lang": "R", "max_stars_repo_path": "R/02Customer.r", "max_stars_repo_name": "felixlindemann/HNUORTools", "max_stars_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/02Customer.r", "max_issues_repo_name": "felixlindemann/HNUORTools", "max_issues_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/02Customer.r", "max_forks_repo_name": "felixlindemann/HNUORTools", "max_forks_repo_head_hexsha": "0cb22cc0da14550b2fb48c996e75dfdad6138904", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.295212766, "max_line_length": 162, "alphanum_fraction": 0.5260913706, "num_tokens": 3562, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "REBOL [\n Title: \"Formatting functions\"\n File: %format.r\n Author: \"Eric Long\"\n Email: kgd03011@nifty.ne.jp\n Date: 20-Feb-2000\n Category: [utility math]\n Version: 1.0.3\n Purpose: {\n A few functions for formatting.\n\n FORMAT aims to be a Reboloid replacement for sprintf.\n It converts data into strings, with rounding of decimal\n values, choice of scientific or fixed-point notation,\n padding to a given length, and right and left justification.\n FORMAT also handles blocks of data, with corresponding blocks\n of formatting options to be applied to successive items.\n\n QT is useful for printing out long blocks of words or numerical\n data, and QC formats and prints out tabular data stored in\n nested blocks.\n\n FULL-FORM returns a string representation of a decimal that will\n load back to exactly the original value. All values that correspond\n to an exact integer up to 2 ** 53 are represented as an integer.\n }\n Acknowledgements: {\n Many thanks to Larry Palmiter and Gerald Goertzel, who tested\n these functions and suggested many improvements. The idea of\n FULL-FORM is originally Larry's, and the development of the\n code was a collaboration among the three of us.\n\n Thanks to Carl for suggesting the best way to pass the options\n argument.\n }\n]\n\ncomment { ================ FORMAT REFORMAT ================\n\nREFORMAT is a shortcut to reduce a block before applying FORMAT.\n\nEXAMPLES:\n\n>> format pi #.4\n== \"3.1416\"\n\n>> reformat/full [pi exp 1] #.15 ; greater precision than FORM\n== [\"3.141592653589793\" \"2.718281828459045\"]\n\n>> format 2 ** 32 #.-6.8 ; round to nearest million\n== \"4,295,000,000\" ; insert commas option\n\n>> reformat [pi exp 1 square-root 2] [#8.4 #6.2] ; width specified\n== [\" 3.1416\" \" 2.72\" \" 1.41\"]\n\n>> format 2 ** 40 #.5.1 ; scientific notation\n== \"1.09951E+012\"\n\n>> reformat [\"pi =\" pi] #7.4.2 ; auto-justification\n== [\"pi = \" \" 3.1416\"]\n\n>> reformat [\"very long string\" 2 ** 32] #6.0.4 ; truncation of string\n== [\"string\" \"4294967296\"]\n\n>> reformat [\"very long string\" 2 ** 32] #4.0.6 ; ... plus auto-justification\n== [\"very\" \"4294967296\"]\n\n>> format $20 / 3 #.4 ; money values handled\n== \"$6.6667\" ; just like number values\n\n>> format now #0 ; inserts zeroes into dates\n== \"06-Feb-2000/13:19\" ; and times for alignment\n}\n\nformat: func [\n {Converts Rebol data into formatted strings.}\n item \"value or block of values to format\"\n options [block! any-string!]\n {single option, or block of options to be applied successively to\n a block of items (with last option used repeatedly if necessary).\n Formed options are parsed to integers: A.B.C\n A width of formatted string\n (negative for left justified)\n B precision to right of decimal point\n (negative for rounding to left of decimal point)\n C and 1 scientific notation\n C and 2 auto justification (number! and money! right justified)\n C and 4 truncate string values\n C and 8 insert commas\n C and 16 prefix value with '$'} ; Added by Mike Yaunish\n\n /full \"use extra precision in forming decimal values\"\n /local n fraction exponent neg decimal\n scientific width left-j trunc commas\n ptr round-off-point format-path prefix\n][\n either data-block? :item [\n ; process block\n if object? :item [ item: next second item ]\n format-path: make path! [format]\n if full [insert tail :format-path 'full]\n if not block? options [ options: reduce [options] ]\n n: to block! :item\n while [not tail? n][\n either data-block? item: first n [\n ; recurse with all format values\n change/only n format-path :item head options\n ][\n ; use current format value\n change/only n format-path :item first options\n ]\n n: next n\n ; get next format values, or reuse the last one\n if 1 < length? options [ options: next options ]\n ]\n ][\n ; process single value\n if block? options [ options: first options ]\n set [width decimal options] parse-integers/def options [0 0 0]\n if negative? width [width: - width left-j: true]\n if not zero? 1 and options [scientific: true decimal: abs decimal]\n if not zero? 2 and options [\n left-j: not any [number? :item money? :item]\n ]\n if all [\n not zero? 4 and options\n not zero? width\n string? :item\n ][trunc: true]\n if not zero? 8 and options [commas: true]\n if not zero? 16 and options [\n prefix: \"$\"\n ]\n if money? :item [\n prefix: append item/1 \"$\"\n item: item/2\n ]\n\n either number? :item [\n ; format number value\n if negative? item [item: - item neg: true]\n n: either full [full-form item][form item]\n\n ; find the power of ten\n either ptr: find n \"E\" [\n exponent: to integer! next ptr\n clear ptr\n ][\n exponent: 0\n ]\n either ptr: find n \".\" [\n remove ptr\n ][\n ptr: tail n\n ]\n exponent: exponent - 2 + index? ptr\n\n ; round off to the desired precision\n round-off-point: either scientific [\n while [ #\"0\" = pick n 1 ] [\n exponent: exponent - 1\n remove n\n ]\n decimal + 1\n ][\n decimal + exponent + 1\n ]\n either round-off-point < length? n [\n either negative? round-off-point [n: copy \"0\"][\n fraction: copy skip n round-off-point\n either zero? round-off-point [n: copy \"0\"][\n clear skip n round-off-point\n ]\n if positive? to integer! .5 +\n to decimal! head insert fraction \".\" [\n n: to string! 1 + to integer! n\n ]\n ]\n ][\n insert/dup tail n \"0\" round-off-point - length? n\n ]\n\n either scientific [\n if positive? decimal [insert next n \".\"]\n insert tail n \"E\"\n either negative? exponent [\n insert tail n \"-\"\n exponent: - exponent\n ][\n insert tail n \"+\"\n ]\n exponent: form exponent\n insert/dup exponent \"0\" 3 - length? exponent\n insert tail n exponent\n ][\n either positive? decimal [\n insert/dup n \"0\" decimal + 1 - length? n\n insert skip tail n (- decimal) \".\"\n ][\n if all [\n negative? decimal\n n <> \"0\"\n ][\n insert/dup tail n \"0\" (- decimal)\n ]\n ]\n ]\n if commas [\n if not ptr: find n \".\" [ ptr: tail n ]\n ptr: skip ptr -3\n while [ not head? ptr ] [\n insert ptr \",\"\n ptr: skip ptr -3\n ]\n ]\n if prefix [insert n prefix]\n if neg [insert n \"-\"]\n ; number value now formatted\n ][\n either any [date? :item time? :item][\n n: form item\n ; remove time zone data if any\n if ptr: find n \"+\" [ clear ptr ]\n ; add zeroes to line up columns\n if #\"-\" = second n [ insert n \"0\" ]\n ptr: either ptr: find n #\"/\" [next ptr][n]\n if #\":\" = second ptr [ insert ptr \"0\" ]\n ; remove seconds if any\n parse n [ thru \":\" to \":\" ptr: (clear ptr)]\n ][\n either any-word? :item [\n n: mold :item\n ][\n n: form :item\n ]\n ]\n ]\n ; pad with spaces\n either left-j [\n if trunc [n: copy/part n width]\n insert/dup tail n \" \" width - length? n\n ][\n if trunc [n: copy skip tail n (- width)]\n insert/dup n \" \" width - length? n\n ]\n ]\n head n\n]\n\nreformat: func [\n {Reduces and formats the items in a block}\n b \"block to format\"\n options [block! any-string!]\n {Formatting options. Do \"help format\" for more information}\n /full \"use extra precision in forming decimal values\"\n][\n either full [\n format/full reduce b options\n ][\n format reduce b options\n ]\n]\n\n\ncomment { ================ QT ================\n\nEXAMPLES:\n\n>> arr: copy [] repeat x 25 [append arr log-e x]\n== [0 0.693147180559945 1.09861228866811 1.38629436111989 ...\n\n>> qt arr\n0.0000 0.6931 1.0986 1.3863 1.6094 1.7918 1.9459 2.0794 2.1972 2.3026 2.3979\n2.4849 2.5649 2.6391 2.7081 2.7726 2.8332 2.8904 2.9444 2.9957 3.0445 3.0910\n3.1355 3.1781 3.2189\n\n>> qt/o arr #.2\n0.00 0.69 1.10 1.39 1.61 1.79 1.95 2.08 2.20 2.30 2.40 2.48 2.56 2.64 2.71\n2.77 2.83 2.89 2.94 3.00 3.04 3.09 3.14 3.18 3.22\n\n>> qt/o arr #.2..2 ; two extra spaces\n0.00 0.69 1.10 1.39 1.61 1.79 1.95 2.08 2.20 2.30 2.40\n2.48 2.56 2.64 2.71 2.77 2.83 2.89 2.94 3.00 3.04 3.09\n3.14 3.18 3.22\n\n\n>> arr: copy [] repeat x 20 [append arr exp x]\n== [2.71828182845905 7.38905609893065 20.0855369231877 ...\n\n>> qt arr\n 2.7183 7.3891 20.0855 54.5982 148.4132\n 403.4288 1096.6332 2980.9580 8103.0839 22026.4658\n 59874.1417 162754.7914 442413.3920 1202604.2842 3269017.3725\n 8886110.5205 24154952.7536 65659969.1373 178482300.9632 485165195.4098\n\n>> qt/o arr #.0 ; /o refinement for options\n 3 7 20 55 148 403 1097\n 2981 8103 22026 59874 162755 442413 1202604\n 3269017 8886111 24154953 65659969 178482301 485165195\n\n>> qt/o arr #.2.1\n2.72E+000 7.39E+000 2.01E+001 5.46E+001 1.48E+002 4.03E+002 1.10E+003\n2.98E+003 8.10E+003 2.20E+004 5.99E+004 1.63E+005 4.42E+005 1.20E+006\n3.27E+006 8.89E+006 2.42E+007 6.57E+007 1.78E+008 4.85E+008\n\n\n>> arr: copy [] repeat x 26 [append arr x * x - 1e-5]\n== [0.99999 3.99999 8.99999 15.99999 24.99999 35.99999 48.99999 63.99999 ...\n\n>> qt arr ; automatic rounding to integer\n 1 4 9 16 25 36 49 64 81 100 121 144 169 196 225 256 289 324 361\n400 441 484 529 576 625 676\n\n\n>> qt system\nself version build product words options user script\nconsole ports network schemes error standard\n\n>> right-margin: 50\n== 50\n>> qt system\nself version build product words\noptions user script console ports\nnetwork schemes error standard\n\n>> s: \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n>> arr: copy [] repeat x 12 [append arr s]\n== [\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\" \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\" ...\n\n>> qt arr ; long values truncated by default 3 to a line\nABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP\nABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP\nABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP\nABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP ABCDEFGHIJKLMNOP\n\n>> qt/o arr #0.0.0 ; no truncation\nABCDEFGHIJKLMNOPQRSTUVWXYZ\nABCDEFGHIJKLMNOPQRSTUVWXYZ\nABCDEFGHIJKLMNOPQRSTUVWXYZ\n.....\n\n>> qt/o arr #5.0.4 ; truncation to five characters\nVWXYZ VWXYZ VWXYZ VWXYZ VWXYZ VWXYZ VWXYZ VWXYZ\nVWXYZ VWXYZ VWXYZ VWXYZ\n\n>> qt/o arr #5.0.6 ; ... plus auto-justification\nABCDE ABCDE ABCDE ABCDE ABCDE ABCDE ABCDE ABCDE\nABCDE ABCDE ABCDE ABCDE\n}\n\nqt: func [\n {Quick table: prints out a simple block in aligned columns}\n b [any-block! object!]\n /o options [block! any-string!]\n /local s rm width decimal out tmp-options max-width extra-width pad\n][\n\n if object? b [\n b: first b\n ]\n rm: either value? 'right-margin [right-margin][77]\n if not options [\n either find b money! [\n options: #0.2.6\n ][\n options: #0.0.6\n if find b decimal! [\n foreach a b [\n if all [decimal? :a not zero? a] [\n if 10 < log-10 abs a [\n options: #0.4.7 break ; use scientific\n ]\n if 1e-4 < ((abs a) + 5e-5 // 1) [ ; if significantly\n options: #0.4.6 ; different from an integer\n ]\n ]\n ]\n ]\n ]\n ]\n set [width decimal tmp-options extra-width]\n parse-integers/def options [0 0 6 0]\n extra-width: extra-width + 1\n options: to issue! rejoin [ width \".\" decimal \".\" tmp-options ]\n if zero? width [\n max-width: to integer! rm + extra-width / 3 - extra-width\n foreach a b [\n if not data-block? :a [\n width: maximum width length? format :a options\n ]\n if width >= max-width [\n width: max-width\n break\n ]\n ]\n ]\n pad: head insert/dup copy \"\" \" \" extra-width\n s: 0\n options: to issue! rejoin [ width \".\" decimal \".\" tmp-options ]\n foreach a b [\n either data-block? :a [\n print \"^/(nested)\"\n qt/o a options\n s: 0\n ][\n s: s + length? out: format :a options\n if s > rm [\n print \"\"\n s: length? out\n ]\n prin out\n s: s + extra-width\n if positive? rm - s - extra-width [\n prin pad\n ]\n ]\n ]\n print \"\"\n exit\n]\n\ncomment { ================ QC ================\n\nEXAMPLES:\n\n>> arr: compose/deep [\n[ [\"pi\" (pi)]\n[ [\"pi * pi\" (pi * pi)]\n[ [\"exp pi\" (exp pi)]\n[ [\"log-e pi\" (log-e pi)]]\n== [[\"pi\" 3.14159265358979] [\"pi * pi\" 9.86960440108936] [\"exp pi\" ...\n\n>> qc arr\npi 3.14\npi * pi 9.87\nexp pi 23.14\nlog-e pi 1.14\n\n>> qc arr #.4 ; with optional options argument\npi 3.1416\npi * pi 9.8696\nexp pi 23.1407\nlog-e pi 1.1447\n\n>> qc arr #.4.2.2 ; two extra spaces\npi 3.1416\npi * pi 9.8696\nexp pi 23.1407\nlog-e pi 1.1447\n\n>> qc arr [#-10 #6.2] ; passing block of options to FORMAT\npi 3.14\npi * pi 9.87\nexp pi 23.14\nlog-e pi 1.14\n}\n\nunset!: (type?)\n\nqc: func [\n {prints out nested blocks in aligned columns}\n b [any-block!]\n options [block! any-string! unset!] \"optional options\"\n /local widths w max-width decimal tmp-options extra-width bb\n][\n\n if not value? 'options [\n options: #20.2.6\n ]\n if not any-block? first b [\n qt/o b options\n exit\n ]\n if data-block? first first b [\n foreach a b [\n either value? 'options [qc a options][qc a]\n print \"\"\n ]\n exit\n ]\n if not block? options [\n set [max-width decimal tmp-options extra-width]\n parse-integers/def options [20 2 6 0]\n if zero? max-width [max-width: 20]\n options: to issue! rejoin [ 0 \".\" decimal \".\" tmp-options ]\n widths: array/initial length? b/1 4\n foreach bb b [\n while [(length? widths) < (length? :bb)] [\n append widths 4\n ]\n while [ not tail? :bb ] [\n w: minimum\n maximum\n length? format first :bb options\n first widths\n max-width\n if w > first widths [\n change widths w\n ]\n bb: next :bb\n widths: next widths\n ]\n widths: head widths\n ]\n while [ not tail? widths ] [\n change widths to issue! rejoin [\n extra-width + first widths \".\"\n decimal \".\"\n tmp-options\n ]\n widths: next widths\n ]\n options: head widths\n ]\n foreach bb b [\n print format :bb options\n ]\n]\n\ncomment { ================ PARSE-INTEGERS ================\n\nNOTE: Used by FORMAT, QT and QC to parse the options argument.\n Any datatype formable to a sequence of periods and integers\n may be used.\n\nEXAMPLES:\n\n>> parse-integers #20.3.-4\n== [20 3 -4]\n>> parse-integers 2.18\n== [2 18]\n>> parse-integers 2.100 ; trailing zeroes disappear when\n== [2 1] ; decimal is formed\n>> parse-integers #4....6\n== [4 0 0 0 6] ; zero is default\n\n>> parse-integers/def #4....6 [11 12 13 14 15 16 17 18 19 20]\n== [4 12 13 14 6 16 17 18 19 20]\n>> parse-integers/def 4....6 [11 12 13 14 15 16 17 18 19 20]\n== [4 0 0 0 6 16 17 18 19 20] ; zeroes appear when tuple is formed\n}\n\nparse-integers: func [\n {parse integers from the form of S}\n s\n /def defaults [block!] \"block of default values\"\n /local item\n][\n defaults: copy either def [defaults][[]]\n s: parse form s \".\"\n while [ not tail? s ] [\n defaults: either integer? item: load first s [\n either tail? defaults [\n insert tail defaults item\n ][\n change defaults item\n ]\n ][\n either tail? defaults [\n insert tail defaults 0\n ][\n next defaults\n ]\n ]\n s: next s\n ]\n head defaults\n]\n\ncomment { ================ DATA-BLOCK? ================\n\nNOTE: Used by FORMAT, QT and QC\n\n}\ndata-block?: func [\n {Returns TRUE if VALUE is a block, list, hash, paren or object}\n value [any-type!]\n][\n all [\n value? 'value\n any [\n block? :value\n list? :value\n hash? :value\n paren? :value\n object? :value\n ]\n ]\n]\n\ncomment { ================ FULL-FORM ================\n\nEXAMPLES:\n\n>> form pi\n== \"3.14159265358979\"\n>> full-form pi\n== \"3.141592653589793\" ; forms to full precision\n\n>> pi - to-decimal form pi\n== 3.10862446895044E-15 ; cannot recover original value\n>> pi - to-decimal full-form pi\n== 0 ; original value restored\n\n>> full-form 2 ** 53 - 1\n== \"9007199254740991\"\n>> full-form 2 ** 53\n== \"9007199254740992\" ; greatest \"integer\"\n>> full-form 2 ** 53 + 2\n== \"9.007199254740994E+15\" ; higher values in scientific notation\n\n>> full-form 2 ** -1074 ; smallest positive decimal value\n== \"4.94065645841247E-324\" ; (excess precision here)\n}\n\nfull-form: func [\n {returns full-precision form of number}\n n [number!]\n /local\n s digit exponent sign formed\n first-n diff form-it savs f-exp too-big big-int\n][\n sign: either negative? n ; save sign as string and\n [n: (- n) \"-\"][\"\"] ; make N positive\n\n too-big: positive? n - 1.79769313486231E+308 ; highest FORM-able number\n\n big-int: all [ ; pseudo-integer, incrementable\n not negative? n - 1E+15 ; by 1, but FORM converts it to\n not positive? n - (2 ** 53) ; scientific format\n ]\n\n if all [\n not big-int ; always take control from 1E+15 to MAXINTEGER\n not too-big ; if TOO-BIG, TO DECIMAL! FORM N overflows\n zero? n - to decimal! form n ; otherwise if FORM is good enough\n ][return head insert form n sign] ; ... use it\n\n first-n: n\n s: copy \"\"\n\n exponent: either any [\n positive? exponent: log-10 n ; get rough value of exponent\n zero? exponent - (to integer! exponent)\n ][ ; get integer at or below rough value\n to integer! exponent\n ][\n -1 + to integer! exponent\n ]\n\n n: n / (10 ** exponent) ; convert N so that (N >= 1) and (N < 10)\n digit: to integer! n ; get the integer portion\n\n if zero? digit [ ; in case LOG-10 rounded up to next integer,\n exponent: exponent - 1 ; making (N < 1) ...\n digit: to integer! n: n * 10 ; adjust and recalculate\n ]\n\n loop 16 [\n insert tail s digit ; append integer portion to N\n n: n - digit * 10 ; increment N by factor of 10\n digit: to integer! n ; to get next integer portion\n ]\n\n insert next s \".\" ; add decimal point to get representation of N\n\n f-exp: either negative? exponent [head insert form exponent \"E\" ][\n either positive? exponent ; make exponent string\n [head insert form exponent \"E+\"][\"\"]\n ]\n form-it: func [][\n formed: copy f-exp ; tack on exponent string to get\n insert formed s ; decimal representation of FIRST-N\n first-n - to decimal! formed ; return the value for DIFF\n ]\n\n if not zero? diff: form-it [ ; if 16 loops didn't work ...\n savs: s\n if not too-big [\n s: string-add s \"1\" ; first try incrementing,\n diff: form-it\n ]\n if not zero? diff [ ; and if that didn't work ...\n s: savs\n insert tail s digit ; tack on 17th digit\n ]\n ]\n if zero? diff [ ; return if we had the exact form\n return head insert either big-int [\n head remove skip s 1 ; without decimal point if possible\n ][formed] sign\n ]\n\n either positive? diff: form-it [ ; now see where we are\n while [ positive? diff ] [ ; increment till it works ...\n s: string-add s \"1\"\n diff: do form-it\n ]\n ][\n while [ negative? diff ] [ ; or decrement ...\n s: string-subtract/full-length s \"1\"\n diff: form-it\n ]\n ]\n head insert formed sign\n]\n\ncomment { ================ STRING-ADD ================\n\nNOTE: Used by FULL-FORM\n\nEXAMPLES:\n\n>> string-add \"123456789999999999875\" \"125\"\n== \"123456790000000000000\"\n\n>> string-add \"2.0000000000000000\" \"-125\"\n== \"1.9999999999999875\" ; decimal point in longer arg ignored\n}\n\nstring-add: func [\n {add two string representations of integers numerically -\n NOTE: decimal points in longer argument are ignored}\n a [string!] b [string!]\n /local c d neg\n][\n a: copy a b: copy b\n either #\"-\" = first a [\n either #\"-\" = first b [neg: true remove a remove b][\n remove a return string-subtract b a\n ]\n ][\n if #\"-\" = first b [remove b return string-subtract a b]\n ]\n if (length? a) < (length? b) [set [a b] reduce [b a]]\n insert/dup b \"0\" (length? a) - (length? b)\n a: tail a b: tail b\n d: 0 while [ not head? b ] [\n a: back a b: back b\n if #\".\" = first a [a: back a]\n c: (first a) + (first b) + d - 48\n d: either c > #\"9\" [c: c - 10 1][0]\n change a c\n ]\n if d > 0 [insert a \"1\"]\n if neg [insert a \"-\"]\n a\n]\n\ncomment { ================ STRING-SUBTRACT ================\n\nNOTE: Used by FULL-FORM\n}\n\nstring-subtract: func [\n {subtract two string representations of integers numerically -\n NOTE: decimal points in longer argument are ignored}\n a [string!] b [string!]\n /full-length\n /local c d neg\n][\n a: copy a b: copy b\n either #\"-\" = first a [\n either #\"-\" = first b [neg: true remove a remove b][\n insert b \"-\" return string-add a b\n ]\n ][\n if #\"-\" = first b [remove b return string-add a b]\n ]\n if any [\n (length? a) < (length? b)\n all [(length? a) = (length? b) a < b]\n ][set [a b] reduce [b a] neg: not neg]\n insert/dup b \"0\" (length? a) - (length? b)\n a: tail a b: tail b\n d: 0 while [ not head? b ] [\n a: back a b: back b\n if #\".\" = first a [a: back a]\n c: to char! (first a) - (first b) - d + 48\n d: either c < #\"0\" [c: c + 10 1][0]\n change a c\n ]\n if not full-length [\n while [all [#\"0\" = first a 1 < length? a]][remove a]\n ]\n if neg [\n either #\"-\" = first a [remove a][insert a \"-\"]\n ]\n a\n]\n\ncomment { ================ PAREN-FORM ================\n\nNOTE: This is the fastest way to make a precise string representation\n of an arbitrary decimal\n\nEXAMPLE:\n\n>> paren-form pi\n== \"(3.14159265358979 + 3.10862446895044E-15)\"\n>> pi - do paren-form pi\n== 0\n}\n\nparen-form: func [\n {returns a string precisely representing numerical value}\n n [number!]\n /local s diff\n][\n diff: n - to decimal! s: form n\n either zero? diff [s][to string! reduce [\"(\" s \" + \" diff \")\"]]\n]\n", "meta": {"hexsha": "416714ff258e185256d99b487281f3ebf6955c0d", "size": 25564, "ext": "r", "lang": "R", "max_stars_repo_path": "community-scripts/format.r", "max_stars_repo_name": "mikeyaunish/DB-Rider", "max_stars_repo_head_hexsha": "d711bcd91bc7f384a3994b367ae484d91b51efb9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2017-11-09T10:45:21.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-31T06:46:19.000Z", "max_issues_repo_path": "community-scripts/format.r", "max_issues_repo_name": "mikeyaunish/DB-Rider", "max_issues_repo_head_hexsha": "d711bcd91bc7f384a3994b367ae484d91b51efb9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "community-scripts/format.r", "max_forks_repo_name": "mikeyaunish/DB-Rider", "max_forks_repo_head_hexsha": "d711bcd91bc7f384a3994b367ae484d91b51efb9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-02-12T08:45:46.000Z", "max_forks_repo_forks_event_max_datetime": "2018-02-12T08:45:46.000Z", "avg_line_length": 31.4440344403, "max_line_length": 78, "alphanum_fraction": 0.5088796745, "num_tokens": 6948, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "\n\n\n\n\n\n\n C\bCH\bHA\bAP\bPT\bTE\bER\bR 8\b8\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs\n\n\n\n\n\n\n 8\b8.\b.1\b1.\b. v\bva\bal\bli\bid\bd f\bfu\bun\bnc\bct\bti\bio\bon\bn o\bob\bbj\bje\bec\bct\bts\bs\n\n There are many different objects which can occupy\n the function field of a symbol object. Table 8.1, on\n the following page, shows all of the possibilities,\n how to recognize them, and where to look for documen-\n tation.\n\n\n\n 8\b8.\b.2\b2.\b. f\bfu\bun\bnc\bct\bti\bio\bon\bns\bs\n\n The basic Lisp function is the lambda function.\n When a lambda function is called, the actual arguments\n are evaluated from left to right and are lambda-bound\n to the formal parameters of the lambda function.\n\n An nlambda function is usually used for functions\n which are invoked by the user at top level. Some\n built-in functions which evaluate their arguments in\n special ways are also nlambdas (e.g _\bc_\bo_\bn_\bd, _\bd_\bo, _\bo_\br).\n When an nlambda function is called, the list of\n unevaluated arguments is lambda bound to the single\n formal parameter of the nlambda function.\n\n Some programmers will use an nlambda function\n when they are not sure how many arguments will be\n passed. Then, the first thing the nlambda function\n does is map _\be_\bv_\ba_\bl over the list of unevaluated argu-\n ments it has been passed. This is usually the wrong\n thing to do, as it will not work compiled if any of\n the arguments are local variables. The solution is to\n use a lexpr. When a lexpr function is called, the\n arguments are evaluated and a fixnum whose value is\n the number of arguments is lambda-bound to the single\n formal parameter of the lexpr function. The lexpr can\n then access the arguments using the _\ba_\br_\bg function.\n\n When a function is compiled, _\bs_\bp_\be_\bc_\bi_\ba_\bl declarations\n may be needed to preserve its behavior. An argument\n is not lambda-bound to the name of the corresponding\n formal parameter unless that formal parameter has been\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b1\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-2\b2\n\n\n\n\n\n +-------------------+------------------------+----------------+\n | informal name | object type | documentation |\n +-------------------+------------------------+----------------+\n | interpreted | list with _\bc_\ba_\br | 8.2 |\n | lambda function | _\be_\bq to lambda | |\n +-------------------+------------------------+----------------+\n | interpreted | list with _\bc_\ba_\br | 8.2 |\n | nlambda function | _\be_\bq to nlambda | |\n +-------------------+------------------------+----------------+\n | interpreted | list with _\bc_\ba_\br | 8.2 |\n | lexpr function | _\be_\bq to lexpr | |\n +-------------------+------------------------+----------------+\n | interpreted | list with _\bc_\ba_\br | 8.3 |\n | macro | _\be_\bq to macro | |\n +-------------------+------------------------+----------------+\n | fclosure | vector with _\bv_\bp_\br_\bo_\bp | 8.4 |\n | | _\be_\bq to fclosure | |\n +-------------------+------------------------+----------------+\n | compiled | binary with discipline | 8.2 |\n | lambda or lexpr | _\be_\bq to lambda | |\n | function | | |\n +-------------------+------------------------+----------------+\n | compiled | binary with discipline | 8.2 |\n | nlambda function | _\be_\bq to nlambda | |\n +-------------------+------------------------+----------------+\n | compiled | binary with discipline | 8.3 |\n | macro | _\be_\bq to macro | |\n +-------------------+------------------------+----------------+\n | foreign | binary with discipline | 8.5 |\n +-----\bs-\bu-\bb-\br-\bo-\bu\bo-\bt\bf-\bi-\bn\b\"-\be\bs-\bu-\bb-\br-\bo-\bu+\bt-\bi-\bn-\be-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b2-\b4-\b0-\b>-\b?-\b1-\b2-\b4-\b0-+----------------+\n | foreign | binary with discipline | 8.5 |\n +------\bf-\bu-\bn-\bc-\bt-\bi-\bo\bo-\bn\bf--\b\"-\bf-\bu-\bn-\bc+\bt-\bi-\bo-\bn-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b3-\b2-\b0-\b>-\b?-\b1-\b3-\b2-\b0-+----------------+\n | foreign | binary with discipline | 8.5 |\n +--\bi-\bn-\bt\bo-\be\bf-\bg-\be\b\"-\br\bi-\bn-\bf\bt-\bu\be-\bn\bg-\bc\be-\bt\br-\bi\b--\bo\bf-\bn\bu-\bn-\bc+\bt-\bi-\bo-\bn-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b4-\b0-\b0-\b>-\b?-\b1-\b4-\b0-\b0-+----------------+\n | foreign | binary with discipline | 8.5 |\n +---\br-\be-\ba-\bl-\bo-\bf\bf-\bu-\bn\b\"-\bc\br-\bt\be-\bi\ba-\bo\bl-\bn\b--\bf-\bu-\bn-\bc+\bt-\bi-\bo-\bn-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b4-\b8-\b0-\b>-\b?-\b1-\b4-\b8-\b0-+----------------+\n | foreign | binary with discipline | 8.5 |\n +-----\bC--\bf-\bu-\bn-\bc\bo-\bt\bf-\bi-\bo\b\"-\bn\bc-\b--\bf-\bu-\bn-\bc+\bt-\bi-\bo-\bn-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b5-\b6-\b0-\b>-\b?-\b1-\b5-\b6-\b0-+----------------+\n | foreign | binary with discipline | 8.5 |\n +--\bd-\bo\bo-\bu\bf-\bb-\bl\b\"-\be\bd-\bo-\bf\bu-\bu\bb-\bn\bl-\bc\be-\bt\b--\bi\bc-\bo\b--\bn\bf-\bu-\bn-\bc+\bt-\bi-\bo-\bn-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b6-\b4-\b0-\b>-\b?-\b1-\b6-\b4-\b0-+----------------+\n | foreign | binary with discipline | 8.5 |\n +-\bs-\bt-\br\bo-\bu\bf-\bc-\bt\b\"-\bu\bv-\br\be-\be\bc-\bt-\bf\bo-\bu\br-\bn\b--\bc\bc-\bt\b--\bi\bf-\bo\bu-\bn\bn-\bc+\bt-\bi-\bo-\bn-\b\"-\b.-\bn-\br--\b3-\bb-\bo-\bt--\b1-\b7-\b2-\b0-\b>-\b?-\b1-\b7-\b2-\b0-+----------------+\n | array | array object | 9 |\n +-------------------+------------------------+----------------+\n Table 8.1\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-3\b3\n\n\n declared _\bs_\bp_\be_\bc_\bi_\ba_\bl (see S12.3.2.2).\n\n Lambda and lexpr functions both compile into a\n binary object with a discipline of lambda. However, a\n compiled lexpr still acts like an interpreted lexpr.\n\n\n\n 8\b8.\b.3\b3.\b. m\bma\bac\bcr\bro\bos\bs\n\n An important feature of Lisp is its ability to\n manipulate programs as data. As a result of this,\n most Lisp implementations have very powerful macro\n facilities. The Lisp language's macro facility can be\n used to incorporate popular features of the other lan-\n guages into Lisp. For example, there are macro pack-\n ages which allow one to create records (ala Pascal)\n and refer to elements of those records by the field\n names. The _\bs_\bt_\br_\bu_\bc_\bt package imported from Maclisp does\n this. Another popular use for macros is to create\n more readable control structures which expand into\n _\bc_\bo_\bn_\bd, _\bo_\br and _\ba_\bn_\bd. One such example is the If macro.\n It allows you to write\n\n _\b(_\bI_\bf _\b(_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b0_\b) _\bt_\bh_\be_\bn _\b(_\bp_\br_\bi_\bn_\bt _\b'_\bz_\be_\br_\bo_\b) _\b(_\bt_\be_\br_\bp_\br_\b)\n _\be_\bl_\bs_\be_\bi_\bf _\b(_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b1_\b) _\bt_\bh_\be_\bn _\b(_\bp_\br_\bi_\bn_\bt _\b'_\bo_\bn_\be_\b) _\b(_\bt_\be_\br_\bp_\br_\b)\n _\be_\bl_\bs_\be _\b(_\bp_\br_\bi_\bn_\bt _\b'_\b|_\bI _\bg_\bi_\bv_\be _\bu_\bp_\b|_\b)_\b)\n\n which expands to\n\n _\b(_\bc_\bo_\bn_\bd\n _\b(_\b(_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b0_\b) _\b(_\bp_\br_\bi_\bn_\bt _\b'_\bz_\be_\br_\bo_\b) _\b(_\bt_\be_\br_\bp_\br_\b)_\b)\n _\b(_\b(_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b1_\b) _\b(_\bp_\br_\bi_\bn_\bt _\b'_\bo_\bn_\be_\b) _\b(_\bt_\be_\br_\bp_\br_\b)_\b)\n _\b(_\bt _\b(_\bp_\br_\bi_\bn_\bt _\b'_\b|_\bI _\bg_\bi_\bv_\be _\bu_\bp_\b|_\b)_\b)_\b)\n\n\n\n\n 8\b8.\b.3\b3.\b.1\b1.\b. m\bma\bac\bcr\bro\bo f\bfo\bor\brm\bms\bs\n\n A macro is a function which accepts a Lisp\n expression as input and returns another Lisp\n expression. The action the macro takes is called\n macro expansion. Here is a simple example:\n\n -> _\b(_\bd_\be_\bf _\bf_\bi_\br_\bs_\bt _\b(_\bm_\ba_\bc_\br_\bo _\b(_\bx_\b) _\b(_\bc_\bo_\bn_\bs _\b'_\bc_\ba_\br _\b(_\bc_\bd_\br _\bx_\b)_\b)_\b)_\b)\n first\n -> _\b(_\bf_\bi_\br_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b)_\b)\n a\n ____________________\n t\bth\bhe\be f\bfi\bir\brs\bst\bt c\bch\bha\bar\bra\bac\bct\bte\ber\br o\bof\bf t\bth\bhe\be s\bst\btr\bri\bin\bng\bg i\bis\bs s\bsi\big\bgn\bni\bif\bfi\bic\bca\ban\bnt\bt (\b(i\bi.\b.e\be \"\b\"s\bs\"\b\"\n i\bis\bs o\bok\bk f\bfo\bor\br \"\b\"s\bsu\bub\bbr\bro\bou\but\bti\bin\bne\be\"\b\")\b)\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-4\b4\n\n\n -> _\b(_\ba_\bp_\bp_\bl_\by _\b'_\bf_\bi_\br_\bs_\bt _\b'_\b(_\bf_\bi_\br_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b)_\b)_\b)\n (car '(a b c))\n\n The first input line defines a macro called _\bf_\bi_\br_\bs_\bt.\n Notice that the macro has one formal parameter, _\bx.\n On the second input line, we ask the interpreter to\n evaluate _\b(_\bf_\bi_\br_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b)_\b). _\bE_\bv_\ba_\bl sees that _\bf_\bi_\br_\bs_\bt\n has a function definition of type macro, so it\n evaluates _\bf_\bi_\br_\bs_\bt's definition, passing to _\bf_\bi_\br_\bs_\bt, as\n an argument, the form _\be_\bv_\ba_\bl itself was trying to\n evaluate: _\b(_\bf_\bi_\br_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b)_\b). The _\bf_\bi_\br_\bs_\bt macro chops\n off the car of the argument with _\bc_\bd_\br, cons' a _\bc_\ba_\br\n at the beginning of the list and returns\n _\b(_\bc_\ba_\br _\b'_\b(_\ba _\bb _\bc_\b)_\b), which _\be_\bv_\ba_\bl evaluates. The value _\ba\n is returned as the value of _\b(_\bf_\bi_\br_\bs_\bt _\b'_\b(_\ba _\bb _\bc_\b)_\b). Thus\n whenever _\be_\bv_\ba_\bl tries to evaluate a list whose car\n has a macro definition it ends up doing (at least)\n two operations, the first of which is a call to the\n macro to let it macro expand the form, and the\n other is the evaluation of the result of the macro.\n The result of the macro may be yet another call to\n a macro, so _\be_\bv_\ba_\bl may have to do even more evalua-\n tions until it can finally determine the value of\n an expression. One way to see how a macro will\n expand is to use _\ba_\bp_\bp_\bl_\by as shown on the third input\n line above.\n\n\n\n 8\b8.\b.3\b3.\b.2\b2.\b. d\bde\bef\bfm\bma\bac\bcr\bro\bo\n\n The macro _\bd_\be_\bf_\bm_\ba_\bc_\br_\bo makes it easier to define\n macros because it allows you to name the arguments\n to the macro call. For example, suppose we find\n ourselves often writing code like\n _\b(_\bs_\be_\bt_\bq _\bs_\bt_\ba_\bc_\bk _\b(_\bc_\bo_\bn_\bs _\bn_\be_\bw_\be_\bl_\bt _\bs_\bt_\ba_\bc_\bk_\b). We could define a\n macro named _\bp_\bu_\bs_\bh to do this for us. One way to\n define it is:\n\n -> _\b(_\bd_\be_\bf _\bp_\bu_\bs_\bh\n _\b(_\bm_\ba_\bc_\br_\bo _\b(_\bx_\b) _\b(_\bl_\bi_\bs_\bt _\b'_\bs_\be_\bt_\bq _\b(_\bc_\ba_\bd_\bd_\br _\bx_\b) _\b(_\bl_\bi_\bs_\bt _\b'_\bc_\bo_\bn_\bs _\b(_\bc_\ba_\bd_\br _\bx_\b) _\b(_\bc_\ba_\bd_\bd_\br _\bx_\b)_\b)_\b)_\b)_\b)\n push\n\n then _\b(_\bp_\bu_\bs_\bh _\bn_\be_\bw_\be_\bl_\bt _\bs_\bt_\ba_\bc_\bk_\b) will expand to the form\n mentioned above. The same macro written using def-\n macro would be:\n\n -> _\b(_\bd_\be_\bf_\bm_\ba_\bc_\br_\bo _\bp_\bu_\bs_\bh _\b(_\bv_\ba_\bl_\bu_\be _\bs_\bt_\ba_\bc_\bk_\b)\n _\b(_\bl_\bi_\bs_\bt _\b'_\bs_\be_\bt_\bq _\b,_\bs_\bt_\ba_\bc_\bk _\b(_\bl_\bi_\bs_\bt _\b'_\bc_\bo_\bn_\bs _\b,_\bv_\ba_\bl_\bu_\be _\b,_\bs_\bt_\ba_\bc_\bk_\b)_\b)_\b)\n push\n\n Defmacro allows you to name the arguments of the\n macro call, and makes the macro definition look\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-5\b5\n\n\n more like a function definition.\n\n\n\n 8\b8.\b.3\b3.\b.3\b3.\b. t\bth\bhe\be b\bba\bac\bck\bkq\bqu\buo\bot\bte\be c\bch\bha\bar\bra\bac\bct\bte\ber\br m\bma\bac\bcr\bro\bo\n\n The default syntax for FRANZ LISP has four\n characters with associated character macros. One\n is semicolon for comments. Two others are the\n backquote and comma which are used by the backquote\n character macro. The fourth is the sharp sign\n macro described in the next section.\n\n The backquote macro is used to create lists\n where many of the elements are fixed (quoted).\n This makes it very useful for creating macro defi-\n nitions. In the simplest case, a backquote acts\n just like a single quote:\n\n ->_\b`_\b(_\ba _\bb _\bc _\bd _\be_\b)\n (a b c d e)\n\n If a comma precedes an element of a backquoted list\n then that element is evaluated and its value is put\n in the list.\n\n ->_\b(_\bs_\be_\bt_\bq _\bd _\b'_\b(_\bx _\by _\bz_\b)_\b)\n (x y z)\n ->_\b`_\b(_\ba _\bb _\bc _\b,_\bd _\be_\b)\n (a b c (x y z) e)\n\n If a comma followed by an at sign precedes an ele-\n ment in a backquoted list, then that element is\n evaluated and spliced into the list with _\ba_\bp_\bp_\be_\bn_\bd.\n\n ->_\b`_\b(_\ba _\bb _\bc _\b,_\b@_\bd _\be_\b)\n (a b c x y z e)\n\n Once a list begins with a backquote, the commas may\n appear anywhere in the list as this example shows:\n\n ->_\b`_\b(_\ba _\bb _\b(_\bc _\bd _\b,_\b(_\bc_\bd_\br _\bd_\b)_\b) _\b(_\be _\bf _\b(_\bg _\bh _\b,_\b@_\b(_\bc_\bd_\bd_\br _\bd_\b) _\b,_\b@_\bd_\b)_\b)_\b)\n (a b (c d (y z)) (e f (g h z x y z)))\n\n It is also possible and sometimes even useful to\n use the backquote macro within itself. As a final\n demonstration of the backquote macro, we shall\n define the first and push macros using all the\n power at our disposal: defmacro and the backquote\n macro.\n\n ->_\b(_\bd_\be_\bf_\bm_\ba_\bc_\br_\bo _\bf_\bi_\br_\bs_\bt _\b(_\bl_\bi_\bs_\bt_\b) _\b`_\b(_\bc_\ba_\br _\b,_\bl_\bi_\bs_\bt_\b)_\b)\n first\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-6\b6\n\n\n ->_\b(_\bd_\be_\bf_\bm_\ba_\bc_\br_\bo _\bp_\bu_\bs_\bh _\b(_\bv_\ba_\bl_\bu_\be _\bs_\bt_\ba_\bc_\bk_\b) _\b`_\b(_\bs_\be_\bt_\bq _\b,_\bs_\bt_\ba_\bc_\bk _\b(_\bc_\bo_\bn_\bs _\b,_\bv_\ba_\bl_\bu_\be _\b,_\bs_\bt_\ba_\bc_\bk_\b)_\b)_\b)\n stack\n\n\n\n 8\b8.\b.3\b3.\b.4\b4.\b. s\bsh\bha\bar\brp\bp s\bsi\big\bgn\bn c\bch\bha\bar\bra\bac\bct\bte\ber\br m\bma\bac\bcr\bro\bo\n\n The sharp sign macro can perform a number of\n different functions at read time. The character\n directly following the sharp sign determines which\n function will be done, and following Lisp s-\n expressions may serve as arguments.\n\n\n\n 8\b8.\b.3\b3.\b.4\b4.\b.1\b1.\b. c\bco\bon\bnd\bdi\bit\bti\bio\bon\bna\bal\bl i\bin\bnc\bcl\blu\bus\bsi\bio\bon\bn\n\n If you plan to run one source file in more than\n one environment then you may want to some pieces\n of code to be included or not included depend-\n ing on the environment. The C language uses\n \"#ifdef\" and \"#ifndef\" for this purpose, and\n Lisp uses \"#+\" and \"#-\". The environment that\n the sharp sign macro checks is the _\b(_\bs_\bt_\ba_\bt_\bu_\bs _\bf_\be_\ba_\b-\n _\bt_\bu_\br_\be_\bs_\b) list which is initialized when the Lisp\n system is built and which may be altered by\n _\b(_\bs_\bs_\bt_\ba_\bt_\bu_\bs _\bf_\be_\ba_\bt_\bu_\br_\be _\bf_\bo_\bo_\b) and\n _\b(_\bs_\bs_\bt_\ba_\bt_\bu_\bs _\bn_\bo_\bf_\be_\ba_\bt_\bu_\br_\be _\bb_\ba_\br_\b) The form of conditional\n inclusion is\n _\b#_\b+_\bw_\bh_\be_\bn _\bw_\bh_\ba_\bt\n where _\bw_\bh_\be_\bn is either a symbol or an expression\n involving symbols and the functions _\ba_\bn_\bd, _\bo_\br, and\n _\bn_\bo_\bt. The meaning is that _\bw_\bh_\ba_\bt will only be read\n in if _\bw_\bh_\be_\bn is true. A symbol in _\bw_\bh_\be_\bn is true\n only if it appears in the _\b(_\bs_\bt_\ba_\bt_\bu_\bs _\bf_\be_\ba_\bt_\bu_\br_\be_\bs_\b)\n list.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-7\b7\n\n\n\n ____________________________________________________\n\n ; suppose we want to write a program which references a file\n ; and which can run at ucb, ucsd and cmu where the file naming conventions\n ; are different.\n ;\n -> _\b(_\bd_\be_\bf_\bu_\bn _\bh_\bo_\bw_\bo_\bl_\bd _\b(_\bn_\ba_\bm_\be_\b)\n _\b(_\bt_\be_\br_\bp_\br_\b)\n _\b(_\bl_\bo_\ba_\bd _\b#_\b+_\b(_\bo_\br _\bu_\bc_\bb _\bu_\bc_\bs_\bd_\b) _\b\"_\b/_\bu_\bs_\br_\b/_\bl_\bi_\bb_\b/_\bl_\bi_\bs_\bp_\b/_\ba_\bg_\be_\bs_\b._\bl_\b\"\n _\b#_\b+_\bc_\bm_\bu _\b\"_\b/_\bu_\bs_\br_\b/_\bl_\bi_\bs_\bp_\b/_\bd_\bo_\bc_\b/_\ba_\bg_\be_\bs_\b._\bl_\b\"_\b)\n _\b(_\bp_\ba_\bt_\bo_\bm _\bn_\ba_\bm_\be_\b)\n _\b(_\bp_\ba_\bt_\bo_\bm _\b\" _\bi_\bs _\b\"_\b)\n _\b(_\bp_\br_\bi_\bn_\bt _\b(_\bc_\bd_\br _\b(_\ba_\bs_\bs_\bo_\bc _\bn_\ba_\bm_\be _\ba_\bg_\be_\bf_\bi_\bl_\be_\b)_\b)_\b)\n _\b(_\bp_\ba_\bt_\bo_\bm _\b\"_\by_\be_\ba_\br_\bs _\bo_\bl_\bd_\b\"_\b)\n _\b(_\bt_\be_\br_\bp_\br_\b)_\b)\n ____________________________________________________\n\n\n\n The form\n _\b#_\b-_\bw_\bh_\be_\bn _\bw_\bh_\ba_\bt\n is equivalent to\n _\b#_\b+_\b(_\bn_\bo_\bt _\bw_\bh_\be_\bn_\b) _\bw_\bh_\ba_\bt\n\n\n\n 8\b8.\b.3\b3.\b.4\b4.\b.2\b2.\b. f\bfi\bix\bxn\bnu\bum\bm c\bch\bha\bar\bra\bac\bct\bte\ber\br e\beq\bqu\bui\biv\bva\bal\ble\ben\bnt\bts\bs\n\n When working with fixnum equivalents of charac-\n ters, it is often hard to remember the number\n corresponding to a character. The form\n _\b#_\b/_\bc\n is equivalent to the fixnum representation of\n character c.\n\n\n ____________________________________________________\n\n ; a function which returns t if the user types y else it returns nil.\n ;\n -> _\b(_\bd_\be_\bf_\bu_\bn _\by_\be_\bs_\bo_\br_\bn_\bo _\bn_\bi_\bl\n _\b(_\bp_\br_\bo_\bg_\bn _\b(_\ba_\bn_\bs_\b)\n _\b(_\bs_\be_\bt_\bq _\ba_\bn_\bs _\b(_\bt_\by_\bi_\b)_\b)\n _\b(_\bc_\bo_\bn_\bd _\b(_\b(_\be_\bq_\bu_\ba_\bl _\ba_\bn_\bs _\b#_\b/_\by_\b) _\bt_\b)\n _\b(_\bt _\bn_\bi_\bl_\b)_\b)_\b)_\b)\n ____________________________________________________\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-8\b8\n\n\n 8\b8.\b.3\b3.\b.4\b4.\b.3\b3.\b. r\bre\bea\bad\bd t\bti\bim\bme\be e\bev\bva\bal\blu\bua\bat\bti\bio\bon\bn\n\n Occasionally you want to express a constant as a\n Lisp expression, yet you don't want to pay the\n penalty of evaluating this expression each time\n it is referenced. The form\n _\b#_\b._\be_\bx_\bp_\br_\be_\bs_\bs_\bi_\bo_\bn\n evaluates the expression at read time and\n returns its value.\n\n\n ____________________________________________________\n\n ; a function to test if any of bits 1 3 or 12 are set in a fixnum.\n ;\n -> _\b(_\bd_\be_\bf_\bu_\bn _\bt_\be_\bs_\bt_\bi_\bt _\b(_\bn_\bu_\bm_\b)\n _\b(_\bc_\bo_\bn_\bd _\b(_\b(_\bz_\be_\br_\bo_\bp _\b(_\bb_\bo_\bo_\bl_\be _\b1 _\bn_\bu_\bm _\b#_\b._\b(_\b+ _\b(_\bl_\bs_\bh _\b1 _\b1_\b) _\b(_\bl_\bs_\bh _\b1 _\b3_\b) _\b(_\bl_\bs_\bh _\b1 _\b1_\b2_\b)_\b)_\b)_\b)\n _\bn_\bi_\bl_\b)\n _\b(_\bt _\bt_\b)_\b)_\b)\n ____________________________________________________\n\n\n\n\n\n\n 8\b8.\b.4\b4.\b. f\bfc\bcl\blo\bos\bsu\bur\bre\bes\bs\n\n Fclosures are a type of functional object. The\n purpose is to remember the values of some variables\n between invocations of the functional object and to\n protect this data from being inadvertently overwritten\n by other Lisp functions. Fortran programs usually\n exhibit this behavior for their variables. (In fact,\n some versions of Fortran would require the variables\n to be in COMMON). Thus it is easy to write a linear\n congruent random number generator in Fortran, merely\n by keeping the seed as a variable in the function. It\n is much more risky to do so in Lisp, since any special\n variable you picked, might be used by some other func-\n tion. Fclosures are an attempt to provide most of the\n same functionality as closures in Lisp Machine Lisp,\n to users of FRANZ LISP. Fclosures are related to clo-\n sures in this way:\n (fclosure '(a b) 'foo) <==>\n (let ((a a) (b b)) (closure '(a b) 'foo))\n\n\n\n 8\b8.\b.4\b4.\b.1\b1.\b. a\ban\bn e\bex\bxa\bam\bmp\bpl\ble\be\n\n ____________________________________________________________\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-9\b9\n\n\n % l\bli\bis\bsp\bp\n Franz Lisp, Opus 38.60\n ->(\b(d\bde\bef\bfu\bun\bn c\bco\bod\bde\be (\b(m\bme\be c\bco\bou\bun\bnt\bt)\b)\n (\b(p\bpr\bri\bin\bnt\bt (\b(l\bli\bis\bst\bt '\b'i\bin\bn x\bx)\b))\b)\n (\b(s\bse\bet\btq\bq x\bx (\b(+\b+ 1\b1 x\bx)\b))\b)\n (\b(c\bco\bon\bnd\bd (\b((\b(g\bgr\bre\bea\bat\bte\ber\brp\bp c\bco\bou\bun\bnt\bt 1\b1)\b) (\b(f\bfu\bun\bnc\bca\bal\bll\bl m\bme\be m\bme\be (\b(s\bsu\bub\bb1\b1 c\bco\bou\bun\bnt\bt)\b))\b))\b))\b)\n (\b(p\bpr\bri\bin\bnt\bt (\b(l\bli\bis\bst\bt '\b'o\bou\but\bt x\bx)\b))\b))\b)\n code\n ->(\b(d\bde\bef\bfu\bun\bn t\bte\bes\bst\bte\ber\br (\b(o\bob\bbj\bje\bec\bct\bt c\bco\bou\bun\bnt\bt)\b)\n (\b(f\bfu\bun\bnc\bca\bal\bll\bl o\bob\bbj\bje\bec\bct\bt o\bob\bbj\bje\bec\bct\bt c\bco\bou\bun\bnt\bt)\b) (\b(t\bte\ber\brp\bpr\bri\bi)\b))\b)\n tester\n ->(\b(s\bse\bet\btq\bq x\bx 0\b0)\b)\n 0\n ->(\b(s\bse\bet\btq\bq z\bz (\b(f\bfc\bcl\blo\bos\bsu\bur\bre\be '\b'(\b(x\bx)\b) '\b'c\bco\bod\bde\be)\b))\b)\n fclosure[8]\n -> (\b(t\bte\bes\bst\bte\ber\br z\bz 3\b3)\b)\n (in 0)(in 1)(in 2)(out 3)(out 3)(out 3)\n nil\n ->x\bx\n 0\n ____________________________________________________________\n\n\n\n\n\n The function _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be creates a new object\n that we will call an fclosure, (although it is\n actually a vector). The fclosure contains a func-\n tional object, and a set of symbols and values for\n the symbols. In the above example, the fclosure\n functional object is the function code. The set of\n symbols and values just contains the symbol `x' and\n zero, the value of `x' when the fclosure was cre-\n ated.\n\n When an fclosure is funcall'ed:\n\n 1) The Lisp system lambda binds the symbols in\n the fclosure to their values in the fclosure.\n\n 2) It continues the funcall on the functional\n object of the fclosure.\n\n 3) Finally, it un-lambda binds the symbols in the\n fclosure and at the same time stores the cur-\n rent values of the symbols in the fclosure.\n\n\n Notice that the fclosure is saving the value\n of the symbol `x'. Each time a fclosure is cre-\n ated, new space is allocated for saving the values\n of the symbols. Thus if we execute fclosure again,\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b10\b0\n\n\n over the same function, we can have two independent\n counters:\n\n ____________________________________________________________\n\n -> (\b(s\bse\bet\btq\bq z\bzz\bz (\b(f\bfc\bcl\blo\bos\bsu\bur\bre\be '\b'(\b(x\bx)\b) '\b'c\bco\bod\bde\be)\b))\b)\n fclosure[1]\n -> (\b(t\bte\bes\bst\bte\ber\br z\bzz\bz 2\b2)\b)\n (in 0)(in 1)(out 2)(out 2)\n -> (\b(t\bte\bes\bst\bte\ber\br z\bzz\bz 2\b2)\b)\n (in 2)(in 3)(out 4)(out 4)\n -> (\b(t\bte\bes\bst\bte\ber\br z\bz 3\b3)\b)\n (in 3)(in 4)(in 5)(out 6)(out 6)(out 6)\n ____________________________________________________________\n\n\n\n\n\n\n\n 8\b8.\b.4\b4.\b.2\b2.\b. u\bus\bse\bef\bfu\bul\bl f\bfu\bun\bnc\bct\bti\bio\bon\bns\bs\n\n Here are some quick some summaries of func-\n tions dealing with closures. They are more for-\n mally defined in S2.8.4. To recap, fclosures are\n made by _\b(_\bf_\bc_\bl_\bo_\bs_\bu_\br_\be _\b'_\bl_\b__\bv_\ba_\br_\bs _\b'_\bg_\b__\bf_\bu_\bn_\bc_\bo_\bb_\bj_\b). l_vars is a\n list of symbols (not containing nil), g_funcobj is\n any object that can be funcalled. (Objects which\n can be funcalled, include compiled Lisp functions,\n lambda expressions, symbols, foreign functions,\n etc.) In general, if you want a compiled function\n to be closed over a variable, you must declare the\n variable to be special within the function.\n Another example would be:\n\n (fclosure '(a b) #'(lambda (x) (plus x a)))\n\n Here, the #' construction will make the compiler\n compile the lambda expression.\n\n There are times when you want to share vari-\n ables between fclosures. This can be done if the\n fclosures are created at the same time using _\bf_\bc_\bl_\bo_\b-\n _\bs_\bu_\br_\be_\b-_\bl_\bi_\bs_\bt. The function _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\b-_\ba_\bl_\bi_\bs_\bt returns an\n assoc list giving the symbols and values in the\n fclosure. The predicate _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\bp returns t iff\n its argument is a fclosure. Other functions\n imported from Lisp Machine Lisp are _\bs_\by_\bm_\be_\bv_\ba_\bl_\b-_\bi_\bn_\b-\n _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\b, _\bl_\be_\bt_\b-_\bf_\bc_\bl_\bo_\bs_\be_\bd_\b, and _\bs_\be_\bt_\b-_\bi_\bn_\b-_\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\b.\n Lastly, the function _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\b-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn returns the\n function argument.\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b11\b1\n\n\n 8\b8.\b.4\b4.\b.3\b3.\b. i\bin\bnt\bte\ber\brn\bna\bal\bl s\bst\btr\bru\buc\bct\btu\bur\bre\be\n\n Currently, closures are implemented as vec-\n tors, with property being the symbol fclosure. The\n functional object is the first entry. The remain-\n ing entries are structures which point to the sym-\n bols and values for the closure, (with a reference\n count to determine if a recursive closure is\n active).\n\n\n\n 8\b8.\b.5\b5.\b. f\bfo\bor\bre\bei\big\bgn\bn s\bsu\bub\bbr\bro\bou\but\bti\bin\bne\bes\bs a\ban\bnd\bd f\bfu\bun\bnc\bct\bti\bio\bon\bns\bs\n\n FRANZ LISP has the ability to dynamically load\n object files produced by other compilers and to call\n functions defined in those files. These functions are\n called _\bf_\bo_\br_\be_\bi_\bg_\bn functions.* There are seven types of\n foreign functions. They are characterized by the type\n of result they return, and by differences in the\n interpretation of their arguments. They come from two\n families: a group suited for languages which pass\n arguments by reference (e.g. Fortran), and a group\n suited for languages which pass arguments by value\n (e.g. C).\n\n\n There are four types in the first group:\n\n s\bsu\bub\bbr\bro\bou\but\bti\bin\bne\be\n This does not return anything. The Lisp system\n always returns t after calling a subroutine.\n\n f\bfu\bun\bnc\bct\bti\bio\bon\bn\n This returns whatever the function returns. This\n must be a valid Lisp object or it may cause the\n Lisp system to fail.\n\n i\bin\bnt\bte\beg\bge\ber\br-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\n This returns an integer which the Lisp system\n makes into a fixnum and returns.\n\n r\bre\bea\bal\bl-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\n This returns a double precision real number which\n the Lisp system makes into a flonum and returns.\n\n ____________________\n *This topic is also discussed in Report PAM-124 of the\n Center for Pure and Applied Mathematics, UCB, entitled\n ``Parlez-Vous Franz? An Informal Introduction to Interfac-\n ing Foreign Functions to Franz LISP'', by James R. Larus\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b12\b2\n\n\n There are three types in the second group:\n\n c\bc-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\n This is like an integer function, except for its\n different interpretation of arguments.\n\n d\bdo\bou\bub\bbl\ble\be-\b-c\bc-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\n This is like a real-function.\n\n v\bve\bec\bct\bto\bor\br-\b-c\bc-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\n This is for C functions which return a structure.\n The first argument to such functions must be a\n vector (of type vectori), into which the result\n is stored. The second Lisp argument becomes the\n first argument to the C function, and so on\n\n A foreign function is accessed through a binary object\n just like a compiled Lisp function. The difference is\n that the discipline field of a binary object for a\n foreign function is a string whose first character is\n given in the following table:\n\n\n +-------+-------------------+\n |letter | type |\n +-------+-------------------+\n | s | subroutine |\n +-------+-------------------+\n | f | function |\n +-------+-------------------+\n | i | integer-function |\n +-------+-------------------+\n | r | real-function. |\n +-------+-------------------+\n | c | c-function |\n +-------+-------------------+\n | v | vector-c-function |\n +-------+-------------------+\n | d | double-c-function |\n +-------+-------------------+\n +-------+-------------------+\n Two functions are provided for setting-up foreign\n functions. _\bC_\bf_\ba_\bs_\bl loads an object file into the Lisp\n system and sets up one foreign function binary object.\n If there are more than one function in an object file,\n _\bg_\be_\bt_\ba_\bd_\bd_\br_\be_\bs_\bs can be used to set up additional foreign\n function objects.\n\n Foreign functions are called just like other\n functions, e.g _\b(_\bf_\bu_\bn_\bn_\ba_\bm_\be _\ba_\br_\bg_\b1 _\ba_\br_\bg_\b2_\b). When a function\n in the Fortran group is called, the arguments are\n evaluated and then examined. List, hunk and symbol\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b13\b3\n\n\n arguments are passed unchanged to the foreign func-\n tion. Fixnum and flonum arguments are copied into a\n temporary location and a pointer to the value is\n passed (this is because Fortran uses call by reference\n and it is dangerous to modify the contents of a fixnum\n or flonum which something else might point to). If\n the argument is an array object, the data field of the\n array object is passed to the foreign function (This\n is the easiest way to send large amounts of data to\n and receive large amounts of data from a foreign func-\n tion). If a binary object is an argument, the entry\n field of that object is passed to the foreign function\n (the entry field is the address of a function, so this\n amounts to passing a function as an argument).\n\n When a function in the C group is called, fixnum\n and flownum arguments are passed by value. For almost\n all other arguments, the address is merely provided to\n the C routine. The only exception arises when you\n want to invoke a C routine which expects a ``struc-\n ture'' argument. Recall that a (rarely used) feature\n of the C language is the ability to pass structures by\n value. This copies the structure onto the stack.\n Since the Franz's nearest equivalent to a C structure\n is a vector, we provide an escape clause to copy the\n contents of an immediate-type vector by value. If the\n property field of a vectori argument, is the symbol\n \"value-structure-argument\", then the binary data of\n this immediate-type vector is copied into the argument\n list of the C routine.\n\n The method a foreign function uses to access the\n arguments provided by Lisp is dependent on the lan-\n guage of the foreign function. The following scripts\n demonstrate how how Lisp can interact with three lan-\n guages: C, Pascal and Fortran. C and Pascal have\n pointer types and the first script shows how to use\n pointers to extract information from Lisp objects.\n There are two functions defined for each language.\n The first (cfoo in C, pfoo in Pascal) is given four\n arguments, a fixnum, a flonum-block array, a hunk of\n at least two fixnums and a list of at least two\n fixnums. To demonstrate that the values were passed,\n each ?foo function prints its arguments (or parts of\n them). The ?foo function then modifies the second\n element of the flonum-block array and returns a 3 to\n Lisp. The second function (cmemq in C, pmemq in Pas-\n cal) acts just like the Lisp _\bm_\be_\bm_\bq function (except it\n won't work for fixnums whereas the lisp _\bm_\be_\bm_\bq will work\n for small fixnums). In the script, typed input is in\n b\bbo\bol\bld\bd, computer output is in roman and comments are in\n _\bi_\bt_\ba_\bl_\bi_\bc_\b.\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b14\b4\n\n\n ____________________________________________________________\n\n _\bT_\bh_\be_\bs_\be _\ba_\br_\be _\bt_\bh_\be _\bC _\bc_\bo_\bd_\be_\bd _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n % c\bca\bat\bt c\bch\bh8\b8a\bau\bux\bxc\bc.\b.c\bc\n /* demonstration of c coded foreign integer-function */\n\n /* the following will be used to extract fixnums out of a list of fixnums */\n struct listoffixnumscell\n { struct listoffixnumscell *cdr;\n int *fixnum;\n };\n\n struct listcell\n { struct listcell *cdr;\n int car;\n };\n\n cfoo(a,b,c,d)\n int *a;\n double b[];\n int *c[];\n struct listoffixnumscell *d;\n {\n printf(\"a: %d, b[0]: %f, b[1]: %f0, *a, b[0], b[1]);\n printf(\" c (first): %d c (second): %d0,\n *c[0],*c[1]);\n printf(\" ( %d %d ... )\", *(d->fixnum), *(d->cdr->fixnum));\n b[1] = 3.1415926;\n return(3);\n }\n\n struct listcell *\n cmemq(element,list)\n int element;\n struct listcell *list;\n {\n for( ; list && element != list->car ; list = list->cdr);\n return(list);\n }\n\n\n _\bT_\bh_\be_\bs_\be _\ba_\br_\be _\bt_\bh_\be _\bP_\ba_\bs_\bc_\ba_\bl _\bc_\bo_\bd_\be_\bd _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n % c\bca\bat\bt c\bch\bh8\b8a\bau\bux\bxp\bp.\b.p\bp\n type pinteger = ^integer;\n realarray = array[0..10] of real;\n pintarray = array[0..10] of pinteger;\n listoffixnumscell = record\n cdr : ^listoffixnumscell;\n fixnum : pinteger;\n end;\n plistcell = ^listcell;\n listcell = record\n cdr : plistcell;\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b15\b5\n\n\n car : integer;\n end;\n\n function pfoo ( var a : integer ;\n var b : realarray;\n var c : pintarray;\n var d : listoffixnumscell) : integer;\n begin\n writeln(' a:',a, ' b[0]:', b[0], ' b[1]:', b[1]);\n writeln(' c (first):', c[0]^,' c (second):', c[1]^);\n writeln(' ( ', d.fixnum^, d.cdr^.fixnum^, ' ...) ');\n b[1] := 3.1415926;\n pfoo := 3\n end ;\n\n { the function pmemq looks for the Lisp pointer given as the first argument\n in the list pointed to by the second argument.\n Note that we declare \" a : integer \" instead of \" var a : integer \" since\n we are interested in the pointer value instead of what it points to (which\n could be any Lisp object)\n }\n function pmemq( a : integer; list : plistcell) : plistcell;\n begin\n while (list <> nil) and (list^.car <> a) do list := list^.cdr;\n pmemq := list;\n end ;\n\n\n _\bT_\bh_\be _\bf_\bi_\bl_\be_\bs _\ba_\br_\be _\bc_\bo_\bm_\bp_\bi_\bl_\be_\bd\n % c\bcc\bc -\b-c\bc c\bch\bh8\b8a\bau\bux\bxc\bc.\b.c\bc\n 1.0u 1.2s 0:15 14% 30+39k 33+20io 147pf+0w\n % p\bpc\bc -\b-c\bc c\bch\bh8\b8a\bau\bux\bxp\bp.\b.p\bp\n 3.0u 1.7s 0:37 12% 27+32k 53+32io 143pf+0w\n\n\n % l\bli\bis\bsp\bp\n Franz Lisp, Opus 38.60\n _\bF_\bi_\br_\bs_\bt _\bt_\bh_\be _\bf_\bi_\bl_\be_\bs _\ba_\br_\be _\bl_\bo_\ba_\bd_\be_\bd _\ba_\bn_\bd _\bw_\be _\bs_\be_\bt _\bu_\bp _\bo_\bn_\be _\bf_\bo_\br_\be_\bi_\bg_\bn _\bf_\bu_\bn_\bc_\b-\n _\bt_\bi_\bo_\bn _\bb_\bi_\bn_\ba_\br_\by_\b. _\bW_\be _\bh_\ba_\bv_\be _\bt_\bw_\bo _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs _\bi_\bn _\be_\ba_\bc_\bh _\bf_\bi_\bl_\be _\bs_\bo _\bw_\be _\bm_\bu_\bs_\bt\n _\bc_\bh_\bo_\bo_\bs_\be _\bo_\bn_\be _\bt_\bo _\bt_\be_\bl_\bl _\bc_\bf_\ba_\bs_\bl _\ba_\bb_\bo_\bu_\bt_\b. _\bT_\bh_\be _\bc_\bh_\bo_\bi_\bc_\be _\bi_\bs _\ba_\br_\bb_\bi_\bt_\br_\ba_\br_\by_\b.\n -> (\b(c\bcf\bfa\bas\bsl\bl '\b'c\bch\bh8\b8a\bau\bux\bxc\bc.\b.o\bo '\b'_\b_c\bcf\bfo\boo\bo '\b'c\bcf\bfo\boo\bo \"\b\"i\bin\bnt\bte\beg\bge\ber\br-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\"\b\")\b)\n /usr/lib/lisp/nld -N -A /usr/local/lisp -T 63000 ch8auxc.o -e _cfoo -o /tmp/Li7055.0 -lc\n #63000-\"integer-function\"\n -> (\b(c\bcf\bfa\bas\bsl\bl '\b'c\bch\bh8\b8a\bau\bux\bxp\bp.\b.o\bo '\b'_\b_p\bpf\bfo\boo\bo '\b'p\bpf\bfo\boo\bo \"\b\"i\bin\bnt\bte\beg\bge\ber\br-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\"\b\" \"\b\"-\b-l\blp\bpc\bc\"\b\")\b)\n /usr/lib/lisp/nld -N -A /tmp/Li7055.0 -T 63200 ch8auxp.o -e _pfoo -o /tmp/Li7055.1 -lpc -lc\n #63200-\"integer-function\"\n _\bH_\be_\br_\be _\bw_\be _\bs_\be_\bt _\bu_\bp _\bt_\bh_\be _\bo_\bt_\bh_\be_\br _\bf_\bo_\br_\be_\bi_\bg_\bn _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn _\bb_\bi_\bn_\ba_\br_\by _\bo_\bb_\bj_\be_\bc_\bt_\bs\n -> (\b(g\bge\bet\bta\bad\bdd\bdr\bre\bes\bss\bs '\b'_\b_c\bcm\bme\bem\bmq\bq '\b'c\bcm\bme\bem\bmq\bq \"\b\"f\bfu\bun\bnc\bct\bti\bio\bon\bn\"\b\" '\b'_\b_p\bpm\bme\bem\bmq\bq '\b'p\bpm\bme\bem\bmq\bq \"\b\"f\bfu\bun\bnc\bct\bti\bio\bon\bn\"\b\")\b)\n #6306c-\"function\"\n _\bW_\be _\bw_\ba_\bn_\bt _\bt_\bo _\bc_\br_\be_\ba_\bt_\be _\ba_\bn_\bd _\bi_\bn_\bi_\bt_\bi_\ba_\bl_\bi_\bz_\be _\ba_\bn _\ba_\br_\br_\ba_\by _\bt_\bo _\bp_\ba_\bs_\bs _\bt_\bo _\bt_\bh_\be\n _\bc_\bf_\bo_\bo _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\b. _\bI_\bn _\bt_\bh_\bi_\bs _\bc_\ba_\bs_\be _\bw_\be _\bc_\br_\be_\ba_\bt_\be _\ba_\bn _\bu_\bn_\bn_\ba_\bm_\be_\bd _\ba_\br_\br_\ba_\by _\ba_\bn_\bd\n _\bs_\bt_\bo_\br_\be _\bi_\bt _\bi_\bn _\bt_\bh_\be _\bv_\ba_\bl_\bu_\be _\bc_\be_\bl_\bl _\bo_\bf _\bt_\be_\bs_\bt_\ba_\br_\br_\b. _\bW_\bh_\be_\bn _\bw_\be _\bc_\br_\be_\ba_\bt_\be _\ba_\bn\n _\ba_\br_\br_\ba_\by _\bt_\bo _\bp_\ba_\bs_\bs _\bt_\bo _\bt_\bh_\be _\bP_\ba_\bs_\bc_\ba_\bl _\bp_\br_\bo_\bg_\br_\ba_\bm _\bw_\be _\bw_\bi_\bl_\bl _\bu_\bs_\be _\ba _\bn_\ba_\bm_\be_\bd\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b16\b6\n\n\n _\ba_\br_\br_\ba_\by _\bj_\bu_\bs_\bt _\bt_\bo _\bd_\be_\bm_\bo_\bn_\bs_\bt_\br_\ba_\bt_\be _\bt_\bh_\be _\bd_\bi_\bf_\bf_\be_\br_\be_\bn_\bt _\bw_\ba_\by _\bt_\bh_\ba_\bt _\bn_\ba_\bm_\be_\bd _\ba_\bn_\bd\n _\bu_\bn_\bn_\ba_\bm_\be_\bd _\ba_\br_\br_\ba_\by_\bs _\ba_\br_\be _\bc_\br_\be_\ba_\bt_\be_\bd _\ba_\bn_\bd _\ba_\bc_\bc_\be_\bs_\bs_\be_\bd_\b.\n -> (\b(s\bse\bet\btq\bq t\bte\bes\bst\bta\bar\brr\br (\b(a\bar\brr\bra\bay\by n\bni\bil\bl f\bfl\blo\bon\bnu\bum\bm-\b-b\bbl\blo\boc\bck\bk 2\b2)\b))\b)\n array[2]\n -> (\b(s\bst\bto\bor\bre\be (\b(f\bfu\bun\bnc\bca\bal\bll\bl t\bte\bes\bst\bta\bar\brr\br 0\b0)\b) 1\b1.\b.2\b23\b34\b4)\b)\n 1.234\n -> (\b(s\bst\bto\bor\bre\be (\b(f\bfu\bun\bnc\bca\bal\bll\bl t\bte\bes\bst\bta\bar\brr\br 1\b1)\b) 5\b5.\b.6\b67\b78\b8)\b)\n 5.678\n -> (\b(c\bcf\bfo\boo\bo 3\b38\b85\b5 t\bte\bes\bst\bta\bar\brr\br (\b(h\bhu\bun\bnk\bk 1\b10\b0 1\b11\b1 1\b13\b3 1\b14\b4)\b) '\b'(\b(1\b15\b5 1\b16\b6 1\b17\b7)\b))\b)\n a: 385, b[0]: 1.234000, b[1]: 5.678000\n c (first): 10 c (second): 11\n ( 15 16 ... )\n 3\n _\bN_\bo_\bt_\be _\bt_\bh_\ba_\bt _\bc_\bf_\bo_\bo _\bh_\ba_\bs _\br_\be_\bt_\bu_\br_\bn_\be_\bd _\b3 _\ba_\bs _\bi_\bt _\bs_\bh_\bo_\bu_\bl_\bd_\b. _\bI_\bt _\ba_\bl_\bs_\bo _\bh_\ba_\bd _\bt_\bh_\be\n _\bs_\bi_\bd_\be _\be_\bf_\bf_\be_\bc_\bt _\bo_\bf _\bc_\bh_\ba_\bn_\bg_\bi_\bn_\bg _\bt_\bh_\be _\bs_\be_\bc_\bo_\bn_\bd _\bv_\ba_\bl_\bu_\be _\bo_\bf _\bt_\bh_\be _\ba_\br_\br_\ba_\by _\bt_\bo\n _\b3_\b._\b1_\b4_\b1_\b5_\b9_\b2_\b6 _\bw_\bh_\bi_\bc_\bh _\bc_\bh_\be_\bc_\bk _\bn_\be_\bx_\bt_\b.\n -> (\b(f\bfu\bun\bnc\bca\bal\bll\bl t\bte\bes\bst\bta\bar\brr\br 1\b1)\b)\n 3.1415926\n\n\n _\bI_\bn _\bp_\br_\be_\bp_\ba_\br_\ba_\bt_\bi_\bo_\bn _\bf_\bo_\br _\bc_\ba_\bl_\bl_\bi_\bn_\bg _\bp_\bf_\bo_\bo _\bw_\be _\bc_\br_\be_\ba_\bt_\be _\ba_\bn _\ba_\br_\br_\ba_\by_\b.\n -> (\b(a\bar\brr\bra\bay\by t\bte\bes\bst\bt f\bfl\blo\bon\bnu\bum\bm-\b-b\bbl\blo\boc\bck\bk 2\b2)\b)\n array[2]\n -> (\b(s\bst\bto\bor\bre\be (\b(t\bte\bes\bst\bt 0\b0)\b) 1\b1.\b.2\b23\b34\b4)\b)\n 1.234\n -> (\b(s\bst\bto\bor\bre\be (\b(t\bte\bes\bst\bt 1\b1)\b) 5\b5.\b.6\b67\b78\b8)\b)\n 5.678\n -> (\b(p\bpf\bfo\boo\bo 3\b38\b85\b5 (\b(g\bge\bet\btd\bd '\b't\bte\bes\bst\bt)\b) (\b(h\bhu\bun\bnk\bk 1\b10\b0 1\b11\b1 1\b13\b3 1\b14\b4)\b) '\b'(\b(1\b15\b5 1\b16\b6 1\b17\b7)\b))\b)\n a: 385 b[0]: 1.23400000000000E+00 b[1]: 5.67800000000000E+00\n c (first): 10 c (second): 11\n ( 15 16 ...)\n 3\n -> (\b(t\bte\bes\bst\bt 1\b1)\b)\n 3.1415926\n\n _\bN_\bo_\bw _\bt_\bo _\bt_\be_\bs_\bt _\bo_\bu_\bt _\bt_\bh_\be _\bm_\be_\bm_\bq_\b'_\bs\n _\b-_\b> (\b(c\bcm\bme\bem\bmq\bq '\b'a\ba '\b'(\b(b\bb c\bc a\ba d\bd e\be f\bf)\b))\b)\n _\b(_\ba _\bd _\be _\bf_\b)\n _\b-_\b> (\b(p\bpm\bme\bem\bmq\bq '\b'e\be '\b'(\b(a\ba d\bd f\bf g\bg a\ba x\bx)\b))\b)\n _\bn_\bi_\bl\n _\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b__\b_\n\n\n\n\n\n The Fortran example will be much shorter since in\n Fortran you can't follow pointers as you can in other\n languages. The Fortran function ffoo is given three\n arguments: a fixnum, a fixnum-block array and a\n flonum. These arguments are printed out to verify\n that they made it and then the first value of the\n array is modified. The function returns a double\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs,\b, F\bFc\bcl\blo\bos\bsu\bur\bre\bes\bs,\b, a\ban\bnd\bd M\bMa\bac\bcr\bro\bos\bs 8\b8-\b-1\b17\b7\n\n\n precision value which is converted to a flonum by lisp\n and printed. Note that the entry point corresponding\n to the Fortran function ffoo is _ffoo_ as opposed to\n the C and Pascal convention of preceding the name with\n an underscore.\n\n ____________________________________________________________\n\n\n % c\bca\bat\bt c\bch\bh8\b8a\bau\bux\bxf\bf.\b.f\bf\n double precision function ffoo(a,b,c)\n integer a,b(10)\n double precision c\n print 2,a,b(1),b(2),c\n 2 format(' a=',i4,', b(1)=',i5,', b(2)=',i5,' c=',f6.4)\n b(1) = 22\n ffoo = 1.23456\n return\n end\n % f\bf7\b77\b7 -\b-c\bc c\bch\bh8\b8a\bau\bux\bxf\bf.\b.f\bf\n ch8auxf.f:\n ffoo:\n 0.9u 1.8s 0:12 22% 20+22k 54+48io 158pf+0w\n % l\bli\bis\bsp\bp\n Franz Lisp, Opus 38.60\n -> (\b(c\bcf\bfa\bas\bsl\bl '\b'c\bch\bh8\b8a\bau\bux\bxf\bf.\b.o\bo '\b'_\b_f\bff\bfo\boo\bo_\b_ '\b'f\bff\bfo\boo\bo \"\b\"r\bre\bea\bal\bl-\b-f\bfu\bun\bnc\bct\bti\bio\bon\bn\"\b\" \"\b\"-\b-l\blF\bF7\b77\b7 -\b-l\blI\bI7\b77\b7\"\b\")\b)\n /usr/lib/lisp/nld -N -A /usr/local/lisp -T 63000 ch8auxf.o -e _ffoo_\n -o /tmp/Li11066.0 -lF77 -lI77 -lc\n #6307c-\"real-function\"\n\n -> (\b(a\bar\brr\bra\bay\by t\bte\bes\bst\bt f\bfi\bix\bxn\bnu\bum\bm-\b-b\bbl\blo\boc\bck\bk 2\b2)\b)\n array[2]\n -> (\b(s\bst\bto\bor\bre\be (\b(t\bte\bes\bst\bt 0\b0)\b) 1\b10\b0)\b)\n 10\n -> (\b(s\bst\bto\bor\bre\be (\b(t\bte\bes\bst\bt 1\b1)\b) 1\b11\b1)\b)\n 11\n -> (\b(f\bff\bfo\boo\bo 3\b38\b85\b5 (\b(g\bge\bet\btd\bd '\b't\bte\bes\bst\bt)\b) 5\b5.\b.6\b67\b78\b8)\b)\n a= 385, b(1)= 10, b(2)= 11 c=5.6780\n 1.234559893608093\n -> (\b(t\bte\bes\bst\bt 0\b0)\b)\n 22\n\n ____________________________________________________________\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n", "meta": {"hexsha": "239c1da1e42c94edfa94a02d13b567a48dc2503a", "size": 51497, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch8.r", "max_stars_repo_name": "krytarowski/franz-lisp-christos", "max_stars_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-17T08:05:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T08:07:14.000Z", "max_issues_repo_path": "lisplib/manual/ch8.r", "max_issues_repo_name": "krytarowski/franz-lisp-christos", "max_issues_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lisplib/manual/ch8.r", "max_forks_repo_name": "krytarowski/franz-lisp-christos", "max_forks_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-15T16:38:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-15T16:38:53.000Z", "avg_line_length": 45.856634016, "max_line_length": 204, "alphanum_fraction": 0.4637357516, "num_tokens": 19557, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "subroutine succ(ksc,i,j,nadj,madj,ntot,nerror)\n\n# Find the successor of j in the adjacency list of i.\n# Called by addpt, initad, trifnd, swap, delout, dirseg, dirout.\n\nimplicit double precision(a-h,o-z)\ndimension nadj(-3:ntot,0:madj)\n\nnerror = -1\n\nn = nadj(i,0)\n\n# If the adjacency list of i is empty, then clearly j has no successor\n# in this adjacency list. Something's wrong; stop.\nif(n==0) {\n\tnerror = 9\n\treturn\n}\n\n# The adjacency list of i is non-empty; search through it until j is found;\n# add 1 to the location of j, and find the contents of this new location.\ndo k = 1,n {\n if(j==nadj(i,k)) {\n kp = k+1\n if(kp>n) kp = 1 # Take kp modulo n. (The adjacency list\n ksc = nadj(i,kp) # is circular.)\n return\n }\n}\n\n# The adjacency list doesn't contain j. Something's wrong.\nnerror = 10\nreturn\nend\n", "meta": {"hexsha": "39da76f3b8b4c45ab9c967a634df3699e79b84b2", "size": 891, "ext": "r", "lang": "R", "max_stars_repo_path": "VisUncertainty/bin/Debug/R-3.4.4/library/deldir/ratfor/succ.r", "max_stars_repo_name": "hyeongmokoo/SAAR_beta1", "max_stars_repo_head_hexsha": "7406f30d7f39a03e8494b2171c2bc09904a36f62", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-08-23T15:35:47.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-24T12:20:59.000Z", "max_issues_repo_path": "VisUncertainty/bin/Debug/R-3.4.4/library/deldir/ratfor/succ.r", "max_issues_repo_name": "hyeongmokoo/SAAR_beta1", "max_issues_repo_head_hexsha": "7406f30d7f39a03e8494b2171c2bc09904a36f62", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-08-17T15:14:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-23T21:55:49.000Z", "max_forks_repo_path": "VisUncertainty/bin/Debug/R-3.4.4/library/deldir/ratfor/succ.r", "max_forks_repo_name": "hyeongmokoo/SAAR_beta1", "max_forks_repo_head_hexsha": "7406f30d7f39a03e8494b2171c2bc09904a36f62", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-04T05:34:16.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-04T05:34:16.000Z", "avg_line_length": 25.4571428571, "max_line_length": 79, "alphanum_fraction": 0.6195286195, "num_tokens": 263, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.2942149721629888, "lm_q2_score": 0.027585283226074335, "lm_q1q2_score": 0.008116003336467623}}
{"text": "#Copyright 2016 Lingjiao Chen, Arun Kumar, Jeffrey Naughton, and Jignesh M. Patel, Version 0.8.\n#All rights reserved.\n#\n#Licensed under the Apache License, Version 2.0 (the \"License\");\n#you may not use this file except in compliance with the License.\n#You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n#Unless required by applicable law or agreed to in writing, software\n#distributed under the License is distributed on an \"AS IS\" BASIS,\n#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n#See the License for the specific language governing permissions and\n#limitations under the License.\n\nlibrary(Matrix);\nNormalMatrix <- setClass (\n\t\"NormalMatrix\",\n\tslots = c (\n\t\tEntTable = \"list\",\n\t\tAttTables = \"list\",\n\t\tKFKDs = \"list\",\n\t\tTrans = \"logical\",\n\t\tSparse = \"logical\"\n\t\t),\n\tprototype=prototype(Trans=FALSE,Sparse=FALSE)\t\n\t)\n\n#############################################\n# Transpose Rule\n't.NormalMatrix' <- function( NM1)\n{\n\tNM1@Trans=!NM1@Trans;\n\treturn(NM1);\n}\n#############################################\n\n#############################################\n# Element-wise Scalar Operators\nsetMethod(\"+\", c(\"numeric\",\"NormalMatrix\"), function(e1,e2) {\n\tRnum = length(e2@AttTables);\n\tSempty = nrow(e2@EntTable[[1]])*ncol(e2@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te2@EntTable[[1]]=e2@EntTable[[1]]+e1;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te2@AttTables[[i]] = e2@AttTables[[i]]+e1;\n\t}\t\n\treturn(e2);\n})\n\nsetMethod(\"+\", c(\"NormalMatrix\",\"numeric\"), function(e1,e2) {\n\tRnum = length(e1@AttTables);\n\tSempty = nrow(e1@EntTable[[1]])*ncol(e1@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te1@EntTable[[1]]=e1@EntTable[[1]]+e2;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te1@AttTables[[i]] = e1@AttTables[[i]]+e2;\n\t}\t\n\treturn(e1);\n})\n\nsetMethod(\"-\", c(\"numeric\",\"NormalMatrix\"), function(e1,e2) {\n\tRnum = length(e2@AttTables);\n\tSempty = nrow(e2@EntTable[[1]])*ncol(e2@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te2@EntTable[[1]]=e2@EntTable[[1]]-e1;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te2@AttTables[[i]] = e2@AttTables[[i]]-e1;\n\t}\t\n\treturn(e2);\n})\n\nsetMethod(\"-\", c(\"NormalMatrix\",\"numeric\"), function(e1,e2) {\n\tRnum = length(e1@AttTables);\n\tSempty = nrow(e1@EntTable[[1]])*ncol(e1@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te1@EntTable[[1]]=e1@EntTable[[1]]-e2;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te1@AttTables[[i]] = e1@AttTables[[i]]-e2;\n\t}\t\n\treturn(e1);\n})\n\nsetMethod(\"*\", c(\"numeric\",\"NormalMatrix\"), function(e1,e2) {\n\tRnum = length(e2@AttTables);\n\tSempty = nrow(e2@EntTable[[1]])*ncol(e2@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te2@EntTable[[1]]=e2@EntTable[[1]]*e1;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te2@AttTables[[i]] = e2@AttTables[[i]]*e1;\n\t}\t\n\treturn(e2);\n})\n\nsetMethod(\"*\", c(\"NormalMatrix\",\"numeric\"), function(e1,e2) {\n\tRnum = length(e1@AttTables);\n\tSempty = nrow(e1@EntTable[[1]])*ncol(e1@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te1@EntTable[[1]]=e1@EntTable[[1]]*e2;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te1@AttTables[[i]] = e1@AttTables[[i]]*e2;\n\t}\t\n\treturn(e1);\n})\n\nsetMethod(\"/\", c(\"numeric\",\"NormalMatrix\"), function(e1,e2) {\n\tRnum = length(e2@AttTables);\n\tSempty = nrow(e2@EntTable[[1]])*ncol(e2@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te2@EntTable[[1]]=e2@EntTable[[1]]/e1;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te2@AttTables[[i]] = e2@AttTables[[i]]/e1;\n\t}\t\n\treturn(e2);\n})\n\nsetMethod(\"/\", c(\"NormalMatrix\",\"numeric\"), function(e1,e2) {\n\tRnum = length(e1@AttTables);\n\tSempty = nrow(e1@EntTable[[1]])*ncol(e1@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te1@EntTable[[1]]=e1@EntTable[[1]]/e2;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te1@AttTables[[i]] = e1@AttTables[[i]]/e2;\n\t}\t\n\treturn(e1);\n})\n\nsetMethod(\"^\", c(\"numeric\",\"NormalMatrix\"), function(e1,e2) {\n\tRnum = length(e2@AttTables);\n\tSempty = nrow(e2@EntTable[[1]])*ncol(e2@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te2@EntTable[[1]]=e2@EntTable[[1]]^e1;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te2@AttTables[[i]] = e2@AttTables[[i]]^e1;\n\t}\t\n\treturn(e2);\n})\n\nsetMethod(\"^\", c(\"NormalMatrix\",\"numeric\"), function(e1,e2) {\n\tRnum = length(e1@AttTables);\n\tSempty = nrow(e1@EntTable[[1]])*ncol(e1@EntTable[[1]]);\n\tif(Sempty!=0)\n\t{\n\t\te1@EntTable[[1]]=e1@EntTable[[1]]^e2;\n\t}\n\tfor(i in 1:Rnum)\n\t{\n\t\te1@AttTables[[i]] = e1@AttTables[[i]]^e2;\n\t}\t\n\treturn(e1);\n})\n\n#############################################\n\n#############################################\n#Aggregation\nsetMethod(\"sum\", c(\"NormalMatrix\"), function(x) {\n\tSempty = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\ty = ifelse(Sempty!=0,sum(x@EntTable[[1]]),0 );\n\tRnum = length(x@AttTables);\n\tfor(i in 1:Rnum)\n\t{\n\t\ty = y + colSums(x@KFKDs[[i]])%*%rowSums(x@AttTables[[i]]);\n\t}\t\n\treturn(y);\n})\n\nsetMethod(\"rowSums\", c(\"NormalMatrix\"), function(x) {\n\tif(x@Trans==TRUE)\n\t{\n\t\treturn(t(NormalMatrixcolSums(x)));\n\t}\n\telse\n\t{\n\t\treturn(NormalMatrixrowSums(x));\n\t}\n})\n\nsetMethod(\"colSums\", c(\"NormalMatrix\"), function(x) {\n\tif(x@Trans==TRUE)\n\t{\n\t\treturn(t(NormalMatrixrowSums(x)));\n\t}\n\telse\n\t{\n\t\treturn(NormalMatrixcolSums(x));\n\t}\n})\n\nNormalMatrixrowSums <- function(x){\n\tSempty = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\ti = 1;\n\tif(Sempty!=0)\n\t{\n\t\ty = rowSums(x@EntTable[[1]])+as.numeric(x@KFKDs[[i]]%*%rowSums(x@AttTables[[i]]));;\n\t}\n\telse\n\t{\n\t\ty = as.numeric(x@KFKDs[[i]]%*%rowSums(x@AttTables[[i]]));\n\t}\n\tRnum = length(x@AttTables);\n\ti = 2;\n\twhile(i <= Rnum)\n\t{\n\t\ty = y + as.numeric(x@KFKDs[[i]]%*%rowSums(x@AttTables[[i]]));\n\t\ti = i + 1;\n\t}\t\n\treturn(y);\n}\n\nNormalMatrixcolSums <- function(x){\n\tSempty = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\tRnum = length(x@AttTables);\n\tif(Sempty!=0)\n\t{\n\t\ty = c(colSums(x@EntTable[[1]]), colSums(x@KFKDs[[1]])%*%x@AttTables[[1]]);\n\t}\n\telse\n\t{\n\t\ty = colSums(x@KFKDs[[1]])%*%x@AttTables[[1]];\n\t}\t\n\ti = 2;\n\twhile(i <= Rnum)\n\t{\n\t\ty = c(y, colSums(x@KFKDs[[i]])%*%x@AttTables[[i]] );\n\t\ti = i + 1;\n\t}\t\n\treturn(y);\n}\n\n\n#############################################\n\n#############################################\n# Multiplication\n\n# Left Matrix Multiplication\nNormalMatrixLMM <- function(x,y)\n{\n\tSempty = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\tRnum = length(x@AttTables);\n\tif(x@Sparse==TRUE) # EntTable is empty\n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = x@KFKDs[[1]] %*% (x@AttTables[[1]] %*% y[1:(ncol(x@AttTables[[1]]) ),] );\n\t\t}\n\t\telse\n\t\t{\n\t\t\tOut1 = x@EntTable[[1]]%*% y[1:(ncol(x@EntTable[[1]]) ),] + x@KFKDs[[1]] %*% (x@AttTables[[1]] %*% y[(1+ncol(x@EntTable[[1]])):(ncol(x@AttTables[[1]])+ncol(x@EntTable[[1]]) ),] );\n\t\t}\t\t\n\t\tStartCol = ncol(x@EntTable[[1]])+ncol(x@AttTables[[1]]);\n\t\ti = 2;\n\t\twhile(i <= Rnum) # multi-table joins\n\t\t{\n\t\t\tOut1 = Out1 + (x@KFKDs[[i]]) %*% (x@AttTables[[i]] %*% y[(1+StartCol):(StartCol+ncol(x@AttTables[[i]]) ),] );\t\n\t\t\tStartCol=StartCol+(ncol(x@AttTables[[i]]) );\n\t\t\ti = i + 1;\n\t\t}\n\t\treturn (Out1);\n\t}\n\telse\n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = as.matrix(x@KFKDs[[1]] %*% (x@AttTables[[1]] %*% y[1:(ncol(x@AttTables[[1]]) ),] ));\n\t\t}\n\t\telse\n\t\t{\n\t\t\tOut1 = x@EntTable[[1]]%*% y[1:(ncol(x@EntTable[[1]]) ),] + as.matrix(x@KFKDs[[1]] %*% (x@AttTables[[1]] %*% y[(1+ncol(x@EntTable[[1]])):(ncol(x@AttTables[[1]])+ncol(x@EntTable[[1]]) ),] ));\n\t\t}\t\t\n\t\tStartCol = ncol(x@EntTable[[1]])+ncol(x@AttTables[[1]]);\n\t\ti = 2;\t\t\t\n\t\twhile(i <= Rnum) # multi-table joins\n\t\t{\n\t\t\tOut1 = Out1 + as.matrix((x@KFKDs[[i]]) %*% (x@AttTables[[i]] %*% y[(1+StartCol):(StartCol+ncol(x@AttTables[[i]]) ),] ));\t\n\t\t\tStartCol=StartCol+(ncol(x@AttTables[[i]]) );\n\t\t\ti = i + 1;\n\t\t}\n\t\treturn (Out1);\n\n\t}\n}\n\n# Right Matrix Multiplication\nNormalMatrixRMM <- function(x,y)\n{\n\tSempty = nrow(y@EntTable[[1]])*ncol(y@EntTable[[1]]);\n\tRnum = length(y@AttTables);\n\tif(y@Sparse==TRUE)# Sparse \n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = (x %*% y@KFKDs[[1]])%*%y@AttTables[[1]];\n\t\t}\n\t\telse\n\t\t{\n\t\t\tOut1 = cbind(x%*% y@EntTable[[1]],(x %*% y@KFKDs[[1]])%*%y@AttTables[[1]]);\t\n\t\t}\t\n\t\ti = 2;\t\n\t\twhile( i <= Rnum) # multi-table joins\n\t\t{\n\t\t\tOut1 = cbind(Out1,(x %*% y@KFKDs[[i]])%*%y@AttTables[[i]]);\n\t\t\ti = i + 1;\t\n\t\t}\t\t\n\t\treturn (Out1);\n\t}\n\telse\n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = as.matrix(x %*% y@KFKDs[[1]])%*%y@AttTables[[1]];\n\t\t}\n\t\telse\n\t\t{\n\t\t\tOut1 = cbind(x%*% y@EntTable[[1]],as.matrix(x %*% y@KFKDs[[1]])%*%y@AttTables[[1]]);\t\n\t\t}\t\t\n\t\ti = 2;\t\n\t\twhile( i <= Rnum) # multi-table joins\n\t\t{\n\t\t\tOut1 = cbind(Out1,as.matrix(x %*% y@KFKDs[[i]])%*%y@AttTables[[i]]);\n\t\t\ti = i + 1;\t\n\t\t}\t\t\n\t\treturn (Out1);\n\t}\n}\n\n# API for LMM and RMM with different matrix types.\n\n#LMM API.\nsetMethod(\"%*%\", c(\"NormalMatrix\",\"dgCMatrix\"), function(x,y) \n{\n\tif(x@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixLMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixRMM(t(y),x)) );\n\t}\n})\n\nsetMethod(\"%*%\", c(\"NormalMatrix\",\"matrix\"), function(x,y) \n{\n\tif(x@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixLMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixRMM(t(y),x)) );\n\t}\n})\n\nsetMethod(\"%*%\", c(\"NormalMatrix\",\"dgeMatrix\"), function(x,y) \n{\n\tif(x@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixLMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixRMM(t(y),x)) );\n\t}\n})\n\nsetMethod(\"%*%\", c(\"NormalMatrix\",\"lgCMatrix\"), function(x,y) \n{\n\tif(x@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixLMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixRMM(t(y),x)) );\n\t}\n})\n\n#RMM API.\nsetMethod(\"%*%\", c(\"dgCMatrix\",\"NormalMatrix\"), function(x,y) {\n\tif(y@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixRMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixLMM(y,t(x))) );\n\t}\n})\n\nsetMethod(\"%*%\", c(\"dgeMatrix\",\"NormalMatrix\"), function(x,y) {\n\tif(y@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixRMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixLMM(y,t(x))) );\n\t}\n})\n\nsetMethod(\"%*%\", c(\"lgCMatrix\",\"NormalMatrix\"), function(x,y) {\n\tif(y@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixRMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixLMM(y,t(x))) );\n\t}\n})\n\nsetMethod(\"%*%\", c(\"matrix\",\"NormalMatrix\"), function(x,y) {\n\tif(y@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixRMM(x,y) );\n\t}\n\telse\n\t{\n\t\treturn( t(NormalMatrixLMM(y,t(x))) );\n\t}\n})\n\n#############################################\n\n\n#############################################\n#Derived Multiplication Operators.\n#Include Crossprod and double multiplication.\n#############################################\n\n#############################################\n# Cross Product\nNormalMatrixCrossprod <- function(x)\n{\n\tSempty = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\tRnum = length(x@AttTables);\n\tif(x@Sparse==TRUE)# Sparse \n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = ( crossprod( Diagonal( x=colSums(x@KFKDs[[1]])^{1/2} ) %*% x@AttTables[[1]]) ) ;\n\t\t\ti = 2;\t\n\t\t\twhile( i <= Rnum) # multi-table joins\n\t\t\t{\n\t\t\t\t# Construct Y_21\n\t\t\t\tj = 1;\n\t\t\t\tY21 = crossprod( x@AttTables[[i]], crossprod(crossprod(x@KFKDs[[j]], x@KFKDs[[i]] ), x@AttTables[[j]]) ) ;\n\t\t\t\tj = 2;\n\t\t\t\twhile( j <=(i-1))\n\t\t\t\t{\n\t\t\t\t\tj = j + 1;\n\t\t\t\t\tY21 = cbind(Y21, crossprod( x@AttTables[[i]], crossprod(crossprod(x@KFKDs[[j]], x@KFKDs[[i]] ), x@AttTables[[j]]) )) ;\n\t\t\t\t}\n\t\t\t\tOut1 = (rbind( cbind(Out1,t(Y21) ) , cbind( Y21, (crossprod( Diagonal( x=colSums(x@KFKDs[[i]])^{1/2} ) %*% x@AttTables[[i]]) ) )) );\n\t\t\t\ti = i + 1;\n\t\t\t}\n\t\t\treturn( Out1 );\n\t\t}\n\t\telse\n\t\t{\n\t\t\tY21 = crossprod(x@AttTables[[1]], ( crossprod(x@KFKDs[[1]], x@EntTable[[1]]) ) );\n\t\t\tOut1 = (rbind( cbind(crossprod(x@EntTable[[1]]),t(Y21) ) , cbind( Y21, (crossprod( Diagonal( x=colSums(x@KFKDs[[1]])^{1/2} ) %*% x@AttTables[[1]]) ) )) );\n\t\t\ti = 2;\t\n\t\t\twhile( i <= Rnum) # multi-table joins\n\t\t\t{\n\t\t\t\t# Construct Y_21\n\t\t\t\tY21 = crossprod(x@AttTables[[i]], ( crossprod(x@KFKDs[[i]], x@EntTable[[1]]) ) );\n\t\t\t\tfor( j in 1:(i-1))\n\t\t\t\t{\n\t\t\t\t\tY21 = cbind(Y21, crossprod( x@AttTables[[i]], crossprod(crossprod(x@KFKDs[[j]], x@KFKDs[[i]] ), x@AttTables[[j]]) )) ;\n\t\t\t\t}\n\t\t\t\tOut1 = (rbind( cbind(Out1,t(Y21) ) , cbind( Y21, (crossprod( Diagonal( x=colSums(x@KFKDs[[i]])^{1/2} ) %*% x@AttTables[[i]]) ) )) );\n\t\t\t\ti = i + 1;\n\t\t\t}\n\t\t\treturn( Out1 );\n\t\t}\t\t\n\t}\n\telse\n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = as.matrix( crossprod( Diagonal( x=colSums(x@KFKDs[[1]])^{1/2} ) %*% x@AttTables[[1]]) ) ;\n\t\t\ti = 2;\t\n\t\t\twhile( i <= Rnum) # multi-table joins\n\t\t\t{\n\t\t\t\t# Construct Y_21\n\t\t\t\tj = 1;\n\t\t\t\tY21 = crossprod( x@AttTables[[i]], as.matrix(crossprod(crossprod(x@KFKDs[[j]], x@KFKDs[[i]] ), x@AttTables[[j]]) )) ;\n\t\t\t\tj = 2;\n\t\t\t\twhile( j <=(i-1))\n\t\t\t\t{\n\t\t\t\t\tj = j + 1;\n\t\t\t\t\tY21 = cbind(Y21, crossprod( x@AttTables[[i]], as.matrix(crossprod(crossprod(x@KFKDs[[j]], x@KFKDs[[i]] ), x@AttTables[[j]]) )) ) ;\n\t\t\t\t}\n\t\t\t\tOut1 = (rbind( cbind(Out1,t(Y21) ) , cbind( Y21, as.matrix(crossprod( Diagonal( x=colSums(x@KFKDs[[i]])^{1/2} ) %*% x@AttTables[[i]]) ) )) );\n\t\t\t\ti = i + 1;\t\t\t\n\t\t\t}\n\t\t\treturn( Out1 );\n\t\t}\n\t\telse\n\t\t{\n\t\t\tY21 = crossprod(x@AttTables[[1]], as.matrix( crossprod(x@KFKDs[[1]], x@EntTable[[1]]) ) );\n\t\t\tOut1 = (rbind( cbind(crossprod(x@EntTable[[1]]),t(Y21) ) , cbind( Y21, as.matrix(crossprod( Diagonal( x=colSums(x@KFKDs[[1]])^{1/2} ) %*% x@AttTables[[1]]) ) )) );\n\t\t\ti = 2;\t\n\t\t\twhile( i <= Rnum) # multi-table joins\n\t\t\t{\n\t\t\t\t# Construct Y_21\n\t\t\t\tY21 = crossprod(x@AttTables[[i]], ( crossprod(x@KFKDs[[i]], x@EntTable[[1]]) ) );\n\t\t\t\tfor( j in 1:(i-1))\n\t\t\t\t{\n\t\t\t\t\tY21 = cbind(Y21, crossprod( x@AttTables[[i]], as.matrix(crossprod(crossprod(x@KFKDs[[j]], x@KFKDs[[i]] ), x@AttTables[[j]]) ))) ;\n\t\t\t\t}\n\t\t\t\tOut1 = (rbind( cbind(Out1,t(Y21) ) , cbind( Y21, as.matrix(crossprod( Diagonal( x=colSums(x@KFKDs[[i]])^{1/2} ) %*% x@AttTables[[i]]) ) )) );\n\t\t\t\ti = i + 1;\t\t\t\n\t\t\t}\n\t\t\treturn( Out1 );\n\t\t}\t\n\t}\n}\n\n# Transpose Cross Product\nNormalMatrixCrossprodTrans <- function(x)\n{\n\tSempty = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\tRnum = length(x@AttTables);\n\tif(x@Sparse==TRUE)# Sparse \n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = tcrossprod(x@KFKDs[[1]] %*% tcrossprod(x@AttTables[[1]]), x@KFKDs[[1]]);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tOut1 = tcrossprod(x@EntTable[[1]]) + tcrossprod(x@KFKDs[[1]] %*% tcrossprod(x@AttTables[[1]]), x@KFKDs[[1]]);\n\t\t}\n\t\ti = 2;\t\n\t\twhile( i <= Rnum) # multi-table joins\n\t\t{\n\t\t\tOut1 = Out1 + tcrossprod(x@KFKDs[[i]] %*% tcrossprod(x@AttTables[[i]]), x@KFKDs[[i]]);\n\t\t\ti = i + 1;\n\t\t}\n\t\treturn( Out1 );\n\t}\n\telse\n\t{\n\t\tif(Sempty==0) # EntTable is empty\n\t\t{\n\t\t\tOut1 = as.matrix(tcrossprod(x@KFKDs[[1]] %*% tcrossprod(x@AttTables[[1]]), x@KFKDs[[1]]));\n\t\t}\n\t\telse\n\t\t{\n\t\t\tOut1 = tcrossprod(x@EntTable[[1]]) + as.matrix(tcrossprod(x@KFKDs[[1]] %*% tcrossprod(x@AttTables[[1]]), x@KFKDs[[1]]));\n\t\t}\n\t\ti = 2;\t\n\t\twhile( i <= Rnum) # multi-table joins\n\t\t{\n\t\t\tOut1 = Out1 + as.matrix(tcrossprod(x@KFKDs[[i]] %*% tcrossprod(x@AttTables[[i]]), x@KFKDs[[i]]));\n\t\t\ti = i + 1;\n\t\t}\n\t\treturn( Out1 );\n\t}\t\n}\n\n# Crossprod API\nsetMethod(\"crossprod\", c(\"NormalMatrix\"), function(x) {\n\tif(x@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixCrossprod(x) );\n\t}\n\telse\n\t{\n\t\treturn( (NormalMatrixCrossprodTrans(x)) );\n\t}\n})\n\nsetMethod(\"tcrossprod\", c(\"NormalMatrix\"), function(x) {\n\tif(x@Trans==FALSE)\n\t{\n\t\treturn( NormalMatrixCrossprodTrans(x) );\n\t}\n\telse\n\t{\n\t\treturn( (NormalMatrixCrossprod(x)) );\n\t}\n})\n#############################################\n\n\n#############################################\n#Double Multiplication\n\n#############################################\n# Cross Product Alike, i.e., t(A)%*% B \n# Warning: Only test the case when A = B.\nNormalMatrixCrossprod2M <- function(x,y)\n{\n\tSempty1 = nrow(x@EntTable[[1]])*ncol(x@EntTable[[1]]);\n\tRnum1 = length(x@AttTables);\n\tSempty2 = nrow(y@EntTable[[1]])*ncol(y@EntTable[[1]]);\n\tRnum2 = length(y@AttTables);\n\tif(x@Sparse==TRUE)# Sparse \n\t{\n\t\t# First compute attribute tables\n\t\ti = 1;\n\t\tj = 1;\n\t\tOut1 = t(x@AttTables[[i]]) %*%( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]]);\n\t\tj = j+1;\n\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t{\n\t\t\tOut1 = cbind(Out1,t(x@AttTables[[i]]) %*%( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]]));\n\t\t\tj = j+1;\n\t\t}\n\t\ti = i+1;\n\t\twhile( 2<=i && i<=Rnum1 ) # Enter this loop only if A is multi-table.\n\t\t{\n\t\t\tj = 1;\n\t\t\tTemp = t(x@AttTables[[i]]) %*%( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]]);\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t\t{\n\t\t\t\tTemp = cbind(Temp,t(x@AttTables[[i]]) %*%( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]]));\n\t\t\t\tj = j+1;\n\t\t\t}\n\t\t\tOut1 = rbind(Out1,Temp);\n\t\t\ti=i+1;\n\t\t}\n\t\t# Finish attribute tables.\n\t\t\n\t\t# Consider Entity table.\n\t\tif(Sempty1 == 0 && Sempty2 != 0 ) # No Entity table in A.\n\t\t{\n\t\t\tj = 1;\n\t\t\tTemp = t(x@AttTables[[j]]) %*%( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum1)# Enter this loop only if A is multi-table.\n\t\t\t{\n\t\t\t\tTemp = rbind(Temp,t(x@AttTables[[j]]) %*%( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ));\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = cbind(Temp,Out1);\n\t\t}\n\t\tif(Sempty1 != 0 && Sempty2 == 0 ) # No Entity table in B.\n\t\t{\n\t\t\tj = 1;\n\t\t\tTemp = (t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t\t{\n\t\t\t\tTemp = cbind(Temp,(t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = rbind(Temp,Out1);\n\t\t}\n\t\tif(Sempty1 != 0 && Sempty2 != 0 ) # Both with Entity table.\n\t\t{\n\t\t\t# EntTable in A.\n\t\t\tj = 1;\n\t\t\tTemp = t(x@AttTables[[j]]) %*%( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum1)# Enter this loop only if A is multi-table.\n\t\t\t{\n\t\t\t\tTemp = rbind(Temp,t(x@AttTables[[j]]) %*%( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = cbind(Temp,Out1);\n\t\t\t# EntTable in B.\n\t\t\tj = 1;\n\t\t\tTemp = cbind( (t(x@EntTable[[1]]) %*% y@EntTable[[1]]) , (t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] ) ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t\t{\n\t\t\t\tTemp = cbind(Temp,(t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = rbind(Temp,Out1);\n\t\t}\n\t\t\t\n\t\t\treturn(Out1);\t\n\t}\n\telse\n\t{\n\t\t# First compute attribute tables\n\t\ti = 1;\n\t\tj = 1;\n\t\tOut1 = t(x@AttTables[[i]]) %*%as.matrix(( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]]));\n\t\tj = j+1;\n\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t{\n\t\t\tOut1 = cbind(Out1,t(x@AttTables[[i]]) %*%as.matrix(( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]])));\n\t\t\tj = j+1;\n\t\t}\n\t\ti = i+1;\n\t\twhile( 2<=i && i<=Rnum1 ) # Enter this loop only if A is multi-table.\n\t\t{\n\t\t\tj = 1;\n\t\t\tTemp = t(x@AttTables[[i]]) %*%as.matrix(( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]]));\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t\t{\n\t\t\t\tTemp = cbind(Temp,t(x@AttTables[[i]]) %*%as.matrix(( t(x@KFKDs[[i]]) %*% y@KFKDs[[j]] ) %*% (y@AttTables[[j]])));\n\t\t\t\tj = j+1;\n\t\t\t}\n\t\t\tOut1 = rbind(Out1,Temp);\n\t\t\ti=i+1;\n\t\t}\n\t\t# Finish attribute tables.\n\t\t\n\t\t# Consider Entity table.\n\t\tif(Sempty1 == 0 && Sempty2 != 0 ) # No Entity table in A.\n\t\t{\n\t\t\tj = 1;\n\t\t\tTemp = t(x@AttTables[[j]]) %*%as.matrix( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum1)# Enter this loop only if A is multi-table.\n\t\t\t{\n\t\t\t\tTemp = rbind(Temp,t(x@AttTables[[j]]) %*%as.matrix( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = cbind(Temp,Out1);\n\t\t}\n\t\tif(Sempty1 != 0 && Sempty2 == 0 ) # No Entity table in B.\n\t\t{\n\t\t\tj = 1;\n\t\t\tTemp = as.matrix(t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t\t{\n\t\t\t\tTemp = cbind(Temp,as.matrix(t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = rbind(Temp,Out1);\n\t\t}\n\t\tif(Sempty1 != 0 && Sempty2 != 0 ) # Both with Entity table.\n\t\t{\n\t\t\t# EntTable in A.\n\t\t\tj = 1;\n\t\t\tTemp = t(x@AttTables[[j]]) %*%as.matrix( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum1)# Enter this loop only if A is multi-table.\n\t\t\t{\n\t\t\t\tTemp = rbind(Temp,t(x@AttTables[[j]]) %*%as.matrix( t(x@KFKDs[[j]]) %*% y@EntTable[[1]] ) );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = cbind(Temp,Out1);\n\t\t\t# EntTable in B.\n\t\t\tj = 1;\n\t\t\tTemp = cbind( (t(x@EntTable[[1]]) %*% y@EntTable[[1]]) , as.matrix(t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] ) ;\n\t\t\tj = j+1;\n\t\t\twhile( 2<=j && j<= Rnum2)# Enter this loop only if B is multi-table.\n\t\t\t{\n\t\t\t\tTemp = cbind(Temp,as.matrix(t(x@EntTable[[1]]) %*% y@KFKDs[[j]]) %*% y@AttTables[[j]] );\n\t\t\t\tj = j+1;\n\t\t\t}\t\t\t\n\t\t\tOut1 = rbind(Temp,Out1);\n\t\t}\n\t\treturn( Out1 );\n\t}\t\n}\n\n#Note: Double Multiplication is not fully optimized.\n#############################################\n\n\n\n\n#############################################\n# M:N joins\nNormalMatrixJoin <- function(S,R,SJA,RJA,IsSparse=FALSE){\n\t# Input : S, R, SJA, RJA\n\tnS = nrow(S);\n\tdS = ncol(S);\n\tnR = nrow(R);\n\tdR = ncol(R);\n\n\tSJAS = sparseMatrix(c(1:nS),SJA );\n\tRJAS = sparseMatrix(c(1:nR),RJA );\n\tif( ncol(SJAS) > ncol(RJAS) )\n\t{\n\t\tSJAS = SJAS[,1:ncol(RJAS)];\n\t}\n\tif( ncol(SJAS) < ncol(RJAS) )\n\t{\n\t\tRJAS = RJAS[,1:ncol(SJAS)];\n\t}\n\tJM = SJAS %*%t(RJAS);\n\tnT = sum(colSums((JM)));\n\tif(nT!=0)\n\t{\n\t\tJMS = summary(JM);\n\t\tSJM = sparseMatrix(c(1:nT),JMS[,1],dims=c(nT,nS),x=1);\n\t\tRJM = sparseMatrix(c(1:nT),JMS[,2],dims=c(nT,nR),x=1);\n\t}\n\tif(nT==0)\n\t{\n\t\tSJM = Matrix(0,nT,nS);\n\t\tRJM = Matrix(0,nT,nR);\n\t}\n\tS0 = matrix(0,0,0);\n\tTNM = NormalMatrix(EntTable=list(S0),AttTables=list(S,R),KFKDs=list(SJM,RJM),Sparse=IsSparse);\n\treturn (TNM);\n}\n#############################################\n#Handle conflicts with regular matrix package\nsetMethod(Ops, c(\"NormalMatrix\",\"NormalMatrix\"), function(e1,e2) {\n\treturn ();\n})\n", "meta": {"hexsha": "792c4d583f556b11647ff742ac411299fc5c42b1", "size": 21190, "ext": "r", "lang": "R", "max_stars_repo_path": "src/NormalMatrix.r", "max_stars_repo_name": "lchen001/Morpheus", "max_stars_repo_head_hexsha": "d94e6487dcf32982fb4322a4ed2a8a40c2319f56", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2017-10-30T18:39:16.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T16:20:23.000Z", "max_issues_repo_path": "src/NormalMatrix.r", "max_issues_repo_name": "lchen001/Morpheus", "max_issues_repo_head_hexsha": "d94e6487dcf32982fb4322a4ed2a8a40c2319f56", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/NormalMatrix.r", "max_forks_repo_name": "lchen001/Morpheus", "max_forks_repo_head_hexsha": "d94e6487dcf32982fb4322a4ed2a8a40c2319f56", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2, 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NO\n2. NO", "lm_q1_score": 0.48047867804790706, "lm_q2_score": 0.016657043182201847, "lm_q1q2_score": 0.008003354088371247}}
{"text": "individuals <- c(\"C010_pennunk\", \"C016_pennunk\", \"C018_pennunk\", \"C036_modoc\", \"C002_modoc\", \"C019_modoc\", \"C046_laevigatus\", \"C003_laevigatus\", \"C049_herculeanus\",\"C050_herculeanus\", \"C056_herculeanus\", \"C039_vicinus\", \"C005_vicinus\", \"C028_vicinus\", \"C024_vicinusunk\", \"C029_vicinusunk\", \"C006_ocreatus\")\n# species\nspecies <- unique(sapply(strsplit(individuals, \"_\"), \"[[\", 2))\nspecies\n# \"pennunk\" \"modoc\" \"laevigatus\" \"herculeanus\" \"vicinus\" \"vicinusunk\" \"ocreatus\" \n\n\n# define parameters\n# gen time = 2 * age of sexual maturity in closely-related species from ant literature (3-4 years depending on estimate)\n# age of sex. maturity = ~3 years\n# method citation = doi: 10.1016/j.cub.2015.03.047\ngen <- 6\nmu <- 1.983877e-09 #* gen # needs to be per generation\n# plotting numbers\nmin_age <- 000\nmax_age <- 1000000\nplotting_age_range <- 1000 # years for x axis\n\n\n# loop through each output directory\noutput <- list()\noutput_bootstraps <- list()\nfor(a in 1:length(individuals)) {\n\t# identify bootstraps \n\ta_bootstraps <- paste(\"output/\", individuals[a], \"_b\", seq(from=1, to=10, by=1), \".final.txt\", sep=\"\")\n\t# identify main output file\n\ta_main <- paste(\"output/\", individuals[a], \".final.txt\", sep=\"\")\n\n\t\n\t# read in main file\n\ta_rep <- read.table(a_main, sep=\"\\t\", header=T)\n\t# rearrange main file for plotting lines\n\tfor(d in 1:nrow(a_rep)) {\n\t\tif(d == 1) {\n\t\t\ta_rep2 <- rbind(c(a_rep[d,2], a_rep[d,4]), c(a_rep[d,3], a_rep[d,4]))\n\t\t} else {\n\t\t\ta_rep2 <- rbind(a_rep2, c(a_rep[d,2], a_rep[d,4]), c(a_rep[d,3], a_rep[d,4]))\n\t\t}\n\t}\n\ta_rep <- a_rep2\n\t# scale by mutation rate\n\ta_rep[,1] <- a_rep[,1] / mu * gen\n\ta_rep[,2] <- (1 / a_rep[,2]) / (2 * mu)\n\t# remove very young and old time frames prone to error\n\ta_rep <- a_rep[a_rep[,1] >= min_age & a_rep[,1] <= max_age,]\n\t# scale by plotting age range and pop size range\n\ta_rep <- a_rep / plotting_age_range\n\t# add to output list\n\toutput[[a]] <- a_rep\n\t\n\t# output for each bootstrap\n\toutput_bootstraps[[a]] <- list()\n\tfor(b in 1:length(a_bootstraps)) {\n\t\tb_rep <- read.table(a_bootstraps[b], sep=\"\\t\", header=T)\n\t\t# rearrange main file for plotting lines\n\t\tfor(d in 1:nrow(b_rep)) {\n\t\t\tif(d == 1) {\n\t\t\t\tb_rep2 <- rbind(c(b_rep[d,2], b_rep[d,4]), c(b_rep[d,3], b_rep[d,4]))\n\t\t\t} else {\n\t\t\t\tb_rep2 <- rbind(b_rep2, c(b_rep[d,2], b_rep[d,4]), c(b_rep[d,3], b_rep[d,4]))\n\t\t\t}\n\t\t}\n\t\tb_rep <- b_rep2\n\t\t# scale by mutation rate\n\t\tb_rep[,1] <- b_rep[,1] / mu * gen\n\t\tb_rep[,2] <- (1 / b_rep[,2]) / (2 * mu)\n\t\t# remove very young and old time frames prone to error\n\t\tb_rep <- b_rep[b_rep[,1] >= min_age & b_rep[,1] <= max_age,]\n\t\t# scale by plotting age range and pop size range\n\t\tb_rep <- b_rep / plotting_age_range\n\t\t# add to output list\n\t\toutput_bootstraps[[a]][[b]] <- b_rep\t\t\n\t}\n}\n\n\n\n# plot all separate\na_col <- \"darkgreen\"\npar(mfrow=c(4,5))\npar(mar=c(4.5,4.5,2,0.2))\nfor(a in 1:length(output)) {\n\tplot_name1 <- individuals[a]\n\tplot(c(-1,1), xlim=c(50, 900), ylim=c(0,500), pch=19, cex=0.01, log=\"x\", xlab=\"kya\", ylab=\"Pop. Size (1000s)\", main=\"\", xaxt=\"n\", yaxt=\"n\")\n\ttitle(main=bquote(italic(.(plot_name1))), adj=0,line=0.5, cex.main=1.5)\n\taxis(side=2, at=c(0, 150, 300, 450), labels=T)\t\t\n\taxis(side=1, at=c(20, 50, 100, 200, 500, 900), labels=T)\t\t\n\t\n\t\n\t# plot bootstraps\n\tfor(b in 1:length(output_bootstraps[[1]])) {\n\t\tlines(output_bootstraps[[a]][[b]][,1], output_bootstraps[[a]][[b]][,2], col=a_col, lwd=0.3)\n\t}\n\tlines(output[[a]][,1], output[[a]][,2], col=a_col, lwd=3)\n}\n\n\n\n\n\n\n# define current pop sizes \ncurrent_pop <- c()\nfor(a in 1:length(output)) {\n\tcurrent_pop <- c(current_pop, output[[a]][1,2])\n}\n\n# harmonic mean of pop sizes from most recent to 500k years ago\nharmonic_pop <- c()\nfor(a in 1:length(output)) {\n\tout_rep <- output[[a]]\n\t\n\t# define time series\n\ttime_series <- seq(from=as.integer(out_rep[2,1])+1, to=500, by=1)\n\t# time series pops\n\ttime_pops <- c()\n\tfor(b in 1:length(time_series)) {\n\t\ttime_pops <- c(time_pops, out_rep[time_series[b] < out_rep[,1],][1,2])\n\t}\n\t# harmonic mean of this individual\n\tharm_rep <- length(time_pops) / sum((1 / time_pops))\n\t\n\t# add to output element\n\tharmonic_pop <- c(harmonic_pop, harm_rep)\n}\n\n# read in diversity table\ndiv <- read.table(\"01_heterozygosity_per_individual.txt\", header=T, stringsAsFactors=F)\n# remove outgroups\ndiv <- div[div$number <= 17, ]\n\n\n# define output to match with diversity table\noutput <- data.frame(individual=as.character(gsub(\"C\", \"C-\", sapply(strsplit(individuals, \"_\"), \"[[\", 1))), current_pop=as.numeric(current_pop), harmonic_pop=as.numeric(harmonic_pop))\n# rearrange\noutput <- output[match(div$individual, output$individual),]\n# combine outputs\ndiv <- cbind(div, output)\n# remove duplicate individuals column after checking\ndiv <- div[,-6]\n\n#write output\nwrite.table(div, file=\"01_heterozygosity_and_pop_sizes.txt\", sep=\"\\t\", row.names=F, quote=F)\n", "meta": {"hexsha": "08bc1042694319f3233dca55e2ffb17ba5900da9", "size": 4802, "ext": "r", "lang": "R", "max_stars_repo_path": "04_msmc2/04_plot_and_calc_harmonic_means.r", "max_stars_repo_name": "jdmanthey/camponotus_genomes1", "max_stars_repo_head_hexsha": "5caf349e3f84548e24da239d6d3fabca4171dcbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "04_msmc2/04_plot_and_calc_harmonic_means.r", "max_issues_repo_name": "jdmanthey/camponotus_genomes1", "max_issues_repo_head_hexsha": "5caf349e3f84548e24da239d6d3fabca4171dcbe", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "04_msmc2/04_plot_and_calc_harmonic_means.r", "max_forks_repo_name": "jdmanthey/camponotus_genomes1", "max_forks_repo_head_hexsha": "5caf349e3f84548e24da239d6d3fabca4171dcbe", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2021-01-29T16:29:49.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-29T16:29:49.000Z", "avg_line_length": 33.3472222222, "max_line_length": 307, "alphanum_fraction": 0.6597251145, "num_tokens": 1659, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.33458944125318596, "lm_q2_score": 0.022977372335312288, "lm_q1q2_score": 0.007687986171138551}}
{"text": "# -------------------------------------------\n# App Title: Benford's Law and Data Examples\n# Author: Jimmy Doi\n# -------------------------------------------\n\nlibrary(RColorBrewer)\nlibrary(rvest)\nlibrary(xml2)\n\n##############################################################\n## US Census ##\n##############################################################\n\n########################################\n# Download complete data set from:\n# https://www.census.gov/data/datasets/2016/demo/popest/counties-total.html\n\ncomp.cens.data <- function(){\n\n vals <- as.matrix(read.csv(\"co-est2016-alldata.csv\", header=T))\n vals[,5] <- as.integer(vals[,5])\n vals <- vals[vals[,5]!=0,] #only leave county data\n\n return(vals)\n}\n\n########################################\ncens.data <- function(census.data,ID){\n\n sequence <- as.numeric(census.data[,ID])\n\n # Convert to scientific notation\n sequence <- format(sequence,scientific=TRUE)\n\n return(sequence)\n}\n\n##############################################################\n## Census Quick Facts ##\n##############################################################\n\n\n\n########################################\n# Download complete data set from:\n# quickfacts.census.gov/qfd/download/DataSet.txt\n\ncomp.quick.data <- function(){\n\n vals <- as.matrix(read.csv(\"DataSet.txt\", header=T))\n vals <- vals[vals[,1]%%1000!=0,] #only leave county data\n\n return(vals)\n}\n\n########################################\nquick.data <- function(census.data,ID){\n\n sequence <- as.numeric(census.data[,ID])\n\n # Convert to scientific notation\n sequence <- format(sequence,scientific=TRUE)\n\n return(sequence)\n}\n\n##############################################################\n## Stock Market ##\n##############################################################\n\n########################################\ncomp.data <- function(mkt){\n\n vals <- as.matrix(read.csv(paste(\"http://online.wsj.com/public/resources/documents/\",mkt,\".csv\",sep=\"\"), header=T,skip=3))\n\n return(vals)\n}\n\n########################################\ndate.data <- function(){\n date1 <- as.matrix(read.csv(\"http://online.wsj.com/public/resources/documents/NYSE.csv\",header=F,skip=1, nrows=1))\n date2 <- as.matrix(read.csv(\"http://online.wsj.com/public/resources/documents/Nasdaq.csv\",header=F,skip=1, nrows=1))\n date3 <- as.matrix(read.csv(\"http://online.wsj.com/public/resources/documents/SCAP.csv\",header=F,skip=1, nrows=1))\n date4 <- as.matrix(read.csv(\"http://online.wsj.com/public/resources/documents/AMEX.csv\",header=F,skip=1, nrows=1))\n\n min.length <- min(nchar(date1),nchar(date2),nchar(date3),nchar(date4))\n\n if (nchar(date1)==min.length) return(date1)\n else if (nchar(date2)==min.length) return(date2)\n else if (nchar(date3)==min.length) return(date3)\n else if (nchar(date4)==min.length) return(date4)\n}\n\n\n##############################################################\n## World Stock Market ##\n##############################################################\n\n########################################\nw.comp.data <- function(mkt){\n url <- paste(\"https://www.investing.com/indices/\",mkt,sep=\"\")\n\n #vals <- as.matrix(read.csv(paste(\"http://online.wsj.com/public/resources/documents/\",mkt,\".csv\",sep=\"\"), header=T,skip=3))\n\n vals <- url %>%\n read_html() %>%\n html_nodes(xpath='//*[@id=\"cr1\"]') %>%\n html_table()\n\n vals <- vals[[1]]\n\n # Following line removes any \"0 volume\" stocks, but at\n # some points of the day volume can be 0 for all stocks\n # and that would lead to NO STOCKS READ\n #\n # vals<-vals[which(vals[,8]!=0),]\n\n return(vals)\n}\n\n########################################\nw.status <- function(mkt){\n url <- paste(\"https://www.investing.com/indices/\",mkt,sep=\"\")\n\n my.status <- url %>%\n read_html() %>%\n html_nodes(xpath='//*[@id=\"quotes_summary_current_data\"]/div[1]/div[2]/div[2]/text()') %>%\n html_text()\n\n status <- \"ACTIVE\"\n if (length(grep(\"Closed\",my.status[3]))) status <- \"CLOSED\"\n\n return(status)\n}\n\n########################################\nseq.data <- function(all.data,var){\n\n # Use gsub() to remove embedded commas if any (for volume data)\n # Will not alter data without embedded commas (open, high, low, close)\n sequence <- as.numeric(gsub(\",\",\"\",all.data[,var]))\n\n # Convert to scientific notation\n sequence <- format(sequence,scientific=TRUE)\n\n return(sequence)\n\n}\n\n########################################\ngoodness.test <- function(sequence){\n\n string <- substr(as.character(sequence),1,1)\n\n first.digits <- as.integer(string)\n\n # In the event any of the 9 possible digits is not observed in the string,\n # table(first.digits) will not display all digits 1, 2, ..., 9. An example is\n #\n # NAME 1 2 3 4 5 6 7 9\n # FREQ 12 11 9 8 6 5 4 2\n #\n # Here, obs <- as.integer(table(first.digits)) would only contain 8 elements.\n # To correct for this, initialize obs to a zero vector, loop through NAME,\n # find FREQ corresponding to NAME,then paste that FREQ in obs[NAME].\n # This will also work for the case when all 9 possible digits are observed.\n\n Obs <- seq(0,0,,9)\n\n for (i in as.numeric(names(table(first.digits)))) {\n pos <- which(as.numeric(names(table(first.digits)))==i)\n Obs[i]<- as.numeric(table(first.digits))[pos]\n }\n i <- seq(1,9)\n\n Exp <- sum(Obs)*log10(1+1/i)\n\n Digit <- seq(1,9)\n\n #print(info)\n\n chisq <- sum(((Obs-Exp)**2)/Exp)\n\n Exp <- round(Exp,3)\n info <- cbind(Digit,Obs,Exp)\n rownames(info)<-rep(\"\",nrow(info))\n\n #print(chisq)\n\n p.value <- 1-pchisq(chisq,8)\n\n text.chi <- paste(\"Chi-Square=\",format(round(chisq,3),nsmall=3),sep=\"\")\n names(text.chi)<-\"\"\n\n text.pval <- paste(\"P-Value=\",format(round(p.value,3),nsmall=3),sep=\"\")\n names(text.pval)<-\"\"\n\n print(info)\n print(text.chi,quote=F)\n print(text.pval,quote=F)\n\n}\n\n############################################\npmf.compare<-function(sequence){\n\n string <- substr(as.character(sequence),1,1)\n\n first.digits <- as.integer(string)\n\n # In the event any of the 9 possible digits is not observed in the string,\n # table(first.digits) will not display all digits 1, 2, ..., 9. An example is\n #\n # NAME 1 2 3 4 5 6 7 9\n # FREQ 12 11 9 8 6 5 4 2\n #\n # Here, obs <- as.integer(table(first.digits)) would only contain 8 elements.\n # To correct for this, initialize obs to a zero vector, loop through NAME,\n # find FREQ corresponding to NAME,then paste that FREQ in obs[NAME].\n # This will also work for the case when all 9 possible digits are observed.\n\n Obs <- seq(0,0,,9)\n\n for (i in as.numeric(names(table(first.digits)))) {\n pos <- which(as.numeric(names(table(first.digits)))==i)\n Obs[i]<- as.numeric(table(first.digits))[pos]\n }\n i <- seq(1,9)\n\n pmf.Exp <- cbind(seq(1,9)+0.065,log10(1+1/i))\n pmf.Obs <- cbind(seq(1,9)-0.065,Obs/sum(Obs))\n\n col1 <- brewer.pal(n = 8, name = \"Dark2\")[6]\n col2 <- brewer.pal(n = 8, name = \"Dark2\")[1]\n\n par(mar=c(3.5,4.5,2,0))\n\n my.lwd <- 3.75\n\n if (max(pmf.Obs[,2])>=max(pmf.Exp[,2])){\n plot(pmf.Obs[,1],pmf.Obs[,2],type=\"n\",col=col1,xlim=c(0.75,9.25),\n ylim=c(0,max(pmf.Obs[,2])),\n xlab=\"\",ylab=\"Proportion\", xaxt=\"n\", main=\"Proportion of First Digits\")\n axis(1, at=seq(1,9), labels=c(\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"))\n mtext(\"First Digit\",1,line=2.5)\n for (i in 1:nrow(pmf.Obs)){\n lines(c(pmf.Obs[i,1],pmf.Obs[i,1]),c(0,pmf.Obs[i,2]),col=col1,lwd=my.lwd)\n }\n for (i in 1:nrow(pmf.Exp)){\n lines(c(pmf.Exp[i,1],pmf.Exp[i,1]),c(0,pmf.Exp[i,2]),col=col2,lwd=my.lwd)\n legend(\"topright\", inset=.02,\n c(\"Observed Proportion\",\"Benford's Law\"), fill=c(col1,col2), horiz=F)\n }\n }\n\n if (max(pmf.Obs[,2])?@^_`{}~\n standard readtable:A-Z a-z ^H !$%&*/:;<=>?@^_{}~\n A normal character.\n\n\n _\bc_\bn_\bu_\bm_\bb_\be_\br raw readtable:0-9\n standard readtable:0-9\n This type is a digit. The syntax for an integer\n (fixnum or bignum) is a string of _\bc_\bn_\bu_\bm_\bb_\be_\br characters\n optionally followed by a _\bc_\bp_\be_\br_\bi_\bo_\bd_\b. If the digits are\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-4\b4\n\n\n not followed by a _\bc_\bp_\be_\br_\bi_\bo_\bd, then they are interpreted\n in base _\bi_\bb_\ba_\bs_\be which must be eight or ten. The syntax\n for a floating point number is either zero or more\n _\bc_\bn_\bu_\bm_\bb_\be_\br's followed by a _\bc_\bp_\be_\br_\bi_\bo_\bd and then followed by\n one or more _\bc_\bn_\bu_\bm_\bb_\be_\br's. A floating point number may\n also be an integer or floating point number followed\n by 'e' or 'd', an optional '+' or '-' and then zero or\n more _\bc_\bn_\bu_\bm_\bb_\be_\br's.\n\n\n _\bc_\bs_\bi_\bg_\bn raw readtable:+-\n standard readtable:+-\n A leading sign for a number. No other characters\n should be given this class.\n\n\n _\bc_\bl_\be_\bf_\bt_\b-_\bp_\ba_\br_\be_\bn raw readtable:(\n standard readtable:(\n A left parenthesis. Tells the reader to begin forming\n a list.\n\n\n _\bc_\br_\bi_\bg_\bh_\bt_\b-_\bp_\ba_\br_\be_\bn raw readtable:)\n standard readtable:)\n A right parenthesis. Tells the reader that it has\n reached the end of a list.\n\n\n _\bc_\bl_\be_\bf_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt raw readtable:[\n standard readtable:[\n A left bracket. Tells the reader that it should begin\n forming a list. See the description of _\bc_\br_\bi_\bg_\bh_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt\n for the difference between cleft-bracket and cleft-\n paren.\n\n\n _\bc_\br_\bi_\bg_\bh_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt raw readtable:]\n standard readtable:]\n A right bracket. A _\bc_\br_\bi_\bg_\bh_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt finishes the forma-\n tion of the current list and all enclosing lists until\n it finds one which begins with a _\bc_\bl_\be_\bf_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt or\n until it reaches the top level list.\n\n\n _\bc_\bp_\be_\br_\bi_\bo_\bd raw readtable:.\n standard readtable:.\n The period is used to separate element of a cons cell\n [e.g. (a . (b . nil)) is the same as (a b)]. _\bc_\bp_\be_\br_\bi_\bo_\bd\n is also used in numbers as described above.\n\n\n _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br raw readtable:^I-^M esc space\n standard readtable:^I-^M esc space\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-5\b5\n\n\n Separates tokens. When the reader is scanning, these\n character are passed over. Note: there is a differ-\n ence between the _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br character class and the\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property of a syntax class.\n\n\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bq_\bu_\bo_\bt_\be raw readtable:'\n standard readtable:'\n This causes _\br_\be_\ba_\bd to be called recursively and the list\n (quote ) to be returned.\n\n\n _\bc_\bs_\by_\bm_\bb_\bo_\bl_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br raw readtable:|\n standard readtable:|\n This causes the reader to begin collecting characters\n and to stop only when another identical _\bc_\bs_\by_\bm_\bb_\bo_\bl_\b-\n _\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br is seen. The only way to escape a _\bc_\bs_\by_\bm_\bb_\bo_\bl_\b-\n _\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br within a symbol name is with a _\bc_\be_\bs_\bc_\ba_\bp_\be char-\n acter. The collected characters are converted into a\n string which becomes the print name of a symbol. If a\n symbol with an identical print name already exists,\n then the allocation is not done, rather the existing\n symbol is used.\n\n\n _\bc_\be_\bs_\bc_\ba_\bp_\be raw readtable:\\\n standard readtable:\\\n This causes the next character to read in to be\n treated as a v\bvc\bch\bha\bar\bra\bac\bct\bte\ber\br. A character whose syntax\n class is v\bvc\bch\bha\bar\bra\bac\bct\bte\ber\br has a character class _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br\n and does not have the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property so it will\n not separate symbols.\n\n\n _\bc_\bs_\bt_\br_\bi_\bn_\bg_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br raw readtable:\"\n standard readtable:\"\n This is the same as _\bc_\bs_\by_\bm_\bb_\bo_\bl_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br except the\n result is returned as a string instead of a symbol.\n\n\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br_\b-_\bs_\by_\bm_\bb_\bo_\bl raw readtable:none\n standard readtable:none\n This returns a symbol whose print name is the the sin-\n gle character which has been collected.\n\n\n _\bc_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:`,\n The reader calls the macro function associated with\n this character and the current readtable, passing it\n no arguments. The result of the macro is added to the\n structure the reader is building, just as if that form\n were directly read by the reader. More details on\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-6\b6\n\n\n macros are provided below.\n\n\n _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:#;\n A _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo differs from a _\bc_\bm_\ba_\bc_\br_\bo in the way the\n result is incorporated in the structure the reader is\n building. A _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo must return a list of\n forms (possibly empty). The reader acts as if it read\n each element of the list itself without the surround-\n ing parenthesis.\n\n\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This causes to reader to check the next character. If\n it is a _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br then this acts like a _\bc_\bm_\ba_\bc_\br_\bo_\b. Oth-\n erwise, it acts like a _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br_\b.\n\n\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This is triggered like a _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bm_\ba_\bc_\br_\bo however the\n result is spliced in like a _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo_\b.\n\n\n _\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This is differs from a _\bc_\bm_\ba_\bc_\br_\bo in that the macro func-\n tion is passed a form representing what the reader has\n read so far. The result of the macro replaces what\n the reader had read so far.\n\n\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This differs from the _\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo in that the macro\n will only be triggered if the character following the\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo character is a _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br.\n\n\n _\bc_\bi_\bl_\bl_\be_\bg_\ba_\bl raw readtable:^@-^G^N-^Z^\\-^_rubout\n standard readtable:^@-^G^N-^Z^\\-^_rubout\n The characters cause the reader to signal an error if\n read.\n\n\n\n 7\b7.\b.5\b5.\b. S\bSy\byn\bnt\bta\bax\bx C\bCl\bla\bas\bss\bse\bes\bs\n\n The readtable maps each character into a syntax\n class. The syntax class contains three pieces of\n information: the character class, whether this is a\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-7\b7\n\n\n separator, and the escape properties. The first two\n properties are used by the reader, the last by the\n printer (and _\be_\bx_\bp_\bl_\bo_\bd_\be). The initial lisp system has\n the following syntax classes defined. The user may\n add syntax classes with _\ba_\bd_\bd_\b-_\bs_\by_\bn_\bt_\ba_\bx_\b-_\bc_\bl_\ba_\bs_\bs. For each\n syntax class, we list the properties of the class and\n which characters have this syntax class by default.\n More information about each syntax class can be found\n under the description of the syntax class's character\n class.\n\n v\bvc\bch\bha\bar\bra\bac\bct\bte\ber\br raw readtable:A-Z a-z ^H !#$%&*,/:;<=>?@^_`{}~\n _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br standard readtable:A-Z a-z ^H !$%&*/:;<=>?@^_{}~\n\n\n\n v\bvn\bnu\bum\bmb\bbe\ber\br raw readtable:0-9\n _\bc_\bn_\bu_\bm_\bb_\be_\br standard readtable:0-9\n\n\n\n v\bvs\bsi\big\bgn\bn raw readtable:+-\n _\bc_\bs_\bi_\bg_\bn standard readtable:+-\n\n\n\n v\bvl\ble\bef\bft\bt-\b-p\bpa\bar\bre\ben\bn raw readtable:(\n _\bc_\bl_\be_\bf_\bt_\b-_\bp_\ba_\br_\be_\bn standard readtable:(\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvr\bri\big\bgh\bht\bt-\b-p\bpa\bar\bre\ben\bn raw readtable:)\n _\bc_\br_\bi_\bg_\bh_\bt_\b-_\bp_\ba_\br_\be_\bn standard readtable:)\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvl\ble\bef\bft\bt-\b-b\bbr\bra\bac\bck\bke\bet\bt raw readtable:[\n _\bc_\bl_\be_\bf_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt standard readtable:[\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvr\bri\big\bgh\bht\bt-\b-b\bbr\bra\bac\bck\bke\bet\bt raw readtable:]\n _\bc_\br_\bi_\bg_\bh_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt standard readtable:]\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvp\bpe\ber\bri\bio\bod\bd raw readtable:.\n _\bc_\bp_\be_\br_\bi_\bo_\bd standard readtable:.\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\n v\bvs\bse\bep\bpa\bar\bra\bat\bto\bor\br raw readtable:^I-^M esc space\n _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br standard readtable:^I-^M esc space\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-8\b8\n\n\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvs\bsi\bin\bng\bgl\ble\be-\b-q\bqu\buo\bot\bte\be raw readtable:'\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bq_\bu_\bo_\bt_\be standard readtable:'\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvs\bsy\bym\bmb\bbo\bol\bl-\b-d\bde\bel\bli\bim\bmi\bit\bte\ber\br raw readtable:|\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br standard readtable:|\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n\n\n v\bve\bes\bsc\bca\bap\bpe\be raw readtable:\\\n _\bc_\be_\bs_\bc_\ba_\bp_\be standard readtable:\\\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n\n\n v\bvs\bst\btr\bri\bin\bng\bg-\b-d\bde\bel\bli\bim\bmi\bit\bte\ber\br raw readtable:\"\n _\bc_\bs_\bt_\br_\bi_\bn_\bg_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br standard readtable:\"\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n\n\n v\bvs\bsi\bin\bng\bgl\ble\be-\b-c\bch\bha\bar\bra\bac\bct\bte\ber\br-\b-s\bsy\bym\bmb\bbo\bol\bl raw readtable:none\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br_\b-_\bs_\by_\bm_\bb_\bo_\bl standard readtable:none\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n\n v\bvm\bma\bac\bcr\bro\bo raw readtable:none\n _\bc_\bm_\ba_\bc_\br_\bo standard readtable:`,\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvs\bsp\bpl\bli\bic\bci\bin\bng\bg-\b-m\bma\bac\bcr\bro\bo raw readtable:none\n _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo standard readtable:#;\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n v\bvs\bsi\bin\bng\bgl\ble\be-\b-m\bma\bac\bcr\bro\bo raw readtable:none\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\n v\bvs\bsi\bin\bng\bgl\ble\be-\b-s\bsp\bpl\bli\bic\bci\bin\bng\bg-\b-m\bma\bac\bcr\bro\bo raw readtable:none\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\n v\bvi\bin\bnf\bfi\bix\bx-\b-m\bma\bac\bcr\bro\bo raw readtable:none\n _\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-9\b9\n\n\n v\bvs\bsi\bin\bng\bgl\ble\be-\b-i\bin\bnf\bfi\bix\bx-\b-m\bma\bac\bcr\bro\bo raw readtable:none\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\n v\bvi\bil\bll\ble\beg\bga\bal\bl raw readtable:^@-^G^N-^Z^\\-^_rubout\n _\bc_\bi_\bl_\bl_\be_\bg_\ba_\bl standard readtable:^@-^G^N-^Z^\\-^_rubout\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n\n\n 7\b7.\b.6\b6.\b. C\bCh\bha\bar\bra\bac\bct\bte\ber\br M\bMa\bac\bcr\bro\bos\bs\n\n Character macros are user written functions which\n are executed during the reading process. The value\n returned by a character macro may or may not be used\n by the reader, depending on the type of macro and the\n value returned. Character macros are always attached\n to a single character with the _\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx function.\n\n\n\n 7\b7.\b.6\b6.\b.1\b1.\b. T\bTy\byp\bpe\bes\bs There are three types of character\n macros: normal, splicing and infix. These types\n differ in the arguments they are given or in what\n is done with the result they return.\n\n\n\n 7\b7.\b.6\b6.\b.1\b1.\b.1\b1.\b. N\bNo\bor\brm\bma\bal\bl\n\n A normal macro is passed no arguments. The\n value returned by a normal macro is simply used\n by the reader as if it had read the value\n itself. Here is an example of a macro which\n returns the abbreviation for a given state.\n\n\n ____________________________________________________\n\n ->_\b(_\bd_\be_\bf_\bu_\bn _\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv _\bn_\bi_\bl\n _\b(_\bc_\bd_\br _\b(_\ba_\bs_\bs_\bq _\b(_\br_\be_\ba_\bd_\b) _\b'_\b(_\b(_\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba _\b. _\bc_\ba_\b) _\b(_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba _\b. _\bp_\ba_\b)_\b)_\b)_\b)_\b)\n stateabbrev\n -> _\b(_\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx _\b'_\b\\_\b! _\b'_\bv_\bm_\ba_\bc_\br_\bo _\b'_\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv_\b)\n t\n -> _\b'_\b( _\b! _\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba _\b! _\bw_\by_\bo_\bm_\bi_\bn_\bg _\b! _\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba_\b)\n (ca nil pa)\n ____________________________________________________\n\n\n\n Notice what happened to\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-1\b10\b0\n\n\n _\b! _\bw_\by_\bo_\bm_\bi_\bn_\bg. Since it wasn't in the table, the associated\n function returned nil. The creator of the macro may have\n wanted to leave the list alone, in such a case, but couldn't\n with this type of reader macro. The splicing macro,\n described next, allows a character macro function to return\n a value that is ignored.\n\n\n\n 7\b7.\b.6\b6.\b.1\b1.\b.2\b2.\b. S\bSp\bpl\bli\bic\bci\bin\bng\bg\n\n The value returned from a splicing macro\n must be a list or nil. If the value is nil,\n then the value is ignored, otherwise the reader\n acts as if it read each object in the list.\n Usually the list only contains one element. If\n the reader is reading at the top level (i.e. not\n collecting elements of list), then it is illegal\n for a splicing macro to return more then one\n element in the list. The major advantage of a\n splicing macro over a normal macro is the abil-\n ity of the splicing macro to return nothing.\n The comment character (usually ;) is a splicing\n macro bound to a function which reads to the end\n of the line and always returns nil. Here is the\n previous example written as a splicing macro\n\n\n ____________________________________________________\n\n -> _\b(_\bd_\be_\bf_\bu_\bn _\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv _\bn_\bi_\bl\n _\b(_\b(_\bl_\ba_\bm_\bb_\bd_\ba _\b(_\bv_\ba_\bl_\bu_\be_\b)\n _\b(_\bc_\bo_\bn_\bd _\b(_\bv_\ba_\bl_\bu_\be _\b(_\bl_\bi_\bs_\bt _\bv_\ba_\bl_\bu_\be_\b)_\b)\n _\b(_\bt _\bn_\bi_\bl_\b)_\b)_\b)\n _\b(_\bc_\bd_\br _\b(_\ba_\bs_\bs_\bq _\b(_\br_\be_\ba_\bd_\b) _\b'_\b(_\b(_\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba _\b. _\bc_\ba_\b) _\b(_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba _\b. _\bp_\ba_\b)_\b)_\b)_\b)_\b)_\b)\n -> _\b(_\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx _\b'_\b! _\b'_\bv_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo _\b'_\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv_\b)\n -> _\b'_\b(_\b!_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba _\b! _\bf_\bo_\bo _\b!_\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba_\b)\n (pa ca)\n -> _\b'_\b!_\bf_\bo_\bo _\b!_\bb_\ba_\br _\b!_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba\n pa\n ->\n ____________________________________________________\n\n\n\n\n\n\n 7\b7.\b.6\b6.\b.1\b1.\b.3\b3.\b. I\bIn\bnf\bfi\bix\bx\n\n Infix macros are passed a _\bc_\bo_\bn_\bc structure\n representing what has been read so far.\n Briefly, a tconc structure is a single list cell\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-1\b11\b1\n\n\n whose car points to a list and whose cdr points\n to the last list cell in that list. The inter-\n pretation by the reader of the value returned by\n an infix macro depends on whether the macro is\n called while the reader is constructing a list\n or whether it is called at the top level of the\n reader. If the macro is called while a list is\n being constructed, then the value returned\n should be a tconc structure. The car of that\n structure replaces the list of elements that the\n reader has been collecting. If the macro is\n called at top level, then it will be passed the\n value nil, and the value it returns should\n either be nil or a tconc structure. If the\n macro returns nil, then the value is ignored and\n the reader continues to read. If the macro\n returns a tconc structure of one element (i.e.\n whose car is a list of one element), then that\n single element is returned as the value of _\br_\be_\ba_\bd_\b.\n If the macro returns a tconc structure of more\n than one element, then that list of elements is\n returned as the value of read.\n\n\n ____________________________________________________\n\n -> _\b(_\bd_\be_\bf_\bu_\bn _\bp_\bl_\bu_\bs_\bo_\bp _\b(_\bx_\b)\n _\b(_\bc_\bo_\bn_\bd _\b(_\b(_\bn_\bu_\bl_\bl _\bx_\b) _\b(_\bt_\bc_\bo_\bn_\bc _\bn_\bi_\bl _\b'_\b\\_\b+_\b)_\b)\n _\b(_\bt _\b(_\bl_\bc_\bo_\bn_\bc _\bn_\bi_\bl _\b(_\bl_\bi_\bs_\bt _\b'_\bp_\bl_\bu_\bs _\b(_\bc_\ba_\ba_\br _\bx_\b) _\b(_\br_\be_\ba_\bd_\b)_\b)_\b)_\b)_\b)_\b)\n\n plusop\n -> _\b(_\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx _\b'_\b\\_\b+ _\b'_\bv_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo _\b'_\bp_\bl_\bu_\bs_\bo_\bp_\b)\n t\n -> _\b'_\b(_\ba _\b+ _\bb_\b)\n (plus a b)\n -> _\b'_\b+\n |+|\n ->\n ____________________________________________________\n\n\n\n\n\n\n 7\b7.\b.6\b6.\b.2\b2.\b. I\bIn\bnv\bvo\boc\bca\bat\bti\bio\bon\bns\bs\n\n There are three different circumstances in\n which you would like a macro function to be trig-\n gered.\n\n _\bA_\bl_\bw_\ba_\by_\bs _\b-\n Whenever the macro character is seen, the\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-1\b12\b2\n\n\n macro should be invoked. This is accomplished\n by using the character classes _\bc_\bm_\ba_\bc_\br_\bo, _\bc_\bs_\bp_\bl_\bi_\bc_\b-\n _\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo, or _\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo, and by using the\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. The syntax classes\n v\bvm\bma\bac\bcr\bro\bo, v\bvs\bsp\bpl\bli\bic\bci\bin\bng\bg-\b-m\bma\bac\bcr\bro\bo, and v\bvs\bsi\bin\bng\bgl\ble\be-\b-m\bma\bac\bcr\bro\bo are\n defined this way.\n\n _\bW_\bh_\be_\bn _\bf_\bi_\br_\bs_\bt _\b-\n The macro should only be triggered when the\n macro character is the first character found\n after the scanning process. A syntax class\n for a _\bw_\bh_\be_\bn _\bf_\bi_\br_\bs_\bt macro would be defined using\n _\bc_\bm_\ba_\bc_\br_\bo, _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo, or _\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo and\n not including the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property.\n\n _\bW_\bh_\be_\bn _\bu_\bn_\bi_\bq_\bu_\be _\b-\n The macro should only be triggered when the\n macro character is the only character col-\n lected in the token collection phase of the\n reader, i.e the macro character is preceeded\n by zero or more _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\brs and followed by a\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br_\b. A syntax class for a _\bw_\bh_\be_\bn _\bu_\bn_\bi_\bq_\bu_\be\n macro would be defined using _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bm_\ba_\bc_\br_\bo,\n _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo, or _\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo\n and not including the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. The\n syntax classes so defined are v\bvs\bsi\bin\bng\bgl\ble\be-\b-m\bma\bac\bcr\bro\bo,\n v\bvs\bsi\bin\bng\bgl\ble\be-\b-s\bsp\bpl\bli\bic\bci\bin\bng\bg-\b-m\bma\bac\bcr\bro\bo, and v\bvs\bsi\bin\bng\bgl\ble\be-\b-i\bin\bnf\bfi\bix\bx-\b-\n m\bma\bac\bcr\bro\bo.\n\n\n\n 7\b7.\b.7\b7.\b. F\bFu\bun\bnc\bct\bti\bio\bon\bns\bs\n\n (\b(s\bse\bet\bts\bsy\byn\bnt\bta\bax\bx 's_symbol 's_synclass ['ls_func])\b)\n\n WHERE: ls_func is the name of a function or a lambda\n body.\n\n RETURNS: t\n\n SIDE EFFECT: S_symbol should be a symbol whose print\n name is only one character. The syntax\n class for that character is set to\n s_synclass in the current readtable. If\n s_synclass is a class that requires a\n character macro, then ls_func must be sup-\n plied.\n\n NOTE: The symbolic syntax codes are new to Opus 38.\n For compatibility, s_synclass can be one of the\n fixnum syntax codes which appeared in older ver-\n sions of the FRANZ LISP Manual. This compatibil-\n ity is only temporary: existing code which uses\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n T\bTh\bhe\be L\bLi\bis\bsp\bp R\bRe\bea\bad\bde\ber\br 7\b7-\b-1\b13\b3\n\n\n the fixnum syntax codes should be converted.\n\n (\b(g\bge\bet\bts\bsy\byn\bnt\bta\bax\bx 's_symbol)\b)\n\n RETURNS: the syntax class of the first character of\n s_symbol's print name. s_symbol's print name\n must be exactly one character long.\n\n NOTE: This function is new to Opus 38. It supercedes\n _\b(_\bs_\bt_\ba_\bt_\bu_\bs _\bs_\by_\bn_\bt_\ba_\bx_\b) which no longer exists.\n\n (\b(a\bad\bdd\bd-\b-s\bsy\byn\bnt\bta\bax\bx-\b-c\bcl\bla\bas\bss\bs 's_synclass 'l_properties)\b)\n\n RETURNS: s_synclass\n\n SIDE EFFECT: Defines the syntax class s_synclass to\n have properties l_properties. The list\n l_properties should contain a character\n classes mentioned above. l_properties may\n contain one of the escape properties:\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs, _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be, or\n _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bf_\bi_\br_\bs_\bt. l_properties may con-\n tain the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. After a syn-\n tax class has been defined with _\ba_\bd_\bd_\b-\n _\bs_\by_\bn_\bt_\ba_\bx_\b-_\bc_\bl_\ba_\bs_\bs, the _\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx function can\n be used to give characters that syntax\n class.\n\n\n ____________________________________________________\n\n ; Define a non-separating macro character.\n ; This type of macro character is used in UCI-Lisp, and\n ; it corresponds to a FIRST MACRO in Interlisp\n -> _\b(_\ba_\bd_\bd_\b-_\bs_\by_\bn_\bt_\ba_\bx_\b-_\bc_\bl_\ba_\bs_\bs _\b'_\bv_\bu_\bc_\bi_\b-_\bm_\ba_\bc_\br_\bo _\b'_\b(_\bc_\bm_\ba_\bc_\br_\bo _\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bf_\bi_\br_\bs_\bt_\b)_\b)\n vuci-macro\n ->\n ____________________________________________________\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n", "meta": {"hexsha": "d092b8035b76f5efacf64f86c2a792ab69d17dc8", "size": 39769, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch7.r", "max_stars_repo_name": "krytarowski/franz-lisp-christos", "max_stars_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-17T08:05:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T08:07:14.000Z", "max_issues_repo_path": "lisplib/manual/ch7.r", "max_issues_repo_name": "krytarowski/franz-lisp-christos", "max_issues_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lisplib/manual/ch7.r", "max_forks_repo_name": "krytarowski/franz-lisp-christos", "max_forks_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-15T16:38:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-15T16:38:53.000Z", "avg_line_length": 46.2968568102, "max_line_length": 192, "alphanum_fraction": 0.4851768966, "num_tokens": 14493, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.1460872582014364, "lm_q2_score": 0.05184547214356044, "lm_q1q2_score": 0.007573962875611692}}
{"text": "\r\nrobu.custom <- function(formula, data, studynum,var.eff.size, userweights,\r\n modelweights = c(\"CORR\", \"HIER\"), rho = 0.8, \r\n small = TRUE, userdfs = NULL, ...) {\r\n \r\n # Evaluate model weighting scheme.\r\n modelweights <- match.arg(modelweights)\r\n \r\n if (modelweights == \"CORR\" && rho > 1 | rho < 0) \r\n stop (\"Rho must be a value between 0 and 1.\")\r\n \r\n if (missing(userweights)){\r\n user_weighting = FALSE \r\n } else { \r\n user_weighting = TRUE\r\n }\r\n \r\n cl <- match.call() # Full model call\r\n mf <- match.call(expand.dots = FALSE)\r\n ml <- mf[[2]] # Model formula \r\n m <- match(c(\"formula\", \"data\", \"studynum\", \r\n \"var.eff.size\", \"userweights\"), names(mf))\r\n mf <- mf[c(1L, m)] \r\n mf$drop.unused.levels <- TRUE\r\n mf[[1L]] <- as.name(\"model.frame\")\r\n mf <- eval(mf, parent.frame()) \r\n \r\n if(!user_weighting){ \r\n \r\n dframe <- data.frame(effect.size = mf[,1],\r\n stats::model.matrix(formula, mf),\r\n studynum = mf[[\"(studynum)\"]],\r\n var.eff.size = mf[[\"(var.eff.size)\"]])\r\n \r\n X.full.names <- names(dframe)[-match(c(\"effect.size\", \r\n \"studynum\", \r\n \"var.eff.size\"), \r\n names(dframe))] \r\n \r\n } else { # Begin userweights\r\n \r\n dframe <- data.frame(effect.size = mf[,1],\r\n stats::model.matrix(formula, mf), \r\n studynum = mf[[\"(studynum)\"]], \r\n var.eff.size = mf[[\"(var.eff.size)\"]],\r\n userweights = mf[[\"(userweights)\"]])\r\n \r\n X.full.names <- names(dframe)[-match(c(\"effect.size\", \r\n \"studynum\", \r\n \"userweights\", \r\n \"var.eff.size\"), \r\n names(dframe))] \r\n } # End userweights\r\n study_orig_id <- dframe$studynum\r\n dframe$study <- as.factor(dframe$studynum)\r\n dframe$study <- as.numeric(dframe$study)\r\n dframe <- dframe[order(dframe$study),]\r\n k_temp <- as.data.frame(unclass(rle(sort(dframe$study))))\r\n dframe$k <- k_temp[[1]][ match(dframe$study, k_temp[[2]])]\r\n dframe$avg.var.eff.size <- stats::ave(dframe$var.eff.size, dframe$study)\r\n dframe$sd.eff.size <- sqrt(dframe$var.eff.size)\r\n \r\n switch(modelweights, \r\n \r\n HIER = { # Begin HIER\r\n \r\n dframe$weights <- 1 / dframe$var.eff.size\r\n \r\n }, # End HIER\r\n \r\n CORR = { # Begin CORR\r\n \r\n dframe$weights <- 1 / (dframe$k * dframe$avg.var.eff.size)\r\n \r\n } # End CORR\r\n \r\n ) \r\n \r\n X.full <- dframe[c(\"study\", X.full.names)]\r\n \r\n data.full.names <- names(dframe)[-match(c(\"studynum\",X.full.names), \r\n names(dframe))] \r\n data.full <- dframe[c(data.full.names)]\r\n k <- data.full[ !duplicated(data.full$study), ]$k\r\n k_list <- as.list(k) \r\n M <- nrow(data.full) # Number of units in analysis\r\n p <- ncol(X.full) - 2 # Number of (non-intercept) covariates \r\n N <- max(data.full$study) # Number of studies\r\n W <- as.matrix(by(data.full$weights, data.full$study, \r\n function(x) diag(x, nrow = length(x)),\r\n simplify = FALSE))\r\n X <- data.matrix(X.full)\r\n X <- lapply(split(X[,2:(p + 2)], X[,1]), matrix, ncol = p + 1)\r\n y <- by(data.full$effect.size, data.full$study, \r\n function(x) matrix(x))\r\n J <- by(rep(1, nrow(X.full)), X.full$study, \r\n function(x) matrix(x, nrow = length(x), \r\n ncol = length(x)))\r\n sigma <- by(data.full$sd.eff.size, data.full$study, \r\n function(x) tcrossprod(x))\r\n vee <- by(data.full$var.eff.size, data.full$study, \r\n function(x) diag(x, nrow = length(x)))\r\n SigmV <- Map(function(sigma, V) \r\n sigma - V, sigma, vee)\r\n sumXWX <- Reduce(\"+\", Map(function(X, W) \r\n t(X) %*% W %*% X, \r\n X, W))\r\n sumXWy <- Reduce(\"+\", Map(function(X, W, y) \r\n t(X) %*% W %*% y, \r\n X, W, y))\r\n sumXWJWX <- Reduce(\"+\", Map(function(X, W, J) \r\n t(X) %*% W %*% J %*% W %*% X, \r\n X, W, J))\r\n \r\n Matrx_WKXX <- Reduce(\"+\", \r\n Map(function(X, W, k) { t(X) %*% (W / k) %*% X}, \r\n X, W, k_list)) \r\n \r\n Matrx_wk_XJX_XX <- Reduce(\"+\", \r\n Map(function(X, W, J, k) {(W / k)[1,1] * ( t(X) %*% J %*% X - t(X) %*% X) }, \r\n X, W, J, k_list)) \r\n \r\n switch(modelweights, \r\n \r\n HIER = { # Begin HIER\r\n \r\n tr.sumJJ <- Reduce(\"+\", Map(function(J) \r\n sum(diag(J %*% J)), \r\n J)) \r\n sumXJX <- Reduce(\"+\", Map(function(X, J) \r\n t(X) %*% J %*% X, \r\n X, J))\r\n sumXWJJX <- Reduce(\"+\", Map(function(X, W, J) \r\n t(X) %*% W %*% J %*% J %*% X, \r\n X, W, J))\r\n sumXJJWX <- Reduce(\"+\", Map(function(X, W, J) \r\n t(X) %*% J %*% J %*% W %*% X, \r\n X, W, J))\r\n sumXWWX <- Reduce(\"+\", Map(function(X, W) \r\n t(X) %*% W %*% W %*% X, \r\n X, W))\r\n sumXJWX <- Reduce(\"+\", Map(function(X, W, J) \r\n t(X) %*% J %*% W %*% X, \r\n X , W, J))\r\n sumXWJX <- Reduce(\"+\", Map(function(X, W, J) \r\n t(X) %*% W %*% J %*% X, \r\n X, W, J))\r\n } # End HIER\r\n \r\n ) \r\n \r\n b <- solve(sumXWX) %*% sumXWy \r\n Xreg <- as.matrix(X.full[-c(1)], dimnames = NULL)\r\n data.full$pred <- Xreg %*% b \r\n data.full$e <- data.full$effect.size - data.full$pred \r\n \r\n if (!user_weighting) { \r\n \r\n switch(modelweights, \r\n \r\n HIER = { # Begin HIER\r\n \r\n # Sigma_aj = tau.sq * J_j + omega.sq * I_j + V_j \r\n # Qe is sum of squares 1\r\n # Qe = Sigma(T'WT)-(Sigma(T'WX)(Sigma(X'WX))^-1(Sigma(X'WT)\r\n # where W = V^(-1) and V = data.full$var.eff.size\r\n # Also, Qe = (y-xb)' W (y-xb)\r\n sumV <- sum(data.full$var.eff.size)\r\n W <- diag(1 / data.full$var.eff.size) \r\n sumW <- sum(W)\r\n Qe <- t(data.full$e) %*% W %*% data.full$e\r\n \r\n # Qa is sum of squares 2\r\n # Qa = sum(T-XB.hat)'J(T-XB.hat)\r\n # where B.hat = (X'WX)^-1(X'WT)\r\n # Also, Qa = (y-xb)'A (y-xb), A=diag(J)\r\n e <- by(data.full$e, data.full$study, function(x) matrix(x))\r\n sumEJE <- Reduce(\"+\", Map(function(e, J) t(e) %*% J %*% e, e, J))\r\n Qa <- sumEJE \r\n \r\n # MoM estimators for tau.sq and omega.sq can be written as\r\n # omega.sq.h = A2(Qa-C1)-A1(Qe-C2) / B1A2-B2A1\r\n # tau.sq.h = Qe-C2/A2 - omega.sq.h(B2/A2) where\r\n \r\n # Vi = (t(X)WX)^-1\r\n V.i <- solve(sumXWX)\r\n \r\n # A1 = Sigma(kj^2) - tr(V*Sigma(kj*t(Xj)*Jj*Wj*Xj)) - \r\n # tr(V*Sigma(kj*t(Xj)*Jj*Wj*Xj)) +\r\n # tr(V*[Sigma(t(Xj)*Jj*Xj)]*V*Sigma(t(Xj)*Wj*Jj*Wj*Xj))\r\n # B1 = Sigma(kj) - tr(V Sigma(t(Xj)*Jj*Wj*Xj)) -\r\n # tr(V Sigma(t(Xj)*Wj*Jj*Xj)) +\r\n # tr(V*[Sigma(t(Xj)*Jj*Xj)]*V*Sigma(t(Xj)*Wj^2*Xj)) \r\n # C1 = tr(W^-1) - tr(V*Sigma(t(X)*Jj*Xj))\r\n \r\n A1 <- tr.sumJJ - sum(diag(V.i %*% sumXJJWX)) - \r\n sum(diag(V.i %*% sumXWJJX)) + \r\n sum(diag(V.i %*% sumXJX %*% V.i %*% sumXWJWX))\r\n \r\n B1 <- length(data.full$study) -\r\n sum(diag(V.i %*% sumXWJX)) -\r\n sum(diag(V.i %*% sumXJWX)) +\r\n sum(diag(V.i %*% sumXJX%*%V.i %*% sumXWWX))\r\n C1 <- sumV - sum(diag(V.i %*% sumXJX))\r\n \r\n # A2 = tr(W) - tr(V*Sigma(t(X)*Wj*Jj*Wj*Xj))\r\n # B2 = tr(W) - tr(V*Sigma(t(X)*Wj^2*Xj))\r\n # C2 = Sigma(kj-p)\r\n \r\n A2 <- sumW - sum(diag(V.i %*% sumXWJWX)) \r\n B2 <- sumW - sum(diag(V.i %*% sumXWWX)) \r\n C2 <- length(data.full$study) - (p + 1) \r\n \r\n # MoM estimator for omega.sq.h = A2(Qa-C1)-A1(Qe-C2) / B1A2-B2A1\r\n # Estimate of between-studies-wthin-cluster variance component\r\n omega.sq1 <- ((Qa - C1) * A2 - (Qe - C2) * A1) / (B1 * A2 - B2 * A1)\r\n omega.sq <- ifelse(omega.sq1 < 0, 0, omega.sq1)\r\n \r\n # MoM estimators for tau.sq: Qe-C2/A2 - omega.sq.h(B2/A2)\r\n # Estimate of between-clusters variance component \r\n tau.sq1 <- ((Qe - C2) / A2) - omega.sq * (B2 / A2)\r\n tau.sq <- ifelse(tau.sq1 < 0, 0, tau.sq1) \r\n \r\n # Approximate inverse variance weights\r\n data.full$r.weights <- (1 / (as.vector(data.full$var.eff.size) + \r\n as.vector(tau.sq) + \r\n as.vector(omega.sq)))\r\n \r\n # Model info list for hierarchical effects\r\n mod_info <- list(omega.sq = omega.sq, tau.sq = tau.sq)\r\n \r\n }, # End HIER\r\n \r\n CORR = { # Begin CORR\r\n \r\n W <- diag (data.full$weights) \r\n sumW <- sum(data.full$weights) # Sum (k.j*w.j)\r\n Qe <- t(data.full$e) %*% W %*% data.full$e \r\n \r\n # The following components (denom, termA, termB, term1, term2)\r\n # are used in the calculation of the estimate of the residual \r\n # variance component tau.sq.hat. \r\n # Note: The effect of correlation on the estimates occurs entirely \r\n # through the rho*term2 component.\r\n \r\n denom <- sumW - sum(diag(solve(sumXWX) %*% sumXWJWX)) \r\n termA <- sum(diag(solve(sumXWX) %*% Matrx_WKXX)) #ZH_edit \r\n termB <- sum(diag(solve(sumXWX) %*% Matrx_wk_XJX_XX ))#ZH_edit \r\n term1 <- (Qe - N + termA) / denom \r\n term2 <- termB / denom \r\n tau.sq1 <- term1 + rho * term2 \r\n tau.sq <- ifelse(tau.sq1 < 0, 0, tau.sq1)\r\n df <- N - termA - rho * (termB) \r\n I.2.1 <- ((Qe - df) / Qe) * 100\r\n I.2 <- ifelse(I.2.1 < 0, 0, I.2.1)\r\n \r\n # Approximate inverse variance weights\r\n data.full$r.weights <- 1 / (as.vector(data.full$k) * \r\n (as.vector(data.full$avg.var.eff.size) + \r\n as.vector(tau.sq)))\r\n \r\n # Model info list for correlated effects\r\n mod_info <- list(rho = rho, I.2 = I.2, tau.sq = tau.sq,\r\n term1 = term1, term2 = term2)\r\n \r\n } # End CORR \r\n \r\n ) \r\n \r\n } else { # Begin userweights\r\n \r\n data.full$r.weights <- data.full$userweights\r\n \r\n # Model info list for userweights\r\n mod_info <- list(k = k, N = N, p = p, M = M)\r\n \r\n } # End userweights\r\n \r\n W.r.big <- diag(data.full$r.weights) # W\r\n W.r <- by(data.full$r.weights, data.full$study, # Wj\r\n function(x) diag(x, nrow = length(x)))\r\n sumXWX.r <- Reduce(\"+\", Map(function(X, W) \r\n t(X) %*% W %*% X, \r\n X, W.r))\r\n sumXWy.r <- Reduce(\"+\", Map(function(X, W, y) \r\n t(X) %*% W %*% y, \r\n X, W.r, y))\r\n b.r <- solve(sumXWX.r) %*% sumXWy.r \r\n data.full$pred.r <- Xreg %*% b.r\r\n data.full$e.r <- cbind(data.full$effect.size) - data.full$pred.r\r\n data.full$e.r <- as.numeric(data.full$e.r)\r\n sigma.hat.r <- by(data.full$e.r, data.full$study, \r\n function(x) tcrossprod(x))\r\n \r\n if (!small) { # Begin small = FALSE\r\n \r\n sumXWeeWX.r <- Reduce(\"+\", Map(function(X, W, V) \r\n t(X) %*% W %*% V %*% W %*% X, \r\n X, W.r, sigma.hat.r))\r\n \r\n VR.r <- solve(sumXWX.r) %*% sumXWeeWX.r %*% solve(sumXWX.r) \r\n SE <- sqrt(diag(VR.r)) * sqrt(N / (N - (p + 1)))\r\n t <- b.r / SE\r\n if (is.null(userdfs))\r\n dfs <- N - (p + 1)\r\n else\r\n dfs <- userdfs\r\n prob <- 2 * (1 - stats::pt(abs(t), dfs))\r\n CI.L <- b.r - stats::qt(.975, dfs) * SE\r\n CI.U <- b.r + stats::qt(.975, dfs) * SE\r\n \r\n } else { # Begin small = TRUE\r\n \r\n Q <- solve(sumXWX.r) # Q = (X'WX)^(-1)\r\n Q.list <- rep(list(Q), N)\r\n H <- Xreg %*% Q %*% t(Xreg) %*% W.r.big # H = X * Q * X' * W\r\n ImH <- diag(c(1), dim(Xreg)[1], dim(Xreg)[1]) - H\r\n data.full$ImH <- cbind(ImH)\r\n \r\n ImHj <- lapply(split(x = ImH,f = as.factor(data.full$study)), \r\n function(x){matrix(x, ncol =M)})\r\n #ImHj <- by(data.full$ImH, data.full$study, \r\n # function(x) as.matrix(x))\r\n \r\n \r\n diag_one <- by(rep(1, M), X.full$study, \r\n function(x) diag(x, nrow = length(x)))\r\n \r\n ImHii <- Map(function(X, Q, W, D) \r\n D - X %*% Q %*% t(X) %*% W,\r\n X, Q.list, W.r, diag_one)\r\n \r\n \r\n if (!user_weighting){\r\n \r\n Working_Matrx_E <- diag(1/data.full$r.weights) #1/W\r\n Working_Matrx_E_j <- by(data.full$r.weights, data.full$study, # Wj\r\n function(x) diag(1/x, nrow = length(x))) #1/W_j\r\n \r\n \r\n switch(modelweights, \r\n HIER = {\r\n # Inside Matrix = E_j^0.5 * ImH_j *E * t(ImH_j) * E_j^0.5\r\n # In this case, the formula can be simplified to \r\n # Inside Matrix = E_j^0.5 * ImH_jj * E_j^1.5\r\n InsideMatrx_list <- Map(\r\n function (W_E_j, ImH_jj) {\r\n sqrt(W_E_j) %*% ImH_jj %*% (W_E_j^1.5)\r\n },\r\n Working_Matrx_E_j, ImHii)\r\n \r\n eigenres_list <- lapply(InsideMatrx_list, function(x) eigen(x))\r\n eigenval_list <- lapply(eigenres_list, function(x) x$values)\r\n eigenvec_list <- lapply(eigenres_list, function(x) x$vectors)\r\n \r\n A.MBB <- Map(function (eigenvec, eigenval, k, W_E_j) {\r\n eigenval_InvSqrt <- ifelse(eigenval< 10^-10, 0, 1/sqrt(eigenval)) # Pseudo_Inverse\r\n sqrt(W_E_j) %*% eigenvec %*% diag(eigenval_InvSqrt, k, k) %*% t(eigenvec) %*%sqrt(W_E_j) # Pseudo_Inverse\r\n },\r\n eigenvec_list, \r\n eigenval_list, \r\n k_list,\r\n Working_Matrx_E_j)\r\n \r\n },\r\n \r\n CORR = {\r\n # In this case, the formula can be simplified to \r\n # A_MBB = ImH_jj ^ (-0.5)\r\n \r\n eigenres_list <- lapply(ImHii, function(x) eigen(x))\r\n eigenval_list <- lapply(eigenres_list, function(x) x$values)\r\n eigenvec_list <- lapply(eigenres_list, function(x) x$vectors)\r\n \r\n A.MBB <- Map(function (eigenvec, eigenval, k, W_E_j) {\r\n eigenval_InvSqrt <- ifelse(eigenval< 10^-10, 0, 1/sqrt(eigenval)) # Pseudo_Inverse\r\n eigenvec %*% diag(eigenval_InvSqrt, k, k) %*% t(eigenvec) # Pseudo_Inverse\r\n },\r\n eigenvec_list, \r\n eigenval_list, \r\n k_list,\r\n Working_Matrx_E_j)\r\n })\r\n \r\n \r\n } else { # Begin userweights\r\n \r\n V.big <- diag(c(1), dim(Xreg)[1], dim(Xreg)[1]) %*% \r\n diag(data.full$avg.var.eff.size)\r\n v.j <- by(data.full$avg.var.eff.size, data.full$study, \r\n function(x) diag(x, nrow = length(x)))\r\n v.j.sqrt_list <- lapply(v.j, function (x) sqrt(x))\r\n \r\n Working_Matrx_E_j <- v.j\r\n Working_Matrx_E <- V.big\r\n \r\n InsideMatrx_list <- Map(\r\n function (ImH_j) {\r\n ImH_j %*% Working_Matrx_E %*% t(ImH_j)\r\n },\r\n ImHj)\r\n \r\n eigenres_list <- lapply(InsideMatrx_list, function(x) eigen(x))\r\n eigenval_list <- lapply(eigenres_list, function(x) x$values)\r\n eigenvec_list <- lapply(eigenres_list, function(x) x$vectors)\r\n \r\n A.MBB <- Map(function (eigenvec, eigenval, k, v.j.sqrt) {\r\n eigenval_InvSqrt <- ifelse(eigenval< 10^-10, 0, 1/sqrt(eigenval)) # Pseudo_Inverse\r\n v.j.sqrt %*% eigenvec %*% diag(eigenval_InvSqrt, k, k) %*% t(eigenvec) # Pseudo_Inverse\r\n },\r\n eigenvec_list, \r\n eigenval_list, \r\n k_list,\r\n v.j.sqrt_list)\r\n \r\n \r\n } # End userweights\r\n \r\n sumXWA.MBBeeA.MBBWX.r <- Map(function(X,W,A,S) \r\n t(X) %*% W %*% A %*% S %*% A %*% W %*%X, \r\n X, W.r, A.MBB, sigma.hat.r)\r\n \r\n \r\n \r\n sumXWA.MBBeeA.MBBWX.r <- Reduce(\"+\", sumXWA.MBBeeA.MBBWX.r) \r\n giTemp <- Map(function(I, A, W, X, Q)\r\n t(I) %*% A %*% W %*% X %*% Q, \r\n ImHj, A.MBB, W.r, X, Q.list)\r\n \r\n \r\n \r\n \r\n giTemp <- do.call(rbind,giTemp)\r\n gi_matrix <- lapply(X = 1:(p+1), FUN = function(i){ matrix(giTemp[,i], nrow = M) })\r\n \r\n \r\n if (!user_weighting) {\r\n W.mat <- matrix(rep(1/sqrt(data.full$r.weights),times = N),nrow = M)\r\n B_matrix_half <- lapply(X = gi_matrix, FUN = function(gi_mat){ W.mat * gi_mat})\r\n }else{\r\n \r\n B_matrix_half <- gi_matrix\r\n # B_matrix_half <- lapply(X = gi_matrix, FUN = function(gi_mat){ solve(sqrt(V.big)) %*% gi_mat})\r\n }\r\n \r\n B_mat <- lapply(X = B_matrix_half, FUN = tcrossprod)\r\n \r\n B_trace_square <- sapply(X = B_mat, FUN = function(B){ (sum(diag(B)))^2})\r\n \r\n B_square_trace <- sapply(X = B_mat, FUN = function(B){sum(B * B)})\r\n \r\n if (is.null(userdfs))\r\n dfs <- B_trace_square/B_square_trace\r\n else\r\n dfs <- userdfs\r\n \r\n \r\n \r\n VR.MBB1 <- solve(sumXWX.r) %*% sumXWA.MBBeeA.MBBWX.r %*% solve(sumXWX.r)\r\n VR.r <- VR.MBB1\r\n SE <- sqrt(diag(VR.r))\r\n t <- b.r / SE\r\n prob <- 2 * (1 - stats::pt(abs(t), df = dfs)) \r\n CI.L <- b.r - stats::qt(.975, dfs) * SE\r\n CI.U <- b.r + stats::qt(.975, dfs) * SE\r\n \r\n } # End small = TRUE\r\n \r\n reg_table <- data.frame(cbind(b.r, SE, t, dfs, prob, CI.L, CI.U))\r\n #names(X.full)[2] <- \"intercept\"\r\n labels <- c(colnames(X.full[2:length(X.full)]))\r\n sig <- ifelse(prob < .01, \"***\", \r\n ifelse(prob > .01 & prob < .05, \"**\",\r\n ifelse(prob > .05 & prob < .10, \"*\", \"\")))\r\n reg_table <- cbind(labels, reg_table, sig)\r\n colnames(reg_table) <- c(\"labels\", \"b.r\", \"SE\", \"t\", \"dfs\", \"prob\", \"CI.L\", \r\n \"CI.U\", \"sig\")\r\n \r\n if (!small) { # Begin small = FALSE\r\n \r\n mod_label_sm <- \"\"\r\n mod_notice <- \"\"\r\n \r\n } else { # Begin small = TRUE\r\n \r\n mod_label_sm <- \"with Small-Sample Corrections\" \r\n mod_notice <- \"Note: If df < 4, do not trust the results\"\r\n \r\n } # End small = TRUE\r\n \r\n \r\n if (!user_weighting) {\r\n \r\n switch(modelweights,\r\n \r\n HIER = { # Begin HIER\r\n \r\n mod_label <- c(\"RVE: Hierarchical Effects Model\", mod_label_sm)\r\n \r\n }, # End HIER\r\n \r\n CORR = { # Begin CORR\r\n \r\n mod_label <- c(\"RVE: Correlated Effects Model\", mod_label_sm)\r\n \r\n } # End CORR\r\n \r\n ) \r\n \r\n } else { # Begin userweights\r\n \r\n mod_label <- c(\"RVE: User Specified Weights\", mod_label_sm)\r\n \r\n } # End userweights\r\n \r\n res <- list(data.full = data.full, X.full = X.full, reg_table = reg_table, \r\n mod_label = mod_label, mod_notice = mod_notice, modelweights = \r\n modelweights, mod_info = mod_info, user_weighting = \r\n user_weighting, ml = ml, cl = cl, N = N, M = M, k = k, \r\n k_list = k_list, p = p, X = X, y = y, Xreg = Xreg, b.r = b.r, \r\n VR.r = VR.r, dfs = dfs, small = small, data = data, labels = \r\n labels, study_orig_id = study_orig_id)\r\n \r\n class(res) <- \"robu\"\r\n res\r\n}\r\n", "meta": {"hexsha": "ace832987bda4d9fa433876bbe149c9da7da6724", "size": 21557, "ext": "r", "lang": "R", "max_stars_repo_path": "Code/Utilities/robu.custom.r", "max_stars_repo_name": "timesler/AttachmentStabilityMetaAnalysis_Opie2018", "max_stars_repo_head_hexsha": "b55a9a635e6a1669fce1a22e37ed0f39a026cd54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/Utilities/robu.custom.r", "max_issues_repo_name": "timesler/AttachmentStabilityMetaAnalysis_Opie2018", "max_issues_repo_head_hexsha": "b55a9a635e6a1669fce1a22e37ed0f39a026cd54", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/Utilities/robu.custom.r", "max_forks_repo_name": "timesler/AttachmentStabilityMetaAnalysis_Opie2018", "max_forks_repo_head_hexsha": "b55a9a635e6a1669fce1a22e37ed0f39a026cd54", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.9051233397, "max_line_length": 123, "alphanum_fraction": 0.4214408313, "num_tokens": 6244, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "# load packages\nsuppressMessages({\n library(CHNOSZ)\n library(dplyr)\n library(comprehenr)\n library(stringr)\n})\n\nknown_oxstates <- c(\"H\"=\"+\", \"O\"=\"-2\", \"F\"=\"-\",\n \"Cl\"=\"-\", \"Na\"=\"+\", \"Li\"=\"+\",\n \"K\"=\"+\", \"Ca\"=\"+2\", \"Mg\"=\"+2\", \"Z\"=\"+\")#,\n# \"Si\"=\"+4\", \"Al\"=\"+3\") # useful for aluminosilicates\n\n############ Helper functions\n\n# print messages if 'verbose' setting >= vlevel of message.\nvmessage <- function(m, vlevel, verbose){\n if(verbose >= vlevel){\n print(m)\n }\n}\n\n# function for inserting a row at index r while shifting other rows down\ninsertRow <- function(existingDF, newrow, r) {\n if(r < nrow(existingDF)){\n existingDF[seq(r+1,nrow(existingDF)+1),] <- existingDF[seq(r,nrow(existingDF)),]\n existingDF[r,] <- newrow\n return(existingDF)\n }else{\n return(rbind(existingDF, newrow))\n }\n}\n\n# checks if sp in a dissrxn all match strict basis names\nall_in_strict_basis <- function(s, strict_basis_names, fixed_species){\n s <- s[c(FALSE, TRUE)]\n s <- s[2:length(s)]\n s <- s[!s %in% fixed_species]\n return(all(s %in% strict_basis_names))\n}\n\n# function to vectorize all_in_strict_basis()\nsplit_and_check_dissrxn <- function(dissrxn, strict_basis_names, fixed_species){\n s <- str_split(dissrxn, \" \")\n s <- unlist(lapply(s, all_in_strict_basis, strict_basis_names, fixed_species))\n return(s)\n}\n\n# order thermo_df to put strict basis species in the front, aux basis species in\n# the middle (and sort them to prevent EQ3 errors), and everything else at the\n# end.\norder_thermo_df <- function(thermo_df, fixed_species, verbose){\n basis_entries <- filter(thermo_df, tag==\"basis\")\n aux_entries <- filter(thermo_df, tag==\"aux\")\n other_entries <- filter(thermo_df, tag!=\"basis\" & tag!=\"aux\")\n \n aux_entries_with_only_strict <- aux_entries[split_and_check_dissrxn(aux_entries[, \"dissrxn\"], basis_entries[, \"name\"], fixed_species), ]\n aux_entries_with_other_aux <- aux_entries[!split_and_check_dissrxn(aux_entries[, \"dissrxn\"], basis_entries[, \"name\"], fixed_species), ]\n aux_entries_with_strict_aux <- aux_entries_with_other_aux[split_and_check_dissrxn(aux_entries_with_other_aux[, \"dissrxn\"], c(aux_entries_with_only_strict[, \"name\"], basis_entries[, \"name\"]), fixed_species), ]\n \n if(!identical(aux_entries_with_other_aux[, \"dissrxn\"], character(0))){\n aux_entries_with_nonstrict_aux <- aux_entries_with_other_aux[!split_and_check_dissrxn(aux_entries_with_other_aux[, \"dissrxn\"], c(aux_entries_with_only_strict[, \"name\"], basis_entries[, \"name\"]), fixed_species), ]\n }else{\n aux_entries_with_nonstrict_aux <- data.frame()\n }\n \n# print(nrow(aux_entries))\n# print(nrow(aux_entries_with_other_aux))\n# print(nrow(aux_entries_with_nonstrict_aux))\n# print(aux_entries_with_nonstrict_aux[, \"name\"])\n\n if(nrow(aux_entries_with_nonstrict_aux) > 0){\n # sort aux species with nonstrict aux so that their order of \n # declaration won't cause EQPT errors.\n counter <- 0\n previous_state <- aux_entries_with_nonstrict_aux[,\"name\"]\n while(counter < 10){\n counter <- counter + 1\n for(i in 1:nrow(aux_entries_with_nonstrict_aux)){\n s <- str_split(aux_entries_with_nonstrict_aux[i, \"dissrxn\"], \" \")[[1]]\n s <- s[2:length(s)]\n s <- s[!s %in% fixed_species]\n for(sp in s){\n for(ii in 1:nrow(aux_entries_with_nonstrict_aux)){\n if(sp == aux_entries_with_nonstrict_aux[ii,\"name\"]){\n aux_entries_with_nonstrict_aux <- insertRow(aux_entries_with_nonstrict_aux, aux_entries_with_nonstrict_aux[i, ], ii+1)\n aux_entries_with_nonstrict_aux <- aux_entries_with_nonstrict_aux[-c(i), ]\n }\n }\n }\n }\n #print(aux_entries_with_nonstrict_aux[, \"name\"])\n if(all(aux_entries_with_nonstrict_aux[,\"name\"] == previous_state)){\n break\n }\n previous_state <- aux_entries_with_nonstrict_aux[,\"name\"]\n \n }\n if(counter >= 10){\n m <- paste(\"Warning: Could not automatically re-order the auxiliary basis species\",\n paste(aux_entries_with_nonstrict_aux[,\"name\"], collapse=\", \"), \". This might happen if there are\",\n \"many interdependencies of auxiliary basis species in their dissociation reactions.\")\n vmessage(m, 1, verbose)\n }\n \n }\n \n thermo_df <- rbind(basis_entries, aux_entries_with_only_strict,\n aux_entries_with_strict_aux, aux_entries_with_nonstrict_aux,\n other_entries)\n\n return(thermo_df)\n}\n\n\n# helper function from Jeff Dick's CHNOSZ\n# to retrieve the coefficients of reactions to form the species from the basis species\nspecies.basis <- function(species=get(\"thermo\", CHNOSZ)$species$ispecies) {\n # current basis elements\n bmat <- basis.elements()\n tbmat <- t(bmat)\n # what are the elements?\n belem <- rownames(tbmat)\n # get the species makeup into a matrix\n mkp <- as.matrix(sapply(makeup(species, count.zero=TRUE), c))\n # the positions of the species elements in the basis elements\n ielem <- match(rownames(mkp), belem)\n # the elements of the species must be contained by the basis species\n if(any(is.na(ielem))) stop(paste(\"element(s) not in the basis:\", \n paste(rownames(mkp)[is.na(ielem)], collapse=\" \")))\n # the positions of the basis elements in the species elements\n jelem <- match(belem, rownames(mkp))\n # keep track of which ones are NA's; \n # index them as one here but turn them to zero later\n ina <- is.na(jelem)\n jelem[ina] <- 1\n # now put the species matrix into the same order as the basis\n mkp <- mkp[jelem, , drop=FALSE]\n # fill zeros for any basis element not in the species\n mkp[ina, ] <- 0\n # solve for the basis coefficients and transpose\n nbasis <- t(apply(mkp, 2, function(x) solve(tbmat, x)))\n # very small numbers are probably a floating point artifact\n # can cause problems in situations where zeros are needed\n # (manifests as issue in longex(\"phosphate\"), where which.balance()\n # identifies H2O as conserved component)\n # 20140201 set digits (to R default) becuase getOption(\"digits\") is changed in knitr\n out <- zapsmall(nbasis, digits=7)\n # add names of species and basis species\n colnames(out) <- colnames(tbmat)\n # add names of species only if it was a character argument\n if(all(is.character(species))) rownames(out) <- species\n return(out)\n} \n\n \n# modified version of subcrt() that only performs balancing\nsubcrt_bal <- function(ispecies, coeff){\n \n thermo <- get(\"thermo\", CHNOSZ)\n species <- as.character(thermo$OBIGT$name[ispecies])\n state <- as.character(thermo$OBIGT$state[ispecies])\n \n # the mass balance; should be zero for a balanced reaction\n mss <- makeup(ispecies, coeff, sum=TRUE)\n # take out very small numbers\n mss[abs(mss) < 1e-7] <- 0\n # report and try to fix any non-zero mass balance\n if(any(mss!=0)) {\n # the missing composition: the negative of the mass balance\n miss <- -mss\n # drop elements that are zero\n miss <- miss[miss!=0]\n # look for basis species that have our compositoin\n tb <- thermo$basis\n if(all(names(miss) %in% colnames(tb)[1:nrow(tb)])) {\n # the missing composition as formula\n ft <- as.chemical.formula(miss)\n # the basis species needed to supply it\n bc <- species.basis(ft)\n # drop zeroes\n bc.new <- bc[,(bc[1,]!=0),drop=FALSE]\n bc <- bc.new\n ispecies.new <- tb$ispecies[match(colnames(bc),rownames(tb))]\n b.species <- thermo$OBIGT$formula[ispecies.new]\n newspecies <- c(ispecies, tb$ispecies[match(colnames(bc), rownames(tb))])\n newcoeff <- c(coeff, as.numeric(bc[1, ]))\n return(list('newspecies'=newspecies, 'newcoeff'=newcoeff))\n }\n } else {\n return(list('newspecies'=ispecies, 'newcoeff'=as.numeric(coeff)))\n }\n}\n\n# print messages if 'verbose' setting >= vlevel of message.\nvmessage <- function(m, vlevel, verbose){\n if(verbose >= vlevel){\n print(m)\n }\n}\n\n\n# function to return 0 if a value is NA\ncheck_get <- function(val){\n ifelse(is.na(val), 0, val)\n}\n\n\nget_oxstate <- function(makeup_list){\n \n ifelse(length(setdiff(names(makeup_list), names(known_oxstates))) == 1,\n {\n ox_state <- c(- check_get(makeup_list[\"F\"])*(-1) -\n check_get(makeup_list[\"Cl\"])*(-1) -\n check_get(makeup_list[\"H\"]) -\n check_get(makeup_list[\"Li\"]) -\n check_get(makeup_list[\"K\"]) -\n check_get(makeup_list[\"Na\"]) -\n check_get(makeup_list[\"Ca\"])*(2) -\n check_get(makeup_list[\"Mg\"])*(2) -\n# check_get(makeup_list[\"Si\"])*(4) - # useful for aluminosilicates\n# check_get(makeup_list[\"Al\"])*(3) - # useful for aluminosilicates\n check_get(makeup_list[\"O\"])*(-2) +\n check_get(makeup_list[\"Z\"]))\n ox_state <- ox_state/check_get(makeup_list[setdiff(names(makeup_list), names(known_oxstates))])\n ox_state <- ifelse(ox_state %% 1 == 0, ox_state, NA) # filter out results with non-integer oxidation states\n names(ox_state) <- setdiff(names(makeup_list), names(known_oxstates))\n return(ox_state)\n },\n {\n return(NA)\n })\n}\n\n\n# function to print charge (e.g. a charge of +1 is displayed as \"+\", a charge of -2 is displayed as \"-2\")\nformat_charge <- function(charge){\n sign <- ifelse(charge >= 0, \"+\", \"-\")\n mag <- ifelse(abs(charge) == 1, \"\", as.character(abs(charge)))\n\n sign <- ifelse(charge == 0, \"\", sign)\n mag <- ifelse(charge == 0, \"\", mag)\n\n return(paste0(sign, mag))\n}\n\n\nerrcheck <- function(expr, me=\"\", mw=\"\", verbose){\n tryCatch(expr,\n error = function(e){\n if(me != \"\"){\n vmessage(paste0(\"An error occurred:\\n\", me, \"Additional detail:\\n\", e), 1, verbose)\n stop()\n }else{\n vmessage(paste0(\"An error occurred:\\n\", e), 1, verbose)\n stop()\n }\n \n },\n warning = function(w){\n if(mw != \"\"){\n vmessage(paste0(\"A warning occurred:\\n\", mw, \"Additional detail:\\n\", w), 1, verbose)\n }else{\n vmessage(paste0(\"A warning occurred:\\n\", w), 1, verbose)\n }\n }\n )\n}\n\n\nmatch_basis_comp <- function(sp_elems, elem){\n if(elem %in% sp_elems){\n match <- all(sp_elems %in% unique(c(elem, \"H\", \"O\", \"Z\")))\n }else{\n match <- FALSE\n }\n return(match)\n}\n\n\n\n\n################################ get_dissrxn\n\n### INPUT EXAMPLE\n# sp_name <- \"tyrosine\"\n# # Preferred basis species for elements\n# # (Not all elements have to be specified)\n# basis_pref <- c(\"C\"=\"HCO3-\", \"N\"=\"NH4+\")\n# HOZ_balancers <- c(\"H+\", \"O2\", \"H2O\") # might be data0-specific (e.g., \"O2(g)\")\n# thermo_df has to be the entire thermo datasheet.\n# Needed to determine whether aux species are better than strict basis depending on ox state.\n# If NULL, just uses strict basis species, or what it determines to be strict basis.\n\nget_dissrxn <- function(sp_name, redox_elem_states, basis_pref=c(), aux_pref=c(),\n HOZ_balancers=c(\"H+\", \"O2\", \"H2O\"),\n thermo_df=NULL, verbose=2){\n \n if(length(sp_name) > 1 & length(unique(sp_name)) == 1){\n sp_name = unique(sp_name)\n }\n \n if(length(sp_name) > 0){\n # get a vector of elements that make up the (non-basis) species\n basis_elem <- (function (x) setdiff(names(unlist(makeup(info(info(x), check.it=F)$formula))), c(\"H\", \"O\", \"Z\"))) (thermo_df[, \"name\"])\n basis_elem <- c(basis_elem, names(basis_pref))\n }else{\n # if there are no species, define basis list in dissrxns and exit the function\n dissrxns <- list(basis_list = basis_pref)\n return(dissrxns)\n }\n \n basis_elem <- unique(basis_elem) \n \n sp <- thermo_df\n sp_formula <- sp$formula_modded\n sp_formula_makeup <- makeup(sp_formula)\n names(sp_formula_makeup) <- sp$name\n \n # loop through each element and assign preferred basis species (if supplied)\n # or automatically choose compatible basis species\n basis_list <- list()\n \n# # check that every basis element has a basis pref\n# if(!setequal(basis_elem, names(basis_pref))){\n# missing_basis <- basis_elem[!(basis_elem %in% names(basis_pref))]\n# stop(paste(\"Error: the element(s)\", paste(missing_basis, collapse=\", \"), \"require strict basis species in the database.\"))\n# }\n\n for(elem in basis_elem){\n # if a preferred basis species exists for this element, assign it and move to next element\n if(elem %in% names(basis_pref)){\n basis_list[[elem]] <- c(info(info(basis_pref[elem]), check.it=F)$name)\n names(basis_list[[elem]]) <- basis_pref[elem]\n next\n }\n\n # get index of species that fulfill requirements of EQ3's definition of a basis species for this element\n idx <- unlist(lapply(lapply(sp_formula_makeup, names), match_basis_comp, elem=elem))\n\n # provide a vector of potential basis species for this element\n basis_list[[elem]] <- sp$name[idx]\n names(basis_list[[elem]]) <- sp$name[idx]\n\n # get formula makeup of potential basis species\n frm_mkup <- lapply(unlist(lapply(lapply(lapply(basis_list[[elem]], FUN=info), FUN=info), `[`, \"formula\")), makeup)\n \n # if possible, narrow down this list to basis species with a matching element abundance of 1\n abund1 <- unlist(lapply(frm_mkup, function(x) x[elem]==1))\n if(sum(abund1 != 0)){\n # assign narrowed list of matches to basis list\n basis_list[[elem]] <- unlist(basis_list[[elem]][abund1])\n }\n\n # filter out species with \"[\" in the name (e.g. \"[-CH3]\"). These kinds of groups are in CHNOSZ's OBIGT.\n basis_list[[elem]] <- unlist(basis_list[[elem]][!grepl(\"\\\\[\", names(basis_list[[elem]]))])\n\n # error handling in case a suitable basis species cannot be found\n errcheck(length(basis_list[[elem]]) == 0, me=paste0(\"A suitable basis species could be found to represent the element \", elem, \".\"))\n }\n \n # further narrow down the list of potential basis species by grabbing the basis species\n # with the fewest elements+charge from available basis species. In the end there should\n # only be one basis species assigned to each element.\n \n # check whether basis species in the basis_list are actually given as basis\n # species in thermo_df.\n elements_without_basis <- c()\n for(elem in names(basis_list)){\n if(length(basis_list[[elem]]) == 0){\n elements_without_basis <- c(elements_without_basis, elem)\n }\n }\n if(length(elements_without_basis) > 0){\n msg <- paste(\"Error during database processing. The following elements\",\n \"appear in chemical species but do not have representative\",\n \"basis species:\", paste(elements_without_basis, collapse=\", \"),\n \". Are necessary basis species being excluded?\")\n stop(msg)\n }\n \n simplest_basis <- c()\n for(elem in basis_elem){\n form <- lapply(info(info(basis_list[[elem]]))$formula, makeup)\n sums <- unlist(lapply(lapply(form, abs), sum))\n idx <- which(sums==min(sums))\n if(length(idx) > 1){\n idx <- idx[1]\n }\n \n simplest_basis[elem] <- basis_list[[elem]][idx]\n }\n\n dissrxns <- lapply(sp_name, spec_diss, simplest_basis, sp_formula_makeup,\n HOZ_balancers, redox_elem_states, aux_pref, thermo_df, verbose)\n \n \n names(dissrxns) <- sp_name\n dissrxns[[\"basis_list\"]] <- simplest_basis\n return(dissrxns)\n\n}\n\n# supply a species name, get a dissrxn.\n# (meant to lapply this over all species names)\nspec_diss <- function(sp, simplest_basis, sp_formula_makeup, HOZ_balancers,\n redox_elem_states, aux_pref, thermo_df=NULL, verbose=2){\n \n # determine a balanced dissociation reaction into basis species\n\n # get the elemental composition\n comp <- makeup(thermo_df[thermo_df[\"name\"]==sp, \"formula_modded\"][1])\n \n # get the elements that are not H, O, Z (charge)\n basis_elem <- setdiff(names(comp), c(\"H\", \"O\", \"Z\"))\n \n # Instantiate a list of chosen basis species and their coeffs in the dissrxns.\n # Start with strict basis species only. E.g., if SO4-2 is a strict basis species:\n # Pb(HS)3-, chosen_basis$Pb is Pb+2:1 and chosen_basis$S is SO4-2:3\n # In the code below, auxiliary basis species can be chosen instead:\n # Pb(HS)3-, chosen_basis$Pb is Pb+2:1 and chosen_basis$S is HS-:3\n chosen_basis <- list()\n \n # Check whether an auxiliary basis species given in thermo_df would work\n # better than a strict basis species.\n \n if(!is.null(thermo_df)){\n \n sp_formula_ox = thermo_df[thermo_df[\"name\"]==sp, \"formula_ox_modded\"][1]\n \n for(elem in basis_elem){\n \n sp_formula_ox_split <- strsplit(sp_formula_ox, \" \")\n \n# print(paste(\"species:\", sp))\n# print(paste(\"elem:\", elem))\n# print(paste(\"formula ox:\", sp_formula_ox))\n# print(\"formula ox split:\")\n \n # get the element plus oxidation state from formula ox\n # e.g., turn \"2Al+3\" into \"Al+3\"\n # or \"0.165Mg+2\" to \"Mg+2\"\n step1 <- sp_formula_ox_split[[1]]\n step2 <- step1[grepl(paste0(elem,\"([^a-z]|$)\"), step1)]\n step3 <- gsub(\"^[[:digit:]]+(\\\\.[[:digit:]]+)?\", \"\", step2)\n \n sp_ox_elem <- makeup(step3)\n\n sp_num_elem <- as.numeric(gsub(\"([0-9]?)[A-Z].*\", \"\\\\1\", sp_formula_ox_split[[1]][grepl(paste0(elem,\"([^a-z]|$)\"), sp_formula_ox_split[[1]], perl=T)], perl=T))\n sp_num_elem <- ifelse(is.na(sp_num_elem), 1, sp_num_elem)\n names(sp_num_elem) <- rep(simplest_basis[[elem]], length(sp_num_elem))\n n_elem_in_basis <- sp_formula_makeup[[simplest_basis[elem]]][elem]\n chosen_basis[[elem]] <- sp_num_elem/n_elem_in_basis\n\n if(elem %in% names(simplest_basis) && elem %in% names(aux_pref)){\n # get oxidation states of this element available in strict and aux basis species\n strict_df = thermo_df %>%\n filter(name == simplest_basis[elem]) %>%\n select(formula_ox_modded)\n aux_df = thermo_df %>%\n filter(name %in% aux_pref[names(aux_pref)==elem]) %>%\n filter(name != sp) %>% # prevents an aux species from dissociating into itself\n select(formula_ox_modded)\n basis_df <- rbind(strict_df, aux_df)\n basis_df[\"ave_ox_state_of_elem\"] <- 0\n basis_formula_ox_split <- lapply(basis_df[\"formula_ox_modded\"], strsplit, \" \")$formula_ox_modded\n \n idx <- 1\n for(line in basis_formula_ox_split){\n basis_num_elem <- as.numeric(gsub(\"([0-9]?)[A-Z].*\", \"\\\\1\", line[grepl(paste0(elem,\"([^a-z]|$)\"), line, perl=T)], perl=T))\n basis_num_elem <- ifelse(is.na(basis_num_elem), 1, basis_num_elem)\n basis_ox_elem <- makeup(gsub(\"^[[:digit:]]+(\\\\.[[:digit:]]+)?\", \"\", line[grepl(paste0(elem,\"([^a-z]|$)\"), line, perl=T)]))\n if(length(basis_num_elem) > 1){\n basis_oxstates <- c()\n basis_total_atoms <- c()\n for(i in 1:length(basis_num_elem)){\n if(!(\"Z\" %in% names(basis_ox_elem[[i]]))){\n basis_ox_elem[[i]][\"Z\"] <- 0\n }\n basis_oxstates <- c(basis_oxstates, basis_ox_elem[[i]][\"Z\"]*basis_num_elem[i])\n basis_total_atoms <- c(basis_total_atoms, basis_num_elem[i])\n }\n basis_ave_ox_state_of_elem <- sum(basis_oxstates)/sum(basis_total_atoms)\n }else{\n if(!(\"Z\" %in% names(basis_ox_elem))){\n basis_ave_ox_state_of_elem <- 0\n }else{\n basis_ave_ox_state_of_elem <- basis_ox_elem[\"Z\"]\n }\n\n }\n basis_df[idx, \"ave_ox_state_of_elem\"] <- basis_ave_ox_state_of_elem\n idx <- idx + 1\n }\n \n # get the oxidation state of the element in the non-basis species\n sp_formula_ox = thermo_df[thermo_df[\"name\"]==sp, \"formula_ox_modded\"]\n \n # For each oxidation state of this element in the non-basis species,\n # pick the closest strict or aux basis species.\n if(is.list(sp_ox_elem)){\n # When there is more than one instance of this element in the non-basis species,\n # find the basis species with the closest oxidation states and assign.\n chosen_basis_species_vec <- c()\n basis_ave_ox_states <- unlist(basis_df[\"ave_ox_state_of_elem\"]) \n \n names(basis_ave_ox_states) <- row.names(basis_df)\n for(i in 1:length(sp_ox_elem)){\n if(!(\"Z\" %in% names(sp_ox_elem[[i]]))){\n sp_ox_elem[[i]][\"Z\"] <- 0\n }\n chosen_basis_species_vec <- c(chosen_basis_species_vec, names(which.min(abs(basis_ave_ox_states-sp_ox_elem[[i]][\"Z\"])))[1])\n }\n\n basis_num_elems <- c()\n for(basis in chosen_basis_species_vec){\n n_elem_in_basis <- sp_formula_makeup[[basis]][elem]\n basis_num_elems <- c(basis_num_elems, n_elem_in_basis)\n }\n names(sp_num_elem) <- chosen_basis_species_vec\n chosen_basis[[elem]] <- sp_num_elem/basis_num_elems\n \n \n }else{\n # Find the basis species with the closest oxidation state to the\n # element inside the non-basis species and assign.\n if(!(\"Z\" %in% names(sp_ox_elem))){\n sp_ox_elem[\"Z\"] <- 0\n }\n basis_ave_ox_states <- unlist(basis_df[\"ave_ox_state_of_elem\"])\n names(basis_ave_ox_states) <- row.names(basis_df)\n \n # if there is more than one basis species matching the oxidation state of\n # this element in the non-basis species, just pick the first one, thus the [1].\n # Maybe there is a better way to decide but I'm going with this for now.\n chosen_basis_name <- names(which.min(abs(basis_ave_ox_states-sp_ox_elem[\"Z\"])))[1]\n \n n_elem_in_basis <- sp_formula_makeup[[chosen_basis_name]][elem]\n \n names(sp_num_elem) <- chosen_basis_name\n \n chosen_basis[[elem]] <- sp_num_elem/n_elem_in_basis\n \n }\n\n }\n\n # sum together basis species coeffs sharing a name (e.g., c(HS-:1, HS-:1) is summed to c(HS-:2))\n chosen_basis[[elem]] <- tapply(unlist(chosen_basis[[elem]]), names(unlist(chosen_basis[[elem]])), sum)\n }\n }\n\n# print(sp)\n# print(\"Chosen basis\")\n# print(chosen_basis)\n \n # set basis species in CHNOSZ so that a dissociation reaction can be autobalanced\n basis(unique(c(simplest_basis[basis_elem], HOZ_balancers)))\n\n basis_to_include <- unlist(lapply(chosen_basis, names))\n coeffs_to_include <- unlist(lapply(chosen_basis, `[`))\n\n isp <- suppressMessages(info(c(sp, basis_to_include)))\n\n autobalance <- subcrt_bal(isp, c(-1, coeffs_to_include))\n spec_names <- thermo()$OBIGT[autobalance$newspecies, \"name\"]\n \n spec_names[spec_names == \"water\"] <- \"H2O\" # prevent CHNOSZ from renaming 'H2O' (name req. by EQ3) to 'water'\n sp_dissrxn <- autobalance$newcoeff \n \n names(sp_dissrxn) <- spec_names\n \n sp_dissrxn <- paste(c(rbind(sprintf(\"%.4f\", round(sp_dissrxn, 4)), names(sp_dissrxn))), collapse=\" \")\n \n return(sp_dissrxn)\n}\n\n\n\n \nsuppress_redox_and_generate_dissrxns <- function(filename,\n filename_ss,\n db,\n water_model,\n template,\n exceed_Ttr,\n data0_formula_ox_name,\n suppress_redox,\n infer_formula_ox,\n exclude_category,\n fixed_species=c(\"H2O\", \"H+\", \"O2(g)\", \"water\", \"Cl-\", \"e-\"),\n verbose=1){\n \n # specify molecules to balance H, O, and charge (Z)\n HOZ_balancers <- c(\"H+\", \"O2(g)\", \"H2O\") # might be dataset-specific (e.g., \"O2(g)\")\n \n # load thermodynamic data\n thermo_df <- read.csv(filename, stringsAsFactors=F)\n \n # exclude rows based on categories\n if(length(exclude_category) > 0){\n for(cat in names(exclude_category)){\n thermo_df <- thermo_df %>%\n filter(!(!!as.name(cat)) %in% exclude_category[[cat]])\n }\n }\n \n # Ensure that Cl- and other EQ3NR-required species are in thermo_df by marking\n # them as \"required\" in category 1 of the thermodynamic db CSV.\n \n # get all unique oxidation states of elements in the entire dataset\n elem_ox <- c()\n for(entry in thermo_df$formula_ox){\n entry <- strsplit(entry, \" \")[[1]]\n for(elem in entry){\n e <- unlist(strsplit(elem, \"[0-9](?=[A-Z])\", perl=T))\n e <- e[length(e)]\n elem_ox <- c(elem_ox, e)\n }\n }\n elem_ox <- unique(elem_ox)\n \n # create pseudoelement names\n redox_elem_states <- list()\n for(elem in suppress_redox){\n # check for multiple oxidation states for this element in thermo_df$formula_ox\n elem_ox_match <- unlist(lapply(lapply(makeup(elem_ox), FUN=names), `[`, 1)) # remove 'Z' in makeup()\n redox_entry <- c()\n\n matches <- elem_ox[elem_ox_match == elem]\n\n for(match in matches){\n if(grepl(\"\\\\+\", match)){\n mag <- tolower(as.roman(as.numeric(strsplit(match, \"\\\\+\")[[1]][2])))\n if(is.na(mag)){\n mag <- \"i\"\n }\n redox_entry[match] <- paste0(elem, mag, \"p\")\n }else if(grepl(\"\\\\-\", match)){\n mag <- tolower(as.roman(as.numeric(strsplit(match, \"\\\\-\")[[1]][2])))\n if(is.na(mag)){\n mag <- \"i\"\n }\n redox_entry[match] <- paste0(elem, mag, \"n\") # n for negative charge\n }else{\n redox_entry[match] <- paste0(elem, \"z\") # z for zero charge\n }\n }\n redox_elem_states[[elem]] <- redox_entry\n\n }\n \n # Assign Americium a mass of 223 in the CHNOSZ element database.\n e <- thermo()$element\n e[e[,\"element\"] == \"Am\", \"mass\"] <- 223\n thermo(element = e)\n \n # Add oxidation-separated elements to CHNOSZ's database of elements\n # element, state, source, mass, s, n\n for(elem in names(redox_elem_states)){\n for(elem_ox in names(redox_elem_states[[elem]])){\n old <- thermo()$element\n elem_entry <- filter(thermo()$element, element==elem)[1, ] # todo: more elegant solution for getting one matching entry, e.g. in the case of matching Cl\n Xprops <- data.frame(element=unname(redox_elem_states[[elem]][elem_ox]),\n state=elem_entry[, \"state\"],\n source=paste(\"redox suppression workaround for\", elem_ox),\n mass=elem_entry[, \"mass\"],\n s=elem_entry[, \"s\"],\n n=elem_entry[, \"n\"])\n new <- rbind(old, Xprops)\n suppressMessages(thermo(element=new))\n }\n }\n \n known_species <- to_vec(for(i in 1:length(known_oxstates)) paste0(names(known_oxstates)[i], known_oxstates[i]))\n\n if(infer_formula_ox){\n \n makeup_list <- makeup(thermo_df$formula)\n names(makeup_list) <- thermo_df$name\n\n result <- lapply(makeup_list, FUN=get_oxstate)\n\n inferred_formula_ox <- c()\n\n for(entry in names(result)){\n if(!is.na(result[[entry]])){\n ox <- result[[entry]]\n elem <- names(ox)\n n_elem <- makeup_list[[entry]][elem]\n if(n_elem == 1){\n n_elem <- \"\"\n }\n out_elem <- paste0(n_elem, elem, format_charge(as.numeric(ox)))\n \n elem_other <- names(makeup_list[[entry]])[!(names(makeup_list[[entry]]) %in% c(\"Z\", elem))]\n out <- to_vec(for(x in elem_other) if(makeup_list[[entry]][x] != 1) paste0(makeup_list[[entry]][x], x, known_oxstates[x]) else(paste0(x, known_oxstates[x])))\n inferred_formula_ox[[entry]] <- paste(out_elem, paste(out, collapse=\" \"))\n\n thermo_df[thermo_df[, \"name\"] == entry, \"formula_ox\"] <- inferred_formula_ox[[entry]]\n } else if (entry %in% known_species){\n thermo_df[thermo_df[, \"name\"] == entry, \"formula_ox\"] <- entry\n }\n }\n write.csv(thermo_df, data0_formula_ox_name, row.names=F, na=\"\")\n }\n \n thermo_df[\"formula_modded\"] <- thermo_df[\"formula\"]\n thermo_df[\"formula_ox_modded\"] <- thermo_df[\"formula_ox\"]\n \n # create pseudoelements and assign to molecular formulae\n if(length(suppress_redox) > 0){\n\n # loop through species\n for(idx in 1:nrow(thermo_df)){\n\n species_name <- thermo_df[idx, \"name\"]\n \n # get makeup and charge of this species\n this_makeup <- makeup(thermo_df[idx, \"formula\"])\n \n elems <- names(this_makeup)\n charge <- this_makeup[\"Z\"]\n if(is.na(charge)){\n charge <- 0\n }\n \n # for each element in this species, see if it matches an entry in redox_elem_states\n this_formula <- c()\n for(elem in elems){\n \n if(elem %in% names(redox_elem_states)){\n \n sp_formula_ox <- thermo_df[thermo_df[, \"name\"] == species_name, \"formula_ox\"]\n\n # get oxidation state info\n formula_ox <- strsplit(sp_formula_ox, \" \")\n names(formula_ox) <- species_name\n \n # assign abundances to each ox elem\n for(elem_ox in formula_ox[[species_name]]){\n\n # determine the formula coefficient\n this_coeff <- as.numeric(gsub(\"([0-9]?)[A-Z].*\", \"\\\\1\", elem_ox, perl=T))\n if(is.na(this_coeff)){\n this_coeff <- 1\n }\n\n # determine the element\n e <- unlist(strsplit(elem_ox, \"[0-9](?=[A-Z])\", perl=T))\n e <- e[length(e)]\n\n this_formula[e] <- this_coeff\n \n }\n \n # create a modified formula_ox column in the modified_thermo_df if there\n # is redox suppression, such that \"Fe+2 2Cl-\" becomes \"Feii+2 2Cl-\"\n this_formula_modded <- this_formula\n for(ox_elem in names(redox_elem_states)){\n for(ox_state in names(this_formula)){\n if(grepl(paste0(ox_elem,\"([^a-z]|$)\"), ox_state)){\n \n # handle positive oxidation states\n splitname <- strsplit(ox_state, \"\\\\+\")[[1]]\n if(length(splitname) > 1){\n ox_charge <- \"+\"\n }else{\n # handle negative oxidation states\n splitname <- strsplit(ox_state, \"\\\\-\")[[1]]\n if(length(splitname) > 1){\n ox_charge <- \"-\"\n }else{\n ox_charge <- \"\"\n }\n }\n \n if(length(splitname) != 1){\n ox_charge_magnitude <- as.numeric(splitname[2])\n }else{\n ox_charge_magnitude <- 0\n }\n\n pseudoelem_ox_charge <- redox_elem_states[[ox_elem]][ox_state]\n if(ox_charge_magnitude !=0){\n pseudoelem_ox_charge <- paste0(pseudoelem_ox_charge, ox_charge, ox_charge_magnitude)\n }\n \n this_formula_names <- names(this_formula_modded)\n this_formula_names[this_formula_names == ox_state] <- pseudoelem_ox_charge\n names(this_formula_modded) <- this_formula_names\n \n }\n }\n }\n \n to_collapse <- c()\n for(x in names(this_formula_modded)){\n formula_ox_vec <- c(rbind(this_formula_modded[x], x))\n this_formula_ox_modded <- paste(formula_ox_vec[formula_ox_vec!=\"1\"], collapse=\"\")\n to_collapse <- c(to_collapse, this_formula_ox_modded)\n }\n this_formula_ox_modded <- paste(to_collapse, collapse=\" \")\n\n thermo_df[thermo_df[, \"name\"] == species_name, \"formula_ox_modded\"] <- this_formula_ox_modded\n \n }\n }\n \n modified_formula <- c()\n previous_elems <- c()\n for(name in names(this_formula)){\n elem <- names(which(sapply(lapply(redox_elem_states, FUN=names), FUN=function(x) name %in% x)))\n if(!identical(elem, character(0))){\n modified_formula[redox_elem_states[[elem]][name]] <- this_formula[name]\n }else{\n e_makeup <- names(makeup(name))\n elem <- e_makeup[!(e_makeup %in% c(\"Z\"))]\n if(elem %in% previous_elems){\n modified_formula[elem] <- modified_formula[elem] + this_formula[name]\n }else{\n modified_formula[elem] <- this_formula[name]\n }\n }\n previous_elems <- c(previous_elems, elem)\n }\n \n formula_vec <- c(rbind(names(modified_formula), modified_formula))\n formula <- paste(formula_vec[formula_vec!=\"1\"], collapse=\"\")\n \n if(formula==\"\"){\n formula <- thermo_df[idx, \"formula_modded\"]\n }else{\n formula <- paste0(formula, format_charge(charge))\n }\n \n thermo_df[thermo_df[, \"name\"] == species_name, \"formula_modded\"] <- formula\n }\n }\n \n # reorder thermo_df so that strict basis are first, then aux that depend only\n # on strict basis, then aux that depend on other aux, then everything else.\n # Prevents EQPT errors.\n thermo_df <- order_thermo_df(thermo_df, fixed_species, verbose)\n\n thermo_df %>% mutate_if(is.factor, as.character) -> thermo_df\n \n # include modified file in CHNOSZ database\n to_mod_OBIGT <- thermo_df[c(\"name\", \"abbrv\", \"formula_modded\",\n \"state\", \"ref1\", \"ref2\", \"date\",\n \"E_units\", \"G\", \"H\", \"S\", \"Cp\",\n \"V\", \"a1.a\", \"a2.b\", \"a3.c\",\n \"a4.d\", \"c1.e\", \"c2.f\",\n \"omega.lambda\", \"z.T\")]\n \n names(to_mod_OBIGT) <- c(\"name\", \"abbrv\", \"formula\",\n \"state\", \"ref1\", \"ref2\", \"date\",\n \"E_units\", \"G\", \"H\", \"S\", \"Cp\",\n \"V\", \"a1.a\", \"a2.b\", \"a3.c\",\n \"a4.d\", \"c1.e\", \"c2.f\",\n \"omega.lambda\", \"z.T\")\n\n suppressMessages({\n thermo(OBIGT=thermo()$OBIGT[unique(info(fixed_species)), ]) # replaces the default OBIGT database with user-supplied database\n mod.OBIGT(to_mod_OBIGT, replace=TRUE) # produces a message\n })\n\n\n # begin handling basis preferences\n basis_df <- thermo_df %>%\n filter(tag==\"basis\")\n aux_df <- thermo_df %>%\n filter(tag==\"aux\")\n\n basis_pref <- basis_df[, \"name\"]\n \n basis_pref_elements <- lapply(lapply(lapply(basis_df[,\"formula_modded\"], makeup), names), setdiff, c(\"Z\", \"O\", \"H\"))\n \n # handle the possibility of a basis species with more than one non-OH element,\n # e.g., glycine as a basis species has C and N.\n for(i in 1:length(basis_pref_elements)){\n \n if(length(basis_pref_elements[[i]]) > 1){\n \n msg <- paste0(\"Error during database processing. The strict basis \",\n \"species '\", basis_pref[i], \"' has more than one \",\n \"element besides O and H. This can be solved by making \",\n \"this species an auxiliary basis species with a dissociation \",\n \"reaction into strict basis species representing the elements \",\n paste(basis_pref_elements[[i]], collapse=\" \"))\n stop(msg)\n\n }\n }\n \n names(basis_pref) <- basis_pref_elements\n \n aux_pref <- unlist(aux_df[, \"name\"])\n aux_pref_names <- lapply(lapply(lapply(aux_df[,\"formula_modded\"], makeup), names), setdiff, c(\"Z\", \"O\", \"H\"))\n \n\n \n # Remove aux basis species from the preferred list if they contain more than\n # one element besides O and H.\n # E.g., remove CN-, OCN-, etc.\n keep <- c()\n for(i in 1:length(aux_pref)){\n if(length(aux_pref_names[[i]])==1){\n keep <- c(keep, i)\n }\n }\n aux_pref <- aux_pref[keep]\n aux_pref_names <- aux_pref_names[keep]\n \n # Also remove aux basis species from the preferred list if they have more than\n # one atom of the same element.\n # E.g., remove S2-2, S2O3-2, etc.\n if(!identical(aux_pref, character(0))){\n keep <- c()\n for(i in 1:length(aux_pref)){\n if(makeup(thermo_df[thermo_df[, \"name\"]==aux_pref[[i]], \"formula_modded\"])[aux_pref_names[[i]]] == 1){\n keep <- c(keep, i)\n }\n }\n aux_pref <- aux_pref[keep]\n aux_pref_names <- aux_pref_names[keep]\n \n names(aux_pref) <- aux_pref_names\n }else{\n aux_pref <- list()\n aux_pref_names <- c()\n }\n \n # EQ3 has Cl-, H2O, and O2(g) hard-coded as basis species for the\n # elements Cl, H, and O, respectively.\n basis_pref[\"Cl\"] <- \"Cl-\"\n basis_pref[\"H\"] <- \"H2O\"\n basis_pref[\"O\"] <- \"O2(g)\"\n \n # handle the rest of the preferred basis species, e.g.,\n # define basis species for redox pseudoelements\n # basis_pref[\"Feiii\"] <- \"Fe+3\"\n # basis_pref[\"Feii\"] <- \"Fe+2\"\n # basis_pref[\"Fez\"] <- \"iron\"\n # basis_pref[\"Siin\"] <- \"HS-\"\n # basis_pref[\"Svi\"] <- \"SO4-2\"\n \n # Ensure that each element has a representative strict basis species.\n # If not, assign one from aux species.\n # If that fails, assign one from a valid non-basis species.\n # First check that each element has only one valid basis species.\n # 1. get a vector of ALL elements except for O and H\n all_elements <- lapply(lapply(lapply(thermo_df[,\"formula_modded\"], makeup), names), setdiff, c(\"Z\", \"O\", \"H\"))\n all_elements <- unique(unlist(all_elements))\n # 2. check that all elements are represented by basis species\n elem_need_basis <- setdiff(all_elements, names(basis_pref))\n # 3. check auxiliary basis species to see if any can be used as strict basis for unrepresented elements\n # and assign to basis_prefs. Remove from aux basis prefs.\n if(length(elem_need_basis) > 0){\n for(elem in elem_need_basis){\n if(elem %in% names(aux_pref)){\n valid_aux <- aux_pref[names(aux_pref) == elem]\n \n basis_pref <- c(basis_pref, valid_aux[1]) # add valid aux to basis prefs. The [1] just selects the first valid aux species if there are multiple. Selection method can probably be improved.\n aux_pref <- aux_pref[aux_pref != valid_aux]\n \n thermo_df[thermo_df[, \"name\"] == valid_aux, \"tag\"] <- \"basis\"\n\n }else{\n # Pick a basis species from valid non-basis...\n # The dissociation reaction generator will do this automatically.\n }\n }\n }\n \n # get names of species that need dissrxns:\n # 1. non-basis species in the datafile lacking a dissociation reaction\n # 2. non-basis species with incorrect or unbalanced dissociation reactions.\n # Note: unbalanced dissociation reactions can occur when suppressing redox on element(s).\n # (e.g., chalcopyrite, CuFeS2, which contains Fe+3, might be written to dissociate into Fe+2.\n # This creates an unbalanced reaction if iron oxidation states are all different pseudoelements)\n \n # for each dissrxn, determine if unbalanced. If so, flag a thermo_df 'need_dissrxn' column.\n thermo_df[, \"regenerate_dissrxn\"] <- FALSE\n all_basis_species <- c()\n for(species in thermo_df[, \"name\"]){\n \n tag <- thermo_df[thermo_df[, \"name\"] == species, \"tag\"]\n if(tag != \"basis\"){\n dissrxn <- thermo_df[thermo_df[, \"name\"] == species, \"dissrxn\"]\n dissrxn <- strsplit(dissrxn, \" \")[[1]] # split the rxn into coeffs and species\n dissrxn_names <- dissrxn[c(FALSE, TRUE)] # get names of reactants and products\n all_basis_species <- c(all_basis_species, dissrxn_names[2:length(dissrxn_names)])\n dissrxn_ispecies <- suppressMessages(info(dissrxn_names))\n dissrxn_coefs <- dissrxn[c(TRUE, FALSE)] # get coeffs of reactants and products\n dissrxn_coefs <- as.numeric(dissrxn_coefs) # convert coeffs from str to numeric\n tryCatch({\n subcrt_bal(dissrxn_ispecies, dissrxn_coefs)\n }, error=function(e){\n thermo_df[thermo_df[, \"name\"] == species, \"regenerate_dissrxn\"] <<- TRUE # assign global variable with <<- because this is within the error function\n })\n }\n }\n all_basis_species <- unique(all_basis_species)\n\n # check whether there are species in dissociation reactions that are missing or\n # not tagged in the database as 'basis' or 'aux'.\n missing_basis_species <- c()\n species_to_blame <- c()\n species_in_dissrxns_that_are_not_basis <- c()\n for(basis in all_basis_species){\n if(!(basis %in% thermo_df$name) && !(basis %in% fixed_species)){\n missing_basis_species <- c(missing_basis_species, basis)\n }else if(!(basis %in% basis_df$name) && !(basis %in% aux_df$name) && !(basis %in% fixed_species)){\n species_in_dissrxns_that_are_not_basis <- c(species_in_dissrxns_that_are_not_basis, basis)\n }\n }\n if(length(missing_basis_species) > 0){\n for(species in thermo_df[, \"name\"]){\n tag <- thermo_df[thermo_df[, \"name\"] == species, \"tag\"]\n if(tag != \"basis\"){\n dissrxn <- thermo_df[thermo_df[, \"name\"] == species, \"dissrxn\"]\n dissrxn <- strsplit(dissrxn, \" \")[[1]] # split the rxn into coeffs and species\n dissrxn_names <- dissrxn[c(FALSE, TRUE)] # get names of reactants and products\n for(n in dissrxn_names){\n if(n %in% missing_basis_species){\n species_to_blame <- c(species_to_blame, species)\n }\n }\n }\n }\n species_to_blame <- unique(species_to_blame)\n \n msg_missing_basis <- paste(\"The following species appear in dissociation\",\n \"reactions but are not included in the database: [\", paste(missing_basis_species, collapse=\", \"),\n \"]. These missing species are found in the dissociation reactions of [\",\n paste(species_to_blame, collapse=\", \"), \"].\")\n }else{\n msg_missing_basis <- \"\"\n }\n if(length(species_in_dissrxns_that_are_not_basis) > 0){\n for(species in thermo_df[, \"name\"]){\n tag <- thermo_df[thermo_df[, \"name\"] == species, \"tag\"]\n if(tag != \"basis\"){\n dissrxn <- thermo_df[thermo_df[, \"name\"] == species, \"dissrxn\"]\n dissrxn <- strsplit(dissrxn, \" \")[[1]] # split the rxn into coeffs and species\n dissrxn_names <- dissrxn[c(FALSE, TRUE)] # get names of reactants and products\n for(n in dissrxn_names){\n if(n %in% missing_basis_species){\n species_to_blame <- c(species_to_blame, species)\n }\n }\n }\n }\n species_to_blame <- unique(species_to_blame)\n msg_not_basis <- paste(\"The following species appear in dissociation\",\n \"reactions but are not tagged as strict or auxiliary basis species: [\",\n paste(species_in_dissrxns_that_are_not_basis, collapse=\", \"),\n \"]. These non-basis species are found in the dissociation reactions of [\",\n paste(species_to_blame, collapse=\", \"), \"].\")\n }else{\n msg_not_basis <- \"\"\n }\n if(length(missing_basis_species) > 0 | length(species_in_dissrxns_that_are_not_basis) > 0){\n stop(paste(\"One or more errors were encountered during database processing.\",\n msg_missing_basis, msg_not_basis))\n }\n \n df_needs_dissrxns <- thermo_df %>%\n filter(tag != \"basis\") %>%\n filter(regenerate_dissrxn == TRUE)\n \n if(nrow(df_needs_dissrxns) > 0){\n if(length(suppress_redox) == 0){\n needs_dissrxns_message <- paste(\"Balanced dissociation reactions are missing for species:\",\n paste(unique(unlist(df_needs_dissrxns[\"name\"])), collapse=\", \"))\n }else{\n needs_dissrxns_message <- paste(\"Balanced and/or redox-suppressed dissociation reactions are missing for species:\",\n paste(unique(unlist(df_needs_dissrxns[\"name\"])), collapse=\", \"))\n }\n \n vmessage(needs_dissrxns_message, 1, verbose)\n vmessage(\"Generating dissociation reactions for these species using strict and auxiliary basis species containing a maximum of one atom of one element besides O and H...\", 1, verbose)\n }\n \n # generate dissociation reactions\n dissrxns <- get_dissrxn(sp_name=unlist(df_needs_dissrxns[\"name\"]),\n basis_pref=basis_pref,\n aux_pref=aux_pref,\n HOZ_balancers=HOZ_balancers,\n thermo_df=thermo_df,\n verbose=verbose,\n redox_elem_states=redox_elem_states)\n \n # Produce a warning message about which dissrxns were (re)generated and what they are.\n if(nrow(df_needs_dissrxns) > 0){\n names <- df_needs_dissrxns[[\"name\"]]\n generated_dissrxns <- dissrxns[names]\n nonbasis_idx <- unlist(lapply(lapply(lapply(generated_dissrxns, strsplit, \" \"), `[[`, 1), FUN=function(x) length(x)!=4))\n basis_idx <- !nonbasis_idx\n nonbasis_names <- names(nonbasis_idx)[nonbasis_idx]\n nonbasis_names <- unique(nonbasis_names)\n basis_names <- names(basis_idx)[basis_idx]\n vmessage(paste(nonbasis_names, \":\", generated_dissrxns[nonbasis_names], \"\\n\"), 1, verbose)\n vmessage(paste(\"Species that have been converted into strict basis:\", paste(unique(basis_names), collapse=\", \")), 1, verbose)\n }\n \n thermo_df[is.na(thermo_df)]='' \n\n out_list = list(\"OBIGT_df\"=thermo_df, \"dissrxns\"=dissrxns, \"basis_pref\"=basis_pref)\n \n return(out_list)\n}", "meta": {"hexsha": "168897ca66e52cf90b14ae9068f6b4916d5e563a", "size": 45591, "ext": "r", "lang": "R", "max_stars_repo_path": "AqEquil/redox_and_dissrxns.r", "max_stars_repo_name": "worm-portal/AqEquil", "max_stars_repo_head_hexsha": "44cf30e779f31ed3fcf57e431ba9e43df99dca94", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2021-11-04T00:44:57.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-14T18:57:43.000Z", "max_issues_repo_path": "AqEquil/redox_and_dissrxns.r", "max_issues_repo_name": "worm-portal/AqEquil", "max_issues_repo_head_hexsha": "44cf30e779f31ed3fcf57e431ba9e43df99dca94", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AqEquil/redox_and_dissrxns.r", "max_forks_repo_name": "worm-portal/AqEquil", "max_forks_repo_head_hexsha": "44cf30e779f31ed3fcf57e431ba9e43df99dca94", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.9622641509, "max_line_length": 216, "alphanum_fraction": 0.6058651927, "num_tokens": 11780, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "######################################################################################################\n# CS171 BioVis Project \n# Spring 2013\n# Author: John Mercer\n# Code: a.) takes the TIM data from the clustal output and \n# processing it to be scored with the principal component scores. It then creates\n# the scored values for all TIMs and the averages for the family to be compared\n# with dTIM and scTIM\n# b.) plots the histrograms of the prin comp scores for both dTIM scTIM\n# and the mean for the family\n# c.) plots all matrix of histograms of (dTIM - family mean) for all characteristics\n# c.) plots a SPLOM (scatter plot matrix) for (dTIM - family mean) across all characteristics\n# Resources:\n# http://stat.ethz.ch/R-manual/R-patched/library/stats/html/princomp.html\n# http://cran.r-project.org/web/packages/psych/index.html\n#######################################################################################################\n\n# real data want to score: dTIMandscTimAllTims_forR.txt\n# subset to do testing: dTIMandscTimAllTims_forR_sub4testing.txt\n# establish a connection to the 544 list (can't use read.table because no commas)\ncon <- file(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/TIMData/dTIMandscTimAllTims_forR.txt\", 'r') \n\n#read all lines into data\ndata <- readLines(con)\nclose(con)\n#prepare two columns \nprotein_id <- c()\nsequence <- c()\nprint(\"done reading...\")\n\n#iterate through the lines and make two columns (index, description)\nfor (line in data){\n protein_id = append(protein_id, gsub(\"(^ +)|( +$)\", \"\",substr(line,1,18)))\n sequence <- append(sequence, substr(line,18,nchar(line)))\n}\n\nprint(\"done parsing...\")\n\nunique_protein_ids <- unique(protein_id)\n\n#put into dataframe\nprotein_list <- data.frame(protein_id=protein_id,sequence=sequence)\n\n#sort the file according to protein_id so I can re-combine the sequences faster\nprotein_list <- protein_list[order(protein_id),]\n\n###############################\n# Combine Sequence Segments #\n###############################\nprotein_count <- 1\nunique_sequences <- c()\nfor (i in 1:nrow(protein_list)){\n if (i==1){\n unique_sequences[protein_count] <-protein_list[i,2]\n }else{\n if (protein_list[i,1] == protein_list[i-1,1]){\n unique_sequences[protein_count] <- paste(gsub(\"(^ +)|( +$)\", \"\",unique_sequences[protein_count]),gsub(\"(^ +)|( +$)\", \"\",protein_list[i,2]), sep = \"\")\n }else{\n protein_count <- protein_count + 1\n unique_sequences[protein_count] <-protein_list[i,2]\n }\n }\n #let me know how it's going\n if (i %% 1000 == 0){\n print(i)\n }\n}\n\n# so now the unique protein id list is unique_protein_ids[]\n# and the corrosponding sequences are in unique_sequences[]\nunique_protein_ids <- sort(unique_protein_ids) #I need to sort them so they still match the sequence\naligned_proteins <- data.frame(protein_id=unique_protein_ids,sequence=unique_sequences)\n\n#write.csv(aligned_proteins,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/TIMData/aligned_TIMs_proper_format.csv\") \n\nprint(\"done fixing...\")\n\n#find where dTIM starts (MART) and ends (DIINSRN)\nstart_seq <- regexpr(\"MART\", aligned_proteins[aligned_proteins$protein_id == \"dTIM\",2], fixed=T)[1] \nend_seq <- regexpr(\"DIINSRN\", aligned_proteins[aligned_proteins$protein_id == \"dTIM\",2], fixed=T)[1] \n\n#remove the superfluous end regions (before dTIM starts and after it ends)\naligned_proteins$sequence <- substr(aligned_proteins$sequence,start_seq,end_seq + nchar(\"DIINSRN\")-1)\n\n#now get the positions we want to keep (non indels)\nx <- as.character(as.vector(aligned_proteins[aligned_proteins$protein_id == \"dTIM\",2]))\nindelIndices <- c()\nposition <- 1\nnumIndels <- 1\nfor (i in 1:nchar(x)){\n if (substr(x,i,i) != \"-\"){\n indelIndices[numIndels] <- i\n numIndels <- numIndels + 1\n }\n}\n\n#now iterate through each protein and only keep the positions that were not indels in the scTIM/dTIM\nfor (r in 1:nrow(aligned_proteins)) {\n temp_string <- as.character(as.vector(aligned_proteins[r,2]))\n new_string <- \"\"\n for (i in 1:length(indelIndices)){\n new_string <- paste(new_string,substr(temp_string, indelIndices[i], indelIndices[i]),sep=\"\") \n }\n aligned_proteins[r,2] <- gsub(\"(^ +)|( +$)\", \"\",new_string)\n}\n\n#output all of the protein sequences after reducing to the region (248 residues) of interest\nwrite.csv(aligned_proteins,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/TIMData/aligned_TIMs_proper_format.csv\") \n\nprint(\"done done!\")\n\n#now compute the matrix of 5508 TIMs without dTIM or scTIM\naligned_proteins_wo_dscTIM <- aligned_proteins[aligned_proteins$protein_id != \"dTIM\",]\naligned_proteins_wo_dscTIM <- aligned_proteins_wo_dscTIM[aligned_proteins_wo_dscTIM$protein_id != \"scTIM\",]\n\nwrite.csv(aligned_proteins_wo_dscTIM,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/TIMData/aligned_TIMs_proper_format_wo_dscTIM.csv\") \n\naligned_proteins_dTIM_only <- aligned_proteins[aligned_proteins$protein_id == \"dTIM\",]\naligned_proteins_scTIM_only <- aligned_proteins[aligned_proteins$protein_id == \"scTIM\",]\n\nwrite.csv(aligned_proteins_dTIM_only,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/TIMData/aligned_TIMs_proper_format_dTIM_only.csv\") \nwrite.csv(aligned_proteins_scTIM_only,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/TIMData/aligned_TIMs_proper_format_scTIM_only.csv\") \n\n#############################################################\n### Now Score the alignments with the Prin Comps ###########\n#############################################################\n\n\n############################################################################\n#### ScoreLookup translates an aa to a prin comp score #####\n############################################################################\n#A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V\nScoreLookup <- function(aa, scores){\n if (aa==\"A\"){\n x<-scores[1]\n }else if (aa==\"R\"){\n x<-scores[2]\n }else if (aa==\"N\"){\n x<-scores[3]\n }else if (aa==\"D\"){\n x<-scores[4]\n }else if (aa==\"C\"){\n x<-scores[5]\n }else if (aa==\"Q\"){\n x<-scores[6]\n }else if (aa==\"E\"){\n x<-scores[7]\n }else if (aa==\"G\"){\n x<-scores[8]\n }else if (aa==\"H\"){\n x<-scores[9]\n }else if (aa==\"I\"){\n x<-scores[10]\n }else if (aa==\"L\"){\n x<-scores[11]\n }else if (aa==\"K\"){\n x<-scores[12]\n }else if (aa==\"M\"){\n x<-scores[13]\n }else if (aa==\"F\"){\n x<-scores[14]\n }else if (aa==\"P\"){\n x<-scores[15]\n }else if (aa==\"S\"){\n x<-scores[16]\n }else if (aa==\"T\"){\n x<-scores[17]\n }else if (aa==\"W\"){\n x<-scores[18]\n }else if (aa==\"Y\"){\n x<-scores[19]\n }else if (aa==\"V\"){\n x<-scores[20]\n }else {\n x<-0\n }\n return (x)\n}\n\nScoreLookup(\"-\",alpha_and_turn_prin_comps$PC1)\n\nlength_of_tims <- nchar(as.character(as.vector(aligned_proteins[aligned_proteins$protein_id == \"dTIM\",2])))\n\n################################# \n### Alpha and Turn ###########\n################################# \n#get the apha prin comps as an example\nalpha_and_turn_prin_comps <- read.table(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/alpha_prin_comps.csv\", header=TRUE, sep=\",\") \n\n#this is the matrix we will fill where M[i,j] = jth prin comp score for the residue in the ith protein\nprin_comp_scored_family <- matrix(0, nrow = nrow(aligned_proteins_wo_dscTIM), ncol = length_of_tims)\n\n##now apply to entire matrix\nfor (r in 1:nrow(aligned_proteins_wo_dscTIM)) {\n temp_string <- as.character(as.vector(aligned_proteins_wo_dscTIM[r,2]))\n for (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n prin_comp_scored_family[r,i] <- ScoreLookup(aa, alpha_and_turn_prin_comps$PC1)\n }\n if (r %% 20 == 0){\n print(r)\n }\n}\n\nalpha_and_turn_fam_means <- colMeans(prin_comp_scored_family)\nwrite.csv(alpha_and_turn_fam_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/alpha_and_turn_fam_means.csv\") \n\n#Score dTIM\nprin_comp_scored_dTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\n\ntemp_string <- as.character(as.vector(aligned_proteins_dTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n prin_comp_scored_dTIM[1,i] <- ScoreLookup(aa, alpha_and_turn_prin_comps$PC1)\n}\nalpha_and_turn_dTIM_means <- colMeans(prin_comp_scored_dTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(alpha_and_turn_dTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/alpha_and_turn_dTIM_scores.csv\") \n\n#Score scTIM\nprin_comp_scored_scTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\ntemp_string <- as.character(as.vector(aligned_proteins_scTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n prin_comp_scored_scTIM[1,i] <- ScoreLookup(aa, alpha_and_turn_prin_comps$PC1)\n}\nalpha_and_turn_scTIM_means <- colMeans(prin_comp_scored_scTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(alpha_and_turn_scTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/alpha_and_turn_scTIM_scores.csv\") \n\nalpha_turn_scTIM_m_mean <-alpha_and_turn_scTIM_means-alpha_and_turn_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/alpha_turn_scTIM_m_mean.pdf\")\nhist( alpha_turn_scTIM_m_mean, \n main=\"Alpha and Turn: scTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\nalpha_turn_dTIM_m_mean <-alpha_and_turn_dTIM_means-alpha_and_turn_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/alpha_turn_dTIM_m_mean.pdf\")\nhist( alpha_turn_dTIM_m_mean, \n main=\"Alpha and Turn: dTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\nalpha_turn_dTIM_m_scTIM <-alpha_and_turn_dTIM_means-alpha_and_turn_scTIM_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/alpha_turn_dTIM_m_scTIM.pdf\")\nhist( alpha_turn_dTIM_m_scTIM, \n main=\"Alpha and Turn: dTIM Minus scTIM\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\n################################# \n### Beta Propensity ##########\n################################# \n#get the apha prin comps as an example\nbeta_prin_comps <- read.table(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/beta_prin_comps.csv\", header=TRUE, sep=\",\") \n\n#this is the matrix we will fill where M[i,j] = jth prin comp score for the residue in the ith protein\nbeta_scored_family <- matrix(0, nrow = nrow(aligned_proteins_wo_dscTIM), ncol = length_of_tims)\n\n##now apply to entire matrix\nfor (r in 1:nrow(aligned_proteins_wo_dscTIM)) {\n temp_string <- as.character(as.vector(aligned_proteins_wo_dscTIM[r,2]))\n for (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n beta_scored_family[r,i] <- ScoreLookup(aa, beta_prin_comps$PC1)\n }\n if (r %% 100 == 0){\n print(r)\n }\n}\n\nbeta_fam_means <- colMeans(beta_scored_family)\nwrite.csv(beta_fam_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/beta_fam_means.csv\") \n\n#Score dTIM\nbeta_scored_dTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\n\ntemp_string <- as.character(as.vector(aligned_proteins_dTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n beta_scored_dTIM[1,i] <- ScoreLookup(aa, beta_prin_comps$PC1)\n}\nbeta_dTIM_means <- colMeans(beta_scored_dTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(beta_dTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/beta_dTIM_scores.csv\") \n\n#Score scTIM\nbeta_scored_scTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\ntemp_string <- as.character(as.vector(aligned_proteins_scTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n beta_scored_scTIM[1,i] <- ScoreLookup(aa, beta_prin_comps$PC1)\n}\nbeta_scTIM_means <- colMeans(beta_scored_scTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(beta_scTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/beta_scTIM_scores.csv\") \n\nbeta_scTIM_m_mean <- beta_scTIM_means - beta_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/beta_scTIM_m_mean.pdf\")\nhist( beta_scTIM_m_mean, \n main=\"Beta: scTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\nbeta_dTIM_m_mean <- beta_dTIM_means - beta_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/beta_dTIM_m_mean.pdf\")\nhist( beta_dTIM_m_mean, \n main=\"Beta: dTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\nbeta_dTIM_m_scTIM <- beta_dTIM_means - beta_scTIM_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/beta_dTIM_m_scTIM.pdf\")\nhist( beta_dTIM_m_scTIM, \n main=\"Beta: dTIM Minus scTIM\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n################################# \n### Composition ##########\n################################# \n#get the comp prin comps \ncomp_prin_comps <- read.table(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/composition_prin_comps.csv\", header=TRUE, sep=\",\") \n\n#this is the matrix we will fill where M[i,j] = jth prin comp score for the residue in the ith protein\ncomp_scored_family <- matrix(0, nrow = nrow(aligned_proteins_wo_dscTIM), ncol = length_of_tims)\n\n##now apply to entire matrix\nfor (r in 1:nrow(aligned_proteins_wo_dscTIM)) {\n temp_string <- as.character(as.vector(aligned_proteins_wo_dscTIM[r,2]))\n for (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n comp_scored_family[r,i] <- ScoreLookup(aa, comp_prin_comps$PC1)\n }\n if (r %% 100 == 0){\n print(r)\n }\n}\n\ncomp_fam_means <- colMeans(comp_scored_family)\nwrite.csv(comp_fam_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/comp_fam_means.csv\") \n\n#Score dTIM\ncomp_scored_dTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\n\ntemp_string <- as.character(as.vector(aligned_proteins_dTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n comp_scored_dTIM[1,i] <- ScoreLookup(aa, comp_prin_comps$PC1)\n}\ncomp_dTIM_means <- colMeans(comp_scored_dTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(comp_dTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/comp_dTIM_scores.csv\") \n\n#Score scTIM\ncomp_scored_scTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\ntemp_string <- as.character(as.vector(aligned_proteins_scTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n comp_scored_scTIM[1,i] <- ScoreLookup(aa, comp_prin_comps$PC1)\n}\ncomp_scTIM_means <- colMeans(comp_scored_scTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(comp_scTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/comp_scTIM_scores.csv\") \n\ncomp_scTIM_m_mean <- comp_scTIM_means - comp_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/comp_scTIM_m_mean.pdf\")\nhist( comp_scTIM_m_mean, \n main=\"Composition: scTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\ncomp_dTIM_m_mean <- comp_dTIM_means - comp_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/comp_dTIM_m_mean.pdf\")\nhist( comp_dTIM_m_mean, \n main=\"Composition: dTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\ncomp_dTIM_m_scTIM <- comp_dTIM_means - comp_scTIM_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/comp_dTIM_m_scTIM.pdf\")\nhist( comp_dTIM_m_scTIM, \n main=\"Composition: dTIM Minus scTIM\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\n################################# \n###### Hydrophobicity ##########\n################################# \n#get the comp prin comps \nhydro_prin_comps <- read.table(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/hydro_prin_comps.csv\", header=TRUE, sep=\",\") \n\n#this is the matrix we will fill where M[i,j] = jth prin comp score for the residue in the ith protein\nhydro_scored_family <- matrix(0, nrow = nrow(aligned_proteins_wo_dscTIM), ncol = length_of_tims)\n\n##now apply to entire matrix\nfor (r in 1:nrow(aligned_proteins_wo_dscTIM)) {\n temp_string <- as.character(as.vector(aligned_proteins_wo_dscTIM[r,2]))\n for (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n hydro_scored_family[r,i] <- ScoreLookup(aa, hydro_prin_comps$PC1)\n }\n if (r %% 100 == 0){\n print(r)\n }\n}\n\nhydro_fam_means <- colMeans(hydro_scored_family)\nwrite.csv(hydro_fam_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/hydro_fam_means.csv\") \n\n#Score dTIM\nhydro_scored_dTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\n\ntemp_string <- as.character(as.vector(aligned_proteins_dTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n hydro_scored_dTIM[1,i] <- ScoreLookup(aa, hydro_prin_comps$PC1)\n}\nhydro_dTIM_means <- colMeans(hydro_scored_dTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(hydro_dTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/hydro_dTIM_scores.csv\") \n\n#Score scTIM\nhydro_scored_scTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\ntemp_string <- as.character(as.vector(aligned_proteins_scTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n hydro_scored_scTIM[1,i] <- ScoreLookup(aa, hydro_prin_comps$PC1)\n}\nhydro_scTIM_means <- colMeans(hydro_scored_scTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(hydro_scTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/hydro_scTIM_scores.csv\") \n\nhydro_scTIM_m_mean <- hydro_scTIM_means - hydro_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/hydro_scTIM_m_mean.pdf\")\nhist( hydro_scTIM_m_mean, \n main=\"Hydrophobicity: scTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\nhydro_dTIM_m_mean <- hydro_dTIM_means - hydro_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/hydro_dTIM_m_mean.pdf\")\nhist( hydro_dTIM_m_mean, \n main=\"Hydrophobicity: dTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\nhydro_dTIM_m_scTIM <- hydro_dTIM_means - hydro_scTIM_means\n#redo this plot\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/hydro_dTIM_m_scTIM.pdf\")\nhist( hydro_dTIM_m_scTIM, \n main=\"Hydrophobicity: dTIM Minus scTIM\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n################################# \n#### Other Properties ##########\n################################# \n#get the comp prin comps \nother_prin_comps <- read.table(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/other_prin_comps.csv\", header=TRUE, sep=\",\") \n\n#this is the matrix we will fill where M[i,j] = jth prin comp score for the residue in the ith protein\nother_scored_family <- matrix(0, nrow = nrow(aligned_proteins_wo_dscTIM), ncol = length_of_tims)\n##now apply to entire matrix\nfor (r in 1:nrow(aligned_proteins_wo_dscTIM)) {\n temp_string <- as.character(as.vector(aligned_proteins_wo_dscTIM[r,2]))\n for (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n other_scored_family[r,i] <- ScoreLookup(aa, other_prin_comps$PC1)\n }\n if (r %% 100 == 0){\n print(r)\n }\n}\n\nother_fam_means <- colMeans(other_scored_family)\nwrite.csv(other_fam_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/other_fam_means.csv\") \n\n#Score dTIM\nother_scored_dTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\n\ntemp_string <- as.character(as.vector(aligned_proteins_dTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n other_scored_dTIM[1,i] <- ScoreLookup(aa, other_prin_comps$PC1)\n}\nother_dTIM_means <- colMeans(other_scored_dTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(other_dTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/other_dTIM_scores.csv\") \n\n#Score scTIM\nother_scored_scTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\ntemp_string <- as.character(as.vector(aligned_proteins_scTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n other_scored_scTIM[1,i] <- ScoreLookup(aa, other_prin_comps$PC1)\n}\nother_scTIM_means <- colMeans(other_scored_scTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(other_scTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/other_scTIM_scores.csv\") \n\nother_scTIM_m_mean <- other_scTIM_means - other_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/other_scTIM_m_mean.pdf\")\nhist( other_scTIM_m_mean, \n main=\"Other Properties: scTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\nother_dTIM_m_mean <- other_dTIM_means - other_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/other_dTIM_m_mean.pdf\")\nhist( other_dTIM_m_mean, \n main=\"Other Properties: dTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\nother_dTIM_m_scTIM <- other_dTIM_means - other_scTIM_means\n\n#redo this plot\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/other_dTIM_m_scTIM.pdf\")\nhist( other_dTIM_m_scTIM, \n main=\"Other Properties: dTIM Minus scTIM\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n\n################################# \n# Physicochemical Properties ###\n################################# \n#get the physico prin comps \nphysico_prin_comps <- read.table(\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/physico_prin_comps.csv\", header=TRUE, sep=\",\") \n\n#this is the matrix we will fill where M[i,j] = jth prin comp score for the residue in the ith protein\nphysico_scored_family <- matrix(0, nrow = nrow(aligned_proteins_wo_dscTIM), ncol = length_of_tims)\n\n##now apply to entire matrix\nfor (r in 1:nrow(aligned_proteins_wo_dscTIM)) {\n temp_string <- as.character(as.vector(aligned_proteins_wo_dscTIM[r,2]))\n for (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n physico_scored_family[r,i] <- ScoreLookup(aa, physico_prin_comps$PC1)\n }\n if (r %% 100 == 0){\n print(r)\n }\n}\n\nphysico_fam_means <- colMeans(physico_scored_family)\nwrite.csv(physico_fam_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/physico_fam_means.csv\") \n\n#Score dTIM\nphysico_scored_dTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\n\ntemp_string <- as.character(as.vector(aligned_proteins_dTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n physico_scored_dTIM[1,i] <- ScoreLookup(aa, physico_prin_comps$PC1)\n}\nphysico_dTIM_means <- colMeans(physico_scored_dTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(physico_dTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/physico_dTIM_scores.csv\") \n\n#Score scTIM\nphysico_scored_scTIM <- matrix(0, nrow = 1, ncol = length_of_tims)\ntemp_string <- as.character(as.vector(aligned_proteins_scTIM_only[1,2]))\nfor (i in 1:nchar(temp_string)){\n aa <- substr(temp_string,i,i)\n physico_scored_scTIM[1,i] <- ScoreLookup(aa, physico_prin_comps$PC1)\n}\nphysico_scTIM_means <- colMeans(physico_scored_scTIM ) #this is just trivial value/1 average to maintain common code pattern\nwrite.csv(physico_scTIM_means,file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/physico_scTIM_scores.csv\") \n\nphysico_scTIM_m_mean <- physico_scTIM_means - physico_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/physico_scTIM_m_mean.pdf\")\nhist( physico_scTIM_m_mean, \n main=\"Physicochemical Properties: scTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\nphysico_dTIM_m_mean <- physico_dTIM_means - physico_fam_means\n\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/physico_dTIM_m_mean.pdf\")\nhist( physico_dTIM_m_mean, \n main=\"Physicochemical Properties: dTIM Minus Family Mean\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\nphysico_dTIM_m_scTIM <- physico_dTIM_means - physico_scTIM_means\n\n#replot this\npdf(file=\"/Users/mercicle/Documents/python_workspace/BioVis/Codes/PrinCompData/ScoredTIMs/physico_dTIM_m_scTIM.pdf\")\nhist( physico_dTIM_m_scTIM, \n main=\"Physicochemical Properties: dTIM Minus scTIM\",\n freq=FALSE,\n breaks = 20, \n col=\"lightgreen\", \n border=\"black\")\ndev.off()\n\n############################################################################\n#### Plot all histograms on One Plot #####\n############################################################################\nalpha_turn_dTIM_m_mean_df <- as.data.frame(alpha_turn_dTIM_m_mean)\nbeta_dTIM_m_mean_df <- as.data.frame(beta_dTIM_m_mean)\ncomp_dTIM_m_mean_df <- as.data.frame(comp_dTIM_m_mean)\nhydro_dTIM_m_mean_df <- as.data.frame(hydro_dTIM_m_mean)\nother_dTIM_m_mean_df <- as.data.frame(other_dTIM_m_mean)\nphysico_dTIM_m_mean_df <- as.data.frame(physico_dTIM_m_mean)\n\n#Now, combine your two dataframes into one. First make a new column in each.\nalpha_turn_dTIM_m_mean_df$characteristic <- 'alpha'\nbeta_dTIM_m_mean_df$characteristic <- 'beta'\ncomp_dTIM_m_mean_df$characteristic <- 'comp'\nhydro_dTIM_m_mean_df$characteristic <- 'hydro'\nother_dTIM_m_mean_df$characteristic <- 'other'\nphysico_dTIM_m_mean_df$characteristic <- 'physico'\n\nnames(alpha_turn_dTIM_m_mean_df)[1] <- \"dTIMmMean\"\nnames(beta_dTIM_m_mean_df)[1] <- \"dTIMmMean\"\nnames(comp_dTIM_m_mean_df)[1] <- \"dTIMmMean\"\nnames(hydro_dTIM_m_mean_df)[1] <- \"dTIMmMean\"\nnames(other_dTIM_m_mean_df)[1] <- \"dTIMmMean\"\nnames(physico_dTIM_m_mean_df)[1] <- \"dTIMmMean\"\n\n\n#and combine into your new data frame vegLengths\nallDists <- rbind(alpha_turn_dTIM_m_mean_df, \n beta_dTIM_m_mean_df,\n comp_dTIM_m_mean_df,\n hydro_dTIM_m_mean_df,\n other_dTIM_m_mean_df,\n physico_dTIM_m_mean_df)\n\n#now make your lovely plot\nggplot(allDists, aes(x = dTIMmMean)) +\ngeom_histogram(data=subset(allDists,characteristic == \"alpha\"),fill = \"red\", alpha = 0.2) +\ngeom_histogram(data=subset(allDists,characteristic == \"beta\"),fill = \"grey\", alpha = 0.2) +\ngeom_histogram(data=subset(allDists,characteristic == \"comp\"),fill = \"blue\", alpha = 0.2) +\ngeom_histogram(data=subset(allDists,characteristic == \"hydro\"),fill = \"green\", alpha = 0.2) +\ngeom_histogram(data=subset(allDists,characteristic == \"other\"),fill = \"purple\", alpha = 0.2) +\ngeom_histogram(data=subset(allDists,characteristic == \"physico\"),fill = \"yellow\", alpha = 0.2)\n\nggplot(allDists, aes(x = dTIMmMean)) +\n geom_histogram(data=subset(allDists,characteristic == \"alpha\"),fill = \"red\", alpha = 0.2) +\n geom_histogram(data=subset(allDists,characteristic == \"beta\"),fill = \"grey\", alpha = 0.2) +\n geom_histogram(data=subset(allDists,characteristic == \"comp\"),fill = \"blue\", alpha = 0.2) +\n geom_histogram(data=subset(allDists,characteristic == \"hydro\"),fill = \"green\", alpha = 0.2) +\n geom_histogram(data=subset(allDists,characteristic == \"other\"),fill = \"purple\", alpha = 0.2) +\n geom_histogram(data=subset(allDists,characteristic == \"physico\"),fill = \"yellow\", alpha = 0.2) + \n geom_histogram(aes(fill = ..count..)) +\n scale_fill_gradient(\"count\", low = \"red\",high = \"green\") +\n facet_wrap(~ characteristic)\n\n# + opts(title=\"Normal Random Sample\")\n\n############################################################################\n#### Scatterplot Matrix #####\n############################################################################\nalpha_turn_dTIM_m_mean_df <- as.data.frame(alpha_turn_dTIM_m_mean)\nbeta_dTIM_m_mean_df <- as.data.frame(beta_dTIM_m_mean)\ncomp_dTIM_m_mean_df <- as.data.frame(comp_dTIM_m_mean)\nhydro_dTIM_m_mean_df <- as.data.frame(hydro_dTIM_m_mean)\nother_dTIM_m_mean_df <- as.data.frame(other_dTIM_m_mean)\nphysico_dTIM_m_mean_df <- as.data.frame(physico_dTIM_m_mean)\n\nallDists <- data.frame(alpha_pc_dTIM_m_mean = alpha_turn_dTIM_m_mean ,\n beta_pc_dTIM_m_mean = beta_dTIM_m_mean ,\n comp_pc_dTIM_m_mean = comp_dTIM_m_mean ,\n hydro_pc_dTIM_m_mean = hydro_dTIM_m_mean ,\n other_pc_dTIM_m_mean = other_dTIM_m_mean ,\n physico_pc_dTIM_m_mean = physico_dTIM_m_mean )\n\npairs(~alpha_pc_dTIM_m_mean\n +beta_pc_dTIM_m_mean \n + comp_pc_dTIM_m_mean \n + hydro_pc_dTIM_m_mean\n + other_pc_dTIM_m_mean\n + physico_pc_dTIM_m_mean\n ,data = allDists, \n main=\"ScatterPlot of AA Characteristics: dTIM Prin Comp - Mean Prin Comp\")\n\n ", "meta": {"hexsha": "ec1a26a23f7c83b3fab1d55fc30c6121c530c1ec", "size": 30270, "ext": "r", "lang": "R", "max_stars_repo_path": "GeneralizedMu8App/Codes/R_Codes/score_TIM_data.r", "max_stars_repo_name": "mercicle/Mu-8", "max_stars_repo_head_hexsha": "e98ce9e3b7c672e0fa9e924ba6815c65a6d34dc0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2016-05-27T08:26:32.000Z", "max_stars_repo_stars_event_max_datetime": "2017-03-15T21:41:27.000Z", "max_issues_repo_path": "OnePageStaticMu8App/R_Codes/score_TIM_data.r", "max_issues_repo_name": "mercicle/Mu-8", "max_issues_repo_head_hexsha": "e98ce9e3b7c672e0fa9e924ba6815c65a6d34dc0", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "OnePageStaticMu8App/R_Codes/score_TIM_data.r", "max_forks_repo_name": "mercicle/Mu-8", "max_forks_repo_head_hexsha": "e98ce9e3b7c672e0fa9e924ba6815c65a6d34dc0", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.7402422611, "max_line_length": 157, "alphanum_fraction": 0.7053187975, "num_tokens": 8541, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "parse_expression <- function(tokens) {\n parse_assignment(tokens)\n}\n\nparse_assignment <- function(tokens) {\n p <- parse_or(tokens)\n expr <- p$expr\n tokens <- p$tokens\n\n # look for assignment token\n if (is_type(tokens[[1]], token_type$EQUAL)) {\n equals <- tokens[[1]]\n p <- parse_assignment(tokens[-1])\n value <- p$expr\n tokens <- p$tokens\n\n if (inherits(expr, \"lox_expr_variable\")) {\n name <- expr$name\n expr <- expr_assignment(name, value)\n return(list(expr = expr, tokens = tokens))\n }\n\n lox_runtime_error(\"Invalid assignment target.\", equals)\n }\n\n # return\n list(expr = expr, tokens = tokens)\n}\n\nparse_or <- function(tokens) {\n p <- parse_and(tokens)\n expr <- p$expr\n tokens <- p$tokens\n\n while(is_type(tokens[[1]], token_type$OR)) {\n operator <- tokens[[1]]\n p <- parse_and(tokens[-1])\n right <- p$expr\n tokens <- p$tokens\n expr <- expr_logical(expr, operator, right)\n }\n\n list(expr = expr, tokens = tokens)\n}\n\nparse_and <- function(tokens) {\n p <- parse_equality(tokens)\n expr <- p$expr\n tokens <- p$tokens\n\n while(is_type(tokens[[1]], token_type$AND)) {\n operator <- tokens[[1]]\n p <- parse_equality(tokens[-1])\n right <- p$expr\n tokens <- p$tokens\n expr <- expr_logical(expr, operator, right)\n }\n\n list(expr = expr, tokens = tokens)\n}\n\nparse_equality <- function(tokens) {\n #cat(\"parse_equality\\n\")\n left <- parse_comparison(tokens)\n expr <- left$expr\n tokens <- left$tokens\n\n while(tokens[[1]]$type %in% c(token_type$BANG_EQUAL, token_type$EQUAL_EQUAL)) {\n operator <- tokens[[1]]\n right <- parse_term(tokens[-1])\n\n expr <- expr_binary(expr, operator, right$expr)\n tokens <- right$tokens\n }\n\n # return\n list(expr = expr, tokens = tokens)\n}\n\nparse_comparison <- function(tokens) {\n #cat(\"parse_comparison\\n\")\n left <- parse_term(tokens)\n expr <- left$expr\n tokens <- left$tokens\n\n while(tokens[[1]]$type %in% c(token_type$GREATER, token_type$GREATER_EQUAL, token_type$LESS, token_type$LESS_EQUAL)) {\n operator <- tokens[[1]]\n right <- parse_term(tokens[-1])\n\n expr <- expr_binary(expr, operator, right$expr)\n tokens <- right$tokens\n }\n\n # return\n list(expr = expr, tokens = tokens)\n}\n\nparse_term <- function(tokens) {\n #cat(\"parse_term\\n\")\n left <- parse_factor(tokens)\n expr <- left$expr\n tokens <- left$tokens\n\n # loop\n while(tokens[[1]]$type %in% c(token_type$MINUS, token_type$PLUS)) {\n operator <- tokens[[1]]\n right <- parse_factor(tokens[-1])\n\n expr <- expr_binary(expr, operator, right$expr)\n tokens <- right$tokens\n }\n\n # return\n list(expr = expr, tokens = tokens)\n}\n\nparse_factor <- function(tokens) {\n #cat(\"parse_factor\\n\")\n left <- parse_unary(tokens)\n expr <- left$expr\n tokens <- left$tokens\n\n # get next token\n while (tokens[[1]]$type %in% c(token_type$SLASH, token_type$STAR)) {\n operator <- tokens[[1]]\n right <- parse_unary(tokens[-1])\n\n expr <- expr_binary(expr, operator, right$expr)\n tokens <- right$tokens\n }\n\n list(expr = expr, tokens = tokens)\n}\n\nparse_unary <- function(tokens) {\n #cat(\"parse_unary\\n\")\n\n # check for unary operators\n if (tokens[[1]]$type %in% c(token_type$BANG, token_type$MINUS)) {\n operator <- tokens[[1]]\n right <- parse_unary(tokens[-1])\n\n expr <- expr_unary(operator, right$expr)\n tokens <- right$tokens\n return(list(expr = expr, tokens = tokens))\n }\n\n # parse as primary expression\n #parse_primary(tokens)\n parse_call(tokens)\n}\n\nparse_call <- function(tokens) {\n p <- parse_primary(tokens)\n expr <- p$expr\n tokens <- p$tokens\n\n while (TRUE) {\n if (is_type(tokens[[1]], token_type$LEFT_PAREN)) {\n p <- parse_call_args(expr, tokens[-1])\n expr <- p$expr\n tokens <- p$tokens\n } else {\n break\n }\n }\n\n list(expr = expr, tokens = tokens)\n}\n\nparse_call_args <- function(callee, tokens) {\n # initialize arguments list\n arguments <- list()\n if (!is_type(tokens[[1]], token_type$RIGHT_PAREN)) {\n while(TRUE) {\n # check argument size\n if (length(arguments) >= 255) {\n lox_parser_error(\"Call cannot have more than 255 arguments\", tokens[[1]]$line)\n }\n\n # parse argument\n p <- parse_expression(tokens)\n expr <- p$expr\n tokens <- p$tokens\n\n # add to arguments list\n arguments <- c(arguments, list(expr))\n\n # check if done\n if (!is_type(tokens[[1]], token_type$COMMA)) break\n tokens <- tokens[-1]\n }\n }\n\n # extract closing paren\n paren <- tokens[[1]]\n tokens <- consume(tokens, token_type$RIGHT_PAREN)\n\n # build call expression and return\n expr <- expr_call(callee, paren, arguments)\n list(expr = expr, tokens = tokens)\n}\n\nparse_primary <- function(tokens) {\n #cat(\"parse_primary\\n\")\n # get next token\n type <- tokens[[1]]$type\n\n # check for literal values\n if (type == token_type$`FALSE`) {\n expr <- expr_literal(FALSE)\n return(list(expr = expr, tokens = tokens[-1]))\n }\n\n if (type == token_type$`TRUE`) {\n expr <- expr_literal(TRUE)\n return(list(expr = expr, tokens = tokens[-1]))\n }\n\n if (type == token_type$NIL) {\n expr <- expr_literal(\"nil\")\n return(list(expr = expr, tokens = tokens[-1]))\n }\n\n if (type %in% c(token_type$NUMBER, token_type$STRING)) {\n expr <- expr_literal(tokens[[1]]$literal)\n return(list(expr = expr, tokens = tokens[-1]))\n }\n\n if (type == token_type$IDENTIFIER) {\n expr <- expr_variable(tokens[[1]])\n return(list(expr = expr, tokens = tokens[-1]))\n }\n\n # check for parenthesis\n if (type == token_type$LEFT_PAREN) {\n r <- parse_expression(tokens[-1])\n tokens <- r$tokens\n expr <- expr_grouping(r$expr)\n\n # check for closing paren\n tokens <- consume(tokens, token_type$RIGHT_PAREN)\n\n # return\n return(list(expr = expr, tokens = tokens))\n }\n\n # otherwise no idea\n token <- tokens[[1]]\n lox_parser_error(sprintf(\"cannot parse token `%s`\", token$lexeme), token$line)\n}\n", "meta": {"hexsha": "12f23729e617b45003e3263fe159b72c972269ee", "size": 5908, "ext": "r", "lang": "R", "max_stars_repo_path": "R/parse-expressions.r", "max_stars_repo_name": "zamorarr/rlox", "max_stars_repo_head_hexsha": "cf11b213d072a7a3c9ccd46921640c8123ec6c91", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/parse-expressions.r", "max_issues_repo_name": "zamorarr/rlox", "max_issues_repo_head_hexsha": "cf11b213d072a7a3c9ccd46921640c8123ec6c91", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/parse-expressions.r", "max_forks_repo_name": "zamorarr/rlox", "max_forks_repo_head_hexsha": "cf11b213d072a7a3c9ccd46921640c8123ec6c91", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.4444444444, "max_line_length": 120, "alphanum_fraction": 0.6313473257, "num_tokens": 1610, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.28140560742914383, "lm_q2_score": 0.025178840557642416, "lm_q1q2_score": 0.007085466921484927}}
{"text": "# Yuan Yuan, 2015-03-11\n# weighted test agains continuous response variables, such as gene expression\n\n\n# drawback: the ylabel of the inversed plot is negative, can be edited in AI\nlibrary(ggplot2)\nlibrary(grid)\nlibrary(gridExtra)\nlibrary(gtable)\nhist.mirror2 <- function(data.plot, label)\n {\n myplot <- ggplot(data.plot, aes(ps, fill=status))+geom_histogram(data=subset(data.plot, status==\"Yes\"), aes(ps, y=..count..))+\n geom_histogram(data=subset(data.plot,status==\"No\"), aes(ps, y= -..count..))+\n scale_fill_hue(label)+\n labs(x=\"Propensity Score\")\n print(myplot)\n }\n\n\nweight.test <- function(data,form, molecular.pri, is.continuous=TRUE, weight=\"ATT\", mirror.plot=FALSE,cancer, data.type, outdir=\".\", perm=FALSE, seed=seed)\n { \n rownames(molecular.pri) <- gsub(\"\\\\.\",\"\\\\-\",rownames(molecular.pri)) \n common <- intersect(rownames(data), rownames(molecular.pri))\n print(paste(\"Number of samples:\", length(common)))\n \n molecular.common <- molecular.pri[match(common, rownames(molecular.pri)),]\n clinical.common <- data[match(common, rownames(data)),]\n print(summary(factor(clinical.common$Z)))\n # write sample info to file\n write.table(t(rbind(rownames(clinical.common),ifelse(clinical.common$Z==1, \"High_Autophagy\",\"Low_Autophagy\"))), file=paste(cancer,\"_\",data.type,\"_samples.txt\", sep=\"\"),quote = F,row.names = F,sep=\"\\t\")\n \n print(\"----------------\")\n\n age.cutoff <- 65\n print(paste(\"age <\", age.cutoff,\":\", length(which(clinical.common$age_at_initial_pathologic_diagnosis< age.cutoff))))\n print(paste(\"age >=\", age.cutoff,\":\", length(which(clinical.common$age_at_initial_pathologic_diagnosis>= age.cutoff))))\n print(\"----------------\")\n \n\n if(perm)\n {\n n <- nrow(clinical.common)\n set.seed(seed)\n perm <- sample(1:n,n)\n clinical.common$Z <- clinical.common$Z[perm]\n }\n \n print(paste(\"Weighting scheme:\", weight))\n source(\"/home/cliu18/liucj/github/RstudioWithGit/autophagy_codes/13_afh_vs_afl/yy_code/GIPW_function_omega.R\")\n ans <- GIPW.std.omega(dat=clinical.common, form.ps=form, weight=weight,trt.est=F)\n # draw mirror plot\n\n if(mirror.plot)\n {\n ps <- ans$ps\n status <- ifelse(clinical.common$Z==1, \"Yes\",\"No\")\n data.plot <- data.frame(ps, status )\n pdf(paste(outdir,\"/\",cancer,\"_\", data.type, ifelse(perm,paste(\"_perm_\",seed, sep=\"\"), \"\"),\"_mirror_raw.pdf\",sep=\"\"))\n if(analysis==\"omt\")\n {\n label=\"OMT\"\n }\n if(analysis==\"gender\")\n {\n label=\"Female\"\n }\n if(analysis==\"race\")\n {\n label=\"Non-White\"\n }\n if(analysis==\"myclusters\"){\n label = \"High_Autophagy\"\n }\n print(hist.mirror2(data.plot, label))\n dev.off()\n }\n wt <- ans$W\n source(\"/home/cliu18/liucj/github/RstudioWithGit/autophagy_codes/13_afh_vs_afl/yy_code/check_balance.r\")\n index.tr <- which(clinical.common$Z==1)\n index.ctr <- which(clinical.common$Z==0)\n\n cutoff=0.1 # 10%\n for (i in 2:(ncol(clinical.common)-1))\n {\n print(paste(\"check\", colnames(clinical.common)[i]))\n if(nrow(clinical.common[which(clinical.common[,i]==1),]) >=3){\n #print(summary(clinical.common[,i]))\n std.diff <- check.balance(index.tr, index.ctr, clinical.common[,i], wt, printout=TRUE)\n if(std.diff>cutoff)\n {\n print(paste(\"Fail: standardized difference= \", std.diff))\n print(\"=======================================\")\n # stop()\n }else{\n print(\"Pass!\")\n }\n }else{\n print(\"too small sample size\")\n }\n \n \n }\n\n \n molecular.pvalues <- c()\n molecular.coefs <- c()\n molecular.0 <- c()\n molecular.1 <- c()\n molecular.0.w <- c()\n molecular.1.w <- c()\n for (i in 1:ncol(molecular.common))\n {\n tmp <- sapply(split(as.numeric(molecular.common[,i]), clinical.common$Z), mean,na.rm=T)\n molecular.0 <- c(molecular.0, tmp[1])\n molecular.1 <- c(molecular.1, tmp[2])\n # get weighted mean\n molecular.0.w <- c(molecular.0.w, sum(as.numeric(molecular.common[index.ctr,i])*wt[index.ctr],na.rm=T)/sum(wt[index.ctr],na.rm=T))\n molecular.1.w <- c(molecular.1.w, sum(as.numeric(molecular.common[index.tr,i])*wt[index.tr],na.rm=T)/sum(wt[index.tr],na.rm=T))\n #print(i)\n if (i%%1000==0)\n {\n print(i)\n }\n \n if(is.continuous)\n {\n #pvalue <- try(summary(lm(clinical.common$Z~molecular.common[,i], weights=wt))$coef[2,4])\n pvalue <- try(summary(lm(molecular.common[,i]~clinical.common$Z, weights=wt))$coef[2,4])\n coef <- try(summary(lm(molecular.common[,i]~clinical.common$Z, weights=wt))$coef[2,1])\n \n if (class(pvalue)==\"try-error\")\n {\n pvalue <- NA\n coef <- NA\n }\n }else{\n \n #pvalue <- summary(glm(clinical.common$Z~molecular.common[,i], family=binomial, weights=wt))$coef[2,4]\n pvalue <- summary(glm(as.numeric(molecular.common[,i])~clinical.common$Z, family=binomial, weights=wt),na.rm=T)$coef[2,4]\n coef <- summary(glm(as.numeric(molecular.common[,i])~clinical.common$Z, family=binomial, weights=wt),na.rm=T)$coef[2,1]\n if (class(pvalue)==\"try-error\")\n {\n pvalue <- NA\n coef <- NA\n }\n }\n molecular.pvalues <- c(molecular.pvalues, pvalue)\n molecular.coefs <- c(molecular.coefs, coef)\n }\n molecular.fdr <- p.adjust(molecular.pvalues,\"fdr\")\n return(list(feature=colnames(molecular.common),coef=molecular.coefs, pvalue=molecular.pvalues,fdr=molecular.fdr , mean.0= molecular.0, mean.1= molecular.1, mean.0.w=molecular.0.w, mean.1.w=molecular.1.w))\n }\n\nsummarize.fdr <- function(molecular.data, molecular.result, cutoff=0.05, print=FALSE )\n {\n pvalue <- molecular.result$pvalue\n fdr <- molecular.result$fdr\n coef <- molecular.result$coef\n mean.0 <- molecular.result$mean.0\n mean.1 <- molecular.result$mean.1\n mean.0.w <- molecular.result$mean.0.w\n mean.1.w <- molecular.result$mean.1.w\n signif.index <- which(fdr0)\n {\n stop(\"Some protein coding genes are not found. Double check!\")\n }\n print(paste(\"After remove non-coding, total genes:\", length(keep.index)))\n ##print(paste(length(rm),\"columns were removed because of non-coding.\"))\n\n molecular.data <- molecular.data[,keep.index]\n }\n \n # remove non-variable genes, i.e., sd=0\n zero.col <- which(apply(molecular.data, 2, sd)==0)\n if (length(zero.col)>0)\n {\n molecular.data <- molecular.data[,-zero.col]\n print(paste(length(zero.col),\"columns were removed because of no variation.\"))\n }\n \n return(molecular.data)\n }\n\nwrite.summary <- function(summary, cancer, analysis, type)\n {\n file <- paste(cancer,\"_\",analysis,\"_summary_\",type,\".txt\", sep=\"\")\n data <- data.frame(summary$feature.sig, summary$fdr.sig, summary$coef.sig, summary$mean0.sig, summary$mean1.sig, summary$mean0.sig.w, summary$mean1.sig.w)\n if (analysis==\"gender\")\n {\n colnames(data) <- c(\"feature\",\"fdr\",\"coef\",\"mean_MALE\", \"mean_FEMALE\", \"mean_MALE_weighted\", \"mean_FEMALE_weighted\")\n }\n if(analysis==\"race\")\n {\n colnames(data) <- c(\"feature\",\"fdr\",\"coef\",\"mean_nonWHITE\", \"mean_WHITE\",\"mean_nonWHITE_weighted\", \"mean_WHITE_weighted\")\n }\n if (analysis==\"omt\")\n {\n colnames(data) <- c(\"feature\",\"fdr\",\"coef\",\"mean_nonOMT\", \"mean_OMT\",\"mean_nonOMT_weighted\", \"mean_OMT_weighted\")\n }\n write.table(data,file=file, quote=FALSE, sep=\"\\t\", col.names=TRUE, row.names=FALSE)\n }\n\nwrite.result <- function(result, cancer, analysis, type, fdr.cutoff=0.05)\n {\n file <- paste(cancer,\"_\",analysis,\"_result_\",type,\".txt\", sep=\"\")\n data <- data.frame(result$feature, result$pvalue, result$fdr, result$coef, result$mean.0, result$mean.1, result$mean.0.w, result$mean.1.w)\n if (analysis==\"gender\")\n {\n colnames(data) <- c(\"feature\",\"pvalue\",\"fdr\",\"coef\",\"mean_MALE\", \"mean_FEMALE\", \"mean_MALE_weighted\", \"mean_FEMALE_weighted\")\n }\n if(analysis==\"race\")\n {\n colnames(data) <- c(\"feature\",\"fdr\",\"coef\",\"mean_nonWHITE\", \"mean_WHITE\",\"mean_nonWHITE_weighted\", \"mean_WHITE_weighted\")\n }\n if (analysis==\"omt\")\n {\n colnames(data) <- c(\"feature\",\"fdr\",\"coef\",\"mean_nonOMT\", \"mean_OMT\",\"mean_nonOMT_weighted\", \"mean_OMT_weighted\")\n }\n write.table(data,file=file, quote=FALSE, sep=\"\\t\", col.names=TRUE, row.names=FALSE) \n }\n\n\nplot.perm <- function(perm, n, cancer, analysis, type, cutoff)\n { \n p=length(which(perm>= n))/length(perm)\n print(paste(\"Permutation P-value =\", p))\n print(paste(type,\": n.sig =\", n))\n print(paste(\"Median perm n.sig =\", median(perm)))\n file <- paste(cancer,\"_\",analysis,\"_perm_\",type,\"_\",cutoff,\".pdf\", sep=\"\")\n pdf(file, width=3, height=3)\n par(mgp=c(2,1,0))\n if(type==\"mut\") {main=\"Mutation\"}\n if(type==\"cnv\") {main=\"SCNA\"}\n if(type==\"methy\") {main=\"Methy\"}\n if(type==\"mRNAseq\") {main=\"mRNA\"}\n if(type==\"miRNA\") {main=\"miRNA\"}\n if(type==\"rppa\") {main=\"Protein\"}\n if (n > max(perm))\n {\n myhist <- hist(perm, xlim=c(0, max(max(perm),n)), main=\"\", xlab=\"# Genes\" )\n \n }else{\n myhist <- hist(perm, main=\"\", xlab=\"# Genes\")\n }\n text(n, max(myhist$counts),paste(\"p-value =\", p), pos=ifelse(n> 0.5* max(perm),2,4), col=ifelse(p<=0.05, \"red\",\"blue\"), cex=1.1, xpd=T)\n abline(v=n, col=ifelse(p<=0.05, \"red\",\"blue\"), lty=2, lwd=2)\n dev.off()\n }\n\nperm.cal <- function(cancer, analysis, type, molecular.pri, cutoff=0.05, seedV=1:100)\n {\n print(paste(\"Calculate permutation for\", cancer,\":\",type,\":\"))\n # load(file=paste(cancer,\"_\", analysis,\"_result.RData\",sep=\"\"))\n result <- get(paste(type,\".result\", sep=\"\"))\n summary <- summarize.fdr(molecular.pri, result, cutoff=cutoff )\n n <- summary$n.sig\n perm <- c()\n print(\"For permutation:\")\n for (seed in seedV)\n {\n ##print(seed)\n perm.result <- perm.summary <- c()\n if(type==\"mut\")\n {\n load(paste(scripts.dir, \"/\",cancer,\"_\",analysis,\"/perm_sig_result_\",seed,\".RData\", sep=\"\"))\n }else if(type==\"pre\")\n {\n load(paste(scripts.dir, \"/\",cancer,\"_\",analysis,\"/perm_sig_result_pre_\",seed,\".RData\", sep=\"\"))\n }else{\n \n load(paste(scripts.dir, \"/\",cancer,\"_\",analysis,\"/perm_sig_result_\",seed,\".RData\", sep=\"\"))\n }\n perm.result <- get(paste(\"perm.\",type,\".result\", sep=\"\"))\n perm.summary <- summarize.fdr(molecular.pri, perm.result, cutoff=cutoff )\n perm <- c(perm, perm.summary$n.sig)\n do.call(rm, list(paste(\"perm.\",type,\".result\", sep=\"\")))\n \n }\n ##print (perm)\n ##print (n)\n plot.perm(perm, n, cancer, analysis, type, cutoff)\n }\n\n# only for mRNAseq, only write the protein coding genes, adjust fdr accordingly\nkeep.coding <- function(mRNAseq.result,cancer)\n {\n genes.raw <- mRNAseq.result$feature\n genes <- unlist(lapply(genes.raw, function(x) unlist(strsplit(x,\"[|]\"))[1]))\n ids <- as.numeric(unlist(lapply(genes.raw, function(x) unlist(strsplit(x,\"[|]\"))[2])))\n gene.data <- read.table(\"/rsrch1/bcb/yyuan3/extraspace/yyuan3/Lab_share/protein_coding_gene_annotation.tsv\",header=TRUE, sep=\"\\t\", quote=\"\")\n name.id <- paste(gene.data$Gene, gene.data$ID, sep=\"|\")\n keep.index <- match(gene.data$ID, ids)\n print(paste(\"After remove non-coding, total genes:\", length(keep.index)))\n out <- data.frame(mRNAseq.result)[keep.index,]\n #out$feature <- as.vector(out$feature)\n out$fdr <- p.adjust(out$pvalue, \"fdr\")\n colnames(out) <- c(\"feature\",\"coef\",\"pvalue\",\"fdr\",\"mean_MALE\", \"mean_FEMALE\", \"mean_MALE_weighted\", \"mean_FEMALE_weighted\")\n write.table(out, file=paste(cancer,\"_gender_result_mRNAseq_coding.txt\", sep=\"\"), sep=\"\\t\", quote=FALSE,row.names=FALSE)\n \n \n }\n\n## t-test\nmyttest <- function(data, molecular.pri, cancer, data.type, perm=FALSE, seed=seed)\n {\n \n common <- intersect(rownames(data), rownames(molecular.pri))\n print(paste(\"Number of samples:\", length(common)))\n molecular.common <- molecular.pri[match(common, rownames(molecular.pri)),]\n clinical.common <- data[match(common, rownames(data)),]\n print(summary(factor(clinical.common$Z)))\n print(\"----------------\")\n\n if(perm)\n {\n n <- nrow(clinical.common)\n set.seed(seed)\n perm <- sample(1:n,n)\n clinical.common$Z <- clinical.common$Z[perm]\n }\n \n molecular.pvalues <- c()\n molecular.coefs <- c()\n molecular.0 <- c()\n molecular.1 <- c()\n for (i in 1:ncol(molecular.common))\n {\n tmp <- by(molecular.common[,i], clinical.common$Z, mean)\n molecular.0 <- c(molecular.0, tmp[1])\n molecular.1 <- c(molecular.1, tmp[2])\n if (i%%1000==0)\n {\n print(i)\n }\n result <- try(t.test(molecular.common[,i]~clinical.common$Z))\n if (class(result)==\"try-error\")\n {\n pvalue <- coef <- NA\n }else{\n \n pvalue <- result$p.value\n coef <- result$statistic\n }\n molecular.pvalues <- c(molecular.pvalues, pvalue)\n molecular.coefs <- c(molecular.coefs, coef)\n }\n molecular.fdr <- p.adjust(molecular.pvalues,\"fdr\")\n return(list(feature=colnames(molecular.common),coef=molecular.coefs, pvalue=molecular.pvalues,fdr=molecular.fdr , mean.0= molecular.0, mean.1= molecular.1))\n \n }\n\nformat.barcode <- function(samples)\n {\n \n samples.new <- unlist(lapply(samples, function(x) paste(unlist(strsplit(x, \"-\"))[1:3], collapse=\"-\")))\n types <- unlist(lapply(samples, function(x) paste(unlist(strsplit(x, \"-\"))[4], collapse=\"-\")))\n print(summary(as.factor(types)))\n types[substr(types, 1,2)==\"01\" | substr(types, 1,2)==\"03\"] <- \"primary\"\n types[substr(types, 1,2)==\"02\"] <- \"recurrent\"\n types[substr(types, 1,2)==\"06\"] <- \"metastatic\"\n types[substr(types, 1,2)==\"11\"| substr(types, 1,2)==\"10\"] <- \"normal\"\n return(list(barcodes=samples.new, types=types))\n \n }\n\nformat.mature <- function(mature.raw)\n {\n samples <- format.barcode(rownames(mature.raw))\n ## only primary cancer will be used for downstream analysis\n ## KIRC has two 05: additinoal- new primary\n mature.pri <- mature.raw[samples$types==\"primary\",,drop=FALSE]\n mature.rec <- mature.raw[samples$types==\"recurrent\",,drop=FALSE]\n mature.met <- mature.raw[samples$types==\"metastatic\",,drop=FALSE]\n mature.normal <- mature.raw[samples$types==\"normal\",,drop=FALSE]\n rownames(mature.pri) <- samples$barcodes[samples$type==\"primary\"]\n rownames(mature.rec) <- samples$barcodes[samples$type==\"recurrent\"]\n rownames(mature.met) <- samples$barcodes[samples$type==\"metastatic\"]\n rownames(mature.normal) <- samples$barcodes[samples$type==\"normal\"]\n print(paste(\"Samples with mature: \", nrow(mature.pri)))\n print(paste(\"Samples with normal mature: \", nrow(mature.normal)))\n ## fix NA\n min.offset <- 2^min(mature.pri[-which(is.na(mature.pri))])/10\n #min.offset <- 10^-6\n mature.pri <- 2^mature.pri\n mature.pri[is.na(mature.pri)] <- 0\n mature.pri <- log2(mature.pri+min.offset)\n # remove features with no variation\n zero.col <- which(apply(mature.pri, 2, sd)==0)\n if(length(zero.col)>0)\n {\n mature.pri <- mature.pri[,-zero.col]\n print(paste(\"features removed because of no variation:\", length(zero.col)))\n }\n return(mature.pri)\n }\n\n\nformat.pre <- function(pre.raw)\n {\n samples <- format.barcode(rownames(pre.raw))\n ## only primary cancer will be used for downstream analysis\n ## KIRC has two 05: additinoal- new primary\n pre.pri <- pre.raw[samples$types==\"primary\",,drop=FALSE]\n pre.rec <- pre.raw[samples$types==\"recurrent\",,drop=FALSE]\n pre.met <- pre.raw[samples$types==\"metastatic\",,drop=FALSE]\n pre.normal <- pre.raw[samples$types==\"normal\",,drop=FALSE]\n rownames(pre.pri) <- samples$barcodes[samples$type==\"primary\"]\n rownames(pre.rec) <- samples$barcodes[samples$type==\"recurrent\"]\n rownames(pre.met) <- samples$barcodes[samples$type==\"metastatic\"]\n rownames(pre.normal) <- samples$barcodes[samples$type==\"normal\"]\n print(paste(\"Samples with pre: \", nrow(pre.pri)))\n print(paste(\"Samples with normal pre: \", nrow(pre.normal)))\n ## fix NA\n min.offset <- min(pre.pri[pre.pri!=0])/10\n #min.offset <- 2^min(pre.pri[-which(is.na(pre.pri))])/10\n #min.offset <- 10^-6\n #pre.pri <- 2^pre.pri\n #pre.pri[is.na(pre.pri)] <- 0\n pre.pri <- log2(pre.pri+min.offset)\n # remove features with no variation\n zero.col <- which(apply(pre.pri, 2, sd)==0)\n if(length(zero.col)>0)\n {\n pre.pri <- pre.pri[,-zero.col]\n print(paste(\"features removed because of no variation:\", length(zero.col)))\n }\n return(pre.pri)\n }\n", "meta": {"hexsha": "5761bc06e2107ab6c867bbad1f0f2e7e01ad1934", "size": 21336, "ext": "r", "lang": "R", "max_stars_repo_path": "13.afh-vs-afl/yy_code/cal.r", "max_stars_repo_name": "zhangyupisa/autophagy-in-cancer", "max_stars_repo_head_hexsha": "7510611e742ba451c9741ff3ca767c84bfce66f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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{"text": "\ncode 'G11'\nname '流动性比例监测表'\ndimensions {\n\trowdim {\n\t\tdummy true\n hierarchical true\n headModels ([\n [id:101,name:'现金',parentId:1],[id:102,name:'黄金',parentId:1],[id:103,name:'超额准备金',parentId:1],\n [id:104,name:'一个月内同行业往来',parentId:1],[id:105,name:'一个月内应收利息',parentId:1],[id:106,name:'一个月内合格贷款',parentId:1],\n [id:107,name:'一个月内债券投资',parentId:1],[id:108,name:'变现证券透支',parentId:1],[id:109,name:'可变现资产',parentId:1],\n [id:110,name:'流动性资产总和',parentId:1],\n [id:201,name:'活期存款',parentId:2],[id:202,name:'一个月内定期存款',parentId:2],[id:203,name:'一个月同业往来款项',parentId:2],\n [id:204,name:'一个月内已发行债券',parentId:2],[id:205,name:'一个月内应付利息',parentId:2],[id:206,name:'一个月内中央银行借款',parentId:2],\n [id:207,name:'一个月内其他负债',parentId:2],[id:208,name:'流动性负债总和',parentId:2], \n [id:1,name:'流动性资产'],[id:2,name:'流动性负债'],[id:3,name:'流动性比例'],[id:4,name:'本月平均流动性资产'],\n [id:5,name:'本月平均流动性负债'],[id:6,name:'本月平均流动性比列'],\n [id:7,name:'月度最低流动性比例'],[id:8,name:'一个月到期存款金额'],[id:9,name:'用于质押的有关贷款金额']\n ]) \n }\n\t\n currency {\n text '余额'\n\t\tloadErate true\n\t\tstaticHeads {\n\t\t\thead {\n\t\t\t\tmodel ([code:'01',name:'人民币'])\n\t\t\t}\n\t\t\thead {\n\t\t\t\tderived true\n\t\t\t\taggregate true\n\t\t\t\tmodel ([code:'foreign',name:'外币折人民币'])\n\t\t\t\taggregator {curvs->\n\t\t\t\t\tcurrencyExchange(curvs,'01')\n\t\t\t\t}\n\t\t\t}\n\t\t\thead {\n\t\t\t\tderived true\n\t\t\t\taggregate true\n\t\t\t\tmodel ([code:'sum',name:'本外币合计'])\n\t\t\t\tformula {\n\t\t\t\t\t111111111\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n}\n\ndataRequest {\n table 'glAllBranch'\n params {\n date 'context[]'\n }\n}\n\ndataRequest {\n table 'cfLiquid',allDimensions: ['currency','item','date']\n fields 'sum(balance) value'\n groupFields 'subject_number,currency_code'\n params {\n date 'context[]'\n\t\tcurrency 'context[]'\n staticModel 'context[]'\n liquidGapTerm cw({[\"<=\",context['liquidGapDays']]})\n }\n}\n//手工维护\ndataRequest ManualDataRequest\n\n\ndataGrids {\n\tapply sel([rowdim:101,currency:['01','foreign']]), {\n params ([item:'S1001',bal:'debit',dataRequest:'glAllBranch'])\n\t}\n\tapply sel([rowdim:102,currency:['01','foreign']]), {\n params ([item:'S1431',bal:'debit',dataRequest:'glAllBranch'])\n\t}\n\tapply sel([rowdim:103,currency:['01','foreign']]), {\n\t\tformula {\n v([item:\"S1002\",bal:'debit',dataRequest:'glAllBranch']) -\n 0.000 //应缴准备金余额\n }\n\t}\n\tapply sel([rowdim:104,currency:['01','foreign']]), {\n formula {sumItemsWithMinus(['S1011','S1012','S1013','S1014','S1031','S1111','1404'],['S1032','S2017','S2018','S2019','S2111','S2020'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:105,currency:['01','foreign']]), {\n\t\tformula {sumItems(['S1132','0220','0221'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:106,currency:['01','foreign']]), {\n\t\tformula {sumItems(['S1301','S1302','S1303','S1304','S1306'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:107,currency:['01','foreign']]), {\n\t\tformula {0.000}\n\t}\n\tapply sel([rowdim:108,currency:['01','foreign']]), {\n\t\tformula {0.000}\n\t}\n\tapply sel([rowdim:109,currency:['01','foreign']]), {\n formula {\n sumItems(['1003','1305','1811','1901'],[bal:'debit',dataRequest:'glAllBranch'])+\n mathPositive(v([item:\"0031\",bal:'debit',dataRequest:'glAllBranch']),v([item:\"2006\",bal:'credit',dataRequest:'glAllBranch']),0)+\n //下面是手工维护\n v0([dict:'rmblccp1',dataRequest:'manualData'])+\n v0([dict:'rmblccp2',dataRequest:'manualData'])+\n v0([dict:'rmblccp3',dataRequest:'manualData'])\n }\n\t}\n\tapply sel([rowdim:110,currency:['01','foreign']]), {\n\t\tformula {\n (-9..-1).sum{rowRel(it)}\n }\n\t} \n\tapply sel([rowdim:201,currency:['01','foreign']]), {\n formula {sumItems(['2001','2003','2005','1404'],[bal:'credit',dataRequest:'glAllBranch'])}\n\t}\n\tapply sel([rowdim:202,currency:['01','foreign']]), {\n\t\tformula {sumItems(['S2002','S2004','S2014'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:203,currency:['01','foreign']]), {\n formula{\n mathPositive(sumItems(['S2017','S2019','S2111'],[dataRequest:'cfLiquid']),sumItems(['S1011','S1013','S1111'],[dataRequest:'cfLiquid']),0)\n }\n\t}\n\tapply sel([rowdim:204,currency:['01','foreign']]), {\n\t\tformula {sumItems(['S2502'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:205,currency:['01','foreign']]), {\n\t\tformula {sumItems(['S2231','0450','1451','3002'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:206,currency:['01','foreign']]), {\n\t\tformula {sumItems(['2015'],[dataRequest:'cfLiquid'])}\n\t}\n\tapply sel([rowdim:207,currency:['01','foreign']]), {\n formula {\n sumItems(['S2007','S2011','SS2012','S2013','S2014','S2101','S2221','S2401','S2711','S2801','S2901','S3031','S30411','S30412','S30413','S30414','S30415','S30416','S30417','S30419','S3051','S3301'],[bal:'credit',dataRequest:'glAllBranch'])+\n mathPositive(sumItems(['S2006'],[bal:'credit',dataRequest:'glAllBranch']),sumItems(['0013'],[bal:'debit',dataRequest:'glAllBranch']),0)-\n sumItems(['S2014'],[dataRequest:'cfLiquid'])\n }\n\t}\n\tapply sel([rowdim:208,currency:['01','foreign']]), {\n\t\tformula {\n (-7..-1).sum{rowRel(it)}\n }\n\t}\n \n //第3列 求和\n apply sel([0..26,2]), {\n formula {colRel(-1)+colRel(-2)}\n\t}\n //设置这行的文本格式 百分比 3.流动性比例 (项目1.10/项目2.8×100％)\n apply sel([rowdim:3,currency:['01','foreign','sum']]),{\n // dataType new PercentType()\n dataType 'percent'\n }\n //3.流动性比例 (项目1.10/项目2.8×100％)\n\tapply sel([rowdim:3,currency:['01','foreign','sum']]),{\n\t\tformula {rowRel(-10)/rowRel(-1)}\n\t}\n //4.本月平均流动性资产\n\tapply sel([rowdim:4,currency:['01','foreign','sum']]),{//目前只是本期的\n\t\tformula {rowRel(-11)}\n\t}\n //5.本月平均流动性负债\n\tapply sel([rowdim:5,currency:['01','foreign','sum']]),{//目前只是本期的\n\t\tformula {rowRel(-3)}\n\t}\n //设置这行的文本格式 百分比 6.本月平均流动性比例(项目4./项目5.×100％)\n apply sel([rowdim:6,currency:['01','foreign','sum']]),{\n // dataType new PercentType()\n dataType 'percent'\n }\n //6.本月平均流动性比例(项目4./项目5.×100％)\n\tapply sel([rowdim:6,currency:['01','foreign','sum']]),{\n\t\tformula {(rowRel(-2)/rowRel(-1))}\n\t}\n //7.月度最低流动性比例（％）\n\tapply sel([24,0..2]), {\n\t\tformula {00000.0}\n\t}\n //8.一个月内到期用于质押的存款金额\n\tapply sel([25,0..2]), {\n\t\tformula {00000.0}\n\t}\n //9.项目8.用于质押的有关贷款金额\n\tapply sel([26,0..2]), {\n\t\tformula {00000.0}\n\t}\n\n //将1.流动性资产 2.流动性负债 所在行，为空 \n apply sel([rowdim:[1,2],currency:['01','foreign','sum']]), {\n\t\tformula {\n \n }\n\t}\n}", "meta": {"hexsha": "9ac17b0626d8112a43bd0cf724d5b92aa3a701df", "size": 6550, "ext": "rd", "lang": "R", "max_stars_repo_path": "demo-untidy/G11.rd", "max_stars_repo_name": "wushexu/jyreport", "max_stars_repo_head_hexsha": "7a4e2beec321aa3244e4ba0636066cd517a1c347", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2018-04-13T01:51:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-20T03:19:05.000Z", "max_issues_repo_path": "demo-untidy/G11.rd", "max_issues_repo_name": 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{"text": "\n\n\n\n\n\n\n CHAPTER 8\n\n\n Functions, Fclosures, and Macros\n\n\n\n\n\n\n 8.1. valid function objects\n\n There are many different objects which can occupy\n the function field of a symbol object. Table 8.1, on\n the following page, shows all of the possibilities,\n how to recognize them, and where to look for documen-\n tation.\n\n\n\n 8.2. functions\n\n The basic Lisp function is the lambda function.\n When a lambda function is called, the actual arguments\n are evaluated from left to right and are lambda-bound\n to the formal parameters of the lambda function.\n\n An nlambda function is usually used for functions\n which are invoked by the user at top level. Some\n built-in functions which evaluate their arguments in\n special ways are also nlambdas (e.g _\bc_\bo_\bn_\bd, _\bd_\bo, _\bo_\br).\n When an nlambda function is called, the list of\n unevaluated arguments is lambda bound to the single\n formal parameter of the nlambda function.\n\n Some programmers will use an nlambda function\n when they are not sure how many arguments will be\n passed. Then, the first thing the nlambda function\n does is map _\be_\bv_\ba_\bl over the list of unevaluated argu-\n ments it has been passed. This is usually the wrong\n thing to do, as it will not work compiled if any of\n the arguments are local variables. The solution is to\n use a lexpr. When a lexpr function is called, the\n arguments are evaluated and a fixnum whose value is\n the number of arguments is lambda-bound to the single\n formal parameter of the lexpr function. The lexpr can\n then access the arguments using the _\ba_\br_\bg function.\n\n When a function is compiled, _\bs_\bp_\be_\bc_\bi_\ba_\bl declarations\n may be needed to preserve its behavior. An argument\n is not lambda-bound to the name of the corresponding\n formal parameter unless that formal parameter has been\n declared _\bs_\bp_\be_\bc_\bi_\ba_\bl (see 12.3.2.2).\n\n\nFunctions, Fclosures, and Macros 8-1\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-2\n\n\n\n\n\n\u001b8_________________________________________________________________\n informal name object type documentation\n\u001b8_________________________________________________________________\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b_________________________________________________________________\n interpreted list with _\bc_\ba_\br 8.2\n lambda function _\be_\bq to lambda\n\u001b8_________________________________________________________________\n interpreted list with _\bc_\ba_\br 8.2\n nlambda function _\be_\bq to nlambda\n\u001b8_________________________________________________________________\n interpreted list with _\bc_\ba_\br 8.2\n lexpr function _\be_\bq to lexpr\n\u001b8_________________________________________________________________\n interpreted list with _\bc_\ba_\br 8.3\n macro _\be_\bq to macro\n\u001b8_________________________________________________________________\n fclosure vector with _\bv_\bp_\br_\bo_\bp 8.4\n _\be_\bq to fclosure\n\u001b8_________________________________________________________________\n compiled binary with discipline 8.2\n lambda or lexpr _\be_\bq to lambda\n function\n\u001b8_________________________________________________________________\n compiled binary with discipline 8.2\n nlambda function _\be_\bq to nlambda\n\u001b8_________________________________________________________________\n compiled binary with discipline 8.3\n macro _\be_\bq to macro\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n subroutine of \"subroutine\"[|\b-]\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n function of \"function\"[|\b-]\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n integer function of \"integer-function\"[|\b-]\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n real function of \"real-function\"[|\b-]\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n C function of \"c-function\"[|\b-]\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n double function of \"double-c-function\"[|\b-]\n\u001b8_________________________________________________________________\n foreign binary with discipline 8.5\n structure function of \"vector-c-function\"[|\b-]\n\u001b8_________________________________________________________________\n array array object 9\n\u001b8_________________________________________________________________\n\u001b7\b|\b\u001b8|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9 |\b\u001b8|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9 |\b\u001b8|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9 |\b\u001b8|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9 Table 8.1\n\n____________________\n\u001b9 [|\b-]Only the first character of the string is significant (i.e \"s\"\nis ok for \"subroutine\")\n\n\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-3\n\n\n Lambda and lexpr functions both compile into a\n binary object with a discipline of lambda. However, a\n compiled lexpr still acts like an interpreted lexpr.\n\n\n\n 8.3. macros\n\n An important feature of Lisp is its ability to\n manipulate programs as data. As a result of this,\n most Lisp implementations have very powerful macro\n facilities. The Lisp language's macro facility can be\n used to incorporate popular features of the other\n languages into Lisp. For example, there are macro\n packages which allow one to create records (ala Pas-\n cal) and refer to elements of those records by the\n field names. The _\bs_\bt_\br_\bu_\bc_\bt package imported from Maclisp\n does this. Another popular use for macros is to\n create more readable control structures which expand\n into _\bc_\bo_\bn_\bd, _\bo_\br and _\ba_\bn_\bd. One such example is the If\n macro. It allows you to write\n\n (_\bI_\bf (_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b0) _\bt_\bh_\be_\bn (_\bp_\br_\bi_\bn_\bt '_\bz_\be_\br_\bo) (_\bt_\be_\br_\bp_\br)\n _\be_\bl_\bs_\be_\bi_\bf (_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b1) _\bt_\bh_\be_\bn (_\bp_\br_\bi_\bn_\bt '_\bo_\bn_\be) (_\bt_\be_\br_\bp_\br)\n _\be_\bl_\bs_\be (_\bp_\br_\bi_\bn_\bt '|_\bI _\bg_\bi_\bv_\be _\bu_\bp|))\n\n which expands to\n\n (_\bc_\bo_\bn_\bd\n ((_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b0) (_\bp_\br_\bi_\bn_\bt '_\bz_\be_\br_\bo) (_\bt_\be_\br_\bp_\br))\n ((_\be_\bq_\bu_\ba_\bl _\bn_\bu_\bm_\bb _\b1) (_\bp_\br_\bi_\bn_\bt '_\bo_\bn_\be) (_\bt_\be_\br_\bp_\br))\n (_\bt (_\bp_\br_\bi_\bn_\bt '|_\bI _\bg_\bi_\bv_\be _\bu_\bp|)))\n\n\n\n\n 8.3.1. macro forms\n\n A macro is a function which accepts a Lisp\n expression as input and returns another Lisp\n expression. The action the macro takes is called\n macro expansion. Here is a simple example:\n\n -> (_\bd_\be_\bf _\bf_\bi_\br_\bs_\bt (_\bm_\ba_\bc_\br_\bo (_\bx) (_\bc_\bo_\bn_\bs '_\bc_\ba_\br (_\bc_\bd_\br _\bx))))\n first\n -> (_\bf_\bi_\br_\bs_\bt '(_\ba _\bb _\bc))\n a\n -> (_\ba_\bp_\bp_\bl_\by '_\bf_\bi_\br_\bs_\bt '(_\bf_\bi_\br_\bs_\bt '(_\ba _\bb _\bc)))\n (car '(a b c))\n\n The first input line defines a macro called _\bf_\bi_\br_\bs_\bt.\n Notice that the macro has one formal parameter, _\bx.\n On the second input line, we ask the interpreter to\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-4\n\n\n evaluate (_\bf_\bi_\br_\bs_\bt '(_\ba _\bb _\bc)). _\bE_\bv_\ba_\bl sees that _\bf_\bi_\br_\bs_\bt\n has a function definition of type macro, so it\n evaluates _\bf_\bi_\br_\bs_\bt's definition, passing to _\bf_\bi_\br_\bs_\bt, as\n an argument, the form _\be_\bv_\ba_\bl itself was trying to\n evaluate: (_\bf_\bi_\br_\bs_\bt '(_\ba _\bb _\bc)). The _\bf_\bi_\br_\bs_\bt macro chops\n off the car of the argument with _\bc_\bd_\br, cons' a _\bc_\ba_\br\n at the beginning of the list and returns\n (_\bc_\ba_\br '(_\ba _\bb _\bc)), which _\be_\bv_\ba_\bl evaluates. The value _\ba\n is returned as the value of (_\bf_\bi_\br_\bs_\bt '(_\ba _\bb _\bc)). Thus\n whenever _\be_\bv_\ba_\bl tries to evaluate a list whose car\n has a macro definition it ends up doing (at least)\n two operations, the first of which is a call to the\n macro to let it macro expand the form, and the\n other is the evaluation of the result of the macro.\n The result of the macro may be yet another call to\n a macro, so _\be_\bv_\ba_\bl may have to do even more evalua-\n tions until it can finally determine the value of\n an expression. One way to see how a macro will\n expand is to use _\ba_\bp_\bp_\bl_\by as shown on the third input\n line above.\n\n\n\n 8.3.2. defmacro\n\n The macro _\bd_\be_\bf_\bm_\ba_\bc_\br_\bo makes it easier to define\n macros because it allows you to name the arguments\n to the macro call. For example, suppose we find\n ourselves often writing code like\n (_\bs_\be_\bt_\bq _\bs_\bt_\ba_\bc_\bk (_\bc_\bo_\bn_\bs _\bn_\be_\bw_\be_\bl_\bt _\bs_\bt_\ba_\bc_\bk). We could define a\n macro named _\bp_\bu_\bs_\bh to do this for us. One way to\n define it is:\n\n -> (_\bd_\be_\bf _\bp_\bu_\bs_\bh\n (_\bm_\ba_\bc_\br_\bo (_\bx) (_\bl_\bi_\bs_\bt '_\bs_\be_\bt_\bq (_\bc_\ba_\bd_\bd_\br _\bx) (_\bl_\bi_\bs_\bt '_\bc_\bo_\bn_\bs (_\bc_\ba_\bd_\br _\bx) (_\bc_\ba_\bd_\bd_\br _\bx)))))\n push\n\n then (_\bp_\bu_\bs_\bh _\bn_\be_\bw_\be_\bl_\bt _\bs_\bt_\ba_\bc_\bk) will expand to the form\n mentioned above. The same macro written using def-\n macro would be:\n\n -> (_\bd_\be_\bf_\bm_\ba_\bc_\br_\bo _\bp_\bu_\bs_\bh (_\bv_\ba_\bl_\bu_\be _\bs_\bt_\ba_\bc_\bk)\n (_\bl_\bi_\bs_\bt '_\bs_\be_\bt_\bq ,_\bs_\bt_\ba_\bc_\bk (_\bl_\bi_\bs_\bt '_\bc_\bo_\bn_\bs ,_\bv_\ba_\bl_\bu_\be ,_\bs_\bt_\ba_\bc_\bk)))\n push\n\n Defmacro allows you to name the arguments of the\n macro call, and makes the macro definition look\n more like a function definition.\n\n\n\n\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-5\n\n\n 8.3.3. the backquote character macro\n\n The default syntax for FRANZ LISP has four\n characters with associated character macros. One\n is semicolon for comments. Two others are the\n backquote and comma which are used by the backquote\n character macro. The fourth is the sharp sign\n macro described in the next section.\n\n The backquote macro is used to create lists\n where many of the elements are fixed (quoted). This\n makes it very useful for creating macro defini-\n tions. In the simplest case, a backquote acts just\n like a single quote:\n\n ->`(_\ba _\bb _\bc _\bd _\be)\n (a b c d e)\n\n If a comma precedes an element of a backquoted list\n then that element is evaluated and its value is put\n in the list.\n\n ->(_\bs_\be_\bt_\bq _\bd '(_\bx _\by _\bz))\n (x y z)\n ->`(_\ba _\bb _\bc ,_\bd _\be)\n (a b c (x y z) e)\n\n If a comma followed by an at sign precedes an ele-\n ment in a backquoted list, then that element is\n evaluated and spliced into the list with _\ba_\bp_\bp_\be_\bn_\bd.\n\n ->`(_\ba _\bb _\bc ,@_\bd _\be)\n (a b c x y z e)\n\n Once a list begins with a backquote, the commas may\n appear anywhere in the list as this example shows:\n\n ->`(_\ba _\bb (_\bc _\bd ,(_\bc_\bd_\br _\bd)) (_\be _\bf (_\bg _\bh ,@(_\bc_\bd_\bd_\br _\bd) ,@_\bd)))\n (a b (c d (y z)) (e f (g h z x y z)))\n\n It is also possible and sometimes even useful to\n use the backquote macro within itself. As a final\n demonstration of the backquote macro, we shall\n define the first and push macros using all the\n power at our disposal: defmacro and the backquote\n macro.\n\n ->(_\bd_\be_\bf_\bm_\ba_\bc_\br_\bo _\bf_\bi_\br_\bs_\bt (_\bl_\bi_\bs_\bt) `(_\bc_\ba_\br ,_\bl_\bi_\bs_\bt))\n first\n ->(_\bd_\be_\bf_\bm_\ba_\bc_\br_\bo _\bp_\bu_\bs_\bh (_\bv_\ba_\bl_\bu_\be _\bs_\bt_\ba_\bc_\bk) `(_\bs_\be_\bt_\bq ,_\bs_\bt_\ba_\bc_\bk (_\bc_\bo_\bn_\bs ,_\bv_\ba_\bl_\bu_\be ,_\bs_\bt_\ba_\bc_\bk)))\n stack\n\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-6\n\n\n 8.3.4. sharp sign character macro\n\n The sharp sign macro can perform a number of\n different functions at read time. The character\n directly following the sharp sign determines which\n function will be done, and following Lisp s-\n expressions may serve as arguments.\n\n\n\n 8.3.4.1. conditional inclusion\n\n If you plan to run one source file in more than\n one environment then you may want to some pieces\n of code to be included or not included depend-\n ing on the environment. The C language uses\n \"#ifdef\" and \"#ifndef\" for this purpose, and\n Lisp uses \"#+\" and \"#-\". The environment that\n the sharp sign macro checks is the\n (_\bs_\bt_\ba_\bt_\bu_\bs _\bf_\be_\ba_\bt_\bu_\br_\be_\bs) list which is initialized when\n the Lisp system is built and which may be\n altered by (_\bs_\bs_\bt_\ba_\bt_\bu_\bs _\bf_\be_\ba_\bt_\bu_\br_\be _\bf_\bo_\bo) and\n (_\bs_\bs_\bt_\ba_\bt_\bu_\bs _\bn_\bo_\bf_\be_\ba_\bt_\bu_\br_\be _\bb_\ba_\br) The form of conditional\n inclusion is\n _\b#_\b+_\bw_\bh_\be_\bn _\bw_\bh_\ba_\bt\n where _\bw_\bh_\be_\bn is either a symbol or an expression\n involving symbols and the functions _\ba_\bn_\bd, _\bo_\br, and\n _\bn_\bo_\bt. The meaning is that _\bw_\bh_\ba_\bt will only be read\n in if _\bw_\bh_\be_\bn is true. A symbol in _\bw_\bh_\be_\bn is true\n only if it appears in the (_\bs_\bt_\ba_\bt_\bu_\bs _\bf_\be_\ba_\bt_\bu_\br_\be_\bs)\n list.\n\n\n ____________________________________________________\n\n ; suppose we want to write a program which references a file\n ; and which can run at ucb, ucsd and cmu where the file naming conventions\n ; are different.\n ;\n -> (_\bd_\be_\bf_\bu_\bn _\bh_\bo_\bw_\bo_\bl_\bd (_\bn_\ba_\bm_\be)\n (_\bt_\be_\br_\bp_\br)\n (_\bl_\bo_\ba_\bd #+(_\bo_\br _\bu_\bc_\bb _\bu_\bc_\bs_\bd) \"/_\bu_\bs_\br/_\bl_\bi_\bb/_\bl_\bi_\bs_\bp/_\ba_\bg_\be_\bs._\bl\"\n #+_\bc_\bm_\bu \"/_\bu_\bs_\br/_\bl_\bi_\bs_\bp/_\bd_\bo_\bc/_\ba_\bg_\be_\bs._\bl\")\n (_\bp_\ba_\bt_\bo_\bm _\bn_\ba_\bm_\be)\n (_\bp_\ba_\bt_\bo_\bm \" _\bi_\bs \")\n (_\bp_\br_\bi_\bn_\bt (_\bc_\bd_\br (_\ba_\bs_\bs_\bo_\bc _\bn_\ba_\bm_\be _\ba_\bg_\be_\bf_\bi_\bl_\be)))\n (_\bp_\ba_\bt_\bo_\bm \"_\by_\be_\ba_\br_\bs _\bo_\bl_\bd\")\n (_\bt_\be_\br_\bp_\br))\n ____________________________________________________\n\n\n\nThe form\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-7\n\n\n _\b#_\b-_\bw_\bh_\be_\bn _\bw_\bh_\ba_\bt\nis equivalent to\n _\b#_\b+_\b(_\bn_\bo_\bt _\bw_\bh_\be_\bn_\b) _\bw_\bh_\ba_\bt\n\n\n\n 8.3.4.2. fixnum character equivalents\n\n When working with fixnum equivalents of charac-\n ters, it is often hard to remember the number\n corresponding to a character. The form\n _\b#_\b/_\bc\n is equivalent to the fixnum representation of\n character c.\n\n\n ____________________________________________________\n\n ; a function which returns t if the user types y else it returns nil.\n ;\n -> (_\bd_\be_\bf_\bu_\bn _\by_\be_\bs_\bo_\br_\bn_\bo _\bn_\bi_\bl\n (_\bp_\br_\bo_\bg_\bn (_\ba_\bn_\bs)\n (_\bs_\be_\bt_\bq _\ba_\bn_\bs (_\bt_\by_\bi))\n (_\bc_\bo_\bn_\bd ((_\be_\bq_\bu_\ba_\bl _\ba_\bn_\bs #/_\by) _\bt)\n (_\bt _\bn_\bi_\bl))))\n ____________________________________________________\n\n\n\n\n\n\n 8.3.4.3. read time evaluation\n\n Occasionally you want to express a constant as a\n Lisp expression, yet you don't want to pay the\n penalty of evaluating this expression each time\n it is referenced. The form\n _\b#_\b._\be_\bx_\bp_\br_\be_\bs_\bs_\bi_\bo_\bn\n evaluates the expression at read time and\n returns its value.\n\n\n\n\n\n\n\n\n\n\n\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-8\n\n\n\n ____________________________________________________\n\n ; a function to test if any of bits 1 3 or 12 are set in a fixnum.\n ;\n -> (_\bd_\be_\bf_\bu_\bn _\bt_\be_\bs_\bt_\bi_\bt (_\bn_\bu_\bm)\n (_\bc_\bo_\bn_\bd ((_\bz_\be_\br_\bo_\bp (_\bb_\bo_\bo_\bl_\be _\b1 _\bn_\bu_\bm #.(+ (_\bl_\bs_\bh _\b1 _\b1) (_\bl_\bs_\bh _\b1 _\b3) (_\bl_\bs_\bh _\b1 _\b1_\b2))))\n _\bn_\bi_\bl)\n (_\bt _\bt)))\n ____________________________________________________\n\n\n\n\n\n\n 8.4. fclosures\n\n Fclosures are a type of functional object. The\n purpose is to remember the values of some variables\n between invocations of the functional object and to\n protect this data from being inadvertently overwritten\n by other Lisp functions. Fortran programs usually\n exhibit this behavior for their variables. (In fact,\n some versions of Fortran would require the variables\n to be in COMMON). Thus it is easy to write a linear\n congruent random number generator in Fortran, merely\n by keeping the seed as a variable in the function. It\n is much more risky to do so in Lisp, since any special\n variable you picked, might be used by some other func-\n tion. Fclosures are an attempt to provide most of the\n same functionality as closures in Lisp Machine Lisp,\n to users of FRANZ LISP. Fclosures are related to clo-\n sures in this way:\n (fclosure '(a b) 'foo) <==>\n (let ((a a) (b b)) (closure '(a b) 'foo))\n\n\n\n 8.4.1. an example\n\n____________________________________________________________\n\n% lisp\nFranz Lisp, Opus 38.60\n->(defun code (me count)\n (print (list 'in x))\n (setq x (+ 1 x))\n (cond ((greaterp count 1) (funcall me me (sub1 count))))\n (print (list 'out x)))\ncode\n->(defun tester (object count)\n (funcall object object count) (terpri))\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-9\n\n\ntester\n->(setq x 0)\n0\n->(setq z (fclosure '(x) 'code))\nfclosure[8]\n-> (tester z 3)\n(in 0)(in 1)(in 2)(out 3)(out 3)(out 3)\nnil\n->x\n0\n____________________________________________________________\n\n\n\n\n\n The function _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be creates a new object\n that we will call an fclosure, (although it is\n actually a vector). The fclosure contains a func-\n tional object, and a set of symbols and values for\n the symbols. In the above example, the fclosure\n functional object is the function code. The set of\n symbols and values just contains the symbol `x' and\n zero, the value of `x' when the fclosure was\n created.\n\n When an fclosure is funcall'ed:\n\n 1) The Lisp system lambda binds the symbols in\n the fclosure to their values in the fclosure.\n\n 2) It continues the funcall on the functional\n object of the fclosure.\n\n 3) Finally, it un-lambda binds the symbols in the\n fclosure and at the same time stores the\n current values of the symbols in the fclosure.\n\n\n Notice that the fclosure is saving the value\n of the symbol `x'. Each time a fclosure is\n created, new space is allocated for saving the\n values of the symbols. Thus if we execute fclosure\n again, over the same function, we can have two\n independent counters:\n\n____________________________________________________________\n\n-> (setq zz (fclosure '(x) 'code))\nfclosure[1]\n-> (tester zz 2)\n(in 0)(in 1)(out 2)(out 2)\n-> (tester zz 2)\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-10\n\n\n(in 2)(in 3)(out 4)(out 4)\n-> (tester z 3)\n(in 3)(in 4)(in 5)(out 6)(out 6)(out 6)\n____________________________________________________________\n\n\n\n\n\n\n\n 8.4.2. useful functions\n\n Here are some quick some summaries of func-\n tions dealing with closures. They are more for-\n mally defined in 2.8.4. To recap, fclosures are\n made by (_\bf_\bc_\bl_\bo_\bs_\bu_\br_\be '_\bl__\bv_\ba_\br_\bs '_\bg__\bf_\bu_\bn_\bc_\bo_\bb_\bj). l_vars is a\n list of symbols (not containing nil), g_funcobj is\n any object that can be funcalled. (Objects which\n can be funcalled, include compiled Lisp functions,\n lambda expressions, symbols, foreign functions,\n etc.) In general, if you want a compiled function\n to be closed over a variable, you must declare the\n variable to be special within the function.\n Another example would be:\n\n (fclosure '(a b) #'(lambda (x) (plus x a)))\n\n Here, the #' construction will make the compiler\n compile the lambda expression.\n\n There are times when you want to share vari-\n ables between fclosures. This can be done if the\n fclosures are created at the same time using\n _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be-_\bl_\bi_\bs_\bt. The function _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be-_\ba_\bl_\bi_\bs_\bt returns\n an assoc list giving the symbols and values in the\n fclosure. The predicate _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be_\bp returns t iff\n its argument is a fclosure. Other functions\n imported from Lisp Machine Lisp are _\bs_\by_\bm_\be_\bv_\ba_\bl-_\bi_\bn-\n _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be, _\bl_\be_\bt-_\bf_\bc_\bl_\bo_\bs_\be_\bd, and _\bs_\be_\bt-_\bi_\bn-_\bf_\bc_\bl_\bo_\bs_\bu_\br_\be.\n Lastly, the function _\bf_\bc_\bl_\bo_\bs_\bu_\br_\be-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn returns the\n function argument.\n\n\n\n 8.4.3. internal structure\n\n Currently, closures are implemented as vec-\n tors, with property being the symbol fclosure. The\n functional object is the first entry. The remain-\n ing entries are structures which point to the sym-\n bols and values for the closure, (with a reference\n count to determine if a recursive closure is\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-11\n\n\n active).\n\n\n\n 8.5. foreign subroutines and functions\n\n FRANZ LISP has the ability to dynamically load\n object files produced by other compilers and to call\n functions defined in those files. These functions are\n called _\bf_\bo_\br_\be_\bi_\bg_\bn functions.* There are seven types of\n foreign functions. They are characterized by the type\n of result they return, and by differences in the\n interpretation of their arguments. They come from two\n families: a group suited for languages which pass\n arguments by reference (e.g. Fortran), and a group\n suited for languages which pass arguments by value\n (e.g. C).\n\n\n There are four types in the first group:\n\n subroutine\n This does not return anything. The Lisp system\n always returns t after calling a subroutine.\n\n function\n This returns whatever the function returns. This\n must be a valid Lisp object or it may cause the\n Lisp system to fail.\n\n integer-function\n This returns an integer which the Lisp system\n makes into a fixnum and returns.\n\n real-function\n This returns a double precision real number which\n the Lisp system makes into a flonum and returns.\n\n\n There are three types in the second group:\n\n c-function\n This is like an integer function, except for its\n different interpretation of arguments.\n\n\n____________________\n\u001b9 *This topic is also discussed in Report PAM-124 of the\nCenter for Pure and Applied Mathematics, UCB, entitled\n``Parlez-Vous Franz? An Informal Introduction to Interfac-\ning Foreign Functions to Franz LISP'', by James R. Larus\n\n\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-12\n\n\n double-c-function\n This is like a real-function.\n\n vector-c-function\n This is for C functions which return a structure.\n The first argument to such functions must be a\n vector (of type vectori), into which the result\n is stored. The second Lisp argument becomes the\n first argument to the C function, and so on\n\n A foreign function is accessed through a binary object\n just like a compiled Lisp function. The difference is\n that the discipline field of a binary object for a\n foreign function is a string whose first character is\n given in the following table:\n\n\n\u001b8 ____________________________\n letter type\n\u001b8 ____________________________\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b____________________________\n s subroutine\n\u001b8 ____________________________\n f function\n\u001b8 ____________________________\n i integer-function\n\u001b8 ____________________________\n r real-function.\n\u001b8 ____________________________\n c c-function\n\u001b8 ____________________________\n v vector-c-function\n\u001b8 ____________________________\n d double-c-function\n\u001b8 ____________________________\n\u001b7 |\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n |\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n |\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\b\u001b7|\n\n\n\n\n\n\n\n\n\n\n\n\n Two functions are provided for setting-up foreign\n functions. _\bC_\bf_\ba_\bs_\bl loads an object file into the Lisp\n system and sets up one foreign function binary object.\n If there are more than one function in an object file,\n _\bg_\be_\bt_\ba_\bd_\bd_\br_\be_\bs_\bs can be used to set up additional foreign\n function objects.\n\n Foreign functions are called just like other\n functions, e.g (_\bf_\bu_\bn_\bn_\ba_\bm_\be _\ba_\br_\bg_\b1 _\ba_\br_\bg_\b2). When a function\n in the Fortran group is called, the arguments are\n evaluated and then examined. List, hunk and symbol\n arguments are passed unchanged to the foreign func-\n tion. Fixnum and flonum arguments are copied into a\n temporary location and a pointer to the value is\n passed (this is because Fortran uses call by reference\n and it is dangerous to modify the contents of a fixnum\n or flonum which something else might point to). If\n the argument is an array object, the data field of the\n array object is passed to the foreign function (This\n is the easiest way to send large amounts of data to\n and receive large amounts of data from a foreign func-\n tion). If a binary object is an argument, the entry\n\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-13\n\n\n field of that object is passed to the foreign function\n (the entry field is the address of a function, so this\n amounts to passing a function as an argument).\n\n When a function in the C group is called, fixnum\n and flownum arguments are passed by value. For almost\n all other arguments, the address is merely provided to\n the C routine. The only exception arises when you\n want to invoke a C routine which expects a ``struc-\n ture'' argument. Recall that a (rarely used) feature\n of the C language is the ability to pass structures by\n value. This copies the structure onto the stack.\n Since the Franz's nearest equivalent to a C structure\n is a vector, we provide an escape clause to copy the\n contents of an immediate-type vector by value. If the\n property field of a vectori argument, is the symbol\n \"value-structure-argument\", then the binary data of\n this immediate-type vector is copied into the argument\n list of the C routine.\n\n The method a foreign function uses to access the\n arguments provided by Lisp is dependent on the\n language of the foreign function. The following\n scripts demonstrate how how Lisp can interact with\n three languages: C, Pascal and Fortran. C and Pascal\n have pointer types and the first script shows how to\n use pointers to extract information from Lisp objects.\n There are two functions defined for each language.\n The first (cfoo in C, pfoo in Pascal) is given four\n arguments, a fixnum, a flonum-block array, a hunk of\n at least two fixnums and a list of at least two fix-\n nums. To demonstrate that the values were passed,\n each ?foo function prints its arguments (or parts of\n them). The ?foo function then modifies the second\n element of the flonum-block array and returns a 3 to\n Lisp. The second function (cmemq in C, pmemq in Pas-\n cal) acts just like the Lisp _\bm_\be_\bm_\bq function (except it\n won't work for fixnums whereas the lisp _\bm_\be_\bm_\bq will work\n for small fixnums). In the script, typed input is in\n bold, computer output is in roman and comments are in\n _\bi_\bt_\ba_\bl_\bi_\bc.\n\n\n____________________________________________________________\n\n_\bT_\bh_\be_\bs_\be _\ba_\br_\be _\bt_\bh_\be _\bC _\bc_\bo_\bd_\be_\bd _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n% cat ch8auxc.c\n/* demonstration of c coded foreign integer-function */\n\n/* the following will be used to extract fixnums out of a list of fixnums */\nstruct listoffixnumscell\n{ struct listoffixnumscell *cdr;\n int *fixnum;\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-14\n\n\n};\n\nstruct listcell\n{ struct listcell *cdr;\n int car;\n};\n\ncfoo(a,b,c,d)\nint *a;\ndouble b[];\nint *c[];\nstruct listoffixnumscell *d;\n{\n printf(\"a: %d, b[0]: %f, b[1]: %f0, *a, b[0], b[1]);\n printf(\" c (first): %d c (second): %d0,\n *c[0],*c[1]);\n printf(\" ( %d %d ... ) \", *(d->fixnum), *(d->cdr->fixnum));\n b[1] = 3.1415926;\n return(3);\n}\n\nstruct listcell *\ncmemq(element,list)\nint element;\nstruct listcell *list;\n{\n for( ; list && element != list->car ; list = list->cdr);\n return(list);\n}\n\n\n_\bT_\bh_\be_\bs_\be _\ba_\br_\be _\bt_\bh_\be _\bP_\ba_\bs_\bc_\ba_\bl _\bc_\bo_\bd_\be_\bd _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs\n% cat ch8auxp.p\ntype pinteger = ^integer;\n realarray = array[0..10] of real;\n pintarray = array[0..10] of pinteger;\n listoffixnumscell = record\n cdr : ^listoffixnumscell;\n fixnum : pinteger;\n end;\n plistcell = ^listcell;\n listcell = record\n cdr : plistcell;\n car : integer;\n end;\n\nfunction pfoo ( var a : integer ;\n var b : realarray;\n var c : pintarray;\n var d : listoffixnumscell) : integer;\nbegin\n writeln(' a:',a, ' b[0]:', b[0], ' b[1]:', b[1]);\n writeln(' c (first):', c[0]^,' c (second):', c[1]^);\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-15\n\n\n writeln(' ( ', d.fixnum^, d.cdr^.fixnum^, ' ...) ');\n b[1] := 3.1415926;\n pfoo := 3\nend ;\n\n{ the function pmemq looks for the Lisp pointer given as the first argument\n in the list pointed to by the second argument.\n Note that we declare \" a : integer \" instead of \" var a : integer \" since\n we are interested in the pointer value instead of what it points to (which\n could be any Lisp object)\n}\nfunction pmemq( a : integer; list : plistcell) : plistcell;\nbegin\n while (list <> nil) and (list^.car <> a) do list := list^.cdr;\n pmemq := list;\nend ;\n\n\n_\bT_\bh_\be _\bf_\bi_\bl_\be_\bs _\ba_\br_\be _\bc_\bo_\bm_\bp_\bi_\bl_\be_\bd\n% cc -c ch8auxc.c\n1.0u 1.2s 0:15 14% 30+39k 33+20io 147pf+0w\n% pc -c ch8auxp.p\n3.0u 1.7s 0:37 12% 27+32k 53+32io 143pf+0w\n\n\n% lisp\nFranz Lisp, Opus 38.60\n_\bF_\bi_\br_\bs_\bt _\bt_\bh_\be _\bf_\bi_\bl_\be_\bs _\ba_\br_\be _\bl_\bo_\ba_\bd_\be_\bd _\ba_\bn_\bd _\bw_\be _\bs_\be_\bt _\bu_\bp _\bo_\bn_\be _\bf_\bo_\br_\be_\bi_\bg_\bn _\bf_\bu_\bn_\bc_\b-\n_\bt_\bi_\bo_\bn _\bb_\bi_\bn_\ba_\br_\by. _\bW_\be _\bh_\ba_\bv_\be _\bt_\bw_\bo _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\bs _\bi_\bn _\be_\ba_\bc_\bh _\bf_\bi_\bl_\be _\bs_\bo _\bw_\be _\bm_\bu_\bs_\bt\n_\bc_\bh_\bo_\bo_\bs_\be _\bo_\bn_\be _\bt_\bo _\bt_\be_\bl_\bl _\bc_\bf_\ba_\bs_\bl _\ba_\bb_\bo_\bu_\bt. _\bT_\bh_\be _\bc_\bh_\bo_\bi_\bc_\be _\bi_\bs _\ba_\br_\bb_\bi_\bt_\br_\ba_\br_\by.\n-> (cfasl 'ch8auxc.o '_cfoo 'cfoo \"integer-function\")\n/usr/lib/lisp/nld -N -A /usr/local/lisp -T 63000 ch8auxc.o -e _cfoo -o /tmp/Li7055.0 -lc\n#63000-\"integer-function\"\n-> (cfasl 'ch8auxp.o '_pfoo 'pfoo \"integer-function\" \"-lpc\")\n/usr/lib/lisp/nld -N -A /tmp/Li7055.0 -T 63200 ch8auxp.o -e _pfoo -o /tmp/Li7055.1 -lpc -lc\n#63200-\"integer-function\"\n_\bH_\be_\br_\be _\bw_\be _\bs_\be_\bt _\bu_\bp _\bt_\bh_\be _\bo_\bt_\bh_\be_\br _\bf_\bo_\br_\be_\bi_\bg_\bn _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn _\bb_\bi_\bn_\ba_\br_\by _\bo_\bb_\bj_\be_\bc_\bt_\bs\n-> (getaddress '_cmemq 'cmemq \"function\" '_pmemq 'pmemq \"function\")\n#6306c-\"function\"\n_\bW_\be _\bw_\ba_\bn_\bt _\bt_\bo _\bc_\br_\be_\ba_\bt_\be _\ba_\bn_\bd _\bi_\bn_\bi_\bt_\bi_\ba_\bl_\bi_\bz_\be _\ba_\bn _\ba_\br_\br_\ba_\by _\bt_\bo _\bp_\ba_\bs_\bs _\bt_\bo _\bt_\bh_\be\n_\bc_\bf_\bo_\bo _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn. _\bI_\bn _\bt_\bh_\bi_\bs _\bc_\ba_\bs_\be _\bw_\be _\bc_\br_\be_\ba_\bt_\be _\ba_\bn _\bu_\bn_\bn_\ba_\bm_\be_\bd _\ba_\br_\br_\ba_\by _\ba_\bn_\bd\n_\bs_\bt_\bo_\br_\be _\bi_\bt _\bi_\bn _\bt_\bh_\be _\bv_\ba_\bl_\bu_\be _\bc_\be_\bl_\bl _\bo_\bf _\bt_\be_\bs_\bt_\ba_\br_\br. _\bW_\bh_\be_\bn _\bw_\be _\bc_\br_\be_\ba_\bt_\be _\ba_\bn\n_\ba_\br_\br_\ba_\by _\bt_\bo _\bp_\ba_\bs_\bs _\bt_\bo _\bt_\bh_\be _\bP_\ba_\bs_\bc_\ba_\bl _\bp_\br_\bo_\bg_\br_\ba_\bm _\bw_\be _\bw_\bi_\bl_\bl _\bu_\bs_\be _\ba _\bn_\ba_\bm_\be_\bd\n_\ba_\br_\br_\ba_\by _\bj_\bu_\bs_\bt _\bt_\bo _\bd_\be_\bm_\bo_\bn_\bs_\bt_\br_\ba_\bt_\be _\bt_\bh_\be _\bd_\bi_\bf_\bf_\be_\br_\be_\bn_\bt _\bw_\ba_\by _\bt_\bh_\ba_\bt _\bn_\ba_\bm_\be_\bd _\ba_\bn_\bd\n_\bu_\bn_\bn_\ba_\bm_\be_\bd _\ba_\br_\br_\ba_\by_\bs _\ba_\br_\be _\bc_\br_\be_\ba_\bt_\be_\bd _\ba_\bn_\bd _\ba_\bc_\bc_\be_\bs_\bs_\be_\bd.\n-> (setq testarr (array nil flonum-block 2))\narray[2]\n-> (store (funcall testarr 0) 1.234)\n1.234\n-> (store (funcall testarr 1) 5.678)\n5.678\n-> (cfoo 385 testarr (hunk 10 11 13 14) '(15 16 17))\na: 385, b[0]: 1.234000, b[1]: 5.678000\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-16\n\n\n c (first): 10 c (second): 11\n ( 15 16 ... )\n 3\n_\bN_\bo_\bt_\be _\bt_\bh_\ba_\bt _\bc_\bf_\bo_\bo _\bh_\ba_\bs _\br_\be_\bt_\bu_\br_\bn_\be_\bd _\b3 _\ba_\bs _\bi_\bt _\bs_\bh_\bo_\bu_\bl_\bd. _\bI_\bt _\ba_\bl_\bs_\bo _\bh_\ba_\bd _\bt_\bh_\be\n_\bs_\bi_\bd_\be _\be_\bf_\bf_\be_\bc_\bt _\bo_\bf _\bc_\bh_\ba_\bn_\bg_\bi_\bn_\bg _\bt_\bh_\be _\bs_\be_\bc_\bo_\bn_\bd _\bv_\ba_\bl_\bu_\be _\bo_\bf _\bt_\bh_\be _\ba_\br_\br_\ba_\by _\bt_\bo\n_\b3._\b1_\b4_\b1_\b5_\b9_\b2_\b6 _\bw_\bh_\bi_\bc_\bh _\bc_\bh_\be_\bc_\bk _\bn_\be_\bx_\bt.\n-> (funcall testarr 1)\n3.1415926\n\n\n_\bI_\bn _\bp_\br_\be_\bp_\ba_\br_\ba_\bt_\bi_\bo_\bn _\bf_\bo_\br _\bc_\ba_\bl_\bl_\bi_\bn_\bg _\bp_\bf_\bo_\bo _\bw_\be _\bc_\br_\be_\ba_\bt_\be _\ba_\bn _\ba_\br_\br_\ba_\by.\n-> (array test flonum-block 2)\narray[2]\n-> (store (test 0) 1.234)\n1.234\n-> (store (test 1) 5.678)\n5.678\n-> (pfoo 385 (getd 'test) (hunk 10 11 13 14) '(15 16 17))\n a: 385 b[0]: 1.23400000000000E+00 b[1]: 5.67800000000000E+00\n c (first): 10 c (second): 11\n ( 15 16 ...)\n3\n-> (test 1)\n3.1415926\n\n _\bN_\bo_\bw _\bt_\bo _\bt_\be_\bs_\bt _\bo_\bu_\bt _\bt_\bh_\be _\bm_\be_\bm_\bq'_\bs\n-> (cmemq 'a '(b c a d e f))\n(_\ba _\bd _\be _\bf)\n-> (pmemq 'e '(a d f g a x))\n_\bn_\bi_\bl\n____________________________________________________________\n\n\n\n\n\n The Fortran example will be much shorter since in\n Fortran you can't follow pointers as you can in other\n languages. The Fortran function ffoo is given three\n arguments: a fixnum, a fixnum-block array and a flo-\n num. These arguments are printed out to verify that\n they made it and then the first value of the array is\n modified. The function returns a double precision\n value which is converted to a flonum by lisp and\n printed. Note that the entry point corresponding to\n the Fortran function ffoo is _ffoo_ as opposed to the\n C and Pascal convention of preceding the name with an\n underscore.\n\n____________________________________________________________\n\n\n% cat ch8auxf.f\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nFunctions, Fclosures, and Macros 8-17\n\n\n double precision function ffoo(a,b,c)\n integer a,b(10)\n double precision c\n print 2,a,b(1),b(2),c\n2 format(' a=',i4,', b(1)=',i5,', b(2)=',i5,' c=',f6.4)\n b(1) = 22\n ffoo = 1.23456\n return\n end\n% f77 -c ch8auxf.f\nch8auxf.f:\n ffoo:\n0.9u 1.8s 0:12 22% 20+22k 54+48io 158pf+0w\n% lisp\nFranz Lisp, Opus 38.60\n-> (cfasl 'ch8auxf.o '_ffoo_ 'ffoo \"real-function\" \"-lF77 -lI77\")\n/usr/lib/lisp/nld -N -A /usr/local/lisp -T 63000 ch8auxf.o -e _ffoo_\n-o /tmp/Li11066.0 -lF77 -lI77 -lc\n#6307c-\"real-function\"\n\n-> (array test fixnum-block 2)\narray[2]\n-> (store (test 0) 10)\n10\n-> (store (test 1) 11)\n11\n-> (ffoo 385 (getd 'test) 5.678)\n a= 385, b(1)= 10, b(2)= 11 c=5.6780\n1.234559893608093\n-> (test 0)\n22\n\n____________________________________________________________\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n", "meta": {"hexsha": "27c19b7ddccc7b02a68bdc5e8b55c5745079fa85", "size": 40009, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch8.r", "max_stars_repo_name": "omasanori/franz-lisp", "max_stars_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2016-11-03T05:46:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-17T06:10:55.000Z", "max_issues_repo_path": "lisplib/manual/ch8.r", "max_issues_repo_name": "omasanori/franz-lisp", "max_issues_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T16:13:04.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-26T20:48:51.000Z", "max_forks_repo_path": "lisplib/manual/ch8.r", "max_forks_repo_name": "omasanori/franz-lisp", "max_forks_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2016-11-09T16:41:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-08T20:56:43.000Z", "avg_line_length": 30.1954716981, "max_line_length": 231, "alphanum_fraction": 0.5853183034, "num_tokens": 14524, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "\n\n\n\n\n\n\n CHAPTER 11\n\n\n The Joseph Lister Trace Package\n\n\n\n\n The Joseph Lister[] Trace package is an important tool\nfor the interactive debugging of a Lisp program. It allows\nyou to examine selected calls to a function or functions,\nand optionally to stop execution of the Lisp program to\nexamine the values of variables.\n\n The trace package is a set of Lisp programs located in\nthe Lisp program library (usually in the file\n/usr/lib/lisp/trace.l). Although not normally loaded in the\nLisp system, the package will be loaded in when the first\ncall to _\bt_\br_\ba_\bc_\be is made.\n\n(trace [ls_arg1 ...])\n\n WHERE: the form of the ls_arg_\bi is described below.\n\n RETURNS: a list of the function sucessfully modified\n for tracing. If no arguments are given to\n _\bt_\br_\ba_\bc_\be, a list of all functions currently being\n traced is returned.\n\n SIDE EFFECT: The function definitions of the functions\n to trace are modified.\n\n\nThe ls_arg_\bi can have one of the following forms:\n\n\n foo - when foo is entered and exited, the trace informa-\n tion will be printed.\n\n\n (foo break) - when foo is entered and exited the trace\n information will be printed. Also, just after the\n trace information for foo is printed upon entry, you\n will be put in a special break loop. The prompt is\n `T>' and you may type any Lisp expression, and see\n its value printed. The _\bith argument to the function\n just called can be accessed as (arg _\bi). To leave the\n trace loop, just type ^D or (tracereturn) and\n____________________\n\u001b9 []_\bL_\bi_\bs_\bt_\be_\br, _\bJ_\bo_\bs_\be_\bp_\bh 1st Baron Lister of Lyme Regis,\n1827-1912; English surgeon: introduced antiseptic surgery.\n\n\n\n\u001b9The Joseph Lister Trace Package 11-1\n\n\n\n\n\n\n\nThe Joseph Lister Trace Package 11-2\n\n\n execution will continue. Note that ^D will work only\n on UNIX systems.\n\n\n (foo if expression) - when foo is entered and the expres-\n sion evaluates to non-nil, then the trace information\n will be printed for both exit and entry. If expres-\n sion evaluates to nil, then no trace information will\n be printed.\n\n\n (foo ifnot expression) - when foo is entered and the\n expression evaluates to nil, then the trace informa-\n tion will be printed for both entry and exit. If\n both if and ifnot are specified, then the if expres-\n sion must evaluate to non nil AND the ifnot expres-\n sion must evaluate to nil for the trace information\n to be printed out.\n\n\n (foo evalin expression) - when foo is entered and after\n the entry trace information is printed, expression\n will be evaluated. Exit trace information will be\n printed when foo exits.\n\n\n (foo evalout expression) - when foo is entered, entry\n trace information will be printed. When foo exits,\n and before the exit trace information is printed,\n expression will be evaluated.\n\n\n (foo evalinout expression) - this has the same effect as\n (trace (foo evalin expression evalout expression)).\n\n\n (foo lprint) - this tells _\bt_\br_\ba_\bc_\be to use the level printer\n when printing the arguments to and the result of a\n call to foo. The level printer prints only the top\n levels of list structure. Any structure below three\n levels is printed as a &. This allows you to trace\n functions with massive arguments or results.\n\n\n\n The following trace options permit one to have\n greater control over each action which takes place when\n a function is traced. These options are only meant to\n be used by people who need special hooks into the trace\n package. Most people should skip reading this section.\n\n\n (foo traceenter tefunc) - this tells _\bt_\br_\ba_\bc_\be that the\n\n\n Printed: July 27, 1983\n\n\n\n\n\n\n\nThe Joseph Lister Trace Package 11-3\n\n\n function to be called when foo is entered is tefunc.\n tefunc should be a lambda of two arguments, the first\n argument will be bound to the name of the function\n being traced, foo in this case. The second argument\n will be bound to the list of arguments to which foo\n should be applied. The function tefunc should print\n some sort of \"entering foo\" message. It should not\n apply foo to the arguments, however. That is done\n later on.\n\n\n (foo traceexit txfunc) - this tells _\bt_\br_\ba_\bc_\be that the func-\n tion to be called when foo is exited is txfunc.\n txfunc should be a lambda of two arguments, the first\n argument will be bound to the name of the function\n being traced, foo in this case. The second argument\n will be bound to the result of the call to foo. The\n function txfunc should print some sort of \"exiting\n foo\" message.\n\n\n (foo evfcn evfunc) - this tells _\bt_\br_\ba_\bc_\be that the form evfunc\n should be evaluated to get the value of foo applied\n to its arguments. This option is a bit different from\n the other special options since evfunc will usually\n be an expression, not just the name of a function,\n and that expression will be specific to the evalua-\n tion of function foo. The argument list to be\n applied will be available as T-arglist.\n\n\n (foo printargs prfunc) - this tells _\bt_\br_\ba_\bc_\be to used prfunc\n to print the arguments to be applied to the function\n foo. prfunc should be a lambda of one argument. You\n might want to use this option if you wanted a print\n function which could handle circular lists. This\n option will work only if you do not specify your own\n traceenter function. Specifying the option lprint is\n just a simple way of changing the printargs function\n to the level printer.\n\n\n (foo printres prfunc) - this tells _\bt_\br_\ba_\bc_\be to use prfunc to\n print the result of evaluating foo. prfunc should be\n a lambda of one argument. This option will work only\n if you do not specify your own traceexit function.\n Specifying the option lprint changes printres to the\n level printer.\n\n\n\n You may specify more than one option for each\n function traced. For example:\n\n\n Printed: July 27, 1983\n\n\n\n\n\n\n\nThe Joseph Lister Trace Package 11-4\n\n\n (_\bt_\br_\ba_\bc_\be (_\bf_\bo_\bo _\bi_\bf (_\be_\bq _\b3 (_\ba_\br_\bg _\b1)) _\bb_\br_\be_\ba_\bk _\bl_\bp_\br_\bi_\bn_\bt) (_\bb_\ba_\br _\be_\bv_\ba_\bl_\bi_\bn\n (_\bp_\br_\bi_\bn_\bt _\bx_\by_\bz_\bz_\by)))\n\n This tells _\bt_\br_\ba_\bc_\be to trace two more functions, foo and\n bar. Should foo be called with the first argument _\be_\bq\n to 3, then the entering foo message will be printed\n with the level printer. Next it will enter a trace\n break loop, allowing you to evaluate any lisp expres-\n sions. When you exit the trace break loop, foo will be\n applied to its arguments and the resulting value will\n be printed, again using the level printer. Bar is also\n traced, and each time bar is entered, an entering bar\n message will be printed and then the value of xyzzy\n will be printed. Next bar will be applied to its argu-\n ments and the result will be printed. If you tell\n _\bt_\br_\ba_\bc_\be to trace a function which is already traced, it\n will first _\bu_\bn_\bt_\br_\ba_\bc_\be it. Thus if you want to specify\n more than one trace option for a function, you must do\n it all at once. The following is _\bn_\bo_\bt equivalent to the\n preceding call to _\bt_\br_\ba_\bc_\be for foo:\n\n (_\bt_\br_\ba_\bc_\be (_\bf_\bo_\bo _\bi_\bf (_\be_\bq _\b3 (_\ba_\br_\bg _\b1))) (_\bf_\bo_\bo _\bb_\br_\be_\ba_\bk) (_\bf_\bo_\bo\n _\bl_\bp_\br_\bi_\bn_\bt))\n\n In this example, only the last option, lprint, will be\n in effect.\n\n If the symbol $tracemute is given a non nil value,\n printing of the function name and arguments on entry\n and exit will be surpressed. This is particularly use-\n ful if the function you are tracing fails after many\n calls to it. In this case you would tell _\bt_\br_\ba_\bc_\be to\n trace the function, set $tracemute to t, and begin the\n computation. When an error occurs you can use _\bt_\br_\ba_\b-\n _\bc_\be_\bd_\bu_\bm_\bp to print out the current trace frames.\n\n Generally the trace package has its own internal\n names for the the lisp functions it uses, so that you\n can feel free to trace system functions like _\bc_\bo_\bn_\bd and\n not worry about adverse interaction with the actions of\n the trace package. You can trace any type of function:\n lambda, nlambda, lexpr or macro whether compiled or\n interpreted and you can even trace array references\n (however you should not attempt to store in an array\n which has been traced).\n\n When tracing compiled code keep in mind that many\n function calls are translated directly to machine\n language or other equivalent function calls. A full\n list of open coded functions is listed at the beginning\n of the liszt compiler source. _\bT_\br_\ba_\bc_\be will do a\n (_\bs_\bs_\bt_\ba_\bt_\bu_\bs _\bt_\br_\ba_\bn_\bs_\bl_\bi_\bn_\bk _\bn_\bi_\bl) to insure that the new traced\n definitions it defines are called instead of the old\n\n\n Printed: July 27, 1983\n\n\n\n\n\n\n\nThe Joseph Lister Trace Package 11-5\n\n\n untraced ones. You may notice that compiled code will\n run slower after this is done.\n\n(traceargs s_func [x_level])\n\n WHERE: if x_level is missing it is assumed to be 1.\n\n RETURNS: the arguments to the x_level_\bt_\bh call to traced\n function s_func are returned.\n\n(tracedump)\n\n SIDE EFFECT: the currently active trace frames are\n printed on the terminal. returns a list\n of functions untraced.\n\n(untrace [s_arg1 ...])\n\n RETURNS: a list of the functions which were untraced.\n\n NOTE: if no arguments are given, all functions are\n untraced.\n\n SIDE EFFECT: the old function definitions of all traced\n functions are restored except in the case\n where it appears that the current defini-\n tion of a function was not created by\n trace.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9\n\n\u001b9 Printed: July 27, 1983\n\n\n\n", "meta": {"hexsha": "bc85282e559fe77003129614559e7a8b3580e0b6", "size": 10861, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch11.r", "max_stars_repo_name": "omasanori/franz-lisp", "max_stars_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2016-11-03T05:46:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-17T06:10:55.000Z", "max_issues_repo_path": "lisplib/manual/ch11.r", "max_issues_repo_name": "omasanori/franz-lisp", "max_issues_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T16:13:04.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-26T20:48:51.000Z", "max_forks_repo_path": "lisplib/manual/ch11.r", "max_forks_repo_name": "omasanori/franz-lisp", "max_forks_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2016-11-09T16:41:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-08T20:56:43.000Z", "avg_line_length": 33.0121580547, "max_line_length": 134, "alphanum_fraction": 0.6429426388, "num_tokens": 3108, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.11920292515117027, "lm_q2_score": 0.052618952976300835, "lm_q1q2_score": 0.0062723331131669365}}
{"text": "# Function definitions\n\n\n#' Crea un dibujo de un gato en ascii\n#'\n#' @param numeroDeGatos el numero de gatos a imprimir\n#'\n#' @return none\n#' @export\n#'\n#' @examples\n#'\n#' crearGato(10)\n#'\ncrearGato <- function(numeroDeGatos){\n\nfor(i in 1:numeroDeGatos){\n cat(\" /\\\\___/\\\\\n | |\n_ * * _\n- /_\\ -\n ---\")\n }\n\n}\n\n\n\n\n#' Calculates the Shannon Diversity Index using ln base e\n#'\n#' @param species vector of abundances of species\n#'\n#' @return the Shannon Diversity Index\n#'\n#' @export\n#'\n#' @examples\n#'\n#' shannon(datosPruebaShannon)\n#'\nshannon <- function(species)\n{\n species <- species[species>0]\n species <- species/sum(species)\n H <- -sum(species* log(species))\n return(H)\n}\n\n\n#' Fix the problems of chlorophyll-a data set\n#'\n#' @param chla data.frame with original data\n#'\n#' @return data.frame with fixed data\n#' @export\n#\n#' @examples\n#'\n#' filename <- system.file(\"extdata\", \"Clorofila.txt\", package = \"PaqueGato\")\n#'\n#' chla <- read.delim(filename)\n#'\n#' fixClorophylData(filename)\n#'\nfixClorophylData <- function(chla)\n{\n actual_year <- chla$year[1]\n chla$Date <- as.Date(\"1900-01-01\")\n\n for(i in 1:nrow(chla))\n {\n if( is.na(chla$Year[i]))\n chla$Year[i] <- actual_year\n else\n actual_year <- chla$Year[i]\n\n fecha <- lubridate::ymd(paste(chla$Year[i],chla$Month[i], 1))\n if( is.na(fecha)){\n if(chla$Month[i]==\"Mar\")\n fecha <- lubridate::ymd(paste(chla$Year[i],3, 1))\n else {\n fecha <- lubridate::dmy(chla$Month[i])\n\n if(is.na(fecha)) {\n fecha <- lubridate::mdy(chla$Month[i])\n\n if(is.na(fecha)) {\n temp <- strsplit(chla$Month[i],\" \")[[1]][3]\n fecha <- lubridate::dmy(temp)\n }\n }\n }\n }\n chla$Date[i] <- fecha\n\n }\n\n chla$IntegE1 <- abs(chla$IntegE1)\n chla$IntegE2 <- abs(as.numeric(chla$IntegE2))\n\n return(chla)\n}\n\n\n#' Read ecological networks in CSV format as edge list or adyacency matrix\n#'\n#' @param fileName Filename of the csv formated network\n#'\n#' @return an igraph object\n#' @export\n#'\n#' @examples readEcoNetwork(\"econetwork.csv\")\nreadEcoNetwork <- function(fileName){\n\n g <- lapply(fileName, function(fname){\n\n web <- read.csv(fname, header = T,check.names = F)\n\n if( ncol(web)==2 ){\n web <- web[,c(2,1)]\n\n g <- graph_from_data_frame(web)\n\n } else {\n if( (ncol(web)-1) == nrow(web) ) { # The adjacency matrix must be square\n g <- graph_from_adjacency_matrix(as.matrix(web[,2:ncol(web)]))\n\n } else {\n g <- NULL\n warning(\"Invalid file format: \",fileName)\n }\n }\n\n })\n return(g)\n}\n\n", "meta": {"hexsha": "5c4098dcffb56fa85c6a9da642d22b1f24902a16", "size": 2624, "ext": "r", "lang": "R", "max_stars_repo_path": "R/functions.r", "max_stars_repo_name": "lsaravia/PaqueGato", "max_stars_repo_head_hexsha": "27827b86abe1aa9a8f13c328d67afd8b648d9a4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "R/functions.r", "max_issues_repo_name": "lsaravia/PaqueGato", "max_issues_repo_head_hexsha": "27827b86abe1aa9a8f13c328d67afd8b648d9a4d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "R/functions.r", "max_forks_repo_name": "lsaravia/PaqueGato", "max_forks_repo_head_hexsha": "27827b86abe1aa9a8f13c328d67afd8b648d9a4d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2018-07-20T18:09:41.000Z", "max_forks_repo_forks_event_max_datetime": "2018-07-20T18:09:41.000Z", "avg_line_length": 18.8776978417, "max_line_length": 97, "alphanum_fraction": 0.5868902439, "num_tokens": 816, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.3276683008207139, "lm_q2_score": 0.01912403549304638, "lm_q1q2_score": 0.0062663402148415304}}
{"text": "#' cpuvec class\n#' \n#' Storage and methods for CPU vector data.\n#' \n#' @details\n#' Data is held in an external pointer.\n#' \n#' @rdname cpuvec-class\n#' @name cpuvec-class\ncpuvecR6 = R6::R6Class(\"cpuvec\",\n public = list(\n #' @details\n #' Class initializer. See also \\code{?cpuvec}.\n #' @param size The length of the vector.\n #' @param type Storage type for the vector. Should be one of 'int', 'float', or 'double'.\n #' @useDynLib fmlr R_cpuvec_init\n initialize = function(size=0, type=\"double\")\n {\n type = match.arg(tolower(type), TYPES_STR)\n \n size = as.integer(size)\n \n private$type_str = type\n private$type = type_str2int(type)\n private$x_ptr = .Call(R_cpuvec_init, private$type, size)\n },\n \n \n \n #' @details\n #' Change the length of the vector object.\n #' @param size The new length.\n #' @useDynLib fmlr R_cpuvec_resize\n resize = function(size)\n {\n size = as.integer(size)\n .Call(R_cpuvec_resize, private$type, private$x_ptr, size)\n invisible(self)\n },\n \n \n \n #' @details\n #' Set the data in the cpuvec object to point to the array in 'data'. See\n #' also \\code{?as_cpuvec}.\n #' @param data R vector.\n #' @useDynLib fmlr R_cpuvec_inherit\n inherit = function(data)\n {\n if (!is.vector(data) && !float::is.float(data))\n stop(\"'data' must be a vector\")\n \n if (float::is.float(data) && private$type == TYPE_FLOAT)\n {\n type = TYPE_FLOAT\n rdata = data@Data\n }\n else if (is.integer(data) && private$type == TYPE_INT)\n {\n type = TYPE_INT\n rdata = data\n }\n else if (is.double(data) && private$type == TYPE_DOUBLE)\n {\n type = TYPE_DOUBLE\n rdata = data\n }\n else\n stop(\"can not mix fundamental types with inherit()\")\n \n .Call(R_cpuvec_inherit, type, private$x_ptr, rdata)\n invisible(self)\n },\n \n \n \n #' @details\n #' Duplicate the vector in a deep copy.\n #' @useDynLib fmlr R_cpuvec_dupe\n dupe = function()\n {\n ret_ptr = .Call(R_cpuvec_dupe, private$type, private$x_ptr)\n \n tmp = private$x_ptr\n private$x_ptr = ret_ptr\n ret = self$clone()\n private$x_ptr = tmp\n \n ret\n },\n \n \n \n #' @details\n #' Print one-line information about the vector.\n #' @useDynLib fmlr R_cpuvec_info\n info = function()\n {\n .Call(R_cpuvec_info, private$type, private$x_ptr)\n invisible(self)\n },\n \n \n \n #' @details\n #' Print the data.\n #' @param ndigits Number of decimal digits to print.\n #' @useDynLib fmlr R_cpuvec_print\n print = function(ndigits=4)\n {\n ndigits = min(as.integer(ndigits), 15L)\n \n .Call(R_cpuvec_print, private$type, private$x_ptr, ndigits)\n invisible(self)\n },\n \n \n \n #' @details\n #' Fill all entries with zero.\n #' @useDynLib fmlr R_cpuvec_fill_zero\n fill_zero = function()\n {\n .Call(R_cpuvec_fill_zero, private$type, private$x_ptr)\n invisible(self)\n },\n \n \n \n #' @details\n #' Fill all entries with supplied value.\n #' @param v Value to set all entries to.\n #' @useDynLib fmlr R_cpuvec_fill_val\n fill_val = function(v)\n {\n v = as.double(v)\n \n .Call(R_cpuvec_fill_val, private$type, private$x_ptr, v)\n invisible(self)\n },\n \n \n \n #' @details\n #' Fill the vector (column-wise) with linearly-spaced values.\n #' @param start,stop Beginning/end of the linear spacing.\n #' @useDynLib fmlr R_cpuvec_fill_linspace\n fill_linspace = function(start, stop)\n {\n if ((missing(start) && !missing(stop)) || (!missing(start) && missing(stop)))\n stop(\"must supply both 'start' and 'stop', or neither\")\n \n if (!missing(start))\n start = check_is_number(start)\n else\n start = NULL\n if (!missing(stop))\n stop = check_is_number(stop)\n else\n stop = NULL\n \n .Call(R_cpuvec_fill_linspace, private$type, private$x_ptr, start, stop)\n invisible(self)\n },\n \n \n \n #' @details\n #' Scale all entries by the supplied value.\n #' @param s Value to scale all entries by.\n #' @useDynLib fmlr R_cpuvec_scale\n scale = function(s)\n {\n s = as.double(s)\n \n .Call(R_cpuvec_scale, private$type, private$x_ptr, s)\n invisible(self)\n },\n \n \n \n #' @details\n #' Exponentiate all entries by the supplied value.\n #' @param p Value to raise all entries to.\n #' @useDynLib fmlr R_cpuvec_pow\n pow = function(p)\n {\n s = as.double(s)\n \n .Call(R_cpuvec_pow, private$type, private$x_ptr, p)\n invisible(self)\n },\n \n \n \n #' @details\n #' Reverse rows.\n #' @useDynLib fmlr R_cpuvec_rev\n rev = function()\n {\n .Call(R_cpuvec_rev, private$type, private$x_ptr)\n invisible(self)\n },\n \n \n \n #' @details\n #' Sum the vector.\n #' @return Returns the sum.\n #' @useDynLib fmlr R_cpuvec_sum\n sum = function()\n {\n .Call(R_cpuvec_sum, private$type, private$x_ptr)\n },\n \n \n \n #' @details\n #' Finds the minimum value of the vector.\n #' @return Returns the min.\n #' @useDynLib fmlr R_cpuvec_min\n min = function()\n {\n .Call(R_cpuvec_min, private$type, private$x_ptr)\n },\n \n \n \n #' @details\n #' Finds the maximum value of the vector.\n #' @return Returns the max.\n #' @useDynLib fmlr R_cpuvec_max\n max = function()\n {\n .Call(R_cpuvec_max, private$type, private$x_ptr)\n },\n \n \n \n #' @details\n #' Get element from the vector.\n #' @param i Index (0-based).\n #' @useDynLib fmlr R_cpuvec_get\n get = function(i)\n {\n i = check_is_natnum(i)\n .Call(R_cpuvec_get, private$type, private$x_ptr, i)\n },\n \n \n \n #' @details\n #' Set element of the vector.\n #' @param i Index (0-based).\n #' @param v Value.\n #' @useDynLib fmlr R_cpuvec_set\n set = function(i, v)\n {\n i = check_is_natnum(i)\n v = check_is_number(v)\n .Call(R_cpuvec_set, private$type, private$x_ptr, i, v)\n invisible(self)\n },\n \n \n \n #' @details\n #' Returns length of the vector.\n #' @useDynLib fmlr R_cpuvec_size\n size = function() .Call(R_cpuvec_size, private$type, private$x_ptr),\n \n \n \n #' @details\n #' Returns the external pointer data. For developers only.\n data_ptr = function() private$x_ptr,\n \n #' @details\n #' Returns the integer code for the underlying storage type. For developers only.\n get_type = function() private$type,\n \n #' @details\n #' Returns the string code for the underlying storage type. For developers only.\n get_type_str = function() private$type_str,\n \n #' @details\n #' Returns the integer code the class type, in this case CLASS_VEC.\n get_class = function() CLASS_VEC,\n \n \n \n #' @details\n #' Returns an R vector containing a copy of the class data.\n #' @useDynLib fmlr R_cpuvec_to_robj\n to_robj = function()\n {\n ret = .Call(R_cpuvec_to_robj, private$type, private$x_ptr)\n if (private$type == TYPE_FLOAT)\n ret = float::float32(ret)\n \n ret\n },\n \n \n \n #' @details\n #' Copies the values of the input to the class data. See also \\code{?as_cpuvec}.\n #' @param robj R vector.\n #' @useDynLib fmlr R_cpuvec_from_robj\n from_robj = function(robj)\n {\n if (!is.vector(robj) && !float::is.float(robj))\n stop(\"'robj' must be a vector\")\n \n if (float::is.float(robj))\n {\n robj_type = TYPE_FLOAT\n rdata = robj@Data\n }\n else if (is.integer(robj))\n {\n robj_type = TYPE_INT\n rdata = robj\n }\n else if (is.double(robj))\n {\n robj_type = TYPE_DOUBLE\n rdata = robj\n }\n else\n stop(\"bad fundamental type\")\n \n .Call(R_cpuvec_from_robj, private$type, private$x_ptr, robj_type, rdata)\n invisible(self)\n }\n ),\n \n \n \n private = list(\n x_ptr = NULL,\n type = -1L,\n type_str = \"\"\n )\n)\n\n\n\n#' cpuvec\n#' \n#' Constructor for cpuvec objects.\n#' \n#' @details\n#' Data is held in an external pointer.\n#' \n#' @param size Length of the vector.\n#' @param type Storage type for the vector. Should be one of 'int', 'float', or 'double'.\n#' @return A cpuvec class object.\n#' \n#' @seealso \\code{\\link{cpuvec-class}}\n#' \n#' @export\ncpuvec = function(size=0, type=\"double\")\n{\n cpuvecR6$new(size=size, type=type)\n}\n\n\n\n#' as_cpuvec\n#' \n#' Convert an R vector to a cpumat object.\n#' \n#' @param x R vector.\n#' @param copy Should the R data be copied? If \\code{FALSE}, be careful!\n#' @return A cpuvec object.\n#' \n#' @export\nas_cpuvec = function(x, copy=TRUE)\n{\n x_type = type_robj2str(x)\n ret = cpuvec(type=x_type)\n \n if (isTRUE(copy))\n ret$from_robj(x)\n else\n ret$inherit(x)\n \n ret\n}\n", "meta": {"hexsha": "9f400f2bd512cac5d7daec9b6386e32cff48caa6", "size": 8971, "ext": "r", "lang": "R", "max_stars_repo_path": "R/cpuvec.r", "max_stars_repo_name": "fml-fam/fmlr", "max_stars_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2020-02-06T21:06:14.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-23T22:34:08.000Z", "max_issues_repo_path": "R/cpuvec.r", "max_issues_repo_name": "wrathematics/fmlr", "max_issues_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-02-19T17:27:46.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-09T00:30:36.000Z", "max_forks_repo_path": "R/cpuvec.r", "max_forks_repo_name": "wrathematics/fmlr", "max_forks_repo_head_hexsha": "7a9c8030435b9921fc832b27ef5f174a40c7792b", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.9437340153, "max_line_length": 93, "alphanum_fraction": 0.5770817077, "num_tokens": 2403, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO", "lm_q1_score": 0.24798743735585302, "lm_q2_score": 0.021948252795223815, "lm_q1q2_score": 0.005442890965125992}}
{"text": "subroutine pred(kpr,i,j,nadj,madj,ntot,nerror)\n\n# Find the predecessor of j in the adjacency list of i.\n# Called by initad, trifnd, swap, dirseg, dirout.\n\nimplicit double precision(a-h,o-z)\ndimension nadj(-3:ntot,0:madj)\n\nnerror = -1\n\nn = nadj(i,0)\n\n# If the adjacency list of i is empty, then clearly j has no predecessor\n# in this adjacency list. Something's wrong; stop.\nif(n==0) {\n\tnerror = 5\n\treturn\n}\n\n# The adjacency list of i is non-empty; search through it until j is found;\n# subtract 1 from the location of j, and find the contents of this new location\ndo k = 1,n {\n if(j==nadj(i,k)) {\n km = k-1\n if(km<1) km = n # Take km modulo n. (The adjacency list\n kpr = nadj(i,km) # is circular.)\n return\n }\n}\n\n# The adjacency list for i doesn't contain j. Something's wrong; stop.\nnerror = 6\nreturn\nend\n", "meta": {"hexsha": "9bfdf82dfb5cc31c5dc9e89ad4b89369ed1c1eda", "size": 895, "ext": "r", "lang": "R", "max_stars_repo_path": "VisUncertainty/bin/Debug/R-3.4.4/library/deldir/ratfor/pred.r", "max_stars_repo_name": "hyeongmokoo/SAAR_beta1", "max_stars_repo_head_hexsha": "7406f30d7f39a03e8494b2171c2bc09904a36f62", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_repo_stars_event_min_datetime": "2018-08-23T15:35:47.000Z", "max_stars_repo_stars_event_max_datetime": "2020-11-24T12:20:59.000Z", "max_issues_repo_path": "VisUncertainty/bin/Debug/R-3.4.4/library/deldir/ratfor/pred.r", "max_issues_repo_name": "hyeongmokoo/SAAR_beta1", "max_issues_repo_head_hexsha": "7406f30d7f39a03e8494b2171c2bc09904a36f62", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2, "max_issues_repo_issues_event_min_datetime": "2020-08-17T15:14:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-23T21:55:49.000Z", "max_forks_repo_path": "VisUncertainty/bin/Debug/R-3.4.4/library/deldir/ratfor/pred.r", "max_forks_repo_name": "hyeongmokoo/SAAR_beta1", "max_forks_repo_head_hexsha": "7406f30d7f39a03e8494b2171c2bc09904a36f62", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2020-11-04T05:34:16.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-04T05:34:16.000Z", "avg_line_length": 25.5714285714, "max_line_length": 79, "alphanum_fraction": 0.6245810056, "num_tokens": 259, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. NO\n2. NO\n\n", "lm_q1_score": 0.22815651265736978, "lm_q2_score": 0.02333076842649296, "lm_q1q2_score": 0.005323066761805304}}
{"text": 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<- function(instructions) {\n currentFloor <- 0\n for(char in 1:nchar(instructions)){\n curr_char = substr(instructions,char,char)\n if(curr_char == \"(\"){\n currentFloor <- currentFloor+1\n }else{\n currentFloor <- currentFloor-1\n }\n }\n return(currentFloor)\n}\n\n\nstopifnot(\n final.floor(\"(())\") == 0,\n final.floor(\"()()\") == 0,\n final.floor(\"(((\") == 3\n)\nfinal.floor(input)\n\n\nbasement.pos <- function(instructions) {\n currentFloor <- 0\n for(char in 1:nchar(instructions)){\n curr_char = substr(instructions,char,char)\n if(curr_char == \"(\"){\n currentFloor <- currentFloor+1\n }else{\n currentFloor <- currentFloor-1\n }\n if(currentFloor < 0){\n return(char)\n }\n }\n}\n\nstopifnot(\n basement.pos(\")\") == 1,\n basement.pos(\"()())\") == 5\n)\nbasement.pos(input)\n", "meta": {"hexsha": "5bb03f9afdcde66bee80b445f9562166eeb7477e", "size": 7921, "ext": "r", "lang": "R", "max_stars_repo_path": "day1/main.r", "max_stars_repo_name": "el-hult/adventofcode2015", 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NO\n2. NO", "lm_q1_score": 0.2720245510940225, "lm_q2_score": 0.01941934867901364, "lm_q1q2_score": 0.005282539606946984}}
{"text": "# Functions that accompany the Supp, Koons, and Ernest rodent movement project using data at the Portal Project 2000-2009\n# These functions were used to create figures and summarizing data, but may not be included in the final paper.\n\n# # # list of dataframes WORKS NOW, TO USE? OR NOT TO USE? \n# datLS = list(het, cricet, foliv, insec)\n# names(datLS) = c('het','cricet', 'foliv', 'insec')\n# for (df in seq_along(datLS)) datLS[[df]]$tag = as.character(datLS[[df]]$tag)\n\n#---------------------------------------------------------------------------------------------------------------\n#################### OLD FUNCTIONS ? TO FIX AND WORK ON...\n#---------------------------------------------------------------------------------------------------------------\n\n### Create a set of capture histories by treatment type\ncreate_trmt_hist = function(dat, tags, prd){\n \n MARK_data = data.frame(\"captures\"=1, \"censored\"=1, \"tags\"=1, \"species\"=1, \"sex\"=1, \"avg_weight\"=1)\n outcount = 0\n \n for (t in 1:length(tags)){\n capture_history = \"\" #create empty string\n for (p in 1:length(prd)){\n tmp<-which(dat$tag==tags[t] & dat$period==prd[p])\n if (nrow(dat[tmp,]) == 0) {\n state = \"0\"\n capture_history = paste(capture_history, state, sep=\"\")}\n else if (nrow(dat[tmp,] > 0)) {\n if (dat[tmp,17]==1){\n state = \"A\"\n capture_history = paste(capture_history, state, sep=\"\")}\n else if (dat[tmp,17]==2){\n state = \"B\"\n capture_history = paste(capture_history, state, sep=\"\")}\n else if (dat[tmp,17]==3){\n state = \"C\"\n capture_history = paste(capture_history, state, sep=\"\")}\n }\n }\n tmp2<-which(dat$tag==tags[t])\n censored = 1\n for (irow in nrow(dat[tmp2,])){\n if (dat[tmp2,17] == 3) {\n censored = -1\n break}}\n spp = unique(dat[which(dat$tag==tags[t]),6])\n sex = unique(dat[which(dat$tag==tags[t]),7])\n avg_mass = mean(dat[which(dat$tag==tags[t]),11])\n outcount = outcount + 1\n MARK_data[outcount,] <- c(capture_history, censored, tags[t], spp, sex, avg_mass)\n }\n return(MARK_data)\n}\n\n## Create a set of capture histories by plot\ncreate_plot_hist = function(dat, tags, prd) {\n MARK_data_by_plot = data.frame(\"captures\"=1, \"censored\"=1, \"tags\"=1, \"species\"=1, \"sex\"=1, \"avg_weight\"=1)\n outcount = 0\n \n for (t in 1:length(tags)){\n capture_history = \"\" #create empty string\n for (p in 1:length(prd)){\n tmp<-which(dat$tag==tags[t] & dat$period==prd[p])\n if (nrow(dat[tmp,]) == 0) {\n state = \"0\"\n capture_history = paste(capture_history, state, sep=\",\")}\n else if (nrow(dat[tmp,] > 0)) {\n state = as.character(dat[tmp,4])\n capture_history = paste(capture_history, state, sep=\",\")}\n }\n tmp2<-which(dat$tag==tags[t])\n censored = 1\n for (irow in nrow(dat[tmp2,])){\n if (dat[tmp2,17] == 3) {\n censored = -1\n break}}\n spp = unique(dat[which(dat$tag==tags[t]),6])\n sex = unique(dat[which(dat$tag==tags[t]),7])\n avg_mass = mean(dat[which(dat$tag==tags[t]),11])\n outcount = outcount + 1\n MARK_data_by_plot[outcount,] <- c(capture_history, censored, tags[t], spp, sex, avg_mass)\n }\n return(MARK_data_by_plot)\n}\n\n# Find rats that are moving around\nfind_rats_that_move = function(dat, tags, spp_col, sex_col, trmt_col, plot_col){\n moving_rats = data.frame(\"tag\"=1, \"spp\"=1, \"sex\"=1, \"loc\"=1, \"occurrences\"=1)\n outcount = 0\n \n for (t in 1:length(tags)){\n tmp <- which(dat$tag == tags[t])\n spp = unique(dat[tmp,spp_col])\n sex = unique(dat[tmp,sex_col])\n if (nrow(dat[tmp,]) > 1) {\n trmt_list = dat[tmp,trmt_col]\n trmt = trmt_list[1]\n for (i in 2:length(trmt_list)){\n if (trmt_list[i] != trmt) {\n outcount = outcount + 1\n moving_rats[outcount,] <- c(tags[t], spp, sex, \"trmt\", nrow(dat[tmp,]))\n break\n }}\n plot_list = dat[tmp,plot_col]\n plot = plot_list[1]\n for (p in 2:length(plot_list)){\n if (plot_list[p] != plot){\n outcount = outcount + 1\n moving_rats[outcount,] <- c(tags[t], spp, sex, \"plot\", nrow(dat[tmp,]))\n break\n }}\n }}\n return(moving_rats)\n}\n\n# find num captures/rat\nnum_captures = function(dat, tags){\n \n rat_catches = data.frame(\"tag\"=1, \"spp\"=1, \"sex\"=1, \"captures\"=1)\n outcount = 0\n \n for (t in 1:length(tags)){\n tmp <- which(dat$tag == tags[t])\n spp = unique(dat[tmp,6])\n sex = unique(dat[tmp,7])\n captures = nrow(dat[tmp,])\n outcount = outcount + 1\n rat_catches[outcount,] <- c(tags[t], spp, sex, captures)\n }\n rat_catches$captures = as.numeric(rat_catches$captures)\n return(rat_catches)\n}\n\n### Creates a dataframe to look at movement\nexamine_trmt_moves = function(dat, tags){\n \n moves_trmt = data.frame(\"tags\"=1, \"sp\"=1, \"sex\"=1, \"num_moves\"=1, \"move_list\"=1, \"censored\"=1,\n \"c2r\"=1, \"r2c\"=1, \"c2e\"=1, \"r2e\"=1)\n outcount = 0\n \n for (t in 1:length(tags)){\n move_list = as.character() #create empty string\n ind_dat=dat[which(dat$tag==tags[t]),] # subset data for individual with tag t\n if (nrow(ind_dat) > 1) { # only record data for indivs with multiple captures\n for (i in 1:nrow(ind_dat)){\n if (ind_dat[i,17]==1){\n state = \"A\"\n move_list <- append(move_list, state)\n censored = 1 }\n else if (ind_dat[i,17]==2){\n state = \"B\"\n move_list <- append(move_list, state)\n censored = 1 }\n else if (ind_dat[i,17]==3){\n state = \"C\"\n move_list <- append(move_list, state)\n censored = -1 }\n }\n type = move_list[1]\n moves = 0\n new_list = \"\"\n for (l in 1:length(move_list)){\n new_list = paste(new_list, move_list[l], sep=\"\")\n c2r = 0\n r2c = 0\n c2e = 0\n r2e = 0\n if (move_list[l]!=type) {\n moves = moves + 1\n if (type == \"A\" & move_list[l] == \"B\") {\n c2r = c2r + 1}\n else if (type == \"A\" & move_list[l] == \"C\") {\n c2e = c2e + 1}\n else if (type == \"B\" & move_list[l] == \"A\") {\n r2c = r2c + 1}\n else if (type == \"B\" & move_list[l] == \"C\") {\n r2e = r2e + 1}\n type = move_list[l] \n }}\n spp = unique(ind_dat[,6])\n sex = unique(ind_dat[,7])\n outcount = outcount + 1\n moves_trmt[outcount,] <- c(tags[t], spp, sex, moves, new_list, censored, c2r, r2c, c2e, r2e)\n }}\n return(moves_trmt)\n}\n\n### Creates a dataframe to look at movement among plots\nexamine_plot_moves = function(dat, tags){\n \n moves_plot = data.frame(\"tags\"=1, \"sp\"=1, \"sex\"=1, \"num_moves\"=1, \"neighborhood\"=1, \"move_list\"=1)\n outcount = 0\n \n for (t in 1:length(tags)){\n move_list = as.numeric() #create empty string\n ind_dat=dat[which(dat$tag==tags[t]),] # subset data for individual with tag t\n if (nrow(ind_dat) > 1) { # only record data for indivs with multiple captures\n for (i in 1:nrow(ind_dat)){\n state = ind_dat[i,4]\n move_list <- append(move_list, state)}\n }\n type = move_list[1]\n neighbor = \"\"\n moves = 0\n new_list = \"\"\n for (l in 1:length(move_list)){\n new_list = paste(new_list, move_list[l], sep=\",\")\n if (move_list[l]!=type) {\n moves = moves + 1\n near = is.neighbor(move_list[l], type) \n neighbor = paste(neighbor, near, sep = \"\")\n type = move_list[l] }}\n spp = unique(ind_dat[,6])\n sex = unique(ind_dat[,7])\n outcount = outcount + 1\n moves_plot[outcount,] <- c(tags[t], spp, sex, moves, neighbor, new_list)\n }\n return(moves_plot)\n}\n\n# determine if a plot is a neighbor\nis.neighbor = function(plotA, plotB){\n \n if (plotA == 1) {\n if (plotB == 2 | plotB == 7) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 2) {\n if (plotB == 1 | plotB == 3 | plotB == 8 ) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 3) {\n if (plotB == 2 | plotB == 4 | plotB == 8 | plotB == 9 | plotB==10) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 4) {\n if (plotB == 3 | plotB == 5 | plotB == 9 | plotB == 10 | plotB == 11) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 5) {\n if (plotB == 4 | plotB == 6 | plotB == 11 | plotB == 12 | plotB == 24) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 6) {\n if (plotB == 5 | plotB==24 | plotB == 12) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 7) {\n if (plotB == 1 | plotB == 2 | plotB == 13 | plotB == 8 ) {neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 8 ) {\n if (plotB == 2 | plotB == 7 | plotB == 9 | plotB == 14 ) { neighbor = \"Y\"}\n else { neighbor = \"N\" }}\n else if (plotA == 9 ) {\n if (plotB == 8 | plotB == 10 | plotB==3 | plotB == 4 | plotB == 16 | plotB == 15) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 10 ) {\n if (plotB == 9 | plotB == 11 | plotB == 16 | plotB == 4 | plotB == 17 ) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 11 ) {\n if (plotB == 10 | plotB == 12 | plotB == 5 | plotB == 16 | plotB == 17 | plotB == 18) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 12 ) {\n if (plotB == 11 | plotB == 5 | plotB == 6 | plotB == 24 | plotB == 19 | plotB == 18) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 13) {\n if (plotB == 7 | plotB == 14 | plotB == 8) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 14) {\n if (plotB == 13 | plotB == 8 | plotB == 15 | plotB == 7) {neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 15) {\n if (plotB == 14 | plotB == 16 | plotB == 9 | plotB == 8 | plotB == 20) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 16) {\n if (plotB == 15 | plotB == 17 | plotB == 9 | plotB == 10 | plotB == 21 | plotB == 20) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 17) {\n if (plotB == 16 | plotB == 18 | plotB == 10 | plotB == 11 | plotB == 21 | plotB == 22) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 18) {\n if (plotB == 17 | plotB == 19 | plotB == 12 | plotB == 22 | plotB == 23) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 19) {\n if (plotB == 12 | plotB == 18 | plotB == 24) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 20) {\n if (plotB == 15 | plotB == 16 | plotB == 21) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 21) {\n if (plotB == 20 | plotB == 22 | plotB == 16 | plotB == 15) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 22) {\n if (plotB == 21 | plotB == 23 | plotB == 17 | plotB == 18) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 23) {\n if (plotB == 22 | plotB == 18) { neighbor = \"Y\" }\n else { neighbor = \"N\" }}\n else if (plotA == 24) {\n if (plotB == 6 | plotB == 19 | plotB == 12) { neighbor == \"Y\" }\n else { neighbor = \"N\" }}\n else { neighbor = \"ERROR\" }\n \n return (neighbor)\n}\n\n### Creates a dataframe to look at movement among plots # FIX ME #WHY ARE THERE MULTIPLE CAPS PER PERIOD?? FIX!\nexamine_stake_moves = function(dat, tags, stake_col, e_col, n_col, spp_col, sex_col){\n \n moves_stake = data.frame(\"tags\"=1, \"sp\"=1, \"sex\"=1, \"avg_mass\"=1, \"num_moves\"=1, \"stake_list\"=1)\n outcount = 0\n #distances = as.numeric()\n \n for (t in 1:length(tags)){\n stake_list = as.numeric() #create empty string\n ind_dat=dat[which(dat$tag==tags[t]),] # subset data for individual with tag t\n ind_dat = ind_dat[order(ind_dat[,2]),] # order by period number so time is sequential\n if (nrow(ind_dat) > 1) { # only record data for indivs with multiple captures\n for (i in 1:nrow(ind_dat)){\n stake = ind_dat[i,stake_col]\n stake_list <- append(stake_list, stake)} \n type = stake_list[1]\n moves = 0\n new_stake_list = \"\"\n for (l in 1:length(stake_list)){\n new_stake_list = paste(new_stake_list, stake_list[l], sep=\" \")\n if (stake_list[l]!=type) {\n moves = moves + 1\n type = stake_list[l] }}\n if (moves > 1) { \n # dist_list = as.numeric()\n # for (i in 1:nrow(ind_dat)){\n # if (i > 1){\n # dist = round(UTM_dist(ind_dat[i,e_col], ind_dat[i-1,e_col], ind_dat[i,n_col], ind_dat[i-1,n_col]),3)\n # dist_list = append(dist_list, dist)\n # }}\n avg_mass = averageMass(ind_dat)\n spp = unique(ind_dat[,spp_col])\n sex = unique(ind_dat[,sex_col])\n outcount = outcount + 1\n moves_stake[outcount,] <- c(tags[t], spp, sex, avg_mass, moves, new_stake_list)\n }}}\n return(moves_stake)\n}\n\n# calculte dist between two UTMs, dist=sqrt((x2-x1)^2 + (y2-y1)^2) \nUTM_dist = function (e1, e2, n1, n2){\n dist = sqrt((e2-e1)^2 + (n2-n1)^2)\n return(dist)\n}\n\n# Get average mass from all rows of data pertaining to individual, don't count Juvenile or pregnant mass\naverageMass = function(data) {\n mass_data = subset(data, reprod != \"J\" & pregnant != \"P\")\n masses = subset(mass_data, mass > 0, na.rm=T)\n avg_mass = sum(masses$mass)/length(masses$mass)\n return(round(avg_mass,3))}\n\n# break data up into x and y coordinates\nxy_data = function (stake) {\n \n if (stake==71|stake==72|stake==73|stake==74|stake==75|stake==76|stake==77) {\n x = 7 }\n else if (stake==61|stake==62|stake==63|stake==64|stake==65|stake==66|stake==67) {\n x = 6 }\n else if (stake==51|stake==52|stake==53|stake==54|stake==55|stake==56|stake==57) {\n x = 5 }\n else if (stake==41|stake==42|stake==43|stake==44|stake==45|stake==46|stake==47) {\n x = 4 }\n else if (stake==31|stake==32|stake==33|stake==34|stake==35|stake==36|stake==37) {\n x = 3 }\n else if (stake==21|stake==22|stake==23|stake==24|stake==25|stake==26|stake==27) {\n x = 2 }\n else { x = 1}\n if (stake==71|stake==61|stake==51|stake==41|stake==31|stake==21|stake==11) {\n y = 1 }\n else if (stake==72|stake==62|stake==52|stake==42|stake==32|stake==22|stake==12) {\n y = 2 }\n else if (stake==73|stake==63|stake==53|stake==43|stake==33|stake==32|stake==31) {\n y = 3 }\n else if (stake==74|stake==64|stake==54|stake==44|stake==34|stake==24|stake==14) {\n y = 4 }\n else if (stake==75|stake==65|stake==55|stake==45|stake==35|stake==25|stake==15) {\n y = 5 }\n else if (stake==76|stake==66|stake==56|stake==46|stake==36|stake==26|stake==16) {\n y = 6 }\n else { y = 7 }\n xy=c(x,y)\n return(xy)\n}\n\n# plot stake moves, takes data from those in one plot that are recaptured at least three times\nplot_stake_moves = function (dat, tags, stake_col, plot_col, spp_col, sex_col) {\n for (t in 1:length(tags)){\n coords = data.frame(\"x\"=1,\"y\"=1)\n outcount = 0\n ind_dat=dat[which(dat$tag==tags[t]),] # subset data for individual with tag t\n ind_dat = ind_dat[order(ind_dat[,2]),] # order by period number so time is sequential\n if (nrow(ind_dat) > 2) { # only record data for indivs with multiple captures\n for (i in 1:nrow(ind_dat)){\n stake = ind_dat[i,stake_col]\n xy = xy_data(stake)\n outcount = outcount + 1\n coords[outcount,]<-xy\n } \n avg_mass = averageMass(ind_dat)\n spp = unique(ind_dat[,spp_col])\n sex = unique(ind_dat[,sex_col])\n plot_num = unique(ind_dat[,plot_col])\n plot(coords$x, coords$y, xlim = c(1,7), ylim = c(1,7), type = \"b\", \n main = paste(avg_mass, \"g\", sex, spp, \"in plot\", plot_num, \",recaptures =\", nrow(coords), sep = \" \"))\n points(coords[1,1], coords[1,2], pch = 17, col = \"indianred\") #start\n points(coords[nrow(coords),1],coords[nrow(coords),2],pch = 15, col = \"blue\") #stop\n }}}\n\n\n# plot number of individuals captured in each period (since each record has its own row, we can just count the number\n### of occurences of each period)\nplot_freq_by_prd = function(data, name) {\n require(plyr)\n \n prdcount = count(data, vars = \"period\")\n plot(prdcount$period, prdcount$freq, type = \"b\", pch = 19, xlab = \"Period\", \n ylab = paste(\"num individual\", name, \"captured\", sep = ' '))\n}\n\n\n#plot number of recaptures as a histogram for each species\nplot_recap_hist = function (data, name) {\n recapcount = count(data, vars = \"tag\")\n recaps = recapcount$freq\n bw = seq(min(recaps), max(recaps), 1)\n hist(recaps, breaks = bw, xlab = paste(\"num\", name, \"recaptures\", sep = \" \"), ylab = \"count\",\n col = \"gray60\", border = \"white\", main = \"\")\n}\n\n# \n# ######################### plotting commands - mostly density plots, not exactly correct ##############################\n# \n# # plot density of movment by guild for 2000-2009 - DENSITY PLOTS AREN'T REALLY \"CORRECT\" BECAUSE DATA ARE DISCRETE\n# plot(density(Hgran), main = 'Portal movement by guild', xlab = 'meters', lwd = 2, col = 'hotpink2')\n# lines(density(Cgran), col = 'deepskyblue3', lwd = 3, lty = 6)\n# lines(density(naometers), col = 'indianred4', lwd = 4, lty = 3)\n# lines(density(foli), col = 'mediumpurple4', lwd = 4, lty = 3)\n# lines(density(insectiv), col = 'darkgreen', lwd = 2)\n# legend('topright', c('Hgran', 'Neotoma', 'foliv', 'Cgran', 'insec'), bty = 'n', lty = c(1,3,6,3,1), lwd = 5, seg.len = 2,\n# col = c('hotpink2', 'indianred4', 'mediumpurple4', 'deepskyblue3', 'darkgreen'))\n# \n# \n# ############## MOVING\n# # get list of indivs that moved plots or treatment, species is included\n# moving_rats = find_rats_that_move(heteros, tags, 8, 9, 3, 4)\n# \n# # subset tags that never leave a plot\n# stationary_hets = heteros\n# plotmovers = unique(moving_rats$tag)\n# for (i in 1:length(plotmovers)) {\n# stationary_hets = subset(stationary_hets, tag != plotmovers[i])\n# }\n# \n# # list the stakes it inhabits\n# tags = unique(stationary_hets$tag)\n# stakemoves = examine_stake_moves(stationary_hets, tags, 5, 6, 7, 8, 9)\n# \n# # calculate the distances between each trapping location\n# plot_stake_moves(stationary_hets, tags, 5, 4, 8, 9)\n# \n# \n# # plot density of all consecutive movement from rodents within a species 2000-2009, NOT A GREAT WAY TO PLOT THIS DATA\n# plot(density(pf_meters), main = paste(\"P. flavus (\", length(pftags), \" = i, \", length(pf_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(pp_meters), main = paste(\"C. penicillatus (\", length(pptags), \" = i, \", length(pp_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(pb_meters), main = paste(\"C. baileyi (\", length(pbtags), \" = i, \", length(pb_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(do_meters), main = paste(\"D. ordii (\", length(dotags), \" = i, \", length(do_meters), \" = N)\", sep = ''), lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(dm_meters), main = paste(\"D. merriami (\", length(dmtags), \"= i, \", length(dm_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# \n# plot(density(pe_meters), main = paste(\"P. eremicus (\", length(petags), \" = i, \", length(pe_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(pm_meters), main = paste(\"P. maniculatus (\", length(pmtags), \" = i, \", length(pm_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(rm_meters), main = paste(\"R. megalotis (\", length(rmtags), \"= i, \", length(rm_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# \n# plot(density(sh_meters), main = paste(\"S. hispidus (\", length(shtags), \" = i, \", length(sh_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(sf_meters), main = paste(\"S. fulviventer (\", length(sftags), \" = i, \", length(sf_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(nao_meters), main = paste(\"N. albigula (\", length(naotags), \" = i, \", length(nao_meters), \" = N)\", sep = ''), lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# \n# plot(density(ot_meters), main = paste(\"O. torridus (\", length(ottags), \" = i, \", length(ot_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# plot(density(ol_meters), main = paste(\"O. leucogaster (\", length(oltags), \" = i, \", length(ol_meters), \" = N)\", sep = ''), xlab = 'meters', lwd = 2, xlim = c(0,500), ylim = c(0,.1))\n# \n# \n# #plot all together using density WHICH IS NOT REALLY A GOOD WAY TO PLOT THIS DATA\n# plot(density(pb_meters), main = 'Portal rodent movement', xlab = 'meters', lwd = 2, col = 'peru')\n# lines(density(pp_meters), col = 'black', lwd = 2)\n# lines(density(do_meters), col = 'mediumpurple4', lwd = 4, lty = 3)\n# lines(density(dm_meters), col = 'maroon4', lwd = 4, lty = 3)\n# lines(density(pf_meters), col = 'hotpink', lwd = 2)\n# \n# lines(density(pe_meters), col = 'deepskyblue3', lwd = 3, lty = 6)\n# lines(density(pm_meters), col = 'royalblue4', lwd = 3, lty = 6)\n# lines(density(rm_meters), col = 'cadetblue', lwd = 3, lty = 6)\n# \n# lines(density(sh_meters), col = 'indianred', lwd = 3)\n# lines(density(sf_meters), col = 'brown', lwd = 3)\n# lines(density(nao_meters), col = 'gray60', lwd = 3)\n# \n# lines(density(ot_meters), col = 'darkgreen', lwd = 2)\n# lines(density(ol_meters), col = 'darkolivegreen', lwd = 2)\n# \n# legend('topright', c('PB','PP','DO','DM','PE','PM','OT','OL'), bty = 'n', lty = c(1,1,3,3,6,6,1,1), lwd = 10, seg.len = 2, \n# col = c('peru', 'black', 'mediumpurple4','maroon4','deepskyblue3', 'royalblue3','darkgreen', 'darkolivegreen'))\n# abline(v = 70.71, lty = 2, col = 'gray60', lwd = 2)\n\n#---------------------------------------------------------------------------------\n# mark winter months\n#---------------------------------------------------------------------------------\n# small-bodied species may be rare during winter months (go into torpor)\n# we want to mark the months that this is, so we can later denote them properly in MARK\npp = subset(het, species == \"PP\")\nbarplot(table(pp$mo))\npf = subset(het, species == \"PF\")\nbarplot(table(pf$mo))\n\n\nprds = c(261:380)\nwinter = c()\n\nfor (p in 1:length(prds)){\n dat = subset(allrats, period == prds[p])\n mo = dat[1,2]\n if (mo %in% list(12, 1, 2)){\n winter = append(winter, prds[p])\n }\n}\n\n", "meta": {"hexsha": "e4a89a1ceba2926d2ae96fc5fb17029537b90404", "size": 22357, "ext": "r", "lang": "R", "max_stars_repo_path": "additional_movement_fxns.r", "max_stars_repo_name": "weecology/portal-rodent-dispersal", "max_stars_repo_head_hexsha": "25f543f08fd58679c848835dd762ca1dfe4f17d3", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, "max_stars_repo_stars_event_min_datetime": "2021-04-04T00:48:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-04T00:48:03.000Z", "max_issues_repo_path": "additional_movement_fxns.r", "max_issues_repo_name": "weecology/portal-rodent-dispersal", "max_issues_repo_head_hexsha": "25f543f08fd58679c848835dd762ca1dfe4f17d3", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, 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{"text": ".II \"awk^tutorial\"\n.ds TL \"The awk Language\"\n.NH \"Introduction to the awk Language\" 1\n.PP\n\\fBawk\\fR is a general-purpose language for processing text.\nWith \\fBawk\\fR, you can manipulate strings,\nprocess records, and generate reports.\n.PP\n.II \"Aho, A.V.\"\n.II \"Weinberger, P.J.\"\n.II \"Kernighan, Brian W.\"\n.B awk\nis named after its creators:\nA. V. Aho, P. J. Weinberger, and Brian W. Kernighan.\nUnfortunately, its name suggests that\n.B awk\nis awkward \\(em whereas in truth the\n.B awk\nlanguage is simple, elegant, and powerful.\nWith it, you can perform many tasks that would otherwise require\nhours of drudgery.\n.PP\n\\fBawk\\fR uses a simple syntax.\nEach statement in an \\fBawk\\fR program contains\neither or both of two elements:\na \\fIpattern\\fR and an \\fIaction\\fR.\nThe pattern tells \\fBawk\\fR what lines to select from the input stream;\nand the action tells \\fBawk\\fR what to do with the selected data.\n.PP\nThis tutorial explains how to write\n.B awk\nprograms.\nIt explains how to describe a pattern to \\fBawk\\fR.\nIt also describes the range of actions that\n.B awk\ncan perform;\nthese include formatted printing of text,\nassigning variables, defining arrays, and controlling the flow of data.\n.SH \"Example Files\"\n.PP\nBefore you begin to study\n.BR awk ,\nplease take the time to type the following text files that\nare used by the examples in this tutorial.\n.PP\nThe first is some text from Shakespeare.\nUse the command \\fBcat\\fR to type it into the file\n.BR text1 ,\nas follows.\nNote that \\fB\\fR means that you should hold down the \\fBCtrl\\fR\n(or \\fBcontrol\\fR) key and simultaneously press `D'.\nDo not type it literally.\n.DM\n.II \"Shakespeare, William\"\n\tcat > text1\n\tWhen, in disgrace with fortune and men's eyes,\n\tI all alone beweep my outcast state,\n\tAnd trouble deaf heaven with my bootless cries,\n\tAnd look upon myself, and curse my fate,\n\tWishing me like to one more rich in hope,\n\tFeatured like him, like him with friends possest,\n\tDesiring this man's art and that man's scope,\n\tWith what I most enjoy contented least.\n\tYet in these thoughts myself almost despising,\n\tHaply I think on thee - and then my state,\n\tLike to the lark at break of day arising\n\tFrom sullen earth, sings hymns at heaven's gate;\n\tFor thy sweet love remember'd such wealth brings\n\tThat then I scorn to change my state with kings.\n\t\n.DE\n.II \"Ruth, Babe\"\nThe second example consists of some of Babe Ruth's batting statistics,\nwhich we will use to demonstrate how \\fBawk\\fR processes tabular input.\nType it into file\n.BR table1 ,\nas follows:\n.DM\n\tcat > table1\n\t1920\t.376\t54\t158\t137\n\t1921\t.378\t59\t177\t171\n\t1922\t.315\t35\t94\t99\n\t1923\t.393\t41\t151\t131\n\t1924\t.378\t46\t143\t121\n\t1925\t.290\t25\t61\t66\n\t1926\t.372\t47\t139\t145\n\t1927\t.356\t60\t158\t164\n\t1928\t.323\t54\t163\t142\n\t1929\t.345\t46\t121\t154\n\t\n.DE\nThe columns give, respectively,\nthe season, the batting average, and the numbers of\nhome runs, runs scored, and runs batted in (RBIs).\n.PP\nThe rest of this tutorial presents many examples that use these files.\nType them in and run them!\nIn that way, you can get a feel for\n.B awk\ninto your fingers.\nExperiment; try some variations on the examples.\nDon't be afraid of making mistakes; this is one good way to learn the\nlimits (and the strengths) of a language.\n.SH \"Using awk\"\n.PP\n\\fBawk\\fR reads input from the standard input\n(entered from your terminal or from a file you specify),\nprocesses each input line according to a specified \\fBawk\\fR program,\nand writes output to the standard output.\nThis section explains the structure of an \\fBawk\\fR program\nand the syntax of \\fBawk\\fR command lines.\n.Sh \"Command-line Options\"\n.PP\n.II \"awk^command-line options\"\nThe complete form for the \\fBawk\\fR command line is as follows:\n.DM\n\t\\fBawk\\fR [\\fB-y\\^\\fR] [\\fB\\-F\\fIc\\^\\fR] [\\fB\\-f \\fIprogfile\\^\\fR] [\\fIprog\\^\\fR] [\\fIfile1\\^\\fR] [\\fIfile2\\^\\fR] ...\n.DE\nThe following describes each element of the command line.\n.IP \\fB\\-y\\fR\nMap\n.I patterns\nfrom lower case to both lower-case and upper-case letters.\nFor example, with this option the string\n.B the\nwould match\n.B the\nor\n.BR The .\n.IP \\fB\\-F\\fIc\\fR\nSet the field-separator character to the character\n.IR c .\nThe field-separator character and its uses are described below.\n.IP \"\\fB\\-f \\fIprogfile\\fR\"\nRead the\n.B awk\nprogram from\n.I progfile .\n.IP \\fIprog\\fR\nAn\n.B awk\nprogram to execute.\nIf you do not use the\n.B -f\noption, you must enter\n.BR awk 's\nstatements on its command line.\n.sp \\n(pDu\nNote that if you include\n.BR awk 's\nprogram on its command line (instead of in a separate file),\nyou must enclose the program between apostrophes.\nOtherwise, some of the\n.B awk\nstatements will be modified by the shell before \\fBawk\\fR ever sees them,\nwhich will make a mess of your program.\nFor example:\n.DM\n\tawk 'BEGIN {print \"sample output file\"}\n\t {print NR, $0}'\n.DE\n(The following sections explain\nwhat the stuff between the apostrophes means.)\nHowever, if you include the statement within a file that you pass to\n.B awk\nvia its \\fB\\-f\\fR option, you must \\fInot\\fR enclose the statements within\nparentheses; otherwise,\n.B awk\nwill become very confused.\nIf you were to put the statements in the above program into an\n.B awk\nprogram file, they would appear as follows:\n.DM\n\tBEGIN {print \"sample output file\"}\n {print NR, $0}\n.DE\n.IP \"\\fIfile1 file2 ...\\fR\"\nThe files whose text you wish to process.\nFor example, the command\n.DM\n\tawk '{print NR, $0}' text1\n.DE\nprints the contents of\n.BR text1 ,\nbut precedes each line with a line number.\n.sp \\n(pDu\nIf you do not name an input file,\n.B awk\nprocesses what it reads from the standard input.\nFor example, the command\n.DM\n\tawk '{print NR, $0}'\n.DE\nreads what you type from the keyboard and echoes it preceded with a line\nnumber.\nTo exit from this program, type\n.BR .\n.SH \"Structure of an awk Program\"\n.PP\n.II \"awk^statement\"\nAn\n.B awk\nprogram consists of one or more statements of the form:\n.DS\n\t\\fIpattern \\fB{ \\fIaction \\fB}\\fR\n.DE\nNote that\n.B awk\ninsists that the\naction\nbe enclosed between braces, so that it can distinguish the action\nfrom the pattern.\n.PP\nA program can contain as many statements\nas you need to accomplish your purposes.\nWhen\n.B awk\nreads a line of input,\nit compares that line with the \\fIpattern\\fR in each statement.\nEach time a line matches \\fIpattern\\fR,\n\\fBawk\\fR performs the corresponding \\fIaction\\fR.\n.B awk\nthen reads the next line of input.\n.PP\nA statement can specify an \\fIaction\\fR without a \\fIpattern\\fR.\nIn this case,\n.B awk\nperforms the action on every line of input.\nFor example, the program\n.DM\n\tawk '{ print }' text1\n.DE\nprints every line of\n.B text1\nonto the standard output.\n.PP\nAn \\fBawk\\fR program may also specify a pattern without an action.\nIn this case, when an input line matches the pattern,\n\\fBawk\\fR prints it on the standard output.\nFor example, the command\n.DM\n\tawk 'NR > 0' table1\n.DE\nprints all of\n.B table1\nonto the standard output.\nNote that you can use the same pattern in more than one statement.\nExamples of this will be given below.\n.PP\n.BR awk 's\nmethod of forming patterns uses\n.I \"regular expressions\"\n(also called\n.IR patterns ),\nlike those used by the \\*(CO commands\n.BR sed ,\n.BR ed ,\nand\n.BR egrep .\nLikewise,\n.BR awk 's\nmethod of constructing actions is modelled after the C programming language.\nIf you are familiar with regular expressions and with C, you should have\nno problem learning how to use\n.BR awk .\nHowever,\nif you are not familiar with them, they will be explained in the following\nsections.\n.Sh \"Records and Fields\"\n.PP\n.II \"awk^records\"\n.II \"awk^fields\"\n\\fBawk\\fR divides its input into\n.IR records .\nIt divides each record, in turn, into\n.IR fields .\n.II \"awk^input-record separator\"\n.II \"awk^input-field separator\"\nRecords are separated by a character called\nthe\n.IR \"input-record separator\" ;\nlikewise, fields are separated by the\n.IR \"input-field separator\" .\n.B awk\nin effect conceives of its input as a table with an indefinite\nnumber of columns.\n.PP\nThe newline character is the default input-field separator,\nso \\fBawk\\fR normally regards each input line as a separate record.\nThe space and the tab characters are\nthe default input-field separator,\nso white space normally separates fields.\n.PP\nTo address a field within a record, use the syntax \\fB$\\fIN\\fR, where\n.I N\nis the number of the field within the current record.\nThe pattern \\fB$0\\fR addresses the entire record.\nExamples of this will be given below.\n.II \"awk^output-record separator\"\n.II \"awk^output-field separator\"\nIn addition to input record and field separators,\n\\fBawk\\fR provides output record and field separators,\nwhich it prints between output records and fields.\nThe default output-field separator\nis the newline character;\n\\fBawk\\fR normally prints each output record as a separate line.\nThe space character is the default output-field separator.\n.SH \"Patterns\"\n.PP\nThis section describes how\n.B awk\ninterprets the pattern section of a statement.\n.Sh \"Special Patterns\"\n.PP\n.II \"awk^special patterns\"\n.II \"awk^pattern, special\"\nTo begin,\n.B awk\ndefines and sets a number of special \\fIpattern\\fRs.\nYou can use these patterns in your program for special purposes.\nYou can also redefine some of these patterns to suit your preferences.\nThe following describes the commonest such special\n.IR patterns ,\nand how they're used:\n.II \"awk^BEGIN\"\n.IP \\fBBEGIN\\fR\nThis pattern matches the beginning of the input file.\n.B awk\nexecutes all\n.I actions\nassociated with this pattern before it begins to read input.\n.II \"awk^END\"\n.IP \\fBEND\\fR\nThis pattern matches the end of the input file.\n.B awk\nexecutes all\n.I actions\nassociated with this pattern after it had read all of its input.\n.II \"awk^FILENAME\"\n.IP \\fBFILENAME\\fR\n.B awk\nsets this pattern to the name of the file\nthat it is currently reading.\nShould you name more than one input file on the command line,\n.B awk\nresets this pattern as it reads each file in turn.\n.II \"awk^FS\"\n.IP \\fBFS\\fR\nInput-field separator.\nThis pattern names the character that\n.B awk\nrecognizes as the field separator for the records it reads.\n.II \"awk^NF\"\n.IP \\fBNF\\fR\nThis pattern gives the number of fields within the current record.\n.II \"awk^NR\"\n.IP \\fBNR\\fR\nThis pattern gives the number of the current record within the\ninput stream.\n.II \"awk^OFS\"\n.IP \\fBOFS\\fR\nOutput-field separator.\n.B awk\nsets this pattern to the character that it writes in its output to\nseparate one field from another.\n.II \"awk^ORS\"\n.IP \\fBORS\\fR\nOutput-record separator.\n.B awk\nsets this pattern to the character that it writes in its output to\nseparate one field from another.\n.II \"awk^RS\"\n.IP \\fBRS\\fR\nInput-record separator.\n.B awk\nsets this pattern to the character by which it separates records\nthat it reads.\n.Sh \"Arithmetic Relational Expressions\"\n.II \"awk^arithmetic operators\"\n.PP\nAn\n.I operator\nmarks a task to be within an expression,\nmuch as the `+' or `/' within an arithmetic expression indicates\nthat the numbers are to be, respectively, added or divided.\nYou can use \\fBawk\\fR's operators to:\n.IP \\(bu 0.3i\nCompare a special pattern with a variable, a field, or a constant.\n.IS \\(bu\nAssign a value to a variable or to a special pattern.\n.IS \\(bu\nDictate the relationship among two or more expressions.\n.PP\nThe first type of operator to be discussed are\n.IR \"arithmetic relational operators\" .\nThese compare the input text with an arithmetic value.\n.B awk\nrecognizes the following arithmetic operators:\n.DS\n\t\\fB<\\fR\tLess than\n\t\\fB<=\\fR\tLess than or equal to\n\t\\fB==\\fR\tEquivalent\n\t\\fB!=\\fR\tNot equal\n\t\\fB>=\\fR\tGreater than or equal to\n\t\\fB>\\fR\tGreater than\n.DE\nWith these operators, you can compare a field with a constant;\ncompare one field with another;\nor compare a special pattern with either a\nfield or a constant.\n.PP\nFor example, the following\n.B awk\nprogram prints all of the years in which Babe Ruth hit more than\n50 home runs:\n.DM\n\tawk '$3 >= 50' table1\n.DE\n(As you recall, column 3 in the file\n.B table1\ngives the number of home runs.)\nThe program prints the following on your screen:\n.DM\n\t1920\t.376\t54\t158\t137\n\t1921\t.378\t59\t177\t171\n\t1927\t.356\t60\t158\t164\n\t1928\t.323\t54\t163\t142\n.DE\nThe following program, however, shows the years in which Babe Ruth scored\nmore runs than he drove in:\n.DM\n\tawk '$4 > $5 { print $1 }' table1\n.DE\nRemember, field 4 in file\n.B table1\ngives the number of runs scored, and field 5 gives the number of\nruns batted in.\nYou should see the following on your screen:\n.DM\n\t1920\n\t1921\n\t1923\n\t1924\n\t1928\n.DE\nIn the above program, expression\n.DM\n\t{print $1}\n.DE\ndefines the action to perform, as noted by the fact that expression is\nenclosed between braces.\nIn this case, the program tells\n.B awk\nthat if the input record matches the pattern, to print only the first\nfield.\nHowever, to print both the season and the number of runs scored, use the\nfollowing program:\n.DM\n\tawk '$4 > $5 { print $1, $4 }' table1\n.DE\nThis prints the following:\n.DM\n\t1920 158\n\t1921 177\n\t1923 151\n\t1924 143\n\t1928 163\n.DE\nNote that\n.B $1\nand\n.B $4\nare separated by a comma.\n.II \"awk^,\"\nThe comma tells\n.B awk\nto print its default output-field separator between columns.\nIf we had left out the comma, the output would have appeared as follows:\n.DM\n\t1920158\n\t1921177\n\t1923151\n\t1924143\n\t1928163\n.DE\nAs we noted above, the special pattern\n.B OFS\ngives the output-field separator.\n.B awk\nby default defines this special pattern to the space character.\nIf we wish to redefine the output-field separator,\nwe can use an operator, plus the special pattern\n.BR BEGIN ,\nas follows:\n.DM\n\tawk 'BEGIN { OFS = \":\" }\n\t\t$4 > $5\t{ print $1, $4 }' table1\n.DE\nThis prints:\n.DM\n\t1920:158\n\t1921:177\n\t1923:151\n\t1924:143\n\t1928:163\n.DE\nThe first statement\n.DM\n\tBEGIN { OFS = \":\"}\n.DE\ntells\n.B awk\nto set the output-field separator (the special pattern \\fBOFS\\fR)\nto `:' before it processes any input (as indicated by the special\npattern \\fBBEGIN\\fR).\n.PP\nAlthough we're getting a little ahead of ourselves, note that there's\nno reason to print the fields in the order in which they appear in\nthe input record.\nFor example, if you wish to print the number of runs scored before the\nseason, use the command:\n.DM\n\tawk 'BEGIN { OFS = \":\"}\n\t\t$4 > $5\t{ print $4, $1 }' table1\n.DE\nThis prints:\n.DM\n\t158:1920\n\t177:1921\n\t151:1923\n\t143:1924\n\t163:1928\n.DE\nAs you recall, the special pattern\n.B NR\ngives the number of the current input record.\nYou can execute an action by comparing this pattern with a constant.\nFor example, the command\n.DM\n\tawk 'NR > 12' text1\n.DE\nprints:\n.DM\n\tFor thy sweet love remember'd such wealth brings\n\tThat then I scorn to change my state with kings.\n.DE\nThat is, the program prints every line after line 12 in the input file.\nAs you recall, a statement that has a pattern but no action\nprints the entire record that matches the pattern.\n.PP\nAs we saw with the special patterns, some patterns can be defined to\nbe numbers and others to be text.\nIf you compare a number with a string,\n.B awk\nby default makes a string comparison.\nThe following example shows how \\fBawk\\fR\ncompares one field to part of the alphabet:\n.DM\n\tawk '$1 <= \"C\"' text1\n.DE\nThis program prints:\n.DM\n\tAnd trouble deaf heaven with my bootless cries,\n\tAnd look upon myself, and curse my fate,\n.DE\nThe statement\n\\fB$1 <= \"C\"\\fR selected all records that begin\nwith an ASCII value less than or equal to that of the letter `C' (0x43) \\(em\nin this case, both lines that begin with `A' (0x41).\nIf we ran this example against\n.BR table1 ,\nit would print every record in the file.\nThis is because each record begins with the character `1' (0x31), which\nmatches the pattern \\fB$1 <= \"C\"\\fR.\n.PP\nFinally, you can use a numeric field plus a constant in a comparison\nstatement.\nFor example, the following program prints all of the seasons in which\nBabe Ruth had at least 100 more runs batted in than home runs:\n.DM\n\tawk '$3 + 100 < $5 {print $1}' table1\n.DE\nThis prints the following:\n.DM\n\t1921\n\t1927\n\t1929\n.DE\n.Sh \"Boolean Combinations of Expressions\"\n.PP\n.II \"awk^Boolean operators\"\n.B awk\nhas a number of operators, called\n.I Boolean\noperators, that let you hook together several small expressions\ninto one large, complex expression.\n.B awk\nrecognizes the following Boolean operators:\n.DS\n\t\\fB||\\fR\tBoolean OR (one expression or the other is true)\n\t\\fB&&\\fR\tBoolean AND (both expressions are true)\n\t\\fB!\\fR\tBoolean NOT (invert the value of an expression)\n.DE\n.II \"Boole, George\"\n(The eponym ``Boolean'' comes from the English mathematician George Boole.)\nIn a Boolean expression,\n.B awk\nevaluates each sub-expression to see if it is true or false;\nthe relationship of sub-expressions (as set by the Boolean operator)\nthen determines whether the entire expression is true or false.\n.PP\nFor example, the following program prints all seasons in which\nBabe Ruth hit between 40 and 50 home runs:\n.DM\n\tawk '$3 >= 40 && $3 <= 50 { print $1, $3 }' table1\n.DE\nThis prints the following:\n.DM\n\t1923 41\n\t1924 46\n\t1926 47\n\t1929 46\n.DE\nIn the above program,\n.B awk\nprinted its output only if the subexpression \\fB$3 >= 40\\fR\nwas true \\fIand\\fR (\\fB&&\\fR) the subexpression \\fB$3 <= 50\\fR\nwas true.\n.PP\nThe next example demonstrates the Boolean OR operator.\nIt prints all seasons for which Babe Ruth hit fewer\nthan 40 home runs or more than 50 home runs:\n.DM\n\tawk '$3 < 40 || $3 > 50 { print $1, $3}' table1\n.DE\nThis example prints the following:\n.DM\n\t1920 54\n\t1921 59\n\t1922 35\n\t1925 25\n\t1927 60\n\t1928 54\n.DE\nIn this example,\n.B awk\nprinted its output if the subexpression \\fB$3 < 40\\fR\nwas true \\fIor\\fR (\\fB||\\fR) the subexpression \\fB$3 > 50\\fR was true.\nNote that the output would also printed if both subexpressions were true\n(although in this case, this is impossible).\n.PP\nFinally, the Boolean operator `!' negates the truth-value of any expression.\nFor example, the expression \\fB$1 = \"And\"\\fR is true if the first field\nin the current record equals ``And''; however, the expression\n\\fB$1 != \"And\"\\fR is true if the first field does \\fInot\\fR equal ``And''.\nFor example, the program\n.DM\n\tawk '$1 != \"And\"' text1\n.DE\nprints:\n.DM\n\tWhen, in disgrace with fortune and men's eyes,\n\tI all alone beweep my outcast state,\n\tWishing me like to one more rich in hope,\n\tFeatured like him, like him with friends possest,\n\tDesiring this man's art and that man's scope,\n\tWith what I most enjoy contented least.\n\tYet in these thoughts myself almost despising,\n\tHaply I think on thee - and then my state,\n\tLike to the lark at break of day arising\n\tFrom sullen earth, sings hymns at heaven's gate;\n\tFor thy sweet love remember'd such wealth brings\n\tThat then I scorn to change my state with kings.\n.DE\nThese are the 12 lines from \\fBtext1\\fR that do not begin with ``And''.\n.PP\nNote that\n.B awk\nevaluates all operators from left to right unless sub-expressions are\ngrouped together with parentheses, as is described in the following section.\n.Sh \"Patterns\"\n.II \"awk^patterns\"\n.PP\nThe previous examples have all matched strings or numbers against predefined\nfields in each input record.\nThis is fine for manipulating tabular information, like our table of\nBabe Ruth's batting statistics, but it is not terribly useful when\nyou are processing free text.\nFree text is not organized into predefined columns, nor are you likely to\nknow which field (that is, which word) will contain the pattern you're seeking.\n.PP\n.II ed\n.II sed\nTo help you manage free text,\n.B awk\nhas a pattern-matching facility that resembles those of the editors\n.B ed\nand\n.BR sed .\n.PP\nThe most common way to search for a pattern is to enclose it between slashes.\nFor example, the program\n.DM\n\tawk '/and/' text1\n.DE\nprints every line in\n.B text1\nthat contains the string ``and''.\n.DM\n\tWhen, in disgrace with fortune and men's eyes,\n\tAnd look upon myself, and curse my fate,\n\tDesiring this man's art and that man's scope,\n\tHaply I think on thee - and then my state,\n.DE\nNote that ``and'' does not have to be a word by itself \\(em it can be\na fragment within a word as well.\nNote, too, that this pattern matches ``and'' but does not match ``And'' \\(em\nbut it would if we were to use the \\fB\\-y\\fR option on the \\fBawk\\fR command\nline (described above).\n.PP\nYou can use Boolean operators to search for more than one string at once.\nFor example, the program\n.DM\n\tawk '/and/ && /or/' text1\n.DE\nfinds every line in \\fBtext1\\fR that contains both ``and'' and ``or''.\nThere is only one:\n.DM\n\tWhen, in disgrace with fortune and men's eyes,\n.DE\nNote that the ``or'' in this line is embedded in the word ``fortune''.\n.PP\n.B awk\ncan also scan for classes and types of characters.\nTo do so, enclose the characters within brackets and place the bracketed\ncharacters between the slashes.\nFor example, the following program looks for every line in \\fBtext1\\fR\nthat contains a capital `A' through a capital `E':\n.DM\n\tawk '/[A-E]/' text1\n.DE\nThis prints the following:\n.DM\n\tAnd trouble deaf heaven with my bootless cries,\n\tAnd look upon myself, and curse my fate,\n\tDesiring this man's art and that man's scope,\n.DE\n.II \"awk^special characters\"\nIn addition, you can use the following\nspecial characters for further flexibility:\n.DS\n\t\\fB[ ]\\fR\tClass of characters\n\t\\fB( )\\fR\tGrouping subexpressions\n\t\\fB|\\fR\tAlternatives among expressions\n\t\\fB+\\fR\tOne or more occurrences of the expression\n\t\\fB?\\fR\tZero or more occurrences of the expression\n\t\\fB*\\fR\tZero, one, or more occurrences of the expression\n\t\\fB.\\fR\tAny non-newline character\n.DE\nWhen adding a special character to a pattern, enclose the special\ncharacter as well as the rest of the pattern\nwithin slashes.\n.PP\nTo search for a string that contains one of\nthe special characters, you must precede the\ncharacter with a backslash.\nFor example, if you are looking for the string ``today?'',\nuse the following pattern:\n.DM\n\t/today\\e?/\n.DE\nWhen you need to find an expression in a\nparticular field, not just anywhere in the\nrecord, you can use one of these operators:\n.DS\n\t\\fB~\\fR\tContains the data in question\n\t\\fB!~\\fR\tDoes not contain the data in question\n.DE\nFor example, if you need to find the digit `9'\nin the fourth field of file \\fBtable1\\fR, use the following\nprogram:\n.DM\n\tawk '$4~/9/ {print $1, $4}' table1\n.DE\nThis prints the following:\n.DM\n\t1922 94\n\t1926 139\n.DE\nAs you can see, the above program found every record with a `9' in its\nfourth field, regardless of whether the `9' came at the beginning of the\nfield or its end.\n.B awk\nalso recognizes two operators that let you set where a pattern is within\na field:\n.DS\n\t\\fB^\\fR\tBeginning of the record or field\n\t\\fB$\\fR\tEnd of the record or field\n.DE\nFor example, to find every record in \\fBtable1\\fR whose fourth field\n\\fIbegins\\fR with a `9', run the following program:\n.DM\n\tawk '$4~/^9/ {print $1, $4}' table1\n.DE\nThis prints:\n.DM\n\t1922 94\n.DE\nFinally, to negate a pattern use the operator \\fB!~\\fR.\nFor example, to print every record in\n.B table1\nwhose fourth column does\n.I not\nbegin with a `9', use the following program:\n.DM\n\tawk '$4!~/^9/ {print $1, $4}' table1\n.DE\nThis prints:\n.DM\n\t1920 158\n\t1921 177\n\t1923 151\n\t1924 143\n\t1925 61\n\t1926 139\n\t1927 158\n\t1928 163\n\t1929 121\n.DE\n.Sh \"Ranges of Patterns\"\n.PP\n.II \"awk^patterns, range\"\nYou can tell \\fBawk\\fR to perform an action\non all records between two patterns.\nFor example, to print all records between the\n.I patterns\n\\fB1925\\fR and \\fB1929\\fR, inclusive,\nenclose the strings in slashes and separate them with a comma,\nthen indicate the\n.B print\naction, as follows:\n.DM\n\tawk '/1925/,/1929/ { print }' table1\n.DE\nYou can also use the special pattern\n.B NR\n(or \\fIrecord number\\^\\fR) to name a range of record numbers.\nFor example, to print records 5 through 10 of file\n.BR text1 ,\nuse the following program:\n.DM\n\tawk 'NR == 5, NR == 10 { print }' text1\n.DE\n.Sh \"Resetting Separators\"\n.PP\n.II \"awk^separators, reset\"\nAs noted above,\n.B awk\nrecognizes certain characters by default to parse its input into\nrecords and fields, and to separate its output into records and fields:\n.II \"awk^FS\"\n.IP \\fBFS\\fR\nInput-field separator.\nBy default, this is one or more white-space characters (tabs or spaces).\n.II \"awk^OFS\"\n.IP \\fBOFS\\fR\nOutput-field separator.\nBy default, this is exactly one space character.\n.II \"awk^ORS\"\n.IP \\fBORS\\fR\nOutput-record separator.\nBy default, this is the newline character.\n.II \"awk^RS\"\n.IP \\fBRS\\fR\nInput-record separator.\nBy default, this is the newline character.\n.PP\nBy resetting any of these special patterns, you can change how\n.B awk\nparses its input or organizes its output.\nConsider, for example, the command:\n.DM\n\tawk 'BEGIN {ORS = \"|\"}\n\t\t/1920/,/1925/ {print $1, $5}' table1\n.DE\nThis prints the following:\n.DM\n\t1920 137|1921 171|1922 99|1923 131|1924 121|1925 66|\n.DE\nAs you can see, this prints the season and the number of runs batted\nin for the 1920 through 1925 season.\nHowever,\n.B awk\nuses the pipe character `|' instead of the newline character to separate\nrecords.\nIf you wish to change the output-field separator as well as the\noutput-record separator, use the program:\n.DM\n\tawk 'BEGIN {ORS = \"|\" ;\tOFS = \":\"}\n\t\t/1920/,/1925/ {print $1, $5}' table1\n.DE\nThis produces:\n.DM\n\t1920:137|1921:171|1922:99|1923:131|1924:121|1925:66|\n.DE\n.II \"awk^;\"\nAs you can see,\n.B awk\nhas used the colon `:' instead of a white-space character to separate\none field from another.\n.PP\nNote, too, that the semicolon `;' character separates expressions\nin the action portion of the statement associated with the\n.B BEGIN\npattern.\nThis lets you associate more than one action with a given pattern, so you\ndo not have to repeat that pattern.\nThis is discussed at greater length below.\n.PP\nYou can also change the input-record separator from the newline\ncharacter to something else that you prefer.\nFor example, the following program changes the input-record separator\nfrom the newline to the comma:\n.DM\n\tawk 'BEGIN {RS = \",\"}\n\t\t{print $0}' text1\n.DE\nThis yields the following:\n.DM\n\tWhen\n\t in disgrace with fortune and men's eyes\n.sp \\n(pDu\n\tI all alone beweep my outcast state\n.sp \\n(pDu\n\tAnd trouble deaf heaven with my bootless cries\n.sp \\n(pDu\n\tAnd look upon myself\n\t and curse my fate\n.sp \\n(pDu\n\tWishing me like to one more rich in hope\n.sp \\n(pDu\n\tFeatured like him\n\t like him with friends possest\n.sp \\n(pDu\n\tDesiring this man's art and that man's scope\n.sp \\n(pDu\n\tWith what I most enjoy contented least.\n\tYet in these thoughts myself almost despising\n.sp \\n(pDu\n\tHaply I think on thee - and then my state\n.sp \\n(pDu\n\tLike to the lark at break of day arising\n\tFrom sullen earth\n\t sings hymns at heaven's gate;\n\tFor thy sweet love remember'd such wealth brings\n\tThat then I scorn to change my state with kings.\n.DE\nThe blank lines resulted from a comma's occurring at the end of a line.\n.PP\n.II \"awk^=\"\nNote that by specifying the null string \\fB(RS=\"\")\\fR,\nyou can make two consecutive newlines the record separator.\nNote, too, that only one character can be the input-record separator.\nIf you try to reset this separator to a string,\n.B awk\nuses the first character in the string as the separator, and ignores the rest.\n.PP\nYou can change the input-field separator by redefining\n.BR FS .\nThe default\n.B FS\nis \\fB\\et\\fR exactly and in that order\n(where \\fB\\fR is the space character).\nIn this case,\n.B awk\nuses its ``white-space rule,'' in which\n.B awk\ntreats any sequence of spaces and tabs as a single separator.\nThis is the default rule for\n.BR FS .\nIf you set\n.B FS\nto anything else, including \\fB\\et\\fR, then each separator is separate.\nFor example, the following program changes the input-field separator\nto the comma and prints the first such field it finds in each line from file\n.BR text1 :\n.DM\n\tawk 'BEGIN {FS = \",\"}\n\t\t{print $1}' text1\n.DE\nThis produces:\n.DM\n\tWhen\n\tI all alone beweep my outcast state\n\tAnd trouble deaf heaven with my bootless cries\n\tAnd look upon myself\n\tWishing me like to one more rich in hope\n\tFeatured like him\n\tDesiring this man's art and that man's scope\n\tWith what I most enjoy contented least.\n\tYet in these thoughts myself almost despising\n\tHaply I think on thee - and then my state\n\tLike to the lark at break of day arising\n\tFrom sullen earth\n\tFor thy sweet love remember'd such wealth brings\n\tThat then I scorn to change my state with kings.\n.DE\nAs you can see, this program prints text up to the first comma in\neach line.\n.B awk\nthrows away the comma itself, because the input-field separator is not\nexplicitly printed.\n.PP\nYou can define several characters to be input-field separators\nsimultaneously.\nWhen you specify several characters within\nquotation marks, each character becomes a field\nseparator, and all separators have equal precedence.\nFor example, you can specify the letters `i', `j', and `k' to be\ninput-field separators.\nThe following program does this, and prints the first field so\ndefined from each record in file\n.BR text1 :\n.DM\n\tawk 'BEGIN {FS = \"ijk\"}\n\t\t{print $1}' text1\n.DE\nThis prints:\n.DM\n\tWhen, \n\tI all alone beweep my outcast state,\n\tAnd trouble deaf heaven w\n\tAnd loo\n\tW\n\tFeatured l\n\tDes\n\tW\n\tYet \n\tHaply I th\n\tL\n\tFrom sullen earth, s\n\tFor thy sweet love remember'd such wealth br\n\tThat then I scorn to change my state w\n.DE\nNote that if you set the input-record separator to a null string, you\ncan use the newline character as the input-field separator.\nThis is a handy way to concatenate clusters of lines into records that\nyou can then manipulate further.\n.PP\nOne last point about the\n.B FS\nseparator.\nIf the white-space rule is not invoked and\nan assignment is made to a nonexistent field,\n.B awk\ncan add the proper number of field separators.\nFor example if\n\\fBFS=\":\"\\fR and the input line is \\fBa:b\\fR, then\nthe command \\fB$5 = \"e\"\\fR produces \\fBa:b:::e\\fR.\nIf the white-space rule were in effect,\n.B awk\nwould add spaces as if each space were a separator,\nand print a warning message.\nIn short, it would try to produce the sanest result from the error.\n.PP\nFinally, the variable\n.B NR\ngives the number of the current record.\nThe next example prints the total number of records in file\n.BR text1 :\n.DM\n\tawk 'END {print NR}' text1\n.DE\nThe output is\n.DM\n\t14\n.DE\nwhich is to be expected, since\n.B text1\nis a sonnet.\n.SH \"Actions\"\n.PP\n.II \"awk^actions\"\nThe previous section described how to construct a\n.I pattern\nfor\n.BR awk .\nFor each pattern, there must be a corresponding\n.IR action .\nSo far, the only action shown has been to print output.\nHowever,\n.B awk\ncan perform many varieties of actions.\nIn addition to printing, \\fBawk\\fR can:\n.DS\n\t\\(bu Execute built-in functions\n\t\\(bu Redirect output\n\t\\(bu Assign variables\n\t\\(bu Use fields as variables\n\t\\(bu Define arrays\n\t\\(bu Use control statements\n.DE\nThese actions are discussed in detail in the following sections.\n.PP\nAs noted above, each\n.B awk\nstatement must have an action.\nIf a statement does not include an action,\n.B awk\nassumes that the action is\n.BR {print} .\n.PP\nWithin each statement,\n.B awk\ndistinguishes an action from its corresponding pattern by the fact\nthat the action is enclosed within braces.\nNote that the action section of a statement may include several\nindividual actions;\nhowever, each action must be separated from the others by\nsemicolons `;' or newlines.\n.PP\n.II \"Free Software Foundation\"\nSome forms of\n.BR awk ,\nsuch as that provided by the Free Software Foundation (FSF),\nallow user-defined functions.\nThe FSF version of\n.B awk\nis available from the MWC BBS as well as via COHware.\nNote that your system must have at least two megabytes of RAM\nto run the FSF version of\n.BR awk .\n.Sh \"awk Functions\"\n.PP\n.II \"awk^functions\"\n\\fBawk\\fR includes the following functions with which you can\nmanipulate input.\nYou can assign a function to any variable or use it in a pattern.\nThe following lists\n.BR awk 's\nfunctions.\nNote that an\n.I argument\ncan be a variable, a field, a constant, or an expression:\n.II \"awk^abs\"\n.IP \\fBabs(\\fIargument\\^\\fB)\\fR\nReturn the absolute value of \\fIargument\\fR.\n.II \"awk^exp\"\n.IP \\fBexp(\\fIargument\\^\\fB)\\fR\n.II \"Euler, Leonhart\"\nReturn Euler's number\n.I e\n(2.178...) to the power of \\fIargument\\fR.\n.II \"awk^index\"\n.IP \\fBindex(\\fIstring1\\^\\fB,\\fIstring2\\^\\fB)\\fR\nReturn the position of \\fIstring2\\fR within\n\\fIstring1\\fR.\nIf \\fIs2\\fR does not occur in \\fIs1\\fR,\n.B awk\nreturns zero.\nThis\n.B awk\nfunction resembles the \\*(CO C function\n.BR index() .\n.II \"awk^int\"\n.IP \\fBint(\\fIargument\\^\\fB)\\fR\nReturn the integer portion of \\fIargument\\fR.\n.II \"awk^length\"\n.IP \\fBlength\\fR\nReturn the length, in bytes, of the current record.\n.IP \\fBlength(\\fIargument\\^\\fB)\\fR\nReturn the length, in bytes, of \\fIargument\\fR.\n.II \"awk^log\"\n.IP \\fBlog(\\fIargument\\^\\fB)\\fR\nReturn the natural logarithm of \\fIargument\\fR.\n.II \"awk^print\"\n.IP \"\\fBprint(\\fIargument1 argument2 ... argumentN\\^\\fB)\\fR\"\nConcatenate and print\n.I argument1\nthrough\n.IR argumentN .\n.IP \"\\fBprint(\\fIargument1\\^\\fB,\\fIargument2\\^\\fB, \\fI... argumentN\\^\\fB)\\fR\"\nPrint\n.I argument1\nthrough\n.IR argumentN .\nSeparate each\n.I argument\nwith the\n.B OFS\ncharacter.\n.II \"awk^printf\"\n.IP \"\\fBprintf(\\fIf\\^\\fB, \\fIargument1\\^\\fB, \\fI... argumentN\\^\\fB)\\fR\"\nFormat and print strings \\fIargument1\\fR through \\fIargument\\fR in the\nmanner set by the formatting string\n.IR f ,\nwhich can use \\fBprintf()\\fR-style formatting codes.\n.IP \"\\fBsplit(\\fIstr\\^\\fB, \\fIarray\\^\\fB, \\fIfs\\^\\fB)\\fR\"\nDivide the string \\fIstr\\fR into fields associated with\n\\fIarray\\fR.\nThe fields are separated by character \\fIfs\\fR or the default field separator.\n.II \"awk^sprintf\"\n.IP \"\\fBsprintf(\\fIf\\^\\fB, \\fIe1\\^\\fB, \\fIe2\\^\\fB)\\fR\"\nFormat strings \\fIe1\\fR and \\fIe2\\fR in the\nmanner set by the formatting string\n.IR f ,\nand return the formatted string.\n.I f\ncan use \\fBprintf()\\fR-style formatting codes.\n.II \"awk^sqrt\"\n.IP \"\\fBsqrt(\\fIargument\\^\\fB)\\fR\"\nReturn the square root of \\fIargument\\fR.\n.II \"awk^substr\"\n.IP \"\\fBsubstr(\\fIstr\\^\\fB, \\fIbeg\\^\\fB, \\fIlen\\^\\fB)\\fR\"\nScan string \\fIstr\\fR for position \\fIbeg\\fR;\nif found, print the next \\fIlen\\fR characters.\nIf \\fIlen\\fR is not included, print from from \\fIbeg\\fR\nto the end of the record.\n.Sh \"Printing with awk\"\n.PP\n.II \"awk^printing\"\nPrinting is the commonest task you will perform in your\n.B awk\nprograms.\n.BR awk 's\nprinting functions\n.B printf\nand\n.B sprintf\nresemble the C functions\n.B printf()\nand\n.BR sprintf() ;\nhowever, there are enough differences to make a close reading of this\nsection worthwhile.\n.PP\n.B print\nis the commonest, and simplest, \\fBawk\\fR function.\nWhen used without any arguments,\n.B print\nprints all of the current record.\nThe following example prints every record in file\n.BR text1 :\n.DM\n\tawk '{print}' text1\n.DE\nYou can print fields in any order you desire.\nFor example, the following program reverses the order of the season\nand batting-average columns from file\n.BR table1 :\n.B\n.DM\n\tawk '/1920/,/1925/ { print $2,$1 }' table1\n.DE\nThe output is as follows:\n.DM\n\t.376 1920\n\t.378 1921\n\t.315 1922\n\t.393 1923\n\t.378 1924\n\t.290 1925\n.DE\nBecause the field names are separated by a comma,\n\\fBawk\\fR inserts the\n.B OFS\nbetween the fields when it prints them.\nIf you do not separate field names with commas,\n\\fBawk\\fR concatenates the fields when it printing them.\nFor example, the program\n.DM\n\tawk '/1920/,/1925/ { print $2 $1 }' table1\n.DE\nproduces:\n.DM\n\t.3761920\n\t.3781921\n\t.3151922\n\t.3931923\n\t.3781924\n\t.2901925\n.DE\nWhen you use \\fBawk\\fR to process a column of text or numbers,\nyou may wish to specify a consistent format for the output.\nThe statement for formatting a column of numbers follows this\n.IR pattern :\n.DM\n\t{printf \"format\", expression}\n.DE\nwhere\n.I format\nprescribes how to format the output, and\n.I expression\nspecifies the fields for \\fBawk\\fR to print.\n.PP\nThe following table names and defines\nthe most commonly used of \\fBawk\\fR's format control characters.\nEach character must be preceded by a percent sign `%'\nand a number in the form of\n.I n\nor\n.IR n.m .\n.DS\n\t\\fB%\\fIn\\fBd\\fR\tDecimal number\n\t\\fB%\\fIn\\^\\fB.\\fIm\\^\\fBf\\fR\tFloating-point number\n\t\\fB%\\fIn\\^\\fB.\\fIm\\^\\fBs\\fR\tString\n\t\\fB%%\\fR\tLiteral `%' character\n.DE\nWhen you use the\n.B printf()\nfunction, you must define the output-record separator within the\nformat string.\nThe following codes are available:\n.DS\n\t\\fB\\en\\fR\tNewline\n\t\\fB\\et\\fR\tTab\n\t\\fB\\ef\\fR\tForm feed\n\t\\fB\\er\\fR\tCarriage return\n\t\\fB\\e\"\\fR\tQuotation mark\n.DE\nFor example, the following program prints Babe Ruth's RBIs unformatted:\n.DM\n\tawk '/1920/,/1925/ { print $1, $5 }' table1\n.DE\nThe output appears as follows:\n.DM\n\t1920 137\n\t1921 171\n\t1922 99\n\t1923 131\n\t1924 121\n\t1925 66\n.DE\nAs you can see,\n.B awk\nright-justifies its output by default.\nTo left-justify the second column, use the following program:\n.DM\n\tawk '/1920/,/1925/ { printf(\"%d %3d\\en\", $1, $5) }' table1\n.DE\nThe output is as follows:\n.DM\n\t1920 137\n\t1921 171\n\t1922 99\n\t1923 131\n\t1924 121\n\t1925 66\n.DE\nNote that the `3' in the string\n.B %3d\nspecifies the minimum number of characters to be displayed.\nIf the size of the number exceeds the space allotted to it,\n.B awk\nprints the entire number.\nA different rule applies when printing strings, as will be\nshown below.\n.PP\nTo print a floating-point number, you must specify the minimum number of\ndigits you wish to appear on either side of the decimal point.\nFor example, the following program gives the average number of RBIs\nBabe Ruth hit in each game between 1920 and 1925:\n.DM\n\tawk '/1920/,/1925/ { printf(\"%d %1.2f\\en\", $1, $5/154.0) }' table1\n.DE\nThis prints the following:\n.DM\n\t1920 0.89\n\t1921 1.11\n\t1922 0.64\n\t1923 0.85\n\t1924 0.79\n\t1925 0.43\n.DE\nNote the following points about the above program:\n.IP \\(bu 0.3i\nTo get the average number of runs batted in, we had to divide the\ntotal number of RBIs in a season by the number of games in a season\n(which in the 1920s was 154).\n.B awk\npermits you to use a constant to perform arithmetic on a field; this will\nbe discussed in more detail below.\n.IP \\(bu\nTo force\n.B awk\nto produce a floating-point number, the constant had to be in the format\nof a floating-point number, i.e., ``154.0'' instead of ``154''.\nDividing an integer by another integer would not have produced what we wanted.\n.PP\n.B awk\nrounds its output to match sensitivity you've requested \\(em that is,\nthe number of digits to the right of the decimal point.\nTo see how sensitivity affects output, run the following program:\n.DM\nawk '/1920/,/1925/{printf(\"%1.2f %1.3f %1.4f\\en\",$5/154.0,$5/154.0,$5/154.0)}'\\e\ntable1\n.DE\nThis prints the following:\n.DM\n\t0.89 0.890 0.8896\n\t1.11 1.110 1.1104\n\t0.64 0.643 0.6429\n\t0.85 0.851 0.8506\n\t0.79 0.786 0.7857\n\t0.43 0.429 0.4286\n.DE\nAs an aside, the above example also shows that\nyou can break \\fBawk\\fR's command line across more than one\nline using a backslash `\\e' at the end of every line but the last.\nNote, however, that you\n.I cannot\nbreak an\n.B awk\nstatement across more than one line, or\n.B awk\nwill complain about a syntax error.\n.PP\nOne last example of floating-point numbers prints Babe Ruth's\nratio of runs scored to runs batted in between 1920 and 1925:\n.DM\n\tawk '/1920/,/1925/{x = ($5*1.0) ; printf(\"%1.3f\\en\", $4/x)}' table1\n.DE\nThis produces the following:\n.DM\n\t1.153\n\t1.035\n\t0.949\n\t1.153\n\t1.182\n\t0.924\n.DE\nThe expression\n.B \"x = ($5*1.0)\"\nwas needed to turn field 5 (the divisor) into a floating-point number,\nso we could obtain the decimal fraction that we wanted.\nThis is discussed further below, when we discuss how to manipulate\nconstants.\n.PP\nThe function\n.B sprintf()\nalso formats expressions; however, instead of printing\nits output, it returns it for assignment to a variable.\nFor example, you could rewrite the previous example program to replace\nthe multiplication operation with a call to\n.BR sprintf() :\n.DM\n\tawk '/1920/,/1925/{x = sprintf(\"%3.1f\", $5)\n\t\tprintf(\"%1.3f\\en\", $4/x)}' table1\n.DE\nThe output is the same as that shown above.\n.PP\nThe\n.B %s\nformatting string can be used to align text in fields.\nThe digit to the left of the period gives the width of the field;\nthat to the right of the period gives the number of characters to write\ninto the field.\nNote that if input is larger than the number of characters allotted to it,\n.B awk\ntruncates the input.\nFor example, the following program aligns on seven-character fields\nsome words from file\n.BR text1 :\n.DM\n\tawk '{x=sprintf(\"%7.5s %7.5s %7.5s %7.5s\", $1, $2, $3, $4)\n\t\tprint x}' text1\n.DE\nThe output is as follows:\n.DM\n\t When, in disgr with\n\t I all alone bewee\n\t And troub deaf heave\n\t And look upon mysel\n\t Wishi me like to\n\t Featu like him, like\n\t Desir this man's art\n\t With what I most\n\t Yet in these thoug\n\t Haply I think on\n\t Like to the lark\n\t From sulle earth sings\n\t For thy sweet love\n\t That then I scorn\n.DE\nNote that fields (words) longer than five characters are truncated;\nand every word is right-justified on a seven-character field.\n.Sh \"Redirecting Output\"\n.PP\n.II \"awk^redirecting output\"\nIn addition to printing to the standard output,\n.B awk\ncan redirect the output of an action into a file, or append it onto\nan existing file.\nWith this feature, you can extract information from\na given file and construct new documents.\nThe following example shows an easy way to sift Babe Ruth's statistics\ninto four separate files, for further processing:\n.DM\n\tawk '{\tprint $1, $2 > \"average\"\n\t\tprint $1, $3 > \"home.runs\"\n\t\tprint $1, $4 > \"runs.scored\"\n\t\tprint $1, $5 > \"rbi\"}' table1\n.DE\nNote like as under the shell, the\noperator `>' creates the named file if it does not exist,\nor replaces its contents if it does.\nTo append\n.BR awk 's\nonto the end of an existing file, use the operator `>>'.\n.PP\n.B awk\ncan also pipe the output of an action to another program.\nAs under the shell, the operator `|' pipes the output of one\nprocess into another process.\nFor example, if it is vital for user\n.B fred\nto know Babe Ruth's batting\naverage for 1925, you can mail it to him with the following command:\n.DM\n\tawk '/1925/ {print $1, $2 | \"mail fred\"}' table1\n.DE\n.Sh \"Assignment of Variables\"\n.PP\n.II \"awk^assigning variables\"\nA number of the previous examples assign values to variables.\n.B awk\nlets you create variables, perform arithmetic upon them,\nand otherwise work with them.\n.PP\nAn\n.B awk\nvariable can be a string or a number,\ndepending upon the context.\nUnlike C,\n.B awk\ndoes not require that you declare a variable.\nBy default, variables are set to the null\nstring (numeric value zero) on start-up of the \\fBawk\\fR program.\nTo set the variable\n.B x\nto the numeric value one, you can use the assignment operator `=':\n.DM\n\tx = 1\n.DE\nTo set x to the string\n.B ted ,\nalso use the assignment operator:\n.DM\n\tx = \"ted\"\n.DE\nWhen the context demands it, \\fBawk\\fR converts strings to numbers or numbers\nto strings.\nFor example, the statement\n.DM\n\tx = \"3\"\n.DE\ninitializes to\n.B x\nto the string ``3''.\nWhen an expression contains an\narithmetic operator such as the `-',\n\\fBawk\\fR interprets the expression as numeric.\n(Alphabetic strings evaluate to zero.)\nTherefore, the expression\n.DM\n\tx = \"3\" - \"1\"\n.DE\nassigns the numeric value two to variable\n.B x ,\nnot the string ``2''.\n.PP\nWhen the operator is included within the\nquotation marks, \\fBawk\\fR treats the operator as a\ncharacter in the string.\nIn the following example\n.DM\n\tx = \"3 - 1\"\n.DE\ninitializes\n.B x\nto the string ``3 - 1''.\n.PP\nA number of examples in the previous section showed you how to perform\narithmetic on fields.\n.II \"awk^arithmetic operators\"\nThe following table gives \\fBawk\\fR's arithmetic operators:\n.DS\n\t\\fB+\\fR\tAddition\n\t\\fB-\\fR\tSubtraction\n\t\\fB*\\fR\tMultiplication\n\t\\fB/\\fR\tDivision\n\t\\fB%\\fR\tModulus\n\t\\fB++\\fR\tIncrement\n\t\\fB--\\fR\tDecrement\n\t\\fB+=\\fR\tAdd and assign value\n\t\\fB-=\\fR\tSubtract and assign value\n\t\\fB*=\\fR\tMultiply and assign value\n\t\\fB/=\\fR\tDivide and assign value\n\t\\fB%=\\fR\tDivide modulo and assign value\n.DE\nVariables are often used with increment operators.\nFor example, the following program computes the average number of home\nruns Babe Ruth hit each season during the 1920s:\n.DM\n\tawk '\t{ x += $3 }\n\t\tEND { y = (NR * 1.0) \n\t\t\tprintf(\"Average for %d years: %2.3f.\\en\", NR, x/y) }' table1\n.DE\nThe output is:\n.DM\n\tAverage for 10 years: 46.700.\n.DE\n.Sh \"Field Variables\"\n.PP\n.II \"awk^field variables\"\n\\fBawk\\fR lets fields receive assignments, be used in arithmetic,\nand be manipulated in string operations.\nOne task that has not yet been demonstrated is using \na variable to address a field.\nFor example, the following program prints the\n.BR NR th\nfield (word) from the first seven lines in file\n.BR text1 :\n.DM\n\tawk 'NR < 8 {print NR, $(NR)}' text1\n.DE\nThe output is:\n.DM\n\t1 When,\n\t2 all\n\t3 deaf\n\t4 myself,\n\t5 one\n\t6 with\n\t7 man's\n.DE\n.Sh \"Control Statements\"\n.PP\n.II \"awk^control statements\"\n\\fBawk\\fR has seven defined control statements.\nThis section explains them and gives examples of their use.\n.II \"awk^if\"\n.II \"awk^else\"\n.IP \"\\fBif (\\fIcondition\\^\\fB) \\fIaction1 \\fB[else\\fI action2 \\fB]\\fR\"\nIf \\fIcondition\\fR is true, then execute\n.IR action1 .\nIf the optional\n.B else\nclause is present and\n.I condition\nis false, then\nexecute\n.IR action2 .\n.sp \\n(pDu\nThe following program keeps running totals of Babe Ruth's RBIs, for both\nthe years where his runs scored exceeded his RBIs and the years where they\ndid not:\n.DM\n\tawk '{ if ( $4 > $5 )\n\t\t gyear++\n\t\telse\n\t\t lyear++\n\t }\n\t END { printf(\"Scored exceed RBIs: %d years.\\n\", gyear)\n\t\t printf(\"Scored not exceed RBIs: %d years.\\n\", lyear)\n\t }' table1\n.DE\nThis produces:\n.DM\n\tScored exceed RBIs: 5 years.\n\tScored not exceed RBIs: 5 years.\n.DE\nNote that if more than one action is associated with an\n.B if\nor\n.B else\nstatement, you must enclose the statements between braces.\nIf you use braces with both the\n.B if\nand\n.B else\nstatements, note that the beginning and closing braces\n.I must\nappear on the same line as the\n.B else\nstatement.\nFor example:\n.DM\n\tif (expr) {\n\t\tstuff\n\t\tstuff\n\t} else {\n\t\tstuff \n\t\tstuff\n\t}\n.DE\n.II \"awk^while\"\n.IS \"\\fBwhile (\\fIcondition\\^\\fB) \\fIaction\\fR\"\nThe\n.B while\nstatement executes\n.I action\nas long as\n.I condition\nis true.\nFor example, the following program counts the number of times the word\n.B the\nappears in file\n.BR text1 .\nThe\n.B while\nloop uses a variable to examine every word in every line:\n.DM\n\tawk ' { i = 1\n\t\twhile (i <= NF ) {\n\t\t\tif ($i == \"the\") j++\n\t\t\ti++\n\t\t}\n\t }\n\tEND { printf (\"The word \\e\"the\\e\" occurs %d times.\\en\", j) }' text1\n.DE\n.II \"Shakespeare, William\"\nThe result, as follows, shows Shakespeare's economy of language:\n.DM\n\tThe word \"the\" occurs 1 times.\n.DE\nBy the way, note that if a control statement has more than one statement\nin its action section,\nenclose the action section between braces.\nIf you do not,\n.B awk\nwill behave erratically or exit with a syntax error.\n.II \"awk^for\"\n.IP \"\\fBfor( \\fIinitial\\fB ; \\fIend\\fB ; \\fIiteration\\fB ) \\fIaction\\fR\"\n.IS \"\\fBfor( \\fIvariable\\fB in \\fIarray\\fB ) \\fIaction\\fR\"\n.BR awk 's\n.B for\nstatement closely resembles the\n.B for\nstatement in the C language.\nThe statement\n.I initial\ndefines actions to be performed before the loop begins; this is usually\nused to initialize variables, especially counters.\nThe statement\n.I end\ndefines when the loop is to end.\nThe statement\n.I iteration\ndefines one or more actions that are performed on every iteration of the\nloop; usually this is used to increment counters.\nFinally,\n.I action\ncan be one or more statements that are executed on every iteration of\nthe loop.\n.I action\nneed not be present, in which case only the action defined in the\n.I iteration\nportion of the\n.B for\nstatement is executed.\n.B for\nis in fact just an elaboration of the\n.B while\nstatement, but adjusted to make it a little easier to use.\nThe following example writes the previous example, but replaces the\n.B while\nloop with a\n.B for\nmechanism:\n.DM\n\tawk ' { for (i = 1 ; i <= NF ; i++)\n\t\t if ($i == \"the\") j++\n\t }\n\tEND { printf (\"The word \\e\"the\\e\" occurs %d times.\\en\", j) }' text1\n.DE\nThe output is the same as the previous example, but the syntax is neater\nand easier to read.\n.sp \\n(pDu\nThe second form of the\n.B for\nloop examines the contents of an array.\nIt is described in the following section, which introduces arrays.\n.IP \\fBbreak\\fR\n.II \"awk^break\"\nThe statement\n.B break\nimmediately interrupts a\n.B while\nor\n.B for\nloop.\nFor example, the following program is the same as the previous example,\nbut counts only the first occurrence of the word\n.B the\nin each line of\n.BR text1 .\nThus, it counts the number of lines in\n.B text1\nthat contain\n.BR the :\n.DM\n\tawk '{ for (i = 1 ; i <= NF ; i++) {\n\t\t if ($i == \"the\") {\n\t\t \tj++\n\t\t\tbreak\n\t\t }\n\t\t }\n\t }\n\tEND {printf (\"The word \\e\"the\\e\" occurs in %d lines.\\en\", j)}' text1\n.DE\n.II \"awk^continue\"\n.IS \\fBcontinue\\fR\nThe statement\n.B continue\nimmediately begins the next iteration of the nearest\n.B while\nor\n.B for\nloop.\nFor example, the following program prints all of Babe Ruth's statistics \\(em\nruns scored, runs batted, and home runs \\(em in which he had more than 59\nin one year:\n.DM\n\tawk ' { for (i = 3 ; i <= NF ; i++)\n\t\t if ($i <= 59)\n\t\t\tcontinue\n\t\t else\n\t\t\tprintf(\"%d, column %d: %d\\en\", $1, i, $i)\n\t } ' table1\n.DE\nThis produces the following:\n.DM\n\t1920, column 4: 158\n\t1920, column 5: 137\n\t1921, column 4: 177\n\t1921, column 5: 171\n\t1922, column 4: 94\n\t1922, column 5: 99\n\t...\n.DE\n.II \"awk^next\"\n.IS \\fBnext\\fR\nThe statement\n.B next\nforces\n.B awk\nabort the processing of the current record and\nskip to the next input record.\nProcessing of the new input record begins with the first pattern, just as\nif the processing of the previous record had concluded normally.\nTo demonstrate this, the following program skips all records in file\n.B text1\nthat have an odd number of fields (words):\n.DM\n\tawk ' { if (NF % 2 == 0) next }\n\t { print $0 } ' text1\n.DE\nThis produces:\n.DM\n\tI all alone beweep my outcast state,\n\tWishing me like to one more rich in hope,\n\tWith what I most enjoy contented least.\n\tYet in these thoughts myself almost despising,\n\tLike to the lark at break of day arising\n.DE\n.II \"awk^exit\"\n.IS \\fBexit\\fR\nFinally, the control statement\n.B exit\nforces the \\fBawk\\fP program to skip all remaining input and\nexecute the \\fIactions\\fR at the \\fBEND\\fR pattern, if any.\nFor example, the following program prints the year in which Babe Ruth\nhit his 300th home run:\n.DM\n\tawk ' { i = $1 }\n\t (j += $3) >= 300 { exit }\n\t END {print \"Babe Ruth hit his 300th homer in\", i \".\"}' table1\n.DE\nThis produces:\n.DM\n\tBabe Ruth hit his 300th homer in 1926.\n.DE\n.SH \"Arrays\"\n.PP\n.II \"awk^arrays\"\n.B awk\nhas a powerful feature for managing arrays.\nUnlike C,\n.B awk\nautomatically manages the size of an array, so you do not have to declare\nthe array's size ahead of time.\nAlso, unlike C,\n.B awk\nlets you address each element within an array by a label, not just by its\noffset within the array.\nThis lets you generate arrays ``on the fly,'' which can be very useful\nin transforming many varieties of data.\n.PP\nTo declare an array, simply name it within a statement.\n.B awk\nrecognizes as an array every variable that is followed by brackets `[\\ ]'.\nTo initialize a row within an array, you must define its value and name its\nlabel.\nA label can be either a number or a string.\nA value, too, can be a number or a string; if the value is a number, then\nyou can perform arithmetic upon it, as will be shown in a following example.\n.Sh \"Initializing an Array\"\n.PP\nTo demonstrate how an array works, use the line editor\n.B ed\nto add a line of text to the beginning of file\n.BR table1 .\nType the following; please note that the token\n.B \nmeans that you should type a tab character:\n.DM\n\ted table1\n\t1i\n\tYear\\fB\\fPBA\\fB\\fPHRs\\fB\\fPScored\\fB\\fPRBIs\n\t.\n\twq\n.DE\nThis change writes a header into \\fBtable1\\fR that names each column.\nNow, we can read these labels into an array and use them to\ndescribe Babe Ruth's statistics.\nFor example, the following prints a summary of Babe Ruth's statistics for\nthe year 1926:\n.DM\n\tawk '\tNR == 1 { for (i=1; i <= NF; i++) header [i] = $i }\n\t\t$1 == 1926 { \n\t\t\tfor (i=1; i <= NF; i++)\n\t\t\t\tprint header[i] \":\\et\", $i\n\t\t} ' table1\n.DE\nThis produces:\n.DM\n.ta 0.5i 1.5i\n\tYear:\t 1926\n\tBA:\t .372\n\tHRs:\t 47\n\tScored:\t 139\n\tRBIs:\t 145\n.DE\nThe statement\n.DM\n\tNR == 1 { for (i=1; i <= NF; i++) header [i] = $i }\n.DE\nreads the first line in\n.BR table1 ,\nwhich contains the column headers, and uses the headers to initialize the array\n.BR header .\nEach row is labeled with the contents of the variable\n.BR i .\n.PP\nThe loop\n.DM\n\tfor (i=1; i <= NF; i++)\n\t\tprint header[i] \":\\et\", $i\n.DE\nprints the contents of\n.BR header .\nBecause we labeled each row within\n.B header\nwith a number, we can use a numeric loop to read its contents.\n.Sh \"The for() Statement With Arrays\"\n.PP\nIn the previous example, each element in the array was labeled with a number.\nThis permitted us to read the array with an ordinary\n.B for\nstatement, which sets and increments a numeric variable.\nHowever, the rows within an array can be labeled with strings, instead of\nnumbers.\nTo read the contents of such an array, you must use a special form of the\n.BR for\nstatement, as follows:\n.DS\n\t\\fBfor ( \\fIoffset \\fBin \\fIarray \\fB)\\fR\n.DE\n.I array\nnames the array in question.\n.I offset\nis a variable that you name at the time of contructing the\n.B for\nstatement.\nYou can use the value of\n.I offset\nin any subordinate printing actions.\n.PP\nThe following program demonstrates this new form of\n.BR for ,\nand (incidentally) to demonstrate the power of\n.BR awk 's\narray-handling feature.\nIt builds an array of each unique word in the file\n.BR text1 ,\nand notes the number of times that word occurs within the file:\n.DM\n\tawk '\t{ for (i = 1 ; i <= NF ; i++)\n\t\t\twords [$i]++ }\n\t\tEND { for (entry in words)\n\t\t\tprint entry \":\", words[entry] }' text1 | sort\n.DE\nThis prints:\n.DM\n\t-: 1\n\tAnd: 2\n\tDesiring: 1\n\tFeatured: 1\n\tFor: 1\n\tFrom: 1\n\tHaply: 1\n\tI: 4\n\tLike: 1\n\tThat: 1\n\tWhen,: 1\n\tWishing: 1\n\tWith: 1\n\tYet: 1\n\tall: 1\n\talmost: 1\n\t...\n.DE\nAs you can imagine, a similar program in C would require many more\nlines of code.\nHowever, a few features of this program are worth noting.\n.PP\nFirst, the expression\n.DM\n\t{ for (i = 1 ; i <= NF ; i++)\n\t\twords [$i]++ }\n.DE\ndeclares the array\n.B words .\nEvery time\n.B awk\nencounters a new field (word), it automatically adds another entry to the\narray, and labels that entry with the word.\nNo work on your part is needed for this to happen.\n.II \"awk^++\"\nThe `++' operator increments the value of the appropriate entry within\n.BR words .\nBecause we did not initialize the entry, it implicitly contains a number.\n.PP\nThe expression\n.DM\n\t{ for (entry in words)\n\t\tprint entry \":\", words[entry] }\n.DE\nwalks through the array\n.BR words .\n.B awk\ninitializes the variable\n.B entry\nto the label for each row in\n.BR words ;\nthe\n.B print\nstatement then prints\n.B entry\nand the contents of that row in the array \\(em in this case, the number of\ntimes the row appears in our input file.\n.PP\nFinally, we piped the output of this program to the command\n.B sort\nto print the words in alphabetical order.\n.SH \"For More Information\"\n.PP\nThis tutorial just gives a brief introduction to the power of\n.BR awk .\nTo explore the language in depth, see \\fIsed & awk\\fR by Dale Dougherty\n(Sebastopol, Calif, O'Reilly & Associates, Inc., 1985).\nThis book, however, describes a more complex version of\n.B awk\nthan that provided with \\*(CO.\n.PP\nThe Lexicon's article on \\fBawk\\fR gives a quick summary of\nits features and options.\n", "meta": {"hexsha": "b0565e664cf4597b6ad038b47d9c8015947cbd49", "size": 55238, "ext": "r", "lang": "R", "max_stars_repo_path": "doc/mwc/doc/coherent/text/awk.r", "max_stars_repo_name": "gspu/Coherent", "max_stars_repo_head_hexsha": "299bea1bb52a4dcc42a06eabd5b476fce77013ef", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 20, "max_stars_repo_stars_event_min_datetime": "2019-10-10T14:14:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-24T02:54:38.000Z", "max_issues_repo_path": 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NO\n2. NO\n\n", "lm_q1_score": 0.07585817375348311, "lm_q2_score": 0.05921025696673772, "lm_q1q2_score": 0.004491581960971174}}
{"text": "\n\n\n\n\n\n\n C\bCH\bHA\bAP\bPT\bTE\bER\bR 5\b5\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt\n\n\n\n\n The following functions are used to read from and write\n to external devices (e.g. files) and programs (through\n pipes). All I/O goes through the lisp data type called the\n port. A port may be open for either reading or writing, but\n usually not both simultaneously (see _\bf_\bi_\bl_\be_\bo_\bp_\be_\bn ). There are\n only a limited number of ports (20) and they will not be\n reclaimed unless they are _\bc_\bl_\bo_\bs_\bed. All ports are reclaimed\n by a _\br_\be_\bs_\be_\bt_\bi_\bo call, but this drastic step won't be necessary\n if the program closes what it uses.\n\n If a port argument is not supplied to a function which\n requires one, or if a bad port argument (such as nil) is\n given, then FRANZ LISP will use the default port according\n to this scheme: If input is being done then the default port\n is the value of the symbol p\bpi\bip\bpo\bor\brt\bt and if output is being\n done then the default port is the value of the symbol\n p\bpo\bop\bpo\bor\brt\bt. Furthermore, if the value of piport or poport is\n not a valid port, then the standard input or standard output\n will be used, respectively.\n\n The standard input and standard output are usually the\n keyboard and terminal display unless your job is running in\n the background and its input or output is connected to a\n pipe. All output which goes to the standard output will\n also go to the port p\bpt\btp\bpo\bor\brt\bt if it is a valid port. Output\n destined for the standard output will not reach the standard\n output if the symbol ^\b^w\bw is non nil (although it will still\n go to p\bpt\btp\bpo\bor\brt\bt if p\bpt\btp\bpo\bor\brt\bt is a valid port).\n\n Some of the functions listed below reference files\n directly. FRANZ LISP has borrowed a convenient shorthand\n notation from _\b/_\bb_\bi_\bn_\b/_\bc_\bs_\bh, concerning naming files. If a file\n name begins with ~ (tilde), and the symbol t\bti\bil\bld\bde\be-\b-e\bex\bxp\bpa\ban\bns\bsi\bio\bon\bn\n\n is bound to something other than nil, then FRANZ LISP\n expands the file name. It takes the string of characters\n between the leading tilde, and the first slash as a user-\n name. Then, that initial segment of the filename is\n replaced by the home directory of the user. The null user-\n name is taken to be the current user.\n\n FRANZ LISP keeps a cache of user home directory infor-\n mation, to minimize searching the password file. Tilde-\n expansion is performed in the following functions: _\bc_\bf_\ba_\bs_\bl_\b,\n _\bc_\bh_\bd_\bi_\br_\b, _\bf_\ba_\bs_\bl_\b, _\bf_\bf_\ba_\bs_\bl_\b, _\bf_\bi_\bl_\be_\bo_\bp_\be_\bn_\b, _\bi_\bn_\bf_\bi_\bl_\be_\b, _\bl_\bo_\ba_\bd_\b, _\bo_\bu_\bt_\bf_\bi_\bl_\be_\b, _\bp_\br_\bo_\bb_\be_\bf_\b,\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-1\b1\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-2\b2\n\n\n _\bs_\by_\bs_\b:_\ba_\bc_\bc_\be_\bs_\bs_\b, _\bs_\by_\bs_\b:_\bu_\bn_\bl_\bi_\bn_\bk.\n\n (\b(c\bcf\bfa\bas\bsl\bl 'st_file 'st_entry 'st_funcname ['st_disc\n ['st_library]])\b)\n\n RETURNS: t\n\n SIDE EFFECT: This is used to load in a foreign function\n (see S8.4). The object file st_file is\n loaded into the lisp system. St_entry\n should be an entry point in the file just\n loaded. The function binding of the sym-\n bol s_funcname will be set to point to\n st_entry, so that when the lisp function\n s_funcname is called, st_entry will be\n run. st_disc is the discipline to be\n given to s_funcname. st_disc defaults to\n \"subroutine\" if it is not given or if it\n is given as nil. If st_library is non-\n null, then after st_file is loaded, the\n libraries given in st_library will be\n searched to resolve external references.\n The form of st_library should be something\n like \"-lm\". The C library (\" -lc \" ) is\n always searched so when loading in a C\n file you probably won't need to specify a\n library. For Fortran files, you should\n specify \"-lF77\" and if you are doing any\n I/O, the library entry should be \"-lI77\n -lF77\". For Pascal files \"-lpc\" is\n required.\n\n NOTE: This function may be used to load the output of\n the assembler, C compiler, Fortran compiler, and\n Pascal compiler but NOT the lisp compiler (use\n _\bf_\ba_\bs_\bl for that). If a file has more than one\n entry point, then use _\bg_\be_\bt_\ba_\bd_\bd_\br_\be_\bs_\bs to locate and\n setup other foreign functions.\n It is an error to load in a file which has a\n global entry point of the same name as a global\n entry point in the running lisp. As soon as you\n load in a file with _\bc_\bf_\ba_\bs_\bl, its global entry\n points become part of the lisp's entry points.\n Thus you cannot _\bc_\bf_\ba_\bs_\bl in the same file twice\n unless you use _\br_\be_\bm_\bo_\bv_\be_\ba_\bd_\bd_\br_\be_\bs_\bs to change certain\n global entry points to local entry points.\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-3\b3\n\n\n (\b(c\bcl\blo\bos\bse\be 'p_port)\b)\n\n RETURNS: t\n\n SIDE EFFECT: the specified port is drained and closed,\n releasing the port.\n\n NOTE: The standard defaults are not used in this case\n since you probably never want to close the stan-\n dard output or standard input.\n\n (\b(c\bcp\bpr\bri\bin\bnt\btf\bf 'st_format 'xfst_val ['p_port])\b)\n\n RETURNS: xfst_val\n\n SIDE EFFECT: The UNIX formatted output function printf\n is called with arguments st_format and\n xfst_val. If xfst_val is a symbol then\n its print name is passed to printf. The\n format string may contain characters which\n are just printed literally and it may con-\n tain special formatting commands preceded\n by a percent sign. The complete set of\n formatting characters is described in the\n UNIX manual. Some useful ones are %d for\n printing a fixnum in decimal, %f or %e for\n printing a flonum, and %s for printing a\n character string (or print name of a sym-\n bol).\n\n EXAMPLE: _\b(_\bc_\bp_\br_\bi_\bn_\bt_\bf _\b\"_\bP_\bi _\be_\bq_\bu_\ba_\bl_\bs _\b%_\bf_\b\" _\b3_\b._\b1_\b4_\b1_\b5_\b9_\b) prints `Pi\n equals 3.14159'\n\n (\b(d\bdr\bra\bai\bin\bn ['p_port])\b)\n\n RETURNS: nil\n\n SIDE EFFECT: If this is an output port then the charac-\n ters in the output buffer are all sent to\n the device. If this is an input port then\n all pending characters are flushed. The\n default port for this function is the\n default output port.\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-4\b4\n\n\n (\b(e\bex\bx [s_filename])\b)\n (\b(v\bvi\bi [s_filename])\b)\n (\b(e\bex\bxl\bl [s_filename])\b)\n (\b(v\bvi\bil\bl [s_filename])\b)\n\n RETURNS: nil\n\n SIDE EFFECT: The lisp system starts up an editor on the\n file named as the argument. It will try\n appending .l to the file if it can't find\n it. The functions _\be_\bx_\bl and _\bv_\bi_\bl will load\n the file after you finish editing it.\n These functions will also remember the\n name of the file so that on subsequent\n invocations, you don't need to provide the\n argument.\n\n NOTE: These functions do not evaluate their argument.\n\n (\b(f\bfa\bas\bsl\bl 'st_name ['st_mapf ['g_warn]])\b)\n\n WHERE: st_mapf and g_warn default to nil.\n\n RETURNS: t if the function succeeded, nil otherwise.\n\n SIDE EFFECT: this function is designed to load in an\n object file generated by the lisp compiler\n Liszt. File names for object files usu-\n ally end in `.o', so _\bf_\ba_\bs_\bl will append `.o'\n to st_name (if it is not already present).\n If st_mapf is non nil, then it is the name\n of the map file to create. _\bF_\ba_\bs_\bl writes in\n the map file the names and addresses of\n the functions it loads and defines. Nor-\n mally the map file is created (i.e. trun-\n cated if it exists), but if\n _\b(_\bs_\bs_\bt_\ba_\bt_\bu_\bs _\ba_\bp_\bp_\be_\bn_\bd_\bm_\ba_\bp _\bt_\b) is done then the map\n file will be appended. If g_warn is non\n nil and if a function is loaded from the\n file which is already defined, then a\n warning message will be printed.\n\n NOTE: _\bf_\ba_\bs_\bl only looks in the current directory for the\n file to load. The function _\bl_\bo_\ba_\bd looks through a\n user-supplied search path and will call _\bf_\ba_\bs_\bl if\n it finds a file with the same root name and a\n `.o' extension. In most cases the user would be\n better off using the function _\bl_\bo_\ba_\bd rather than\n calling _\bf_\ba_\bs_\bl directly.\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-5\b5\n\n\n (\b(f\bff\bfa\bas\bsl\bl 'st_file 'st_entry 'st_funcname ['st_discipline\n ['st_library]])\b)\n\n RETURNS: the binary object created.\n\n SIDE EFFECT: the Fortran object file st_file is loaded\n into the lisp system. St_entry should be\n an entry point in the file just loaded. A\n binary object will be created and its\n entry field will be set to point to\n st_entry. The discipline field of the\n binary will be set to st_discipline or\n \"subroutine\" by default. If st_library is\n present and non-null, then after st_file\n is loaded, the libraries given in\n st_library will be searched to resolve\n external references. The form of\n st_library should be something like \"-lS\n -ltermcap\". In any case, the standard\n Fortran libraries will be searched also to\n resolve external references.\n\n NOTE: in F77 on Unix, the entry point for the fortran\n function foo is named `_foo_'.\n\n (\b(f\bfi\bil\ble\bep\bpo\bos\bs 'p_port ['x_pos])\b)\n\n RETURNS: the current position in the file if x_pos is\n not given or else x_pos if x_pos is given.\n\n SIDE EFFECT: If x_pos is given, the next byte to be\n read or written to the port will be at\n position x_pos.\n\n (\b(f\bfi\bil\ble\bes\bst\bta\bat\bt 'st_filename)\b)\n\n RETURNS: a vector containing various numbers which the\n UNIX operating system assigns to files. if\n the file doesn't exist, an error is invoked.\n Use _\bp_\br_\bo_\bb_\be_\bf to determine if the file exists.\n\n NOTE: The individual entries can be accesed by mnemonic\n functions of the form filestat:_\bf_\bi_\be_\bl_\bd, where field\n may be any of atime, ctime, dev, gid, ino,\n mode,mtime, nlink, rdev, size, type, uid. See\n the UNIX programmers manual for a more detailed\n description of these quantities.\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-6\b6\n\n\n (\b(f\bfl\bla\bat\btc\bc 'g_form ['x_max])\b)\n\n RETURNS: the number of characters required to print\n g_form using _\bp_\ba_\bt_\bo_\bm. If x_max is given and if\n _\bf_\bl_\ba_\bt_\bc determines that it will return a value\n greater than x_max, then it gives up and\n returns the current value it has computed.\n This is useful if you just want to see if an\n expression is larger than a certain size.\n\n (\b(f\bfl\bla\bat\bts\bsi\biz\bze\be 'g_form ['x_max])\b)\n\n RETURNS: the number of characters required to print\n g_form using _\bp_\br_\bi_\bn_\bt. The meaning of x_max is\n the same as for flatc.\n\n NOTE: Currently this just _\be_\bx_\bp_\bl_\bo_\bd_\be's g_form and checks\n its length.\n\n (\b(f\bfi\bil\ble\beo\bop\bpe\ben\bn 'st_filename 'st_mode)\b)\n\n RETURNS: a port for reading or writing (depending on\n st_mode) the file st_name.\n\n SIDE EFFECT: the given file is opened (or created if\n opened for writing and it doesn't yet\n exist).\n\n NOTE: this function call provides a direct interface to\n the operating system's fopen function. The mode\n may be more than just \"r\" for read, \"w\" for write\n or \"a\" for append. The modes \"r+\", \"w+\" and \"a+\"\n permit both reading and writing on a port pro-\n vided that _\bf_\bs_\be_\be_\bk is done between changes in\n direction. See the UNIX manual description of\n fopen for more details. This routine does not\n look through a search path for a given file.\n\n (\b(f\bfs\bse\bee\bek\bk 'p_port 'x_offset 'x_flag)\b)\n\n RETURNS: the position in the file after the function is\n performed.\n\n SIDE EFFECT: this function positions the read/write\n pointer before a certain byte in the file.\n If x_flag is 0 then the pointer is set to\n x_offset bytes from the beginning of the\n file. If x_flag is 1 then the pointer is\n set to x_offset bytes from the current\n location in the file. If x_flag is 2 then\n the pointer is set to x_offset bytes from\n the end of the file.\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-7\b7\n\n\n (\b(i\bin\bnf\bfi\bil\ble\be 's_filename)\b)\n\n RETURNS: a port ready to read s_filename.\n\n SIDE EFFECT: this tries to open s_filename and if it\n cannot or if there are no ports available\n it gives an error message.\n\n NOTE: to allow your program to continue on a file-not-\n found error, you can use something like:\n _\b(_\bc_\bo_\bn_\bd _\b(_\b(_\bn_\bu_\bl_\bl _\b(_\bs_\be_\bt_\bq _\bm_\by_\bp_\bo_\br_\bt _\b(_\bc_\ba_\br _\b(_\be_\br_\br_\bs_\be_\bt _\b(_\bi_\bn_\bf_\bi_\bl_\be\n _\bn_\ba_\bm_\be_\b) _\bn_\bi_\bl_\b)_\b)_\b)_\b)\n _\b(_\bp_\ba_\bt_\bo_\bm _\b'_\b\"_\bc_\bo_\bu_\bl_\bd_\bn_\b'_\bt _\bo_\bp_\be_\bn _\bt_\bh_\be _\bf_\bi_\bl_\be_\b\"_\b)_\b)_\b)\n which will set myport to the port to read from if\n the file exists or will print a message if it\n couldn't open it and also set myport to nil. To\n simply determine if a file exists, use _\bp_\br_\bo_\bb_\be_\bf.\n\n (\b(l\blo\boa\bad\bd 's_filename ['st_map ['g_warn]])\b)\n\n RETURNS: t\n\n NOTE: The function of _\bl_\bo_\ba_\bd has changed since previous\n releases of FRANZ LISP and the following descrip-\n tion should be read carefully.\n\n SIDE EFFECT: _\bl_\bo_\ba_\bd now serves the function of both _\bf_\ba_\bs_\bl\n and the old _\bl_\bo_\ba_\bd. _\bL_\bo_\ba_\bd will search a user\n defined search path for a lisp source or\n object file with the filename s_filename\n (with the extension .l or .o added as\n appropriate). The search path which _\bl_\bo_\ba_\bd\n uses is the value of _\b(_\bs_\bt_\ba_\bt_\bu_\bs _\bl_\bo_\ba_\bd_\b-_\bs_\be_\ba_\br_\bc_\bh_\b-\n _\bp_\ba_\bt_\bh_\b). The default is (|.| /usr/lib/lisp)\n which means look in the current directory\n first and then /usr/lib/lisp. The file\n which _\bl_\bo_\ba_\bd looks for depends on the last\n two characters of s_filename. If\n s_filename ends with \".l\" then _\bl_\bo_\ba_\bd will\n only look for a file name s_filename and\n will assume that this is a FRANZ LISP\n source file. If s_filename ends with \".o\"\n then _\bl_\bo_\ba_\bd will only look for a file named\n s_filename and will assume that this is a\n FRANZ LISP object file to be _\bf_\ba_\bs_\bled in.\n Otherwise, _\bl_\bo_\ba_\bd will first look for\n s_filename.o, then s_filename.l and\n finally s_filename itself. If it finds\n s_filename.o it will assume that this is\n an object file, otherwise it will assume\n that it is a source file. An object file\n is loaded using _\bf_\ba_\bs_\bl and a source file is\n loaded by reading and evaluating each form\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-8\b8\n\n\n in the file. The optional arguments\n st_map and g_warn are passed to _\bf_\ba_\bs_\bl\n should _\bf_\ba_\bs_\bl be called.\n\n NOTE: _\bl_\bo_\ba_\bd requires a port to open the file s_filename.\n It then lambda binds the symbol piport to this\n port and reads and evaluates the forms.\n\n (\b(m\bma\bak\bke\ber\bre\bea\bad\bdt\bta\bab\bbl\ble\be ['s_flag])\b)\n\n WHERE: if s_flag is not present it is assumed to be\n nil.\n\n RETURNS: a readtable equal to the original readtable if\n s_flag is non-null, or else equal to the cur-\n rent readtable. See chapter 7 for a descrip-\n tion of readtables and their uses.\n\n (\b(m\bms\bsg\bg [l_option ...] ['g_msg ...])\b)\n\n NOTE: This function is intended for printing short mes-\n sages. Any of the arguments or options presented\n can be used any number of times, in any order.\n The messages themselves (g_msg) are evaluated,\n and then they are transmitted to _\bp_\ba_\bt_\bo_\bm. Typi-\n cally, they are strings, which evaluate to them-\n selves. The options are interpreted specially:\n\n\n ____________________________________________________\n\n _\bm_\bs_\bg _\bO_\bp_\bt_\bi_\bo_\bn _\bS_\bu_\bm_\bm_\ba_\br_\by\n\n _\b(_\bP _\bp_\b__\bp_\bo_\br_\bt_\bn_\ba_\bm_\be_\b) causes subsequent output to go to the port p_portname\n (port should be opened previously)\n\n _\bB print a single blank.\n\n _\b(_\bB _\b'_\bn_\b__\bb_\b) evaluate n_b and print that many blanks.\n\n _\bN print a single by calling _\bt_\be_\br_\bp_\br.\n\n _\b(_\bN _\b'_\bn_\b__\bn_\b) evaluate n_n and transmit\n that many newlines to the stream.\n\n _\bD _\bd_\br_\ba_\bi_\bn the current port.\n ____________________________________________________\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-9\b9\n\n\n (\b(n\bnw\bwr\bri\bit\btn\bn ['p_port])\b)\n\n RETURNS: the number of characters in the buffer of the\n given port but not yet written out to the file\n or device. The buffer is flushed automati-\n cally when filled, or when _\bt_\be_\br_\bp_\br is called.\n\n (\b(o\bou\but\btf\bfi\bil\ble\be 's_filename ['st_type])\b)\n\n RETURNS: a port or nil\n\n SIDE EFFECT: this opens a port to write s_filename. If\n st_type is given and if it is a symbol or\n string whose name begins with `a', then\n the file will be opened in append mode,\n that is the current contents will not be\n lost and the next data will be written at\n the end of the file. Otherwise, the file\n opened is truncated by _\bo_\bu_\bt_\bf_\bi_\bl_\be if it\n existed beforehand. If there are no free\n ports, outfile returns nil. If one cannot\n write on s_filename, an error is sig-\n nalled.\n\n (\b(p\bpa\bat\bto\bom\bm 'g_exp ['p_port])\b)\n\n RETURNS: g_exp\n\n SIDE EFFECT: g_exp is printed to the given port or the\n default port. If g_exp is a symbol or\n string, the print name is printed without\n any escape characters around special char-\n acters in the print name. If g_exp is a\n list then _\bp_\ba_\bt_\bo_\bm has the same effect as\n _\bp_\br_\bi_\bn_\bt.\n\n (\b(p\bpn\bnt\btl\ble\ben\bn 'xfs_arg)\b)\n\n RETURNS: the number of characters needed to print\n xfs_arg.\n\n (\b(p\bpo\bor\brt\btp\bp 'g_arg)\b)\n\n RETURNS: t iff g_arg is a port.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-1\b10\b0\n\n\n (\b(p\bpp\bp [l_option] s_name1 ...)\b)\n\n RETURNS: t\n\n SIDE EFFECT: If s_name_\bi has a function binding, it is\n pretty-printed, otherwise if s_name_\bi has a\n value then that is pretty-printed. Nor-\n mally the output of the pretty-printer\n goes to the standard output port poport.\n The options allow you to redirect it.\n\n\n ____________________________________________________\n\n _\bP_\bP _\bO_\bp_\bt_\bi_\bo_\bn _\bS_\bu_\bm_\bm_\ba_\br_\by\n\n _\b(_\bF _\bs_\b__\bf_\bi_\bl_\be_\bn_\ba_\bm_\be_\b) direct future printing to s_filename\n\n _\b(_\bP _\bp_\b__\bp_\bo_\br_\bt_\bn_\ba_\bm_\be_\b) causes output to go to the port p_portname\n (port should be opened previously)\n\n _\b(_\bE _\bg_\b__\be_\bx_\bp_\br_\be_\bs_\bs_\bi_\bo_\bn_\b) evaluate g_expression and don't print\n ____________________________________________________\n\n\n\n\n (\b(p\bpr\bri\bin\bnc\bc 'g_arg ['p_port])\b)\n\n EQUIVALENT TO: patom.\n\n (\b(p\bpr\bri\bin\bnt\bt 'g_arg ['p_port])\b)\n\n RETURNS: nil\n\n SIDE EFFECT: prints g_arg on the port p_port or the\n default port.\n\n (\b(p\bpr\bro\bob\bbe\bef\bf 'st_file)\b)\n\n RETURNS: t iff the file st_file exists.\n\n NOTE: Just because it exists doesn't mean you can read\n it.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-1\b11\b1\n\n\n (\b(p\bpp\bp-\b-f\bfo\bor\brm\bm 'g_form ['p_port])\b)\n\n RETURNS: t\n\n SIDE EFFECT: g_form is pretty-printed to the port\n p_port (or poport if p_port is not given).\n This is the function which _\bp_\bp uses. _\bp_\bp_\b-\n _\bf_\bo_\br_\bm does not look for function defini-\n tions or values of variables, it just\n prints out the form it is given.\n\n NOTE: This is useful as a top-level-printer, c.f. _\bt_\bo_\bp_\b-\n _\bl_\be_\bv_\be_\bl in Chapter 6.\n\n (\b(r\bra\bat\bto\bom\bm ['p_port ['g_eof]])\b)\n\n RETURNS: the next atom read from the given or default\n port. On end of file, g_eof (default nil) is\n returned.\n\n (\b(r\bre\bea\bad\bd ['p_port ['g_eof]])\b)\n\n RETURNS: the next lisp expression read from the given\n or default port. On end of file, g_eof\n (default nil) is returned.\n\n NOTE: An error will occur if the reader is given an ill\n formed expression. The most common error is too\n many right parentheses (note that this is not\n considered an error in Maclisp).\n\n (\b(r\bre\bea\bad\bdc\bc ['p_port ['g_eof]])\b)\n\n RETURNS: the next character read from the given or\n default port. On end of file, g_eof (default\n nil) is returned.\n\n (\b(r\bre\bea\bad\bdl\bli\bis\bst\bt 'l_arg)\b)\n\n RETURNS: the lisp expression read from the list of\n characters in l_arg.\n\n (\b(r\bre\bem\bmo\bov\bve\bea\bad\bdd\bdr\bre\bes\bss\bs 's_name1 ['s_name2 ...])\b)\n\n RETURNS: nil\n\n SIDE EFFECT: the entries for the s_name_\bi in the Lisp\n symbol table are removed. This is useful\n if you wish to _\bc_\bf_\ba_\bs_\bl or _\bf_\bf_\ba_\bs_\bl in a file\n twice, since it is illegal for a symbol in\n the file you are loading to already exist\n in the lisp symbol table.\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-1\b12\b2\n\n\n (\b(r\bre\bes\bse\bet\bti\bio\bo)\b)\n\n RETURNS: nil\n\n SIDE EFFECT: all ports except the standard input, out-\n put and error are closed.\n\n (\b(s\bse\bet\bts\bsy\byn\bnt\bta\bax\bx 's_symbol 's_synclass ['ls_func])\b)\n\n RETURNS: t\n\n SIDE EFFECT: this sets the code for s_symbol to sx_code\n in the current readtable. If s_synclass\n is _\bm_\ba_\bc_\br_\bo or _\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg then ls_func is the\n associated function. See Chapter 7 on\n the reader for more details.\n\n (\b(s\bsl\blo\boa\bad\bd 's_file)\b)\n\n SIDE EFFECT: the file s_file (in the current directory)\n is opened for reading and each form is\n read, printed and evaluated. If the form\n is recognizable as a function definition,\n only its name will be printed, otherwise\n the whole form is printed.\n\n NOTE: This function is useful when a file refuses to\n load because of a syntax error and you would like\n to narrow down where the error is.\n\n (\b(t\bta\bab\bb 'x_col ['p_port])\b)\n\n SIDE EFFECT: enough spaces are printed to put the cur-\n sor on column x_col. If the cursor is\n beyond x_col to start with, a _\bt_\be_\br_\bp_\br is\n done first.\n\n (\b(t\bte\ber\brp\bpr\br ['p_port])\b)\n\n RETURNS: nil\n\n SIDE EFFECT: a terminate line character sequence is\n sent to the given port or the default\n port. This will also drain the port.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-1\b13\b3\n\n\n (\b(t\bte\ber\brp\bpr\bri\bi ['p_port])\b)\n\n EQUIVALENT TO: terpr.\n\n (\b(t\bti\bil\bld\bde\be-\b-e\bex\bxp\bpa\ban\bnd\bd 'st_filename)\b)\n\n RETURNS: a symbol whose pname is the tilde-expansion of\n the argument, (as discussed at the beginning\n of this chapter). If the argument does not\n begin with a tilde, the argument itself is\n returned.\n\n (\b(t\bty\byi\bi ['p_port])\b)\n\n RETURNS: the fixnum representation of the next charac-\n ter read. On end of file, -1 is returned.\n\n (\b(t\bty\byi\bip\bpe\bee\bek\bk ['p_port])\b)\n\n RETURNS: the fixnum representation of the next charac-\n ter to be read.\n\n NOTE: This does not actually read the character, it\n just peeks at it.\n\n (\b(t\bty\byo\bo 'x_char ['p_port])\b)\n\n RETURNS: x_char.\n\n SIDE EFFECT: the character whose fixnum representation\n is x_code, is printed as a on the given\n output port or the default output port.\n\n (\b(u\bun\bnt\bty\byi\bi 'x_char ['p_port])\b)\n\n SIDE EFFECT: x_char is put back in the input buffer so\n a subsequent _\bt_\by_\bi or _\br_\be_\ba_\bd will read it\n first.\n\n NOTE: a maximum of one character may be put back.\n\n (\b(u\bus\bse\ber\brn\bna\bam\bme\be-\b-t\bto\bo-\b-d\bdi\bir\br 'st_name)\b)\n\n RETURNS: the home directory of the given user. The\n result is stored, to avoid unnecessarily\n searching the password file.\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n I\bIn\bnp\bpu\but\bt/\b/O\bOu\but\btp\bpu\but\bt 5\b5-\b-1\b14\b4\n\n\n (\b(z\bza\bap\bpl\bli\bin\bne\be)\b)\n\n RETURNS: nil\n\n SIDE EFFECT: all characters up to and including the\n line termination character are read and\n discarded from the last port used for\n input.\n\n NOTE: this is used as the macro function for the semi-\n colon character when it acts as a comment charac-\n ter.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n", "meta": {"hexsha": "2529e0b642398c0b2cb71152462ab47843648645", "size": 34632, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch5.r", "max_stars_repo_name": "krytarowski/franz-lisp-christos", "max_stars_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-17T08:05:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T08:07:14.000Z", "max_issues_repo_path": "lisplib/manual/ch5.r", "max_issues_repo_name": "krytarowski/franz-lisp-christos", "max_issues_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lisplib/manual/ch5.r", "max_forks_repo_name": "krytarowski/franz-lisp-christos", "max_forks_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-15T16:38:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-15T16:38:53.000Z", "avg_line_length": 37.44, "max_line_length": 176, "alphanum_fraction": 0.4713270963, "num_tokens": 9229, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "\n\n\n\n\n\n\n CHAPTER 7\n\n\n The Lisp Reader\n\n\n\n\n\n\n 7.1. Introduction\n\n The _\br_\be_\ba_\bd function is responsible for converting a\n stream of characters into a Lisp expression. _\bR_\be_\ba_\bd is\n table driven and the table it uses is called a _\br_\be_\ba_\bd_\bt_\b-\n _\ba_\bb_\bl_\be. The _\bp_\br_\bi_\bn_\bt function does the inverse of _\br_\be_\ba_\bd; it\n converts a Lisp expression into a stream of charac-\n ters. Typically the conversion is done in such a way\n that if that stream of characters were read by _\br_\be_\ba_\bd,\n the result would be an expression equal to the one\n _\bp_\br_\bi_\bn_\bt was given. _\bP_\br_\bi_\bn_\bt must also refer to the readt-\n able in order to determine how to format its output.\n The _\be_\bx_\bp_\bl_\bo_\bd_\be function, which returns a list of charac-\n ters rather than printing them, must also refer to\n the readtable.\n\n A readtable is created with the _\bm_\ba_\bk_\be_\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be\n function, modified with the _\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx function and\n interrogated with the _\bg_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx function. The struc-\n ture of a readtable is hidden from the user - a\n readtable should only be manipulated with the three\n functions mentioned above.\n\n There is one distinguished readtable called the\n _\bc_\bu_\br_\br_\be_\bn_\bt _\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be whose value determines what _\br_\be_\ba_\bd,\n _\bp_\br_\bi_\bn_\bt and _\be_\bx_\bp_\bl_\bo_\bd_\be do. The current readtable is the\n value of the symbol _\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be. Thus it is possible\n to rapidly change the current syntax by lambda binding\n a different readtable to the symbol _\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be. When\n the binding is undone, the syntax reverts to its old\n form.\n\n\n\n 7.2. Syntax Classes\n\n The readtable describes how each of the 128 ascii\n characters should be treated by the reader and\n printer. Each character belongs to a _\bs_\by_\bn_\bt_\ba_\bx _\bc_\bl_\ba_\bs_\bs\n which has three properties:\n\n character class -\n Tells what the reader should do when it sees this\n\n\nThe Lisp Reader 7-1\n\n\n\n\n\n\n\nThe Lisp Reader 7-2\n\n\n character. There are a large number of character\n classes. They are described below.\n\n separator -\n Most types of tokens the reader constructs are\n one character long. Four token types have an\n arbitrary length: number (1234), symbol print\n name (franz), escaped symbol print name\n (|franz|), and string (\"franz\"). The reader can\n easily determine when it has come to the end of\n one of the last two types: it just looks for the\n matching delimiter (| or \"). When the reader is\n reading a number or symbol print name, it stops\n reading when it comes to a character with the\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. The separator character is\n pushed back into the input stream and will be the\n first character read when the reader is called\n again.\n\n escape -\n Tells the printer when to put escapes in front\n of, or around, a symbol whose print name contains\n this character. There are three possibilities:\n always escape a symbol with this character in it,\n only escape a symbol if this is the only charac-\n ter in the symbol, and only escape a symbol if\n this is the first character in the symbol.\n [note: The printer will always escape a symbol\n which, if printed out, would look like a valid\n number.]\n\n When the Lisp system is built, Lisp code is added\n to a C-coded kernel and the result becomes the stan-\n dard lisp system. The readtable present in the C-\n coded kernel, called the _\br_\ba_\bw _\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be, contains the\n bare necessities for reading in Lisp code. During the\n construction of the complete Lisp system, a copy is\n made of the raw readtable and then the copy is modi-\n fied by adding macro characters. The result is what\n is called the _\bs_\bt_\ba_\bn_\bd_\ba_\br_\bd _\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be. When a new readt-\n able is created with _\bm_\ba_\bk_\be_\br_\be_\ba_\bd_\bt_\ba_\bb_\bl_\be, a copy is made of\n either the raw readtable or the current readtable\n (which is likely to be the standard readtable).\n\n\n\n 7.3. Reader Operations\n\n The reader has a very simple algorithm. It is\n either _\bs_\bc_\ba_\bn_\bn_\bi_\bn_\bg for a token, _\bc_\bo_\bl_\bl_\be_\bc_\bt_\bi_\bn_\bg a token, or\n _\bp_\br_\bo_\bc_\be_\bs_\bs_\bi_\bn_\bg a token. Scanning involves reading charac-\n ters and throwing away those which don't start tokens\n (such as blanks and tabs). Collecting means gathering\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-3\n\n\n the characters which make up a token into a buffer.\n Processing may involve creating symbols, strings,\n lists, fixnums, bignums or flonums or calling a user\n written function called a character macro.\n\n The components of the syntax class determine when\n the reader switches between the scanning, collecting\n and processing states. The reader will continue scan-\n ning as long as the character class of the characters\n it reads is _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br. When it reads a character\n whose character class is not _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br it stores that\n character in its buffer and begins the collecting\n phase.\n\n If the character class of that first character is\n _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br, _\bc_\bn_\bu_\bm_\bb_\be_\br, _\bc_\bp_\be_\br_\bi_\bo_\bd, or _\bc_\bs_\bi_\bg_\bn. then it will\n continue collecting until it runs into a character\n whose syntax class has the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. (That\n last character will be pushed back into the input\n buffer and will be the first character read next\n time.) Now the reader goes into the processing phase,\n checking to see if the token it read is a number or\n symbol. It is important to note that after the first\n character is collected the component of the syntax\n class which tells the reader to stop collecting is\n the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property, not the character class.\n\n If the character class of the character which\n stopped the scanning is not _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br, _\bc_\bn_\bu_\bm_\bb_\be_\br,\n _\bc_\bp_\be_\br_\bi_\bo_\bd, or _\bc_\bs_\bi_\bg_\bn. then the reader processes that\n character immediately. The character classes\n _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bm_\ba_\bc_\br_\bo, _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo, and _\bc_\bs_\bi_\bn_\bg_\bl_\be-\n _\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo will act like _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br if the following\n token is not a _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br. The processing which is\n done for a given character class is described in\n detail in the next section.\n\n\n\n 7.4. Character Classes\n\n\n_\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br raw readtable:A-Z a-z ^H !#$%&*,/:;<=>?@^_`{}~\n standard readtable:A-Z a-z ^H !$%&*/:;<=>?@^_{}~\n A normal character.\n\n\n_\bc_\bn_\bu_\bm_\bb_\be_\br raw readtable:0-9\n standard readtable:0-9\n This type is a digit. The syntax for an integer (fix-\n num or bignum) is a string of _\bc_\bn_\bu_\bm_\bb_\be_\br characters\n optionally followed by a _\bc_\bp_\be_\br_\bi_\bo_\bd. If the digits are\n not followed by a _\bc_\bp_\be_\br_\bi_\bo_\bd, then they are interpreted\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-4\n\n\n in base _\bi_\bb_\ba_\bs_\be which must be eight or ten. The syntax\n for a floating point number is either zero or more\n _\bc_\bn_\bu_\bm_\bb_\be_\br's followed by a _\bc_\bp_\be_\br_\bi_\bo_\bd and then followed by\n one or more _\bc_\bn_\bu_\bm_\bb_\be_\br's. A floating point number may\n also be an integer or floating point number followed\n by 'e' or 'd', an optional '+' or '-' and then zero or\n more _\bc_\bn_\bu_\bm_\bb_\be_\br's.\n\n\n_\bc_\bs_\bi_\bg_\bn raw readtable:+-\n standard readtable:+-\n A leading sign for a number. No other characters\n should be given this class.\n\n\n_\bc_\bl_\be_\bf_\bt_\b-_\bp_\ba_\br_\be_\bn raw readtable:(\n standard readtable:(\n A left parenthesis. Tells the reader to begin forming\n a list.\n\n\n_\bc_\br_\bi_\bg_\bh_\bt_\b-_\bp_\ba_\br_\be_\bn raw readtable:)\n standard readtable:)\n A right parenthesis. Tells the reader that it has\n reached the end of a list.\n\n\n_\bc_\bl_\be_\bf_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt raw readtable:[\n standard readtable:[\n A left bracket. Tells the reader that it should begin\n forming a list. See the description of _\bc_\br_\bi_\bg_\bh_\bt-_\bb_\br_\ba_\bc_\bk_\be_\bt\n for the difference between cleft-bracket and cleft-\n paren.\n\n\n_\bc_\br_\bi_\bg_\bh_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt raw readtable:]\n standard readtable:]\n A right bracket. A _\bc_\br_\bi_\bg_\bh_\bt-_\bb_\br_\ba_\bc_\bk_\be_\bt finishes the forma-\n tion of the current list and all enclosing lists until\n it finds one which begins with a _\bc_\bl_\be_\bf_\bt-_\bb_\br_\ba_\bc_\bk_\be_\bt or\n until it reaches the top level list.\n\n\n_\bc_\bp_\be_\br_\bi_\bo_\bd raw readtable:.\n standard readtable:.\n The period is used to separate element of a cons cell\n [e.g. (a . (b . nil)) is the same as (a b)]. _\bc_\bp_\be_\br_\bi_\bo_\bd\n is also used in numbers as described above.\n\n\n_\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br raw readtable:^I-^M esc space\n standard readtable:^I-^M esc space\n Separates tokens. When the reader is scanning, these\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-5\n\n\n character are passed over. Note: there is a differ-\n ence between the _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br character class and the\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property of a syntax class.\n\n\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bq_\bu_\bo_\bt_\be raw readtable:'\n standard readtable:'\n This causes _\br_\be_\ba_\bd to be called recursively and the list\n (quote ) to be returned.\n\n\n_\bc_\bs_\by_\bm_\bb_\bo_\bl_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br raw readtable:|\n standard readtable:|\n This causes the reader to begin collecting characters\n and to stop only when another identical _\bc_\bs_\by_\bm_\bb_\bo_\bl-\n _\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br is seen. The only way to escape a _\bc_\bs_\by_\bm_\bb_\bo_\bl-\n _\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br within a symbol name is with a _\bc_\be_\bs_\bc_\ba_\bp_\be char-\n acter. The collected characters are converted into a\n string which becomes the print name of a symbol. If a\n symbol with an identical print name already exists,\n then the allocation is not done, rather the existing\n symbol is used.\n\n\n_\bc_\be_\bs_\bc_\ba_\bp_\be raw readtable:\\\n standard readtable:\\\n This causes the next character to read in to be\n treated as a vcharacter. A character whose syntax\n class is vcharacter has a character class _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br\n and does not have the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property so it will\n not separate symbols.\n\n\n_\bc_\bs_\bt_\br_\bi_\bn_\bg_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br raw readtable:\"\n standard readtable:\"\n This is the same as _\bc_\bs_\by_\bm_\bb_\bo_\bl-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br except the\n result is returned as a string instead of a symbol.\n\n\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br_\b-_\bs_\by_\bm_\bb_\bo_\bl raw readtable:none\n standard readtable:none\n This returns a symbol whose print name is the the sin-\n gle character which has been collected.\n\n\n_\bc_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:`,\n The reader calls the macro function associated with\n this character and the current readtable, passing it\n no arguments. The result of the macro is added to the\n structure the reader is building, just as if that form\n were directly read by the reader. More details on\n macros are provided below.\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-6\n\n\n_\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:#;\n A _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo differs from a _\bc_\bm_\ba_\bc_\br_\bo in the way the\n result is incorporated in the structure the reader is\n building. A _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo must return a list of\n forms (possibly empty). The reader acts as if it read\n each element of the list itself without the surround-\n ing parenthesis.\n\n\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This causes to reader to check the next character. If\n it is a _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br then this acts like a _\bc_\bm_\ba_\bc_\br_\bo. Oth-\n erwise, it acts like a _\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br.\n\n\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This is triggered like a _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bm_\ba_\bc_\br_\bo however the\n result is spliced in like a _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo.\n\n\n_\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This is differs from a _\bc_\bm_\ba_\bc_\br_\bo in that the macro func-\n tion is passed a form representing what the reader has\n read so far. The result of the macro replaces what the\n reader had read so far.\n\n\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo raw readtable:none\n standard readtable:none\n This differs from the _\bc_\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo in that the macro\n will only be triggered if the character following the\n _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo character is a _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br.\n\n\n_\bc_\bi_\bl_\bl_\be_\bg_\ba_\bl raw readtable:^@-^G^N-^Z^\\-^_rubout\n standard readtable:^@-^G^N-^Z^\\-^_rubout\n The characters cause the reader to signal an error if\n read.\n\n\n\n 7.5. Syntax Classes\n\n The readtable maps each character into a syntax\n class. The syntax class contains three pieces of\n information: the character class, whether this is a\n separator, and the escape properties. The first two\n properties are used by the reader, the last by the\n printer (and _\be_\bx_\bp_\bl_\bo_\bd_\be). The initial lisp system has\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-7\n\n\n the following syntax classes defined. The user may\n add syntax classes with _\ba_\bd_\bd-_\bs_\by_\bn_\bt_\ba_\bx-_\bc_\bl_\ba_\bs_\bs. For each\n syntax class, we list the properties of the class and\n which characters have this syntax class by default.\n More information about each syntax class can be found\n under the description of the syntax class's character\n class.\n\nvcharacter raw readtable:A-Z a-z ^H !#$%&*,/:;<=>?@^_`{}~\n_\bc_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br standard readtable:A-Z a-z ^H !$%&*/:;<=>?@^_{}~\n\n\n\nvnumber raw readtable:0-9\n_\bc_\bn_\bu_\bm_\bb_\be_\br standard readtable:0-9\n\n\n\nvsign raw readtable:+-\n_\bc_\bs_\bi_\bg_\bn standard readtable:+-\n\n\n\nvleft-paren raw readtable:(\n_\bc_\bl_\be_\bf_\bt_\b-_\bp_\ba_\br_\be_\bn standard readtable:(\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvright-paren raw readtable:)\n_\bc_\br_\bi_\bg_\bh_\bt_\b-_\bp_\ba_\br_\be_\bn standard readtable:)\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvleft-bracket raw readtable:[\n_\bc_\bl_\be_\bf_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt standard readtable:[\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvright-bracket raw readtable:]\n_\bc_\br_\bi_\bg_\bh_\bt_\b-_\bb_\br_\ba_\bc_\bk_\be_\bt standard readtable:]\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvperiod raw readtable:.\n_\bc_\bp_\be_\br_\bi_\bo_\bd standard readtable:.\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\nvseparator raw readtable:^I-^M esc space\n_\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br standard readtable:^I-^M esc space\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-8\n\n\nvsingle-quote raw readtable:'\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bq_\bu_\bo_\bt_\be standard readtable:'\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvsymbol-delimiter raw readtable:|\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br standard readtable:|\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n\n\nvescape raw readtable:\\\n_\bc_\be_\bs_\bc_\ba_\bp_\be standard readtable:\\\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n\n\nvstring-delimiter raw readtable:\"\n_\bc_\bs_\bt_\br_\bi_\bn_\bg_\b-_\bd_\be_\bl_\bi_\bm_\bi_\bt_\be_\br standard readtable:\"\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n\n\nvsingle-character-symbol raw readtable:none\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bc_\bh_\ba_\br_\ba_\bc_\bt_\be_\br_\b-_\bs_\by_\bm_\bb_\bo_\bl standard readtable:none\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n\nvmacro raw readtable:none\n_\bc_\bm_\ba_\bc_\br_\bo standard readtable:`,\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvsplicing-macro raw readtable:none\n_\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo standard readtable:#;\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvsingle-macro raw readtable:none\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\nvsingle-splicing-macro raw readtable:none\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\nvinfix-macro raw readtable:none\n_\bc_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\nvsingle-infix-macro raw readtable:none\n_\bc_\bs_\bi_\bn_\bg_\bl_\be_\b-_\bi_\bn_\bf_\bi_\bx_\b-_\bm_\ba_\bc_\br_\bo standard readtable:none\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\bw_\bh_\be_\bn_\b-_\bu_\bn_\bi_\bq_\bu_\be\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-9\n\n\n\n\nvillegal raw readtable:^@-^G^N-^Z^\\-^_rubout\n_\bc_\bi_\bl_\bl_\be_\bg_\ba_\bl standard readtable:^@-^G^N-^Z^\\-^_rubout\n_\be_\bs_\bc_\ba_\bp_\be_\b-_\ba_\bl_\bw_\ba_\by_\bs\n_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br\n\n\n\n 7.6. Character Macros\n\n Character macros are user written functions which\n are executed during the reading process. The value\n returned by a character macro may or may not be used\n by the reader, depending on the type of macro and the\n value returned. Character macros are always attached\n to a single character with the _\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx function.\n\n\n\n 7.6.1. Types There are three types of character\n macros: normal, splicing and infix. These types\n differ in the arguments they are given or in what\n is done with the result they return.\n\n\n\n 7.6.1.1. Normal\n\n A normal macro is passed no arguments. The\n value returned by a normal macro is simply used\n by the reader as if it had read the value\n itself. Here is an example of a macro which\n returns the abbreviation for a given state.\n\n\n ____________________________________________________\n\n ->(_\bd_\be_\bf_\bu_\bn _\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv _\bn_\bi_\bl\n (_\bc_\bd_\br (_\ba_\bs_\bs_\bq (_\br_\be_\ba_\bd) '((_\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba . _\bc_\ba) (_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba . _\bp_\ba)))))\n stateabbrev\n -> (_\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx '_\b\\! '_\bv_\bm_\ba_\bc_\br_\bo '_\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv)\n t\n -> '( ! _\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba ! _\bw_\by_\bo_\bm_\bi_\bn_\bg ! _\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba)\n (ca nil pa)\n ____________________________________________________\n\n\n\nNotice what happened to\n ! _\bw_\by_\bo_\bm_\bi_\bn_\bg. Since it wasn't in the table, the associated\nfunction returned nil. The creator of the macro may have\nwanted to leave the list alone, in such a case, but couldn't\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-10\n\n\nwith this type of reader macro. The splicing macro,\ndescribed next, allows a character macro function to return\na value that is ignored.\n\n\n\n 7.6.1.2. Splicing\n\n The value returned from a splicing macro\n must be a list or nil. If the value is nil,\n then the value is ignored, otherwise the reader\n acts as if it read each object in the list.\n Usually the list only contains one element. If\n the reader is reading at the top level (i.e. not\n collecting elements of list), then it is illegal\n for a splicing macro to return more then one\n element in the list. The major advantage of a\n splicing macro over a normal macro is the abil-\n ity of the splicing macro to return nothing. The\n comment character (usually ;) is a splicing\n macro bound to a function which reads to the end\n of the line and always returns nil. Here is the\n previous example written as a splicing macro\n\n\n ____________________________________________________\n\n -> (_\bd_\be_\bf_\bu_\bn _\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv _\bn_\bi_\bl\n ((_\bl_\ba_\bm_\bb_\bd_\ba (_\bv_\ba_\bl_\bu_\be)\n (_\bc_\bo_\bn_\bd (_\bv_\ba_\bl_\bu_\be (_\bl_\bi_\bs_\bt _\bv_\ba_\bl_\bu_\be))\n (_\bt _\bn_\bi_\bl)))\n (_\bc_\bd_\br (_\ba_\bs_\bs_\bq (_\br_\be_\ba_\bd) '((_\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba . _\bc_\ba) (_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba . _\bp_\ba))))))\n -> (_\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx '! '_\bv_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo '_\bs_\bt_\ba_\bt_\be_\ba_\bb_\bb_\br_\be_\bv)\n -> '(!_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba ! _\bf_\bo_\bo !_\bc_\ba_\bl_\bi_\bf_\bo_\br_\bn_\bi_\ba)\n (pa ca)\n -> '!_\bf_\bo_\bo !_\bb_\ba_\br !_\bp_\be_\bn_\bn_\bs_\by_\bl_\bv_\ba_\bn_\bi_\ba\n pa\n ->\n ____________________________________________________\n\n\n\n\n\n\n 7.6.1.3. Infix\n\n Infix macros are passed a _\bc_\bo_\bn_\bc structure\n representing what has been read so far.\n Briefly, a tconc structure is a single list cell\n whose car points to a list and whose cdr points\n to the last list cell in that list. The\n interpretation by the reader of the value\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-11\n\n\n returned by an infix macro depends on whether\n the macro is called while the reader is con-\n structing a list or whether it is called at the\n top level of the reader. If the macro is called\n while a list is being constructed, then the\n value returned should be a tconc structure.\n The car of that structure replaces the list of\n elements that the reader has been collecting.\n If the macro is called at top level, then it\n will be passed the value nil, and the value it\n returns should either be nil or a tconc struc-\n ture. If the macro returns nil, then the value\n is ignored and the reader continues to read. If\n the macro returns a tconc structure of one ele-\n ment (i.e. whose car is a list of one element),\n then that single element is returned as the\n value of _\br_\be_\ba_\bd. If the macro returns a tconc\n structure of more than one element, then that\n list of elements is returned as the value of\n read.\n\n\n ____________________________________________________\n\n -> (_\bd_\be_\bf_\bu_\bn _\bp_\bl_\bu_\bs_\bo_\bp (_\bx)\n (_\bc_\bo_\bn_\bd ((_\bn_\bu_\bl_\bl _\bx) (_\bt_\bc_\bo_\bn_\bc _\bn_\bi_\bl '_\b\\+))\n (_\bt (_\bl_\bc_\bo_\bn_\bc _\bn_\bi_\bl (_\bl_\bi_\bs_\bt '_\bp_\bl_\bu_\bs (_\bc_\ba_\ba_\br _\bx) (_\br_\be_\ba_\bd))))))\n\n plusop\n -> (_\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx '_\b\\+ '_\bv_\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo '_\bp_\bl_\bu_\bs_\bo_\bp)\n t\n -> '(_\ba + _\bb)\n (plus a b)\n -> '+\n |+|\n ->\n ____________________________________________________\n\n\n\n\n\n\n 7.6.2. Invocations\n\n There are three different circumstances in\n which you would like a macro function to be trig-\n gered.\n\n _\bA_\bl_\bw_\ba_\by_\bs -\n Whenever the macro character is seen, the\n macro should be invoked. This is accomplished\n by using the character classes _\bc_\bm_\ba_\bc_\br_\bo,\n\n\n Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-12\n\n\n _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo, or _\bc_\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo, and by using\n the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. The syntax classes\n vmacro, vsplicing-macro, and vsingle-macro are\n defined this way.\n\n _\bW_\bh_\be_\bn _\bf_\bi_\br_\bs_\bt -\n The macro should only be triggered when the\n macro character is the first character found\n after the scanning process. A syntax class\n for a _\bw_\bh_\be_\bn _\bf_\bi_\br_\bs_\bt macro would be defined using\n _\bc_\bm_\ba_\bc_\br_\bo, _\bc_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo, or _\bc_\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo and\n not including the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property.\n\n _\bW_\bh_\be_\bn _\bu_\bn_\bi_\bq_\bu_\be -\n The macro should only be triggered when the\n macro character is the only character col-\n lected in the token collection phase of the\n reader, i.e the macro character is preceeded\n by zero or more _\bc_\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\brs and followed by a\n _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br. A syntax class for a _\bw_\bh_\be_\bn _\bu_\bn_\bi_\bq_\bu_\be\n macro would be defined using _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bm_\ba_\bc_\br_\bo,\n _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bs_\bp_\bl_\bi_\bc_\bi_\bn_\bg-_\bm_\ba_\bc_\br_\bo, or _\bc_\bs_\bi_\bn_\bg_\bl_\be-_\bi_\bn_\bf_\bi_\bx-_\bm_\ba_\bc_\br_\bo\n and not including the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. The\n syntax classes so defined are vsingle-macro,\n vsingle-splicing-macro, and vsingle-infix-\n macro.\n\n\n\n 7.7. Functions\n\n(setsyntax 's_symbol 's_synclass ['ls_func])\n\n WHERE: ls_func is the name of a function or a lambda\n body.\n\n RETURNS: t\n\n SIDE EFFECT: S_symbol should be a symbol whose print\n name is only one character. The syntax\n class for that character is set to\n s_synclass in the current readtable. If\n s_synclass is a class that requires a\n character macro, then ls_func must be sup-\n plied.\n\n NOTE: The symbolic syntax codes are new to Opus 38.\n For compatibility, s_synclass can be one of the\n fixnum syntax codes which appeared in older ver-\n sions of the FRANZ LISP Manual. This compatibil-\n ity is only temporary: existing code which uses\n the fixnum syntax codes should be converted.\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n\n\n\n\nThe Lisp Reader 7-13\n\n\n(getsyntax 's_symbol)\n\n RETURNS: the syntax class of the first character of\n s_symbol's print name. s_symbol's print name\n must be exactly one character long.\n\n NOTE: This function is new to Opus 38. It supercedes\n (_\bs_\bt_\ba_\bt_\bu_\bs _\bs_\by_\bn_\bt_\ba_\bx) which no longer exists.\n\n(add-syntax-class 's_synclass 'l_properties)\n\n RETURNS: s_synclass\n\n SIDE EFFECT: Defines the syntax class s_synclass to\n have properties l_properties. The list\n l_properties should contain a character\n classes mentioned above. l_properties may\n contain one of the escape properties:\n _\be_\bs_\bc_\ba_\bp_\be-_\ba_\bl_\bw_\ba_\by_\bs, _\be_\bs_\bc_\ba_\bp_\be-_\bw_\bh_\be_\bn-_\bu_\bn_\bi_\bq_\bu_\be, or\n _\be_\bs_\bc_\ba_\bp_\be-_\bw_\bh_\be_\bn-_\bf_\bi_\br_\bs_\bt. l_properties may con-\n tain the _\bs_\be_\bp_\ba_\br_\ba_\bt_\bo_\br property. After a syn-\n tax class has been defined with _\ba_\bd_\bd-\n _\bs_\by_\bn_\bt_\ba_\bx-_\bc_\bl_\ba_\bs_\bs, the _\bs_\be_\bt_\bs_\by_\bn_\bt_\ba_\bx function can\n be used to give characters that syntax\n class.\n\n\n ____________________________________________________\n\n ; Define a non-separating macro character.\n ; This type of macro character is used in UCI-Lisp, and\n ; it corresponds to a FIRST MACRO in Interlisp\n -> (_\ba_\bd_\bd-_\bs_\by_\bn_\bt_\ba_\bx-_\bc_\bl_\ba_\bs_\bs '_\bv_\bu_\bc_\bi-_\bm_\ba_\bc_\br_\bo '(_\bc_\bm_\ba_\bc_\br_\bo _\be_\bs_\bc_\ba_\bp_\be-_\bw_\bh_\be_\bn-_\bf_\bi_\br_\bs_\bt))\n vuci-macro\n ->\n ____________________________________________________\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u001b9\n\n\u001b9 Printed: January 31, 1984\n\n\n\n", "meta": {"hexsha": "29637afe8b25e60d3a27bd8ba12ad335d5e84ceb", "size": 32487, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch7.r", "max_stars_repo_name": "omasanori/franz-lisp", "max_stars_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 35, "max_stars_repo_stars_event_min_datetime": "2016-11-03T05:46:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-17T06:10:55.000Z", "max_issues_repo_path": "lisplib/manual/ch7.r", "max_issues_repo_name": "omasanori/franz-lisp", "max_issues_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": 3, "max_issues_repo_issues_event_min_datetime": "2017-05-19T16:13:04.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-26T20:48:51.000Z", "max_forks_repo_path": "lisplib/manual/ch7.r", "max_forks_repo_name": "omasanori/franz-lisp", "max_forks_repo_head_hexsha": "64e037d26cd382c0b88e0a5dc261bd2c29191af8", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 7, "max_forks_repo_forks_event_min_datetime": "2016-11-09T16:41:33.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-08T20:56:43.000Z", "avg_line_length": 37.9521028037, "max_line_length": 153, "alphanum_fraction": 0.5651799181, "num_tokens": 12416, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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{"text": "\n\n\n\n\n\n\n C\bCH\bHA\bAP\bPT\bTE\bER\bR 1\b1\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP\n\n\n\n\n\n\n 1\b1.\b.1\b1.\b. FRANZ LISPwas algebraic manipulation, artificial\n intelligence, and programming languages at the Univer-\n sity of California at Berkeley. Its roots are in a\n PDP-11 Lisp system which originally came from Harvard.\n As it grew it adopted features of Maclisp and Lisp\n Machine Lisp. Substantial compatibility with other\n Lisp dialects (Interlisp, UCILisp, CMULisp) is\n achieved by means of support packages and compiler\n switches. The heart of FRANZ LISP is written almost\n entirely in the programming language C. Of course, it\n has been greatly extended by additions written in\n Lisp. A small part is written in the assembly lan-\n guage for the current host machines, VAXen and a cou-\n ple of flavors of 68000. Because FRANZ LISP is writ-\n ten in C, it is relatively portable and easy to com-\n prehend.\n\n FRANZ LISP is capable of running large lisp pro-\n grams in a timesharing environment, has facilities for\n arrays and user defined structures, has a user con-\n trolled reader with character and word macro capabil-\n ities, and can interact directly with compiled Lisp,\n C, Fortran, and Pascal code.\n\n This document is a reference manual for the FRANZ\n LISP system. It is not a Lisp primer or introduction\n to the language. Some parts will be of interest pri-\n marily to those maintaining FRANZ LISP at their com-\n puter site. There is an additional document entitled\n _\bT_\bh_\be _\bF_\br_\ba_\bn_\bz _\bL_\bi_\bs_\bp _\bS_\by_\bs_\bt_\be_\bm_\b, _\bb_\by _\bJ_\bo_\bh_\bn _\bF_\bo_\bd_\be_\br_\ba_\br_\bo_\b, _\bw_\bh_\bi_\bc_\bh _\bp_\ba_\br_\b-\n _\bt_\bi_\ba_\bl_\bl_\by _\bd_\be_\bs_\bc_\br_\bi_\bb_\be_\bs _\bt_\bh_\be _\bs_\by_\bs_\bt_\be_\bm _\bi_\bm_\bp_\bl_\be_\bm_\be_\bn_\bt_\ba_\bt_\bi_\bo_\bn_\b. _\bF_\bR_\bA_\bN_\bZ\n _\bL_\bI_\bS_\bP_\b, _\ba_\bs _\bd_\be_\bl_\bi_\bv_\be_\br_\be_\bd _\bb_\by _\bB_\be_\br_\bk_\be_\bl_\be_\by_\b, _\bi_\bn_\bc_\bl_\bu_\bd_\be_\bs _\ba_\bl_\bl _\bs_\bo_\bu_\br_\bc_\be\n _\bc_\bo_\bd_\be _\ba_\bn_\bd _\bm_\ba_\bc_\bh_\bi_\bn_\be _\br_\be_\ba_\bd_\ba_\bb_\bl_\be _\bv_\be_\br_\bs_\bi_\bo_\bn _\bo_\bf _\bt_\bh_\bi_\bs _\bm_\ba_\bn_\bu_\ba_\bl _\ba_\bn_\bd\n _\bs_\by_\bs_\bt_\be_\bm _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt_\b. _\bT_\bh_\be _\bs_\by_\bs_\bt_\be_\bm _\bd_\bo_\bc_\bu_\bm_\be_\bn_\bt _\bi_\bs _\bi_\bn _\ba _\bs_\bi_\bn_\bg_\bl_\be\n _\bf_\bi_\bl_\be _\bn_\ba_\bm_\be_\bd _\b\"_\bf_\br_\ba_\bn_\bz_\b._\bn_\b\" _\bi_\bn _\bt_\bh_\be _\b\"_\bd_\bo_\bc_\b\" _\bs_\bu_\bb_\bd_\bi_\br_\be_\bc_\bt_\bo_\br_\by_\b.\n\n ____________________\n is rumored that this name has something to do with Franz\n Liszt [F_\brants List] (1811-1886) a Hungarian composer and\n keyboard virtuoso. These allegations have never been\n proven.\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-1\b1\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-2\b2\n\n\n This document is divided into four Movements. In\n the first one we will attempt to describe the language\n of FRANZ LISP precisely and completely as it now\n stands (Opus 38.69, June 1983). In the second Move-\n ment we will look at the reader, function types,\n arrays and exception handling. In the third Movement\n we will look at several large support packages written\n to help the FRANZ LISP user, namely the trace package,\n compiler, fixit and stepping package. Finally the\n fourth movement contains an index into the other\n movements. In the rest of this chapter we shall exam-\n ine the data types of FRANZ LISP. The conventions\n used in the description of the FRANZ LISP functions\n will be given in S1.3 -- it is very important that\n these conventions are understood.\n\n\n\n 1\b1.\b.2\b2.\b. D\bDa\bat\bta\ba T\bTy\byp\bpe\bes\bs FRANZ LISP has fourteen data types.\n In this section we shall look in detail at each type\n and if a type is divisible we shall look inside it.\n There is a Lisp function _\bt_\by_\bp_\be which will return the\n type name of a lisp object. This is the official\n FRANZ LISP name for that type and we will use this\n name and this name only in the manual to avoid confus-\n ing the reader. The types are listed in terms of\n importance rather than alphabetically.\n\n\n\n 1\b1.\b.2\b2.\b.0\b0.\b. l\bli\bis\bsp\bpv\bva\bal\bl This is the name we use to describe\n any Lisp object. The function _\bt_\by_\bp_\be will never\n return `lispval'.\n\n\n\n 1\b1.\b.2\b2.\b.1\b1.\b. s\bsy\bym\bmb\bbo\bol\bl This object corresponds to a variable\n in most other programming languages. It may have a\n value or may be `unbound'. A symbol may be _\bl_\ba_\bm_\bb_\bd_\ba\n _\bb_\bo_\bu_\bn_\bd meaning that its current value is stored away\n somewhere and the symbol is given a new value for\n the duration of a certain context. When the Lisp\n processor leaves that context, the symbol's cur-\n rent value is thrown away and its old value is\n restored.\n A symbol may also have a _\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn _\bb_\bi_\bn_\bd_\bi_\bn_\bg. This\n function binding is static; it cannot be lambda\n bound. Whenever the symbol is used in the func-\n tional position of a Lisp expression the function\n binding of the symbol is examined (see Chapter 4\n for more details on evaluation).\n A symbol may also have a _\bp_\br_\bo_\bp_\be_\br_\bt_\by _\bl_\bi_\bs_\bt, another\n static data structure. The property list consists\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-3\b3\n\n\n of a list of an even number of elements, considered\n to be grouped as pairs. The first element of the\n pair is the _\bi_\bn_\bd_\bi_\bc_\ba_\bt_\bo_\br the second the _\bv_\ba_\bl_\bu_\be of that\n indicator.\n Each symbol has a print name _\b(_\bp_\bn_\ba_\bm_\be_\b) which is how\n this symbol is accessed from input and referred to\n on (printed) output.\n A symbol also has a hashlink used to link symbols\n together in the oblist -- this field is inaccessi-\n ble to the lisp user.\n Symbols are created by the reader and by the func-\n tions _\bc_\bo_\bn_\bc_\ba_\bt, _\bm_\ba_\bk_\bn_\ba_\bm and their derivatives. Most\n symbols live on FRANZ LISP's sole _\bo_\bb_\bl_\bi_\bs_\bt, and\n therefore two symbols with the same print name are\n usually the exact same object (they are _\be_\bq). Sym-\n bols which are not on the oblist are said to be\n _\bu_\bn_\bi_\bn_\bt_\be_\br_\bn_\be_\bd_\b. The function _\bm_\ba_\bk_\bn_\ba_\bm creates uninterned\n symbols while _\bc_\bo_\bn_\bc_\ba_\bt creates _\bi_\bn_\bt_\be_\br_\bn_\be_\bd ones.\n\n\n +-------------+-----------+-----------+---------------------+\n |Subpart name | Get value | Set value | Type |\n | | | | |\n +-------------+-----------+-----------+---------------------+\n | value | eval | set | lispval |\n | | | setq | |\n +-------------+-----------+-----------+---------------------+\n | property | plist | setplist | list or nil |\n | list | get | putprop | |\n | | | defprop | |\n +-------------+-----------+-----------+---------------------+\n | function | getd | putd | array, binary, list |\n | binding | | def | or nil |\n +-------------+-----------+-----------+---------------------+\n | print name | get_pname | | string |\n +-------------+-----------+-----------+---------------------+\n | hash link | | | |\n +-------------+-----------+-----------+---------------------+\n\n\n\n 1\b1.\b.2\b2.\b.2\b2.\b. l\bli\bis\bst\bt A list cell has two parts, called the\n car and cdr. List cells are created by the func-\n tion _\bc_\bo_\bn_\bs.\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-4\b4\n\n\n +-------------+-----------+-----------+---------+\n |Subpart name | Get value | Set value | Type |\n | | | | |\n +-------------+-----------+-----------+---------+\n | car | car | rplaca | lispval |\n +-------------+-----------+-----------+---------+\n | cdr | cdr | rplacd | lispval |\n +-------------+-----------+-----------+---------+\n\n\n\n 1\b1.\b.2\b2.\b.3\b3.\b. b\bbi\bin\bna\bar\bry\by This type acts as a function header\n for machine coded functions. It has two parts, a\n pointer to the start of the function and a symbol\n whose print name describes the argument _\bd_\bi_\bs_\bc_\bi_\bp_\bl_\bi_\bn_\be.\n The discipline (if _\bl_\ba_\bm_\bb_\bd_\ba, _\bm_\ba_\bc_\br_\bo or _\bn_\bl_\ba_\bm_\bb_\bd_\ba) deter-\n mines whether the arguments to this function will\n be evaluated by the caller before this function is\n called. If the discipline is a string (specifi-\n cally \"_\bs_\bu_\bb_\br_\bo_\bu_\bt_\bi_\bn_\be\", \"_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn\", \"_\bi_\bn_\bt_\be_\bg_\be_\br_\b-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn\",\n \"_\br_\be_\ba_\bl_\b-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn\", \"_\bc_\b-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn\", \"_\bd_\bo_\bu_\bb_\bl_\be_\b-_\bc_\b-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn\",\n or \"_\bv_\be_\bc_\bt_\bo_\br_\b-_\bc_\b-_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn\" ) then this function is a\n foreign subroutine or function (see S8.5 for more\n details on this). Although the type of the _\be_\bn_\bt_\br_\by\n field of a binary type object is usually s\bst\btr\bri\bin\bng\bg or\n o\bot\bth\bhe\ber\br, the object pointed to is actually a sequence\n of machine instructions.\n Objects of type binary are created by _\bm_\bf_\bu_\bn_\bc_\bt_\bi_\bo_\bn_\b,\n _\bc_\bf_\ba_\bs_\bl_\b, and _\bg_\be_\bt_\ba_\bd_\bd_\br_\be_\bs_\bs_\b.\n\n\n +-------------+-----------+-----------+------------------+\n |Subpart name | Get value | Set value | Type |\n | | | | |\n +-------------+-----------+-----------+------------------+\n | entry | getentry | | string or fixnum |\n +-------------+-----------+-----------+------------------+\n | discipline | getdisc | putdisc | symbol or fixnum |\n +-------------+-----------+-----------+------------------+\n\n\n\n 1\b1.\b.2\b2.\b.4\b4.\b. f\bfi\bix\bxn\bnu\bum\bm A fixnum is an integer constant in\n the range -2to 2Small fixnums (-1024 to 1023) are\n stored in a special table so they needn't be allo-\n cated each time one is needed. In principle, the\n range for fixnums is machine dependent, although\n all current implementations for franz have this\n range.\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-5\b5\n\n\n 1\b1.\b.2\b2.\b.5\b5.\b. f\bfl\blo\bon\bnu\bum\bm A flonum is a double precision real\n number. On the VAX, the range is +-2.9x10to\n +-1.7x10There are approximately sixteen decimal\n digits of precision. Other machines may have other\n ranges.\n\n\n\n 1\b1.\b.2\b2.\b.6\b6.\b. b\bbi\big\bgn\bnu\bum\bm A bignum is an integer of potentially\n unbounded size. When integer arithmetic exceeds\n the limits of fixnums mentioned above, the calcula-\n tion is automatically done with bignums. Should\n calculation with bignums give a result which can be\n represented as a fixnum, then the fixnum represen-\n tation will be used.(f current algorithms for inte-\n ger arithmetic operations will return (in certain\n cases) a result between +-2and 2as a bignum\n although this could be represented as a fixnum.\n This contraction is known as _\bi_\bn_\bt_\be_\bg_\be_\br _\bn_\bo_\br_\bm_\ba_\bl_\bi_\bz_\ba_\bt_\bi_\bo_\bn.\n Many Lisp functions assume that integers are nor-\n malized. Bignums are composed of a sequence of\n l\bli\bis\bst\bt cells and a cell known as an s\bsd\bdo\bot\bt.\b. The user\n should consider a b\bbi\big\bgn\bnu\bum\bm structure indivisible and\n use functions such as _\bh_\ba_\bi_\bp_\ba_\br_\bt, and _\bb_\bi_\bg_\bn_\bu_\bm_\b-_\bl_\be_\bf_\bt_\bs_\bh_\bi_\bf_\bt\n to extract parts of it.\n\n\n\n 1\b1.\b.2\b2.\b.7\b7.\b. s\bst\btr\bri\bin\bng\bg A string is a null terminated\n sequence of characters. Most functions of symbols\n which operate on the symbol's print name will also\n work on strings. The default reader syntax is set\n so that a sequence of characters surrounded by dou-\n ble quotes is a string.\n\n\n\n 1\b1.\b.2\b2.\b.8\b8.\b. p\bpo\bor\brt\bt A port is a structure which the system\n I/O routines can reference to transfer data between\n the Lisp system and external media. Unlike other\n Lisp objects there are a very limited number of\n ports (20). Ports are allocated by _\bi_\bn_\bf_\bi_\bl_\be and _\bo_\bu_\bt_\b-\n _\bf_\bi_\bl_\be and deallocated by _\bc_\bl_\bo_\bs_\be and _\br_\be_\bs_\be_\bt_\bi_\bo. The\n _\bp_\br_\bi_\bn_\bt function prints a port as a percent sign fol-\n lowed by the name of the file it is connected to\n (if the port was opened by _\bf_\bi_\bl_\be_\bo_\bp_\be_\bn_\b, _\bi_\bn_\bf_\bi_\bl_\be_\b, _\bo_\br\n _\bo_\bu_\bt_\bf_\bi_\bl_\be). During initialization, FRANZ LISP binds\n the symbol p\bpi\bip\bpo\bor\brt\bt to a port attached to the stan-\n dard input stream. This port prints as %$stdin.\n There are ports connected to the standard output\n and error streams, which print as %$stdout and\n %$stderr. This is discussed in more detail at the\n beginning of Chapter 5.\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-6\b6\n\n\n 1\b1.\b.2\b2.\b.9\b9.\b. v\bve\bec\bct\bto\bor\br Vectors are indexed sequences of\n data. They can be used to implement a notion of\n user-defined types via their associated property\n list. They make h\bhu\bun\bnk\bks\bs (see below) logically unnec-\n essary, although hunks are very efficiently garbage\n collected. There is a second kind of vector,\n called an immediate-vector, which stores binary\n data. The name that the function _\bt_\by_\bp_\be returns for\n immediate-vectors is v\bve\bec\bct\bto\bor\bri\bi. Immediate-vectors\n could be used to implement strings and block-flonum\n arrays, for example. Vectors are discussed in\n chapter 9. The functions _\bn_\be_\bw_\b-_\bv_\be_\bc_\bt_\bo_\br, and _\bv_\be_\bc_\bt_\bo_\br,\n can be used to create vectors.\n\n\n +-------------+-----------+-----------+---------+\n |Subpart name | Get value | Set value | Type |\n | | | | |\n +-------------+-----------+-----------+---------+\n | datum[_\bi] | vref | vset | lispval |\n +-------------+-----------+-----------+---------+\n | property | vprop | vsetprop | lispval |\n | | | vputprop | |\n +-------------+-----------+-----------+---------+\n | size | vsize | - | fixnum |\n +-------------+-----------+-----------+---------+\n\n\n\n 1\b1.\b.2\b2.\b.1\b10\b0.\b. a\bar\brr\bra\bay\by Arrays are rather complicated types\n and are fully described in Chapter 9. An array\n consists of a block of contiguous data, a function\n to access that data, and auxiliary fields for use\n by the accessing function. Since an array's\n accessing function is created by the user, an array\n can have any form the user chooses (e.g. n-\n dimensional, triangular, or hash table).\n Arrays are created by the function _\bm_\ba_\br_\br_\ba_\by.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-7\b7\n\n\n +----------------+-----------+-----------+---------------------+\n | Subpart name | Get value | Set value | Type |\n | | | | |\n +----------------+-----------+-----------+---------------------+\n |access function | getaccess | putaccess | binary, list |\n | | | | or symbol |\n +----------------+-----------+-----------+---------------------+\n | auxiliary | getaux | putaux | lispval |\n +----------------+-----------+-----------+---------------------+\n | data | arrayref | replace | block of contiguous |\n | | | set | lispval |\n +----------------+-----------+-----------+---------------------+\n | length | getlength | putlength | fixnum |\n +----------------+-----------+-----------+---------------------+\n | delta | getdelta | putdelta | fixnum |\n +----------------+-----------+-----------+---------------------+\n\n\n\n 1\b1.\b.2\b2.\b.1\b11\b1.\b. v\bva\bal\blu\bue\be A value cell contains a pointer to a\n lispval. This type is used mainly by arrays of\n general lisp objects. Value cells are created with\n the _\bp_\bt_\br function. A value cell containing a\n pointer to the symbol `foo' is printed as\n `(ptr to)foo'\n\n\n\n 1\b1.\b.2\b2.\b.1\b12\b2.\b. h\bhu\bun\bnk\bk A hunk is a vector of from 1 to 128\n lispvals. Once a hunk is created (by _\bh_\bu_\bn_\bk or\n _\bm_\ba_\bk_\bh_\bu_\bn_\bk) it cannot grow or shrink. The access time\n for an element of a hunk is slower than a list cell\n element but faster than an array. Hunks are really\n only allocated in sizes which are powers of two,\n but can appear to the user to be any size in the 1\n to 128 range. Users of hunks must realize that\n _\b(_\bn_\bo_\bt _\b(_\ba_\bt_\bo_\bm _\b'_\bl_\bi_\bs_\bp_\bv_\ba_\bl_\b)_\b) will return true if _\bl_\bi_\bs_\bp_\bv_\ba_\bl\n is a hunk. Most lisp systems do not have a direct\n test for a list cell and instead use the above test\n and assume that a true result means _\bl_\bi_\bs_\bp_\bv_\ba_\bl is a\n list cell. In FRANZ LISP you can use _\bd_\bt_\bp_\br to check\n for a list cell. Although hunks are not list\n cells, you can still access the first two hunk ele-\n ments with _\bc_\bd_\br and _\bc_\ba_\br and you can access any hunk\n element with _\bc_\bx_\br.(f a hunk, the function _\bc_\bd_\br refer-\n ences the first element and _\bc_\ba_\br the second.\n You can set the value of the first two elements of\n a hunk with _\br_\bp_\bl_\ba_\bc_\bd and _\br_\bp_\bl_\ba_\bc_\ba and you can set the\n value of any element of the hunk with _\br_\bp_\bl_\ba_\bc_\bx. A\n hunk is printed by printing its contents surrounded\n by { and }. However a hunk cannot be read in in\n this way in the standard lisp system. It is easy\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-8\b8\n\n\n to write a reader macro to do this if desired.\n\n\n\n 1\b1.\b.2\b2.\b.1\b13\b3.\b. o\bot\bth\bhe\ber\br Occasionally, you can obtain a\n pointer to storage not allocated by the lisp sys-\n tem. One example of this is the entry field of\n those FRANZ LISP functions written in C. Such\n objects are classified as of type o\bot\bth\bhe\ber\br. Foreign\n functions which call malloc to allocate their own\n space, may also inadvertantly create such objects.\n The garbage collector is supposed to ignore such\n objects.\n\n\n\n 1\b1.\b.3\b3.\b. D\bDo\boc\bcu\bum\bme\ben\bnt\bta\bat\bti\bio\bon\bn The conventions used in the follow-\n ing chapters were designed to give a great deal of\n information in a brief space. The first line of a\n function description contains the function name in\n b\bbo\bol\bld\bd f\bfa\bac\bce\be and then lists the arguments, if any. The\n arguments all have names which begin with a letter or\n letters and an underscore. The letter(s) gives the\n allowable type(s) for that argument according to this\n table.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Printed: October 16, 1993\n\n\n\n\n\n\n\n F\bFR\bRA\bAN\bNZ\bZ L\bLI\bIS\bSP\bP 1\b1-\b-9\b9\n\n\n +-------+----------------------------------------------+\n |Letter | Allowable type(s) |\n | | |\n +-------+----------------------------------------------+\n |g | any type |\n +-------+----------------------------------------------+\n |s | symbol (although nil may not be allowed) |\n +-------+----------------------------------------------+\n |t | string |\n +-------+----------------------------------------------+\n |l | list (although nil may be allowed) |\n +-------+----------------------------------------------+\n |n | number (fixnum, flonum, bignum) |\n +-------+----------------------------------------------+\n |i | integer (fixnum, bignum) |\n +-------+----------------------------------------------+\n |x | fixnum |\n +-------+----------------------------------------------+\n |b | bignum |\n +-------+----------------------------------------------+\n |f | flonum |\n +-------+----------------------------------------------+\n |u | function type (either binary or lambda body) |\n +-------+----------------------------------------------+\n |y | binary |\n +-------+----------------------------------------------+\n |v | vector |\n +-------+----------------------------------------------+\n |V | vectori |\n +-------+----------------------------------------------+\n |a | array |\n +-------+----------------------------------------------+\n |e | value |\n +-------+----------------------------------------------+\n |p | port (or nil) |\n +-------+----------------------------------------------+\n |h | hunk |\n +-------+----------------------------------------------+\n\n In the first line of a function description, those\n arguments preceded by a quote mark are evaluated (usu-\n ally before the function is called). The quoting con-\n vention is used so that we can give a name to the\n result of evaluating the argument and we can describe\n the allowable types. If an argument is not quoted it\n does not mean that that argument will not be evalu-\n ated, but rather that if it is evaluated, the time at\n which it is evaluated will be specifically mentioned\n in the function description. Optional arguments are\n surrounded by square brackets. An ellipsis (...)\n means zero or more occurrences of an argument of the\n directly preceding type.\n\n\n\n Printed: October 16, 1993\n\n\n\n", "meta": {"hexsha": "270fef9c0ff6ebec8d4f7ee43b4e7c4b1dbbd552", "size": 28381, "ext": "r", "lang": "R", "max_stars_repo_path": "lisplib/manual/ch1.r", "max_stars_repo_name": "krytarowski/franz-lisp-christos", "max_stars_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_stars_repo_licenses": ["BSD-4-Clause-UC"], "max_stars_count": 2, "max_stars_repo_stars_event_min_datetime": "2021-08-17T08:05:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-17T08:07:14.000Z", "max_issues_repo_path": "lisplib/manual/ch1.r", "max_issues_repo_name": "krytarowski/franz-lisp-christos", "max_issues_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_issues_repo_licenses": ["BSD-4-Clause-UC"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lisplib/manual/ch1.r", "max_forks_repo_name": "krytarowski/franz-lisp-christos", "max_forks_repo_head_hexsha": "7f529800f415d583b4c1f0ae90dec19ad3fb1a8a", "max_forks_repo_licenses": ["BSD-4-Clause-UC"], "max_forks_count": 1, "max_forks_repo_forks_event_min_datetime": "2019-07-15T16:38:53.000Z", "max_forks_repo_forks_event_max_datetime": "2019-07-15T16:38:53.000Z", "avg_line_length": 47.6991596639, "max_line_length": 158, "alphanum_fraction": 0.4171805081, "num_tokens": 7898, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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