A lot of working components of the new layout

4 files changed, 1132 insertions, 1027 deletions

diff --git a/inst/GUI/gmkin.R b/inst/GUI/gmkin.R
index 7fef7a1..0b615c4 100644
--- a/inst/GUI/gmkin.R
+++ b/inst/GUI/gmkin.R
@@ -1,12 +1,12 @@
 # gWidgetsWWW2 GUI for mkin {{{1

 

-# Copyright (C) 2013,2014 Johannes Ranke

+# Copyright (C) 2013,2014,2015 Johannes Ranke

 # Portions of this file are copyright (C) 2013 Eurofins Regulatory AG, Switzerland

 # Contact: jranke@uni-bremen.de

 

-# This file is part of the R package mkin

+# This file is part of the R package gmkin

 

-# mkin is free software: you can redistribute it and/or modify it under the

+# gmkin is free software: you can redistribute it and/or modify it under the

 # terms of the GNU General Public License as published by the Free Software

 # Foundation, either version 3 of the License, or (at your option) any later

 # version.

@@ -20,1061 +20,377 @@
 # this program. If not, see <http://www.gnu.org/licenses/>

 

 # Set the GUI title and create the basic widget layout {{{1

-w      <- gwindow("gmkin - Browser based GUI for kinetic evaluations using mkin")

-sb     <- gstatusbar(paste("Powered by gWidgetsWWW2 (ExtJS, Rook)",

-                           "and mkin (FME, deSolve and minpack.lm)",

-                           "--- Working directory is", getwd()), cont = w)

-pg     <- gpanedgroup(cont = w, default.size = 260)

-center <- gnotebook(cont = pg)

-left   <- gvbox(cont = pg, use.scrollwindow = TRUE)

-# Set initial values {{{1

-# Initial project workspace contents {{{2

-project_name <- "FOCUS_2006_gmkin"

-project_file <- paste0(project_name, ".RData")

-workspace <- get(project_name)     # From dataset distributed with mkin

-studies.df <- workspace$studies.df # dataframe containing study titles

-ds <- workspace$ds                 # list of datasets

-ds.cur <- workspace$ds.cur         # current dataset index

-m <- workspace$m                   # list with mkinmod models, amended with mkinmod$name

-m.cur <- workspace$m.cur           # m.cur current model index

-f <- workspace$f                   # f list of fitted mkinfit objects

-f.cur <- workspace$f.cur           # current fit index

-s <- workspace$s                   # list of summaries of the fitted mkinfit objects

-# Initialise meta data objects so assignments within functions using <<- will {{{2

-# update them in the right environment

-observed.all <- vector()           # vector of names of observed variables in datasets

-ds.df <- data.frame()

-m.df <- data.frame()

-f.df <- data.frame()

-# Empty versions of meta data {{{2

-f.df.empty <- data.frame(Fit = as.integer(0), 

-                         Dataset = "", 

-                         Model = "",

-                         stringsAsFactors = FALSE)

+# Configuration {{{2

+left_width = 250

+right_width = 500

+save_keybinding = "Ctrl-X"

+# Widgets {{{2

+window_title <- paste0("gmkin ", packageVersion("gmkin"),

+                       "- Browser based GUI for kinetic evaluations using mkin")

+w  <- gwindow(window_title)

+sb <- gstatusbar(paste("Powered by gWidgetsWWW2 (ExtJS, Rook)",

+                       "and mkin (FME, deSolve and minpack.lm)",

+                       "--- Working directory is", getwd()), cont = w)

+

+bl <- gborderlayout(cont = w,

+                    #title = list(center = "Work", east = "Results"),

+                    panels = c("center", "west", "east"),

+                    collapsible = list(west = FALSE))

+

+bl$set_panel_size("west", left_width)

+bl$set_panel_size("east", right_width)

+

+center <- gnotebook(cont = bl, where = "center")

+left   <- gvbox(cont = bl, use.scrollwindow = TRUE, where = "west")

+right   <- gnotebook(cont = bl, use.scrollwindow = TRUE, where = "east")

+

 # Helper functions {{{1

 # Override function for making it possible to override original data points using the GUI {{{2

 override <- function(d) {

-  data.frame(name = d$name, time = d$time, 

-             value = ifelse(is.na(d$override), d$value, d$override),

-             err = d$err)

+  if (!is.null(d$override)) {

+    d_new <- data.frame(name = d$name, time = d$time, 

+                        value = ifelse(is.na(d$override), d$value, d$override),

+                        err = d$err)

+    return(d_new)

+  } else {

+    return(d)

+  }

+}

+# Update dataframe with projects {{{2

+update_p.df <- function() {

+  wd_projects <- gsub(".gmkinws", "", dir(pattern = ".gmkinws$"))

+  if (length(wd_projects) > 0) {

+    p.df.wd <- data.frame(Name = wd_projects, 

+                          Source = rep("working directory",

+                                       length(wd_projects)),

+                          stringsAsFactors = FALSE)

+    p.df <<- rbind(p.df.package, p.df.wd)

+  } else {

+    p.df <<- p.df.package

+  }

 }

 # Update dataframe with datasets {{{2

 update_ds.df <- function() {

-  ds.n <- length(ds)

-  ds.df <<- data.frame(Index = 1:ds.n, 

-                       Title = character(ds.n),

-                       Study = character(ds.n), 

-                       stringsAsFactors = FALSE)

-  for (i in 1:ds.n)

-  {

-    ds.index <- names(ds)[[i]]

-    ds.df[i, "Title"] <<- ds[[ds.index]]$title

-    ds.df[i, "Study"] <<- ds[[ds.index]]$study_nr

-    observed = as.character(unique(ds[[ds.index]]$data$name))

-    observed.all <<- union(observed, observed.all)

-  }

+  ds.df <<- data.frame(Title = sapply(ws$ds, function(x) x$title))

 }

 # Update dataframe with models {{{2

 update_m.df <- function() {

-  m.n <- length(m)

-  m.df <<- data.frame(Index = 1:m.n, 

-                      Name = character(m.n),

-                      stringsAsFactors = FALSE)

-  for (i in 1:m.n) {

-    m.index <- names(m)[[i]]

-    m.df[i, "Name"] <<- m[[m.index]]$name

-  }

+  m.df <<- data.frame(Name = names(ws$m))

 }

 # Update dataframe with fits {{{2

 update_f.df <- function() {

-  f.df <<- f.df.empty

-  f.count <- 0

-  for (fit.index in names(f)) {

-    f.count <- f.count + 1

-    ftmp <- f[[fit.index]]

-    f.df[f.count, "Fit"] <<- as.integer(f.count)

-    f.df[f.count, c("Dataset", "Model")] <<- c(ftmp$ds.index, ftmp$mkinmod$name)

-  }

-}

-# Initialise meta data objects {{{1

-update_ds.df()

-update_m.df()

-update_f.df()

-# Widgets and handlers for project data {{{1

-prg <- gexpandgroup("Project file management", cont = left, horizontal = FALSE)

-# Project data management handler functions {{{2

-upload_file_handler <- function(h, ...)

-{

-  # General

-  tmpfile <- normalizePath(svalue(h$obj), winslash = "/")

-  project_file <<- pr.gf$filename

-  project_name <<- try(load(tmpfile))

-  if (inherits(project_name, "try-error")) {

-    galert(paste("Failed to load", project_file), parent = w)

-  }

-

-  workspace <- get(project_name)

-

-  # Check for old workspaces created using mkin < 0.9-32 that aren't loaded properly

-  workspace_old <- FALSE

-  if (length(workspace$f) > 0) {

-    if (is.null(workspace$f[[1]]$method.modFit)) {

-      stopmessage <- paste("Could not load workspace ",

-                           project_file,

-                           ". It seems it was created using mkin with version < 0.9-32", 

-                           sep = "")

-      galert(stopmessage, parent = w)

-      svalue(sb) <- stopmessage

-      workspace_old <- TRUE

-    }

+  f.df <- f.df.empty

+  if (!is.na(ftmp[1])) {

+    f.df[1, "Name"] <- c("Temporary (not fitted)")

   }

-

-  if (!workspace_old) {

-    # Update project file name and status bar

-    svalue(pr.ge) <- project_name

-    svalue(sb) <- paste("Loaded project file", project_file)

-

-    # Studies

-    studies.gdf[,] <- studies.df <- workspace$studies.df

-

-    # Datasets

-    ds.cur <<- workspace$ds.cur

-    ds <<- workspace$ds

-    update_ds.df()

-    ds.gtable[,] <- ds.df

-    update_ds_editor()

-

-    # Models

-    m.cur <<- workspace$m.cur

-    m <<- workspace$m

-    update_m.df()

-    m.gtable[,] <- m.df

-    update_m_editor()

-

-    # Fits

-    f.cur <<- workspace$f.cur

-    f <<- workspace$f

-    s <<- workspace$s

-    if (length(f) > 0) {

-      update_f.df()

-      ftmp <<- f[[f.cur]]

-      stmp <<- s[[f.cur]]

-      ds.i <<- ds.cur

-      delete(f.gg.plotopts, f.gg.po.obssel)

-      f.gg.po.obssel <<- gcheckboxgroup(names(ftmp$mkinmod$spec), cont = f.gg.plotopts, 

-                                       checked = TRUE)

-      update_plotting_and_fitting()

-    } else {

-      f.df <<- f.df.empty

-      update_ds_editor()

-      svalue(center) <- 1

-    }

-    f.gtable[,] <- f.df

+  if (!is.na(ws$f)) {

+    f.df.ws <- data.frame(Name = names(ws$f), stringsAsFactors = FALSE)

+    f.df <- rbind(f.df, f.df.ws)

   }

+  f.df <<- f.df

 }

-save_to_file_handler <- function(h, ...)

-{

-  studies.df <- data.frame(studies.gdf[,], stringsAsFactors = FALSE)

-  workspace <- list(

-                    studies.df = studies.df,

-

-                    ds = ds,

-                    ds.cur = ds.cur,

-

-                    m = m,

-                    m.cur = m.cur,

-

-                    f = f,

-                    f.cur = f.cur,

-

-                    s = s)

-  assign(project_name, workspace)

-  save(list = project_name, file = project_file)

-  svalue(sb) <- paste("Saved project contents to", project_file, "in working directory", getwd())

-}

-change_project_name_handler = function(h, ...) {

-  project_name <<- as.character(svalue(h$obj))

-  project_file <<- paste0(project_name, ".RData")

-}

-# Project data management GUI elements {{{2

-pr.gf <- gfile(text = "Select project file", cont = prg,

-               handler = upload_file_handler)

-pr.ge <- gedit(project_name, cont = prg, label = "Project",

-               handler = change_project_name_handler)

-# The save button is always visible {{{2

-gbutton("Save current project contents", cont = left,

-        handler = save_to_file_handler)

-

-# Widget and handler for Studies {{{1

-stg <- gexpandgroup("Studies", cont = left)

-visible(stg) <- FALSE

-update_study_selector <- function(h, ...) {

-  delete(ds.e.1, ds.study.gc)

-  ds.study.gc <<- gcombobox(paste("Study", studies.gdf[,1]), cont = ds.e.1) 

-  svalue(ds.study.gc, index = TRUE) <- ds[[ds.cur]]$study_nr

-}

-studies.gdf <- gdf(studies.df, name = "Edit studies in the project",

-                   width = 235,

-                   height = 180, cont = stg)

-studies.gdf$set_column_width(1, 40)

-addHandlerChanged(studies.gdf, update_study_selector)

-# Widgets and handlers for datasets and models {{{1

-dsm <- gframe("Datasets and models", cont = left, horizontal = FALSE)

-# Widget for dataset table with handler {{{2

-ds.switcher <- function(h, ...) {

-  ds.cur <<- as.character(svalue(h$obj))

-  update_ds_editor()

+# Generate the initial workspace {{{1

+ws <- gmkinws$new()

+ws.import <- NA

+# Initialise meta data objects so assignments within functions using <<- will {{{2

+# update them in the right environment.

+# Also create initial versions of meta data in order to be able to clear the workspace

+p.df <- p.df.package <- data.frame(Name = c("FOCUS_2006", "FOCUS_2006_Z"),

+                                   Source = rep("gmkin package", 2), stringsAsFactors = FALSE)

+

+update_p.df()

+ds.df <- ds.df.empty <- data.frame(Title = "", stringsAsFactors = FALSE)

+m.df <- m.df.empty <- data.frame(Name = "", stringsAsFactors = FALSE)

+f.df <- f.df.empty <- data.frame(Name = "", stringsAsFactors = FALSE)

+ftmp <- NA

+# left: Explorer tables {{{1

+# Frames {{{2

+p.gf  <- gframe("Projects", cont = left, horizontal = FALSE)

+ds.gf <- gframe("Datasets", cont = left)

+m.gf <-  gframe("Models", cont = left)

+c.gf <-  gframe("Configuration", cont = left, horizontal = FALSE)

+f.gf <-  gframe("Results", cont = left)

+

+# Project explorer {{{2

+# Initialize project list from the gmkin package and the current working directory

+# The former must be manually amended if additional workspaces should be available

+p.gtable <- gtable(p.df, cont = p.gf, width = left_width - 10, height = 120)

+size(p.gtable) <- list(columnWidths = c(130, 100))

+p.switcher <- function(h, ...) {

+  p.cur <- h$row_index

+  Name <- p.df[p.cur, "Name"]

+  if (p.df[p.cur, "Source"] == "working directory") {

+    load(paste0(Name, ".gmkinws"))

+    ws <<- ws

+  } else {

+    ws <<-  get(Name)

+  }

   svalue(center) <- 1

+  svalue(c.ds) <- empty_conf_labels[1]

+  svalue(c.m) <- empty_conf_labels[2]

+  update_p_editor(p.cur)

+  update_ds.df()

+  ds.gtable[,] <<- ds.df

+  update_m.df()

+  m.gtable[,] <<- m.df

+  update_f.df()

+  f.gtable[,] <<- f.df

 }

-ds.gtable <- gtable(ds.df, cont = dsm)

-addHandlerDoubleClick(ds.gtable, ds.switcher)

-size(ds.gtable) <- list(columnWidths = c(40, 150, 30))

-ds.gtable$value <- 1

-

-# Model table with handler {{{2

-m.switcher <- function(h, ...) {

-  m.cur <<- as.character(svalue(h$obj))

-  update_m_editor()

+addHandlerClicked(p.gtable, p.switcher)

+# Dataset explorer {{{2

+ds.switcher <- function(h, ...) {

+  ws$ds.cur <<- h$row_index

+  svalue(c.ds) <- ds.df[ws$ds.cur, "Title"]

+  #update_ds_editor()

   svalue(center) <- 2

 }

-m.gtable <- gtable(m.df, cont = dsm)

-addHandlerDoubleClick(m.gtable, m.switcher)

-m.gtable$set_column_width(1, 40)

-m.gtable$value <- 1

-

-# Button for setting up a fit for the selected dataset and model {{{2

-configure_fit_handler = function(h, ...) {

-          ds.i <<- as.character(svalue(ds.gtable))

-          m.i <<- as.character(svalue(m.gtable))

-          ftmp <<- suppressWarnings(mkinfit(m[[m.i]],

-                                            override(ds[[ds.i]]$data),

-                                            method.modFit = "Marq",

-                                            err = "err", 

-                                            control.modFit = list(maxiter = 0)))

-          ftmp$ds.index <<- ds.i

-          ftmp$ds <<- ds[[ds.i]]

-          stmp <<- summary(ftmp)

-          svalue(pf) <- paste0("Dataset ", ds.i, ", Model ", m[[m.i]]$name)

-          svalue(f.gg.opts.plot) <<- FALSE

-          svalue(f.gg.opts.st) <<- ftmp$solution_type

-          svalue(f.gg.opts.weight) <<- ftmp$weight

-          svalue(f.gg.opts.atol) <<- ftmp$atol

-          svalue(f.gg.opts.rtol) <<- ftmp$rtol

-          svalue(f.gg.opts.transform_rates) <<- ftmp$transform_rates

-          svalue(f.gg.opts.transform_fractions) <<- ftmp$transform_fractions

-          svalue(f.gg.opts.reweight.method) <<- ifelse(

-                                         is.null(ftmp$reweight.method),

-                                         "none", ftmp$reweight.method)

-          svalue(f.gg.opts.reweight.tol) <<- ftmp$reweight.tol

-          svalue(f.gg.opts.reweight.max.iter) <<- ftmp$reweight.max.iter

-          svalue(f.gg.opts.method.modFit) <<- "Port"

-          svalue(f.gg.opts.maxit.modFit) <<- ftmp$maxit.modFit

-          f.gg.parms[,] <- get_Parameters(stmp, FALSE)

-          delete(f.gg.plotopts, f.gg.po.obssel)

-          f.gg.po.obssel <<- gcheckboxgroup(names(ftmp$mkinmod$spec), cont = f.gg.plotopts, 

-                                           checked = TRUE)

-          show_plot("Initial", default = TRUE)

-          svalue(plot.ftmp.savefile) <<- paste(ftmp$ds$title, "_", ftmp$mkinmod$name,

-                                               ".", plot_format, sep = "")

-          oldwidth <<- options()$width

-          options(width = 90)

-          svalue(f.gg.summary.filename) <<- ""

-          svalue(f.gg.summary.listing) <<- c("<pre>", capture.output(stmp), "</pre>")

-          options(width = oldwidth)

-          svalue(center) <- 3

+ds.gtable <- gtable(ds.df, cont = ds.gf, width = left_width - 10, height = 160)

+addHandlerClicked(ds.gtable, ds.switcher)

+# Model explorer {{{2

+m.switcher <- function(h, ...) {

+  ws$m.cur <<- h$row_index

+  svalue(c.m) <- m.df[ws$m.cur, "Name"]

+  #update_m_editor()

+  svalue(center) <- 3

 }

-gbutton("Configure fit for selected model and dataset", cont = dsm, 

-        handler = configure_fit_handler)

-

-# Widget and handler for fits {{{1

-f.gf <- gframe("Fits", cont = left, horizontal = FALSE)

+m.gtable <- gtable(m.df, cont = m.gf, width = left_width - 10, height = 160)

+addHandlerClicked(m.gtable, m.switcher)

+# Fit explorer {{{2

 f.switcher <- function(h, ...) {

-  if (svalue(h$obj) != 0) {

-    f.cur <<- svalue(h$obj)

-    ftmp <<- f[[f.cur]]

-    stmp <<- s[[f.cur]]

-    ds.i <<- ftmp$ds.index

-    update_plotting_and_fitting()

+  ws$f.cur <<- h$row_index - 1

+  if (ws$f.cur > 0) {

+    ftmp <<- ws$f[[ws$f.cur]]

+    stmp <<- ws$s[[ws$f.cur]]

+    c.ds$call_Ext("setText", 

+       paste0("<font color='gray'>", ftmp$ds$title, "</font>"), FALSE)

+    c.m$call_Ext("setText", 

+       paste0("<font color='gray'>", ftmp$m$name, "</font>"), FALSE)

   }

-  svalue(center) <- 3

-}

-f.gtable <- gtable(f.df, cont = f.gf)

-addHandlerDoubleClick(f.gtable, f.switcher)

-f.gtable$set_column_width(1, 40)

-f.gtable$set_column_width(2, 60)

-

-# Dataset editor {{{1

-ds.editor <- gframe("Dataset 1", horizontal = FALSE, cont = center, 

-                    label = "Dataset editor")

-# Handler functions {{{2

-ds.empty <- list(

-                 study_nr = 1,

-                 title = "",

-                 sampling_times = c(0, 1),

-                 time_unit = "",

-                 observed = "parent",

-                 unit = "",

-                 replicates = 1,

-                 data = data.frame(

-                                   name = "parent",

-                                   time = c(0, 1),

-                                   value = c(100, NA),

-                                   override = "NA",

-                                   err = 1,

-                                   stringsAsFactors = FALSE))

-

-copy_dataset_handler <- function(h, ...) {

-  ds.old <- ds.cur

-  ds.cur <<- as.character(1 + length(ds))

-  svalue(ds.editor) <- paste("Dataset", ds.cur)

-  ds[[ds.cur]] <<- ds[[ds.old]]

-  update_ds.df()

-  ds.gtable[,] <- ds.df

-}

- 

-delete_dataset_handler <- function(h, ...) {

-  if (length(ds) > 1) {

-    ds[[ds.cur]] <<- NULL

-    names(ds) <<- as.character(1:length(ds))

-    ds.cur <<- names(ds)[[1]]

-    update_ds.df()

-    ds.gtable[,] <- ds.df

-    update_ds_editor()

+  #update_f_conf()

+  #update_f_results()

+  svalue(center) <- 5

+}

+f.gtable <- gtable(f.df, cont = f.gf, width = left_width - 10, height = 160)

+addHandlerClicked(f.gtable, f.switcher)

+# Configuration {{{2

+empty_conf_labels <- paste0("<font color='gray'>Current ", c("dataset", "model"), "</font>")

+c.ds <- glabel(empty_conf_labels[1], cont = c.gf)

+c.m <- glabel(empty_conf_labels[2], cont = c.gf)

+c.conf <- gbutton("Configure fit", cont = c.gf, handler = function(h, ...) svalue(center) <- 4)

+# center: Project editor {{{1

+p.editor  <- gframe("", horizontal = FALSE, cont = center, 

+                     label = "Project")

+# Working directory {{{2

+p.line.wd <- ggroup(cont = p.editor, horizontal = TRUE)

+wd_handler <- function(h, ...) {

+ target_wd <- svalue(p.wde)

+ wd <- try(setwd(target_wd))

+ if (inherits(wd, "try-error")) {

+   gmessage(paste("Could not set working directory to", target_wd), parent = w)

+ } else {

+   svalue(sb) <- paste("Changed working directory to", wd)

+   update_p.df()

+   p.gtable[,] <- p.df

+   p.line.import.p[,] <- c("", p.df$Name)

+ }

+}

+p.wde <- gedit(getwd(), cont = p.line.wd, label = "Working directory", width = 50)

+p.wde$add_handler_enter(wd_handler)

+p.wdb <- gbutton("Change", cont = p.line.wd, handler = wd_handler)

+tooltip(p.wdb) <- "Edit the box on the left and press enter to change"

+# Project name {{{2

+p.line.name <- ggroup(cont = p.editor, horizontal = TRUE)

+p.name  <- gedit("New project", label = "Project name",

+                 width = 50, cont = p.line.name)

+p.save <- gaction("Save", parent = w,

+                  handler = function(h, ...) {

+                    filename <- paste0(svalue(p.name), ".gmkinws")

+                    try_to_save <- function (filename) {

+                      if (!inherits(try(save(ws, file = filename)),

+                                    "try-error")) {

+                        svalue(sb) <- paste("Saved project to file", filename,

+                                            "in working directory", getwd())

+                        update_p.df()

+                        p.gtable[,] <- p.df

+                      } else {

+                        gmessage("Saving failed for an unknown reason", parent = w)

+                      }

+                    }

+                    if (file.exists(filename)) {

+                      gconfirm(paste("File", filename, "exists. Overwrite?"),

+                               parent = w, 

+                               handler = function(h, ...) {

+                        try_to_save(filename)

+                      })

+                    } else {

+                      try_to_save(filename)

+                    }

+                  })

+p.save.button <- gbutton(action = p.save, cont = p.line.name)

+p.save$add_keybinding(save_keybinding)

+tooltip(p.save.button) <- paste("Press", save_keybinding, "to save")

+

+update_p_editor <- function(p.cur) {

+  project_name <-  as.character(p.df[p.cur, "Name"])

+  svalue(p.name)  <- project_name

+  if (p.df[p.cur, "Source"] == "gmkin package") {

+    svalue(p.filename) <- ""

+    p.delete$call_Ext("disable")

   } else {

-    galert("Deleting the last dataset is not supported", parent = w)

+    svalue(p.filename) <- file.path(getwd(), paste0(project_name, ".gmkinws"))

+    p.delete$call_Ext("enable")

   }

 }

- 

-new_dataset_handler <- function(h, ...) {

-  ds.cur <<- as.character(1 + length(ds))

-  ds[[ds.cur]] <<- ds.empty

-  update_ds.df()

-  ds.gtable[,] <- ds.df

-  update_ds_editor()

-}

-

-tmptextheader <- character(0)

-load_text_file_with_data <- function(h, ...) {

-  tmptextfile <<- normalizePath(svalue(h$obj), winslash = "/")

-  tmptext <- readLines(tmptextfile, warn = FALSE)

-  tmptextskip <<- 0

-  for (tmptextline in tmptext) {

-    if (grepl(":|#|/", tmptextline)) tmptextskip <<- tmptextskip + 1

-    else break()

-  }

-  svalue(ds.e.up.skip) <- tmptextskip

-  if (svalue(ds.e.up.header)) {

-    tmptextheader <<- strsplit(tmptext[tmptextskip + 1], 

-                             " |\t|;|,")[[1]]

-  }

-  svalue(ds.e.up.wide.time) <- tmptextheader[[1]]

-  svalue(ds.e.up.long.time) <- tmptextheader[[2]]

-  svalue(ds.e.up.text) <- c("<pre>", tmptext, "</pre>")

-  svalue(ds.e.stack) <- 2

-}

- 

-new_ds_from_csv_handler <- function(h, ...) {

-   tmpd <- try(read.table(tmptextfile,

-                          skip = as.numeric(svalue(ds.e.up.skip)), 

-                          dec = svalue(ds.e.up.dec),

-                          sep = switch(svalue(ds.e.up.sep), 

-                                       whitespace = "", 

-                                       ";" = ";",

-                                       "," = ","),

-                          header = svalue(ds.e.up.header),

-                          stringsAsFactors = FALSE))

-  if(svalue(ds.e.up.widelong) == "wide") {

-    tmpdl <- mkin_wide_to_long(tmpd, time = as.character(svalue(ds.e.up.wide.time)))

-  } else {

-    tmpdl <- data.frame(

-      name = tmpd[[svalue(ds.e.up.long.name)]],

-      time = tmpd[[svalue(ds.e.up.long.time)]],

-      value = tmpd[[svalue(ds.e.up.long.value)]])

-    tmpderr <- tmpd[[svalue(ds.e.up.long.err)]]

-    if (!is.null(tmpderr)) tmpdl$err <- tmpderr

-  }

-  if (class(tmpd) != "try-error") {

-    ds.cur <<- as.character(1 + length(ds))

-    ds[[ds.cur]] <<- list(

-                          study_nr = NA,

-                          title = "New import",

-                          sampling_times = sort(unique(tmpdl$time)),

-                          time_unit = "",

-                          observed = unique(tmpdl$name),

-                          unit = "",

-                          replicates = max(aggregate(tmpdl$time,

-                                                       list(tmpdl$time,

-                                                            tmpdl$name),

-                                                     length)$x),

-                          data = tmpdl)

-    ds[[ds.cur]]$data$override <<- as.numeric(NA)

-    if (is.null(ds[[ds.cur]]$data$err)) ds[[ds.cur]]$data$err <<- 1

+# File name {{{2

+p.line.file <- ggroup(cont = p.editor, horizontal = TRUE)

+p.filename.gg  <- ggroup(width = 400, cont = p.line.file)

+p.filename  <- glabel("", cont = p.filename.gg)

+p.delete.handler = function(h, ...) {

+  filename <- file.path(getwd(), paste0(svalue(p.name), ".gmkinws"))

+  gconfirm(paste0("Are you sure you want to delete ", filename, "?"),

+           parent = w,

+           handler = function(h, ...) {

+             if (inherits(try(unlink(filename)), "try-error")) {

+               gmessage("Deleting failed for an unknown reason", cont = w)

+             } else {

+               svalue(sb) <- paste("Deleted", filename)

+               svalue(p.filename) <- ""

+               p.delete$call_Ext("disable")

+               update_p.df()

+               p.gtable[,] <- p.df

+             }

+           })

+}

+p.delete <- gbutton("Delete", cont = p.line.file,

+                    handler = p.delete.handler)

+p.delete$call_Ext("disable")

+# Import {{{2

+p.line.import <- ggroup(cont = p.editor, horizontal = TRUE)

+p.line.import.p <- gcombobox(c("", p.df$Name), label = "Import from", cont = p.line.import,

+  handler = function(h, ...) {

+    p.import <- svalue(h$obj, index = TRUE) - 1

+    Name <- p.df[p.import, "Name"]

+    if (p.df[p.import, "Source"] == "working directory") {

+      load(paste0(Name, ".gmkinws"))

+      ws.import <<- ws

+    } else {

+      ws.import <<- get(Name)

+    }

+    p.line.import.dst[,] <- data.frame(Title = sapply(ws.import$ds, function(x) x$title),

+                                       stringsAsFactors = FALSE)

+    p.line.import.mt[,] <- data.frame(Name = names(ws.import$m),

+                                      stringsAsFactors = FALSE)

+  })

+p.line.import.frames <- ggroup(cont = p.editor, horizontal = TRUE)

+

+p.line.import.dsf <- gframe("Datasets for import", cont = p.line.import.frames, horizontal = FALSE)

+p.line.import.dst <- gtable(ds.df.empty, cont = p.line.import.dsf, multiple = TRUE,

+                            width = left_width - 10, height = 160)

+p.line.import.dsb <- gbutton("Import selected", cont = p.line.import.dsf,

+  handler = function(h, ...) {

+    i <- svalue(p.line.import.dst, index = TRUE)

+    ws$ds <<- append(ws$ds, ws.import$ds[i])

     update_ds.df()

     ds.gtable[,] <- ds.df

-    update_ds_editor()

-  } else {

-    galert("Uploading failed", parent = "w")

   }

-}

- 

-empty_grid_handler <- function(h, ...) {

-  obs <- strsplit(svalue(ds.e.obs), ", ")[[1]]

-  sampling_times <- strsplit(svalue(ds.e.st), ", ")[[1]]

-  replicates <- as.numeric(svalue(ds.e.rep))

-  new.data = data.frame(

-    name = rep(obs, each = replicates * length(sampling_times)),

-    time = as.numeric(rep(sampling_times, each = replicates, times = length(obs))),

-    value = as.numeric(NA),

-    override = as.numeric(NA),

-    err = 1,

-    stringsAsFactors = FALSE

-  )

-  ds.e.gdf[,] <- new.data

-}

-

-keep_ds_changes_handler <- function(h, ...) {

-  ds[[ds.cur]]$title <<- svalue(ds.title.ge)

-  ds[[ds.cur]]$study_nr <<- as.numeric(gsub("Study ", "", svalue(ds.study.gc)))

-  update_ds.df()

-  ds.gtable[,] <- ds.df

-  tmpd <- ds.e.gdf[,]

-  ds[[ds.cur]]$data <<- tmpd

-  ds[[ds.cur]]$sampling_times <<- sort(unique(tmpd$time))

-  ds[[ds.cur]]$time_unit <<- svalue(ds.e.stu)

-  ds[[ds.cur]]$observed <<- unique(tmpd$name)

-  ds[[ds.cur]]$unit <<- svalue(ds.e.obu)

-  ds[[ds.cur]]$replicates <<- max(aggregate(tmpd$time, 

-                                            list(tmpd$time, tmpd$name), length)$x)

-  update_ds_editor()

-  observed.all <<- union(observed.all, ds[[ds.cur]]$observed)

-  update_m_editor()

-}

- 

-# Widget setup {{{2

-# Line 1 {{{3

-ds.e.1 <- ggroup(cont = ds.editor, horizontal = TRUE)

-glabel("Title: ", cont = ds.e.1) 

-ds.title.ge <- gedit(ds[[ds.cur]]$title, cont = ds.e.1) 

-glabel(" from ", cont = ds.e.1) 

-ds.study.gc <- gcombobox(paste("Study", studies.gdf[,1]), cont = ds.e.1) 

-

-# Line 2 {{{3

-ds.e.2 <- ggroup(cont = ds.editor, horizontal = TRUE)

-ds.e.2a <- ggroup(cont = ds.e.2, horizontal = FALSE)

-gbutton("Copy dataset", cont = ds.e.2a, handler = copy_dataset_handler)

-gbutton("Delete dataset", cont = ds.e.2a, handler = delete_dataset_handler)

-gbutton("New dataset", cont = ds.e.2a, handler = new_dataset_handler)

-

-ds.e.2b <- ggroup(cont = ds.e.2)

-tmptextfile <- "" # Initialize file name for imported data

-tmptextskip <- 0 # Initialize number of lines to be skipped

-tmptexttime <- "V1" # Initialize name of time variable if no header row

-upload_dataset.gf <- gfile(text = "Upload text file", cont = ds.e.2b,

-        handler = load_text_file_with_data)

-

-gbutton("Keep changes", cont = ds.e.2, handler = keep_ds_changes_handler)

-

-# Line 3 with forms or upload area {{{3

-ds.e.stack <- gstackwidget(cont = ds.editor)

-# Forms for meta data {{{4

-ds.e.forms <- ggroup(cont = ds.e.stack, horizontal = TRUE)

-

-ds.e.3a <- gvbox(cont = ds.e.forms)

-ds.e.3a.gfl <- gformlayout(cont = ds.e.3a)

-ds.e.st  <- gedit(paste(ds[[ds.cur]]$sampling_times, collapse = ", "),

-                  width = 40,

-                  label = "Sampling times", 

-                  cont = ds.e.3a.gfl)

-ds.e.stu <- gedit(ds[[ds.cur]]$time_unit, 

-                  width = 20,

-                  label = "Unit", cont = ds.e.3a.gfl)

-ds.e.rep <- gedit(ds[[ds.cur]]$replicates, 

-                  width = 20,

-                  label = "Replicates", cont = ds.e.3a.gfl)

-

-ds.e.3b <- gvbox(cont = ds.e.forms)

-ds.e.3b.gfl <- gformlayout(cont = ds.e.3b)

-ds.e.obs <- gedit(paste(ds[[ds.cur]]$observed, collapse = ", "),

-                  width = 50,

-                  label = "Observed", cont = ds.e.3b.gfl)

-ds.e.obu <- gedit(ds[[ds.cur]]$unit,

-                  width = 20, label = "Unit", 

-                  cont = ds.e.3b.gfl)

-generate_grid.gb <- gbutton("Generate empty grid for kinetic data", cont = ds.e.3b, 

-        handler = empty_grid_handler)

-tooltip(generate_grid.gb) <- "Overwrites the kinetic data shown below"

-# Data upload area {{{4

-ds.e.upload <- ggroup(cont = ds.e.stack, horizontal = TRUE)

-ds.e.up.text <- ghtml("<pre></pre>", cont = ds.e.upload, width = 400, height = 400)

-ds.e.up.options <- ggroup(cont = ds.e.upload, horizontal = FALSE)

-ds.e.up.import <- gbutton("Import using options specified below", cont = ds.e.up.options,

-                          handler = new_ds_from_csv_handler)

-ds.e.up.skip <- gedit(tmptextskip, label = "Comment lines", cont = ds.e.up.options)

-ds.e.up.header <- gcheckbox(cont = ds.e.up.options, label = "Column names", 

-                            checked = TRUE)

-ds.e.up.sep <- gcombobox(c("whitespace", ";", ","), cont = ds.e.up.options,

-                         selected = 1, label = "Separator")

-ds.e.up.dec <- gcombobox(c(".", ","), cont = ds.e.up.options,

-                         selected = 1, label = "Decimal")

-ds.e.up.widelong <- gradio(c("wide", "long"), horizontal = TRUE, 

-                           label = "Format", cont = ds.e.up.options,

-                           handler = function(h, ...) {

-                             widelong = svalue(h$obj, index = TRUE)

-                             svalue(ds.e.up.wlstack) <- widelong

-                           })

-ds.e.up.wlstack <- gstackwidget(cont = ds.e.up.options)

-ds.e.up.wide <- ggroup(cont = ds.e.up.wlstack, horizontal = FALSE, width = 300)

-ds.e.up.wide.time <- gedit(tmptexttime, cont = ds.e.up.wide, label = "Time column")

-ds.e.up.long <- ggroup(cont = ds.e.up.wlstack, horizontal = FALSE, width = 300)

-ds.e.up.long.name <- gedit("name", cont = ds.e.up.long, label = "Observed variables")

-ds.e.up.long.time <- gedit(tmptexttime, cont = ds.e.up.long, label = "Time column")

-ds.e.up.long.value <- gedit("value", cont = ds.e.up.long, label = "Value column")

-ds.e.up.long.err <- gedit("err", cont = ds.e.up.long, label = "Relative errors")

-svalue(ds.e.up.wlstack) <- 1

-

-svalue(ds.e.stack) <- 1

-

-# Kinetic Data {{{3

-ds.e.gdf <- gdf(ds[[ds.cur]]$data, name = "Kinetic data", 

-                width = 500, height = 700, cont = ds.editor)

-ds.e.gdf$set_column_width(2, 70)

-

-# Update the dataset editor {{{3

-update_ds_editor <- function() {

-  svalue(ds.editor) <- paste("Dataset", ds.cur)

-  svalue(ds.title.ge) <- ds[[ds.cur]]$title

-  svalue(ds.study.gc, index = TRUE) <- ds[[ds.cur]]$study_nr

-

-  svalue(ds.e.st) <- paste(ds[[ds.cur]]$sampling_times, collapse = ", ")

-  svalue(ds.e.stu) <- ds[[ds.cur]]$time_unit

-  svalue(ds.e.obs) <- paste(ds[[ds.cur]]$observed, collapse = ", ")

-  svalue(ds.e.obu) <- ds[[ds.cur]]$unit

-  svalue(ds.e.rep) <- ds[[ds.cur]]$replicates

-  svalue(ds.e.stack) <- 1

-  ds.e.gdf[,] <- ds[[ds.cur]]$data

-}

-# Model editor {{{1

-m.editor <- gframe("Model 1", horizontal = FALSE, cont = center, label = "Model editor")

-# Handler functions {{{3

-copy_model_handler <- function(h, ...) {

-  m.old <- m.cur

-  m.cur <<- as.character(1 + length(m))

-  svalue(m.editor) <- paste("Model", m.cur)

-  m[[m.cur]] <<- m[[m.old]]

-  update_m.df()

-  m.gtable[,] <- m.df

-}

- 

-delete_model_handler <- function(h, ...) {

-  if (length(m) > 1) {

-    m[[m.cur]] <<- NULL

-    names(m) <<- as.character(1:length(m))

-    m.cur <<- "1"

+)

+

+p.line.import.mf <- gframe("Models for import", cont = p.line.import.frames, horizontal = FALSE)

+p.line.import.mt <- gtable(m.df.empty, cont = p.line.import.mf, multiple = TRUE,

+                            width = left_width - 10, height = 160)

+p.line.import.mb <- gbutton("Import selected", cont = p.line.import.mf,

+  handler = function(h, ...) {

+    i <- svalue(p.line.import.mt, index = TRUE)

+    ws$m <<- append(ws$m, ws.import$m[i])

     update_m.df()

     m.gtable[,] <- m.df

-    update_m_editor()

-  } else {

-    galert("Deleting the last model is not supported", parent = w)

-  }

-}

-

-add_observed_handler <- function(h, ...) {

-  obs.i <- length(m.e.rows) + 1

-  m.e.rows[[obs.i]] <<- ggroup(cont = m.editor, horizontal = TRUE)

-  m.e.obs[[obs.i]] <<- gcombobox(observed.all, selected = obs.i, 

-                                cont = m.e.rows[[obs.i]])

-  m.e.type[[obs.i]] <<- gcombobox(c("SFO", "SFORB"),

-                                 cont = m.e.rows[[obs.i]])

-  svalue(m.e.type[[obs.i]]) <- "SFO"

-  glabel("to", cont = m.e.rows[[obs.i]]) 

-  m.e.to[[obs.i]] <<- gedit("",

-                            initial.msg = "Optional list of target variables, e.g. 'm1, m2'",

-                            width = 40, cont = m.e.rows[[obs.i]])

-  m.e.sink[[obs.i]] <<- gcheckbox("Path to sink", 

-                                  checked = TRUE, cont = m.e.rows[[obs.i]]) 

-  gbutton("Remove compound", handler = remove_observed_handler, 

-          action = obs.i, cont = m.e.rows[[obs.i]])

-}

-

-remove_observed_handler <- function(h, ...) {

-  m[[m.cur]]$spec[[h$action]] <<- NULL

-  update_m_editor()

-}

-

-keep_m_changes_handler <- function(h, ...) {

-  spec <- list()

-  for (obs.i in 1:length(m.e.rows)) {

-    to_vector = strsplit(svalue(m.e.to[[obs.i]]), ", ")[[1]]

-    if (length(to_vector) == 0) to_vector = ""

-    spec[[obs.i]] <- list(type = svalue(m.e.type[[obs.i]]),

-                          to = to_vector,

-                          sink = svalue(m.e.sink[[obs.i]]))

-    if(spec[[obs.i]]$to[[1]] == "") spec[[obs.i]]$to = NULL

-    names(spec)[[obs.i]] <- svalue(m.e.obs[[obs.i]])

-  }

-  m[[m.cur]] <<- mkinmod(use_of_ff = svalue(m.ff.gc), 

-                         speclist = spec)

-  m[[m.cur]]$name <<- svalue(m.name.ge) 

-  update_m.df()

-  m.gtable[,] <- m.df

-}

- 

-# Widget setup {{{3

-m.e.0 <- ggroup(cont = m.editor, horizontal = TRUE)

-glabel("Model name: ", cont = m.e.0) 

-m.name.ge <- gedit(m[[m.cur]]$name, cont = m.e.0) 

-glabel("Use of formation fractions: ", cont = m.e.0) 

-m.ff.gc <- gcombobox(c("min", "max"), cont = m.e.0)

-svalue(m.ff.gc) <- m[[m.cur]]$use_of_ff

-

-# Model handling buttons {{{4

-m.e.b <- ggroup(cont = m.editor, horizontal = TRUE)

-gbutton("Copy model", cont = m.e.b, handler = copy_model_handler)

-gbutton("Delete model", cont = m.e.b, handler = delete_model_handler)

-gbutton("Add observed variable", cont = m.e.b, 

-        handler = add_observed_handler)

-gbutton("Keep changes", cont = m.e.b, handler = keep_m_changes_handler)

-

-

-m.observed <- names(m[[m.cur]]$spec)

-m.e.rows <- m.e.obs <- m.e.type <- m.e.to <- m.e.sink <- list()

-obs.to <- ""

-

-# Show the model specification {{{4

-show_m_spec <- function() {

-  for (obs.i in 1:length(m[[m.cur]]$spec)) {

-    obs.name <- names(m[[m.cur]]$spec)[[obs.i]]

-    m.e.rows[[obs.i]] <<- ggroup(cont = m.editor, horizontal = TRUE)

-    m.e.obs[[obs.i]] <<- gcombobox(observed.all, selected = 0, 

-                                  cont = m.e.rows[[obs.i]])

-    svalue(m.e.obs[[obs.i]]) <<- obs.name

-    if (obs.i == 1) {

-      m.e.type[[obs.i]] <<- gcombobox(c("SFO", "FOMC", "DFOP", "HS", "SFORB"),

-                                     cont = m.e.rows[[obs.i]])

-    } else {

-      m.e.type[[obs.i]] <<- gcombobox(c("SFO", "SFORB"),

-                                     cont = m.e.rows[[obs.i]])

-    }

-    svalue(m.e.type[[obs.i]]) <<- m[[m.cur]]$spec[[obs.i]]$type

-    glabel("to", cont = m.e.rows[[obs.i]]) 

-    obs.to <<- ifelse(is.null(m[[m.cur]]$spec[[obs.i]]$to), "",

-                 paste(m[[m.cur]]$spec[[obs.i]]$to, collapse = ", "))

-    m.e.to[[obs.i]] <<- gedit(obs.to, 

-                              initial.msg = "Optional list of target variables, e.g. 'm1, m2'",

-                              width = 40, cont = m.e.rows[[obs.i]])

-    m.e.sink[[obs.i]] <<- gcheckbox("Path to sink", checked = m[[m.cur]]$spec[[obs.i]]$sink,

-              cont = m.e.rows[[obs.i]]) 

-    if (obs.i > 1) {

-      gbutton("Remove observed variable", handler = remove_observed_handler, 

-              action = obs.i, cont = m.e.rows[[obs.i]])

-    }

-  }

-}

-show_m_spec()

-

-# Update the model editor {{{3

-update_m_editor <- function() {

-  svalue(m.editor) <- paste("Model", m.cur)

-  svalue(m.name.ge) <- m[[m.cur]]$name

-  svalue(m.ff.gc) <- m[[m.cur]]$use_of_ff

-  for (oldrow.i in 1:length(m.e.rows)) {

-    delete(m.editor, m.e.rows[[oldrow.i]])

-  }

-  m.observed <<- names(m[[m.cur]]$spec)

-  m.e.rows <<- m.e.obs <<- m.e.type <<- m.e.to <<- m.e.sink <<- list()

-  show_m_spec()

-}

-

-# 3}}}

-# 2}}}

-# Plotting and fitting {{{1

-show_plot <- function(type, default = FALSE) {

-  Parameters <- f.gg.parms[,]

-  Parameters.de <- subset(Parameters, Type == "deparm", type)

-  stateparms <- subset(Parameters, Type == "state")[[type]]

-  deparms <- as.numeric(Parameters.de[[type]])

-  names(deparms) <- rownames(Parameters.de)

-  if (type == "Initial" & default == FALSE) {

-    ftmp <<- suppressWarnings(mkinfit(ftmp$mkinmod, 

-                                      override(ds[[ds.i]]$data),

-                                      parms.ini = deparms,

-                                      state.ini = stateparms, 

-                                      fixed_parms = names(deparms),

-                                      fixed_initials = names(stateparms),

-                                      err = "err", 

-                                      method.modFit = "Marq",

-                                      control.modFit = list(maxiter = 0)))

-    ftmp$ds.index <<- ds.i

-    ftmp$ds <<- ds[[ds.i]]

-  } 

-  svalue(plot.ftmp.gi) <<- plot_ftmp_png()

-  svalue(plot.confint.gi) <<- plot_confint_png()

-}

-get_Parameters <- function(stmp, optimised)

-{

-  pars <- rbind(stmp$start[1:2], stmp$fixed)

-

-  pars$fixed <- c(rep(FALSE, length(stmp$start$value)),

-                  rep(TRUE, length(stmp$fixed$value)))

-  pars$name <- rownames(pars)

-  Parameters <- data.frame(Name = pars$name,

-                           Type = pars$type,

-                           Initial = pars$value,

-                           Fixed = pars$fixed,

-                           Optimised = as.numeric(NA))

-  Parameters <- rbind(subset(Parameters, Type == "state"),

-                      subset(Parameters, Type == "deparm"))

-  rownames(Parameters) <- Parameters$Name

-  if (optimised) {

-    Parameters[rownames(stmp$bpar), "Optimised"] <- stmp$bpar[, "Estimate"]

-  }

-  return(Parameters)

-}

-run_fit <- function() {

-  Parameters <- f.gg.parms[,]

-  Parameters.de <- subset(Parameters, Type == "deparm")

-  deparms <- Parameters.de$Initial

-  names(deparms) <- Parameters.de$Name

-  defixed <- names(deparms[Parameters.de$Fixed])

-  Parameters.ini <- subset(Parameters, Type == "state")

-  iniparms <- Parameters.ini$Initial

-  names(iniparms) <- sub("_0", "", Parameters.ini$Name)

-  inifixed <- names(iniparms[Parameters.ini$Fixed])

-  weight <- svalue(f.gg.opts.weight)

-  if (weight == "manual") {

-    err = "err"

-  } else {

-    err = NULL

   }

-  reweight.method <- svalue(f.gg.opts.reweight.method)

-  if (reweight.method == "none") reweight.method = NULL

-  ftmp <<- mkinfit(ftmp$mkinmod, override(ds[[ds.i]]$data),

-                   state.ini = iniparms,

-                   fixed_initials = inifixed,

-                   parms.ini = deparms, 

-                   fixed_parms = defixed,

-                   plot = svalue(f.gg.opts.plot),

-                   solution_type = svalue(f.gg.opts.st),

-                   atol = as.numeric(svalue(f.gg.opts.atol)),

-                   rtol = as.numeric(svalue(f.gg.opts.rtol)),

-                   transform_rates = svalue(f.gg.opts.transform_rates),

-                   transform_fractions = svalue(f.gg.opts.transform_fractions),

-                   weight = weight,

-                   err = err,

-                   reweight.method = reweight.method,

-                   reweight.tol = as.numeric(svalue(f.gg.opts.reweight.tol)),

-                   reweight.max.iter = as.numeric(svalue(f.gg.opts.reweight.max.iter)),

-                   method.modFit = svalue(f.gg.opts.method.modFit),

-                   maxit.modFit = svalue(f.gg.opts.maxit.modFit)

-                   )

-  ftmp$ds.index <<- ds.i

-  ftmp$ds <<- ds[[ds.i]]

-  stmp <<- summary(ftmp)

-  show_plot("Optimised")

-  svalue(f.gg.opts.st) <- ftmp$solution_type

-  svalue(f.gg.opts.weight) <- ftmp$weight.ini

-  f.gg.parms[,] <- get_Parameters(stmp, TRUE)

-  svalue(f.gg.summary.filename) <<- paste(ftmp$ds$title, "_", ftmp$mkinmod$name, ".txt", sep = "")

-  svalue(f.gg.summary.listing) <<- c("<pre>", capture.output(stmp), "</pre>")

-}

-ds.i <- m.i <- "1"

-f.cur <- "0"

+)

+

+

+# center: Dataset editor {{{1

+ds.editor <- gframe("", horizontal = FALSE, cont = center, 

+                     label = "Dataset editor")

+m.editor  <- gframe("", horizontal = FALSE, cont = center, 

+                    label = "Model editor")

+f.config  <- gframe("", horizontal = FALSE, cont = center, 

+                    label = "Fit configuration")

+r.viewer  <- gframe("", horizontal = FALSE, cont = center, 

+                    label = "Result viewer")

+svalue(center) <- 1

+# right: Viewing area {{{1

+# Workflow {{{2

+workflow.gg <- ggroup(cont = right, label = "Workflow", width = 480,  height = 600,

+                      ext.args = list(layout = list(type="vbox", align = "center")))

+

+workflow.png <- get_tempfile(ext = ".png")

+file.copy(system.file("GUI/gmkin_workflow_434x569.png", package = "gmkin"), workflow.png)

+workflow.gi <- gimage(workflow.png, size = c(434, 569), label = "Workflow", cont = workflow.gg)

+

+# Manual {{{2

+gmkin_manual <- readLines(system.file("GUI/gmkin_manual.html", package = "gmkin"))

+gmb_start <- grep("<body>", gmkin_manual)

+gmb_end <- grep("</body>", gmkin_manual)

+gmkin_manual_body <- gmkin_manual[gmb_start:gmb_end]

+

+manual.gh <- ghtml(label = "Manual", paste0("<div class = 'manual' style = 'margin: 20px'>

+<style>

+.manual h1{

+  font-size: 14px;

+  line-height: 20px;

+}

+.manual h2{

+  font-size: 14px;

+  line-height: 20px;

+}

+.manual h3{

+  font-size: 12px;

+  line-height: 18px;

+}

+.manual ul{

+  font-size: 12px;

+  line-height: 12px;

+}

+.manual li{

+  font-size: 12px;

+  line-height: 12px;

+}

+</style>

+", paste(gmkin_manual_body, collapse = '\n'), "

+</div>"), width = 460, cont = right)

+

+# Changes {{{2

+gmkin_news <- markdownToHTML(system.file("NEWS.md", package = "gmkin"), 

+                             fragment.only = TRUE,

+                             )

+

+changes.gh <- ghtml(label = "Changes", paste0("<div class = 'news' style = 'margin: 20px'>

+<style>

+.news h1{

+  font-size: 14px;

+  line-height: 20px;

+}

+.news h2{

+  font-size: 14px;

+  line-height: 20px;

+}

+.news h3{

+  font-size: 12px;

+  line-height: 18px;

+}

+.news ul{

+  font-size: 12px;

+  line-height: 12px;

+}

+.news li{

+  font-size: 12px;

+  line-height: 12px;

+}

+</style>

+", gmkin_news, "

+</div>"), width = 460, cont = right)

+

+svalue(right) <- 1

 

-# GUI widgets {{{2

-pf <- gframe("Dataset 1, Model SFO", horizontal = TRUE, 

-             cont = center, label = "Plotting and fitting")

 

-# Plot area to the left {{{3

-pf.p <- ggroup(cont = pf, horizontal = FALSE)

-ftmp <- suppressWarnings(mkinfit(m[[m.cur]], override(ds[[ds.i]]$data), 

-                                 err = "err", 

-                                 method.modFit = "Marq",

-                                 control.modFit = list(maxiter = 0)))

-ftmp$ds.index = ds.i

-ftmp$ds = ds[[ds.i]]

-stmp <- summary(ftmp)

-Parameters <- get_Parameters(stmp, FALSE)

-

-plot_ftmp <- function() {

-  if(exists("f.gg.po.obssel")) {

-    obs_vars_plot = svalue(f.gg.po.obssel)

-  } else {

-    obs_vars_plot = names(ftmp$mkinmod$spec)

-  }

-  if(exists("f.gg.po.legend")) {

-    plot_legend = svalue(f.gg.po.legend)

-  } else {

-    plot_legend = TRUE

-  }

-  plot(ftmp, main = ftmp$ds$title, obs_vars = obs_vars_plot,

-       xlab = ifelse(ftmp$ds$time_unit == "", "Time", 

-                     paste("Time in", ftmp$ds$time_unit)),

-       ylab = ifelse(ds[[ds.i]]$unit == "", "Observed", 

-                     paste("Observed in", ftmp$ds$unit)),

-       legend = plot_legend,

-       show_residuals = TRUE)

-}

-

-plot_ftmp_png <- function() {

-  tf <- get_tempfile(ext=".png")

-  png(tf, width = 400, height = 400)

-  plot_ftmp()

-  dev.off()

-  return(tf)

-}

-

-plot_ftmp_save <- function(filename) {

-  switch(plot_format,

-         png = png(filename, width = 400, height = 400),

-         pdf = pdf(filename),

-         wmf = win.metafile(filename))

-  plot_ftmp()

-  dev.off()

-  svalue(sb) <- paste("Saved plot to", filename, "in working directory", getwd())

-}

-

-plot_confint_png <- function() {

-  tf <- get_tempfile(ext=".png")

-  png(tf, width = 400, height = 400)

-  mkinparplot(ftmp) 

-  dev.off()

-  return(tf)

-}

-

-plot_formats <- c("png", "pdf")

-if (exists("win.metafile", "package:grDevices", inherits = FALSE)) {

-  plot_formats = c("wmf", plot_formats)

-}

-plot_format <- plot_formats[[1]]

-

-plot.ftmp.gi <- gimage(plot_ftmp_png(), container = pf.p, width = 400, height = 400)

-plot.ftmp.saveline <- ggroup(cont = pf.p, horizontal = TRUE)

-plot.ftmp.savefile <- gedit(paste(ds[[ds.cur]]$title, "_", m[[m.cur]]$name, ".",

-                                     plot_format, sep = ""),

-                               width = 40, cont = plot.ftmp.saveline)

-plot.ftmp.savebutton <-  gbutton("Save plot", cont = plot.ftmp.saveline,

-                                    handler = function(h, ...) {

-                                      filename <- svalue(plot.ftmp.savefile)

-                                      if (file.exists(filename))

-                                      {

-                                        gconfirm(paste("File", filename, 

-                                                       "exists. Overwrite?"),

-                                                 parent = w,

-                                                 handler = function(h, ...) {

-                                            plot_ftmp_save(filename)

-                                          }

-                                        )

-                                      } else {

-                                        plot_ftmp_save(filename)

-                                      }

-                                    })

-plot.space <- ggroup(cont = pf.p, horizontal = TRUE, height = 18)

-plot.confint.gi <- gimage(plot_confint_png(), container = pf.p, width = 400, height = 400)

-

-# Buttons and notebook area to the right {{{3

-p.gg <- ggroup(cont = pf, horizontal = FALSE)

-# Row with buttons {{{4

-f.gg.buttons <- ggroup(cont = p.gg)

-run.fit.gb <- gbutton("Run", width = 100,

-                      handler = function(h, ...) run_fit(), cont =

-                      f.gg.buttons)

-tooltip(run.fit.gb) <- "Fit with current settings on the current dataset, with the original model"

-

-keep.fit.gb <- gbutton("Keep fit", 

-                       handler = function(h, ...) {

-                            f.cur <<- as.character(length(f) + 1)

-                            f[[f.cur]] <<- ftmp

-                            s[[f.cur]] <<- stmp

-                            update_f.df()

-                            f.gtable[,] <<- f.df

-                            delete(f.gg.plotopts, f.gg.po.obssel)

-                            f.gg.po.obssel <<- gcheckboxgroup(names(ftmp$mkinmod$spec), 

-                                                              cont = f.gg.plotopts, 

-                                                              checked = TRUE)

-                            delete(f.gg.buttons, get.initials.gc)

-                            get.initials.gc <<- gcombobox(paste("Fit", f.df$Fit), 

-                                                          cont = f.gg.buttons)

-                          }, cont = f.gg.buttons)

-tooltip(keep.fit.gb) <- "Store the optimised model with all settings and the current dataset in the fit list"

-

-delete_fit_handler <- function(h, ...) {

-  if (length(f) > 0) {

-    f[[f.cur]] <<- NULL

-    s[[f.cur]] <<- NULL

-  }

-  if (length(f) > 0) {

-    names(f) <<- as.character(1:length(f))

-    names(s) <<- as.character(1:length(f))

-    update_f.df()

-    f.cur <<- "1"

-    ftmp <<- f[[f.cur]]

-    stmp <<- s[[f.cur]]

-    ds.i <<- ftmp$ds.index

-    update_plotting_and_fitting()

-  } else {

-    f.df <<- f.df.empty

-    f.cur <<- "0"

-  }

-  f.gtable[,] <<- f.df

-}

-

-delete.fit.gb <- gbutton("Delete fit", handler = delete_fit_handler,

-                         cont = f.gg.buttons)

-tooltip(delete.fit.gb) <- "Delete the currently loaded fit from the fit list"

-

-show.initial.gb <- gbutton("Show initial", 

-                           handler = function(h, ...) show_plot("Initial"),

-                           cont = f.gg.buttons)

-tooltip(show.initial.gb) <- "Show model with current inital settings for current dataset"

-

-get_initials_handler <- function(h, ...)

-{

-  f.i <- svalue(get.initials.gc, index = TRUE)

-  if (length(f) > 0) {

-    got_initials <- c(f[[f.i]]$bparms.fixed, f[[f.i]]$bparms.optim)

-    parnames <- f.gg.parms[,"Name"]

-    newparnames <- names(got_initials)

-    commonparnames <- intersect(parnames, newparnames)

-    f.gg.parms[commonparnames, "Initial"] <<- got_initials[commonparnames]

-  }

-}

-get.initials.gb <- gbutton("Get initials from", cont = f.gg.buttons,

-                           handler = get_initials_handler)

-get.initials.gc <- gcombobox(paste("Fit", f.df$Fit), cont = f.gg.buttons)

-

-# Notebook to the right {{{3

-f.gn <- gnotebook(cont = p.gg, width = 680, height = 790)

-# Dataframe with initial and optimised parameters {{{4

-f.gg.parms <- gdf(Parameters, cont = f.gn, 

-                 width = 670, height = 750,

-                 do_add_remove_buttons = FALSE, label = "Parameters")

-f.gg.parms$set_column_width(1, 200)

-f.gg.parms$set_column_width(2, 50)

-f.gg.parms$set_column_width(3, 60)

-f.gg.parms$set_column_width(4, 50)

-f.gg.parms$set_column_width(5, 60)

-

-# Fit options form {{{4

-f.gg.opts <- gformlayout(cont = f.gn, label = "Fit options")

-solution_types <- c("auto", "analytical", "eigen", "deSolve")

-f.gg.opts.plot <- gcheckbox("plot",

-                         cont = f.gg.opts, checked = FALSE)

-f.gg.opts.st <- gcombobox(solution_types, selected = 1,

-                          label = "solution_type", width = 200,

-                          cont = f.gg.opts)

-f.gg.opts.atol <- gedit(ftmp$atol, label = "atol", width = 20,

-                         cont = f.gg.opts)

-f.gg.opts.rtol <- gedit(ftmp$rtol, label = "rtol", width = 20,

-                         cont = f.gg.opts)

-f.gg.opts.transform_rates <- gcheckbox("transform_rates",

-                         cont = f.gg.opts, checked = TRUE)

-f.gg.opts.transform_fractions <- gcheckbox("transform_fractions",

-                         cont = f.gg.opts, checked = TRUE)

-weights <- c("manual", "none", "std", "mean")

-f.gg.opts.weight <- gcombobox(weights, selected = 1, label = "weight",

-                              width = 200, cont = f.gg.opts)

-f.gg.opts.reweight.method <- gcombobox(c("none", "obs"), selected = 1,

-                                       label = "reweight.method",

-                                       width = 200,

-                                       cont = f.gg.opts)

-f.gg.opts.reweight.tol <- gedit(1e-8, label = "reweight.tol",

-                                 width = 20, cont = f.gg.opts)

-f.gg.opts.reweight.max.iter <- gedit(10, label = "reweight.max.iter",

-                                 width = 20, cont = f.gg.opts)

-optimisation_methods <- c("Port", "Marq", "SANN")

-f.gg.opts.method.modFit <- gcombobox(optimisation_methods, selected = 1,

-                                     label = "method.modFit",

-                                     width = 200,

-                                     cont = f.gg.opts)

-f.gg.opts.maxit.modFit <- gedit("auto", label = "maxit.modFit",

-                                 width = 20, cont = f.gg.opts)

-

-# Summary {{{3

-oldwidth <- options()$width

-options(width = 90)

-f.gg.summary <- ggroup(label = "Summary", cont = f.gn, horizontal = FALSE)

-f.gg.summary.topline <- ggroup(cont = f.gg.summary, horizontal = TRUE)

-f.gg.summary.filename <- gedit(paste(ds[[ds.cur]]$title, "_", m[[m.cur]]$name,

-                                     ".txt", sep = ""),

-                               width = 50, cont = f.gg.summary.topline)

-f.gg.summary.savebutton <-  gbutton("Save summary", cont = f.gg.summary.topline,

-                                    handler = function(h, ...) {

-                                      filename <- svalue(f.gg.summary.filename)

-                                      if (file.exists(filename))

-                                      {

-                                        gconfirm(paste("File", filename, "exists. Overwrite?"),

-                                                 parent = w,

-                                                 handler = function(h, ...) {

-                                                   capture.output(stmp,  file = filename)

-                                                 })

-                                      } else {

-                                        capture.output(stmp,  file = filename)

-                                      }

-                                    })

-f.gg.summary.listing <- ghtml(c("<pre>", capture.output(stmp), "</pre>"),

-                        cont = f.gg.summary, label = "Summary")

-options(width = oldwidth)

-

-# Plot options {{{4

-f.gg.plotopts <- ggroup(cont = f.gn, label = "Plot options", horizontal = FALSE,

-                        width = 200)

-

-f.gg.po.format <- gcombobox(plot_formats, selected = 1, label = "File format",

-                  cont = f.gg.plotopts,

-                  handler = function(h, ...) {

-                    plot_format <<- svalue(h$obj)

-                    svalue(plot.ftmp.savefile) <<- gsub("...$", plot_format,

-                                                       svalue(plot.ftmp.savefile))

-                  })

-plot_format <- svalue(f.gg.po.format)

-f.gg.po.legend <- gcheckbox("legend", cont = f.gg.plotopts, checked = TRUE)

-f.gg.po.update <- gbutton("Update plot",

-                          handler = function(h, ...) show_plot("Optimised"), 

-                          cont = f.gg.plotopts)

-f.gg.po.obssel <- gcheckboxgroup(names(m[[m.cur]]$spec), cont = f.gg.plotopts, 

-                                 checked = TRUE)

-svalue(f.gn) <- 1

-

-# Update the plotting and fitting area {{{3

-update_plotting_and_fitting <- function() {

-  svalue(pf) <- paste0("Fit ", f.cur, ": Dataset ", ftmp$ds.index, 

-                       ", Model ", ftmp$mkinmod$name)

-  # Parameters

-  f.gg.parms[,] <- get_Parameters(stmp, TRUE)

-

-  # Fit options

-  delete(f.gg.buttons, get.initials.gc)

-  get.initials.gc <<- gcombobox(paste("Fit", f.df$Fit), cont = f.gg.buttons)

-

-  svalue(f.gg.opts.st) <- ftmp$solution_type

-  svalue(f.gg.opts.atol) <- ftmp$atol

-  svalue(f.gg.opts.rtol) <- ftmp$rtol

-  svalue(f.gg.opts.transform_rates) <- ftmp$transform_rates

-  svalue(f.gg.opts.transform_fractions) <- ftmp$transform_fractions

-  svalue(f.gg.opts.weight) <- ftmp$weight.ini

-  svalue(f.gg.opts.reweight.method) <- ifelse(is.null(ftmp$reweight.method),

-                                                      "none", 

-                                                      ftmp$reweight.method)

-  svalue(f.gg.opts.reweight.tol) <- ftmp$reweight.tol

-  svalue(f.gg.opts.reweight.max.iter) <- ftmp$reweight.max.iter

-  svalue(f.gg.opts.method.modFit) <- ftmp$method.modFit

-  svalue(f.gg.opts.maxit.modFit) <- ftmp$maxit.modFit

-

-  # Summary

-  oldwidth <<- options()$width

-  options(width = 90)

-  svalue(f.gg.summary.filename) <<- paste(ftmp$ds$title, "_", ftmp$mkinmod$name, ".txt", sep = "")

-  svalue(f.gg.summary.listing) <<- c("<pre>", capture.output(stmp), "</pre>")

-  options(width = oldwidth)

-

-  # Plot options

-  delete(f.gg.plotopts, f.gg.po.obssel)

-  f.gg.po.obssel <<- gcheckboxgroup(names(ftmp$mkinmod$spec), cont = f.gg.plotopts, 

-                                   checked = TRUE)

-  # Plot

-  svalue(plot.ftmp.savefile) <<- paste(ftmp$ds$title, "_", ftmp$mkinmod$name,

-                                       ".", plot_format, sep = "")

-  show_plot("Optimised")  

- 

-}

 # vim: set foldmethod=marker ts=2 sw=2 expandtab: {{{1

diff --git a/inst/GUI/gmkin_manual.html b/inst/GUI/gmkin_manual.html
new file mode 100644
index 0000000..588f580
--- /dev/null
+++ b/inst/GUI/gmkin_manual.html
@@ -0,0 +1,233 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+  <meta http-equiv="Content-Style-Type" content="text/css" />
+  <meta name="generator" content="pandoc" />
+  <title>Manual for gmkin</title>
+  <style type="text/css">code{white-space: pre;}</style>
+  <style type="text/css">
+table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {
+  margin: 0; padding: 0; vertical-align: baseline; border: none; }
+table.sourceCode { width: 100%; line-height: 100%; }
+td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }
+td.sourceCode { padding-left: 5px; }
+code > span.kw { color: #007020; font-weight: bold; }
+code > span.dt { color: #902000; }
+code > span.dv { color: #40a070; }
+code > span.bn { color: #40a070; }
+code > span.fl { color: #40a070; }
+code > span.ch { color: #4070a0; }
+code > span.st { color: #4070a0; }
+code > span.co { color: #60a0b0; font-style: italic; }
+code > span.ot { color: #007020; }
+code > span.al { color: #ff0000; font-weight: bold; }
+code > span.fu { color: #06287e; }
+code > span.er { color: #ff0000; font-weight: bold; }
+  </style>
+</head>
+<body>
+<div id="header">
+<h1 class="title">Manual for gmkin</h1>
+</div>
+<div id="TOC">
+<ul>
+<li><a href="#introduction">Introduction</a></li>
+<li><a href="#starting-gmkin">Starting gmkin</a></li>
+<li><a href="#project-file-management">Project file management</a></li>
+<li><a href="#studies">Studies</a></li>
+<li><a href="#datasets-and-models">Datasets and Models</a></li>
+<li><a href="#dataset-editor">Dataset editor</a><ul>
+<li><a href="#entering-data-directly">Entering data directly</a></li>
+<li><a href="#importing-data-from-text-files">Importing data from text files</a></li>
+</ul></li>
+<li><a href="#model-editor">Model editor</a></li>
+<li><a href="#plotting-and-fitting">Plotting and fitting</a><ul>
+<li><a href="#parameters">Parameters</a></li>
+<li><a href="#fit-options">Fit options</a></li>
+<li><a href="#fitting-the-model">Fitting the model</a></li>
+<li><a href="#summary">Summary</a></li>
+<li><a href="#plot-options">Plot options</a></li>
+<li><a href="#confidence-interval-plots">Confidence interval plots</a></li>
+</ul></li>
+</ul>
+</div>
+<!--
+%\VignetteEngine{knitr::rmarkdown}
+%\VignetteIndexEntry{Manual for gmkin}
+-->
+<h2 id="introduction">Introduction</h2>
+<p>The R add-on package gmkin provides a browser based graphical interface for performing kinetic evaluations of degradation data using the mkin package. While the use of gmkin should be largely self-explanatory, this manual may serve as a functionality overview and reference.</p>
+<p>For system requirements and installation instructions, please refer to the <a href="http://kinfit.r-forge.r-project.org/gmkin_static">gmkin homepage</a></p>
+<h2 id="starting-gmkin">Starting gmkin</h2>
+<p>As gmkin is an R package, you need to start R and load the gmkin package before you can run gmkin. This can be achieved by entering the command</p>
+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(gmkin)</code></pre>
+<p>into the R console. This will also load the packages that gmkin depends on, most notably gWidgetsWWW2 and mkin. Loading the package only has to be done once after you have started R.</p>
+<p>Before you start gmkin, you should make sure that R is using the working directory that you would like to keep your gmkin project file(s) in. If you use the standard R application on windows, you can change the working directory from the File menu.</p>
+<p>Once you are sure that the working directory is what you want it to be, gmkin can be started by entering the R command</p>
+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">gmkin</span>()</code></pre>
+<p>This will cause the default browser to start up or, if it is already running, to pop up and open a new tab for displaying the gmkin user interface.</p>
+<p>In the R console, you should see some messages, telling you if the local R help server, which also serves the gmkin application, has been started, which port it is using and that it is starting an app called gmkin.</p>
+<p>Finally, it should give a message like</p>
+<pre class="sourceCode r"><code class="sourceCode r">Model cost at call <span class="dv">1</span>:<span class="st"> </span><span class="fl">2388.077</span></code></pre>
+<p>which means that the first kinetic evaluation has been configured for fitting.</p>
+<p>In the browser, you should see something like the screenshot below.</p>
+<div class="figure">
+<img 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alt="gmkin start" />
+<p class="caption">gmkin start</p>
+</div>
+<p>The statusbar at the bottom of the gmkin window shows, among others, the working directory that gmkin uses.</p>
+<p>Note that the project file management area described below can be minimized by clicking on the arrows on the right hand side of its title bar. This may be helpful if the vertical size of your browser window is restricted.</p>
+<h2 id="project-file-management">Project file management</h2>
+<p>At startup, gmkin loads a project called &quot;FOCUS_2006_gmkin&quot; which is distributed with the gmkin package. A gmkin project contains datasets, kinetic models for fitting, and so-called fits, i.e. the results of fitting models to data. These gmkin projects can be saved and restored using the project file management area in the top left.</p>
+<div class="figure">
+<img 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WZ9H62XO3RhjOToqsjwyNV/SWMWhWmMTn7TLcW86QEuANAwYDpPbDndz0mTFTUx9NrWhRHDPpoyf3NI9FcAMHTJHHbqlsjw8EU7M8LGD9M2Wl6iQkz4bhxj3hS9znJwMPoa5DpJXl6e3nfjeD6NKUXYsKWqtmN4C4QN7fUseO6BMTewpjHmBtY0xtzAmsaYG/ge0eyob0tlK0l5K2oNwZo2O+rbUtlKUt6KWkPw3MPcqHdLZStJeStqDcHjtLlRH5fKVhLGFbUMxd5BKJh3jFkKuvLguQfG3MDjtBmCx2mMuWHq8iddunXrZuS0QrtJzt+CynQfaxpjbmBNmyFVHAj1T2m1f4++N/cuHhQU0G/IqLi0fG0xvQW2CKFDq+aFDgz29/fvP2TEukP3EEKFd/4cG9o/IGjQd/syCIKoSmwIj9OY6oFg0ZQ0njsw/njS+k88t34Vq83ZM3OZcNjCxJSTS0c1WDX7VwAInbPiUMI/Z8+e2bvhkz/WfgYAOz//ufnUtScTDw62PPJutn/F94hmCONS2beurjmN8HMT5+XZtB0uL5wsEkVp7IYLbPPSjq7dmfBElC9TqGgaiUSiY3nyTe1sc3MLnDuF0+ivKsZWGbCmMWWwY5M5KtqRQwIApXzB4pSuuXojFFr3b7vhAtvlK3Z3nbVsvldjAVfx0UdjAIACeLPzcWV3QK4ieO5hhpi6TFWXka3ttp+6Li5RK6R5lxO2OviMJQiCIMidKfcohJ5c2M2zDdAuszVcYOvtatnY3UVAyM7u327DJp/LVb2EvD2XniKkvnNyJ1mVyMpZUWsIHqcxZQj6etGTtRtn7flBzrLx8AlYOrc7ACBEBxclToz8WmXhNuaLxdrCQxfGLFuyKnJzoV0jL80C22HzR06NGafguw6b8c38kGczx47fvnjkgh++HL6B2zPiC2sylUJQ0y8nw2tszY2aWCobGhp6+PDh6vX5duitqDXE/PefxryH4LmHGVLJeWflOXLkSPU6rFHwOI0xN/A4bW6QJIkQIkkzHK1omq5Mv8yw5+85XL6VRCIxs8WIAEDTtEQi4fKtKiyJx2lzg2fthBQFYrG4MktC6hFsNtvCyobg2VX4mw+saXMDEQTw7S0qWohaT6nMj5jw3ANjbmBNY8wNZk2zSEKhfq/X/2DqIwoVYrMIZk072bBfFijfcUAYTBV5Wah0EnKYNe3XUvgkV/koR6FQ4dEaUw9QqNCjHMWTXKWfhzXzc48G1uwPfe3P3pGcy5KqKSxrTF2HzSKchJwhnewdrNjlPsuzsWAP7mj3LsPCYKoF/NwDY25gTWPMDaxpjLmBvxuvZvAr2WsdrOnqp41no9oO4f3lduZTrOnqx97WurZDeK/Bmq5+WOb4e/x6BNZ09WOWa0zqEVjT1Q/WdO1S7tXX7nzD4lq08Qs99KCokh5NXbS8vJmtSeXrPmRZBDye7qG1Z+5f1rtdU1srm+YdAlceuKVb5emJzSF+bRysLBycmwwaMy+9SK11ZehckyhI++1/vbw/aGBjLXTw7jXsYFYRWT7SR0fCurWysbRq7Nnrlyu5GiNSPp3S38de6Og3cMozFTJiJAn655hQVzvrD1p2WZ300khDb4eRblYIAMCWpNeao7w9WylF0cnYKCvX8VXcZbU8ugl5NeS5Vjh2/KJaTekeAKBnUaupVxcXCWw6/3bmdrG85FbKHm9rwaLLrzVZ2elrLC1bbjh6WVyizH9xd8v8vq0Hby7PldYSbMffcOKGuEQlK8o5vnGslcsYw0a1x1hXqwnbT4tLpJfjYgQNwjTGc5949d90KleSf2rLAM+Z540Y7+wa3G1JvKhIfi95TZe+s4w09HYH4xWr5HHs+MWKNY0QotVFJMsSIURTJWxBi78/GyDg8BFCspzToZ2bcVncxt4Dj78q1quoKLwU0cNLyBO06D7imkSpMUqeHvRv4ci3afxl/COE0Oo3g/QeUTFUbePkOsKx4xcpmtY9AEDPQtH0Kk+HiONPtaePjg5r0G6dJr3B23HI7w8MqzC60lo6WXN/vV+otRcpKUYPmuPGlfMKiqZoWq0SkywrjTHSyfJgTglF08U5f1o6jzVi/KaJzdZXUiP+tUfhg7juzRvwhK4xv6Q7cVlFaoqiZGx+k1t75jW3Fwjs3Gdsv31ly8wmdgK+0GXSpnS9Tp1fO7xl2MJ8FVVqoWQci1YZO+c0tePzha6T35TXPSqlaUopSf5lnLDJJ5pTkmUxeNlxhVqJEPrF3yVg8ZEihSxpbbCz72a9irsD3fwWHJAoSo59599k6EGNcWNHp6iDd7MzYq1chuiVNw+OHb+oZ2HsoIeAk1ms0p4qpekci9aadBsLzg2pktG5oSut5f5v4wV8lw8nzNoWfzJXQVUyWtHFz+3bfKNJO3NZOSoKIUQps1lcFyNGDwHn2vEVLRwsnFr2/OOB2Ij/1e0aBK85JZPlxk4JYL/ZUJ0geSEL9hfKVfdPfcMWNA9a8LsmzbX21e3Ugz/ndp24QUGX6SbJshz41X698rpUoOnS2SGb79Et7O9nUq1de8VbW3BuFasQQqriTI5FK92AEEJtLDiP5GqEkKo4nSf01xhbCNiP5Wq9hoxclHoHo6Z10Rh5JKH5tEqhFQTJ1yT5JCGnESNGNI0QKrx/efv678eE9nay8/gh6UWFoUqfJXZ2aLT3cZHmlEMSbz4YNUFyjRh5JDFwwe9iuSxp7UC7ll8baaIZn635rysvPKUNFQCyZGqEEE1JAeDBmzRBsLQFcq78NPyLOO1lYKyrLa9LZeceenbtIMAjCZWmWVpFkDy9ijzyv5tF7QfGIcp+lu+Hpg2LeVpyrkr+G4yV0hscy3aadAcr7vkiBaNzFkGUGQ9oBUFyDIuJH+zn2wYZj1N8/2BHB/cfz/73ubtwWTlKzZAsYvFcjRgdOay/82UIIUr5imTbGmmFQ2gVotDVpbYAYxoAPGyco3ffZsxirKvl2PGLb/PUSVunhYB9R6YGALXsDpvfTK+Yh4D9Slmqf5qSaYzufNZjuVntO/F2xHjYfnviufb0Vcp3dq1jNOlPvewX7HvIWKu7kHssT649LRbF8W37adK3Lp+Sv9mmxtLNTykx9rMTpeTf/l2nRB35d4b/f5vZDrTnpxYpAEAhThXYDTJiHGDPfyynAAAQTbAsjDTkzGU9UVAAYDweQzZk3G++68Mt6fkm1SrlLcZpbXp3b7eA7/+WKiSJy3u79tyhVyCuv/vA1SdkKsW1+LnunT/VGNe0azB8b2Zu5nZLp9KBpI0FJ0tazNhWfaSS43Re+gqBldee1LsKlSLr3L6OQqvVmfmarMKszdYCl5Xx5yUKtSTv8f6Vk5oHLtNk3d010aXHjCsPc9Sq4gc3kkZ52X/8x0NNVkwzm4Hf/ZEtVcgLX++c09Wu1RdGgtwY6Bay7Zae8cr8Dn1+TBLLCk/+HOSz4KpRo3eHL+PzSooSVw5w6rzeSEPfNLcdGnteLsuLnRoEpozTCCGl5GoHJ9/zBXLDLMO0lrece2jT8vzUoZ2acFjc5l0+TM2T6xVQFl0bHeDJZ7MaNPNdf/qVxlj0MM63kR1P6P7576UfxqPfY/jWDY20WL+opKYRQjfjlnT1cOGyuC6t/ZYduK2b9fj4hiCfphYcloWta/DI2dcK5Nqs20fWBbZ357I4jk3az/nptNZe/CIpso+3kM/m8G069xuXkl1iJEh22e8QjuXJEEJq+dPoIB8bgW2n/lNfvbnLZDRSytczBvkI2Bx370FJImMN5V7f6NlQyLX+4LPdaQRLYHhBjGgaIfT02Ce2rcbkqCiTNM28p/pbIy+4aunYm1JLqu6qnpKQeGlQ/661HUVd4eKpv208e7ZqwP53/8zgeUTR80013WJC4qVq/hbX1qn7oK/2VK9PTP3l/t61/T0bcrjC4cuzNp1Y9m4arebfe8hViup1iKkKjL9TQDXwNvLyGhr9y4nRv1R7axWAf8NkztSEfGu3ocqAf0GGMTewpjHmBp57VD94mW3tgjVdzeAHebUOnntgzA2saYy5gTWNMTewpjHmBtY0xtzAmsaYG1jTGHMDaxpjbmBNY8wNrGmMuaH/3ThN08+ePZNIJDRNM1bAYOoyJEnqa1okEtE07eHhweFwaiUmDKYqqFQq/blHQUGBm5sbFjSmnsLhcPTHabVazWaz69SyBQzGJBh+a4oFjanX4OceGHMDj9MYc6NOa7rg5l9L1v1269FLqYrl0rzduLmLQtqa/yvQ/fz8Lly4UNtR1GPq9Nzji1krO45fdPB4ckrS0QWRHr+u3VXbEVXMksF9quih8oKueltv4ae6Gq05GDRtZPuzd0yeimrYyI3PJkiORfvgaXGxnyKEDq2aFzow2N/fv/+QEesO3VOV3A8OiiihaIQQTZVEBAXfL1GVZJ+bFRXep1fA0Mjp/zyR6PpUFFz7ekpk7549B4VPPnKrACHk5+enzfXz86MpWY/ew5NXxQT0CNBNI4T03VLynoERWUfWhvUN6BU0aOdj8enIgQn5Mj8/v5faXc1ptX+Pvjf3Lh4UFNBvyKi4tHw9n9IXyTNGh/by7zk0clri4yJtPIZd0IucoS2m3un7NxqzSR1Mi1sZHtLP37/H4PDouKu5NScDU9HfAzItLU1RZ7j/17qQXoFR0z5bF/vbxczXZTNlr+6d9vUboFAo9o4MXn4rW6FQZN9aETziN4VCsfF/gbEpmdISyZ3U2ICQVbrVYsP7fP77RUmJ9E7Kpl4DPlcoFJ06ddLmatKdff0+33+pWFaslzZ029m3+/zdpwuksqdXd/ccME/Pm9bnV3vPiYuL044t6tlvsZ7PH0N7z91/UVwivXZwfkDIWq0Hw7aMR15e7wz9G4nZpA4Gd/M99zhHJpNm/PNjYMhnJn62NQjDfFokEtX834dKwW3/v5929bufefv27Ywf52xqOmfT5FY2eWlH1+5MeCLKlylUNI1EIpHnuPa716ZEft318trk9uOWi0Si/c+l0tljtwAAAMkSiEThWp+/PZb85NuwIDffoungPbGlndXtskgkQrRyhK9zXk4eAOimDd0iWhHVq6kkPwca9CzJWS8SxQDTBYzwcxPn5dm0HS4vnCwSRen6jH9V/HOXhuLcfHvv0SXZ00WiYRoPhm1VGDlj7wz9G4nZpA7OmxC8cM5cv47tvTp03LExrO7Ipk7vhUCrpATHyqO9r0d735DQPiMmLJm8d9XyFbu7zlo236uxgKv46KMxAGDbKpp1d16Juv2uO8Ty1rYAwALYcSDelsUws2IBqA3ugSkELAIQVay1OLBJwzSjWwvN6xAIlpGOvLnKtHayp/VJALwJp0xYhm0xRq6HYRlG/+XFbFIHWw+Yuqnri/SMzKsJG/64OmbN+I4VBPeuYPjUiTrD+kkfb/j7WrGSRmp51tUzJMeeIAhvV8vG7i4CQnZ2/3YbNvlcriLZ1tFeVOyVWLVntJDNIggixNVyQ8JNNU09urj747nbdX0OcbOMTbyppunn6Qcixy8lCMKRwzr+MB/RihsJsVySIAhC9yLopg3dGpYUsMhclapsP8idKfcohJ5c2M2zDdD32dBi66l7Skp57/RWC+fB2lzDtgwjN2zLsEx5/hljNqmDa2Ki4h+ofXr2HTMx+snfGyv1ib4T6vRzj6nLZslSd308Onx45ISNx159+v1MABg2f+TGmePGTFmQ2ypyfkizmWPHA0Db8cGp6870imqrqTh0yRx26pbI8PBFOzPCxg/T9Tnk2zno9ObI8OELNv0b9UUMAHwzJ+r08hkRo2OuCv5nzyap8sdCI261rJgcOG3cJF0LQnRwUeLEyPAFOx6MmR+h73NhjOToqsjwyNV/SWMWhRlpyzByw7YMy5TnnzFmkzoYNTH02taFEcM+mjJ/c0j0V+VetXeO/p7q6enpDg4OtRqSuREaGnr48OFKFlaXPP1o1OxDBw/UaEjmTZ0ep99DRoyeHTh5QW1HUb9huEckTHxhOMY4R44cqXzh+IPxNRfJewIepzHmhv44TZIkQogksdYx9RV97QqFQrwYEVUElMIAAAAxSURBVFN/oWlaf5x2d3d//fp1YWGhSqWqlZgwmKrAsHaLIAgXFxcXF5daCQiDqTr/B5WjFnY6YdCCAAAAAElFTkSuQmCC" alt="projects" />
+<p class="caption">projects</p>
+</div>
+<p>If you would like to save these items for reference or for the purpose of continuing your work at a later time, you can modify the project name and press the button below it. The full name of the project file created and the working directory will be displayed in the gmkin status bar.</p>
+<p>For restoring a previously saved project file, use the Browse button to locate it, and the &quot;Upload&quot; button to load its contents into gmkin.</p>
+<h2 id="studies">Studies</h2>
+<p>The &quot;Studies&quot; area directly below the &quot;Project file management&quot; area can be expanded by clicking on the arrows on the right hand side of its title bar. Studies in gmkin are simply a numbered list of sources for the datasets in a project. You can edit the titles directly by clicking on them. If you would like to add a new data source, use the &quot;Add&quot; button above the table containing the list. If there are more than one studies in the list, you can also remove them using the &quot;Remove&quot; button.</p>
+<div class="figure">
+<img 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xghgHWMEQJYxxghUImOnZwci4qKLbuwxsYyvDp+dPy7Dxeue1IEbbq99c3Wn/p42VlfUc1DwjQuKh+PrRksazigmhZPPb7ozRX5+/+419lHcuPYhjnTdt8+Nr2wsAghNKuVd0QC/5v6ul2cZU1CwjQuDN8zNQEAEKNy9371we4loX6eHj4tv08oQAjl39nWO6S5Z7OWszddJwgCIVSW8cfIvp2aengEdx9yOKGI0eZ8GBL06+MihFBhwo5WHSfnaRlT/+VVmLDmkz1zD63v1sJNLHV8bdSyW1HTEELOzk7Hu7XYn6N0dnZKUdE7F40JCWzm5urq36bL5zvv6+9ydnZCCBUnR73To62HW5Pgbm//llDIeyAYAWCdjgkprX22vqT/5YS0m0cXrB++ECH0/QdftP3iyLOkux847iQBEEI/Dp/ZfemupLSnR8L7LRz5LSFuEnFyzeohU9NL06a9u3nzqU2uYsJcEKaZJ/LVk5vJTC0HX3kMAApFgZ+UHL9m7/3E1Ny8nKtRq7YsGaW/i/O54b15TlMjkzPTds/3Chu1nvdAMAIAjP6+9MhOUqPh2s3VJV9R4ObqkpyrcBQRgCg3N498RUGgh9vNjFwXMcnSCnePwHxFQZCnWwHFlg/yYnluTioAPNoz+V8bzs74/tqnfb3MXREexse1CW5rlBno4XYtPbeJxOA00wWTrygAgMy/I2Ys+yU2JbNUpaFZpL+L2wjydLuWnusmJlk639OnS252kumBVHK5wjQGHsbHWTs/lpPcRvnqPgwAzSKEEGIpLocEIi4rz6NCeVwpRXqWk5fj49QihDwtxGFa71BXu00JBcvbuvJacv9+OP7LwVtO7urdVmaj8vJqob+L2yCAQOVBsuYOxIpWwjQCrJhXGF76ue133exWRj+gafWVvctEBIEQmhnoNC/yvJrS3Du+vP0bnyGEChN2Tjrkd+G3f8h1o/73rNTCRcE08+P143eNnnvxUTalUd49Hdl34CqdpVxMPlOpEEKvBzoGt2puh4oOf/eZu43oYbFKt4uznNZcvmj/NaVGeePAIkffqbwHghEAYP3zCv1dCKEl25aMmDLUd57N8E9+dRYfoVg0/fCWmHGLWnz1VObzStiPexltzqx316w+dd1B4vDd0c+6DZnV4/oOo3mChXp9Bq38g1o3fljnpCJo3fXNFdt/0IUR/e2Y7m06PkuOm7fz0649glWywPkbDuyYltCvTdtzFbs4yxmHNkwYOy3wk1yP1j1/+O8c3gOx4lTHNAIM5sfvvMKzfOqL5nFiQlDLVnVfL0YwPE5MwN/nYYQA1jFGCODfbWKEAB6PMUIA6xgjBLCOMULAQMepT5PrJYj6qhcjGAx03KNHj/qKA4OpCXg9IYwQwDrGCAGsY4wQwDrGCAGsY4wQwDrGCIHKdZx77+TsYV1Jsk4VTxjCZf4VsaRzkJdULPUK6rz453M64/gj4cGecrFU3r7/2HOZSq64kTcAyL26/e3ubZs42kmkspavDtp+Pdeo0oyz4YG9wwpppLi3Z8ArfjZiiUeLV7+JfgYAVOntf/doZSeVh/YafaeM4s1BTMmKcf1d7G1cfUK/OJKE6MKwXoHh53he7Y7ZE96nvb+DVGzv5Nlz6LTzWUpzjVBpTv3SgOLR/ah+y5ks3h/bhw6cdOxWFtTt2xOm1SUdmiDz+dfxm0lqSv3kRtRAL4fxvyUhhJIPz3VtNfpiQo5WWXB+z+eB/TeaFuc+9nKSrjkRU6phKFXRxX2LW3dfoG+jLb3Vxq19bBmFEBrsZhd26GqpVhX35zc28i4IoRMjAjp/frRMqzq2rEuLkb/z5tz+qttr4UfyyrSp13eNnrYaIUSVPXjFLfhOqVa/otTouXbufQ9ceKDUMqV5T/eGj/Lv83W1m6V+aTjxVK7jcrv61vFMb9nSB/m6j3n3FsuazkYITfJyCH9caLk49zHITnwwschcjZfmhXT99i63TauKKzYSxXYtEEL9nG3PFqgRQuqCM7bO/Xlzxnk6bM8qM3J795uuIfMv6+csaCZfcifP8iGD3ms4eTERHXxdpHLPiWuvcEssRM4d7tvEUUSKXJq2WRh5BzFKiX3ru1sX+DnZ2sg8V8UrEEKq3L/e79XWTiLxbNVz641chFBp2om3OrV0sJH6hLy+/6FBixU9+e+Adr42IolP27574wt4HeowjcfUc+7NiE5+rhI75+Gf/+kuEWnpMrFd0OE5/W3Ftjzx1wYNV8dGFw0XMZlPPV8Bg6FySbELQshZTBZQrOVouY8J+z71d3R57c2Rn4b/+Ndd44Md2cT+QI7SMI/ZPjV0xJobCCGZiFRylTNKUiTjzXGVkEe/megps3H177zxcjbnoixnr737SH2nrhIyj+JZysOcjj/ylY/bc4OiVBe2TuJehdSFlxkfLZL6IIQIkd2YddHFGvrplXUyn38jhMLbub0feV5FaWJPrnBsNhoh9GUb1/Df72oodeypcCf/MP2qlwY5j9p8vozSXN05xilgCa9DHabxmHqe20w+ZsdltbrgwLK3SIJACBGkdPTmC1pa/9L0PP6a03B1bJTTRCLK1lvJhdFmiyTuCCEXMZltssILr44RQgyluHom6vvlH3X3k8+6YnC8jmKymH5+PrCUYtWYjjMiyodSnWpZRkmK5Lw5diQxLuJMmVZzdc9M+ybvlfuhi0ixo35F7hJRZkXA+ieqOR3rTmCGyuNy0k6v7/tKoIvMTkwSOstCLnhWy+W4Ssgsw2Zx1Xs50igkVwmZW15FLilx5XWowzQeU8/O4vJzlVan6CJM09CcB9P4a06j0fFCX/nCmBzdx7x7ix39PkEITfWSLb5nfJn2thGlqMtbjVLGi239EUKMWqHTqbb0ro28s34Ruei5jhkqb36vFmE7b+v2DnCx/btQgxBSK07ZugzizenjJD1ToEYIsXQJKbLnCrJ0EadyHZ/6O80xPIV0Pc1dV1iqQF/HjhUnKqPN5HJelduEH7uhKFFRWoW5c8BdInpWoRsO/fPHCDeJKKe8ihydjo0c6jCNx9SzTFR+kaQ1z0wjNI2/5jQaHaf8b4qde7+j15M0tPbp7ehB3g5zTz5DCD09ttDJd+i5uExKUxZz4sf2XZcihCL7+bz784nMAlVpXupv3w7wH74fITTWy2H8puj8Mi2tKv5n/yK7JsP0/evPK/aODpqwJ15/7+mxQUO2XixSFhwKa99qwlnenD/GBg3cfL5Yrby5b6rMZzpXUJmzz2hekXZmoa1Ltz1/P1BRTHFu8sEfFoilfgghP1vxhutpDFV6cv1YO5LQNcJ0b9mUQ7dpWvX35g+46/jSzu7HEvM1JVm7/zPe00YUW6Q0ld3XoW5DN5xRUdq40ytlXsMQQuEhbm+tPa2iNLcOL/J91eAed0Vrl1GRF8s0Zf9sHeXS8gtkUcem8Zh6nuDpMOXQHa2m6PCqkaY6No3fjAqqQOU6Nvd844XCW9G5nxd1auEpEUk8g179fOvzm6eHR1a19nAgJQ7t+o0+lVqCEKLKYue929PbxU7u7jdw3NJUNY0QKko8+m6vULlULJbKQnq9eyjWYH23S/NCuq29z22LDR8nXSrSUGX3x/YOdbRzbN9vfLySQgjx5CgfTno91FYs9g7uuyeu3Pm9b43v8xBCMXtW9m7X3E4ilrt69Xtv1p9PihFCcUd+6OLrJHX0nffL3UA7McWWN0LWxbUh3o4SB/cP11xyl4goFhU/jfCwl8g8Q1adePrPJ13Ets6msivLODWsS5BULHIP7L75Sg5CqCzz1PCuLaViUZMWXTb8nakfT3HKkf4hzSQiie8rA48kFyOLOjaNx9Rz+pnwlu4yG7n3v5eeJgixkRPT+C3UaKXkrB2PBY+29FYbtw5xZVQt+qTKYtubPHd7Gbhy9ve4rBKGVl3fP9vObWgd1Ii/zytH4tDx7MH3hry5pJCunZdTEF24ZNDQUYfOtneQ1IrDRsTjfevfDPGS2Mjf+fLOd1G/1EGNBuuwTH/D0ipsGEyDBY/HGCGAdYwRAljHGCFg8J6pQqGorzgwmJpgoGNXV+N1szGYRgGeV2CEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEANYxRghgHWOEgFj/Q+TZ7PqKA4OpCQY6Hhgqq684MJiagOcVGCFgMB6j+ooCg6kZYt5cgqUIRAOqorAJAhFiREpq3U+98PDOgxfkuU2H0Bfk2RyC71BDHSMALlZgWcIWSKJqzhAikRYYiou4tvzUI8EhfrXuMz42tY4vfC9Dh/LMK0hEs6QtIqoYKwAQBAs2BKtGIKlFP/WIq7P8Rbit4/nby9ChvPMKVJ1YuZIEQT7vptryU2+ISGHcBwu/Qw3HY4QAAKHyjeqhK15bfs6mymZ0g8eZpdV2VW3IF6PjmjSLleg3WmPs0OiHpScea5IylLZA+8vo2f2atW5qZ8Heqn5SZKUW5aXnpCWZ7kpLfmiNB3PUsPiLhnwB1PcxPUeRlfoiPFhQi5X8EEN/f88lg2rmFRDs3bpdSOeOYUdzLzwssVDEoFlRxdQNGSYWIRcXFy2lNcrPeJrAMIxRpgU/1hTn9VOpqxeRwETHKVeOzBre1d7WtoY6rpvgK+0IFqGMpwnWuDLnh9eDObVY2aGX0+ijsflzO6m+7qvq5ZyVnZaRWgQjBrVd+2cORRFLf8tZsDPLtJRVwwPLsgzDMAyjn5n5NNGnqQ/DsNZ4MKWGxQHg4+ljPp4+JmzG2KWLw6KvZ1bbjwVEBKmfhn1x7I1FBxFCRvlVSlWN4cbxdUtmTTTKLE2/c3jTf8JmjK3J0bEs29S7aebThNr1wKsW67mVUnZzbuCIIHFbV5jaRbZ5hMuTxGc0A+Pfbd9jydmBPQPyixSmpQyb1cx5x9A0RdEMw+py0pMe+jRtmp+fzzBMFc5fa4pbf/4CrNuyf+3mvYtmDft793cvYkwjSEI/Pfx779jXW5nmVylZaBbThFjN/uM3Pl3/i1F+xKGrbYYtQghZar3KOoKhaQBwcXFNuB9juR0sCMPUg6larO1QBIBgTk+n0qJS3UcPW2ZMa0RoNDISvlkyzN2NkJFa01I8z9306+JgEaJpmpsDAEB68kNfX1+lSqXWaHSZ5s4FUywXN/WjH5upDQAgoIEQIYCrB77/80ZccZlG6ujVZej84b2blz09E7n5YGYJ22bgR09PfbP8p7100e1dP+1OSs+382w1dObHHT3tzdQPAECauT03l28l5g5ZnXt9V8TuJ5lFMs+WQ2Z+0snL/uCSGQCwcv74tVv261t+8tFMy67095rrCBYhLUWpVCp3D49HD2JahXY258SCMEw9GKnF1I81ketwldupS8r+SYidPrITyQJBgJOE52aT//s8IxiG0Wq13JUiLSne19dXpVKpVCqKomiati6ecmpY3IiwGWMAwEbu3XfcYgDoPvqT7qMBAJVk3flq5bfDe/90OmKv+/DP53XxefD7xjgWkQT8vf7ngFHLJgd75z2K3rjhaMevP6hJALXLXxu22L35yaoeLTOvR/yyMapT+Jj3v915fcYYIxHXFgzDcO2fl5fHMMztaxc6dutTcw/6aqkSdzM1u2LLpCQokb2YYW1A+5+BTX489KhL77Zf9QMxApoFlgQ/J56H0DzP3QAho8crFEVxmkMI+QS0SYy/4+bmplarNRoNlwlGXsz4AYDKi5v60Y/NkO8379M3KIo/ufe/pzPzCjRaikUIIRRTrPmsS6BIRLR7azKKno8Quphbpty45CQAABAiO4RqNMWsHuYO+bJCteS11hKS8O0yQZWIOukAAAPzSURBVP1rGEKjLdtb3gXmO5SDoiilUqlQKLRabWFh4Wt9B/F7M9+hvB701cLvx0zk/yQWRo4ITCmAYi3kqOFJAey8nDLldb+Ic0k5Cg3FIoASdRmaM8jXtKxV4zHLsizL6sbOwOAOcXeuSaVSbkZvjQd9aljcAju37A+dvGJSax+pmFo8ZxoAMAjKZwAVMwESiOU/7ZGLajQxeGEQFf1j5VW3RrAsW1ZWppNgbXkwUov1ZJRSeUpILgSKgbXR98Z3dB3bww0Ahb3lrrPxd7dPyVWaluV57maaGIZhWZbWe0YW3KGbWq02yrQ+Vam4udhMM1t52nt5u0tAdTt6t0xEZqu0r8hsjt1JpRnVg9N7OPteHvYH/4rVMnT67X0rV++utN4XgbkaezexO3olQctoU67stHUbZO4wLbRApY1m1K1KlaqwsLB730HV6ERzHkzVYmVsShXKU4GWARUDr7bwkUgkpjbJuUpen9Y+d6MoymjsbNuxO1ODAbWGxXl5fcbI31bO/mJZeEHAvyf091+7ePZbk4cnH/hyadgnd7VvEIQIAPosnCe6uf2L+RMjD8f1GzmsSv4JgiAIQn+jduk9f3rZ2U3L5k3d81fpvxcMsWC5aObYRTPH6m9UA5Zl8/PyuldrJLbggVct1lCshqup7OK9V9QMNPFy++Vqcb7quT5lUumRSzxP3Dh4fu9msFERGU3TDM0Y5Qe/0vXqhdM8Y5cZP0aYLW7qh8/VdxH7jDKlLgOWbxhQ/iF01ZoRkPro7sRlmz0ciLRbe+Ps2wMCibzdxCXrLLi1QK19n2zGjdS1y4wvuhiZmR5meaYVDi13BMuy3fsMsqoFzAvD1IM5tRgXN9lrY2MXm5p/4eMO7/x4p3ffDu07tJy489E7bcTdA+Txz4p/ik5dOzXYXLT8v6M3MhaJxOkZGXxVQ7c+g6orY7PFTf1Y44qX3OvRJ+/+UKyk5R6Bg2ctfKGzBeupszAsd0TrV7paGYk5P7weLKjFcodues8NAIChI8YHzd77oE3n0KBOra9p4ff7UJgter8f6hgoMxewVfd5QSGdgqyxa3h0Gvd5p3H1HcRLRs3V4i1Hx+cEfnHg1t1npJpBLWXa2d3c+3fyslDEzO/dajBk6IrXlp/eTYvjnlXbTUOkDn7vpt9ojbFDKZpaNtLgdLDcaLz3eQRR3XAJQAC6G6Da8oOpIcLvUJ75MQMiEaJoQoKqWDEBSIQoBkS160d41PFxvQwdyjM/pgmxGNESpK3qyYeAYEFEE+La9VOP/H7qWn2HUAu8DB3K/9yNBrGVt4A86B1jbfmpF5oHB78o13V+aILvULx+BUYINKDXbDCYavN/N7aw2+DCkB8AAAAASUVORK5CYII=" alt="studies" />
+<p class="caption">studies</p>
+</div>
+<p>Note that the user is responsible to keep the study list consistent with the numbers that are used in the list of datasets described below.</p>
+<h2 id="datasets-and-models">Datasets and Models</h2>
+<p>The project loaded at the start of gmkin contains two datasets and four kinetic models. These are listed to the left under the heading &quot;Datasets and Models&quot;, together with a button for setting up fits as shown below.</p>
+<div class="figure">
+<img 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" alt="datasets and models" />
+<p class="caption">datasets and models</p>
+</div>
+<p>For editing, adding or removing datasets or models, you need to double-click on an entry in the respective list.</p>
+<p>For setting up a fit of a specific model to a specific dataset, the model and the dataset should be selected by clicking on them. If they are compatible, clicking the button &quot;Configure fit for selected dataset and model&quot; will set up the fit and open the &quot;Plotting and Fitting&quot; tab to the right.</p>
+<h2 id="dataset-editor">Dataset editor</h2>
+<p>The dataset editor allows for editing datasets, entering new datasets, uploading data from text files and deleting datasets.</p>
+<div class="figure">
+<img 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" alt="dataset editor" />
+<p class="caption">dataset editor</p>
+</div>
+<p>If you want to create (enter or load) a new dataset, it is wise to first edit the list of data sources in the &quot;Studies&quot; area as described above.</p>
+<h3 id="entering-data-directly">Entering data directly</h3>
+<p>For entering new data manually, click on &quot;New dataset&quot;, enter a title and select the study from which the dataset is taken. At this stage, you may already want to press &quot;Keep changes&quot;, so the dataset appears in the list of datasets.</p>
+<p>In order to generate a table suitable for entering the data, enter a comma separated list of sampling times, optionally the time unit, and the number of replicate measurements at each sampling time. Also, add a comma separated list of short names of the relevant compounds in your dataset. A unit can be specified for the observed values. An example of filling out the respective fields is shown below.</p>
+<div class="figure">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAvoAAABhCAIAAAAoQ5IrAAAAA3NCSVQICAjb4U/gAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAczklEQVR4nO3dd1wT9/8H8M9dEpKAQERkiCiCopRaraOKo6hYUVy4FRdV6yraiha1WrVq1bZardo6ah046sRRpaLi+NU6+7WWqrgrLozsmX33+yOQxiQkrJAQXs9HHj4un/vc59655P3h7eWSUCzLEgAAAADbxdW+szlRbKk4AKAKTAxxN9iO3AcAW6I/13FN9gAA22C8pkHuA4BtMDjX0VUfBwAAAEBVQrkDAAAANg7lDgAAANg4lDsAAABg43QvVYbKEp9w1dIhQE0XFtrO0iHURMh9gCpWmrkO5Y4ZBQQ2sHQIUHMl335q6RBqLuQ+QJUp5VyHcseMXESOlg4BACwAuQ9gbVDumBGHxqVRADURch/A2qDcMSMaUx5AjYTcB7A2KHfMCFMeQM2E3AewNqZzMuNGbJ+gQFcnIU/gGBDU78Dd7EoPgqIonYVyWOErqvgglYt+Eyt/Ojn0XRenukG9Jj9TsHQJnlyOm9L/PXuBoKQOat82dRPy+erb22P+KNNo+f8eC2/f1NmhVsPA97dcTze+I6imKuU1nLRnSVAzL4GdsP5bnZbt+0fTbsEss54EN6KKn+tVTd3KvW0pJxwAq1XKrDR9dmdE90nhB67t6xxop8w+vzN6YLdPhrzcUbGpoEQV+Xn2oxnSORUepHJxqDeehj/nDXg1bP2zX1sm7YkYMP/G3ysNf3Cu34JjCxbu236ymc7mOlKkqtPphV1FfOMxGBxtZrcPvZYeTxve5t6xOcHhH095dajUjwlqkLTri9tP/XXriXMD2jVMuXJoeFg7VeOUL1rXtXRc1YDx5K10v2ZK55R3j6WccACqPVbLpjOvWD2tHe12PcrR3M1XMOqFF6e/a9fEk8fh1mnYYumvKSzLsoyUK/C5uzvGz0UorO09fdvd/23+1Ke2UODkOWnjPyzLsqpCmut878BsN0c7lwatd9zNVg+lCaNogZHy7Jve3jGzUW2BwKneZPW2LJv7eF8HP1e+U71pP//jZsfJVzGaqFYVn9rZJS7QDGIiGJaVZV8d1ultJ76wcYfhN/Lk6sbbO+a87V2Hy+F6NGm3+uxLnQhL78TJKyqG0b5FuDkcTitUMUxBWpyD+xidtTo3QojxDiG1BQ8LFcb7lDTazeuXZCpGxTBKRQ7NqVXKQXCrXrcTJ6/ovCYNJriRVasC6wxPeKa5m3J8kGvz7zUZ8ThujpcT36VB6+3JWUZyRz/LGFUhV9j4t1k9OYRweaI0uYplWUaRVYfHu5orM5iVuY/3tfd1FdZuOP/gQ93pwvro5r6ygOY6J+8vmve2J2ep25+dWqWZQpcce6JiGKWygCtsHD+rp5AnUDGMJOvK0OJD8WeuTMUwKpWEZ9/0n+1Fc+OkDUkqhllZPPvtfJWv+zJQSbgCnzu7iqbBaVuTr28qmgYnbkjS7mlywsENN6u9lXKuM13uPNz9oVDgOWDcjJ8PJabLVJr2Pl5O352/J1cp/j4yg+8UpG6kaH6f+fuypYqHZxdwhX4h8/erl+0c2xbtj5CeCw/mSKUX1vd2abpM06izQHMces3bp7Ptquau3b87K5Gkb54czKUo3UeiN4jJYGK7egXNP5gnKzyxtKNPv8PqRh8B99jtVJVKduPw57V9BusfkFLSfwLc7ThpChXLsir5a46dp/HNTU7lfkLu4I5N+Vw737b9E8WF5RtNfGW2S8AC49tCNVXxcsdfyLtdoNDclecn8eybqZcpiuq35Gi+Qv77D2EuASvUjQZzx2CW0Rz73stPypTyqHq1pt/JYFk2695MR69pJfVf/Y5rz3UX5IrC374eQOnlvrXRP/IG5z2DU6jmyLAlHjoDc6OR6cLkNGhyBAArV2nlDsuy2Q+vbf3+q9H9u7jV9v/mzAvd1YxMMwERQh5IlCzLMqp8Qsij4mWK4mg6qP/Hpii8xxU01DTqLzzQ29ZXwFVPvtLss/rJaXwQg8EE2PP+lSpZllUUJPGdOqobw+oIRyz+KelJlsFDUXr6TwCPppRFi0qKtjO+ucnZZ/qUKfuv/iuX5x5aGOza4utyjJb/LKFNnQZ7nuQa3xaqqYqXO3yakjFa9xkZRQvUi4SQm/lylmUVBXe4Ah91o8HcMZhlms1vLm3ddPxFlmUvT32r9Vc3S+rfWMhNLlSwLKsouGX9f5gNljv6895/3pxC1UeGLfnQ6c+NRo6JyWnQ5AgAVq4yyx2NnEf7BKIQ9fLzU6uD3/GrXUvIpSmDp5cNLhNCit4NY6QUzdNfa2RbHkUVbysrZbljPBg+/d81j5p5PP/Z73PGD2hcV+jdovvG300cECP0nwBPO476vL1KLubw6xnfvPSzj0r2gua5lHW0nIeHW9XxXnux/A8QrFzFy51AB97/it9OYllWnn+T59BcvayVyDJN7W4wdwxmGSFEfaI4P3WLvdsIlmU/9HDY8qqgpP48TeHFyK3/D7PBckd/3itpCtWcQi/p0GkPq9+ov2uTy8ZHALBypZzrTF+bdufaWSlTtOzgFSTPK/o5mPBBs3ss3fsoNVMizTA5iLYHEiUhRCl9wuGX7XvW3e04KTIVIUQTQwX5C7mp8qK5hVFJ1I0O9Tst3xL34HXekcXB08P6VcqO1Hq5CH7PlRFCZDm/C2uHVXA0RparXmAZGU3XKtO28rw/Q9tNjjz257SO7hUMA2xYlL/oy1PPNXdTLyyt3SxKc7c4kR9rEtlg7hjMMlL8oVAHj/Ft5Ycvpl7dK20z3t2+pP5edpxHEiUhRH12pzrSn/dKmkI1k3JJhw4AysF0ubNhxMCByw+mFchlOeJd84Y4N56mbg/1d24e4OtAcvYunevN597IlpZmfxRFLdqQKFHKL/0c7ew3tUyxjqtfK3rnNZk0c8ecL/XXBtjzHhYUlmnAeZ08xq1LlCrlf8XFNGj7qboxopnbF8eTZCrW3acZyxSUaUDjpkQ2Xr/nUq405/L+DU0mTKngaIPqe3xy4IZclnPkq9GurT8v07Y/9w93/ebctA5uFYwBbNvQHbNOj+23++J9uVL+8NLeASNOzo4dol5VnMjKq9tnipp8rG40mDsGs0zbwl715/0S49VroZH+Exs6xcReUyolp9ct5FSHD6LrMDjvmZxCTR46jXLMfgA1jsnzPwUvzkR0a+kk4PIEzm16jL3wuuiq2NyUDW72PKd6LVYmvrw8+z2uQMSW7s2sx3FzvRz5dRq121v8gS9Suveh0v/6MdDDyc6x/qzYvymOUCfOf/dHCRw9TA6ivSzPvTEqOFDA5bj6tv3+fKq68dlvK1o1qsuhOXUatFh08JH+5qWkf3pNKX06IeRdZ6GodeiU1OKLvvVHNvgE6XdLu765o78Hh2Pn32HopSxpmUbjvvkH40SGpKyPDqxfxd/MYln21t4l7fw97Th2ns2Clh9MLmpVFXJ4dZ8c/byeI9/F5z1NIhvMHYNZpv1CzUyOprl0dHKmkf5Zd7a18hYJRN5z9t13s+MUqBjWit9/Mfhmlv68Z3IKNXnoNMv6s59+n5KWjfxFAKgWzHLtTsVVJJ0uJ8bfeZWnUkqu7p7k6DW5EqMyB/0nQJ887zpX0KiKu0ENUSnlDpSDwXKnCvaLGQBqpkq7dsd6PNyzOjTQg2fnNGTFgw2nlls6nEqw++ORvePOV3E3ALBJmAEAjKjq38xiK/CVx6O2nBq1pRJjsbzIHfciq7wbAFSxisx7pYcZAMCI6nR2BwAAAKAcUO4AAACAjUO5AwAAADauqq/dqVHiEyrn6xABoHpB7gNYG5Q75hIW2s7SIQCABSD3AawQ3swCAAAAG4dyBwAAAGwcyh0AAACwcSh3AAAAwMbhUmVzqRYfzcA1lQCVDrkPYIVQ7phRQGADS4dgTPLtp5YOAcA2IfcBrA3KHTNyETlaOgQAsADkPoC1QbljRhwal0YB1ETIfQBrg3LHjGhMeQA1EnIfwNqg3DEjTHkANRNyH8DamM5JiqJMthhcu8JXVO6wbANdMiGfb2Rt1bD04QFrh9wvN51cy7h+ILylt/c7fQ9dT6dpmmJyu3u3eKZgtftk3/3M3l7w8c0MTYuQz9fcnETuHcIiz76UIPcByqfyX/csy6oXjmZIK33w6oVD0SXdjK+tmpulDw/YGuS+hk6ujeozoWfsjaS9fSf0Gcmh6IfbRiin7/IV8LT73Fjwa+j3oae/uKqdoUqlSn3LTk1e0Es5/P3PkPsA5VOB1z0rs3Nodid2lq+LUOjsNWXTLXWz+n943/nVvpIroyhq9+tC4/8jtGEUTWnf8lMOdPJ3d6zru/DwY/21VX+z9OGBagu5b4pOrl3JlY1/19Pt7bGy3CsskzF+UdqBWS3f6EMp518o2DjmJ/mVuVLyX4ZqOgic3PtO31Tw8hfkPkD5VKDcofgq2fNZ99/7KzXv1pEJWz8bp70y+lEWIYRl2ZFu9pr/89U0OpPMtgFRohmHcl4nd3ocQ1m41KHomvp3CCoBct8UnVwbWa/Wz3+/fn1zE1/U5f7GYXazd3rzOdod8lJWPPdZ3NCx/pImmV89ztFkqKaDSpJxdNUYUdNpyH2A8qnQWU1GVbB2/iBnPtc3OEaRf6OyYrJVPzzIXj2+A48r7B61pMb+GQDbgNwvkxVHv1zXs8k7o8/s/G3lmK/y17f6OzTAzS2g597Lr9UdkpbuCVoWRgjptaLTwS9vajakKIqiKJrDb9Wpd+xtv4SLCy3zAACqP9OfzOJQlIoQjuY+K6donuZeYwGHEELRDiyrMkd8tiRFpvIVcAkhXKG/pWMBMA25X1lcW8149HoGISRpVWfnBTuiwt8dfPbfWE6cT5fhw7POEsIuiEs5v7WB+qwL30nMkIvq/4mq/1/EKNL2nJOP6uFluUcAUO2ZPrvTwcnuhNaFhwXivQJRD3OGZLO87DiPJEpCiKLglqVjATANuV+5VPIXo79lDkxsdi1PPr6lh9vbY2W5lwkhBakb/6o9gy021+32+hf52hvSvLpdyYZjLwssFDiALTBd7mxZO2pyeMyf/6arlIWP/06c9MGMUZvWlWboAHvew4LCCkdoOyY2dIqJvaZUSk6vW8jB2+dg9ZD7lStp5eB6y7a5cKlOTvyfbopTb2wUiLoRQpJXb2y95L/rn0Yve2/LmmSdbb16LLk+LjpdwVRpxAA2xHS54z9my9kYv5jwVvYCUfvwaI/JcZsHNyrN0PHbJzX39COmvquj5phyeNXLZb0d6zb93W9VHR5dyODyHbBqyP1KpJI9GbVWuG9sE0LI3lM74kc0fzfy3J5zOwkh3+54uiq8oaZng96rX+78Vm8A6rMNbYLHx1ZdxAC2hdK+ZnZzonhiiLsFo7El8QlXw0LbWToKY6w/QqhcRhIcuV+JrD+zrD9CgIowOKHh+6YAAADAxqHcAQAAABuHcgcAAABsHModAAAAsHEodwAAAMDGmf5WZSi3+ISrlg4BACwAuQ9gbVDumAs+5wlQMyH3AawQ3swCAAAAG4dyBwAAAGwcyh0AAACwcSh3AAAAwMbhUmVzqRYfzcA1lQAAUBOg3DGjgMAGlg7BmOTbTy0dAgAAQFVAuWNGLiJHS4cAAAAAKHfMiUPj0igAAADLQ7ljRjTKHQAAACuAcseMUO5ANbI5UWzpEAAAzMV0uUNRlGaZJxQ17xi2fNvmHvUdyrQbiqJYltVeKKUVvqI5j7PLtC/roVPu5D46MmrEZ+f+eSHyafXF9riJ7dwsFRiAjokh7pYOAQDAjEp1+oEtlvf63sIw5eAO0eXeX5lqHULI0QxpufdlcRyK1r7N7Pah17TtablZx5e2jQn/WGdt1d8sfXgAAACqSNn+5vFrufWdtqng5R71XXnOteGdmzsL7Jt0HPFXvoIQQhgJhye6f3COuxO/TsM2sfdydEbQnCvKf3akUxM3ochnXtwTdcvLM6vb+9ez4/JcfVp+dfwpIeQ7v9pXcmUURe1+XWhgX4TciZ3bvIErj8vz9G+/5lyqzi4sjqIp7Vv00VMbxr5fS2DfatBXsqwzOmur/mbpwwMAAFBFylbuKArTD68cLWo6TX1334CBT7ssepGb8X3Ys4EjTxBCCC1klDmf3Gr7IC33cIzHjP4/ljTUzvBJTb69+PTivLVR09UtkyIXDfvpfIFccvb7bktGDieERD/KIoSwLDvSzd7AvgjpPWnlspO3ZPKC+G9CFo8rGqesJ5DMR6fAaNEmyI6maIrK+N8ykX+0pasdlDsAAFBTvHElzeZEsf5b+JqTJRRtF9iipV9g0Py1X7epzSeEvOVgF58p8eFzlIX/1PKcIs25qO5/I0/+bi2eUnJf6NJDIXlCDF2708SedyZL2pDPMRAUK6c5AoZhtPsb3FdvV3vnT9bOHTO4eUNRZR+ZiopPuGrwO4sLnp/q0vKj6P/dGtHQwt/KU1KEYKsMJrgawzDPnj3Ly8tT5x0AQHVE07Sjo6O3t7f+R4VK9cksdcHBKNL2nJOP6uGlaX8sVTYSFI1A0QJNe3MHHiGEK2iokr8sacwUqcrT7o1a58XpNSNnrU96/DKvUKp/hsbgvvbfPLV00XcD206X1es4b/2uSZ2s/XLL3EdHurabHnn0usVrHQBtYrGYYRh/f38ej2fpWAAAykmhULx48UIsFnt6euqsKsObWTSvbley4djLAk2Lv5CbKlepr2JmVBJN+wOJkhCilD7h8Ev8FQVvAeeJVKndEj5odo+lex+lZkqkGfr9De7LoX6n5VviHrzOO7I4eHpYv9I/FouQ5/0Z2m5y5LE/p3W09rIMapqsrCwvLy/UOgBQrfF4PC8vr+xsAx/oLtu1O149llwfF52uKDrdPa+Tx7h1iVKl/K+4mAZtP1U3UhS1aEOiRCm/9HO0s9/UkoaKaiyaf+R+xp1ttdy7q1tC/Z2bB/g6kJy9S+d687k3sqWEkAB73sOCwpL2FdHM7YvjSTIV6+7TjGUKStqXlfi5f7jrN+emdcDnz8HqKJVKLpfLAgBUc1wuV6FQ6M9yZf2aQeqzDW2Cxsfejo0khAw8cPR439G1Z9+t1aDVF1uPqXuwLLvC90ITl/5S15Y/nJlQ0kATjq7/pUsnr+xan265oG6ZHbe4cYCHVPTWgp2/7Zf91c7TUyHJit8+KcDTT5KbanBf36yZ2X9q9+UpmaL6b8/bUdRY1q/2qTLTz79UnnuLGl9090SGJMxFYHQLgKpjnVkDAFApTF+qXOYRrbXaqGLWfyGw9UcIlctIgiclJQUGBlZxPAAA5nD79u133nlHpxE/IgEAhODsDgDYtMr/al1MmgDVkQXfa69Rkn/bNnlUeJfOHYJDwj6atexGusTSEVlM+/btTbac2DI7uHPXco+pP2A5oqqszlBlDE5x+CUBAIAq8vrSqslrrw2a+d3Js7//dmjr4MD8JV/sM9O+lvTuZqaRK2v8y5cvG+/AMpIlWy78knDSfLvQ0Dyc0m9SmtHAeqDcAQBCcHanSuxeET9kzbKQFj52HErg5PbB2KWHfhjDsmzh6z9mRA7t9n5wv4iPT6fksSpp567DHhxbHf5B8PshYduf5Oj3YVSSTl2GnFsZFdwpmGXZIytj+vfq3rFjx9C+w9ccuX8+old8piQoKOilTKU7uBb9/XYKHnhtw8zQsMg9556eXT09pGvYptP3WEbZsdMHt/YsDgsJ7tF35N6/MzXjP8158EHI0HwVw7IsoyocFhL6WKLUjJ+VHBcZHtq5S4+YjdfDOndUvBlzUFAQy7LZd+PG9A8NDglb+ss/6ks/NZb37U0IGdgtmGXZ/Bfnpo3q/37Hzv0ipiY8ydV5+BrqV3LW3V/CRizJUqqCgoJKczC1D5c6KlnWjS8mR3Tp3Dls6KRjd7K0d6EfsJGDr7OqMl5EYJrBKQ7lDgBAFTmZJR3pa+ArRvdMX9xi0lcnz55a80nbb2ZsIzRfJRfvLGy3+8TZ/WvG7Jz+rX4fihYwitdn/cafuXCGENJ/5tdH4k9fvPh/e9ZNP7B6VvDueELIpUuXPO1o3cGN7pdRZlwJ/Hj/phE/Lor6q82MuNhPYpdEE4rDqPIP2fU6dPLM99MDf5q3WTO+t1PjKL+CtcnZhJCcRz8WNI5qJPjv+2N/nv1Dgwnfnkk4+AG9J0fFct+MWW377B/8pqxOTDjc2+GYzk/bzD2eqN4LIWTXp8udBi1MuJC4bKTryuidlKGh1FSylM9jzn69ebaIQxNCSnMwtQ+XepBfpn1Oes5KOJe4fsZ7a+d8pz2+fsBGDr7OqtK8SMBMcKmyGcUnXLV0CAClJRaLLR2C7aNY8koslnJoQsiAAQPUjYcPH973PD8/eswmQgghNEcoFg9lGVnk+43yMtOIa+fCtO/F4ihDfeTD27pnpGUQQjL+/nX19vgUcaZEpmAYVv1sqv/V31ATj8Ex+zQWSujmKnnaQD+BhG7OKDLV4wwL8srJyHB+a4g0e5JYHKkZv9nY9htX/jZuRcilVec7RvbTfiGdzJRueNc1K6swsNc4ZtsVsVisHbN6hBMZ0g3NRenpWe6thzLscf3XobrlUGrBD+955KRnurQcVfj6Y7F4kM5QGhs/iXKbubJuQaa4oGjz0hxMopUCYrF495O89W09stIz7Rv13rX5jezQD9jIwTe4CiwC5Y654DPeAKAjpI7gwMO8j5o6E0IOHz5MioseDiHbDh4qOhtRzJ6mCCGEKjpZYrBPHW7R3RVfx7absXzO2w2FdrLBg0dr9zG4oZFV9jRFCEUIERYvqBX/tWB03hZwbhTp+nzCa0mbHSkeP/o4aa9iWEKrR6D+20QTs5qKENV/iyWiCCl+i+K/typ0hlITB/jd2HmeLA3Xe1AmDqY2DiHKEj51ox+wkYNvZBVUMbyZBQCEEEKB+fX+uPvppcv+785zJcNK89L+OL6Vw6tLUVSfeg7r4m8pGdW/V2I/+mwrRVHaz4h62XiflvUcGnp7CinJxX1bnbn0c6lCyKHTFQqDG2oYGVNvgd5+4b6KZVMux/JFwRRFacanOHYTgp1XH1vl0P1DLv3G4w1ytNt+NYVRSa8d3UqKf3Ba53G978TfdfUpyyrvJm5/c+s3wujjYf/T2ftylfz++Z/s3XvrDKXdP2rE58F5cduSsjR9SnMwNQ9H3aGvl8PmhFtKhnmedDDiw2Xau9AP2MjB11+lHzNUOoNTHModAIAqUqfFuBUftU3YvGTk0CHjpsacvKNYsH4tIaTfkpnc3zdFDB26aPs/4R8OMrit8T6D5oz48dOxoyfPT28aMaeP76djPvx6UtepYyca37A0+1VjWaZ7bsL4iKHztz0aPWcYIUQzPiHEd9iwe/tujx7QUGerUdGD7mz+fNjoqX9IQymKozsoIYSQiJgR97Z8PiRiwunCXo40pSrhnEq/hVF5v66MGBqx6nh+1KJww52KDV0w/vQ332YqmZI66D9w7YdDCOn75Uz2/MaIoUPmb/gzcm6U8YCNHHz9VcYjB/Op/G9VBgDrZPxblevUqVPF8UA10r9//6NHjxpcxSgLkk79tOZik+3Leuusupd0w75BgJcT9/EfW+duSj+wa575IwUgGRkZ+FZlAACoTMOGjHbyfW/+0p76q1L/7+jOK19nFihEnk0i582t+tgANFDuAAAhxZc4ABh07NixklYdPHK4pFVdpy3uOs08AQGUEa7dAQAAABuHszsAQGiaZlmWpvH/HwCo3hiGMTiVYXYDAOLk5JSXl8cwJX6SBQDA+jEMk5eX5+TkpL8KZ3cAgHh7e7969So7O1uhUFg6FgCAcuLxeCKRyMPDQ3+VbrmzORFfcQ1Q41AU5enp6enpaelAAADMgirpt0MBAAAAbAOu3QEAAAAbh3IHAAAAbNz/A2FaVQFNeKYrAAAAAElFTkSuQmCC" alt="generate data grid" />
+<p class="caption">generate data grid</p>
+</div>
+<p>Once everyting is filled out to your satisfaction, press the button &quot;Generate empty grid for kinetic data&quot;. In our example, this would result in the data grid shown below. You can enter the observed data into the value column, as shown in the screenshot below.</p>
+<div class="figure">
+<img 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" alt="data grid" />
+<p class="caption">data grid</p>
+</div>
+<p>The column with title override serves to override data points from the original datasets, without loosing the information which value was originally reported.</p>
+<p>If everything is OK, press &quot;Keep changes&quot; to save the dataset in the current workspace. Note that you need to save the project file (see above) in order to be able to use the dataset that you created in a future gmkin session.</p>
+<h3 id="importing-data-from-text-files">Importing data from text files</h3>
+<p>In case you want to work with a larger dataset that is already available as a computer file e.g. in a spreadsheet application, you can export these data as a tab separated or comma separated text file and import it using the &quot;Browse&quot; and &quot;Upload&quot; buttons in the dataset editor.</p>
+<p>As an example, we can create a text file from one of the datasets shipped with the mkin package using the following R command:</p>
+<pre class="sourceCode r"><code class="sourceCode r"><span class="kw">write.table</span>(schaefer07_complex_case, <span class="dt">sep =</span> <span class="st">&quot;,&quot;</span>, <span class="dt">dec =</span> <span class="st">&quot;.&quot;</span>, 
+            <span class="dt">row.names =</span> <span class="ot">FALSE</span>, <span class="dt">quote =</span> <span class="ot">FALSE</span>, 
+            <span class="dt">file =</span> <span class="st">&quot;schaefer07.csv&quot;</span>)</code></pre>
+<p>This produces a text file with comma separated values in the current working directory of R.</p>
+<p>Loading this text file into gmkin using the &quot;Browse&quot; and &quot;Upload&quot; buttons results in an import configuration area like this, with the uploaded text file displayed to the left, and the import options to the right.</p>
+<div class="figure">
+<img 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" alt="upload area" />
+<p class="caption">upload area</p>
+</div>
+<p>In the import configuration area, the following options can be specified. In the field &quot;Comment lines&quot;, the number of lines in the beginning of the file that should be ignored can be specified.</p>
+<p>The checkbox on the next line should be checked if the first line of the file contains the column names, i.e. the names of the observed variables when the data are in wide format.</p>
+<p>As &quot;Separator&quot;, whitespace, semicolon or comma can be chosen. If whitespace is selected, files in which the values are separated by a fixed or varying number of whitespace characters should be read in correctly. As the tabulator counts as a whitespace character, this is also the option to choose for tabulator separated values.</p>
+<p>As the &quot;Decimal&quot; separator, comma &quot;,&quot; or period &quot;.&quot; can be selected.</p>
+<p>In the next line, it can be specified if the data are in wide or in long format. If in wide format, the only option left to specify is the title of the column containing the sampling times. If the data is in long format, the column headings specifying the columns containing the observed variables (default is &quot;name&quot;), the sampling times (default is &quot;time&quot;), the observed values (default is &quot;value&quot;) and, if present in the data, the relative errors (default is &quot;err&quot;) can be adapted. The default settings appearing if the long format is selected are shown below.</p>
+<div class="figure">
+<img src="data:image/png;base64,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" alt="long" />
+<p class="caption">long</p>
+</div>
+<p>In our example we have data in the wide format, and after adapting the &quot;Separator&quot; to a comma, we can press the button &quot;Import using options specified below&quot;, and the data should be imported. If successful, the data editor should show the sampling times and the names of the observed variables, as well as the imported data in a grid for further editing or specifying overrides.</p>
+<p>After editing the title of the dataset and selecting the correct study as the source of the data, the dataset editor should look like shown below.</p>
+<div class="figure">
+<img 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" alt="successful upload" />
+<p class="caption">successful upload</p>
+</div>
+<p>If everything is OK, press &quot;Keep changes&quot; to save the dataset in the current workspace. Again, you need to save the project file in order to be able to use the dataset that you created in a future gmkin session.</p>
+<h2 id="model-editor">Model editor</h2>
+<p>The following screenshot shows the model editor for the model number 4 in the list of models that are in the initial workspace.</p>
+<div class="figure">
+<img 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" alt="model editor" />
+<p class="caption">model editor</p>
+</div>
+<p>In the first line the name of the model can be edited. You can also specify &quot;min&quot; or &quot;max&quot; for minimum or maximum use of formation fractions. Maximum use of formation fractions means that the differential equations in the degradation model are formulated using formation fractions. When you specify &quot;min&quot;, then formation fractions are only used for the parent compound when you use the FOMC, DFOP or the HS model for it.</p>
+<p>Pressing &quot;Copy model&quot; keeps the model name, so you should change it for the newly generated copy. Pressing &quot;Add observed variable&quot; adds a line in the array of state variable specifications below. The observed variables to be added are usually transformation products (usually termed metabolites), but can also be the parent compound in a different compartment (e.g. &quot;parent_sediment&quot;).</p>
+<p>Only observed variable names that occur in previously defined datasets can be selected. For any observed variable other than the first one, only the SFO or the SFORB model can be selected. For each observed variables, a comma separated list of target variables can be specified. In addition, a pathway to the sink compartment can be selected. If too many observed variables have been added, complete lines can be removed from the model definition by pressing the button &quot;Remove observed variable&quot;.</p>
+<p>If the model definition is supposedly correct, press &quot;Keep changes&quot; to make it possible to select it for fitting in the listing of models to the left.</p>
+<h2 id="plotting-and-fitting">Plotting and fitting</h2>
+<p>If the dataset(s) to be used in a project are created, and suitable kinetic models have been defined, kinetic evaluations can be configured by selecting one dataset and one model in the lists to the left, and the pressing the button &quot;Configure fit for selected dataset and model&quot; below these lists.</p>
+<p>This opens the &quot;Plotting and fitting&quot; tab area to the right, consisting of a graphical window showing the data points in the selected dataset and the model, evaluated with the initial parameters defined by calling <code>mkinfit</code> without defining starting parameters. The value of the objective function to be minimized for these default parameters can be seen in the R console, e.g. as</p>
+<pre class="sourceCode r"><code class="sourceCode r">Model cost at call <span class="dv">1</span>:<span class="st"> </span><span class="fl">15156.12</span></code></pre>
+<p>for the example shown below, where the FOCUS example dataset D and the model SFO_SFO were selected.</p>
+<div class="figure">
+<img 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" alt="plotting and fitting" />
+<p class="caption">plotting and fitting</p>
+</div>
+<h3 id="parameters">Parameters</h3>
+<p>In the right hand area, initially the tab with the parameter list is displayed. While name and type of the parameters should not be edited, their initial values can be edited by clicking on a row. Also, it can be specified if the parameters should be fixed in the optimisation process.</p>
+<p>If the initial values for the parameters were changed, the resulting model solution can be visually checked by pressing the button &quot;Show initial&quot;. This will update the plot of the model and the data using the specified initial parameter values.</p>
+<p>If a similar model with a partially overlapping model definition has already be fitted, initial values for parameters with the same name in both models can also be retrieved from previous fits by selecting the fit and pressing the button &quot;Get initials from&quot;. This facilitates stepwise fitting of more complex degradation pathways.</p>
+<p>After the model has been successfully fitted by pressing the &quot;Run&quot; button, the optimised parameter values are added to the parameter table.</p>
+<h3 id="fit-options">Fit options</h3>
+<p>The most important fit options of the <code>mkinfit</code> function can be set via the &quot;Fit option&quot; tab shown below. If the &quot;plot&quot; checkbox is checked, an R graphics device started via the R console shows the fitting progress, i.e. the change of the model solution together with the data during the optimisation.</p>
+<div class="figure">
+<img 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" alt="fit options" />
+<p class="caption">fit options</p>
+</div>
+<p>The &quot;solution_type&quot; can either be &quot;auto&quot;, which means that the most effective solution method is chosen for the model, in the order of &quot;analytical&quot; (for parent only degradation data), &quot;eigen&quot; (for differential equation models with only linear terms, i.e. without FOMC, DFOP or HS submodels) or &quot;deSolve&quot;, which can handle all model definitions generated by the <code>mkin</code> package.</p>
+<p>The parameters &quot;atol&quot; and &quot;rtol&quot; are only effective if the solution type is &quot;deSolve&quot;. They control the precision of the iterative numerical solution of the differential equation model.</p>
+<p>The checkboxes &quot;transform_rates&quot; and &quot;transform_fractions&quot; control if the parameters are fitted as defined in the model, or if they are internally transformed during the fitting process in order to improve the estimation of standard errors and confidence intervals which are based on a linear approximation at the optimum found by the fitting algorithm.</p>
+<p>If fitting with transformed fractions leads to a suboptimal fit, doing a first run without transforming fractions may help. A final run using the optimised parameters from the previous run as starting values (see comment on &quot;Get initials from&quot; above) can then be performed with transformed fractions.</p>
+<p>The dropdown box &quot;weight&quot; specifies if and how the observed values should be weighted in the fitting process. If &quot;manual&quot; is chosen, the values in the &quot;err&quot; column of the dataset are used, which are set to unity by default. Setting these to higher values gives lower weight and vice versa. If &quot;none&quot; is chosen, observed values are not weighted. Please refer to the documentation of the <code>modFit</code> function from the <code>FME</code> package for the meaning of options &quot;std&quot; and &quot;mean&quot;.</p>
+<p>The options &quot;reweight.method&quot;, &quot;reweight.tol&quot; and &quot;reweight.max.iter&quot; enable the use of iteratively reweighted least squares fitting, if the reweighting method is set to &quot;obs&quot;. Please refer to the <code>mkinfit</code> <a href="http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html">documentation</a> for more details.</p>
+<p>The drop down box &quot;method.modFit&quot; makes it possible to choose between the optimisation algorithms &quot;Port&quot; (the default in mkin versions &gt; 0.9-33, a local optimisation algorithm using a model/trust region approach), &quot;Marq&quot; (the former default in mkin, a Levenberg-Marquardt variant from the R package <code>minpack.lm</code>), and &quot;SANN&quot; (the simulated annealing method - robust but inefficient and without a convergence criterion).</p>
+<p>Finally, the maximum number of iterations for the optimisation can be adapted using the &quot;maxit.modFit&quot; field.</p>
+<h3 id="fitting-the-model">Fitting the model</h3>
+<p>In many cases the starting parameters and the fit options do not need to be modified and the model fitting process can simply be started by pressing the &quot;Run&quot; button. In the R console, the progressive reduction in the model cost can be monitored and will be displayed like this:</p>
+<pre class="sourceCode r"><code class="sourceCode r">Model cost at call  <span class="dv">1</span> :<span class="st">  </span><span class="fl">15156.12</span> 
+Model cost at call  <span class="dv">3</span> :<span class="st">  </span><span class="fl">15156.12</span> 
+Model cost at call  <span class="dv">7</span> :<span class="st">  </span><span class="fl">14220.79</span> 
+Model cost at call  <span class="dv">8</span> :<span class="st">  </span><span class="fl">14220.79</span> 
+Model cost at call  <span class="dv">11</span> :<span class="st">  </span><span class="fl">14220.79</span> 
+Model cost at call  <span class="dv">12</span> :<span class="st">  </span><span class="fl">3349.268</span> 
+Model cost at call  <span class="dv">15</span> :<span class="st">  </span><span class="fl">3349.268</span> 
+Model cost at call  <span class="dv">17</span> :<span class="st">  </span><span class="fl">788.6367</span> 
+Model cost at call  <span class="dv">18</span> :<span class="st">  </span><span class="fl">788.6366</span> 
+Model cost at call  <span class="dv">22</span> :<span class="st">  </span><span class="fl">374.0575</span> 
+Model cost at call  <span class="dv">23</span> :<span class="st">  </span><span class="fl">374.0575</span> 
+Model cost at call  <span class="dv">27</span> :<span class="st">  </span><span class="fl">371.2135</span> 
+Model cost at call  <span class="dv">28</span> :<span class="st">  </span><span class="fl">371.2135</span> 
+Model cost at call  <span class="dv">32</span> :<span class="st">  </span><span class="fl">371.2134</span> 
+Model cost at call  <span class="dv">36</span> :<span class="st">  </span><span class="fl">371.2134</span> 
+Model cost at call  <span class="dv">37</span> :<span class="st">  </span><span class="fl">371.2134</span> </code></pre>
+<p>If plotting of the fitting progress was selecte in the &quot;Fit options&quot; tab, a new separate graphics window should either pop up, or a graphics window previously started for this purpose will be reused.</p>
+<h3 id="summary">Summary</h3>
+<p>Once a fit has successfully been performed by pressing the &quot;Run&quot; button, the summary as displayed below can be accessed via the &quot;Summary&quot; tab.</p>
+<div class="figure">
+<img src="data:image/png;base64,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" alt="summary" />
+<p class="caption">summary</p>
+</div>
+<p>The complete summary can be saved into a text file by specifying a suitable file name and pressing the button &quot;Save summary&quot;.</p>
+<h3 id="plot-options">Plot options</h3>
+<p>In the tab &quot;Plot options&quot;, the file format can be chosen, the legend can be turned off, and the observed variables for which the data and the model fit should be plotted can be selected as shown below.</p>
+<div class="figure">
+<img 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" alt="plot options" />
+<p class="caption">plot options</p>
+</div>
+<p>On systems running the Windows operating system, the windows metafile (wmf) format can be additionally chosen. Chaning the file format for plotting will also change the extension of the proposed filename for saving the plot.</p>
+<h3 id="confidence-interval-plots">Confidence interval plots</h3>
+<p>Whenever a new fit has been configured or a run of a fit has been completed, the plotting area is updated with the abovementioned plot of the data and the current model solution.</p>
+<p>In addition, a confidence interval plot is shown below this conventional plot. In case a fit has been run and confidence intervals were successfully calculated for the fit (i.e. if the model was not overparameterised and no other problems occurred), the confidence intervals are graphically displayed as bars as shown below.</p>
+<div class="figure">
+<img 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alt="confidence" />
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+                 fill="none"
+                 stroke="rgb(52,101,164)"
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+                   font-weight="700"
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+                     class="TextPosition"
+                     x="7429"
+                     y="14615"
+                     id="tspan221"><tspan
+                       fill="rgb(0,0,0)"
+                       stroke="none"
+                       id="tspan223">Model management:</tspan></tspan><tspan
+                     class="TextPosition"
+                     x="4491"
+                     y="15326"
+                     id="tspan225"><tspan
+                       font-weight="400"
+                       fill="rgb(0,0,0)"
+                       stroke="none"
+                       id="tspan227">Select, create, edit, move or delete models</tspan></tspan></tspan></text>
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+             class="com.sun.star.drawing.CustomShape"
+             id="g229"><g
+               id="id6"><path
+                 fill="rgb(114,159,207)"
+                 stroke="none"
+                 d="M 10500,21500 L 2000,21500 2000,18200 19000,18200 19000,21500 10500,21500 Z"
+                 id="path232" /><path
+                 fill="none"
+                 stroke="rgb(52,101,164)"
+                 d="M 10500,21500 L 2000,21500 2000,18200 19000,18200 19000,21500 10500,21500 Z"
+                 id="path234" /><text
+                 class="TextShape"
+                 id="text236"><tspan
+                   class="TextParagraph"
+                   font-family="Liberation Sans, sans-serif"
+                   font-size="635px"
+                   font-weight="700"
+                   id="tspan238"><tspan
+                     class="TextPosition"
+                     x="7882"
+                     y="19715"
+                     id="tspan240"><tspan
+                       fill="rgb(0,0,0)"
+                       stroke="none"
+                       id="tspan242">Fit configuration:</tspan></tspan><tspan
+                     class="TextPosition"
+                     x="2698"
+                     y="20426"
+                     id="tspan244"><tspan
+                       font-weight="400"
+                       fill="rgb(0,0,0)"
+                       stroke="none"
+                       id="tspan246">Combine model with data, set fit options and start the fit</tspan></tspan></tspan></text>
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+             id="g248"><g
+               id="id7"><path
+                 fill="rgb(114,159,207)"
+                 stroke="none"
+                 d="M 10463,26663 L 1313,26663 1313,23363 19613,23363 19613,26663 10463,26663 Z"
+                 id="path251" /><path
+                 fill="none"
+                 stroke="rgb(52,101,164)"
+                 d="M 10463,26663 L 1313,26663 1313,23363 19613,23363 19613,26663 10463,26663 Z"
+                 id="path253" /><text
+                 class="TextShape"
+                 id="text255"><tspan
+                   class="TextParagraph"
+                   font-family="Liberation Sans, sans-serif"
+                   font-size="635px"
+                   font-weight="700"
+                   id="tspan257"><tspan
+                     class="TextPosition"
+                     x="8311"
+                     y="24878"
+                     id="tspan259"><tspan
+                       fill="rgb(0,0,0)"
+                       stroke="none"
+                       id="tspan261">Result viewer:</tspan></tspan><tspan
+                     class="TextPosition"
+                     x="1660"
+                     y="25589"
+                     id="tspan263"><tspan
+                       font-weight="400"
+                       fill="rgb(0,0,0)"
+                       stroke="none"
+                       id="tspan265">Check graphs and statistics, view endpoints, save or delete fits</tspan></tspan></tspan></text>
+</g></g><g
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+             id="g267"><g
+               id="id8"><path
+                 fill="none"
+                 stroke="rgb(0,0,0)"
+                 d="M 10500,6200 L 10500,7570"
+                 id="path270" /><path
+                 fill="rgb(0,0,0)"
+                 stroke="none"
+                 d="M 10500,8000 L 10650,7550 10350,7550 10500,8000 Z"
+                 id="path272" /></g></g><g
+             class="com.sun.star.drawing.LineShape"
+             id="g274"><g
+               id="id9"><path
+                 fill="none"
+                 stroke="rgb(0,0,0)"
+                 d="M 10500,11300 L 10500,12670"
+                 id="path277" /><path
+                 fill="rgb(0,0,0)"
+                 stroke="none"
+                 d="M 10500,13100 L 10650,12650 10350,12650 10500,13100 Z"
+                 id="path279" /></g></g><g
+             class="com.sun.star.drawing.LineShape"
+             id="g281"><g
+               id="id10"><path
+                 fill="none"
+                 stroke="rgb(0,0,0)"
+                 d="M 10500,16400 L 10500,17770"
+                 id="path284" /><path
+                 fill="rgb(0,0,0)"
+                 stroke="none"
+                 d="M 10500,18200 L 10650,17750 10350,17750 10500,18200 Z"
+                 id="path286" /></g></g><g
+             class="com.sun.star.drawing.LineShape"
+             id="g288"><g
+               id="id11"><path
+                 fill="none"
+                 stroke="rgb(0,0,0)"
+                 d="M 10500,21500 L 10500,22970"
+                 id="path291" /><path
+                 fill="rgb(0,0,0)"
+                 stroke="none"
+                 d="M 10500,23400 L 10650,22950 10350,22950 10500,23400 Z"
+                 id="path293" /></g></g></g></g></g></g></svg>
\ No newline at end of file