From c1adac6647612046082771b76b024e004a26dc2e Mon Sep 17 00:00:00 2001
From: Kiweler <mkiwele@kerckhoff.mpg.de>
Date: Fri, 7 Jun 2019 09:17:15 +0200
Subject: [PATCH] RSLC gradient included

---
 R/gradient_functions.R                    | 150 ++++++++++++++++------
 R/sysdata.rda                             | Bin 2735 -> 3765 bytes
 R/term_matching_functions.R               |   3 +-
 data/tmt_list.rda                         | Bin 2732 -> 3765 bytes
 vignettes/manipulate_term_match_table.Rmd |   5 +-
 vignettes/using_MARMoSET.Rmd              |   2 +-
 6 files changed, 117 insertions(+), 43 deletions(-)

diff --git a/R/gradient_functions.R b/R/gradient_functions.R
index 4161f85..285a23e 100644
--- a/R/gradient_functions.R
+++ b/R/gradient_functions.R
@@ -5,16 +5,27 @@
 #'
 #' @return Gradient information as sentence.
 
-gradient_as_string <- function(flat_json, group_number)
+gradient_as_string <- function(flat_json, group_number, lc_pump)
 {
   assertive.types::assert_is_list(flat_json)
   assertive.types::assert_is_a_number(group_number)
+  assertive.types::assert_is_a_string(lc_pump)
 
-  grad_tab <- one_gradient_table(flat_json, group_number)
-  grad_tab$`Duration[mm:ss]` %<>%
-    stringi::stri_replace_all_regex(pattern =  ":.*", replacement = "") %<>%
-    stringi::stri_replace_all_regex(pattern =  "^0", replacement = "")
+  grad_tab <- one_gradient_table(flat_json, group_number, lc_pump)
 
+  {
+    if(lc_pump == "Thermo EASY-nLC")
+    {
+      grad_tab$`Time[mm:ss]` %<>%
+        stringi::stri_replace_all_regex(pattern =  ":0.*", replacement = "") %<>%
+        stringi::stri_replace_all_regex(pattern =  "^0", replacement = "")
+      time <- grad_tab$`Time[mm:ss]`
+    }
+    else if(lc_pump == "RSLCnano")
+    {
+      time <- grad_tab$`Time[min]`
+    }
+  }
 
   if(dim(table(grad_tab$`Flow[nl/min]`)) == 1)
   {
@@ -27,12 +38,11 @@ gradient_as_string <- function(flat_json, group_number)
                   sep = "")
   }
 
-  first <- paste("The quantity of B in percent is from ", grad_tab$`Mixture[%B]`[1], "% B in ",
-        grad_tab$`Duration[mm:ss]`[2], " minutes to ",
-        grad_tab$`Mixture[%B]`[2], "% B.",
+  first <- paste("The quantity of B in percent is from ", grad_tab$`Mixture[%B]`[1], "% B to ",
+        grad_tab$`Mixture[%B]`[2], "% B at minute ", time[2], ".",
         sep = "")
 
-  following <- paste("In ", grad_tab$`Duration[mm:ss]`[3:nrow(grad_tab)], " minutes to ",
+  following <- paste("At minute ", time[3:nrow(grad_tab)], " to ",
         grad_tab$`Mixture[%B]`[3:nrow(grad_tab)], "% B.", sep = "", collapse = " ")
 
   paste(flow, first, following,  sep=" ") %>%
@@ -40,13 +50,12 @@ gradient_as_string <- function(flat_json, group_number)
 }
 
 
-#' Gradient.
+#' Create a list with all gradient tables.
 #'
 #' @param flat_json A flattened JSON file, created by "prepare_json()".
-#' @param group_number An index that determines the group.
-#' @param lc_pump Name of the used lc_pump, default = "Thermo EASY-nLC".
+#' @param lc_pump Name of the used lc pump, default = "Thermo EASY-nLC"
 #'
-#' @return A table with gradient.
+#' @return List of gradients for all groups
 #'
 #' @examples
 #' # Real-time JSON-generation: `json <- generate_json(data)`
@@ -54,39 +63,30 @@ gradient_as_string <- function(flat_json, group_number)
 #'
 #' flat_json <- flatten_json(json)
 #'
-#' one_gradient_table(flat_json, 1)
+#' gradient_tables(flat_json)
 #'
 #' @export
 
-one_gradient_table <- function(flat_json, group_number, lc_pump = "Thermo EASY-nLC")
+gradient_tables <- function(flat_json, lc_pump = "Thermo EASY-nLC")
 {
   assertive.types::assert_is_list(flat_json)
-  assertive.types::assert_is_a_number(group_number)
-  assertive.properties::assert_is_not_null(flat_json[[paste("Group" , group_number , "Instruments", lc_pump, "InstrumentFriendName", sep = ".")]])
+  assertive.types::assert_is_a_string(lc_pump)
 
-  time <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Time [mm:ss]")]]
-  duration <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Duration [mm:ss]")]]
-  flow <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Flow [nl/min]")]]
-  mixture <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Mixture [%B]")]]
+  group_count <- flat_json %>% names() %>% stringi::stri_extract_first_regex('^Group\\.\\d+\\.') %>% table() %>% length()
 
-  gradient_tab <- data.frame(
-   "Time[mm:ss]" = time,
-   "Duration[mm:ss]" = duration,
-   "Flow[nl/min]" = flow,
-   "Mixture[%B]" = mixture,
-   check.names = FALSE,
-   stringsAsFactors = FALSE)
+  vector_of_gradient_tables <- lapply(1:group_count, function(group) one_gradient_table(flat_json, group, lc_pump))
 
-  return(gradient_tab)
+  return(vector_of_gradient_tables)
 }
 
 
-#' Create a list with all gradient tables.
+#' Gradient.
 #'
 #' @param flat_json A flattened JSON file, created by "prepare_json()".
-#' @param lc_pump Name of the used lc pump, default = "Thermo EASY-nLC"
+#' @param group_number An index that determines the group.
+#' @param lc_pump Name of the used lc_pump, default = "Thermo EASY-nLC".
 #'
-#' @return List of gradients for all groups
+#' @return A table with gradient.
 #'
 #' @examples
 #' # Real-time JSON-generation: `json <- generate_json(data)`
@@ -94,18 +94,92 @@ one_gradient_table <- function(flat_json, group_number, lc_pump = "Thermo EASY-n
 #'
 #' flat_json <- flatten_json(json)
 #'
-#' gradient_tables(flat_json)
+#' one_gradient_table(flat_json, 1)
 #'
 #' @export
 
-gradient_tables <- function(flat_json, lc_pump = "Thermo EASY-nLC")
+one_gradient_table <- function(flat_json, group_number, lc_pump = "Thermo EASY-nLC")
 {
   assertive.types::assert_is_list(flat_json)
-  assertive.types::assert_is_a_string(lc_pump)
+  assertive.types::assert_is_a_number(group_number)
 
-  group_count <- flat_json %>% names() %>% stringi::stri_extract_first_regex('^Group\\.\\d+\\.') %>% table() %>% length()
+  {
+    if(lc_pump == "Thermo EASY-nLC")
+    {
+      gradient_table <- EASYnLC_g_tab(flat_json = flat_json, group_number = group_number)
+    }
+    else if(lc_pump == "RSLCnano")
+    {
+      gradient_table <- RSLC_g_tab(flat_json = flat_json, group_number = group_number)
+    }
+  }
+  return(gradient_table)
+}
 
-  vector_of_gradient_tables <- lapply(1:group_count, function(group) one_gradient_table(flat_json, group, lc_pump))
+######################################################################################
+#' Create gradient table for RSLCpump.
+#'
+#' @param flat_json A flattened JSON file, created by "prepare_json()".
+#'
+#' @return gradient table for RSLCpump
 
-  return(vector_of_gradient_tables)
+RSLC_g_tab <- function(flat_json, group_number)
+{
+  group_regex <- paste0("^Group.", group_number, ".Instruments.RSLCnano.Method.[0-9]+\\.[0-9]{3}")
+  RSLC_gradient <- stringi::stri_subset(names(flat_json), regex = group_regex)
+  # get values for these keys
+  gradient_values <- lapply(1:length(RSLC_gradient), function(key) flat_json[[RSLC_gradient[key]]] %>%stringi::stri_split(regex = " ")) %>%
+    as.data.frame() %>% t()
+
+  # shortening the rows
+  gradient_time <- stringi::stri_replace_all_regex(RSLC_gradient, pattern = "^Group.[:digit:].Instruments.RSLCnano.Method.", replacement = "") %>%
+    stringi::stri_split(regex = "\\[min\\]") %>%
+    as.data.frame() %>% t()
+
+  # combine values and keys
+  gradient_table <- as.data.frame(cbind(gradient_time, gradient_values[,1])) %>%
+    # tidy up the entries
+    dplyr::filter(!stringr::str_detect(V2, 'Equilibration|LoadingPump|ColumnOven.Valve')) %<>%
+    dplyr::mutate(V2 = V2 %>% stringi::stri_replace_first_fixed('Run','')) %<>%
+    dplyr::mutate(V2 = V2 %>% stringi::stri_trim()) %>%
+    # spread
+    tidyr::spread(key = V2, value = V3)
+
+  col2 <- colnames(gradient_table[2:length(gradient_table)]) %>% stringi::stri_replace_all_regex(pattern = '.PumpModule.NC_Pump.', replacement = '') %>%
+    stringi::stri_replace_all_regex(pattern = '%B.Value', replacement = 'Mixture[%B]') %>%
+    stringi::stri_replace_all_regex(pattern = 'Flow.Nominal', replacement = 'Flow[nl/min]') %>%
+    stringi::stri_trim()
+  colnames(gradient_table) <- c("Time[min]", col2)
+
+  # microliter to nl:
+  gradient_table$`Flow[nl/min]` %<>%  as.character() %>% as.numeric() %>% magrittr::multiply_by(1000)
+  gradient_table$`Time[min]` %<>%  as.character() %>% as.numeric()
+  gradient_table %<>% dplyr::arrange(`Time[min]`)
+
+  return(gradient_table)
+}
+
+
+#' Create gradient table for RSLCpump.
+#'
+#' @param flat_json A flattened JSON file, created by "prepare_json()".
+#'
+#' @return gradient table for RSLCpump
+
+EASYnLC_g_tab <- function(flat_json, group_number)
+{
+  time <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Time [mm:ss]")]]
+  duration <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Duration [mm:ss]")]]
+  flow <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Flow [nl/min]")]]
+  mixture <- flat_json[[paste0("Group.",group_number,".Instruments.Thermo EASY-nLC.Method.Gradient.Mixture [%B]")]]
+
+  gradient_table <- data.frame(
+    "Time[mm:ss]" = time,
+    "Duration[mm:ss]" = duration,
+    "Flow[nl/min]" = flow,
+    "Mixture[%B]" = mixture,
+    check.names = FALSE,
+    stringsAsFactors = FALSE)
+
+  return(gradient_table)
 }
diff --git a/R/sysdata.rda b/R/sysdata.rda
index ba1d347b194bae9c0742a3d52e55aad5e59c0024..7100a3b0c7506546a3bd47b0f53b63d64687e578 100644
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diff --git a/R/term_matching_functions.R b/R/term_matching_functions.R
index b2e079c..61d6aa0 100644
--- a/R/term_matching_functions.R
+++ b/R/term_matching_functions.R
@@ -81,7 +81,8 @@ one_group_match_terms <- function(flat_json, term_matching_table, group_number)
     }
     else if (handle_type == "function")
     {
-      meta_data_table[["Value"]][[this_row]] <- gradient_as_string(flat_json, group_number)
+      lc_pump <- term_matching_table[which(term_matching_table$term == "hplc_instrument"),]$example
+      meta_data_table[["Value"]][[this_row]] <- gradient_as_string(flat_json, group_number, lc_pump )
     }
   }
 
diff --git a/data/tmt_list.rda b/data/tmt_list.rda
index b02ff96a0570e4628b5c4bffbf6fa08a9a98e782..7100a3b0c7506546a3bd47b0f53b63d64687e578 100644
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diff --git a/vignettes/manipulate_term_match_table.Rmd b/vignettes/manipulate_term_match_table.Rmd
index aa06f85..5912b61 100644
--- a/vignettes/manipulate_term_match_table.Rmd
+++ b/vignettes/manipulate_term_match_table.Rmd
@@ -14,7 +14,7 @@ knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
-library(MARMoSET)
+library("MARMoSET")
 ```
 
 ## Structure
@@ -121,8 +121,7 @@ subset_tmt <- rbind(subset_tmt, new_row)
 
 This so changed term matching table can be used with the function `match_terms()`.
 
-```{R}
-
+```{r}
 subset_table <- one_group_match_terms(flat_json = flat_json, 
                                          term_matching_table = subset_tmt, 
                                          group_number = 1)
diff --git a/vignettes/using_MARMoSET.Rmd b/vignettes/using_MARMoSET.Rmd
index 39b6692..3b0511d 100644
--- a/vignettes/using_MARMoSET.Rmd
+++ b/vignettes/using_MARMoSET.Rmd
@@ -116,7 +116,7 @@ gradient_table <- one_gradient_table(flat_json = flat_json,
 
 vector_of_gradient_tables <- gradient_tables(flat_json = flat_json)
 
-head(gradient_table, 3)
+head(gradient_table, 2)
 ```
 
 Finally to export all tables in a vector or one specific, the functions `save_group_table()` and `save_all_groups()`are helpful. The output of `gradient_tables()` or `match_terms()` goes in as `groups_vector` whereas the second argument, `output_path` expects a path with filename. `save_all_groups()` adds the group numbers automatically to each filename, `save_group_table()` takes it as third argument but doesn't add.