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Merge pull request #15 from jporobicg/master
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update to allow different combination of Cap-Low case. Adding ice groups
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@jporobicg
jporobicg committed Oct 18, 2023
2 parents 0f972c7 + 423753b commit 47d305a
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111 changes: 55 additions & 56 deletions R/initGen.R
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Expand Up @@ -90,85 +90,84 @@ make.map.data.init <- function(bgm.file, cum.depths){
generate.vars.init <- function(grp.file, cum.depths, df.atts, ice_model) {
## read in group data from group csv file
df.grp <- read.csv(file = grp.file, header = TRUE, stringsAsFactors = FALSE)
## make sure GroupType column title exists
col.titles <- names(df.grp)
col.InvertType <- which(col.titles == "InvertType")
## make sure grouptype column title exists
col.titles <- tolower(names(df.grp))
names(df.grp) <- col.titles
col.InvertType <- which(col.titles == "inverttype")
if (!length(col.InvertType) == 0) {
names(df.grp)[col.InvertType] <- "GroupType"
names(df.grp)[col.InvertType] <- "grouptype"
}
df.grp$GroupType <- as.character(df.grp$GroupType)
df.grp$grouptype <- as.character(df.grp$grouptype)
## find epibenthos groups
epi.grps.def <- c("SED_EP_FF", "SED_EP_OTHER", "EP_OTHER", "MOB_EP_OTHER",
"MICROPHTYBENTHOS", "PHYTOBEN", "SEAGRASS", "CORAL", 'LG_INF', 'SM_INF')
epi.grps <- df.grp$Name[df.grp$GroupType %in% epi.grps.def]
df.grp <- df.grp %>% mutate(isEpiGrp = GroupType %in% epi.grps.def)
epi.grps <- df.grp$name[df.grp$grouptype %in% epi.grps.def]
df.grp <- df.grp %>% mutate(isEpiGrp = grouptype %in% epi.grps.def)

## find cover groups. These groups need _cover in boxTracers
cover.grps <- df.grp$Name[df.grp$IsCover == 1]
cover.grps <- df.grp$name[df.grp$iscover == 1]

## set up flags for groups that need multiple N values (e.g. _N1, _N2, ...)
df.grp <- df.grp %>% mutate(multiN =
(IsCover == 1 & (NumCohorts > 1)) |
(GroupType == "PWN" & (NumCohorts > 1)) |
(GroupType == "CEP" & (NumCohorts > 1)))
(iscover == 1 & (numcohorts > 1)) |
(grouptype == "PWN" & (numcohorts > 1)) |
(grouptype == "CEP" & (numcohorts > 1)))

## groups with nums, structural and reserve N values
sr.grps <- c("FISH", "BIRD", "SHARK", "MAMMAL", "REPTILE", "FISH_INVERT")
## set up flags for groups that need _Nums, _ResN, _StructN
df.grp <- df.grp %>% mutate(needsNums = GroupType %in% sr.grps)
df.grp <- df.grp %>% mutate(needsNums = grouptype %in% sr.grps)

## set up a flag for groups that need light adaptation
light.adpn.grps <- c("DINOFLAG", "MICROPHTYBENTHOS", "SM_PHY",
"MED_PHY", "LG_PHY")
df.grp <- df.grp %>% mutate(needsLight = GroupType %in% light.adpn.grps)
df.grp <- df.grp %>% mutate(needsLight = grouptype %in% light.adpn.grps)

## set up a flag for groups that need live in the ice
ice.grp <- c('ICE_DIATOMS', 'ICE_MIXOTROPHS', 'ICE_ZOOBIOTA')
df.grp <- df.grp %>% mutate(live_ice = GroupType %in% ice.grp)
df.grp <- df.grp %>% mutate(live_ice = grouptype %in% ice.grp)

## set up a flag for groups that Fe producer groups
if(ice_model) {
fe.grp <- c('SM_PHY', 'LG_PHY', "DINOFLAG", 'FISH', 'MAMMAL')
df.grp <- df.grp %>% mutate(needsFe = GroupType %in% fe.grp )
}
fe.grp <- c('SM_PHY', 'LG_PHY', "DINOFLAG", 'FISH', 'MAMMAL')
df.grp <- df.grp %>% mutate(needsFe = grouptype %in% fe.grp )

#### Donnuts model
#### create data frame for invert biological variables (ignores multiple stocks)
Variable <- NULL ## variable name
long_name <- NULL ## long name
att.index <- NULL ## corresponding row of df.atts
count = 1
for (grp in 1:length(df.grp$Name)) {
for (grp in 1:length(df.grp$name)) {
if (!df.grp$needsNums[grp]) {
if (!df.grp$multiN[grp]) { ## single group
Variable <- c(Variable, paste(df.grp$Name[grp], "_N", sep = ""))
indx <- which(df.atts$name==paste(df.grp$GroupType[grp], "_N", sep = ""))
long_name <- c(long_name, paste(df.grp$Name[grp],
Variable <- c(Variable, paste(df.grp$name[grp], "_N", sep = ""))
indx <- which(df.atts$name==paste(df.grp$grouptype[grp], "_N", sep = ""))
long_name <- c(long_name, paste(df.grp$name[grp],
df.atts$long_name[indx], sep = " "))
att.index <- c(att.index, indx)
} else { ## multiple groups
for (j in 1:df.grp$NumCohorts[grp]) {
Variable <- c(Variable, paste(df.grp$Name[grp], "_N", as.character(j),
for (j in 1:df.grp$numcohorts[grp]) {
Variable <- c(Variable, paste(df.grp$name[grp], "_N", as.character(j),
sep = ""))
indx <- which(df.atts$name==paste(df.grp$GroupType[grp], "_N",
indx <- which(df.atts$name==paste(df.grp$grouptype[grp], "_N",
sep = ""))
long_name <- c(long_name, paste(df.grp$Name[grp], "cohort",
long_name <- c(long_name, paste(df.grp$name[grp], "cohort",
as.character(j), df.atts$long_name[indx], sep = " "))
att.index <- c(att.index, indx)
}
}
if (df.grp$IsCover[grp]) { ## single cover group
Variable <- c(Variable, paste(df.grp$Name[grp], "_Cover", sep = ""))
if (df.grp$iscover[grp]) { ## single cover group
Variable <- c(Variable, paste(df.grp$name[grp], "_Cover", sep = ""))
indx <- which(df.atts$name == "Cover")
long_name <- c(long_name, paste("Percent cover by",
df.grp$Name[grp], sep = " "))
df.grp$name[grp], sep = " "))
att.index <- c(att.index, indx)
}
if (df.grp$IsSiliconDep[grp]) { ## single silicon group
Variable <- c(Variable, paste(df.grp$Name[grp], "_S", sep = ""))
if (df.grp$issilicondep[grp]) { ## single silicon group
Variable <- c(Variable, paste(df.grp$name[grp], "_S", sep = ""))
Siname <- ifelse(df.grp$live_ice[grp], "Si2D", "Si3D")
indx <- which(df.atts$name == Siname)
long_name <- c(long_name, paste(df.grp$Name[grp],
long_name <- c(long_name, paste(df.grp$name[grp],
"Silicon", sep = " "))
att.index <- c(att.index, indx)
}
Expand All @@ -177,20 +176,20 @@ generate.vars.init <- function(grp.file, cum.depths, df.atts, ice_model) {
sep = ""))
indx <- which(df.atts$name == "Light3D")
long_name <- c(long_name, paste("Light adaption of",
df.grp$Name[grp], sep = " "))
df.grp$name[grp], sep = " "))
att.index <- c(att.index, indx)
}
if (df.grp$live_ice[grp]) { ## Ice dependent groups
Variable <- c(Variable, paste(df.grp$Name[grp], "_F", sep = ""))
Variable <- c(Variable, paste(df.grp$name[grp], "_F", sep = ""))
indx <- which(df.atts$name == "Fe3D_ice")
long_name <- c(long_name, paste(df.grp$Name[grp],
long_name <- c(long_name, paste(df.grp$name[grp],
"Iron", sep = " "))
att.index <- c(att.index, indx)
}
if (ice_model & df.grp$needsFe[grp]) { ## Fe dependent groups
Variable <- c(Variable, paste(df.grp$Name[grp], "_F", sep = ""))
Variable <- c(Variable, paste(df.grp$name[grp], "_F", sep = ""))
indx <- which(df.atts$name == "Fe3D")
long_name <- c(long_name, paste(df.grp$Name[grp],
long_name <- c(long_name, paste(df.grp$name[grp],
"Iron", sep = " "))
att.index <- c(att.index, indx)
}
Expand All @@ -204,44 +203,44 @@ generate.vars.init <- function(grp.file, cum.depths, df.atts, ice_model) {
Variable <- NULL ## variable name
long_name <- NULL ## Long name
att.index <- NULL ## corresponding row of df.atts
for (grp in 1:length(df.grp$Name)) {
for (grp in 1:length(df.grp$name)) {
if (df.grp$needsNums[grp]) {
Variable <- c(Variable, paste(df.grp$Name[grp], "_N", sep = ""))
indx <- which(df.atts$name==paste(df.grp$GroupType[grp], "_N", sep = ""))
long_name <- c(long_name, paste(df.grp$Name[grp],
Variable <- c(Variable, paste(df.grp$name[grp], "_N", sep = ""))
indx <- which(df.atts$name==paste(df.grp$grouptype[grp], "_N", sep = ""))
long_name <- c(long_name, paste(df.grp$name[grp],
df.atts$long_name[indx], sep = " "))
att.index <- c(att.index, indx)

if (ice_model & df.grp$needsFe[grp]) { ## Fe dependent groups
Variable <- c(Variable, paste(df.grp$Name[grp], "_F", sep = ""))
Variable <- c(Variable, paste(df.grp$name[grp], "_F", sep = ""))
indx <- which(df.atts$name == "Fe3D")
long_name <- c(long_name, paste(df.grp$Name[grp],
long_name <- c(long_name, paste(df.grp$name[grp],
"Iron", sep = " "))
att.index <- c(att.index, indx)
}

for (j in 1:df.grp$NumCohorts[grp]) {
Variable <- c(Variable, paste(df.grp$Name[grp], as.character(j),
for (j in 1:df.grp$numcohorts[grp]) {
Variable <- c(Variable, paste(df.grp$name[grp], as.character(j),
"_Nums", sep = ""))
indx <- which(df.atts$name=="Nums3D")
long_name <- c(long_name, paste("Numbers of", df.grp$Name[grp], "cohort",
long_name <- c(long_name, paste("Numbers of", df.grp$name[grp], "cohort",
as.character(j), sep = " "))
att.index <- c(att.index, indx)
}
for (j in 1:df.grp$NumCohorts[grp]) {
Variable <- c(Variable, paste(df.grp$Name[grp], as.character(j),
for (j in 1:df.grp$numcohorts[grp]) {
Variable <- c(Variable, paste(df.grp$name[grp], as.character(j),
"_StructN", sep = ""))
indx <- which(df.atts$name=="StructN3D")
long_name <- c(long_name, paste("Individual structural N for",
df.grp$Name[grp], "cohort", as.character(j), sep = " "))
df.grp$name[grp], "cohort", as.character(j), sep = " "))
att.index <- c(att.index, indx)
}
for (j in 1:df.grp$NumCohorts[grp]) {
Variable <- c(Variable, paste(df.grp$Name[grp], as.character(j),
for (j in 1:df.grp$numcohorts[grp]) {
Variable <- c(Variable, paste(df.grp$name[grp], as.character(j),
"_ResN", sep = ""))
indx <- which(df.atts$name=="ResN3D")
long_name <- c(long_name, paste("Individual reserve N for",
df.grp$Name[grp], "cohort", as.character(j), sep = " "))
df.grp$name[grp], "cohort", as.character(j), sep = " "))
att.index <- c(att.index, indx)
}
}
Expand Down Expand Up @@ -363,8 +362,8 @@ make.init.csv <- function(grp.file, bgm.file, cum.depths, csv.name, ice_model =
##' @param cum.depths Vector of cumulative depths (starting with value zero).
##' @param init.file csv file containing all variable names and their attributes. Also includes how the values are distributed in space and the vertical.
##' @param horiz.file csv file containing box-defined values if customised flag is set for the horizontal distribution in \code{init.file}.
##' @param nc.file Name of the NetCDF file generated which contains the initial conditions. This file can be used as input to Atlantis.
##' @param vert Name and location of the csv file containing the functional groups' vertical distribution
##' @param nc.file name of the NetCDF file generated which contains the initial conditions. This file can be used as input to Atlantis.
##' @param vert name and location of the csv file containing the functional groups' vertical distribution
##' @param ice_model Boolean string. Is the model using Fe and Ice dependent species?
##' @return Null (always). Produces a NetCDF file with the name \code{nc.file}.
##'
Expand Down Expand Up @@ -645,8 +644,8 @@ make.init.nc <- function(bgm.file, cum.depths, init.file, horiz.file, nc.file, v
##' This function is useful when collecting data from Atlantis models in order to parameterise new Atlantis models.
##' Incorrect output may be produced if the number of boxes equals the number of time steps or water layers.
##'
##' @param nc.file Name of the NetCDF file containing the initial conditions.
##' @param output.file Name of the csv file where data is written.
##' @param nc.file name of the NetCDF file containing the initial conditions.
##' @param output.file name of the csv file where data is written.
##'
##' @return Null (always). Produces a csv file with the name \code{ouput.file}.
##'
Expand Down
4 changes: 2 additions & 2 deletions man/get.init.nc.Rd
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4 changes: 2 additions & 2 deletions man/make.init.nc.Rd
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