Merge branch '47-automatically-update-website-from-package-repository' into 'dev'

Resolve "Automatically update website from package repository"

Closes #47

See merge request !15

.gitlab-ci.yml

 stages:
     - checks
+    - website
 
 default:
   tags: [docker]
@@ -23,12 +24,11 @@ before_script:
   - mkdir -p $R_LIBS_USER $BUILD_LOGS_DIR
   - echo "R_LIBS='$R_LIBS_USER'" > .Renviron
   - R -e 'devtools::install_deps(dep = T)'
-  - R -e 'devtools::install_dev_deps()'
 
 .check:
   stage: checks
   script:
-    - if [[ $NOT_CRAN == "false" ]]; then sudo apt-get install -y qpdf; fi
+    - if [[ $NOT_CRAN == "false" ]]; then sudo apt-get update && sudo apt-get install -y qpdf; fi
     - R -e 'devtools::check(check_dir = Sys.getenv("CHECK_DIR"), cran = !as.logical(Sys.getenv("NOT_CRAN")), env_vars = c(NOT_CRAN = Sys.getenv("NOT_CRAN")))'
     - R -e 'if (length(devtools::check_failures(path = Sys.getenv("BUILD_LOGS_DIR"), note = FALSE)) > 0) stop()'
 
@@ -41,3 +41,15 @@ check_as_cran:
   variables:
     NOT_CRAN: "false"
   extends: .check
+
+website:
+  stage: website
+  # only:
+  #   refs:
+  #     - master
+  #     - tags
+  script:
+    - R -e 'remotes::install_gitlab("in-wop/seinebasin", host = "gitlab.irstea.fr")'
+    - R -e 'pkgdown::build_site()'
+    - sudo apt-get update && sudo apt-get install -y sshpass rsync
+    - sshpass -p "${OVH_PASS}" rsync -a -e "ssh -o StrictHostKeyChecking=no" docs/ ${OVH_LOGIN}@${OVH_SFTP}:/home/${OVH_LOGIN}/airgriwrm/

vignettes/seinebasin/V01_First_network.Rmd

@@ -5,25 +5,29 @@ vignette: >
   %\VignetteIndexEntry{Seine_01: Structuration of a semi-distributive GR4J model network}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
+bibliography: seinebasin.bib
 ---
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-```{r}
+```{r library}
 library(airGRiwrm)
 ```
 
+The example below is inspired by the semi-distributed model developed in the ClimAware project [@theobaldClimAwareImpactsClimate2014a].
 
 ## Semi-distributive network description
 
+The model is distributed according to the gauging stations described in @dorchiesClimateChangeImpacts2014.
+
 List of nodes
 
-```{r}
+```{r seine_nodes}
 seine_nodes <- read.table(
   file = system.file("seine_data", "network_gauging_stations.txt", package = "seinebasin"),
-  sep = ";", header = TRUE, fileEncoding = "UTF-8", quote = "\""
+  sep = ";", header = TRUE, fileEncoding = "UTF-8", quote = "\"", stringsAsFactors = FALSE
 )
 seine_nodes
 ```
@@ -38,7 +42,7 @@ Create the GRiwrm object which lists the nodes and describes the network diagram
 
 `GRiwrm` function helps to rename the columns of the dataframe and assign the variable classes.
 
-```{r}
+```{r griwrm}
 seine_nodes$id_aval[seine_nodes$id_aval == ""] <- NA
 seine_nodes$distance_aval <- as.double(seine_nodes$distance_aval) / 1000
 seine_nodes$model <- "RunModel_GR4J"
@@ -49,7 +53,7 @@ griwrm
 
 The diagram of the network structure is represented below with in blue the upstream nodes with a GR4J model and in green the intermediate nodes with an SD (GR4J + LAG) model.
 
-```{r fig.height = 7, fig.width = 8}
+```{r DiagramGRiwrm, fig.height = 7, fig.width = 8}
 DiagramGRiwrm(griwrm)
 ```
 
@@ -57,48 +61,15 @@ DiagramGRiwrm(griwrm)
 
 ## Observation time series
 
-Loading hydrometeorological data on the Seine river basin from the ClimAware project:
-
-```{r, warning=FALSE, message=FALSE}
-
-Precip <- NULL
-PotEvap <- NULL
-Qobs <- NULL
-
-MergeTS <- function(dfOld, id, dfNew) {
-    names(dfNew) <- c("DatesR", id) # Renaming columns of the new input into date and sub-basin ID
-    if(is.null(dfOld)) {
-    dfOut <- dfNew # Creation of the first column
-  } else {
-    dfOut <- merge(dfOld, dfNew, by = "DatesR", all = TRUE) # Merge the new columns respecting to date column
-  }
-  return(dfOut)
-}
-
-for(id in griwrm$id) {
-  url <-
-    file.path(
-      "https://stratus.irstea.fr/d/0b18e688851a45478f7a/files/?p=/climaware_hydro/Q_OBS_NAT",
-      paste0(id, "_NAT.txt&dl=1")
-    )
-  ts <- read.table(url, sep = ";", skip = 16, header = TRUE)
-  # Date conversion to POSIX
-  ts$Date <- as.POSIXct(as.character(ts$Date), format = "%Y%m%d")
-  # Ptot column  is merged into Precip dataframe
-  Precip <- MergeTS(Precip, id, ts[,c("Date", "Ptot")])
-  # ETP column is merged into PotEvap dataframe
-  PotEvap <- MergeTS(PotEvap, id, ts[,c("Date", "ETP")])
-  # Convert Qobs from m3/s to mm/time step
-  ts$Qnat <- ts$Qnat * 86.4 / griwrm$area[griwrm$id == id]
-  # Setting data gaps to NA
-  ts$Qnat[ts$Qnat <= 0] <- NA
-  # Qnat column is merged into Qobs dataframe
-  Qobs <- MergeTS(Qobs, id, ts[,c("Date", "Qnat")])
-}
-DatesR <- Precip$DatesR
-Precip$DatesR <- NULL
-PotEvap$DatesR <- NULL
-Qobs$DatesR <- NULL
+The daily mean precipitation and potential evaporation at the scale of the intermediate sub-basins are extracted from the SAFRAN reanalysis [@vidal50yearHighresolutionAtmospheric2010]. 
+
+The daily naturalised flow is provided by @hydratecActualisationBaseDonnees2011a.
+
+These data are embedded in a the R package 'seinebasin' which is not publicly available.
+
+```{r QNAT, warning=FALSE, message=FALSE}
+library(seinebasin)
+data(QNAT)
 ```
 
 ## Generate the GRIWRM InputsModel object
@@ -108,17 +79,17 @@ The GRIWRM InputsModel object is a list of **airGR** InputsModel. The identifier
 The **airGR** CreateInputsModel function is extended in order to handle the griwrm object which describe the basin diagram:
 
 
-```{r}
-InputsModel <- CreateInputsModel(griwrm, DatesR, Precip, PotEvap, Qobs)
+```{r CreateInputsModel}
+InputsModel <- CreateInputsModel(griwrm, DatesR, Precip, PotEvap, Qnat)
 ```
 
 
 
 ## Save data for next vignettes
 
-```{r}
+```{r save}
 dir.create("_cache", showWarnings = FALSE)
-save(griwrm, Qobs, InputsModel, file = "_cache/V01.RData")
-save(griwrm, DatesR, Precip, PotEvap, Qobs, MergeTS, file = "_cache/V01b.RData")
+save(griwrm, InputsModel, file = "_cache/V01.RData")
 ```
 
+# References

vignettes/seinebasin/V02_First_run.Rmd

@@ -13,7 +13,7 @@ knitr::opts_chunk$set(echo = TRUE)
 
 ## Load libraries
 
-```{r}
+```{r library}
 library(airGRiwrm)
 ```
 
@@ -24,7 +24,7 @@ library(airGRiwrm)
 
 Run `vignette("01_First_network", package = "airGRiwrm")` before this one in order to create the Rdata file loaded below:
 
-```{r}
+```{r load}
 load("_cache/V01.RData")
 ```
 
@@ -32,11 +32,9 @@ load("_cache/V01.RData")
 
 Data comes from calibration of ClimAware project with naturalised flows.
 
-```{r}
-ClimAwareParams <- read.csv(
-  file = "https://stratus.irstea.fr/d/0b18e688851a45478f7a/files/?p=/climaware_hydro/Ident_NAT_P0P0.txt&dl=1"
-)
-
+```{r ClimAwareParams}
+library(seinebasin)
+data(ClimAwareParams)
 ClimAwareParams
 ```
 
@@ -45,7 +43,7 @@ Almost all sub basin has only a lag parameter. The only exception is for La Marn
 
 This lag parameter has to be converted in a speed in m/s used in the **airGR** lag model:
 
-```{r}
+```{r ParamClimAware}
 # Convert TGR routing parameter into speed
 params <- merge(griwrm, ClimAwareParams, by.x = "id", by.y = "id_sgl")
 
@@ -77,7 +75,7 @@ The user must at least define the following arguments:
 * IndPeriod_Run: the period on which the model is run
 
 
-```{r}
+```{r IndPeriod_Run}
 IndPeriod_Run <- seq(
   which(InputsModel[[1]]$DatesR == (InputsModel[[1]]$DatesR[1] + 365*24*60*60)), # Set aside warm-up period
   length(InputsModel[[1]]$DatesR) # Until the end of the time series
@@ -86,13 +84,13 @@ IndPeriod_Run <- seq(
 
 The warmup period could also be defined as is:
 
-```{r}
+```{r IndPeriod_WarmUp}
 IndPeriod_WarmUp = seq(1,IndPeriod_Run[1]-1)
 ```
 
 
 
-```{r}
+```{r CreateRunOptions}
 RunOptions <- CreateRunOptions(
   InputsModel = InputsModel,
   IndPeriod_WarmUp = IndPeriod_WarmUp,
@@ -104,7 +102,7 @@ RunOptions <- CreateRunOptions(
 
 ## Run the SD model for the whole basin
 
-```{r}
+```{r RunModel}
 OutputsModelsClimAware <- RunModel(
   InputsModel,
   RunOptions = RunOptions,
@@ -114,14 +112,15 @@ OutputsModelsClimAware <- RunModel(
 
 ## Save data for next vignettes
 
-```{r}
+```{r save}
 save(RunOptions, ParamClimAware, IndPeriod_Run, file = "_cache/V02.RData")
 ```
 
 ## Plot the result for each basin
 
-```{r, fig.height = 5, fig.width = 8}
-plot(OutputsModelsClimAware, Qobs = Qobs[IndPeriod_Run,])
+```{r plot, fig.height = 5, fig.width = 8}
+data(QNAT)
+plot(OutputsModelsClimAware, Qobs = Qnat[IndPeriod_Run,])
 ```
 
 

vignettes/seinebasin/V03_First_Calibration.Rmd

@@ -15,44 +15,41 @@ knitr::opts_chunk$set(echo = TRUE)
 
 Run `vignette("01_First_network", package = "airGRiwrm")` and `vignette("02_First_run", package = "airGRiwrm")` before this one in order to create the Rdata files loaded below:
 
-```{r}
+```{r load}
 library(airGRiwrm)
 load("_cache/V01.RData")
 load("_cache/V02.RData")
+library(seinebasin)
+data(QNAT)
 ```
 
 ## InputsCrit object
 
-```{r}
+```{r CreateInputsCrit}
 InputsCrit <- CreateInputsCrit(
   InputsModel = InputsModel,
   FUN_CRIT = airGR::ErrorCrit_KGE2,
-  RunOptions = RunOptions, Obs = Qobs[IndPeriod_Run,]
+  RunOptions = RunOptions, Obs = Qnat[IndPeriod_Run,]
 )
 str(InputsCrit)
 ```
 
 ## GRiwrmCalibOptions object
 
-```{r}
+```{r CreateCalibOptions}
 CalibOptions <- CreateCalibOptions(InputsModel)
 str(CalibOptions)
 ```
 
 ## Calibration
 
-```{r}
+```{r Calibration}
 OutputsCalib <- Calibration(InputsModel, RunOptions, InputsCrit, CalibOptions)
 ```
 
-```{r}
-save(OutputsCalib, file = "_cache/V03.RData")
-```
-
-
 ## Run model with Michel calibration
 
-```{r}
+```{r RunModel}
 ParamMichel <- sapply(griwrm$id, function(x) {OutputsCalib[[x]]$Param})
 
 OutputsModels <- RunModel(
@@ -64,14 +61,14 @@ OutputsModels <- RunModel(
 
 ## Save calibration data for next vignettes
 
-```{r}
+```{r save}
 save(ParamMichel, file = "_cache/V03.RData")
 ```
 
 
 ## Plot the result for each basin
 
-```{r, fig.height = 5, fig.width = 8}
-plot(OutputsModels, Qobs = Qobs[IndPeriod_Run,])
+```{r plot, fig.height = 5, fig.width = 8}
+plot(OutputsModels, Qobs = Qnat[IndPeriod_Run,])
 ```
 

vignettes/seinebasin/V04_Open-loop_influenced_flow.Rmd

@@ -6,27 +6,24 @@ vignette: >
   %\VignetteIndexEntry{Seine_04: Running open-loop influenced flow semi-distributed model network}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
+bibliography: seinebasin.bib
 ---
 
-```{r, include = FALSE}
+```{r setup, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
+library(DiagrammeR)
 ```
 
-```{r setup}
+```{r library}
 library(airGRiwrm)
-library(DiagrammeR)
 ```
 
 ## Integration of the reservoir connections into the model
 
-### Loading naturalised data
-
-```{r}
-load("_cache/V01b.RData")
-```
+The goal of this vignette is to add reservoirs connections to the Seine River model previously set up. A complete description of the reservoir system of the Seine River can be found in @dehayEtudeImpactChangement2012
 
 ### Add connections to the reservoirs in the gauging station network
 
@@ -34,7 +31,7 @@ Because of the **airGR** SD model structure, the topology of the network will di
 
 For example, the physical topology of the Aube lake is represented below:
 
-```{r echo = FALSE, fig.height = 3}
+```{r mmd_aube_true, echo = FALSE, fig.height = 3}
 
 mmd <- function(x, ...) {
   # For avoiding crash of R CMD build in console mode
@@ -57,7 +54,7 @@ style AUBE fill:#ccf
 
 In the SD model, we do not model intermediate flows as well as the reservoir in the catchment. As a result, an equivalent topology compatible with **airGR** will be the one below:
 
-```{r echo = FALSE}
+```{r mmd_aube_model, echo = FALSE}
 mmd("
 graph LR
 TRANN_01 -->|68km| ARCIS_24
@@ -72,7 +69,7 @@ style AUBE_R3 fill:#fcc
 
 Configuration on lake Seine is similar:
 
-```{r echo = FALSE, fig.height = 3}
+```{r mmd_seine_true, echo = FALSE, fig.height = 3}
 mmd("
 graph LR
   BAR-S_06 -->|6km| SEINE_P7
@@ -88,7 +85,7 @@ graph LR
 
 which can be translate as:
 
-```{r echo = FALSE}
+```{r mmd_seine_model, echo = FALSE}
 mmd("
 graph LR
   BAR-S_06 -->|79.7km| MERY-_22
@@ -101,7 +98,7 @@ graph LR
 
 Pannecière is an inline reservoir:
 
-```{r echo = FALSE, fig.height = 2}
+```{r mmd_pannec_true, echo = FALSE, fig.height = 2}
 mmd("
 graph LR
 P(PANNECIERE)
@@ -113,7 +110,7 @@ style P fill:#ccf
 
 It can be modelled as:
 
-```{r echo = FALSE}
+```{r mmd_pannec_model, echo = FALSE}
 mmd("
 graph LR
 CHAUM_07 --> |153km| GURGY_02
@@ -128,7 +125,7 @@ With $Q_{PANNEC\_P} = - Q_{CHAUM\_07}$ as all the upstream flow fills the reserv
 
 Marne lake can be mapped as:
 
-```{r echo = FALSE}
+```{r mmd_marne_true, echo = FALSE}
 mmd("
 graph LR
 LOUVE_19 -->|0.5km| MARNE_P28
@@ -147,7 +144,7 @@ style M fill:#ccf
 ```
 And can be modeled as:
 
-```{r echo = FALSE}
+```{r mmd_marne_model, echo = FALSE}
 mmd("
 graph LR
 MARNE_P23 -->|3km| STDIZ_04
@@ -163,7 +160,7 @@ style MARNE_R25 fill:#fcc
 
 Hence the topological connection to the reservoirs is described in the model as below:
 
-```{r}
+```{r reservoir_connections}
 reservoir_connections <- read.table(
   file = system.file("seine_data", "network_reservoir_connections.txt", package = "seinebasin"),
   sep = ";", header = TRUE
@@ -173,10 +170,11 @@ reservoir_connections
 
 Reservoir connections are added to the GRiwrm object:
 
-```{r}
+```{r griwrm2}
 reservoir_connections$length <- reservoir_connections$length / 1000
 reservoir_connections$model <- NA
 reservoir_connections$area <- NA
+load("_cache/V01.RData")
 griwrm2 <- rbind(griwrm, reservoir_connections[, names(griwrm)])
 DiagramGRiwrm(griwrm2)
 ```
@@ -186,41 +184,18 @@ DiagramGRiwrm(griwrm2)
 
 Description of the files, the columns and the type of connection (inlet / outlet) are defined in the list below:
 
-```{r}
-
+```{r lCfgReservoirs}
 lCfgReservoirs <- jsonlite::read_json(system.file("seine_data", "config_reservoirs.json", package = "seinebasin"))
 str(lCfgReservoirs)
 ```
 
 Then, we load observation data for each reservoir and each connection:
 
-```{r}
-
-for(reservoir_id in names(lCfgReservoirs)) {
-  df <- read.table(
-    file = file.path(
-      "https://stratus.irstea.fr/d/0b18e688851a45478f7a/files/?p=/Reservoir_current_rules",
-      paste0(lCfgReservoirs[[reservoir_id]]$file, "&dl=1")
-    ),
-    sep = "\t", header = TRUE, fill = TRUE
-  )
-  df$DatesR <- as.POSIXct(df$Date, format = "%d/%m/%Y")
-  Qreservoir <- merge(data.frame(DatesR = DatesR), df, by = "DatesR", all.x = TRUE)
-  for(connect in names(lCfgReservoirs[[reservoir_id]]$connections)) {
-    message("Processing ", connect)
-    # Replace data gap by zeros (should be interpolated ?)
-    Qreservoir[is.na(Qreservoir[,lCfgReservoirs[[reservoir_id]]$connections[[connect]]$col]),lCfgReservoirs[[reservoir_id]]$connections[[connect]]$col] <- 0
-    if(lCfgReservoirs[[reservoir_id]]$connections[[connect]]$type == "in") {
-      # Reservoir inlet flow is a withdrawal for the catchment: this is a negative flow to route
-      Qreservoir[,lCfgReservoirs[[reservoir_id]]$connections[[connect]]$col] <- -Qreservoir[,lCfgReservoirs[[reservoir_id]]$connections[[connect]]$col]
-    }
-    Qobs <- cbind(
-      Qobs,
-      Qreservoir[,lCfgReservoirs[[reservoir_id]]$connections[[connect]]$col] * 86400
-    )
-    names(Qobs)[ncol(Qobs)] <- connect
-  }
-}
+```{r seinebasin}
+library(seinebasin)
+data(QNAT)
+data(Qreservoirs)
+Qnat <- cbind(Qnat, Qreservoirs)
 ```
 
 ## How to handle online reservoir? The Pannecière lake case.
@@ -232,22 +207,23 @@ There are two possibilities:
 
 If we know in advance the flow released by the reservoir, upstream flow informations is not usefull for the simulation. But reservoir management simulation would need upstream flow informations for simulating the reservoir state during simulation. The second alternative will be useful for the next phases.
 
-```{r}
-Qobs$PANNEC_P <- -Qobs$CHAUM_07 * griwrm2$area[griwrm2$id == "CHAUM_07"] * 1e3
+```{r Qnat}
+Qnat <- cbind(Qnat, -Qnat[,"CHAUM_07"] * griwrm2$area[griwrm2$id == "CHAUM_07"] * 1e3)
+colnames(Qnat)[ncol(Qnat)] <- "PANNEC_P"
 ```
 
 
 ## Create the InputsModel object
 
-```{r}
-InputsModel2 <- CreateInputsModel(griwrm2, DatesR, Precip, PotEvap, Qobs)
+```{r CreateInputsModel}
+InputsModel2 <- CreateInputsModel(griwrm2, DatesR, Precip, PotEvap, Qnat)
 ```
 
 ## Run simulation with naturalised flow parameters
 
 ### Load calibration parameters
 
-```{r}
+```{r load}
 # Load RunOptions
 load("_cache/V02.RData")
 # Load calibrated parameters with Michel's method
@@ -259,14 +235,14 @@ load("_cache/V03.RData")
 
 A lag parameter is now mandatory for these sub-basins. As no calibration is possible at that stage an arbitrary one will be used.
 
-```{r}
+```{r ParamMichel}
 ParamMichel$STDIZ_04 <- c(1, ParamMichel$STDIZ_04)
 ```
 
 
 ### Run simulation
 
-```{r}
+```{r RunModel}
 OutputsModels2 <- RunModel(
   InputsModel2,
   RunOptions = RunOptions,
@@ -278,36 +254,12 @@ OutputsModels2 <- RunModel(
 
 ### Load observed flow
 
-```{r, warning=FALSE, message=FALSE}
-
-Qobs2 <- NULL
-
-# Files of observed flows doesn't exist for some basins
-NatIds <- c("CHAUM_07", "MONTE_15", "NOGEN_13", "STGER_09", "TRANN_01")
-
-ids <- griwrm$id[!griwrm$id %in% NatIds]
-
-for(id in ids) {
-  url <-
-    file.path(
-      "https://stratus.irstea.fr/d/0b18e688851a45478f7a/files/?p=/climaware_hydro/Q_OBS_NAT",
-      paste0(id, "_BV.txt&dl=1")
-    )
-  ts <- read.table(file = url,
-                   sep = ";", skip = 24, header = TRUE)
-  # Date conversion to POSIX
-  ts$Date <- as.POSIXct(as.character(ts$Date), format = "%Y%m%d")
-    # Convert Qobs from l/s to mm/time step
-  ts$Q <- ts$Q * 86.4 / griwrm$area[griwrm$id == id] / 1000
-  # Setting data gaps to NA
-  ts$Q[ts$Q <= 0] <- NA
-  # Column is merged into Qobs dataframe
-  Qobs2 <- MergeTS(Qobs2, id, ts[,c("Date", "Q")])
-}
+```{r QOBS, warning=FALSE, message=FALSE}
+data(QOBS)
 ```
 
 ## Save data for next vignettes
-```{r}
+```{r save}
 ReduceOutputsModel <- function(OutputsModels, IndPeriod) {
   items <- names(OutputsModels)
   OutputsModelsOut <- sapply(items, function(x) {OutputsModels[[x]] <- OutputsModels[[x]][IndPeriod]})
@@ -316,17 +268,18 @@ ReduceOutputsModel <- function(OutputsModels, IndPeriod) {
   return(OutputsModelsOut)
 }
 
-QobsReservoirs <- Qobs[DatesR < "2008-08-01", reservoir_connections$id]
-save(QobsReservoirs, Qobs2, griwrm2, ReduceOutputsModel, file = "_cache/V04.RData")
+save(griwrm2, ReduceOutputsModel, file = "_cache/V04.RData")
 ```
 
 ### Comparison with simulated flows
 
-```{r, fig.height = 5, fig.width = 8}
+```{r plot, fig.height = 5, fig.width = 8}
+IndPeriod <- RunOptions[[1]]$IndPeriod_Run
+IndPeriod <- IndPeriod[IndPeriod <= nrow(Qobs)]
 htmltools::tagList(lapply(
-  ids,
+  colnames(Qobs),
   function(x) {
-    Q2 <- Qobs2[RunOptions[[1]]$IndPeriod_Run,x]
+    Q2 <- Qobs[IndPeriod,x]
     IndPeriod_Obs <- which(!is.na(Q2))
     OutputsModels <- ReduceOutputsModel(OutputsModels2[[x]], IndPeriod_Obs)
     plot(OutputsModels, Qobs = Q2[IndPeriod_Obs], main = x, which = c("Regime"))
@@ -334,5 +287,4 @@ htmltools::tagList(lapply(
 ))
 ```
 
-
-
+# References

vignettes/seinebasin/V05_Open-loop_influenced_flow_calibration.Rmd

@@ -8,31 +8,32 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, include = FALSE}
+```{r setup, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>"
 )
 ```
 
-```{r setup}
+```{r library}
 library(airGRiwrm)
 ```
 
-This vignette proposes an example of calibration of influenced flow with the Marne reservoir. It will used influenced observation flows directly measured at gauging stations and flows recorded at reservoir inlets and outlet.
+This vignette proposes an example of calibration of influenced flow with the Marne reservoir.
+It will used influenced observation flows directly measured at gauging stations and flows recorded at reservoir inlets and outlet.
 
 ## Set the data
 
 Loading naturalised data and influenced flow configuration:
 
-```{r}
-load("_cache/V01b.RData")
+```{r load}
+#load("_cache/V01.RData")
 load("_cache/V04.RData")
 ```
 
 Remove e