diff --git a/plotting/layout.R b/plotting/layout.R
index 9b36075641441d7f21305d7c62701e098e5c98e1..0ebbee1ab4fcd06819e0ba5029512d4e0a3089b7 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -215,6 +215,12 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
             var = replicate(nbp, var)
         }}
 
+    if (all(class(glose) != 'list')) {
+        glose = list(glose)
+        if (length(glose) == 1) {
+            glose = replicate(nbp, glose)
+        }}
+    
     if (all(class(type) != 'list')) {
         type = list(type)
         if (length(type) == 1) {
@@ -238,6 +244,7 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
                        alpha=alpha[[i]],
                        unit2day=unit2day[[i]],
                        var=var[[i]], type=type[[i]],
+                       glose=glose[[i]],
                        missRect=missRect[[i]])
         # Stores it
         list_df2plot[[i]] = df2plot
diff --git a/plotting/map.R b/plotting/map.R
index 603ea24d01955f96de816e5e24323995ed441f69..1c3ae24dcfecaa7ef4083f0bf95c362af46d2092 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -315,6 +315,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
             var = list_df2plot[[i]]$var
             # Extracts the type of variable of the plot
             type = list_df2plot[[i]]$type
+            # Explanations about the variable
+            glose = list_df2plot[[i]]$glose
             
             # Createsa name for the map
             if (j > 1) {
@@ -404,7 +406,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
                            390000)
                 yBasin = c(6280000, 6290000, 6320000,
                            6385000, 6450000, 6530000,
-                           6360000)
+                           6361000)
                 nameBasin = c('Adour', 'Garonne', 'Tarn-Aveyron',
                               'Lot', 'Dordogne', 'Charente',
                               'Fleuves-\nCôtiers')
@@ -714,19 +716,59 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
             plot_palette = tibble(xtick=xtick, ytick=ytick,
                                   colTick=colTick, labTick=labTick)
 
+            
+            nbLine = as.integer(nchar(glose)/40) + 1
+
+
+            nbNewline = 0
+
+            nbLim = 40
+            gloseName = glose
+            nbChar = nchar(gloseName)
+            while (nbChar > nbLim) {
+                nbNewline = nbNewline + 1
+                posSpace = which(strsplit(gloseName, "")[[1]] == " ")
+                idNewline = which.min(abs(posSpace - nbLim * nbNewline))
+                posNewline = posSpace[idNewline]
+                gloseName = paste(substring(gloseName,
+                                            c(1, posNewline + 1),
+                                            c(posNewline,
+                                              nchar(gloseName))),
+                                  collapse="\n")
+                Newline = substr(gloseName,
+                                 posNewline + 2,
+                                 nchar(gloseName))
+                nbChar = nchar(Newline)
+            }
+
+            Yline = 0.6 + 0.47*nbNewline
+            Ytitle = Yline + 0.15
+            
             # New plot with void theme
             title = ggplot() + theme_void() +
-                # Plots separation line
+                # Plots separation lines
                 geom_line(aes(x=c(-0.3, 3.7), y=c(0.05, 0.05)),
                           size=0.6, color="#00A3A8") +
+                geom_line(aes(x=c(-0.3, 3.7), y=c(Yline, Yline)),
+                          size=0.6, color="#00A3A8") +
                 # Writes title
-                geom_shadowtext(data=tibble(x=-0.3, y=0.2,
+                geom_shadowtext(data=tibble(x=-0.3, y=Ytitle,
                                             label=var),
                                 aes(x=x, y=y, label=label),
                                 fontface="bold",
                                 color="#00A3A8",
                                 bg.colour="white",
                                 hjust=0, vjust=0, size=10) +
+                
+                # Writes title
+                geom_shadowtext(data=tibble(x=-0.3, y=0.2,
+                                            label=gloseName),
+                                aes(x=x, y=y, label=label),
+                                fontface="bold",
+                                color="#00A3A8",
+                                bg.colour="white",
+                                hjust=0, vjust=0, size=3) +
+                
                 # X axis
                 scale_x_continuous(limits=c(-0.3, 1 + 3),
                                    expand=c(0, 0)) +
diff --git a/processing/extract.R b/processing/extract.R
index 07cb39fa816cec0ad3d2988d4290e59fb8a68ffe..ef5c105bee491d2ffd042272cf6990b1d99dbe98 100644
--- a/processing/extract.R
+++ b/processing/extract.R
@@ -130,7 +130,7 @@ iRegHydro = c('D'='Affluents du Rhin',
 ### 2.1. Creation of selection
 # Create a txt file that resume all the station data files present
 # in a filedir
-create_selection = function (computer_data_path, filedir, outname) {
+create_selection = function (computer_data_path, filedir, outname, optname='_HYDRO_QJM') {
 
     # Out file for store results
     outfile = file.path(computer_data_path, outname)
@@ -146,18 +146,24 @@ create_selection = function (computer_data_path, filedir, outname) {
     for (f in filelist_tmp) {
         # If the filename is a 'txt' file
             if (file_ext(f) == 'txt') {
+                # Extracts the station code
+                code = gsub("[^[:alnum:] ].*$", '', f)
                 # Then the station code is stored
-                codelist = c(codelist, gsub('.txt', '', f)) 
+                codelist = c(codelist, code)
             }
         }  
     # Create a tibble to store the data to write
     df_file = tibble(code=codelist,
-                     filename=paste(codelist,
-                                    '_HYDRO_QJM.txt', sep=''),
+                     filename=paste(codelist, optname, '.txt',
+                                    sep=''),
                      ok=TRUE)
     # Write the data in a txt file
     write.table(df_file, outfile, sep=";", col.names=TRUE, quote=FALSE)
-    return (NULL)
+    # Returns that it is done with the path
+    print('Done')
+    print(paste('path : ', outfile, sep=''))
+    print('example of file : ')
+    print(head(df_file))
 }
 # Example
 # create_selection(
@@ -167,7 +173,7 @@ create_selection = function (computer_data_path, filedir, outname) {
 
 ### 2.2. Agence de l'eau Adour-Garonne selection
 # Gets the selection of station from the 'Liste-station_RRSE.docx' file
-get_selection_AG = function (computer_data_path, listdir, listname,
+get_selection_AEAG = function (computer_data_path, listdir, listname,
                              cnames=c('code','station', 'BV_km2',
                                       'axe_principal_concerne',
                                       'longueur_serie', 'commentaires',
@@ -212,7 +218,7 @@ get_selection_AG = function (computer_data_path, listdir, listname,
     return (df_selec)
 }
 # Example
-# df_selec_AG = get_selection_AG(
+# df_selec_AEAG = get_selection_AEAG(
     # "/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data",
     # "",
     # "Liste-station_RRSE.docx",
@@ -228,7 +234,7 @@ get_selection_AG = function (computer_data_path, listdir, listname,
 ### 2.3. INRAE selection
 # Gets the selection of station from the selection txt file generated
 # by the 'create_selection' function
-get_selection_IN = function (computer_data_path, listdir, listname) {
+get_selection_INRAE = function (computer_data_path, listdir, listname) {
     
     # Gets the file path to the data
     list_path = file.path(computer_data_path, listdir, listname)
@@ -244,7 +250,7 @@ get_selection_IN = function (computer_data_path, listdir, listname) {
     return (df_selec)
 }
 # Example
-# df_selec_IN = get_selection_IN(
+# df_selec_INRAE = get_selection_INRAE(
     # "/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data",
     # "",
     # "INRAE_selection.txt")
diff --git a/processing/format.R b/processing/format.R
index 316169a0f80cf0b853d61d704e8740ae87e6c7d1..175109e3532b00ff25ab5eb3c046d535c1cca660 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -37,42 +37,42 @@ library(Hmisc)
 ## 1. BEFORE TREND ANALYSE
 ### 1.1. Joining selection
 # Joins tibbles of different selection of station as a unique one
-join = function (df_data_AG, df_data_IN, df_meta_AG, df_meta_IN) {
+join = function (df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_INRAE) {
 
     # If there is an INRAE and an Agence de l'eau Adour-Garonne selection
-    if (!is.null(df_data_IN) & !is.null(df_data_AG)) {
+    if (!is.null(df_data_INRAE) & !is.null(df_data_AEAG)) {
 
         # Gets the station in common
-        common = levels(factor(df_meta_IN[df_meta_IN$code %in% df_meta_AG$code,]$code)) 
+        common = levels(factor(df_meta_INRAE[df_meta_INRAE$code %in% df_meta_AEAG$code,]$code)) 
         # Gets the Nv station to add
-        INadd = levels(factor(df_meta_IN[!(df_meta_IN$code %in% df_meta_AG$code),]$code))
+        INRAEadd = levels(factor(df_meta_INRAE[!(df_meta_INRAE$code %in% df_meta_AEAG$code),]$code))
 
-        # Selects only the IN meta to add
-        df_meta_INadd = df_meta_IN[df_meta_IN$code %in% INadd,]
+        # Selects only the INRAE meta to add
+        df_meta_INRAEadd = df_meta_INRAE[df_meta_INRAE$code %in% INRAEadd,]
 
         # Names the source of the selection
-        df_meta_AG$source = 'AG'
-        df_meta_INadd$source = 'IN'
+        df_meta_AEAG$source = 'AEAG'
+        df_meta_INRAEadd$source = 'INRAE'
         
-        # Joins IN data to AG data
-        df_meta = full_join(df_meta_AG, df_meta_INadd)
+        # Joins INRAE data to AEAG data
+        df_meta = full_join(df_meta_AEAG, df_meta_INRAEadd)
 
-        # Selects only the IN data to add
-        df_data_INadd = df_data_IN[df_data_IN$code %in% INadd,]
-        # Joins IN meta to AG meta
-        df_data = full_join(df_data_AG, df_data_INadd)
+        # Selects only the INRAE data to add
+        df_data_INRAEadd = df_data_INRAE[df_data_INRAE$code %in% INRAEadd,]
+        # Joins INRAE meta to AEAG meta
+        df_data = full_join(df_data_AEAG, df_data_INRAEadd)
 
     # If there is just an Agence de l'eau Adour-Garonne selection
-    } else if (is.null(df_data_IN) & !is.null(df_data_AG)) {
-        df_meta_AG$source = 'AG'
-        df_meta = df_meta_AG
-        df_data = df_data_AG
+    } else if (is.null(df_data_INRAE) & !is.null(df_data_AEAG)) {
+        df_meta_AEAG$source = 'AEAG'
+        df_meta = df_meta_AEAG
+        df_data = df_data_AEAG
         
     # If there is just an INRAE selection
-    } else if (!is.null(df_data_IN) & is.null(df_data_AG)) {
-        df_meta_IN$source = 'IN'
-        df_meta = df_meta_IN
-        df_data = df_data_IN
+    } else if (!is.null(df_data_INRAE) & is.null(df_data_AEAG)) {
+        df_meta_INRAE$source = 'INRAE'
+        df_meta = df_meta_INRAE
+        df_data = df_data_INRAE
 
     # If there is no selection
     } else {
diff --git a/script.R b/script.R
index d9952242776c3ec4f44b051e5151cb5a64aebbf9..20de3817c1e23a30cfe28728e94eddcca47a3f8a 100644
--- a/script.R
+++ b/script.R
@@ -55,40 +55,44 @@ filedir =
 # Name of the file that will be analysed from the BH directory
 # (if 'all', all the file of the directory will be chosen)
 filename =
-    # ""
-    c(
+    ""
+    "all"
+    # c(
         # "S2235610_HYDRO_QJM.txt",
         # "P1712910_HYDRO_QJM.txt",
         # "P0885010_HYDRO_QJM.txt",
         # "O5055010_HYDRO_QJM.txt",
         # "O0384010_HYDRO_QJM.txt",
         # "S4214010_HYDRO_QJM.txt",
-        "Q7002910_HYDRO_QJM.txt"
+        # "Q7002910_HYDRO_QJM.txt"
         # "Q0214010_HYDRO_QJM.txt"
-        # "O3035210_HYDRO_QJM.txt"
+        # "O3035210_HYDRO_QJM.txt",
         # "O0554010_HYDRO_QJM.txt",
         # "O1584610_HYDRO_QJM.txt"
-    )
+    # )
 
 
 ## AGENCE EAU ADOUR GARONNE SELECTION
 # Path to the 'docx' list file of station from the Agence de l'eau
 # Adour-Garonne that will be analysed
-AGlistdir = 
+AEAGlistdir = 
     ""
 
-AGlistname = 
-    ""
-    # "Liste-station_RRSE.docx" 
+AEAGlistname = 
+    # ""
+    "Liste-station_RRSE.docx" 
 
 
 ## NIVALE SELECTION
 # Path to the 'txt' list file of station from INRAE that will be analysed
-INlistdir =
+# Generated with :
+# create_selection(computer_data_path, 'dirname', 'example.txt')
+INRAElistdir =
     ""
 
-INlistname = 
+INRAElistname = 
     ""
+    # "example.txt"
     # "INRAE_selection.txt"
 
 
@@ -177,18 +181,18 @@ rv_shpname = 'CoursEau_FXX.shp'
 
 ## 2. SELECTION OF STATION
 # Initialization of null data frame if there is no data selected
-df_data_AG = NULL
-df_data_IN = NULL
-df_meta_AG = NULL
-df_meta_IN = NULL
+df_data_AEAG = NULL
+df_data_INRAE = NULL
+df_meta_AEAG = NULL
+df_meta_INRAE = NULL
 
 ### 2.1. Selection of the Agence de l'eau Adour-Garonne 
-if (AGlistname != ""){
+if (AEAGlistname != ""){
     
     # Get only the selected station from a list station file
-    df_selec_AG = get_selection_AG(computer_data_path, 
-                             AGlistdir,
-                             AGlistname,
+    df_selec_AEAG = get_selection_AEAG(computer_data_path, 
+                             AEAGlistdir,
+                             AEAGlistname,
                              cnames=c('code',
                                       'station', 
                                       'BV_km2',
@@ -200,39 +204,39 @@ if (AGlistname != ""){
                                       'longueur_serie'))
     
     # Get filenames of the selection
-    filename = df_selec_AG[df_selec_AG$ok,]$filename 
+    filename = df_selec_AEAG[df_selec_AEAG$ok,]$filename 
     # Extract metadata about selected stations
-    df_meta_AG = extract_meta(computer_data_path, filedir, filename)
+    df_meta_AEAG = extract_meta(computer_data_path, filedir, filename)
     # Extract data about selected stations
-    df_data_AG = extract_data(computer_data_path, filedir, filename)
+    df_data_AEAG = extract_data(computer_data_path, filedir, filename)
 }
 
 ### 2.2. INRAE selection 
-if (INlistname != ""){
+if (INRAElistname != ""){
     
     # Get only the selected station from a list station file
-    df_selec_IN = get_selection_IN(computer_data_path, 
-                                   INlistdir,
-                                   INlistname)
+    df_selec_INRAE = get_selection_INRAE(computer_data_path, 
+                                   INRAElistdir,
+                                   INRAElistname)
 
     # Get filenames of the selection
-    filename = df_selec_IN[df_selec_IN$ok,]$filename
+    filename = df_selec_INRAE[df_selec_INRAE$ok,]$filename
     # Extract metadata about selected stations
-    df_meta_IN = extract_meta(computer_data_path, filedir, filename)
+    df_meta_INRAE = extract_meta(computer_data_path, filedir, filename)
     # Extract data about selected stations
-    df_data_IN = extract_data(computer_data_path, filedir, filename)
+    df_data_INRAE = extract_data(computer_data_path, filedir, filename)
 } 
 
 ### 2.3. Manual selection 
-if (AGlistname == "" & INlistname == "") {
+if (AEAGlistname == "" & INRAElistname == "") {
     # Extract metadata about selected stations
-    df_meta_AG = extract_meta(computer_data_path, filedir, filename)
+    df_meta_AEAG = extract_meta(computer_data_path, filedir, filename)
     # Extract data about selected stations
-    df_data_AG = extract_data(computer_data_path, filedir, filename)
+    df_data_AEAG = extract_data(computer_data_path, filedir, filename)
 }
 
 ### 2.4. Data join
-df_join = join(df_data_AG, df_data_IN, df_meta_AG, df_meta_IN)
+df_join = join(df_data_AEAG, df_data_INRAE, df_meta_AEAG, df_meta_INRAE)
 df_data = df_join$data
 df_meta = df_join$meta
 
@@ -318,40 +322,40 @@ df_shapefile = ini_shapefile(resources_path,
 ### 4.2. Analysis layout 
 datasheet_layout(toplot=c(
                      # 'datasheet',
-                     # 'matrix',
-                     'map'
+                     'matrix'
+                     # 'map'
                  ),
                  df_meta=df_meta,
                  
                  df_data=list(
-                     # res_QAtrend$data,
-                     # res_QMNAtrend$data,
-                     # res_VCN10trend$data,
-                     # res_tDEBtrend$data,
+                     res_QAtrend$data,
+                     res_QMNAtrend$data,
+                     res_VCN10trend$data,
+                     res_tDEBtrend$data,
                      res_tCENtrend$data
                  ),
                  
                  df_trend=list(
-                     # res_QAtrend$trend,
-                     # res_QMNAtrend$trend,
-                     # res_VCN10trend$trend,
-                     # res_tDEBtrend$trend,
+                     res_QAtrend$trend,
+                     res_QMNAtrend$trend,
+                     res_VCN10trend$trend,
+                     res_tDEBtrend$trend,
                      res_tCENtrend$trend
                  ),
                  
                  var=list(
-                     # 'QA',
-                     # 'QMNA',
-                     # 'VCN10',
-                     # 'tDEB',
+                     'QA',
+                     'QMNA',
+                     'VCN10',
+                     'tDEB',
                      'tCEN'
                  ),
                  
                  type=list(
-                     # 'sévérité',
-                     # 'sévérité',
-                     # 'sévérité',
-                     # 'saisonnalité',
+                     'sévérité',
+                     'sévérité',
+                     'sévérité',
+                     'saisonnalité',
                      'saisonnalité'
                  ),
 
@@ -359,9 +363,9 @@ datasheet_layout(toplot=c(
                      "Moyenne annuelle du débit journalier",
                      "Minimum annuel de la moyenne menusuelle du débit journalier",
                      "Minimum annuel de la moyenne sur 10 jours du débit journalier",
-                     "Début d'étiage (jour de l'année de la première moyenne sur 10 sous le maximum des VCN10)",
+                     "Début d'étiage (jour de l'année de la première moyenne sur 10 jours sous le maximum des VCN10)",
                      "Centre d'étiage (jour de l'année du VCN10)"
-                 )
+                 ),
                  
                  layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),