summary table txt

plotting/matrix.R

@@ -838,6 +838,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period,
                             # significant figures
                             trendValueC = signif(trendValue, 2)
                         }
+                        trendValueC = as.character(trendValueC)
                         
                         # If it is significative
                         if (Alpha_trend_per[i] == 'TRUE') {
@@ -860,7 +861,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period,
                         mat = mat +
                             # Writes the mean trend
                             annotate('text', x=X[i], y=Y[i],
-                                     label=trendValueC,
+                                     label=bquote(bold(.(trendValueC))),
                                      hjust=0.5, vjust=0.5, 
                                      size=3, color=Tcolor) + 
                             # Writes the mean of the associated variable
@@ -1165,11 +1166,12 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period,
                                 # significant figures
                                 breakValueC = signif(breakValue, 2)
                             }
+                            breakValueC = as.character(breakValueC)
 
                             # Writes breaking values
                             mat = mat +
                                 annotate('text', x=Xr_mean[i], y=Y[i],
-                                         label=breakValueC,
+                                    label=bquote(bold(.(breakValueC))),
                                          hjust=0.5, vjust=0.5, 
                                          size=3, color='white')   
                         }

processing/analyse.R

@@ -97,7 +97,7 @@ get_intercept = function (df_Xtrend, df_Xlist, unit2day=365.25) {
 ### 1.1. QA __________________________________________________________
 # Realise the trend analysis of the average annual flow (QA)
 # hydrological variable
-get_QAtrend = function (df_data, df_meta, period, alpha, dayLac_lim, yearNA_lim, df_flag, df_mod=tibble()) {
+get_QAtrend = function (df_data, df_meta, period, per.start, alpha, dayLac_lim, yearNA_lim, df_flag, df_mod=tibble()) {
 
     # Local corrections if needed
     res = flag_data(df_data, df_meta,
@@ -132,8 +132,12 @@ get_QAtrend = function (df_data, df_meta, period, alpha, dayLac_lim, yearNA_lim,
                               funct=mean,
                               timestep='year',
                               period=per,
+                              per.start=per.start,
                               pos.datetime=1,
                               na.rm=TRUE)
+
+        # df_QAEx = date_correction(df_QAEx, per)
+        
         # Compute the trend analysis
         df_QAtrend = Estimate.stats(data.extract=df_QAEx,
                                     level=alpha,

processing/extract.R

@@ -136,7 +136,7 @@ iRegHydro = c('D'='Affluents du Rhin',
 
 
 ## 2. SELECTION ______________________________________________________
-### 2.1. Creation of selection _______________________________________
+### 2.1. Creation of a selection _____________________________________
 # Create a txt file that resume all the station data files present
 # in a filedir
 create_selection = function (computer_data_path, filedir, outname, optname='_HYDRO_QJM') {
@@ -178,11 +178,11 @@ create_selection = function (computer_data_path, filedir, outname, optname='_HYD
 # create_selection(
 #     "/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data",
 #     "France207",
-#     "INRAE_selection.txt")
+#     "selection.txt")
 
 ### 2.2. Agence de l'eau Adour-Garonne selection _____________________
 # Gets the selection of station from the 'Liste-station_RRSE.docx' file
-get_selection_AEAG = function (computer_data_path, listdir, listname,
+get_selection_DOCX = function (computer_data_path, listdir, listname,
                              cnames=c('code','station', 'BV_km2',
                                       'axe_principal_concerne',
                                       'longueur_serie', 'commentaires',
@@ -227,7 +227,7 @@ get_selection_AEAG = function (computer_data_path, listdir, listname,
     return (df_selec)
 }
 # Example
-# df_selec_AEAG = get_selection_AEAG(
+# df_selec_DOCX = get_selection_DOCX(
     # "/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data",
     # "",
     # "Liste-station_RRSE.docx",
@@ -240,10 +240,10 @@ get_selection_AEAG = function (computer_data_path, listdir, listname,
     # c_num=c('BV_km2',
             # 'longueur_serie'))
 
-### 2.3. INRAE selection _____________________________________________
+### 2.3. TXT selection _____________________________________________
 # Gets the selection of station from the selection txt file generated
 # by the 'create_selection' function
-get_selection_INRAE = function (computer_data_path, listdir, listname) {
+get_selection_TXT = function (computer_data_path, listdir, listname) {
     
     # Gets the file path to the data
     list_path = file.path(computer_data_path, listdir, listname)
@@ -259,10 +259,10 @@ get_selection_INRAE = function (computer_data_path, listdir, listname) {
     return (df_selec)
 }
 # Example
-# df_selec_INRAE = get_selection_INRAE(
+# df_selec_TXT = get_selection_TXT(
     # "/home/louis/Documents/bouleau/INRAE/CDD_stationnarite/data",
     # "",
-    # "INRAE_selection.txt")
+    # "selection.txt")
 
 
 ## 3. EXTRACTION _____________________________________________________

processing/format.R

@@ -306,6 +306,38 @@ sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code
 
 
 ## 2. DURING TREND ANALYSE ___________________________________________
+
+
+date_correction = function (df_XEx, per) {
+
+    # Takes the first date as example
+    exDate = df_XEx$datetime[1]
+    # Finds the number of dash in the date
+    nbt = lengths(regmatches(exDate, gregexpr('-', exDate)))
+
+    # If there is only one dash
+    if (nbt == 1) {
+        # Converts it to date from a year and a month
+        Date = paste(df_XEx$datetime, '01', sep='-')
+    # If there is no dash
+    } else if (nbt == 0) {
+        # Converts it to date from only a year
+        Date = paste(df_XEx$datetime, '01', '01', sep='-')
+    # If there is more than 2 dashes
+    } else if (nbt != 2) {
+        # This is not a classical date
+        stop('erreur of date format')
+    }
+
+    Start = per[1]
+    End = per[2]
+    
+    df_XEx = df_XEx[Date >= Start & Date <= End,]
+
+    return (df_XEx)
+}
+
+
 ### 2.1. Preparation _________________________________________________
 # Prepares the data in order to have a list of a data tibble with
 # date, group and flow column and a info tibble with the station code
@@ -452,7 +484,7 @@ prepare_date = function(df_XEx, df_Xlist, per.start="01-01") {
 ### 3.1. Period of trend _____________________________________________
 # Compute the start and the end of the period for a trend analysis
 # according to the accessible data 
-get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
+get_period = function (per, df_Xtrend, df_XEx, df_Xlist, per.start='01-01') {
 
     # Converts results of trend to tibble
     df_Xtrend = tibble(df_Xtrend)
@@ -461,38 +493,30 @@ get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
     df_Xtrend$period_start = as.Date("1970-01-01")
     df_Xtrend$period_end = as.Date("1970-01-01")
 
-    # Changes the format of the date variable to date
-    df_Xlisttmp = reprepare(df_XEx, df_Xlist, colnamegroup=c('code'))
-    df_XExtmp = df_Xlisttmp$data
+    # print(df_XEx)
+    # print(head(df_XEx))
+    # print(tail(df_XEx))
+    # print('')
 
+    df_data = df_Xlist$data
+    df_info = df_Xlist$info
+    
     # For all the different group
-    for (g in df_Xlisttmp$info$group) {
+    for (g in df_info$group) {
         # Gets the analyse data associated to the group
-        df_XExtmp_code = df_XExtmp[df_XExtmp$group == g,]
+        df_data_code = df_data[df_data$group == g,]
         # Gets the id in the trend result associated to the group
         id = which(df_Xtrend$group1 == g)
-
-        # Computes index of the nearest accessible start and end date
-        OkStart = df_XExtmp_code$Date >= as.Date(per[1])
-        OkEnd = df_XExtmp_code$Date <= as.Date(per[2])
-
-        # DateStart = df_XExtmp_code$Date[OkStart]
-        # DateEnd = df_XExtmp_code$Date[OkEnd]
-        DateStart = df_XExtmp_code$Date
-        DateEnd = df_XExtmp_code$Date
         
-        iStart = which.min(abs(DateStart - as.Date(per[1])))
-        iEnd = which.min(abs(DateEnd - as.Date(per[2])))
-
-        # print(nrow(df_XEx))
-        # print(as.Date(DateStart[iStart]))
-        # print(head(df_XEx))
-        # print(tail(df_XEx))
-        # print(as.Date(DateEnd[iEnd]))
+        Date = df_data_code$Date
+        Date = df_data_code$Date
+        
+        iStart = which.min(abs(Date - as.Date(per[1])))
+        iEnd = which.min(abs(Date - as.Date(per[2])))
 
         # Stores the start and end of the trend analysis
-        df_Xtrend$period_start[id] = as.Date(DateStart[iStart])
-        df_Xtrend$period_end[id] = as.Date(DateEnd[iEnd])
+        df_Xtrend$period_start[id] = as.Date(Date[iStart])
+        df_Xtrend$period_end[id] = as.Date(Date[iEnd])
     }
     return (df_Xtrend)
 }

script.R

@@ -66,8 +66,10 @@ filename =
         # "Q6332510_HYDRO_QJM.txt",
         # "Q7002910_HYDRO_QJM.txt",
         # "Q0214010_HYDRO_QJM.txt",
-        # "O3035210_HYDRO_QJM.txt",
-        # "O0554010_HYDRO_QJM.txt",
+        
+        # "O0362510_HYDRO_QJM.txt",
+        # "O0554010_HYDRO_QJM.txt"
+
         # "Q6332510_HYDRO_QJM.txt"
         # "O0362510_HYDRO_QJM.txt"
     # )
@@ -76,24 +78,25 @@ filename =
 ## 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
-AEAGlistdir = 
+DOCXlistdir = 
     ""
 
-AEAGlistname = 
+DOCXlistname = 
     # ""
     "Liste-station_RRSE.docx" 
 
 
-## NIVALE SELECTION
-# Path to the 'txt' list file of station from INRAE that will be analysed
+## MANUAL SELECTION
+# Path to the 'txt' list file of station from manualy created that
+# will be analysed
 # Generated with :
 # create_selection(computer_data_path, 'dirname', 'example.txt')
-INRAElistdir =
+TXTlistdir =
     ""
 
-INRAElistname = 
+TXTlistname = 
     ""
-    # "INRAE_selection.txt"
+    # "selection.txt"
 
 
 which_layout =
@@ -151,10 +154,10 @@ df_flag = data.frame(
 # Sampling span of the data
 sampleSpan = c('05-01', '11-30')
 
-# Is the hydrological network needs to be plot
+# If the hydrological network needs to be plot
 show_river = FALSE
 
-# If results and data used in the analysis needs to be written
+# If results and dresata used in the analysis needs to be written
 saving = FALSE
 
 ############### END OF REGION TO MODIFY (without risk) ###############
@@ -217,18 +220,18 @@ rv_shpname = 'CoursEau_FXX.shp'
 
 ## 2. SELECTION OF STATION ___________________________________________
 # Initialization of null data frame if there is no data selected
-df_data_AEAG = NULL
-df_data_INRAE = NULL
-df_meta_AEAG = NULL
-df_meta_INRAE = NULL
+df_data_DOCX = NULL
+df_data_TXT = NULL
+df_meta_DOCX = NULL
+df_meta_TXT = NULL
 
 ### 2.1. Selection of the Agence de l'eau Adour-Garonne ______________
-if (AEAGlistname != "") {
+if (DOCXlistname != "") {
     
     # Get only the selected station from a list station file
-    df_selec_AEAG = get_selection_AEAG(computer_data_path, 
-                             AEAGlistdir,
-                             AEAGlistname,
+    df_selec_DOCX = get_selection_DOCX(computer_data_path, 
+                             DOCXlistdir,
+                             DOCXlistname,
                              cnames=c('code',
                                       'station', 
                                       'BV_km2',
@@ -240,40 +243,40 @@ if (AEAGlistname != "") {
                                       'longueur_serie'))
     
     # Get filenames of the selection
-    filename = df_selec_AEAG[df_selec_AEAG$ok,]$filename 
+    filename = df_selec_DOCX[df_selec_DOCX$ok,]$filename 
     # Extract metadata about selected stations
-    df_meta_AEAG = extract_meta(computer_data_path, filedir, filename)
+    df_meta_DOCX = extract_meta(computer_data_path, filedir, filename)
     # Extract data about selected stations
-    df_data_AEAG = extract_data(computer_data_path, filedir, filename)
+    df_data_DOCX = extract_data(computer_data_path, filedir, filename)
 }
 
-### 2.2. INRAE selection _____________________________________________
-if (INRAElistname != ""){
+### 2.2. TXT selection _____________________________________________
+if (TXTlistname != ""){
     
     # Get only the selected station from a list station file
-    df_selec_INRAE = get_selection_INRAE(computer_data_path, 
-                                   INRAElistdir,
-                                   INRAElistname)
+    df_selec_TXT = get_selection_TXT(computer_data_path, 
+                                   TXTlistdir,
+                                   TXTlistname)
 
     # Get filenames of the selection
-    filename = df_selec_INRAE[df_selec_INRAE$ok,]$filename
+    filename = df_selec_TXT[df_selec_TXT$ok,]$filename
     # Extract metadata about selected stations
-    df_meta_INRAE = extract_meta(computer_data_path, filedir, filename)
+    df_meta_TXT = extract_meta(computer_data_path, filedir, filename)
     # Extract data about selected stations
-    df_data_INRAE = extract_data(computer_data_path, filedir, filename)
+    df_data_TXT = extract_data(computer_data_path, filedir, filename)
 } 
 
 ### 2.3. Manual selection ____________________________________________
-if (AEAGlistname == "" & INRAElistname == "") {
+if (DOCXlistname == "" & TXTlistname == "") {
     # Extract metadata about selected stations
-    df_meta_AEAG = extract_meta(computer_data_path, filedir, filename)
+    df_meta_DOCX = extract_meta(computer_data_path, filedir, filename)
     # Extract data about selected stations
-    df_data_AEAG = extract_data(computer_data_path, filedir, filename)
+    df_data_DOCX = extract_data(computer_data_path, filedir, filename)
 }
 
 ### 2.4. Data join ___________________________________________________
-df_join = join_selection(df_data_AEAG, df_data_INRAE,
-                         df_meta_AEAG, df_meta_INRAE)
+df_join = join_selection(df_data_DOCX, df_data_TXT,
+                         df_meta_DOCX, df_meta_TXT)
 df_data = df_join$data
 df_meta = df_join$meta
 
@@ -310,68 +313,69 @@ df_meta = get_hydrograph(df_data, df_meta, period=mean_period[[1]])$meta
 df_sqrt = compute_sqrt(df_data)
 
 ### 3.2. Trend analysis ______________________________________________
-# if ('analyse' %in% which_layout) {
-#     # QA trend
-#     res = get_QAtrend(df_data, df_meta,
-#                       period=trend_period,
-#                       alpha=alpha,
-#                       dayLac_lim=dayLac_lim,
-#                       yearNA_lim=yearNA_lim,
-#                       df_flag=df_flag)
-#     df_QAdata = res$data
-#     df_QAmod = res$mod
-#     res_QAtrend = res$analyse
-
-#     # QMNA tend
-#     res = get_QMNAtrend(df_data, df_meta,
-#                         period=trend_period,
-#                         alpha=alpha,
-#                         sampleSpan=sampleSpan,
-#                         dayLac_lim=dayLac_lim,
-#                         yearNA_lim=yearNA_lim,
-#                         df_flag=df_flag)
-#     df_QMNAdata = res$data
-#     df_QMNAmod = res$mod
-#     res_QMNAtrend = res$analyse
-
-#     # VCN10 trend
-#     res = get_VCN10trend(df_data, df_meta,
-#                          period=trend_period,
-#                          alpha=alpha,
-#                          sampleSpan=sampleSpan,
-#                          dayLac_lim=dayLac_lim,
-#                          yearNA_lim=yearNA_lim,
-#                          df_flag=df_flag)
-#     df_VCN10data = res$data
-#     df_VCN10mod = res$mod
-#     res_VCN10trend = res$analyse
-
-#     # Start date for low water trend
-#     res = get_tDEBtrend(df_data, df_meta, 
-#                         period=trend_period,
-#                         alpha=alpha,
-#                         sampleSpan=sampleSpan,
-#                         thresold_type='VCN10',
-#                         select_longest=TRUE,
-#                         dayLac_lim=dayLac_lim,
-#                         yearNA_lim=yearNA_lim,
-#                         df_flag=df_flag)
-#     df_tDEBdata = res$data
-#     df_tDEBmod = res$mod
-#     res_tDEBtrend = res$analyse
-
-#     # Center date for low water trend
-#     res = get_tCENtrend(df_data, df_meta, 
-#                         period=trend_period,
-#                         alpha=alpha,
-#                         sampleSpan=sampleSpan,
-#                         dayLac_lim=dayLac_lim,
-#                         yearNA_lim=yearNA_lim,
-#                         df_flag=df_flag)
-#     df_tCENdata = res$data
-#     df_tCENmod = res$mod
-#     res_tCENtrend = res$analyse
-# }
+if ('analyse' %in% which_layout) {
+    # QA trend
+    res = get_QAtrend(df_data, df_meta,
+                      period=trend_period,
+                      per.start='09-01',
+                      alpha=alpha,
+                      dayLac_lim=dayLac_lim,
+                      yearNA_lim=yearNA_lim,
+                      df_flag=df_flag)
+    df_QAdata = res$data
+    df_QAmod = res$mod
+    res_QAtrend = res$analyse
+
+    # QMNA tend
+    res = get_QMNAtrend(df_data, df_meta,
+                        period=trend_period,
+                        alpha=alpha,
+                        sampleSpan=sampleSpan,
+                        dayLac_lim=dayLac_lim,
+                        yearNA_lim=yearNA_lim,
+                        df_flag=df_flag)
+    df_QMNAdata = res$data
+    df_QMNAmod = res$mod
+    res_QMNAtrend = res$analyse
+
+    # VCN10 trend
+    res = get_VCN10trend(df_data, df_meta,
+                         period=trend_period,
+                         alpha=alpha,
+                         sampleSpan=sampleSpan,
+                         dayLac_lim=dayLac_lim,
+                         yearNA_lim=yearNA_lim,
+                         df_flag=df_flag)
+    df_VCN10data = res$data
+    df_VCN10mod = res$mod
+    res_VCN10trend = res$analyse
+
+    # Start date for low water trend
+    res = get_tDEBtrend(df_data, df_meta, 
+                        period=trend_period,
+                        alpha=alpha,
+                        sampleSpan=sampleSpan,
+                        thresold_type='VCN10',
+                        select_longest=TRUE,
+                        dayLac_lim=dayLac_lim,
+                        yearNA_lim=yearNA_lim,
+                        df_flag=df_flag)
+    df_tDEBdata = res$data
+    df_tDEBmod = res$mod
+    res_tDEBtrend = res$analyse
+
+    # Center date for low water trend
+    res = get_tCENtrend(df_data, df_meta, 
+                        period=trend_period,
+                        alpha=alpha,
+                        sampleSpan=sampleSpan,
+                        dayLac_lim=dayLac_lim,
+                        yearNA_lim=yearNA_lim,
+                        df_flag=df_flag)
+    df_tCENdata = res$data
+    df_tCENmod = res$mod
+    res_tCENtrend = res$analyse
+}
 
 ### 3.3. Break analysis ______________________________________________
 # df_break = get_break(res_QAtrend$data, df_meta)