diff --git a/plotting/map.R b/plotting/map.R
index 7b62fa990c2f2bcc168005d0381649dfd7a7fadb..257bc7abacac12dda2b94cb99e22c2c6a5b13069 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -277,7 +277,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
# If there is a specified station code
} else {
# Same but with less graduation and smaller size
- xmin = gpct(5, xlim, shift=TRUE)
+ xmin = gpct(2, xlim, shift=TRUE)
xint = c(0, 100*1E3)
ymin = gpct(1, ylim, shift=TRUE)
ymax = ymin + gpct(3, ylim)
diff --git a/processing/analyse.R b/processing/analyse.R
index 6c429525f9d6cd670b973d4d9587714d1e732c9f..cda2470bdfec193ef759cccdfb51b0202d4da69b 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -327,13 +327,19 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
# Get the data associated to the code
df_data_roll_code = df_data_roll[df_data_roll$code == code,]
-
+ # print('aa')
+
# Get the data associated to the code
df_data_code = df_data[df_data$code == code,]
+ # print('bb')
+
# Prepare the data to fit the entry of extract.Var
df_QNAlist_code = prepare(df_data_code,
colnamegroup=c('code'))
+
+ # print('cc')
+
# Compute the yearly mean over the data
df_QNAEx_code = extract.Var(data.station=df_QNAlist_code,
@@ -344,18 +350,17 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
pos.datetime=1,
na.rm=TRUE)
- # print(code)
- # print(df_QNAEx_code)
+ # print('dd')
QNAmax = max(df_QNAEx_code$values, na.rm=TRUE)
- # print(QNAmax)
- # print(per.start)
-
-
+ # print('ee')
+
# Prepare the data to fit the entry of extract.Var
df_tINIlist_code = prepare(df_data_roll_code,
colnamegroup=c('code'))
+
+ # print('ff')
# Compute the yearly min over the averaged data
df_tINIEx_code = extract.Var(data.station=df_tINIlist_code,
@@ -366,28 +371,34 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
pos.datetime=1,
UpLim=QNAmax)
+ # print('gg')
+ # print(df_tINIEx_code)
+
+
df_tINIEx_code$group1 = k
df_tINIlist_code$data$group = k
df_tINIlist_code$info$group = k
+
+
# Converts index of the tINI to the julian date associated
df_tINIEx_code = prepare_date(df_tINIEx_code,
df_tINIlist_code,
per.start=per.start)
+
+ # print('hh')
# Store the results
- df_tINIEx = bind_rows(df_tINIEx, df_tINIEx_code)
+ df_tINIEx = bind_rows(df_tINIEx, df_tINIEx_code)
df_tINIlist$data = bind_rows(df_tINIlist$data,
df_tINIlist_code$data)
df_tINIlist$info = bind_rows(df_tINIlist$info,
df_tINIlist_code$info)
+
+ # print('ii')
}
-
- # print(df_tINIEx)
- # print(df_tINIlist)
-
# Compute the trend analysis
df_tINItrend = Estimate.stats(data.extract=df_tINIEx,
@@ -403,6 +414,8 @@ get_tINItrend = function (df_data, df_meta, period, p_thresold) {
df_tINIExB = df_tINIEx
}
+ print(per.start)
+
# Specify the period of analyse
df_tINItrend = get_period(per, df_tINItrend, df_tINIEx,
df_tINIlist)
@@ -601,7 +614,8 @@ get_hydrograph = function (df_data, period=NULL, df_meta=NULL) {
# Stores result of the hydrological regime
df_meta$regime_hydro[df_meta$code == code] = classRegime
# Computes the month of the max QM
- maxMonth = which.max(df_QM$QM)
+ maxMonth = which.max(QM_code)
+
# Stores it as the start of the hydrological year
df_meta$start_year[df_meta$code == code] = maxMonth
# Otherwise
diff --git a/processing/format.R b/processing/format.R
index 84c928fa63bded3f2d411c7b6e5e95aed7f3ceba..fde65227d705f8194503192f024b2373cee39d10 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -285,19 +285,24 @@ get_period = function (per, df_Xtrend, df_XEx, df_Xlist) {
# Computes index of the nearest accessible start and end date
OkStart = df_XExtmp_code$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$Date
- distStart = abs(df_XExtmp_code$Date[OkStart] - as.Date(per[1]))
- distEnd = abs(df_XExtmp_code$Date[OkEnd] - as.Date(per[2]))
-
- iStart = which.min(distStart)
- iEnd = which.min(distEnd)
+ iStart = which.min(abs(DateStart - as.Date(per[1])))
+ iEnd = which.min(abs(DateEnd - as.Date(per[2])))
+ print(nrow(df_XEx))
+ print(as.Date(DateStart[iStart]))
+ print(head(df_XEx))
+ print(tail(df_XEx))
+ print(as.Date(DateEnd[iEnd]))
# Stores the start and end of the trend analysis
- df_Xtrend$period_start[id] =
- as.Date(df_XExtmp_code$Date[iStart])
- df_Xtrend$period_end[id] =
- as.Date(df_XExtmp_code$Date[iEnd])
+ df_Xtrend$period_start[id] = as.Date(DateStart[iStart])
+ df_Xtrend$period_end[id] = as.Date(DateEnd[iEnd])
}
return (df_Xtrend)
}
diff --git a/script.R b/script.R
index f478638708fdb5a5e885fad611550ecef3cd2927..8a1f5805f75ea37376a92e0af7d6f743483bcd9a 100644
--- a/script.R
+++ b/script.R
@@ -62,14 +62,12 @@ filename =
# "P1712910_HYDRO_QJM.txt",
# "P0885010_HYDRO_QJM.txt",
# "O5055010_HYDRO_QJM.txt",
- # "O0384010_HYDRO_QJM.txt",
- # "S4214010_HYDRO_QJM.txt",
- "Q7002910_HYDRO_QJM.txt"
+ "O0384010_HYDRO_QJM.txt"
+ # "S4214010_HYDRO_QJM.txt"
+ # "Q7002910_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