diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index 56ecfb63ba9c28ec4dd6895e475f379e19aa9640..0cc1f5ede05c6c54ec2851818fea37e49db79ea8 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -429,7 +429,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
lim_pct=lim_pct)
# Stores the plot
- P[[i+nbh]] = p
+ P[[i+nbh]] = p
}
if (!is.null(df_page)) {
diff --git a/plotting/layout.R b/plotting/layout.R
index cb2437bb9983b85542d4f9e1710cdda1e6ab8523..e0c659253eb640c0340fc6ec3e253229620780ef 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -488,7 +488,7 @@ palette_tester = function (n=256) {
### Summary panel
summary_panel = function (df_page, foot_note, foot_height, resources_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp) {
-
+
text_title = paste(
"<b>Analyse de Stationnarité Hydrologique</b>",
sep='')
@@ -499,8 +499,6 @@ summary_panel = function (df_page, foot_note, foot_height, resources_path, AEAGl
Sec_name = rle(df_page$section)$values
nSec = length(Sec_name)
-
- nlim = 50
text_sum1 = ''
text_page1 = ''
@@ -508,7 +506,7 @@ summary_panel = function (df_page, foot_note, foot_height, resources_path, AEAGl
text_page2 = ''
nline = 0
- nline_max = 25
+ nline_max = 58
for (idS in 1:nSec) {
sec_name = Sec_name[idS]
subSec_name = rle(df_page$subsection[df_page$section == sec_name])$values
@@ -534,7 +532,7 @@ summary_panel = function (df_page, foot_note, foot_height, resources_path, AEAGl
n_page = df_page$n[df_page$section == sec_name &
df_page$subsection == subsec_name][1]
- line = paste(" ", idS, ".", idSS, ". ",
+ line = paste(idS, ".", idSS, ". ",
subsec_name, "<br>", sep='')
page = paste("p.", n_page, "<br>", sep='')
@@ -549,16 +547,23 @@ summary_panel = function (df_page, foot_note, foot_height, resources_path, AEAGl
nline = nline + 1
}
}
+ if (nline <= nline_max) {
+ text_sum1 = paste(text_sum1, "<br>", sep='')
+ text_page1 = paste(text_page1, "<br>", sep='')
+ } else {
+ text_sum2 = paste(text_sum2, "<br>", sep='')
+ text_page2 = paste(text_page2, "<br>", sep='')
+ }
+ nline = nline + 1
}
- # text_sum1 = gsub("é", "é", text_sum1)
- text_sum1 = gsub(" ", "<span style='color:white'>_</span>",
- text_sum1)
+
+ # text_sum1 = gsub(" ", "<span style='color:white'>_</span>",
+ # text_sum1)
text_sum1 = gsub('[.]', '.', text_sum1)
text_page1 = gsub('[.]', '.', text_page1)
- # text_sum2 = gsub("é", "é", text_sum2)
- text_sum2 = gsub(" ", "<span style='color:white'>_</span>",
- text_sum2)
+ # text_sum2 = gsub(" ", "<span style='color:white'>_</span>",
+ # text_sum2)
text_sum2 = gsub('[.]', '.', text_sum2)
text_page2 = gsub('[.]', '.', text_page2)
@@ -640,7 +645,7 @@ summary_panel = function (df_page, foot_note, foot_height, resources_path, AEAGl
title_height = 0.75
subtitle_height = 1.25
margin_size = 0.5
- page_width = 0.5
+ page_width = 2
height = 29.7
width = 21
diff --git a/plotting/map.R b/plotting/map.R
index 991e0c3354f7887d8f586f4c03359002ec95c990..d86006ad4c3b09575f021b5fca9b9764aaa8e2a1 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -629,8 +629,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# For the station to highlight
geom_point(data=plot_map_code,
aes(x=lon, y=lat),
- shape=21, size=1.5, stroke=0.5,
- color='#00A3A8', fill='#00A3A8')
+ shape=21, size=1.5, stroke=0.25,
+ color='grey97', fill='#00A3A8')
}
# Extracts the position of the tick of the colorbar
diff --git a/processing/analyse.R b/processing/analyse.R
index b0b841a7b6826cedd55d8a2822b0d22ffa72c5a9..6ee5525f3be62205b29a3c63b268581f0fb8eba6 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -97,11 +97,12 @@ 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) {
+get_QAtrend = function (df_data, df_meta, period, alpha, yearLac_day) {
# Removes incomplete data from time series
- df_data = remove_incomplete_data(df_data, df_meta,
- yearLac_pct=1, yearStart='01-01')
+ df_data = missing_data(df_data, df_meta,
+ yearLac_day=yearLac_day,
+ yearStart='01-01')
# Make sure to convert the period to a list
period = as.list(period)
@@ -150,11 +151,11 @@ get_QAtrend = function (df_data, df_meta, period, alpha) {
### 1.2. QMNA
# Realise the trend analysis of the monthly minimum flow in the
# year (QMNA) hydrological variable
-get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
+get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
# Removes incomplete data from time series
- df_data = remove_incomplete_data(df_data, df_meta,
- yearLac_pct=1, yearStart='01-01')
+ df_data = missing_data(df_data, df_meta,
+ yearLac_day=yearLac_day, yearStart='01-01')
# Samples the data
df_data = sampling_data(df_data, df_meta,
sampleSpan=sampleSpan)
@@ -219,11 +220,11 @@ get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
### 1.3. VCN10
# Realises the trend analysis of the minimum 10 day average flow
# over the year (VCN10) hydrological variable
-get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan) {
+get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
# Removes incomplete data from time series
- df_data = remove_incomplete_data(df_data, df_meta,
- yearLac_pct=1, yearStart='01-01')
+ df_data = missing_data(df_data, df_meta,
+ yearLac_day=yearLac_day, yearStart='01-01')
# Samples the data
df_data = sampling_data(df_data, df_meta,
@@ -327,7 +328,7 @@ which_underfirst = function (L, UpLim, select_longest=TRUE) {
return (id)
}
-get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, thresold_type='VCN10', select_longest=TRUE) {
+get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, thresold_type='VCN10', select_longest=TRUE) {
# Get all different stations code
Code = levels(factor(df_meta$code))
@@ -352,18 +353,18 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, thresold_t
}
# Removes incomplete data from time series
- df_data = remove_incomplete_data(df_data,
- df_meta=df_meta,
- yearLac_pct=1)
+ df_data = missing_data(df_data,
+ df_meta=df_meta,
+ yearLac_day=yearLac_day)
# Samples the data
df_data = sampling_data(df_data,
df_meta=df_meta,
sampleSpan=sampleSpan)
# Removes incomplete data from the averaged time series
- df_data_roll = remove_incomplete_data(df_data_roll,
- df_meta=df_meta,
- yearLac_pct=1)
+ df_data_roll = missing_data(df_data_roll,
+ df_meta=df_meta,
+ yearLac_day=yearLac_day)
# Samples the data
df_data_roll = sampling_data(df_data_roll,
df_meta=df_meta,
@@ -481,7 +482,7 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, thresold_t
### 1.5. CEN date
# Realises the trend analysis of the date of the minimum 10 day
# average flow over the year (VCN10) hydrological variable
-get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
+get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
# Get all different stations code
Code = levels(factor(df_meta$code))
@@ -503,9 +504,9 @@ get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
}
# Removes incomplete data from time series
- df_data_roll = remove_incomplete_data(df_data_roll, df_meta,
- yearLac_pct=1,
- yearStart='01-01')
+ df_data_roll = missing_data(df_data_roll, df_meta,
+ yearLac_day=yearLac_day,
+ yearStart='01-01')
# Samples the data
df_data_roll = sampling_data(df_data_roll, df_meta,
sampleSpan=sampleSpan)
@@ -523,10 +524,10 @@ get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
df_CENlist = prepare(df_data_roll, colnamegroup=c('code'))
# Compute the yearly min over the averaged data
df_CENEx = extract.Var(data.station=df_CENlist,
- funct=which.min,
- period=per,
- timestep='year',
- pos.datetime=1)
+ funct=which.min,
+ period=per,
+ timestep='year',
+ pos.datetime=1)
# Converts index of the CEN to the julian date associated
df_CENEx = prepare_date(df_CENEx, df_CENlist)
diff --git a/processing/format.R b/processing/format.R
index 731578bd173017a45b3233e1f306faa003f3e43b..316169a0f80cf0b853d61d704e8740ae87e6c7d1 100644
--- a/processing/format.R
+++ b/processing/format.R
@@ -31,6 +31,7 @@
# Usefull library
library(dplyr)
+library(Hmisc)
## 1. BEFORE TREND ANALYSE
@@ -80,8 +81,8 @@ join = function (df_data_AG, df_data_IN, df_meta_AG, df_meta_IN) {
return (list(data=df_data, meta=df_meta))
}
-### 1.2. Remove incomplete data
-remove_incomplete_data = function (df_data, df_meta, yearLac_pct=1, yearStart='01-01', Code=NULL) {
+### 1.2. Manages missing data
+missing_data = function (df_data, df_meta, yearLac_day=3, yearStart='01-01', Code=NULL) {
if (is.null(Code)) {
# Get all different stations code
@@ -129,9 +130,20 @@ remove_incomplete_data = function (df_data, df_meta, yearLac_pct=1, yearStart='0
yearLacMiss_pct = nbNA/nbDate * 100
- if (yearLacMiss_pct > yearLac_pct) {
+ if (nbNA > yearLac_day) {
df_data_code_year$Value = NA
df_data_code[OkYear,] = df_data_code_year
+
+ } else if (nbNA <= yearLac_day & nbNA > 1) {
+ DateJ = as.numeric(df_data_code_year$Date)
+ Value = df_data_code_year$Value
+
+ Value = approxExtrap(x=DateJ,
+ y=Value,
+ xout=DateJ,
+ method="linear",
+ na.rm=TRUE)$y
+ df_data_code$Value[OkYear] = Value
}
}
df_data[df_data$code == code,] = df_data_code
@@ -150,19 +162,23 @@ sampling_data = function (df_data, df_meta, sampleSpan=c('05-01', '11-30'), Code
} else {
nCode = length(Code)
}
-
- sampleStart = as.Date(paste('1970', sampleSpan[1], sep='-'))
- sampleEnd = as.Date(paste('1970', sampleSpan[2], sep='-'))
+
+ # 1972 is leap year reference is case of leap year comparison
+ sampleStart = as.Date(paste('1972', sampleSpan[1], sep='-'))
+ sampleEnd = as.Date(paste('1972', sampleSpan[2], sep='-'))
for (code in Code) {
# Extracts the data corresponding to the code
df_data_code = df_data[df_data$code == code,]
DateMD = substr(df_data_code$Date, 6, 10)
- Date = paste('1970', DateMD, sep='-')
+ Date = paste('1972', DateMD, sep='-')
df_data_code$Value[Date < sampleStart | Date > sampleEnd] = NA
+ # Leap year verification
+ # print(df_data_code[df_data_code$Date > as.Date("1992-02-25"),])
+
df_data[df_data$code == code,] = df_data_code
}
diff --git a/script.R b/script.R
index 6eb0fb9ea5d2aed0900c1a2e6ffd78f5bbea4257..a8ce827b0026981f29f7c456b3aff311f3d4b7de 100644
--- a/script.R
+++ b/script.R
@@ -55,19 +55,20 @@ filedir =
# Name of the file that will be analysed from the BH directory
# (if 'all', all the file of the directory will be chosen)
filename =
- # ""
- c(
+ ""
+ # c(
# "S2235610_HYDRO_QJM.txt",
# "P1712910_HYDRO_QJM.txt",
# "P0885010_HYDRO_QJM.txt",
# "O5055010_HYDRO_QJM.txt",
# "O0384010_HYDRO_QJM.txt",
# "S4214010_HYDRO_QJM.txt",
- "Q7002910_HYDRO_QJM.txt"
+ # "Q7002910_HYDRO_QJM.txt"
+ # "Q0214010_HYDRO_QJM.txt"
# "O3035210_HYDRO_QJM.txt"
# "O0554010_HYDRO_QJM.txt",
# "O1584610_HYDRO_QJM.txt"
- )
+ # )
## AGENCE EAU ADOUR GARONNE SELECTION
@@ -77,8 +78,8 @@ AGlistdir =
""
AGlistname =
- ""
- # "Liste-station_RRSE.docx"
+ # ""
+ "Liste-station_RRSE.docx"
## NIVALE SELECTION
@@ -105,6 +106,9 @@ mean_period = list(period1, period2)
# alpha the risk
alpha = 0.1
+# Number of missing days per year before remove the year
+yearLac_day = 3
+
# Sampling span of the data
sampleSpan = c('05-01', '11-30')
@@ -239,19 +243,22 @@ df_meta = get_hydrograph(df_data, df_meta, period=mean_period[[1]])$meta
# QA trend
res_QAtrend = get_QAtrend(df_data, df_meta,
period=trend_period,
- alpha=alpha)
+ alpha=alpha,
+ yearLac_day=yearLac_day)
# QMNA tend
res_QMNAtrend = get_QMNAtrend(df_data, df_meta,
period=trend_period,
alpha=alpha,
- sampleSpan=sampleSpan)
+ sampleSpan=sampleSpan,
+ yearLac_day=yearLac_day)
# VCN10 trend
res_VCN10trend = get_VCN10trend(df_data, df_meta,
period=trend_period,
alpha=alpha,
- sampleSpan=sampleSpan)
+ sampleSpan=sampleSpan,
+ yearLac_day=yearLac_day)
# Start date for low water trend
res_DEBtrend = get_DEBtrend(df_data, df_meta,
@@ -259,14 +266,16 @@ res_DEBtrend = get_DEBtrend(df_data, df_meta,
alpha=alpha,
sampleSpan=sampleSpan,
thresold_type='VCN10',
- select_longest=TRUE)
+ select_longest=TRUE,
+ yearLac_day=yearLac_day)
# res_DEBtrend = read_listofdf(resdir, 'res_DEBtrend')
# Center date for low water trend
res_CENtrend = get_CENtrend(df_data, df_meta,
period=trend_period,
alpha=alpha,
- sampleSpan=sampleSpan)
+ sampleSpan=sampleSpan,
+ yearLac_day=yearLac_day)
### 3.3. Break analysis
# df_break = get_break(res_QAtrend$data, df_meta)
@@ -286,7 +295,7 @@ df_shapefile = ini_shapefile(computer_data_path,
fr_shpdir, fr_shpname,
bs_shpdir, bs_shpname,
sbs_shpdir, sbs_shpname,
- rv_shpdir, rv_shpname, riv=FALSE)
+ rv_shpdir, rv_shpname, riv=TRUE)
### 4.1. Simple time panel to criticize station data
# Plot time panel of debit by stations
diff --git a/script_install.R b/script_install.R
index 762033e8f281c249e65199797f045d7bb31b418d..6f05ba5956e5ffb38e99716ab0d32e240fdecd2a 100644
--- a/script_install.R
+++ b/script_install.R
@@ -16,6 +16,7 @@ install.packages("RColorBrewer")
install.packages('trend')
install.packages("shadowtext")
install.packages("png")
+install.packages("Hmisc")
# linux