diff --git a/plotting/datasheet.R b/plotting/datasheet.R
index d8ae947504f20c291ed74ff2dd188336b1e55160..1c34cf2aee01ec686e9b6676a46679158307711e 100644
--- a/plotting/datasheet.R
+++ b/plotting/datasheet.R
@@ -34,7 +34,7 @@ source('processing/analyse.R', encoding='UTF-8')
# Manages datasheets creations for all stations. Makes the call to
# the different headers, trend analysis graphs and realises arranging
# every plots.
-datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, time_header, foot_note, layout_matrix, info_ratio, time_ratio, var_ratio, foot_height, resources_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp) {
+datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, time_header, foot_note, layout_matrix, info_ratio, time_ratio, var_ratio, foot_height, resources_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, outdirTmp, df_page=NULL) {
# The percentage of augmentation and diminution of the min
# and max limits for y axis
@@ -432,12 +432,28 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
P[[i+nbh]] = p
}
- foot = foot_panel('fiche station', k, nCode, resources_path,
- AEAGlogo_file, INRAElogo_file,
- FRlogo_file, foot_height)
-
- P[[nbg]] = foot
-
+ if (!is.null(df_page)) {
+ section = 'fiche station'
+ subsection = code
+ n_page = df_page$n[nrow(df_page)] + 1
+ df_page = bind_rows(
+ df_page,
+ tibble(section=section,
+ subsection=subsection,
+ n=n_page))
+ }
+
+ if (foot_note) {
+ footName = 'fiche station'
+ if (is.null(df_page)) {
+ n_page = k
+ }
+
+ foot = foot_panel(footName, n_page, resources_path,
+ AEAGlogo_file, INRAElogo_file,
+ FRlogo_file, foot_height)
+ P[[nbg]] = foot
+ }
# Convert the 'layout_matrix' to a matrix if it is not already
layout_matrix = as.matrix(layout_matrix)
@@ -517,6 +533,7 @@ datasheet_panel = function (list_df2plot, df_meta, trend_period, info_header, ti
width=width, height=height, units='cm', dpi=100)
}
+ return (df_page)
}
diff --git a/plotting/layout.R b/plotting/layout.R
index a31cdd89c9e8e25e70a5d7593cebf45585a5b734..4fb5889e2246cfe15e884dbcf2eda2b8bfc26e44 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -242,35 +242,74 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
list_df2plot[[i]] = df2plot
}
- # If datasheets needs to be plot
- if ('datasheet' %in% toplot) {
-
- datasheet_panel(list_df2plot, df_meta, trend_period, info_header=info_header, time_header=time_header, foot_note=foot_note, layout_matrix=layout_matrix, info_ratio=info_ratio, time_ratio=time_ratio, var_ratio=var_ratio, foot_height=foot_height, resources_path=resources_path, AEAGlogo_file=AEAGlogo_file, INRAElogo_file=INRAElogo_file, FRlogo_file=FRlogo_file, outdirTmp=outdirTmp)
-
+ df_page = tibble(section='sommaire', subsection=NA, n=1)
+
+ # If map needs to be plot
+ if ('map' %in% toplot) {
+ df_page = map_panel(list_df2plot,
+ df_meta,
+ idPer_trend=length(trend_period),
+ mean_period=mean_period,
+ df_shapefile=df_shapefile,
+ foot_note=foot_note,
+ foot_height=foot_height,
+ resources_path=resources_path,
+ AEAGlogo_file=AEAGlogo_file,
+ INRAElogo_file=INRAElogo_file,
+ FRlogo_file=FRlogo_file,
+ outdirTmp=outdirTmp,
+ df_page=df_page)
}
# If summarize matrix needs to be plot
if ('matrix' %in% toplot) {
- matrix_panel(list_df2plot, df_meta, trend_period, mean_period,
- slice=19, outdirTmp=outdirTmp, A3=TRUE,
- foot_note=foot_note, foot_height=foot_height, resources_path=resources_path, AEAGlogo_file=AEAGlogo_file, INRAElogo_file=INRAElogo_file, FRlogo_file=FRlogo_file,)
+ df_page = matrix_panel(list_df2plot,
+ df_meta,
+ trend_period,
+ mean_period,
+ slice=19,
+ outdirTmp=outdirTmp,
+ A3=TRUE,
+ foot_note=foot_note,
+ foot_height=foot_height,
+ resources_path=resources_path,
+ AEAGlogo_file=AEAGlogo_file,
+ INRAElogo_file=INRAElogo_file,
+ FRlogo_file=FRlogo_file,
+ df_page=df_page)
}
- # If map needs to be plot
- if ('map' %in% toplot) {
- map_panel(list_df2plot,
- df_meta,
- idPer_trend=length(trend_period),
- mean_period=mean_period,
- df_shapefile=df_shapefile,
- foot_note=foot_note,
- foot_height=foot_height,
- resources_path=resources_path,
- AEAGlogo_file=AEAGlogo_file,
- INRAElogo_file=INRAElogo_file,
- FRlogo_file=FRlogo_file,
- outdirTmp=outdirTmp)
+ # If datasheets needs to be plot
+ if ('datasheet' %in% toplot) {
+ df_page = datasheet_panel(list_df2plot,
+ df_meta,
+ trend_period,
+ info_header=info_header,
+ time_header=time_header,
+ foot_note=foot_note,
+ layout_matrix=layout_matrix,
+ info_ratio=info_ratio,
+ time_ratio=time_ratio,
+ var_ratio=var_ratio,
+ foot_height=foot_height,
+ resources_path=resources_path,
+ AEAGlogo_file=AEAGlogo_file,
+ INRAElogo_file=INRAElogo_file,
+ FRlogo_file=FRlogo_file,
+ outdirTmp=outdirTmp,
+ df_page=df_page)
}
+
+ print(df_page)
+
+ summary_panel(df_page,
+ foot_note,
+ foot_height,
+ resources_path,
+ AEAGlogo_file,
+ INRAElogo_file,
+ FRlogo_file,
+ outdirTmp)
# Combine independant pages into one PDF
details = file.info(list.files(outdirTmp, full.names=TRUE))
@@ -444,11 +483,128 @@ 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 </b>",
+ sep='')
+
+ Sec_name = rle(df_page$section)$values
+ nSec = length(Sec_name)
+
+ text_sum = ''
+ for (idS in 1:nSec) {
+ sec_name = Sec_name[idS]
+ subSec_name = rle(df_page$subsection[df_page$section == sec_name])$values
+ n_page = df_page$n[df_page$section == sec_name][1]
+
+ text_sum = paste(text_sum,
+ idS, ". ", "<b>", sec_name, "</b>",
+ ' ', 'p.', n_page, "<br>",
+ sep='')
+
+ nSSec = length(subSec_name)
+ for (idSS in 1:nSSec) {
+ subsec_name = subSec_name[idSS]
+ if (!is.na(subsec_name)) {
+ n_page = df_page$n[df_page$section == sec_name &
+ df_page$subsection == subsec_name][1]
+
+ text_sum = paste(text_sum,
+ idS, ".", idSS, ". ", subsec_name,
+ ' ', 'p.', n_page, "<br>",
+ sep='')
+ }
+ }
+ }
+
+ text_sum = gsub('<ol>', '', text_sum)
+
+ text_sum = "1. <b>sommaire</b><br>2. <b>carte des tendances observées</b><br>2.1. QA p.2<br>2.2. QMNA p.3<br>2.3. VCN10 p.4<br>2.4. DEB p.5<br>2.5. CEN p.6<br>3. <b>carte des écarts observés</b><br>3.1. QA p.7<br>3.2. QMNA p.8<br>3.3. VCN10 p.9<br>3.4. DEB p.10<br>3.5. CEN p.11<br>4. <b>tableau récapitulatif de saisonnalité</b><br>4.1. Adour p.12<br>5. <b>tableau récapitulatif de sévérité</b><br>5.1. Adour p.13<br>6. <b>fiche station</b><br>6.1. Q7002910 p.14<br>"
+
+ print(text_title)
+ print(text_sum)
+
+ # text_sum = 'test<br>test<br><b>test</b>'
+
+ # Converts all texts to graphical object in the right position
+ gtitle = richtext_grob(text_title,
+ x=0, y=1,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="#00A3A8", fontsize=20))
+
+ gsum = richtext_grob(text_sum,
+ x=0, y=1,
+ margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
+ hjust=0, vjust=1,
+ gp=gpar(col="#00A3A8", fontsize=10))
+
+ # If there is a foot note
+ if (foot_note) {
+ footName = 'sommaire'
+ foot = foot_panel(footName,
+ 1, resources_path,
+ AEAGlogo_file, INRAElogo_file,
+ FRlogo_file, foot_height)
+
+ P = list(gtitle, gsum, foot)
+ LM = matrix(c(1,
+ 2,
+ 3),
+ nrow=3, byrow=TRUE)
+ } else {
+ foot_height = 0
+ P = list(gtitle, gsum)
+ LM = matrix(c(1,
+ 2),
+ nrow=2, byrow=TRUE)
+ }
+ id_foot = 2
+
+ LMcol = ncol(LM)
+ LMrow = nrow(LM)
+
+ LM = rbind(rep(99, times=LMcol), LM, rep(99, times=LMcol))
+ LMrow = nrow(LM)
+ LM = cbind(rep(99, times=LMrow), LM, rep(99, times=LMrow))
+ LMcol = ncol(LM)
+
+ margin_height = 0.5
+ height = 29.7
+ width = 21
+
+ row_height = (height - 2*margin_height - foot_height) / (LMrow - 3)
+
+ Hcut = LM[, 2]
+ heightLM = rep(row_height, times=LMrow)
+ heightLM[Hcut == id_foot] = foot_height
+ heightLM[Hcut == 99] = margin_height
+
+ col_width = (width - 2*margin_height) / (LMcol - 2)
+
+ Wcut = LM[(nrow(LM)-1),]
+ widthLM = rep(col_width, times=LMcol)
+ widthLM[Wcut == 99] = margin_height
+
+ # Arranges the graphical object
+ plot = grid.arrange(grobs=P, layout_matrix=LM,
+ heights=heightLM, widths=widthLM)
+
+ # Saves the plot
+ ggsave(plot=plot,
+ path=outdirTmp,
+ filename=paste('sommaire', '.pdf', sep=''),
+ width=width, height=height, units='cm', dpi=100)
+}
+
+
### Foot note panel
-foot_panel = function (name, n_page, N_page, resources_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, foot_height) {
+foot_panel = function (name, n_page, resources_path, AEAGlogo_file, INRAElogo_file, FRlogo_file, foot_height) {
text_page = paste(
- name, " <b>p. ", n_page, "/", N_page, "</b>",
+ name, " <b>p. ", n_page, "</b>",
sep='')
text_date = paste (
diff --git a/plotting/map.R b/plotting/map.R
index 5c4dcf9b556b4fd5dfa2a88b08f767f6c35e279f..5403fc0ae98ba8b75b9090865b2991143bee9dfe 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -34,7 +34,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
foot_note=FALSE,
foot_height=0, resources_path=NULL,
AEAGlogo_file=NULL, INRAElogo_file=NULL,
- FRlogo_file=NULL,
+ FRlogo_file=NULL, df_page=NULL,
verbose=TRUE) {
# Extract shapefiles
@@ -325,13 +325,20 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
outname = paste('map_', var, sep='')
}
- n_page = i + nbp*(j-1)
- N_page = nbp*nPeriod_mean
+ n_loop = i + nbp*(j-1)
+ N_loop = nbp*nPeriod_mean
# If there is the verbose option
if (verbose) {
+ if (j > 1) {
+ mapName = 'difference'
+ } else {
+ mapName = 'tendence'
+ }
# Prints the name of the map
- print(paste('Map for variable : ', var,
- " (", round(n_page/N_page*100, 0), " %)",
+ print(paste('Map of ', mapName, ' for : ', var,
+ " (",
+ round(n_loop/N_loop*100, 0),
+ " %)",
sep=''))
}
@@ -641,11 +648,11 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# If it is a flow variable
if (type == 'sévérité') {
# Formatting of label in pourcent
- labTick = as.character(round(labTick*100, 2))
+ labTick = as.character(signif(labTick*100, 2))
# If it is a date variable
} else if (type == 'saisonnalité') {
# Formatting of label
- labTick = as.character(round(labTick, 2))
+ labTick = as.character(signif(labTick, 2))
}
# X position of ticks all similar
@@ -686,7 +693,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
color='white', fill=colTick)
if (j > 1) {
- ValueName = "Écarts observées"
+ ValueName = "Écarts observés"
# If it is a flow variable
if (type == 'sévérité') {
unit = bquote(bold("(%)"))
@@ -794,7 +801,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
yValue = yValue[alpha_Ok]
# Histogram distribution
- # Computes the histogram of the trend
+ # Computes the histogram of values
res_hist = hist(yValue, breaks=ytick, plot=FALSE)
# Extracts the number of counts per cells
counts = res_hist$counts
@@ -808,15 +815,27 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
xValue = c()
yValue = c()
# Start X position of the distribution
- start_hist = 1.25
+ start_hist = 1
+
# X separation bewteen point
hist_sep = 0.15
+
+ # Gets the maximun number of point of the distribution
+ maxCount = max(counts, na.rm=TRUE)
+ # Limit of the histogram
+ lim_hist = 2
+ # If the number of point will exceed the limit
+ if (maxCount * hist_sep > lim_hist) {
+ # Computes the right amount of space between points
+ hist_sep = lim_hist / maxCount
+ }
+
# For all cells of the histogram
for (ii in 1:length(mids)) {
# If the count in the current cell is not zero
if (counts[ii] != 0) {
- # Stores the X positions of points of the distribution
- # for the current cell
+ # Stores the X positions of points of the
+ # distribution for the current cell
xValue = c(xValue,
seq(start_hist,
start_hist+(counts[ii]-1)*hist_sep,
@@ -827,16 +846,16 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
yValue = c(yValue, rep(mids[ii], times=counts[ii]))
}
- # Makes a tibble to plot the trend distribution
+ # Makes a tibble to plot the distribution
plot_value = tibble(xValue=xValue, yValue=yValue)
pal = pal +
- # Plots the point of the trend distribution
+ # Plots the point of the distribution
geom_point(data=plot_value,
aes(x=xValue, y=yValue),
- # shape=21, size=1,
- # color="grey20", fill="grey20")
- alpha=0.4)
+ shape=21, color='white',
+ fill='grey50', stroke=0.4,
+ alpha=1)
if (type == 'sévérité') {
labelArrow = 'Plus sévère'
@@ -844,6 +863,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
labelArrow = 'Plus tôt'
}
+ # Position of the arrow
xArrow = 3.2
pal = pal +
@@ -870,16 +890,35 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# Margin of the colorbar
theme(plot.margin=margin(t=0, r=0, b=0, l=0, unit="mm"))
- if (j > 1) {
- footName = 'carte des écarts observés'
- } else {
- footName = 'carte des tendances observées'
+ if (!is.null(df_page)) {
+ if (j > 1) {
+ section = 'carte des écarts observés'
+ } else {
+ section = 'carte des tendances observées'
+ }
+ subsection = var
+ n_page = df_page$n[nrow(df_page)] + 1
+ df_page = bind_rows(
+ df_page,
+ tibble(section=section,
+ subsection=subsection,
+ n=n_page))
}
# If there is a foot note
if (foot_note) {
+ if (j > 1) {
+ footName = 'carte des écarts observés'
+ } else {
+ footName = 'carte des tendances observées'
+ }
+
+ if (is.null(df_page)) {
+ n_page = n_loop
+ }
+
foot = foot_panel(footName,
- n_page, N_page, resources_path,
+ n_page, resources_path,
AEAGlogo_file, INRAElogo_file,
FRlogo_file, foot_height)
@@ -929,7 +968,8 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
heights=heightLM, widths=widthLM)
- # If there is no specified station code to highlight (mini map)
+ # If there is no specified station code to highlight
+ # (mini map)
if (is.null(codeLight)) {
# Saving matrix plot
ggsave(plot=plot,
@@ -939,7 +979,13 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
}
}
}
+ # If there is no specified station code to highlight
+ # (mini map)
+ if (is.null(codeLight)) {
+ return (df_page)
# Returns the map object
- return (map)
+ } else {
+ return (map)
+ }
}
diff --git a/plotting/matrix.R b/plotting/matrix.R
index 679482fd716badb97e441763ba6a5b43f6302403..65ce7c9e43f34c1f71e6198a6455b8044ca464c7 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -32,7 +32,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
foot_note=FALSE,
foot_height=0, resources_path=NULL,
AEAGlogo_file=NULL, INRAElogo_file=NULL,
- FRlogo_file=NULL) {
+ FRlogo_file=NULL, df_page=NULL) {
# Number of variable/plot
nbp = length(list_df2plot)
@@ -207,7 +207,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Var_trend = c()
Type_trend = c()
Code_trend = c()
- Pthresold_trend = c()
+ Alpha_trend = c()
TrendValue_trend = c()
DataMean_trend = c()
Fill_trend = c()
@@ -282,12 +282,12 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# table cells
fill = color_res
color = 'white'
- Pthresold = p_thresold
+ Alpha = TRUE
# Otherwise it is not significative
} else {
fill = 'white'
color = 'grey85'
- Pthresold = NA
+ Alpha = FALSE
}
# Stores info needed to plot
@@ -296,7 +296,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
Var_trend = append(Var_trend, var)
Type_trend = append(Type_trend, type)
Code_trend = append(Code_trend, code)
- Pthresold_trend = append(Pthresold_trend, Pthresold)
+ Alpha_trend = append(Alpha_trend, Alpha)
TrendValue_trend = append(TrendValue_trend, trendValue)
DataMean_trend = append(DataMean_trend, dataMean)
Fill_trend = append(Fill_trend, fill)
@@ -463,6 +463,33 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Gets all the different type of plots
Type = levels(factor(allType))
nbType = length(Type)
+
+ # Number of pages
+ N_loop = 0
+ # For all the type of plots
+ for (itype in 1:nbType) {
+ # Gets the type
+ type = Type[itype]
+ # Extracts each possibilities of first letter of station code
+ firstLetter = levels(factor(substr(Code, 1, 1)))
+ # Number of different first letters
+ nfL = length(firstLetter)
+ # For all the available first letter
+ for (ifL in 1:nfL) {
+ # Gets the first letter
+ fL = firstLetter[ifL]
+
+ # Get only station code with the same first letter
+ subCodefL = Code[substr(Code, 1, 1) == fL]
+ # Counts the number of station in it
+ nsubCodefL = length(subCodefL)
+ # Computes the number of pages needed to plot all stations
+ nMat = as.integer(nsubCodefL/slice) + 1
+ # Counts the number of pages
+ N_loop = N_loop + nMat
+ }
+ }
+
# For all the type of plots
for (itype in 1:nbType) {
# Gets the type
@@ -485,15 +512,15 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
nMat = as.integer(nsubCodefL/slice) + 1
# For all the pages
for (iMat in 1:nMat) {
- n_page = ifL + nfL*(itype-1)
- N_page = nfL*2
+ n_loop = ifL + nfL*(itype-1) + (iMat-1)
+ # N_loop = nfL*nbType
# Print the matrix name
print(paste('Matrix ', iMat, '/', nMat,
' of ', type,
' for region : ', fL,
" (",
- round(n_page / N_page * 100,
+ round(n_loop / N_loop * 100,
0),
" %)",
sep=''))
@@ -517,7 +544,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subVar_trend = Var_trend[CodefL_trend]
subType_trend = Type_trend[CodefL_trend]
subCode_trend = Code_trend[CodefL_trend]
- subPthresold_trend = Pthresold_trend[CodefL_trend]
+ subAlpha_trend = Alpha_trend[CodefL_trend]
subTrendValue_trend = TrendValue_trend[CodefL_trend]
subDataMean_trend = DataMean_trend[CodefL_trend]
subFill_trend = Fill_trend[CodefL_trend]
@@ -599,8 +626,8 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subType_trend[subNPeriod_trend == j]
Code_trend_per =
subCode_trend[subNPeriod_trend == j]
- Pthresold_trend_per =
- subPthresold_trend[subNPeriod_trend == j]
+ Alpha_trend_per =
+ subAlpha_trend[subNPeriod_trend == j]
TrendValue_trend_per =
subTrendValue_trend[subNPeriod_trend == j]
DataMean_trend_per =
@@ -696,7 +723,7 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
# If it is significative
- if (!is.na(Pthresold_trend_per[i])) {
+ if (Alpha_trend_per[i]) {
# The text color is white
Tcolor = 'white'
# Otherwise
@@ -1113,22 +1140,43 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
dpi = 100
}
+ if (!is.null(df_page)) {
+ section = paste('tableau récapitulatif de ',
+ type, sep='')
+ subsection = title
+ n_page = df_page$n[nrow(df_page)] + 1
+ df_page = bind_rows(
+ df_page,
+ tibble(section=section,
+ subsection=subsection,
+ n=n_page))
+ }
+
# If there is a foot note
if (foot_note) {
- foot = foot_panel('tableau récapitulatif',
- n_page, N_page,
+ footName = paste('tableau récapitulatif de ',
+ type, sep='')
+
+ if (is.null(df_page)) {
+ n_page = n_loop
+ }
+
+ foot = foot_panel(footName,
+ n_page,
resources_path,
AEAGlogo_file, INRAElogo_file,
FRlogo_file, foot_height)
- # Stores the map, the title and the colorbar in a list
+ # Stores the map, the title and the colorbar
+ # in a list
P = list(mat, foot)
LM = matrix(c(1,
2),
nrow=2, byrow=TRUE)
} else {
foot_height = 0
- # Stores the map, the title and the colorbar in a list
+ # Stores the map, the title and the colorbar
+ # in a list
P = list(mat)
LM = matrix(c(1),
nrow=1, byrow=TRUE)
@@ -1177,4 +1225,5 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
}
+ return (df_page)
}
diff --git a/processing/analyse.R b/processing/analyse.R
index 8bc6530a716b3358fb2109c3febbf6507f77d318..b0b841a7b6826cedd55d8a2822b0d22ffa72c5a9 100644
--- a/processing/analyse.R
+++ b/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, p_thresold) {
+get_QAtrend = function (df_data, df_meta, period, alpha) {
# Removes incomplete data from time series
df_data = remove_incomplete_data(df_data, df_meta,
@@ -125,7 +125,7 @@ get_QAtrend = function (df_data, df_meta, period, p_thresold) {
na.rm=TRUE)
# Compute the trend analysis
df_QAtrend = Estimate.stats(data.extract=df_QAEx,
- level=p_thresold)
+ level=alpha)
# Get the associated time interval
I = interval(per[1], per[2])
@@ -150,7 +150,7 @@ get_QAtrend = function (df_data, df_meta, period, p_thresold) {
### 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, p_thresold, sampleSpan) {
+get_QMNAtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
# Removes incomplete data from time series
df_data = remove_incomplete_data(df_data, df_meta,
@@ -192,7 +192,7 @@ get_QMNAtrend = function (df_data, df_meta, period, p_thresold, sampleSpan) {
na.rm=TRUE)
# Compute the trend analysis
df_QMNAtrend = Estimate.stats(data.extract=df_QMNAEx,
- level=p_thresold)
+ level=alpha)
# Get the associated time interval
I = interval(per[1], per[2])
@@ -219,7 +219,7 @@ get_QMNAtrend = function (df_data, df_meta, period, p_thresold, 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, p_thresold, sampleSpan) {
+get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan) {
# Removes incomplete data from time series
df_data = remove_incomplete_data(df_data, df_meta,
@@ -268,7 +268,7 @@ get_VCN10trend = function (df_data, df_meta, period, p_thresold, sampleSpan) {
na.rm=TRUE)
# Compute the trend analysis
df_VCN10trend = Estimate.stats(data.extract=df_VCN10Ex,
- level=p_thresold)
+ level=alpha)
# Get the associated time interval
I = interval(per[1], per[2])
@@ -327,7 +327,7 @@ which_underfirst = function (L, UpLim, select_longest=TRUE) {
return (id)
}
-get_DEBtrend = function (df_data, df_meta, period, p_thresold, sampleSpan, thresold_type='VCN10', select_longest=TRUE) {
+get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, thresold_type='VCN10', select_longest=TRUE) {
# Get all different stations code
Code = levels(factor(df_meta$code))
@@ -455,7 +455,7 @@ get_DEBtrend = function (df_data, df_meta, period, p_thresold, sampleSpan, thres
# Compute the trend analysis
df_DEBtrend = Estimate.stats(data.extract=df_DEBEx,
- level=p_thresold)
+ level=alpha)
# Get the associated time interval
I = interval(per[1], per[2])
@@ -481,7 +481,7 @@ get_DEBtrend = function (df_data, df_meta, period, p_thresold, sampleSpan, thres
### 1.5. CEN date
# Realises the trend analysis of the date of the minimum 10 day
# average flow over the year (VCN10) hydrological variable
-get_CENtrend = function (df_data, df_meta, period, p_thresold, sampleSpan) {
+get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan) {
# Get all different stations code
Code = levels(factor(df_meta$code))
@@ -533,7 +533,7 @@ get_CENtrend = function (df_data, df_meta, period, p_thresold, sampleSpan) {
# Compute the trend analysis
df_CENtrend = Estimate.stats(data.extract=df_CENEx,
- level=p_thresold)
+ level=alpha)
# Get the associated time interval
I = interval(per[1], per[2])
@@ -690,7 +690,7 @@ get_hydrograph = function (df_data, period=NULL, df_meta=NULL) {
### 2.2. Break date
# Compute the break date of the flow data by station
-get_break = function (df_data, df_meta, p_thresold=0.05) {
+get_break = function (df_data, df_meta, alpha=0.05) {
# Get all different stations code
Code = levels(factor(df_meta$code))
@@ -719,7 +719,7 @@ get_break = function (df_data, df_meta, p_thresold=0.05) {
ibreak = res_break$estimate
# If the p value results is under the thresold
- if (p_value <= p_thresold) {
+ if (p_value <= alpha) {
# Get the mean of the index break if there is several
ibreak = round(mean(ibreak), 0)
# Store the date break with its associated code
diff --git a/script.R b/script.R
index 99f6c6777004e9f8e9a236a3fd8dcc506bd831e3..f4550cdf600b9f2fa52f30ba3e8863af0e389d9a 100644
--- a/script.R
+++ b/script.R
@@ -55,19 +55,19 @@ 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"
# "O3035210_HYDRO_QJM.txt"
# "O0554010_HYDRO_QJM.txt",
# "O1584610_HYDRO_QJM.txt"
- # )
+ )
## AGENCE EAU ADOUR GARONNE SELECTION
@@ -77,8 +77,8 @@ AGlistdir =
""
AGlistname =
- # ""
- "Liste-station_RRSE.docx"
+ ""
+ # "Liste-station_RRSE.docx"
## NIVALE SELECTION
@@ -236,37 +236,37 @@ df_meta = get_lacune(df_data, df_meta)
df_meta = get_hydrograph(df_data, df_meta, period=mean_period[[1]])$meta
### 3.2. Trend analysis
-# # QA trend
-# res_QAtrend = get_QAtrend(df_data, df_meta,
-# period=trend_period,
-# alpha=alpha)
-
-# # QMNA tend
-# res_QMNAtrend = get_QMNAtrend(df_data, df_meta,
-# period=trend_period,
-# alpha=alpha,
-# sampleSpan=sampleSpan)
-
-# # VCN10 trend
-# res_VCN10trend = get_VCN10trend(df_data, df_meta,
-# period=trend_period,
-# alpha=alpha,
-# sampleSpan=sampleSpan)
-
-# # Start date for low water trend
-# res_DEBtrend = get_DEBtrend(df_data, df_meta,
-# period=trend_period,
-# alpha=alpha,
-# sampleSpan=sampleSpan,
-# thresold_type='VCN10',
-# select_longest=TRUE)
-# # res_DEBtrend = read_listofdf(resdir, 'res_DEBtrend')
-
-# # Center date for low water trend
-# res_CENtrend = get_CENtrend(df_data, df_meta,
-# period=trend_period,
-# alpha=alpha,
-# sampleSpan=sampleSpan)
+# QA trend
+res_QAtrend = get_QAtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha)
+
+# QMNA tend
+res_QMNAtrend = get_QMNAtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan)
+
+# VCN10 trend
+res_VCN10trend = get_VCN10trend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan)
+
+# Start date for low water trend
+res_DEBtrend = get_DEBtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan,
+ thresold_type='VCN10',
+ select_longest=TRUE)
+# res_DEBtrend = read_listofdf(resdir, 'res_DEBtrend')
+
+# Center date for low water trend
+res_CENtrend = get_CENtrend(df_data, df_meta,
+ period=trend_period,
+ alpha=alpha,
+ sampleSpan=sampleSpan)
### 3.3. Break analysis
# df_break = get_break(res_QAtrend$data, df_meta)
@@ -286,7 +286,7 @@ df_shapefile = ini_shapefile(computer_data_path,
fr_shpdir, fr_shpname,
bs_shpdir, bs_shpname,
sbs_shpdir, sbs_shpname,
- rv_shpdir, rv_shpname, riv=TRUE)
+ rv_shpdir, rv_shpname, riv=FALSE)
### 4.1. Simple time panel to criticize station data
# Plot time panel of debit by stations