diff --git a/plotting/layout.R b/plotting/layout.R
index e0c659253eb640c0340fc6ec3e253229620780ef..ec53ea93d97ba5f8ac6167fb4a6e59bc2c002885 100644
--- a/plotting/layout.R
+++ b/plotting/layout.R
@@ -143,7 +143,7 @@ datasheet_layout = function (df_data, df_meta, layout_matrix,
missRect=FALSE, time_header=NULL,
info_header=TRUE, foot_note=FALSE,
info_ratio=1, time_ratio=2,
- var_ratio=3, foot_height=0.5,
+ var_ratio=3, foot_height=1,
df_shapefile=NULL,
resources_path=NULL,
AEAGlogo_file=NULL,
@@ -532,7 +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("<b>", idS, ".", idSS, ".</b> ",
subsec_name, "<br>", sep='')
page = paste("p.", n_page, "<br>", sep='')
@@ -696,7 +696,7 @@ foot_panel = function (name, n_page, resources_path, AEAGlogo_file, INRAElogo_fi
gp=gpar(col="#00A3A8", fontsize=8))
gtext_date = richtext_grob(text_date,
- x=1, y=0.4,
+ x=1, y=0.55,
margin=unit(c(t=0, r=0, b=0, l=0), "mm"),
hjust=1, vjust=0.5,
gp=gpar(col="#00A3A8", fontsize=6))
@@ -707,13 +707,15 @@ foot_panel = function (name, n_page, resources_path, AEAGlogo_file, INRAElogo_fi
AEAGlogo_img = readPNG(AEAGlogo_path)
AEAGlogo_grob = rasterGrob(AEAGlogo_img,
+ y=0.49,
+ vjust=0.5,
width=unit(0.7*foot_height, "cm"))
INRAElogo_img = readPNG(INRAElogo_path)
INRAElogo_grob = rasterGrob(INRAElogo_img,
- y=0.57,
+ y=0.565,
vjust=0.5,
- width=unit(1.1*foot_height, "cm"))
+ width=unit(1.08*foot_height, "cm"))
FRlogo_img = readPNG(FRlogo_path)
FRlogo_grob = rasterGrob(FRlogo_img,
@@ -722,7 +724,7 @@ foot_panel = function (name, n_page, resources_path, AEAGlogo_file, INRAElogo_fi
P = list(void,
FRlogo_grob, INRAElogo_grob, AEAGlogo_grob,
- gtext_page, gtext_date)
+ gtext_page, gtext_date)
# Creates the matrix layout
LM = matrix(c(1, 2, 3, 4, 5,
@@ -732,7 +734,7 @@ foot_panel = function (name, n_page, resources_path, AEAGlogo_file, INRAElogo_fi
# And sets the relative width of each plot
widths = rep(1, times=ncol(LM))
- widths[2] = 0.18
+ widths[2] = 0.2
widths[3] = 0.25
widths[4] = 0.2
diff --git a/plotting/map.R b/plotting/map.R
index d86006ad4c3b09575f021b5fca9b9764aaa8e2a1..5f6ede5dfda8aa7536e6d8852a7cba87431aae36 100644
--- a/plotting/map.R
+++ b/plotting/map.R
@@ -200,10 +200,6 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
minTrendValue = apply(TrendValue_code, c(1, 2), min, na.rm=TRUE)
maxTrendValue = apply(TrendValue_code, c(1, 2), max, na.rm=TRUE)
-
-
-
-
# If there is a 'mean_period'
if (!is.null(mean_period)) {
# Blank vectors to store info about breaking analysis
@@ -362,8 +358,15 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
# Open a new plot with the personalise theme
map = ggplot() + theme_void() +
- # theme(plot.background=element_rect(fill=NA,
- # color="#EC4899")) +
+
+ # theme(
+ # # Ticks marker
+ # axis.ticks.x=element_line(color='grey75', size=0.3),
+ # axis.ticks.y=element_line(color='grey75', size=0.3),
+ # # Ticks label
+ # axis.text.x=element_text(color='grey40'),
+ # axis.text.y=element_text(color='grey40')) +
+
# Fixed coordinate system (remove useless warning)
cf +
# Plot the background of France
@@ -393,6 +396,29 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
aes(x=long, y=lat, group=group),
color="grey40", fill=NA, size=sizefr)
+ if (is.null(codeLight)) {
+ xBasin = c(410000, 520000, 630000,
+ 620000, 510000, 450000,
+ 390000)
+ yBasin = c(6280000, 6290000, 6320000,
+ 6385000, 6450000, 6530000,
+ 6360000)
+ nameBasin = c('Adour', 'Garonne', 'Tarn-Aveyron',
+ 'Lot', 'Dordogne', 'Charente',
+ 'Fleuves-\nCôtiers')
+ nBasin = length(xBasin)
+
+ plot_basin = tibble(x=xBasin, y=yBasin, label=nameBasin)
+
+ map = map +
+ geom_shadowtext(data=plot_basin,
+ aes(x=x, y=y, label=label),
+ fontface="bold",
+ color="grey85",
+ bg.colour="grey97",
+ hjust=0.5, vjust=0.5, size=5)
+ }
+
# If the sea needs to be shown
if (showSea) {
# Leaves space around the France
@@ -478,7 +504,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
fill = c()
shape = c()
Value = c()
- alpha_Ok = c()
+ OkVal = c()
# For all code
for (k in 1:nCode) {
# Gets the code
@@ -560,27 +586,35 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
ncolor=256,
nbTick=nbTick)
- # If it is significative
- if (pvalue <= alpha){
+ if (j > 1) {
# The computed color is stored
filltmp = color_res
- # If the mean tend is positive
- if (value >= 0) {
- # Uses a triangle up for the shape
- # of the marker
- shapetmp = 24
- # If negative
+ # The marker is a circle
+ shapetmp = 21
+
+ } else {
+ # If it is significative
+ if (pvalue <= alpha){
+ # The computed color is stored
+ filltmp = color_res
+ # If the mean tend is positive
+ if (value >= 0) {
+ # Uses a triangle up for the shape
+ # of the marker
+ shapetmp = 24
+ # If negative
+ } else {
+ # Uses a triangle down for the shape
+ # of the marker
+ shapetmp = 25
+ }
+ # If it is not significative
} else {
- # Uses a triangle down for the shape
- # of the marker
- shapetmp = 25
+ # The fill color is grey
+ filltmp = 'grey97'
+ # The marker is a circle
+ shapetmp = 21
}
- # If it is not significative
- } else {
- # The fill color is grey
- filltmp = 'grey97'
- # The marker is a circle
- shapetmp = 21
}
# Extracts the localisation of the current station
@@ -596,23 +630,39 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
shape = c(shape, shapetmp)
Value = c(Value, value)
# If the trend analysis is significative a TRUE is stored
- alpha_Ok = c(alpha_Ok,
- pvalue <= alpha)
+ OkVal = c(OkVal, pvalue <= alpha)
}
# Creates a tibble to stores all the data to plot
plot_map = tibble(lon=lon, lat=lat, fill=fill,
- shape=shape, code=Code)
+ shape=shape, code=Code, OkVal=OkVal)
# If there is no specified station code to highlight
# (mini map)
if (is.null(codeLight)) {
- map = map +
- # Plots the trend point
- geom_point(data=plot_map,
- aes(x=lon, y=lat),
- shape=shape, size=5, stroke=1,
- color='grey50', fill=fill)
- # If there is a specified station code
+
+ plot_map_NOk = plot_map[!plot_map$OkVal,]
+ plot_map_Ok = plot_map[plot_map$OkVal,]
+
+ if (nrow(plot_map_NOk) > 0) {
+ map = map +
+ # Plots the point that are not
+ # significant first
+ geom_point(data=plot_map_NOk,
+ aes(x=lon, y=lat),
+ shape=shape[!OkVal],
+ size=5, stroke=1,
+ color='grey50', fill=fill[!OkVal])
+ }
+ if (nrow(plot_map_Ok) > 0) {
+ map = map +
+ # Plots the point that are significant last
+ geom_point(data=plot_map_Ok,
+ aes(x=lon, y=lat),
+ shape=shape[OkVal], size=5, stroke=1,
+ color='grey50', fill=fill[OkVal])
+ }
+
+ # If there is a specified station code
} else {
# Extract data of all stations not to highlight
plot_map_codeNo = plot_map[plot_map$code != codeLight,]
@@ -629,7 +679,7 @@ 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=0.25,
+ shape=21, size=2, stroke=0.5,
color='grey97', fill='#00A3A8')
}
@@ -736,34 +786,27 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
size=3, color='grey40')
}
- yUp = -20
- yNone = -29
-
- if (j > 1) {
- upLabel = bquote(bold("Hausse"))
- noneLabel = NULL
- downLabel = bquote(bold("Baisse"))
- yDown = -29
- } else {
+ if (j == 1) {
upLabel = bquote(bold("Hausse significative à 10%"))
noneLabel = bquote(bold("Non significatif à 10%"))
downLabel = bquote(bold("Baisse significative à 10%"))
+
+ yUp = -20
+ yNone = -29
yDown = -40
- }
- pal = pal +
- # Up triangle in the marker legend
- geom_point(aes(x=0, y=yUp),
- shape=24, size=4, stroke=1,
- color='grey50', fill='grey97') +
- # Up triangle text legend
- annotate('text',
- x=0.3, y=yUp,
- label=upLabel,
- hjust=0, vjust=0.5,
- size=3, color='grey40')
+ pal = pal +
+ # Up triangle in the marker legend
+ geom_point(aes(x=0, y=yUp),
+ shape=24, size=4, stroke=1,
+ color='grey50', fill='grey97') +
+ # Up triangle text legend
+ annotate('text',
+ x=0.3, y=yUp,
+ label=upLabel,
+ hjust=0, vjust=0.5,
+ size=3, color='grey40')
- if (!is.null(noneLabel)) {
pal = pal +
# Circle in the marker legend
geom_point(aes(x=0, y=yNone),
@@ -775,20 +818,21 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
label=noneLabel,
hjust=0, vjust=0.7,
size=3, color='grey40')
+
+ pal = pal +
+ # Down triangle in the marker legend
+ geom_point(aes(x=0, y=yDown),
+ shape=25, size=4, stroke=1,
+ color='grey50', fill='grey97') +
+ # Down triangle text legend
+ annotate('text',
+ x=0.3, y=yDown,
+ label=downLabel,
+ hjust=0, vjust=0.5,
+ size=3, color='grey40')
+
}
- pal = pal +
- # Down triangle in the marker legend
- geom_point(aes(x=0, y=yDown),
- shape=25, size=4, stroke=1,
- color='grey50', fill='grey97') +
- # Down triangle text legend
- annotate('text',
- x=0.3, y=yDown,
- label=downLabel,
- hjust=0, vjust=0.5,
- size=3, color='grey40')
-
# Normalises all the trend values for each station
# according to the colorbar
if (j > 1) {
@@ -798,7 +842,7 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer_trend=1,
}
# Takes only the significative ones
- yValue = yValue[alpha_Ok]
+ yValue = yValue[OkVal]
# Histogram distribution
# Computes the histogram of values
diff --git a/processing/analyse.R b/processing/analyse.R
index 6ee5525f3be62205b29a3c63b268581f0fb8eba6..267b891d953ac72eb36d511ca33d7028a95623ca 100644
--- a/processing/analyse.R
+++ b/processing/analyse.R
@@ -292,7 +292,7 @@ get_VCN10trend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_
return (res_VCN10trend)
}
-### 1.4. DEB date
+### 1.4. tDEB date
which_underfirst = function (L, UpLim, select_longest=TRUE) {
ID = which(L <= UpLim)
@@ -328,7 +328,7 @@ which_underfirst = function (L, UpLim, select_longest=TRUE) {
return (id)
}
-get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day, thresold_type='VCN10', select_longest=TRUE) {
+get_tDEBtrend = 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$code))
@@ -375,7 +375,7 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
# Set the max interval period as the minimal possible
Imax = 0
# Blank tibble for data to return
- df_DEBtrendB = tibble()
+ df_tDEBtrendB = tibble()
# For all periods
for (per in period) {
@@ -408,8 +408,8 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
# print(df_QT)
- df_DEBEx = tibble()
- df_DEBlist = list(data=tibble(), info=tibble())
+ df_tDEBEx = tibble()
+ df_tDEBlist = list(data=tibble(), info=tibble())
# For all the code
for (k in 1:nCode) {
@@ -422,13 +422,13 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
df_data_roll_code = df_data_roll[df_data_roll$code == code,]
# Prepare the data to fit the entry of extract.Var
- df_DEBlist_code = prepare(df_data_roll_code,
+ df_tDEBlist_code = prepare(df_data_roll_code,
colnamegroup=c('code'))
QT_code = df_QT$Thresold[df_QT$code == code]
# Compute the yearly min over the averaged data
- df_DEBEx_code = extract.Var(data.station=df_DEBlist_code,
+ df_tDEBEx_code = extract.Var(data.station=df_tDEBlist_code,
funct=which_underfirst,
period=per,
timestep='year',
@@ -436,26 +436,26 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
UpLim=QT_code,
select_longest=select_longest)
- df_DEBEx_code$group1 = k
- df_DEBlist_code$data$group = k
- df_DEBlist_code$info$group = k
+ df_tDEBEx_code$group1 = k
+ df_tDEBlist_code$data$group = k
+ df_tDEBlist_code$info$group = k
- # Converts index of the DEB to the julian date associated
- df_DEBEx_code = prepare_date(df_DEBEx_code,
- df_DEBlist_code)
+ # Converts index of the tDEB to the julian date associated
+ df_tDEBEx_code = prepare_date(df_tDEBEx_code,
+ df_tDEBlist_code)
# Store the results
- df_DEBEx = bind_rows(df_DEBEx, df_DEBEx_code)
+ df_tDEBEx = bind_rows(df_tDEBEx, df_tDEBEx_code)
- df_DEBlist$data = bind_rows(df_DEBlist$data,
- df_DEBlist_code$data)
+ df_tDEBlist$data = bind_rows(df_tDEBlist$data,
+ df_tDEBlist_code$data)
- df_DEBlist$info = bind_rows(df_DEBlist$info,
- df_DEBlist_code$info)
+ df_tDEBlist$info = bind_rows(df_tDEBlist$info,
+ df_tDEBlist_code$info)
}
# Compute the trend analysis
- df_DEBtrend = Estimate.stats(data.extract=df_DEBEx,
+ df_tDEBtrend = Estimate.stats(data.extract=df_tDEBEx,
level=alpha)
# Get the associated time interval
@@ -464,25 +464,25 @@ get_DEBtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
if (I > Imax) {
# Store it and the associated data and info
Imax = I
- df_DEBlistB = df_DEBlist
- df_DEBExB = df_DEBEx
+ df_tDEBlistB = df_tDEBlist
+ df_tDEBExB = df_tDEBEx
}
# Specify the period of analyse
- df_DEBtrend = get_period(per, df_DEBtrend, df_DEBEx,
- df_DEBlist)
+ df_tDEBtrend = get_period(per, df_tDEBtrend, df_tDEBEx,
+ df_tDEBlist)
# Store the trend
- df_DEBtrendB = bind_rows(df_DEBtrendB, df_DEBtrend)
+ df_tDEBtrendB = bind_rows(df_tDEBtrendB, df_tDEBtrend)
}
# Clean results of trend analyse
- res_DEBtrend = clean(df_DEBtrendB, df_DEBExB, df_DEBlistB)
- return (res_DEBtrend)
+ res_tDEBtrend = clean(df_tDEBtrendB, df_tDEBExB, df_tDEBlistB)
+ return (res_tDEBtrend)
}
-### 1.5. CEN date
+### 1.5. tCEN 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, yearLac_day) {
+get_tCENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_day) {
# Get all different stations code
Code = levels(factor(df_meta$code))
@@ -516,24 +516,24 @@ get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
# Set the max interval period as the minimal possible
Imax = 0
# Blank tibble for data to return
- df_CENtrendB = tibble()
+ df_tCENtrendB = tibble()
# For all periods
for (per in period) {
# Prepare the data to fit the entry of extract.Var
- df_CENlist = prepare(df_data_roll, colnamegroup=c('code'))
+ df_tCENlist = prepare(df_data_roll, colnamegroup=c('code'))
# Compute the yearly min over the averaged data
- df_CENEx = extract.Var(data.station=df_CENlist,
+ df_tCENEx = extract.Var(data.station=df_tCENlist,
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)
+ # Converts index of the tCEN to the julian date associated
+ df_tCENEx = prepare_date(df_tCENEx, df_tCENlist)
# Compute the trend analysis
- df_CENtrend = Estimate.stats(data.extract=df_CENEx,
+ df_tCENtrend = Estimate.stats(data.extract=df_tCENEx,
level=alpha)
# Get the associated time interval
@@ -542,19 +542,19 @@ get_CENtrend = function (df_data, df_meta, period, alpha, sampleSpan, yearLac_da
if (I > Imax) {
# Store it and the associated data and info
Imax = I
- df_CENlistB = df_CENlist
- df_CENExB = df_CENEx
+ df_tCENlistB = df_tCENlist
+ df_tCENExB = df_tCENEx
}
# Specify the period of analyse
- df_CENtrend = get_period(per, df_CENtrend, df_CENEx,
- df_CENlist)
+ df_tCENtrend = get_period(per, df_tCENtrend, df_tCENEx,
+ df_tCENlist)
# Store the trend
- df_CENtrendB = bind_rows(df_CENtrendB, df_CENtrend)
+ df_tCENtrendB = bind_rows(df_tCENtrendB, df_tCENtrend)
}
# Clean results of trend analyse
- res_CENtrend = clean(df_CENtrendB, df_CENExB, df_CENlistB)
- return (res_CENtrend)
+ res_tCENtrend = clean(df_tCENtrendB, df_tCENExB, df_tCENlistB)
+ return (res_tCENtrend)
}
diff --git a/processing/extract.R b/processing/extract.R
index fc4f61ffedaacbf9df1ac8b5235a765b15ce75a0..aac50b48f9d6d5deaf167919361989a6781e5b15 100644
--- a/processing/extract.R
+++ b/processing/extract.R
@@ -477,7 +477,7 @@ extract_data = function (computer_data_path, filedir, filename,
## 4. SHAPEFILE MANAGEMENT
# Generates a list of shapefiles to draw a hydrological map of
# the France
-ini_shapefile = function (computer_data_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, sbs_shpdir, sbs_shpname, rv_shpdir, rv_shpname, riv=TRUE) {
+ini_shapefile = function (computer_data_path, fr_shpdir, fr_shpname, bs_shpdir, bs_shpname, sbs_shpdir, sbs_shpname, rv_shpdir, rv_shpname, is_river=TRUE) {
# Path for shapefile
fr_shppath = file.path(computer_data_path, fr_shpdir, fr_shpname)
@@ -501,7 +501,7 @@ ini_shapefile = function (computer_data_path, fr_shpdir, fr_shpname, bs_shpdir,
df_subbassin = tibble(fortify(subbassin))
# If the river shapefile needs to be load
- if (riv) {
+ if (is_river) {
# Hydrographic network
river = readOGR(dsn=rv_shppath, verbose=FALSE) ### trop long ###
river = river[which(river$Classe == 1),]
diff --git a/script.R b/script.R
index a8ce827b0026981f29f7c456b3aff311f3d4b7de..b049e48badc65443583ac29ef1fb7b91c57f939b 100644
--- a/script.R
+++ b/script.R
@@ -114,6 +114,9 @@ sampleSpan = c('05-01', '11-30')
## MAP
+# Is the hydrological network needs to be plot
+is_river = TRUE
+
# Path to the shapefile for france contour from 'computer_data_path'
fr_shpdir = 'map/france'
fr_shpname = 'gadm36_FRA_0.shp'
@@ -261,17 +264,17 @@ res_VCN10trend = get_VCN10trend(df_data, df_meta,
yearLac_day=yearLac_day)
# Start date for low water trend
-res_DEBtrend = get_DEBtrend(df_data, df_meta,
+res_tDEBtrend = get_tDEBtrend(df_data, df_meta,
period=trend_period,
alpha=alpha,
sampleSpan=sampleSpan,
thresold_type='VCN10',
select_longest=TRUE,
yearLac_day=yearLac_day)
-# res_DEBtrend = read_listofdf(resdir, 'res_DEBtrend')
+# res_tDEBtrend = read_listofdf(resdir, 'res_tDEBtrend')
# Center date for low water trend
-res_CENtrend = get_CENtrend(df_data, df_meta,
+res_tCENtrend = get_tCENtrend(df_data, df_meta,
period=trend_period,
alpha=alpha,
sampleSpan=sampleSpan,
@@ -295,7 +298,7 @@ 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, is_river=is_river)
### 4.1. Simple time panel to criticize station data
# Plot time panel of debit by stations
@@ -322,29 +325,33 @@ datasheet_layout(toplot=c(
res_QAtrend$data,
res_QMNAtrend$data,
res_VCN10trend$data,
- res_DEBtrend$data,
- res_CENtrend$data),
+ res_tDEBtrend$data,
+ res_tCENtrend$data
+ ),
df_trend=list(
res_QAtrend$trend,
res_QMNAtrend$trend,
res_VCN10trend$trend,
- res_DEBtrend$trend,
- res_CENtrend$trend),
+ res_tDEBtrend$trend,
+ res_tCENtrend$trend
+ ),
var=list(
'QA',
'QMNA',
'VCN10',
- 'DEB',
- 'CEN'),
+ 'tDEB',
+ 'tCEN'
+ ),
type=list(
'sévérité',
'sévérité',
'sévérité',
'saisonnalité',
- 'saisonnalité'),
+ 'saisonnalité'
+ ),
layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),
@@ -357,7 +364,7 @@ datasheet_layout(toplot=c(
info_ratio=2,
time_ratio=2,
var_ratio=3,
- foot_height=1,
+ foot_height=1.25,
df_shapefile=df_shapefile,
figdir=figdir,
filename_opt='',