From bae48daeb272a1ad040c698679a661aa0e6c6a8e Mon Sep 17 00:00:00 2001
From: "louis.heraut" <louis.heraut@inrae.fr>
Date: Tue, 11 Jan 2022 12:27:01 +0100
Subject: [PATCH] Cleaned and commented
---
plotting/matrix.R | 91 +++++++++++++++++++++++++++++++----------------
1 file changed, 61 insertions(+), 30 deletions(-)
diff --git a/plotting/matrix.R b/plotting/matrix.R
index ba442bb..3811360 100644
--- a/plotting/matrix.R
+++ b/plotting/matrix.R
@@ -27,6 +27,7 @@
## 1. MATRIX PANEL
+# Generates a summarizing matrix of the trend analyses of all station for different hydrological variables and periods. Also shows difference of means between specific periods.
matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice=NULL, outdirTmp='', outnameTmp='matrix', title=NULL, A3=FALSE) {
# Number of variable/plot
@@ -171,11 +172,12 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
}
- # Compute the min and the max of the mean trend for all the station
+ # Computes the min and the max of the mean trend for
+ # all the station
minTrendMean = apply(TrendMean_code, c(1, 2), min, na.rm=TRUE)
maxTrendMean = apply(TrendMean_code, c(1, 2), max, na.rm=TRUE)
-
+ # Blank vectors to store info about trend analyses
Periods_trend = c()
NPeriod_trend = c()
Type_trend = list()
@@ -204,42 +206,54 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Extracts the trend corresponding to the code
df_trend_code = df_trend[df_trend$code == code,]
+ # Gets the associated time info
Start = Start_code[Code_code == code][[1]][j]
End = End_code[Code_code == code][[1]][j]
Periods = Periods_code[Code_code == code][[1]][j]
+ # Extracts the corresponding data for the period
df_data_code_per =
df_data_code[df_data_code$Date >= Start
& df_data_code$Date <= End,]
-
+ # Same for trend
df_trend_code_per =
df_trend_code[df_trend_code$period_start == Start
& df_trend_code$period_end == End,]
+ # Computes the number of trend analysis selected
Ntrend = nrow(df_trend_code_per)
+ # If there is more than one trend on the same period
if (Ntrend > 1) {
+ # Takes only the first because they are similar
df_trend_code_per = df_trend_code_per[1,]
}
-
+
+ # Computes the mean of the data on the period
dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+ # Normalises the trend value by the mean of the data
trendMean = df_trend_code_per$trend / dataMean
+ # If the p value is under the threshold
if (df_trend_code_per$p <= p_threshold){
+ # Gets the color associated to the mean trend
color_res = get_color(trendMean,
minTrendMean[j, i],
maxTrendMean[j, i],
palette_name='perso',
reverse=TRUE)
-
+ # Specifies the color fill and contour of
+ # table cells
fill = color_res
color = 'white'
Pthresold = p_thresold
+ # Otherwise it is not significative
} else {
fill = 'white'
color = 'grey85'
Pthresold = NA
}
+ # Stores info needed to plot
Periods_trend = append(Periods_trend, Periods)
NPeriod_trend = append(NPeriod_trend, j)
Type_trend = append(Type_trend, type)
@@ -253,10 +267,9 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
-
# If there is a 'mean_period'
if (!is.null(mean_period)) {
-
+ # Blank vectors to store info about mean analyses
Periods_mean = c()
NPeriod_mean = c()
Type_mean = list()
@@ -269,9 +282,11 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
# Number of mean period
nPeriod_mean = length(mean_period)
+ # Blank array to store difference of mean between two periods
BreakMean_code = array(rep(1, nPeriod_mean*nbp*nCode),
dim=c(nPeriod_mean, nbp, nCode))
-
+ # Blank array to store mean for a temporary period in order
+ # to compute the difference of mean with a second period
dataMeantmp = array(rep(NA, nbp*nCode),
dim=c(nbp, nCode))
@@ -311,18 +326,24 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
dataMean = mean(df_data_code_per$Qm3s,
na.rm=TRUE)
+ # If this in not the first period
if (j > 1) {
+ # Compute the difference of mean
Break = dataMean - dataMeantmp[i, k]
+ # Otherwise for the first period
} else {
+ # Stocks NA
Break = NA
}
-
+ # Normalises the break by the mean of the
+ # initial period
BreakMean = Break / dataMeantmp[i, k]
+ # Stores the result
BreakMean_code[j, i, k] = BreakMean
-
+ # Stores temporarily the mean of the current period
dataMeantmp[i, k] = dataMean
-
+ # Stores info needed to plot
Periods_mean = append(Periods_mean, Periods)
NPeriod_mean = append(NPeriod_mean, j)
Type_mean = append(Type_mean, type)
@@ -333,15 +354,16 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
}
}
}
-
+ # Computes the min and the max of the mean trend for
+ # all the station
minBreakMean = apply(BreakMean_code, c(1, 2),
min, na.rm=TRUE)
maxBreakMean = apply(BreakMean_code, c(1, 2),
max, na.rm=TRUE)
-
+ # Blanks vector to store color info
Fill_mean = c()
Color_mean = c()
-
+ # Index to count over all break computed
ii = 1
for (j in 1:nPeriod_mean) {
# For all the code
@@ -350,52 +372,59 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
code = Code[k]
# For all variable
for (i in 1:nbp) {
-
+ # Extracts break mean
BreakMean = BreakMean_mean[ii]
-
+ # Gets the color associated
color_res = get_color(BreakMean,
minBreakMean[j, i],
maxBreakMean[j, i],
palette_name='perso',
reverse=TRUE)
-
+ # Gets the fill and contour color
fill = color_res
color = 'white'
-
+ # Stores it
Fill_mean = append(Fill_mean, fill)
Color_mean = append(Color_mean, color)
-
+ # Passes to the next index
ii = ii + 1
}
}
}
}
-
+ # If the slice option is not specified, the info for all
+ # stations will be draw on the same page
if (is.null(slice)) {
slice = nCode
}
+ # Extracts each possibilities of first letter of station code
firstLetter = levels(factor(substr(Code, 1, 1)))
-
+ # For all the available first letter
for (fL in firstLetter) {
-
+ # Print the matrix name
print(paste('Matrix for region :', fL))
# 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
-
+ # For all the pages
for (imat in 1:nMat) {
+ # Extracts the station for the current page
subCode = subCodefL[(slice*(imat-1)+1):(slice*imat)]
-
+ # Removes NA stations
subCode = subCode[!is.na(subCode)]
+ # Gets the number of station for the page
nsubCode = length(subCode)
+ # Creates logical vector to select only info about
+ # stations that will be plot on the page
CodefL_trend = Code_trend %in% subCode
-
+ # Extracts those info
subPeriods_trend = Periods_trend[CodefL_trend]
subNPeriod_trend = NPeriod_trend[CodefL_trend]
subType_trend = Type_trend[CodefL_trend]
@@ -405,9 +434,10 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subDataMean_trend = DataMean_trend[CodefL_trend]
subFill_trend = Fill_trend[CodefL_trend]
subColor_trend = Color_trend[CodefL_trend]
-
+
+ # Same for mean difference analysis
CodefL_mean = Code_mean %in% subCode
-
+ # Extracts right info
subPeriods_mean = Periods_mean[CodefL_mean]
subNPeriod_mean = NPeriod_mean[CodefL_mean]
subType_mean = Type_mean[CodefL_mean]
@@ -416,9 +446,10 @@ matrix_panel = function (list_df2plot, df_meta, trend_period, mean_period, slice
subBreakMean_mean = BreakMean_mean[CodefL_mean]
subFill_mean = Fill_mean[CodefL_mean]
subColor_mean = Color_mean[CodefL_mean]
-
- title = df_meta[df_meta$code == subCode[1],]$region_hydro
+ # Extracts the name of the currently hydrological
+ # region plotted
+ title = df_meta[df_meta$code == subCode[1],]$region_hydro
### Plot ###
height = nsubCode
--
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