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Commit b0d43708 authored by Heraut Louis's avatar Heraut Louis
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INI variable

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with 119 additions and 93 deletions
+119 -93
processing/analyse.R
+ 111
− 85
View file @ b0d43708
......@@ -216,19 +216,17 @@ get_VCN10trend = function (df_data, df_meta, period, p_thresold) {
df_data_roll = tibble()
# For all the code
for (c in Code) {
for (code in Code) {
# Get the data associated to the code
df_data_code = df_data[df_data$code == c,]
df_data_code = df_data[df_data$code == code,]
# Perform the roll mean of the flow over 10 days
df_data_code = tibble(Date=rollmean(df_data_code$Date,
10,
fill=NA),
Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=c)
df_data_roll_code = tibble(Date=df_data_code$Date,
Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=code)
# Store the results
df_data_roll = bind_rows(df_data_roll, df_data_code)
df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
}
# Make sure to convert the period to a list
......@@ -274,77 +272,100 @@ get_VCN10trend = function (df_data, df_meta, period, p_thresold) {
return (res_VCN10trend)
}
### 1.4. tINI date
get_tINItrend = function (df_data, df_meta, period, p_thresold) {
# Get all different stations code
Code = levels(factor(df_meta$code))
# Gets the number of station
nCode = length(Code)
# Blank tibble to store the data averaged
df_data_roll = tibble()
### 1.4. tINI date
# get_tINItrend = function (df_data, df_meta, period, p_thresold) {
# # Get all different stations code
# Code = levels(factor(df_meta$code))
# # Blank tibble to store the data averaged
# df_data_roll = tibble()
# # For all the code
# for (c in Code) {
# # Get the data associated to the code
# df_data_code = df_data[df_data$code == c,]
# # Perform the roll mean of the flow over 10 days
# df_data_code = tibble(Date=df_data_code$Date,
# Value=rollmean(df_data_code$Value,
# 10,
# fill=NA),
# code=c)
# # Store the results
# df_data_roll = bind_rows(df_data_roll, df_data_code)
# }
# # Make sure to convert the period to a list
# period = as.list(period)
# # Set the max interval period as the minimal possible
# Imax = 0
# # Blank tibble for data to return
# df_tMIDtrendB = tibble()
# # For all periods
# for (per in period) {
# # Prepare the data to fit the entry of extract.Var
# df_tMIDlist = prepare(df_data_roll, colnamegroup=c('code'))
# # Compute the yearly min over the averaged data
# df_tMIDEx = extract.Var(data.station=df_tMIDlist,
# funct=which.min,
# period=per,
# timestep='year',
# pos.datetime=1)
# # Converts index of the tMID to the julian date associated
# df_tMIDEx = prepare_date(df_tMIDEx, df_tMIDlist)
# For all the code
for (code in Code) {
# Get the data associated to the code
df_data_code = df_data[df_data$code == code,]
# Perform the roll mean of the flow over 10 days
df_data_roll_code = tibble(Date=df_data_code$Date,
Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=code)
# Store the results
df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
}
# Make sure to convert the period to a list
period = as.list(period)
# Set the max interval period as the minimal possible
Imax = 0
# Blank tibble for data to return
df_tINItrendB = tibble()
# For all periods
for (per in period) {
df_tINIEx = tibble()
# For all the code
for (k in 1:nCode) {
# Gets the code
code = Code[k]
# Get the data associated to the code
df_data_roll_code = df_data_roll[df_data_roll$code == code,]
# Prepare the data to fit the entry of extract.Var
df_tINIlist_code = prepare(df_data_roll_code,
colnamegroup=c('code'))
per.start = df_meta$start_year[df_meta$code == code]
per.start = paste(sprintf("%02d", per.start), '-01', sep='')
# Compute the yearly min over the averaged data
df_tINIEx_code = extract.Var(data.station=df_tINIlist_code,
funct=which.min,
period=per,
per.start=per.start,
timestep='year',
pos.datetime=1)
#######
df_tINIEx_code$group1 = k
# Store the results
df_tINIEx = bind_rows(df_tINIEx, df_tINIEx_code)
}
# Converts index of the tINI to the julian date associated
df_tINIEx = prepare_date(df_tINIEx, df_tINIlist)
# # Compute the trend analysis
# df_tMIDtrend = Estimate.stats(data.extract=df_tMIDEx,
# level=p_thresold)
# # Get the associated time interval
# I = interval(per[1], per[2])
# # If it is the largest interval
# if (I > Imax) {
# # Store it and the associated data and info
# Imax = I
# df_tMIDlistB = df_tMIDlist
# df_tMIDExB = df_tMIDEx
# }
# # Specify the period of analyse
# df_tMIDtrend = get_period(per, df_tMIDtrend, df_tMIDEx,
# df_tMIDlist)
# # Store the trend
# df_tMIDtrendB = bind_rows(df_tMIDtrendB, df_tMIDtrend)
# }
# # Clean results of trend analyse
# res_tMIDtrend = clean(df_tMIDtrendB, df_tMIDExB, df_tMIDlistB)
# return (res_tMIDtrend)
# }
# Compute the trend analysis
df_tINItrend = Estimate.stats(data.extract=df_tINIEx,
level=p_thresold)
# Get the associated time interval
I = interval(per[1], per[2])
# If it is the largest interval
if (I > Imax) {
# Store it and the associated data and info
Imax = I
df_tINIlistB = df_tINIlist
df_tINIExB = df_tINIEx
}
# Specify the period of analyse
df_tINItrend = get_period(per, df_tINItrend, df_tINIEx,
df_tINIlist)
# Store the trend
df_tINItrendB = bind_rows(df_tINItrendB, df_tINItrend)
}
# Clean results of trend analyse
res_tINItrend = clean(df_tINItrendB, df_tINIExB, df_tINIlistB)
return (res_tINItrend)
}
### 1.5. tMID date
# Realises the trend analysis of the date of the minimum 10 day
......@@ -357,17 +378,17 @@ get_tMIDtrend = function (df_data, df_meta, period, p_thresold) {
df_data_roll = tibble()
# For all the code
for (c in Code) {
for (code in Code) {
# Get the data associated to the code
df_data_code = df_data[df_data$code == c,]
df_data_code = df_data[df_data$code == code,]
# Perform the roll mean of the flow over 10 days
df_data_code = tibble(Date=df_data_code$Date,
Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=c)
df_data_roll_code = tibble(Date=df_data_code$Date,
Value=rollmean(df_data_code$Value,
10,
fill=NA),
code=code)
# Store the results
df_data_roll = bind_rows(df_data_roll, df_data_code)
df_data_roll = bind_rows(df_data_roll, df_data_roll_code)
}
# Make sure to convert the period to a list
......@@ -465,6 +486,8 @@ get_hydrograph = function (df_data, period=NULL, df_meta=NULL) {
if (!is.null(df_meta)) {
# New column in metadata for hydrological regime
df_meta$regime_hydro = NA
# New column in metadata for the start of the hydrological year
df_meta$start_year = NA
# Get all different stations code
Code = levels(factor(df_meta$code))
......@@ -530,7 +553,10 @@ get_hydrograph = function (df_data, period=NULL, df_meta=NULL) {
df_QM = bind_rows(df_QM, df_QMtmp)
# Stores result of the hydrological regime
df_meta$regime_hydro[df_meta$code == code] = classRegime
# Computes the month of the max QM
maxMonth = which.max(df_QM$QM)
# Stores it as the start of the hydrological year
df_meta$start_year[df_meta$code == code] = maxMonth
# Otherwise
} else {
# No tibble needed
......
script.R
+ 8
− 8
View file @ b0d43708
......@@ -244,9 +244,9 @@ res_VCN10trend = get_VCN10trend(df_data, df_meta,
p_thresold=p_thresold)
# Start date for low water trend
# res_tINItrend = get_tINItrend(df_data, df_meta,
# period=trend_period,
# p_thresold=p_thresold)
res_tINItrend = get_tINItrend(df_data, df_meta,
period=trend_period,
p_thresold=p_thresold)
# Center date for low water trend
res_tMIDtrend = get_tMIDtrend(df_data, df_meta,
......@@ -297,28 +297,28 @@ datasheet_layout(toplot=c(
df_data=list(res_QAtrend$data,
res_QMNAtrend$data,
res_VCN10trend$data,
# res_tINItrend$data,
res_tINItrend$data,
res_tMIDtrend$data),
df_trend=list(res_QAtrend$trend,
res_QMNAtrend$trend,
res_VCN10trend$trend,
# res_tINItrend$trend,
res_tINItrend$trend,
res_tMIDtrend$trend),
var=list('QA',
'QMNA',
'VCN10',
# 'tINI',
'tINI',
'tMID'),
type=list('flow',
'flow',
'flow',
# 'date',
'date',
'date'),
layout_matrix=matrix(c(1, 2, 3, 4), ncol=1),
layout_matrix=matrix(c(1, 2, 3, 4, 5), ncol=1),
missRect=TRUE,
trend_period=trend_period,
......
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