R/plot_output.R
In streampulse/StreamPULSE: Run Stream Metabolism Models on StreamPULSE data
Defines functions
plot_output
Documented in
plot_output
#' Visualize metabolism model results
#'
#' Generates a Shiny R visualization similar to those found on the
#' \href{http://data.streampulse.org:3838/streampulse_diagnostic_plots/}{StreamPULSE data portal}.
#'
#' @author Mike Vlah, \email{vlahm13@gmail.com}
#' @param model_out the output of \link{fit_metabolism}.
#' @return Opens a shiny app in default browser via localhost.
#' @seealso \code{\link{request_data}} for acquiring StreamPULSE data;
#' \code{\link{prep_metabolism}} for organizing data and acquiring additional
#' variables, and \code{\link{fit_metabolism}} for fitting models to data and
#' predicting metabolism.
#' @export
#' @examples
#' streampulse_data = request_data(sitecode='NC_Eno',
#' startdate='2016-06-10', enddate='2016-10-23')
#'
#' fitdata = prep_metabolism(d=streampulse_data, type='bayes',
#' model='streamMetabolizer', interval='15 min',
#' rm_flagged=list('Bad Data', 'Questionable'), fillgaps=fillgaps,
#' zq_curve=list(sensor_height=NULL, Z=Z_data, Q=Q_data,
#' fit='power', plot=TRUE), estimate_areal_depth=TRUE)
#'
#' modelfit = fit_metabolism(fitdata)
#'
#' plot_output(modelfit)
plot_output = function(model_out){
options(shiny.usecairo=TRUE)
#create data objects
modOut = list(data=model_out$fit@data,
data_daily=model_out$fit@data_daily, fit=model_out$fit@fit)
predictions = model_out$predictions
fitpred = list('mod_out'=modOut, 'predictions'=predictions)
#create mapping of input data fields and their pretty equivalents
varmap = list('DO.sat'=list('DO sat', 'DO sat (%)'),
'depth'=list('Depth', 'Depth (m)'),
'temp.water'=list('Water temp',
expression(paste('Water temp (', degree, 'C)'))),
'light'=list('PAR', 'Light (PAR)'),
'discharge'=list('Discharge',
expression(paste('Discharge (m'^3, 's'^-1, ')'))))
#populate overlay selector
vars_etc = colnames(fitpred$mod_out$data)
varinds = ! vars_etc %in% c('date', 'solar.time', 'DO.obs', 'DO.mod')
vars = vars_etc[varinds]
select_vars = vector('character', length=length(vars))
for(i in 1:length(vars)){
if(vars[i] %in% names(varmap)){
select_vars[i] = varmap[[vars[i]]][[1]]
}
}
#initiate time slider:
#convert POSIX time to DOY and UNIX time
DOY = as.numeric(gsub('^0+', '',
strftime(fitpred$mod_out$data$solar.time, format="%j")))
#get DOY bounds for slider
DOYmin = ifelse(DOY[1] %in% 365:366, 1, DOY[1])
DOYmax = DOY[length(DOY)]
ui = fluidPage(
#screen shouldn't go gray when plots are updating.
tags$style(type="text/css", ".recalculating { opacity: 1.0; }" ),
#hide error messages on screen
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
),
navbarPage(title=p(strong(a('StreamPULSE',
href='https://data.streampulse.org/'))), inverse=TRUE,
windowTitle='StreamPULSE Diagnostics',
tabPanel('Model Performance',
sidebarLayout(
sidebarPanel(
p(strong('Select DOY range:')),
p('Drag blue bar to move fixed range',
style=paste0('color:gray; font-size:80%;',
'padding:0; margin:0')),
p('Press play to autoscroll*',
style='color:gray; font-size:80%'),
htmlOutput('MPtime_slider'),
p('Click any point to view its date.',
style=paste0('color:gray; font-size:80%;')),
p(paste('*Residuals based on linear relationship',
'between daily mean K600 and log daily mean Q.'),
style=paste0('color:gray; font-size:80%;')),
width = 3
),
mainPanel(
fluidRow(
column(6, align='left',
plotOutput('KvER', height='300px', width='auto',
click='KvER_click'),
plotOutput('KvGPP', height='300px', width='auto',
click='KvGPP_click')
),
column(6, align='left',
plotOutput('KvQ', height='300px', width='auto',
click='KvQ_click'),
plotOutput('QvKres', height='300px', width='auto',
click='QvKres_click')
)
)
)
)
),
tabPanel(HTML('O<sub>2</sub> and Metabolism'),
fluidRow(
column(12, align='left',
div(align='center', style=paste0(
'display: inline-block;',
'vertical-align:middle;',
'margin-right:1em'),
div(align='center', style=paste0(
'display: inline-block;',
'vertical-align:middle;',
'margin-right:1em'),
p(strong('Select DOY range:')),
p('Drag blue bar to move fixed range',
style=paste0(
'color:gray; font-size:80%;',
'padding:0; margin:0')),
p('Press play to autoscroll*',
style='color:gray; font-size:80%')
),
div(align='left', style=paste0(
'display: inline-block;',
'vertical-align:middle;'),
htmlOutput('time_slider')
)
),
hr()
)
),
fluidRow(
column(9, align='center',
plotOutput('metab_legend', height='20px',
width='auto'),
plotOutput('metab_plot', height='300px', width='auto'),
plotOutput('O2_legend', height='20px',
width='auto'),
plotOutput('O2_plot', brush='O2_brush',
height='300px', width='auto')
),
column(3, align='center',
p(strong(HTML('Cumulative O<sub>2</sub> (gm<sup>-2</sup>d<sup>-1</sup>)'))),
tableOutput('cumul_metab'),
br(),
# plotOutput('cumul_legend', height='20px',
# width='auto'),
# plotOutput('cumul_plot', height='200px', width='auto'),
plotOutput('kernel_legend', height='20px',
width='auto'),
plotOutput('kernel_plot', height='200px', width='auto'),
br(),
selectInput('metab_overlay', 'Model param overlay',
list('None', 'mean daily K600'), selected='None'),
selectInput('O2_overlay', 'Input data overlay',
as.list(c('None', select_vars)), selected='None'),
radioButtons('xformat', 'Series x-axis', inline=TRUE,
list('DOY', 'Date'), selected='DOY')
)
),
br(),
p(paste("*At this time, plotting may fail if your data",
"do not all occur within the same calendar year.",
"If plots do not load, try toggling between tabs."),
style='color:gray; font-size:100%')
)
)
)
server = function(input, output, session){
output$time_slider = renderUI({
if(!is.null(fitpred)){
#convert POSIX time to DOY and UNIX time
DOY = as.numeric(gsub('^0+', '',
strftime(fitpred$mod_out$data$solar.time, format="%j")))
#get DOY bounds for slider
DOYmin = ifelse(DOY[1] %in% 365:366, 1, DOY[1])
DOYmax = DOY[length(DOY)]
sliderInput("range", label=NULL,
min=DOYmin, max=DOYmax, value=c(DOYmin, DOYmax),
ticks=TRUE, step=3,
animate=animationOptions(interval=2000)
)
}
})
output$MPtime_slider = renderUI({
if(!is.null(fitpred)){
#convert POSIX time to DOY and UNIX time
MPDOY = as.numeric(gsub('^0+', '',
strftime(fitpred$mod_out$data$solar.time, format="%j")))
#get DOY bounds for slider
MPDOYmin = ifelse(MPDOY[1] %in% 365:366, 1, MPDOY[1])
MPDOYmax = MPDOY[length(MPDOY)]
sliderInput("MPrange", label=NULL,
min=MPDOYmin, max=MPDOYmax, value=c(MPDOYmin, MPDOYmax),
ticks=TRUE, step=3,
animate=animationOptions(interval=2000)
)
}
})
#update model performance data frames based on time range selection
get_slices = eventReactive({
input$MPrange
}, {
if(! is.null(fitpred)){
mod_out = fitpred$mod_out
MPstart = input$MPrange[1]
MPend = input$MPrange[2]
#convert POSIX time to DOY and UNIX time
DOY = as.numeric(gsub('^0+', '', strftime(mod_out$data$solar.time,
format="%j")))
date = as.Date(gsub('^0+', '', strftime(mod_out$data$solar.time,
format="%Y-%m-%d")))
# replace initial DOYs of 365 or 366 (solar date in previous calendar year) with 1
if(DOY[1] %in% 365:366){
DOY[DOY %in% 365:366 & 1:length(DOY) < length(DOY)/2] = 1
}
#filter data by date bounds specified in time slider
xmin_ind = match(MPstart, DOY)
if(is.na(xmin_ind)) xmin_ind = 1
xmin = date[xmin_ind]
xmax_ind = length(DOY) - match(MPend, rev(DOY)) + 1
if(is.na(xmax_ind)) xmax_ind = nrow(mod_out$data)
xmax = date[xmax_ind]
daily_slice = mod_out$fit$daily[mod_out$fit$daily$date <= xmax &
mod_out$fit$daily$date >= xmin,]
data_daily_slice = mod_out$data_daily[mod_out$data_daily$date <= xmax &
mod_out$data_daily$date >= xmin,]
out = list(daily_slice=daily_slice, data_daily_slice=data_daily_slice,
mod_out=mod_out)
}
})
observeEvent({
input$range
}, {
start = input$range[1]
end = input$range[2]
if(!is.null(start) && !is.null(end)){
output$metab_legend = renderPlot({
if(input$metab_overlay != 'None'){
metab_legend(show_K600=TRUE)
} else {
metab_legend(show_K600=FALSE)
}
})
output$metab_plot = renderPlot({
ts_full = processing_func(fitpred$predictions, st=start,
en=end)
par(mar=c(1,4,0,4), oma=rep(0,4))
daily = fitpred$mod_out$fit$daily
daily$doy = as.numeric(gsub('^0+', '',
strftime(daily$date, format="%j")))
daily = daily[daily$doy > start & daily$doy < end,]
season_ts_func(ts_full, daily, st=start, en=end,
input$metab_overlay)
})
output$kernel_legend = renderPlot({
kernel_legend()
})
output$kernel_plot = renderPlot({
ts_full = processing_func(fitpred$predictions, st=start,
en=end)
par(mar=c(3,3.5,0,.5), oma=rep(0,4))
kernel_func(ts_full, 'Name and Year')
})
output$O2_legend = renderPlot({
O2_legend(overlay=input$O2_overlay, varmap=varmap)
})
output$O2_plot = renderPlot({
par(mar=c(3,4,0,4), oma=rep(0,4))
O2_plot(mod_out=fitpred$mod_out, st=start, en=end,
brush=input$O2_brush, overlay=input$O2_overlay,
xformat=input$xformat, varmap=varmap)
})
output$cumul_metab = renderTable({
ts_full = processing_func(fitpred$predictions, st=start,
en=end)
na_rm = na.omit(ts_full)
gppsum = sum(na_rm$GPP, na.rm=TRUE)
ersum = sum(na_rm$ER, na.rm=TRUE)
nepsum = sum(na_rm$NPP, na.rm=TRUE)
return(data.frame('GPP'=gppsum, 'ER'=ersum, 'NEP'=nepsum))
}, striped=TRUE)
}
})
observeEvent({
input$MPrange
}, {
slices = get_slices()
mod_out = slices$mod_out
MPstart = input$MPrange[1]
MPend = input$MPrange[2]
if(!is.null(MPstart) && !is.null(MPend)){
output$KvER = renderPlot({
if(!is.null(fitpred$mod_out)){
KvER_plot(mod_out=fitpred$mod_out,
slice=slices$daily_slice)
}
})
output$KvQ = renderPlot({
if(!is.null(fitpred$mod_out)){
KvQ_plot(mod_out=fitpred$mod_out,
slicex=slices$data_daily_slice,
slicey=slices$daily_slice)
}
})
output$KvGPP = renderPlot({
if(!is.null(fitpred$mod_out)){
KvGPP_plot(mod_out=fitpred$mod_out,
slice=slices$daily_slice)
}
})
output$QvKres = renderPlot({
if(!is.null(fitpred$mod_out)){
QvKres_plot(mod_out=fitpred$mod_out,
slicex=slices$data_daily_slice,
slicey=slices$daily_slice)
}
})
}
})
#replot when a click is registered; don't react when click handler flushes
observeEvent({
if (! is.null(input$KvER_click$x) ||
! is.null(input$KvQ_click$x) ||
! is.null(input$QvKres_click$x) ||
! is.null(input$KvGPP_click$x)) TRUE
else NULL
}, {
slices = get_slices()
mod_out = slices$mod_out
MPstart = input$MPrange[1]
MPend = input$MPrange[2]
output$KvER = renderPlot({
KvER_plot(mod_out=mod_out,
slice=slices$daily_slice, click=isolate(input$KvER_click))
})
output$KvQ = renderPlot({
KvQ_plot(mod_out=mod_out, slicex=slices$data_daily_slice,
slicey=slices$daily_slice, click=isolate(input$KvQ_click))
})
output$KvGPP = renderPlot({
KvGPP_plot(mod_out=mod_out,
slice=slices$daily_slice, click=isolate(input$KvGPP_click))
})
output$QvKres = renderPlot({
if(!is.null(mod_out)){
QvKres_plot(mod_out=mod_out, slicex=slices$data_daily_slice,
slicey=slices$daily_slice, click=isolate(input$QvKres_click))
}
})
}, ignoreNULL=TRUE)
}
shinyApp(ui=ui, server=server, options=list(launch.browser=TRUE))
}
streampulse/StreamPULSE documentation built on Nov. 2, 2024, 9:54 p.m.
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Defines functions plot_output
Documented in plot_output
#' Visualize metabolism model results
#'
#' Generates a Shiny R visualization similar to those found on the
#' \href{http://data.streampulse.org:3838/streampulse_diagnostic_plots/}{StreamPULSE data portal}.
#'
#' @author Mike Vlah, \email{vlahm13@gmail.com}
#' @param model_out the output of \link{fit_metabolism}.
#' @return Opens a shiny app in default browser via localhost.
#' @seealso \code{\link{request_data}} for acquiring StreamPULSE data;
#' \code{\link{prep_metabolism}} for organizing data and acquiring additional
#' variables, and \code{\link{fit_metabolism}} for fitting models to data and
#' predicting metabolism.
#' @export
#' @examples
#' streampulse_data = request_data(sitecode='NC_Eno',
#' startdate='2016-06-10', enddate='2016-10-23')
#'
#' fitdata = prep_metabolism(d=streampulse_data, type='bayes',
#' model='streamMetabolizer', interval='15 min',
#' rm_flagged=list('Bad Data', 'Questionable'), fillgaps=fillgaps,
#' zq_curve=list(sensor_height=NULL, Z=Z_data, Q=Q_data,
#' fit='power', plot=TRUE), estimate_areal_depth=TRUE)
#'
#' modelfit = fit_metabolism(fitdata)
#'
#' plot_output(modelfit)
plot_output = function(model_out){
options(shiny.usecairo=TRUE)
#create data objects
modOut = list(data=model_out$fit@data,
data_daily=model_out$fit@data_daily, fit=model_out$fit@fit)
predictions = model_out$predictions
fitpred = list('mod_out'=modOut, 'predictions'=predictions)
#create mapping of input data fields and their pretty equivalents
varmap = list('DO.sat'=list('DO sat', 'DO sat (%)'),
'depth'=list('Depth', 'Depth (m)'),
'temp.water'=list('Water temp',
expression(paste('Water temp (', degree, 'C)'))),
'light'=list('PAR', 'Light (PAR)'),
'discharge'=list('Discharge',
expression(paste('Discharge (m'^3, 's'^-1, ')'))))
#populate overlay selector
vars_etc = colnames(fitpred$mod_out$data)
varinds = ! vars_etc %in% c('date', 'solar.time', 'DO.obs', 'DO.mod')
vars = vars_etc[varinds]
select_vars = vector('character', length=length(vars))
for(i in 1:length(vars)){
if(vars[i] %in% names(varmap)){
select_vars[i] = varmap[[vars[i]]][[1]]
}
}
#initiate time slider:
#convert POSIX time to DOY and UNIX time
DOY = as.numeric(gsub('^0+', '',
strftime(fitpred$mod_out$data$solar.time, format="%j")))
#get DOY bounds for slider
DOYmin = ifelse(DOY[1] %in% 365:366, 1, DOY[1])
DOYmax = DOY[length(DOY)]
ui = fluidPage(
#screen shouldn't go gray when plots are updating.
tags$style(type="text/css", ".recalculating { opacity: 1.0; }" ),
#hide error messages on screen
tags$style(type="text/css",
".shiny-output-error { visibility: hidden; }",
".shiny-output-error:before { visibility: hidden; }"
),
navbarPage(title=p(strong(a('StreamPULSE',
href='https://data.streampulse.org/'))), inverse=TRUE,
windowTitle='StreamPULSE Diagnostics',
tabPanel('Model Performance',
sidebarLayout(
sidebarPanel(
p(strong('Select DOY range:')),
p('Drag blue bar to move fixed range',
style=paste0('color:gray; font-size:80%;',
'padding:0; margin:0')),
p('Press play to autoscroll*',
style='color:gray; font-size:80%'),
htmlOutput('MPtime_slider'),
p('Click any point to view its date.',
style=paste0('color:gray; font-size:80%;')),
p(paste('*Residuals based on linear relationship',
'between daily mean K600 and log daily mean Q.'),
style=paste0('color:gray; font-size:80%;')),
width = 3
),
mainPanel(
fluidRow(
column(6, align='left',
plotOutput('KvER', height='300px', width='auto',
click='KvER_click'),
plotOutput('KvGPP', height='300px', width='auto',
click='KvGPP_click')
),
column(6, align='left',
plotOutput('KvQ', height='300px', width='auto',
click='KvQ_click'),
plotOutput('QvKres', height='300px', width='auto',
click='QvKres_click')
)
)
)
)
),
tabPanel(HTML('O<sub>2</sub> and Metabolism'),
fluidRow(
column(12, align='left',
div(align='center', style=paste0(
'display: inline-block;',
'vertical-align:middle;',
'margin-right:1em'),
div(align='center', style=paste0(
'display: inline-block;',
'vertical-align:middle;',
'margin-right:1em'),
p(strong('Select DOY range:')),
p('Drag blue bar to move fixed range',
style=paste0(
'color:gray; font-size:80%;',
'padding:0; margin:0')),
p('Press play to autoscroll*',
style='color:gray; font-size:80%')
),
div(align='left', style=paste0(
'display: inline-block;',
'vertical-align:middle;'),
htmlOutput('time_slider')
)
),
hr()
)
),
fluidRow(
column(9, align='center',
plotOutput('metab_legend', height='20px',
width='auto'),
plotOutput('metab_plot', height='300px', width='auto'),
plotOutput('O2_legend', height='20px',
width='auto'),
plotOutput('O2_plot', brush='O2_brush',
height='300px', width='auto')
),
column(3, align='center',
p(strong(HTML('Cumulative O<sub>2</sub> (gm<sup>-2</sup>d<sup>-1</sup>)'))),
tableOutput('cumul_metab'),
br(),
# plotOutput('cumul_legend', height='20px',
# width='auto'),
# plotOutput('cumul_plot', height='200px', width='auto'),
plotOutput('kernel_legend', height='20px',
width='auto'),
plotOutput('kernel_plot', height='200px', width='auto'),
br(),
selectInput('metab_overlay', 'Model param overlay',
list('None', 'mean daily K600'), selected='None'),
selectInput('O2_overlay', 'Input data overlay',
as.list(c('None', select_vars)), selected='None'),
radioButtons('xformat', 'Series x-axis', inline=TRUE,
list('DOY', 'Date'), selected='DOY')
)
),
br(),
p(paste("*At this time, plotting may fail if your data",
"do not all occur within the same calendar year.",
"If plots do not load, try toggling between tabs."),
style='color:gray; font-size:100%')
)
)
)
server = function(input, output, session){
output$time_slider = renderUI({
if(!is.null(fitpred)){
#convert POSIX time to DOY and UNIX time
DOY = as.numeric(gsub('^0+', '',
strftime(fitpred$mod_out$data$solar.time, format="%j")))
#get DOY bounds for slider
DOYmin = ifelse(DOY[1] %in% 365:366, 1, DOY[1])
DOYmax = DOY[length(DOY)]
sliderInput("range", label=NULL,
min=DOYmin, max=DOYmax, value=c(DOYmin, DOYmax),
ticks=TRUE, step=3,
animate=animationOptions(interval=2000)
)
}
})
output$MPtime_slider = renderUI({
if(!is.null(fitpred)){
#convert POSIX time to DOY and UNIX time
MPDOY = as.numeric(gsub('^0+', '',
strftime(fitpred$mod_out$data$solar.time, format="%j")))
#get DOY bounds for slider
MPDOYmin = ifelse(MPDOY[1] %in% 365:366, 1, MPDOY[1])
MPDOYmax = MPDOY[length(MPDOY)]
sliderInput("MPrange", label=NULL,
min=MPDOYmin, max=MPDOYmax, value=c(MPDOYmin, MPDOYmax),
ticks=TRUE, step=3,
animate=animationOptions(interval=2000)
)
}
})
#update model performance data frames based on time range selection
get_slices = eventReactive({
input$MPrange
}, {
if(! is.null(fitpred)){
mod_out = fitpred$mod_out
MPstart = input$MPrange[1]
MPend = input$MPrange[2]
#convert POSIX time to DOY and UNIX time
DOY = as.numeric(gsub('^0+', '', strftime(mod_out$data$solar.time,
format="%j")))
date = as.Date(gsub('^0+', '', strftime(mod_out$data$solar.time,
format="%Y-%m-%d")))
# replace initial DOYs of 365 or 366 (solar date in previous calendar year) with 1
if(DOY[1] %in% 365:366){
DOY[DOY %in% 365:366 & 1:length(DOY) < length(DOY)/2] = 1
}
#filter data by date bounds specified in time slider
xmin_ind = match(MPstart, DOY)
if(is.na(xmin_ind)) xmin_ind = 1
xmin = date[xmin_ind]
xmax_ind = length(DOY) - match(MPend, rev(DOY)) + 1
if(is.na(xmax_ind)) xmax_ind = nrow(mod_out$data)
xmax = date[xmax_ind]
daily_slice = mod_out$fit$daily[mod_out$fit$daily$date <= xmax &
mod_out$fit$daily$date >= xmin,]
data_daily_slice = mod_out$data_daily[mod_out$data_daily$date <= xmax &
mod_out$data_daily$date >= xmin,]
out = list(daily_slice=daily_slice, data_daily_slice=data_daily_slice,
mod_out=mod_out)
}
})
observeEvent({
input$range
}, {
start = input$range[1]
end = input$range[2]
if(!is.null(start) && !is.null(end)){
output$metab_legend = renderPlot({
if(input$metab_overlay != 'None'){
metab_legend(show_K600=TRUE)
} else {
metab_legend(show_K600=FALSE)
}
})
output$metab_plot = renderPlot({
ts_full = processing_func(fitpred$predictions, st=start,
en=end)
par(mar=c(1,4,0,4), oma=rep(0,4))
daily = fitpred$mod_out$fit$daily
daily$doy = as.numeric(gsub('^0+', '',
strftime(daily$date, format="%j")))
daily = daily[daily$doy > start & daily$doy < end,]
season_ts_func(ts_full, daily, st=start, en=end,
input$metab_overlay)
})
output$kernel_legend = renderPlot({
kernel_legend()
})
output$kernel_plot = renderPlot({
ts_full = processing_func(fitpred$predictions, st=start,
en=end)
par(mar=c(3,3.5,0,.5), oma=rep(0,4))
kernel_func(ts_full, 'Name and Year')
})
output$O2_legend = renderPlot({
O2_legend(overlay=input$O2_overlay, varmap=varmap)
})
output$O2_plot = renderPlot({
par(mar=c(3,4,0,4), oma=rep(0,4))
O2_plot(mod_out=fitpred$mod_out, st=start, en=end,
brush=input$O2_brush, overlay=input$O2_overlay,
xformat=input$xformat, varmap=varmap)
})
output$cumul_metab = renderTable({
ts_full = processing_func(fitpred$predictions, st=start,
en=end)
na_rm = na.omit(ts_full)
gppsum = sum(na_rm$GPP, na.rm=TRUE)
ersum = sum(na_rm$ER, na.rm=TRUE)
nepsum = sum(na_rm$NPP, na.rm=TRUE)
return(data.frame('GPP'=gppsum, 'ER'=ersum, 'NEP'=nepsum))
}, striped=TRUE)
}
})
observeEvent({
input$MPrange
}, {
slices = get_slices()
mod_out = slices$mod_out
MPstart = input$MPrange[1]
MPend = input$MPrange[2]
if(!is.null(MPstart) && !is.null(MPend)){
output$KvER = renderPlot({
if(!is.null(fitpred$mod_out)){
KvER_plot(mod_out=fitpred$mod_out,
slice=slices$daily_slice)
}
})
output$KvQ = renderPlot({
if(!is.null(fitpred$mod_out)){
KvQ_plot(mod_out=fitpred$mod_out,
slicex=slices$data_daily_slice,
slicey=slices$daily_slice)
}
})
output$KvGPP = renderPlot({
if(!is.null(fitpred$mod_out)){
KvGPP_plot(mod_out=fitpred$mod_out,
slice=slices$daily_slice)
}
})
output$QvKres = renderPlot({
if(!is.null(fitpred$mod_out)){
QvKres_plot(mod_out=fitpred$mod_out,
slicex=slices$data_daily_slice,
slicey=slices$daily_slice)
}
})
}
})
#replot when a click is registered; don't react when click handler flushes
observeEvent({
if (! is.null(input$KvER_click$x) ||
! is.null(input$KvQ_click$x) ||
! is.null(input$QvKres_click$x) ||
! is.null(input$KvGPP_click$x)) TRUE
else NULL
}, {
slices = get_slices()
mod_out = slices$mod_out
MPstart = input$MPrange[1]
MPend = input$MPrange[2]
output$KvER = renderPlot({
KvER_plot(mod_out=mod_out,
slice=slices$daily_slice, click=isolate(input$KvER_click))
})
output$KvQ = renderPlot({
KvQ_plot(mod_out=mod_out, slicex=slices$data_daily_slice,
slicey=slices$daily_slice, click=isolate(input$KvQ_click))
})
output$KvGPP = renderPlot({
KvGPP_plot(mod_out=mod_out,
slice=slices$daily_slice, click=isolate(input$KvGPP_click))
})
output$QvKres = renderPlot({
if(!is.null(mod_out)){
QvKres_plot(mod_out=mod_out, slicex=slices$data_daily_slice,
slicey=slices$daily_slice, click=isolate(input$QvKres_click))
}
})
}, ignoreNULL=TRUE)
}
shinyApp(ui=ui, server=server, options=list(launch.browser=TRUE))
}
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