R/shinyAppUI.R
In predsci/solarExtremes: solarExtremes - Estimating Space Weather Benchmarks
#' Shiny app server object
#'
#' @import From ggplot
#' @import shiny
# create the shiny application user interface
shinyAppUI <- fluidPage(# App title ----
tags$hr(),
titlePanel("Estimating Space Weather Benchmarks", windowTitle = "solarExtremes"),
tags$hr(),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Select the random distribution type ----
selectInput(
inputId = "var",
label = "Select Space Weather Parameter:",
list("Geomagnetic Indices" = c("Dst" = 'dst', 'AE' = 'ae', "AL" = 'al', "AU" = 'au', "AP" = 'ap', 'Electric Field (Ey)' = 'ey'),
"Interplanetary Indices" = c("CME" = 'cme', "IMF Bn" = "bn", "Bz (GSM)" = "bz", "Solar Wind Speed" = 'sw', "Plasma Beta" = 'beta'),
"Solar Indices" = c("Sunspot Numbers "="sunspot", "F10.7" = 'f107'),
"User Data" = c('gnr')), selected = 'dst'),
tags$hr(),
conditionalPanel(
condition = "input.var == 'dst'",
dateRangeInput(inputId = "tIntDst",
label = "Date Range:",
start = "1957-01-01 00:00:00 GMT",
end = "2019-12-31 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'ae'",
dateRangeInput(inputId = "tIntAE",
label = "Date Range:",
start = "1964-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'al'",
dateRangeInput(inputId = "tIntAL",
label = "Date Range:",
start = "1966-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'au'",
dateRangeInput(inputId = "tIntAU",
label = "Date Range:",
start = "1966-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'ap'",
dateRangeInput(inputId = "tIntAP",
label = "Date Range:",
start = "1964-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'ey'",
dateRangeInput(inputId = "tIntEy",
label = "Date Range:",
start = "1964-1-01 17:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'bz'",
dateRangeInput(inputId = "tIntBz",
label = "Date Range:",
start = "1963-11-28 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'sw'",
dateRangeInput(inputId = "tIntSW",
label = "Date Range:",
start = "1964-01-01 12:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'beta'",
dateRangeInput(inputId = "tIntBeta",
label = "Date Range:",
start = "1964-1-01 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'sunspot'",
dateRangeInput(inputId = "tIntSunSpot",
label = "Date Range:",
start = "1964-01-01 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'f107'",
dateRangeInput(inputId = "tIntF107",
label = "Date Range:",
start = "1964-1-02 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'cme'",
dateRangeInput(inputId = "tIntCME",
label = "Date Range:",
start = "1996-01-11 00:14:36 GMT",
end = "2018-12-31 09:48:05 GMT")),
conditionalPanel(
condition = "input.var == 'bn'",
dateRangeInput(inputId = "tIntBn",
label = "Date Range:",
start = "1963-11-27 19:00:00 GMT",
end = "2019-11-05 14:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'gnr'",
fileInput(inputId = "file1",
label = "Choose a CSV file",
placeholder = 'Select File..Click Submit Button',
accept = c("text/csv", "text/comma=separated values, text/plain",".csv"))),
tags$hr(),
conditionalPanel(
condition = "input.var == 'dst'",
sliderInput(inputId = "evntMaxDst",
label = "Define Minimum Storm Size:",
min=-150, max = -50, step = 5, value = -100.)),
conditionalPanel(
condition = "input.var == 'ae'",
sliderInput(inputId = "evntMaxdAE",
label = "Define Minimum Storm Size:",
min=800, max = 1200, step = 50, value = 1000.)),
conditionalPanel(
condition = "input.var == 'al'",
sliderInput(inputId = "evntMaxdAL",
label = "Define Minimum Storm Size:",
min=-800, max = -400, step = 50, value = -600.)),
conditionalPanel(
condition = "input.var == 'au'",
sliderInput(inputId = "evntMaxdAU",
label = "Define Minimum Storm Size:",
min=300, max = 500, step = 20, value = 400.)),
conditionalPanel(
condition = "input.var == 'ap'",
sliderInput(inputId = "evntMaxdAP",
label = "Define Minimum Storm Size:",
min=80, max = 120, step = 5, value = 100.)),
conditionalPanel(
condition = "input.var == 'ey'",
sliderInput(inputId = "evntMaxdEy",
label = "Define Minimum Storm Size:",
min=6, max = 10, step = 1, value = 8.)),
conditionalPanel(
condition = "input.var == 'bz'",
sliderInput(inputId = "evntMaxdBz",
label = "Define Minimum Storm Size:",
min=-12, max = -8, step = 2, value = -10.)),
conditionalPanel(
condition = "input.var == 'sw'",
sliderInput(inputId = "evntMaxSW",
label = "Define Minimum Storm Size:",
min= 500, max = 700, step = 50, value = 600.)),
conditionalPanel(
condition = "input.var == 'beta'",
sliderInput(inputId = "evntMaxBeta",
label = "Define Minimum Storm Size:",
min = 10, max = 30, step = 5, value = 20.)),
conditionalPanel(
condition = "input.var == 'sunspot'",
sliderInput(inputId = "evntMaxSunSpot",
label = "Define Minimum Storm Size:",
min = 120, max = 170, step = 5, value = 150.)),
conditionalPanel(
condition = "input.var == 'f107'",
sliderInput(inputId = "evntMaxF107",
label = "Define Minimum Storm Size:",
min = 90, max = 100, step = 5, value = 100.)),
conditionalPanel(
condition = "input.var == 'Storm Threshold'",
sliderInput(inputId = "evntMaxBn",
label = "Date Range:",
min = -12, max = -8, step = 1, value = -10.)),
tags$hr(),
radioButtons(
inputId = 'tailmethod',
label = 'Set Tail of Distribution:',
choices = list('Automatically (Kolmogorov-Smirnov Goodness of Fit)' = 1,
"Manually" = 2), selected = 1
),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'dst'",
sliderInput(inputId = 'xminDst',
label = 'Tail Value',
min = 80, max = 140, step = 5, value = 110)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'ae'",
sliderInput(inputId = 'xminAE',
label = 'Tail Value',
min = 700, max = 1500, step = 50, value = 1150)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'al'",
sliderInput(inputId = 'xminAL',
label = 'Tail Value',
min = 400, max = 800, step = 25, value = 600)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'au'",
sliderInput(inputId = 'xminAU',
label = 'Tail Value',
min = 300, max = 500, step = 10, value = 400)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'ap'",
sliderInput(inputId = 'xminAP',
label = 'Tail Value',
min = 60, max = 150, step = 10, value = 110)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'ey'",
sliderInput(inputId = 'xminEy',
label = 'Tail Value',
min = 6., max = 12, step = 0.5, value = 8.5)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'bz'",
sliderInput(inputId = 'xminBz',
label = 'Tail Value',
min = 8., max = 20, step = 1., value = 10)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'sw'",
sliderInput(inputId = 'xminSW',
label = 'Tail Value',
min = 400., max = 900., step = 25., value = 650)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'beta'",
sliderInput(inputId = 'xminBeta',
label = 'Tail Value',
min = 25., max = 50, step = 1., value = 38)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'sunspot'",
sliderInput(inputId = 'xminSunSpot',
label = 'Tail Value',
min = 100, max = 200, step = 5., value = 150)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'f107'",
sliderInput(inputId = 'xminF107',
label = 'Tail Value',
min = 80., max = 150, step = 5., value = 110)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'cme'",
sliderInput(inputId = 'xminCME',
label = 'Tail Value',
min = 1000, max = 2000, step = 50, value = 1500)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'bn'",
sliderInput(inputId = 'xminBn',
label = 'Tail Value',
min = 8., max = 20, step = 1, value = 10)),
helpText("Resetting the Tail of Distribution",
" affects the Standard Analysis and POT Tabs"),
tags$hr(),
textInput("xcrit", label = "Abs. Magnitude of Critical Event:",
placeholder = 'Default is maximum value in dataset.', value = ""),
textInput('dt', label ="Time (years) for Critical Event:",
placeholder =" Enter Time for Critical Event to Occur:",value = 10),
helpText("Resetting the Critical Event and Time ",
"only affects the Standard Analysis Tab"),
submitButton("Submit", icon("refresh")),
verbatimTextOutput("value")
#verbatimTextOutput("value")
),
# Main panel for displaying outputs ----
mainPanel(# Output: Tabset w/ plot, summary, and table ----
tabsetPanel(
type = "tabs",
tabPanel(
"Visualizations",
tags$hr(),
withLoader(plotlyOutput("Plot1", height = "auto", width = "auto"), type ='html', loader = 'loader4'),
withLoader(plotlyOutput("Plot2", height = "auto", width = "auto"), type ='html', loader ='loader4')
),
tabPanel(
"Events Table",
tags$hr(),
htmlOutput("headerTableEvents"),
br(),
dataTableOutput("table", width = "70%", height = "auto")
),
tabPanel(
"Standard Analysis",
br(),
br(),
withLoader(plotlyOutput("ClausetPlot1", height = "auto", width = "auto"), type = 'html', loader = 'loader4'),
br(),
withLoader(plotlyOutput("ClausetPlot2", height = "auto", width = "auto"), type = 'html', loader = 'loader4'),
br(),
br(),
htmlOutput("headerTableEvent"),
br(),
br(),
tableOutput("pEventXyears"),
br(),
br(),
withLoader(plotlyOutput("compDistPlot", height = "auto", width = "auto"), type ='html', loader ='loader4'),
br(),
htmlOutput("headerCompareModels"),
br(),
tableOutput("compareModels"),
br(),
htmlOutput("footnoteCompareModels")
),
tabPanel(
"POT Analysis",
br(),
br(),
withLoader(plotlyOutput("POTPlot", height = "auto", width = "auto"), type ='html', loader ='loader4'),
br(),
htmlOutput("headerTablePOT"),
br(),
tableOutput("summaryPOT")
),
tabPanel("Documentation",
br(),
h3("Background"),
tags$p("Space weather refers to the state and future conditions of the space environment surrounding the Earth, including within the Earth's magnetosphere. Extreme Space Weather refers to conditions that are so far removed from the norm, that they are rare. This page provides a prototype tool for estimating the probability of an extreme solar event, together with the uncertainties associated with that estimate."),
br(),
h3("Methods"),
tags$p("Below we briefly describe what each tab does. For more details about the models see our ",tags$a("2012", target="_blank",href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011SW000734"),",", tags$a("2017",target="_blank",href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016SW001470"), "and ", tags$a("2018",target="_blank",href="https://www.sciencedirect.com/science/article/pii/B9780128127001000054?via%3Dihub"), " publications. For the Peak Over Threshold (POT) method we use the ",tags$a("extRemes 2.0",target="_blank",href="https://www.jstatsoft.org/article/view/v072i08") , " R package."),
br(),
tags$ul(tags$li("In the ", tags$b("Standard Analysis"), " tab, we use three distributions: power-law, log-normal and exponential to fit the tail of the distribution of different space weather parameters: geomagnetic indices (Dst, AE, AL, AU, AP, and Ey), interplanetary indices (CME speed, Bn, Bz (GSM), solar wind speed, and plasma beta), solar indices (sun spot number and F10.7 index). For each distribution, we estimate the best model parameters and generate an ensemble of possible realizations using nonparametric bootstrapping to compute the uncertainty associated with a particular forecast."),
tags$li(" We also use the Vuong's test, which relies on a likelihood ratio test to see if one model is more favorable than another. We compare the Power-Law model to the Log-Normal and Exponential models, and the Log-Normal model to the Exponential model."),
tags$li("The ", tags$b("POT"), " (Peak Over Threshold) tab uses a Generalized Pareto distibution to fit the values that exceed a certain threshold. The threshold is set either manually or by minimizing the Kolmogotov-Smirnov (KS) goodness-of-fit statistic. The same threshold is used in the Standard Analysis and POT tabs."),
tags$li("The ", tags$b("Events Table"), "tab provides a searchable table of events that were identified for a continuous time series.")),
br(),
h3("Data"),
tags$ul(
tags$li("Dst Data are obtained from the ", tags$a(href="http://wdc.kugi.kyoto-u.ac.jp/index.html", "World Data Center for Geomagnetism, Kyoto ", target="_blank"),";"),
tags$li("AE, AL, AU, AP, Ey, Bz, Solar Wind, Plasma Beta and F10.7 data are obtained from NASA's OMNI 2 ", tags$a(href="https://omniweb.gsfc.nasa.gov/form/dx1.html", "data web server", target="_blank"),";"),
tags$li("CME data are obtained from SOHO's ", tags$a(href="https://cdaw.gsfc.nasa.gov/CME_list/","LASCO CME database", target="_blank"), "; and"),
tags$li("IMF Bn Data is obtained from: (a) Historic data from the early 1970's to the end of 2016 were obtained from NASA's OMNI_M dataset,
using the ",tags$a(href="ftp://ftp.swpc.noaa.gov/pub/lists/ace2/", "COHOWeb data server",target="_blank"), " (b) Past, 1-minute high resolution data for 2017, are obtained from NOAA's DSCOVR Space Weather ", tags$a(href="https://www.ngdc.noaa.gov/dscovr/portal/index.html#/","Data Portal",target="_blank"), " and (c) Real time data are obtained from NOAA's Space Weather Prediction Center",
tags$a(href="http://services.swpc.noaa.gov/products/solar-wind/","(SWPC)",target="_blank"),". The high-resolution 1-minute datasets are averaged over 1-hour.")
),
br(),
h3("User Uploaded Data"),
tags$p("The user can upload a table of events for analysis. Currently we only support 'csv' format. The csv file should have columns of: year, month, day, hour, minute, second and event magnitude. Only the first (year) and last (event magnitude) columns are required. All other columns are optional. The magnitude of all the events in the table must be positive. Please note that not all extreme event data can be modeled properly with exponential, log-normal and power-law distributions. To upload your data:"),
tags$ul(
tags$li("Select the ", tags$b("User Data"), " button and click on the ", tags$b("Submit"), " button."),
tags$li("Use the", tags$b("Browse")," window to select and upload your csv file. Once the file is loaded you should see an ", tags$b("upload complete")," bar and shortly after the data will be displayed. "),
tags$li("If the dataset was correctly uploaded it can be modeled as described above.")),
br(),
h3("Technology"),
tags$p("This package was written in R, and displayed using R shinyApp and plotly."),
br(),
br()
),
tabPanel("Contact",
column(width=7,
br(),
br(),
tags$p(tags$em("For questions about this web site please contact:")),
br(),
tags$p("Dr. Pete Riley: pete@predsci.com"),
tags$p("Dr. M. Ben-Nun: mbennun@predsci.com"),
br(),
br(),
includeHTML("address.html")))
))
))
predsci/solarExtremes documentation built on Feb. 18, 2020, 1:49 a.m.
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#' Shiny app server object
#'
#' @import From ggplot
#' @import shiny
# create the shiny application user interface
shinyAppUI <- fluidPage(# App title ----
tags$hr(),
titlePanel("Estimating Space Weather Benchmarks", windowTitle = "solarExtremes"),
tags$hr(),
# Sidebar layout with input and output definitions ----
sidebarLayout(
# Sidebar panel for inputs ----
sidebarPanel(
# Input: Select the random distribution type ----
selectInput(
inputId = "var",
label = "Select Space Weather Parameter:",
list("Geomagnetic Indices" = c("Dst" = 'dst', 'AE' = 'ae', "AL" = 'al', "AU" = 'au', "AP" = 'ap', 'Electric Field (Ey)' = 'ey'),
"Interplanetary Indices" = c("CME" = 'cme', "IMF Bn" = "bn", "Bz (GSM)" = "bz", "Solar Wind Speed" = 'sw', "Plasma Beta" = 'beta'),
"Solar Indices" = c("Sunspot Numbers "="sunspot", "F10.7" = 'f107'),
"User Data" = c('gnr')), selected = 'dst'),
tags$hr(),
conditionalPanel(
condition = "input.var == 'dst'",
dateRangeInput(inputId = "tIntDst",
label = "Date Range:",
start = "1957-01-01 00:00:00 GMT",
end = "2019-12-31 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'ae'",
dateRangeInput(inputId = "tIntAE",
label = "Date Range:",
start = "1964-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'al'",
dateRangeInput(inputId = "tIntAL",
label = "Date Range:",
start = "1966-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'au'",
dateRangeInput(inputId = "tIntAU",
label = "Date Range:",
start = "1966-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'ap'",
dateRangeInput(inputId = "tIntAP",
label = "Date Range:",
start = "1964-1-01 00:00:00 GMT",
end = "2018-02-28 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'ey'",
dateRangeInput(inputId = "tIntEy",
label = "Date Range:",
start = "1964-1-01 17:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'bz'",
dateRangeInput(inputId = "tIntBz",
label = "Date Range:",
start = "1963-11-28 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'sw'",
dateRangeInput(inputId = "tIntSW",
label = "Date Range:",
start = "1964-01-01 12:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'beta'",
dateRangeInput(inputId = "tIntBeta",
label = "Date Range:",
start = "1964-1-01 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'sunspot'",
dateRangeInput(inputId = "tIntSunSpot",
label = "Date Range:",
start = "1964-01-01 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'f107'",
dateRangeInput(inputId = "tIntF107",
label = "Date Range:",
start = "1964-1-02 00:00:00 GMT",
end = "2019-01-30 23:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'cme'",
dateRangeInput(inputId = "tIntCME",
label = "Date Range:",
start = "1996-01-11 00:14:36 GMT",
end = "2018-12-31 09:48:05 GMT")),
conditionalPanel(
condition = "input.var == 'bn'",
dateRangeInput(inputId = "tIntBn",
label = "Date Range:",
start = "1963-11-27 19:00:00 GMT",
end = "2019-11-05 14:00:00 GMT")),
conditionalPanel(
condition = "input.var == 'gnr'",
fileInput(inputId = "file1",
label = "Choose a CSV file",
placeholder = 'Select File..Click Submit Button',
accept = c("text/csv", "text/comma=separated values, text/plain",".csv"))),
tags$hr(),
conditionalPanel(
condition = "input.var == 'dst'",
sliderInput(inputId = "evntMaxDst",
label = "Define Minimum Storm Size:",
min=-150, max = -50, step = 5, value = -100.)),
conditionalPanel(
condition = "input.var == 'ae'",
sliderInput(inputId = "evntMaxdAE",
label = "Define Minimum Storm Size:",
min=800, max = 1200, step = 50, value = 1000.)),
conditionalPanel(
condition = "input.var == 'al'",
sliderInput(inputId = "evntMaxdAL",
label = "Define Minimum Storm Size:",
min=-800, max = -400, step = 50, value = -600.)),
conditionalPanel(
condition = "input.var == 'au'",
sliderInput(inputId = "evntMaxdAU",
label = "Define Minimum Storm Size:",
min=300, max = 500, step = 20, value = 400.)),
conditionalPanel(
condition = "input.var == 'ap'",
sliderInput(inputId = "evntMaxdAP",
label = "Define Minimum Storm Size:",
min=80, max = 120, step = 5, value = 100.)),
conditionalPanel(
condition = "input.var == 'ey'",
sliderInput(inputId = "evntMaxdEy",
label = "Define Minimum Storm Size:",
min=6, max = 10, step = 1, value = 8.)),
conditionalPanel(
condition = "input.var == 'bz'",
sliderInput(inputId = "evntMaxdBz",
label = "Define Minimum Storm Size:",
min=-12, max = -8, step = 2, value = -10.)),
conditionalPanel(
condition = "input.var == 'sw'",
sliderInput(inputId = "evntMaxSW",
label = "Define Minimum Storm Size:",
min= 500, max = 700, step = 50, value = 600.)),
conditionalPanel(
condition = "input.var == 'beta'",
sliderInput(inputId = "evntMaxBeta",
label = "Define Minimum Storm Size:",
min = 10, max = 30, step = 5, value = 20.)),
conditionalPanel(
condition = "input.var == 'sunspot'",
sliderInput(inputId = "evntMaxSunSpot",
label = "Define Minimum Storm Size:",
min = 120, max = 170, step = 5, value = 150.)),
conditionalPanel(
condition = "input.var == 'f107'",
sliderInput(inputId = "evntMaxF107",
label = "Define Minimum Storm Size:",
min = 90, max = 100, step = 5, value = 100.)),
conditionalPanel(
condition = "input.var == 'Storm Threshold'",
sliderInput(inputId = "evntMaxBn",
label = "Date Range:",
min = -12, max = -8, step = 1, value = -10.)),
tags$hr(),
radioButtons(
inputId = 'tailmethod',
label = 'Set Tail of Distribution:',
choices = list('Automatically (Kolmogorov-Smirnov Goodness of Fit)' = 1,
"Manually" = 2), selected = 1
),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'dst'",
sliderInput(inputId = 'xminDst',
label = 'Tail Value',
min = 80, max = 140, step = 5, value = 110)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'ae'",
sliderInput(inputId = 'xminAE',
label = 'Tail Value',
min = 700, max = 1500, step = 50, value = 1150)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'al'",
sliderInput(inputId = 'xminAL',
label = 'Tail Value',
min = 400, max = 800, step = 25, value = 600)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'au'",
sliderInput(inputId = 'xminAU',
label = 'Tail Value',
min = 300, max = 500, step = 10, value = 400)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'ap'",
sliderInput(inputId = 'xminAP',
label = 'Tail Value',
min = 60, max = 150, step = 10, value = 110)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'ey'",
sliderInput(inputId = 'xminEy',
label = 'Tail Value',
min = 6., max = 12, step = 0.5, value = 8.5)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'bz'",
sliderInput(inputId = 'xminBz',
label = 'Tail Value',
min = 8., max = 20, step = 1., value = 10)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'sw'",
sliderInput(inputId = 'xminSW',
label = 'Tail Value',
min = 400., max = 900., step = 25., value = 650)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'beta'",
sliderInput(inputId = 'xminBeta',
label = 'Tail Value',
min = 25., max = 50, step = 1., value = 38)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'sunspot'",
sliderInput(inputId = 'xminSunSpot',
label = 'Tail Value',
min = 100, max = 200, step = 5., value = 150)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'f107'",
sliderInput(inputId = 'xminF107',
label = 'Tail Value',
min = 80., max = 150, step = 5., value = 110)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'cme'",
sliderInput(inputId = 'xminCME',
label = 'Tail Value',
min = 1000, max = 2000, step = 50, value = 1500)),
conditionalPanel(
condition = "input.tailmethod == '2' && input.var == 'bn'",
sliderInput(inputId = 'xminBn',
label = 'Tail Value',
min = 8., max = 20, step = 1, value = 10)),
helpText("Resetting the Tail of Distribution",
" affects the Standard Analysis and POT Tabs"),
tags$hr(),
textInput("xcrit", label = "Abs. Magnitude of Critical Event:",
placeholder = 'Default is maximum value in dataset.', value = ""),
textInput('dt', label ="Time (years) for Critical Event:",
placeholder =" Enter Time for Critical Event to Occur:",value = 10),
helpText("Resetting the Critical Event and Time ",
"only affects the Standard Analysis Tab"),
submitButton("Submit", icon("refresh")),
verbatimTextOutput("value")
#verbatimTextOutput("value")
),
# Main panel for displaying outputs ----
mainPanel(# Output: Tabset w/ plot, summary, and table ----
tabsetPanel(
type = "tabs",
tabPanel(
"Visualizations",
tags$hr(),
withLoader(plotlyOutput("Plot1", height = "auto", width = "auto"), type ='html', loader = 'loader4'),
withLoader(plotlyOutput("Plot2", height = "auto", width = "auto"), type ='html', loader ='loader4')
),
tabPanel(
"Events Table",
tags$hr(),
htmlOutput("headerTableEvents"),
br(),
dataTableOutput("table", width = "70%", height = "auto")
),
tabPanel(
"Standard Analysis",
br(),
br(),
withLoader(plotlyOutput("ClausetPlot1", height = "auto", width = "auto"), type = 'html', loader = 'loader4'),
br(),
withLoader(plotlyOutput("ClausetPlot2", height = "auto", width = "auto"), type = 'html', loader = 'loader4'),
br(),
br(),
htmlOutput("headerTableEvent"),
br(),
br(),
tableOutput("pEventXyears"),
br(),
br(),
withLoader(plotlyOutput("compDistPlot", height = "auto", width = "auto"), type ='html', loader ='loader4'),
br(),
htmlOutput("headerCompareModels"),
br(),
tableOutput("compareModels"),
br(),
htmlOutput("footnoteCompareModels")
),
tabPanel(
"POT Analysis",
br(),
br(),
withLoader(plotlyOutput("POTPlot", height = "auto", width = "auto"), type ='html', loader ='loader4'),
br(),
htmlOutput("headerTablePOT"),
br(),
tableOutput("summaryPOT")
),
tabPanel("Documentation",
br(),
h3("Background"),
tags$p("Space weather refers to the state and future conditions of the space environment surrounding the Earth, including within the Earth's magnetosphere. Extreme Space Weather refers to conditions that are so far removed from the norm, that they are rare. This page provides a prototype tool for estimating the probability of an extreme solar event, together with the uncertainties associated with that estimate."),
br(),
h3("Methods"),
tags$p("Below we briefly describe what each tab does. For more details about the models see our ",tags$a("2012", target="_blank",href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011SW000734"),",", tags$a("2017",target="_blank",href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2016SW001470"), "and ", tags$a("2018",target="_blank",href="https://www.sciencedirect.com/science/article/pii/B9780128127001000054?via%3Dihub"), " publications. For the Peak Over Threshold (POT) method we use the ",tags$a("extRemes 2.0",target="_blank",href="https://www.jstatsoft.org/article/view/v072i08") , " R package."),
br(),
tags$ul(tags$li("In the ", tags$b("Standard Analysis"), " tab, we use three distributions: power-law, log-normal and exponential to fit the tail of the distribution of different space weather parameters: geomagnetic indices (Dst, AE, AL, AU, AP, and Ey), interplanetary indices (CME speed, Bn, Bz (GSM), solar wind speed, and plasma beta), solar indices (sun spot number and F10.7 index). For each distribution, we estimate the best model parameters and generate an ensemble of possible realizations using nonparametric bootstrapping to compute the uncertainty associated with a particular forecast."),
tags$li(" We also use the Vuong's test, which relies on a likelihood ratio test to see if one model is more favorable than another. We compare the Power-Law model to the Log-Normal and Exponential models, and the Log-Normal model to the Exponential model."),
tags$li("The ", tags$b("POT"), " (Peak Over Threshold) tab uses a Generalized Pareto distibution to fit the values that exceed a certain threshold. The threshold is set either manually or by minimizing the Kolmogotov-Smirnov (KS) goodness-of-fit statistic. The same threshold is used in the Standard Analysis and POT tabs."),
tags$li("The ", tags$b("Events Table"), "tab provides a searchable table of events that were identified for a continuous time series.")),
br(),
h3("Data"),
tags$ul(
tags$li("Dst Data are obtained from the ", tags$a(href="http://wdc.kugi.kyoto-u.ac.jp/index.html", "World Data Center for Geomagnetism, Kyoto ", target="_blank"),";"),
tags$li("AE, AL, AU, AP, Ey, Bz, Solar Wind, Plasma Beta and F10.7 data are obtained from NASA's OMNI 2 ", tags$a(href="https://omniweb.gsfc.nasa.gov/form/dx1.html", "data web server", target="_blank"),";"),
tags$li("CME data are obtained from SOHO's ", tags$a(href="https://cdaw.gsfc.nasa.gov/CME_list/","LASCO CME database", target="_blank"), "; and"),
tags$li("IMF Bn Data is obtained from: (a) Historic data from the early 1970's to the end of 2016 were obtained from NASA's OMNI_M dataset,
using the ",tags$a(href="ftp://ftp.swpc.noaa.gov/pub/lists/ace2/", "COHOWeb data server",target="_blank"), " (b) Past, 1-minute high resolution data for 2017, are obtained from NOAA's DSCOVR Space Weather ", tags$a(href="https://www.ngdc.noaa.gov/dscovr/portal/index.html#/","Data Portal",target="_blank"), " and (c) Real time data are obtained from NOAA's Space Weather Prediction Center",
tags$a(href="http://services.swpc.noaa.gov/products/solar-wind/","(SWPC)",target="_blank"),". The high-resolution 1-minute datasets are averaged over 1-hour.")
),
br(),
h3("User Uploaded Data"),
tags$p("The user can upload a table of events for analysis. Currently we only support 'csv' format. The csv file should have columns of: year, month, day, hour, minute, second and event magnitude. Only the first (year) and last (event magnitude) columns are required. All other columns are optional. The magnitude of all the events in the table must be positive. Please note that not all extreme event data can be modeled properly with exponential, log-normal and power-law distributions. To upload your data:"),
tags$ul(
tags$li("Select the ", tags$b("User Data"), " button and click on the ", tags$b("Submit"), " button."),
tags$li("Use the", tags$b("Browse")," window to select and upload your csv file. Once the file is loaded you should see an ", tags$b("upload complete")," bar and shortly after the data will be displayed. "),
tags$li("If the dataset was correctly uploaded it can be modeled as described above.")),
br(),
h3("Technology"),
tags$p("This package was written in R, and displayed using R shinyApp and plotly."),
br(),
br()
),
tabPanel("Contact",
column(width=7,
br(),
br(),
tags$p(tags$em("For questions about this web site please contact:")),
br(),
tags$p("Dr. Pete Riley: pete@predsci.com"),
tags$p("Dr. M. Ben-Nun: mbennun@predsci.com"),
br(),
br(),
includeHTML("address.html")))
))
))
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