gui4gmwm/app.R
In SMAC-Group/gmwm_gui: Graphical User Interface for the Inertial Sensors Calibration
library(gui4gmwm)
################
# CONSTANTS
################
const.RENDER_PLOT_WIDTH = 1000
const.RENDER_PLOT_HEIGHT = 600
const.RENDER_PLOT_RES = 100 # default is 72
const.FIGURE_PLOT_HEIGHT = "600px"
const.FIGURE_PLOT_HEIGHT_REDUCED = "400px"
const.FIGURE_PLOT_HEIGHT_LOGO = "100px"
const.nb_of_digits = 7
# convert degrees-per-second to radians-per-second
const.degps_2_radps = 1/360 * 2*pi
# constant default frequency for custom data
const.DEFAULT_FREQ = 1 # [Hz]
# constants for the custom datasheet
const.DEFAULT_WN = 5.304377e-07
const.DEFAULT_QN = 1.681227e-06
const.DEFAULT_SIGMA2_GM = 7.348293e-09
const.DEFAULT_BETA_GM = 3.526037e-01
const.DEFAULT_RW = 1.268314e-12
const.DEFAULT_DR = 3.913529e-09
const.DEFAULT_BI = const.DEFAULT_WN
const.DEFAULT_BIF0 = NA
# source: https://www.xsens.com/wp-content/uploads/2013/11/MTi-G_User_Manual_and_Technical_Documentation.pdf
# https://www.xsens.com/tags/accelerometers/
# https://www.xsens.com/tags/gyroscopes/
# the frequency here is 100, because the dataset was acquired at this rate
const.MTIG.GYRO_WN = (0.05 * const.degps_2_radps * sqrt(100))^2
const.MTIG.GYRO_BI = (20 / 3600 * const.degps_2_radps )^2
const.MTIG.GYRO_BIF0 = 1/40 #Hz
const.MTIG.ACC_WN = (0.002 * sqrt(100))^2
const.MTIG.ACC_BI = (30 * 1e-6 * 10)^2
const.MTIG.ACC_BIF0 = 1/0.5 #Hz
# source: http://cdn-docs.av-iq.com/dataSheet//NavChip_Product_Brief.pdf,
# the frequency here is 250, because the dataset was acquired at this rate
const.NAVCHIP.GYRO_WN = (0.003 * const.degps_2_radps * sqrt(250))^2 # [(rad/s)^2]
const.NAVCHIP.GYRO_BI = (10 / 3600 * const.degps_2_radps)^2
const.NAVCHIP.GYRO_BIF0 = 1/2 #Hz
const.NAVCHIP.ACC_WN = (50 * 1e-6 * 10 * sqrt(250))^2 # [(m/s^2)^2]
const.NAVCHIP.ACC_BI = (0.05 * 1e-3 * 10)^2
const.NAVCHIP.ACC_BIF0 = 1/5 #Hz
# source: http://www.northropgrumman.com/Capabilities/LN200FOG/Documents/ln200.pdf
# the frequency here is 400, because the dataset was acquired at this rate
const.LN200.GYRO_WN = (0.05 / 60 * sqrt(400) * const.degps_2_radps)^2
const.LN200.GYRO_BI = NA
const.LN200.GYRO_BIF0 = NA #Hz
const.LN200.ACC_WN = const.DEFAULT_WN
const.LN200.ACC_BI = NA
const.LN200.ACC_BIF0 = NA #Hz
# source: http://www.imar-navigation.de/downloads/IMU_FSAS.pdf
# the frequency here is 400, because the dataset was acquired at this rate
const.IMAR.GYRO_WN = (0.15 / 60 * sqrt(400) * const.degps_2_radps)^2
const.IMAR.GYRO_BI = (0.1 /3600 * const.degps_2_radps)^2
const.IMAR.GYRO_BIF0 = 1/100 #Hz
const.IMAR.ACC_WN = const.DEFAULT_WN
const.IMAR.ACC_BI = NA
const.IMAR.ACC_BIF0 = NA #Hz
################
# FUNCTIONS for COSINE INTEGRAL calculations
################
cos_function <- function(t){
cos(t)/t
}
Ci <- function(x){
-integrate(f = cos_function, lower = x, upper = 2e3, subdivisions=10000)$value
}
VCi <- Vectorize(Ci, c("x"))
sigma2_T <- function(T, f0, B){
2*B*B/pi * ( log(2) - ( (sin(pi*f0*T))^3 ) / (2*(pi*f0*T)^2) * ( sin(pi*f0*T)+4*pi*f0*T*cos(pi*f0*T) ) + VCi(2*pi*f0*T) - VCi(4*pi*f0*T) )
}
# loading the four internal datasets
data("navchip") # NAVCHIP
imu6 = imu(imu6, gyros = 1:3, accels = 4:6, axis = c('X', 'Y', 'Z', 'X', 'Y', 'Z'), freq = 100) #MTIG
data("imar.gyro") #IMAR
data("ln200.gyro") #LN200
smac_url <- a("https://smac-group.github.io/gui4gmwm/", href="https://smac-group.github.io/gui4gmwm/")
smac_url_description <- "gui4gmwm Overview:"
# increses file limit from default-5MB to 100MB
options(shiny.maxRequestSize=100*1024^2)
ui <- shinyUI(fluidPage(
shinyjs::useShinyjs(),
tags$style(HTML(".js-irs-0 .irs-single, .js-irs-0 .irs-bar-edge, .js-irs-0 .irs-bar {background: red}")),
tags$style(HTML(".js-irs-1 .irs-single, .js-irs-1 .irs-bar-edge, .js-irs-1 .irs-bar {background: green}")),
tags$style(type='text/css', '#summ {background-color: rgba(0,0,200,0.02); color: black; width: 500px; font-size: 14px;}'),
title = "GMWM GUI",
tabsetPanel(id = "tabs",
tabPanel("Model Data", plotOutput(outputId = "plot", height = const.FIGURE_PLOT_HEIGHT)),
tabPanel("Selected Sensor", plotOutput(outputId = "plot2", height = const.FIGURE_PLOT_HEIGHT)),
tabPanel("Summary", verbatimTextOutput(outputId = "summ", placeholder = FALSE)),
tabPanel("Help",
# fluidPage("cluster"),
h4("Help Tab" ),
br(),
# actionButton("subClust", label = "Create Subcluster"),
#
uiOutput(outputId = "tabhelpurl"),
br(),br(),
fluidRow(
column(5,
plotOutput(outputId = "tabhelpplotlogo_pennstate", height = const.FIGURE_PLOT_HEIGHT_LOGO)
),
column(5,
plotOutput(outputId = "tabhelpplotlogo_epfl", height = const.FIGURE_PLOT_HEIGHT_LOGO)
)
)
)
),
hr(),
fluidRow(
column(4,
radioButtons("data_input_choice", "Select data input:", choices = c("from library" = "library", "custom" = "custom")),
conditionalPanel(
condition = "input.data_input_choice == 'library'",
selectInput("imu_obj", "Select IMU file:",
c("MTi-G" = "imu6",
"Navchip" = "navchip",
"LN-200" = "ln200.gyro",
"IMAR" = "imar.gyro"),
selected = 1),
selectInput("sensors", "Select sensor", c("1"="1","2"="2", selected = 1))
),
conditionalPanel(
condition = "input.data_input_choice == 'custom'",
fileInput("user_defined_txt_file", "Select INPUT file (max 100MB):",
accept = c(
"text/txt",
"text/comma-separated-values,text/plain",
".txt",
".imu",
placeholder = "No file selected")
),
sliderInput("user_defined_txt_file_column", "Select column number:",
min=1, max=6, value=1),
numericInput("user_defined_txt_frequency", label = "Set frequency of dataset", value = const.DEFAULT_FREQ), # frequency defined by the user
textInput("user_defined_units", "Define units of active dataset", "rad/s")
),
column(7, radioButtons("robust", "Select estimator:", choices = c("Classic WV" = "classic", "Robust WV" = "robust"))),
br(),
actionButton("fit1", label = "Plot WV"),
br(),
uiOutput("choose_columns")
),
column(4,
checkboxGroupInput("model", "Select Model",
c("Quantization Noise" = "QN",
"White Noise" = "WN",
"Random Walk" = "RW",
"Drift" = "DR",
"Gauss-Markov" = "GM"),
selected = "WN"),
conditionalPanel(
condition = "input.model.indexOf('GM')>-1",
sliderInput("gm_nb", "Number of Gauss-Markov Processes", 1, 5, 2)
),
actionButton("fit3", label = "Fit Model"),
br(),
br(),
br(),
br(),
actionButton("fit2", label = "Reduce Model Automatically")
),
column(4,
checkboxGroupInput("option_plot", label = "Plot options:",
c("Process Decomp." = "process_decomp",
"Add Datasheet WV" = "datasheet"
#"Show CI of empirical WV" = "ci"
),
selected = c("datasheet")),
checkboxInput("overlay_datasheet", label = "Show Datasheet Specifications", value = FALSE),
conditionalPanel(
condition = "input.overlay_datasheet",
numericInput("dsv_wn", label = " WN", value = format(const.DEFAULT_WN, digits = const.nb_of_digits)),
numericInput("dsv_bi", label = " BIAS INSTABILITY", format(const.DEFAULT_BI, digits = const.nb_of_digits)),
numericInput("dsv_bif0", label = " BIAS INSTABILITY CUTOFF FREQUENCY", format(const.DEFAULT_BIF0, digits = const.nb_of_digits)),
conditionalPanel(
condition = "input.data_input_choice == 'custom'",
numericInput("dsv_qn", label = " QN", value = format(const.DEFAULT_QN, digits = const.nb_of_digits)),
column(6,
numericInput("dsv_sigma2_gm", label = " sigma2 GM", value = format(const.DEFAULT_SIGMA2_GM, digits = const.nb_of_digits))
),
column(5,
numericInput("dsv_beta_gm", label = " beta GM", value = format(const.DEFAULT_BETA_GM, digits = const.nb_of_digits))
),
numericInput("dsv_rw", label = " RW", value = format(const.DEFAULT_RW, digits = const.nb_of_digits)),
numericInput("dsv_dr", label = " DR", value = format(const.DEFAULT_DR, digits = const.nb_of_digits)),
column(2),
column(10,
actionButton("button_reset_noise_params", label = "Reset Noise Params")
)
)
),
br(),
checkboxGroupInput("summary_plot", label = "Summary options:",
c("Show CI of parameters" = "ci"),
selected = c("sum")),
checkboxInput("edit_intern", label = "Edit Optimization Parameters", value = FALSE),
conditionalPanel(
condition = "input.edit_intern == 1",
numericInput("num", label = "Number of Simu. for Starting Values", value = 10^5),
numericInput("seed", label = "Simulation seed", value = 1982)
)
)
)
))
server <- function(input, output, session) {
# library or custom dataset
v <- reactiveValues(plot = FALSE,
fit = FALSE,
gmwm = NULL,
form = NULL,
freq = 100,
first_gmwm = NULL,
n = NULL,
sensor_name = NULL,
sensor_column = NULL,
overlap_datasheet = FALSE,
y_label_with_dataunits = NA,
actual_datasheet_WN_parameter = const.DEFAULT_WN,
actual_datasheet_BI_parameter = NA,
actual_datasheet_BIF0_parameter = NA,
actual_datasheet_QN_parameter = NA,
actual_datasheet_SIGMA2_GM_parameter = NA,
actual_datasheet_BETA_GM_parameter = NA,
actual_datasheet_RW_parameter = NA,
actual_datasheet_DR_parameter = NA,
first_time_plotting_6_pack = TRUE,
custom_data = FALSE,
custom_data_name = NULL,
custom_data_type = NULL,
custom_data_size = NULL,
custom_data_tot_colums = NULL,
datasheet_noise_model = NULL,
datasheet_values_make_sense = FALSE)
# PUSHING ON BUTTON "Plot WV"
observeEvent(input$fit1, {
withProgress(message = 'Calculating empirical WV...', value = 0, {
v$plot = TRUE
v$fit = FALSE
v$overlap_datasheet = "datasheet" %in% input$option_plot
if ("library" %in% input$data_input_choice){ #using library data
my_data = get(input$imu_obj)
Xt = my_data[, input$sensors]
v$sensor_name = input$imu_obj
v$sensor_column = input$sensors
v$freq = attr(my_data, 'freq')
v$custom_data = FALSE
if (input$sensors == "Gyro. X" || input$sensors == "Gyro. Y" || input$sensors == "Gyro. Z"){
v$y_label_with_dataunits = expression(paste("Wavelet Variance ", nu, " [", rad^2/s^2, "]"))
} else if (input$sensors == "Accel. X" || input$sensors == "Accel. Y" || input$sensors == "Accel. Z"){
v$y_label_with_dataunits = expression(paste("Wavelet Variance ", nu, " [", m^2/s^4, "]"))
}
v$actual_datasheet_WN_parameter = input$dsv_wn
v$actual_datasheet_BI_parameter = input$dsv_bi
v$actual_datasheet_BIF0_parameter = input$dsv_bif0
# reset other noise values in case custom data was loaded previosuly
v$actual_datasheet_QN_parameter = const.DEFAULT_QN
v$actual_datasheet_SIGMA2_GM_parameter = const.DEFAULT_SIGMA2_GM
v$actual_datasheet_BETA_GM_parameter = const.DEFAULT_BETA_GM
v$actual_datasheet_RW_parameter = const.DEFAULT_RW
v$actual_datasheet_DR_parameter = const.DEFAULT_DR
# reset other variables in case custom data was loaded previously
v$custom_data_name = NULL
v$custom_data_type = NULL
v$custom_data_size = NULL
v$custom_data_tot_colums = NULL
} else{ #using custom data
inFile <- input$user_defined_txt_file
if (is.null(inFile))
return(NULL)
my_data = read.csv(inFile$datapath, header = FALSE, sep = ",")
if(input$user_defined_txt_file_column > ncol(my_data)){
updateSliderInput(session, "user_defined_txt_file_column", value = ncol(my_data))
the_column_number = ncol(my_data)
} else{
the_column_number = input$user_defined_txt_file_column
}
# update the slider number with the current number of colums
updateSliderInput(session, "user_defined_txt_file_column", max = ncol(my_data))
v$sensor_column = the_column_number
Xt = my_data[, the_column_number]
v$freq = input$user_defined_txt_frequency
v$y_label_with_dataunits = bquote(paste("Wavelet Variance ", nu, " [(", .(input$user_defined_units[1]), ")^2]"))
v$custom_data = TRUE
v$custom_data_name = inFile$name
v$custom_data_type = inFile$type
v$custom_data_size = inFile$size
v$custom_data_tot_colums = ncol(my_data)
v$actual_datasheet_WN_parameter = input$dsv_wn
v$actual_datasheet_BI_parameter = input$dsv_bi
v$actual_datasheet_BIF0_parameter = input$dsv_bif0
v$actual_datasheet_QN_parameter = input$dsv_qn
v$actual_datasheet_SIGMA2_GM_parameter = input$dsv_sigma2_gm
v$actual_datasheet_BETA_GM_parameter = input$dsv_beta_gm
v$actual_datasheet_RW_parameter = input$dsv_rw
v$actual_datasheet_DR_parameter = input$dsv_dr
}
updateNumericInput(session, "dsv_wn", value = format(v$actual_datasheet_WN_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bi", value = format(v$actual_datasheet_BI_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bif0", value = format(v$actual_datasheet_BIF0_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_qn", value = format(v$actual_datasheet_QN_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_sigma2_gm", value = format(v$actual_datasheet_SIGMA2_GM_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_beta_gm", value = format(v$actual_datasheet_BETA_GM_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_rw", value = format(v$actual_datasheet_RW_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_dr", value = format(v$actual_datasheet_DR_parameter, digits = const.nb_of_digits))
v$n = length(Xt)
v$form = wvar(as.numeric(Xt), robust = (input$robust=="robust") , freq = v$freq)
if (v$overlap_datasheet == TRUE){
v$datasheet_values_make_sense = TRUE
if ("library" %in% input$data_input_choice){ #using library data there is only WN, the BI is plotted separately directly in the plot
extranoise <- wn_to_wv(sigma2 = v$actual_datasheet_WN_parameter, tau = v$form$scales*v$freq)
if (!is.na(v$actual_datasheet_BI_parameter)){
extranoise <- extranoise + abs(sigma2_T(v$form$scales, v$actual_datasheet_BIF0_parameter, sqrt(v$actual_datasheet_BI_parameter)/0.664))
}
v$datasheet_noise_model = extranoise
} else{
intermediate = gm_to_ar1(theta = c(v$actual_datasheet_BETA_GM_parameter,
v$actual_datasheet_SIGMA2_GM_parameter),
freq = v$freq)
v$datasheet_noise_model = wn_to_wv(sigma2 = v$actual_datasheet_WN_parameter, tau = v$form$scales) +
qn_to_wv(q2 = v$actual_datasheet_QN_parameter, tau = v$form$scales) +
rw_to_wv(gamma2 = v$actual_datasheet_RW_parameter, tau = v$form$scales) +
dr_to_wv(omega = v$actual_datasheet_DR_parameter, tau = v$form$scales) +
ar1_to_wv(phi = intermediate[1], sigma2 = intermediate[2], tau = v$form$scales)
}
} else{
v$datasheet_values_make_sense = FALSE
}
updateNavbarPage(session, "tabs", selected = "Selected Sensor")
})
})
#PUSHED THE BUTTON "FIT MODEL"
observeEvent(input$fit3, {
withProgress(message = 'Fitting desired model...', value = 0, {
if (is.null(v$first_gmwm)){
v$first_gmwm = TRUE
}
v$fit = TRUE
v$plot = FALSE
if ("library" %in% input$data_input_choice){ #using library data
my_data = get(input$imu_obj)
Xt = my_data[, input$sensors]
v$freq = attr(my_data, 'freq')
v$custom_data = FALSE
} else{ #using custom data
inFile <- input$user_defined_txt_file
if (is.null(inFile))
return(NULL)
my_data = read.csv(inFile$datapath, header = FALSE, sep = ",")
Xt = my_data[, input$user_defined_txt_file_column]
v$freq = input$user_defined_txt_frequency
v$custom_data = TRUE
}
v$n = length(Xt)
first = TRUE
counter_model_size = 0
if ("GM" %in% input$model){
for (i in 1:input$gm_nb){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = GM()
first = FALSE
}else{
model = model + GM()
}
}
}
if ("WN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = WN()
first = FALSE
}else{
model = model + WN()
}
}
if ("QN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = QN()
first = FALSE
}else{
model = model + QN()
}
}
if ("RW" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = RW()
first = FALSE
}else{
model = model + RW()
}
}
if ("DR" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = DR()
first = FALSE
}else{
model = model + DR()
}
}
if (is.null(model)){
model = 3*GM()
}
if (is.null(input$seed)){
input$seed = 1982
}
if (is.null(input$num)){
input$num = 10^5
}
v$gmwm = gmwm_imu(model, Xt, G = input$num, seed = input$seed, robust = (input$robust=="robust"), freq = v$freq)
v$form = v$gmwm
v$first_gmwm = FALSE
updateNavbarPage(session, "tabs", selected = "Selected Sensor")
})
})
# BUTTON REDUCE MODEL WHICH WILL USE THE AUTOIMU FUNCTION
observeEvent(input$fit2, {
withProgress(message = 'Reducing model automatically...', value = 0, {
if (is.null(v$first_gmwm)){
v$first_gmwm = TRUE
}
v$fit = TRUE
v$plot = FALSE
if ("library" %in% input$data_input_choice){ #using library data
my_data = get(input$imu_obj)
Xt = my_data[, input$sensors]
v$freq = attr(my_data, 'freq')
v$custom_data = FALSE
} else{ #using custom data
inFile <- input$user_defined_txt_file
if (is.null(inFile))
return(NULL)
my_data = read.csv(inFile$datapath, header = FALSE, sep = ",")
Xt = my_data[, input$user_defined_txt_file_column]
Xt = imu(data = Xt, freq = v$freq, gyros = 1)
v$freq = input$user_defined_txt_frequency
v$custom_data = TRUE
}
v$n = length(Xt)
first = TRUE
counter_model_size = 0
if ("GM" %in% input$model){
for (i in 1:input$gm_nb){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = GM()
first = FALSE
}else{
model = model + GM()
}
}
}
if ("WN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = WN()
first = FALSE
}else{
model = model + WN()
}
}
if ("QN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = QN()
first = FALSE
}else{
model = model + QN()
}
}
if ("RW" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = RW()
first = FALSE
}else{
model = model + RW()
}
}
if ("DR" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = DR()
first = FALSE
}else{
model = model + DR()
}
}
if (is.null(model)){
model = 3*GM()
}
a = auto_imu(Xt, model = model)
v$form = a[[1]][[2]]
updateNavbarPage(session, "tabs", selected = "Selected Sensor")
})
})
# BUTTON RESET-NOISE-PARAMS
observeEvent(input$button_reset_noise_params, {
withProgress(message = 'Resetting to default noise parameters...', value = 0, {
updateNumericInput(session, "dsv_wn", value = format(const.DEFAULT_WN, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bi", value = format(const.DEFAULT_BI, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bif0", value = format(const.DEFAULT_BIF0, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_qn", value = format(const.DEFAULT_QN, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_sigma2_gm", value = format(const.DEFAULT_SIGMA2_GM, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_beta_gm", value = format(const.DEFAULT_BETA_GM, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_rw", value = format(const.DEFAULT_RW, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_dr", value = format(const.DEFAULT_DR, digits = const.nb_of_digits))
})
})
# SELECTING CUSTOM OR LIBRARY DATA VIA THE RADIOBUTTONS RESULTS IN DISABELING/ENABELING NOISE PARAMETERS
observeEvent(input$data_input_choice, {
if ("library" %in% input$data_input_choice){
shinyjs::disable("dsv_wn")
shinyjs::disable("dsv_bi")
shinyjs::disable("dsv_bif0")
} else {
shinyjs::enable("dsv_wn")
shinyjs::enable("dsv_bi")
shinyjs::enable("dsv_bif0")
}
})
dsnames <- c()
data_set <- reactive({
inFile <- input$imu_obj
if (is.null(inFile))
return(imu6)
data_set <- get(input$imu_obj)
})
observe({
dsnames <- colnames(data_set())
cb_options <- list()
cb_options[ dsnames] <- dsnames
updateSelectInput(session, "sensors",
label = "Selected sensor",
choices = cb_options,
selected = "")
})
# NOISE VALUES UPDATE AUTOMATICALLY IN GUI ACCORDING TO THE SELECTED SENSOR
observeEvent(input$sensors, {
actual_datasheet_WN_parameter <- NA
actual_datasheet_BI_parameter <- NA
actual_datasheet_BIF0_parameter <- NA
if( input$sensors == "Gyro. X" || input$sensors == "Gyro. Y" || input$sensors == "Gyro. Z"){
if (input$imu_obj == "navchip"){
actual_datasheet_WN_parameter = const.NAVCHIP.GYRO_WN
actual_datasheet_BI_parameter = const.NAVCHIP.GYRO_BI
actual_datasheet_BIF0_parameter = const.NAVCHIP.GYRO_BIF0
} else {
if(input$imu_obj == "imu6"){
actual_datasheet_WN_parameter = const.MTIG.GYRO_WN
actual_datasheet_BI_parameter = const.MTIG.GYRO_BI
actual_datasheet_BIF0_parameter = const.MTIG.GYRO_BIF0
} else {
if(input$imu_obj == "imar.gyro"){
actual_datasheet_WN_parameter = const.IMAR.GYRO_WN
actual_datasheet_BI_parameter = const.IMAR.GYRO_BI
actual_datasheet_BIF0_parameter = const.IMAR.GYRO_BIF0
} else{
if(input$imu_obj == "ln200.gyro"){
actual_datasheet_WN_parameter = const.LN200.GYRO_WN
actual_datasheet_BI_parameter = const.LN200.GYRO_BI
actual_datasheet_BIF0_parameter = const.LN200.GYRO_BIF0
} else{
actual_datasheet_WN_parameter = const.DEFAULT_WN
actual_datasheet_BI_parameter = NA
actual_datasheet_BIF0_parameter = NA
}
}
}
}
}
if( input$sensors == "Accel. X" || input$sensors == "Accel. Y" || input$sensors == "Accel. Z"){
if (input$imu_obj == "navchip"){
actual_datasheet_WN_parameter = const.NAVCHIP.ACC_WN
actual_datasheet_BI_parameter = const.NAVCHIP.ACC_BI
actual_datasheet_BIF0_parameter = const.NAVCHIP.ACC_BIF0
} else {
if(input$imu_obj == "imu6"){
actual_datasheet_WN_parameter = const.MTIG.ACC_WN
actual_datasheet_BI_parameter = const.MTIG.ACC_BI
actual_datasheet_BIF0_parameter = const.MTIG.ACC_BIF0
} else {
actual_datasheet_WN_parameter = const.DEFAULT_WN
actual_datasheet_BI_parameter = NA
actual_datasheet_BIF0_parameter = NA
}
}
}
updateNumericInput(session, "dsv_wn", value = format(actual_datasheet_WN_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bi", value = format(actual_datasheet_BI_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bif0", value = format(actual_datasheet_BIF0_parameter, digits = const.nb_of_digits))
})
# calc a specific VW and plot it in the tab "Selected Sensor"
output$plot2 <- renderPlot({
if (v$fit || v$plot){
# for the real data
a = v$form
freq_a = v$freq
a$scales = a$scales#/freq_a
duration_a = v$n/(freq_a*60*60)
if (v$plot){ # should i plot just the real data?
if (v$custom_data){ # is it custom data from a txt file?
title = paste("Haar Wavelet Variance of TXT-FILE:\n", v$custom_data_name, " (column # ", input$user_defined_txt_file_column, " / ", v$custom_data_tot_colums,
") - Filesize: ", round(v$custom_data_size/1024/1024,2), " [MB] - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}else{ # it is NOT custom data
title = paste("Haar Wavelet Variance of DATASET:\n", input$imu_obj, " (", input$sensors,
") - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}
if ("datasheet" %in% input$option_plot){
plot_wv_and_datasheet(a,
v$datasheet_noise_model,
# v$actual_datasheet_BI_parameter,
expression(paste("Scale ", tau, " [s]")),
v$y_label_with_dataunits,
prov_title = title)
} else {
plot(a,
axis.x.label = expression(paste("Scale ", tau, " [s]")),
axis.y.label = v$y_label_with_dataunits,
title = title,
CI = T, #"ci" %in% input$option_plot,
title.size = 22,
axis.label.size = 20,
axis.tick.size = 17,
legend.title.size = 19,
legend.text.size = 19) + theme(legend.position = c(0, 0))
}
}else{ # when doing the "gmwm modeling" plot
if (v$custom_data){ # is it custom data from a txt file?
title = paste("Haar Wavelet Variance of TXT-FILE-DATA:\n", v$custom_data_name, " (column number ", input$user_defined_txt_file_column,
") - Filesize: ", round(v$custom_data_size/1024/1024,2), " [MB] - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}else{ # it is NOT custom data
title = paste("Haar Wavelet Variance of DATASET:\n", input$imu_obj, " (", input$sensors,
") - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}
if ("datasheet" %in% input$option_plot & !"process_decomp" %in% input$option_plot){
plot_gmwm_and_datasheet(object = a,
datasheet = v$datasheet_noise_model,
# v$actual_datasheet_BI_parameter,
axis.x.label = expression(paste("Scale ", tau, " [s]")),
v$y_label_with_dataunits,
prov_title = title)
}else{
plot(a,
axis.x.label = expression(paste("Scale ", tau, " [s]")),
axis.y.label = v$y_label_with_dataunits,
process.decomp = "process_decomp" %in% input$option_plot,
CI = T, #"ci" %in% input$option_plot,
title = title,
title.size = 22,
axis.label.size = 20,
axis.tick.size = 17,
legend.title.size = 19,
legend.text.size = 19) #+ theme(legend.position = c(0.1, 0.1))
}
}
}else{
plot(NA)
}
}, height = const.RENDER_PLOT_HEIGHT, width = const.RENDER_PLOT_WIDTH, res = const.RENDER_PLOT_RES)
# calc the 6 WV from the dataset and plot it in the tab "Model Data"
output$plot <- renderImage({
width <- session$clientData$output_plot2_width
height <- session$clientData$output_plot2_height
if(input$data_input_choice == 'library'){
if(input$imu_obj == 'imu6'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('imu6', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT
} else{
if(input$imu_obj == 'navchip'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('navchip', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT
} else{
if(input$imu_obj == 'ln200.gyro'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('ln200', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT
} else{
if(input$imu_obj == 'imar.gyro'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('imar_gyro', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT_REDUCED
}
}
}
}
} else{
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('custom_dataset', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT_REDUCED
}
list(src = filename, height = model_data_height)
}, deleteFile = FALSE)
# print the summary in the summary-tab
output$summ <- renderPrint({
if (v$fit){
if("ci" %in% input$summary_plot){
summary_message = 'Generating summary with Confidence Intervals...'
} else {
summary_message = 'Generating summary...'
}
withProgress(message = summary_message, value = 0, {
summmary_of_gmwm = summary(v$form, inference = "ci" %in% input$summary_plot)
if("ci" %in% input$summary_plot){
# summmary_of_gmwm
cat("Objective Function: ", summmary_of_gmwm$obj.fun, "\n" )
cat("Seed Number: ", summmary_of_gmwm$seed, "\n\n")
summmary_of_gmwm$estimate[,cbind(1,2,3)]
} else {
cat("Objective Function: ", summmary_of_gmwm$obj.fun, "\n" )
cat("Seed Number: ", summmary_of_gmwm$seed, "\n\n")
summmary_of_gmwm$estimate
}
})
}
})
# print info URL in the help tab
output$tabhelpurl <- renderUI({
tagList(smac_url_description, smac_url)
})
# print info logos in the help tab
output$tabhelpplotlogo_pennstate <- renderImage({
filename <- normalizePath(file.path('./logo', paste('logo_penn_state', '.png', sep='')))
# list(src = bind(filename, filename), height = const.FIGURE_PLOT_HEIGHT_SUPER_REDUCED)
list(src = filename, height = const.FIGURE_PLOT_HEIGHT_LOGO)
}, deleteFile = FALSE)
# print info logos in the help tab
output$tabhelpplotlogo_epfl <- renderImage({
filename <- normalizePath(file.path('./logo', paste('logo_epfl', '.jpg', sep='')))
list(src = filename, height = const.FIGURE_PLOT_HEIGHT_LOGO)
}, deleteFile = FALSE)
}
shinyApp(ui, server)
SMAC-Group/gmwm_gui documentation built on May 21, 2019, 7:54 a.m.
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library(gui4gmwm)
################
# CONSTANTS
################
const.RENDER_PLOT_WIDTH = 1000
const.RENDER_PLOT_HEIGHT = 600
const.RENDER_PLOT_RES = 100 # default is 72
const.FIGURE_PLOT_HEIGHT = "600px"
const.FIGURE_PLOT_HEIGHT_REDUCED = "400px"
const.FIGURE_PLOT_HEIGHT_LOGO = "100px"
const.nb_of_digits = 7
# convert degrees-per-second to radians-per-second
const.degps_2_radps = 1/360 * 2*pi
# constant default frequency for custom data
const.DEFAULT_FREQ = 1 # [Hz]
# constants for the custom datasheet
const.DEFAULT_WN = 5.304377e-07
const.DEFAULT_QN = 1.681227e-06
const.DEFAULT_SIGMA2_GM = 7.348293e-09
const.DEFAULT_BETA_GM = 3.526037e-01
const.DEFAULT_RW = 1.268314e-12
const.DEFAULT_DR = 3.913529e-09
const.DEFAULT_BI = const.DEFAULT_WN
const.DEFAULT_BIF0 = NA
# source: https://www.xsens.com/wp-content/uploads/2013/11/MTi-G_User_Manual_and_Technical_Documentation.pdf
# https://www.xsens.com/tags/accelerometers/
# https://www.xsens.com/tags/gyroscopes/
# the frequency here is 100, because the dataset was acquired at this rate
const.MTIG.GYRO_WN = (0.05 * const.degps_2_radps * sqrt(100))^2
const.MTIG.GYRO_BI = (20 / 3600 * const.degps_2_radps )^2
const.MTIG.GYRO_BIF0 = 1/40 #Hz
const.MTIG.ACC_WN = (0.002 * sqrt(100))^2
const.MTIG.ACC_BI = (30 * 1e-6 * 10)^2
const.MTIG.ACC_BIF0 = 1/0.5 #Hz
# source: http://cdn-docs.av-iq.com/dataSheet//NavChip_Product_Brief.pdf,
# the frequency here is 250, because the dataset was acquired at this rate
const.NAVCHIP.GYRO_WN = (0.003 * const.degps_2_radps * sqrt(250))^2 # [(rad/s)^2]
const.NAVCHIP.GYRO_BI = (10 / 3600 * const.degps_2_radps)^2
const.NAVCHIP.GYRO_BIF0 = 1/2 #Hz
const.NAVCHIP.ACC_WN = (50 * 1e-6 * 10 * sqrt(250))^2 # [(m/s^2)^2]
const.NAVCHIP.ACC_BI = (0.05 * 1e-3 * 10)^2
const.NAVCHIP.ACC_BIF0 = 1/5 #Hz
# source: http://www.northropgrumman.com/Capabilities/LN200FOG/Documents/ln200.pdf
# the frequency here is 400, because the dataset was acquired at this rate
const.LN200.GYRO_WN = (0.05 / 60 * sqrt(400) * const.degps_2_radps)^2
const.LN200.GYRO_BI = NA
const.LN200.GYRO_BIF0 = NA #Hz
const.LN200.ACC_WN = const.DEFAULT_WN
const.LN200.ACC_BI = NA
const.LN200.ACC_BIF0 = NA #Hz
# source: http://www.imar-navigation.de/downloads/IMU_FSAS.pdf
# the frequency here is 400, because the dataset was acquired at this rate
const.IMAR.GYRO_WN = (0.15 / 60 * sqrt(400) * const.degps_2_radps)^2
const.IMAR.GYRO_BI = (0.1 /3600 * const.degps_2_radps)^2
const.IMAR.GYRO_BIF0 = 1/100 #Hz
const.IMAR.ACC_WN = const.DEFAULT_WN
const.IMAR.ACC_BI = NA
const.IMAR.ACC_BIF0 = NA #Hz
################
# FUNCTIONS for COSINE INTEGRAL calculations
################
cos_function <- function(t){
cos(t)/t
}
Ci <- function(x){
-integrate(f = cos_function, lower = x, upper = 2e3, subdivisions=10000)$value
}
VCi <- Vectorize(Ci, c("x"))
sigma2_T <- function(T, f0, B){
2*B*B/pi * ( log(2) - ( (sin(pi*f0*T))^3 ) / (2*(pi*f0*T)^2) * ( sin(pi*f0*T)+4*pi*f0*T*cos(pi*f0*T) ) + VCi(2*pi*f0*T) - VCi(4*pi*f0*T) )
}
# loading the four internal datasets
data("navchip") # NAVCHIP
imu6 = imu(imu6, gyros = 1:3, accels = 4:6, axis = c('X', 'Y', 'Z', 'X', 'Y', 'Z'), freq = 100) #MTIG
data("imar.gyro") #IMAR
data("ln200.gyro") #LN200
smac_url <- a("https://smac-group.github.io/gui4gmwm/", href="https://smac-group.github.io/gui4gmwm/")
smac_url_description <- "gui4gmwm Overview:"
# increses file limit from default-5MB to 100MB
options(shiny.maxRequestSize=100*1024^2)
ui <- shinyUI(fluidPage(
shinyjs::useShinyjs(),
tags$style(HTML(".js-irs-0 .irs-single, .js-irs-0 .irs-bar-edge, .js-irs-0 .irs-bar {background: red}")),
tags$style(HTML(".js-irs-1 .irs-single, .js-irs-1 .irs-bar-edge, .js-irs-1 .irs-bar {background: green}")),
tags$style(type='text/css', '#summ {background-color: rgba(0,0,200,0.02); color: black; width: 500px; font-size: 14px;}'),
title = "GMWM GUI",
tabsetPanel(id = "tabs",
tabPanel("Model Data", plotOutput(outputId = "plot", height = const.FIGURE_PLOT_HEIGHT)),
tabPanel("Selected Sensor", plotOutput(outputId = "plot2", height = const.FIGURE_PLOT_HEIGHT)),
tabPanel("Summary", verbatimTextOutput(outputId = "summ", placeholder = FALSE)),
tabPanel("Help",
# fluidPage("cluster"),
h4("Help Tab" ),
br(),
# actionButton("subClust", label = "Create Subcluster"),
#
uiOutput(outputId = "tabhelpurl"),
br(),br(),
fluidRow(
column(5,
plotOutput(outputId = "tabhelpplotlogo_pennstate", height = const.FIGURE_PLOT_HEIGHT_LOGO)
),
column(5,
plotOutput(outputId = "tabhelpplotlogo_epfl", height = const.FIGURE_PLOT_HEIGHT_LOGO)
)
)
)
),
hr(),
fluidRow(
column(4,
radioButtons("data_input_choice", "Select data input:", choices = c("from library" = "library", "custom" = "custom")),
conditionalPanel(
condition = "input.data_input_choice == 'library'",
selectInput("imu_obj", "Select IMU file:",
c("MTi-G" = "imu6",
"Navchip" = "navchip",
"LN-200" = "ln200.gyro",
"IMAR" = "imar.gyro"),
selected = 1),
selectInput("sensors", "Select sensor", c("1"="1","2"="2", selected = 1))
),
conditionalPanel(
condition = "input.data_input_choice == 'custom'",
fileInput("user_defined_txt_file", "Select INPUT file (max 100MB):",
accept = c(
"text/txt",
"text/comma-separated-values,text/plain",
".txt",
".imu",
placeholder = "No file selected")
),
sliderInput("user_defined_txt_file_column", "Select column number:",
min=1, max=6, value=1),
numericInput("user_defined_txt_frequency", label = "Set frequency of dataset", value = const.DEFAULT_FREQ), # frequency defined by the user
textInput("user_defined_units", "Define units of active dataset", "rad/s")
),
column(7, radioButtons("robust", "Select estimator:", choices = c("Classic WV" = "classic", "Robust WV" = "robust"))),
br(),
actionButton("fit1", label = "Plot WV"),
br(),
uiOutput("choose_columns")
),
column(4,
checkboxGroupInput("model", "Select Model",
c("Quantization Noise" = "QN",
"White Noise" = "WN",
"Random Walk" = "RW",
"Drift" = "DR",
"Gauss-Markov" = "GM"),
selected = "WN"),
conditionalPanel(
condition = "input.model.indexOf('GM')>-1",
sliderInput("gm_nb", "Number of Gauss-Markov Processes", 1, 5, 2)
),
actionButton("fit3", label = "Fit Model"),
br(),
br(),
br(),
br(),
actionButton("fit2", label = "Reduce Model Automatically")
),
column(4,
checkboxGroupInput("option_plot", label = "Plot options:",
c("Process Decomp." = "process_decomp",
"Add Datasheet WV" = "datasheet"
#"Show CI of empirical WV" = "ci"
),
selected = c("datasheet")),
checkboxInput("overlay_datasheet", label = "Show Datasheet Specifications", value = FALSE),
conditionalPanel(
condition = "input.overlay_datasheet",
numericInput("dsv_wn", label = " WN", value = format(const.DEFAULT_WN, digits = const.nb_of_digits)),
numericInput("dsv_bi", label = " BIAS INSTABILITY", format(const.DEFAULT_BI, digits = const.nb_of_digits)),
numericInput("dsv_bif0", label = " BIAS INSTABILITY CUTOFF FREQUENCY", format(const.DEFAULT_BIF0, digits = const.nb_of_digits)),
conditionalPanel(
condition = "input.data_input_choice == 'custom'",
numericInput("dsv_qn", label = " QN", value = format(const.DEFAULT_QN, digits = const.nb_of_digits)),
column(6,
numericInput("dsv_sigma2_gm", label = " sigma2 GM", value = format(const.DEFAULT_SIGMA2_GM, digits = const.nb_of_digits))
),
column(5,
numericInput("dsv_beta_gm", label = " beta GM", value = format(const.DEFAULT_BETA_GM, digits = const.nb_of_digits))
),
numericInput("dsv_rw", label = " RW", value = format(const.DEFAULT_RW, digits = const.nb_of_digits)),
numericInput("dsv_dr", label = " DR", value = format(const.DEFAULT_DR, digits = const.nb_of_digits)),
column(2),
column(10,
actionButton("button_reset_noise_params", label = "Reset Noise Params")
)
)
),
br(),
checkboxGroupInput("summary_plot", label = "Summary options:",
c("Show CI of parameters" = "ci"),
selected = c("sum")),
checkboxInput("edit_intern", label = "Edit Optimization Parameters", value = FALSE),
conditionalPanel(
condition = "input.edit_intern == 1",
numericInput("num", label = "Number of Simu. for Starting Values", value = 10^5),
numericInput("seed", label = "Simulation seed", value = 1982)
)
)
)
))
server <- function(input, output, session) {
# library or custom dataset
v <- reactiveValues(plot = FALSE,
fit = FALSE,
gmwm = NULL,
form = NULL,
freq = 100,
first_gmwm = NULL,
n = NULL,
sensor_name = NULL,
sensor_column = NULL,
overlap_datasheet = FALSE,
y_label_with_dataunits = NA,
actual_datasheet_WN_parameter = const.DEFAULT_WN,
actual_datasheet_BI_parameter = NA,
actual_datasheet_BIF0_parameter = NA,
actual_datasheet_QN_parameter = NA,
actual_datasheet_SIGMA2_GM_parameter = NA,
actual_datasheet_BETA_GM_parameter = NA,
actual_datasheet_RW_parameter = NA,
actual_datasheet_DR_parameter = NA,
first_time_plotting_6_pack = TRUE,
custom_data = FALSE,
custom_data_name = NULL,
custom_data_type = NULL,
custom_data_size = NULL,
custom_data_tot_colums = NULL,
datasheet_noise_model = NULL,
datasheet_values_make_sense = FALSE)
# PUSHING ON BUTTON "Plot WV"
observeEvent(input$fit1, {
withProgress(message = 'Calculating empirical WV...', value = 0, {
v$plot = TRUE
v$fit = FALSE
v$overlap_datasheet = "datasheet" %in% input$option_plot
if ("library" %in% input$data_input_choice){ #using library data
my_data = get(input$imu_obj)
Xt = my_data[, input$sensors]
v$sensor_name = input$imu_obj
v$sensor_column = input$sensors
v$freq = attr(my_data, 'freq')
v$custom_data = FALSE
if (input$sensors == "Gyro. X" || input$sensors == "Gyro. Y" || input$sensors == "Gyro. Z"){
v$y_label_with_dataunits = expression(paste("Wavelet Variance ", nu, " [", rad^2/s^2, "]"))
} else if (input$sensors == "Accel. X" || input$sensors == "Accel. Y" || input$sensors == "Accel. Z"){
v$y_label_with_dataunits = expression(paste("Wavelet Variance ", nu, " [", m^2/s^4, "]"))
}
v$actual_datasheet_WN_parameter = input$dsv_wn
v$actual_datasheet_BI_parameter = input$dsv_bi
v$actual_datasheet_BIF0_parameter = input$dsv_bif0
# reset other noise values in case custom data was loaded previosuly
v$actual_datasheet_QN_parameter = const.DEFAULT_QN
v$actual_datasheet_SIGMA2_GM_parameter = const.DEFAULT_SIGMA2_GM
v$actual_datasheet_BETA_GM_parameter = const.DEFAULT_BETA_GM
v$actual_datasheet_RW_parameter = const.DEFAULT_RW
v$actual_datasheet_DR_parameter = const.DEFAULT_DR
# reset other variables in case custom data was loaded previously
v$custom_data_name = NULL
v$custom_data_type = NULL
v$custom_data_size = NULL
v$custom_data_tot_colums = NULL
} else{ #using custom data
inFile <- input$user_defined_txt_file
if (is.null(inFile))
return(NULL)
my_data = read.csv(inFile$datapath, header = FALSE, sep = ",")
if(input$user_defined_txt_file_column > ncol(my_data)){
updateSliderInput(session, "user_defined_txt_file_column", value = ncol(my_data))
the_column_number = ncol(my_data)
} else{
the_column_number = input$user_defined_txt_file_column
}
# update the slider number with the current number of colums
updateSliderInput(session, "user_defined_txt_file_column", max = ncol(my_data))
v$sensor_column = the_column_number
Xt = my_data[, the_column_number]
v$freq = input$user_defined_txt_frequency
v$y_label_with_dataunits = bquote(paste("Wavelet Variance ", nu, " [(", .(input$user_defined_units[1]), ")^2]"))
v$custom_data = TRUE
v$custom_data_name = inFile$name
v$custom_data_type = inFile$type
v$custom_data_size = inFile$size
v$custom_data_tot_colums = ncol(my_data)
v$actual_datasheet_WN_parameter = input$dsv_wn
v$actual_datasheet_BI_parameter = input$dsv_bi
v$actual_datasheet_BIF0_parameter = input$dsv_bif0
v$actual_datasheet_QN_parameter = input$dsv_qn
v$actual_datasheet_SIGMA2_GM_parameter = input$dsv_sigma2_gm
v$actual_datasheet_BETA_GM_parameter = input$dsv_beta_gm
v$actual_datasheet_RW_parameter = input$dsv_rw
v$actual_datasheet_DR_parameter = input$dsv_dr
}
updateNumericInput(session, "dsv_wn", value = format(v$actual_datasheet_WN_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bi", value = format(v$actual_datasheet_BI_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bif0", value = format(v$actual_datasheet_BIF0_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_qn", value = format(v$actual_datasheet_QN_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_sigma2_gm", value = format(v$actual_datasheet_SIGMA2_GM_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_beta_gm", value = format(v$actual_datasheet_BETA_GM_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_rw", value = format(v$actual_datasheet_RW_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_dr", value = format(v$actual_datasheet_DR_parameter, digits = const.nb_of_digits))
v$n = length(Xt)
v$form = wvar(as.numeric(Xt), robust = (input$robust=="robust") , freq = v$freq)
if (v$overlap_datasheet == TRUE){
v$datasheet_values_make_sense = TRUE
if ("library" %in% input$data_input_choice){ #using library data there is only WN, the BI is plotted separately directly in the plot
extranoise <- wn_to_wv(sigma2 = v$actual_datasheet_WN_parameter, tau = v$form$scales*v$freq)
if (!is.na(v$actual_datasheet_BI_parameter)){
extranoise <- extranoise + abs(sigma2_T(v$form$scales, v$actual_datasheet_BIF0_parameter, sqrt(v$actual_datasheet_BI_parameter)/0.664))
}
v$datasheet_noise_model = extranoise
} else{
intermediate = gm_to_ar1(theta = c(v$actual_datasheet_BETA_GM_parameter,
v$actual_datasheet_SIGMA2_GM_parameter),
freq = v$freq)
v$datasheet_noise_model = wn_to_wv(sigma2 = v$actual_datasheet_WN_parameter, tau = v$form$scales) +
qn_to_wv(q2 = v$actual_datasheet_QN_parameter, tau = v$form$scales) +
rw_to_wv(gamma2 = v$actual_datasheet_RW_parameter, tau = v$form$scales) +
dr_to_wv(omega = v$actual_datasheet_DR_parameter, tau = v$form$scales) +
ar1_to_wv(phi = intermediate[1], sigma2 = intermediate[2], tau = v$form$scales)
}
} else{
v$datasheet_values_make_sense = FALSE
}
updateNavbarPage(session, "tabs", selected = "Selected Sensor")
})
})
#PUSHED THE BUTTON "FIT MODEL"
observeEvent(input$fit3, {
withProgress(message = 'Fitting desired model...', value = 0, {
if (is.null(v$first_gmwm)){
v$first_gmwm = TRUE
}
v$fit = TRUE
v$plot = FALSE
if ("library" %in% input$data_input_choice){ #using library data
my_data = get(input$imu_obj)
Xt = my_data[, input$sensors]
v$freq = attr(my_data, 'freq')
v$custom_data = FALSE
} else{ #using custom data
inFile <- input$user_defined_txt_file
if (is.null(inFile))
return(NULL)
my_data = read.csv(inFile$datapath, header = FALSE, sep = ",")
Xt = my_data[, input$user_defined_txt_file_column]
v$freq = input$user_defined_txt_frequency
v$custom_data = TRUE
}
v$n = length(Xt)
first = TRUE
counter_model_size = 0
if ("GM" %in% input$model){
for (i in 1:input$gm_nb){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = GM()
first = FALSE
}else{
model = model + GM()
}
}
}
if ("WN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = WN()
first = FALSE
}else{
model = model + WN()
}
}
if ("QN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = QN()
first = FALSE
}else{
model = model + QN()
}
}
if ("RW" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = RW()
first = FALSE
}else{
model = model + RW()
}
}
if ("DR" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = DR()
first = FALSE
}else{
model = model + DR()
}
}
if (is.null(model)){
model = 3*GM()
}
if (is.null(input$seed)){
input$seed = 1982
}
if (is.null(input$num)){
input$num = 10^5
}
v$gmwm = gmwm_imu(model, Xt, G = input$num, seed = input$seed, robust = (input$robust=="robust"), freq = v$freq)
v$form = v$gmwm
v$first_gmwm = FALSE
updateNavbarPage(session, "tabs", selected = "Selected Sensor")
})
})
# BUTTON REDUCE MODEL WHICH WILL USE THE AUTOIMU FUNCTION
observeEvent(input$fit2, {
withProgress(message = 'Reducing model automatically...', value = 0, {
if (is.null(v$first_gmwm)){
v$first_gmwm = TRUE
}
v$fit = TRUE
v$plot = FALSE
if ("library" %in% input$data_input_choice){ #using library data
my_data = get(input$imu_obj)
Xt = my_data[, input$sensors]
v$freq = attr(my_data, 'freq')
v$custom_data = FALSE
} else{ #using custom data
inFile <- input$user_defined_txt_file
if (is.null(inFile))
return(NULL)
my_data = read.csv(inFile$datapath, header = FALSE, sep = ",")
Xt = my_data[, input$user_defined_txt_file_column]
Xt = imu(data = Xt, freq = v$freq, gyros = 1)
v$freq = input$user_defined_txt_frequency
v$custom_data = TRUE
}
v$n = length(Xt)
first = TRUE
counter_model_size = 0
if ("GM" %in% input$model){
for (i in 1:input$gm_nb){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = GM()
first = FALSE
}else{
model = model + GM()
}
}
}
if ("WN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = WN()
first = FALSE
}else{
model = model + WN()
}
}
if ("QN" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = QN()
first = FALSE
}else{
model = model + QN()
}
}
if ("RW" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = RW()
first = FALSE
}else{
model = model + RW()
}
}
if ("DR" %in% input$model){
counter_model_size = counter_model_size + 1
if (first == TRUE){
model = DR()
first = FALSE
}else{
model = model + DR()
}
}
if (is.null(model)){
model = 3*GM()
}
a = auto_imu(Xt, model = model)
v$form = a[[1]][[2]]
updateNavbarPage(session, "tabs", selected = "Selected Sensor")
})
})
# BUTTON RESET-NOISE-PARAMS
observeEvent(input$button_reset_noise_params, {
withProgress(message = 'Resetting to default noise parameters...', value = 0, {
updateNumericInput(session, "dsv_wn", value = format(const.DEFAULT_WN, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bi", value = format(const.DEFAULT_BI, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bif0", value = format(const.DEFAULT_BIF0, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_qn", value = format(const.DEFAULT_QN, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_sigma2_gm", value = format(const.DEFAULT_SIGMA2_GM, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_beta_gm", value = format(const.DEFAULT_BETA_GM, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_rw", value = format(const.DEFAULT_RW, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_dr", value = format(const.DEFAULT_DR, digits = const.nb_of_digits))
})
})
# SELECTING CUSTOM OR LIBRARY DATA VIA THE RADIOBUTTONS RESULTS IN DISABELING/ENABELING NOISE PARAMETERS
observeEvent(input$data_input_choice, {
if ("library" %in% input$data_input_choice){
shinyjs::disable("dsv_wn")
shinyjs::disable("dsv_bi")
shinyjs::disable("dsv_bif0")
} else {
shinyjs::enable("dsv_wn")
shinyjs::enable("dsv_bi")
shinyjs::enable("dsv_bif0")
}
})
dsnames <- c()
data_set <- reactive({
inFile <- input$imu_obj
if (is.null(inFile))
return(imu6)
data_set <- get(input$imu_obj)
})
observe({
dsnames <- colnames(data_set())
cb_options <- list()
cb_options[ dsnames] <- dsnames
updateSelectInput(session, "sensors",
label = "Selected sensor",
choices = cb_options,
selected = "")
})
# NOISE VALUES UPDATE AUTOMATICALLY IN GUI ACCORDING TO THE SELECTED SENSOR
observeEvent(input$sensors, {
actual_datasheet_WN_parameter <- NA
actual_datasheet_BI_parameter <- NA
actual_datasheet_BIF0_parameter <- NA
if( input$sensors == "Gyro. X" || input$sensors == "Gyro. Y" || input$sensors == "Gyro. Z"){
if (input$imu_obj == "navchip"){
actual_datasheet_WN_parameter = const.NAVCHIP.GYRO_WN
actual_datasheet_BI_parameter = const.NAVCHIP.GYRO_BI
actual_datasheet_BIF0_parameter = const.NAVCHIP.GYRO_BIF0
} else {
if(input$imu_obj == "imu6"){
actual_datasheet_WN_parameter = const.MTIG.GYRO_WN
actual_datasheet_BI_parameter = const.MTIG.GYRO_BI
actual_datasheet_BIF0_parameter = const.MTIG.GYRO_BIF0
} else {
if(input$imu_obj == "imar.gyro"){
actual_datasheet_WN_parameter = const.IMAR.GYRO_WN
actual_datasheet_BI_parameter = const.IMAR.GYRO_BI
actual_datasheet_BIF0_parameter = const.IMAR.GYRO_BIF0
} else{
if(input$imu_obj == "ln200.gyro"){
actual_datasheet_WN_parameter = const.LN200.GYRO_WN
actual_datasheet_BI_parameter = const.LN200.GYRO_BI
actual_datasheet_BIF0_parameter = const.LN200.GYRO_BIF0
} else{
actual_datasheet_WN_parameter = const.DEFAULT_WN
actual_datasheet_BI_parameter = NA
actual_datasheet_BIF0_parameter = NA
}
}
}
}
}
if( input$sensors == "Accel. X" || input$sensors == "Accel. Y" || input$sensors == "Accel. Z"){
if (input$imu_obj == "navchip"){
actual_datasheet_WN_parameter = const.NAVCHIP.ACC_WN
actual_datasheet_BI_parameter = const.NAVCHIP.ACC_BI
actual_datasheet_BIF0_parameter = const.NAVCHIP.ACC_BIF0
} else {
if(input$imu_obj == "imu6"){
actual_datasheet_WN_parameter = const.MTIG.ACC_WN
actual_datasheet_BI_parameter = const.MTIG.ACC_BI
actual_datasheet_BIF0_parameter = const.MTIG.ACC_BIF0
} else {
actual_datasheet_WN_parameter = const.DEFAULT_WN
actual_datasheet_BI_parameter = NA
actual_datasheet_BIF0_parameter = NA
}
}
}
updateNumericInput(session, "dsv_wn", value = format(actual_datasheet_WN_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bi", value = format(actual_datasheet_BI_parameter, digits = const.nb_of_digits))
updateNumericInput(session, "dsv_bif0", value = format(actual_datasheet_BIF0_parameter, digits = const.nb_of_digits))
})
# calc a specific VW and plot it in the tab "Selected Sensor"
output$plot2 <- renderPlot({
if (v$fit || v$plot){
# for the real data
a = v$form
freq_a = v$freq
a$scales = a$scales#/freq_a
duration_a = v$n/(freq_a*60*60)
if (v$plot){ # should i plot just the real data?
if (v$custom_data){ # is it custom data from a txt file?
title = paste("Haar Wavelet Variance of TXT-FILE:\n", v$custom_data_name, " (column # ", input$user_defined_txt_file_column, " / ", v$custom_data_tot_colums,
") - Filesize: ", round(v$custom_data_size/1024/1024,2), " [MB] - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}else{ # it is NOT custom data
title = paste("Haar Wavelet Variance of DATASET:\n", input$imu_obj, " (", input$sensors,
") - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}
if ("datasheet" %in% input$option_plot){
plot_wv_and_datasheet(a,
v$datasheet_noise_model,
# v$actual_datasheet_BI_parameter,
expression(paste("Scale ", tau, " [s]")),
v$y_label_with_dataunits,
prov_title = title)
} else {
plot(a,
axis.x.label = expression(paste("Scale ", tau, " [s]")),
axis.y.label = v$y_label_with_dataunits,
title = title,
CI = T, #"ci" %in% input$option_plot,
title.size = 22,
axis.label.size = 20,
axis.tick.size = 17,
legend.title.size = 19,
legend.text.size = 19) + theme(legend.position = c(0, 0))
}
}else{ # when doing the "gmwm modeling" plot
if (v$custom_data){ # is it custom data from a txt file?
title = paste("Haar Wavelet Variance of TXT-FILE-DATA:\n", v$custom_data_name, " (column number ", input$user_defined_txt_file_column,
") - Filesize: ", round(v$custom_data_size/1024/1024,2), " [MB] - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}else{ # it is NOT custom data
title = paste("Haar Wavelet Variance of DATASET:\n", input$imu_obj, " (", input$sensors,
") - Duration: ", round(duration_a,1), "(h) @", freq_a, "(Hz)", sep = "")
}
if ("datasheet" %in% input$option_plot & !"process_decomp" %in% input$option_plot){
plot_gmwm_and_datasheet(object = a,
datasheet = v$datasheet_noise_model,
# v$actual_datasheet_BI_parameter,
axis.x.label = expression(paste("Scale ", tau, " [s]")),
v$y_label_with_dataunits,
prov_title = title)
}else{
plot(a,
axis.x.label = expression(paste("Scale ", tau, " [s]")),
axis.y.label = v$y_label_with_dataunits,
process.decomp = "process_decomp" %in% input$option_plot,
CI = T, #"ci" %in% input$option_plot,
title = title,
title.size = 22,
axis.label.size = 20,
axis.tick.size = 17,
legend.title.size = 19,
legend.text.size = 19) #+ theme(legend.position = c(0.1, 0.1))
}
}
}else{
plot(NA)
}
}, height = const.RENDER_PLOT_HEIGHT, width = const.RENDER_PLOT_WIDTH, res = const.RENDER_PLOT_RES)
# calc the 6 WV from the dataset and plot it in the tab "Model Data"
output$plot <- renderImage({
width <- session$clientData$output_plot2_width
height <- session$clientData$output_plot2_height
if(input$data_input_choice == 'library'){
if(input$imu_obj == 'imu6'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('imu6', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT
} else{
if(input$imu_obj == 'navchip'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('navchip', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT
} else{
if(input$imu_obj == 'ln200.gyro'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('ln200', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT
} else{
if(input$imu_obj == 'imar.gyro'){
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('imar_gyro', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT_REDUCED
}
}
}
}
} else{
filename <- normalizePath(file.path('./initial_6_pack_plots', paste('custom_dataset', '.png', sep='')))
model_data_height = const.FIGURE_PLOT_HEIGHT_REDUCED
}
list(src = filename, height = model_data_height)
}, deleteFile = FALSE)
# print the summary in the summary-tab
output$summ <- renderPrint({
if (v$fit){
if("ci" %in% input$summary_plot){
summary_message = 'Generating summary with Confidence Intervals...'
} else {
summary_message = 'Generating summary...'
}
withProgress(message = summary_message, value = 0, {
summmary_of_gmwm = summary(v$form, inference = "ci" %in% input$summary_plot)
if("ci" %in% input$summary_plot){
# summmary_of_gmwm
cat("Objective Function: ", summmary_of_gmwm$obj.fun, "\n" )
cat("Seed Number: ", summmary_of_gmwm$seed, "\n\n")
summmary_of_gmwm$estimate[,cbind(1,2,3)]
} else {
cat("Objective Function: ", summmary_of_gmwm$obj.fun, "\n" )
cat("Seed Number: ", summmary_of_gmwm$seed, "\n\n")
summmary_of_gmwm$estimate
}
})
}
})
# print info URL in the help tab
output$tabhelpurl <- renderUI({
tagList(smac_url_description, smac_url)
})
# print info logos in the help tab
output$tabhelpplotlogo_pennstate <- renderImage({
filename <- normalizePath(file.path('./logo', paste('logo_penn_state', '.png', sep='')))
# list(src = bind(filename, filename), height = const.FIGURE_PLOT_HEIGHT_SUPER_REDUCED)
list(src = filename, height = const.FIGURE_PLOT_HEIGHT_LOGO)
}, deleteFile = FALSE)
# print info logos in the help tab
output$tabhelpplotlogo_epfl <- renderImage({
filename <- normalizePath(file.path('./logo', paste('logo_epfl', '.jpg', sep='')))
list(src = filename, height = const.FIGURE_PLOT_HEIGHT_LOGO)
}, deleteFile = FALSE)
}
shinyApp(ui, server)
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