/
server_ts_gauge.R
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/
server_ts_gauge.R
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# library(reshape2)
library(magrittr)
library(saves)
library(shiny)
library(dplyr)
library(curl) # make the jsonlite suggested dependency explicit
library(ggplot2)
library(xts)
library(plotly)
# Create function for estimating likelihood of greater increase/decrease
make_temp_pdf <- function(offset,sigma){
pdfvals <- c()
for (i in 0:101){
pdfvals <- append(pdfvals, exp(-(i-offset)**2 / (2 * sigma**2)))
}
return(pdfvals)
}
# Simulate temperatures for input to test algorithm
boundedMarkovChain <- function(offset,maxStepSize,upperBound,lowerBound,pdfvals,n_sec=86400,full_output=FALSE){
bmc <- c()
times <- c()
bmchist <- integer(1000)
val <- offset
for (i in 0:(n_sec-1)){
# Add next value as dependent on current state
val <- val + (runif(1,-0.5,.5)*maxStepSize*(1.01-pdfvals[round(val)+1]))
# Check bounds
val <- min(c(max(c(val,lowerBound)),upperBound))
bmchist[min(c(floor(val*10.0)+1,999))] <- bmchist[min(c(floor(val*10.0)+1,999))]+1
if (full_output){
bmc <- append(bmc,val)
times <- append(times,i)
} else {
if (mod(i,max(c(round(n_sec/1000),1)))==0){
bmc <- append(bmc,val)
times <- append(times,i)
}
}
}
return(list("times"=times,"bmc"=bmc,"bmchist"=bmchist))
}
shinyServer(function(input, output, session) {
#---------------------------------------------------
# Add functionality for plotting data using dygraph
#---------------------------------------------------
# Reactive data for updated time series
pltdata <- eventReactive(input$gettraining,{
# Some Info to construct Bounded Markov Chain
stepSize=2.0 #log10(input$n_sec)
sigma=stepSize*7.5
offset = 25
bmc <- boundedMarkovChain(offset,
maxStepSize=5.5,
upperBound=100.0,
lowerBound=0.0,
make_temp_pdf(offset,sigma),
n_sec=input$n_sec
)
})
train_ranges <- reactive({
tmean<-sum(pltdata()$bmchist*seq(0.05, 99.95, 0.1))/sum(pltdata()$bmchist)
tstd <-sqrt(sum(pltdata()$bmchist*((seq(0.05, 99.95, 0.1)-tmean)**2))/sum(pltdata()$bmchist))
c(tmean,tstd)
})
output$trainplot <- renderPlot({
# Create dataframe for time series plots
mc <- data.frame(cbind(pltdata()$times,pltdata()$bmc))
colnames(mc) <- c("times","bmc")
#Calculate basic statistics (mean, std)
tmean<-train_ranges()[1]
tstd <-train_ranges()[2]
# Set up some plotting params
tlims<-c(max(c( 1.0,min(which(pltdata()$bmchist>0),(tmean-tstd*2)*10)/10.0-0.5)),
min(c(99.9,max(which(pltdata()$bmchist>0),(tmean+tstd*2)*10)/10.0+0.5)))
# At most 20 ticks
tickints <- c(0.25,0.5,1,5,15,30,60,90,120,150,180,240,300,360,720,1440,2880)
tickint <- tickints[min(which(tickints-(tail(mc$times,1)/720.0)>=0))]
if (tickint < 1.0){
tickunit <- "sec"
ticklabs <- tickint
} else if (tickint > 15.0) {
tickunit <- "hrs"
ticklabs <- tickint/60.0
} else {
tickunit <- "mins"
ticklabs <- tickint
}
# Plot time series data
ggplot(mc, aes(x=times,y=bmc)) +
geom_hline(yintercept=tmean+tstd*2,color='blue',linetype="dashed")+
geom_hline(yintercept=tmean-tstd*2,color='blue',linetype="dashed")+
geom_line(aes(y=bmc),color='red') +
scale_x_continuous(name = paste("Time since start (",tickunit,")"), breaks= 0:12*tickint*60,
labels=0:12*ticklabs,limits=c(0,mc$times[length(mc$times)])) +
scale_y_continuous(name = "Temperature (C)",limits = tlims) +
theme_minimal() + # start with a minimal theme and add what we need
theme(text = element_text(color = "gray10"),
axis.text = element_text(face = "italic",size=10),
axis.title.x = element_text(vjust = -3, size=14), # move title away from axis
axis.title.y = element_text(vjust = -1, size=14),# move away for axis
panel.grid.major.y=element_line(colour="black", linetype = "dashed"),
panel.grid.major.x=element_blank()
)
})
output$trainhist <- renderPlot({
newhist<-data.frame(cbind(seq(0,99.9,0.1),pltdata()$bmchist))
colnames(newhist) <- c("temp","bmchist")
tmean<-sum(pltdata()$bmchist*seq(0.05, 99.95, 0.1))/sum(pltdata()$bmchist)
tstd <-sqrt(sum(pltdata()$bmchist*((seq(0.05, 99.95, 0.1)-tmean)**2))/sum(pltdata()$bmchist))
tlims<-c(max(c( 1.0,min(which(pltdata()$bmchist>0),(tmean-tstd*2)*10)/10.0-0.5)),
min(c(99.9,max(which(pltdata()$bmchist>0),(tmean+tstd*2)*10)/10.0+0.5)))
ggplot(newhist, aes(y=newhist$bmc,x=newhist$temp)) +
geom_bar(stat="identity",fill="red",width=0.1)+
geom_vline(xintercept=tmean-tstd*2,color='blue',linetype="dashed")+
geom_vline(xintercept=tmean+tstd*2,color='blue',linetype="dashed")+
annotate("text", label = "Max range", x = tmean+tstd*2, y = max(pltdata()$bmchist),
size = 5, colour = "blue",vjust=-0.5,hjust=1,fontface=3)+
annotate("text", label = "Min range", x = tmean-tstd*2, y = max(pltdata()$bmchist),
size = 5, colour = "blue",vjust=1.25,hjust=1,fontface=3)+
coord_flip()+
scale_x_continuous(name = "",limits = tlims) +
scale_y_continuous(name = "Observation count") +
theme_minimal() +
theme(text = element_text(color = "gray10"),
axis.text = element_text(face = "italic",size=10),
axis.title.x = element_text(vjust = -3, size=14), # move title away from axis
axis.title.y = element_blank(),# move away for axis
axis.text.y = element_blank(),# remove y ticks
panel.grid.major.y=element_line(colour="black", linetype = "dashed"),
panel.grid.major.x=element_blank()
)
})
# Update the slider for the normal operating range
output$opranges <- renderUI({
#Calculate basic statistics (mean, std)
#tmean<-sum(pltdata()$bmchist*seq(0.05, 99.95, 0.1))/sum(pltdata()$bmchist)
#tstd <-sqrt(sum(pltdata()$bmchist*((seq(0.05, 99.95, 0.1)-tmean)**2))/sum(pltdata()$bmchist))
sliderInput("slider2", "Operating range (C)", min = 0, max = 100,
value = c(train_ranges()[1]-(train_ranges()[2]*2),
train_ranges()[1]+(train_ranges()[2]*2)))
})
# Run test -> must simulate sensor data. Run first, plot as if "real time"
# Generate new data and test if within normal operating range
rtdata <- eventReactive(input$startsim,{
# Some Info to construct Bounded Markov Chain
stepSize=2.0 #log10(input$n_sec)
sigma=stepSize*7.5
offset = 25
bmc <- boundedMarkovChain(offset,
maxStepSize=5.5,
upperBound=100.0,
lowerBound=0.0,
make_temp_pdf(offset,sigma),
n_sec=7200, #2 hours
full_output=TRUE
)
})
values <- reactiveValues(a=0,run=0)
observeEvent(input$stopsim,{
values$a <-0
values$run <- abs(isolate(values$run)-1)
})
observeEvent(input$startsim,{
values$run <- abs(isolate(values$run)-1)
})
# Plot time series chart
output$timeseries <- renderPlotly({
p <- plot_ly(rtdata(),x = times/60, y = bmc,
mode = "lines",
hovermode = "closest",
source = "source",
name="temperature",
line=list(color="rgb(250,0,0)")
)
p <- add_trace(p, x=c(rtdata()$times[1]/60,rtdata()$times[length(rtdata()$times)]/60),
y=c(train_ranges()[1]+(2*train_ranges()[2]),
train_ranges()[1]+(2*train_ranges()[2])),
name="Max range",
line = list( # line is a named list, valid keys: /r/reference/#scatter-line
color = "rgb(0, 0, 250,1)", # line's "color": /r/reference/#scatter-line-color
dash = 5,
width=1 # line's "dash" property: /r/reference/#scatter-line-dash
)
)
p <- add_trace(p, x=c(rtdata()$times[1]/60,rtdata()$times[length(rtdata()$times)]/60),
y=c(train_ranges()[1]-(2*train_ranges()[2]),
train_ranges()[1]-(2*train_ranges()[2])),
name="Min range",
line = list( # line is a named list, valid keys: /r/reference/#scatter-line
color = "rgb(0, 0, 250,1)", # line's "color": /r/reference/#scatter-line-color
dash = 5,
width=1 # line's "dash" property: /r/reference/#scatter-line-dash
)
) %>%
layout(title = "Simulated temperature data",
xaxis = list(title = "Time (mins)",
gridcolor = "#bfbfbf",
domain = c(0, 0.98),
range = c(rtdata()$times/60,rtdata()$times[length(rtdata()$times)]/60),
tickfont = list(family='Helvetica', face='italic'),
showline=FALSE,
zeroline=FALSE,
showgrid=FALSE
),
yaxis = list(title = "Temperature (C)",
range = c(max(c( 1.0,min(which(pltdata()$bmchist>0),
(train_ranges()[1]-(2*train_ranges()[2]))*10)/10.0-train_ranges()[2])),
min(c(99.9,max(which(pltdata()$bmchist>0),
(train_ranges()[1]+(2*train_ranges()[2]))*10)/10.0+(2*train_ranges()[2])))),
zeroline=FALSE,
tickfont = list(family='Helvetica',style='italic'),
gridcolor = "#bfbfbf",
linetype="dashed"
),
showlegend=FALSE,
font = list(family='Helvetica',style='italic')
)
p
})
# Coupled hover event
output$gague <- renderPlotly({
# Read in hover data
eventdata <- event_data("plotly_hover", source = "source")
validate(need(!is.null(eventdata), "Hover over the time series to see operation level"))
# Get point number
datapoint <- as.numeric(eventdata$pointNumber)[1]
rotangle <- ((rtdata()$bmc[datapoint]-train_ranges()[1])/(2*train_ranges()[2]))*(25/3.0)
base_plot <- plot_ly(
type = "pie",
values = c(50, 16.67, 16.67, 16.67),
labels = c("Error Log Level Meter", "Cold", "Normal", "Hot"),
rotation = 90,
direction = "clockwise",
hole = 0.3,
textinfo = "label",
textposition = "inside",
hoverinfo = "none",
domain = list(x = c(0, 0.48), y = c(0, 1)),
marker = list(colors = c('rgb(255, 255, 255)', 'rgb(100,100,250)', 'rgb(100,250,100)', 'rgb(250,100,100)')),
showlegend= FALSE
)
base_plot <- layout(
base_plot,
shapes = list(
list(
type = 'path',
path = paste('M',#x2 = x cos f - y sin f, y2 = y cos f + x sin f
toString(0.2345),#+(-0.005*cospi(rotangle))),
toString(0.5),#+(-0.005*sinpi(rotangle))),
'L',
toString(0.21),#4.65,25.01826,25.9489,21.69281
toString(0.55),
'L',
toString(0.245),#+(0.005*cospi(rotangle))),
toString(0.5),#25.40873+(0.005*sinpi(rotangle))),
'Z'),
xref = 'paper',
yref = 'paper',
fillcolor = 'rgba(0, 0, 0, 0.5)'
)
),
annotations = list(
list(
xref = 'paper',
yref = 'paper',
x = 0.21,
y = 0.45,
showarrow = FALSE,
text = format(round(rotangle,2),nsmall=2)#rtdata()$bmc[datapoint],2), nsmall = 2)
)
)
)
})
})