collision_script.R

In JannisGottwald/N40RTS: What the Package Does (One Line, Title Case)

library(N40RTS)
library(data.table)
library(ggplot2)

#initialize Project
plst<-initProject(projroot = "H:/projects/repositories/Duration_RTS/", logger_data_raw = "H:/projects/repositories/Duration_Testlauf/data/")

#dataframe with all individuals
tags<-data.frame(
  ID=c("150007_10ms", "150007_20ms", "150007_40ms"), 
  freq=c(150007, 150007, 150007), 
  start=c("2020-09-21", "2020-09-21", "2020-09-21"),
  end=  c("2020-10-01", "2020-10-01", "2020-10-01"),
  min_dur=c(0.0096, 0.016, 0.0288), 
  max_dur=c(0.0128, 0.0192, 0.0352))


#initialize colliding individuals and define station
anml10<-initAnimal(projList = plst, animalID = tags$ID[1], freq = tags$freq[1], start=tags$start[1], end=tags$end[1])
d10lst<-list.files(anml10$path$filtered, pattern="mof_rts_00009",full.names = T)
anml20<-initAnimal(projList = plst, animalID = tags$ID[2], freq = tags$freq[2], start=tags$start[2], end=tags$end[2])
d20lst<-list.files(anml20$path$filtered,pattern="mof_rts_00009", full.names = T)
anml40<-initAnimal(projList = plst, animalID = tags$ID[3], freq = tags$freq[3], start=tags$start[3], end=tags$end[3])
d40lst<-list.files(anml40$path$filtered, pattern="mof_rts_00009",full.names = T)

#data path
path_to_d10<-d10lst[1]
path_to_d20<-d20lst[1]
path_to_d40<-d40lst[1]

#read data
d10<-data.table::fread(path_to_d10)
d20<-data.table::fread(path_to_d20)
d40<-data.table::fread(path_to_d40)
  
#convert timestamps
d10$timestamp<-as.POSIXct(d10$timestamp)
d20$timestamp<-as.POSIXct(d20$timestamp)
d40$timestamp<-as.POSIXct(d40$timestamp)

#create data frames for rbinding  
data10<-data.frame()
data20<-data.frame()
data40<-data.frame()
  
for ( i in c(0,1,2,3)){
#subset to receiver
  
  #10ms    
    d10_r<-d10[d10$receiver==i,]
    d10_r<-d10_r[order(d10_r$timestamp),]
    d10_r$collision<-"no"
  #20ms
    d20_r<-d20[d20$receiver==i,]
    d20_r<-d20_r[order(d20_r$timestamp),]
    d20_r$ID<-1:nrow(d20_r)
    d20_r$collision<-"no"
    d20_r$timeControld20<-d20_r$timestamp
   #40ms
    d40_r<-d40[d40$receiver==i,]
    d40_r<-d40_r[order(d40_r$timestamp),]
    d40_r$ID<-1:nrow(d40_r)
    d40_r$collision<-"no"
    d40_r$timeControld40<-d40_r$timestamp
    
#calculate timediff between consecutive signals of 10ms and 20ms tag
    d10_r$timediff<-abs(difftime( d10_r$timestamp, d10_r$timestamp[2:length(d10_r$timestamp)], units="secs" ))
    d20_r$timediff<-abs(difftime( d20_r$timestamp, d20_r$timestamp[2:length(d20_r$timestamp)], units="secs" ))

#set ecpected timedifference in seconds    
  #10ms
    td10<-0.44
  #20ms
    td20<-0.99
    
#subset to timediffs that indicate a missing signal
    d10_c<-d10_r[d10_r$timediff>=td10+(td10*0.2) & d10_r$timediff<=2*td10+(td10*0.2), ]
    d20_c<-d20_r[d20_r$timediff>=td20+(td20*0.5) & d20_r$timediff<=2*td20+(td20*0.5), ]
    
#create data.frame with timestamps of missing signals 
    c10<-data.frame(timestamp=d10_c$timestamp+td10, timestamp_control=d10_c$timestamp+td10 )
    c20<-data.frame(timestamp=d20_c$timestamp+td20, timestamp_control=d20_c$timestamp+td20)
    
#convert to data.table and set key for matching
    prepD10<-data.table::data.table(c10)
    prepD20<-data.table::data.table(c20)
    data.table::setkey(prepD10,timestamp)
    data.table::setkey(prepD20,timestamp)
    
    victimD20<-data.table::data.table(d20_r)
    victimD40<-data.table::data.table(d40_r)
    data.table::setkey(victimD20,timestamp)
    data.table::setkey(victimD40,timestamp)
    
#match nearest
    p10v20 <- victimD20[prepD10, roll = "nearest"]
    p10v40 <- victimD40[prepD10, roll = "nearest"]
    p20v40 <- victimD40[prepD20, roll = "nearest"]
    
#calculate timediff between signals of affected receiver and potential collisions 
    p10v20$timediff<-abs(difftime(p10v20$timeControld20, p10v20$timestamp_control, units="secs" ))
    p10v40$timediff<-abs(difftime(p10v40$timeControld40, p10v40$timestamp_control, units="secs" ))
    p20v40$timediff<-abs(difftime(p20v40$timeControld40, p20v40$timestamp_control, units="secs" ))
    
#set threshold (abritrary)
    p10v20<-p10v20[p10v20$timediff<=0.06,]
    p10v40<-p10v40[p10v40$timediff<=0.06,]
    p20v40<-p20v40[p20v40$timediff<=0.06,]
    
#classify collisions
    d20_r$collision[d20_r$ID %in% p10v20$ID]<-"yes"
    d40_r$collision[d40_r$ID %in% p10v40$ID]<-"yes"
    d40_r$collision[d40_r$ID %in% p20v40$ID]<-"yes"
    
    
#rbind by receiver   
    data10<-rbind(data10, d10_r)
    data20<-rbind(data20, d20_r)
    data40<-rbind(data40, d40_r)
  }
#write to individual folder @animal$path$filtered  
  data.table::fwrite(data10, paste0(anml10$path$filtered, "/",gsub(".csv", "", basename(path_to_d10)), "_collision.csv" ))
  data.table::fwrite(data20[data20$collision=="no",], paste0(anml20$path$filtered, "/",gsub(".csv", "", basename(path_to_d20)), "_collision.csv" ))
  data.table::fwrite(data40[data40$collision=="no",], paste0(anml40$path$filtered, "/",gsub(".csv", "", basename(path_to_d40)), "_collision.csv" ))
  
  

  
#check if it works
  ggplot(d20_r[d20_r$timestamp<="2020-09-23 10:20:00", ], aes(x=timestamp, y=max_signal, color=receiver))+geom_point()
  ggplot(d20_r[d20_r$timestamp<="2020-09-23 10:20:00" & d20_r$collision=="no", ], aes(x=timestamp, y=max_signal, color=collision))+geom_point()
  
  clsn<-d20_r[d20_r$timestamp>="2020-09-22 15:00:00" & d20_r$timestamp<="2020-09-23 08:20:00" & d20_r$max_signal>60, ]
  nrow(clsn[clsn$collision=="yes",])/nrow(clsn)
  
  signls<-d20_r[d20_r$timestamp>="2020-09-22 15:00:00" & d20_r$timestamp<="2020-09-23 08:20:00" & d20_r$max_signal<59.7 & d20_r$max_signal>58.5, ]
  
  nrow(signls[signls$collision=="yes",])/nrow(signls)
  
  
  ggplot(d40_r[d40_r$timestamp>="2020-09-25", ], aes(x=timestamp, y=max_signal, color=receiver))+geom_point()
  ggplot(d40_r[d40_r$timestamp>="2020-09-25", ], aes(x=timestamp, y=max_signal, color=collision))+geom_point()