#
#
#currently set to only add tod and filter for date/min spm
#
#
rm(list=ls())

#packages
library(dplyr)
library(ggplot2)
library(sp)
library(maptools)

#file list
file_list <- list.files("data/find ladder/SSF data with tod filtered for migration and min spm over 2/",full.names=T)
#keep only tracks where max_nsd >40


#get list of Good track ids and fish

#need to do filtering in track_straightness as well to get Same Tracks

for(p in 1:length(file_list)){ 
  data <-  read.csv(file_list[[p]])
  
  id <- data$fish_id[1]
  
  data$t1_ <- as.POSIXct(data$t1_)
  data$t2_ <- as.POSIXct(data$t2_)
  

  
  if(length(data$fish_id)>1){
    
    #calculate nsd
    
    X <- split(data, list(data$fish_id,data$track),drop=T)
    
    for (d in 1:length(X)) { #ok getting errors as assuming levels for all parts of track
      track <- X[[d]]   
      #save starting coord
      start <- track[1,c("x1_","y1_")] #start coords
      track$nsd <- with(track, sqrt((x2_ - start[[1]])^2 + (y2_ - start[[2]])^2))
      
      if(d==1){
        data_filt <- track
      } else{
        data_filt <- rbind(data_filt,track)
      }
      
    }
    
    #now summarise nsd per track
    
    sum_nsd <- data_filt %>% group_by(track) %>% summarise(max_nsd=max(nsd))
    
    
    #now filter to remove tracks nsd<40
    
    tracks_max40 <- sum_nsd %>% filter(max_nsd>=40)
    
    #now filter data
    
    dat_filt <- data %>% filter(track %in% unique(tracks_max40$track))
    
    write.csv(dat_filt_final,
              paste0("data/find ladder/FINAL final SSF dataframes - filtered - decision tree/",id,".csv"),
              row.names = F)
  } else{
    print(paste0("fish id ",id," no data post date filtering"))
  }
  
}