---
title: "Lineup"
author: "Rachel Mawer"
date: "2023-12-14"
output: 
  html_document:
    toc: true
    toc_float: true
    theme: cerulean
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = F,message=F)

#main packages #delete/add as needed
library(dplyr)
library(ggplot2)
library(knitr)
library(ggpubr)
library(plotly)
library(pracma) #for standard error
library(reactable)
library(amt)
```

## Code for all

Code to create line ups for all fish of a data type (e.g. all with HMMs). The code will

* randomly assign fish a new ID
* plot all specified parameters with real data randomly located
* save a DF containing real and fake ID, and location of real data in plots
* save a blinded DF for analysis

For each observer, this file is knitted and the folder name at the end of the document changed to save outputs to a specific folder per observer.

```{r get data stuff}
fish_files <- list.files("agents + real data - pooled",full.names = T)

#create fake IDs to assign
fake_ids <- sample(1:length(fish_files),length(fish_files)) %>% as.numeric()

p <- cbind(fish_files,fake_ids) %>% as.data.frame() 

p$fake_ids <- as.numeric(p$fake_ids)

p <- p %>% arrange(fake_ids)
```

```{r make plots}

for(i in 1:length(fish_files)){
  dat_all <- read.csv(p$fish_files[i])
  
  #assign a fake ID, 1:31
  fake_fish_id <- p$fake_ids[i]
  
  #repeat below bit for every plot
  agents_id <- data.frame(agent=unique(dat_all$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "tracks"
  
  dat_id <- dat_id1
  
  dat2 <- dat %>% filter(x!=0 &y!=0)
  #tracks
  print(ggplot(dat2,aes(x=x,y=y,col=as.factor(approach)))+
    geom_path()+
    facet_wrap(~plot_id)+
    theme(axis.text.x = element_blank(),axis.text.y = element_blank(),
          axis.ticks = element_blank())+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Tracks")))
  
  #repeat below bit for every plot
  agents_id <- data.frame(agent=unique(dat$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "heatmap"
  
  dat_id <- rbind(dat_id,dat_id1)
  dat2 <- dat %>% filter(x!=0 &y!=0)

  
  #heatmp
  print(ggplot(dat2,aes(x=x,y=y))+
    geom_bin2d()+
    #stat_bin_2d(aes(fill=log(..density..)))+
    facet_wrap(~plot_id)+
    scale_fill_continuous(type = "viridis",trans="log")+
    theme(axis.text.x = element_blank(),axis.text.y = element_blank(),
          axis.ticks = element_blank())+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Heatmaps")))
  
  
  
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(dat$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "flow velocity"
  
  dat_id <- rbind(dat_id,dat_id1)
  #wv
  print(ggplot(dat,aes(x=water_velocity))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Flow velocity")))
  
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(dat$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "svg"
  
  dat_id <- rbind(dat_id,dat_id1)
  
  #svg
  print(ggplot(dat,aes(x=svg))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  SVG")))
  
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(dat$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "depth"
  
  dat_id <- rbind(dat_id,dat_id1) #here add in fish id somehow and fake fish id
  
  #depth
  print(ggplot(dat,aes(x=depth))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Depth")))
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(dat$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "diffsvgang"
  
  dat_id <- rbind(dat_id,dat_id1)
  
  #diffsvgang
  print(ggplot(dat,aes(x=diff_svg_abs_ang_end))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Diff SVG - Fish angle")))
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(dat$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  dat <- merge(dat_all,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "diffvang"
  
  dat_id <- rbind(dat_id,dat_id1)
  
  #diffvelang
  print(ggplot(dat,aes(x=diff_vel_abs_ang_end))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Diff Vel - Fish angle")))
  
  #DO FOR STEP LENGTH AND TURNING ANGLE
 
  l <- dat_all %>% make_track(.x=x,.y=y,all_cols=T) %>% steps(keep_cols="start")
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(l$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  l2 <- merge(l,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "Step length"
  
  dat_id <- rbind(dat_id,dat_id1)
  
  l2 <- l2 %>% filter(x1_!=0 & x2_!=0 &y1_!=0 &y2_!=0)
  
  print(ggplot(l2,aes(x=sl_))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," -  Step lengths")))
  #add code to limit x axis, e.g. max 500 m?
  
  #turning angle
  
  #repeat below bit for every plot...
  agents_id <- data.frame(agent=unique(l$agent),plot_id=sample(1:20,20))
  #this assigns a random number 1 - 20 to dictate the plot position
  
  
  l2 <- merge(l,agents_id) #do unique for each plot LMAO !!
  
  dat_id1 <- agents_id %>% filter(agent=="real")
  dat_id1$plot_type <- "Turning angle"
  
  dat_id <- rbind(dat_id,dat_id1)
  
  print(ggplot(l2,aes(x=ta_))+
    geom_histogram()+
    facet_wrap(~plot_id)+
    labs(title=paste0("Plot for fish id ",fake_fish_id," - Turning angle")))
  
  
  #save df with all data
  
  dat_id$fish_id <- unique(dat$fish_id)
  dat_id$fake_fish_id <- fake_fish_id
  
  
  dat_id_blind <- dat_id %>% dplyr::select(fake_fish_id,plot_type)
  dat_id_blind$plot_id_guess <- NA
  
if(i==1){
  dat_id_all <- dat_id
  dat_id_blind_all <- dat_id_blind
} else{
  dat_id_all <- bind_rows(dat_id_all,dat_id)
  dat_id_blind_all <- bind_rows(dat_id_blind_all,dat_id_blind)
}

}  

write.csv(dat_id_all,"line up pooled/all_dat.csv",row.names = F)
write.csv(dat_id_blind_all,"line up pooled/blinded.csv",row.names = F)

```