plots.Rmd

---
title: "plots"
author: "K. Enevoldsen & P. Waade"
date: "12/10/2018"
output: html_document
editor_options: 
  chunk_output_type: console
---

#Setup
```{r setup, include=FALSE}
# !diagnostics off
#^the above argument prevents the mulitple 'unknown column' warnings (harmless warnings)
knitr::opts_chunk$set(echo = TRUE)
#devtools::install_github("thomasp85/patchwork")
pacman::p_load(pacman, plyr, tidyverse, raster, reshape2, knitr, brms, boot, rethinking, groupdata2, patchwork, RColorBrewer, Hmisc)
p_load(gmodels)
```

#add ID
```{r add_ID} 
add_ID <- function(result_df, add_op = F){
  #DESCRIPTION
  #INPUT
  #OUTPUT
  
  n_rounds <- max(result_df$round_nr)
  n_players <- nrow(result_df)/n_rounds
  result_df$ID <- as.numeric( #as. numeric since sapply returns a matrix
                    sapply(seq(1, n_players, by = 2), #creating a list of the first agent in each pair (jumping by two since it is a pair) 
                    function(x) rep(c(x, x+1), n_rounds))) #creating ID for each pair equal to x and x+1 and repeating for each round
  
  if (add_op){
    result_df$op <- sapply(seq(nrow(result_df)), 
                          function(x) 
                          str_split(result_df$pair[x], " / ")[[1]][str_split(result_df$pair[x], " / ")[[1]] !=  result_df$player[x]])
  }
  
  
  return(result_df)
}
```

#heatmap
```{r heatmap} 

  #create a compete matrix
quick_heatmap <- function(result_df, return_plot_df = F){
  #DESCRIPTION
  #INPUT
  #OUTPUT
  
  tmp_list <- NULL; i = 1
  for (c_pair in unique(result_df$pair)){ #loop through all the pairs
    p_subset <- subset(result_df, pair == c_pair)
    
      #Player 1
    player <- p_subset$player[1]
    op <- p_subset$player[2]
    points <- sum(p_subset$points[seq(1, nrow(p_subset), by = 2)]) #calculate points
    
    tmp_list[[i]] <- data_frame(player = player, op = op, points = points) #save the variables
    
      #Player 2
    player <- p_subset$player[2]
    op <- p_subset$player[1]
    points <- sum(p_subset$points[seq(2, nrow(p_subset), by = 2)]) #calculate points
    
    tmp_list[[i+1]] <- data_frame(player = player, op = op, points = points) #save the variables
    i = i + 2
  }
  i
  tmp_df <- tmp_list %>% bind_rows() #save the variables
  
  unique(tmp_df$player)
  
  p <- ggplot(tmp_df, aes(player, op)) + 
      geom_tile(aes(fill = points), colour = "white") + 
      scale_fill_gradient(name = "Points", low = "lightsteelblue1", high = "steelblue") +  
      theme_classic() + labs(x = "Player 1", y = "Player 2") 
  
  if (return_plot_df){
    return(list(plot = p, plot_df = tmp_df))
  } else{
    return(p)
  }
}
```

#P(k) plot
```{r quick_p_k_plot} 
 
quick_p_k_plot <- function(result_df, ID, blue = T){
  
  p_k_v <- c()
  hidden_states <- result_df$hidden_states[result_df$ID == ID]
  for (i in 1:length(hidden_states)){
    p_k_v <- c(p_k_v, hidden_states[[i]]$own_hidden_states$p_k)
  }
  level <- length(p_k_v)/length(hidden_states)
  plot_df <- data_frame(round = seq(length(hidden_states)))
  
  for (i in 1:level){
    plot_df[,i+1] <- p_k_v[seq(i, length(p_k_v), by= level)]
    colnames(plot_df)[i+1] <- paste("P(", i-1, ")", sep = "")
  }
  
  plot_df1 <- melt(plot_df ,  id.vars = 'round', variable.name = "level")
  p <- ggplot(plot_df1, aes(round, value, color = level)) + geom_line() + labs(x = "Round", y = "P(k)") + guides(color=guide_legend(title=" "))
  
  if (blue){ #add color palette
    p <- p + scale_color_brewer(palette="Blues", direction = -1) + theme_linedraw()
  }
  return(p)
  
}


```

#p_k again (repeated simulations)
```{r}
#repeated sims. 
p_k_rep_sim <- function(d, agent, examined_level = "\\d"){
  #examined level can be regex - i.e "\\d" gets all levels
  d <- subset(d, player == agent)

  tmp <- lapply(d$hidden_states, function(x) as_tibble(t(c(x$own_hidden_states$p_k)))) %>% bind_rows()
  colnames(tmp) <- paste("P(",(seq(length(colnames(tmp)))-1), ")", sep = "")
  d <- bind_cols(d, tmp)
  d <- dplyr::select(d, round_nr, sim, colnames(tmp))
  dp <- reshape2::melt(d, id.vars = c("round_nr", "sim"),value.name = "prop" )
  dp <- filter(dp, grepl(examined_level, variable)) #remove everything beside the chosen values 
  
  dp <- dp %>% group_by(round_nr, variable) %>% mutate(mean_p = mean(prop)) %>% merge(., dp)
  ggplot(dp, aes(round_nr, prop)) + 
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
      stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5))  + geom_line(aes(round_nr, mean_p), size = 0.3) + labs(x = "Round", y = "P(k)") + theme_light() + facet_wrap(~variable)
}
```

#Win % plot
```{r quick_pwon_plot}
quick_pwon_plot <- function(d, agent){ 
  d <- dplyr::select(d, -c(hidden_states))
  
  #create a commulative sum and percentage won
  d <- d %>% 
    group_by(sim, player) %>% 
    mutate(cumsum = cumsum(points), p_won = ((round_nr + cumsum(points))/2)/round_nr)
  
  dp <- subset(d, player == agent)
  
  dp <- dp %>% group_by(round_nr) %>% summarise(m_p_won = mean(p_won)) %>% merge(., dp)
  ggplot(dp, aes(round_nr, p_won)) + 
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
    geom_line(aes(round_nr, m_p_won), size = 0.3) + labs(x = "Round", y = "Win %") + theme_linedraw() + geom_hline(yintercept = 0.5, color = "firebrick") + coord_cartesian(ylim = c(0.3, 0.8))
}
```


#0-ToM plot
```{r}


#fixing sim
oToM_plot <- function(d){
  
  d <- subset(d1, player == "0-ToM")
  d$mean_p <- sapply(d$hidden_states, function(x) x$own_hidden_states$mean_basic)
  d$variance <- sapply(d$hidden_states, function(x) x$own_hidden_states$variance_basic)
  
  dp <- d %>% 
    group_by(round_nr) %>% 
    summarise(m_m = mean(inv.logit(mean_p)), m_v = mean(exp(variance)) ) %>% 
    merge(., d) %>% 
    mutate(mean_p = inv.logit(mean_p),  variance = exp(variance))
  
    #mean
  p_mean <- ggplot(dp, aes(round_nr, mean_p)) + 
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", 
                 fun.args = list(conf.int = .5)) + 
    geom_line(aes(round_nr, m_m)) + theme_light() + 
    labs(title = "", y = "Mean for probability estimation", x = "Round")
  
  
    #variance
  p_var <- ggplot(dp, aes(round_nr, variance)) + 
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
    stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", 
                 fun.args = list(conf.int = .5)) + 
    geom_line(aes(round_nr, m_v)) + theme_light() + 
    labs(title = "", y = "Variance for probability estimation", x = "Round")
  
  return(list(p_mean, p_var))
}
```


#plotting all the things
```{r}
load(file = "result_df_1.rda")
d <- result_df
load(file = "result_df_2.rda")
result_df$sim <- result_df$sim + 25
d <- rbind(d, result_df)
load(file = "result_df_3.rda")
result_df$sim <- result_df$sim + 50
d <- rbind(d, result_df)
load(file = "result_df_4.rda")
result_df$sim <- result_df$sim + 75
d <- rbind(d, result_df)
rm(result_df)
unique(d$sim)

sump <- d %>% group_by(sim, pair, player) %>% summarise(points = sum(points), total_rounds =  n())
  #the following command extract the op from the pair by only choosing the agent in the pair which is not the agent (notice that the result are transposed so that the op. is in the same order as players)
sump$op <- t(str_split(sump$pair, pattern = " / ", simplify = T))[t(str_split(sump$pair, pattern = " / ", simplify = T) != sump$player)]

p_df <- sump %>% mutate(p_won = ((total_rounds + points)/2)/total_rounds) %>% group_by(pair, player, op) %>% summarise(mean_pwon = mean(p_won), sd_p_won = sd(p_won), ci_lower = ci(p_won)[2], ci_upper = ci(p_won)[3])

#all
heat1_all <- ggplot(p_df, aes(player, op)) + 
  geom_tile(aes(fill = mean_pwon), colour = "white") + 
  scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") + 
  geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) + 
  theme_classic() + labs(x = "Player 1", y = "Player 2")

p_df <- p_df %>% filter(!(player %in% c("WSLS", "RB")) & !(op %in% c("WSLS", "RB")))

#ToM's
heat2_all <- ggplot(p_df, aes(player, op)) + 
  geom_tile(aes(fill = mean_pwon), colour = "white") + 
  scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") + 
  geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) + 
  theme_classic() + labs(x = "Player 1", y = "Player 2")

  #doing it with only 30 trials
sump <- d %>% filter(round_nr < 31) %>% group_by(sim, pair, player) %>% summarise(points = sum(points), total_rounds =  n())
sump$op <- t(str_split(sump$pair, pattern = " / ", simplify = T))[t(str_split(sump$pair, pattern = " / ", simplify = T) != sump$player)]

head(d)
p_df <- sump %>% mutate(p_won = ((total_rounds + points)/2)/total_rounds) %>% group_by(pair, player, op) %>% summarise(mean_pwon = mean(p_won), sd_p_won = sd(p_won), ci_lower = ci(p_won)[2], ci_upper = ci(p_won)[3], n = n())

#with only 30 trials
heat1_30 <- ggplot(p_df, aes(player, op)) + 
  geom_tile(aes(fill = mean_pwon), colour = "white") + 
  scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") + 
  geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) + 
  theme_classic() + labs(x = "Player 1", y = "Player 2")

p_df <- p_df %>% filter(!(player %in% c("WSLS", "RB")) & !(op %in% c("WSLS", "RB")))

#with only 30 trials - ToMs
heat2_30 <- ggplot(p_df, aes(player, op)) + 
  geom_tile(aes(fill = mean_pwon), colour = "white") + 
  scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") + 
  geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) + 
  theme_classic() + labs(x = "Player 1", y = "Player 2")

unique(d$pair)
d1 <- filter(d, pair == "0-ToM / 1-ToM")
pwon1v0 <- quick_pwon_plot(d = d1, agent = "1-ToM")
d1 <- filter(d, pair == "1-ToM / 2-ToM")
pwon2v1 <- quick_pwon_plot(d = d1, agent = "2-ToM")
d1 <- filter(d, pair == "2-ToM / 4-ToM")
pwon4v2 <- quick_pwon_plot(d = d1, agent = "4-ToM")
d1 <- filter(d, pair == "4-ToM / 5-ToM")
pwon5v4 <- quick_pwon_plot(d = d1, agent = "5-ToM")

d1 <- filter(d, pair == "RB / 0-ToM")
plots <- oToM_plot(d = d1)
otom_mean <- plots[[1]]
otom_var <- plots[[2]]

d1 <- filter(d, pair == "0-ToM / 1-ToM")
plots <- oToM_plot(d = d1)
otom_mean_v1 <- plots[[1]]
otom_var_v1 <- plots[[2]]

d1 <- filter(d, pair == "1-ToM / 2-ToM")
p_k2v1 <- p_k_rep_sim(d = d1, agent = "2-ToM")

d1 <- filter(d, pair == "2-ToM / 5-ToM")
p_k5v2 <- p_k_rep_sim(d = d1, agent = "5-ToM")

d1 <- filter(d, pair == "2-ToM / 3-ToM")
p_k3v2 <- p_k_rep_sim(d = d1, agent = "3-ToM") 

d1 <- filter(d, pair == "0-ToM / 2-ToM")
p_k2v0 <- p_k_rep_sim(d = d1, agent = "2-ToM")

d1 <- filter(d, pair == "0-ToM / 3-ToM")
p_k3v0 <- p_k_rep_sim(d = d1, agent = "3-ToM")

#all all the plots 
plot_list <- c("heat1_all", "heat2_all","heat1_30", "heat2_30", "pwon1v0", "pwon2v1", "pwon4v2", "pwon5v4", "otom_mean", "otom_var", "p_k2v1", "p_k5v2", "p_k3v2")
plot_list <- c("heat1_all", "heat2_30")
plot_list <- c("p_k2v0", "p_k3v0")
i = "heat1_all"

for (i in plot_list){
  assign("tmpplot", eval(parse(text = i)))
  filename <- paste(i, ".png", sep = "")
  if (i == "p_k5v2"){
    ggsave(filename = filename, plot = tmpplot, path = "plots", width = 30, height = 20, units = "cm")
  } else if (grepl("heat",i)){
    ggsave(filename = filename, plot = tmpplot, path = "plots", width = 30, height = 30, units = "cm")
  } else {
    ggsave(filename = filename, plot = tmpplot, path = "plots", width = 20, height = 20, units = "cm")
  }
}

```

#extracting parameters
```{r} 

d1 <- d %>% filter(pair == "4-ToM / 5-ToM", player == "4-ToM")

tmp <- lapply(d1$hidden_states, function(x) as_tibble(t(c( x$own_hidden_states$param_mean)))) %>% bind_rows()
colnames(tmp) <- paste("mean_", seq(2), sep = "")
d1 <- bind_cols(d1, tmp)
d1 <- d1 %>% group_by(round_nr) %>% summarise(mean_1_m = mean(mean_1), mean_2_m = mean(mean_2)) %>% merge(.,d1)

mu_1_2v1 <- ggplot(d1, aes(round_nr, mean_1))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_1_m), size = 0.3) + theme_light() + labs(x= "Trial", y = "Volatility estimate")

mu_2_2v1 <- ggplot(d1, aes(round_nr, mean_2))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_2_m), size = 0.3)+ theme_light() + labs(x= "Trial", y = "Tempature estimate")

ggsave(filename = "mu_1_2v1.png", plot = mu_1_2v1, path = "plots", width = 20, height = 20, units = "cm")
ggsave(filename = "mu_2_2v1.png", plot = mu_2_2v1, path = "plots", width = 20, height = 20, units = "cm")


d1 <- d %>% filter(pair == "0-ToM / 1-ToM", player == "1-ToM")

tmp <- lapply(d1$hidden_states, function(x) as_tibble(t(c( x$own_hidden_states$param_mean)))) %>% bind_rows()
colnames(tmp) <- paste("mean_", seq(2), sep = "")
d1 <- bind_cols(d1, tmp)
d1 <- d1 %>% group_by(round_nr) %>% summarise(mean_1_m = mean(mean_1), mean_2_m = mean(mean_2)) %>% merge(.,d1)

mu_1_1v0 <- ggplot(d1, aes(round_nr, mean_1))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_1_m), size = 0.3) + theme_light() + labs(x= "Trial", y = "Volatility estimate")

mu_2_1v0 <- ggplot(d1, aes(round_nr, mean_2))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_2_m), size = 0.3)+ theme_light() + labs(x= "Trial", y = "Tempature estimate")

ggsave(filename = "mu_1_1v0.png", plot = mu_1_1v0, path = "plots", width = 20, height = 20, units = "cm")
ggsave(filename = "mu_2_1v0.png", plot = mu_2_1v0, path = "plots", width = 20, height = 20, units = "cm")

```


#plotting all the things - Accurate priors
```{r}
load(file = "result_df_selfsim_1.rda")
d <- result_df
load(file = "result_df_selfsim_2.rda")
result_df$sim <- result_df$sim + 25
d <- rbind(d, result_df)

sump <- d %>% group_by(sim, pair, player) %>% summarise(points = sum(points), total_rounds =  n())
  #the following command extract the op from the pair by only choosing the agent in the pair which is not the agent (notice that the result are transposed so that the op. is in the same order as players)
sump$op <- t(str_split(sump$pair, pattern = " / ", simplify = T))[t(str_split(sump$pair, pattern = " / ", simplify = T) != sump$player)]

p_df <- sump %>% mutate(p_won = ((total_rounds + points)/2)/total_rounds) %>% group_by(pair, player, op) %>% summarise(mean_pwon = mean(p_won), sd_p_won = sd(p_won), ci_lower = ci(p_won)[2], ci_upper = ci(p_won)[3])

# #all
# heat1_all <- ggplot(p_df, aes(player, op)) + 
#   geom_tile(aes(fill = mean_pwon), colour = "white") + 
#   scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") + 
#   geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) + 
#   theme_classic() + labs(x = "Player 1", y = "Player 2")
# 
# p_df <- p_df %>% filter(!(player %in% c("WSLS", "RB")) & !(op %in% c("WSLS", "RB")))
# 
# #ToM's
# heat2_all <- ggplot(p_df, aes(player, op)) + 
#   geom_tile(aes(fill = mean_pwon), colour = "white") + 
#   scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") + 
#   geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) + 
#   theme_classic() + labs(x = "Player 1", y = "Player 2")
# 
  #doing it with only 30 trials
sump <- d %>% filter(round_nr < 31) %>% group_by(sim, pair, player) %>% summarise(points = sum(points), total_rounds =  n())
sump$op <- t(str_split(sump$pair, pattern = " / ", simplify = T))[t(str_split(sump$pair, pattern = " / ", simplify = T) != sump$player)]

p_df <- sump %>% mutate(p_won = ((total_rounds + points)/2)/total_rounds) %>% group_by(pair, player, op) %>% summarise(mean_pwon = mean(p_won), sd_p_won = sd(p_won), ci_lower = ci(p_won)[2], ci_upper = ci(p_won)[3])

#with only 30 trials
heat1_30 <- ggplot(p_df, aes(player, op)) +
  geom_tile(aes(fill = mean_pwon), colour = "white") +
  scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") +
  geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) +
  theme_classic() + labs(x = "Player 1", y = "Player 2")

p_df <- p_df %>% filter(!(player %in% c("WSLS", "RB")) & !(op %in% c("WSLS", "RB")))

#with only 30 trials - ToMs
heat2_30 <- ggplot(p_df, aes(player, op)) +
  geom_tile(aes(fill = mean_pwon), colour = "white") +
  scale_fill_gradient(name = "Win %", low = "lightsteelblue1", high = "steelblue") +
  geom_text(aes(label = paste(round(mean_pwon, 2)," (", round(ci_lower, 2), ", ", round(ci_upper, 2), ")", sep = "")  )) +
  theme_classic() + labs(x = "Player 1", y = "Player 2")
# 
# unique(d$pair)
# d1 <- filter(d, pair == "0-ToM / 1-ToM")
# pwon1v0 <- quick_pwon_plot(d = d1, agent = "1-ToM")
# d1 <- filter(d, pair == "1-ToM / 2-ToM")
# pwon2v1 <- quick_pwon_plot(d = d1, agent = "2-ToM")
# d1 <- filter(d, pair == "2-ToM / 4-ToM")
# pwon4v2 <- quick_pwon_plot(d = d1, agent = "4-ToM")
# d1 <- filter(d, pair == "4-ToM / 5-ToM")
# pwon5v4 <- quick_pwon_plot(d = d1, agent = "5-ToM")

# d1 <- filter(d, pair == "RB / 0-ToM")
# plots <- oToM_plot(d = d1)
# otom_mean <- plots[[1]]
# otom_var <- plots[[2]]
# 
# d1 <- filter(d, pair == "1-ToM / 2-ToM")
# p_k2v1 <- p_k_rep_sim(d = d1, agent = "2-ToM")
# 
# d1 <- filter(d, pair == "2-ToM / 5-ToM")
# p_k5v2 <- p_k_rep_sim(d = d1, agent = "5-ToM")
# 
# d1 <- filter(d, pair == "2-ToM / 3-ToM")
# p_k3v2 <- p_k_rep_sim(d = d1, agent = "3-ToM")

#all all the plots 
plot_list <- c("heat1_all", "heat2_all","heat1_30", "heat2_30", "pwon1v0", "pwon2v1", "pwon4v2", "pwon5v4", "otom_mean", "otom_var", "p_k2v1", "p_k5v2", "p_k3v2")
plot_list <- c("heat1_30", "heat2_30")

for (i in plot_list){
  assign("tmpplot", eval(parse(text = i)))
  filename <- paste(i, "selfsim.png", sep = "")
  if (i == "p_k5v2"){
    ggsave(filename = filename, plot = tmpplot, path = "plots", width = 30, height = 20, units = "cm")
  } else if (grepl("heat",i)){
    ggsave(filename = filename, plot = tmpplot, path = "plots", width = 30, height = 30, units = "cm")
  } else {
    ggsave(filename = filename, plot = tmpplot, path = "plots", width = 20, height = 20, units = "cm")
  }
}

```


#extracting parameters - selfim
```{r}
d1 <- d %>% filter(pair == "0-ToM / 1-ToM", player == "1-ToM")

tmp <- lapply(d1$hidden_states, function(x) as_tibble(t(c( x$own_hidden_states$param_mean)))) %>% bind_rows()
colnames(tmp) <- paste("mean_", seq(2), sep = "")
d1 <- bind_cols(d1, tmp)
d1 <- d1 %>% group_by(round_nr) %>% summarise(mean_1_m = mean(mean_1), mean_2_m = mean(mean_2)) %>% merge(.,d1)

mu_1_1v0_selfsim = ggplot(d1, aes(round_nr, mean_1))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_1_m), size = 0.3) + theme_light() + labs(x="Trial", y="Volatility estimate")

mu_2_1v0_selfsim = ggplot(d1, aes(round_nr, mean_2))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_2_m), size = 0.3) + theme_light() + labs(x="Trial", y="Temperature estimate")

d1 <- d %>% filter(pair == "1-ToM / 2-ToM", player == "2-ToM")

tmp <- lapply(d1$hidden_states, function(x) as_tibble(t(c( x$own_hidden_states$param_mean)))) %>% bind_rows()
colnames(tmp) <- paste("mean_", seq(2), sep = "")
d1 <- bind_cols(d1, tmp)
d1 <- d1 %>% group_by(round_nr) %>% summarise(mean_1_m = mean(mean_1), mean_2_m = mean(mean_2)) %>% merge(.,d1)

mu_1_2v1_selfsim = ggplot(d1, aes(round_nr, mean_1))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_1_m), size = 0.3) + theme_light() + labs(x="Trial", y="Volatility estimate")

mu_2_2v1_selfsim = ggplot(d1, aes(round_nr, mean_2))  +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.25, fill = "steelblue") +
  stat_summary(fun.data=mean_cl_boot, geom="ribbon", alpha = 0.30, fill = "steelblue", fun.args = list(conf.int = .5)) +
  geom_line(aes(round_nr, mean_2_m), size = 0.3) + theme_light() + labs(x="Trial", y="Temperature estimate")

ggsave(filename = "mu_1_1v0_selfsim.png", plot = mu_1_1v0_selfsim, path = "plots", width = 20, height = 20, units = "cm")
ggsave(filename = "mu_2_1v0_selfsim.png", plot = mu_2_1v0_selfsim, path = "plots", width = 20, height = 20, units = "cm")
ggsave(filename = "mu_1_2v1_selfsim.png", plot = mu_1_1v0_selfsim, path = "plots", width = 20, height = 20, units = "cm")
ggsave(filename = "mu_2_2v1_selfsim.png", plot = mu_2_1v0_selfsim, path = "plots", width = 20, height = 20, units = "cm")
```



```{r}
tmpplot <- quick_pwon_plot(result_df, agent = "0-ToM")
ggsave(filename = "RBvotom_lowB.png", plot = tmpplot, path = "plots", width = 20, height = 20, units = "cm")
d_save <- d

beepr::beep(5)
```


# tjek SD på heatmap
#lav heatmap om til percentage won
#omregn på prop på 0 tom'en