library(plyr)
library(dplyr)
library(MPDiR)
library(quickpsy)
library(fitdistrplus)
library(ggplot2)
library(tidyverse)  

source("1-read-and-calculate_errors_time_1.R") # to read global values
source("CI-Functions-Bonferroni.R")

# set directory where script is
sourceDir <- dirname (rstudioapi::getActiveDocumentContext()$path) 
defaultpath <- sourceDir

#remove(list = ls())
print(defaultpath)
setwd(defaultpath)

filename <- "results/aggregated_per_percentage.csv"
dataFile <- read.csv(filename)

aggregatedstable <-dataFile

if (exists ("all_percentage_CIs")) { rm(all_percentage_CIs) }


# calculate CIs for each percentage and vis speed

# tmp_data <- aggregatedstable [ which ((aggregatedstable$correct_answer == '18') & 
#                                         (aggregatedstable$speed_duration == '0-static')),]
# 
# tmp_abs_error <- bootstrapMeanCI(tmp_data[,"mean_abs_error"])
# tmp = data.frame (correct_answer = '18',  speed_duration = '0-static', 
#         mean_abs_error = tmp_abs_error[1], 
#         lowci_abs_error = tmp_abs_error[2], 
#         upci_abs_error = tmp_abs_error[3])
# 
# if ( !exists("all_percentage_CIs") ){
#   all_percentage_CIs <- tmp
#   }else{
#   all_percentage_CIs <- rbind(all_percentage_CIs,tmp)
# }

correct_answer_interest <- c(18,32,43,58,72,83)

for(per in correct_answer_interest)
  for (vis in speed_duration)
  {
    tmp_data <- aggregatedstable [ which ((aggregatedstable$correct_answer == per) & 
                                            (aggregatedstable$speed_duration == vis)),]
    
    tmp_abs_error <- bootstrapMeanCI(tmp_data[,"mean_abs_error"])
    tmp_true_error <- bootstrapMeanCI(tmp_data[,"mean_true_error"])
    tmp = data.frame (correct_answer = per,  speed_duration = vis, 
                      mean_abs_error = tmp_abs_error[1], 
                      lowci_abs_error = tmp_abs_error[2], 
                      upci_abs_error = tmp_abs_error[3],
                      mean_true_error = tmp_true_error[1], 
                      lowci_true_error = tmp_true_error[2], 
                      upci_true_error = tmp_true_error[3])
    
    if ( !exists("all_percentage_CIs") ){
      all_percentage_CIs <- tmp
    }else{
      all_percentage_CIs <- rbind(all_percentage_CIs,tmp)
    }
  }




ggplot(all_percentage_CIs, aes(correct_answer, mean_abs_error, colour=speed_duration)) + 
  theme_bw()+
  geom_line() + 
  geom_point() +
  xlab('True percentage')+ ylab('Absolute Error')+
  geom_linerange (aes(ymin=lowci_abs_error,ymax=upci_abs_error))
#  geom_ribbon (aes(ymin=lowci_abs_error,ymax=upci_abs_error, fill = speed_duration),alpha = 0.3)
ggsave("plots/abs-errors-per-percentage.pdf",device = pdf, width=5, height=2)

ggplot(all_percentage_CIs, aes(correct_answer, mean_true_error, colour=speed_duration)) + 
  theme_bw()+
  geom_line() + 
  geom_point() +
  xlab('True percentage')+ ylab('Error')+
  geom_linerange (aes(ymin=lowci_true_error,ymax=upci_true_error))
#  geom_ribbon (aes(ymin=lowci_true_error,ymax=upci_true_error, fill = speed_duration),alpha = 0.3)
ggsave("plots/true-errors-per-percentage.pdf",device = pdf, width=5, height=2)