#This is just some practice attempts at making histograms of the various states that ebFRET identifies in a given Hidden Markov Model.


library(ggplot2)
library(hrbrthemes)
library(dplyr)
library(tidyr)
library(viridis)
library(ggridges)

#Load in the Full Report Trace outputs from ebFRET

TypeAll_traces_2states_ebFRET <- read.table("F:/Conditions/1uMeEF2;15mMMg2+(WT)/Compiled/Saved_Data/T1_to_T2_Synchronized_secondAttempt/ebFRET_Results/1e15Mg_2states.dat", quote="\"", comment.char="")
TypeAll_traces_3states_ebFRET <- read.table("F:/Conditions/1uMeEF2;15mMMg2+(WT)/Compiled/Saved_Data/T1_to_T2_Synchronized_secondAttempt/ebFRET_Results/1e15Mg_3states.dat", quote="\"", comment.char="")
TypeAll_traces_4states_ebFRET <- read.table("F:/Conditions/1uMeEF2;15mMMg2+(WT)/Compiled/Saved_Data/T1_to_T2_Synchronized_secondAttempt/ebFRET_Results/1e15Mg_4states.dat", quote="\"", comment.char="")

#Name the columns for each dataframe

colnames(TypeAll_traces_2states_ebFRET) <- c("Trace", "Donor", "Acceptor", "FRET", "Viterbi_State", "Viterbi_Mean")
colnames(TypeAll_traces_3states_ebFRET) <- c("Trace", "Donor", "Acceptor", "FRET", "Viterbi_State", "Viterbi_Mean")
colnames(TypeAll_traces_4states_ebFRET) <- c("Trace", "Donor", "Acceptor", "FRET", "Viterbi_State", "Viterbi_Mean")

#Use dplyr to filter out a particular state for each state in the 2-state HMM

State1_2HMM <- filter(TypeAll_traces_2states_ebFRET, Viterbi_State == 1)
State2_2HMM <- filter(TypeAll_traces_2states_ebFRET, Viterbi_State == 2)

#Use dplyr to filter out a particular state for each state in the 3-state HMM

State1_3HMM <- filter(TypeAll_traces_3states_ebFRET, Viterbi_State == 1)
State2_3HMM <- filter(TypeAll_traces_3states_ebFRET, Viterbi_State == 2)
State3_3HMM <- filter(TypeAll_traces_3states_ebFRET, Viterbi_State == 3)

#Use dplyr to filter out a particular state for each state in the 4-state HMM

State1_4HMM <- filter(TypeAll_traces_4states_ebFRET, Viterbi_State == 1)
State2_4HMM <- filter(TypeAll_traces_4states_ebFRET, Viterbi_State == 2)
State3_4HMM <- filter(TypeAll_traces_4states_ebFRET, Viterbi_State == 3)
State4_4HMM <- filter(TypeAll_traces_4states_ebFRET, Viterbi_State == 4)


#Use dplyr to select the Viterbi_Mean values for each state in the 2-state HMM

FRET_State1_2HMM <- select(State1_2HMM, Viterbi_Mean)
FRET_State2_2HMM <- select(State2_2HMM, Viterbi_Mean)

#Use dplyr to select the Viterbi_Mean values for each state in the 3-state HMM

FRET_State1_3HMM <- select(State1_3HMM, Viterbi_Mean)
FRET_State2_3HMM <- select(State2_3HMM, Viterbi_Mean)
FRET_State3_3HMM <- select(State3_3HMM, Viterbi_Mean)

#Use dplyr to select the Viterbi_Mean values for each state in the 4-state HMM

FRET_State1_4HMM <- select(State1_4HMM, Viterbi_Mean)
FRET_State2_4HMM <- select(State2_4HMM, Viterbi_Mean)
FRET_State3_4HMM <- select(State3_4HMM, Viterbi_Mean)
FRET_State4_4HMM <- select(State4_4HMM, Viterbi_Mean)


#Faceted for each state in the 2-state HMM

ggplot(TypeAll_traces_2states_ebFRET, aes(x=Viterbi_Mean))+
  geom_histogram(color="black", fill="white")+
  facet_grid(Viterbi_State ~ .)

#Faceted for each state in the 3-state HMM

ggplot(TypeAll_traces_3states_ebFRET, aes(x=Viterbi_Mean))+
  geom_histogram(color="black", fill="white")+
  facet_grid(Viterbi_State ~ .)

#Faceted for each state in the 4-state HMM

ggplot(TypeAll_traces_4states_ebFRET, aes(x=Viterbi_Mean))+
  geom_histogram(color="black", fill="white")+
  facet_grid(Viterbi_State ~ .)