#<number_id>_<tone>_<rhymetype>_<trialNumber>
setwd("~/Desktop/Falam Recordings/PitchTier")
#this removes thigns frm rg\ightside thingy
rm(list=ls())

library(rPraat)
library(reshape2)
library(dplyr)
library(dygraphs)
library(tuneR)
ptFiles <- dir(pattern = "*.PitchTier")
numFiles <- length(ptFiles)
numSamples <- 100

f0Contour <- matrix(numeric(numSamples*numFiles), ncol = numSamples)


for(fIdx in 1:numFiles)  {

  pitchData <- pt.read(ptFiles[fIdx])
  numTimeObs <- length(pitchData$t)
  timeVecInterp <- seq(pitchData$t[1], pitchData$t[numTimeObs], length = numSamples)
  f0Contour[fIdx, ] <- approx(pitchData$t, pitchData$f, timeVecInterp)$y
}


id <- as.factor(regmatches(ptFiles, regexpr("^\\d*(?=\\_)", ptFiles, perl = TRUE)))
wordType <- as.factor(regmatches(ptFiles, regexpr("[A-Za-z]+(?=_[012].PitchTier$)", ptFiles, perl = TRUE)))
tone <- as.factor(regmatches(ptFiles, regexpr("(?<=\\d\\_)(rising|falling|high|low)", ptFiles, perl = TRUE)))
trialNum <- as.factor(regmatches(ptFiles, regexpr("[0123].PitchTier$", ptFiles, perl = TRUE)))



dfMaster <- data.frame(id, wordType, trialNum, tone, f0Contour)
dfMelt <- melt(dfMaster, id.vars = c("id", "wordType", "trialNum", "tone"))

library(mgcv)
dfMelt$time <- as.numeric(dfMelt$variable)
fit <- bam(value ~ tone +
             s(time, by = tone, bs = "cr"), data = dfMelt, method = "fREML")

library(itsadug)
plot_smooth(fit, view= "time" , plot_all = c("tone") , rug = F)