# Code based on MW 

print("Frequencies of MCMC simulations exceeding modern warmth for different time periods:")

# Read file with data filenames
setwd("bayesmodel")
load(paste("bayes_recon_",numpc,".R",sep=""))

all_idxD <- c(in_idxD, out_idxD)
yhat <- list(yhat1, yhat2, yhat3)

# Get max temp:
MaxTemp <- max(instrument[,2])
tmp <- substr(instrument[,1], 1, 3)
DecAvgTemp <- as.vector( by(instrument[,2], tmp, mean) )
names(DecAvgTemp) <- paste(unique(tmp),"0",sep="")
MaxDec <- max(DecAvgTemp)
SecMaxDec <- sort(DecAvgTemp, decreasing=TRUE)[2]

#########
# YEARS #
#########
# Which recons have a year with temp >= MaxTemp;
# And how many such years do they have?
print("Years")
NumBigger <- rep(NA, dim(yhat[[3]])[2])
for(i in 1:dim(yhat[[3]])[2]){
  NumBigger[i] <- sum(yhat[[3]][,i] >= MaxTemp)
}
print(mean(NumBigger>0))
summary(NumBigger)

################
# TRUE DECADES #
################
# Which recons have a true decade with average temp >= MaxDec (and SecMaxDec);
# And how many such true decades do they have?
print("true decades")
NumBiggerDec <- rep(NA, dim(yhat[[3]])[2])
NumBiggerDec2 <- rep(NA, dim(yhat[[3]])[2])
for(i in 1:dim(yhat[[3]])[2]){
  tmp <- substr(all_idxD, 1, nchar(all_idxD)-1)
  tmp2 <- as.vector( by(yhat[[3]][,i], tmp, mean) )
  NumBiggerDec[i] <- sum(tmp2 >= MaxDec)
  NumBiggerDec2[i] <- sum(tmp2 >= SecMaxDec)
}
print(mean(NumBiggerDec>0))
summary(NumBiggerDec)
print(mean(NumBiggerDec2>0))
summary(NumBiggerDec2)

##################
# ROLLING BLOCKS #
##################
# Which recons have a rolling decades with average temp >= max
# And how many such rolling decades do they have?
print("Rolling decades")
library(zoo)
DecMean <- rollmean(instrument[,2], k=10)
MM <- max(DecMean)
NumBiggerMM <- rep(NA, dim(yhat[[3]])[2])
for(i in 1:dim(yhat[[3]])[2]){
  tmp <- rollmean(yhat[[3]][,i], k=10)
  NumBiggerMM[i] <- sum(tmp >= MM)
}
print(mean(NumBiggerMM>0))
summary(NumBiggerMM)

# Which recons have a rolling 30-year block with average temp >= max
# And how many such rolling 30-year blocks do they have?
print("Rolling 30-year blocks")
library(zoo)
DecMean <- rollmean(instrument[,2], k=30)
MM <- max(DecMean)
NumBiggerMM <- rep(NA, dim(yhat[[3]])[2])
for(i in 1:dim(yhat[[3]])[2]){
  tmp <- rollmean(yhat[[3]][,i], k=30)
  NumBiggerMM[i] <- sum(tmp >= MM)
}
print(mean(NumBiggerMM>0))
summary(NumBiggerMM)

# Which recons have a rolling 60-year block with average temp >= max
# And how many such rolling 60-year blocks do they have?
print("Rolling 60-year blocks")
library(zoo)
DecMean <- rollmean(instrument[,2], k=60)
MM <- max(DecMean)
NumBiggerMM <- rep(NA, dim(yhat[[3]])[2])
for(i in 1:dim(yhat[[3]])[2]){
  tmp <- rollmean(yhat[[3]][,i], k=60)
  NumBiggerMM[i] <- sum(tmp >= MM)
}
print(mean(NumBiggerMM>0))
summary(NumBiggerMM)

##############################
# ROLLING BLOCKS: DERIVATIVE #
##############################
# Which recons have a rolling decades with "derivative" >= "max derivative"
# And how many such rolling decades do they have?
print("Rolling decades/derivative")
library(zoo)
tmplo <- loess(instrument[,2]~instrument[,1], span=.33)
tmpy <- predict(tmplo, instrument[,1], se=FALSE) 
#tmpMM <- rollmax(tmpy, 10)
#tmpmm <- -rollmax(-tmpy, 10)
kk <- 10
tmpMM <- tmpy[(kk+1):length(tmpy)]
tmpmm <- tmpy[1:(length(tmpy)-kk)]
MM <- max(tmpMM-tmpmm)
MMAbs <- max(abs(tmpMM-tmpmm))
NumBiggerMM <- rep(NA, dim(yhat[[3]])[2])
NumBiggerMMAbs <- rep(NA, dim(yhat[[3]])[2])
tmpxx <- 1:dim(yhat[[3]])[1]
for(i in 1:dim(yhat[[3]])[2]){
  tmplo <- loess(yhat[[3]][,i]~tmpxx, span=.33)
  tmpy <- predict(tmplo, tmpxx, se=FALSE) 
  tmpMM <- tmpy[(kk+1):length(tmpy)]
  tmpmm <- tmpy[1:(length(tmpy)-kk)]
  tmp <- tmpMM - tmpmm  
  NumBiggerMM[i] <- sum(tmp >= MM)
  tmp <- abs(tmpMM - tmpmm)
  NumBiggerMMAbs[i] <- sum(tmp >= MMAbs)
}
print(mean(NumBiggerMM>0))
summary(NumBiggerMM)
print(mean(NumBiggerMMAbs>0))
summary(NumBiggerMMAbs)

# Which recons have a rolling 30-year block with "derivative" >= "max derivative"
# And how many such rolling 30-year blocks do they have?
print("Rolling 30-year blocks/derivative")
tmplo <- loess(instrument[,2]~instrument[,1], span=.33)
tmpy <- predict(tmplo, instrument[,1], se=FALSE) 
#tmpMM <- rollmax(tmpy, 10)
#tmpmm <- -rollmax(-tmpy, 10)
kk <- 30
tmpMM <- tmpy[(kk+1):length(tmpy)]
tmpmm <- tmpy[1:(length(tmpy)-kk)]
MM <- max(tmpMM-tmpmm)
MMAbs <- max(abs(tmpMM-tmpmm))
NumBiggerMM <- rep(NA, dim(yhat[[3]])[2])
NumBiggerMMAbs <- rep(NA, dim(yhat[[3]])[2])
tmpxx <- 1:dim(yhat[[3]])[1]
for(i in 1:dim(yhat[[3]])[2]){
  tmplo <- loess(yhat[[3]][,i]~tmpxx, span=.33)
  tmpy <- predict(tmplo, tmpxx, se=FALSE) 
  tmpMM <- tmpy[(kk+1):length(tmpy)]
  tmpmm <- tmpy[1:(length(tmpy)-kk)]
  tmp <- tmpMM - tmpmm  
  NumBiggerMM[i] <- sum(tmp >= MM)
  tmp <- abs(tmpMM - tmpmm)
  NumBiggerMMAbs[i] <- sum(tmp >= MMAbs)
}
print(mean(NumBiggerMM>0))
summary(NumBiggerMM)
print(mean(NumBiggerMMAbs>0))
summary(NumBiggerMMAbs)

# Which recons have a rolling 60-year block with "derivative" >= "max derivative"
# And how many such rolling 60-year blocks do they have?
print("Rolling 60-year blocks/derivative")
library(zoo)
tmplo <- loess(instrument[,2]~instrument[,1], span=.33)
tmpy <- predict(tmplo, instrument[,1], se=FALSE) 
kk <- 60
tmpMM <- tmpy[(kk+1):length(tmpy)]
tmpmm <- tmpy[1:(length(tmpy)-kk)]
MM <- max(tmpMM-tmpmm)
MMAbs <- max(abs(tmpMM-tmpmm))
NumBiggerMM <- rep(NA, dim(yhat[[3]])[2])
NumBiggerMMAbs <- rep(NA, dim(yhat[[3]])[2])
tmpxx <- 1:dim(yhat[[3]])[1]
for(i in 1:dim(yhat[[3]])[2]){
  tmplo <- loess(yhat[[3]][,i]~tmpxx, span=.33)
  tmpy <- predict(tmplo, tmpxx, se=FALSE) 
  tmpMM <- tmpy[(kk+1):length(tmpy)]
  tmpmm <- tmpy[1:(length(tmpy)-kk)]
  tmp <- tmpMM - tmpmm  
  NumBiggerMM[i] <- sum(tmp >= MM)
  tmp <- abs(tmpMM - tmpmm)
  NumBiggerMMAbs[i] <- sum(tmp >= MMAbs)
}
print(mean(NumBiggerMM>0))
summary(NumBiggerMM)
print(mean(NumBiggerMMAbs>0))
summary(NumBiggerMMAbs)


setwd("../")