#Looking at data from Franfjorden
#Run netcdf.R

library(ggplot2)
library(reshape2)

##############################################
#Just some bit of the area:
relevant_lon_pos = which(lon>7.11 & lon<7.13)
relevant_lat_pos = which(lat<62.85 & lat>62.84)

#If we want the whole area:
relevant_lon_pos = 1:length(lon)
relevant_lat_pos = 1:length(lat)

####################
rel_lon = lon[relevant_lon_pos]
rel_lat = lat[relevant_lat_pos]

#Making one large vector for plotting
lonVec = rep(rel_lon, length(rel_lat))
latVec = rep(rel_lat, each = length(rel_lon))

################################################
#Testing for one (high tide) time and one depth:
#total_concentration[longitude, latitude, depth, time]
snapshot = total_concentration[relevant_lon_pos,relevant_lat_pos,which(depth==35),211]

#Making df fit for plotting
consVec = melt(snapshot, value.name = 'cons')

ggplot(data.frame(consVec), aes(x=lonVec, y=latVec))+ 
  geom_raster(aes(fill = cons)) + 
  scale_fill_gradientn(colors =c('blue4','red'))+
  ggtitle("") +
  theme(plot.title = element_text(hjust = 0.5))

#############################################
#Use snapshots from one set of tidal times
#Found tidal times manually.
hourcorr = 60*60
mincorr = 60
#correction from Kristiansund
correction = -7.5*mincorr
#Tides
tidevann0610to0910High = c(08*hourcorr +54*mincorr,
                           21*hourcorr + 05*mincorr,
                           24*hourcorr + 09*hourcorr + 43*mincorr,
                          24*hourcorr + 21*hourcorr + 52*mincorr,
                          2*24*hourcorr + 10*hourcorr + 26*mincorr,
                          2*24*hourcorr + 22*hourcorr + 35*mincorr,
                          3*24*hourcorr + 11*hourcorr + 06*mincorr,
                          3*24*hourcorr + 23*hourcorr + 16*mincorr)+ correction
tidevann0610to0910Low = c(02*hourcorr + 40*mincorr,
                          15*hourcorr + 02*mincorr,
                          24*hourcorr + 03*hourcorr + 33*mincorr,
                          24*hourcorr + 15*hourcorr + 50*mincorr,
                          2*24*hourcorr + 04*hourcorr + 19*mincorr,
                          2*24*hourcorr + 16*hourcorr + 33*mincorr,
                          3*24*hourcorr + 05*hourcorr + 02*mincorr,
                          3*24*hourcorr + 17*hourcorr + 13*mincorr) + correction

highTides = as.POSIXct(tidevann0610to0910High,origin = "2018-10-06",tz = "GMT")
lowTides = as.POSIXct(tidevann0610to0910Low,origin = "2018-10-06",tz = "GMT")


#FIND CONCENTRATIONS AT RELEVANT TIMES
#Testing highTides
#Need to save the model at times closest to the tidal times

#Make replicates of times as many times as we have tidal information
timeMat = matrix(rep(time, times = length(tidevann0610to0910High)), byrow = TRUE, ncol= length(time)) 
timeDiff = abs(sweep(timeMat, 1, tidevann0610to0910High)) #subtracting tidal times

highTidesPos = apply(timeDiff, 1, which.min) #Choosing the times with smallest difference

#concentrations = 
#  total_concentration[relevant_lon_pos,relevant_lat_pos,which(depth==25),highTidesPos]
#consVec = melt(concentrations, value.name = 'cons', id = highTidesPos)

#plots=list()
#for (i in 1:length(highTidesPos)){
#  plots[[i]]= ggplot(data.frame(lon=lonVec, lat=latVec, cons = consVec$cons[consVec$Var3==i]), aes(x=lon, y=lat)) + 
#    geom_raster(aes(fill = cons)) + 
#   scale_fill_gradientn(colors =c('blue4','red'))+
#   ggtitle("") +
#    theme(plot.title = element_text(hjust = 0.5))
#}
 
#library(gridExtra)
#grid.arrange(grobs = plots, ncol = 2)

#check cross section: 
#Don't manage to choose a good area..
#cons_depth = total_concentration[relevant_lon_pos,110,,highTidesPos[1]]
#cross_sect = melt(cons_depth, value.name = "cons", na.rm = TRUE)
#ggplot(cross_sect, aes(x = Var1, y = -Var2)) + 
#  geom_raster(aes(fill = cons)) + 
#  scale_fill_gradientn(colors =c('blue4','red'))