#MODIFIED ON: 05/23/2016            

df_data_v_fname$file <- as.character(df_data_v_fname$file)

df_data_s_fname$file <- as.character(df_data_s_fname$file)

### Needs to make this a function!!!

#Step 1 for a list of dates, load relevant files with year matching,

#Step 2 for giving dates subset the data.frame

#Step 3: find duplicates, create return object and return given station data for the date

list_df_v_stations <- vector("list",length=length(list_year_str))

list_df_s_stations <- vector("list",length=length(list_year_str))

  filename_tmp<- df_data_v_fname$file[df_data_v_fname$year==list_year_str[i]]

  list_df_v_stations[[i]] <- read.table(filename_tmp,stringsAsFactors=F,sep=",")

  filename_tmp<- df_data_s_fname$file[df_data_s_fname$year==list_year_str[i]]

  list_df_s_stations[[i]] <- read.table(filename_tmp,stringsAsFactors=F,sep=",")

#function(x){x$date==}

df_points_day <- remove.duplicates(df_points) #remove duplicates...

dim(data_stations) #one station only but repitition of records because of tiles and dates!!!

#> dim(data_stations)

#[1] 100458     90

#This is a lot of replication!!! okay cut it down

#data_stations_temp <- aggregate(id ~ date, data = data_stations, min)

#data_stations_temp <- aggregate(id ~ x + y + date + dailyTmax + mod1 + res_mod1 , data = data_stations, min)

data_stations_temp <- aggregate(id ~ x + y + date + dailyTmax,data = data_stations, min ) #+ mod1 + res_mod1 , data = data_stations, min)

dim(data_stations_temp)

#> dim(data_stations_temp)

#[1] 11171     5

############### PART3: Make raster stack and display maps #############

if(is.null(r_mosaic_fname)){

  pattern_str <-"*.tif"

  lf_mosaic_list <- list.files(path=in_dir_mosaic,pattern=pattern_str,recursive=F,full.names=T)

  r_mosaic <- stack(lf_mosaic_list)

  save(r_mosaic,file="r_mosaic.RData")

}else{

  r_mosaic <- load_obj(r_mosaic_fname) #load raster stack of images

}

matching <- FALSE #to be added after mask_pred option

###############  PART4: Checking for mosaic produced for given region ##############

## From list of mosaic files predicted extract dates

## Check dates predicted against date range for a given date range

## Join file information to centroids of tiles data.frame

#in_dir_mosaic <- "/nobackupp6/aguzman4/climateLayers/out/reg4/mosaics2/mosaic"

############### PART5: Extraction of temporal profile #############

#### Extract time series from raster stack

### Add dates to mkae it a date object?

#-65,-22

#Use the global output??

##23.09 (on 05/22)

#df_points_day_extracted <- extract(r_mosaic,data_stations,df=T)

#df_points_day_extracted_fname <- paste0("df_points_day_extracted_fname",".txt") 

#write.table(df_points_day_extracted,file=df_points_day_extracted_fname) #5.1 Giga

#4.51 (on 05/23)

if(is.null(df_points_extracted_fname)){

  ##10.41 (on 05/22)

  df_points_day_extracted <- extract(r_mosaic,df_points_day,df=T)

  #17.19 (on 05/23)

  df_points_day_extracted_fname <- paste0("df_points_day_extracted_fname2",".txt")

  #17.27 (on 05/23)

  write.table(df_points_day_extracted,file=df_points_day_extracted_fname,sep=",",row.names = F) 

  #17.19 (on 05/23)

}else{

  df_points_day_extracted <- read.table(df_points_extracted_fname,sep=",")

}

pix_ts <- as.data.frame(t(df_points_day_extracted))

rownames(df_points_day_extracted)

lf_var <- names(r_mosaic)

### Not get the data from the time series

data_pixel <- df_ts_pix[id_selected,]

data_pixel <- as.data.frame(data_pixel)

pix_ts <- t(as.data.frame(subset(data_pixel,select=r_ts_name))) #can subset to range later

#pix_ts <- subset(as.data.frame(pix_ts),select=r_ts_name)

pix_ts <- (as.data.frame(pix_ts))

## Process the coliform data

  #there are several measurements per day for some stations !!!

  #id_name <- data_pixel[[var_ID]]

  #df_tmp  <-data_var[data_var$LOCATION_ID==id_name,]

  df_tmp <- subset(data_var,data_var$ID_stat==id_name)

#df_points_extracted <- extract(r_mosaic,data_stations,df=T)

#df_points_extracted_fname <- paste0("df_points_extracted_fname",".txt")

#write.table(df_points_extracted,file=df_points_extracted_fname) 

#df_points <- read.table(df_points_extracted_fname,sep=",") 

#df_points_tmp <- df_points

#df_points <- as.data.frame(t(df_points))

#names(df_points) <- paste0("ID_",1:ncol(df_points))

#df_centroids <- read.table(df_centroids_fname,sep=",")