#Combining tables and figures for individual runs for years and tiles.

#AUTHOR: Benoit Parmentier 

#CREATED ON: 05/15/2016  

#Version: 1

#PROJECT: Environmental Layers project     

#COMMENTS: Initial commit, script based on part NASA biodiversity conferenc 

l_dates <- c("19920101","19920102","19920103","19920701","19920702","19920703") #dates to plot and analze

df_points_extracted_fname <- "/data/project/layers/commons/NEX_data/climateLayers/out/reg4/mosaic/int_mosaics/data_points_extracted.txt"

in_dir_list_filename <- NULL #if NULL, use the in_dir directory to search for info

##################### START SCRIPT #################

###########  ####################

if(!is.null(in_dir_list_filename)){

  in_dir_list <- as.list(read.table(in_dir_list_filename,stringsAsFactors=F)[,1])

}else{

  #in_dir_shp <- file.path(in_dir_list_all,"shapefiles")

}

list_tb_fname <- list.files(path=in_dir_list,"tb_diagnostic_v_NA_.*.txt",full.names=T)

list_df_fname <- list.files(path=in_dir_list,"df_tile_processed_.*..txt",full.names=T)

list_summary_metrics_v_fname <- list.files(path=in_dir_list,"summary_metrics_v2_NA_.*.txt",full.names=T)

list_pred_data_month_info_fname <- list.files(path=in_dir_list,"pred_data_month_info.*.txt",full.names=T)

list_pred_data_day_info_fname <- list.files(path=in_dir_list,"pred_data_day_info.*.txt",full.names=T)

data_date <- unlist(lapply(list_data_s_fname, function(x){unlist(strsplit(basename(x),"_"))[5]}))

df_data_s_fname <- data.frame(data_date,region_name,dataset="data_s",file=list_data_s_fname)

df_data_v_fname$year <- as.character(df_data_v_fname$year_str)

## Use station from specific year and date?

list_dates_val <- as.Date(strptime(l_dates,"%Y%m%d"))

  filename_tmp <- as.character(df_data_v_fname$file[9])

  df_stations_tmp <- read.table(filename_tmp,stringsAsFactors=F,sep=",")

}

df_points <- subset(df_stations,df_stations_tmp$date==l_dates[1])

plot(df_points,add=T)

coordinates(df_points) <- cbind(df_points$x,df_points$y)

proj4string(df_points) <- CRS_locs_WGS84

#####################

#select one station based on id or coordinates and find that in the list of data.frame??

#rbind.fill(mtcars[c("mpg", "wt")], mtcars[c("wt", "cyl")])

data_stations <- rbind.fill(data_v_subset, data_s_subset)

coordinates(data_stations) <- cbind(data_stations$x,data_stations$y)

proj4string(data_stations) <- CRS_locs_WGS84

dim(data_stations)

##Now grab year year 1992 or matching year...maybe put this in a data.frame??

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)

lf_mosaic_plot_fig <- mclapply(1:length(lf_mosaic_scaled),FUN=plot_raster_mosaic,list_param=list_param_plot_raster_mosaic,mc.preschedule=FALSE,mc.cores = num_cores)                         

############### PART2: temporal profile #############

###

#-65,-22

#Use the global output??

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

## Checking new files:

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

#/nobackupp6/aguzman4/climateLayers/out/reg4/mosaics2/mosaic/output_reg4_*/r_m_use_edge_weights_weighted_mean_mask_gam_CAI_dailyTmax_*_reg4_*.tif