#MODIFIED ON: 10/24/2016            

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

##### Creating animation based on prediction

#####

NAvalue(r_stack)

plot(r_stack,y=6,zlim=c(-10000,10000)) #this is not rescaled

#plot(r_stack,zlim=c(-50,50),col=matlab.like(255))

var_name <- "dailyTmax"

#debug(plot_and_animate_raster_time_series)

#metric_name <- "var_pred" #use RMSE if accuracy

#df_raster <- read.table("df_raster_global_assessment_reg6_10102016.txt",sep=",",header=T)

#plot_figure <- 

#function_product_assessment_part2_functions <- "global_product_assessment_part2_functions_10222016.R"

#source(file.path(script_path,function_product_assessment_part2_functions)) #source all functions used in this script 

#undebug(plot_and_animate_raster_time_series)

range_year <- c(1984,1985)

subset_df_time_series <- subset(df_time_series,year%in% range_year)

subset_df_time_series <- subset_df_time_series[!is.na(subset_df_time_series$lf),]

lf_subset <- file.path(subset_df_time_series$dir,subset_df_time_series$lf)

range_year_str <- paste(range_year, sep = "_", collapse = "_")

out_suffix_str <- paste(range_year_str,out_suffix,sep="_")

#started on 10/22/2016 at 9.57

animation_obj <- plot_and_animate_raster_time_series(lf_subset, 

                                                     item_no,

                                                     region_name,

                                                     var_name,

                                                     metric_name,

                                                     NA_flag_val,

                                                     filenames_figures=NULL,

                                                     frame_speed=60,

                                                     animation_format=".gif",

                                                     zlim_val=NULL,

                                                     plot_figure=T,

                                                     generate_animation=T,

                                                     num_cores=num_cores,

                                                     out_suffix=out_suffix_str,

                                                     out_dir=out_dir)

zlim_val <- c(-2000,5000)

animation_obj <- plot_and_animate_raster_time_series(lf_subset, 

                                                     item_no,

                                                     region_name,

                                                     var_name,

                                                     metric_name,

                                                     NA_flag_val,

                                                     filenames_figures=NULL,

                                                     frame_speed=60,

                                                     animation_format=".gif",

                                                     zlim_val=zlim_val,

                                                     plot_figure=T,

                                                     generate_animation=T,

                                                     num_cores=num_cores,

                                                     out_suffix=out_suffix_str,

                                                     out_dir=out_dir)

#ffmpeg -i yeay.gif outyeay.mp4

#/Applications/ffmpeg -r 25 -i input%3d.png -vcodec libx264 -x264opts keyint=25 -pix_fmt yuv420p -r 25 ../output.mp4

#ffmpeg -f gif -i file.gif -c:v libx264 outfile.mp4

#ffmpeg -i animation_frame_60_-2500_6000_.gif animation_frame_60_-2500_6000_.mp4

#ffmpeg -i animation_frame_60_-2500_6000_.gif animation_frame_60_-2500_6000_.mp4

#ffmpeg -f gif -i animation_frame_60_-2500_6000_.gif -vcodec libx264 -x264opts keyint=25 -pix_fmt yuv420p -r 25 outfile.mp4

#ffmpeg -f gif -i animation_frame_60_-2500_6000_.gif -vcodec libx264 -x264opts keyint=11 -pix_fmt yuv420p -r 11 outfile.mp4

#ffmpeg -r 10 -i animation_frame_60_-2500_6000_.gif animation.avi

#ffmpeg -f gif -i animation_frame_60_-2500_6000_.gif -vcodec libx264 -x264opts -pix_fmt yuv420p outfile.mp4

#in_dir1 <- " /nobackupp6/aguzman4/climateLayers/out_15x45/" #On NEX

#in_dir <- "" #PARAM 0

#y_var_name <- "dailyTmax" #PARAM1

#interpolation_method <- c("gam_CAI") #PARAM2

#out_suffix <- "global_analyses_overall_assessment_reg4_02072016"

#NA_value <- -9999 #PARAM10

#NA_flag_val <- NA_value

#region_name <- "world" #PARAM 13

#countries_shp <-"/data/project/layers/commons/NEX_data/countries.shp" #PARAM 13, copy this on NEX too

#num_cores <- 6 #PARAM 14

#region_name <- c("reg4") #reference region to merge if necessary, if world all the regions are together #PARAM 16

#use previous files produced in step 1a and stored in a data.frame

#df_assessment_files <- "df_assessment_files_reg4_1984_run_global_analyses_pred_12282015.txt" #PARAM 17

#threshold_missing_day <- c(367,365,300,200) #PARM18

#list_param_run_assessment_plottingin_dir <- list(in_dir,y_var_name, interpolation_method, out_suffix, 

#                      out_dir, create_out_dir_param, mosaic_plot, proj_str, file_format, NA_value,

#                      multiple_region, countries_shp, plot_region, num_cores, 

#                      region_name, df_assessment_files, threshold_missing_day) 

#names(list_param_run_assessment_plottingin_dir) <- c("in_dir","y_var_name","interpolation_method","out_suffix", 

#                      "out_dir","create_out_dir_param","mosaic_plot","proj_str","file_format","NA_value",

#                      "multiple_region","countries_shp","plot_region","num_cores", 

#                      "region_name","df_assessment_files","threshold_missing_day") 

#run_assessment_plotting_prediction_fun(list_param_run_assessment_plottingin_dir) 

  ####### PARSE INPUT ARGUMENTS/PARAMETERS #####

  in_dir_list_filename <- list_param_run_assessment_plotting$in_dir_list_filename #PARAM 0

  in_dir <- list_param_run_assessment_plotting$in_dir #PARAM 1

  y_var_name <- list_param_run_assessment_plotting$y_var_name #PARAM2

  interpolation_method <- list_param_run_assessment_plotting$interpolation_method #c("gam_CAI") #PARAM3

  out_suffix <- list_param_run_assessment_plotting$out_suffix #PARAM4

  out_dir <- list_param_run_assessment_plotting$out_dir # PARAM5

  create_out_dir_param <- list_param_run_assessment_plotting$create_out_dir_param # FALSE #PARAM 6

  mosaic_plot <- list_param_run_assessment_plotting$mosaic_plot #FALSE #PARAM7

  proj_str<- list_param_run_assessment_plotting$proj_str #CRS_WGS84 #PARAM 8 #check this parameter

  file_format <- list_param_run_assessment_plotting$file_format #".rst" #PARAM 9

  NA_flag_val <- list_param_run_assessment_plotting$NA_flag_val #-9999 #PARAM10

  multiple_region <- list_param_run_assessment_plotting$multiple_region # <- TRUE #PARAM 11

  countries_shp <- list_param_run_assessment_plotting$countries_shp #<- "world" #PARAM 12

  plot_region <- list_param_run_assessment_plotting$plot_region # PARAM13 

  num_cores <- list_param_run_assessment_plotting$num_cores # 6 #PARAM 14

  region_name <- list_param_run_assessment_plotting$region_name #<- "world" #PARAM 15

  #df_assessment_files_name <- list_param_run_assessment_plotting$df_assessment_files_name #PARAM 16

  threshold_missing_day <- list_param_run_assessment_plotting$threshold_missing_day #PARM17

  year_predicted <- list_param_run_assessment_plotting$year_predicted

  NA_value <- NA_flag_val 

  metric_name <- "rmse" #to be added to the code later...

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

  ####### PART 1: Read in data ########

  out_dir <- in_dir

  if(create_out_dir_param==TRUE){

    out_dir <- create_dir_fun(out_dir,out_suffix)

    setwd(out_dir)

  }else{

    setwd(out_dir) #use previoulsy defined directory

  }

  setwd(out_dir)

  counter_fig <- 0 #index of figure to collect outputs

  #i <- year_predicted

  ###Table 1: Average accuracy metrics

  ###Table 2: daily accuracy metrics for all tiles

  if(!is.null(in_dir_list_filename)){

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

    pattern_str <- paste0("^output_",region_name,".*.")

    in_dir_list_all <- list.dirs(path=in_dir,recursive = T)

    in_dir_list <- in_dir_list_all[grep(pattern_str,basename(in_dir_list_all),invert=FALSE)] #select directory with shapefiles...

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

  #pattern_str <- file.path(in_dir,paste0("output_",region_name,".*."))

  #test <- Sys.glob(pattern_str,FALSE)

  #  searchStr = paste(in_dir_tiles_tmp,"/*/",year_processed,"/gam_CAI_dailyTmax_predicted_",pred_mod_name,"*",day_to_mosaic[i],"*.tif",sep="")

  #  #print(searchStr)

  #  Sys.glob(searchStr)})

  #lf_mosaic <- lapply(1:length(day_to_mosaic),FUN=function(i){

  #  searchStr = paste(in_dir_tiles_tmp,"/*/",year_processed,"/gam_CAI_dailyTmax_predicted_",pred_mod_name,"*",day_to_mosaic[i],"*.tif",sep="")

  #  #print(searchStr)

  #  Sys.glob(searchStr)})

  ##Read in data list from in_dir_list

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

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

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

  #list_tb_s_fname <- list.files(path=file.path(in_dir,in_dir_list),"tb_diagnostic_s_NA.*.txt",full.names=T)

  #list_tb_month_s_fname <- list.files(path=file.path(in_dir,in_dir_list),"tb_month_diagnostic_s.*.txt",full.names=T)

  #list_data_month_s_fname <- list.files(path=file.path(in_dir,in_dir_list),"data_month_s.*.txt",full.names=T)

  #list_data_s_fname <- list.files(path=file.path(in_dir,in_dir_list),"data_day_s.*.txt",full.names=T)

  #list_data_v_fname <- list.files(path=file.path(in_dir,in_dir_list),"data_day_v.*.txt",full.names=T)

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

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

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

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

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

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

  list_data_v_fname <- list.files(path=in_dir_list,"data_day_v.*.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)

  #need to fix this !! has all of the files in one list (for a region)

  #list_shp <- list.files(path=file.path(in_dir,file.path(in_dir_list,"shapefiles")),"*.shp",full.names=T)