##################    Validation and analyses of results  #######################################

############################ Covariate production for a given tile/region ##########################################

#This script examines inputs and outputs from the interpolation step.                             

#Part 1: Script produces plots for every selected date

#Part 2: Examine 

#AUTHOR: Benoit Parmentier                                                                       

#DATE: 03/18/2013                                                                                 

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#???--   

##Comments and TODO:

#Separate inteprolation results analyses from covariates analyses 

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

###Loading R library and packages   

library(RPostgreSQL)

library(sp)                                             # Spatial pacakge with class definition by Bivand et al.

library(spdep)                                          # Spatial pacakge with methods and spatial stat. by Bivand et al.

library(rgdal)                                          # GDAL wrapper for R, spatial utilities

library(raster)

library(gtools)

library(rasterVis)

library(graphics)

library(grid)

library(lattice)

### Parameters and arguments

##Paths to inputs and output

#Select relevant dates and load R objects created during the interpolation step

##Paths to inputs and output

script_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/"

in_path <- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data/"

out_path<- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data/"

infile_covar<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script

date_selected<-c("20000101") ##This is for year 2000!!!

raster_prediction_obj<-"raster_prediction_obj__365d_GAM_fus5_all_lstd_03132013.RData"

#out_prefix<-"_365d_GAM_fus5_all_lstd_03132013"

#out_prefix<-"_365d_GAM_fus5_all_lstd_03142013"                #User defined output prefix

out_prefix<-"_365d_GAM_fus5_all_lstd_03132013"                #User defined output prefix

var<-"TMAX"

#gam_fus_mod<-load_obj("gam_fus_mod_365d_GAM_fus5_all_lstd_02202013.RData")

#validation_obj<-load_obj("gam_fus_validation_mod_365d_GAM_fus5_all_lstd_02202013.RData")

#clim_obj<-load_obj("gamclim_fus_mod_365d_GAM_fus5_all_lstd_02202013.RData")

rnames<-c("x","y","lon","lat","N","E","N_w","E_w","elev","slope","aspect","CANHEIGHT","DISTOC")

lc_names<-c("LC1","LC2","LC3","LC4","LC5","LC6","LC7","LC8","LC9","LC10","LC11","LC12")

lst_names<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12",

             "nobs_01","nobs_02","nobs_03","nobs_04","nobs_05","nobs_06","nobs_07","nobs_08",

             "nobs_09","nobs_10","nobs_11","nobs_12")

covar_names<-c(rnames,lc_names,lst_names)

list_param<-list(in_path,out_path,script_path,raster_prediction_obj,infile_covar,covar_names,date_selected,var,out_prefix)

names(list_param)<-c("in_path","out_path","script_path","raster_prediction_obj",

                     "infile_covar","covar_names","date_selected","var","out_prefix")

setwd(in_path)

## make this a script that calls several function:

#1) covariate script

#2) plots by dates

#3) number of data points monthly and daily

### Functions used in the script

load_obj <- function(f) 

{

  env <- new.env()

  nm <- load(f, env)[1]	

  env[[nm]]

}

extract_number_obs<-function(list_param){

  method_mod_obj<-list_param$method_mod_obj

  #Change to results_mod_obj[[i]]$data_s to make it less specific

  lapply(1:length(method_obj),function(k) nrow(method_mod_obj[[k]]$data_s))

  lapply(1:length(method_obj),function(k) nrow(method_mod_obj[[k]]$data_v))

  lapply(1:length(clim_obj),function(k) nrow(method_mod_obj[[k]]$data_v))

  return()

}

### PLOTTING RESULTS FROM VENEZUELA INTERPOLATION FOR ANALYSIS

#source(file.path(script_path,"results_interpolation_date_output_analyses_03182013.R"))

#j=1

#plots_assessment_by_date(1,list_param)

plots_assessment_by_date<-function(j,list_param){

  date_selected<-list_param$date_selected

  var<-list_param$var

  #gam_fus_mod<-load_obj("gam_fus_mod_365d_GAM_fus5_all_lstd_02202013.RData")

  #validation_obj<-load_obj("gam_fus_validation_mod_365d_GAM_fus5_all_lstd_02202013.RData")

  #clim_obj<-load_obj("gamclim_fus_mod_365d_GAM_fus5_all_lstd_02202013.RData")

  raster_prediction_obj<-load_obj(list_param$raster_prediction_obj)

  #method_mod_obj<-raster_prediction_obj$method_mod_obj

  method_mod_obj<-raster_prediction_obj$gam_fus_mod #change later for any model type

  #validation_obj<-raster_prediction_obj$validation_obj

  validation_obj<-raster_prediction_obj$gam_fus_validation_mod #change later for any model type

  #clim_obj<-raster_prediction_obj$clim_obj

  clim_obj<-raster_prediction_obj$gamclim_fus_mod #change later for any model type

  if (var=="TMAX"){

    y_var_name<-"dailyTmax"                                       

  }

  if (var=="TMIN"){

    y_var_name<-"dailyTmin"                                       

  }

  ## Read covariate stack...

  covar_names<-list_param$covar_names

  s_raster<-brick(infile_covar)                   #read in the data stack

  names(s_raster)<-covar_names               #Assigning names to the raster layers: making sure it is included in the extraction

  ## Figure 0: study area based on LC12 (water) mask

  LC_mask<-subset(s_raster,"LC12")

  LC_mask[LC_mask==100]<-NA

  LC_mask <- LC_mask < 100

  LC_mask_rec<-LC_mask

  LC_mask_rec[is.na(LC_mask_rec)]<-0

  #Add proportion covered by study area+ total of image pixels

  tmp_tb<-freq(LC_mask_rec)

  tmp_tb[2,2]/sum(tmp_tb[,2])*100

  png(paste("Study_area_",

            out_prefix,".png", sep=""))

  plot(LC_mask_rec,legend=FALSE,col=c("black","red"))

  legend("topright",legend=c("Outside","Inside"),title="Study area",

         pt.cex=0.9,fill=c("black","red"),bty="n")

  title("Study area")

  dev.off()

  #determine index position matching date selected

  for (j in 1:length(date_selected)){

    for (i in 1:length(method_mod_obj)){

      if(method_mod_obj[[i]]$sampling_dat$date==date_selected[j]){  

        i_dates[[j]]<-i

      }

    }

  }

  #Examine the first select date add loop or function later

  #j=1

  date<-strptime(date_selected[j], "%Y%m%d")   # interpolation date being processed

  month<-strftime(date, "%m")          # current month of the date being processed

  #Get raster stack of interpolated surfaces

  index<-i_dates[[j]]

  pred_temp<-as.character(method_mod_obj[[index]]$dailyTmax) #list of files

  rast_pred_temp<-stack(pred_temp) #stack of temperature predictions from models 

  #Get validation metrics, daily spdf training and testing stations, monthly spdf station input

  sampling_dat<-method_mod_obj[[index]]$sampling_dat

  metrics_v<-validation_obj[[index]]$metrics_v

  metrics_s<-validation_obj[[index]]$metrics_s

  data_v<-validation_obj[[index]]$data_v

  data_s<-validation_obj[[index]]$data_s

  data_month<-clim_obj[[index]]$data_month

  formulas<-clim_obj[[index]]$formulas

  #Adding layer LST to the raster stack of covariates

  #The names of covariates can be changed...

  LST_month<-paste("mm_",month,sep="") # name of LST month to be matched

  pos<-match("LST",layerNames(s_raster)) #Find the position of the layer with name "LST", if not present pos=NA

  s_raster<-dropLayer(s_raster,pos)      # If it exists drop layer

  pos<-match(LST_month,layerNames(s_raster)) #Find column with the current month for instance mm12

  r1<-raster(s_raster,layer=pos)             #Select layer from stack

  layerNames(r1)<-"LST"

  #Get mask image!!

  date_proc<-strptime(sampling_dat$date, "%Y%m%d")   # interpolation date being processed

  mo<-as.integer(strftime(date_proc, "%m"))          # current month of the date being processed

  day<-as.integer(strftime(date_proc, "%d"))

  year<-as.integer(strftime(date_proc, "%Y"))

  datelabel=format(ISOdate(year,mo,day),"%b %d, %Y")

  ## Figure 1: LST_TMax_scatterplot 

  rmse<-metrics_v$rmse[nrow(metrics_v)]

  rmse_f<-metrics_s$rmse[nrow(metrics_s)]  

  png(paste("LST_TMax_scatterplot_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,

            out_prefix,".png", sep=""))

  plot(data_month$TMax,data_month$LST,xlab="Station mo Tmax",ylab="LST mo Tmax")

  title(paste("LST vs TMax for",datelabel,sep=" "))

  abline(0,1)

  nb_point<-paste("n=",length(data_month$TMax),sep="")

  mean_bias<-paste("Mean LST bias= ",format(mean(data_month$LSTD_bias,na.rm=TRUE),digits=3),sep="")

  #Add the number of data points on the plot

  legend("topleft",legend=c(mean_bias,nb_point),bty="n")

  dev.off()

  ## Figure 2: Daily_tmax_monthly_TMax_scatterplot, modify for TMin!!

  png(paste("Daily_tmax_monthly_TMax_scatterplot_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,

            out_prefix,".png", sep=""))

  plot(dailyTmax~TMax,data=data_s,xlab="Mo Tmax",ylab=paste("Daily for",datelabel),main="across stations in VE")

  nb_point<-paste("ns=",length(data_s$TMax),sep="")

  nb_point2<-paste("ns_obs=",length(data_s$TMax)-sum(is.na(data_s[[y_var_name]])),sep="")

  nb_point3<-paste("n_month=",length(data_month$TMax),sep="")

  #Add the number of data points on the plot

  legend("topleft",legend=c(nb_point,nb_point2,nb_point3),bty="n",cex=0.8)

  dev.off()

  ## Figure 3: Predicted_tmax_versus_observed_scatterplot 

  #This is for mod_kr!! add other models later...

  png(paste("Predicted_tmax_versus_observed_scatterplot_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,

            out_prefix,".png", sep=""))

  #plot(data_s$mod_kr~data_s[[y_var_name]],xlab=paste("Actual daily for",datelabel),ylab="Pred daily")

  y_range<-range(c(data_s$mod_kr,data_v$mod_kr),na.rm=T)

  x_range<-range(c(data_s[[y_var_name]],data_v[[y_var_name]]),na.rm=T)

  col_t<- c("black","red")

  pch_t<- c(1,2)

  plot(data_s$mod_kr,data_s[[y_var_name]], 

       xlab=paste("Actual daily for",datelabel),ylab="Pred daily", 

       ylim=y_range,xlim=x_range,col=col_t[1],pch=pch_t[1])

  points(data_v$mod_kr,data_v[[y_var_name]],col=col_t[2],pch=pch_t[2])

  grid(lwd=0.5, col="black")

  #plot(data_v$mod_kr~data_v[[y_var_name]],xlab=paste("Actual daily for",datelabel),ylab="Pred daily")

  abline(0,1)

  legend("topleft",legend=c("training","testing"),pch=pch_t,col=col_t,bty="n",cex=0.8)

  title(paste("Predicted_tmax_versus_observed_scatterplot for",datelabel,sep=" "))

  nb_point1<-paste("ns_obs=",length(data_s$TMax)-sum(is.na(data_s[[y_var_name]])),sep="")

  rmse_str1<-paste("RMSE= ",format(rmse,digits=3),sep="")

  rmse_str2<-paste("RMSE_f= ",format(rmse_f,digits=3),sep="")

  #Add the number of data points on the plot

  legend("bottomright",legend=c(nb_point1,rmse_str1,rmse_str2),bty="n",cex=0.8)

  dev.off()

  ## Figure 5: prediction raster images

  png(paste("Raster_prediction_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,

            out_prefix,".png", sep=""))

  #paste(metrics_v$pred_mod,format(metrics_v$rmse,digits=3),sep=":")

  names(rast_pred_temp)<-paste(metrics_v$pred_mod,format(metrics_v$rmse,digits=3),sep=":")

  #plot(rast_pred_temp)

  levelplot(rast_pred_temp)

  dev.off()

  ## Figure 5b: prediction raster images

  png(paste("Raster_prediction_plot",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,

            out_prefix,".png", sep=""))

  #paste(metrics_v$pred_mod,format(metrics_v$rmse,digits=3),sep=":")

  names(rast_pred_temp)<-paste(metrics_v$pred_mod,format(metrics_v$rmse,digits=3),sep=":")

  #plot(rast_pred_temp)

  plot(rast_pred_temp)

  dev.off()

  ## Figure 6: training and testing stations used

  png(paste("Training_testing_stations_map_",sampling_dat$date,"_",sampling_dat$prop,"_",sampling_dat$run_samp,

            out_prefix,".png", sep=""))

  plot(raster(rast_pred_temp,layer=5))

  plot(data_s,col="black",cex=1.2,pch=2,add=TRUE)

  plot(data_v,col="red",cex=1.2,pch=1,add=TRUE)

  legend("topleft",legend=c("training stations", "testing stations"), 

         cex=1, col=c("black","red"),

         pch=c(2,1),bty="n")

  dev.off()

  ## Figure 7: monthly stations used

  png(paste("Monthly_data_study_area",

            out_prefix,".png", sep=""))

  plot(raster(rast_pred_temp,layer=5))

  plot(data_month,col="black",cex=1.2,pch=4,add=TRUE)

  title("Monthly ghcn station in Venezuela for January")

  dev.off()

  ## Figure 8: delta surface and bias

  png(paste("Bias_delta_surface_",sampling_dat$date[i],"_",sampling_dat$prop[i],

            "_",sampling_dat$run_samp[i],out_prefix,".png", sep=""))

  bias_rast<-stack(clim_obj[[index]]$bias)

  delta_rast<-raster(method_mod_obj[[index]]$delta) #only one delta image!!!

  names(delta_rast)<-"delta"

  rast_temp_date<-stack(bias_rast,delta_rast)

  rast_temp_date<-mask(rast_temp_date,LC_mask,file="test.tif",overwrite=TRUE)

  #bias_d_rast<-raster("fusion_bias_LST_20100103_30_1_10d_GAM_fus5_all_lstd_02082013.rst")

  plot(rast_temp_date)

  dev.off()

  #Figure 9: histogram for all images...

  #histogram(rast_pred_temp)

  list_output_analyses<-list(metrics_s,metrics_v)

  return(list_output_analyses)

}

## Summarize information for the day: write out textfile...

#Number of station per month

#Number of station per day (training, testing,NA)

#metrics_v,metrics_s

#

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

# #PART 2: Region Covariate analyses ###

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

# 

# # This should be in a separate script to analyze covariates from region.

# 

# #MAP1:Study area with LC mask and tiles/polygon outline

# 

# 

# #MAP 2: plotting land cover in the study region:

# 

# l1<-"LC1,Evergreen/deciduous needleleaf trees"

# l2<-"LC2,Evergreen broadleaf trees"

# l3<-"LC3,Deciduous broadleaf trees"

# l4<-"LC4,Mixed/other trees"

# l5<-"LC5,Shrubs"

# l6<-"LC6,Herbaceous vegetation"

# l7<-"LC7,Cultivated and managed vegetation"

# l8<-"LC8,Regularly flooded shrub/herbaceous vegetation"

# l9<-"LC9,Urban/built-up"

# l10<-"LC10,Snow/ice"

# l11<-"LC11,Barren lands/sparse vegetation"

# l12<-"LC12,Open water"

# lc_names_str<-c(l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12)

# 

# names(lc_reg_s)<-lc_names_str

# 

# png(paste("LST_TMax_scatterplot_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i], out_prefix,".png", sep=""))

# plot(modst$TMax,sta_tmax_from_lst,xlab="Station mo Tmax",ylab="LST mo Tmax",main=paste("LST vs TMax for",datelabel,sep=" "))

# abline(0,1)

# nb_point<-paste("n=",length(modst$TMax),sep="")

# mean_bias<-paste("LST bigrasas= ",format(mean(modst$LSTD_bias,na.rm=TRUE),digits=3),sep="")

# #Add the number of data points on the plot

# legend("topleft",legend=c(mean_bias,nb_point),bty="n")

# dev.off()

# 

# #Map 3: Elevation and LST in January

# tmp_s<-stack(LST,elev_1)

# png(paste("LST_elev_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i], out_prefix,".png", sep=""))

# plot(tmp_s)

# 

# #Map 4: LST climatology per month

#         

# names_tmp<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12")

# LST_s<-subset(s_raster,names_tmp)

# names_tmp<-c("nobs_01","nobs_02","nobs_03","nobs_04","nobs_05","nobs_06","nobs_07","nobs_08",

#              "nobs_09","nobs_10","nobs_11","nobs_12")

# LST_nobs<-subset(s_raster,names_tmp)

# 

# LST_nobs<-mask(LST_nobs,LC_mask,filename="test2.tif")

# LST_s<-mask(LST_s,LC_mask,filename="test3.tif")

# c("Jan","Feb")

# plot(LST_s)

# plot(LST_nobs)

# 

# #Map 5: LST and TMax

# 

# #note differnces in patternin agricultural areas and 

# min_values<-cellStats(LST_s,"min")

# max_values<-cellStats(LST_s,"max")

# mean_values<-cellStats(LST_s,"mean")

# sd_values<-cellStats(LST_s,"sd")

# #median_values<-cellStats(molst,"median") Does not extist

# statistics_LST_s<-cbind(min_values,max_values,mean_values,sd_values) #This shows that some values are extremes...especially in October

# LST_stat_data<-as.data.frame(statistics_LST_s)

# names(LST_stat_data)<-c("min","max","mean","sd")

# # Statistics for number of valid observation stack

# min_values<-cellStats(nobslst,"min")

# max_values<-cellStats(nobslst,"max")

# mean_values<-cellStats(nobslst,"mean")

# sd_values<-cellStats(nobslst,"sd")

# statistics_LSTnobs_s<-cbind(min_values,max_values,mean_values,sd_values) #This shows that some values are extremes...especially in October

# LSTnobs_stat_data<-as.data.frame(statistics_LSTnobs_s)

# 

# X11(width=12,height=12)

# #Plot statiscs (mean,min,max) for monthly LST images

# plot(1:12,LST_stat_data$mean,type="b",ylim=c(-15,60),col="black",xlab="month",ylab="tmax (degree C)")

# lines(1:12,LST_stat_data$min,type="b",col="blue")

# lines(1:12,LST_stat_data$max,type="b",col="red")

# text(1:12,LST_stat_data$mean,rownames(LST_stat_data),cex=1,pos=2)

# 

# legend("topleft",legend=c("min","mean","max"), cex=1.5, col=c("blue","black","red"),

#        lty=1)

# title(paste("LST statistics for Oregon", "2010",sep=" "))

# savePlot("lst_statistics_OR.png",type="png")

# 

# #Plot number of valid observations for LST

# plot(1:12,LSTnobs_stat_data$mean,type="b",ylim=c(0,280),col="black",xlab="month",ylab="tmax (degree C)")

# lines(1:12,LSTnobs_stat_data$min,type="b",col="blue")

# lines(1:12,LSTnobs_stat_data$max,type="b",col="red")

# text(1:12,LSTnobs_stat_data$mean,rownames(LSTnobs_stat_data),cex=1,pos=2)

# 

# legend("topleft",legend=c("min","mean","max"), cex=1.5, col=c("blue","black","red"),

#        lty=1)

# title(paste("LST number of valid observations for Oregon", "2010",sep=" "))

# savePlot("lst_nobs_OR.png",type="png")

# 

# plot(data_month$TMax,add=TRUE)

# 

# ### Map 6: station in the region

# 

# plot(tmax_predicted)

# plot(data_s,col="black",cex=1.2,pch=4,add=TRUE)

# plot(data_v,col="blue",cex=1.2,pch=2,add=TRUE)

# 

# plot(tmax_predicted)

# plot(data_month,col="black",cex=1.2,pch=4,add=TRUE)

# title("Monthly ghcn station in Venezuela for 2000-2010")

# 

#png...output?

# plot(interp_area, axes =TRUE)

# plot(stat_reg, pch=1, col="red", cex= 0.7, add=TRUE)

# plot(data_reg,pch=2,col="blue",cex=2,add=TRUE)

#### End of script ####