##################    Master script for temperature predictions  #######################################

############################ TMIN AND TMAX predictions ##########################################

#                           

##This script produces intperpolated surface of TMIN and TMAX for specified processing region given sets 

#of inputs and parameters.

#STAGE 1: LST climatology calculation

#STAGE 2: Covariates preparation: aspect, land cover, distance to coast etc.

#STAGE 3: Data preparation: database query, extraction of covariates from stack

#STAGE 4: Raster prediction: run interpolation method -- gam fusion, gam CAI, ...

#STAGE 5: Output analyses-visualization of results for specific dates...

#

#AUTHOR: Benoit Parmentier                                                                       

#DATE: 03/14/2013                                                                                 

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   

##Comments and TODO:

# Modify code for stage 1 and call python script from R

# Modify code for stage 2, make it a function and fully automated (distoc var)

# Add options to run only specific stage + additional out_suffix?

# Make master script a function?

# Add log file for master script,add function to collect inputs and outputs

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

###Loading R library and packages   

library(RPostgreSQL)

library(maps)

library(maptools)

library(parallel)

library(gtools)                              # loading some useful tools 

library(mgcv)                                # GAM package by Simon Wood

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(gstat)                               # Kriging and co-kriging by Pebesma et al.

library(fields)                              # NCAR Spatial Interpolation methods such as kriging, splines

library(raster)                              # Hijmans et al. package for raster processing

library(rasterVis)

library(reshape)

library(plotrix)

### Parameters and arguments

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

#list_script_files<-

stages_to_run<-c(1,2,3,4,5) #May decide on antoher strategy later on...

#####SCRIPT USED FOR THE PREDICTIONS

#master_script_temp_03052013.R

#IN MASTER SCRIPT:

#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/climatology_01252013b.py

#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/covariates_production_temperatures_02062013.R

#source(file.path(script_path,"Database_stations_covariates_processing_function_03052013.R"))

#source(file.path(script_path,"GAM_fusion_analysis_raster_prediction_multisampling_03052013.R"))

#source(file.path(script_path,"results_interpolation_date_output_analyses_03052013.R"))

#CALLED FROM GAM FUSION ANALYSIS RASTER PREDICTION

#source(file.path(script_path,"sampling_script_functions_03052013.R"))

#source(file.path(script_path,"GAM_fusion_function_multisampling_03052013.R")) #Include GAM_CAI

#source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_02262013.R"))

############ STAGE 1: LST Climatology ###############

if (stages_to_run[1]==1){

  #Call run through python

  #/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/climatology_01252013b.py

}

############ STAGE 2: Covariate production ################

if (stages_to_run[2]==2){

  #Transform into function...

  #/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/covariates_production_temperatures_02062013.R

}

############# STAGE 3: Data preparation ###############

source(file.path(script_path,"Database_stations_covariates_processing_function_03132013.R"))

#Setting up input argurments for script function...

db.name <- "ghcn"       # name of the Postgres database

var <- "TMAX"           # name of the variables to keep: TMIN, TMAX or PRCP

range_years<-c("2000","2001") #right bound not included in the range!!

range_years_clim<-c("2000","2011") #right bound not included in the range!!

infile1<- "outline_venezuela_region__VE_01292013.shp"      #This is the shape file of outline of the study area                                                      #It is an input/output of the covariate script

infile2<-"/home/layers/data/climate/ghcn/v2.92-upd-2012052822/ghcnd-stations.txt"                              #This is the textfile of station locations from GHCND

infile_covariates<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script and used in stage 3 and stage 4

CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84: same as earlier

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

out_prefix<-"_365d_GAM_fus5_all_lstd_03142013"                #User defined output prefix

#qc_flags<-    flags allowe for the query from the GHCND??

#The names of covariates can be changed...these names should be output/input from covar script!!!

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 of 11 parameters for input in the function...

list_param_prep<-list(db.name,var,range_years,range_years_clim,infile1,infile2,infile_covariates,CRS_locs_WGS84,in_path,covar_names,out_prefix)

cnames<-c("db.name","var","range_years","range_years_clim","infile1","infile2","infile_covariates","CRS_locs_WGS84","in_path","covar_names","out_prefix")

names(list_param_prep)<-cnames

##### RUN SCRIPT TO GET STATION DATA WITH COVARIATES #####

list_outfiles<-database_covariates_preparation(list_param_prep)

############### STAGE 4: RASTER PREDICTION #################

#Prepare parameters for for raster prediction... 

#Collect parameters from the previous stage: data preparation stage

#3 parameters from output

infile_monthly<-list_outfiles$monthly_covar_ghcn_data #outile4 from database_covar script

infile_daily<-list_outfiles$daily_covar_ghcn_data  #outfile3 from database_covar script

infile_locs<- list_outfiles$loc_stations_ghcn #outfile2? from database covar script

#names(outfiles_obj)<- c("loc_stations","loc_stations_ghcn","daily_covar_ghcn_data","monthly_covar_ghcn_data")

list_param_data_prep<-list(infile_monthly,infile_daily,infile_locs,infile_covariates,covar_names,var,out_prefix,CRS_locs_WGS84)

names(list_param_data_prep)<-c("infile_monthly","infile_daily","infile_locs","infile_covariates","covar_names","var","out_prefix","CRS_locs_WGS84")

#Set additional parameters

#Input for sampling function...

seed_number<- 100  #if seed zero then no seed?     

nb_sample<-1           #number of time random sampling must be repeated for every hold out proportion

step<-0         

constant<-0             #if value 1 then use the same samples as date one for the all set of dates

prop_minmax<-c(0.3,0.3)  #if prop_min=prop_max and step=0 then predicitons are done for the number of dates...

#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd

dates_selected<-"" # if empty string then predict for the full year specified earlier

#Models to run...this can be change for each run

list_models<-c("y_var ~ s(elev_1)",

               "y_var ~ s(LST)",

               "y_var ~ s(elev_1,LST)",

               "y_var ~ s(lat) + s(lon)+ s(elev_1)",

               "y_var ~ s(lat,lon,elev_1)",

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST)", 

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC2)",

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC6)", 

               "y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(DISTOC)")

#Choose interpolation method...

interpolation_method<-c("gam_fusion","gam_CAI") #other otpions to be added later

#Default name of LST avg to be matched               

lst_avg<-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")  

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

#Create on the fly output folder...

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

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

#Collect all parameters in a list

list_param_raster_prediction<-list(list_param_data_prep,

                                seed_number,nb_sample,step,constant,prop_minmax,dates_selected,

                                list_models,lst_avg,in_path,out_path,script_path,

                                interpolation_method)

names(list_param_raster_prediction)<-c("list_param_data_prep",

                                "seed_number","nb_sample","step","constant","prop_minmax","dates_selected",

                                "list_models","lst_avg","in_path","out_path","script_path",

                                "interpolation_method")

#Source file

source(file.path(script_path,"GAM_fusion_analysis_raster_prediction_multisampling_03132013.R"))

#Make the function call

raster_prediction_gam_fus_obj <-raster_prediction_gam_fusion(list_param_raster_prediction)

############## STAGE 5: OUTPUT ANALYSES ##################

source(file.path(script_path,"results_interpolation_date_output_analyses_03052013.R"))

###############   END OF SCRIPT   ###################

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