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

#

#AUTHOR: Benoit Parmentier                                                                       

#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(0,0,3,4,5) #May decide on antoher strategy later on...

source(file.path(script_path,"covariates_production_temperatures_03212013.R"))

source(file.path(script_path,"results_interpolation_date_output_analyses_03272013.R"))

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

if (stages_to_run[1]==1){

  #Call run through python

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

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

lc_path<-"/home/layers/data/land-cover/lc-consensus-global"

infile_modis_grid<-"modis_sinusoidal_grid_world.shp"

infile_elev<-"/home/layers/data/terrain/dem-cgiar-srtm-1km-tif/srtm_1km.tif"  #this is the global file: replace later with the input produced by the DEM team

infile_canheight<-"Simard_Pinto_3DGlobalVeg_JGR.tif"              #Canopy height

list_tiles_modis = c('h11v08','h11v07','h12v07','h12v08','h10v07','h10v08') #tile for Venezuel and surrounding area

infile_reg_outline=""  #input region outline defined by polygon

CRS_interp<-"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs";

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

out_region_name<-"_venezuela_region" #generated on the fly

out_suffix<-"_VE_03192013"

ref_rast_name<-""  #local raster name defining resolution, exent, local projection--. set on the fly??

#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_param_covar_production<-list(var,in_path,out_path,lc_path,infile_modis_grid,infile_elev,infile_canheight,

                                  list_tiles_modis,infile_reg_outline,CRS_interp,CRS_locs_WGS84,out_region_name,

                                  out_suffix,ref_rast_name,covar_names) 

names(list_param_covar_production)<-c("var","in_path","out_path","lc_path","infile_modis_grid","infile_elev","infile_canheight",

                                      "list_tiles_modis","infile_reg_outline","CRS_interp","CRS_locs_WGS84","out_region_name",

                                      "out_suffix","ref_rast_name","covar_names") 

  #Transform into function...

  infile_covar_brick<-covariates_production_temperature(list_param_covar_production)

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

#Setting up input argurments for script function...

infile_covariates<-"covariates__venezuela_region_TMIN__VE_03192013.tif" #covariates stack for TMIN

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

#covar_names see stage 2

range_years_clim<-c("1981","2011") #right bound not included in the range!!

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

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

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

list_outfiles<-database_covariates_preparation(list_param_prep)

############### STAGE 4: 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

#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<-"" # 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,

                                interpolation_method)

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

                                "interpolation_method")

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

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

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