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################## Master script for temperature predictions #######################################
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############################ TMIN AND TMAX predictions ##########################################
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#
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##This script produces intperpolated surface of TMIN and TMAX for specified processing region given sets
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#of inputs and parameters.
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#STAGE 1: LST climatology calculation
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#STAGE 2: Covariates preparation: aspect, land cover, distance to coast etc.
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#STAGE 3: Data preparation: database query, extraction of covariates from stack
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#STAGE 4: Raster prediction: run interpolation method -- gam fusion, gam CAI, ...
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#STAGE 5: Output analyses-visualization of results for specific dates...
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#
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#AUTHOR: Benoit Parmentier
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#DATE: 03/28/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--
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##Comments and TODO:
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# Modify code for stage 1 and call python script from R
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# Modify code for stage 2, make it a function and fully automated (distoc var)
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# Add options to run only specific stage + additional out_suffix?
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# Make master script a function?
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# Add log file for master script,add function to collect inputs and outputs
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##################################################################################################
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###Loading R library and packages
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library(RPostgreSQL)
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library(maps)
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library(maptools)
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library(parallel)
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library(gtools) # loading some useful tools
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library(mgcv) # GAM package by Simon Wood
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library(sp) # Spatial pacakge with class definition by Bivand et al.
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library(spdep) # Spatial pacakge with methods and spatial stat. by Bivand et al.
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library(rgdal) # GDAL wrapper for R, spatial utilities
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library(gstat) # Kriging and co-kriging by Pebesma et al.
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library(fields) # NCAR Spatial Interpolation methods such as kriging, splines
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library(raster) # Hijmans et al. package for raster processing
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library(rasterVis)
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library(reshape)
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library(plotrix)
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### Parameters and arguments
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script_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/"
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#list_script_files<-
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#stages_to_run<-c(1,2,3,4,5) #May decide on antoher strategy later on...
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stages_to_run<-c(0,0,3,4,5) #May decide on antoher strategy later on...
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#####SCRIPT USED FOR THE PREDICTIONS: Source all scripts here to avoid confusion on versions being run!!!!
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#source(file.path(script_path,"master_script_temp_03282013.R")) #Master script can be run directly...
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#CALLED FROM MASTER SCRIPT:
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#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/climatology_03192013.py
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source(file.path(script_path,"covariates_production_temperatures_03212013.R"))
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source(file.path(script_path,"Database_stations_covariates_processing_function_03242013.R"))
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source(file.path(script_path,"GAM_fusion_analysis_raster_prediction_multisampling_03272013.R"))
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source(file.path(script_path,"results_interpolation_date_output_analyses_03272013.R"))
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#source(file.path(script_path,"results_covariates_database_stations_output_analyses_03272013.R"))
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#FUNCTIONS CALLED FROM GAM FUSION ANALYSIS RASTER PREDICTION ARE FOUND IN...
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source(file.path(script_path,"sampling_script_functions_03122013.R"))
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source(file.path(script_path,"GAM_fusion_function_multisampling_03192013.R")) #Include GAM_CAI
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source(file.path(script_path,"GAM_fusion_function_multisampling_validation_metrics_03272013.R"))
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############ STAGE 1: LST Climatology ###############
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if (stages_to_run[1]==1){
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#Call run through python
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#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/climatology_03182013.py
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}
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############ STAGE 2: Covariate production ################
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##Paths to inputs and output
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#var<-"TMIN"
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var<-"TMAX"
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in_path <- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data/"
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out_path<- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data/"
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lc_path<-"/home/layers/data/land-cover/lc-consensus-global"
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infile_modis_grid<-"modis_sinusoidal_grid_world.shp"
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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
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infile_canheight<-"Simard_Pinto_3DGlobalVeg_JGR.tif" #Canopy height
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list_tiles_modis = c('h11v08','h11v07','h12v07','h12v08','h10v07','h10v08') #tile for Venezuel and surrounding area
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infile_reg_outline="" #input region outline defined by polygon
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CRS_interp<-"+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +a=6371007.181 +b=6371007.181 +units=m +no_defs";
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84
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out_region_name<-"_venezuela_region" #generated on the fly
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out_suffix<-"_VE_03192013"
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ref_rast_name<-"" #local raster name defining resolution, exent, local projection--. set on the fly??
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#The names of covariates can be changed...these names should be output/input from covar script!!!
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rnames<-c("x","y","lon","lat","N","E","N_w","E_w","elev","slope","aspect","CANHEIGHT","DISTOC")
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lc_names<-c("LC1","LC2","LC3","LC4","LC5","LC6","LC7","LC8","LC9","LC10","LC11","LC12")
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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",
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"nobs_01","nobs_02","nobs_03","nobs_04","nobs_05","nobs_06","nobs_07","nobs_08",
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"nobs_09","nobs_10","nobs_11","nobs_12")
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covar_names<-c(rnames,lc_names,lst_names)
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list_param_covar_production<-list(var,in_path,out_path,lc_path,infile_modis_grid,infile_elev,infile_canheight,
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list_tiles_modis,infile_reg_outline,CRS_interp,CRS_locs_WGS84,out_region_name,
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out_suffix,ref_rast_name,covar_names)
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names(list_param_covar_production)<-c("var","in_path","out_path","lc_path","infile_modis_grid","infile_elev","infile_canheight",
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"list_tiles_modis","infile_reg_outline","CRS_interp","CRS_locs_WGS84","out_region_name",
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"out_suffix","ref_rast_name","covar_names")
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if (stages_to_run[2]==2){
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#Transform into function...
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#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/covariates_production_temperatures_03212013.R
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infile_covar_brick<-covariates_production_temperature(list_param_covar_production)
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}
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############# STAGE 3: Data preparation ###############
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#Setting up input argurments for script function...
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#set up earlier
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var <- "TMAX" # name of the variables to keep: TMIN, TMAX or PRCP --already set up earlier
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infile_covariates<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script and used in stage 3 and stage 4
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#infile_covariates<-"covariates__venezuela_region_TMIN__VE_03192013.tif" #covariates stack for TMIN
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CRS_locs_WGS84<-CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +towgs84=0,0,0") #Station coords WGS84: same as earlier
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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
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#covar_names see stage 2
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#specific to this stage
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db.name <- "ghcn" # name of the Postgres database
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range_years<-c("2000","2001") #right bound not included in the range!!
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range_years_clim<-c("1981","2011") #right bound not included in the range!!
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infile2<-"/home/layers/data/climate/ghcn/v2.92-upd-2012052822/ghcnd-stations.txt" #This is the textfile of station locations from GHCND
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in_path <- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data/"
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out_prefix<-"_365d_GAM_fus5_all_lstd_03282013" #User defined output prefix
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qc_flags_stations<-c("0","S") #flags allowed for screening after the query from the GHCND??
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#list of 12 parameters for input in the function...
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list_param_prep<-list(db.name,var,range_years,range_years_clim,infile1,infile2,infile_covariates,CRS_locs_WGS84,in_path,covar_names,qc_flags_stations,out_prefix)
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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")
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names(list_param_prep)<-cnames
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##### RUN SCRIPT TO GET STATION DATA WITH COVARIATES #####
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list_outfiles<-database_covariates_preparation(list_param_prep)
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############### STAGE 4: RASTER PREDICTION #################
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#Prepare parameters for for raster prediction...
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#Collect parameters from the previous stage: data preparation stage
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#3 parameters from output
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infile_monthly<-list_outfiles$monthly_covar_ghcn_data #outile4 from database_covar script
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infile_daily<-list_outfiles$daily_covar_ghcn_data #outfile3 from database_covar script
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infile_locs<- list_outfiles$loc_stations_ghcn #outfile2? from database covar script
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#names(outfiles_obj)<- c("loc_stations","loc_stations_ghcn","daily_covar_ghcn_data","monthly_covar_ghcn_data")
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list_param_data_prep<-list(infile_monthly,infile_daily,infile_locs,infile_covariates,covar_names,var,out_prefix,CRS_locs_WGS84)
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names(list_param_data_prep)<-c("infile_monthly","infile_daily","infile_locs","infile_covariates","covar_names","var","out_prefix","CRS_locs_WGS84")
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#Set additional parameters
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#Input for sampling function...
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seed_number<- 100 #if seed zero then no seed?
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nb_sample<-1 #number of time random sampling must be repeated for every hold out proportion
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step<-0
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constant<-0 #if value 1 then use the same samples as date one for the all set of dates
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prop_minmax<-c(0.3,0.3) #if prop_min=prop_max and step=0 then predicitons are done for the number of dates...
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#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd
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dates_selected<-"" # if empty string then predict for the full year specified earlier
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#Models to run...this can be change for each run
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list_models<-c("y_var ~ s(elev_1)",
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"y_var ~ s(LST)",
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"y_var ~ s(elev_1,LST)",
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"y_var ~ s(lat) + s(lon)+ s(elev_1)",
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"y_var ~ s(lat,lon,elev_1)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC2)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(LC6)",
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"y_var ~ s(lat,lon) + s(elev_1) + s(N_w,E_w) + s(LST) + s(DISTOC)")
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#Choose interpolation method...
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interpolation_method<-c("gam_fusion","gam_CAI") #other otpions to be added later
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#Default name of LST avg to be matched
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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")
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in_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data"
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#Create on the fly output folder...
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out_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/output_data"
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script_path<-"/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/"
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#Collect all parameters in a list
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list_param_raster_prediction<-list(list_param_data_prep,
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seed_number,nb_sample,step,constant,prop_minmax,dates_selected,
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list_models,lst_avg,in_path,out_path,script_path,
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interpolation_method)
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names(list_param_raster_prediction)<-c("list_param_data_prep",
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"seed_number","nb_sample","step","constant","prop_minmax","dates_selected",
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"list_models","lst_avg","in_path","out_path","script_path",
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"interpolation_method")
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#Make the function call--names to be change to raster_prediction_obj and raster_prediction_fun
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raster_prediction_gam_fus_obj <-raster_prediction_gam_fusion(list_param_raster_prediction)
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############## STAGE 5: OUTPUT ANALYSES ##################
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#source(file.path(script_path,"results_interpolation_date_output_analyses_03052013.R"))
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#Call as function...
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############### END OF SCRIPT ###################
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#####################################################
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