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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 methode -- 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: 03/05/2013
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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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############ STAGE 1: LST Climatology ###############
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#Call run through python
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#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/climatology_01252013b.py
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############ STAGE 2: Covariate production ################
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#Transform into function...
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#/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/covariates_production_temperatures_02062013.R
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############# STAGE 3: Data preparation ###############
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source(file.path(script_path,"Database_stations_covariates_processing_function_03052013.R"))
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#Setting up input argurments for script function...
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db.name <- "ghcn" # name of the Postgres database
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var <- "TMAX" # name of the variables to keep: TMIN, TMAX or PRCP
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range_years<-c("2010","2011") #right bound not included in the range!!
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range_years_clim<-c("2000","2011") #right bound not included in the range!!
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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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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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infile_covariates<-"covariates__venezuela_region__VE_01292013.tif" #this is an output from covariate script
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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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in_path <- "/home/parmentier/Data/IPLANT_project/Venezuela_interpolation/Venezuela_01142013/input_data/"
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out_prefix<-"_365d_GAM_fus5_all_lstd_03052013" #User defined output prefix
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#qc_flags<- flags allowe for the query from the GHCND??
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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 of 11 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,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","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_covaratiates_preparation(list_param_prep)
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############### STAGE 4: RASTER PREDICTION #################
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#Prepare parameters for for raster prediction... Turn this into a function
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source(file.path(script_path,"GAM_fusion_analysis_raster_prediction_multisampling_03052013.R"))
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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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############### END OF SCRIPT ###################
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#####################################################
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