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master_code_monthly_per_zones.R
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master_code_monthly_per_zones.R
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# -------------------------------------------------- #
# Climate Risk Profiles -- Master code monthly runs
# A. Esquivel, C. Saavedra, H. Achicanoy & J. Ramirez-Villegas
# Alliance Bioversity-CIAT, 2021
# -------------------------------------------------- #
# Sourcing functions
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/_main_functions.R') # Main functions
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/_get_soil_data.R') # Get soil data
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/_calc_indices.R') # Calculating agro-indices
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/_calc_indices2.R') # Calculating agro-indices
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/_calc_spi_drought.R') # SPI calculation
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/time_series_plot.R') # Time series graphs
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/time_series_plot_region.R') # Time series graphs by region
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/index_climatology_plot.R') # Bar Graphs
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/summary.R') # summary indices (mean, median...)
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/org_tables.R') # Tablas Julian
source('https://raw.githubusercontent.com/CIAT-DAPA/WFP-profiles/main/migration/_get_climate4regions_districts.R') # Filter climate for districts of interest
OSys <<- Sys.info()[1]
root <<- switch(OSys,
'Linux' = '/dapadfs/workspace_cluster_14/WFP_ClimateRiskPr',
'Windows' = '//CATALOGUE/Workspace14/WFP_ClimateRiskPr')
## Defining country parameters
# Country
country <- 'Nepal'
iso <- 'NPL'
seasons <- list(s1=1,s2=2,s3=3,s4=4,s5=5,s6=6,s7=7,s8=8,s9=9,s10=10,s11=11,s12=12)
# Get historical climate data (done)
# # Get soil data
# crd <- paste0(root,"/1.Data/observed_data/",iso,"/year/climate_1981_mod.fst") %>%
# tidyft::parse_fst(path = .) %>%
# tidyft::select_fst(id,x,y) %>%
# base::as.data.frame()
# crd <- unique(crd[,c('id','x','y')])
# get_soil(crd = crd,
# root_depth = 60,
# outfile = paste0(root,"/1.Data/soil/",iso,"/soilcp_data.fst"))
# Get future climate data
# Calc agro-climatic indices (past)
districts <- list.files(path = paste0(root,'/1.Data/shps/',tolower(country),'/',tolower(iso),'_zones'), pattern = '.shp$', full.names = F) %>% gsub('.shp','',.) %>% gtools::mixedsort()
for(i in 1:length(districts)){
soilfl <- paste0(root,"/1.Data/soil/",iso,"/soilcp_data.fst")
outfile <- paste0(root,"/7.Results/",country,"/past/",districts[i],"_indices_monthly.fst")
spi_out <- paste0(root,"/7.Results/",country,"/past/",districts[i],"_spi.fst")
if(!file.exists(outfile)){
cat(paste0('Processing district: ',districts[i],'\n'))
infile <- flt_clm_subunits2(iso = iso, country = country, district = districts[i])
tryCatch(expr={
calc_indices(climate = infile,
soil = soilfl,
seasons = seasons,
subset = F,
ncores = 15,
outfile = outfile,
spi_out = spi_out)
},
error=function(e){
cat(paste0("Modeling process failed in district: ",districts[i],"\n"))
return("Done\n")
})
cat(paste0('District: ',districts[i],' finished successfully\n'))
} else {
cat(paste0('Seasonal indices are already calculated for district: ',districts[i],'\n'))
}
}
if(!file.exists(paste0(root,"/7.Results/",country,"/past/",iso,"_indices_monthly.fst"))){
indices <- list.files(path = paste0(root,"/7.Results/",country,"/past"), pattern = "[0-9]_indices_monthly.fst", full.names = T) %>%
purrr::map(.f = function(f){
df <- f %>%
tidyft::parse_fst() %>%
base::as.data.frame()
return(df)
}) %>%
dplyr::bind_rows()
tidyft::export_fst(indices, path = paste0(root,"/7.Results/",country,"/past/",iso,"_indices_monthly.fst"))
}
# if(!file.exists(paste0(root,"/7.Results/",country,"/past/",iso,"_spi.fst"))){
# spis <- list.files(path = paste0(root,"/7.Results/",country,"/past"), pattern = "[0-9]_spi.fst", full.names = T) %>%
# purrr::map(.f = function(f){
# df <- f %>%
# tidyft::parse_fst() %>%
# base::as.data.frame()
# return(df)
# }) %>%
# dplyr::bind_rows()
# tidyft::export_fst(spis, path = paste0(root,"/7.Results/",country,"/past/",iso,"_spi.fst"))
# }
# # How much area per municipality is on average subject to ‘Major droughts’ (SPI < -1.5)
# infile <- paste0(root,"/7.Results/",country,"/past/",iso,"_spi.fst")
# outfile <- paste0(root,'/7.Results/',country,'/past/',iso,'_spi_drought.fst')
# calc_spi_drought(spi_data = infile,
# output = outfile,
# country = country,
# iso = iso,
# seasons = seasons)
# Calc agro-climatic indices (future)
models <- c('ACCESS-ESM1-5', 'EC-Earth3-Veg','INM-CM5-0','MPI-ESM1-2-HR','MRI-ESM2-0')
periods <- c('2021-2040','2041-2060')
for(m in models){
for(p in periods){
districts <- list.files(path = paste0(root,'/1.Data/shps/',tolower(country),'/',tolower(iso),'_zones'), pattern = '.shp$', full.names = F) %>% gsub('.shp','',.) %>% gtools::mixedsort()
for(i in 1:length(districts)){
soilfl <- paste0(root,"/1.Data/soil/",iso,"/soilcp_data.fst")
outfile <- paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",districts[i],"_indices_monthly.fst")
spi_out <- paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",districts[i],"_spi.fst")
if(!file.exists(outfile)){
cat(paste0('Processing district: ',districts[i],'\n'))
infile <- flt_clm_subunits3(iso = iso, country = country, district = districts[i], model = m, period = p)
tryCatch(expr={
calc_indices(climate = infile,
soil = soilfl,
seasons = seasons,
subset = F,
ncores = 15,
outfile = outfile,
spi_out = spi_out)
},
error=function(e){
cat(paste0("Modeling process failed in district: ",districts[i],"\n"))
return("Done\n")
})
cat(paste0('District: ',districts[i],' finished successfully\n'))
} else {
cat(paste0('Seasonal indices are already calculated for district: ',districts[i],'\n'))
}
}
if(!file.exists(paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",iso,"_indices_monthly.fst"))){
indices <- list.files(path = paste0(root,"/7.Results/",country,"/future/",m,"/",p), pattern = "[0-9]_indices_monthly.fst", full.names = T) %>%
purrr::map(.f = function(f){
df <- f %>%
tidyft::parse_fst() %>%
base::as.data.frame()
return(df)
}) %>%
dplyr::bind_rows()
tidyft::export_fst(indices, path = paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",iso,"_indices_monthly.fst"))
}
# if(!file.exists(paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",iso,"_spi.fst"))){
# spis <- list.files(path = paste0(root,"/7.Results/",country,"/future/",m,"/",p), pattern = "[0-9]_spi.fst", full.names = T) %>%
# purrr::map(.f = function(f){
# df <- f %>%
# tidyft::parse_fst() %>%
# base::as.data.frame()
# return(df)
# }) %>%
# dplyr::bind_rows()
# tidyft::export_fst(spis, path = paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",iso,"_spi.fst"))
# }
#
# infile <- paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",iso,"_spi.fst")
# outfile <- paste0(root,"/7.Results/",country,"/future/",m,"/",p,"/",iso,"_spi_drought.fst")
# calc_spi_drought(spi_data = infile,
# output = outfile,
# country = country,
# iso = iso,
# seasons = seasons)
}
}
## Graphs
# 1. Barplot series de tiempo
bar_graphs(country = country, iso = iso)
# Fix desde aqui...
# 4. Summary tablas
Other_parameters(country = country, iso3 = iso)
# 5. Summary index.
tictoc::tic()
monthly_data <- read_monthly_data(country = country , iso3 = iso)
tictoc::toc() # 10 Min. --- Pakistan
gc(reset = TRUE)
tictoc::tic()
# Run the process in parallel for the 30% of the pixels
# ncores <- 3
# options(future.global.maxSize = 768 * 1024^2)
# future::plan(cluster, workers = ncores, gc = TRUE)
index_mod <- tibble(Zone = c('all', regions_all$region) ) %>%
dplyr::mutate(index = purrr::map(.x = Zone, .f = function(w){run_by_seasons(Zone = w, data_init = monthly_data, Period = seasons)})) %>%
tidyr::unnest() %>%
dplyr::select(-cod_name )
# future:::ClusterRegistry("stop")
# gc(reset = T)
tictoc::toc() # 40 min
write_csv(x = index_mod, file = glue::glue('//dapadfs/workspace_cluster_14/WFP_ClimateRiskPr/7.Results/{country}/monthly_ind.csv'))
# 4. Julian tables order.
changes_paths(country = country, iso = iso)