R/annual_model_tax_0.R
In manirouhirad/MODSSAT: What the Package Does (One Line, Title Case)
Defines functions
annual_model_tax_0
Documented in
annual_model_tax_0
#' This function produces annual amounts of water use and profits.
#' @param tax_amount is the amount of tax on the unit of groundwater extracted. Defaults to 1.1.
#' @param well_soil_file is the file that contains the soil types for each well in the region. Defaults to "C:/Users/manirad/Dropbox/DSSAT subregions work pre-2018 annual meeting/subregion KS files/outputs_for_econ/Well_Soil Type.csv".
#' @param well_capacity_files is the directory where well capacity files are located. Defaults to "C:/Users/manirad/Downloads/test/Well Capacity".
#' @param econ_output_file is the name of the output file that contains irrigated acres, irrigation, and profits for each well. Defaults to "C:/Users/manirad/Downloads/test/Econ_output/KS_DSSAT_output.csv",
#' @param well_capacity_file_year is the name of the output file that contains irrigation for each well which will be used by MODFLOW. Defaults to "C:/Users/manirad/Downloads/test/KS_DSSAT_output.csv",
#' @param first_year_of_simulation is the first year that the hydro-economic simulation starts. Defaults to 2000.
#' @param default_well_capacity_col_name is the name of the well capacity column generated from the MODFLOW model. Defaults to 'Well_Capacity(gpm)' as this is the original column name we started with.
#' @param missing_soil_types is the soil type that is assigned to missing soil types for wells. Defaults to "KS00000007".
#' @param minimum_well_capacity is the minimum well capacity in the model. If well capacity falls below this capacity, it is set to this minimum. Defaults to 100 gallons per minute.
#' @param maximum_well_capacity is the maximum well capacity in the model. If well capacity falls above this capacity, it is set to this maximum. Defaults to 3000 gallons per minute.
#' @param first_year_of_GW is the first year that the GW model exists. This may be different than the first year of simulation. Defaults to 1997.
#' @param last_year_of_GW is the last year that the GW model exists. This may be different than the last year of simulation. Defaults to 2007.
#' @param irrigation_season_days Number of days in an irrigation season. Defaults to 70.
#' @return returns the output table.
#' @examples
#' \dontrun{
#' gen_lookup_tax(tax_amount = 2)
#' }
#' @export
annual_model_tax_0 = function(tax_amount = 1,
# well_soil_file = "./input_files/Well_Soil Type.csv",
well_soil_file = "./input_files/Well_SoilType_WeatherStation.csv",
well_capacity_files = "./Well Capacity",
econ_output_file = "./Econ_output/KS_DSSAT_output.csv",
well_capacity_file_year = "./KS_DSSAT_output.csv",
first_year_of_simulation = 2000,
default_well_capacity_col_name = "Well_Capacity(gpm)",
missing_soil_types = "KS00000007",
minimum_well_capacity = 1,
maximum_well_capacity = 1000,
first_year_of_GW = 1997,
last_year_of_GW = 2007,
irrigation_season_days = 70)
{
tax_amount = (tax_amount - 1)/10
soil_type = fread(well_soil_file)
soil_type[, `:=`(Soil_Type, gsub("KSFC00000", "KS0000000",
Soil_Type))]
soil_type = soil_type[complete.cases(Well_ID)]
filenames = list.files(path = well_capacity_files, pattern = "*.csv",
full.names = TRUE)
ldf <- lapply(filenames, fread, fill = T)
ldf <- mapply(cbind, ldf, file_name = filenames, SIMPLIFY = F)
year_dt = rbindlist(ldf)
foo = data.table::data.table(Well_ID = 1, V1 = 1, file_name = paste0(well_capacity_files,
"/", first_year_of_simulation, "_Capacity.csv"))
setnames(foo, old = "V1", new = default_well_capacity_col_name)
year_dt = rbind(year_dt, foo)
year_dt[, `:=`(file_name, substr(file_name, nchar(file_name) -
16, nchar(file_name) - 13))]
year_dt[, `:=`(file_name, as.integer(file_name))]
setkey(year_dt, file_name)
year_dt = year_dt[nrow(year_dt)]
year_dt = year_dt$file_name
year_2 = year_dt
well_capacity = data.table(Well_ID = NA, V1 = NA)
setnames(well_capacity, old = "V1", new = default_well_capacity_col_name)
well_capacity = ifelse(year_dt == first_year_of_simulation,
list(rbind(well_capacity, fread(paste0(first_year_of_simulation,
"_Well_Capacity.csv")))), list(rbind(well_capacity,
fread(paste0("./Well Capacity/", year_dt, "_Capacity.csv")))))
well_capacity = data.table(well_capacity[[1]])
well_capacity = well_capacity[complete.cases(Well_ID)]
setkey(soil_type, Well_ID)
setkey(well_capacity, Well_ID)
well_capacity_data = soil_type[well_capacity]
setnames(well_capacity_data, default_well_capacity_col_name,
"Well_capacity")
well_capacity_data[, `:=`(Well_capacity, mean(Well_capacity)),
by = "Well_ID"]
well_capacity_data = unique(well_capacity_data, by = "Well_ID")
setkey(well_capacity_data, Well_ID)
well_capacity_data[is.na(Soil_Type), `:=`(Soil_Type, missing_soil_types)]
well_capacity_data = well_capacity_data[!is.na(Soil_Type)]
# well_capacity_data[, `:=`(Well_capacity, round(Well_capacity))]
well_capacity_data[is.na(weather_station), `:=`(weather_station, well_capacity_data[1,weather_station])]
well_capacity_data = well_capacity_data[!is.na(weather_station)]
well_capacity_data[, `:=`(Well_capacity, round(Well_capacity))]
# soil_type = fread(well_soil_file)
# soil_type[, `:=`(Soil_Type, gsub("KSFC00000", "KS0000000",
# Soil_Type))]
# soil_type = soil_type[complete.cases(Well_ID)]
# soil_type = unique(soil_type, by = "Well_ID")
well_capacity_data[Well_capacity <= minimum_well_capacity,
`:=`(Well_capacity, minimum_well_capacity)]
well_capacity_data[Well_capacity >= maximum_well_capacity,
`:=`(Well_capacity, maximum_well_capacity)]
lookup_table_well_2 = fread("lookup_table_well_2.csv")
lookup_table_all_years_2 = readRDS("lookup_table_all_years_2.rds")
filenames = list.files(path = well_capacity_files, pattern = "*.csv",
full.names = TRUE)
ldf <- lapply(filenames, fread, fill = T)
ldf <- mapply(cbind, ldf, file_name = filenames, SIMPLIFY = F)
year_dt = rbindlist(ldf)
foo = data.table(Well_ID = 1, V1 = 1, file_name = paste0(well_capacity_files,
"/", first_year_of_simulation, "_Capacity.csv"))
setnames(foo, old = "V1", new = default_well_capacity_col_name)
year_dt = rbind(year_dt, foo)
year_dt[, `:=`(file_name, substr(file_name, nchar(file_name) -
16, nchar(file_name) - 13))]
year_dt[, `:=`(file_name, as.integer(file_name))]
setkey(year_dt, file_name)
year_dt = year_dt[nrow(year_dt)]
# year_dt[, `:=`(file_name, ifelse(file_name < last_year_of_GW +
# 1, file_name, ifelse(file_name > last_year_of_GW & file_name <=
# last_year_of_GW + (last_year_of_GW - first_year_of_GW +
# 1), file_name - (last_year_of_GW - first_year_of_GW +
# 1), ifelse(file_name > last_year_of_GW + (last_year_of_GW -
# first_year_of_GW + 1) & file_name <= last_year_of_GW +
# 2 * (last_year_of_GW - first_year_of_GW + 1), file_name -
# 2 * (last_year_of_GW - first_year_of_GW), ifelse(file_name >
# last_year_of_GW + 2 * (last_year_of_GW - first_year_of_GW +
# 1) & file_name < last_year_of_GW + 3 * (last_year_of_GW -
# first_year_of_GW + 1), file_name - 3 * (last_year_of_GW -
# first_year_of_GW), file_name - 4 * (last_year_of_GW -
# first_year_of_GW))))))]
# year_dt[, `:=`(file_name, ifelse(file_name < 2010, file_name, ifelse(file_name > 2009 & file_name <= 2022, file_name - 13,
# ifelse(file_name > 2022 & file_name <= 2035, file_name - 26,
# ifelse(file_name > 2035 & file_name < 2049, file_name - 39, file_name - 52)))))]
# year_dt[, `:=`(file_name, ifelse(file_name <= 2006, file_name+1,
# ifelse(file_name > 2006 & file_name <= 2017, file_name - 10, ifelse(file_name > 2017 & file_name <= 2028,
# file_name - 21, ifelse(file_name > 2028 & file_name <= 2039, file_name - 32, file_name - 43)))))]
#
# year_dt = year_dt$file_name
year_dt[, `:=`(file_name, ifelse(file_name <= (last_year_of_GW-1), file_name +
1, ifelse(file_name > (last_year_of_GW-1) & file_name <= (last_year_of_GW-1 + last_year_of_GW - first_year_of_GW+1), file_name -
(-1 + last_year_of_GW - first_year_of_GW+1), ifelse(file_name > (last_year_of_GW-1 + last_year_of_GW - first_year_of_GW+1) & file_name <= (last_year_of_GW-1 + 2*(last_year_of_GW - first_year_of_GW+1)), file_name -
(-1 + 2*(last_year_of_GW - first_year_of_GW+1)), ifelse(file_name > (last_year_of_GW-1 + 2*(last_year_of_GW - first_year_of_GW+1)) & file_name <= (last_year_of_GW-1 + 3*(last_year_of_GW - first_year_of_GW+1)), file_name -
(-1 + 3*(last_year_of_GW - first_year_of_GW+1)), file_name - (-1 + 4*(last_year_of_GW - first_year_of_GW+1)))))))]
year_dt = year_dt$file_name
lookup_table_all_years_2 = lookup_table_all_years_2[SDAT == year_dt]
lookup_table_all_years_2[, `:=`(Well_ID, NULL)]
setkey(lookup_table_all_years_2, WSTA, SOIL_ID, Well_capacity)
setkey(lookup_table_well_2, WSTA, SOIL_ID, Well_capacity)
setkey(well_capacity_data, weather_station, Soil_Type, Well_capacity)
lookup_table_all_years_2 = lookup_table_all_years_2[well_capacity_data]
lookup_table_well_2 = lookup_table_well_2[well_capacity_data]
lookup_table_all_years_2[, `:=`(irr_tot_acres, sum(irrigation)),
by = "Well_ID"]
lookup_table_all_years_2[, `:=`(profit_Well_ID, sum(profit)),
by = "Well_ID"]
lookup_table_all_years_2 = unique(lookup_table_all_years_2,
by = "Well_ID")
lookup_table_all_years_2[, `:=`(output_rate_acin_day, irr_tot_acres/irrigation_season_days)]
econ_output = lookup_table_all_years_2[, .(Well_ID, Well_capacity,
tot_acres, irr_tot_acres, profit_Well_ID, output_rate_acin_day)]
econ_output[, `:=`(row, 1:.N)]
econ_output[, `:=`(tax, tax_amount)]
econ_output_in = fread("./Econ_output/KS_DSSAT_output.csv")
econ_output = rbind(econ_output_in, econ_output)
# econ_output[is.na(output_rate_acin_day), `:=`(output_rate_acin_day, 0)]
# well_capacity_data = lookup_table_all_years_2[, .(Well_ID, output_rate_acin_day)]
econ_output[, `:=`(output_rate_acin_day, 0)]
well_capacity_data = lookup_table_all_years_2[, .(Well_ID, output_rate_acin_day=0)]
print(well_capacity_data)
write.csv(econ_output, econ_output_file, row.names = FALSE)
write.csv(well_capacity_data, well_capacity_file_year, row.names = FALSE)
}
manirouhirad/MODSSAT documentation built on April 15, 2024, 11:31 p.m.
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Defines functions annual_model_tax_0
Documented in annual_model_tax_0
#' This function produces annual amounts of water use and profits.
#' @param tax_amount is the amount of tax on the unit of groundwater extracted. Defaults to 1.1.
#' @param well_soil_file is the file that contains the soil types for each well in the region. Defaults to "C:/Users/manirad/Dropbox/DSSAT subregions work pre-2018 annual meeting/subregion KS files/outputs_for_econ/Well_Soil Type.csv".
#' @param well_capacity_files is the directory where well capacity files are located. Defaults to "C:/Users/manirad/Downloads/test/Well Capacity".
#' @param econ_output_file is the name of the output file that contains irrigated acres, irrigation, and profits for each well. Defaults to "C:/Users/manirad/Downloads/test/Econ_output/KS_DSSAT_output.csv",
#' @param well_capacity_file_year is the name of the output file that contains irrigation for each well which will be used by MODFLOW. Defaults to "C:/Users/manirad/Downloads/test/KS_DSSAT_output.csv",
#' @param first_year_of_simulation is the first year that the hydro-economic simulation starts. Defaults to 2000.
#' @param default_well_capacity_col_name is the name of the well capacity column generated from the MODFLOW model. Defaults to 'Well_Capacity(gpm)' as this is the original column name we started with.
#' @param missing_soil_types is the soil type that is assigned to missing soil types for wells. Defaults to "KS00000007".
#' @param minimum_well_capacity is the minimum well capacity in the model. If well capacity falls below this capacity, it is set to this minimum. Defaults to 100 gallons per minute.
#' @param maximum_well_capacity is the maximum well capacity in the model. If well capacity falls above this capacity, it is set to this maximum. Defaults to 3000 gallons per minute.
#' @param first_year_of_GW is the first year that the GW model exists. This may be different than the first year of simulation. Defaults to 1997.
#' @param last_year_of_GW is the last year that the GW model exists. This may be different than the last year of simulation. Defaults to 2007.
#' @param irrigation_season_days Number of days in an irrigation season. Defaults to 70.
#' @return returns the output table.
#' @examples
#' \dontrun{
#' gen_lookup_tax(tax_amount = 2)
#' }
#' @export
annual_model_tax_0 = function(tax_amount = 1,
# well_soil_file = "./input_files/Well_Soil Type.csv",
well_soil_file = "./input_files/Well_SoilType_WeatherStation.csv",
well_capacity_files = "./Well Capacity",
econ_output_file = "./Econ_output/KS_DSSAT_output.csv",
well_capacity_file_year = "./KS_DSSAT_output.csv",
first_year_of_simulation = 2000,
default_well_capacity_col_name = "Well_Capacity(gpm)",
missing_soil_types = "KS00000007",
minimum_well_capacity = 1,
maximum_well_capacity = 1000,
first_year_of_GW = 1997,
last_year_of_GW = 2007,
irrigation_season_days = 70)
{
tax_amount = (tax_amount - 1)/10
soil_type = fread(well_soil_file)
soil_type[, `:=`(Soil_Type, gsub("KSFC00000", "KS0000000",
Soil_Type))]
soil_type = soil_type[complete.cases(Well_ID)]
filenames = list.files(path = well_capacity_files, pattern = "*.csv",
full.names = TRUE)
ldf <- lapply(filenames, fread, fill = T)
ldf <- mapply(cbind, ldf, file_name = filenames, SIMPLIFY = F)
year_dt = rbindlist(ldf)
foo = data.table::data.table(Well_ID = 1, V1 = 1, file_name = paste0(well_capacity_files,
"/", first_year_of_simulation, "_Capacity.csv"))
setnames(foo, old = "V1", new = default_well_capacity_col_name)
year_dt = rbind(year_dt, foo)
year_dt[, `:=`(file_name, substr(file_name, nchar(file_name) -
16, nchar(file_name) - 13))]
year_dt[, `:=`(file_name, as.integer(file_name))]
setkey(year_dt, file_name)
year_dt = year_dt[nrow(year_dt)]
year_dt = year_dt$file_name
year_2 = year_dt
well_capacity = data.table(Well_ID = NA, V1 = NA)
setnames(well_capacity, old = "V1", new = default_well_capacity_col_name)
well_capacity = ifelse(year_dt == first_year_of_simulation,
list(rbind(well_capacity, fread(paste0(first_year_of_simulation,
"_Well_Capacity.csv")))), list(rbind(well_capacity,
fread(paste0("./Well Capacity/", year_dt, "_Capacity.csv")))))
well_capacity = data.table(well_capacity[[1]])
well_capacity = well_capacity[complete.cases(Well_ID)]
setkey(soil_type, Well_ID)
setkey(well_capacity, Well_ID)
well_capacity_data = soil_type[well_capacity]
setnames(well_capacity_data, default_well_capacity_col_name,
"Well_capacity")
well_capacity_data[, `:=`(Well_capacity, mean(Well_capacity)),
by = "Well_ID"]
well_capacity_data = unique(well_capacity_data, by = "Well_ID")
setkey(well_capacity_data, Well_ID)
well_capacity_data[is.na(Soil_Type), `:=`(Soil_Type, missing_soil_types)]
well_capacity_data = well_capacity_data[!is.na(Soil_Type)]
# well_capacity_data[, `:=`(Well_capacity, round(Well_capacity))]
well_capacity_data[is.na(weather_station), `:=`(weather_station, well_capacity_data[1,weather_station])]
well_capacity_data = well_capacity_data[!is.na(weather_station)]
well_capacity_data[, `:=`(Well_capacity, round(Well_capacity))]
# soil_type = fread(well_soil_file)
# soil_type[, `:=`(Soil_Type, gsub("KSFC00000", "KS0000000",
# Soil_Type))]
# soil_type = soil_type[complete.cases(Well_ID)]
# soil_type = unique(soil_type, by = "Well_ID")
well_capacity_data[Well_capacity <= minimum_well_capacity,
`:=`(Well_capacity, minimum_well_capacity)]
well_capacity_data[Well_capacity >= maximum_well_capacity,
`:=`(Well_capacity, maximum_well_capacity)]
lookup_table_well_2 = fread("lookup_table_well_2.csv")
lookup_table_all_years_2 = readRDS("lookup_table_all_years_2.rds")
filenames = list.files(path = well_capacity_files, pattern = "*.csv",
full.names = TRUE)
ldf <- lapply(filenames, fread, fill = T)
ldf <- mapply(cbind, ldf, file_name = filenames, SIMPLIFY = F)
year_dt = rbindlist(ldf)
foo = data.table(Well_ID = 1, V1 = 1, file_name = paste0(well_capacity_files,
"/", first_year_of_simulation, "_Capacity.csv"))
setnames(foo, old = "V1", new = default_well_capacity_col_name)
year_dt = rbind(year_dt, foo)
year_dt[, `:=`(file_name, substr(file_name, nchar(file_name) -
16, nchar(file_name) - 13))]
year_dt[, `:=`(file_name, as.integer(file_name))]
setkey(year_dt, file_name)
year_dt = year_dt[nrow(year_dt)]
# year_dt[, `:=`(file_name, ifelse(file_name < last_year_of_GW +
# 1, file_name, ifelse(file_name > last_year_of_GW & file_name <=
# last_year_of_GW + (last_year_of_GW - first_year_of_GW +
# 1), file_name - (last_year_of_GW - first_year_of_GW +
# 1), ifelse(file_name > last_year_of_GW + (last_year_of_GW -
# first_year_of_GW + 1) & file_name <= last_year_of_GW +
# 2 * (last_year_of_GW - first_year_of_GW + 1), file_name -
# 2 * (last_year_of_GW - first_year_of_GW), ifelse(file_name >
# last_year_of_GW + 2 * (last_year_of_GW - first_year_of_GW +
# 1) & file_name < last_year_of_GW + 3 * (last_year_of_GW -
# first_year_of_GW + 1), file_name - 3 * (last_year_of_GW -
# first_year_of_GW), file_name - 4 * (last_year_of_GW -
# first_year_of_GW))))))]
# year_dt[, `:=`(file_name, ifelse(file_name < 2010, file_name, ifelse(file_name > 2009 & file_name <= 2022, file_name - 13,
# ifelse(file_name > 2022 & file_name <= 2035, file_name - 26,
# ifelse(file_name > 2035 & file_name < 2049, file_name - 39, file_name - 52)))))]
# year_dt[, `:=`(file_name, ifelse(file_name <= 2006, file_name+1,
# ifelse(file_name > 2006 & file_name <= 2017, file_name - 10, ifelse(file_name > 2017 & file_name <= 2028,
# file_name - 21, ifelse(file_name > 2028 & file_name <= 2039, file_name - 32, file_name - 43)))))]
#
# year_dt = year_dt$file_name
year_dt[, `:=`(file_name, ifelse(file_name <= (last_year_of_GW-1), file_name +
1, ifelse(file_name > (last_year_of_GW-1) & file_name <= (last_year_of_GW-1 + last_year_of_GW - first_year_of_GW+1), file_name -
(-1 + last_year_of_GW - first_year_of_GW+1), ifelse(file_name > (last_year_of_GW-1 + last_year_of_GW - first_year_of_GW+1) & file_name <= (last_year_of_GW-1 + 2*(last_year_of_GW - first_year_of_GW+1)), file_name -
(-1 + 2*(last_year_of_GW - first_year_of_GW+1)), ifelse(file_name > (last_year_of_GW-1 + 2*(last_year_of_GW - first_year_of_GW+1)) & file_name <= (last_year_of_GW-1 + 3*(last_year_of_GW - first_year_of_GW+1)), file_name -
(-1 + 3*(last_year_of_GW - first_year_of_GW+1)), file_name - (-1 + 4*(last_year_of_GW - first_year_of_GW+1)))))))]
year_dt = year_dt$file_name
lookup_table_all_years_2 = lookup_table_all_years_2[SDAT == year_dt]
lookup_table_all_years_2[, `:=`(Well_ID, NULL)]
setkey(lookup_table_all_years_2, WSTA, SOIL_ID, Well_capacity)
setkey(lookup_table_well_2, WSTA, SOIL_ID, Well_capacity)
setkey(well_capacity_data, weather_station, Soil_Type, Well_capacity)
lookup_table_all_years_2 = lookup_table_all_years_2[well_capacity_data]
lookup_table_well_2 = lookup_table_well_2[well_capacity_data]
lookup_table_all_years_2[, `:=`(irr_tot_acres, sum(irrigation)),
by = "Well_ID"]
lookup_table_all_years_2[, `:=`(profit_Well_ID, sum(profit)),
by = "Well_ID"]
lookup_table_all_years_2 = unique(lookup_table_all_years_2,
by = "Well_ID")
lookup_table_all_years_2[, `:=`(output_rate_acin_day, irr_tot_acres/irrigation_season_days)]
econ_output = lookup_table_all_years_2[, .(Well_ID, Well_capacity,
tot_acres, irr_tot_acres, profit_Well_ID, output_rate_acin_day)]
econ_output[, `:=`(row, 1:.N)]
econ_output[, `:=`(tax, tax_amount)]
econ_output_in = fread("./Econ_output/KS_DSSAT_output.csv")
econ_output = rbind(econ_output_in, econ_output)
# econ_output[is.na(output_rate_acin_day), `:=`(output_rate_acin_day, 0)]
# well_capacity_data = lookup_table_all_years_2[, .(Well_ID, output_rate_acin_day)]
econ_output[, `:=`(output_rate_acin_day, 0)]
well_capacity_data = lookup_table_all_years_2[, .(Well_ID, output_rate_acin_day=0)]
print(well_capacity_data)
write.csv(econ_output, econ_output_file, row.names = FALSE)
write.csv(well_capacity_data, well_capacity_file_year, row.names = FALSE)
}
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