Workflow Eisenstadt 2005
Source:vignettes/workflow_eisenstadt-2005.Rmd
workflow_eisenstadt-2005.RmdInput data
The HDF5 model template (base.h5) ships with the package
under inst/extdata/models/eisenstadt-2005/ and is produced
with the Tandler “Regenwasserbewirtschaftung” calculation engine; the
engine itself is downloaded from the
KWB-R/kwb.raindrop.binaries GitHub Release on demand via
kwb.raindrop::download_engine().
Define Paths and Scenarios
library(kwb.raindrop)
path_list <- list(
modelname = "Eisenstadt_2005",
root_path = file.path(tempdir(), "raindrop_eisenstadt_2005"),
dir_input = "<root_path>/models/<modelname>/input",
dir_output = "<root_path>/models/<modelname>/output",
dir_target_output = "<dir_output>/<dir_target>",
file_errors_hdf5 = "Fehlerprotokoll.h5",
file_results_hdf5_element = "Mulde_Rigole.h5",
file_results_hdf5_flaeche = "Dach.h5",
file_results_hdf5_verschaltungen = "<dir_target>_Verschaltungen.h5",
file_results_txt = "Mulde_Rigole_RAINDROP.txt",
file_results_txt_multilayer = "Mulde_Rigole_RAINDROP_multi_layer.txt",
file_target = "<dir_target>.h5",
path_base = system.file("extdata/models/eisenstadt-2005/base.h5", package = "kwb.raindrop"),
path_exe = if (is_windows && !is_ghactions) kwb.raindrop::download_engine() else NA_character_,
path_errors_hdf5 = "<dir_target_output>/<file_errors_hdf5>",
path_results_hdf5_element = "<dir_target_output>/<file_results_hdf5_element>",
path_results_hdf5_flaeche = "<dir_target_output>/<file_results_hdf5_flaeche>",
path_results_hdf5_verschaltungen = "<dir_target_output>/<file_results_hdf5_verschaltungen>",
path_results_txt = "<dir_target_output>/<file_results_txt>",
path_results_txt_multilayer = "<dir_target_output>/<file_results_txt_multilayer>",
path_target_input = "<dir_input>/<file_target>"
)
parameters <- tibble::tibble(
para_nama_short = c(
"connected_area",
"mulde_area",
"mulde_height",
"filter_hydraulicconductivity",
# "filter_height",
"storage_height"#,
# "bottom_hydraulicconductivity"
),
para_name_long = c(
"/Massnahmenelemente/Dach/Allgemein/Flaeche",
"/Massnahmenelemente/Mulde_Rigole/Allgemein/Flaeche",
"/Massnahmenelemente/Mulde_Rigole/Eigenschaften_Oberflaeche/Ueberlaufhoehe",
"Bodenarten/Bodenfilter/Ks_HydraulicConductivity",
#"/Massnahmenelemente/Mulde_Rigole/Bodenschichtung/Schichtdicken",
"/Massnahmenelemente/Mulde_Rigole/Bodenschichtung/Schichtdicken"#,
# "/Massnahmenelemente/Mulde_Rigole/Allgemein/Endversickerungsrate"
),
index = c(1L,
1L,
1L,
1L,
2)
)
DT::datatable(parameters,
filter = "top",
options = list(pageLength = 25,
autoWidth = TRUE))
connected_area <- 1000
mulde_area <- c(25, 50, 75, 100, 125, 150, 175, 200)
mulde_height <- c(100, 200, 300)
filter_hydraulicconductivity <- c(36, 180, 360)
filter_height <- 300
storage_height <- c(100, 500, 1000)
rain_factor <- 1
bottom_hydraulicconductivity <- 12 #c(1,5,10,20,45,90,180,270,360,1860,3600)
# Alle Kombinationen erzeugen
param_grid_all_combinations <- expand.grid(
connected_area = connected_area,
mulde_area = mulde_area,
mulde_height = mulde_height,
filter_hydraulicconductivity = filter_hydraulicconductivity,
filter_height = filter_height,
storage_height = storage_height,
bottom_hydraulicconductivity = bottom_hydraulicconductivity,
rain_factor = rain_factor
)
param_grid_all_combinations <- param_grid_all_combinations %>%
dplyr::bind_cols(tibble::tibble(scenario_name = sprintf("s%05d",
seq_len(nrow(param_grid_all_combinations)))))
ref_scenario <- param_grid_all_combinations %>%
dplyr::filter(connected_area == min(unique(param_grid_all_combinations$connected_area)),
mulde_area == min(unique(param_grid_all_combinations$mulde_area)),
filter_height == min(filter_height),
filter_hydraulicconductivity == min(param_grid_all_combinations$filter_hydraulicconductivity),
bottom_hydraulicconductivity == min(unique(param_grid_all_combinations$bottom_hydraulicconductivity)),
mulde_height == min(param_grid_all_combinations$mulde_height),
storage_height == min(param_grid_all_combinations$storage_height)) %>%
dplyr::pull(scenario_name)
stopifnot(length(ref_scenario)==1)
scenarios_with_single_parameter_variation <- kwb.raindrop::find_single_param_variations(
data = param_grid_all_combinations,
ref_scenario = ref_scenario
) %>%
dplyr::pull(scenario_name) %>% unique()
#> Rows with exactly one differing parameter: 13 of 216
#> Single-parameter variations per parameter: connected_area=0, mulde_area=7, mulde_height=2, filter_hydraulicconductivity=2, filter_height=0, storage_height=2, bottom_hydraulicconductivity=0, rain_factor=0
# param_grid <- param_grid_all_combinations %>%
# dplyr::filter(scenario_name %in% scenarios_with_single_parameter_variation)
param_grid <- param_grid_all_combinations
DT::datatable(param_grid,
filter = "top",
options = list(pageLength = 25,
autoWidth = TRUE))
htmlwidgets::saveWidget(DT::datatable(parameters,
filter = "top",
options = list(pageLength = 25,
autoWidth = TRUE)), "parameters.html")
htmlwidgets::saveWidget(DT::datatable(param_grid,
filter = "top",
options = list(pageLength = 25,
autoWidth = TRUE)), "param_grid.html")
psi_s_mm <- function(kf_mmh) (3.237 * (kf_mmh/25.4)^(-0.328)) * 25.4
paths <- kwb.utils::resolve(path_list)Run Model
run_one <- function(i,
timestep_hours,
debug = FALSE,
...) {
param_grid_tmp <- param_grid[i, ]
paths <- kwb.utils::resolve(path_list,
dir_target = param_grid_tmp$scenario_name)
fs::dir_create(paths$dir_input, recurse = TRUE)
fs::dir_create(paths$dir_output, recurse = TRUE)
fs::dir_create(paths$dir_target_output, recurse = TRUE)
fs::file_copy(path = paths$path_base,
new_path = paths$path_target_input,
overwrite = TRUE)
h5 <- hdf5r::H5File$new(paths$path_target_input, mode = "a")
new_path <- stringr::str_c(normalizePath(fs::path_abs(paths$dir_target_output)),
"\\")
vals <- kwb.raindrop::h5_read_values(h5)
vals$`//Berechnungsparameter/Ergebnispfad` <- new_path
vals$`//Berechnungsparameter/Zeitschritt_Infiltration` <- timestep_hours
vals$`//Berechnungsparameter/Zeitschritt_ET` <- timestep_hours
vals$`//Berechnungsparameter/Zeitschritt_Verschaltungen` <- timestep_hours
vals$`//Berechnungsparameter/R-Plots` <- 0
vals$`//Berechnungsparameter/Ausgabemodus` <- "Optimierung"
vals$`//Berechnungsparameter/Evapotranspiration_aktiv` <- 1
vals$`//Massnahmenelemente/Dach/Berechnungsparameter/Evapotranspiration_aktiv` <- 1
vals$`//Massnahmenelemente/Dach/Allgemein/Flaeche` <- param_grid_tmp$connected_area
vals$`//Massnahmenelemente/Mulde_Rigole/Berechnungsparameter/Evapotranspiration_aktiv` <- 1
vals$`//Massnahmenelemente/Mulde_Rigole/Allgemein/Regen-Skalierungsfaktor` <- 1
vals$`//Massnahmenelemente/Mulde_Rigole/Allgemein/Flaeche` <- param_grid_tmp$mulde_area
vals$`//Massnahmenelemente/Mulde_Rigole/Eigenschaften_Oberflaeche/Ueberlaufhoehe` <- param_grid_tmp$mulde_height
vals$`//Massnahmenelemente/Mulde_Rigole/Bodenschichtung/Startwerte_theta_ActualSoilMoisture` <- c(0.3, 0)
vals$`//Massnahmenelemente/Mulde_Rigole/Bodenschichtung/Schichtdicken` <- c(param_grid_tmp$filter_height,
param_grid_tmp$storage_height)
vals$`//Massnahmenelemente/Mulde_Rigole/Allgemein/Endversickerungsrate` <- param_grid_tmp$bottom_hydraulicconductivity
vals$`//Bodenarten/Bodenfilter/Ks_HydraulicConductivity` <- param_grid_tmp$filter_hydraulicconductivity
vals$`//Bodenarten/Bodenfilter/Psi_Saugspannung_CapillarySuction` <- psi_s_mm(param_grid_tmp$filter_hydraulicconductivity)
# Timeseries (2×N) als tibble?
if (is.data.frame(vals[["//Kurven/Regen"]])) {
vals[["//Kurven/Regen"]]$value <- vals[["//Kurven/Regen"]]$value * param_grid_tmp$rain_factor
}
kwb.raindrop::h5_write_values(h5, vals, resize = TRUE,
scalar_strategy = "error",
verbose = FALSE)
h5$close_all()
kwb.raindrop::run_model(path_exe = paths$path_exe,
path_input = paths$path_target_input,
debug = debug)
invisible(NULL)
}
n_cores <- parallel::detectCores()
system.time(expr = {
kwb.raindrop::run_scenarios(indices = seq_len(nrow(param_grid)),
run_one_scenario = run_one,
timestep_hours = 0.1,
debug = FALSE,
parallel = TRUE,
workers = n_cores,
show_progress = TRUE
)
}
)
### Read results for first run
if(FALSE) {
paths <- kwb.utils::resolve(path_list,
dir_target = sprintf("s%05d", i = 1))
#simulation_names <- basename(fs::dir_ls(paths$dir_output))
simulation_names <- param_grid$scenario_name
debug <- TRUE
errors_df <- kwb.raindrop::read_raindrop_errors(simulation_names, path_list)
x <- tidyr::unnest(errors_df, errors)
x$Fehlerbeschreibung
}Analyse Results
system.time(
simulation_results <- kwb.raindrop::get_simulation_results_optim_parallel(
paths = paths,
path_list = path_list,
simulation_names = param_grid$scenario_name,
debug = FALSE)
)
system.time(
simulation_results_optimisation <- kwb.raindrop::add_overflow_events_and_waterbalance(
simulation_results = simulation_results,
event_separation_hours = 4
)
)
simulation_results_optimisation <- param_grid %>%
dplyr::left_join(simulation_results_optimisation,
by = c("scenario_name" = "s_name")) %>%
dplyr::relocate(scenario_name, .before = connected_area)
readr::write_csv(simulation_results_optimisation,
file = sprintf("simulation_results_optimisation_%s.csv",
paths$modelname)
)
htmlwidgets::saveWidget(DT::datatable(simulation_results_optimisation,
filter = "top",
options = list(pageLength = 25,
autoWidth = TRUE)),
file = sprintf("simulation_results_optimisation_%s.html",
paths$modelname),
title = sprintf("RAINDROP - '%s': Solution Space",
paths$modelname)
)
### Plot results
params <- c(
#"connected_area",
"mulde_area",
"mulde_height",
"filter_hydraulicconductivity",
#"filter_height",
"storage_height"
#"bottom_hydraulicconductivity",
#"rain_factor"
)
lang <- "de"
max_n_overflows <- 1
pdff <- sprintf("simulation_results_optimisation_%s_main-effects.pdf",
paths$modelname)
gg <- kwb.raindrop::plot_main_effects(
df = simulation_results_optimisation,
y = "n_overflows",
params = params,
lang = lang,
ylim_lower = 0
)
grDevices::pdf(pdff, width = 9, height = 3, onefile = TRUE)
print(gg)
dev.off()
#kwb.utils::finishAndShowPdf(pdff)
pdff <- sprintf("simulation_results_optimisation_%s_design-space_mulde-area_vs_mulde-height_single.pdf",
paths$modelname)
# kwb.utils::preparePdf(pdfFile = pdff)
# on.exit(dev.off(), add = TRUE)
grDevices::pdf(pdff, width = 9, height = 4, onefile = TRUE)
p <- kwb.raindrop::plot_valid_design_space(
param_grid = param_grid,
sim_results = simulation_results_optimisation,
x = "mulde_area",
y = "mulde_height",
valid_max = max_n_overflows,
jitter = TRUE,
alpha_mode = "duplicates",
alpha_min = 0.25,
alpha_max = 1,
drop_overflow_gt_valid_max = FALSE,
keep_param_grid_limits = TRUE,
lang = lang,
subtitle = ""
)
suppressWarnings(print(p))
dev.off()
# --- 2) Interaktiv als HTML: nach dem PDF
plotly_gg <- plotly::ggplotly(gg)
htmlwidgets::saveWidget(
widget = plotly_gg,
file = sprintf("simulation_results_optimisation_%s_main-effects.html",
paths$modelname),
selfcontained = TRUE,
title = sprintf("RAINDROP - '%s': Main Effects",
paths$modelname)
)
pdff <- sprintf("simulation_results_optimisation_%s_design-space_mulde-area_vs_parameters.pdf",
paths$modelname)
kwb.utils::preparePdf(pdfFile = pdff)
for (y in c("mulde_height", "filter_hydraulicconductivity", "storage_height")) {
p <- kwb.raindrop::plot_valid_design_space(
param_grid = param_grid,
sim_results = simulation_results_optimisation,
x = "mulde_area",
y = y,
valid_max = max_n_overflows,
jitter = TRUE,
alpha_mode = "duplicates",
alpha_min = 0.25,
alpha_max = 1,
drop_overflow_gt_valid_max = TRUE,
keep_param_grid_limits = TRUE
)
# interaktiv als HTML
plotly_p <- suppressWarnings(plotly::ggplotly(p, tooltip = "text"))
htmlwidgets::saveWidget(
widget = plotly_p,
file = sprintf("simulation_results_optimisation_%s_design-space_mulde-area_vs_%s.html",
paths$modelname,
y),
selfcontained = TRUE,
title = sprintf("'%s' - Design Space: mulde_area vs. %s",
paths$modelname,
y)
)
# statisch ins PDF (WICHTIG!)
suppressWarnings(print(p))
}
dev.off()
#kwb.utils::finishAndShowPdf(pdff)
pdff <- sprintf("simulation_results_optimisation_%s_water-balance.pdf",
paths$modelname)
kwb.utils::preparePdf(pdfFile = pdff)
p <- kwb.raindrop::plot_wb_tradeoff_overflows(
simulation_results_optimisation = simulation_results_optimisation,
x = max_n_overflows,
param_grid = param_grid,
filter_n_gtx = TRUE,
use_jitter = TRUE
)
# interaktiv als HTML
plotly_p <- suppressWarnings(plotly::ggplotly(p, tooltip = "text"))
htmlwidgets::saveWidget(
widget = plotly_p,
file = sprintf("simulation_results_optimisation_%s_water-balance.html",
paths$modelname),
selfcontained = TRUE,
title = sprintf("'%s' - Water balance vs overflows",
paths$modelname)
)
# statisch ins PDF (WICHTIG!)
suppressWarnings(print(p))
dev.off()
#kwb.utils::finishAndShowPdf(pdff)