diff --git a/R/.virtual_storage.R b/R/.virtual_storage.R index 6e10693..0b87316 100644 --- a/R/.virtual_storage.R +++ b/R/.virtual_storage.R @@ -1,4 +1,5 @@ remotes::install_github("kwb-r/kwb.hydrus1d@dev") +remotes::install_github("kwb-r/flextreat.hydrus1d@dev") paths_list <- list( #extdata = system.file("extdata", package = "flextreat.hydrus1d"), @@ -7,7 +8,7 @@ paths_list <- list( #root_local = "C:/kwb/projects/flextreat/hydrus/Szenarien_10day", #root_local = system.file("extdata/model", package = "flextreat.hydrus1d"), exe_dir = "", - model_name = "test_fracht1", #"1a2a_BTA_korr_test_40d", + model_name = "1a2a_tracer_3140", #"1a2a_BTA_korr_test_40d", model_gui_path = "/.h1d", modelvs_gui_path = "/_vs.h1d", model_dir = "/", @@ -30,15 +31,73 @@ paths_list <- list( paths <- kwb.utils::resolve(paths_list) +paths$solute fs::dir_copy(paths$model_dir, paths$model_dir_vs, overwrite = TRUE) fs::file_copy(paths$model_gui_path, paths$modelvs_gui_path, overwrite = TRUE) +library(flextreat.hydrus1d) +atm <- flextreat.hydrus1d::prepare_atmosphere_data() +atm_selected <- flextreat.hydrus1d::select_hydrologic_years(atm) +# atm_prep <- flextreat.hydrus1d::prepare_atmosphere(atm = atm_selected, +# conc_irrig_clearwater = c(6738, +# 875, +# 4291, +# 2884, +# 1062), +# conc_irrig_groundwater = 0, +# conc_rain = 0 +# ) +atm <- atm_selected +days_monthy <- lubridate::days_in_month(seq.Date(from = min(atm$date), + to = max(atm$date), + by = "month")) + +days_total <- cumsum(days_monthy) + +indeces <- 31:40 + +c_tops <- lapply(indeces, function(i) { + + x <- rep(0, nrow(atm)) + if(i == 1) { + x_min = 1 + } else { + x_min = days_total[i - 1] + 1 + } + x[x_min:days_total[i]] <- rep(100, days_monthy[i]) + + + tib <- data.frame(x) + colnames(tib) <- if(i == indeces[1]) { + "cTop"} else { + sprintf("cTop%d", which(indeces %in% i)) + } + + tib +}) %>% dplyr::bind_cols() + + + + +atm_prep <- flextreat.hydrus1d::prepare_atmosphere(atm = atm_selected, + conc_irrig_clearwater = c_tops, + conc_irrig_groundwater = 0, + conc_rain = 0 + ) + + +writeLines(kwb.hydrus1d::write_atmosphere(atm = atm_prep), + paths$atmosphere) + kwb.hydrus1d::run_model(model_path = paths$model_dir) atmos <- kwb.hydrus1d::read_atmosph(paths$atmosphere) + +atmos$data[names(c_tops)] <- c_tops + atm_default <- atmos tlevel <- kwb.hydrus1d::read_tlevel(paths$t_level) @@ -49,8 +108,7 @@ vs_atm <- flextreat.hydrus1d::recalculate_ctop_with_virtualstorage( crit_v_top = - 0.05 ) -atmos$data$cTop <- vs_atm$cTop - +atmos$data[names(vs_atm$df)] <- vs_atm$df writeLines(kwb.hydrus1d::write_atmosphere(atm = atmos$data), paths$atmosphere_vs) @@ -58,15 +116,20 @@ writeLines(kwb.hydrus1d::write_atmosphere(atm = atmos$data), kwb.hydrus1d::run_model(model_path = paths$model_dir_vs) -solute <- kwb.hydrus1d::read_solute(paths$solute) %>% +solute <- kwb.hydrus1d::read_solute(paths$solute_vs) %>% dplyr::mutate(difftime = c(0,diff(time))) +plot(solute$time, solute$c_top) +points(solute$c_bot, col = "red") (1 - max(solute$sum_cv_top)/sum(atmos$data$Prec*atmos$data$cTop)) * 100 -solute <- kwb.hydrus1d::read_solute(paths$solute_vs) %>% + +paths$solute_vs2 <- "C:/kwb/projects/flextreat/3_1_4_Prognosemodell/Vivian/Rohdaten/H1D/1a2a_tracer_vs/solute2.out" + +solute <- kwb.hydrus1d::read_solute(paths$solute) %>% dplyr::mutate(difftime = c(0,diff(time))) -(1 - max(solute$sum_cv_top)/sum(atmos$data$Prec*atmos$data$cTop)) * 100 +(1 - max(solute$sum_cv_top)/sum(atmos$data$Prec*atmos$data$cTop2)) * 100 sum(atmos$data$Prec) @@ -139,6 +202,118 @@ p <- obsnode %>% plotly::ggplotly(p) -ssolute_aggr_date +solute_aggr_date View(tlevel_aggr_date) View(solute_aggr_date) + + +t_level <- kwb.hydrus1d::read_tlevel(paths$t_level) +t_level + +## t_level aggregate +tlevel_aggr_date <- flextreat.hydrus1d::aggregate_tlevel(t_level) +tlevel_aggr_yearmonth <- flextreat.hydrus1d::aggregate_tlevel(t_level, + col_aggr = "yearmonth") +tlevel_aggr_year_hydrologic <- flextreat.hydrus1d::aggregate_tlevel(t_level, + col_aggr = "year_hydrologic") %>% + dplyr::filter(.data$diff_time >= 364) ### filter out as only may-october + + +wb_date_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_date) +wb_yearmonth_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_yearmonth) +wb_yearhydrologic_plot <- flextreat.hydrus1d::plot_waterbalance(tlevel_aggr_year_hydrologic) + +wb_date_plot +wb_yearmonth_plot +wb_yearhydrologic_plot + + +solute$time[min(which(solute$sum_cv_top == max(solute$sum_cv_top)))] + +paths$model_dir_vs + +solute_files <- fs::dir_ls(paths$exe_dir, + regexp = "1a2a_tracer.*_vs/solute\\d\\d?.out", + recurse = TRUE) + + + +sol_travel <- lapply(solute_files, function(path) { + + solute <- kwb.hydrus1d::read_solute(path) + +tibble::tibble( + model_name = basename(dirname(path)), + solute_name = kwb.utils::removeExtension(basename(path)), + p01_top = mean(solute$time[which(max(solute$sum_cv_top)*0.005 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.015)], na.rm = TRUE), + p01_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.015 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.005)], na.rm = TRUE), + p01_diff = p01_bot - p01_top, + p05_top = mean(solute$time[which(max(solute$sum_cv_top)*0.045 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.055)], na.rm = TRUE), + p05_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.055 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.045)], na.rm = TRUE), + p05_diff = p05_bot - p05_top, + p10_top = mean(solute$time[which(max(solute$sum_cv_top)*0.09 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.11)], na.rm = TRUE), + p10_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.11 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.09)], na.rm = TRUE), + p10_diff = p10_bot - p10_top, + p25_top = mean(solute$time[which(max(solute$sum_cv_top)*0.24 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.26)], na.rm = TRUE), + p25_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.26 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.24)], na.rm = TRUE), + p25_diff = p25_bot - p25_top, + p50_top = mean(solute$time[which(max(solute$sum_cv_top)*0.48 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.52)], na.rm = TRUE), + p50_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.52 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.48)], na.rm = TRUE), + p50_diff = p50_bot - p50_top, + p75_top = mean(solute$time[which(max(solute$sum_cv_top)*0.73 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.77)], na.rm = TRUE), + p75_bot =mean(solute$time[which(- max(solute$sum_cv_top)*0.77 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.73)], na.rm = TRUE), + p75_diff = p75_bot - p75_top, + p90_top = mean(solute$time[which(max(solute$sum_cv_top)*0.88 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.92)], na.rm = TRUE), + p90_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.92 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.88)], na.rm = TRUE), + p90_diff = p90_bot - p90_top, + p95_top = mean(solute$time[which(max(solute$sum_cv_top)*0.935 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*0.965)], na.rm = TRUE), + p95_bot = mean(solute$time[which(- max(solute$sum_cv_top)*0.965 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.935)], na.rm = TRUE), + p95_diff = p95_bot - p95_top, + p99_top = mean(solute$time[which(max(solute$sum_cv_top)*0.98 <= solute$sum_cv_top & solute$sum_cv_top <= max(solute$sum_cv_top)*1)], na.rm = TRUE), + p99_bot = mean(solute$time[which(- max(solute$sum_cv_top)*1 >= solute$sum_cv_bot & solute$sum_cv_bot <= - max(solute$sum_cv_top)*0.98)], na.rm = TRUE), + p99_diff = p99_bot - p99_top + ) +}) %>% dplyr::bind_rows() + + +sol_travel <- sol_travel %>% + dplyr::mutate(solute_name = stringr::str_remove(solute_name, "solute") %>% as.integer()) %>% + dplyr::rename(solute_id = solute_name) %>% + dplyr::arrange(model_name, solute_id) + +lookup_model <- sol_travel %>% + dplyr::group_by(model_name) %>% + dplyr::summarise(n = dplyr::n()) %>% + dplyr::mutate(n_cum = cumsum(n), + n_cum_fix = n_cum - dplyr::first(n)) + + +library(lubridate) + +get_last_day_of_months <- function(ids) { + sapply(ids, function(id) { + start_date <- lubridate::ymd("2017-05-01") + month_date <- start_date %m+% months(id - 1) + last_day <- lubridate::ceiling_date(month_date, "month") - days(1) + + return(last_day) +}) %>% as.Date() +} + + +sol_travel_tot <- lookup_model %>% + dplyr::left_join(sol_travel) %>% + dplyr::mutate(month_id = solute_id + n_cum_fix, + date = get_last_day_of_months(month_id)) + + +p1 <- sol_travel_tot %>% + dplyr::select(date, tidyselect::ends_with("diff")) %>% + tidyr::pivot_longer(- date) %>% + dplyr::mutate(name = stringr::str_remove(name, "_diff")) %>% + ggplot2::ggplot(ggplot2::aes(x = date, y = value, col = name)) + + ggplot2::geom_point() + + ggplot2::geom_line() + + ggplot2::theme_bw() + +plotly::ggplotly(p1)