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https://git.gfz-potsdam.de/naaice/poet.git
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c5a991c4c9
feat: implement NamedVector based on Rcpp::NumericVector feat: remove hard coded checks and substitute by R hook functions, defined in the input script refactor: modify API of DHT_Wrapper/InterpolationModule to expect a work package structure, where input/output values are stored as 2D vectors test: add tests for NamedVector test: extend tests for Field
191 lines
4.2 KiB
R
191 lines
4.2 KiB
R
## Time-stamp: "Last modified 2023-08-16 17:04:42 mluebke"
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database <- normalizePath("../share/poet/bench/dolo/phreeqc_kin.dat")
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input_script <- normalizePath("../share/poet/bench/dolo/dolo_inner.pqi")
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#################################################################
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## Section 1 ##
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## Grid initialization ##
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#################################################################
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n <- 100
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m <- 100
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types <- c("scratch", "phreeqc", "rds")
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init_cell <- list(
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"H" = 110.683,
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"O" = 55.3413,
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"Charge" = -5.0822e-19,
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"C(4)" = 1.2279E-4,
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"Ca" = 1.2279E-4,
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"Cl" = 0,
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"Mg" = 0,
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"O2g" = 0.499957,
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"Calcite" = 2.07e-4,
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"Dolomite" = 0
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)
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grid <- list(
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n_cells = c(n, m),
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s_cells = c(1, 1),
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type = types[1]
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)
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##################################################################
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## Section 2 ##
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## Diffusion parameters and boundary conditions ##
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##################################################################
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## initial conditions
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init_diffu <- list(
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"H" = 110.683,
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"O" = 55.3413,
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"Charge" = -5.0822e-19,
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"C(4)" = 1.2279E-4,
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"Ca" = 1.2279E-4,
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"Cl" = 0,
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"Mg" = 0
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)
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## diffusion coefficients
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alpha_diffu <- c(
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"H" = 1E-6,
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"O" = 1E-6,
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"Charge" = 1E-6,
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"C(4)" = 1E-6,
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"Ca" = 1E-6,
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"Cl" = 1E-6,
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"Mg" = 1E-6
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)
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## list of boundary conditions/inner nodes
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vecinj_diffu <- list(
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list(
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"H" = 110.683,
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"O" = 55.3413,
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"Charge" = 1.90431e-16,
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"C(4)" = 0,
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"Ca" = 0,
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"Cl" = 0.002,
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"Mg" = 0.001
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),
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list(
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"H" = 110.683,
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"O" = 55.3413,
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"Charge" = 1.90431e-16,
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"C(4)" = 0,
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"Ca" = 0.0,
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"Cl" = 0.004,
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"Mg" = 0.002
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)
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)
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vecinj_inner <- list(
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l1 = c(1, 20, 20),
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l2 = c(2, 80, 80),
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l3 = c(2, 60, 80)
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)
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boundary <- list(
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# "N" = c(1, rep(0, n-1)),
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"N" = rep(0, n),
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"E" = rep(0, n),
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"S" = rep(0, n),
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"W" = rep(0, n)
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)
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diffu_list <- names(alpha_diffu)
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vecinj <- do.call(rbind.data.frame, vecinj_diffu)
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names(vecinj) <- names(init_diffu)
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diffusion <- list(
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init = as.data.frame(init_diffu, check.names = FALSE),
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vecinj = vecinj,
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vecinj_inner = vecinj_inner,
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vecinj_index = boundary,
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alpha = alpha_diffu
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)
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#################################################################
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## Section 3 ##
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## Chemistry module (Phreeqc) ##
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#################################################################
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## # Needed when using DHT
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dht_species <- c(
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"H" = 10,
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"O" = 10,
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"Charge" = 3,
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"C(4)" = 5,
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"Ca" = 5,
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"Cl" = 5,
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"Mg" = 5,
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"Calcite" = 5,
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"Dolomite" = 5
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)
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check_sign_cal_dol_dht <- function(old, new) {
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if ((old["Calcite"] == 0) != (new["Calcite"] == 0)) {
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return(TRUE)
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}
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if ((old["Dolomite"] == 0) != (new["Dolomite"] == 0)) {
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return(TRUE)
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}
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return(FALSE)
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}
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fuzz_input_dht_keys <- function(input) {
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return(input[names(dht_species)])
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}
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check_sign_cal_dol_interp <- function(to_interp, data_set) {
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data_set <- as.data.frame(do.call(rbind, data_set), check.names = FALSE, optional = TRUE)
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names(data_set) <- names(dht_species)
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cal <- (data_set$Calcite == 0) == (to_interp["Calcite"] == 0)
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dol <- (data_set$Dolomite == 0) == (to_interp["Dolomite"] == 0)
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cal_dol_same_sig <- cal == dol
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return(rev(which(!cal_dol_same_sig)))
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}
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check_neg_cal_dol <- function(result) {
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neg_sign <- (result["Calcite"] <- 0) || (result["Dolomite"] < 0)
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return(any(neg_sign))
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}
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hooks <- list(
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dht_fill = check_sign_cal_dol_dht,
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dht_fuzz = fuzz_input_dht_keys,
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interp_pre_func = check_sign_cal_dol_interp,
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interp_post_func = check_neg_cal_dol
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)
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chemistry <- list(
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database = database,
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input_script = input_script,
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dht_species = dht_species,
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hooks = hooks
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)
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#################################################################
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## Section 4 ##
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## Putting all those things together ##
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#################################################################
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iterations <- 10
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dt <- 200
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setup <- list(
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grid = grid,
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diffusion = diffusion,
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chemistry = chemistry,
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iterations = iterations,
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timesteps = rep(dt, iterations),
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store_result = TRUE
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)
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