diff --git a/.Rbuildignore b/.Rbuildignore index f0e55e2..154dd6e 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -26,3 +26,4 @@ renv\_instructions\.Rmd ^\.Renviron$ ^Dockerfile$ ^\.dockerignore$ +^bdd_connect\.sh$ diff --git a/.gitignore b/.gitignore index 6dc5c95..3641738 100644 --- a/.gitignore +++ b/.gitignore @@ -18,3 +18,4 @@ deliverables data-raw/translation.Rmd dev/translation.html dev/data-docker/ +bdd_connect.sh diff --git a/NAMESPACE b/NAMESPACE index 94090d8..3f0aaa1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -17,6 +17,7 @@ export(nit_feature_species_basin) export(plot_hsi) export(plot_hsi_nit) export(plot_nit) +export(runSimulation) export(run_app) export(tm_draw) import(Matrix) diff --git a/R/get_data_simulation.R b/R/get_data_simulation.R index 0613e14..4013bba 100644 --- a/R/get_data_simulation.R +++ b/R/get_data_simulation.R @@ -39,7 +39,9 @@ ORDER BY departure, distance")) # HyDiaD parameters ---- hydiad_parameter <- tbl(conn_eurodiad, sql(" SELECT s.latin_name AS \"latin_name_s\", s.local_name AS \"Lname_s\", h.* FROM diadesatlas.hydiadparameter h -INNER JOIN diadesatlas.species s USING (species_id)")) +INNER JOIN diadesatlas.species s USING (species_id)")) %>% + rename(latin_name = latin_name_s, + Lname = Lname_s) # HSI abd Nmax ---- # a query to load HSI for only 8.5 scenario (which do not change between simulations) @@ -54,8 +56,8 @@ WHERE year > 0 AND climatic_scenario = 'rcp85'" # compute the maximum abundance (#) according to hsi, # maximal density (Dmax) , catchment area (ccm_area) inner_join(hydiad_parameter %>% - select("latin_name_s", "Dmax"), - by = c('latin_name' = "latin_name_s")) %>% + select("latin_name", "Dmax"), + by = c('latin_name' = "latin_name")) %>% mutate(Nmax = hsi * Dmax * surface_area) %>% select(-c(surface_area, Dmax)) @@ -86,8 +88,8 @@ WHERE climatic_scenario = 'rcp85'" filter(year == 0) %>% arrange(latin_name, basin_id, climatic_model_code) %>% inner_join(hydiad_parameter %>% - select(latin_name_s, Dmax), - by = c('latin_name' = "latin_name_s")) %>% + select(latin_name, Dmax), + by = c('latin_name' = "latin_name")) %>% mutate(Nmax = hsi * Dmax * surface_area) %>% select(-c(surface_area, Dmax)) diff --git a/R/globals.R b/R/globals.R index 8e88562..7f64668 100644 --- a/R/globals.R +++ b/R/globals.R @@ -31,7 +31,8 @@ globalVariables(unique(c( # generate_datasets: "country", # get_data_simulation: - "climatic_model_code", "Dmax", "hsi", "latin_name_s", "surface_area", + "climatic_model_code", "Dmax", "hsi", + "latin_name_s", "surface_area", "Lname_s", # mod_fourth_server : : "X2", "X3", # runSimulation: diff --git a/R/mod_c_third_fct_query_and_plot.R b/R/mod_c_third_fct_query_and_plot.R index ff54357..49dfbb0 100644 --- a/R/mod_c_third_fct_query_and_plot.R +++ b/R/mod_c_third_fct_query_and_plot.R @@ -185,7 +185,8 @@ plot_nit <- function(model_res_filtered, ), alpha = 0.3) + geom_line( aes(y = nit_movingavg, - colour = .data[[with_colour_source]] + colour = .data[[with_colour_source]], + linetype = .data[[with_colour_source]] ) ) + scale_colour_viridis_d() + diff --git a/R/mod_d_fourth_fct.R b/R/mod_d_fourth_fct.R index 4871306..37ae2bb 100644 --- a/R/mod_d_fourth_fct.R +++ b/R/mod_d_fourth_fct.R @@ -29,20 +29,23 @@ multi_sliders <- function(ns, countries, prefix = "period1") { #' Run simulation #' #' @param selected_latin_name Species latin name -#' @param hydiad_parameter -#' @param anthropogenic_mortality -#' @param catchment_surface -#' @param data_hsi_nmax -#' @param data_ni0 -#' @param outlet_distance -#' @param verbose +#' @param hydiad_parameter Hydiad model parameters +#' @param anthropogenic_mortality table of anthropogenic mortalities +#' @param catchment_surface Surface of basins +#' @param data_hsi_nmax HSI Nmax values +#' @param data_ni0 ni0 values +#' @param outlet_distance distance from outlet +#' @param scenario Climatic scenario. e.g. "rcp85" +#' @param verbose Logical. #' #' @importFrom tidyr pivot_wider expand_grid #' @importFrom tibble column_to_rownames #' @importFrom utils txtProgressBar setTxtProgressBar #' @importFrom methods as #' @import Matrix -#' @noRd +#' +#' @return List of models outputs +#' @export runSimulation <- function(selected_latin_name, hydiad_parameter, anthropogenic_mortality, @@ -50,6 +53,7 @@ runSimulation <- function(selected_latin_name, data_hsi_nmax, data_ni0, outlet_distance, + scenario = "rcp85", verbose = FALSE) { # if (verbose) tic() @@ -75,7 +79,7 @@ runSimulation <- function(selected_latin_name, ## HyDiaD parameters for the selected species ---- parameter <- hydiad_parameter %>% - filter(latin_name_s == !!selected_latin_name ) %>% + filter(latin_name == !!selected_latin_name ) %>% collect() results[['param']][['hydiad_parameter']] <- parameter @@ -99,11 +103,19 @@ runSimulation <- function(selected_latin_name, # Local matrices ---- ## the spawnerTo that are half active in reproduction (Allee effect) ---- # spawnersTo_50 <- parameter$lambda * parameter$Dmax * catchment_surface$surface_area + # browser() + # spawnersTo_50 <- parameter$lambda * parameter$Dmax * catchment_surface %>% pull(surface_area) - catchment_spawnersTo_50 <- catchment_surface %>% + catchment_spawnersTo_50 <- catchment_surface %>% mutate(spawnersTo_50 = !!parameter$lambda * !!parameter$Dmax * surface_area) + # Be sure to be in correct order + spawnersTo_50 <- catchment_spawnersTo_50 %>% + collect() %>% + arrange(basin_name) %>% + pull(spawnersTo_50) + # spawnersTo_50 <- catchment_spawnersTo_50 %>% collect() %>% arrange(basin_name) %>% pull(spawnersTo_50) ## update Nmax according to anthropogenic mortality eh1 = exp(-h1) ---- @@ -139,6 +151,7 @@ runSimulation <- function(selected_latin_name, bind_rows(anticipation) %>% arrange(basin_id, climatic_model_code, year) + # expect_equal(extendedNit, extendedNit_PML) ## list years in simulation ---- years <- extendedNit %>% @@ -150,10 +163,14 @@ runSimulation <- function(selected_latin_name, # if (verbose) tic() # ------------------------------------------------------------------------------- # - - results[["model"]] <- lapply(models, compute_nmax_eh1, extendedNit = extendedNit) + + # Create dput for tests + # dput(head(extendedNit), file = "tests/testthat/extendedNit_dput") + # + results[["model"]] <- lapply(models, compute_nmax_eh1, extendedNit = extendedNit, scenario = scenario) names(results[["model"]]) <- models + # expect_equal(results[["model"]], resultsPM) # OK # --------------------------------------------------------------------------------------- # ## r * exp(-h2) matrix ---- @@ -170,6 +187,8 @@ runSimulation <- function(selected_latin_name, arrange(basin_name) %>% column_to_rownames('basin_name') %>% as.matrix() + + # expect_equal(eh2, eh2_PML) # OK # replace NA (from the populate and burnin periods) with first-year value eh2[, as.character(min(years[["year"]]):(firstYear - 1))] <- eh2[, as.character(firstYear)] # store r_eh2 in results @@ -211,6 +230,7 @@ runSimulation <- function(selected_latin_name, # transform into a sparse matrix to speed up the calculation as("sparseMatrix") + # expect_equal(results[["other"]][['survivingProportion']], survivingProportion_PML) # OK #Rq: transpose of Besty's matrix (not sure now) # if (verbose) toc() @@ -248,6 +268,8 @@ runSimulation <- function(selected_latin_name, incProgress(prog, detail = paste("Doing part", currentYear)) } } + + results <- computeEffective(currentYear, results = results, generationtime = generationtime, @@ -256,7 +278,8 @@ runSimulation <- function(selected_latin_name, parameter = parameter, cohortWeight = cohortWeight, models = models, - catchment_spawnersTo_50 = catchment_spawnersTo_50) + # catchment_spawnersTo_50 = catchment_spawnersTo_50, + spawnersTo_50 = spawnersTo_50) } cat('\n') # if (verbose) toc() @@ -266,16 +289,18 @@ runSimulation <- function(selected_latin_name, #' Compute Nmax_eh1 matrix and prepare Nit matrix #' -#' @param model model -#' @param extendedNit extendedNit +#' @param model model, e.g. "cnrmcm5" +#' @param scenario Global warming scenario e.g "rcp85" +#' @param extendedNit extendedNit data input #' #' @noRd -compute_nmax_eh1 <- function(model, extendedNit) { +compute_nmax_eh1 <- function(model, scenario, extendedNit) { out <- list() out[['HSI']] <- extendedNit %>% - filter(climatic_model_code == model) %>% + filter(climatic_model_code == model, + climatic_scenario == scenario) %>% pivot_wider(id_cols = basin_name, names_from = year, values_from = hsi) %>% @@ -285,7 +310,8 @@ compute_nmax_eh1 <- function(model, extendedNit) { out[['Nmax_eh1']] <- extendedNit %>% - filter(climatic_model_code == model) %>% + filter(climatic_model_code == model, + climatic_scenario == scenario) %>% pivot_wider(id_cols = basin_name, names_from = year, values_from = Nmax_eh1) %>% @@ -295,7 +321,8 @@ compute_nmax_eh1 <- function(model, extendedNit) { out[['Nit']] <- extendedNit %>% - filter(climatic_model_code == model) %>% + filter(climatic_model_code == model, + climatic_scenario == scenario) %>% pivot_wider(id_cols = basin_name, names_from = year, values_from = Nit) %>% @@ -332,6 +359,7 @@ compute_nmax_eh1 <- function(model, extendedNit) { #' @param results #' @param generationtime #' @param nbCohorts +#' @param spawnersTo_50 #' #' @importFrom tibble rownames_to_column #' @@ -344,7 +372,8 @@ computeEffectiveForModel <- function(model, years, parameter, cohortWeight, - catchment_spawnersTo_50) { + # catchment_spawnersTo_50, + spawnersTo_50) { #cat(model, "\t", currentYear, "\n") currentYear_str <- as.character(currentYear) @@ -378,27 +407,27 @@ computeEffectiveForModel <- function(model, # survival offspring if (parameter$withAllee) { - # Safe Join Spawners to be sure that basins are in the same order - catchment_spawnersTo_50_collect <- catchment_spawnersTo_50 %>% collect() - # Keep in order of `spawnersTo` - survivalOffsprings <- as.data.frame(spawnersTo) %>% - # get back rownames as column for the join - rownames_to_column(var = "basin_name") %>% # count() # 134 - rename(spawnersTo = V1) %>% - # Join - left_join(catchment_spawnersTo_50_collect, by = "basin_name") %>% # %>% count() - # Calculate survivalOffsprings with correct basin - mutate( - survivalOffsprings = parameter$r * - (spawnersTo^2 / (spawnersTo_50^2 + spawnersTo^2)) * spawnersTo) %>% - # Back to matrix for the calculations - column_to_rownames(var = "basin_name") %>% - select(survivalOffsprings) %>% - as.matrix() #%>% + # # Safe Join Spawners to be sure that basins are in the same order + # catchment_spawnersTo_50_collect <- catchment_spawnersTo_50 %>% collect() + # # Keep in order of `spawnersTo` + # survivalOffsprings <- as.data.frame(spawnersTo) %>% + # # get back rownames as column for the join + # rownames_to_column(var = "basin_name") %>% # count() # 134 + # rename(spawnersTo = V1) %>% + # # Join + # left_join(catchment_spawnersTo_50_collect, by = "basin_name") %>% # %>% count() + # # Calculate survivalOffsprings with correct basin + # mutate( + # survivalOffsprings = parameter$r * + # (spawnersTo^2 / (spawnersTo_50^2 + spawnersTo^2)) * spawnersTo) %>% + # # Back to matrix for the calculations + # column_to_rownames(var = "basin_name") %>% + # select(survivalOffsprings) %>% + # as.matrix() #%>% # head() %>% is() # calculate the proportion of active spawners - # survivalOffsprings <- parameter$r * (spawnersTo^2 / (spawnersTo_50^2 + spawnersTo^2)) * spawnersTo + survivalOffsprings <- parameter$r * (spawnersTo^2 / (spawnersTo_50^2 + spawnersTo^2)) * spawnersTo } else { survivalOffsprings <- parameter$r * spawnersTo } @@ -417,6 +446,7 @@ computeEffectiveForModel <- function(model, #' @param results #' @param generationtime #' @param nbCohorts +#' @param spawnersTo_50 #' #' @noRd computeEffective <- function(currentYear, @@ -426,7 +456,8 @@ computeEffective <- function(currentYear, years, parameter, cohortWeight, - catchment_spawnersTo_50, + spawnersTo_50, + # catchment_spawnersTo_50, models) { # loop over models @@ -440,7 +471,9 @@ computeEffective <- function(currentYear, years = years, parameter = parameter, cohortWeight = cohortWeight, - catchment_spawnersTo_50 = catchment_spawnersTo_50) + spawnersTo_50 = spawnersTo_50 + # catchment_spawnersTo_50 = catchment_spawnersTo_50 + ) names(provResults) <- models # store the provisional results in results @@ -551,12 +584,14 @@ nit_feature_species_basin <- function(Nit_list, bind_rows( # reference for this species reference_results %>% - filter(latin_name == selected_latin_name) %>% + filter(latin_name == !!selected_latin_name) %>% + collect() %>% group_by(basin_name, year) %>% summarise(min = min(nit), max = max(nit), - mean = mean(nit), .groups = 'drop') %>% - collect() %>% + mean = mean(nit), + .groups = 'drop') %>% + # collect() %>% group_by(basin_name) %>% mutate(rolling_mean = frollmean(mean, n = 10, align = 'center')) %>% mutate(source = 'reference') %>% diff --git a/data-raw/altas_simulation.R b/data-raw/altas_simulation.R index 4882037..4a7a5e9 100644 --- a/data-raw/altas_simulation.R +++ b/data-raw/altas_simulation.R @@ -1,13 +1,14 @@ +# ==== Run this to create the dput for unit tests ==== library(tictoc) library(purrr) -library(Rfast) +# library(Rfast) library(Matrix) library(data.table) -library(tidyverse) +# library(tidyverse) rm(list = ls()) -source('preparation_atlas_simulation.R') +source('data-raw/preparation_atlas_simulation.R') # data upload ---- # ---------------------------------------------------------------------- # @@ -22,8 +23,10 @@ hydiad_parameter %>% # here fake data anthropogenic_mortality = expand_grid(data_hsi_nmax %>% + collect() %>% distinct(year), data_hsi_nmax %>% + collect() %>% distinct(country)) %>% mutate( h1 = 0, h2 = 0) %>% mutate(h1 = ifelse(between(year, 2001, 2050) & country == 'France', @@ -31,23 +34,26 @@ anthropogenic_mortality = h1 = ifelse(between(year, 2051, 2100) & country == 'France', -log(.75), h1)) +dput(anthropogenic_mortality, file = "tests/testthat/anthropogenic_mortality_dput") + # anthropogenic_mortality %>% # filter(country == 'France') %>% # ggplot(aes(x = year, y = exp(-h1))) + # geom_path() #=> In get_data_simulation() -# catchment_surface <- data_hsi_nmax %>% -# distinct(basin_name) %>% -# arrange(basin_name) %>% -# inner_join(data_catchment %>% select(basin_name, surface_area), -# by = 'basin_name') +catchment_surface <- data_hsi_nmax %>% + distinct(basin_name) %>% + arrange(basin_name) %>% + inner_join(data_catchment %>% select(basin_name, surface_area), + by = 'basin_name') +dput(catchment_surface, file = "tests/testthat/catchment_surface_dput") # ========================================================================================= # run simulation ---- selected_latin_name = "Alosa alosa" -runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mortality, +runSimulation_pml = function(selected_latin_name, hydiad_parameter, anthropogenic_mortality, catchment_surface, data_hsi_nmax, data_ni0, outlet_distance, verbose = FALSE) { if (verbose) tic() @@ -58,19 +64,23 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo # Local variables ---- ## ordered list of basin s---- basins <- data_hsi_nmax %>% + collect() %>% distinct(basin_name) %>% arrange(basin_name) %>% pull(basin_name) ## list of models ---- models <- data_hsi_nmax %>% + collect() %>% distinct(climatic_model_code) %>% arrange(climatic_model_code) %>% pull(climatic_model_code) ## HyDiaD parameters for the selected species ---- parameter <- hydiad_parameter %>% + collect() %>% filter(latin_name == selected_latin_name ) + # filter(latin_name_s == selected_latin_name ) results[['param']][['hydiad_parameter']] <- parameter ## cohorts in spawner run (number and weights) ---- @@ -85,6 +95,7 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo ## first year of simulation ---- firstYear = min(data_hsi_nmax$year, na.rm = TRUE) + # data_hsi_nmax %>% collect() %>% pull(year) %>% min(., na.rm = TRUE) # ---------------------------------------------------------------------- # # Local matrices ---- @@ -93,6 +104,7 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo ## update Nmax according to anthropogenic mortality eh1 = exp(-h1) ---- Nit <- data_hsi_nmax %>% + collect() %>% filter(latin_name == selected_latin_name) %>% mutate(phase = 'simul') %>% # update maximal abundance (Nmax) with anthropogenic mortality (h1) @@ -104,7 +116,9 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo # anticipate simulation to initialise model and to burn-in run anticipation <- - expand_grid( data_ni0 %>% filter(latin_name == selected_latin_name) %>% + expand_grid( data_ni0 %>% + collect() %>% + filter(latin_name == selected_latin_name) %>% select(-c(year) ), tibble(year = seq(firstYear - burnin - generationtime, firstYear - 1), phase = c(rep('initial', generationtime), rep('burnin', burnin)))) %>% @@ -112,10 +126,14 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo mutate(Nmax_eh1 = Nmax , Nit = ifelse(phase == 'initial', Nmax, 0)) - extendedNit <- Nit %>% + extendedNit_PML <- extendedNit <- Nit %>% bind_rows(anticipation) %>% arrange(basin_id, climatic_model_code, year) + # extendedNit %>% + # filter(basin_name == "Aa", year == 1951) %>% + # View() + # ## list years in simulation ---- years <- extendedNit %>% @@ -128,7 +146,7 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo if (verbose) tic() # ------------------------------------------------------------------------------- # ## compute Nmax_eh1 matrix and prepare Nit matrix ---- - results[["model"]] <- lapply(models, function(model) { + resultsPM <- results[["model"]] <- lapply(models, function(model) { out = list() out[['HSI']] <- @@ -185,7 +203,7 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo ## r * exp(-h2) matrix ---- # compute exp(-h2) - eh2 <- extendedNit %>% distinct(year, basin_name, country) %>% + eh2_PML <- eh2 <- extendedNit %>% distinct(year, basin_name, country) %>% left_join(anthropogenic_mortality , by = c('country', 'year')) %>% select(-c(country, h1)) %>% @@ -205,7 +223,8 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo ## matrix of survival proportion between catchments among emigrants ---- # row: arrival basin # colum: departure basin - results[["other"]] [['survivingProportion']] <- outlet_distance %>% + survivingProportion_PML <- results[["other"]] [['survivingProportion']] <- outlet_distance %>% + collect() %>% # filter basins filter(departure %in% basins, arrival %in% basins) %>% @@ -240,7 +259,7 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo # for testing: resultsModel <- results[['model']][[1]] # compute effective for 1 model ---- - computeEffectiveForModel = function(model, currentYear, results, generationtime, nbCohorts){ + computeEffectiveForModel_PML = function(model, currentYear, results, generationtime, nbCohorts){ #cat(model, "\t", currentYear, "\n") currentYear_str = as.character(currentYear) @@ -289,11 +308,11 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo } # compute effective for all models ---- - computeEffective = function(currentYear, results, generationtime, nbCohorts) { + computeEffective_PML = function(currentYear, results, generationtime, nbCohorts) { # loop over models provResults <- lapply(names(results[['model']]), - computeEffectiveForModel, currentYear, results, generationtime, nbCohorts) + computeEffectiveForModel_PML, currentYear, results, generationtime, nbCohorts) names(provResults) <- models # store the provisional results in results @@ -316,10 +335,13 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo # run simulation over years if (verbose) tic() for (currentYear in yearsToRun) { + # currentYear <- yearsToRun[1] ## print a progress bar to the console if (verbose) setTxtProgressBar(progbar, currentYear) - results <- computeEffective(currentYear, results, generationtime, nbCohorts) + results <- computeEffective_PML(currentYear, results, generationtime, nbCohorts) } + + # dput(results, file = "tests/testthat/results_pml_dput") cat('\n') if (verbose) toc() @@ -329,11 +351,13 @@ runSimulation = function(selected_latin_name, hydiad_parameter, anthropogenic_mo # ======================================================================================================= # run simulation ---- tic() -results <- runSimulation(selected_latin_name, hydiad_parameter, anthropogenic_mortality, +results <- runSimulation_pml(selected_latin_name, hydiad_parameter, anthropogenic_mortality, catchment_surface, data_hsi_nmax, data_ni0, outlet_distance, verbose = FALSE) toc() - +dput(results, file = "tests/testthat/results_pml_dput") +utils::zip("tests/testthat/results_pml_dput", zipfile = "tests/testthat/results_pml_dput.zip") +file.remove("tests/testthat/results_pml_dput") # ================================================================== # # graphics ---- Nit_list <- results[['model']] %>% @@ -346,7 +370,7 @@ Nit_list <- results[['model']] %>% -nit_feature = function(data_list){ +nit_feature_pml = function(data_list){ return( data_list %>% reduce(pmin) %>% as_tibble(rownames = 'basin_name') %>% pivot_longer(cols = -basin_name, names_to = 'year', values_to = 'min') %>% @@ -367,9 +391,10 @@ nit_feature = function(data_list){ mutate(rolling_mean = frollmean(mean, n = 10, align = 'center'))) } -basin = 'Mondego' +# basin = 'Mondego' +basin = 'Adour' -# nit_feature(Nit_ref) %>% +# nit_feature_pml(Nit_ref) %>% # filter(basin_name == basin) %>% # print(n = Inf) %>% # ggplot(aes(x=year)) + @@ -392,17 +417,20 @@ basin = 'Mondego' # geom_line(aes(y=rolling_mean), col ='red') # # -# nit_feature(Nit_list) %>% +# nit_feature_pml(Nit_list) %>% # filter(basin_name == basin) %>% # print(n = Inf) %>% # ggplot(aes(x=year)) + # geom_ribbon(aes(ymin = min, ymax = max), alpha = .5) + # geom_line(aes(y=rolling_mean), col ='red') +model_nit_outputs <- nit_feature_pml(Nit_list) +dput(model_nit_outputs, file = "tests/testthat/model_nit_outputs_dput") -nit_feature(Nit_list) %>% +model_res_filtered_pml <- model_nit_outputs %>% mutate(source = 'simul') %>% bind_rows(reference_results %>% + collect() %>% filter(latin_name == selected_latin_name) %>% group_by(basin_name, year) %>% summarise(min = min(nit), @@ -413,10 +441,15 @@ nit_feature(Nit_list) %>% mutate(source = 'reference')) %>% suppressWarnings() %>% filter(basin_name == basin, - year >= 1951) %>% + year >= 1951) + +dput(model_res_filtered_pml, file = "tests/testthat/model_res_filtered_dput") + +model_res_filtered_pml %>% ggplot(aes(x = year)) + geom_ribbon(aes(ymin = min, ymax = max, fill = source), alpha = .5) + - geom_line(aes(y = rolling_mean, col = source)) + + geom_line(aes(y = rolling_mean, colour = source, linetype = source), + alpha = 0.9) + ylab('Nit') #================================================================================= diff --git a/data-raw/be-page4-future.Rmd b/data-raw/be-page4-future.Rmd index 772a2a2..e557d84 100644 --- a/data-raw/be-page4-future.Rmd +++ b/data-raw/be-page4-future.Rmd @@ -65,15 +65,24 @@ datasets <- generate_datasets(con = conn_eurodiad) lang <- "fr" # Generate all inputs as in the Shiny application -countries <- c("all", datasets[["countries_mortalities_list"]]) +countries <- datasets[["countries_mortalities_list"]] -mortalities <- data.frame( +mortalities <- tibble::tibble( # country = golem::get_golem_options('countries_mortalities_list'), country = datasets[["countries_mortalities_list"]], yearsimubegin = - rep(0.1, length(datasets[["countries_mortalities_list"]])), + case_when( + country == "France" ~ -log(.5), + # country == "France" ~ -5, + TRUE ~ 0 + ), + # rep(-log(.5), length(datasets[["countries_mortalities_list"]])), yearsimuend = - rep(0.5, length(datasets[["countries_mortalities_list"]])) + case_when( + country == "France" ~ -log(.75), + TRUE ~ 0 + ) + # rep(-log(.75), length(datasets[["countries_mortalities_list"]])) ) mortalities @@ -87,21 +96,24 @@ selected_latin_name = "Alosa alosa" # TODO - In Shiny only - # Use https://shiny.rstudio.com/articles/progress.html -shiny::withProgress(message = 'Making Simu', value = 0, - session = session, { -# browser() -results <- runSimulation( - selected_latin_name, - data_simulation[["hydiad_parameter"]], # 11 rows - # Smaller for example - anthropogenic_mortality %>% filter(year <= 1955), # 1800 rows - data_simulation[["catchment_surface"]], # 134 rows - data_simulation[["data_hsi_nmax"]], # 663300 rows - data_simulation[["data_ni0"]], # 4422 rows - data_simulation[["outlet_distance"]], # 18225 rows - verbose = TRUE -) -}) +shiny::withProgress( + message = 'Making Simu', value = 0, + session = session, { + + results <- runSimulation( + selected_latin_name, + data_simulation[["hydiad_parameter"]], # 11 rows + # Smaller for example + anthropogenic_mortality, # 1800 rows + data_simulation[["catchment_surface"]], # 134 rows + data_simulation[["data_hsi_nmax"]], # 663300 rows + data_simulation[["data_ni0"]], # 4422 rows + data_simulation[["outlet_distance"]], # 18225 rows + # Add scenario + scenario = "rcp85", + verbose = FALSE + ) + }) # Les 5 premières lignes des sous-listes # results %>% purrr::map(~purrr::map(.x, head)) @@ -113,7 +125,7 @@ results <- runSimulation( # graphics ---- Nit_list <- get_model_nit(results) -basin <- 'Mondego' +basin <- 'Adour' #' Plot Nit predictions model_res_filtered <- nit_feature_species_basin( diff --git a/data-raw/preparation_atlas_simulation.R b/data-raw/preparation_atlas_simulation.R index 3edd9f8..bb05940 100644 --- a/data-raw/preparation_atlas_simulation.R +++ b/data-raw/preparation_atlas_simulation.R @@ -1,7 +1,7 @@ library(DBI) library(tictoc) -library(tidyverse) +# library(tidyverse) # rm(list = ls()) connection_sql = TRUE @@ -9,12 +9,12 @@ connection_sql = TRUE # connection to the data base if (connection_sql) - conn_eurodiad <- dbConnect(RPostgres::Postgres(), dbname = 'eurodiad', - host = 'citerne.bordeaux.irstea.priv', - port = 5432, - user = 'patrick.lambert', - password = rstudioapi::askForPassword("Database password")) - + # conn_eurodiad <- dbConnect(RPostgres::Postgres(), dbname = 'eurodiad', + # host = 'citerne.bordeaux.irstea.priv', + # port = 5432, + # user = 'patrick.lambert', + # password = rstudioapi::askForPassword("Database password")) + conn_eurodiad <- connect() # data upload ---- @@ -25,14 +25,15 @@ if (connection_sql) { INNER JOIN diadesatlas.basin_outlet bo USING (basin_id);" ) %>% tibble() - write_rds(data_catchment, './data_input/data_catchment.rds') + + # write_rds(data_catchment, './data_input/data_catchment.rds') } else { data_catchment <- read_rds('./data_input/data_catchment.rds') } # ---------------------------------------------------------------------- # ## Distances between catchment ---- if ( connection_sql) { - outletDistance = dbGetQuery(conn_eurodiad,"SELECT + outlet_distance = dbGetQuery(conn_eurodiad,"SELECT b.basin_name AS departure, od.departure AS departure_id, b2.basin_name AS arrival, @@ -47,22 +48,23 @@ INNER JOIN diadesatlas.basin b2 ON ORDER BY departure, distance ;") %>% tibble() - write_rds(outletDistance, "./data_input/outletDistance.rds") + # write_rds(outlet_distance, "./data_input/outletDistance.rds") } else { - outletDistance <- read_rds( "./data_input/outletDistance.rds") + outlet_distance <- read_rds( "./data_input/outletDistance.rds") } # ---------------------------------------------------------------------- # # HyDiaD parameters ---- if (connection_sql) { - HyDiaDParameter <- dbGetQuery(conn_eurodiad, " + hydiad_parameter <- dbGetQuery(conn_eurodiad, " SELECT s.latin_name, s.local_name AS \"Lname\", h.* FROM diadesatlas.hydiadparameter h INNER JOIN diadesatlas.species s USING (species_id);") %>% - tibble() %>% - write_rds("./data_input/HyDiaDParameter.rds") + tibble() + + # hydiad_parameter %>% write_rds("./data_input/HyDiaDParameter.rds") } else { - HyDiaDParameter <- read_rds("./data_input/HyDiaDParameter.rds") + hydiad_parameter <- read_rds("./data_input/HyDiaDParameter.rds") } @@ -76,21 +78,21 @@ INNER JOIN diadesatlas.basin b USING (basin_id) INNER JOIN diadesatlas.climatic_model cm USING (climatic_model_id) WHERE year > 0 AND climatic_scenario = 'rcp85'" - data_hsi_Nmax <- dbGetQuery(conn_eurodiad, query) %>% + data_hsi_nmax <- dbGetQuery(conn_eurodiad, query) %>% tibble() %>% # compute the maximum abundance (#) according to hsi, # maximal density (Dmax) , catchment area (ccm_area) - inner_join(HyDiaDParameter %>% + inner_join(hydiad_parameter %>% select(latin_name, Dmax), by = 'latin_name') %>% mutate(Nmax = hsi * Dmax * surface_area) %>% select(-c(surface_area, Dmax)) - write_rds(data_hsi_Nmax, './data_input/data_hsi_Nmax.rds') + # write_rds(data_hsi_nmax, './data_input/data_hsi_Nmax.rds') rm(query) } else { - data_hsi_Nmax <- read_rds('./data_input/data_hsi_Nmax.rds') + data_hsi_nmax <- read_rds('./data_input/data_hsi_Nmax.rds') } @@ -98,7 +100,7 @@ WHERE year > 0 AND climatic_scenario = 'rcp85'" # reference results if (connection_sql) { - referenceResults <- dbGetQuery(conn_eurodiad, + reference_results <- dbGetQuery(conn_eurodiad, "SELECT s.latin_name, basin_id, basin_name, year, climatic_scenario, climatic_model_code, nit FROM diadesatlas.hybrid_model_result hmr INNER JOIN diadesatlas.species s USING (species_id) INNER JOIN diadesatlas.basin b USING (basin_id) @@ -107,9 +109,9 @@ WHERE year > 0 AND climatic_scenario = 'rcp85' ORDER BY latin_name, basin_id, climatic_model_code") %>% tibble() - write_rds(referenceResults, './data_input/referenceResults.rds') + # write_rds(reference_results, './data_input/referenceResults.rds') } else { - referenceResults <- read_rds('./data_input/referenceResults.rds') + reference_results <- read_rds('./data_input/referenceResults.rds') } @@ -123,13 +125,13 @@ WHERE climatic_scenario = 'rcp85' AND year = 0 ORDER BY latin_name, basin_id, climatic_model_code") %>% tibble() %>% - inner_join(HyDiaDParameter %>% + inner_join(hydiad_parameter %>% select(latin_name, Dmax), by = 'latin_name') %>% mutate(Nmax = hsi * Dmax * surface_area) %>% select(-c(surface_area, Dmax)) - write_rds(data_ni0, './data_input/data_ni0.rds') + # write_rds(data_ni0, './data_input/data_ni0.rds') } else { data_ni0 <- read_rds('./data_input/data_ni0.rds') } diff --git a/man/runSimulation.Rd b/man/runSimulation.Rd new file mode 100644 index 0000000..1107863 --- /dev/null +++ b/man/runSimulation.Rd @@ -0,0 +1,43 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/mod_d_fourth_fct.R +\name{runSimulation} +\alias{runSimulation} +\title{Run simulation} +\usage{ +runSimulation( + selected_latin_name, + hydiad_parameter, + anthropogenic_mortality, + catchment_surface, + data_hsi_nmax, + data_ni0, + outlet_distance, + scenario = "rcp85", + verbose = FALSE +) +} +\arguments{ +\item{selected_latin_name}{Species latin name} + +\item{hydiad_parameter}{Hydiad model parameters} + +\item{anthropogenic_mortality}{table of anthropogenic mortalities} + +\item{catchment_surface}{Surface of basins} + +\item{data_hsi_nmax}{HSI Nmax values} + +\item{data_ni0}{ni0 values} + +\item{outlet_distance}{distance from outlet} + +\item{scenario}{Climatic scenario. e.g. "rcp85"} + +\item{verbose}{Logical.} +} +\value{ +List of models outputs +} +\description{ +Run simulation +} diff --git a/tests/testthat/anthropogenic_mortality_dput b/tests/testthat/anthropogenic_mortality_dput new file mode 100644 index 0000000..0f6b9a6 --- /dev/null +++ b/tests/testthat/anthropogenic_mortality_dput @@ -0,0 +1,697 @@ +structure(list(year = c(1974L, 1974L, 1974L, 1974L, 1974L, 1974L, +1974L, 1974L, 1974L, 1974L, 1974L, 1974L, 1975L, 1975L, 1975L, +1975L, 1975L, 1975L, 1975L, 1975L, 1975L, 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"Morocco", "Scotland", "Norway", "Germany", +"Sweden", "Denmark", "Wales"), h1 = c(0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, +0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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c("Aa", "Adour", "Anllons", "Artibai", +"Ason", "Aulne", "Authie", "Auzance", "Ave", "Avon", "Bann", +"Barbadun", "Barbate", "Barrow", "Bidassoa", "Blackwater", "Blavet", +"Bou_Regreg", "Bresle", "Butron", "Canche", "Cavado", "Charente", +"Clyde", "Couesnon", "Deba", "Dee", "Deva_Cares", "Dives", "Dordogne", +"Dorset Frome", "Douro", "Douve", "Drammens", "Eden", "Eider", +"Elbe", "Elorn", "Ems", "Eo", "Erne", "Escaut", "Esva", "Eume", +"Forth", "Foyle", "Garonne", "Glomma", "Gotaalv", "Grande", "Great_Ouse", +"Guadalete", "Guadalquivir", "Guadiana", "Gudenaa", "Hampshire Avon", +"Imsa", "Lagan", "Lagen", "Landro", "Lay", "Lea", "Leguer", "Lerez", +"Leyre", "Lima", "Loire", "Loukkos", "Mandeo", "Masma", "Mersey", +"Meuse", "Minho", "Mira", "Mondego", "Nalon_Narcea", "Namsen", +"Nansa", "Navia", "Nervion", "Nivelle", "Odet", "Odiel", "Oiartzun", +"Oka", "Oria", "Orkla", "Orne", "Otra", "Oum_er_Rbia", "Ouro", +"Pas", "Piedras", "Rance", "Rhine", "Sado", "Scorff", "Sebou", +"Seine", "Sella", "Selune", "Seudre", "Severn", "Sevre Niortaise", +"Shannon", "Slaney", "Somme", "Sor", "Spey", "Taff", "Tagus", +"Tamar", "Tambre", "Tay", "Thames", "Tinto", "Touques", "Towy", +"Trent", "Trieux", "Tweed", "Tyne", "Ulla", "Umia", "Urola", +"Urumea", "Vefsna", "Vida", "Vilaine", "Vire", "Vouga", "Weser", +"Wye", "Yser"), surface_area = c(1215, 16880, 516, 110, 640, +1495, 1305, 620, 1395, 2808, 5957, 135, 1290, 2983, 750, 2184, +2060, 10000, 780, 237, 1274, 1648, 10000, 2956, 1134, 555, 2235, +1131, 638, 24000, 548, 97595, 1670, 17000, 2769, 3300, 144055, +379, 17934, 1050, 4374, 21861, 465, 470, 1683, 2927, 55000, 42441, +50200, 283, 8254, 3677, 56966, 55422, 3200, 1749, 128, 6440, +5554, 258, 2190, 128, 494, 453, 2661, 2470, 117000, 3730, 368, +301, 4065, 36011, 17067, 1600, 6644, 6749, 6000, 428, 2584, 1801, +138, 715, 2111, 94, 220, 873, 3092, 1268, 3740, 35000, 189, 662, +286, 1195, 185000, 7692, 300, 39000, 78000, 1297, 1000, 236, +11165, 3580, 11700, 1943, 5560, 202, 3060, 529, 80775, 1820, +1530, 5021, 13331, 1039, 1350, 1388, 10249, 846, 5079, 2940, +2817, 440, 350, 273, 4122, 1081, 10475, 1170, 3635, 48800, 4141, +1300)), row.names = c(NA, -134L), class = c("tbl_df", "tbl", +"data.frame")) diff --git a/tests/testthat/extendedNit_dput b/tests/testthat/extendedNit_dput new file mode 100644 index 0000000..b37305d --- /dev/null +++ b/tests/testthat/extendedNit_dput @@ -0,0 +1,20 @@ +structure(list(latin_name = c("Alosa alosa", "Alosa alosa", "Alosa alosa", +"Alosa alosa", "Alosa alosa", "Alosa alosa"), basin_id = c(1L, +1L, 1L, 1L, 1L, 1L), basin_name = c("Aa", "Aa", "Aa", "Aa", "Aa", +"Aa"), country = c(NA_character_, NA_character_, NA_character_, +NA_character_, NA_character_, NA_character_), year = 1935:1940, + climatic_scenario = c("rcp85", "rcp85", "rcp85", "rcp85", + "rcp85", "rcp85"), climatic_model_code = c("cnrmcm5", "cnrmcm5", + "cnrmcm5", "cnrmcm5", "cnrmcm5", "cnrmcm5"), hsi = c(0.167542178551514, + 0.167542178551514, 0.167542178551514, 0.167542178551514, + 0.167542178551514, 0.167542178551514), Nmax = c(1446.68561647354, + 1446.68561647354, 1446.68561647354, 1446.68561647354, 1446.68561647354, + 1446.68561647354), phase = c("initial", "initial", "initial", + "initial", "initial", "initial"), h1 = c(NA_real_, NA_real_, + NA_real_, NA_real_, NA_real_, NA_real_), Nmax_eh1 = c(1446.68561647354, + 1446.68561647354, 1446.68561647354, 1446.68561647354, 1446.68561647354, + 1446.68561647354), Nit = c(1446.68561647354, 1446.68561647354, + 1446.68561647354, 1446.68561647354, 1446.68561647354, 1446.68561647354 + ), nit = c(1446.68561647354, 1446.68561647354, 1446.68561647354, + 1446.68561647354, 1446.68561647354, 1446.68561647354)), row.names = c(NA, +-6L), class = c("tbl_df", "tbl", "data.frame")) diff --git a/tests/testthat/model_nit_outputs_dput b/tests/testthat/model_nit_outputs_dput new file mode 100644 index 0000000..7559317 --- /dev/null +++ b/tests/testthat/model_nit_outputs_dput @@ -0,0 +1,31426 @@ +structure(list(basin_name = c("Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", +"Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", "Aa", 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"Avon", "Avon", +"Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Avon", +"Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Avon", +"Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Avon", +"Avon", "Avon", "Avon", "Avon", "Avon", "Avon", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", "Bann", +"Bann", "Bann", "Bann", "Bann", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", "Barbadun", +"Barbadun", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barbate", +"Barbate", "Barbate", "Barbate", "Barbate", "Barbate", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", "Barrow", +"Barrow", "Barrow", "Barrow", "Barrow", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", "Bidassoa", +"Bidassoa", "Bidassoa", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blackwater", "Blackwater", +"Blackwater", "Blackwater", "Blackwater", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", "Blavet", +"Blavet", "Blavet", "Blavet", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bou_Regreg", +"Bou_Regreg", "Bou_Regreg", "Bou_Regreg", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", "Bresle", +"Bresle", "Bresle", "Bresle", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", "Butron", "Butron", +"Butron", "Butron", "Butron", "Butron", "Butron", 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"reference", "reference", "reference", "reference", + "reference", "reference")), class = c("grouped_df", "tbl_df", +"tbl", "data.frame"), row.names = c(NA, -300L), groups = structure(list( + basin_name = "Adour", .rows = structure(list(1:300), ptype = integer(0), class = c("vctrs_list_of", + "vctrs_vctr", "list"))), row.names = c(NA, -1L), class = c("tbl_df", +"tbl", "data.frame"), .drop = TRUE)) diff --git a/tests/testthat/results_model_small_dput b/tests/testthat/results_model_small_dput new file mode 100644 index 0000000..a69e3dd --- /dev/null +++ b/tests/testthat/results_model_small_dput @@ -0,0 +1,16 @@ +list(HSI = structure(c(0.167542178551514, 0.167542178551514, +0.167542178551514, 0.167542178551514, 0.167542178551514, 0.167542178551514 +), dim = c(1L, 6L), dimnames = list("Aa", c("1935", "1936", "1937", +"1938", "1939", "1940"))), Nmax_eh1 = structure(c(1446.68561647354, +1446.68561647354, 1446.68561647354, 1446.68561647354, 1446.68561647354, +1446.68561647354), dim = c(1L, 6L), dimnames = list("Aa", c("1935", +"1936", "1937", "1938", "1939", "1940"))), Nit = structure(c(1446.68561647354, +1446.68561647354, 1446.68561647354, 1446.68561647354, 1446.68561647354, +1446.68561647354), dim = c(1L, 6L), dimnames = list("Aa", c("1935", +"1936", "1937", "1938", "1939", "1940"))), emigrants = structure(c(0, +0, 0, 0, 0, 0), dim = c(1L, 6L), dimnames = list("Aa", c("1935", +"1936", "1937", "1938", "1939", "1940"))), immigrants = structure(c(0, +0, 0, 0, 0, 0), dim = c(1L, 6L), dimnames = list("Aa", c("1935", +"1936", "1937", "1938", "1939", "1940"))), spawnersTo = structure(c(0, +0, 0, 0, 0, 0), dim = c(1L, 6L), dimnames = list("Aa", c("1935", +"1936", "1937", "1938", "1939", "1940")))) diff --git a/tests/testthat/results_pml_dput.zip b/tests/testthat/results_pml_dput.zip new file mode 100644 index 0000000..1cd81d7 Binary files /dev/null and b/tests/testthat/results_pml_dput.zip differ diff --git a/tests/testthat/test-mod_d_fourth_fct.R b/tests/testthat/test-mod_d_fourth_fct.R index 9f5ddfd..5fb891f 100644 --- a/tests/testthat/test-mod_d_fourth_fct.R +++ b/tests/testthat/test-mod_d_fourth_fct.R @@ -1,3 +1,208 @@ +# compute_nmax_eh1 ---- +test_that("compute_nmax_eh1 works", { + # dput(models, file = "tests/testthat/models_dput") + if (FALSE) { + # To debug tests only + extendedNit_entry_small <- eval(parse(file = "tests/testthat/extendedNit_dput")) + } + extendedNit_entry_small <- eval(parse(file = "extendedNit_dput")) + + results_model_small <- compute_nmax_eh1(model = "cnrmcm5", + scenario = "rcp85", + extendedNit = extendedNit_entry_small) + # Create expected + # dput(results_model_small, file = "tests/testthat/results_model_small_dput") + # rms_expected <- eval(parse(file = "tests/testthat/results_model_small_dput")) + rms_expected <- eval(parse(file = "results_model_small_dput")) + expect_equal(results_model_small, rms_expected) +}) + +# runSimulation ---- +# Generate expected dput for the same set +# of species and basin in data-raw/altas_simulation.R +test_that("runSimulation works", { + + skip_if_not_connectable(session_globale) + + conn_eurodiad <- get_con(session_globale) + + # Use case + selected_latin_name = "Alosa alosa" + basin <- 'Adour' + + # get_data_simulation - Test inputs ---- + data_simulation <- get_data_simulation(conn_eurodiad) + data_catchment <- data_simulation[["data_catchment"]] + #' @description data_catchment exists + expect_false(is.null(data_catchment)) + expect_equal(ncol(data_catchment), 5) + expect_equal(count(data_catchment) %>% pull(), 135) + expect_equal(ncol(head(data_catchment) %>% collect()), 5) + #' @description outlet_distance exists + outlet_distance <- data_simulation[["outlet_distance"]] + expect_false(is.null(outlet_distance)) + expect_equal(ncol(outlet_distance), 5) + expect_equal(count(outlet_distance) %>% pull(), 18225) + #' @description hydiad_parameter exists + hydiad_parameter <- data_simulation[["hydiad_parameter"]] + expect_false(is.null(hydiad_parameter)) + expect_equal(ncol(hydiad_parameter), 18) + expect_equal(count(hydiad_parameter) %>% pull(), 11) + expect_true(all(c("latin_name", "Lname") %in% colnames(hydiad_parameter))) + #' @description data_hsi_nmax exists + data_hsi_nmax <- data_simulation[["data_hsi_nmax"]] + expect_false(is.null(data_hsi_nmax)) + expect_equal(ncol(data_hsi_nmax), 9) + expect_equal(count(data_hsi_nmax) %>% pull(), 663300) + #' @description reference_results exists + reference_results <- data_simulation[["reference_results"]] + expect_false(is.null(reference_results)) + expect_equal(ncol(reference_results), 7) + expect_equal(count(reference_results) %>% pull(), 663300) + #' @description data_ni0 exists + data_ni0 <- data_simulation[["data_ni0"]] + expect_false(is.null(data_ni0)) + expect_equal(ncol(data_ni0), 9) + expect_equal(count(data_ni0) %>% pull(), 4422) + #' @description catchment_surface exists + catchment_surface <- data_simulation[["catchment_surface"]] + expect_false(is.null(catchment_surface)) + expect_equal(ncol(catchment_surface), 2) + expect_equal(count(catchment_surface) %>% pull(), 134) + cs_object <- catchment_surface %>% collect() %>% arrange(basin_name) + if (FALSE) { + # to debug tests only + cs_expected <- eval(parse( + file = "tests/testthat/catchment_surface_dput")) %>% + arrange(basin_name) + } + cs_expected <- eval(parse(file = "catchment_surface_dput")) %>% arrange(basin_name) + expect_equal(cs_object, cs_expected) + + # generate_datasets ---- + datasets <- generate_datasets(con = conn_eurodiad) + countries <- datasets[["countries_mortalities_list"]] + + # Example of mortalities table ---- + # France only + mortalities <- tibble::tibble( + # country = golem::get_golem_options('countries_mortalities_list'), + country = datasets[["countries_mortalities_list"]], + yearsimubegin = + case_when( + country == "France" ~ -log(.5), + TRUE ~ 0 + ), + # rep(-log(.5), length(datasets[["countries_mortalities_list"]])), + yearsimuend = + case_when( + country == "France" ~ -log(.75), + TRUE ~ 0 + ) + # rep(-log(.75), length(datasets[["countries_mortalities_list"]])) + ) + + # expand_anthropogenic_mortality works ---- + anthropogenic_mortality <- expand_anthropogenic_mortality( + data_hsi_nmax, mortalities) + am_object <- anthropogenic_mortality %>% + select(year, country, h1, h2) %>% + arrange(year, country) + if (FALSE) { + # For tests debug only + am_expected <- eval(parse(file = "tests/testthat/anthropogenic_mortality_dput")) %>% + arrange(year, country) + } + am_expected <- eval(parse(file = "anthropogenic_mortality_dput")) %>% + arrange(year, country) + + expect_equal(am_object, am_expected) + expect_equal(am_object[["h1"]], am_expected[["h1"]]) + + # #' @description anthropogenic_mortality has all countries and 6 columns + # expect_equal(length(sort(unique(anthropogenic_mortality[["country"]]))), + # length(sort(countries))) + # + + # runSimulation ---- + results <- runSimulation( + selected_latin_name, + data_simulation[["hydiad_parameter"]], # 11 rows + # Smaller for example + anthropogenic_mortality, #%>% filter(year <= 1955), # 1800 rows + data_simulation[["catchment_surface"]], # 134 rows + data_simulation[["data_hsi_nmax"]], # 663300 rows + data_simulation[["data_ni0"]], # 4422 rows + data_simulation[["outlet_distance"]], # 18225 rows + verbose = TRUE + ) + + if (FALSE) { + # for debug tests only + utils::unzip(zipfile = "tests/testthat/results_pml_dput.zip") + resultsPML <- eval(parse(file = "tests/testthat/results_pml_dput")) + file.remove("tests/testthat/results_pml_dput") + } + + dirzip <- tempfile() + utils::unzip(zipfile = "results_pml_dput.zip", exdir = dirzip, junkpaths = TRUE) + resultsPML <- eval(parse(file = file.path(dirzip, "results_pml_dput"))) + file.remove(file.path(dirzip, "results_pml_dput")) + + expect_equal(results[["param"]][["hydiad_parameter"]], + resultsPML[["param"]][["hydiad_parameter"]]) + expect_equal(results, resultsPML) + + # Get NIT ---- + Nit_list <- get_model_nit(results) + + # nit_feature ---- + model_nit_outputs <- nit_feature(Nit_list) + if (FALSE) { + # for debug tests only + mno_expected <- eval(parse(file = "tests/testthat/model_nit_outputs_dput")) %>% + ungroup() + } + mno_expected <- eval(parse(file = "model_nit_outputs_dput")) %>% + ungroup() + expect_equal(model_nit_outputs, mno_expected) + + # nit_feature_species_basin ---- + model_res_filtered <- nit_feature_species_basin( + Nit_list = Nit_list, + reference_results = reference_results, + selected_latin_name = selected_latin_name, + basin = basin) + + mrf_object <- model_res_filtered %>% + arrange(basin_name, year) + + if (FALSE) { + # for debug test only + mrf_expected <- eval(parse(file = "tests/testthat/model_res_filtered_dput")) %>% + rename( + nit_min = min, + nit_max = max, + nit_mean = mean, + nit_movingavg = rolling_mean + ) %>% + arrange(basin_name, year) %>% + ungroup() + } + mrf_expected <- eval(parse(file = "model_res_filtered_dput")) %>% + rename( + nit_min = min, + nit_max = max, + nit_mean = mean, + nit_movingavg = rolling_mean + ) %>% + arrange(basin_name, year) %>% + ungroup() + + expect_equivalent(mrf_object, mrf_expected) + # waldo::compare(mrf_object, mrf_expected) +}) + # slugify ---- test_that("slugify works", { expect_equal(slugify("United Kingdom"), "united-kingdom") @@ -8,10 +213,10 @@ test_that("multi_sliders works", { skip_if_not_connectable(session_globale) ns <- session_globale$ns output <- multi_sliders(ns = ns, countries = c("France", "United Kingdom")) - + # long output ---- expected_output <- -'
+ '