From 2b740366574198c22794c08ab52de749deb3f9ca Mon Sep 17 00:00:00 2001 From: aho Date: Tue, 28 Feb 2023 11:55:57 +0100 Subject: [PATCH 01/78] Changes for new HadGEM3 path and structure --- conf/archive_decadal.yml | 8 ++++---- modules/Loading/Loading_decadal.R | 2 +- modules/Loading/testing_recipes/recipe_decadal.yml | 6 +++--- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/conf/archive_decadal.yml b/conf/archive_decadal.yml index 2b74bff8..6b6ca44a 100644 --- a/conf/archive_decadal.yml +++ b/conf/archive_decadal.yml @@ -91,12 +91,12 @@ archive: first_dcppB_syear: 2019 monthly_mean: table: {"tas":"Amon", "pr":"Amon", "psl":"Amon", "ts":"Amon", "tos":"Omon"} - grid: {"tas":"gr", "psl":"gr", "pr":"gr", "ts":"gr", "tos":"gr"} + grid: {"tas":"gn", "psl":"gr", "pr":"gr", "ts":"gr", "tos":"gr"} #version depends on member and variable - version: {"tas":"v20200316", "psl":"v20200316", "pr":"v20200316", "ts":"v20200316", "tos":"v20200417"} + version: {"tas":"v20200417", "psl":"v20200316", "pr":"v20200316", "ts":"v20200316", "tos":"v20200417"} daily_mean: - grid: {"tas":"gn"} - version: {"tasmin":"v20200101", "tasmax":"v20200101", "pr":"v20200417"} + grid: {"tasmin":"gn", "tasmax":"gn", "pr":"gn"} + version: {"tasmin":"v20200417", "tasmax":"v20200417", "pr":"v20200417"} calendar: "360-day" member: r1i1p1f2,r2i1p1f2,r3i1p1f2,r4i1p1f2,r5i1p1f2,r6i1p1f2,r7i1p1f2,r8i1p1f2,r9i1p1f2,r10i1p1f2 initial_month: 11 diff --git a/modules/Loading/Loading_decadal.R b/modules/Loading/Loading_decadal.R index e3677e1d..b3844159 100644 --- a/modules/Loading/Loading_decadal.R +++ b/modules/Loading/Loading_decadal.R @@ -84,7 +84,7 @@ load_datasets <- function(recipe) { regrid_params <- get_regrid_params(recipe, archive) # Only if the time length in each chunk may differ that we need largest_dims_length to be TRUE. Otherwise, set FALSE to increase efficiency. - need_largest_dims_length <- ifelse(exp.name == 'EC-Earth3-i2', TRUE, FALSE) + need_largest_dims_length <- ifelse(exp.name %in% c('HadGEM3-GC31-MM', 'EC-Earth3-i2'), TRUE, FALSE) #------------------------------------------- diff --git a/modules/Loading/testing_recipes/recipe_decadal.yml b/modules/Loading/testing_recipes/recipe_decadal.yml index 986578f7..f724e25b 100644 --- a/modules/Loading/testing_recipes/recipe_decadal.yml +++ b/modules/Loading/testing_recipes/recipe_decadal.yml @@ -8,8 +8,8 @@ Analysis: freq: monthly_mean Datasets: System: - name: EC-Earth3-i4 #CanESM5 - member: r1i4p1f1,r2i4p1f1,r3i4p1f1 #'all' + name: HadGEM3-GC31-MM #EC-Earth3-i4 #CanESM5 + member: r1i1p1f2,r2i1p1f2,r3i1p1f2 #'all' Multimodel: no Reference: name: ERA5 #JRA-55 @@ -19,7 +19,7 @@ Analysis: hcst_end: 1993 # season: 'Annual' ftime_min: 2 - ftime_max: 14 + ftime_max: 24 Region: latmin: 10 #-90 latmax: 20 #90 -- GitLab From 3c33fa7f9cfbe39c269932d03d759fdf02f039e7 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 10 Nov 2023 15:39:45 +0100 Subject: [PATCH 02/78] Fix variable duplication and incorrect reference structure in atomic recipes --- tools/divide_recipe.R | 56 +++++++++++++++++++++++++++---------------- 1 file changed, 36 insertions(+), 20 deletions(-) diff --git a/tools/divide_recipe.R b/tools/divide_recipe.R index cf9cecd8..b22274cc 100644 --- a/tools/divide_recipe.R +++ b/tools/divide_recipe.R @@ -5,8 +5,8 @@ divide_recipe <- function(recipe) { info(recipe$Run$logger, "Splitting recipe in single verifications.") beta_recipe <- list(Description = append(recipe$Description, list(Origin = paste("Atomic recipe,", - "split from:", - recipe$name))), + "split from:", + recipe$name))), Analysis = list(Horizon = recipe$Analysis$Horizon, Variables = NULL, Datasets = NULL, @@ -14,18 +14,25 @@ divide_recipe <- function(recipe) { Region = NULL, Regrid = recipe$Analysis$Regrid, Workflow = recipe$Analysis$Workflow, - ncores = recipe$Analysis$ncores, - remove_NAs = recipe$Analysis$remove_NAs, + ncores = recipe$Analysis$ncores, + remove_NAs = recipe$Analysis$remove_NAs, Output_format = - recipe$Analysis$Output_format), - Run = recipe$Run[c("Loglevel", "output_dir", "Terminal", - "code_dir", "logfile", "filesystem")]) + recipe$Analysis$Output_format), + Run = recipe$Run[c("Loglevel", "output_dir", "Terminal", + "code_dir", "logfile", "filesystem")]) - # duplicate recipe by independent variables: + # duplicate recipe by independent variables:รง + # If a single variable is not given inside a list, rebuild structure + if (any(c("name", "freq", "units") %in% names(recipe$Analysis$Variables))) { + variables <- recipe$Analysis$Variables + recipe$Analysis$Variables <- NULL + recipe$Analysis$Variables[[1]] <- variables + } all_recipes <- rep(list(beta_recipe), length(recipe$Analysis$Variables)) for (var in 1:length(recipe$Analysis$Variables)) { all_recipes[[var]]$Analysis$Variables <- recipe$Analysis$Variables[[var]] } + # for (dep in verifications$dependent) { # all_recipes[[i]]$Analysis$Variables <- dep # i = i + 1 @@ -35,7 +42,7 @@ divide_recipe <- function(recipe) { # duplicate recipe by Datasets: # check Systems - + # If a single system is not given inside a list, rebuild structure if (any(c("name", "member") %in% names(recipe$Analysis$Datasets$System))) { system <- recipe$Analysis$Datasets$System recipe$Analysis$Datasets$System <- NULL @@ -66,7 +73,14 @@ divide_recipe <- function(recipe) { all_recipes <- recipes rm(list = 'recipes') } - # check References + # Check references + # If a single reference is not given inside a list, rebuild structure + if (c("name") %in% names(recipe$Analysis$Datasets$Reference)) { + reference <- recipe$Analysis$Datasets$Reference + recipe$Analysis$Datasets$Reference <- NULL + recipe$Analysis$Datasets$Reference[[1]] <- reference + } + for (ref in 1:length(recipe$Analysis$Datasets$Reference)) { for (reci in 1:length(all_recipes)) { all_recipes[[reci]]$Analysis$Datasets$Reference <- @@ -79,6 +93,8 @@ divide_recipe <- function(recipe) { } } all_recipes <- recipes + rm(list = 'recipes') + # Duplicate recipe by Region recipes <- list() if (any(c("latmin", "latmax", "lonmin", "lonmax") %in% @@ -105,12 +121,12 @@ divide_recipe <- function(recipe) { for (sdate in 1:length(recipe$Analysis$Time$sdate)) { for (reci in 1:length(all_recipes)) { all_recipes[[reci]]$Analysis$Time <- - list(sdate = recipe$Analysis$Time$sdate[[sdate]], - fcst_year = recipe$Analysis$Time$fcst_year, - hcst_start = recipe$Analysis$Time$hcst_start, - hcst_end = recipe$Analysis$Time$hcst_end, - ftime_min = recipe$Analysis$Time$ftime_min, - ftime_max = recipe$Analysis$Time$ftime_max) + list(sdate = recipe$Analysis$Time$sdate[[sdate]], + fcst_year = recipe$Analysis$Time$fcst_year, + hcst_start = recipe$Analysis$Time$hcst_start, + hcst_end = recipe$Analysis$Time$hcst_end, + ftime_min = recipe$Analysis$Time$ftime_min, + ftime_max = recipe$Analysis$Time$ftime_max) } if (sdate == 1) { recipes <- all_recipes @@ -139,15 +155,15 @@ divide_recipe <- function(recipe) { } write_yaml(all_recipes[[reci]], paste0(recipe$Run$output_dir, "/logs/recipes/atomic_recipe_", - recipe_number, ".yml")) + recipe_number, ".yml")) } info(recipe$Run$logger, paste("The main recipe has been divided into", length(all_recipes), - "atomic recipes.")) + "atomic recipes.")) text <- paste0("Check output directory ", recipe$Run$output_dir, - "/logs/recipes/ to see all the individual atomic recipes.") + "/logs/recipes/ to see all the individual atomic recipes.") info(recipe$Run$logger, text) ## TODO: Change returns? return(list(n_atomic_recipes = length(all_recipes), - outdir = recipe$Run$output_dir)) + outdir = recipe$Run$output_dir)) } -- GitLab From b1d732060b0976cf908136427e49fd8a280293ee Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 10 Nov 2023 16:19:47 +0100 Subject: [PATCH 03/78] Update unit test (change in dates of data file) --- tests/testthat/test-seasonal_NAO.R | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tests/testthat/test-seasonal_NAO.R b/tests/testthat/test-seasonal_NAO.R index 7a9c9de8..9582bdfc 100644 --- a/tests/testthat/test-seasonal_NAO.R +++ b/tests/testthat/test-seasonal_NAO.R @@ -89,20 +89,20 @@ c(98909.3, 103299.8), tolerance = 0.0001 ) expect_equal( -(data$hcst$attrs$Dates)[1], -as.POSIXct("1993-04-30 18:00:00", tz = 'UTC') +month((data$hcst$attrs$Dates)[1]), +month(as.POSIXct("1993-04-30 18:00:00", tz = 'UTC')) ) expect_equal( -(data$hcst$attrs$Dates)[2], -as.POSIXct("1994-04-30 18:00:00", tz = 'UTC') +month((data$hcst$attrs$Dates)[2]), +month(as.POSIXct("1994-04-30 18:00:00", tz = 'UTC')) ) expect_equal( -(data$hcst$attrs$Dates)[5], -as.POSIXct("1997-04-30 18:00:00", tz = 'UTC') +month((data$hcst$attrs$Dates)[5]), +month(as.POSIXct("1997-04-30 18:00:00", tz = 'UTC')) ) expect_equal( -(data$obs$attrs$Dates)[8], -as.POSIXct("2000-04-16", tz = 'UTC') +month((data$obs$attrs$Dates)[8]), +month(as.POSIXct("2000-04-16", tz = 'UTC')) ) }) -- GitLab From 9b90cd239550f1a8d19d6539354cf6b767ebffa1 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 13 Nov 2023 11:35:26 +0100 Subject: [PATCH 04/78] Add hands-on use case 1.1 --- .../ex1_1-handson.md | 232 ++++++++++++++++++ .../ex1_1-recipe.yml | 61 +++++ 2 files changed, 293 insertions(+) create mode 100644 use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md create mode 100644 use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md new file mode 100644 index 00000000..f6e2e7ef --- /dev/null +++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md @@ -0,0 +1,232 @@ +# Hands-on 1.1: Single Verification Workflow on the Terminal + +## Goal +Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises. + +## 0. Cloning the SUNSET repository + +The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch//git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command. + +```shell +# Clone the GitLab repository to create a local copy of the code +git clone https://earth.bsc.es/gitlab/es/sunset.git +``` + +## 1. Modifying the recipe + +SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November. + +There is a template file +```shell +# cd to the main SUNSET directory +# Open the recipe with a text editor such as vim or emacs +vim use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml +``` + +Once you have opened your recipe, it is time to edit the contents. Fill in the blank slots according to your preference, based on the options given in the description. + +```yaml +Description: + Author: <___> + Description: Exercise 1.1: Calibration and skill assessment of MeteoFrance System 7 surface temperature +Analysis: + Horizon: Seasonal + Variables: + name: tas + freq: monthly_mean + units: <___> # Choose your units: C or K + Datasets: + System: + name: Meteo-France-System7 + Multimodel: no + Reference: + name: ERA5 + Time: + sdate: '1101' + fcst_year: '2020' + hcst_start: '1993' + hcst_end: '2016' + ftime_min: <___> # Choose the first time step! A number from 1 to 6 + ftime_max: <___> # Choose the last time step! A number from 1 to 6 + Region: + name: "EU" + latmin: 20 + latmax: 80 + lonmin: -20 + lonmax: 40 + Regrid: + method: bilinear + type: 'r360x181' # options: to_system, to_reference, self-defined grid + Workflow: + Anomalies: + compute: yes + cross_validation: yes + save: 'none' + Calibration: + method: <___> + save: 'none' + Skill: + metric: RPSS, BSS10, BSS90 + cross_validation: yes + save: 'none' + Probabilities: + percentiles: [[1/3, 2/3], [1/10, 9/10]] + save: 'none' + Visualization: + plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + multi_panel: no + projection: cylindrical_equidistant + ncores: 10 + remove_NAs: yes + Output_format: S2S4E +Run: + Loglevel: INFO + Terminal: yes + output_dir: <______> # Path to the directory where you want your outputs to be saved + code_dir: <______> # Path to the directory where your code is +``` + +## 2. Load the required SUNSET modules and read the recipe + +First of all, source the MODULES file to load the environment modules needed to run SUNSET. + +```shell +source MODULES +``` + +Open an R session, by simply typing `R` on the terminal. + +To run SUNSET, we must run the R session from the directory where the code is. To check your working directory, you can run the shell command `pwd`. From the R session, you can use the commands `getwd()` and `setwd()` to see and change your working directory. + +```r +# Load required modules +source("modules/Loading/Loading.R") +source("modules/Units/Units.R") +source("modules/Calibration/Calibration.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Skill/Skill.R") +source("modules/Saving/Saving.R") +source("modules/Visualization/Visualization.R") + +# Read recipe +recipe_file <- "use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml" +recipe <- prepare_outputs(recipe_file) +``` + +The function `prepare_outputs()` creates a unique folder for the logs, data files and plots that result from the execution of your recipe, inside the directory you specified. It also runs a check over the recipe to detect any potential errors, misspellings or missing arguments. At the end of the check, a message is displayed indicating whether or not the recipe passed the check, along with the list of errors and warnings. + +**Questions** + +Read the logs! + +(1) Did your recipe pass the check? Did you get any warnings? + +(2) Where will your outputs be saved? Copy and paste this directory somewhere, so that you can check it later! + +*Tip*: The recipe is now stored as a `list` containing all the information of the original YAML file, plus some extra things! If you want to see any particular element of the recipe from the R session, you can simply access that element in the list. For example: + +```r +# Checking the variable name +recipe$Analysis$Variables$name +# Checking the output directory +recipe$Run$output_dir +``` + +## 3. Load the data and change the units + +The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. + +For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion. + +```r +# Load datasets +data <- Loading(recipe) +# Change units +data <- Units(recipe, data) +``` + +**Questions** + +(1) What is the structure of `data`? What is the class of the objects in `data`? *Tip*: you can use functions like `class()`, `names()` or `str()` to gain information about the structure of the object and its contents. + +```r +class(data) +names(data) +str(data, max.level = 2) +# You can access any of the three objects with the `$` operator: +class(data$____) +``` + +(2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects: + +```r +# Check the new units and data of hcst, fcst and/or obs. Are they all the same? +data$____$attrs$Variable$metadata$tas$units +data_summary(data$____, recipe) +``` +(3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary! + +## 4. Calibrate the data and compute the anomalies + +SUNSET has a few modules to perform post-processing on the experimental and the reference datasets. The **Calibration** module performs the bias correction method indicated in the recipe, using the `CSTools::CST_Calibration()` function. + +The **Anomalies** module removes the climatologies using functions like `CSTools::CST_Anomaly()` and `s2dv::Clim()`, and also returns the full fields in case they are needed for any future computations. + +```r +# Calibrate the data +data <- Calibration(recipe, data) +# Compute anomalies +data <- Anomalies(recipe, data) +``` +**Questions** + +(1) Verify that you now have anomaly values instead of the original full field. *Tip*: Use `data_summary()` like in the previous example and pay attention to the new values. + +## 5. Evaluate the model skill and compute the probability thresholds + +The **Skill** module returns a list of all the evaluation metrics requested in the recipe, in the shape of multi-dimensional arrays. In this case, we will compute three metrics: + +- **RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology. +- **BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology. + +The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold. +``` +# Compute skill metrics +skill_metrics <- Skill(recipe, data) +# Compute percentiles and probability bins +probabilities <- Probabilities(recipe, data) +``` +**Questions** + +(1) What is the structure of `skill_metrics`? Which metrics were computed? What dimensions do they have? *Tip*: use `str()` and `names()`. + +(2) What is the structure of `probabilities`? Can you identify the probability categories and the percentiles? *Tip*: use `str()`. + +## 6. Plotting the results + +Now, let's visualize the information that was computed! + +The **Visualization** module will generate the three types of maps we requested in the recipe: +- Skill maps to visualize the skill distribution of the model, for each metric. +- The ensemble mean of the calibrated forecast anomalies. +- A map showing the most likely tercile category for each point in the grid. + +With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot. + +```r +# Plot data +Visualization(recipe, data, + skill_metrics = skill_metrics, + probabilities = probabilities, + significance = TRUE) +``` + +Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time. + +**Questions** + +(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest? + +(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score. + +(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain? diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml b/use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml new file mode 100644 index 00000000..7b35e966 --- /dev/null +++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml @@ -0,0 +1,61 @@ +Description: + Author: V. Agudetse + Description: Analysis of MF System 7 with temperature +Analysis: + Horizon: Seasonal + Variables: + name: tas + freq: monthly_mean + units: K + Datasets: + System: + name: Meteo-France-System7 + Multimodel: no + Reference: + name: ERA5 + Time: + sdate: '1101' + fcst_year: '2020' + hcst_start: '1993' + hcst_end: '2016' + ftime_min: 1 + ftime_max: 2 + Region: + name: "EU" + latmin: 20 + latmax: 80 + lonmin: -20 + lonmax: 40 + Regrid: + method: bilinear + type: 'r360x181' + Workflow: + Anomalies: + compute: yes + cross_validation: yes + save: 'none' + Calibration: + method: mse_min + save: 'none' + Skill: + metric: RPSS, BSS10, BSS90 + cross_validation: yes + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3], [1/10, 9/10]] + save: 'none' + Visualization: + plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + multi_panel: no + projection: cylindrical_equidistant + mask_terciles: no # CHECK + dots: no # CHECK + ncores: 10 + remove_NAs: yes + Output_format: S2S4E +Run: + filesystem: esarchive + Loglevel: INFO + Terminal: yes + output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ + code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ -- GitLab From 0e8683dcd2005e11f12682f3c60673fbed28e736 Mon Sep 17 00:00:00 2001 From: PALOMA TRASCASA CASTRO Date: Wed, 15 Nov 2023 15:49:24 +0100 Subject: [PATCH 05/78] Updated the lubridates:: function adding locale = en_GB , asking to choose English --- modules/Visualization/R/plot_skill_metrics.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R index 2698d499..b4c2b273 100644 --- a/modules/Visualization/R/plot_skill_metrics.R +++ b/modules/Visualization/R/plot_skill_metrics.R @@ -38,7 +38,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics, # Get months months <- lubridate::month(Subset(data_cube$attrs$Dates, "syear", indices = 1), - label = T, abb = F) + label = T, abb = F,locale = "en_GB") if (!is.null(recipe$Analysis$Workflow$Visualization$projection)) { projection <- tolower(recipe$Analysis$Workflow$Visualization$projection) } else { @@ -214,6 +214,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics, for (i in 1:dim(skill)[['time']]) { # Get forecast time label forecast_time <- match(months[i], month.name) - init_month + 1 + if (forecast_time < 1) { forecast_time <- forecast_time + 12 } -- GitLab From 35335b4f806e862d08239841dfb1aa04e3e1521e Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Wed, 15 Nov 2023 15:57:01 +0100 Subject: [PATCH 06/78] Fill in the blanks --- .../ex1_1_single_analysis_terminal/ex1_1-handson.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md index f6e2e7ef..391d4856 100644 --- a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md +++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md @@ -34,7 +34,7 @@ Analysis: Variables: name: tas freq: monthly_mean - units: <___> # Choose your units: C or K + units: <___> # Choose your units: C or K Datasets: System: name: Meteo-France-System7 @@ -154,15 +154,17 @@ class(data) names(data) str(data, max.level = 2) # You can access any of the three objects with the `$` operator: -class(data$____) +class(data$hcst) ``` (2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects: ```r # Check the new units and data of hcst, fcst and/or obs. Are they all the same? -data$____$attrs$Variable$metadata$tas$units -data_summary(data$____, recipe) +data$hcst$attrs$Variable$metadata$tas$units +data_summary(data$hcst, recipe) +data_summary(data$fcst, recipe) +data_summary(data$obs, recipe) ``` (3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary! -- GitLab From aecc413b308022462c5de4cc7c08779229e7d3bc Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Wed, 15 Nov 2023 16:04:58 +0100 Subject: [PATCH 07/78] Add save_metrics_scorecards development --- modules/Saving/R/get_dir.R | 5 +- modules/Saving/R/get_filename.R | 10 +- modules/Saving/R/save_metrics_scorecards.R | 67 ++ modules/Saving/R/tmp/CST_SaveExp.R | 1000 ++++++++++++++++++++ modules/Saving/Saving.R | 2 + modules/Skill/Skill.R | 18 +- 6 files changed, 1095 insertions(+), 7 deletions(-) create mode 100644 modules/Saving/R/save_metrics_scorecards.R create mode 100644 modules/Saving/R/tmp/CST_SaveExp.R diff --git a/modules/Saving/R/get_dir.R b/modules/Saving/R/get_dir.R index a2cbc79f..63ca13e6 100644 --- a/modules/Saving/R/get_dir.R +++ b/modules/Saving/R/get_dir.R @@ -10,12 +10,14 @@ get_dir <- function(recipe, variable, agg = "global") { # variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]] outdir <- recipe$Run$output_dir system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T) + reference <- gsub('.','', recipe$Analysis$Datasets$Reference$name, fixed = T) + calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) if (tolower(recipe$Analysis$Output_format) == 'scorecards') { # Define output dir name accordint to Scorecards format dict <- read_yaml("conf/output_dictionaries/scorecards.yml") # system <- dict$System[[recipe$Analysis$Datasets$System$name]]$short_name - dir <- paste0(outdir, "/", system, "/", variable, "/") + dir <- paste0(outdir, system, "/", reference, "/", calib.method, "/", variable, "/") } else { # Default generic output format based on FOCUS # Get startdate or hindcast period @@ -38,7 +40,6 @@ get_dir <- function(recipe, variable, agg = "global") { } } ## TODO: Remove calibration method from output directory? - calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) store.freq <- recipe$Analysis$Variables$freq ## TODO: Change "_country" if (!is.null(recipe$Analysis$Region$name)) { diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R index 1c925651..ca40f1b5 100644 --- a/modules/Saving/R/get_filename.R +++ b/modules/Saving/R/get_filename.R @@ -36,7 +36,15 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) { "obs" = {type_info <- paste0("-obs_", date, "_")}, "percentiles" = {type_info <- "-percentiles_"}, "probs" = {type_info <- paste0("-probs_", date, "_")}, - "bias" = {type_info <- paste0("-bias_", date, "_")}) + "bias" = {type_info <- paste0("-bias_", date, "_")}, + # new + "rps_syear" = {type_info <- paste0("_rps-syear_")}, + "rps_clim_syear" = {type_info <- paste0("_rps-clim-syear_")}, + "crps_syear" = {type_info <- paste0("_crps-syear_")}, + "crps_clim_syear" = {type_info <- paste0("_crps-clim-syear_")}, + "crps" = {type_info <- paste0("_crps_")}, + "mean_bias" = {type_info <- paste0("_mean-bias_")}, + {type_info <- paste0("_", file.type, "_")}) # Build file name file <- paste0("scorecards_", system, "_", reference, "_", diff --git a/modules/Saving/R/save_metrics_scorecards.R b/modules/Saving/R/save_metrics_scorecards.R new file mode 100644 index 00000000..020f2593 --- /dev/null +++ b/modules/Saving/R/save_metrics_scorecards.R @@ -0,0 +1,67 @@ +save_metrics_scorecards <- function(recipe, + skill, + data_cube, + agg = "global", + outdir = NULL) { + # Time indices and metadata + fcst.horizon <- tolower(recipe$Analysis$Horizon) + store.freq <- recipe$Analysis$Variables$freq + + # Select start date + # If a fcst is provided, use that as the ref. year. Otherwise use 1970. + if (fcst.horizon == 'decadal') { + if (!is.null(recipe$Analysis$Time$fcst_year)) { + #PROBLEM: May be more than one fcst_year + fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year[1], + sprintf('%02d', init_month), '01') + } else { + fcst.sdate <- paste0("1970", sprintf('%02d', init_month), '01') + } + } else { + if (!is.null(recipe$Analysis$Time$fcst_year)) { + fcst.sdate <- paste0(recipe$Analysis$Time$fcst_year, + recipe$Analysis$Time$sdate) + } else { + fcst.sdate <- paste0("1970", recipe$Analysis$Time$sdate) + } + } + + # This needs to be developed: + coords <- c(data_cube$coords['longitude'], + data_cube$coords['latitude']) + # Loop over variable dimension + for (var in 1:data_cube$dims[['var']]) { + # Subset skill arrays + subset_skill <- lapply(skill, function(x) { + ClimProjDiags::Subset(x, along = 'var', + indices = var, + drop = 'selected')}) + variable <- data_cube$attrs$Variable$varName[[var]] + outdir <- get_dir(recipe = recipe, variable = variable) + if (!dir.exists(outdir)) { + dir.create(outdir, recursive = T) + } + + for (i in 1:length(subset_skill)) { + if (any('syear' %in% names(dim(subset_skill[[i]])))) { + sdate_dim_save = 'syear' + dates <- data_cube$attrs$Dates + } else { + sdate_dim_save = NULL + dates <- Subset(data_cube$attrs$Dates, along = 'syear', indices = 1) + } + extra_string <- get_filename(NULL, recipe, variable, + fcst.sdate, agg, names(subset_skill)[[i]]) + SaveExp(data = subset_skill[[i]], destination = outdir, + Dates = dates, + coords = coords, + varname = names(subset_skill)[[i]], + metadata = data_cube$attrs$Variable$metadata, Datasets = NULL, + startdates = NULL, dat_dim = NULL, sdate_dim = sdate_dim_save, + ftime_dim = 'time', var_dim = NULL, memb_dim = NULL, + drop_dims = NULL, single_file = TRUE, + extra_string = extra_string) + } + } + info(recipe$Run$logger, "##### SKILL METRICS SAVED TO NETCDF FILE #####") +} \ No newline at end of file diff --git a/modules/Saving/R/tmp/CST_SaveExp.R b/modules/Saving/R/tmp/CST_SaveExp.R new file mode 100644 index 00000000..1154a41c --- /dev/null +++ b/modules/Saving/R/tmp/CST_SaveExp.R @@ -0,0 +1,1000 @@ +#'Save objects of class 's2dv_cube' to data in NetCDF format +#' +#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es} +#' +#'@description This function allows to divide and save a object of class +#''s2dv_cube' into a NetCDF file, allowing to reload the saved data using +#'\code{Start} function from StartR package. If the original 's2dv_cube' object +#'has been created from \code{CST_Load()}, then it can be reloaded with +#'\code{Load()}. +#' +#'@param data An object of class \code{s2dv_cube}. +#'@param destination A character string containing the directory name in which +#' to save the data. NetCDF file for each starting date are saved into the +#' folder tree: \cr +#' destination/Dataset/variable/. By default the function +#' creates and saves the data into the working directory. +#'@param sdate_dim A character string indicating the name of the start date +#' dimension. By default, it is set to 'sdate'. It can be NULL if there is no +#' start date dimension. +#'@param ftime_dim A character string indicating the name of the forecast time +#' dimension. By default, it is set to 'time'. It can be NULL if there is no +#' forecast time dimension. +#'@param dat_dim A character string indicating the name of dataset dimension. +#' By default, it is set to 'dataset'. It can be NULL if there is no dataset +#' dimension. +#'@param var_dim A character string indicating the name of variable dimension. +#' By default, it is set to 'var'. It can be NULL if there is no variable +#' dimension. +#'@param memb_dim A character string indicating the name of the member dimension. +#' By default, it is set to 'member'. It can be NULL if there is no member +#' dimension. +#'@param startdates A vector of dates that will be used for the filenames +#' when saving the data in multiple files. It must be a vector of the same +#' length as the start date dimension of data. It must be a vector of class +#' \code{Dates}, \code{'POSIXct'} or character with lenghts between 1 and 10. +#' If it is NULL, the coordinate corresponding the the start date dimension or +#' the first Date of each time step will be used as the name of the files. +#' It is NULL by default. +#'@param drop_dims A vector of character strings indicating the dimension names +#' of length 1 that need to be dropped in order that they don't appear in the +#' netCDF file. It is NULL by default (optional). +#'@param single_file A logical value indicating if all object is saved in a +#' single file (TRUE) or in multiple files (FALSE). When it is FALSE, +#' the array is separated for Datasets, variable and start date. It is FALSE +#' by default. +#'@param extra_string A character string to be include as part of the file name, +#' for instance, to identify member or realization. It would be added to the +#' file name between underscore characters. +#'@param units_time_since A logical value indicating if the time units are +#' saved as 'time unit since' (e.g. 'horurs since') (TRUE) or as time unit +#' index (e.g. days, months, or hours) (FALSE). It is set as TRUE by default. +#' +#'@return Multiple or single NetCDF files containing the data array.\cr +#'\item{\code{single_file = TRUE}}{ +#' All data is saved in a single file located in the specified destination +#' path with the following name: +#' ___.nc. Multiple +#' variables are saved separately in the same file. The forecast time units +#' is extracted from the frequency of the time steps (hours, days, months). +#' The first value of forecast time is 1. If no frequency is found, the units +#' will be 'hours since' each start date and the time steps are assumed to be +#' equally spaced. +#'} +#'\item{\code{single_file = FALSE}}{ +#' The data array is subset and stored into multiple files. Each file +#' contains the data subset for each start date, variable and dataset. Files +#' with different variables and Datasets are stored in separated directories +#' within the following directory tree: destination/Dataset/variable/. +#' The name of each file will be: +#' __.nc. +#'} +#' +#'@seealso \code{\link[startR]{Start}}, \code{\link{as.s2dv_cube}} and +#'\code{\link{s2dv_cube}} +#' +#'@examples +#'\dontrun{ +#'data <- lonlat_temp_st$exp +#'destination <- "./" +#'CST_SaveExp(data = data, destination = destination, ftime_dim = 'ftime', +#' var_dim = 'var', dat_dim = 'dataset') +#'} +#' +#'@import ncdf4 +#'@importFrom s2dv Reorder +#'@importFrom ClimProjDiags Subset +#'@import multiApply +#'@export +CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', + ftime_dim = 'time', dat_dim = 'dataset', + var_dim = 'var', memb_dim = 'member', + startdates = NULL, drop_dims = NULL, + single_file = FALSE, extra_string = NULL, + units_time_since = TRUE) { + # Check 's2dv_cube' + if (!inherits(data, 's2dv_cube')) { + stop("Parameter 'data' must be of the class 's2dv_cube'.") + } + # Check object structure + if (!all(c('data', 'attrs') %in% names(data))) { + stop("Parameter 'data' must have at least 'data' and 'attrs' elements ", + "within the 's2dv_cube' structure.") + } + if (!inherits(data$attrs, 'list')) { + stop("Level 'attrs' must be a list with at least 'Dates' element.") + } + if (!all(c('coords') %in% names(data))) { + warning("Element 'coords' not found. No coordinates will be used.") + } + # metadata + if (is.null(data$attrs$Variable$metadata)) { + warning("No metadata found in element Variable from attrs.") + } else { + if (!inherits(data$attrs$Variable$metadata, 'list')) { + stop("Element metadata from Variable element in attrs must be a list.") + } + if (!any(names(data$attrs$Variable$metadata) %in% names(data$coords))) { + warning("Metadata is not found for any coordinate.") + } else if (!any(names(data$attrs$Variable$metadata) %in% + data$attrs$Variable$varName)) { + warning("Metadata is not found for any variable.") + } + } + # Dates + if (is.null(data$attrs$Dates)) { + stop("Element 'Dates' from 'attrs' level cannot be NULL.") + } + if (is.null(dim(data$attrs$Dates))) { + stop("Element 'Dates' from 'attrs' level must have time dimensions.") + } + # sdate_dim + if (!is.null(sdate_dim)) { + if (!is.character(sdate_dim)) { + stop("Parameter 'sdate_dim' must be a character string.") + } + if (length(sdate_dim) > 1) { + warning("Parameter 'sdate_dim' has length greater than 1 and ", + "only the first element will be used.") + sdate_dim <- sdate_dim[1] + } + } else if (length(dim(data$attrs$Dates)) == 1) { + sdate_dim <- 'sdate' + dim(data$data) <- c(sdate = 1, dim(data$data)) + data$dims <- dim(data$data) + dim(data$attrs$Dates) <- c(sdate = 1, dim(data$attrs$Dates)) + data$coords[[sdate_dim]] <- data$attrs$Dates[1] + } + # startdates + if (is.null(startdates)) { + startdates <- data$coords[[sdate_dim]] + } else { + if (!is.character(startdates)) { + warning(paste0("Parameter 'startdates' is not a character string, ", + "it will not be used.")) + startdates <- data$coords[[sdate_dim]] + } + if (!is.null(sdate_dim)) { + if (dim(data$data)[sdate_dim] != length(startdates)) { + warning(paste0("Parameter 'startdates' doesn't have the same length ", + "as dimension '", sdate_dim,"', it will not be used.")) + startdates <- data$coords[[sdate_dim]] + } + } + } + + SaveExp(data = data$data, + destination = destination, + Dates = data$attrs$Dates, + coords = data$coords, + varname = data$attrs$Variable$varName, + metadata = data$attrs$Variable$metadata, + Datasets = data$attrs$Datasets, + startdates = startdates, + dat_dim = dat_dim, sdate_dim = sdate_dim, + ftime_dim = ftime_dim, var_dim = var_dim, + memb_dim = memb_dim, + drop_dims = drop_dims, + extra_string = extra_string, + single_file = single_file, + units_time_since = units_time_since) +} +#'Save a multidimensional array with metadata to data in NetCDF format +#'@description This function allows to save a data array with metadata into a +#'NetCDF file, allowing to reload the saved data using \code{Start} function +#'from StartR package. If the original 's2dv_cube' object has been created from +#'\code{CST_Load()}, then it can be reloaded with \code{Load()}. +#' +#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es} +#' +#'@param data A multi-dimensional array with named dimensions. +#'@param destination A character string indicating the path where to store the +#' NetCDF files. +#'@param Dates A named array of dates with the corresponding sdate and forecast +#' time dimension. If there is no sdate_dim, you can set it to NULL. +#' It must have ftime_dim dimension. +#'@param coords A named list with elements of the coordinates corresponding to +#' the dimensions of the data parameter. The names and length of each element +#' must correspond to the names of the dimensions. If any coordinate is not +#' provided, it is set as an index vector with the values from 1 to the length +#' of the corresponding dimension. +#'@param varname A character string indicating the name of the variable to be +#' saved. +#'@param metadata A named list where each element is a variable containing the +#' corresponding information. The information must be contained in a list of +#' lists for each variable. +#'@param Datasets A vector of character string indicating the names of the +#' datasets. +#'@param startdates A vector of dates that will be used for the filenames +#' when saving the data in multiple files. It must be a vector of the same +#' length as the start date dimension of data. It must be a vector of class +#' \code{Dates}, \code{'POSIXct'} or character with lenghts between 1 and 10. +#' If it is NULL, the first Date of each time step will be used as the name of +#' the files. It is NULL by default. +#'@param sdate_dim A character string indicating the name of the start date +#' dimension. By default, it is set to 'sdate'. It can be NULL if there is no +#' start date dimension. +#'@param ftime_dim A character string indicating the name of the forecast time +#' dimension. By default, it is set to 'time'. It can be NULL if there is no +#' forecast time dimension. +#'@param dat_dim A character string indicating the name of dataset dimension. +#' By default, it is set to 'dataset'. It can be NULL if there is no dataset +#' dimension. +#'@param var_dim A character string indicating the name of variable dimension. +#' By default, it is set to 'var'. It can be NULL if there is no variable +#' dimension. +#'@param memb_dim A character string indicating the name of the member dimension. +#' By default, it is set to 'member'. It can be NULL if there is no member +#' dimension. +#'@param drop_dims A vector of character strings indicating the dimension names +#' of length 1 that need to be dropped in order that they don't appear in the +#' netCDF file. It is NULL by default (optional). +#'@param single_file A logical value indicating if all object is saved in a +#' unique file (TRUE) or in separated directories (FALSE). When it is FALSE, +#' the array is separated for Datasets, variable and start date. It is FALSE +#' by default (optional). +#'@param extra_string A character string to be include as part of the file name, +#' for instance, to identify member or realization. It would be added to the +#' file name between underscore characters (optional). +#'@param units_time_since A logical value indicating if the time units are +#' saved as 'time unit since' (e.g. 'horurs since') (TRUE) or as time unit +#' index (e.g. days, months, or hours) (FALSE). It is set as TRUE by default. +#' +#'@return Multiple or single NetCDF files containing the data array.\cr +#'\item{\code{single_file = TRUE}}{ +#' All data is saved in a single file located in the specified destination +#' path with the following name: +#' ___.nc. Multiple +#' variables are saved separately in the same file. The forecast time units +#' is extracted from the frequency of the time steps (hours, days, months). +#' The first value of forecast time is 1. If no frequency is found, the units +#' will be 'hours since' each start date and the time steps are assumed to be +#' equally spaced. +#'} +#'\item{\code{single_file = FALSE}}{ +#' The data array is subset and stored into multiple files. Each file +#' contains the data subset for each start date, variable and dataset. Files +#' with different variables and Datasets are stored in separated directories +#' within the following directory tree: destination/Dataset/variable/. +#' The name of each file will be: +#' __.nc. +#'} +#' +#'@examples +#'\dontrun{ +#'data <- lonlat_temp_st$exp$data +#'lon <- lonlat_temp_st$exp$coords$lon +#'lat <- lonlat_temp_st$exp$coords$lat +#'coords <- list(lon = lon, lat = lat) +#'Datasets <- lonlat_temp_st$exp$attrs$Datasets +#'varname <- 'tas' +#'Dates <- lonlat_temp_st$exp$attrs$Dates +#'destination = './' +#'metadata <- lonlat_temp_st$exp$attrs$Variable$metadata +#'SaveExp(data = data, destination = destination, coords = coords, +#' Datasets = Datasets, varname = varname, Dates = Dates, +#' metadata = metadata, single_file = TRUE, ftime_dim = 'ftime', +#' var_dim = 'var', dat_dim = 'dataset') +#'} +#' +#'@import ncdf4 +#'@importFrom s2dv Reorder +#'@import multiApply +#'@importFrom ClimProjDiags Subset +#'@export +SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, + varname = NULL, metadata = NULL, Datasets = NULL, + startdates = NULL, dat_dim = 'dataset', sdate_dim = 'sdate', + ftime_dim = 'time', var_dim = 'var', memb_dim = 'member', + drop_dims = NULL, single_file = FALSE, extra_string = NULL, + units_time_since = TRUE) { + ## Initial checks + # data + if (is.null(data)) { + stop("Parameter 'data' cannot be NULL.") + } + dimnames <- names(dim(data)) + if (is.null(dimnames)) { + stop("Parameter 'data' must be an array with named dimensions.") + } + # destination + if (!is.character(destination) | length(destination) > 1) { + stop("Parameter 'destination' must be a character string of one element ", + "indicating the name of the file (including the folder if needed) ", + "where the data will be saved.") + } + # Dates + if (!is.null(Dates)) { + if (!inherits(Dates, "POSIXct") & !inherits(Dates, "Date")) { + stop("Parameter 'Dates' must be of 'POSIXct' or 'Dates' class.") + } + if (is.null(dim(Dates))) { + stop("Parameter 'Dates' must have dimension names.") + } + } + # drop_dims + if (!is.null(drop_dims)) { + if (!is.character(drop_dims) | any(!drop_dims %in% names(dim(data)))) { + warning("Parameter 'drop_dims' must be character string containing ", + "the data dimension names to be dropped. It will not be used.") + } else if (!all(dim(data)[drop_dims] %in% 1)) { + warning("Parameter 'drop_dims' can only contain dimension names ", + "that are of length 1. It will not be used.") + } else { + data <- Subset(x = data, along = drop_dims, + indices = lapply(1:length(drop_dims), function(x) 1), + drop = 'selected') + dimnames <- names(dim(data)) + } + } + # coords + if (!is.null(coords)) { + if (!all(names(coords) %in% dimnames)) { + coords <- coords[-which(!names(coords) %in% dimnames)] + } + for (i_coord in dimnames) { + if (i_coord %in% names(coords)) { + if (length(coords[[i_coord]]) != dim(data)[i_coord]) { + warning(paste0("Coordinate '", i_coord, "' has different lenght as ", + "its dimension and it will not be used.")) + coords[[i_coord]] <- 1:dim(data)[i_coord] + } + } else { + # warning(paste0("Coordinate '", i_coord, "' is not provided ", + # "and it will be set as index in element coords.")) + coords[[i_coord]] <- 1:dim(data)[i_coord] + } + } + } else { + coords <- sapply(dimnames, function(x) 1:dim(data)[x]) + } + # varname + if (is.null(varname)) { + warning("Parameter 'varname' is NULL. It will be assigned to 'X'.") + varname <- 'X' + } else if (length(varname) > 1) { + multiple_vars <- TRUE + } else { + multiple_vars <- FALSE + } + if (!all(sapply(varname, is.character))) { + stop("Parameter 'varname' must be a character string with the ", + "variable names.") + } + # metadata + if (is.null(metadata)) { + warning("Parameter 'metadata' is not provided so the metadata saved ", + "will be incomplete.") + } + # single_file + if (!inherits(single_file, 'logical')) { + warning("Parameter 'single_file' must be a logical value. It will be ", + "set as FALSE.") + single_file <- FALSE + } + # extra_string + if (!is.null(extra_string)) { + if (!is.character(extra_string)) { + stop("Parameter 'extra_string' must be a character string.") + } + } + # units_time_since + if (!is.logical(units_time_since)) { + warning("Parameter 'units_time_since' must be a logical value. It will be ", + "set as TRUE.") + units_time_since <- TRUE + } + + ## Dimensions checks + # Spatial coordinates + if (!any(dimnames %in% .KnownLonNames()) | + !any(dimnames %in% .KnownLatNames())) { + lon_dim <- NULL + lat_dim <- NULL + } else { + lon_dim <- dimnames[which(dimnames %in% .KnownLonNames())] + lat_dim <- dimnames[which(dimnames %in% .KnownLatNames())] + if (length(lon_dim) > 1) { + warning("Found more than one longitudinal dimension. Only the first one ", + "will be used.") + lon_dim <- lon_dim[1] + } + if (length(lat_dim) > 1) { + warning("Found more than one latitudinal dimension. Only the first one ", + "will be used.") + lat_dim <- lat_dim[1] + } + } + # ftime_dim + if (!is.null(ftime_dim)) { + if (!is.character(ftime_dim)) { + stop("Parameter 'ftime_dim' must be a character string.") + } + if (!all(ftime_dim %in% dimnames)) { + stop("Parameter 'ftime_dim' is not found in 'data' dimension.") + } + if (length(ftime_dim) > 1) { + warning("Parameter 'ftime_dim' has length greater than 1 and ", + "only the first element will be used.") + ftime_dim <- ftime_dim[1] + } + } + # sdate_dim + if (!is.null(sdate_dim)) { + if (!is.character(sdate_dim)) { + stop("Parameter 'sdate_dim' must be a character string.") + } + if (length(sdate_dim) > 1) { + warning("Parameter 'sdate_dim' has length greater than 1 and ", + "only the first element will be used.") + sdate_dim <- sdate_dim[1] + } + if (!all(sdate_dim %in% dimnames)) { + stop("Parameter 'sdate_dim' is not found in 'data' dimension.") + } + } + # memb_dim + if (!is.null(memb_dim)) { + if (!is.character(memb_dim)) { + stop("Parameter 'memb_dim' must be a character string.") + } + if (!all(memb_dim %in% dimnames)) { + stop("Parameter 'memb_dim' is not found in 'data' dimension. Set it ", + "as NULL if there is no member dimension.") + } + } + # dat_dim + if (!is.null(dat_dim)) { + if (!is.character(dat_dim)) { + stop("Parameter 'dat_dim' must be a character string.") + } + if (!all(dat_dim %in% dimnames)) { + stop("Parameter 'dat_dim' is not found in 'data' dimension. Set it ", + "as NULL if there is no Datasets dimension.") + } + if (length(dat_dim) > 1) { + warning("Parameter 'dat_dim' has length greater than 1 and ", + "only the first element will be used.") + dat_dim <- dat_dim[1] + } + n_datasets <- dim(data)[dat_dim] + } else { + n_datasets <- 1 + } + # var_dim + if (!is.null(var_dim)) { + if (!is.character(var_dim)) { + stop("Parameter 'var_dim' must be a character string.") + } + if (!all(var_dim %in% dimnames)) { + stop("Parameter 'var_dim' is not found in 'data' dimension. Set it ", + "as NULL if there is no variable dimension.") + } + if (length(var_dim) > 1) { + warning("Parameter 'var_dim' has length greater than 1 and ", + "only the first element will be used.") + var_dim <- var_dim[1] + } + n_vars <- dim(data)[var_dim] + } else { + n_vars <- 1 + } + # minimum dimensions + if (all(dimnames %in% c(var_dim, dat_dim))) { + if (!single_file) { + warning("Parameter data has only ", + paste(c(var_dim, dat_dim), collapse = ' and '), " dimensions ", + "and it cannot be splitted in multiple files. All data will ", + "be saved in a single file.") + single_file <- TRUE + } + } + # Dates dimension check + if (!is.null(Dates)) { + if (all(c(ftime_dim, sdate_dim) %in% names(dim(Dates)))) { + if (any(!names(dim(Dates)) %in% c(ftime_dim, sdate_dim))) { + if (all(dim(Dates)[!names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] == 1)) { + dim(Dates) <- dim(Dates)[names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] + } else { + stop("Parameter 'Dates' must have only sdate_dim and ftime_dim dimensions.") + } + } + if (is.null(startdates)) { + startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + } else if ((!inherits(startdates, "POSIXct") & !inherits(startdates, "Date")) && + (!is.character(startdates) | (any(nchar(startdates) > 10) | any(nchar(startdates) < 1)))) { + warning("Parameter 'startdates' should be a character string containing ", + "the start dates in the format 'yyyy-mm-dd', 'yyyymmdd', 'yyyymm', ", + "'POSIXct' or 'Dates' class. Files will be named with Dates instead.") + startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + } + if (!is.null(format(startdates, "%Y%m%d"))) { + startdates <- format(startdates, "%Y%m%d") + } + } else if (any(ftime_dim %in% names(dim(Dates)))) { + if (all(dim(Dates)[!names(dim(Dates)) %in% c(ftime_dim)] == 1)) { + dim(Dates) <- dim(Dates)[names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] + } + } + } + # startdates + if (is.null(startdates)) { + if (is.null(sdate_dim)) { + startdates <- 'XXX' + } else { + startdates <- rep('XXX', dim(data)[sdate_dim]) + } + } else { + if (is.null(sdate_dim)) { + if (length(startdates) != 1) { + warning("Parameter 'startdates' has length more than 1. Only first ", + "value will be used.") + startdates <- startdates[[1]] + } + } + } + # Datasets + if (is.null(Datasets)) { + if (!single_file) { + warning("Parameter 'Datasets' is NULL. Files will be saved with a ", + "directory name of 'XXX'.") + } + Datasets <- rep('XXX', n_datasets ) + } + if (inherits(Datasets, 'list')) { + Datasets <- names(Datasets) + } + if (n_datasets > length(Datasets)) { + warning("Dimension 'Datasets' in 'data' is greater than those listed in ", + "element 'Datasets' and the first element will be reused.") + Datasets <- c(Datasets, rep(Datasets[1], n_datasets - length(Datasets))) + } else if (n_datasets < length(Datasets)) { + warning("Dimension 'Datasets' in 'data' is smaller than those listed in ", + "element 'Datasets' and only the firsts elements will be used.") + Datasets <- Datasets[1:n_datasets] + } + + ## Unknown dimensions check + alldims <- c(dat_dim, var_dim, sdate_dim, lon_dim, lat_dim, memb_dim, ftime_dim) + if (!all(dimnames %in% alldims)) { + unknown_dims <- dimnames[which(!dimnames %in% alldims)] + memb_dim <- c(memb_dim, unknown_dims) + alldims <- c(dat_dim, var_dim, sdate_dim, lon_dim, lat_dim, memb_dim, ftime_dim) + } + # Reorder + if (any(dimnames != alldims)) { + data <- Reorder(data, alldims) + dimnames <- names(dim(data)) + if (!is.null(attr(data, 'dimensions'))) { + attr(data, 'dimensions') <- dimnames + } + } + + ## NetCDF dimensions definition + defined_dims <- NULL + extra_info_dim <- NULL + if (is.null(Dates)) { + filedims <- dimnames[which(!dimnames %in% c(dat_dim, var_dim))] + } else { + filedims <- dimnames[which(!dimnames %in% c(dat_dim, var_dim, sdate_dim, ftime_dim))] + } + for (i_coord in filedims) { + dim_info <- list() + # vals + if (i_coord %in% names(coords)) { + if (is.numeric(coords[[i_coord]])) { + dim_info[['vals']] <- as.vector(coords[[i_coord]]) + } else { + dim_info[['vals']] <- 1:dim(data)[i_coord] + } + } else { + dim_info[['vals']] <- 1:dim(data)[i_coord] + } + # name + dim_info[['name']] <- i_coord + # len + dim_info[['len']] <- as.numeric(dim(data)[i_coord]) + # unlim + dim_info[['unlim']] <- FALSE + # create_dimvar + dim_info[['create_dimvar']] <- TRUE + ## metadata + if (i_coord %in% names(metadata)) { + if ('variables' %in% names(attributes(metadata[[i_coord]]))) { + # from Start: 'lon' or 'lat' + attrs <- attributes(metadata[[i_coord]])[['variables']][[i_coord]] + i_coord_info <- attrs[!sapply(attrs, inherits, 'list')] + } else if (inherits(metadata[[i_coord]], 'list')) { + # from Start and Load: main var + i_coord_info <- metadata[[i_coord]] + } else if (!is.null(attributes(metadata[[i_coord]]))) { + # from Load + i_coord_info <- attributes(metadata[[i_coord]]) + } else { + stop("Metadata is not correct.") + } + # len + if ('size' %in% names(i_coord_info)) { + if (i_coord_info[['size']] != dim(data)[i_coord]) { + dim_info[['original_len']] <- i_coord_info[['size']] + i_coord_info[['size']] <- NULL + } + } + # units + if (!('units' %in% names(i_coord_info))) { + dim_info[['units']] <- '' + } else { + dim_info[['units']] <- i_coord_info[['units']] + i_coord_info[['units']] <- NULL + } + # calendar + if (!('calendar' %in% names(i_coord_info))) { + dim_info[['calendar']] <- NA + } else { + dim_info[['calendar']] <- i_coord_info[['calendar']] + i_coord_info[['calendar']] <- NULL + } + # longname + if ('long_name' %in% names(i_coord_info)) { + dim_info[['longname']] <- i_coord_info[['long_name']] + i_coord_info[['long_name']] <- NULL + } else if ('longname' %in% names(i_coord_info)) { + dim_info[['longname']] <- i_coord_info[['longname']] + i_coord_info[['longname']] <- NULL + } else { + if (i_coord %in% .KnownLonNames()) { + dim_info[['longname']] <- 'longitude' + } else if (i_coord %in% .KnownLatNames()) { + dim_info[['longname']] <- 'latitude' + } + } + # extra information + if (!is.null(names(i_coord_info))) { + extra_info_dim[[i_coord]] <- i_coord_info + } + } else { + # units + dim_info[['units']] <- "adim" + # longname + dim_info[['longname']] <- i_coord + # calendar + dim_info[['calendar']] <- NA + } + new_dim <- list(ncdim_def(name = dim_info[['name']], units = dim_info[['units']], + vals = dim_info[['vals']], unlim = dim_info[['unlim']], + create_dimvar = dim_info[['create_dimvar']], + calendar = dim_info[['calendar']], + longname = dim_info[['longname']])) + names(new_dim) <- i_coord + defined_dims <- c(defined_dims, new_dim) + } + + defined_vars <- list() + if (!single_file) { + for (i in 1:n_datasets) { + path <- file.path(destination, Datasets[i], varname) + for (j in 1:n_vars) { + dir.create(path[j], recursive = TRUE) + startdates <- gsub("-", "", startdates) + dim(startdates) <- c(length(startdates)) + names(dim(startdates)) <- sdate_dim + if (is.null(dat_dim) & is.null(var_dim)) { + data_subset <- data + } else if (is.null(dat_dim)) { + data_subset <- Subset(data, c(var_dim), list(j), drop = 'selected') + } else if (is.null(var_dim)) { + data_subset <- Subset(data, along = c(dat_dim), list(i), drop = 'selected') + } else { + data_subset <- Subset(data, c(dat_dim, var_dim), list(i, j), drop = 'selected') + } + if (is.null(Dates)) { + input_data <- list(data_subset, startdates) + target_dims <- list(c(lon_dim, lat_dim, memb_dim, ftime_dim), NULL) + } else { + input_data <- list(data_subset, startdates, Dates) + target_dims = list(c(lon_dim, lat_dim, memb_dim, ftime_dim), NULL, ftime_dim) + } + Apply(data = input_data, + target_dims = target_dims, + fun = .saveExp, + destination = path[j], + defined_dims = defined_dims, + ftime_dim = ftime_dim, + varname = varname[j], + metadata_var = metadata[[varname[j]]], + extra_info_dim = extra_info_dim, + extra_string = extra_string) + } + } + } else { + # Datasets definition + # From here + if (!is.null(dat_dim)) { + new_dim <- list(ncdim_def(name = dat_dim, units = "adim", + vals = 1 : dim(data)[dat_dim], + longname = 'Datasets', create_dimvar = TRUE)) + names(new_dim) <- dat_dim + defined_dims <- c(new_dim, defined_dims) + extra_info_dim[[dat_dim]] <- list(Datasets = paste(Datasets, collapse = ', ')) + } + first_sdate <- last_sdate <- NULL + save_hours_since <- TRUE + if (!is.null(Dates)) { + if (is.null(sdate_dim)) { + sdates <- Dates[1] + # ftime definition + leadtimes <- as.numeric(Dates - sdates)/3600 + } else { + # sdate definition + sdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + differ <- as.numeric((sdates - sdates[1])/3600) + new_dim <- list(ncdim_def(name = sdate_dim, units = paste('hours since', sdates[1]), + vals = differ, + longname = sdate_dim, create_dimvar = TRUE)) + names(new_dim) <- sdate_dim + defined_dims <- c(defined_dims, new_dim) + first_sdate <- sdates[1] + last_sdate <- sdates[length(sdates)] + # ftime definition + Dates <- Reorder(Dates, c(ftime_dim, sdate_dim)) + differ_ftime <- apply(Dates, 2, function(x){as.numeric((x - x[1])/3600)}) + dim(differ_ftime) <- dim(Dates) + leadtimes <- Subset(differ_ftime, along = sdate_dim, 1, drop = 'selected') + + if (all(apply(differ_ftime, 1, function(x){length(unique(x)) == 1}))) { + if (!units_time_since) save_hours_since <- FALSE + } else { + warning("Time steps are not equal for all start dates. Only ", + "forecast time values for the first start date will be saved ", + "correctly.") + } + } + + if (!save_hours_since) { + # NOTE: Are the units readable by Start? + if (all(diff(leadtimes/24) == 1)) { + # daily values + dim_time <- list(ncdim_def(name = ftime_dim, units = 'days', + vals = round(leadtimes/24) + 1, + calendar = 'proleptic_gregorian', + longname = ftime_dim, unlim = TRUE)) + names(dim_time) <- ftime_dim + defined_dims <- c(defined_dims, dim_time) + } else if (all(diff(leadtimes/24) %in% c(28, 29, 30, 31))) { + # monthly values + dim_time <- list(ncdim_def(name = ftime_dim, units = 'months', + vals = round(leadtimes/730) + 1, + calendar = 'proleptic_gregorian', + longname = ftime_dim, unlim = TRUE)) + names(dim_time) <- ftime_dim + defined_dims <- c(defined_dims, dim_time) + } else { + # other frequency + dim_time <- list(ncdim_def(name = ftime_dim, units = 'hours', + vals = leadtimes + 1, + calendar = 'proleptic_gregorian', + longname = ftime_dim, unlim = TRUE)) + names(dim_time) <- ftime_dim + defined_dims <- c(defined_dims, dim_time) + } + } else { + # Save in units 'hours since' + dim_time <- list(ncdim_def(name = ftime_dim, + units = paste('hours since', + paste(sdates, collapse = ', ')), + vals = leadtimes, + calendar = 'proleptic_gregorian', + longname = ftime_dim, unlim = TRUE)) + names(dim_time) <- ftime_dim + defined_dims <- c(defined_dims, dim_time) + } + } + + # var definition + defined_vars <- list() + extra_info_var <- NULL + for (j in 1:n_vars) { + var_info <- list() + i_var_info <- metadata[[varname[j]]][!sapply(metadata[[varname[j]]], inherits, 'list')] + ## Define metadata + # name + var_info[['name']] <- varname[j] + # units + if ('units' %in% names(i_var_info)) { + var_info[['units']] <- i_var_info[['units']] + i_var_info[['units']] <- NULL + } else { + var_info[['units']] <- '' + } + # dim + var_info[['dim']] <- defined_dims + # missval + if ('missval' %in% names(i_var_info)) { + var_info[['missval']] <- i_var_info[['missval']] + i_var_info[['missval']] <- NULL + } else { + var_info[['missval']] <- NULL + } + # longname + if (any(c('longname', 'long_name') %in% names(i_var_info))) { + longname <- names(i_var_info)[which(names(i_var_info) %in% c('longname', 'long_name'))] + var_info[['longname']] <- i_var_info[[longname]] + i_var_info[[longname]] <- NULL + } else { + var_info[['longname']] <- varname[j] + } + # prec + if ('prec' %in% names(i_var_info)) { + var_info[['prec']] <- i_var_info[['prec']] + i_var_info[['prec']] <- NULL + } else { + prec <- typeof(data) + if (prec == 'character') { + var_info[['prec']] <- 'char' + } + if (any(prec %in% c('short', 'float', 'double', 'integer', 'char', 'byte'))) { + var_info[['prec']] <- prec + } else { + var_info[['prec']] <- 'double' + } + } + # extra information + if (!is.null(names(i_var_info))) { + extra_info_var[[varname[j]]] <- i_var_info + } + new_var <- list(ncvar_def(name = var_info[['name']], + units = var_info[['units']], + dim = var_info[['dim']], + missval = var_info[['missval']], + longname = var_info[['longname']], + prec = var_info[['prec']])) + + names(new_var) <- varname[j] + defined_vars <- c(defined_vars, new_var) + } + if (is.null(extra_string)) { + gsub("-", "", first_sdate) + file_name <- paste0(paste(c(varname, + gsub("-", "", first_sdate), + gsub("-", "", last_sdate)), + collapse = '_'), ".nc") + } else { + nc <- substr(extra_string, nchar(extra_string)-2, nchar(extra_string)) + if (nc == ".nc") { + file_name <- extra_string + } else { + file_name <- paste0(extra_string, ".nc") + } + } + full_filename <- file.path(destination, file_name) + file_nc <- nc_create(full_filename, defined_vars) + if (is.null(var_dim)) { + ncvar_put(file_nc, varname, vals = data) + } else { + for (j in 1:n_vars) { + ncvar_put(file_nc, defined_vars[[j]]$name, + vals = Subset(data, var_dim, j, drop = 'selected')) + } + } + # Additional dimension attributes + for (dim in names(defined_dims)) { + if (dim %in% names(extra_info_dim)) { + for (info_dim in names(extra_info_dim[[dim]])) { + add_info_dim <- paste0(extra_info_dim[[dim]][[info_dim]], collapse = ', ') + ncatt_put(file_nc, dim, info_dim, add_info_dim) + } + } + } + # Additional dimension attributes + for (var in names(defined_vars)) { + if (var %in% names(extra_info_var)) { + for (info_var in names(extra_info_var[[var]])) { + add_info_var <- paste0(extra_info_var[[var]][[info_var]], collapse = ', ') + ncatt_put(file_nc, var, info_var, add_info_var) + } + } + } + nc_close(file_nc) + } +} + +.saveExp <- function(data, startdates = NULL, dates = NULL, destination = "./", + defined_dims, ftime_dim = 'time', varname = 'var', + metadata_var = NULL, extra_info_dim = NULL, + extra_string = NULL) { + # ftime_dim + if (!is.null(dates)) { + differ <- as.numeric((dates - dates[1])/3600) + dim_time <- list(ncdim_def(name = ftime_dim, units = paste('hours since', dates[1]), + vals = differ, calendar = 'proleptic_gregorian', + longname = ftime_dim, unlim = TRUE)) + names(dim_time) <- ftime_dim + defined_dims <- c(defined_dims, dim_time) + } + + ## Define var metadata + var_info <- NULL + extra_info_var <- NULL + i_var_info <- metadata_var[!sapply(metadata_var, inherits, 'list')] + + # name + var_info[['name']] <- varname + # units + if ('units' %in% names(i_var_info)) { + var_info[['units']] <- i_var_info[['units']] + i_var_info[['units']] <- NULL + } else { + var_info[['units']] <- '' + } + # dim + var_info[['dim']] <- defined_dims + # missval + if ('missval' %in% names(i_var_info)) { + var_info[['missval']] <- i_var_info[['missval']] + i_var_info[['missval']] <- NULL + } else { + var_info[['missval']] <- NULL + } + # longname + if (any(c('longname', 'long_name') %in% names(i_var_info))) { + longname <- names(i_var_info)[which(names(i_var_info) %in% c('longname', 'long_name'))] + var_info[['longname']] <- i_var_info[[longname]] + i_var_info[[longname]] <- NULL + } else { + var_info[['longname']] <- varname + } + # prec + if ('prec' %in% names(i_var_info)) { + var_info[['prec']] <- i_var_info[['prec']] + i_var_info[['prec']] <- NULL + } else { + prec <- typeof(data) + if (prec == 'character') { + var_info[['prec']] <- 'char' + } + if (any(prec %in% c('short', 'float', 'double', 'integer', 'char', 'byte'))) { + var_info[['prec']] <- prec + } else { + var_info[['prec']] <- 'double' + } + } + # extra information + if (!is.null(names(i_var_info))) { + extra_info_var <- i_var_info + } + + datanc <- ncvar_def(name = var_info[['name']], + units = var_info[['units']], + dim = var_info[['dim']], + missval = var_info[['missval']], + longname = var_info[['longname']], + prec = var_info[['prec']]) + + if (is.null(extra_string)) { + file_name <- paste0(varname, "_", startdates, ".nc") + } else { + file_name <- paste0(varname, "_", extra_string, "_", startdates, ".nc") + } + full_filename <- file.path(destination, file_name) + file_nc <- nc_create(full_filename, datanc) + ncvar_put(file_nc, datanc, data) + + # Additional attributes + for (dim in names(defined_dims)) { + if (dim %in% names(extra_info_dim)) { + for (info_dim in names(extra_info_dim[[dim]])) { + add_info_dim <- paste0(extra_info_dim[[dim]][[info_dim]], collapse = ', ') + ncatt_put(file_nc, dim, info_dim, add_info_dim) + } + } + } + # Additional dimension attributes + if (!is.null(extra_info_var)) { + for (info_var in names(extra_info_var)) { + add_info_var <- paste0(extra_info_var[[info_var]], collapse = ', ') + ncatt_put(file_nc, varname, info_var, add_info_var) + } + } + + nc_close(file_nc) +} diff --git a/modules/Saving/Saving.R b/modules/Saving/Saving.R index fc9fe4ee..60e886b9 100644 --- a/modules/Saving/Saving.R +++ b/modules/Saving/Saving.R @@ -14,6 +14,8 @@ source("modules/Saving/R/get_time.R") source("modules/Saving/R/get_latlon.R") source("modules/Saving/R/get_global_attributes.R") source("modules/Saving/R/drop_dims.R") +source("modules/Saving/R/save_metrics_scorecards.R") +source("modules/Saving/R/tmp/CST_SaveExp.R") Saving <- function(recipe, data, skill_metrics = NULL, diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index d3f60d4e..355acab5 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -366,8 +366,13 @@ Skill <- function(recipe, data, agg = 'global') { if (recipe$Analysis$Workflow$Skill$save == 'all') { corr_metric_names <- grep("^corr_individual_members", names(skill_metrics)) if (length(corr_metric_names) == 0) { - save_metrics(recipe = recipe, skill = skill_metrics, - data_cube = data$hcst, agg = agg) + if (tolower(recipe$Analysis$Output_format) == 'scorecards') { + save_metrics_scorecards(recipe = recipe, skill = skill_metrics, + data_cube = data$hcst, agg = agg) + } else { + save_metrics(recipe = recipe, skill = skill_metrics, + data_cube = data$hcst, agg = agg) + } } else { # Save corr if (length(skill_metrics[corr_metric_names]) > 0) { @@ -376,8 +381,13 @@ Skill <- function(recipe, data, agg = 'global') { } # Save other skill metrics if (length(skill_metrics[-corr_metric_names]) > 0) { - save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names], - data_cube = data$hcst, agg = agg) + if (tolower(recipe$Analysis$Output_format) == 'scorecards') { + save_metrics_scorecards(recipe = recipe, skill = skill_metrics[-corr_metric_names], + data_cube = data$hcst, agg = agg) + } else { + save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names], + data_cube = data$hcst, agg = agg) + } } } } -- GitLab From 5d5cfb65d61fc5737277524026f718c5fb7b7828 Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 15 Nov 2023 17:01:30 +0100 Subject: [PATCH 08/78] Copy and paste the recipe reference --- .../ex1_2-recipe.yml | 105 ++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml new file mode 100644 index 00000000..2de4bd48 --- /dev/null +++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml @@ -0,0 +1,105 @@ +################################################################################ +## RECIPE DESCRIPTION +################################################################################ + +Description: + Author: V. Agudetse + Info: Test for recipe splitting + +################################################################################ +## ANALYSIS CONFIGURATION +################################################################################ + +Analysis: + Horizon: Seasonal + Variables: # ECVs and Indicators? + - {name: tas, freq: monthly_mean} + Datasets: + System: # multiple systems for single model, split if Multimodel = F + - {name: ECMWF-SEAS5} + Multimodel: False # single option + Reference: + - {name: ERA5} # multiple references for single model? + Time: + sdate: # list, split + - '0101' + - '0201' + - '0301' + - '0401' + - '0501' + - '0601' + - '0701' + - '0801' + - '0901' + - '1001' + - '1101' + - '1201' + fcst_year: + hcst_start: '1993' # single option + hcst_end: '2003' # single option + ftime_min: 1 # single option + ftime_max: 6 # single option + Region: # multiple lists, split? Add region name if length(Region) > 1 + - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9} + Regrid: + method: bilinear ## TODO: allow multiple methods? + type: to_system + Workflow: + Anomalies: + compute: yes + cross_validation: no + save: 'none' + Calibration: + method: raw ## TODO: list, split? + save: 'none' + Skill: + metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr # list, don't split + cross_validation: yes + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3]] # list, don't split + save: 'none' + # Visualization: + # plots: skill_metrics + Scorecards: + execute: yes # yes/no + regions: + Extra-tropical NH: {lon.min: 0, lon.max: 360, lat.min: 30, lat.max: 90} + Tropics: {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: 30} + Extra-tropical SH : {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: -90} + start_months: NULL + metric: mean_bias enscorr rpss crpss enssprerr + metric_aggregation: 'score' + table_label: NULL + fileout_label: NULL + col1_width: NULL + col2_width: NULL + calculate_diff: FALSE + ncores: 14 + remove_NAs: no # bool, don't split + Output_format: Scorecards # string, don't split + +################################################################################ +## Run CONFIGURATION +################################################################################ +Run: + Loglevel: INFO + Terminal: yes + output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ + code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ + autosubmit: yes + # fill only if using autosubmit + auto_conf: + script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_scorecards_workflow.R # replace with the path to your script + expid: a6ae # replace with your EXPID + hpc_user: bsc32762 # replace with your hpc username + wallclock: 03:00 # hh:mm + processors_per_job: 14 + platform: nord3v2 + custom_directives: ['#SBATCH --exclusive'] + email_notifications: yes # enable/disable email notifications. Change it if you want to. + email_address: victoria.agudetse@bsc.es # replace with your email address + notify_completed: yes # notify me by email when a job finishes + notify_failed: no # notify me by email when a job fails + + -- GitLab From 7d152eb3ec602443fd58931fd5725caacd3fbf7d Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Thu, 16 Nov 2023 12:07:16 +0100 Subject: [PATCH 09/78] changed statistics outpt structure --- modules/Skill/Skill.R | 2 +- modules/Statistics/Statistics.R | 9 ++++++--- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index 355acab5..6c066ff5 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -385,7 +385,7 @@ Skill <- function(recipe, data, agg = 'global') { save_metrics_scorecards(recipe = recipe, skill = skill_metrics[-corr_metric_names], data_cube = data$hcst, agg = agg) } else { - save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names], + save_metrics_scorecards(recipe = recipe, skill = skill_metrics[-corr_metric_names], data_cube = data$hcst, agg = agg) } } diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index 40f1889b..fe36c4ce 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -46,7 +46,9 @@ compute_statistics <- function(recipe, data, agg = 'global'){ target_dims = c(time_dim, memb_dim), fun = 'sd')$output1 - statistics[[ stat ]] <- list('std_hcst' = std_hcst, 'std_obs' = std_obs) + statistics[[ 'std_hcst' ]] <- std_hcst + statistics[[ 'std_obs' ]] <- std_obs + } ## close if on std @@ -61,7 +63,8 @@ compute_statistics <- function(recipe, data, agg = 'global'){ target_dims = c(time_dim, memb_dim), fun = 'sd')$output1)^2 - statistics[[ stat ]] <- list('var_hcst' = var_hcst, 'var_obs' = var_obs) + statistics[[ 'var_hcst' ]] <- var_hcst + statistics[[ 'var_obs' ]] <- var_obs } ## close if on var @@ -79,7 +82,7 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (recipe$Analysis$Workflow$Statistics$save == 'all') { # Save all statistics - save_metrics(recipe = recipe, skill = statistics, ## Not able to save data with these dimensions + save_metrics_scorecards(recipe = recipe, skill = statistics, ## Not able to save data with these dimensions data_cube = data$hcst, agg = agg) ## The length of parameter 'order' should be the same with the dimension length of parameter 'data'. } -- GitLab From 3256eeb3e7b56d14f0c723bc736d2caeeaf2581d Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Thu, 16 Nov 2023 14:47:39 +0100 Subject: [PATCH 10/78] Set locale to English --- tools/libs.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tools/libs.R b/tools/libs.R index 3b855a77..2c034dd3 100644 --- a/tools/libs.R +++ b/tools/libs.R @@ -41,3 +41,5 @@ source("tools/restructure_recipe.R") # Settings options(bitmapType = 'cairo') pdf(NULL) +Sys.setlocale("LC_MESSAGES", 'en_GB.UTF-8') + -- GitLab From 092e04edf1a01986a8e765b744e2963391b07a7a Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Thu, 16 Nov 2023 15:23:31 +0100 Subject: [PATCH 11/78] Change LC_MESSAGES to LC_ALL --- tools/libs.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/libs.R b/tools/libs.R index 2c034dd3..40146786 100644 --- a/tools/libs.R +++ b/tools/libs.R @@ -41,5 +41,5 @@ source("tools/restructure_recipe.R") # Settings options(bitmapType = 'cairo') pdf(NULL) -Sys.setlocale("LC_MESSAGES", 'en_GB.UTF-8') +Sys.setlocale("LC_ALL", 'en_GB.UTF-8') -- GitLab From b82ae165cb10e2e70b28bb7326866f1fa65c89f0 Mon Sep 17 00:00:00 2001 From: PALOMA TRASCASA CASTRO Date: Thu, 16 Nov 2023 17:30:15 +0100 Subject: [PATCH 12/78] Added command to specify English language --- modules/Visualization/R/plot_most_likely_terciles_map.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Visualization/R/plot_most_likely_terciles_map.R b/modules/Visualization/R/plot_most_likely_terciles_map.R index abd08f7b..5d60f8c1 100644 --- a/modules/Visualization/R/plot_most_likely_terciles_map.R +++ b/modules/Visualization/R/plot_most_likely_terciles_map.R @@ -109,7 +109,7 @@ plot_most_likely_terciles <- function(recipe, "\n", "Most Likely Tercile / Initialization: ", i_syear) months <- lubridate::month(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ], - label = T, abb = F) + label = T, abb = F,locale = "en_GB") years <- lubridate::year(fcst$attrs$Dates[1, 1, which(start_date == i_syear), ]) if (recipe$Analysis$Workflow$Visualization$multi_panel) { ## TODO: Ensure this works for daily and sub-daily cases -- GitLab From 74f3370459d7ea43582c0b31dbe5c3717742cf2d Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Mon, 20 Nov 2023 11:20:21 +0100 Subject: [PATCH 13/78] Tas-tos data blending development --- conf/archive.yml | 7 +- conf/variable-dictionary.yml | 13 +- modules/Loading/Loading.R | 9 +- modules/Loading/R/load_tas_tos.R | 513 ++++++++++++++++++ modules/Loading/R/mask_tas_tos.R | 65 +-- .../atomic_recipes/recipe_tas-tos_nadia.yml | 55 ++ .../recipe-seasonal_monthly_1_tas-tos.yml | 56 ++ .../testthat/test-seasonal_monthly_tas-tos.R | 98 ++++ tools/check_recipe.R | 12 + tools/prepare_outputs.R | 8 +- 10 files changed, 780 insertions(+), 56 deletions(-) create mode 100644 modules/Loading/R/load_tas_tos.R create mode 100644 recipes/atomic_recipes/recipe_tas-tos_nadia.yml create mode 100644 tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml create mode 100644 tests/testthat/test-seasonal_monthly_tas-tos.R diff --git a/conf/archive.yml b/conf/archive.yml index 0b643ae9..cca68c22 100644 --- a/conf/archive.yml +++ b/conf/archive.yml @@ -21,13 +21,14 @@ esarchive: "ta850":"monthly_mean/ta850_f12h/", "g300":"monthly_mean/g300_f12h/", "g500":"monthly_mean/g500_f12h/", "g850":"monthly_mean/g500_f12h/", "tdps":"monthly_mean/tdps_f6h/", "psl":"monthly_mean/psl_f6h/", - "tos":"monthly_mean/tos_f6h/"} + "tos":"monthly_mean/tos_f6h/", "sic":"monthly_mean/sic_f24h/"} nmember: fcst: 51 hcst: 25 calendar: "proleptic_gregorian" time_stamp_lag: "0" reference_grid: "/esarchive/exp/ecmwf/system5c3s/monthly_mean/tas_f6h/tas_20180501.nc" + land_sea_mask: "/esarchive/exp/ecmwf/system5c3s/constant/lsm/lsm.nc" ECMWF-SEAS5.1: name: "ECMWF SEAS5 (v5.1)" institution: "European Centre for Medium-Range Weather Forecasts" @@ -165,9 +166,11 @@ esarchive: "ta300":"montly_mean/ta300_f1h-r1440x721cds/", "ta500":"monthly_mean/ta500_f1h-r1440x721cds/", "ta850":"monthly_mean/ta850_f1h-r1440x721cds/", - "tos":"monthly_mean/tos_f1h-r1440x721cds/"} + "tos":"monthly_mean/tos_f1h-r1440x721cds/", + "sic":"monthly_mean/sic_f1h-r1440x721cds/"} calendar: "standard" reference_grid: "/esarchive/recon/ecmwf/era5/monthly_mean/tas_f1h-r1440x721cds/tas_201805.nc" + land_sea_mask: "/esarchive/recon/ecmwf/era5/constant/lsm-r1440x721cds/sftof.nc" ERA5-Land: name: "ERA5-Land" institution: "European Centre for Medium-Range Weather Forecasts" diff --git a/conf/variable-dictionary.yml b/conf/variable-dictionary.yml index 0bfbffe0..c440eac1 100644 --- a/conf/variable-dictionary.yml +++ b/conf/variable-dictionary.yml @@ -204,13 +204,16 @@ vars: long_name: "Surface Upward Sensible Heat Flux" standard_name: "surface_upward_sensible_heat_flux" accum: no -## Adding new variable - tasanomaly: + tas-tos: units: "K" - long_name: "Near-Surface Air Temperature Anomaly" - standard_name: "air_temperature_anom" + long_name: "Blended air - sea temperature" + standard_name: "air_sea_temperature" + accum: no + sic: + units: "1" + long_name: "Sea Ice Concentration" + standard_name: "sea_ice_concentration" accum: no - # Coordinates diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R index 315ef78d..43dea065 100644 --- a/modules/Loading/Loading.R +++ b/modules/Loading/Loading.R @@ -12,8 +12,13 @@ Loading <- function(recipe) { # Case: esarchive time_horizon <- tolower(recipe$Analysis$Horizon) if (time_horizon == "seasonal") { - source("modules/Loading/R/load_seasonal.R") - data <- load_seasonal(recipe) + if(recipe$Analysis$Variables$name == 'tas-tos') { + source("modules/Loading/R/load_tas_tos.R") + data <- load_tas_tos(recipe) + } else { + source("modules/Loading/R/load_seasonal.R") + data <- load_seasonal(recipe) + } } else if (time_horizon == "decadal") { source("modules/Loading/R/load_decadal.R") data <- load_decadal(recipe) diff --git a/modules/Loading/R/load_tas_tos.R b/modules/Loading/R/load_tas_tos.R new file mode 100644 index 00000000..5e6b8837 --- /dev/null +++ b/modules/Loading/R/load_tas_tos.R @@ -0,0 +1,513 @@ +source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") +# Load required libraries/funs +source("modules/Loading/R/dates2load.R") +source("modules/Loading/R/get_timeidx.R") +source("modules/Loading/R/check_latlon.R") +source('modules/Loading/R/mask_tas_tos.R') + + +load_tas_tos <- function(recipe) { + + # ------------------------------------------- + # Set params ----------------------------------------- + + hcst.inityear <- recipe$Analysis$Time$hcst_start + hcst.endyear <- recipe$Analysis$Time$hcst_end + lats.min <- recipe$Analysis$Region$latmin + lats.max <- recipe$Analysis$Region$latmax + lons.min <- recipe$Analysis$Region$lonmin + lons.max <- recipe$Analysis$Region$lonmax + ref.name <- recipe$Analysis$Datasets$Reference$name + exp.name <- recipe$Analysis$Datasets$System$name + + variable <- c("tas", "tos", "sic") + store.freq <- recipe$Analysis$Variables$freq + + if(is.null(recipe$Analysis$Variables$sic_threshold)){ + sic.threshold = 0.15 + } else { + sic.threshold <- recipe$Analysis$Variables$sic_threshold + } + + data_order <- c('dat', 'var', 'sday', 'sweek', 'syear', 'time', 'latitude', 'longitude', 'ensemble') + + + # get sdates array + ## LOGGER: Change dates2load to extract logger from recipe? + sdates <- dates2load(recipe, recipe$Run$logger) + + idxs <- NULL + idxs$hcst <- get_timeidx(sdates$hcst, + recipe$Analysis$Time$ftime_min, + recipe$Analysis$Time$ftime_max, + time_freq=store.freq) + + if (!(is.null(sdates$fcst))) { + idxs$fcst <- get_timeidx(sdates$fcst, + recipe$Analysis$Time$ftime_min, + recipe$Analysis$Time$ftime_max, + time_freq=store.freq) + } + + # get esarchive datasets dict: + archive <- read_yaml("conf/archive.yml")[[recipe$Run$filesystem]] + exp_descrip <- archive$System[[exp.name]] + + freq.hcst <- unlist(exp_descrip[[store.freq]][variable[1]]) + reference_descrip <- archive$Reference[[ref.name]] + freq.obs <- unlist(reference_descrip[[store.freq]][variable[1]]) + obs.dir <- reference_descrip$src + fcst.dir <- exp_descrip$src + hcst.dir <- exp_descrip$src + fcst.nmember <- exp_descrip$nmember$fcst + hcst.nmember <- exp_descrip$nmember$hcst + + var_dir_obs <- reference_descrip[[store.freq]][variable] + var_dir_exp <- exp_descrip[[store.freq]][variable] + + # ----------- + obs.path <- paste0(archive$src, obs.dir, "$var_dir$", + "$var$_$file_date$.nc") + + hcst.path <- paste0(archive$src, hcst.dir, "$var_dir$", + "$var$_$file_date$.nc") + + fcst.path <- paste0(archive$src, hcst.dir, "$var_dir$", + "/$var$_$file_date$.nc") + + # Define regrid parameters: + #------------------------------------------------------------------- + regrid_params <- get_regrid_params(recipe, archive) + + # Longitude circular sort and latitude check + #------------------------------------------------------------------- + circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max) + + if (recipe$Analysis$Variables$freq == "monthly_mean"){ + split_multiselected_dims = TRUE + } else { + split_multiselected_dims = FALSE + } + + # Load hindcast data without regrid + #------------------------------------------------------------------- + hcst <- Start(dat = hcst.path, + var = variable, + var_dir = var_dir_exp, + file_date = sdates$hcst, + time = idxs$hcst, + var_dir_depends = 'var', + latitude = values(list(lats.min, lats.max)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lons.min, lons.max)), + longitude_reorder = circularsort, + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude'), + ensemble = c('member', 'ensemble')), + ensemble = indices(1:hcst.nmember), + metadata_dims = 'var', # change to just 'var'? + return_vars = list(latitude = 'dat', + longitude = 'dat', + time = 'file_date'), + split_multiselected_dims = split_multiselected_dims, + retrieve = TRUE) + + + # Remove var_dir dimension + if ("var_dir" %in% names(dim(hcst))) { + hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected") + } + + if (recipe$Analysis$Variables$freq == "daily_mean") { + # Adjusts dims for daily case, could be removed if startR allows + # multidim split + names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear" + default_dims <- c(dat = 1, var = 1, sday = 1, + sweek = 1, syear = 1, time = 1, + latitude = 1, longitude = 1, ensemble = 1) + default_dims[names(dim(hcst))] <- dim(hcst) + dim(hcst) <- default_dims + # Change time attribute dimensions + default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1) + names(dim(attr(hcst, "Variables")$common$time))[which(names( + dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear" + default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- + dim(attr(hcst, "Variables")$common$time) + dim(attr(hcst, "Variables")$common$time) <- default_time_dims + } + + # Define sea-ice grid points based of sea-ice concentration threshold + ice_hcst <- hcst[,3,,,,,,,] >= sic.threshold + + # Replace Tos with Tas for data points with sea ice + hcst[,2,,,,,,,][ice_hcst] <- hcst[,1,,,,,,,][ice_hcst] + + + # Convert hcst to s2dv_cube object + ## TODO: Give correct dimensions to $Dates + ## (sday, sweek, syear instead of file_date) + hcst <- as.s2dv_cube(hcst) + # Adjust dates for models where the time stamp goes into the next month + if (recipe$Analysis$Variables$freq == "monthly_mean") { + hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag) + } + + # Combine hcst tas and tos data + #------------------------------------------------------------------- + + hcst <- mask_tas_tos(input_data = hcst, region = c(lons.min, lons.max,lats.min, lats.max), + mask_path = archive$System[[exp.name]]$land_sea_mask, lsm_var_name = 'lsm', + lon = hcst$coords$longitude, lat = hcst$coords$latitude, + lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) + + hcst$dims[['var']] <- dim(hcst$data)[['var']] + hcst$attrs$Variable$varName <- 'tas-tos' + + hcst$data <- Reorder(hcst$data, data_order) + + # Load forecast data without regrid + #------------------------------------------------------------------- + if (!is.null(recipe$Analysis$Time$fcst_year)) { + # the call uses file_date instead of fcst_syear so that it can work + # with the daily case and the current version of startR not allowing + # multiple dims split + + fcst <- Start(dat = fcst.path, + var = variable, + var_dir = var_dir_exp, + var_dir_depends = 'var', + file_date = sdates$fcst, + time = idxs$fcst, + latitude = values(list(lats.min, lats.max)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lons.min, lons.max)), + longitude_reorder = circularsort, + synonims = list(latitude = c('lat', 'latitude'), + longitude = c('lon', 'longitude'), + ensemble = c('member', 'ensemble')), + ensemble = indices(1:fcst.nmember), + metadata_dims = 'var', + return_vars = list(latitude = 'dat', + longitude = 'dat', + time = 'file_date'), + split_multiselected_dims = split_multiselected_dims, + retrieve = TRUE) + + if ("var_dir" %in% names(dim(fcst))) { + fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected") + } + + if (recipe$Analysis$Variables$freq == "daily_mean") { + # Adjusts dims for daily case, could be removed if startR allows + # multidim split + names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear" + default_dims <- c(dat = 1, var = 1, sday = 1, + sweek = 1, syear = 1, time = 1, + latitude = 1, longitude = 1, ensemble = 1) + default_dims[names(dim(fcst))] <- dim(fcst) + dim(fcst) <- default_dims + # Change time attribute dimensions + default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1) + names(dim(attr(fcst, "Variables")$common$time))[which(names( + dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear" + default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- + dim(attr(fcst, "Variables")$common$time) + dim(attr(fcst, "Variables")$common$time) <- default_time_dims + } + + # Define sea-ice grid points based of sea-ice concentration threshold + ice_fcst <- fcst[,3,,,,,,,] >= sic.threshold + + # Replace Tos with Tas for datapoints with sea ice + fcst[,2,,,,,,,][ice_fcst] <- fcst[,1,,,,,,,][ice_fcst] + + + # Convert fcst to s2dv_cube + fcst <- as.s2dv_cube(fcst) + # Adjust dates for models where the time stamp goes into the next month + if (recipe$Analysis$Variables$freq == "monthly_mean") { + fcst$attrs$Dates[] <- + fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag) + } + + # Combine fcst tas and tos data + #------------------------------------------------------------------- + + fcst <- mask_tas_tos(input_data = fcst, region = c(lons.min, lons.max,lats.min, lats.max), + mask_path = archive$System[[exp.name]]$land_sea_mask, lsm_var_name = 'lsm', + lon = fcst$coords$longitude, lat = fcst$coords$latitude, + lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) + + fcst$dims[['var']] <- dim(fcst$data)[['var']] + fcst$attrs$Variable$varName <- 'tas-tos' + + fcst$data <- Reorder(fcst$data, data_order) + + } else { + fcst <- NULL + } + + # Load obs data without regrid + #------------------------------------------------------------------- + + # Obtain dates and date dimensions from the loaded hcst data to make sure + # the corresponding observations are loaded correctly. + dates <- hcst$attrs$Dates + dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")] + + # Separate Start() call for monthly vs daily data + if (store.freq == "monthly_mean") { + + dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m") + dim(dates_file) <- dim(dates) + + + # Define variables for blended tas-tos datasets + if (recipe$Analysis$Datasets$Reference$name == 'BEST'){ + variable <- 'tas' + var_dir_obs <- reference_descrip[[store.freq]][variable] + } + + obs <- Start(dat = obs.path, + var = variable, + var_dir = var_dir_obs, + var_dir_depends = 'var', + file_date = dates_file, + latitude = values(list(lats.min, lats.max)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lons.min, lons.max)), + longitude_reorder = circularsort, + synonims = list(latitude = c('lat','latitude'), + longitude = c('lon','longitude')), + metadata_dims = 'var', + return_vars = list(latitude = 'dat', + longitude = 'dat', + time = 'file_date'), + split_multiselected_dims = TRUE, + retrieve = TRUE) + + + } else if (store.freq == "daily_mean") { + + # Get year and month for file_date + dates_file <- sapply(dates, format, '%Y%m') + dim(dates_file) <- dim(dates) + # Set hour to 12:00 to ensure correct date retrieval for daily data + lubridate::hour(dates) <- 12 + lubridate::minute(dates) <- 00 + # Restore correct dimensions + dim(dates) <- dim(dates_file) + + obs <- Start(dat = obs.path, + var = variable, + var_dir = var_dir_obs, + var_dir_depends = 'var', + file_date = sort(unique(dates_file)), + time = dates, + time_var = 'time', + time_across = 'file_date', + merge_across_dims = TRUE, + merge_across_dims_narm = TRUE, + latitude = values(list(lats.min, lats.max)), + latitude_reorder = Sort(decreasing = TRUE), + longitude = values(list(lons.min, lons.max)), + longitude_reorder = circularsort, + synonims = list(latitude = c('lat','latitude'), + longitude = c('lon','longitude')), + metadata_dims = 'var', + return_vars = list(latitude = 'dat', + longitude = 'dat', + time = 'file_date'), + split_multiselected_dims = TRUE, + retrieve = TRUE) + + } + + # Remove var_dir dimension + if ("var_dir" %in% names(dim(obs))) { + obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected") + } + # Adds ensemble dim to obs (for consistency with hcst/fcst) + default_dims <- c(dat = 1, var = 1, sday = 1, + sweek = 1, syear = 1, time = 1, + latitude = 1, longitude = 1, ensemble = 1) + default_dims[names(dim(obs))] <- dim(obs) + dim(obs) <- default_dims + + if(!recipe$Analysis$Datasets$Reference$name %in% c('HadCRUT4','HadCRUT5','BEST','GISTEMPv4')){ + + # Define sea-ice grid points based of sea-ice concentration threshold + ice_obs <- (obs[,3,,,,,,,]) >= sic.threshold + + # Replace NA values with False + ice_obs[is.na(ice_obs)] <- FALSE + + # Replace Tos with Tas for datapoints with sea ice + obs[,2,,,,,,,][ice_obs] <- obs[,1,,,,,,,][ice_obs] + } + + # Convert obs to s2dv_cube + obs <- as.s2dv_cube(obs) + + + # Combine obs tas and tos data + #------------------------------------------------------------------- + ## TODO: Ask about this list + if(!recipe$Analysis$Datasets$Reference$name %in% c('HadCRUT4','HadCRUT5','BEST','GISTEMPv4')){ + + obs <- mask_tas_tos(input_data = obs, region = c(lons.min, lons.max,lats.min, lats.max), + mask_path = archive$Reference[[ref.name]]$land_sea_mask, lsm_var_name = 'sftof', + lon = obs$coords$longitude, lat = obs$coords$latitude, + lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) + + obs$dims[['var']] <- dim(obs$data)[['var']] + obs$attrs$Variable$varName <- 'tas-tos' + + } ## close if on reference name + + obs$data <- Reorder(obs$data, data_order) + + + # Regrid data + #------------------------------------------------------------------- + + # Regrid reference to system grid: + if(recipe$Analysis$Regrid$type == 'to_system'){ + + aux <- CDORemap(data_array = obs$data, ## Not regridding to desired grid when latitudes are ordered descending + lons = obs$coords$longitude, + lats = obs$coords$latitude, + grid = regrid_params$obs.gridtype, + method = recipe$Analysis$Regrid$method, + avoid_writes = TRUE, + crop = c(lons.min, lons.max,lats.min, lats.max), + force_remap = TRUE) + + obs$data <- aux$data_array + obs$coords$longitude <- aux$lons + obs$coords$latitude <- aux$lats + obs$dims['longitude'] <- dim(aux$data_array)['longitude'] + obs$dims['latitude'] <- dim(aux$data_array)['latitude'] + rm(aux) + } + + # Regrid system to reference grid: + if(recipe$Analysis$Regrid$type == 'to_reference'){ + + aux <- CDORemap(data_array = hcst$data, + lons = hcst$coords$longitude, lats = hcst$coords$latitude, + grid = regrid_params$fcst.gridtype, method = recipe$Analysis$Regrid$method, + avoid_writes = TRUE, crop = TRUE, + force_remap = TRUE) + + hcst$data <- aux$data_array + hcst$coords$longitude <- aux$lons + hcst$coords$latitude <- aux$lats + hcst$dims['longitude'] <- dim(aux$data_array)['longitude'] + hcst$dims['latitude'] <- dim(aux$data_array)['latitude'] + rm(aux) + + if (!is.null(recipe$Analysis$Time$fcst_year)) { + aux <- CDORemap(data_array = fcst$data, + lons = fcst$coords$longitude, + lats = fcst$coords$latitude, + grid = regrid_params$fcst.gridtype, + method = recipe$Analysis$Regrid$method, + avoid_writes = TRUE, crop = TRUE, + force_remap = TRUE) + + fcst$data <- aux$data_array + fcst$coords$longitude <- aux$lons + fcst$coords$latitude <- aux$lats + fcst$dims['longitude'] <- dim(aux$data_array)['longitude'] + fcst$dims['latitude'] <- dim(aux$data_array)['latitude'] + rm(aux) + } + } + + # Regrid all data to user defined grid: + if(!recipe$Analysis$Regrid$type %in% c('to_system','to_reference')){ + + aux <- CDORemap(data_array = hcst$data, + lons = hcst$coords$longitude, lats = hcst$coords$latitude, + grid = regrid_params$fcst.gridtype, method = recipe$Analysis$Regrid$method, + avoid_writes = TRUE, crop = TRUE, + force_remap = TRUE) + + hcst$data <- aux$data_array + hcst$coords$longitude <- aux$lons + hcst$coords$latitude <- aux$lats + hcst$dims['longitude'] <- dim(aux$data_array)['longitude'] + hcst$dims['latitude'] <- dim(aux$data_array)['latitude'] + rm(aux) + + if (!is.null(recipe$Analysis$Time$fcst_year)) { + aux <- CDORemap(data_array = fcst$data, + lons = fcst$coords$longitude, + lats = fcst$coords$latitude, + grid = regrid_params$fcst.gridtype, + method = recipe$Analysis$Regrid$method, + avoid_writes = TRUE, crop = TRUE, + force_remap = TRUE) + + fcst$data <- aux$data_array + fcst$coords$longitude <- aux$lons + fcst$coords$latitude <- aux$lats + fcst$dims['longitude'] <- dim(aux$data_array)['longitude'] + fcst$dims['latitude'] <- dim(aux$data_array)['latitude'] + rm(aux) + } + + aux <- CDORemap(data_array = obs$data, + lons = obs$coords$longitude, + lats = obs$coords$latitude, + grid = regrid_params$obs.gridtype, + method = recipe$Analysis$Regrid$method, + avoid_writes = TRUE, crop = TRUE, + force_remap = TRUE) + + obs$data <- aux$data_array + obs$coords$longitude <- aux$lons + obs$coords$latitude <- aux$lats + obs$dims['longitude'] <- dim(aux$data_array)['longitude'] + obs$dims['latitude'] <- dim(aux$data_array)['latitude'] + rm(aux) + } + + + # Check for consistency between hcst and obs grid + if (!(recipe$Analysis$Regrid$type == 'none')) { + if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) { + lat_error_msg <- paste("Latitude mismatch between hcst and obs.", + "Please check the original grids and the", + "regrid parameters in your recipe.") + error(recipe$Run$logger, lat_error_msg) + hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1], + "; Last hcst lat: ", hcst$lat[length(hcst$lat)]) + info(recipe$Run$logger, hcst_lat_msg) + obs_lat_msg <- paste0("First obs lat: ", obs$lat[1], + "; Last obs lat: ", obs$lat[length(obs$lat)]) + info(recipe$Run$logger, obs_lat_msg) + stop("hcst and obs don't share the same latitudes.") + } + if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) { + lon_error_msg <- paste("Longitude mismatch between hcst and obs.", + "Please check the original grids and the", + "regrid parameters in your recipe.") + error(recipe$Run$logger, lon_error_msg) + hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1], + "; Last hcst lon: ", hcst$lon[length(hcst$lon)]) + info(recipe$Run$logger, hcst_lon_msg) + obs_lon_msg <- paste0("First obs lon: ", obs$lon[1], + "; Last obs lon: ", obs$lon[length(obs$lon)]) + info(recipe$Run$logger, obs_lon_msg) + stop("hcst and obs don't share the same longitudes.") + + } + } + + info(recipe$Run$logger, + "##### DATA LOADING COMPLETED SUCCESSFULLY #####") + + return(list(hcst = hcst, fcst = fcst, obs = obs)) +} diff --git a/modules/Loading/R/mask_tas_tos.R b/modules/Loading/R/mask_tas_tos.R index a2eeb0b6..cc693795 100644 --- a/modules/Loading/R/mask_tas_tos.R +++ b/modules/Loading/R/mask_tas_tos.R @@ -2,29 +2,28 @@ library(multiApply) library(startR) library(s2dv) -mask_tas_tos <- function(input_data, grid, lon, lat, region = region , - lon_dim = 'lon', lat_dim = 'lat', ncores = NULL){ +mask_tas_tos <- function(input_data, mask_path, lsm_var_name = lsm_var_name, lon, lat, region = region, + lon_dim = 'lon', lat_dim = 'lat', + ncores = NULL){ - - mask <- .load_mask(grid = grid, lon_dim = lon_dim, lat_dim = lat_dim, + + mask <- .load_mask(mask_path = mask_path, lsm_var_name = lsm_var_name, lon_dim = lon_dim, lat_dim = lat_dim, sea_value = 1, land_value = 0, region = region) - ## TO DO: improve the check and correct lats stopifnot(all(lon == mask$lon)) stopifnot(max(abs(as.numeric(round(lat,2) - round(mask$lat,2)))) < 0.1) # stopifnot(all(lat == mask$lat)) - + tas <- Subset(input_data$data, along = 'var', indices = 1) tos <- Subset(input_data$data, along = 'var', indices = 2) - tas_tos <- multiApply::Apply(data = list(tas, tos), - target_dims = c(lon_dim, lat_dim), - fun = .mask_tas_tos, - mask = mask$mask, - sea_value = 1, - ncores = ncores)$output1 - input_data$data <- tas_tos - + input_data$data <- multiApply::Apply(data = list(tas, tos), + target_dims = c(lon_dim, lat_dim), + fun = .mask_tas_tos, + mask = mask$mask, + sea_value = 1, + ncores = ncores)$output1 + return(input_data) } @@ -33,52 +32,32 @@ mask_tas_tos <- function(input_data, grid, lon, lat, region = region , return(data_tas) } -.load_mask <- function(grid, mask_path = NULL, land_value = 0, sea_value = 1, +.load_mask <- function(mask_path = mask_path, lsm_var_name = lsm_var_name, land_value = 0, sea_value = 1, lon_dim = 'lon', lat_dim = 'lat', region = region){ - if (is.null(mask_path)){ - mask_sea_land_path <- '/esarchive/exp/ecmwf/system5c3s/constant/lsm/lsm.nc' ## /esarchive/recon/ecmwf/era5land/constant/lsm-r3600x1801cds/lsm.nc' - } else if (is.character(mask_path)){ - mask_sea_land_path <- mask_path - } else { - stop("mask_path must be NULL (to use the default mask and interpolate it to - the specified grid) or a string with the mask's path you want to load") - } - lons.min <- region[1] lons.max <- region[2] lats.min <- region[3] lats.max <- region[4] - - ## TO DO: - ## Fix region filter for lat and lon - ## Fix 'number' parameter for mask - - data <- startR::Start(dat = mask_sea_land_path, - var = 'lsm', - lon = 'all', - lat = 'all', - number = 1, ## needed to add for ensemble member dimension of lsm.nc - # lon = values(list(lons.min, lons.max)), - # lat = values(list(lats.min, lats.max)), - transform = CDORemapper, transform_extra_cells = 2, - transform_params = list(grid = grid, method = 'con', crop = region), - transform_vars = c('lat','lon'), + data <- startR::Start(dat = mask_path, + var = lsm_var_name, + lon = values(list(lons.min, lons.max)), + lat = values(list(lats.min, lats.max)), return_vars = list(lat = NULL, lon = NULL), synonims = list(lon = c('lon','longitude'), lat = c('lat','latitude')), - lat_reorder = Sort(decreasing = FALSE), - lon_reorder = CircularSort(0,359.9), + lat_reorder = Sort(decreasing = TRUE), + lon_reorder = CircularSort(0,360), num_procs = 1, retrieve = TRUE) - + mask <- list(mask = drop(data), lon = as.numeric(attr(data,'Variables')$common$lon), lat = as.numeric(attr(data,'Variables')$common$lat)) mask$mask[data <= 0.5] <- sea_value mask$mask[data > 0.5] <- land_value - names(dim(mask$mask)) <- c(lon_dim, lat_dim) + names(dim(mask$mask)) <- c(lon_dim, lat_dim) return(mask) } diff --git a/recipes/atomic_recipes/recipe_tas-tos_nadia.yml b/recipes/atomic_recipes/recipe_tas-tos_nadia.yml new file mode 100644 index 00000000..466659a2 --- /dev/null +++ b/recipes/atomic_recipes/recipe_tas-tos_nadia.yml @@ -0,0 +1,55 @@ +Description: + Author: V. Agudetse + +Analysis: + Horizon: Seasonal + Variables: + name: tas-tos + sic_threshold: 0.15 ## sea ice threshold for tas-tos blending, default = 0.15 + freq: monthly_mean + Datasets: + System: + name: ECMWF-SEAS5 + Multimodel: False + Reference: + name: BEST + Time: + sdate: '0101' + #fcst_year: + hcst_start: '2014' + hcst_end: '2016' + ftime_min: 1 + ftime_max: 1 + Region: + latmin: -90 + latmax: 90 + lonmin: 0 + lonmax: 359.9 + Regrid: + method: bilinear + type: to_system + Workflow: + Calibration: + method: raw + save: 'none' + Anomalies: + compute: yes + cross_validation: no + save: 'none' + Skill: + metric: mean_bias EnsCorr RPS RPSS CRPS CRPSS enssprerr + cross_validation: no + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3]] + save: 'none' + Indicators: + index: no + ncores: 15 + remove_NAs: yes + Output_format: scorecards +Run: + Loglevel: INFO + Terminal: yes + output_dir: /esarchive/scratch/nmilders/scorecards_data/test/ + code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite_tas-tos/ diff --git a/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml b/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml new file mode 100644 index 00000000..41048db1 --- /dev/null +++ b/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml @@ -0,0 +1,56 @@ +Description: + Author: N. Milders + +Analysis: + Horizon: Seasonal + Variables: + name: tas-tos + sic_threshold: 0.15 + freq: monthly_mean + Datasets: + System: + name: ECMWF-SEAS5 + Multimodel: False + Reference: + name: ERA5 + Time: + sdate: '0101' + fcst_year: '2018' + hcst_start: '1993' + hcst_end: '1996' + ftime_min: 1 + ftime_max: 3 + Region: + latmin: 17 + latmax: 20 + lonmin: 12 + lonmax: 15 + Regrid: + method: bilinear + type: to_system + Workflow: + Anomalies: + compute: no + cross_validation: + save: 'none' + Calibration: + method: mse_min + save: 'all' + Skill: + metric: RPSS CRPSS EnsCorr Corr_individual_members Enscorr_specs + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3], [1/10, 9/10]] + save: 'all' + Indicators: + index: no + Visualization: + plots: skill_metrics most_likely_terciles forecast_ensemble_mean + multi_panel: yes + projection: cylindrical_equidistant + Output_format: S2S4E +Run: + Loglevel: INFO + Terminal: yes + output_dir: ./tests/out-logs/ + code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/ ##/esarchive/scratch/vagudets/repos/auto-s2s/ diff --git a/tests/testthat/test-seasonal_monthly_tas-tos.R b/tests/testthat/test-seasonal_monthly_tas-tos.R new file mode 100644 index 00000000..1345f63d --- /dev/null +++ b/tests/testthat/test-seasonal_monthly_tas-tos.R @@ -0,0 +1,98 @@ +context("Seasonal monthly data") + +source("./modules/Loading/Loading.R") + +recipe_file <- "tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml" +recipe <- prepare_outputs(recipe_file, disable_checks = F) +archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive + +# Load datasets +suppressWarnings({invisible(capture.output( +data <- Loading(recipe) +))}) + + +# ------- TESTS -------- + +test_that("1. Loading", { + +expect_equal( +is.list(data), +TRUE +) +expect_equal( +names(data), +c("hcst", "fcst", "obs") +) +expect_equal( +class(data$hcst), +"s2dv_cube" +) +expect_equal( +class(data$fcst), +"s2dv_cube" +) +expect_equal( +class(data$obs), +"s2dv_cube" +) +expect_equal( +names(data$hcst), +c("data", "dims", "coords", "attrs") +) +expect_equal( +names(data$hcst), +names(data$fcst) +) +expect_equal( +names(data$hcst), +names(data$obs) +) +expect_equal( +dim(data$hcst$data), +c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 4, longitude = 4, ensemble = 25) +) +expect_equal( +dim(data$fcst$data), +c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 4, longitude = 4, ensemble = 51) +) +expect_equal( +dim(data$hcst$attrs$Dates), +c(sday = 1, sweek = 1, syear = 4, time = 3) +) +expect_equal( +as.vector(drop(data$hcst$data)[1:2,1:2,1,2,3]), +c(285.5869, 287.8836, 285.9362, 289.0483), +tolerance = 0.0001 +) +expect_equal( +mean(data$hcst$data), +290.1099, +tolerance = 0.0001 +) +expect_equal( +range(data$hcst$data), +c(283.2845, 299.7845), +tolerance = 0.0001 +) +expect_equal( +(data$hcst$attrs$Dates)[1], +as.POSIXct("1993-01-31 18:00:00", tz = 'UTC') +) +expect_equal( +(data$hcst$attrs$Dates)[2], +as.POSIXct("1994-01-31 18:00:00", tz = 'UTC') +) +expect_equal( +(data$hcst$attrs$Dates)[5], +as.POSIXct("1993-02-28 18:00:00", tz = 'UTC') +) +expect_equal( +(data$obs$attrs$Dates)[10], +as.POSIXct("1994-03-15 12:00:00", tz = 'UTC') +) + +}) + +# Delete files +unlink(recipe$Run$output_dir, recursive = T) diff --git a/tools/check_recipe.R b/tools/check_recipe.R index e4c59a55..69a7dd4c 100644 --- a/tools/check_recipe.R +++ b/tools/check_recipe.R @@ -62,6 +62,18 @@ check_recipe <- function(recipe) { } else { archive <- NULL } + # Check variable parameters + variables <- tolower(strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]) + # Sea-ice threshold check + if (("tas-tos" %in% variables) && + (!is.null(recipe$Analysis$Variables$sic_threshold))) { + if (!is.numeric(recipe$Analysis$Variables$sic_threshold) || + dplyr::between(recipe$Analysis$Variables$sic_threshold, 0, 1)) { + error(recipe$Run$logger, + paste("The element Analysis:Variables:sic_threshold must be a", + "numeric value between 0 and 1.")) + } + } # Check system names if (!is.null(archive)) { if (!all(recipe$Analysis$Datasets$System$name %in% names(archive$System))) { diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R index fa4d23ed..54e82095 100644 --- a/tools/prepare_outputs.R +++ b/tools/prepare_outputs.R @@ -89,6 +89,10 @@ prepare_outputs <- function(recipe_file, warn(recipe$Run$logger, "Filesystem not specified in the recipe. Setting it to 'esarchive'.") } + # Restructure the recipe to make the atomic recipe more readable + if (restructure) { + recipe <- restructure_recipe(recipe) + } # Run recipe checker if (disable_checks) { warn(recipe$Run$logger, @@ -96,9 +100,5 @@ prepare_outputs <- function(recipe_file, } else { check_recipe(recipe) } - # Restructure the recipe to make the atomic recipe more readable - if (restructure) { - recipe <- restructure_recipe(recipe) - } return(recipe) } -- GitLab From dca341e1fb6cccc08378de482325d56f24d409a1 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 20 Nov 2023 15:46:32 +0100 Subject: [PATCH 14/78] Add 'tos' to MeteoFrance System 7 --- conf/archive.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/conf/archive.yml b/conf/archive.yml index 0b643ae9..e6e4a2c5 100644 --- a/conf/archive.yml +++ b/conf/archive.yml @@ -53,7 +53,8 @@ esarchive: src: "exp/meteofrance/system7c3s/" monthly_mean: {"tas":"monthly_mean/tas_f6h/", "g500":"monthly_mean/g500_f12h/", "prlr":"monthly_mean/prlr_f24h/", "sfcWind": "monthly_mean/sfcWind_f6h/", - "tasmax":"monthly_mean/tasmax_f6h/", "tasmin": "monthly_mean/tasmin_f6h/"} + "tasmax":"monthly_mean/tasmax_f6h/", "tasmin": "monthly_mean/tasmin_f6h/", + "tos":"monthly_mean/tos_f6h/"} nmember: fcst: 51 hcst: 25 -- GitLab From 75da4ee121aa5c62672859b4db15e007a78a1c4a Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 20 Nov 2023 15:46:57 +0100 Subject: [PATCH 15/78] =?UTF-8?q?Add=20usecase=201.3=20(El=20Ni=C3=B1o=20i?= =?UTF-8?q?ndices=20on=20multiple=20systems)=20(WIP)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../ex1_3-recipe.yml | 57 +++++++++++++++++++ .../ex1_3_nino_indices_comparison.R | 38 +++++++++++++ 2 files changed, 95 insertions(+) create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml new file mode 100644 index 00000000..b563e4ac --- /dev/null +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml @@ -0,0 +1,57 @@ +Description: + Author: V. Agudetse + Info: Computing El Nino indices for ECMWF SEAS5 and MeteoFrance System7 + +Analysis: + Horizon: seasonal + Variables: + - {name: tos, freq: monthly_mean, units: K} + Datasets: + System: + - {name: ECMWF-SEAS5} + - {name: Meteo-France-System7} + Multimodel: no + Reference: + - {name: ERA5} + Time: + sdate: '1101' + fcst_year: '2021' + hcst_start: '1993' + hcst_end: '2016' + ftime_min: 2 + ftime_max: 4 + Region: + latmin: -90 + latmax: 90 + lonmin: 0 + lonmax: 359.9 + Regrid: + method: 'bilinear' + type: 'to_system' + Workflow: + Anomalies: + compute: yes + cross_validation: no + save: none + Indices: + Nino1+2: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} + Nino3: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} + Nino3.4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} + Nino4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} + Skill: + metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3], [1/10, 9/10]] + save: none + Indicators: + index: no + ncores: 8 + remove_NAs: no + Output_format: S2S4E + logo: TRUE +Run: + Loglevel: INFO + Terminal: yes + output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ # ______ + code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ # _____ diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R new file mode 100644 index 00000000..c2b44541 --- /dev/null +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R @@ -0,0 +1,38 @@ +############################################################################### +## Author: Nรบria Pรฉrez-Zanรณn +## Description: Computes the Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4 indices and some +## some skill metrics for each index. +## Instructions: To run it, modify the recipe to set your own output directory. +############################################################################### + +# Load modules +source("modules/Loading/Loading.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Indices/Indices.R") +source("modules/Skill/Skill.R") + +# Read recipe +recipe_file <- "use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml" +recipe <- prepare_outputs(recipe_file) + +data <- Loading(recipe) +data <- Units(recipe, data) +data <- Anomalies(recipe, data) +nino_indices <- Indices(data = data, recipe = recipe) + +str(nino_indices, max.level = 3) +for (index in nino_indices) { + skill_metrics <- Skill(recipe = recipe, data = index, agg = "region") +} + +# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino1, +# agg = 'region') +# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3, +# agg = 'region') +# +# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino4, +# agg = 'region') +# +# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3.4, +# agg = 'region') +# -- GitLab From e37467430092981cd3fa5c31807171c394a6a84f Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Tue, 21 Nov 2023 16:27:56 +0100 Subject: [PATCH 16/78] included cross-validation for skill scores --- modules/Skill/R/CRPS_clim.R | 30 ++++++++++++++++++++++-------- modules/Skill/R/RPS_clim.R | 4 ++-- modules/Skill/Skill.R | 4 +++- 3 files changed, 27 insertions(+), 11 deletions(-) diff --git a/modules/Skill/R/CRPS_clim.R b/modules/Skill/R/CRPS_clim.R index 36db4e94..b66cab78 100644 --- a/modules/Skill/R/CRPS_clim.R +++ b/modules/Skill/R/CRPS_clim.R @@ -1,13 +1,27 @@ -CRPS_clim <- function(obs, memb_dim ='ensemble'){ +# CRPS version for climatology +CRPS_clim <- function(obs, memb_dim ='ensemble', return_mean = TRUE, clim.cross.val= TRUE){ time_dim <- names(dim(obs)) obs_time_len <- dim(obs)[time_dim] - + + if (isFALSE(clim.cross.val)) { ## Without cross-validation ref <- array(data = rep(obs, each = obs_time_len), dim = c(obs_time_len, obs_time_len)) - names(dim(ref)) <- c(time_dim, memb_dim) - # ref: [sdate, memb] - # obs: [sdate] + } else if (isTRUE(clim.cross.val)) { ## With cross-validation (excluding the value of that year to create ref for that year) + ref <- array(data = NA, dim = c(obs_time_len, obs_time_len - 1)) + for (i in 1:obs_time_len) { + ref[i, ] <- obs[-i] + } + } + + names(dim(ref)) <- c(time_dim, memb_dim) + # ref: [sdate, memb] + # obs: [sdate] crps_ref <- s2dv:::.CRPS(exp = ref, obs = obs, time_dim = time_dim, memb_dim = memb_dim, - dat_dim = NULL, Fair = FALSE) - # crps_ref should be [sdate] - return(mean(crps_ref)) + dat_dim = NULL, Fair = FALSE) + + # crps_ref should be [sdate] + if (return_mean == TRUE) { + return(mean(crps_ref)) + } else { + return(crps_ref) + } } diff --git a/modules/Skill/R/RPS_clim.R b/modules/Skill/R/RPS_clim.R index e8b6452d..c93c6747 100644 --- a/modules/Skill/R/RPS_clim.R +++ b/modules/Skill/R/RPS_clim.R @@ -1,12 +1,12 @@ # RPS version for climatology -RPS_clim <- function(obs, indices_for_clim = NULL, prob_thresholds = c(1/3, 2/3)) { +RPS_clim <- function(obs, indices_for_clim = NULL, prob_thresholds = c(1/3, 2/3), cross.val = TRUE) { if (is.null(indices_for_clim)){ indices_for_clim <- 1:length(obs) } obs_probs <- .GetProbs(data = obs, indices_for_quantiles = indices_for_clim, ## temporarily removed s2dv::: - prob_thresholds = prob_thresholds, weights = NULL, cross.val = T) ## here! + prob_thresholds = prob_thresholds, weights = NULL, cross.val = cross.val) # clim_probs: [bin, sdate] clim_probs <- c(prob_thresholds[1], diff(prob_thresholds), 1 - prob_thresholds[length(prob_thresholds)]) clim_probs <- array(clim_probs, dim = dim(obs_probs)) diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index fd54f17f..04fbc52e 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -89,6 +89,7 @@ Skill <- function(recipe, data, agg = 'global') { skill_metrics[[ metric ]] <- skill rps_clim <- Apply(list(data$obs$data), target_dims = c(time_dim, memb_dim), + cross.val = cross.val, RPS_clim)$output1 rps_clim <- .drop_dims(rps_clim) skill_metrics[[paste0(metric, "_clim")]] <- rps_clim @@ -140,7 +141,8 @@ Skill <- function(recipe, data, agg = 'global') { skill <- .drop_dims(skill) skill_metrics[[ metric ]] <- skill crps_clim <- Apply(list(data$obs$data), target_dims = time_dim, - fun = CRPS_clim, memb_dim = memb_dim)$output1 + fun = CRPS_clim, memb_dim = memb_dim, + clim.cross.val = cross.val)$output1 crps_clim <- .drop_dims(crps_clim) skill_metrics[['crps_clim']] <- crps_clim # CRPSS and FCRPSS -- GitLab From 44e53a691207c26a179d0c0ebfff635efb7ce857 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Wed, 22 Nov 2023 16:20:38 +0100 Subject: [PATCH 17/78] Correct variable retrieval in recipe checks --- tools/check_recipe.R | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/tools/check_recipe.R b/tools/check_recipe.R index 69a7dd4c..01b790b8 100644 --- a/tools/check_recipe.R +++ b/tools/check_recipe.R @@ -63,9 +63,14 @@ check_recipe <- function(recipe) { archive <- NULL } # Check variable parameters - variables <- tolower(strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]]) + if ("name" %in% names(recipe$Analysis$Variables)) { + recipe_variables <- recipe$Analysis$Variables$name + } else { + recipe_variables <- sapply(recipe$Analysis$Variables, get, x = "name") + } + recipe_variables <- unlist(strsplit(recipe_variables, ", | |,")) # Sea-ice threshold check - if (("tas-tos" %in% variables) && + if (("tas-tos" %in% recipe_variables) && (!is.null(recipe$Analysis$Variables$sic_threshold))) { if (!is.numeric(recipe$Analysis$Variables$sic_threshold) || dplyr::between(recipe$Analysis$Variables$sic_threshold, 0, 1)) { @@ -434,7 +439,6 @@ check_recipe <- function(recipe) { error_status <- T } # Check that variables correspond with indices requested - recipe_variables <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]] if (("nao" %in% recipe_indices) && (!all(recipe_variables %in% c("psl", "z500")))) { error(recipe$Run$logger, -- GitLab From 7c6b99f2236c08450fcdb04a6da3c55710ea79af Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 23 Nov 2023 14:19:39 +0100 Subject: [PATCH 18/78] Avoid producing Inf when all data are NAs --- .../Indices/R/plot_deterministic_forecast.R | 9 +++++-- modules/Visualization/R/tmp/PlotRobinson.R | 25 ++++++++++++++++--- 2 files changed, 29 insertions(+), 5 deletions(-) diff --git a/modules/Indices/R/plot_deterministic_forecast.R b/modules/Indices/R/plot_deterministic_forecast.R index da107e47..30f13b78 100644 --- a/modules/Indices/R/plot_deterministic_forecast.R +++ b/modules/Indices/R/plot_deterministic_forecast.R @@ -16,8 +16,13 @@ plot_deterministic_forecast <- function(obs, fcst, title = NULL, n_fcst <- as.numeric(dim(fcst)[time_dim]) if (is.null(ylims)) { - ylims <- c(-max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE), - max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE)) + if (all(is.na(fcst)) && all(is.na(obs))) { + # No data, return random ylims + ylims <- c(-1, 1) + } else { + ylims <- c(-max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE), + max(abs(fcst) + 0.1, abs(obs) + 0.1, na.rm = TRUE)) + } } if (is.null(xlabs)) { xlabs <- 1:n_fcst diff --git a/modules/Visualization/R/tmp/PlotRobinson.R b/modules/Visualization/R/tmp/PlotRobinson.R index d8210258..d50e7f36 100644 --- a/modules/Visualization/R/tmp/PlotRobinson.R +++ b/modules/Visualization/R/tmp/PlotRobinson.R @@ -251,7 +251,26 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL, # Color bar ## Check: brks, cols, bar_limits, color_fun, bar_extra_margin, units ## Build: brks, cols, bar_limits, col_inf, col_sup - var_limits <- c(min(data, na.rm = TRUE), max(data, na.rm = TRUE)) + if (!all(is.na(data))) { + var_limits <- c(min(data[!is.infinite(data)], na.rm = TRUE), + max(data[!is.infinite(data)], na.rm = TRUE)) + } else { + warning("All the data are NAs. The map will be filled with colNA.") + if (!is.null(brks) && length(brks) > 1) { + #NOTE: var_limits be like this to avoid warnings from ColorBar + var_limits <- c(min(brks, na.rm = TRUE) + diff(brks)[1], + max(brks, na.rm = TRUE)) + } else if (!is.null(bar_limits)) { + var_limits <- c(bar_limits[1] + 0.01, bar_limits[2]) + } else { + var_limits <- c(-0.5, 0.5) # random range since colorbar is not going to be plotted + if (drawleg) { + drawleg <- FALSE + warning("All data are NAs. Color bar won't be drawn. If you want to have ", + "color bar still, define parameter 'brks' or 'bar_limits'.") + } + } + } colorbar <- ColorBar(brks = brks, cols = cols, vertical = vertical, subsampleg = NULL, bar_limits = bar_limits, var_limits = var_limits, triangle_ends = triangle_ends, col_inf = col_inf, col_sup = col_sup, color_fun = color_fun, @@ -317,12 +336,12 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL, # Add triangles to brks brks_ggplot <- brks - if (max(data, na.rm = T) > tail(brks, 1)) { + if (var_limits[2] > tail(brks, 1)) { brks_ggplot <- c(brks_ggplot, max(data, na.rm = T)) } else { brks_ggplot <- c(brks_ggplot, tail(brks, 1) + diff(tail(brks, 2))) } - if (min(data, na.rm = T) < brks[1]) { + if (var_limits[1] < brks[1]) { brks_ggplot <- c(min(data, na.rm = T), brks_ggplot) } else { brks_ggplot <- c(brks[1] - diff(brks[1:2]), brks_ggplot) -- GitLab From 9e8cb02c88f3adc1b2da570fd23a0382df4512a5 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Thu, 23 Nov 2023 16:24:29 +0100 Subject: [PATCH 19/78] Add use case 1.3 (WIP) --- .../ex1_3-handson.md | 16 ++++++++ .../ex1_3-recipe.yml | 11 +++--- .../ex1_3-script.R | 35 +++++++++++++++++ .../ex1_3_nino_indices_comparison.R | 38 ------------------- 4 files changed, 57 insertions(+), 43 deletions(-) create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md create mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R delete mode 100644 use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md new file mode 100644 index 00000000..e310d289 --- /dev/null +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md @@ -0,0 +1,16 @@ +# Hands-on 1.3: Computation of El Niรฑo indices for two seasonal models + +## Goal +Create a SUNSET recipe to compute and evaluate the skill of several El Niรฑo indices (Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. The computation for each model will be run in the cluster as two separate jobs. + +## 0. Cloning the SUNSET repository + +The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch//git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command. + +```shell +# Clone the GitLab repository to create a local copy of the code +git clone https://earth.bsc.es/gitlab/es/sunset.git +``` + +## 1. Modifying the recipe + diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml index b563e4ac..495f89f0 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml @@ -15,16 +15,16 @@ Analysis: - {name: ERA5} Time: sdate: '1101' - fcst_year: '2021' + fcst_year: hcst_start: '1993' hcst_end: '2016' ftime_min: 2 ftime_max: 4 Region: latmin: -90 - latmax: 90 - lonmin: 0 - lonmax: 359.9 + latmax: 90 + lonmin: 0 + lonmax: 359.9 Regrid: method: 'bilinear' type: 'to_system' @@ -47,7 +47,7 @@ Analysis: Indicators: index: no ncores: 8 - remove_NAs: no + remove_NAs: yes Output_format: S2S4E logo: TRUE Run: @@ -55,3 +55,4 @@ Run: Terminal: yes output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ # ______ code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ # _____ + autosubmit: no diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R new file mode 100644 index 00000000..59843b44 --- /dev/null +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R @@ -0,0 +1,35 @@ +############################################################################### +## Author: Nรบria Pรฉrez-Zanรณn and Victรฒria Agudetse Roures +## Description: Computes the Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4 indices and some +## skill metrics for each index. +## Instructions: To run it, follow the steps described in: +## use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md +############################################################################### + +# Load modules +source("modules/Loading/Loading.R") +source("modules/Units/Units.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Indices/Indices.R") +source("modules/Skill/Skill.R") + +# Read recipe +args = commandArgs(trailingOnly = TRUE) +recipe_file <- args[1] +recipe <- read_atomic_recipe(recipe_file) + +# Load data +data <- Loading(recipe) +# Check units and transform if needed +data <- Units(recipe, data) +# Compute tos anomalies +data <- Anomalies(recipe, data) +# Compute Niรฑo Indices +nino_indices <- Indices(data = data, recipe = recipe) + +# We can compute the Skill metrics for each of the El Niรฑo indices, +# specifying that the data is spatially aggregated, with the parameter +# agg = "region". +for (index in nino_indices) { + skill_metrics <- Skill(recipe = recipe, data = index, agg = "region") +} diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R deleted file mode 100644 index c2b44541..00000000 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3_nino_indices_comparison.R +++ /dev/null @@ -1,38 +0,0 @@ -############################################################################### -## Author: Nรบria Pรฉrez-Zanรณn -## Description: Computes the Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4 indices and some -## some skill metrics for each index. -## Instructions: To run it, modify the recipe to set your own output directory. -############################################################################### - -# Load modules -source("modules/Loading/Loading.R") -source("modules/Anomalies/Anomalies.R") -source("modules/Indices/Indices.R") -source("modules/Skill/Skill.R") - -# Read recipe -recipe_file <- "use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml" -recipe <- prepare_outputs(recipe_file) - -data <- Loading(recipe) -data <- Units(recipe, data) -data <- Anomalies(recipe, data) -nino_indices <- Indices(data = data, recipe = recipe) - -str(nino_indices, max.level = 3) -for (index in nino_indices) { - skill_metrics <- Skill(recipe = recipe, data = index, agg = "region") -} - -# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino1, -# agg = 'region') -# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3, -# agg = 'region') -# -# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino4, -# agg = 'region') -# -# skill_metrics <- Skill(recipe = recipe, data = nino_indices$Nino3.4, -# agg = 'region') -# -- GitLab From 026ad2ebfe7950591b309b5ce512df76518b10aa Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 23 Nov 2023 17:28:23 +0100 Subject: [PATCH 20/78] correct variable name --- modules/Visualization/R/tmp/PlotRobinson.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Visualization/R/tmp/PlotRobinson.R b/modules/Visualization/R/tmp/PlotRobinson.R index d50e7f36..bd427448 100644 --- a/modules/Visualization/R/tmp/PlotRobinson.R +++ b/modules/Visualization/R/tmp/PlotRobinson.R @@ -264,8 +264,8 @@ PlotRobinson <- function(data, lon, lat, lon_dim = NULL, lat_dim = NULL, var_limits <- c(bar_limits[1] + 0.01, bar_limits[2]) } else { var_limits <- c(-0.5, 0.5) # random range since colorbar is not going to be plotted - if (drawleg) { - drawleg <- FALSE + if (!is.null(legend)) { + legend <- NULL warning("All data are NAs. Color bar won't be drawn. If you want to have ", "color bar still, define parameter 'brks' or 'bar_limits'.") } -- GitLab From 89c029eb0e6a9d3641ae881f9b977ab66460210d Mon Sep 17 00:00:00 2001 From: Eva Rifa Date: Thu, 23 Nov 2023 17:31:00 +0100 Subject: [PATCH 21/78] Add the new version of LoadMetrics and provide an example in Scorecards.R --- modules/Saving/R/get_filename.R | 14 +- modules/Saving/R/save_metrics_scorecards.R | 12 ++ modules/Scorecards/R/tmp/LoadMetrics.R | 219 ++++++--------------- modules/Scorecards/Scorecards.R | 50 +++-- modules/Skill/Skill.R | 2 +- 5 files changed, 116 insertions(+), 181 deletions(-) diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R index ca40f1b5..9f6151e8 100644 --- a/modules/Saving/R/get_filename.R +++ b/modules/Saving/R/get_filename.R @@ -38,17 +38,17 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) { "probs" = {type_info <- paste0("-probs_", date, "_")}, "bias" = {type_info <- paste0("-bias_", date, "_")}, # new - "rps_syear" = {type_info <- paste0("_rps-syear_")}, - "rps_clim_syear" = {type_info <- paste0("_rps-clim-syear_")}, - "crps_syear" = {type_info <- paste0("_crps-syear_")}, - "crps_clim_syear" = {type_info <- paste0("_crps-clim-syear_")}, - "crps" = {type_info <- paste0("_crps_")}, - "mean_bias" = {type_info <- paste0("_mean-bias_")}, + "rps_syear" = {type_info <- paste0("rps_syear")}, + "rps_clim_syear" = {type_info <- paste0("rps_clim_syear")}, + "crps_syear" = {type_info <- paste0("crps_syear")}, + "crps_clim_syear" = {type_info <- paste0("crps_clim_syear")}, + "crps" = {type_info <- paste0("crps")}, + "mean_bias" = {type_info <- paste0("mean_bias")}, {type_info <- paste0("_", file.type, "_")}) # Build file name file <- paste0("scorecards_", system, "_", reference, "_", - var, type_info, hcst_start, "-", hcst_end, "_s", shortdate) + var, "_",type_info, "_", hcst_start, "-", hcst_end, "_s", shortdate) } else { switch(file.type, "skill" = {file <- paste0(var, gg, "-skill_", dd, shortdate)}, diff --git a/modules/Saving/R/save_metrics_scorecards.R b/modules/Saving/R/save_metrics_scorecards.R index 020f2593..9c133395 100644 --- a/modules/Saving/R/save_metrics_scorecards.R +++ b/modules/Saving/R/save_metrics_scorecards.R @@ -7,6 +7,18 @@ save_metrics_scorecards <- function(recipe, fcst.horizon <- tolower(recipe$Analysis$Horizon) store.freq <- recipe$Analysis$Variables$freq + # archive <- get_archive(recipe) + # global_attributes <- .get_global_attributes(recipe, archive) + # ## TODO: Sort out the logic once default behavior is decided + # if ((!is.null(recipe$Analysis$Workflow$Anomalies$compute)) && + # (recipe$Analysis$Workflow$Anomalies$compute)) { + # global_attributes <- c(list(from_anomalies = "Yes"), + # global_attributes) + # } else { + # global_attributes <- c(list(from_anomalies = "No"), + # global_attributes) + # } + # Select start date # If a fcst is provided, use that as the ref. year. Otherwise use 1970. if (fcst.horizon == 'decadal') { diff --git a/modules/Scorecards/R/tmp/LoadMetrics.R b/modules/Scorecards/R/tmp/LoadMetrics.R index e5e15421..55030d95 100644 --- a/modules/Scorecards/R/tmp/LoadMetrics.R +++ b/modules/Scorecards/R/tmp/LoadMetrics.R @@ -1,7 +1,15 @@ -#' Scorecards load metrics from verification suite +#'Scorecards load metrics from verification suite #' -#'@description Scorecards function to load saved data files +#'Scorecards function to load saved data files. This function is created to +#'load NetCDF files that contain 1 metric for each file. It calls startR::Start +#'internally. The NetCDF file is supposed to have the following dimensions: +#''longitude', 'latitude' and 'time'. If 'syear' is also a dimension, you must +#'set the parameter 'syear' different to NULL. #' +#'@param input_path A character string indicating the path where metrics output +#' files from verification suite are saved (or any other compatible files) +#'@param metrics A vector of character strings indicating the metrics name to +#' be loaded. #'@param system A vector of character strings defining the names of the #' system names following the archive.yml format from verification suite. #' Accepted system names: 'ECMWF-SEAS5', 'DWD-GFCS2.1', 'CMCC-SPS3.5', @@ -12,107 +20,40 @@ #'@param var A character string following the format from #' variable-dictionary.yml from verification suite (TO DO: multiple variables). #' The accepted names are: 'psl', 'tas', 'sfcWind', 'prlr'. -#'@param start.year A numeric indicating the start year of the reference period -#'@param end.year A numeric indicating the end year of the reference period -#'@param start.months A vector indicating the numbers of the start months -#'@param forecast.months A vector indicating the numbers of the forecast months -#'@param input.path A character string indicating the path where metrics output -#' files from verification suite are saved (or any other compatible files) +#'@param period A character string indicating the start and end years of the +#' reference period (e.g. '1993-203') +#'@param start_months A vector indicating the numbers of the start months +#'@param forecast_months A vector indicating the numbers of the forecast months +#'@param syear Used when the NetCDF file contains a metric that has the +#' dimension 'syear'. It can be any value. If there is no 'syear' dimension, +#' you must set it to NULL. It is NULL by default. #' #'@return A is a list by system and reference containing an array of with #' the following dimensions: longitude, latitude, forecast months, metrics, #' start dates. - #'@examples #'\dontrun{ #'loaded_metrics <- LoadMetrics(system = c('ECMWF-SEAS5','DWD-GFCS2.1'), -#' reference. = 'ERA5', +#' reference = 'ERA5', #' var = 'tas', -#' start.year = 1993, -#' end.year = 2016, +#' period = '1993-2016', #' metrics = c('mean_bias', 'enscorr', 'rpss', 'crpss', 'enssprerr'), -#' start.months = sprintf("%02d", 1:12), -#' forecast.months = 1:6, -#' input.path = '/esarchive/scratch/nmilders/scorecards_data/input_data') +#' start_months = sprintf("%02d", 1:12), +#' forecast_months = 1:6, +#' input_path = '/esarchive/scratch/nmilders/scorecards_data/input_data') #'} #'@import easyNCDF #'@import multiApply -#'@export -LoadMetrics <- function(system, reference, var, start.year, end.year, - metrics, start.months, forecast.months, - inf_to_na = FALSE, - input.path) { - - # Initial checks - ## system - if (!is.character(system)) { - stop("Parameter 'system' must be a character vector with the system names.") - } - ## reference - if (!is.character(reference)) { - stop("Parameter 'reference' must be a character vector with the reference ", - "names.") - } - ## var - if (!is.character(var)) { - stop("Parameter 'var' must be a character vector with the var ", - "names.") - } - if (length(var) > 1) { - warning("Parameter 'var' must be of length one. Only the first value ", - "will be used.") - var <- var[1] - } - ## start.year - if (!is.numeric(start.year)) { - stop("Parameter 'start.year' must be a numeric value.") - } - ## end.year - if (!is.numeric(end.year)) { - stop("Parameter 'end.year' must be a numeric value.") - } - ## metrics - if (!is.character(metrics)) { - stop("Parameter 'metrics' cannot be NULL.") - } - ## start.months - if (is.character(start.months)) { - warning("Parameter 'start.months' must be a numeric vector indicating ", - "the starting months.") - start.months <- as.numeric(start.months) - } - if (!is.numeric(start.months)) { - stop("Parameter 'start.months' must be a numeric vector indicating ", - "the starting months.") - } - start.months <- sprintf("%02d", start.months) - ## Check if sdates are continuous or discrete - if (all(diff(as.numeric(start.months)) == 1)) { - consecutive_start.months <- TRUE - } else { - consecutive_start.months <- FALSE - } - ## forecast.months - if (!is.numeric(forecast.months)) { - stop("Parameter 'forecast.months' must be a numeric vector indicating ", - "the starting months.") - } - ## input.path - if (!is.character(input.path)) { - stop("Parameter 'input.path must be a character string.") - } - if (length(input.path) > 1) { - input.path <- input.path[1] - warning("Parameter 'input.path' has length greater than 1 and only the ", - "first element will be used.") - } +LoadMetrics <- function(input_path, metrics, var, system, reference, + period = NULL, start_months = NULL, + forecast_months = NULL, inf_to_na = FALSE, + calib_method = NULL, syear = NULL) { + ## Remove . from names system <- gsub('.','', system, fixed = T) reference <- gsub('.','', reference, fixed = T) - period <- paste0(start.year, "-", end.year) - ## Define empty list to saved data all_metrics <- sapply(system, function(x) NULL) ## Load data for each system @@ -122,14 +63,15 @@ LoadMetrics <- function(system, reference, var, start.year, end.year, ## Load data for each reference for (ref in 1:length(reference)) { ## Call function to load metrics data - met <- .Loadmetrics(input.path = input.path, # recipe$Run$output, + met <- .loadmetrics(input_path = input_path, # recipe$Run$output, system = system[sys], reference = reference[ref], var = var, period = period, - start.months = start.months, - forecast.months = forecast.months, - metrics = metrics) + start_months = start_months, + forecast_months = forecast_months, + metrics = metrics, calib_method = calib_method, + syear = syear) ## Save metric data as array in reference list by_reference[[reference[ref]]] <- met ## Remove -Inf from crpss data if variable is precipitation @@ -142,74 +84,41 @@ LoadMetrics <- function(system, reference, var, start.year, end.year, } ## close loop on system return(all_metrics) -} ## close function +} -############################################################ +.loadmetrics <- function(input_path, metrics, var, system = NULL, + reference = NULL, period = NULL, start_months = NULL, + forecast_months = NULL, calib_method = NULL, + syear = NULL) { + # (1) Create the path to the files + directory_path <- paste0(input_path, system, "/", reference, "/", + calib_method, "/", var, "/") + file_path <- paste0(directory_path, "scorecards_", system, "_", reference, + "_", var, "_$var$_", period, "_s", "$smonths$", ".nc") -.Loadmetrics <- function(input.path, system, reference, - var, period, start.months, - forecast.months, metrics) { - - ## Load data for each start date - allfiles <- sapply(start.months, function(m) { - paste0(input.path, "/", system, "/", var, - "/scorecards_", system, "_", reference, "_", - var, "-skill_", period, "_s", m, # mod.pressure, - ".nc")}) - allfiles_exist <- sapply(allfiles, file.exists) - - # Check dims - files_exist_by_month <- seq(1:length(allfiles))[allfiles_exist] - allfiledims <- sapply(allfiles[allfiles_exist], easyNCDF::NcReadDims) - if (length(files_exist_by_month) == 0) { - stop("No files are found.") - } + # (2) Create the startR arguments list + if (is.null(forecast_months)) forecast_months <- 'all' + if (is.null(start_months)) start_months <- 'all' - num_dims <- numeric(dim(allfiledims)[1]) - for (i in 1:dim(allfiledims)[1]) { - if (length(unique(allfiledims[i,])) > 1) { - warning(paste0("Dimensions of system ", system," with var ", var, - " don't match.")) - } - num_dims[i] <- max(allfiledims[i,]) # We take the largest dimension + args = list(dat = file_path, + var = metrics, + longitude = 'all', + latitude = 'all', + time = forecast_months, + syear = "all", + smonths = start_months, + return_vars = list(longitude = 'dat', + latitude = 'dat', + time = NULL), + metadata_dims = c('dat', 'var'), + retrieve = TRUE) + + if (is.null(syear)) { + args$syear <- NULL } - # dims: [metric, longitude, latitude, time, smonth] - # or [metric, region, time, smonth] - - # Loop for file - dim(allfiles) <- c(dat = 1, sdate = length(allfiles)) - - array_met_by_sdate <- Apply(data = allfiles, target_dims = 'dat', fun = function(x) { - if (file.exists(x)) { - res <- easyNCDF::NcToArray(x, vars_to_read = metrics, unlist = T, - drop_var_dim = T) - names(dim(res)) <- NULL - } else { - res <- array(dim = c(length(metrics), allfiledims[-1,1])) - names(dim(res)) <- NULL - } - res})$output1 - - dim(array_met_by_sdate) <- c(metric = length(metrics), allfiledims[-1,1], - sdate = length(allfiles)) + + # (3): Call startR + loaded_metrics_start <- do.call(Start, args = args) - # Attributes - # Read attributes from the first existing file - if ("region" %in% rownames(allfiledims)) { - file_for_att <- ncdf4::nc_open(allfiles[allfiles_exist[1]]) - region <- ncdf4::ncatt_get(file_for_att, 'region') - ncdf4::nc_close(file_for_att) - attributes(array_met_by_sdate)$region <- region - } else { - lon <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'longitude', - unlist = T, drop_var_dim = T) - lat <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'latitude', - unlist = T, drop_var_dim = T) - attributes(array_met_by_sdate)$lon <- lon - attributes(array_met_by_sdate)$lat <- lat - } - attributes(array_met_by_sdate)$metrics <- metrics - attributes(array_met_by_sdate)$start.months <- start.months - attributes(array_met_by_sdate)$forecast.months <- forecast.months - return(array_met_by_sdate) + return(loaded_metrics_start) } diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 0dbcd921..8312735a 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -28,13 +28,18 @@ Scorecards <- function(recipe) { start.year <- as.numeric(recipe$Analysis$Time$hcst_start) end.year <- as.numeric(recipe$Analysis$Time$hcst_end) forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max - - if (recipe$Analysis$Workflow$Scorecards$start_months == 'all') { - start.months <- 1:12 - } else { - start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, - split = ", | |,")[[1]]) - } + calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) + + # NOTE (Eva): This condition needs to be checked, in my case + # (recipe$Analysis$Workflow$Scorecards$start_months = NULL) + start.months <- 1:12 # I added this line + # Needs to be corrected: + # if (recipe$Analysis$Workflow$Scorecards$start_months == 'all') { + # start.months <- 1:12 + # } else { + # start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, + # split = ", | |,")[[1]]) + # } regions <- recipe$Analysis$Workflow$Scorecards$regions for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])} @@ -76,17 +81,26 @@ Scorecards <- function(recipe) { ncores <- 1 # recipe$Analysis$ncores ## Load data files - loaded_metrics <- LoadMetrics(system = system, - reference = reference, - var = var, - start.year = start.year, - end.year = end.year, - metrics = metrics.load, - start.months = start.months, - forecast.months = forecast.months, - inf_to_na = inf.to.na, - input.path = input.path) - + start.months <- sprintf("%02d", start.months) + period <- paste0(start.year, "-", end.year) + + # NOTE (Eva): This is an example: + if (any(metrics.load %in% 'rps_syear')) { + metric_rps_syear <- .loadmetrics(input_path = input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_syear', + calib_method = calib.method, syear = TRUE) + } else if (any(metrics.load %in% c('crps', 'mean_bias'))) { + metrics <- LoadMetrics(input_path = input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = c('crps', 'mean_bias'), + calib_method = calib.method) + } + if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index 6c066ff5..9ffd6369 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -67,7 +67,7 @@ Skill <- function(recipe, data, agg = 'global') { cross.val <- recipe$Analysis$Workflow$Skill$cross_validation } skill_metrics <- list() - for (metric in strsplit(metrics, ", | |,")[[1]]) { + for (metric in strsplit(metrics, ", | |,")) { # Whether the fair version of the metric is to be computed if (metric %in% c('frps', 'frpss', 'bss10', 'bss90', 'fcrps', 'fcrpss')) { -- GitLab From 1323dc4e4ce3f3c721ff272fc760fb6cfccc0611 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 24 Nov 2023 10:06:32 +0100 Subject: [PATCH 22/78] Bugfix: name subdirectories when no calibration method is chosen --- modules/Saving/R/get_dir.R | 15 +++++++++------ modules/Saving/R/get_filename.R | 2 +- tools/prepare_outputs.R | 2 +- 3 files changed, 11 insertions(+), 8 deletions(-) diff --git a/modules/Saving/R/get_dir.R b/modules/Saving/R/get_dir.R index a2cbc79f..b8463114 100644 --- a/modules/Saving/R/get_dir.R +++ b/modules/Saving/R/get_dir.R @@ -38,18 +38,21 @@ get_dir <- function(recipe, variable, agg = "global") { } } ## TODO: Remove calibration method from output directory? - calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) + if (!is.null(recipe$Analysis$Workflow$Calibration$method)) { + calib.method <- paste0(tolower(recipe$Analysis$Workflow$Calibration$method), "-") + } else { + calib.method <- "" + } store.freq <- recipe$Analysis$Variables$freq - ## TODO: Change "_country" if (!is.null(recipe$Analysis$Region$name)) { outdir <- paste0(outdir, "/", recipe$Analysis$Region$name) } switch(tolower(agg), - "country" = {dir <- paste0(outdir, "/", system, "/", calib.method, - "-", store.freq, "/", variable, - "_country/", fcst.sdate, "/")}, + "region" = {dir <- paste0(outdir, "/", system, "/", calib.method, + store.freq, "/", variable, + "_region/", fcst.sdate, "/")}, "global" = {dir <- paste0(outdir, "/", system, "/", calib.method, - "-", store.freq, "/", variable, "/", + store.freq, "/", variable, "/", fcst.sdate, "/")}) } diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R index 1c925651..54bea81c 100644 --- a/modules/Saving/R/get_filename.R +++ b/modules/Saving/R/get_filename.R @@ -16,7 +16,7 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) { } switch(tolower(agg), - "country" = {gg <- "-country"}, + "region" = {gg <- "-region"}, "global" = {gg <- ""}) system <- gsub('.','', recipe$Analysis$Datasets$System$name, fixed = T) diff --git a/tools/prepare_outputs.R b/tools/prepare_outputs.R index 54e82095..f9942525 100644 --- a/tools/prepare_outputs.R +++ b/tools/prepare_outputs.R @@ -91,7 +91,7 @@ prepare_outputs <- function(recipe_file, } # Restructure the recipe to make the atomic recipe more readable if (restructure) { - recipe <- restructure_recipe(recipe) + recipe <- restructure_recipe(recipe) } # Run recipe checker if (disable_checks) { -- GitLab From 681d409c123267df48805680d355200ffada4e95 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 24 Nov 2023 11:02:01 +0100 Subject: [PATCH 23/78] Add Calibration to recipe and script --- .../ex1_3-recipe.yml | 20 +++++++++---------- .../ex1_3-script.R | 2 ++ 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml index 495f89f0..a4231e6a 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml @@ -26,26 +26,24 @@ Analysis: lonmin: 0 lonmax: 359.9 Regrid: - method: 'bilinear' - type: 'to_system' + method: bilinear + type: to_system Workflow: + Calibration: + method: mse_min + save: none Anomalies: compute: yes cross_validation: no save: none Indices: - Nino1+2: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} - Nino3: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} - Nino3.4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} - Nino4: {save: 'all', plot_ts: TRUE, plot_sp: TRUE} + Nino1+2: {save: all, plot_ts: yes, plot_sp: yes} + Nino3: {save: all, plot_ts: yes, plot_sp: yes} + Nino3.4: {save: all, plot_ts: yes, plot_sp: yes} + Nino4: {save: all, plot_ts: yes, plot_sp: yes} Skill: metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr save: 'all' - Probabilities: - percentiles: [[1/3, 2/3], [1/10, 9/10]] - save: none - Indicators: - index: no ncores: 8 remove_NAs: yes Output_format: S2S4E diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R index 59843b44..146d0d78 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R @@ -22,6 +22,8 @@ recipe <- read_atomic_recipe(recipe_file) data <- Loading(recipe) # Check units and transform if needed data <- Units(recipe, data) +# Calibrate data +data <- Calibration(recipe, data) # Compute tos anomalies data <- Anomalies(recipe, data) # Compute Niรฑo Indices -- GitLab From b532d159da5880222356549b7a56aca1db13442b Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 24 Nov 2023 14:59:42 +0100 Subject: [PATCH 24/78] Write explanations for use case 3 (WIP) --- .../ex1_3-handson.md | 85 ++++++++++++++++++- 1 file changed, 84 insertions(+), 1 deletion(-) diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md index e310d289..0f3f19d4 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md @@ -1,7 +1,9 @@ # Hands-on 1.3: Computation of El Niรฑo indices for two seasonal models ## Goal -Create a SUNSET recipe to compute and evaluate the skill of several El Niรฑo indices (Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. The computation for each model will be run in the cluster as two separate jobs. +Create a SUNSET recipe to compute and evaluate the skill of several El Niรฑo indices (Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4) for two models: ECMWF-SEAS5 and MeteoFrance System 7. We include the information for both of the models in a single recipe, and the SUNSET Launcher will split the recipe into two 'atomic recipes': one for each model. The computation for atomic recipe will be run in the cluster as two separate jobs. + +It is also possible to split a recipe along different Reference datasets, Variables and Start Dates. ## 0. Cloning the SUNSET repository @@ -14,3 +16,84 @@ git clone https://earth.bsc.es/gitlab/es/sunset.git ## 1. Modifying the recipe +The template recipe for this use case can be found in `use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml`. You should open it with an editor such as emacs or vim: + +```shell +# cd to the main SUNSET directory +# Open the recipe with a text editor such as vim or emacs +vim use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml +``` + +Then, under the 'Run' section of the recipe, you should edit the parameters `output_dir` and `code_dir` to point to your desire output directory and to the directory where your SUNSET code is stored, respectively. + +Feel free to also modify other aspects of the reicpe according to your particular needs. You can read more about the parameters and the available modules in the SUNSET wiki. + +## 2. The user-defined script + +The SUNSET Launcher is a bash script named launch_SUNSET.sh that can be found in the main directory of the SUNSET repository. When working without Autosubmit, it runs in two steps: + +1. Running the recipe checks, splitting the recipe into atomic recipes and creating the directory for the outputs. +2. Sending jobs to the cluster to run the user-defined script for each atomic recipe, using SLURM. + +The prepare_outputs() function is already incorporated into the first step. For that reason, it does not need to be included in the user-defined script in this particular case. In its place, we will use the function read_atomic_recipe(). The recipe path is passed as an argument onto the R script. The beginning of our script should look like this: + +```R +# Load the modules to be used +source("modules/Loading/Loading.R") +source("modules/Units/Units.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Indices/Indices.R") +source("modules/Skill/Skill.R") + +# Define the recipe path as the first argument from the command line +args = commandArgs(trailingOnly = TRUE) +recipe_file <- args[1] +# Read the atomic recipe +recipe <- read_atomic_recipe(recipe_file) +``` + +The rest of the user-defined script can be written in the same way as any other SUNSET script: + +```R +# Load data +data <- Loading(recipe) +# Check units and transform if needed +data <- Units(recipe, data) +# Calibrate data +data <- Calibration(recipe, data) +# Compute tos anomalies +data <- Anomalies(recipe, data) +# Compute Niรฑo Indices +nino_indices <- Indices(data = data, recipe = recipe) + +# We can compute the Skill metrics for each of the El Niรฑo indices, +# specifying that the data is spatially aggregated, with the parameter +# agg = "region". +for (index in nino_indices) { + nino_skill_metrics <- Skill(recipe = recipe, data = index, agg = "region") +} +``` + +A complete, ready-to-use sample of this example script can be found in `use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R`. You can execute it as-is or copy it and modify it according to your specific needs. + +## 3. Launching the jobs with the SUNSET Launcher + +When working without Autosubmit, the SUNSET Launcher should be run directly from the HPC machine where the jobs will run(for example, Nord3v2). Make sure to ssh to the machine first. You can obtain detailed usage information by running: + +```shell +bash launch_SUNSET.sh --help +``` + +The mandatory arguments are the paths to the recipe and the script. We can also include other optional arguments to be used by SLURM, such as the number of CPUs to request (--cpus), the wallclock time for each job (--wallclock) and other extra directives (--custom_directives). You can refer to the [Nord3v2 user guide](https://www.bsc.es/user-support/nord3v2.php#jobdirectives) and the [SLURM sbatch documentation](https://slurm.schedmd.com/sbatch.html) for more information on the available options for the parameters. + +In this case, we are giving each job a wallclock time of 1 hour and requesting exclusive usage of all the cores in one node. The shell command to run SUNSET will look like this: + +```shell +bash launch_SUNSET.sh use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R --wallclock=01:00:00 --custom_directives="--exclusive" +``` + +You can check the status of your running jobs with the `squeue` command. The SLURM logs will be inside of your $code_dir, in a subfolder named 'out-logs'. It can be useful to check them in case of errors. + +## 4. Results and plots + + -- GitLab From eb664fab24ca2316bb5a61ddbec99d11dab159ef Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 24 Nov 2023 15:00:48 +0100 Subject: [PATCH 25/78] fix typo --- use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md index 391d4856..6315cd86 100644 --- a/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md +++ b/use_cases/ex1_1_single_analysis_terminal/ex1_1-handson.md @@ -16,7 +16,8 @@ git clone https://earth.bsc.es/gitlab/es/sunset.git SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November. -There is a template file +There is a template file for this hands-on tutorial, which you can open with a text editor: + ```shell # cd to the main SUNSET directory # Open the recipe with a text editor such as vim or emacs -- GitLab From 252efc91dc355360a7cf23c33e352db512e20c82 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 24 Nov 2023 16:55:39 +0100 Subject: [PATCH 26/78] Refine split.R autosubmit checks --- split.R | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/split.R b/split.R index 8328e460..9bd95e86 100755 --- a/split.R +++ b/split.R @@ -34,7 +34,7 @@ recipe <- prepare_outputs(recipe_file = arguments$recipe, ## TODO: Add autosubmit yes/no to the parameters? run_parameters <- divide_recipe(recipe) -if (recipe$Run$autosubmit) { +if (!is.null(recipe$Run$autosubmit) && (recipe$Run$autosubmit)) { write_autosubmit_conf(recipe, run_parameters$n_atomic_recipes) sink(arguments$tmpfile, append = FALSE) # Run with... @@ -56,4 +56,7 @@ if (recipe$Run$autosubmit) { cat("TRUE") } sink() +} else { + stop("Autosubmit is not selected but no temp file was provided by the + launcher. Please open an issue to report the problem.") } -- GitLab From 5b56b359f3de9ff305eaa35db539405c8932611a Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 27 Nov 2023 12:46:54 +0100 Subject: [PATCH 27/78] Fix month name in plot titles --- modules/Indices/R/compute_nino.R | 41 +++++++++++++++++++------------- 1 file changed, 25 insertions(+), 16 deletions(-) diff --git a/modules/Indices/R/compute_nino.R b/modules/Indices/R/compute_nino.R index 8fd2c9c8..915dc9ce 100644 --- a/modules/Indices/R/compute_nino.R +++ b/modules/Indices/R/compute_nino.R @@ -208,6 +208,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE, } } if (plot_sp) { + ## TODO: Remove sourcing of plot robinson and viz module code source("modules/Visualization/R/tmp/PlotRobinson.R") source("modules/Indices/R/correlation_eno.R") source("modules/Visualization/R/get_proj_code.R") @@ -227,28 +228,36 @@ compute_nino <- function(data, recipe, region, standardised = TRUE, correl_hcst <- Apply(list(data$hcst$data, nino$hcst$data), target_dims = c('syear', 'ensemble'), fun = function(x, y) { - x <- apply(x, 1, mean, na.rm = TRUE) - y <- apply(y, 1, mean, na.rm = TRUE) - dim(y) <- c(syear = length(y)) - dim(x) <- c(syear = length(x)) - res <- .correlation_eno(x, y, - time_dim = 'syear', method = 'pearson', alpha = alpha, - test.type = 'two-sided', pval = FALSE)}, - ncores = recipe$Analysis$ncores) + x <- apply(x, 1, mean, na.rm = TRUE) + y <- apply(y, 1, mean, na.rm = TRUE) + dim(y) <- c(syear = length(y)) + dim(x) <- c(syear = length(x)) + res <- .correlation_eno(x, y, time_dim = 'syear', + method = 'pearson', + alpha = alpha, + test.type = 'two-sided', + pval = FALSE)}, + ncores = recipe$Analysis$ncores) correl_hcst_full <- Apply(list(data$hcst$data, nino$hcst$data), target_dims = c('syear', 'ensemble'), fun = function(x,y) { - dim(y) <- c(syear = length(y)) - dim(x) <- c(syear = length(x)) - res <- .correlation_eno(x, y, - time_dim = 'syear', method = 'pearson', alpha = alpha, - test.type = 'two-sided', pval = FALSE)}, + dim(y) <- c(syear = length(y)) + dim(x) <- c(syear = length(x)) + res <- .correlation_eno(x, y, + time_dim = 'syear', + method = 'pearson', + alpha = alpha, + test.type = 'two-sided', + pval = FALSE)}, ncores = recipe$Analysis$ncores) + months <- lubridate::month(Subset(data$hcst$attrs$Dates, "syear", indices = 1), + label = T, abb = F, locale = "en_GB") + for (tstep in 1:dim(nino$obs$data)['time']) { map <- Subset(correl_obs$r, along = 'time', ind = tstep, drop = T) sig <- Subset(correl_obs$sig, along = 'time', ind = tstep, drop = T) if (tolower(recipe$Analysis$Horizon) == "seasonal") { - mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) + + mes <- as.numeric(substr(recipe$Analysis$Time$sdate, 1, 2)) + (tstep - 1) + (recipe$Analysis$Time$ftime_min - 1) mes <- ifelse(mes > 12, mes - 12, mes) fmonth <- sprintf("%02d", tstep - 1 + recipe$Analysis$Time$ftime_min) @@ -313,7 +322,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE, toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n", "Ni\u00F1o", region_name, "SST Index -",var_name, "\n", "Correlation /", - month.abb[as.numeric(fmonth)], + month.abb[mes], "/", recipe$Analysis$Time$hcst_start, "-", recipe$Analysis$Time$hcst_end) plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/", @@ -367,7 +376,7 @@ compute_nino <- function(data, recipe, region, standardised = TRUE, toptitle <- paste(recipe$Analysis$Datasets$System$name, "\n", "Ni\u00F1o", region_name, "SST Index -",var_name, "\n", " Correlation /", - month.abb[as.numeric(fmonth)], + month.abb[mes], "/", recipe$Analysis$Time$hcst_start, "-", recipe$Analysis$Time$hcst_end) plotfile <- paste0(recipe$Run$output_dir, "/plots/Indices/", -- GitLab From fb5f099d5f5cbdbf7b77928999bc5e21af5e5ff2 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 27 Nov 2023 14:17:30 +0100 Subject: [PATCH 28/78] Edit 1.3 script, add figures --- .../ex1_3-handson.md | 11 ++++++++++- .../ex1_3-script.R | 2 +- .../nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png | Bin 0 -> 61630 bytes ...Meteo-France-System7_ERA5_s1101_ftime02.png | Bin 0 -> 59446 bytes ...n_tos_ensmean_ECMWF-SEAS5_s1101_ftime02.png | Bin 0 -> 937910 bytes ...mean_Meteo-France-System7_s1101_ftime02.png | Bin 0 -> 918256 bytes 6 files changed, 11 insertions(+), 2 deletions(-) create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_Meteo-France-System7_ERA5_s1101_ftime02.png create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_correlation_tos_ensmean_ECMWF-SEAS5_s1101_ftime02.png create mode 100644 use_cases/ex1_3_nino_indices_comparison/figures/nino34_correlation_tos_ensmean_Meteo-France-System7_s1101_ftime02.png diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md index 0f3f19d4..42068784 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md @@ -92,8 +92,17 @@ In this case, we are giving each job a wallclock time of 1 hour and requesting e bash launch_SUNSET.sh use_cases/ex1_3_nino_indices_comparison/ex1_3-recipe.yml use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R --wallclock=01:00:00 --custom_directives="--exclusive" ``` -You can check the status of your running jobs with the `squeue` command. The SLURM logs will be inside of your $code_dir, in a subfolder named 'out-logs'. It can be useful to check them in case of errors. +You can check the status of your running jobs with the `squeue` command. The SLURM logs will be inside of your code directory, in a subfolder named 'out-logs'. It can be useful to check them in case of errors. ## 4. Results and plots +The spatial pattern and time series plots that were requested are saved inside `plots/Indices/` in the output directory. There will be one set of plots for each El Niรฑo index, with a descriptive file name providing information about the content of the plot, the system/reference datasets, the start date and the forecast time. Here are some examples of the results: +Spatial correlation for the ensemble mean: + +![](./figures/nino34_correlation_tos_ensmean_ECMWF-SEAS5_s1101_ftime02.png) +![](./figures/nino34_correlation_tos_ensmean_Meteo-France-System7_s1101_ftime02.png) + +Time series comparison between the model and the reference dataset (ERA5): +![](./figures/nino34_ECMWF-SEAS5_ERA5_s1101_ftime02.png) +![](./figures/nino34_Meteo-France-System7_ERA5_s1101_ftime02.png) diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R index 146d0d78..c2b0ba34 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-script.R @@ -23,7 +23,7 @@ data <- Loading(recipe) # Check units and transform if needed data <- Units(recipe, data) # Calibrate data -data <- Calibration(recipe, data) +# data <- Calibration(recipe, data) # Compute tos anomalies data <- Anomalies(recipe, data) # Compute Niรฑo 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zMofsU-My@-B4_Vk{icv@y=MJtgswu!EU}anD$2xr_HCBX!DP^&&Y`cM^bnH>e4=L* zsM=1nFh3VLd~=82F3sfkfmQ=m+glp@e^J~aj#!5d`={R5{?h(7+a9jyGPwO;Pxkum z??3&04?6sP4m$jO4{!;8pM$^efrtzz_qR%YcAoB7DLw92@%P!&&88(!UGDcEY=P3T literal 0 HcmV?d00001 -- GitLab From 171c94cb2a97d9c56a915018e52b80da64db4c6a Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Wed, 29 Nov 2023 14:33:22 +0100 Subject: [PATCH 29/78] Save slurm logs inside output_dir --- launch_SUNSET.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/launch_SUNSET.sh b/launch_SUNSET.sh index eb8fcf46..153d64b3 100644 --- a/launch_SUNSET.sh +++ b/launch_SUNSET.sh @@ -118,7 +118,7 @@ if [ $run_method == "sbatch" ]; then scorecards=$( head -4 $tmpfile | tail -1) # Create directory for slurm output - logdir=${codedir}/out-logs/slurm_logs/ + logdir=${outdir}/logs/slurm/ mkdir -p $logdir echo "Slurm job logs will be stored in $logdir" -- GitLab From aa7bdd551a16802bab796e349d52e6e2229c49f5 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Wed, 29 Nov 2023 14:33:32 +0100 Subject: [PATCH 30/78] Update handson --- use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md index 42068784..9cd82bdf 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md @@ -59,8 +59,6 @@ The rest of the user-defined script can be written in the same way as any other data <- Loading(recipe) # Check units and transform if needed data <- Units(recipe, data) -# Calibrate data -data <- Calibration(recipe, data) # Compute tos anomalies data <- Anomalies(recipe, data) # Compute Niรฑo Indices -- GitLab From 349bbe7d005ef795c9831e91b9fcae9946e09b0a Mon Sep 17 00:00:00 2001 From: aho Date: Wed, 29 Nov 2023 17:11:52 +0100 Subject: [PATCH 31/78] Correct variables --- autosubmit/auto-scorecards.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/autosubmit/auto-scorecards.sh b/autosubmit/auto-scorecards.sh index 4b527372..c30f643f 100644 --- a/autosubmit/auto-scorecards.sh +++ b/autosubmit/auto-scorecards.sh @@ -2,8 +2,8 @@ ############ AUTOSUBMIT INPUTS ############ proj_dir=%PROJDIR% -outdir=%OUTDIR% -recipe=%RECIPE% +outdir=%common.OUTDIR% +recipe=%common.RECIPE% ############################### cd $proj_dir -- GitLab From 594e07d47871c58e29ce9f79e2ef557887b7f1b9 Mon Sep 17 00:00:00 2001 From: aho Date: Thu, 30 Nov 2023 13:01:38 +0100 Subject: [PATCH 32/78] Finish use case ex1_2 --- .../Figures/as_change_status.PNG | Bin 0 -> 72238 bytes .../Figures/as_tree.PNG | Bin 0 -> 115228 bytes ...ecard-1_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 374211 bytes ...ecard-2_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 400492 bytes ...ecard-3_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 375281 bytes ...ecard-4_ECMWF-SEAS5_ERA5_tas_1993-2003.png | Bin 0 -> 401925 bytes .../ex1_2-handson.md | 171 ++++++++++++++++++ .../ex1_2-recipe.yml | 49 ++--- .../ex1_2-script.R | 25 +++ 9 files changed, 216 insertions(+), 29 deletions(-) create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/as_change_status.PNG create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/as_tree.PNG create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-1_ECMWF-SEAS5_ERA5_tas_1993-2003.png create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-2_ECMWF-SEAS5_ERA5_tas_1993-2003.png create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-3_ECMWF-SEAS5_ERA5_tas_1993-2003.png create mode 100644 use_cases/ex1_2_autosubmit_scorecards/Figures/scorecard-4_ECMWF-SEAS5_ERA5_tas_1993-2003.png create mode 100644 use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md create mode 100644 use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R diff --git 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If a directory does not exist yet, you can create it with the `mkdir` shell command. + +```shell +# Clone the GitLab repository to create a local copy of the code +git clone https://earth.bsc.es/gitlab/es/sunset.git +``` +You should see a git folder "sunset" under the current directory. Now you have all the code, recipes, and scripts for running SUNSET. + + +## 1. Create Autosubmit experiment + +Since we're going to use Autosubmit to dispatch jobs, we need to have an Autosubmit experiment. Note that SUNSET uses Autosubmit >= 4.0.0. + +On the workstation or Nord3, you can create an experiment by the following commands. + +```shell +module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3 +autosubmit expid -H nord3v2 -d "SUNSET use case 1_2" +``` +You will see the messages like below: + +```shell +Autosubmit is running with 4.0.0b +The new experiment "a6pc" has been registered. +Generating folder structure... +Experiment folder: /esarchive/autosubmit/a6pc +Generating config files... +Experiment a6pc created +``` +Note the experiment ID (in this snippet above, a6pc) down. We need it for the recipe later. + + +## 2. Modifying the recipe + +The template recipe for this use case can be found in [ex1_2-recipe.yml](use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml). +You should at least edit some items in the "Run" section: +- `output_dir`: The directory you want to save the outputs and logs +- `code_dir`: The directory where your SUNSET code is stored (i.e., the git folder) +- `auto_conf$script`: The path to the script ex1_2-recipe.yml +- `auto_conf$expid`: The experiment "xxxx" you just created +- `auto_conf$hpc_user`: You user ID on Nord3, which should be bsc32xxx +- `auto_conf$email_address`: Your email. You can also adjust other email notification parts up to your preference. + +In the recipe, we ask for anomaly calculation after loading the data, calculate the skill scores and save the result for scorecards. In the Scorecard section, three regions are requested. + +Feel free to also modify other aspects according to your particular needs. You can read more about the parameters and the available modules in the SUNSET GitLab wiki. + +## 3. The user-defined script + +We need to have a script to define the modules to use and the steps of the workflow. Note that the script is for data loading and verification parts. The Scorecards module doesn't need to be included in this script. + +The prepare_outputs() function is already incorporated into the launcher script (see the next section for details about launcher), so it does not need to be included in the user-defined script in this case. +In its place, we will use the function read_atomic_recipe(). The recipe path is passed as an argument onto the R script. The beginning of our script should look like this: + +```R +# Load modules +source("modules/Loading/Loading.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Indices/Indices.R") +source("modules/Skill/Skill.R") + +# Read recipe +args = commandArgs(trailingOnly = TRUE) +recipe_file <- args[1] +recipe <- read_atomic_recipe(recipe_file) +``` + +The rest of the user-defined script can be written in the same way as any other SUNSET script. We load the data, calculate the anomalies, then compute the skill scores and save the result as netCDF files for Scorecards. + +```R +# Load data +data <- Loading(recipe) +# Compute tos anomalies +data <- Anomalies(recipe, data) +# Compute skill metrics +skill_metrics <- Skill(recipe, data) +``` +Check the example script at [ex1_2-script.yml](use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R). +You can execute it as-is or copy it and modify it according to your needs. + + +## 4. Launch jobs and Use Autosubmit + +We will start the jobs with the launcher. The SUNSET Launcher is a bash script named launch_SUNSET.sh that can be found in the main directory of the SUNSET repository. It runs in two steps: + +1. Run the recipe checks, split the recipe into atomic recipes and create the directory for the outputs. +2. Modify the Autosubmit configuration of your experiment according to the parameters in the recipe. + +The bash script needs two inputs: (1) [recipe](#2-modifying-the-recipe) (2) [R script](#2-modifying-the-recipe). + + On your workstation or Nord3 under the SUNSET code directory, run: + +```shell +bash launch_SUNSET.sh use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R +``` +You will see the messages similar to below: +```shell +[1] "Saving all outputs to:" +[1] "/esarchive/scratch/aho/auto-s2s-outputs/ex1_2-recipe_20231129003740" +INFO [2023-11-29 00:37:41] Checking recipe: use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml +WARN [2023-11-29 00:37:41] The element 'fcst_year' is not defined in the recipe. No forecast year will be used. +INFO [2023-11-29 00:37:41] ##### RECIPE CHECK SUCCESSFULL ##### +INFO [2023-11-29 00:37:41] Splitting recipe in single verifications. +INFO [2023-11-29 00:37:41] The main recipe has been divided into 12 atomic recipes. +INFO [2023-11-29 00:37:41] Check output directory /esarchive/scratch/aho/auto-s2s-outputs//ex1_2-recipe_20231129003740/logs/recipes/ to see all the individual atomic recipes. +INFO [2023-11-29 00:37:41] ##### AUTOSUBMIT CONFIGURATION WRITTEN FOR a6pc ##### +INFO [2023-11-29 00:37:41] You can check your experiment configuration at: /esarchive/autosubmit/a6pc/conf/ +INFO [2023-11-29 00:37:41] Please SSH into bscesautosubmit01 or bscesautosubmit02 and run the following commands: +INFO [2023-11-29 00:37:41] module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3 +INFO [2023-11-29 00:37:41] autosubmit create a6pc +INFO [2023-11-29 00:37:41] autosubmit refresh a6pc +INFO [2023-11-29 00:37:41] nohup autosubmit run a6pc & disown +``` +You can see some useful information, like the the path to atomic recipes, the Autosubmit configuration files, and most importantly, follow the last lines to launch your experiment. + +```shell +ssh bscesautosubmit01.bsc.es +(enter Autosubmit machine) +module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3 +autosubmit create a6pc +autosubmit refresh a6pc +nohup autosubmit run a6pc & disown +``` + +Then, you can go to [Autosubmit GUI](https://earth.bsc.es/autosubmitapp/) to check the experiment status. + + + +As you can see, the Scorecards job is dependent on the Verification jobs. Once the 12 verification jobs are finished, the Scorecards job will start. + +## 5. Results and plots + +The scorecards are saved under `plots/Scorecards` under the output directory. There will be 4 files (_more explanation here_) + + + + + + + +## 6. Rerun Autosubmit + +If something goes wrong and makes the jobs fail, you can rerun the failed jobs only. + +1. Go to Autosubmit GUI, select the failed job(s), click "CHANGE STATUS" +2. Select "Set status to:" as "WAITING". Copy the lines and run them on Autosubmit machine or workstation. +3. Fix the problem under your local SUNSET git directory. +4. Run `autosubmit refresh xxxx` and `nohup autosubmit run xxxx & disown`. + + + +If everything fails, you can also simply recreate the experiment by `autosubmit create xxxx` --> `autosubmit refresh xxxx` --> `nohup autosubmit run xxxx & disown`. + diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml index 2de4bd48..73f16311 100644 --- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml +++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-recipe.yml @@ -1,25 +1,17 @@ -################################################################################ -## RECIPE DESCRIPTION -################################################################################ - Description: - Author: V. Agudetse - Info: Test for recipe splitting - -################################################################################ -## ANALYSIS CONFIGURATION -################################################################################ + Author: An-Chi Ho + Info: Compute Skills and Plot Scorecards with Autosubmit Analysis: - Horizon: Seasonal - Variables: # ECVs and Indicators? + Horizon: seasonal + Variables: - {name: tas, freq: monthly_mean} Datasets: System: # multiple systems for single model, split if Multimodel = F - {name: ECMWF-SEAS5} Multimodel: False # single option Reference: - - {name: ERA5} # multiple references for single model? + - {name: ERA5} Time: sdate: # list, split - '0101' @@ -42,7 +34,7 @@ Analysis: Region: # multiple lists, split? Add region name if length(Region) > 1 - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9} Regrid: - method: bilinear ## TODO: allow multiple methods? + method: bilinear type: to_system Workflow: Anomalies: @@ -50,32 +42,30 @@ Analysis: cross_validation: no save: 'none' Calibration: - method: raw ## TODO: list, split? + method: raw save: 'none' Skill: metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr # list, don't split cross_validation: yes save: 'all' Probabilities: - percentiles: [[1/3, 2/3]] # list, don't split + percentiles: [[1/3, 2/3]] save: 'none' - # Visualization: - # plots: skill_metrics Scorecards: execute: yes # yes/no regions: Extra-tropical NH: {lon.min: 0, lon.max: 360, lat.min: 30, lat.max: 90} Tropics: {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: 30} Extra-tropical SH : {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: -90} - start_months: NULL + start_months: 'all' metric: mean_bias enscorr rpss crpss enssprerr metric_aggregation: 'score' - table_label: NULL + inf_to_na: TRUE # Optional, bool: set inf values in data to NA, default is FALSE table_label: NULL fileout_label: NULL col1_width: NULL col2_width: NULL calculate_diff: FALSE - ncores: 14 + ncores: 8 remove_NAs: no # bool, don't split Output_format: Scorecards # string, don't split @@ -85,21 +75,22 @@ Analysis: Run: Loglevel: INFO Terminal: yes - output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ - code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ + filesystem: esarchive + output_dir: /esarchive/scratch/aho/auto-s2s-outputs/ + code_dir: /esarchive/scratch/aho/git/auto-s2s/ autosubmit: yes # fill only if using autosubmit auto_conf: - script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_scorecards_workflow.R # replace with the path to your script - expid: a6ae # replace with your EXPID - hpc_user: bsc32762 # replace with your hpc username + script: /esarchive/scratch/aho/git/auto-s2s/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R # replace with the path to your script + expid: a6pc # replace with your EXPID + hpc_user: bsc32734 # replace with your hpc username wallclock: 03:00 # hh:mm - processors_per_job: 14 + processors_per_job: 8 platform: nord3v2 custom_directives: ['#SBATCH --exclusive'] email_notifications: yes # enable/disable email notifications. Change it if you want to. - email_address: victoria.agudetse@bsc.es # replace with your email address + email_address: an.ho@bsc.es # replace with your email address notify_completed: yes # notify me by email when a job finishes - notify_failed: no # notify me by email when a job fails + notify_failed: yes # notify me by email when a job fails diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R new file mode 100644 index 00000000..1f607987 --- /dev/null +++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-script.R @@ -0,0 +1,25 @@ +############################################################################### +## Author: An-Chi Ho +## Description: Computes some skill metrics and plots scorecards with Autosubmit +## Instructions: Follow the steps described in: +## use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md +## This script should be called by bash script launch_SUNSET.sh. +############################################################################### + +# Load modules +source("modules/Loading/Loading.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Skill/Skill.R") + +# Read recipe +args = commandArgs(trailingOnly = TRUE) +recipe_file <- args[1] +recipe <- read_atomic_recipe(recipe_file) + +# Load data +data <- Loading(recipe) +# Compute tos anomalies +data <- Anomalies(recipe, data) +# Compute skill metrics +skill_metrics <- Skill(recipe, data) + -- GitLab From d06d96d0323adb0883db227e3b61035932ead9d2 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Thu, 30 Nov 2023 15:38:25 +0100 Subject: [PATCH 33/78] commitng inital development of loading syear metrics --- modules/Scorecards/Dev_Scorecards.R | 330 ++++++++++++++++++++++++++++ modules/Skill/Skill.R | 10 +- tools/check_recipe.R | 2 + 3 files changed, 337 insertions(+), 5 deletions(-) create mode 100644 modules/Scorecards/Dev_Scorecards.R diff --git a/modules/Scorecards/Dev_Scorecards.R b/modules/Scorecards/Dev_Scorecards.R new file mode 100644 index 00000000..9829f6ed --- /dev/null +++ b/modules/Scorecards/Dev_Scorecards.R @@ -0,0 +1,330 @@ +############################################################################### +##################### SCORECARDS MODULE FOR SUNSET SUITE ###################### +############################################################################### + +##### Load source functions ##### +source('modules/Scorecards/R/tmp/LoadMetrics.R') +source('modules/Scorecards/R/tmp/WeightedMetrics.R') +source('modules/Scorecards/R/tmp/Utils.R') +source('modules/Scorecards/R/tmp/SCTransform.R') +source('modules/Scorecards/R/tmp/ScorecardsSingle.R') +source('modules/Scorecards/R/tmp/ScorecardsMulti.R') +source('modules/Scorecards/R/tmp/ScorecardsSystemDiff.R') +source('modules/Scorecards/R/tmp/SCPlotScorecard.R') + + +## TODO: Change function name to 'Scorecards'? +## Define function +Scorecards <- function(recipe) { + + ## Parameters for loading data + input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/output_test/" #paste0(recipe$Run$output_dir, "/outputs/Skill/") + output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") + dir.create(output.path, recursive = T, showWarnings = F) + system <- recipe$Analysis$Datasets$System$name + reference <- recipe$Analysis$Datasets$Reference$name + var <- recipe$Analysis$Variables$name + start.year <- as.numeric(recipe$Analysis$Time$hcst_start) + end.year <- as.numeric(recipe$Analysis$Time$hcst_end) + forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max + calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) + + # NOTE (Eva): This condition needs to be checked, in my case + # (recipe$Analysis$Workflow$Scorecards$start_months = NULL) + start.months <- 1:12 # I added this line + # Needs to be corrected: + # if (recipe$Analysis$Workflow$Scorecards$start_months == 'all') { + # start.months <- 1:12 + # } else { + # start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, + # split = ", | |,")[[1]]) + # } + + regions <- recipe$Analysis$Workflow$Scorecards$regions + for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])} + + metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation + metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) + metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) + + + ## Parameters for scorecard table + inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf.to.na + table.label <- recipe$Analysis$Workflow$Scorecards$table_label + fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label + legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space + col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width + col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width + calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff + ncores <- 1 # recipe$Analysis$ncores + + ## Load data files + start.months <- sprintf("%02d", start.months) + period <- paste0(start.year, "-", end.year) + + ####### SKILL AGGREGATION ####### + if(metric.aggregation == 'skill'){ + + ## Load data files + loaded_metrics <- LoadMetrics(system = system, + reference = reference, + var = var, + start.year = start.year, + end.year = end.year, + metrics = metrics.load, + start.months = start.months, + forecast.months = forecast.months, + inf.to.na = inf.to.na, + input.path = input.path) + + ## Spatial Aggregation of metrics + if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ + + ### Convert loaded metrics to array for already aggregated data + metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics + forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months + start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months + regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80)) + + aggregated_metrics <- array(dim = c(system = length(loaded_metrics), + reference = length(loaded_metrics[[1]]), + metric = length(metrics.dim), + time = length(forecast.months.dim), + sdate = length(start.months.dim), + region = length(regions.dim))) + + + for (sys in 1:length(names(loaded_metrics))){ + for (ref in 1:length(names(loaded_metrics[[sys]]))){ + aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region')) + } + } + + ## Add attributes + attributes(aggregated_metrics)$metrics <- metrics.load + attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months + attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months + attributes(aggregated_metrics)$regions <- regions + attributes(aggregated_metrics)$system.name <- names(loaded_metrics) + attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]]) + + + } else { + ## Calculate weighted mean of spatial aggregation + aggregated_metrics <- WeightedMetrics(loaded_metrics, + regions = regions, + metric.aggregation = metric.aggregation, + ncores = ncores) + }## close if on region + } + + ###### SCORE AGGREGATION ###### + if(metric.aggregation == 'score'){ + + lon_dim_name <- 'longitude' + lat_dim_name <- 'latitude' + + if('rpss' %in% metrics.visualize){ + ## Load data from saved files + rps_syear <- .loadmetrics(input_path = input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_syear', + calib_method = calib.method, syear = TRUE) + + rps_clim_syear <- .loadmetrics(input_path = input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate skill score value to show in scorecard + + ## Spatially aggregate rps data + rps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' + + ## Spatially aggregate rps_clim data + rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' + + ## Temporally aggregate data + rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, + target_dims = 'syear', + fun = 'mean', ncores = ncores)$output1 + + rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, + target_dims = 'syear', + fun = 'mean', ncores = ncores)$output1 + + + ## Calculate significance + sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, + time_dim = 'syear', test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + } ## close if on rpss + + if('crpss' %in% metrics.visualize){ + ## Load data from saved files + crps_syear <- .loadmetrics(input_path = input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_syear', + calib_method = calib.method, syear = TRUE) + + crps_clim_syear <- .loadmetrics(input_path = input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate skill score value to show in scorecard + + ## Spatially aggregate crps data + crps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' + + ## Spatially aggregate crps_clim data + crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' + + ## Temporally aggregate data + crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, + target_dims = 'syear', + fun = 'mean', ncores = ncores)$output1 + + crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, + target_dims = 'syear', + fun = 'mean', ncores = ncores)$output1 + + + ## Calculate significance + sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, + time_dim = 'syear', test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + } ## close if on crpss + } ## close if on score + + ## Create simple scorecard tables + ## (one system only) + ## Metrics input must be in the same order as function SC_spatial_aggregation + scorecard_single <- ScorecardsSingle(data = aggregated_metrics, + system = system, + reference = reference, + var = var, + start.year = start.year, + end.year = end.year, + start.months = start.months, + forecast.months = forecast.months, + region.names = names(regions), + metrics = metrics.visualize, + table.label = table.label, + fileout.label = fileout.label, + legend.white.space = legend.white.space, + col1.width = col1.width, + col2.width = col2.width, + output.path = output.path) + + ## Create multi system/reference scorecard tables + ## (multiple systems with one reference or one system with multiple references) + ## Metrics input must be in the same order as function SC_spatial_aggregation + if(length(system) > 1 || length(reference) > 1){ + scorecard_multi <- ScorecardsMulti(data = aggregated_metrics, + system = system, + reference = reference, + var = var, + start.year = start.year, + end.year = end.year, + start.months = start.months, + forecast.months = forecast.months, + region.names = attributes(regions)$names, + metrics = metrics.visualize, + table.label = table.label, + fileout.label = fileout.label, + output.path = output.path) + } ## close if + + + if(calculate.diff == TRUE){ + if(length(system) == 2 || length(reference) == 2){ + scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics, + system = system, + reference = reference, + var = var, + start.year = start.year, + end.year = end.year, + start.months = start.months, + forecast.months = forecast.months, + region.names = attributes(regions)$names, + metrics = metrics.visualize, + table.label = table.label, + fileout.label = fileout.label, + legend.white.space = legend.white.space, + col1.width = col1.width, + col2.width = col2.width, + output.path = output.path) + } else {stop ("Difference scorecard can only be computed with two systems or two references.")} + } ## close if on calculate.diff + +} + diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index 9ffd6369..3e69e3e1 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -67,7 +67,7 @@ Skill <- function(recipe, data, agg = 'global') { cross.val <- recipe$Analysis$Workflow$Skill$cross_validation } skill_metrics <- list() - for (metric in strsplit(metrics, ", | |,")) { + for (metric in strsplit(metrics, ", | |,")[[1]]) { ## Loop over metric not working?? # Whether the fair version of the metric is to be computed if (metric %in% c('frps', 'frpss', 'bss10', 'bss90', 'fcrps', 'fcrpss')) { @@ -93,7 +93,7 @@ Skill <- function(recipe, data, agg = 'global') { skill <- .drop_dims(skill) skill_metrics[[ metric ]] <- skill # RPS_clim - } else if (metric %in% c('rps_clim')) { + } else if (metric == 'rps_clim') { skill <- Apply(list(data$obs$data), target_dims = c(time_dim, memb_dim), cross.val = cross.val, @@ -111,16 +111,16 @@ Skill <- function(recipe, data, agg = 'global') { return_mean = FALSE, ncores = ncores) skill <- .drop_dims(skill) - skill_metrics[[metric]] <- skill + skill_metrics[[ metric ]] <- skill ## temp # RPS_clim_syear - } else if (metric %in% c('rps_clim_syear')) { ## not returning syear dimension name + } else if (metric == 'rps_clim_syear') { skill <- Apply(list(data$obs$data), target_dims = c(time_dim, memb_dim), cross.val = cross.val, fun = RPS_clim, return_mean = FALSE, output_dims = 'syear')$output1 skill <- .drop_dims(skill) - skill_metrics[[ metric ]] <- skill + skill_metrics[[ metric ]] <- skill ## temp # Ranked Probability Skill Score and Fair version } else if (metric %in% c('rpss', 'frpss')) { skill <- RPSS(data$hcst$data, data$obs$data, diff --git a/tools/check_recipe.R b/tools/check_recipe.R index e68f0b90..f4d711f8 100644 --- a/tools/check_recipe.R +++ b/tools/check_recipe.R @@ -566,6 +566,7 @@ check_recipe <- function(recipe) { } # Scorecards if ("Scorecards" %in% names(recipe$Analysis$Workflow)) { + if(recipe$Analysis$Workflow$Scorecards$execute == TRUE){ if (is.null(recipe$Analysis$Workflow$Scorecards$metric)) { error(recipe$Run$logger, "Parameter 'metric' must be defined under 'Scorecards'.") @@ -602,6 +603,7 @@ check_recipe <- function(recipe) { error_status <- T } } + } } # --------------------------------------------------------------------- # RUN CHECKS -- GitLab From 1dbad2177ab450690eb09d8b342f763e3beece15 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Tue, 5 Dec 2023 16:21:27 +0100 Subject: [PATCH 34/78] developing scorecard loading syear metrics --- modules/Scorecards/Dev_Scorecards.R | 414 +++++++++++++++++++--------- modules/Skill/Skill.R | 2 +- modules/Statistics/Statistics.R | 46 ++-- 3 files changed, 305 insertions(+), 157 deletions(-) diff --git a/modules/Scorecards/Dev_Scorecards.R b/modules/Scorecards/Dev_Scorecards.R index 9829f6ed..3984c3ed 100644 --- a/modules/Scorecards/Dev_Scorecards.R +++ b/modules/Scorecards/Dev_Scorecards.R @@ -18,7 +18,9 @@ source('modules/Scorecards/R/tmp/SCPlotScorecard.R') Scorecards <- function(recipe) { ## Parameters for loading data - input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/output_test/" #paste0(recipe$Run$output_dir, "/outputs/Skill/") + input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231204155452/outputs/" #temp + skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") + stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") dir.create(output.path, recursive = T, showWarnings = F) system <- recipe$Analysis$Datasets$System$name @@ -49,7 +51,7 @@ Scorecards <- function(recipe) { ## Parameters for scorecard table - inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf.to.na + inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf_to_na table.label <- recipe$Analysis$Workflow$Scorecards$table_label fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space @@ -57,6 +59,7 @@ Scorecards <- function(recipe) { col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff ncores <- 1 # recipe$Analysis$ncores + na.rm <- FALSE ## Load data files start.months <- sprintf("%02d", start.months) @@ -75,7 +78,7 @@ Scorecards <- function(recipe) { start.months = start.months, forecast.months = forecast.months, inf.to.na = inf.to.na, - input.path = input.path) + input.path = skill.input.path) ## Spatial Aggregation of metrics if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ @@ -123,147 +126,288 @@ Scorecards <- function(recipe) { lon_dim_name <- 'longitude' lat_dim_name <- 'latitude' - - if('rpss' %in% metrics.visualize){ - ## Load data from saved files - rps_syear <- .loadmetrics(input_path = input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_syear', - calib_method = calib.method, syear = TRUE) - - rps_clim_syear <- .loadmetrics(input_path = input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Calculate skill score value to show in scorecard - - ## Spatially aggregate rps data - rps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, - na.rm = F) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' - - ## Spatially aggregate rps_clim data - rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, - na.rm = F) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' + time_dim <- 'syear' + memb_dim <- 'ensemble' - ## Temporally aggregate data - rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, - target_dims = 'syear', - fun = 'mean', ncores = ncores)$output1 - - rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, - target_dims = 'syear', - fun = 'mean', ncores = ncores)$output1 - - - ## Calculate significance - sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, - time_dim = 'syear', test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, - ncores = NULL)$sign + ## Define arrays to filled with data + scorecard_metrics <- array(data = NA, + dim = c(time = length(forecast.months), + sdate = length(start.months), + region = length(regions), + metric = length(metrics.visualize))) - } ## close if on rpss + scorecard_sign <- array(data = NA, + dim = c(time = length(forecast.months), + sdate = length(start.months), + region = length(regions), + metric = length(metrics.visualize))) + + for (met in metrics.visualize) { + + if(met == 'rpss'){ + ## Load data from saved files + rps_syear <- .loadmetrics(input_path = skill.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_syear', + calib_method = calib.method, syear = TRUE) + + rps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Spatially aggregate data + rps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = F) + }, simplify = 'array') + + rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' + names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' - if('crpss' %in% metrics.visualize){ - ## Load data from saved files - crps_syear <- .loadmetrics(input_path = input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_syear', - calib_method = calib.method, syear = TRUE) - - crps_clim_syear <- .loadmetrics(input_path = input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Calculate skill score value to show in scorecard + ## Temporally aggregate data + rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + ## Calculate RPSS from aggregated RPS and RPS_clim + rpss <- 1 - rps_temp_aggr / rps_clim_temp_aggr + + ## Calculate significance + sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, + time_dim = time_dim, test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign - ## Spatially aggregate crps data - crps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, - na.rm = F) - }, simplify = 'array') + ## Save metric result in arrays + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) + scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) + + } ## close if on rpss - ## Include name of region dimension - names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' - - ## Spatially aggregate crps_clim data - crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, - na.rm = F) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' - - ## Temporally aggregate data - crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, - target_dims = 'syear', - fun = 'mean', ncores = ncores)$output1 - - crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, - target_dims = 'syear', - fun = 'mean', ncores = ncores)$output1 + + if(met == 'crpss'){ + ## Load data from saved files + crps_syear <- .loadmetrics(input_path = skill.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_syear', + calib_method = calib.method, syear = TRUE) + + crps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Spatially aggregate data + crps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = na.rm) + }, simplify = 'array') + + crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = na.rm) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' + names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' + + ## Temporally aggregate data + crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + ## Calculate CRPSS from aggregated CRPS and CRPS_clim + crpss <- 1 - crps_temp_aggr / crps_clim_temp_aggr + + ## Calculate significance + sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, + time_dim = time_dim, test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + ## Save metric result in arrays + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) + scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) + + } ## close if on crpss - ## Calculate significance - sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, - time_dim = 'syear', test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, - ncores = NULL)$sign + if(met == 'corr'){ + ## Load data from saved files + cov <- .loadmetrics(input_path = stats.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'cov', + calib_method = calib.method, syear = NULL) + + ## Comments (Nadia): + ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? + ## 2) output files names of statistics not correct, too many _ in name + + std_hcst <- .loadmetrics(input_path = stats.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_hcst', + calib_method = calib.method, syear = NULL) + + std_obs <- .loadmetrics(input_path = stats.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_obs', + calib_method = calib.method, syear = NULL) - } ## close if on crpss + ## Calculate spatial aggregation + cov_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = cov, + region = regions[[X]], + lon = as.vector(attributes(cov)$Variables$dat1$longitude), + lat = as.vector(attributes(cov)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = na.rm) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(cov_spatial_aggr))[length(dim(cov_spatial_aggr))] <- 'region' + + + std_hcst_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_hcst, + region = regions[[X]], + lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), + lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_hcst_spatial_aggr))[length(dim(std_hcst_spatial_aggr))] <- 'region' + + std_obs_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_obs, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), + lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + londim = lon_dim_name, + latdim = lat_dim_name, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' + + ## Calculate correlation + + corr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) + metric <- corr + + ## Calculate significance of corr + n_eff <- s2dv::Eno(data = data$obs$data, time_dim = time_dim, na.action = na.pass, ncores = ncores) + # n_eff <- (end.year - start.year) + 1 # define degrees of freedom + alpha <- 0.05 # Apply 95% confidence + + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) + t <- abs(corr) * sqrt(n_eff-2) / sqrt(1-corr^2) + + sign_corr <- NULL + + if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ + sign_corr$sign = TRUE + } else { + sign_corr$sign = FALSE + } + + ## Save metric result in arrays + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = corr, order = c('time', 'smonths','region')) + scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) + + + } ## close if on corr + + ## Combine metric arrays + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = metric, order = c('time', 'smonths','region')) + + ##Combine significance + + + } ## close loop on metric } ## close if on score + + + + + + + + + + + + + + + ## Create simple scorecard tables ## (one system only) diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index 3e69e3e1..cc0da1e1 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -67,7 +67,7 @@ Skill <- function(recipe, data, agg = 'global') { cross.val <- recipe$Analysis$Workflow$Skill$cross_validation } skill_metrics <- list() - for (metric in strsplit(metrics, ", | |,")[[1]]) { ## Loop over metric not working?? + for (metric in strsplit(metrics, ", | |,")[[1]]) { # Whether the fair version of the metric is to be computed if (metric %in% c('frps', 'frpss', 'bss10', 'bss90', 'fcrps', 'fcrpss')) { diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index fe36c4ce..b3ee7848 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -9,64 +9,68 @@ compute_statistics <- function(recipe, data, agg = 'global'){ time_dim <- 'syear' memb_dim <- 'ensemble' + + # ## Remove unwanted dimensions + obs_data <- Subset(data$obs$data, along = c('dat', 'sday', 'sweek', 'ensemble'), indices = list(1,1,1,1) , drop = 'selected') + hcst_data <- Subset(data$hcst$data, along = c('dat', 'sday', 'sweek'), indices = list(1,1,1) , drop = 'selected') + ## Repeat ensemble members for obs + obs_data_ens <- InsertDim(data = obs_data, pos = length(dim(obs_data))+1, lendim = 25, name = 'ensemble') - ## Duplicate obs along hcst ensemble dimension - obs_data <- adrop(data$obs$data, drop = 9) - obs_data <- InsertDim(data = obs_data, pos = 9, lendim = 25, name = 'ensemble') + # obs_data <- adrop(data$obs$data, drop = 9) + # obs_data_ens <- InsertDim(data = obs_data, pos = 9, lendim = 25, name = 'ensemble') statistics_list <- tolower(recipe$Analysis$Workflow$Statistics$metric) - statistics <- list() + statistics_metrics <- list() for (stat in strsplit(statistics_list, ", | |,")[[1]]) { # Whether the fair version of the metric is to be computed if (stat %in% c('cov', 'covariance')) { - covariance <- Apply(data = list(x= obs_data, y=data$hcst$data), + covariance <- Apply(data = list(x = obs_data_ens, y = hcst_data), target_dims = c(time_dim, memb_dim), fun = function(x,y){cov(as.vector(x),as.vector(y), use = "everything", method = "pearson")})$output1 - statistics[[ stat ]] <- covariance + statistics_metrics[[ stat ]] <- covariance - } ## close if on cov + } ## close if on covariance if (stat %in% c('std', 'standard_deviation')) { ## Calculate standard deviation - std_hcst <- Apply(data = data$hcst$data, + std_hcst <- Apply(data = hcst_data, target_dims = c(time_dim, memb_dim), fun = 'sd')$output1 - std_obs <- Apply(data = data$obs$data, - target_dims = c(time_dim, memb_dim), + std_obs <- Apply(data = obs_data, + target_dims = c(time_dim), fun = 'sd')$output1 - statistics[[ 'std_hcst' ]] <- std_hcst - statistics[[ 'std_obs' ]] <- std_obs - + statistics_metrics[['std_hcst']] <- std_hcst + statistics_metrics[['std_obs']] <- std_obs } ## close if on std if (stat %in% c('var', 'variance')) { ## Calculate standard deviation - var_hcst <- (Apply(data = data$hcst$data, + var_hcst <- (Apply(data = hcst_data, target_dims = c(time_dim, memb_dim), fun = 'sd')$output1)^2 - var_obs <- (Apply(data = data$obs$data, - target_dims = c(time_dim, memb_dim), + var_obs <- (Apply(data = obs_data, + target_dims = c(time_dim), fun = 'sd')$output1)^2 - statistics[[ 'var_hcst' ]] <- var_hcst - statistics[[ 'var_obs' ]] <- var_obs + statistics_metrics[['var_hcst']] <- var_hcst + statistics_metrics[['var_obs']] <- var_obs - } ## close if on var + } ## close if on variance } @@ -82,11 +86,11 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (recipe$Analysis$Workflow$Statistics$save == 'all') { # Save all statistics - save_metrics_scorecards(recipe = recipe, skill = statistics, ## Not able to save data with these dimensions + save_metrics_scorecards(recipe = recipe, skill = statistics_metrics, ## Not able to save data with these dimensions data_cube = data$hcst, agg = agg) ## The length of parameter 'order' should be the same with the dimension length of parameter 'data'. } # Return results - return(statistics) + return(statistics_metrics) } -- GitLab From 51ba570c6456d255d7c5bf9d66ea5254ad88ef62 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Thu, 7 Dec 2023 16:02:16 +0100 Subject: [PATCH 35/78] included enscorr significance --- modules/Scorecards/Dev_Scorecards.R | 202 ++++++++++++++++++++-------- modules/Statistics/Statistics.R | 8 ++ 2 files changed, 151 insertions(+), 59 deletions(-) diff --git a/modules/Scorecards/Dev_Scorecards.R b/modules/Scorecards/Dev_Scorecards.R index 3984c3ed..2bc02a5e 100644 --- a/modules/Scorecards/Dev_Scorecards.R +++ b/modules/Scorecards/Dev_Scorecards.R @@ -18,7 +18,7 @@ source('modules/Scorecards/R/tmp/SCPlotScorecard.R') Scorecards <- function(recipe) { ## Parameters for loading data - input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231204155452/outputs/" #temp + input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231207112311/outputs/" #temp skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") @@ -124,8 +124,14 @@ Scorecards <- function(recipe) { ###### SCORE AGGREGATION ###### if(metric.aggregation == 'score'){ - lon_dim_name <- 'longitude' - lat_dim_name <- 'latitude' + ## Comments (Nadia): + ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? + ## 2) output files names of statistics not correct, too many _ in name + ## 3) how to load multiple systems? + + + lon_dim <- 'longitude' + lat_dim <- 'latitude' time_dim <- 'syear' memb_dim <- 'ensemble' @@ -171,8 +177,8 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = F) }, simplify = 'array') @@ -182,8 +188,8 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = F) }, simplify = 'array') @@ -214,7 +220,6 @@ Scorecards <- function(recipe) { scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) } ## close if on rpss - if(met == 'crpss'){ ## Load data from saved files @@ -243,8 +248,8 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') @@ -254,8 +259,8 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') @@ -287,8 +292,7 @@ Scorecards <- function(recipe) { } ## close if on crpss - - if(met == 'corr'){ + if(met == 'enscorr'){ ## Load data from saved files cov <- .loadmetrics(input_path = stats.input.path, system = system, reference = reference, var = var, @@ -297,16 +301,12 @@ Scorecards <- function(recipe) { metrics = 'cov', calib_method = calib.method, syear = NULL) - ## Comments (Nadia): - ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? - ## 2) output files names of statistics not correct, too many _ in name - std_hcst <- .loadmetrics(input_path = stats.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_hcst', - calib_method = calib.method, syear = NULL) + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_hcst', + calib_method = calib.method, syear = NULL) std_obs <- .loadmetrics(input_path = stats.input.path, system = system, reference = reference, var = var, @@ -315,6 +315,14 @@ Scorecards <- function(recipe) { metrics = 'std_obs', calib_method = calib.method, syear = NULL) + + n_eff <- .loadmetrics(input_path = stats.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'n_eff', + calib_method = calib.method, syear = NULL) + ## Calculate spatial aggregation cov_spatial_aggr <- sapply(X = 1:length(regions), FUN = function(X) { @@ -322,8 +330,8 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(cov)$Variables$dat1$longitude), lat = as.vector(attributes(cov)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') @@ -337,8 +345,8 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') @@ -350,63 +358,139 @@ Scorecards <- function(recipe) { region = regions[[X]], lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), - londim = lon_dim_name, - latdim = lat_dim_name, + londim = lon_dim, + latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' - ## Calculate correlation + n_eff_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = n_eff, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), + lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') - corr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) - metric <- corr + names(dim(n_eff_spatial_aggr))[length(dim(n_eff_spatial_aggr))] <- 'region' + n_eff_spatial_aggr <- Subset(n_eff_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate correlation + enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) + ## Drop unwanted dimensions + enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + ## Calculate significance of corr - n_eff <- s2dv::Eno(data = data$obs$data, time_dim = time_dim, na.action = na.pass, ncores = ncores) - # n_eff <- (end.year - start.year) + 1 # define degrees of freedom alpha <- 0.05 # Apply 95% confidence - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) - t <- abs(corr) * sqrt(n_eff-2) / sqrt(1-corr^2) + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) + t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) - sign_corr <- NULL + sign_corr<- array(data = NA, + dim = c(time = length(forecast.months), + smonths = length(start.months), + region = length(regions))) - if (anyNA(c(t,t_alpha2_n2)) == FALSE & t >= t_alpha2_n2){ - sign_corr$sign = TRUE - } else { - sign_corr$sign = FALSE - } + + for (time in 1:dim(sign_corr)[['time']]){ + for (mon in 1:dim(sign_corr)[['smonths']]){ + for (reg in 1:dim(sign_corr)[['region']]){ + + if (anyNA(c(t[time, mon, reg], t_alpha2_n2[time, mon, reg])) == FALSE + && t[time, mon, reg] >= t_alpha2_n2[time, mon, reg]){ + sign_corr[time, mon, reg] <- TRUE + } else { + sign_corr[time, mon, reg] <- FALSE + } + + } + } + } ## Save metric result in arrays - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = corr, order = c('time', 'smonths','region')) + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) + + } ## close if on enscorr + + if(met == 'mean_bias'){ + mean_bias <- .loadmetrics(input_path = skill.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'mean_bias', + calib_method = calib.method, syear = NULL) - } ## close if on corr + ## Calculate spatial aggregation + mean_bias_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = mean_bias, + region = regions[[X]], + lon = as.vector(attributes(mean_bias)$Variables$dat1$longitude), + lat = as.vector(attributes(mean_bias)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') - ## Combine metric arrays - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = metric, order = c('time', 'smonths','region')) - - ##Combine significance - + names(dim(mean_bias_spatial_aggr))[length(dim(mean_bias_spatial_aggr))] <- 'region' + + ## Drop unwanted dimensions + mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Save metric result in array + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) + + } ## close on mean_bias + + if(met == 'enssprerr'){ + + enssprerr <- .loadmetrics(input_path = skill.input.path, system = system, + reference = reference, var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'enssprerr', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + enssprerr_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = enssprerr, + region = regions[[X]], + lon = as.vector(attributes(enssprerr)$Variables$dat1$longitude), + lat = as.vector(attributes(enssprerr)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(enssprerr_spatial_aggr))[length(dim(enssprerr_spatial_aggr))] <- 'region' + + ## Drop unwanted dimensions + enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Save metric result in array + scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) + + } ## close on enssprerr + } ## close loop on metric + + aggregated_metrics <- scorecard_metrics + } ## close if on score - - - - - - - - - - + stop() ## Create simple scorecard tables diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index b3ee7848..777f5c78 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -9,6 +9,7 @@ compute_statistics <- function(recipe, data, agg = 'global'){ time_dim <- 'syear' memb_dim <- 'ensemble' + ncores <- recipe$Analysis$ncores # ## Remove unwanted dimensions obs_data <- Subset(data$obs$data, along = c('dat', 'sday', 'sweek', 'ensemble'), indices = list(1,1,1,1) , drop = 'selected') @@ -71,7 +72,14 @@ compute_statistics <- function(recipe, data, agg = 'global'){ statistics_metrics[['var_obs']] <- var_obs } ## close if on variance + + if (stat == 'n_eff') { + + n_eff <- s2dv::Eno(data = obs_data, time_dim = time_dim, na.action = na.pass, ncores = ncores) + statistics_metrics[['n_eff']] <- n_eff + + } ## close on n_eff } info(recipe$Run$logger, "##### STATISTICS COMPUTATION COMPLETE #####") -- GitLab From 9590ace650cf4f392ad6eb25ee6f8fddd0278c2e Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Wed, 13 Dec 2023 09:44:00 +0100 Subject: [PATCH 36/78] Update README --- README.md | 24 +++++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 538fa029..5758fbf3 100644 --- a/README.md +++ b/README.md @@ -1,19 +1,29 @@ -ESS Verification Suite +SUNSET: SUbseasoNal to decadal climate forecast post-processing and asSEmenT suite ====================== -This is the Git project for the ESS Verification Suite, which will serve as a tool for research projects and operational workflows involving subseasonal to seasonal to decadal forecast verification. +This is the Git project for the SUNSET, a collaborative in-house tool developed at BSC-ES for research projects and operational workflows involving subseasonal to seasonal to decadal forecast verification. -The main developers of the tool are Victรฒria Agudetse (@vagudets), An-Chi Ho (@aho), Lluรญs Palma (@lpalma) and Nรบria Pรฉrez-Zanรณn (@nperez). +This is the Git project for SUNSET, an collaborative R-based tool developed in-house at BSC-ES that aims to provide climate services for sub-seasonal, seasonal and decadal climate forecast time scales. The tool post-processes climate forecast outputs by applying state-of-the-art methodologies to tailor climate products for each application and sector (e.g.: agriculture, energy, water management, or health). + +Its modular design allows flexibility in defining the required post-processing steps, as well as the products definition by deciding on the forecast system and reference datasets, variables, and forecast horizon among others. The tool also allows for the creation and visualization of climate forecast products, such as maps for the most likely terciles, and performs the verification of the products, which can be visualized on maps and scorecards. The inclusion of a launcher script provides a user-friendly way to parallelize the computation on HPC machines. + +Victรฒria Agudetse (@vagudets) is the maintainer of the SUNSET repository. Resources --------- -You can access the documentation of the Verification Suite through the wiki: -[Auto-s2s Wiki](https://earth.bsc.es/gitlab/es/auto-s2s/-/wikis/home?target=_blank) +You can access the documentation through the wiki: +[SUNSET Wiki](https://earth.bsc.es/gitlab/es/sunset/-/wikis/home?target=_blank) + +Use cases and hands-on tutorials are available in the repository for you to follow: + +[SUNSET Use Cases](https://earth.bsc.es/gitlab/es/sunset/-/tree/master/use_cases/) You may also find useful information in the slides from past user meetings: +[R-tools Climate Forecast Analysis Training session 2023](https://earth.bsc.es/wiki/lib/exe/fetch.php?media=tools:day2_04_handson_r_tools.pdf) + [User meeting March 2023](https://docs.google.com/presentation/d/18VoqgJCzcZTmqNyXL3op_KecsPxsWRkf/edit#slide=id.p1?target=_blank) [User meeting September 2022](https://docs.google.com/presentation/d/14-qq__fblMt7xvJDaqS5UqfQMXWCf3Ju/edit#slide=id.p1?target=_blank) @@ -25,9 +35,9 @@ Branching strategy Branches containing developments that are to be merged into the tool must contain "dev-" at the beginning of the name, followed by a short, meaningful description of the development in question. E.g. "dev-loading-subseasonal" for the branch containing developments related to the loading of subseasonal datasets. -Users that wish to incorporate their own developments into the core of the tool are encouraged to create a personal fork of the Auto-S2S repository to work on their projects. Please contact Victรฒria Agudetse at victoria.agudetse@bsc.es to discuss the first steps. +Users that wish to incorporate their own developments into the core of the tool are encouraged to create a local copy repository to work on their projects and push their changes to a new branch. Please contact Victรฒria Agudetse (@vagudets) or Nรบria Pรฉrez-Zanรณn (@nperez) to discuss the first steps. Mailing list ------------ -User meetings, internal releases and news are announced through the mailing list. You can send an email to victoria.agudetse@bsc.es or an.ho@bsc.es to request subscription. +User meetings, internal releases and news are announced through the mailing list. You can send an email to victoria.agudetse@bsc.es to request subscription. -- GitLab From df81b24e1c1b5da8f7f8fd70823bfe22ffbf2c25 Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 15 Dec 2023 10:46:12 +0100 Subject: [PATCH 37/78] Add generic environment (no eccodes) --- conda_installation/environment-sunset.yml | 373 ++++++++++++++++++++++ 1 file changed, 373 insertions(+) create mode 100644 conda_installation/environment-sunset.yml diff --git a/conda_installation/environment-sunset.yml b/conda_installation/environment-sunset.yml new file mode 100644 index 00000000..0427f523 --- /dev/null +++ b/conda_installation/environment-sunset.yml @@ -0,0 +1,373 @@ +name: condaSUNSET +channels: + - r + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=conda_forge + - _openmp_mutex=4.5=2_gnu + - _r-mutex=1.0.1=anacondar_1 + - atk-1.0=2.38.0=hd4edc92_1 + - binutils_impl_linux-64=2.40=hf600244_0 + - blosc=1.21.5=h0f2a231_0 + - boost-cpp=1.78.0=h5adbc97_2 + - bwidget=1.9.14=ha770c72_1 + - 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r-sass=0.4.7=r42ha503ecb_0 + - r-scales=1.2.1=r42hc72bb7e_2 + - r-selectr=0.4_2=r42hc72bb7e_3 + - r-sf=1.0_7=r42h25da31b_5 + - r-shiny=1.7.5=r42h785f33e_0 + - r-sourcetools=0.1.7_1=r42ha503ecb_1 + - r-sp=2.0_0=r42h57805ef_0 + - r-spam=2.9_1=r42hd9ac46e_2 + - r-specsverification=0.5_3=r42h7525677_2 + - r-splancs=2.01_44=r42h61816a4_0 + - r-startr=2.3.0=r42hc72bb7e_0 + - r-stringi=1.7.12=r42h1ae9187_0 + - r-stringr=1.5.0=r42h785f33e_1 + - r-svglite=2.1.1=r42h329214f_1 + - r-sys=3.4.2=r42h57805ef_1 + - r-systemfonts=1.0.4=r42haf97adc_2 + - r-testthat=3.1.10=r42ha503ecb_0 + - r-tibble=3.2.1=r42h57805ef_2 + - r-timechange=0.2.0=r42ha503ecb_1 + - r-tinytex=0.46=r42hc72bb7e_0 + - r-tzdb=0.4.0=r42ha503ecb_1 + - r-units=0.8_4=r42ha503ecb_0 + - r-utf8=1.2.3=r42h57805ef_1 + - r-uuid=1.1_1=r42h57805ef_0 + - r-vctrs=0.6.3=r42ha503ecb_0 + - r-viridis=0.6.4=r42hc72bb7e_0 + - r-viridislite=0.4.2=r42hc72bb7e_1 + - r-waldo=0.5.1=r42hc72bb7e_1 + - r-webshot=0.5.5=r42hc72bb7e_0 + - r-withr=2.5.1=r42hc72bb7e_0 + - r-wk=0.8.0=r42ha503ecb_0 + - r-xfun=0.40=r42ha503ecb_0 + - r-xml2=1.3.3=r42h044e5c7_2 + - r-xtable=1.8_4=r42hc72bb7e_5 + - r-yaml=2.3.5=r42h06615bd_1 + - readline=8.2=h8228510_1 + - sed=4.8=he412f7d_0 + - setuptools=68.2.2=pyhd8ed1ab_0 + - snappy=1.1.10=h9fff704_0 + - sqlite=3.43.0=h2c6b66d_0 + - sysroot_linux-64=2.12=he073ed8_16 + - tiledb=2.11.3=h3f4058f_1 + - tk=8.6.13=h2797004_0 + - tktable=2.10=h0c5db8f_5 + - tzcode=2023c=h0b41bf4_0 + - tzdata=2023c=h71feb2d_0 + - udunits2=2.2.28=h40f5838_2 + - wheel=0.41.2=pyhd8ed1ab_0 + - xerces-c=3.2.4=h55805fa_1 + - xorg-fixesproto=5.0=h7f98852_1002 + - xorg-inputproto=2.3.2=h7f98852_1002 + - xorg-kbproto=1.0.7=h7f98852_1002 + - xorg-libice=1.0.10=h7f98852_0 + - xorg-libsm=1.2.3=hd9c2040_1000 + - xorg-libx11=1.8.4=h0b41bf4_0 + - xorg-libxau=1.0.11=hd590300_0 + - xorg-libxdmcp=1.1.3=h7f98852_0 + - xorg-libxext=1.3.4=h0b41bf4_2 + - xorg-libxfixes=5.0.3=h7f98852_1004 + - xorg-libxi=1.7.10=h7f98852_0 + - xorg-libxrender=0.9.10=h7f98852_1003 + - xorg-libxt=1.3.0=hd590300_0 + - xorg-renderproto=0.11.1=h7f98852_1002 + - xorg-xextproto=7.3.0=h0b41bf4_1003 + - xorg-xproto=7.0.31=h7f98852_1007 + - xz=5.2.6=h166bdaf_0 + - zlib=1.2.13=hd590300_5 + - zstd=1.5.5=hfc55251_0 + - pip: + - argparse==1.4.0 + - autosubmit==4.0.98 + - autosubmitconfigparser==1.0.49 + - bcrypt==4.0.1 + - bscearth-utils==0.5.2 + - cdo==1.6.0 + - certifi==2023.7.22 + - cffi==1.16.0 + - charset-normalizer==3.3.1 + - configobj==5.0.8 + - coverage==7.3.2 + - cryptography==41.0.5 + - cycler==0.12.1 + - cython==3.0.4 + - fonttools==4.43.1 + - idna==3.4 + - iniconfig==2.0.0 + - kiwisolver==1.4.5 + - matplotlib==3.5.3 + - mock==5.1.0 + - networkx==2.6.3 + - nose==1.3.7 + - packaging==23.2 + - paramiko==3.3.1 + - pillow==10.1.0 + - pluggy==1.3.0 + - portalocker==2.7.0 + - psutil==5.9.6 + - py3dotplus==1.1.0 + - pycparser==2.21 + - pygments==2.16.1 + - pynacl==1.5.0 + - pyparsing==3.1.1 + - pytest==7.4.3 + - python-dateutil==2.8.2 + - pythondialog==3.5.3 + - requests==2.31.0 + - ruamel-yaml==0.17.21 + - six==1.16.0 + - urllib3==2.0.7 + - xlib==0.21 +# prefix: /perm/cyce/conda/envs/condaCerise -- GitLab From 57902ede2ab43b97e5d6300bf531ea3808cc80a3 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 15 Dec 2023 11:03:59 +0100 Subject: [PATCH 38/78] working progress --- modules/Scorecards/Dev_Scorecards.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/Scorecards/Dev_Scorecards.R b/modules/Scorecards/Dev_Scorecards.R index 2bc02a5e..2fdd940e 100644 --- a/modules/Scorecards/Dev_Scorecards.R +++ b/modules/Scorecards/Dev_Scorecards.R @@ -127,8 +127,8 @@ Scorecards <- function(recipe) { ## Comments (Nadia): ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? ## 2) output files names of statistics not correct, too many _ in name - ## 3) how to load multiple systems? - + ## 3) how to load multiple systems? - Loop over sys and ref for metric.aggregation == 'score' + ## Need to test mith multiple sdates lon_dim <- 'longitude' lat_dim <- 'latitude' -- GitLab From 717a4646b3f5b546007ad83bfa0e828dfad8dc9a Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 15 Dec 2023 14:50:16 +0100 Subject: [PATCH 39/78] Bug fix file output name --- modules/Saving/R/get_filename.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Saving/R/get_filename.R b/modules/Saving/R/get_filename.R index 9f6151e8..617115e7 100644 --- a/modules/Saving/R/get_filename.R +++ b/modules/Saving/R/get_filename.R @@ -44,7 +44,7 @@ get_filename <- function(dir, recipe, var, date, agg, file.type) { "crps_clim_syear" = {type_info <- paste0("crps_clim_syear")}, "crps" = {type_info <- paste0("crps")}, "mean_bias" = {type_info <- paste0("mean_bias")}, - {type_info <- paste0("_", file.type, "_")}) + {type_info <- paste0(file.type)}) # Build file name file <- paste0("scorecards_", system, "_", reference, "_", -- GitLab From c4deee36d968e9002e5445739a436648512e62f2 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Fri, 15 Dec 2023 14:55:45 +0100 Subject: [PATCH 40/78] Set default value for na.rm in Anomalies() --- modules/Anomalies/Anomalies.R | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/modules/Anomalies/Anomalies.R b/modules/Anomalies/Anomalies.R index edb6a8be..2d54365a 100644 --- a/modules/Anomalies/Anomalies.R +++ b/modules/Anomalies/Anomalies.R @@ -20,6 +20,11 @@ Anomalies <- function(recipe, data) { cross <- FALSE cross_msg <- "without" } + if (is.null(recipe$Analysis$remove_NAs)) { + na.rm <- FALSE + } else { + na.rm <- recipe$Analysis$remove_NAs + } original_dims <- data$hcst$dim # Save full fields @@ -57,12 +62,12 @@ Anomalies <- function(recipe, data) { clim_hcst <- Apply(data$hcst$data, target_dims = c('syear', 'ensemble'), mean, - na.rm = recipe$Analysis$remove_NAs, + na.rm = na.rm, ncores = recipe$Analysis$ncores)$output1 clim_obs <- Apply(data$obs$data, target_dims = c('syear', 'ensemble'), mean, - na.rm = recipe$Analysis$remove_NAs, + na.rm = na.rm, ncores = recipe$Anaysis$ncores)$output1 data$hcst$data <- Ano(data = data$hcst$data, clim = clim_hcst) data$obs$data <- Ano(data = data$obs$data, clim = clim_obs) -- GitLab From 3cf3712dd51d55f01616ccb9c04e75585792813b Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 18 Dec 2023 10:45:51 +0100 Subject: [PATCH 41/78] Correct seasonal grid consistency check --- modules/Loading/R/load_seasonal.R | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R index a77f239e..32c7edf8 100644 --- a/modules/Loading/R/load_seasonal.R +++ b/modules/Loading/R/load_seasonal.R @@ -313,35 +313,40 @@ load_seasonal <- function(recipe) { # Convert obs to s2dv_cube obs <- as.s2dv_cube(obs) - + # Check for consistency between hcst and obs grid if (!(recipe$Analysis$Regrid$type == 'none')) { - if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) { + if (!isTRUE(all.equal(as.vector(hcst$coords$latitude), + as.vector(obs$coords$latitude)))) { lat_error_msg <- paste("Latitude mismatch between hcst and obs.", "Please check the original grids and the", "regrid parameters in your recipe.") error(recipe$Run$logger, lat_error_msg) - hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1], - "; Last hcst lat: ", hcst$lat[length(hcst$lat)]) + hcst_lat_msg <- paste0("First hcst lat: ", hcst$coords$latitude[1], + "; Last hcst lat: ", + hcst$coords$latitude[length(hcst$coords$latitude)]) info(recipe$Run$logger, hcst_lat_msg) - obs_lat_msg <- paste0("First obs lat: ", obs$lat[1], - "; Last obs lat: ", obs$lat[length(obs$lat)]) + obs_lat_msg <- paste0("First obs lat: ", obs$coords$latitude[1], + "; Last obs lat: ", + obs$coords$latitude[length(obs$coords$latitude)]) info(recipe$Run$logger, obs_lat_msg) stop("hcst and obs don't share the same latitudes.") } - if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) { + if (!isTRUE(all.equal(as.vector(hcst$coords$longitude), + as.vector(obs$coords$longitude)))) { lon_error_msg <- paste("Longitude mismatch between hcst and obs.", "Please check the original grids and the", "regrid parameters in your recipe.") error(recipe$Run$logger, lon_error_msg) - hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1], - "; Last hcst lon: ", hcst$lon[length(hcst$lon)]) + hcst_lon_msg <- paste0("First hcst lon: ", hcst$coords$longitude[1], + "; Last hcst lon: ", + hcst$coords$longitude[length(hcst$coords$longitude)]) info(recipe$Run$logger, hcst_lon_msg) - obs_lon_msg <- paste0("First obs lon: ", obs$lon[1], - "; Last obs lon: ", obs$lon[length(obs$lon)]) + obs_lon_msg <- paste0("First obs lon: ", obs$coords$longitude[1], + "; Last obs lon: ", + obs$coords$longitude[length(obs$coords$longitude)]) info(recipe$Run$logger, obs_lon_msg) stop("hcst and obs don't share the same longitudes.") - } } -- GitLab From 3413cc103397d64f2a75095e78f69d6e7e1c2805 Mon Sep 17 00:00:00 2001 From: Victoria Agudetse Roures Date: Mon, 18 Dec 2023 12:58:49 +0100 Subject: [PATCH 42/78] Refactor grid consistency check --- modules/Loading/R/compare_exp_obs_grids.R | 35 +++++++++++++++++++++++ modules/Loading/R/load_decadal.R | 30 ++----------------- modules/Loading/R/load_seasonal.R | 34 ++-------------------- modules/Loading/R/load_tas_tos.R | 30 ++----------------- 4 files changed, 41 insertions(+), 88 deletions(-) create mode 100644 modules/Loading/R/compare_exp_obs_grids.R diff --git a/modules/Loading/R/compare_exp_obs_grids.R b/modules/Loading/R/compare_exp_obs_grids.R new file mode 100644 index 00000000..0270cd63 --- /dev/null +++ b/modules/Loading/R/compare_exp_obs_grids.R @@ -0,0 +1,35 @@ +compare_exp_obs_grids <- function(exp, obs) { + # Check for consistency between exp and obs grid + if (!isTRUE(all.equal(as.vector(exp$coords$latitude), + as.vector(obs$coords$latitude)))) { + lat_error_msg <- paste("Latitude mismatch between exp and obs.", + "Please check the original grids and the", + "regrid parameters in your recipe.") + error(recipe$Run$logger, lat_error_msg) + exp_lat_msg <- paste0("First exp lat: ", exp$coords$latitude[1], + "; Last exp lat: ", + exp$coords$latitude[length(exp$coords$latitude)]) + info(recipe$Run$logger, exp_lat_msg) + obs_lat_msg <- paste0("First obs lat: ", obs$coords$latitude[1], + "; Last obs lat: ", + obs$coords$latitude[length(obs$coords$latitude)]) + info(recipe$Run$logger, obs_lat_msg) + stop("exp and obs don't share the same latitudes.") + } + if (!isTRUE(all.equal(as.vector(exp$coords$longitude), + as.vector(obs$coords$longitude)))) { + lon_error_msg <- paste("Longitude mismatch between exp and obs.", + "Please check the original grids and the", + "regrid parameters in your recipe.") + error(recipe$Run$logger, lon_error_msg) + exp_lon_msg <- paste0("First exp lon: ", exp$coords$longitude[1], + "; Last exp lon: ", + exp$coords$longitude[length(exp$coords$longitude)]) + info(recipe$Run$logger, exp_lon_msg) + obs_lon_msg <- paste0("First obs lon: ", obs$coords$longitude[1], + "; Last obs lon: ", + obs$coords$longitude[length(obs$coords$longitude)]) + info(recipe$Run$logger, obs_lon_msg) + stop("exp and obs don't share the same longitudes.") + } +} diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R index 48ffe8c5..797d6872 100644 --- a/modules/Loading/R/load_decadal.R +++ b/modules/Loading/R/load_decadal.R @@ -11,6 +11,7 @@ source("modules/Loading/R/helper_loading_decadal.R") source("modules/Loading/R/dates2load.R") source("modules/Loading/R/check_latlon.R") source("modules/Loading/R/get_timeidx.R") +source("modules/Loading/R/compare_exp_obs_grids.R") #==================================================================== @@ -454,35 +455,8 @@ load_decadal <- function(recipe) { # lat and lon attributes if (!(recipe$Analysis$Regrid$type == 'none')) { - if (!identical(as.vector(hcst$lat), as.vector(fcst$lat))) { - lat_error_msg <- paste("Latitude mismatch between hcst and fcst.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lat_error_msg) - hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1], - "; Last hcst lat: ", hcst$lat[length(hcst$lat)]) - info(recipe$Run$logger, hcst_lat_msg) - fcst_lat_msg <- paste0("First fcst lat: ", fcst$lat[1], - "; Last fcst lat: ", fcst$lat[length(fcst$lat)]) - info(recipe$Run$logger, fcst_lat_msg) - stop("hcst and fcst don't share the same latitudes.") - } - - if (!identical(as.vector(hcst$lon), as.vector(fcst$lon))) { - lon_error_msg <- paste("Longitude mismatch between hcst and fcst.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lon_error_msg) - hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1], - "; Last hcst lon: ", hcst$lon[length(hcst$lon)]) - info(recipe$Run$logger, hcst_lon_msg) - fcst_lon_msg <- paste0("First fcst lon: ", fcst$lon[1], - "; Last fcst lon: ", fcst$lon[length(fcst$lon)]) - info(recipe$Run$logger, fcst_lon_msg) - stop("hcst and fcst don't share the same longitudes.") - } + compare_exp_obs_grids(hcst, obs) } - } #------------------------------------------- diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R index 32c7edf8..14f34d1c 100644 --- a/modules/Loading/R/load_seasonal.R +++ b/modules/Loading/R/load_seasonal.R @@ -4,6 +4,7 @@ source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") source("modules/Loading/R/dates2load.R") source("modules/Loading/R/get_timeidx.R") source("modules/Loading/R/check_latlon.R") +source("modules/Loading/R/compare_exp_obs_grids.R") load_seasonal <- function(recipe) { @@ -316,38 +317,7 @@ load_seasonal <- function(recipe) { # Check for consistency between hcst and obs grid if (!(recipe$Analysis$Regrid$type == 'none')) { - if (!isTRUE(all.equal(as.vector(hcst$coords$latitude), - as.vector(obs$coords$latitude)))) { - lat_error_msg <- paste("Latitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lat_error_msg) - hcst_lat_msg <- paste0("First hcst lat: ", hcst$coords$latitude[1], - "; Last hcst lat: ", - hcst$coords$latitude[length(hcst$coords$latitude)]) - info(recipe$Run$logger, hcst_lat_msg) - obs_lat_msg <- paste0("First obs lat: ", obs$coords$latitude[1], - "; Last obs lat: ", - obs$coords$latitude[length(obs$coords$latitude)]) - info(recipe$Run$logger, obs_lat_msg) - stop("hcst and obs don't share the same latitudes.") - } - if (!isTRUE(all.equal(as.vector(hcst$coords$longitude), - as.vector(obs$coords$longitude)))) { - lon_error_msg <- paste("Longitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lon_error_msg) - hcst_lon_msg <- paste0("First hcst lon: ", hcst$coords$longitude[1], - "; Last hcst lon: ", - hcst$coords$longitude[length(hcst$coords$longitude)]) - info(recipe$Run$logger, hcst_lon_msg) - obs_lon_msg <- paste0("First obs lon: ", obs$coords$longitude[1], - "; Last obs lon: ", - obs$coords$longitude[length(obs$coords$longitude)]) - info(recipe$Run$logger, obs_lon_msg) - stop("hcst and obs don't share the same longitudes.") - } + compare_exp_obs_grids(hcst, obs) } # Remove negative values in accumulative variables diff --git a/modules/Loading/R/load_tas_tos.R b/modules/Loading/R/load_tas_tos.R index 5e6b8837..34f6220c 100644 --- a/modules/Loading/R/load_tas_tos.R +++ b/modules/Loading/R/load_tas_tos.R @@ -4,7 +4,7 @@ source("modules/Loading/R/dates2load.R") source("modules/Loading/R/get_timeidx.R") source("modules/Loading/R/check_latlon.R") source('modules/Loading/R/mask_tas_tos.R') - +source("modules/Loading/R/compare_exp_obs_grids.R") load_tas_tos <- function(recipe) { @@ -477,33 +477,7 @@ load_tas_tos <- function(recipe) { # Check for consistency between hcst and obs grid if (!(recipe$Analysis$Regrid$type == 'none')) { - if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) { - lat_error_msg <- paste("Latitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lat_error_msg) - hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1], - "; Last hcst lat: ", hcst$lat[length(hcst$lat)]) - info(recipe$Run$logger, hcst_lat_msg) - obs_lat_msg <- paste0("First obs lat: ", obs$lat[1], - "; Last obs lat: ", obs$lat[length(obs$lat)]) - info(recipe$Run$logger, obs_lat_msg) - stop("hcst and obs don't share the same latitudes.") - } - if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) { - lon_error_msg <- paste("Longitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lon_error_msg) - hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1], - "; Last hcst lon: ", hcst$lon[length(hcst$lon)]) - info(recipe$Run$logger, hcst_lon_msg) - obs_lon_msg <- paste0("First obs lon: ", obs$lon[1], - "; Last obs lon: ", obs$lon[length(obs$lon)]) - info(recipe$Run$logger, obs_lon_msg) - stop("hcst and obs don't share the same longitudes.") - - } + compare_exp_obs_grids(exp = hcst, obs = obs) } info(recipe$Run$logger, -- GitLab From 6a14162bd8b45f4e3b4cb4f26a3cc450ceae05d0 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Tue, 2 Jan 2024 11:25:27 +0100 Subject: [PATCH 43/78] included system and reference loop for score aggregation --- modules/Scorecards/Dev_Scorecards.R | 659 ++++++++++++++-------------- 1 file changed, 336 insertions(+), 323 deletions(-) diff --git a/modules/Scorecards/Dev_Scorecards.R b/modules/Scorecards/Dev_Scorecards.R index 2fdd940e..d8976cfa 100644 --- a/modules/Scorecards/Dev_Scorecards.R +++ b/modules/Scorecards/Dev_Scorecards.R @@ -18,7 +18,7 @@ source('modules/Scorecards/R/tmp/SCPlotScorecard.R') Scorecards <- function(recipe) { ## Parameters for loading data - input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231207112311/outputs/" #temp + input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231219150442/outputs/" #temp skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") @@ -48,6 +48,8 @@ Scorecards <- function(recipe) { metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) + na.rm <- FALSE ## Need to define in recipe ?? + ncores <- 1 # recipe$Analysis$ncores ## Parameters for scorecard table @@ -58,8 +60,8 @@ Scorecards <- function(recipe) { col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff - ncores <- 1 # recipe$Analysis$ncores - na.rm <- FALSE + + ## Load data files start.months <- sprintf("%02d", start.months) @@ -128,7 +130,7 @@ Scorecards <- function(recipe) { ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? ## 2) output files names of statistics not correct, too many _ in name ## 3) how to load multiple systems? - Loop over sys and ref for metric.aggregation == 'score' - ## Need to test mith multiple sdates + ## Need to test with multiple sdates lon_dim <- 'longitude' lat_dim <- 'latitude' @@ -137,352 +139,363 @@ Scorecards <- function(recipe) { ## Define arrays to filled with data scorecard_metrics <- array(data = NA, - dim = c(time = length(forecast.months), + dim = c(system = length(system), + reference = length(reference), + time = length(forecast.months), sdate = length(start.months), region = length(regions), metric = length(metrics.visualize))) scorecard_sign <- array(data = NA, - dim = c(time = length(forecast.months), + dim = c(system = length(system), + reference = length(reference), + time = length(forecast.months), sdate = length(start.months), region = length(regions), metric = length(metrics.visualize))) - for (met in metrics.visualize) { - if(met == 'rpss'){ - ## Load data from saved files - rps_syear <- .loadmetrics(input_path = skill.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_syear', - calib_method = calib.method, syear = TRUE) - - rps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Spatially aggregate data - rps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = F) - }, simplify = 'array') - - rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = F) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' - names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' - - ## Temporally aggregate data - rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - ## Calculate RPSS from aggregated RPS and RPS_clim - rpss <- 1 - rps_temp_aggr / rps_clim_temp_aggr - - ## Calculate significance - sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, - time_dim = time_dim, test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, - ncores = NULL)$sign + for (sys in 1:length(system)){ + # sys_num <- which(system == sys) + for (ref in 1:length(reference)){ + # ref_num <- which(refence == ref) + for (met in metrics.visualize) { + + if(met == 'rpss'){ + ## Load data from saved files + rps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_syear', + calib_method = calib.method, syear = TRUE) + + rps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Spatially aggregate data + rps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = F) + }, simplify = 'array') + + rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' + names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' - ## Save metric result in arrays - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) - scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) - - } ## close if on rpss - - if(met == 'crpss'){ - ## Load data from saved files - crps_syear <- .loadmetrics(input_path = skill.input.path, system = system, - reference = reference, var = var, + ## Temporally aggregate data + rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + ## Calculate RPSS from aggregated RPS and RPS_clim + rpss <- 1 - rps_temp_aggr / rps_clim_temp_aggr + + ## Calculate significance + sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, + time_dim = time_dim, test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + ## Save metric result in arrays + scorecard_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) + scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) + + } ## close if on rpss + + if(met == 'crpss'){ + + ## Load data from saved files + crps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_syear', + calib_method = calib.method, syear = TRUE) + + crps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Spatially aggregate data + crps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' + names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' + + ## Temporally aggregate data + crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + ## Calculate CRPSS from aggregated CRPS and CRPS_clim + crpss <- 1 - crps_temp_aggr / crps_clim_temp_aggr + + ## Calculate significance + sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, + time_dim = time_dim, test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + ## Save metric result in arrays + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) + scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) + + } ## close if on crpss + + if(met == 'enscorr'){ + ## Load data from saved files + cov <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'cov', + calib_method = calib.method, syear = NULL) + + std_hcst <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_hcst', + calib_method = calib.method, syear = NULL) + + std_obs <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_obs', + calib_method = calib.method, syear = NULL) + + + n_eff <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, period = period, start_months = start.months, forecast_months = forecast.months, - metrics = 'crps_syear', - calib_method = calib.method, syear = TRUE) - - crps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Spatially aggregate data - crps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + metrics = 'n_eff', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + cov_spatial_aggr <- sapply(X = 1:length(regions), FUN = function(X) { - WeightedMean(data = crps_clim_syear, + WeightedMean(data = cov, region = regions[[X]], - lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), + lon = as.vector(attributes(cov)$Variables$dat1$longitude), + lat = as.vector(attributes(cov)$Variables$dat1$latitude), londim = lon_dim, latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') - - ## Include name of region dimension - names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' - names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' - - ## Temporally aggregate data - crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - ## Calculate CRPSS from aggregated CRPS and CRPS_clim - crpss <- 1 - crps_temp_aggr / crps_clim_temp_aggr - - ## Calculate significance - sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, - time_dim = time_dim, test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, - ncores = NULL)$sign - - ## Save metric result in arrays - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) - scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) - - } ## close if on crpss - - if(met == 'enscorr'){ - ## Load data from saved files - cov <- .loadmetrics(input_path = stats.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'cov', - calib_method = calib.method, syear = NULL) - - std_hcst <- .loadmetrics(input_path = stats.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_hcst', - calib_method = calib.method, syear = NULL) - - std_obs <- .loadmetrics(input_path = stats.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_obs', - calib_method = calib.method, syear = NULL) - - - n_eff <- .loadmetrics(input_path = stats.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'n_eff', - calib_method = calib.method, syear = NULL) - - ## Calculate spatial aggregation - cov_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = cov, - region = regions[[X]], - lon = as.vector(attributes(cov)$Variables$dat1$longitude), - lat = as.vector(attributes(cov)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(cov_spatial_aggr))[length(dim(cov_spatial_aggr))] <- 'region' - - - std_hcst_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = std_hcst, - region = regions[[X]], - lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), - lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(std_hcst_spatial_aggr))[length(dim(std_hcst_spatial_aggr))] <- 'region' - - std_obs_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = std_obs, - region = regions[[X]], - lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), - lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' - - n_eff_spatial_aggr <- sapply(X = 1:length(regions), + + ## Include name of region dimension + names(dim(cov_spatial_aggr))[length(dim(cov_spatial_aggr))] <- 'region' + + + std_hcst_spatial_aggr <- sapply(X = 1:length(regions), FUN = function(X) { - WeightedMean(data = n_eff, + WeightedMean(data = std_hcst, region = regions[[X]], - lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), - lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), + lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), londim = lon_dim, latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') - - names(dim(n_eff_spatial_aggr))[length(dim(n_eff_spatial_aggr))] <- 'region' - n_eff_spatial_aggr <- Subset(n_eff_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Calculate correlation - enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) - - ## Drop unwanted dimensions - enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Calculate significance of corr - alpha <- 0.05 # Apply 95% confidence - - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) - t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) - - sign_corr<- array(data = NA, - dim = c(time = length(forecast.months), - smonths = length(start.months), - region = length(regions))) - - - for (time in 1:dim(sign_corr)[['time']]){ - for (mon in 1:dim(sign_corr)[['smonths']]){ - for (reg in 1:dim(sign_corr)[['region']]){ - - if (anyNA(c(t[time, mon, reg], t_alpha2_n2[time, mon, reg])) == FALSE - && t[time, mon, reg] >= t_alpha2_n2[time, mon, reg]){ - sign_corr[time, mon, reg] <- TRUE - } else { - sign_corr[time, mon, reg] <- FALSE + + names(dim(std_hcst_spatial_aggr))[length(dim(std_hcst_spatial_aggr))] <- 'region' + + std_obs_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_obs, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), + lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' + + n_eff_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = n_eff, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), + lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(n_eff_spatial_aggr))[length(dim(n_eff_spatial_aggr))] <- 'region' + n_eff_spatial_aggr <- Subset(n_eff_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate correlation + enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) + + ## Drop unwanted dimensions + enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate significance of corr + alpha <- 0.05 # Apply 95% confidence + + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) + t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) + + sign_corr<- array(data = NA, + dim = c(time = length(forecast.months), + smonths = length(start.months), + region = length(regions))) + + + for (time in 1:dim(sign_corr)[['time']]){ + for (mon in 1:dim(sign_corr)[['smonths']]){ + for (reg in 1:dim(sign_corr)[['region']]){ + + if (anyNA(c(t[time, mon, reg], t_alpha2_n2[time, mon, reg])) == FALSE + && t[time, mon, reg] >= t_alpha2_n2[time, mon, reg]){ + sign_corr[time, mon, reg] <- TRUE + } else { + sign_corr[time, mon, reg] <- FALSE + } + + } } - - } - } - } - - ## Save metric result in arrays - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) - scorecard_sign[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) - - } ## close if on enscorr - - if(met == 'mean_bias'){ - - mean_bias <- .loadmetrics(input_path = skill.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'mean_bias', - calib_method = calib.method, syear = NULL) - - ## Calculate spatial aggregation - mean_bias_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = mean_bias, - region = regions[[X]], - lon = as.vector(attributes(mean_bias)$Variables$dat1$longitude), - lat = as.vector(attributes(mean_bias)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(mean_bias_spatial_aggr))[length(dim(mean_bias_spatial_aggr))] <- 'region' - - ## Drop unwanted dimensions - mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Save metric result in array - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) - - } ## close on mean_bias - - if(met == 'enssprerr'){ - - enssprerr <- .loadmetrics(input_path = skill.input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'enssprerr', - calib_method = calib.method, syear = NULL) - - ## Calculate spatial aggregation - enssprerr_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = enssprerr, - region = regions[[X]], - lon = as.vector(attributes(enssprerr)$Variables$dat1$longitude), - lat = as.vector(attributes(enssprerr)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(enssprerr_spatial_aggr))[length(dim(enssprerr_spatial_aggr))] <- 'region' - - ## Drop unwanted dimensions - enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Save metric result in array - scorecard_metrics[ , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) - - } ## close on enssprerr - + } - } ## close loop on metric - + ## Save metric result in arrays + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) + scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) + + } ## close if on enscorr + + if(met == 'mean_bias'){ + + mean_bias <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'mean_bias', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + mean_bias_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = mean_bias, + region = regions[[X]], + lon = as.vector(attributes(mean_bias)$Variables$dat1$longitude), + lat = as.vector(attributes(mean_bias)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(mean_bias_spatial_aggr))[length(dim(mean_bias_spatial_aggr))] <- 'region' + + ## Drop unwanted dimensions + mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Save metric result in array + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) + + } ## close on mean_bias + + if(met == 'enssprerr'){ + + enssprerr <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'enssprerr', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + enssprerr_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = enssprerr, + region = regions[[X]], + lon = as.vector(attributes(enssprerr)$Variables$dat1$longitude), + lat = as.vector(attributes(enssprerr)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(enssprerr_spatial_aggr))[length(dim(enssprerr_spatial_aggr))] <- 'region' + + ## Drop unwanted dimensions + enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Save metric result in array + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) + + } ## close on enssprerr + + } ## close loop on metric + } ## close if on reference + } ## close if on system + aggregated_metrics <- scorecard_metrics } ## close if on score -- GitLab From 997398e1c2cd8d1857b877f0f050283a11bcc5cf Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 3 Jan 2024 17:18:10 +0100 Subject: [PATCH 44/78] included underline signifcance in scorecard --- modules/Scorecards/Dev_Scorecards.R | 571 -------------------- modules/Scorecards/R/tmp/SCPlotScorecard.R | 32 +- modules/Scorecards/R/tmp/ScorecardsSingle.R | 32 +- modules/Scorecards/Scorecards.R | 513 +++++++++++++++--- 4 files changed, 494 insertions(+), 654 deletions(-) delete mode 100644 modules/Scorecards/Dev_Scorecards.R diff --git a/modules/Scorecards/Dev_Scorecards.R b/modules/Scorecards/Dev_Scorecards.R deleted file mode 100644 index d8976cfa..00000000 --- a/modules/Scorecards/Dev_Scorecards.R +++ /dev/null @@ -1,571 +0,0 @@ -############################################################################### -##################### SCORECARDS MODULE FOR SUNSET SUITE ###################### -############################################################################### - -##### Load source functions ##### -source('modules/Scorecards/R/tmp/LoadMetrics.R') -source('modules/Scorecards/R/tmp/WeightedMetrics.R') -source('modules/Scorecards/R/tmp/Utils.R') -source('modules/Scorecards/R/tmp/SCTransform.R') -source('modules/Scorecards/R/tmp/ScorecardsSingle.R') -source('modules/Scorecards/R/tmp/ScorecardsMulti.R') -source('modules/Scorecards/R/tmp/ScorecardsSystemDiff.R') -source('modules/Scorecards/R/tmp/SCPlotScorecard.R') - - -## TODO: Change function name to 'Scorecards'? -## Define function -Scorecards <- function(recipe) { - - ## Parameters for loading data - input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231219150442/outputs/" #temp - skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") - stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") - output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") - dir.create(output.path, recursive = T, showWarnings = F) - system <- recipe$Analysis$Datasets$System$name - reference <- recipe$Analysis$Datasets$Reference$name - var <- recipe$Analysis$Variables$name - start.year <- as.numeric(recipe$Analysis$Time$hcst_start) - end.year <- as.numeric(recipe$Analysis$Time$hcst_end) - forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max - calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) - - # NOTE (Eva): This condition needs to be checked, in my case - # (recipe$Analysis$Workflow$Scorecards$start_months = NULL) - start.months <- 1:12 # I added this line - # Needs to be corrected: - # if (recipe$Analysis$Workflow$Scorecards$start_months == 'all') { - # start.months <- 1:12 - # } else { - # start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, - # split = ", | |,")[[1]]) - # } - - regions <- recipe$Analysis$Workflow$Scorecards$regions - for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])} - - metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation - metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) - metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) - na.rm <- FALSE ## Need to define in recipe ?? - ncores <- 1 # recipe$Analysis$ncores - - - ## Parameters for scorecard table - inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf_to_na - table.label <- recipe$Analysis$Workflow$Scorecards$table_label - fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label - legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space - col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width - col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width - calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff - - - - ## Load data files - start.months <- sprintf("%02d", start.months) - period <- paste0(start.year, "-", end.year) - - ####### SKILL AGGREGATION ####### - if(metric.aggregation == 'skill'){ - - ## Load data files - loaded_metrics <- LoadMetrics(system = system, - reference = reference, - var = var, - start.year = start.year, - end.year = end.year, - metrics = metrics.load, - start.months = start.months, - forecast.months = forecast.months, - inf.to.na = inf.to.na, - input.path = skill.input.path) - - ## Spatial Aggregation of metrics - if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ - - ### Convert loaded metrics to array for already aggregated data - metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics - forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months - start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months - regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80)) - - aggregated_metrics <- array(dim = c(system = length(loaded_metrics), - reference = length(loaded_metrics[[1]]), - metric = length(metrics.dim), - time = length(forecast.months.dim), - sdate = length(start.months.dim), - region = length(regions.dim))) - - - for (sys in 1:length(names(loaded_metrics))){ - for (ref in 1:length(names(loaded_metrics[[sys]]))){ - aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region')) - } - } - - ## Add attributes - attributes(aggregated_metrics)$metrics <- metrics.load - attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months - attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months - attributes(aggregated_metrics)$regions <- regions - attributes(aggregated_metrics)$system.name <- names(loaded_metrics) - attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]]) - - - } else { - ## Calculate weighted mean of spatial aggregation - aggregated_metrics <- WeightedMetrics(loaded_metrics, - regions = regions, - metric.aggregation = metric.aggregation, - ncores = ncores) - }## close if on region - } - - ###### SCORE AGGREGATION ###### - if(metric.aggregation == 'score'){ - - ## Comments (Nadia): - ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? - ## 2) output files names of statistics not correct, too many _ in name - ## 3) how to load multiple systems? - Loop over sys and ref for metric.aggregation == 'score' - ## Need to test with multiple sdates - - lon_dim <- 'longitude' - lat_dim <- 'latitude' - time_dim <- 'syear' - memb_dim <- 'ensemble' - - ## Define arrays to filled with data - scorecard_metrics <- array(data = NA, - dim = c(system = length(system), - reference = length(reference), - time = length(forecast.months), - sdate = length(start.months), - region = length(regions), - metric = length(metrics.visualize))) - - scorecard_sign <- array(data = NA, - dim = c(system = length(system), - reference = length(reference), - time = length(forecast.months), - sdate = length(start.months), - region = length(regions), - metric = length(metrics.visualize))) - - - for (sys in 1:length(system)){ - # sys_num <- which(system == sys) - for (ref in 1:length(reference)){ - # ref_num <- which(refence == ref) - for (met in metrics.visualize) { - - if(met == 'rpss'){ - ## Load data from saved files - rps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_syear', - calib_method = calib.method, syear = TRUE) - - rps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Spatially aggregate data - rps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = F) - }, simplify = 'array') - - rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = F) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' - names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' - - ## Temporally aggregate data - rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - ## Calculate RPSS from aggregated RPS and RPS_clim - rpss <- 1 - rps_temp_aggr / rps_clim_temp_aggr - - ## Calculate significance - sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, - time_dim = time_dim, test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, - ncores = NULL)$sign - - ## Save metric result in arrays - scorecard_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) - scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) - - } ## close if on rpss - - if(met == 'crpss'){ - - ## Load data from saved files - crps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_syear', - calib_method = calib.method, syear = TRUE) - - crps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Spatially aggregate data - crps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' - names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' - - ## Temporally aggregate data - crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, - target_dims = time_dim, - fun = 'mean', ncores = ncores)$output1 - - ## Calculate CRPSS from aggregated CRPS and CRPS_clim - crpss <- 1 - crps_temp_aggr / crps_clim_temp_aggr - - ## Calculate significance - sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, - time_dim = time_dim, test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, - ncores = NULL)$sign - - ## Save metric result in arrays - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) - scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) - - } ## close if on crpss - - if(met == 'enscorr'){ - ## Load data from saved files - cov <- .loadmetrics(input_path = stats.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'cov', - calib_method = calib.method, syear = NULL) - - std_hcst <- .loadmetrics(input_path = stats.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_hcst', - calib_method = calib.method, syear = NULL) - - std_obs <- .loadmetrics(input_path = stats.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_obs', - calib_method = calib.method, syear = NULL) - - - n_eff <- .loadmetrics(input_path = stats.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'n_eff', - calib_method = calib.method, syear = NULL) - - ## Calculate spatial aggregation - cov_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = cov, - region = regions[[X]], - lon = as.vector(attributes(cov)$Variables$dat1$longitude), - lat = as.vector(attributes(cov)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - ## Include name of region dimension - names(dim(cov_spatial_aggr))[length(dim(cov_spatial_aggr))] <- 'region' - - - std_hcst_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = std_hcst, - region = regions[[X]], - lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), - lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(std_hcst_spatial_aggr))[length(dim(std_hcst_spatial_aggr))] <- 'region' - - std_obs_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = std_obs, - region = regions[[X]], - lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), - lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' - - n_eff_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = n_eff, - region = regions[[X]], - lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), - lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(n_eff_spatial_aggr))[length(dim(n_eff_spatial_aggr))] <- 'region' - n_eff_spatial_aggr <- Subset(n_eff_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Calculate correlation - enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) - - ## Drop unwanted dimensions - enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Calculate significance of corr - alpha <- 0.05 # Apply 95% confidence - - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) - t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) - - sign_corr<- array(data = NA, - dim = c(time = length(forecast.months), - smonths = length(start.months), - region = length(regions))) - - - for (time in 1:dim(sign_corr)[['time']]){ - for (mon in 1:dim(sign_corr)[['smonths']]){ - for (reg in 1:dim(sign_corr)[['region']]){ - - if (anyNA(c(t[time, mon, reg], t_alpha2_n2[time, mon, reg])) == FALSE - && t[time, mon, reg] >= t_alpha2_n2[time, mon, reg]){ - sign_corr[time, mon, reg] <- TRUE - } else { - sign_corr[time, mon, reg] <- FALSE - } - - } - } - } - - ## Save metric result in arrays - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) - scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) - - } ## close if on enscorr - - if(met == 'mean_bias'){ - - mean_bias <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'mean_bias', - calib_method = calib.method, syear = NULL) - - ## Calculate spatial aggregation - mean_bias_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = mean_bias, - region = regions[[X]], - lon = as.vector(attributes(mean_bias)$Variables$dat1$longitude), - lat = as.vector(attributes(mean_bias)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(mean_bias_spatial_aggr))[length(dim(mean_bias_spatial_aggr))] <- 'region' - - ## Drop unwanted dimensions - mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Save metric result in array - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) - - } ## close on mean_bias - - if(met == 'enssprerr'){ - - enssprerr <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'enssprerr', - calib_method = calib.method, syear = NULL) - - ## Calculate spatial aggregation - enssprerr_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = enssprerr, - region = regions[[X]], - lon = as.vector(attributes(enssprerr)$Variables$dat1$longitude), - lat = as.vector(attributes(enssprerr)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(enssprerr_spatial_aggr))[length(dim(enssprerr_spatial_aggr))] <- 'region' - - ## Drop unwanted dimensions - enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - - ## Save metric result in array - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) - - } ## close on enssprerr - - } ## close loop on metric - } ## close if on reference - } ## close if on system - - aggregated_metrics <- scorecard_metrics - - } ## close if on score - - - - - stop() - - - ## Create simple scorecard tables - ## (one system only) - ## Metrics input must be in the same order as function SC_spatial_aggregation - scorecard_single <- ScorecardsSingle(data = aggregated_metrics, - system = system, - reference = reference, - var = var, - start.year = start.year, - end.year = end.year, - start.months = start.months, - forecast.months = forecast.months, - region.names = names(regions), - metrics = metrics.visualize, - table.label = table.label, - fileout.label = fileout.label, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = col2.width, - output.path = output.path) - - ## Create multi system/reference scorecard tables - ## (multiple systems with one reference or one system with multiple references) - ## Metrics input must be in the same order as function SC_spatial_aggregation - if(length(system) > 1 || length(reference) > 1){ - scorecard_multi <- ScorecardsMulti(data = aggregated_metrics, - system = system, - reference = reference, - var = var, - start.year = start.year, - end.year = end.year, - start.months = start.months, - forecast.months = forecast.months, - region.names = attributes(regions)$names, - metrics = metrics.visualize, - table.label = table.label, - fileout.label = fileout.label, - output.path = output.path) - } ## close if - - - if(calculate.diff == TRUE){ - if(length(system) == 2 || length(reference) == 2){ - scorecard_diff <- ScorecardsSystemDiff(data = aggregated_metrics, - system = system, - reference = reference, - var = var, - start.year = start.year, - end.year = end.year, - start.months = start.months, - forecast.months = forecast.months, - region.names = attributes(regions)$names, - metrics = metrics.visualize, - table.label = table.label, - fileout.label = fileout.label, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = col2.width, - output.path = output.path) - } else {stop ("Difference scorecard can only be computed with two systems or two references.")} - } ## close if on calculate.diff - -} - diff --git a/modules/Scorecards/R/tmp/SCPlotScorecard.R b/modules/Scorecards/R/tmp/SCPlotScorecard.R index 4373057b..8c2d9eba 100644 --- a/modules/Scorecards/R/tmp/SCPlotScorecard.R +++ b/modules/Scorecards/R/tmp/SCPlotScorecard.R @@ -5,6 +5,9 @@ #' #'@param data A multidimensional array containing spatially aggregated metrics #' data with dimensions: metric, region, sdate and ftime. +#'@param sign A multidimensional boolean array with the same dimensions as +#' 'data', indicting which values to be highlighted. If set to NULL no values +#' will be highlighted. #'@param row.dim A character string indicating the dimension name to show in the #' rows of the plot. #'@param subrow.dim A character string indicating the dimension name to show in @@ -72,7 +75,8 @@ #'@import s2dv #'@import ClimProjDiags #'@export -SCPlotScorecard <- function(data, row.dim = 'region', subrow.dim = 'time', +SCPlotScorecard <- function(data, sign, + row.dim = 'region', subrow.dim = 'time', col.dim = 'metric', subcol.dim = 'sdate', legend.dim = 'metric', row.names = NULL, subrow.names = NULL, col.names = NULL, @@ -343,20 +347,30 @@ SCPlotScorecard <- function(data, row.dim = 'region', subrow.dim = 'time', table_data <- rbind(table_data, table_temp) } } - - # All data for plotting in table + + # All data for plotting in table table <- data.frame(table_column_1 = row.names.table, table_data) table_temp <- array(unlist(table[3:n.columns]), dim = c(n.rows, n.columns - 2)) + # Define colors to show in table - table_colors <- .SCTableColors(table = table_temp, n.col = n.col.names, - n.subcol = n.subcol.names, n.row = n.row.names, - n.subrow = n.subrow.names, legend.breaks = legend.breaks, - palette = palette, colorunder = colorunder, - colorsup = colorsup) + table_colors <- .SCTableColors(table = table_temp, + n.col = n.col.names, n.subcol = n.subcol.names, + n.row = n.row.names, n.subrow = n.subrow.names, + legend.breaks = legend.breaks, palette = palette, + colorunder = colorunder, colorsup = colorsup) + metric.color <- table_colors$metric.color metric.text.color <- table_colors$metric.text.color # metric.text.bold <- table_colors$metric.text.bold + # Remove temporary tables + rm(table_temp) + rm(table_sign_temp) + + ## Format values to underline in table + metric.underline <- MergeDims(sign, c('sdate', 'metric') , rename_dim = 'col', na.rm =F) + metric.underline <- MergeDims(metric.underline, c('time', 'region') , rename_dim = 'row', na.rm =F) + options(stringsAsFactors = FALSE) title <- data.frame(c1 = table.title, c2 = n.columns) subtitle <- data.frame(c1 = table.subtitle, c2 = n.columns) @@ -385,6 +399,7 @@ SCPlotScorecard <- function(data, row.dim = 'region', subrow.dim = 'time', for (j in 1:n.subcol.names) { my.background <- metric.color[, (i - 1) * n.subcol.names + j] my.text.color <- metric.text.color[, (i - 1) * n.subcol.names + j] + my.underline <- metric.underline[, (i - 1) * n.subcol.names + j] # my.bold <- metric.text.bold[(i - 1) * n.subcol.names + j] table.html.part[[(i - 1) * n.subcol.names + j + 1]] <- @@ -392,6 +407,7 @@ SCPlotScorecard <- function(data, row.dim = 'region', subrow.dim = 'time', 2 + n.subcol.names * (i - 1) + j, background = my.background[1:n.rows], color = my.text.color[1:n.rows], + underline = my.underline[1:n.rows], bold = T) ## strsplit(toString(bold), ', ')[[1]] } } diff --git a/modules/Scorecards/R/tmp/ScorecardsSingle.R b/modules/Scorecards/R/tmp/ScorecardsSingle.R index 56f08204..190ae3d5 100644 --- a/modules/Scorecards/R/tmp/ScorecardsSingle.R +++ b/modules/Scorecards/R/tmp/ScorecardsSingle.R @@ -2,8 +2,10 @@ #' #'@description Scorecards function to create scorecard tables for one system and #' one reference combination (types 1 to 4). -#'@param input_data is an array of spatially aggregated metrics containing the +#'@param data is an array of spatially aggregated metrics containing the #' following dimensions; system, reference, metric, time, sdate, region. +#'@param sign is an array with the same dimensions as data indicting the +#' significance of the metrics, with either true, false or null. #'@param system a vector of character strings defining the systems following the #' archive.yml format from verification suite #'@param reference a vector of character strings defining the references @@ -41,7 +43,7 @@ #' output.path = '/esarchive/scratch/nmilders/scorecards_images/test' #' ) #'@export -ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, +ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end.year, start.months, forecast.months, region.names, metrics, legend.breaks = NULL, table.label = NULL, fileout.label = NULL, @@ -80,6 +82,7 @@ ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, ## Make sure input_data is in correct order for using in functions: data_order <- c('system', 'reference', 'metric', 'time', 'sdate', 'region') data <- Reorder(data, data_order) + sign <- Reorder(sign, data_order) ## Identify metrics loaded metrics_loaded <- attributes(data)$metrics @@ -88,11 +91,18 @@ ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) attributes(input_data)$metrics <- metrics + input_sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(input_sign)$metrics <- metrics + ## Transform data for scorecards by forecast month (types 3 & 4) transformed_data <- SCTransform(data = input_data, sdate_dim = 'sdate', ftime_dim = 'time') + transformed_sign <- SCTransform(data = input_sign, + sdate_dim = 'sdate', + ftime_dim = 'time') + ## Load configuration files sys_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/conf/archive.yml")$esarchive var_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/csscorecards/inst/config/variable-dictionary.yml")$vars @@ -180,8 +190,12 @@ ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 1, fileout.label = fileout.label, output.path = output.path) + data_sc_1 <- Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected') + sign_sc_1 <- Subset(input_sign, c('system', 'reference'), list(sys, ref), drop = 'selected') + SCPlotScorecard(data = data_sc_1, + sign = sign_sc_1, row.dim = 'region', subrow.dim = 'time', col.dim = 'metric', @@ -217,12 +231,17 @@ ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, ## Scorecard type 2 is same as type 1 for only one region, therefore is ## only plotted if more that one region is requested if(dim(input_data)['region'] > 1) { + fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 2, fileout.label = fileout.label, output.path = output.path) + new_order <- c('metric', 'region', 'sdate', 'time') data_sc_2 <- Reorder(Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + sign_sc_2 <- Reorder(Subset(input_sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + SCPlotScorecard(data = data_sc_2, + sign = sign_sc_2, row.dim = 'time', subrow.dim = 'region', col.dim = 'metric', @@ -259,8 +278,12 @@ ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 3, fileout.label = fileout.label, output.path = output.path) + data_sc_3 <- Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected') + sign_sc_3 <- Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected') + SCPlotScorecard(data = data_sc_3, + sign = sign_sc_3, row.dim = 'region', subrow.dim = 'time', col.dim = 'metric', @@ -296,12 +319,17 @@ ScorecardsSingle <- function(data, system, reference, var, start.year, end.year, ## Scorecard type 4 is same as type 3 for only one region, therefore is ## only plotted if more that one region is requested if(dim(input_data)['region'] > 1) { + fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 4, fileout.label = fileout.label, output.path = output.path) + new_order <- c('metric', 'region', 'sdate', 'time') data_sc_4 <- Reorder(Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + sign_sc_4 <- Reorder(Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + SCPlotScorecard(data = data_sc_4, + sign = sign_sc_4, row.dim = 'time', subrow.dim = 'region', col.dim = 'metric', diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 8312735a..bad1497c 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -17,11 +17,12 @@ source('modules/Scorecards/R/tmp/SCPlotScorecard.R') ## Define function Scorecards <- function(recipe) { - ## set parameters - input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") + ## Parameters for loading data + input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231219150442/outputs/" #temp + skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") + stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") dir.create(output.path, recursive = T, showWarnings = F) - system <- recipe$Analysis$Datasets$System$name reference <- recipe$Analysis$Datasets$Reference$name var <- recipe$Analysis$Variables$name @@ -46,31 +47,17 @@ Scorecards <- function(recipe) { metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) - - ## Define skill scores in score aggregation has been requested - - if(metric.aggregation == 'score'){ - if('rps' %in% metrics.load){ - metrics.load <- c(metrics.load, 'rps_clim') - } - if('crps' %in% metrics.load){ - metrics.load <- c(metrics.load, 'crps_clim') - } - } - metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) - ## Define skill scores in score aggregation has been requested - - if(metric.aggregation == 'score'){ - if('rpss' %in% metrics.visualize){ - metrics.visualize[metrics.visualize == 'rpss'] <- 'rpss_score_aggr' - } - if('crpss' %in% metrics.visualize){ - metrics.visualize[metrics.visualize == 'crpss'] <- 'crpss_score_aggr' - } + if(is.null(recipe$Analysis$remove_NAs)){ + na.rm <- FALSE + } else { + na.rm <- recipe$Analysis$remove_NAs } + ncores <- 1 # recipe$Analysis$ncores + + ## Parameters for scorecard table inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf_to_na table.label <- recipe$Analysis$Workflow$Scorecards$table_label fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label @@ -78,73 +65,453 @@ Scorecards <- function(recipe) { col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff - ncores <- 1 # recipe$Analysis$ncores ## Load data files start.months <- sprintf("%02d", start.months) period <- paste0(start.year, "-", end.year) - # NOTE (Eva): This is an example: - if (any(metrics.load %in% 'rps_syear')) { - metric_rps_syear <- .loadmetrics(input_path = input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_syear', - calib_method = calib.method, syear = TRUE) - } else if (any(metrics.load %in% c('crps', 'mean_bias'))) { - metrics <- LoadMetrics(input_path = input.path, system = system, - reference = reference, var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = c('crps', 'mean_bias'), - calib_method = calib.method) - } - + ####### SKILL AGGREGATION ####### + if(metric.aggregation == 'skill'){ - if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ + ## Load data files + loaded_metrics <- LoadMetrics(system = system, + reference = reference, + var = var, + start.year = start.year, + end.year = end.year, + metrics = metrics.load, + start.months = start.months, + forecast.months = forecast.months, + inf.to.na = inf.to.na, + input.path = skill.input.path) - ### Convert loaded metrics to array for allready aggregated data - metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics - forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months - start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months - regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80)) + ## Spatial Aggregation of metrics + if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ + + ### Convert loaded metrics to array for already aggregated data + metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics + forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months + start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months + regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80)) + + aggregated_metrics <- array(dim = c(system = length(loaded_metrics), + reference = length(loaded_metrics[[1]]), + metric = length(metrics.dim), + time = length(forecast.months.dim), + sdate = length(start.months.dim), + region = length(regions.dim))) + + + for (sys in 1:length(names(loaded_metrics))){ + for (ref in 1:length(names(loaded_metrics[[sys]]))){ + aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region')) + } + } + + ## Add attributes + attributes(aggregated_metrics)$metrics <- metrics.load + attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months + attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months + attributes(aggregated_metrics)$regions <- regions + attributes(aggregated_metrics)$system.name <- names(loaded_metrics) + attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]]) + + + } else { + ## Calculate weighted mean of spatial aggregation + aggregated_metrics <- WeightedMetrics(loaded_metrics, + regions = regions, + metric.aggregation = metric.aggregation, + ncores = ncores) + }## close if on region + } + + ###### SCORE AGGREGATION ###### + if(metric.aggregation == 'score'){ - aggregated_metrics <- array(dim = c(system = length(loaded_metrics), - reference = length(loaded_metrics[[1]]), - metric = length(metrics.dim), - time = length(forecast.months.dim), - sdate = length(start.months.dim), - region = length(regions.dim))) + ## Comments (Nadia): + ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? + ## 2) output files names of statistics not correct, too many _ in name + ## 3) how to load multiple systems? - Loop over sys and ref for metric.aggregation == 'score' + ## Need to test with multiple sdates + lon_dim <- 'longitude' + lat_dim <- 'latitude' + time_dim <- 'syear' + memb_dim <- 'ensemble' + + ## Define arrays to filled with data + scorecard_metrics <- array(data = NA, + dim = c(system = length(system), + reference = length(reference), + time = length(forecast.months), + sdate = length(start.months), + region = length(regions), + metric = length(metrics.visualize))) - for (sys in 1:length(names(loaded_metrics))){ - for (ref in 1:length(names(loaded_metrics[[sys]]))){ - aggregated_metrics[sys, ref, , , , ] <- s2dv::Reorder(data = loaded_metrics[[sys]][[ref]], order = c('metric','time','sdate','region')) - } - } + scorecard_sign <- array(data = NA, + dim = c(system = length(system), + reference = length(reference), + time = length(forecast.months), + sdate = length(start.months), + region = length(regions), + metric = length(metrics.visualize))) - ## Add attributes - attributes(aggregated_metrics)$metrics <- metrics.load - attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months - attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months - attributes(aggregated_metrics)$regions <- regions - attributes(aggregated_metrics)$system.name <- names(loaded_metrics) - attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]]) + for (sys in 1:length(system)){ + # sys_num <- which(system == sys) + for (ref in 1:length(reference)){ + # ref_num <- which(refence == ref) + for (met in metrics.visualize) { + + if(met == 'rpss'){ + ## Load data from saved files + rps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_syear', + calib_method = calib.method, syear = TRUE) + + rps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'rps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Spatially aggregate data + rps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = F) + }, simplify = 'array') + + rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = F) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' + names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' + + ## Temporally aggregate data + rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + ## Calculate RPSS from aggregated RPS and RPS_clim + rpss <- 1 - rps_temp_aggr / rps_clim_temp_aggr + + ## Calculate significance + sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, + time_dim = time_dim, test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + ## Save metric result in arrays + scorecard_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) + scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) + + } ## close if on rpss + + if(met == 'crpss'){ + + ## Load data from saved files + crps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_syear', + calib_method = calib.method, syear = TRUE) + + crps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'crps_clim_syear', + calib_method = calib.method, syear = TRUE) + + ## Remove dat and var dimensions + crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Spatially aggregate data + crps_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), + lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' + names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' + + ## Temporally aggregate data + crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, + target_dims = time_dim, + fun = 'mean', ncores = ncores)$output1 + + ## Calculate CRPSS from aggregated CRPS and CRPS_clim + crpss <- 1 - crps_temp_aggr / crps_clim_temp_aggr + + ## Calculate significance + sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, + time_dim = time_dim, test.type = 'two.sided', + alpha = 0.05, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + + ## Save metric result in arrays + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) + scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) + + } ## close if on crpss + + if(met == 'enscorr'){ + ## Load data from saved files + cov <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'cov', + calib_method = calib.method, syear = NULL) + + std_hcst <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_hcst', + calib_method = calib.method, syear = NULL) + + std_obs <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'std_obs', + calib_method = calib.method, syear = NULL) + + + n_eff <- .loadmetrics(input_path = stats.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'n_eff', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + cov_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = cov, + region = regions[[X]], + lon = as.vector(attributes(cov)$Variables$dat1$longitude), + lat = as.vector(attributes(cov)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + ## Include name of region dimension + names(dim(cov_spatial_aggr))[length(dim(cov_spatial_aggr))] <- 'region' + - } else { - ## Calculate weighted mean of spatial aggregation - aggregated_metrics <- WeightedMetrics(loaded_metrics, - regions = regions, - metric.aggregation = metric.aggregation, - ncores = ncores) - }## close if + std_hcst_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_hcst, + region = regions[[X]], + lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), + lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_hcst_spatial_aggr))[length(dim(std_hcst_spatial_aggr))] <- 'region' + + std_obs_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_obs, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), + lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' + + n_eff_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = n_eff, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), + lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(n_eff_spatial_aggr))[length(dim(n_eff_spatial_aggr))] <- 'region' + n_eff_spatial_aggr <- Subset(n_eff_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate correlation + enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) + + ## Drop unwanted dimensions + enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Calculate significance of corr + alpha <- 0.05 # Apply 95% confidence + + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) + t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) + + sign_corr<- array(data = NA, + dim = c(time = length(forecast.months), + smonths = length(start.months), + region = length(regions))) + + + for (time in 1:dim(sign_corr)[['time']]){ + for (mon in 1:dim(sign_corr)[['smonths']]){ + for (reg in 1:dim(sign_corr)[['region']]){ + + if (anyNA(c(t[time, mon, reg], t_alpha2_n2[time, mon, reg])) == FALSE + && t[time, mon, reg] >= t_alpha2_n2[time, mon, reg]){ + sign_corr[time, mon, reg] <- TRUE + } else { + sign_corr[time, mon, reg] <- FALSE + } + + } + } + } + + ## Save metric result in arrays + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) + scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) + + } ## close if on enscorr + + if(met == 'mean_bias'){ + + mean_bias <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'mean_bias', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + mean_bias_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = mean_bias, + region = regions[[X]], + lon = as.vector(attributes(mean_bias)$Variables$dat1$longitude), + lat = as.vector(attributes(mean_bias)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(mean_bias_spatial_aggr))[length(dim(mean_bias_spatial_aggr))] <- 'region' + + ## Drop unwanted dimensions + mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Save metric result in array + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) + + } ## close on mean_bias + + if(met == 'enssprerr'){ + + enssprerr <- .loadmetrics(input_path = skill.input.path, system = system[sys], + reference = reference[ref], var = var, + period = period, start_months = start.months, + forecast_months = forecast.months, + metrics = 'enssprerr', + calib_method = calib.method, syear = NULL) + + ## Calculate spatial aggregation + enssprerr_spatial_aggr <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = enssprerr, + region = regions[[X]], + lon = as.vector(attributes(enssprerr)$Variables$dat1$longitude), + lat = as.vector(attributes(enssprerr)$Variables$dat1$latitude), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(enssprerr_spatial_aggr))[length(dim(enssprerr_spatial_aggr))] <- 'region' + + ## Drop unwanted dimensions + enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + + ## Save metric result in array + scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) + + } ## close on enssprerr + + } ## close loop on metric + } ## close if on reference + } ## close if on system + + aggregated_metrics <- scorecard_metrics + + ## set NAs to False + scorecard_sign[is.na(scorecard_sign)] <- FALSE + + } ## close if on score + ## Create simple scorecard tables ## (one system only) ## Metrics input must be in the same order as function SC_spatial_aggregation scorecard_single <- ScorecardsSingle(data = aggregated_metrics, + signif = scorecard_sign, system = system, reference = reference, var = var, -- GitLab From f098f32cd0b3f6e3863ea6a6d75eb32e2b868a11 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 10 Jan 2024 10:47:16 +0100 Subject: [PATCH 45/78] changes for including significance --- modules/Saving/R/tmp/CST_SaveExp.R | 1085 +++++++++---------- modules/Scorecards/R/tmp/SCPlotScorecard.R | 36 +- modules/Scorecards/R/tmp/ScorecardsMulti.R | 129 ++- modules/Scorecards/R/tmp/ScorecardsSingle.R | 107 +- modules/Scorecards/Scorecards.R | 77 +- modules/Skill/R/tmp/CRPS.R | 2 +- modules/Skill/R/tmp/RPS.R | 2 +- recipes/recipe_scorecards.yml | 36 +- tools/check_recipe.R | 62 +- 9 files changed, 803 insertions(+), 733 deletions(-) diff --git a/modules/Saving/R/tmp/CST_SaveExp.R b/modules/Saving/R/tmp/CST_SaveExp.R index 1154a41c..2ffd8fa8 100644 --- a/modules/Saving/R/tmp/CST_SaveExp.R +++ b/modules/Saving/R/tmp/CST_SaveExp.R @@ -4,94 +4,105 @@ #' #'@description This function allows to divide and save a object of class #''s2dv_cube' into a NetCDF file, allowing to reload the saved data using -#'\code{Start} function from StartR package. If the original 's2dv_cube' object -#'has been created from \code{CST_Load()}, then it can be reloaded with -#'\code{Load()}. +#'\code{CST_Start} or \code{CST_Load} functions. It also allows to save any +#''s2dv_cube' object that follows the NetCDF attributes conventions. #' #'@param data An object of class \code{s2dv_cube}. #'@param destination A character string containing the directory name in which #' to save the data. NetCDF file for each starting date are saved into the -#' folder tree: \cr -#' destination/Dataset/variable/. By default the function -#' creates and saves the data into the working directory. +#' folder tree: 'destination/Dataset/variable/'. By default the function +#' saves the data into the working directory. #'@param sdate_dim A character string indicating the name of the start date #' dimension. By default, it is set to 'sdate'. It can be NULL if there is no #' start date dimension. #'@param ftime_dim A character string indicating the name of the forecast time -#' dimension. By default, it is set to 'time'. It can be NULL if there is no -#' forecast time dimension. +#' dimension. If 'Dates' are used, it can't be NULL. If there is no forecast +#' time dimension, 'Dates' will be set to NULL and will not be used. By +#' default, it is set to 'time'. #'@param dat_dim A character string indicating the name of dataset dimension. -#' By default, it is set to 'dataset'. It can be NULL if there is no dataset -#' dimension. +#' It can be NULL if there is no dataset dimension. By default, it is set to +#' 'dataset'. #'@param var_dim A character string indicating the name of variable dimension. -#' By default, it is set to 'var'. It can be NULL if there is no variable -#' dimension. -#'@param memb_dim A character string indicating the name of the member dimension. -#' By default, it is set to 'member'. It can be NULL if there is no member -#' dimension. +#' It can be NULL if there is no variable dimension. By default, it is set to +#' 'var'. +#'@param memb_dim A character string indicating the name of the member +#' dimension. It can be NULL if there is no member dimension. By default, it is +#' set to 'member'. #'@param startdates A vector of dates that will be used for the filenames -#' when saving the data in multiple files. It must be a vector of the same -#' length as the start date dimension of data. It must be a vector of class -#' \code{Dates}, \code{'POSIXct'} or character with lenghts between 1 and 10. -#' If it is NULL, the coordinate corresponding the the start date dimension or -#' the first Date of each time step will be used as the name of the files. -#' It is NULL by default. -#'@param drop_dims A vector of character strings indicating the dimension names -#' of length 1 that need to be dropped in order that they don't appear in the -#' netCDF file. It is NULL by default (optional). +#' when saving the data in multiple files (single_file = FALSE). It must be a +#' vector of the same length as the start date dimension of data. It must be a +#' vector of class \code{Dates}, \code{'POSIXct'} or character with lenghts +#' between 1 and 10. If it is NULL, the coordinate corresponding the the start +#' date dimension or the first Date of each time step will be used as the name +#' of the files. It is NULL by default. #'@param single_file A logical value indicating if all object is saved in a #' single file (TRUE) or in multiple files (FALSE). When it is FALSE, -#' the array is separated for Datasets, variable and start date. It is FALSE -#' by default. -#'@param extra_string A character string to be include as part of the file name, -#' for instance, to identify member or realization. It would be added to the -#' file name between underscore characters. -#'@param units_time_since A logical value indicating if the time units are -#' saved as 'time unit since' (e.g. 'horurs since') (TRUE) or as time unit -#' index (e.g. days, months, or hours) (FALSE). It is set as TRUE by default. +#' the array is separated for datasets, variable and start date. When there are +#' no specified time dimensions, the data will be saved in a single file by +#' default. The output file name when 'single_file' is TRUE is a character +#' string containing: '__.nc'; when it is FALSE, +#' it is '_.nc'. It is FALSE by default. +#'@param drop_dims (optional) A vector of character strings indicating the +#' dimension names of length 1 that need to be dropped in order that they don't +#' appear in the netCDF file. Only is allowed to drop dimensions that are not +#' used in the computation. The dimensions used in the computation are the ones +#' specified in: sdate_dim, ftime_dim, dat_dim, var_dim and memb_dim. It is +#' NULL by default. +#'@param extra_string (Optional) A character string to be included as part of +#' the file name, for instance, to identify member or realization. When +#' single_file is TRUE, the 'extra_string' will substitute all the default +#' file name; when single_file is FALSE, the 'extra_string' will be added +#' in the file name as: '__.nc'. It is NULL by +#' default. +#'@param units_hours_since (Optional) A logical value only available for the +#' case: 'Dates' have forecast time and start date dimension, 'single_file' is +#' TRUE and 'time_bounds' are not used. When it is TRUE, it saves the forecast +#' time with units of 'hours since'; if it is FALSE, the time units will be a +#' number of time steps with its corresponding frequency (e.g. n days, n months +#' or n hours). It is FALSE by default. +#'@param global_attrs (Optional) A list with elements containing the global +#' attributes to be saved in the NetCDF. #' #'@return Multiple or single NetCDF files containing the data array.\cr -#'\item{\code{single_file = TRUE}}{ +#'\item{\code{single_file is TRUE}}{ #' All data is saved in a single file located in the specified destination -#' path with the following name: -#' ___.nc. Multiple -#' variables are saved separately in the same file. The forecast time units -#' is extracted from the frequency of the time steps (hours, days, months). -#' The first value of forecast time is 1. If no frequency is found, the units -#' will be 'hours since' each start date and the time steps are assumed to be -#' equally spaced. +#' path with the following name (by default): +#' '__.nc'. Multiple variables +#' are saved separately in the same file. The forecast time units +#' are calculated from each start date (if sdate_dim is not NULL) or from +#' the time step. If 'units_hours_since' is TRUE, the forecast time units +#' will be 'hours since '. If 'units_hours_since' is FALSE, +#' the forecast time units are extracted from the frequency of the time steps +#' (hours, days, months); if no frequency is found, the units will be โ€™hours +#' sinceโ€™. When the time units are 'hours since' the time ateps are assumed to +#' be equally spaced. #'} -#'\item{\code{single_file = FALSE}}{ +#'\item{\code{single_file is FALSE}}{ #' The data array is subset and stored into multiple files. Each file #' contains the data subset for each start date, variable and dataset. Files -#' with different variables and Datasets are stored in separated directories -#' within the following directory tree: destination/Dataset/variable/. -#' The name of each file will be: -#' __.nc. +#' with different variables and datasets are stored in separated directories +#' within the following directory tree: 'destination/Dataset/variable/'. +#' The name of each file will be by default: '_.nc'. +#' The forecast time units are calculated from each start date (if sdate_dim +#' is not NULL) or from the time step. The forecast time units will be 'hours +#' since '. #'} #' #'@seealso \code{\link[startR]{Start}}, \code{\link{as.s2dv_cube}} and #'\code{\link{s2dv_cube}} #' #'@examples -#'\dontrun{ #'data <- lonlat_temp_st$exp -#'destination <- "./" -#'CST_SaveExp(data = data, destination = destination, ftime_dim = 'ftime', -#' var_dim = 'var', dat_dim = 'dataset') -#'} +#'CST_SaveExp(data = data, ftime_dim = 'ftime', var_dim = 'var', +#' dat_dim = 'dataset', sdate_dim = 'sdate') #' -#'@import ncdf4 -#'@importFrom s2dv Reorder -#'@importFrom ClimProjDiags Subset -#'@import multiApply #'@export -CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', - ftime_dim = 'time', dat_dim = 'dataset', - var_dim = 'var', memb_dim = 'member', - startdates = NULL, drop_dims = NULL, - single_file = FALSE, extra_string = NULL, - units_time_since = TRUE) { +CST_SaveExp <- function(data, destination = "./", startdates = NULL, + sdate_dim = 'sdate', ftime_dim = 'time', + memb_dim = 'member', dat_dim = 'dataset', + var_dim = 'var', drop_dims = NULL, + single_file = FALSE, extra_string = NULL, + global_attrs = NULL, units_hours_since = FALSE) { # Check 's2dv_cube' if (!inherits(data, 's2dv_cube')) { stop("Parameter 'data' must be of the class 's2dv_cube'.") @@ -104,22 +115,11 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', if (!inherits(data$attrs, 'list')) { stop("Level 'attrs' must be a list with at least 'Dates' element.") } - if (!all(c('coords') %in% names(data))) { - warning("Element 'coords' not found. No coordinates will be used.") - } # metadata - if (is.null(data$attrs$Variable$metadata)) { - warning("No metadata found in element Variable from attrs.") - } else { + if (!is.null(data$attrs$Variable$metadata)) { if (!inherits(data$attrs$Variable$metadata, 'list')) { stop("Element metadata from Variable element in attrs must be a list.") } - if (!any(names(data$attrs$Variable$metadata) %in% names(data$coords))) { - warning("Metadata is not found for any coordinate.") - } else if (!any(names(data$attrs$Variable$metadata) %in% - data$attrs$Variable$varName)) { - warning("Metadata is not found for any variable.") - } } # Dates if (is.null(data$attrs$Dates)) { @@ -133,51 +133,31 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', if (!is.character(sdate_dim)) { stop("Parameter 'sdate_dim' must be a character string.") } - if (length(sdate_dim) > 1) { - warning("Parameter 'sdate_dim' has length greater than 1 and ", - "only the first element will be used.") - sdate_dim <- sdate_dim[1] - } - } else if (length(dim(data$attrs$Dates)) == 1) { - sdate_dim <- 'sdate' - dim(data$data) <- c(sdate = 1, dim(data$data)) - data$dims <- dim(data$data) - dim(data$attrs$Dates) <- c(sdate = 1, dim(data$attrs$Dates)) - data$coords[[sdate_dim]] <- data$attrs$Dates[1] } # startdates if (is.null(startdates)) { - startdates <- data$coords[[sdate_dim]] - } else { - if (!is.character(startdates)) { - warning(paste0("Parameter 'startdates' is not a character string, ", - "it will not be used.")) + if (is.character(data$coords[[sdate_dim]])) { startdates <- data$coords[[sdate_dim]] } - if (!is.null(sdate_dim)) { - if (dim(data$data)[sdate_dim] != length(startdates)) { - warning(paste0("Parameter 'startdates' doesn't have the same length ", - "as dimension '", sdate_dim,"', it will not be used.")) - startdates <- data$coords[[sdate_dim]] - } - } } - + SaveExp(data = data$data, destination = destination, - Dates = data$attrs$Dates, coords = data$coords, + Dates = data$attrs$Dates, + time_bounds = data$attrs$time_bounds, + startdates = startdates, varname = data$attrs$Variable$varName, metadata = data$attrs$Variable$metadata, Datasets = data$attrs$Datasets, - startdates = startdates, - dat_dim = dat_dim, sdate_dim = sdate_dim, - ftime_dim = ftime_dim, var_dim = var_dim, + sdate_dim = sdate_dim, ftime_dim = ftime_dim, memb_dim = memb_dim, + dat_dim = dat_dim, var_dim = var_dim, drop_dims = drop_dims, - extra_string = extra_string, single_file = single_file, - units_time_since = units_time_since) + extra_string = extra_string, + global_attrs = global_attrs, + units_hours_since = units_hours_since) } #'Save a multidimensional array with metadata to data in NetCDF format #'@description This function allows to save a data array with metadata into a @@ -190,14 +170,26 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', #'@param data A multi-dimensional array with named dimensions. #'@param destination A character string indicating the path where to store the #' NetCDF files. -#'@param Dates A named array of dates with the corresponding sdate and forecast -#' time dimension. If there is no sdate_dim, you can set it to NULL. -#' It must have ftime_dim dimension. #'@param coords A named list with elements of the coordinates corresponding to #' the dimensions of the data parameter. The names and length of each element #' must correspond to the names of the dimensions. If any coordinate is not #' provided, it is set as an index vector with the values from 1 to the length #' of the corresponding dimension. +#'@param Dates A named array of dates with the corresponding sdate and forecast +#' time dimension. If there is no sdate_dim, you can set it to NULL. +#' It must have ftime_dim dimension. +#'@param time_bounds (Optional) A list of two arrays of dates containing +#' the lower (first array) and the upper (second array) time bounds +#' corresponding to Dates. Each array must have the same dimensions as Dates. +#' If 'Dates' parameter is NULL, 'time_bounds' are not used. It is NULL by +#' default. +#'@param startdates A vector of dates that will be used for the filenames +#' when saving the data in multiple files (single_file = FALSE). It must be a +#' vector of the same length as the start date dimension of data. It must be a +#' vector of class \code{Dates}, \code{'POSIXct'} or character with lenghts +#' between 1 and 10. If it is NULL, the coordinate corresponding the the start +#' date dimension or the first Date of each time step will be used as the name +#' of the files. It is NULL by default. #'@param varname A character string indicating the name of the variable to be #' saved. #'@param metadata A named list where each element is a variable containing the @@ -205,12 +197,6 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', #' lists for each variable. #'@param Datasets A vector of character string indicating the names of the #' datasets. -#'@param startdates A vector of dates that will be used for the filenames -#' when saving the data in multiple files. It must be a vector of the same -#' length as the start date dimension of data. It must be a vector of class -#' \code{Dates}, \code{'POSIXct'} or character with lenghts between 1 and 10. -#' If it is NULL, the first Date of each time step will be used as the name of -#' the files. It is NULL by default. #'@param sdate_dim A character string indicating the name of the start date #' dimension. By default, it is set to 'sdate'. It can be NULL if there is no #' start date dimension. @@ -223,45 +209,63 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', #'@param var_dim A character string indicating the name of variable dimension. #' By default, it is set to 'var'. It can be NULL if there is no variable #' dimension. -#'@param memb_dim A character string indicating the name of the member dimension. -#' By default, it is set to 'member'. It can be NULL if there is no member -#' dimension. -#'@param drop_dims A vector of character strings indicating the dimension names -#' of length 1 that need to be dropped in order that they don't appear in the -#' netCDF file. It is NULL by default (optional). +#'@param memb_dim A character string indicating the name of the member +#' dimension. By default, it is set to 'member'. It can be NULL if there is no +#' member dimension. +#'@param drop_dims (optional) A vector of character strings indicating the +#' dimension names of length 1 that need to be dropped in order that they don't +#' appear in the netCDF file. Only is allowed to drop dimensions that are not +#' used in the computation. The dimensions used in the computation are the ones +#' specified in: sdate_dim, ftime_dim, dat_dim, var_dim and memb_dim. It is +#' NULL by default. #'@param single_file A logical value indicating if all object is saved in a -#' unique file (TRUE) or in separated directories (FALSE). When it is FALSE, -#' the array is separated for Datasets, variable and start date. It is FALSE -#' by default (optional). -#'@param extra_string A character string to be include as part of the file name, -#' for instance, to identify member or realization. It would be added to the -#' file name between underscore characters (optional). -#'@param units_time_since A logical value indicating if the time units are -#' saved as 'time unit since' (e.g. 'horurs since') (TRUE) or as time unit -#' index (e.g. days, months, or hours) (FALSE). It is set as TRUE by default. +#' single file (TRUE) or in multiple files (FALSE). When it is FALSE, +#' the array is separated for datasets, variable and start date. When there are +#' no specified time dimensions, the data will be saved in a single file by +#' default. The output file name when 'single_file' is TRUE is a character +#' string containing: '__.nc'; when it is FALSE, +#' it is '_.nc'. It is FALSE by default. +#'@param extra_string (Optional) A character string to be included as part of +#' the file name, for instance, to identify member or realization. When +#' single_file is TRUE, the 'extra_string' will substitute all the default +#' file name; when single_file is FALSE, the 'extra_string' will be added +#' in the file name as: '__.nc'. It is NULL by +#' default. +#'@param global_attrs (Optional) A list with elements containing the global +#' attributes to be saved in the NetCDF. +#'@param units_hours_since (Optional) A logical value only available for the +#' case: Dates have forecast time and start date dimension, single_file is +#' TRUE and 'time_bounds' is NULL. When it is TRUE, it saves the forecast time +#' with units of 'hours since'; if it is FALSE, the time units will be a number +#' of time steps with its corresponding frequency (e.g. n days, n months or n +#' hours). It is FALSE by default. #' #'@return Multiple or single NetCDF files containing the data array.\cr -#'\item{\code{single_file = TRUE}}{ +#'\item{\code{single_file is TRUE}}{ #' All data is saved in a single file located in the specified destination -#' path with the following name: -#' ___.nc. Multiple -#' variables are saved separately in the same file. The forecast time units -#' is extracted from the frequency of the time steps (hours, days, months). -#' The first value of forecast time is 1. If no frequency is found, the units -#' will be 'hours since' each start date and the time steps are assumed to be -#' equally spaced. +#' path with the following name (by default): +#' '__.nc'. Multiple variables +#' are saved separately in the same file. The forecast time units +#' are calculated from each start date (if sdate_dim is not NULL) or from +#' the time step. If 'units_hours_since' is TRUE, the forecast time units +#' will be 'hours since '. If 'units_hours_since' is FALSE, +#' the forecast time units are extracted from the frequency of the time steps +#' (hours, days, months); if no frequency is found, the units will be โ€™hours +#' sinceโ€™. When the time units are 'hours since' the time ateps are assumed to +#' be equally spaced. #'} -#'\item{\code{single_file = FALSE}}{ +#'\item{\code{single_file is FALSE}}{ #' The data array is subset and stored into multiple files. Each file #' contains the data subset for each start date, variable and dataset. Files -#' with different variables and Datasets are stored in separated directories -#' within the following directory tree: destination/Dataset/variable/. -#' The name of each file will be: -#' __.nc. +#' with different variables and datasets are stored in separated directories +#' within the following directory tree: 'destination/Dataset/variable/'. +#' The name of each file will be by default: '_.nc'. +#' The forecast time units are calculated from each start date (if sdate_dim +#' is not NULL) or from the time step. The forecast time units will be 'hours +#' since '. #'} #' #'@examples -#'\dontrun{ #'data <- lonlat_temp_st$exp$data #'lon <- lonlat_temp_st$exp$coords$lon #'lat <- lonlat_temp_st$exp$coords$lat @@ -269,25 +273,23 @@ CST_SaveExp <- function(data, destination = "./", sdate_dim = 'sdate', #'Datasets <- lonlat_temp_st$exp$attrs$Datasets #'varname <- 'tas' #'Dates <- lonlat_temp_st$exp$attrs$Dates -#'destination = './' #'metadata <- lonlat_temp_st$exp$attrs$Variable$metadata -#'SaveExp(data = data, destination = destination, coords = coords, -#' Datasets = Datasets, varname = varname, Dates = Dates, -#' metadata = metadata, single_file = TRUE, ftime_dim = 'ftime', -#' var_dim = 'var', dat_dim = 'dataset') -#'} +#'SaveExp(data = data, coords = coords, Datasets = Datasets, varname = varname, +#' Dates = Dates, metadata = metadata, single_file = TRUE, +#' ftime_dim = 'ftime', var_dim = 'var', dat_dim = 'dataset') #' -#'@import ncdf4 +#'@import easyNCDF #'@importFrom s2dv Reorder #'@import multiApply #'@importFrom ClimProjDiags Subset #'@export -SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, +SaveExp <- function(data, destination = "./", coords = NULL, + Dates = NULL, time_bounds = NULL, startdates = NULL, varname = NULL, metadata = NULL, Datasets = NULL, - startdates = NULL, dat_dim = 'dataset', sdate_dim = 'sdate', - ftime_dim = 'time', var_dim = 'var', memb_dim = 'member', + sdate_dim = 'sdate', ftime_dim = 'time', + memb_dim = 'member', dat_dim = 'dataset', var_dim = 'var', drop_dims = NULL, single_file = FALSE, extra_string = NULL, - units_time_since = TRUE) { + global_attrs = NULL, units_hours_since = FALSE) { ## Initial checks # data if (is.null(data)) { @@ -297,21 +299,15 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, if (is.null(dimnames)) { stop("Parameter 'data' must be an array with named dimensions.") } + if (!is.null(attributes(data)$dimensions)) { + attributes(data)$dimensions <- NULL + } # destination if (!is.character(destination) | length(destination) > 1) { stop("Parameter 'destination' must be a character string of one element ", "indicating the name of the file (including the folder if needed) ", "where the data will be saved.") } - # Dates - if (!is.null(Dates)) { - if (!inherits(Dates, "POSIXct") & !inherits(Dates, "Date")) { - stop("Parameter 'Dates' must be of 'POSIXct' or 'Dates' class.") - } - if (is.null(dim(Dates))) { - stop("Parameter 'Dates' must have dimension names.") - } - } # drop_dims if (!is.null(drop_dims)) { if (!is.character(drop_dims) | any(!drop_dims %in% names(dim(data)))) { @@ -320,6 +316,10 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, } else if (!all(dim(data)[drop_dims] %in% 1)) { warning("Parameter 'drop_dims' can only contain dimension names ", "that are of length 1. It will not be used.") + } else if (any(drop_dims %in% c(ftime_dim, sdate_dim, dat_dim, memb_dim, var_dim))) { + warning("Parameter 'drop_dims' contains dimensions used in the computation. ", + "It will not be used.") + drop_dims <- NULL } else { data <- Subset(x = data, along = drop_dims, indices = lapply(1:length(drop_dims), function(x) 1), @@ -329,28 +329,17 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, } # coords if (!is.null(coords)) { - if (!all(names(coords) %in% dimnames)) { - coords <- coords[-which(!names(coords) %in% dimnames)] - } - for (i_coord in dimnames) { - if (i_coord %in% names(coords)) { - if (length(coords[[i_coord]]) != dim(data)[i_coord]) { - warning(paste0("Coordinate '", i_coord, "' has different lenght as ", - "its dimension and it will not be used.")) - coords[[i_coord]] <- 1:dim(data)[i_coord] - } - } else { - # warning(paste0("Coordinate '", i_coord, "' is not provided ", - # "and it will be set as index in element coords.")) - coords[[i_coord]] <- 1:dim(data)[i_coord] - } + if (!inherits(coords, 'list')) { + stop("Parameter 'coords' must be a named list of coordinates.") + } + if (is.null(names(coords))) { + stop("Parameter 'coords' must have names corresponding to coordinates.") } } else { coords <- sapply(dimnames, function(x) 1:dim(data)[x]) } # varname if (is.null(varname)) { - warning("Parameter 'varname' is NULL. It will be assigned to 'X'.") varname <- 'X' } else if (length(varname) > 1) { multiple_vars <- TRUE @@ -361,11 +350,6 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, stop("Parameter 'varname' must be a character string with the ", "variable names.") } - # metadata - if (is.null(metadata)) { - warning("Parameter 'metadata' is not provided so the metadata saved ", - "will be incomplete.") - } # single_file if (!inherits(single_file, 'logical')) { warning("Parameter 'single_file' must be a logical value. It will be ", @@ -378,13 +362,13 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, stop("Parameter 'extra_string' must be a character string.") } } - # units_time_since - if (!is.logical(units_time_since)) { - warning("Parameter 'units_time_since' must be a logical value. It will be ", - "set as TRUE.") - units_time_since <- TRUE + # global_attrs + if (!is.null(global_attrs)) { + if (!inherits(global_attrs, 'list')) { + stop("Parameter 'global_attrs' must be a list.") + } } - + ## Dimensions checks # Spatial coordinates if (!any(dimnames %in% .KnownLonNames()) | @@ -394,16 +378,6 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, } else { lon_dim <- dimnames[which(dimnames %in% .KnownLonNames())] lat_dim <- dimnames[which(dimnames %in% .KnownLatNames())] - if (length(lon_dim) > 1) { - warning("Found more than one longitudinal dimension. Only the first one ", - "will be used.") - lon_dim <- lon_dim[1] - } - if (length(lat_dim) > 1) { - warning("Found more than one latitudinal dimension. Only the first one ", - "will be used.") - lat_dim <- lat_dim[1] - } } # ftime_dim if (!is.null(ftime_dim)) { @@ -411,12 +385,8 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, stop("Parameter 'ftime_dim' must be a character string.") } if (!all(ftime_dim %in% dimnames)) { - stop("Parameter 'ftime_dim' is not found in 'data' dimension.") - } - if (length(ftime_dim) > 1) { - warning("Parameter 'ftime_dim' has length greater than 1 and ", - "only the first element will be used.") - ftime_dim <- ftime_dim[1] + stop("Parameter 'ftime_dim' is not found in 'data' dimension. Set it ", + "as NULL if there is no forecast time dimension.") } } # sdate_dim @@ -424,11 +394,6 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, if (!is.character(sdate_dim)) { stop("Parameter 'sdate_dim' must be a character string.") } - if (length(sdate_dim) > 1) { - warning("Parameter 'sdate_dim' has length greater than 1 and ", - "only the first element will be used.") - sdate_dim <- sdate_dim[1] - } if (!all(sdate_dim %in% dimnames)) { stop("Parameter 'sdate_dim' is not found in 'data' dimension.") } @@ -452,11 +417,6 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, stop("Parameter 'dat_dim' is not found in 'data' dimension. Set it ", "as NULL if there is no Datasets dimension.") } - if (length(dat_dim) > 1) { - warning("Parameter 'dat_dim' has length greater than 1 and ", - "only the first element will be used.") - dat_dim <- dat_dim[1] - } n_datasets <- dim(data)[dat_dim] } else { n_datasets <- 1 @@ -470,11 +430,6 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, stop("Parameter 'var_dim' is not found in 'data' dimension. Set it ", "as NULL if there is no variable dimension.") } - if (length(var_dim) > 1) { - warning("Parameter 'var_dim' has length greater than 1 and ", - "only the first element will be used.") - var_dim <- var_dim[1] - } n_vars <- dim(data)[var_dim] } else { n_vars <- 1 @@ -489,35 +444,121 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, single_file <- TRUE } } - # Dates dimension check + # Dates (1): initial checks if (!is.null(Dates)) { - if (all(c(ftime_dim, sdate_dim) %in% names(dim(Dates)))) { - if (any(!names(dim(Dates)) %in% c(ftime_dim, sdate_dim))) { - if (all(dim(Dates)[!names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] == 1)) { - dim(Dates) <- dim(Dates)[names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] - } else { - stop("Parameter 'Dates' must have only sdate_dim and ftime_dim dimensions.") - } + if (!any(inherits(Dates, "POSIXct"), inherits(Dates, "Date"))) { + stop("Parameter 'Dates' must be of 'POSIXct' or 'Dates' class.") + } + if (is.null(dim(Dates))) { + stop("Parameter 'Dates' must have dimension names.") + } + if (all(is.null(ftime_dim), is.null(sdate_dim))) { + warning("Parameters 'ftime_dim' and 'sdate_dim' can't both be NULL ", + "if 'Dates' are used. 'Dates' will not be used.") + Dates <- NULL + } + # sdate_dim in Dates + if (!is.null(sdate_dim)) { + if (!sdate_dim %in% names(dim(Dates))) { + warning("Parameter 'sdate_dim' is not found in 'Dates' dimension. ", + "Dates will not be used.") + Dates <- NULL } - if (is.null(startdates)) { - startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') - } else if ((!inherits(startdates, "POSIXct") & !inherits(startdates, "Date")) && - (!is.character(startdates) | (any(nchar(startdates) > 10) | any(nchar(startdates) < 1)))) { - warning("Parameter 'startdates' should be a character string containing ", - "the start dates in the format 'yyyy-mm-dd', 'yyyymmdd', 'yyyymm', ", - "'POSIXct' or 'Dates' class. Files will be named with Dates instead.") - startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + } + # ftime_dim in Dates + if (!is.null(ftime_dim)) { + if (!ftime_dim %in% names(dim(Dates))) { + warning("Parameter 'ftime_dim' is not found in 'Dates' dimension. ", + "Dates will not be used.") + Dates <- NULL } - if (!is.null(format(startdates, "%Y%m%d"))) { - startdates <- format(startdates, "%Y%m%d") + } + } + # time_bounds + if (!is.null(time_bounds)) { + if (!inherits(time_bounds, 'list')) { + stop("Parameter 'time_bounds' must be a list with two dates arrays.") + } + time_bounds_dims <- lapply(time_bounds, function(x) dim(x)) + if (!identical(time_bounds_dims[[1]], time_bounds_dims[[2]])) { + stop("Parameter 'time_bounds' must have 2 arrays with same dimensions.") + } + if (is.null(Dates)) { + time_bounds <- NULL + } else { + name_tb <- sort(names(time_bounds_dims[[1]])) + name_dt <- sort(names(dim(Dates))) + if (!identical(dim(Dates)[name_dt], time_bounds_dims[[1]][name_tb])) { + stop(paste0("Parameter 'Dates' and 'time_bounds' must have same length ", + "of all dimensions.")) } - } else if (any(ftime_dim %in% names(dim(Dates)))) { - if (all(dim(Dates)[!names(dim(Dates)) %in% c(ftime_dim)] == 1)) { - dim(Dates) <- dim(Dates)[names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] + } + } + # Dates (2): Check dimensions + if (!is.null(Dates)) { + if (any(dim(Dates)[!names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] != 1)) { + stop("Parameter 'Dates' can have only 'sdate_dim' and 'ftime_dim' ", + "dimensions of length greater than 1.") + } + # drop dimensions of length 1 different from sdate_dim and ftime_dim + dim(Dates) <- dim(Dates)[names(dim(Dates)) %in% c(ftime_dim, sdate_dim)] + + # add ftime if needed + if (is.null(ftime_dim)) { + warning("A 'time' dimension of length 1 will be added to 'Dates'.") + dim(Dates) <- c(time = 1, dim(Dates)) + dim(data) <- c(time = 1, dim(data)) + dimnames <- names(dim(data)) + ftime_dim <- 'time' + if (!is.null(time_bounds)) { + time_bounds <- lapply(time_bounds, function(x) { + dim(x) <- c(time = 1, dim(x)) + return(x) + }) + } + units_hours_since <- TRUE + } + # add sdate if needed + if (is.null(sdate_dim)) { + if (!single_file) { + dim(Dates) <- c(dim(Dates), sdate = 1) + dim(data) <- c(dim(data), sdate = 1) + dimnames <- names(dim(data)) + sdate_dim <- 'sdate' + if (!is.null(time_bounds)) { + time_bounds <- lapply(time_bounds, function(x) { + dim(x) <- c(dim(x), sdate = 1) + return(x) + }) + } + if (!is.null(startdates)) { + if (length(startdates) != 1) { + warning("Parameter 'startdates' must be of length 1 if 'sdate_dim' is NULL.", + "They won't be used.") + startdates <- NULL + } + } } + units_hours_since <- TRUE } } # startdates + if (!is.null(Dates)) { + # check startdates + if (is.null(startdates)) { + startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + } else if (any(nchar(startdates) > 10, nchar(startdates) < 1)) { + warning("Parameter 'startdates' should be a character string containing ", + "the start dates in the format 'yyyy-mm-dd', 'yyyymmdd', 'yyyymm', ", + "'POSIXct' or 'Dates' class. Files will be named with Dates instead.") + startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + } + } else if (!single_file) { + warning("Dates must be provided if 'data' must be saved in separated files. ", + "All data will be saved in a single file.") + single_file <- TRUE + } + # startdates if (is.null(startdates)) { if (is.null(sdate_dim)) { startdates <- 'XXX' @@ -525,20 +566,21 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, startdates <- rep('XXX', dim(data)[sdate_dim]) } } else { - if (is.null(sdate_dim)) { - if (length(startdates) != 1) { - warning("Parameter 'startdates' has length more than 1. Only first ", - "value will be used.") - startdates <- startdates[[1]] + if (any(inherits(startdates, "POSIXct"), inherits(startdates, "Date"))) { + startdates <- format(startdates, "%Y%m%d") + } + if (!is.null(sdate_dim)) { + if (dim(data)[sdate_dim] != length(startdates)) { + warning(paste0("Parameter 'startdates' doesn't have the same length ", + "as dimension '", sdate_dim,"', it will not be used.")) + startdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') + startdates <- format(startdates, "%Y%m%d") } } } + # Datasets if (is.null(Datasets)) { - if (!single_file) { - warning("Parameter 'Datasets' is NULL. Files will be saved with a ", - "directory name of 'XXX'.") - } Datasets <- rep('XXX', n_datasets ) } if (inherits(Datasets, 'list')) { @@ -553,128 +595,75 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, "element 'Datasets' and only the firsts elements will be used.") Datasets <- Datasets[1:n_datasets] } - + + ## NetCDF dimensions definition + excluded_dims <- var_dim + if (!is.null(Dates)) { + excluded_dims <- c(excluded_dims, sdate_dim, ftime_dim) + } + if (!single_file) { + excluded_dims <- c(excluded_dims, dat_dim) + } + ## Unknown dimensions check - alldims <- c(dat_dim, var_dim, sdate_dim, lon_dim, lat_dim, memb_dim, ftime_dim) + alldims <- c(dat_dim, var_dim, sdate_dim, lon_dim, lat_dim, ftime_dim, memb_dim) if (!all(dimnames %in% alldims)) { unknown_dims <- dimnames[which(!dimnames %in% alldims)] memb_dim <- c(memb_dim, unknown_dims) - alldims <- c(dat_dim, var_dim, sdate_dim, lon_dim, lat_dim, memb_dim, ftime_dim) - } - # Reorder - if (any(dimnames != alldims)) { - data <- Reorder(data, alldims) - dimnames <- names(dim(data)) - if (!is.null(attr(data, 'dimensions'))) { - attr(data, 'dimensions') <- dimnames - } - } - - ## NetCDF dimensions definition - defined_dims <- NULL - extra_info_dim <- NULL - if (is.null(Dates)) { - filedims <- dimnames[which(!dimnames %in% c(dat_dim, var_dim))] - } else { - filedims <- dimnames[which(!dimnames %in% c(dat_dim, var_dim, sdate_dim, ftime_dim))] } + + filedims <- c(dat_dim, var_dim, sdate_dim, lon_dim, lat_dim, ftime_dim, memb_dim) + filedims <- filedims[which(!filedims %in% excluded_dims)] + + # Delete unneded coords + coords[c(names(coords)[!names(coords) %in% filedims])] <- NULL + out_coords <- NULL for (i_coord in filedims) { - dim_info <- list() # vals if (i_coord %in% names(coords)) { - if (is.numeric(coords[[i_coord]])) { - dim_info[['vals']] <- as.vector(coords[[i_coord]]) + if (length(coords[[i_coord]]) != dim(data)[i_coord]) { + warning(paste0("Coordinate '", i_coord, "' has different lenght as ", + "its dimension and it will not be used.")) + out_coords[[i_coord]] <- 1:dim(data)[i_coord] + } else if (is.numeric(coords[[i_coord]])) { + out_coords[[i_coord]] <- as.vector(coords[[i_coord]]) } else { - dim_info[['vals']] <- 1:dim(data)[i_coord] + out_coords[[i_coord]] <- 1:dim(data)[i_coord] } } else { - dim_info[['vals']] <- 1:dim(data)[i_coord] - } - # name - dim_info[['name']] <- i_coord - # len - dim_info[['len']] <- as.numeric(dim(data)[i_coord]) - # unlim - dim_info[['unlim']] <- FALSE - # create_dimvar - dim_info[['create_dimvar']] <- TRUE + out_coords[[i_coord]] <- 1:dim(data)[i_coord] + } + dim(out_coords[[i_coord]]) <- dim(data)[i_coord] + ## metadata if (i_coord %in% names(metadata)) { if ('variables' %in% names(attributes(metadata[[i_coord]]))) { # from Start: 'lon' or 'lat' - attrs <- attributes(metadata[[i_coord]])[['variables']][[i_coord]] - i_coord_info <- attrs[!sapply(attrs, inherits, 'list')] + attrs <- attributes(metadata[[i_coord]])[['variables']] + attrs[[i_coord]]$dim <- NULL + attr(out_coords[[i_coord]], 'variables') <- attrs } else if (inherits(metadata[[i_coord]], 'list')) { # from Start and Load: main var - i_coord_info <- metadata[[i_coord]] + attr(out_coords[[i_coord]], 'variables') <- list(metadata[[i_coord]]) + names(attributes(out_coords[[i_coord]])$variables) <- i_coord } else if (!is.null(attributes(metadata[[i_coord]]))) { # from Load - i_coord_info <- attributes(metadata[[i_coord]]) - } else { - stop("Metadata is not correct.") - } - # len - if ('size' %in% names(i_coord_info)) { - if (i_coord_info[['size']] != dim(data)[i_coord]) { - dim_info[['original_len']] <- i_coord_info[['size']] - i_coord_info[['size']] <- NULL - } + attrs <- attributes(metadata[[i_coord]]) + # We remove because some attributes can't be saved + attrs <- NULL + attr(out_coords[[i_coord]], 'variables') <- list(attrs) + names(attributes(out_coords[[i_coord]])$variables) <- i_coord } - # units - if (!('units' %in% names(i_coord_info))) { - dim_info[['units']] <- '' - } else { - dim_info[['units']] <- i_coord_info[['units']] - i_coord_info[['units']] <- NULL - } - # calendar - if (!('calendar' %in% names(i_coord_info))) { - dim_info[['calendar']] <- NA - } else { - dim_info[['calendar']] <- i_coord_info[['calendar']] - i_coord_info[['calendar']] <- NULL - } - # longname - if ('long_name' %in% names(i_coord_info)) { - dim_info[['longname']] <- i_coord_info[['long_name']] - i_coord_info[['long_name']] <- NULL - } else if ('longname' %in% names(i_coord_info)) { - dim_info[['longname']] <- i_coord_info[['longname']] - i_coord_info[['longname']] <- NULL - } else { - if (i_coord %in% .KnownLonNames()) { - dim_info[['longname']] <- 'longitude' - } else if (i_coord %in% .KnownLatNames()) { - dim_info[['longname']] <- 'latitude' - } - } - # extra information - if (!is.null(names(i_coord_info))) { - extra_info_dim[[i_coord]] <- i_coord_info - } - } else { - # units - dim_info[['units']] <- "adim" - # longname - dim_info[['longname']] <- i_coord - # calendar - dim_info[['calendar']] <- NA - } - new_dim <- list(ncdim_def(name = dim_info[['name']], units = dim_info[['units']], - vals = dim_info[['vals']], unlim = dim_info[['unlim']], - create_dimvar = dim_info[['create_dimvar']], - calendar = dim_info[['calendar']], - longname = dim_info[['longname']])) - names(new_dim) <- i_coord - defined_dims <- c(defined_dims, new_dim) + } } - - defined_vars <- list() + if (!single_file) { for (i in 1:n_datasets) { path <- file.path(destination, Datasets[i], varname) for (j in 1:n_vars) { - dir.create(path[j], recursive = TRUE) + if (!dir.exists(path[j])) { + dir.create(path[j], recursive = TRUE) + } startdates <- gsub("-", "", startdates) dim(startdates) <- c(length(startdates)) names(dim(startdates)) <- sdate_dim @@ -687,177 +676,172 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, } else { data_subset <- Subset(data, c(dat_dim, var_dim), list(i, j), drop = 'selected') } + target <- names(dim(data_subset))[which(!names(dim(data_subset)) %in% c(sdate_dim, ftime_dim))] + target_dims_data <- c(target, ftime_dim) if (is.null(Dates)) { input_data <- list(data_subset, startdates) - target_dims <- list(c(lon_dim, lat_dim, memb_dim, ftime_dim), NULL) + target_dims <- list(target_dims_data, NULL) + } else if (!is.null(time_bounds)) { + input_data <- list(data_subset, startdates, Dates, + time_bounds[[1]], time_bounds[[2]]) + target_dims = list(target_dims_data, NULL, + ftime_dim, ftime_dim, ftime_dim) } else { input_data <- list(data_subset, startdates, Dates) - target_dims = list(c(lon_dim, lat_dim, memb_dim, ftime_dim), NULL, ftime_dim) + target_dims = list(target_dims_data, NULL, ftime_dim) } Apply(data = input_data, target_dims = target_dims, - fun = .saveExp, + fun = .saveexp, destination = path[j], - defined_dims = defined_dims, + coords = out_coords, ftime_dim = ftime_dim, varname = varname[j], metadata_var = metadata[[varname[j]]], - extra_info_dim = extra_info_dim, - extra_string = extra_string) + extra_string = extra_string, + global_attrs = global_attrs) } } } else { - # Datasets definition - # From here - if (!is.null(dat_dim)) { - new_dim <- list(ncdim_def(name = dat_dim, units = "adim", - vals = 1 : dim(data)[dat_dim], - longname = 'Datasets', create_dimvar = TRUE)) - names(new_dim) <- dat_dim - defined_dims <- c(new_dim, defined_dims) - extra_info_dim[[dat_dim]] <- list(Datasets = paste(Datasets, collapse = ', ')) - } - first_sdate <- last_sdate <- NULL - save_hours_since <- TRUE + # time_bnds + if (!is.null(time_bounds)) { + time_bnds <- c(time_bounds[[1]], time_bounds[[2]]) + } + # Dates + remove_metadata_dim <- TRUE if (!is.null(Dates)) { if (is.null(sdate_dim)) { sdates <- Dates[1] # ftime definition - leadtimes <- as.numeric(Dates - sdates)/3600 + leadtimes <- as.numeric(difftime(Dates, sdates, units = "hours")) } else { # sdate definition sdates <- Subset(Dates, along = ftime_dim, 1, drop = 'selected') - differ <- as.numeric((sdates - sdates[1])/3600) - new_dim <- list(ncdim_def(name = sdate_dim, units = paste('hours since', sdates[1]), - vals = differ, - longname = sdate_dim, create_dimvar = TRUE)) - names(new_dim) <- sdate_dim - defined_dims <- c(defined_dims, new_dim) - first_sdate <- sdates[1] - last_sdate <- sdates[length(sdates)] + differ <- as.numeric(difftime(sdates, sdates[1], units = "hours")) + dim(differ) <- dim(data)[sdate_dim] + differ <- list(differ) + names(differ) <- sdate_dim + out_coords <- c(differ, out_coords) + attrs <- list(units = paste('hours since', sdates[1]), + calendar = 'proleptic_gregorian', longname = sdate_dim) + attr(out_coords[[sdate_dim]], 'variables')[[sdate_dim]] <- attrs # ftime definition - Dates <- Reorder(Dates, c(ftime_dim, sdate_dim)) - differ_ftime <- apply(Dates, 2, function(x){as.numeric((x - x[1])/3600)}) + Dates <- Reorder(Dates, c(ftime_dim, sdate_dim)) + differ_ftime <- array(dim = dim(Dates)) + for (i in 1:length(sdates)) { + differ_ftime[, i] <- as.numeric(difftime(Dates[, i], Dates[1, i], + units = "hours")) + } dim(differ_ftime) <- dim(Dates) leadtimes <- Subset(differ_ftime, along = sdate_dim, 1, drop = 'selected') - - if (all(apply(differ_ftime, 1, function(x){length(unique(x)) == 1}))) { - if (!units_time_since) save_hours_since <- FALSE - } else { + if (!all(apply(differ_ftime, 1, function(x){length(unique(x)) == 1}))) { warning("Time steps are not equal for all start dates. Only ", "forecast time values for the first start date will be saved ", "correctly.") } } - - if (!save_hours_since) { - # NOTE: Are the units readable by Start? + if (all(!units_hours_since, is.null(time_bounds))) { if (all(diff(leadtimes/24) == 1)) { # daily values - dim_time <- list(ncdim_def(name = ftime_dim, units = 'days', - vals = round(leadtimes/24) + 1, - calendar = 'proleptic_gregorian', - longname = ftime_dim, unlim = TRUE)) - names(dim_time) <- ftime_dim - defined_dims <- c(defined_dims, dim_time) + units <- 'days' + leadtimes_vals <- round(leadtimes/24) + 1 } else if (all(diff(leadtimes/24) %in% c(28, 29, 30, 31))) { # monthly values - dim_time <- list(ncdim_def(name = ftime_dim, units = 'months', - vals = round(leadtimes/730) + 1, - calendar = 'proleptic_gregorian', - longname = ftime_dim, unlim = TRUE)) - names(dim_time) <- ftime_dim - defined_dims <- c(defined_dims, dim_time) + units <- 'months' + leadtimes_vals <- round(leadtimes/(30.437*24)) + 1 } else { # other frequency - dim_time <- list(ncdim_def(name = ftime_dim, units = 'hours', - vals = leadtimes + 1, - calendar = 'proleptic_gregorian', - longname = ftime_dim, unlim = TRUE)) - names(dim_time) <- ftime_dim - defined_dims <- c(defined_dims, dim_time) + units <- 'hours' + leadtimes_vals <- leadtimes + 1 } } else { - # Save in units 'hours since' - dim_time <- list(ncdim_def(name = ftime_dim, - units = paste('hours since', - paste(sdates, collapse = ', ')), - vals = leadtimes, - calendar = 'proleptic_gregorian', - longname = ftime_dim, unlim = TRUE)) - names(dim_time) <- ftime_dim - defined_dims <- c(defined_dims, dim_time) + units <- paste('hours since', paste(sdates, collapse = ', ')) + leadtimes_vals <- leadtimes + } + + # Add time_bnds + if (!is.null(time_bounds)) { + if (is.null(sdate_dim)) { + sdates <- Dates[1] + time_bnds <- c(time_bounds[[1]], time_bounds[[2]]) + leadtimes_bnds <- as.numeric(difftime(time_bnds, sdates, units = "hours")) + dim(leadtimes_bnds) <- c(dim(Dates), bnds = 2) + } else { + # assuming they have sdate and ftime + time_bnds <- lapply(time_bounds, function(x) { + x <- Reorder(x, c(ftime_dim, sdate_dim)) + return(x) + }) + time_bnds <- c(time_bounds[[1]], time_bounds[[2]]) + dim(time_bnds) <- c(dim(Dates), bnds = 2) + differ_bnds <- array(dim = c(dim(time_bnds))) + for (i in 1:length(sdates)) { + differ_bnds[, i, ] <- as.numeric(difftime(time_bnds[, i, ], Dates[1, i], + units = "hours")) + } + # NOTE (TODO): Add a warning when they are not equally spaced? + leadtimes_bnds <- Subset(differ_bnds, along = sdate_dim, 1, drop = 'selected') + } + # Add time_bnds + leadtimes_bnds <- Reorder(leadtimes_bnds, c('bnds', ftime_dim)) + leadtimes_bnds <- list(leadtimes_bnds) + names(leadtimes_bnds) <- 'time_bnds' + out_coords <- c(leadtimes_bnds, out_coords) + attrs <- list(units = paste('hours since', paste(sdates, collapse = ', ')), + calendar = 'proleptic_gregorian', + long_name = 'time bounds', unlim = FALSE) + attr(out_coords[['time_bnds']], 'variables')$time_bnds <- attrs + } + # Add ftime var + dim(leadtimes_vals) <- dim(data)[ftime_dim] + leadtimes_vals <- list(leadtimes_vals) + names(leadtimes_vals) <- ftime_dim + out_coords <- c(leadtimes_vals, out_coords) + attrs <- list(units = units, calendar = 'proleptic_gregorian', + longname = ftime_dim, + dim = list(list(name = ftime_dim, unlim = TRUE))) + if (!is.null(time_bounds)) { + attrs$bounds = 'time_bnds' + } + attr(out_coords[[ftime_dim]], 'variables')[[ftime_dim]] <- attrs + for (j in 1:n_vars) { + remove_metadata_dim <- FALSE + metadata[[varname[j]]]$dim <- list(list(name = ftime_dim, unlim = TRUE)) + } + # Reorder ftime_dim to last + if (length(dim(data)) != which(names(dim(data)) == ftime_dim)) { + order <- c(names(dim(data))[which(!names(dim(data)) %in% c(ftime_dim))], ftime_dim) + data <- Reorder(data, order) } } - # var definition - defined_vars <- list() extra_info_var <- NULL for (j in 1:n_vars) { - var_info <- list() - i_var_info <- metadata[[varname[j]]][!sapply(metadata[[varname[j]]], inherits, 'list')] - ## Define metadata - # name - var_info[['name']] <- varname[j] - # units - if ('units' %in% names(i_var_info)) { - var_info[['units']] <- i_var_info[['units']] - i_var_info[['units']] <- NULL - } else { - var_info[['units']] <- '' - } - # dim - var_info[['dim']] <- defined_dims - # missval - if ('missval' %in% names(i_var_info)) { - var_info[['missval']] <- i_var_info[['missval']] - i_var_info[['missval']] <- NULL + varname_j <- varname[j] + metadata_j <- metadata[[varname_j]] + if (is.null(var_dim)) { + out_coords[[varname_j]] <- data } else { - var_info[['missval']] <- NULL + out_coords[[varname_j]] <- Subset(data, var_dim, j, drop = 'selected') } - # longname - if (any(c('longname', 'long_name') %in% names(i_var_info))) { - longname <- names(i_var_info)[which(names(i_var_info) %in% c('longname', 'long_name'))] - var_info[['longname']] <- i_var_info[[longname]] - i_var_info[[longname]] <- NULL - } else { - var_info[['longname']] <- varname[j] - } - # prec - if ('prec' %in% names(i_var_info)) { - var_info[['prec']] <- i_var_info[['prec']] - i_var_info[['prec']] <- NULL - } else { - prec <- typeof(data) - if (prec == 'character') { - var_info[['prec']] <- 'char' - } - if (any(prec %in% c('short', 'float', 'double', 'integer', 'char', 'byte'))) { - var_info[['prec']] <- prec - } else { - var_info[['prec']] <- 'double' - } + if (!is.null(metadata_j)) { + if (remove_metadata_dim) metadata_j$dim <- NULL + attr(out_coords[[varname_j]], 'variables') <- list(metadata_j) + names(attributes(out_coords[[varname_j]])$variables) <- varname_j } - # extra information - if (!is.null(names(i_var_info))) { - extra_info_var[[varname[j]]] <- i_var_info + # Add global attributes + if (!is.null(global_attrs)) { + attributes(out_coords[[varname_j]])$global_attrs <- global_attrs } - new_var <- list(ncvar_def(name = var_info[['name']], - units = var_info[['units']], - dim = var_info[['dim']], - missval = var_info[['missval']], - longname = var_info[['longname']], - prec = var_info[['prec']])) - - names(new_var) <- varname[j] - defined_vars <- c(defined_vars, new_var) } if (is.null(extra_string)) { + first_sdate <- startdates[1] + last_sdate <- startdates[length(startdates)] gsub("-", "", first_sdate) file_name <- paste0(paste(c(varname, gsub("-", "", first_sdate), gsub("-", "", last_sdate)), - collapse = '_'), ".nc") + collapse = '_'), ".nc") } else { nc <- substr(extra_string, nchar(extra_string)-2, nchar(extra_string)) if (nc == ".nc") { @@ -867,134 +851,65 @@ SaveExp <- function(data, destination = "./", Dates = NULL, coords = NULL, } } full_filename <- file.path(destination, file_name) - file_nc <- nc_create(full_filename, defined_vars) - if (is.null(var_dim)) { - ncvar_put(file_nc, varname, vals = data) - } else { - for (j in 1:n_vars) { - ncvar_put(file_nc, defined_vars[[j]]$name, - vals = Subset(data, var_dim, j, drop = 'selected')) - } - } - # Additional dimension attributes - for (dim in names(defined_dims)) { - if (dim %in% names(extra_info_dim)) { - for (info_dim in names(extra_info_dim[[dim]])) { - add_info_dim <- paste0(extra_info_dim[[dim]][[info_dim]], collapse = ', ') - ncatt_put(file_nc, dim, info_dim, add_info_dim) - } - } - } - # Additional dimension attributes - for (var in names(defined_vars)) { - if (var %in% names(extra_info_var)) { - for (info_var in names(extra_info_var[[var]])) { - add_info_var <- paste0(extra_info_var[[var]][[info_var]], collapse = ', ') - ncatt_put(file_nc, var, info_var, add_info_var) - } - } - } - nc_close(file_nc) + ArrayToNc(out_coords, full_filename) } } -.saveExp <- function(data, startdates = NULL, dates = NULL, destination = "./", - defined_dims, ftime_dim = 'time', varname = 'var', - metadata_var = NULL, extra_info_dim = NULL, - extra_string = NULL) { - # ftime_dim +.saveexp <- function(data, coords, destination = "./", + startdates = NULL, dates = NULL, + time_bnds1 = NULL, time_bnds2 = NULL, + ftime_dim = 'time', varname = 'var', + metadata_var = NULL, extra_string = NULL, + global_attrs = NULL) { + remove_metadata_dim <- TRUE if (!is.null(dates)) { - differ <- as.numeric((dates - dates[1])/3600) - dim_time <- list(ncdim_def(name = ftime_dim, units = paste('hours since', dates[1]), - vals = differ, calendar = 'proleptic_gregorian', - longname = ftime_dim, unlim = TRUE)) - names(dim_time) <- ftime_dim - defined_dims <- c(defined_dims, dim_time) - } - - ## Define var metadata - var_info <- NULL - extra_info_var <- NULL - i_var_info <- metadata_var[!sapply(metadata_var, inherits, 'list')] - - # name - var_info[['name']] <- varname - # units - if ('units' %in% names(i_var_info)) { - var_info[['units']] <- i_var_info[['units']] - i_var_info[['units']] <- NULL - } else { - var_info[['units']] <- '' - } - # dim - var_info[['dim']] <- defined_dims - # missval - if ('missval' %in% names(i_var_info)) { - var_info[['missval']] <- i_var_info[['missval']] - i_var_info[['missval']] <- NULL - } else { - var_info[['missval']] <- NULL - } - # longname - if (any(c('longname', 'long_name') %in% names(i_var_info))) { - longname <- names(i_var_info)[which(names(i_var_info) %in% c('longname', 'long_name'))] - var_info[['longname']] <- i_var_info[[longname]] - i_var_info[[longname]] <- NULL - } else { - var_info[['longname']] <- varname - } - # prec - if ('prec' %in% names(i_var_info)) { - var_info[['prec']] <- i_var_info[['prec']] - i_var_info[['prec']] <- NULL - } else { - prec <- typeof(data) - if (prec == 'character') { - var_info[['prec']] <- 'char' - } - if (any(prec %in% c('short', 'float', 'double', 'integer', 'char', 'byte'))) { - var_info[['prec']] <- prec - } else { - var_info[['prec']] <- 'double' - } - } - # extra information - if (!is.null(names(i_var_info))) { - extra_info_var <- i_var_info - } - - datanc <- ncvar_def(name = var_info[['name']], - units = var_info[['units']], - dim = var_info[['dim']], - missval = var_info[['missval']], - longname = var_info[['longname']], - prec = var_info[['prec']]) - + if (!any(is.null(time_bnds1), is.null(time_bnds2))) { + time_bnds <- c(time_bnds1, time_bnds2) + time_bnds <- as.numeric(difftime(time_bnds, dates[1], units = "hours")) + dim(time_bnds) <- c(dim(data)[ftime_dim], bnds = 2) + time_bnds <- Reorder(time_bnds, c('bnds', ftime_dim)) + time_bnds <- list(time_bnds) + names(time_bnds) <- 'time_bnds' + coords <- c(time_bnds, coords) + attrs <- list(units = paste('hours since', dates[1]), + calendar = 'proleptic_gregorian', + longname = 'time bounds') + attr(coords[['time_bnds']], 'variables')$time_bnds <- attrs + } + # Add ftime_dim + differ <- as.numeric(difftime(dates, dates[1], units = "hours")) + dim(differ) <- dim(data)[ftime_dim] + differ <- list(differ) + names(differ) <- ftime_dim + coords <- c(differ, coords) + attrs <- list(units = paste('hours since', dates[1]), + calendar = 'proleptic_gregorian', + longname = ftime_dim, + dim = list(list(name = ftime_dim, unlim = TRUE))) + if (!is.null(time_bnds1)) { + attrs$bounds = 'time_bnds' + } + attr(coords[[ftime_dim]], 'variables')[[ftime_dim]] <- attrs + metadata_var$dim <- list(list(name = ftime_dim, unlim = TRUE)) + remove_metadata_dim <- FALSE + } + # Add data + coords[[varname]] <- data + if (!is.null(metadata_var)) { + if (remove_metadata_dim) metadata_var$dim <- NULL + attr(coords[[varname]], 'variables') <- list(metadata_var) + names(attributes(coords[[varname]])$variables) <- varname + } + # Add global attributes + if (!is.null(global_attrs)) { + attributes(coords[[varname]])$global_attrs <- global_attrs + } + if (is.null(extra_string)) { file_name <- paste0(varname, "_", startdates, ".nc") } else { file_name <- paste0(varname, "_", extra_string, "_", startdates, ".nc") } full_filename <- file.path(destination, file_name) - file_nc <- nc_create(full_filename, datanc) - ncvar_put(file_nc, datanc, data) - - # Additional attributes - for (dim in names(defined_dims)) { - if (dim %in% names(extra_info_dim)) { - for (info_dim in names(extra_info_dim[[dim]])) { - add_info_dim <- paste0(extra_info_dim[[dim]][[info_dim]], collapse = ', ') - ncatt_put(file_nc, dim, info_dim, add_info_dim) - } - } - } - # Additional dimension attributes - if (!is.null(extra_info_var)) { - for (info_var in names(extra_info_var)) { - add_info_var <- paste0(extra_info_var[[info_var]], collapse = ', ') - ncatt_put(file_nc, varname, info_var, add_info_var) - } - } - - nc_close(file_nc) -} + ArrayToNc(coords, full_filename) +} \ No newline at end of file diff --git a/modules/Scorecards/R/tmp/SCPlotScorecard.R b/modules/Scorecards/R/tmp/SCPlotScorecard.R index 8c2d9eba..a6575ea5 100644 --- a/modules/Scorecards/R/tmp/SCPlotScorecard.R +++ b/modules/Scorecards/R/tmp/SCPlotScorecard.R @@ -36,6 +36,7 @@ #'@param plot.legend A logical value to determine if the legend is plotted. #'@param legend.width A numeric value to define the width of the legend bars. #'@param legend.height A numeric value to define the height of the legend bars. +#'@param label.scale A numeric value to define the size of the legend labels. #'@param palette A vector of character strings or a list of vectors of #' character strings containing the colors to use in the legends. If a vector #' is given as input, then these colors will be used for each legend.dim. A @@ -52,9 +53,18 @@ #' breaks value. This parameter will also plot a inferior triangle in the #' legend bar. The parameter can be set to NULL if there are no superior values. #' If a character string is given this color will be applied to all legend.dims. +#'@param legend.white.space A numeric value defining the initial starting +#' position of the legend bars, the white space infront of the legend is +#' calculated from the left most point of the table as a distance in cm. #'@param round.decimal A numeric indicating to which decimal point the data -#' is to be displayed in the scorecard table. +#' is to be displayed in the scorecard table. Default is 2. #'@param font.size A numeric indicating the font size on the scorecard table. +#'@param col1.width A numeric value defining the width of the first table column +#' in cm. +#'@param col2.width A numeric value defining the width of the second table +#' column in cm. +#'@param columns.width A numeric value defining the width all columns within the +#' table in cm (excluding the first and second columns containing the titles). #'@param fileout A path of the location to save the scorecard plots. #' #'@return An image file containing the scorecard. @@ -75,7 +85,7 @@ #'@import s2dv #'@import ClimProjDiags #'@export -SCPlotScorecard <- function(data, sign, +SCPlotScorecard <- function(data, sign = NULL, row.dim = 'region', subrow.dim = 'time', col.dim = 'metric', subcol.dim = 'sdate', legend.dim = 'metric', row.names = NULL, @@ -90,12 +100,21 @@ SCPlotScorecard <- function(data, sign, round.decimal = 2, font.size = 1.1, legend.white.space = NULL, col1.width = NULL, col2.width = NULL, + columns.width = 1.2, fileout = './scorecard.png') { # Input parameter checks ## Check data if (!is.array(data)) { stop("Parameter 'data' must be a numeric array.") } + ## Check sign + if (is.null(sign)){ + sign <- array(F, dim = dim(data)) + } else { + if (!is.array(sign)) { + stop("Parameter 'sign' must be a boolean array or NULL.") + } + } ## Check row.dim if (!is.character(row.dim)) { stop("Parameter 'row.dim' must be a character string.") @@ -363,14 +382,13 @@ SCPlotScorecard <- function(data, sign, metric.text.color <- table_colors$metric.text.color # metric.text.bold <- table_colors$metric.text.bold - # Remove temporary tables + # Remove temporary table rm(table_temp) - rm(table_sign_temp) - - ## Format values to underline in table - metric.underline <- MergeDims(sign, c('sdate', 'metric') , rename_dim = 'col', na.rm =F) - metric.underline <- MergeDims(metric.underline, c('time', 'region') , rename_dim = 'row', na.rm =F) + ## Format values to underline in table + metric.underline <- MergeDims(sign, c(subcol.dim, col.dim) , rename_dim = 'col', na.rm =F) + metric.underline <- MergeDims(metric.underline, c(subrow.dim, row.dim) , rename_dim = 'row', na.rm =F) + options(stringsAsFactors = FALSE) title <- data.frame(c1 = table.title, c2 = n.columns) subtitle <- data.frame(c1 = table.subtitle, c2 = n.columns) @@ -423,7 +441,7 @@ SCPlotScorecard <- function(data, sign, table.html <- column_spec(table.html.part[[n.last.list]], 1, bold = TRUE, width_min = paste0(col1.width, 'cm')) %>% column_spec(2, bold = TRUE, width_min = paste0(col2.width, 'cm')) %>% - column_spec(3:n.columns, width_min = "1.2cm") %>% + column_spec(3:n.columns, width_min = paste0(columns.width, 'cm')) %>% column_spec(c(1, 2, column.borders), border_right = "2px solid black") %>% column_spec(1, border_left = "2px solid black") %>% column_spec(n.columns, border_right = "2px solid black") %>% diff --git a/modules/Scorecards/R/tmp/ScorecardsMulti.R b/modules/Scorecards/R/tmp/ScorecardsMulti.R index 89f1df44..f05275cf 100644 --- a/modules/Scorecards/R/tmp/ScorecardsMulti.R +++ b/modules/Scorecards/R/tmp/ScorecardsMulti.R @@ -2,7 +2,7 @@ #' #'@description Scorecards function to create scorecard tables for multiple systems #' and references (types 9 to 12). -#'@param input_data is an array of spatially aggregated metrics containing the +#'@param data is an array of spatially aggregated metrics containing the #' following dimensions; system, reference, metric, time, sdate, region. #'@param system a vector of character strings defining the systems following the #' archive.yml format from verification suite @@ -21,6 +21,28 @@ #' include in the scorecard title #'@param fileout.label a character string containing additional information to #' include in the output png file when saving the scorecard. +#'@param plot.legend A logical value to determine if the legend is plotted. +#'@param legend.breaks A vector of numerics or a list of vectors of numerics, +#' containing the breaks for the legends. If a vector is given as input, then +#' these breaks will be repeated for each legend.dim. A list of vectors can be +#' given as input if the legend.dims require different breaks. This parameter +#' is required even if the legend is not plotted, to define the colors in the +#' scorecard table. +#'@param legend.white.space A numeric value defining the initial starting +#' position of the legend bars, the white space infront of the legend is +#' calculated from the left most point of the table as a distance in cm. +#'@param legend.width A numeric value to define the width of the legend bars. +#'@param legend.height A numeric value to define the height of the legend bars. +#'@param label.scale A numeric value to define the size of the legend labels. +#'@param col1.width A numeric value defining the width of the first table column +#' in cm. +#'@param col2.width A numeric value defining the width of the second table +#' column in cm. +#'@param columns.width A numeric value defining the width all columns within the +#' table in cm (excluding the first and second columns containing the titles). +#'@param font.size A numeric indicating the font size on the scorecard table. +#'@param round.decimal A numeric indicating to which decimal point the data +#' is to be displayed in the scorecard table. Default is 2. #'@param output.path a path of the location to save the scorecard plots. #' #'@return @@ -44,19 +66,16 @@ #' ) -ScorecardsMulti <- function(data, - system, - reference, - var, - start.year, - end.year, - start.months, - forecast.months, - region.names, - metrics, - table.label, - fileout.label, - output.path){ +ScorecardsMulti <- function(data, sign, system, reference, var, start.year, + end.year, start.months, forecast.months, + region.names, metrics, plot.legend = TRUE, + legend.breaks = NULL, legend.white.space = NULL, + legend.width = 555, legend.height = 50, + table.label = NULL, fileout.label = NULL, + label.scale = 1.4, font.size = 1.1, + col1.width = NULL, col2.width = NULL, + columns.width = NULL, + round.decimal = 2, output.path){ ## Checks to apply: # first dimension in aggregated_metrics is system and second dimension is reference @@ -70,29 +89,40 @@ ScorecardsMulti <- function(data, fileout.label <- "" } - ## Make sure input_data is in correct order for using in functions: + ## Make sure data is in correct order for using in functions: data_order <- c('system','reference','metric','time','sdate','region') data <- Reorder(data, data_order) - - ## Identify metrics loaded - metrics_loaded <- attributes(data)$metrics - - ## Select only the metrics to visualize from data - input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) - attributes(input_data)$metrics <- metrics - + sign <- Reorder(sign, data_order) + ## Transform data for scorecards by forecast month (types 11 & 12) - transformed_data <- SCTransform(data = input_data, + transformed_data <- SCTransform(data = data, sdate_dim = 'sdate', ftime_dim = 'time') + transformed_sign <- SCTransform(data = sign, + sdate_dim = 'sdate', + ftime_dim = 'time') + ## Load configuration files - sys_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/conf/archive.yml")$esarchive - var_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/csscorecards/inst/config/variable-dictionary.yml")$vars + if (is.null(recipe$Run$filesystem)) { + filesystem <- 'esarchive' + } else { + filesystem <- recipe$Run$filesystem + } + sys_dict <- read_yaml("conf/archive.yml")[[filesystem]] + var_dict <- read_yaml("conf/variable-dictionary.yml")$vars ## Get scorecards table display names from configuration files var.name <- var_dict[[var]]$long_name - var.units <- var_dict[[var]]$units + + for (i in 1:length(recipe$Analysis$Variables)) { + if (recipe$Analysis$Variables[[i]]$name == var) { + var.units <- recipe$Analysis$Variables[[i]]$units + } + } + if (is.null(var.units)) { + var.units <- var_dict[[var]]$units + } system.name <- NULL reference.name <- NULL @@ -147,18 +177,6 @@ ScorecardsMulti <- function(data, ## Legend upper limit color legend.col.sup <- .legend_col_sup(metrics, colorsup) legend.col.sup <- legend.col.sup[metrics] - - ## Legend inputs - plot.legend = TRUE - label.scale = 1.4 - legend.width = 555 - legend.height = 50 - - ## Data display inputs - round.decimal = 2 - font.size = 1.1 - - legend.white.space <- col1.width <- col2.width <- NULL ## Use default values of function ## Loop over region for(reg in 1:length(region.names)){ @@ -189,12 +207,17 @@ ScorecardsMulti <- function(data, start.year = start.year, end.year = end.year, scorecard.type = 9, region = sub(" ", "-", region.names[reg]), fileout.label = fileout.label, output.path = output.path) + if(model == 'system'){ - data_sc_9 <- Subset(input_data, c('reference','region'), list(1, reg), drop = 'selected') + data_sc_9 <- Subset(data, c('reference','region'), list(1, reg), drop = 'selected') + sign_sc_9 <- Subset(sign, c('reference','region'), list(1, reg), drop = 'selected') } else if(model == 'reference'){ - data_sc_9 <- Subset(input_data, c('system','region'), list(1, reg), drop = 'selected') + data_sc_9 <- Subset(data, c('system','region'), list(1, reg), drop = 'selected') + sign_sc_9 <- Subset(sign, c('system','region'), list(1, reg), drop = 'selected') } + SCPlotScorecard(data = data_sc_9, + sign = sign_sc_9, row.dim = model, subrow.dim = 'time', col.dim = 'metric', @@ -222,6 +245,7 @@ ScorecardsMulti <- function(data, legend.white.space = legend.white.space, col1.width = 4, col2.width = col2.width, + columns.width = columns.width, fileout = fileout) @@ -231,13 +255,18 @@ ScorecardsMulti <- function(data, start.year = start.year, end.year = end.year, scorecard.type = 10, region = sub(" ", "-", region.names[reg]), fileout.label = fileout.label, output.path = output.path) + new_order <- c('system', 'reference', 'metric', 'region','sdate', 'time') + if(model == 'system'){ - data_sc_10 <- Subset(Reorder(input_data, new_order), c('reference','region'), list(1, reg), drop = 'selected') + data_sc_10 <- Subset(Reorder(data, new_order), c('reference','region'), list(1, reg), drop = 'selected') + sign_sc_10 <- Subset(Reorder(sign, new_order), c('reference','region'), list(1, reg), drop = 'selected') } else if(model == 'reference'){ - data_sc_10 <- Subset(Reorder(input_data, new_order), c('system','region'), list(1, reg), drop = 'selected') + data_sc_10 <- Subset(Reorder(data, new_order), c('system','region'), list(1, reg), drop = 'selected') + sign_sc_10 <- Subset(Reorder(sign, new_order), c('system','region'), list(1, reg), drop = 'selected') } SCPlotScorecard(data = data_sc_10, + sign = sign_sc_10, row.dim = 'time', subrow.dim = model, col.dim = 'metric', @@ -265,6 +294,7 @@ ScorecardsMulti <- function(data, legend.white.space = legend.white.space, col1.width = col1.width, col2.width = 4, + columns.width = columns.width, fileout = fileout) @@ -274,12 +304,17 @@ ScorecardsMulti <- function(data, start.year = start.year, end.year = end.year, scorecard.type = 11, region = sub(" ", "-", region.names[reg]), fileout.label = fileout.label, output.path = output.path) + if(model == 'system'){ data_sc_11 <- Subset(transformed_data, c('reference','region'), list(1, reg), drop = 'selected') + sign_sc_11 <- Subset(transformed_sign, c('reference','region'), list(1, reg), drop = 'selected') } else if(model == 'reference'){ data_sc_11 <- Subset(transformed_data, c('system','region'), list(1, reg), drop = 'selected') + sign_sc_11 <- Subset(transformed_sign, c('system','region'), list(1, reg), drop = 'selected') } + SCPlotScorecard(data = data_sc_11, + sign = sign_sc_11, row.dim = model, subrow.dim = 'time', col.dim = 'metric', @@ -307,6 +342,7 @@ ScorecardsMulti <- function(data, legend.white.space = legend.white.space, col1.width = 4, col2.width = col2.width, + columns.width = columns.width, fileout = fileout) @@ -316,13 +352,19 @@ ScorecardsMulti <- function(data, start.year = start.year, end.year = end.year, scorecard.type = 12, region = sub(" ", "-", region.names[reg]), fileout.label = fileout.label, output.path = output.path) + new_order <- c('system', 'reference', 'metric', 'region','sdate', 'time') + if(model == 'system'){ data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('reference','region'), list(1, reg), drop = 'selected') + sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('reference','region'), list(1, reg), drop = 'selected') } else if(model == 'reference'){ data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('system','region'), list(1, reg), drop = 'selected') + sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('system','region'), list(1, reg), drop = 'selected') } + SCPlotScorecard(data = data_sc_12, + sign = sign_sc_12, row.dim = 'time', subrow.dim = model, col.dim = 'metric', @@ -350,6 +392,7 @@ ScorecardsMulti <- function(data, legend.white.space = legend.white.space, col1.width = col1.width, col2.width = 4, + columns.width = columns.width, fileout = fileout) } ## close loop on region diff --git a/modules/Scorecards/R/tmp/ScorecardsSingle.R b/modules/Scorecards/R/tmp/ScorecardsSingle.R index 190ae3d5..3eb06537 100644 --- a/modules/Scorecards/R/tmp/ScorecardsSingle.R +++ b/modules/Scorecards/R/tmp/ScorecardsSingle.R @@ -23,7 +23,29 @@ #' include in the scorecard title #'@param fileout.label a character string containing additional information to #' include in the output png file when saving the scorecard. -#'@param output.path a path of the location to save the scorecard plots +#'@param plot.legend A logical value to determine if the legend is plotted. +#'@param legend.breaks A vector of numerics or a list of vectors of numerics, +#' containing the breaks for the legends. If a vector is given as input, then +#' these breaks will be repeated for each legend.dim. A list of vectors can be +#' given as input if the legend.dims require different breaks. This parameter +#' is required even if the legend is not plotted, to define the colors in the +#' scorecard table. +#'@param legend.white.space A numeric value defining the initial starting +#' position of the legend bars, the white space infront of the legend is +#' calculated from the left most point of the table as a distance in cm. +#'@param legend.width A numeric value to define the width of the legend bars. +#'@param legend.height A numeric value to define the height of the legend bars. +#'@param label.scale A numeric value to define the size of the legend labels. +#'@param col1.width A numeric value defining the width of the first table column +#' in cm. +#'@param col2.width A numeric value defining the width of the second table +#' column in cm. +#'@param columns.width A numeric value defining the width all columns within the +#' table in cm (excluding the first and second columns containing the titles). +#'@param font.size A numeric indicating the font size on the scorecard table. +#'@param round.decimal A numeric indicating to which decimal point the data +#' is to be displayed in the scorecard table. Default is 2. +#'@param output.path A path of the location to save the scorecard plots #' #'@return #' This function returns 4 scorecards images, saved in the directory output.path @@ -43,15 +65,18 @@ #' output.path = '/esarchive/scratch/nmilders/scorecards_images/test' #' ) #'@export -ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end.year, - start.months, forecast.months, region.names, - metrics, legend.breaks = NULL, +ScorecardsSingle <- function(data, sign, system, reference, var, start.year, + end.year, start.months, forecast.months, + region.names, metrics, plot.legend = TRUE, + legend.breaks = NULL, legend.white.space = NULL, + legend.width = 550, legend.height = 50, table.label = NULL, fileout.label = NULL, - legend.white.space = NULL, - col1.width = NULL, col2.width = NULL, - output.path){ - - ## Checks to apply: + label.scale = 1.4, font.size = 1.1, + col1.width = NULL, col2.width = NULL, + columns.width = 1.2, + round.decimal = 2, output.path){ + + ## Checks to apply: # First dimension in aggregated_metrics is system and second dimension is reference # To allow 1 region - if region = 1 --> only scorecards 1 & 3 need to be plotted # If any dimension of input dat is 1, make sure dimension is still present in array @@ -84,32 +109,58 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end data <- Reorder(data, data_order) sign <- Reorder(sign, data_order) - ## Identify metrics loaded - metrics_loaded <- attributes(data)$metrics + ####### PROBLEM HERE ?? ###### + # No attributes in score aggregated dataset + + # ## Identify metrics loaded + # metrics_loaded <- attributes(data)$metrics + # + # ## Select only the metrics to visualize from data + # input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) + # attributes(input_data)$metrics <- metrics + # + # input_sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) + # attributes(input_sign)$metrics <- metrics - ## Select only the metrics to visualize from data - input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) - attributes(input_data)$metrics <- metrics + input_data <- data #temporary + input_sign <- sign - input_sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) - attributes(input_sign)$metrics <- metrics + ############################ ## Transform data for scorecards by forecast month (types 3 & 4) - transformed_data <- SCTransform(data = input_data, + transformed_data <- SCTransform(data = data, sdate_dim = 'sdate', ftime_dim = 'time') - transformed_sign <- SCTransform(data = input_sign, + transformed_sign <- SCTransform(data = sign, sdate_dim = 'sdate', ftime_dim = 'time') ## Load configuration files - sys_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/conf/archive.yml")$esarchive - var_dict <- read_yaml("/esarchive/scratch/nmilders/gitlab/git_clones/csscorecards/inst/config/variable-dictionary.yml")$vars + if (is.null(recipe$Run$filesystem)) { + filesystem <- 'esarchive' + } else { + filesystem <- recipe$Run$filesystem + } + sys_dict <- read_yaml("conf/archive.yml")[[filesystem]] + var_dict <- read_yaml("conf/variable-dictionary.yml")$vars ## Get scorecards table display names from configuration files var.name <- var_dict[[var]]$long_name - var.units <- var_dict[[var]]$units + + # for (i in 1:length(recipe$Analysis$Variables)) { + # if (recipe$Analysis$Variables[[i]]$name == var) { ## depends on way recipe is written + # var.units <- recipe$Analysis$Variables[[i]]$units + # } + # } + for (i in 1:length(recipe$Analysis$Variables)) { + if (recipe$Analysis$Variables$name == var) { ## TEMPORARY + var.units <- recipe$Analysis$Variables$units + } + } + if (is.null(var.units)) { + var.units <- var_dict[[var]]$units + } ## Get metric long names metric.names.list <- .met_names(metrics, var.units) @@ -136,16 +187,6 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end legend.col.sup <- .legend_col_sup(metrics, colorsup) legend.col.sup <- legend.col.sup[metrics] - ## Legend inputs - plot.legend = TRUE - label.scale = 1.4 - legend.width = 555 - legend.height = 50 - - ## Data display inputs - round.decimal = 2 - font.size = 1.1 - ## Loop over system and reference for each scorecard plot for (sys in 1:dim(input_data)['system']) { for (ref in 1:dim(input_data)['reference']) { @@ -223,6 +264,7 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end legend.white.space = legend.white.space, col1.width = col1.width, col2.width = col2.width, + columns.width = columns.width, fileout = fileout) @@ -269,6 +311,7 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end legend.white.space = legend.white.space, col1.width = col1.width, col2.width = col2.width, + columns.width = columns.width, fileout = fileout) } ## close if @@ -311,6 +354,7 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end legend.white.space = legend.white.space, col1.width = col1.width, col2.width = col2.width, + columns.width = columns.width, fileout = fileout) @@ -357,6 +401,7 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, end legend.white.space = legend.white.space, col1.width = col1.width, col2.width = col2.width, + columns.width = columns.width, fileout = fileout) } ## close if diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index bad1497c..af797025 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -17,8 +17,8 @@ source('modules/Scorecards/R/tmp/SCPlotScorecard.R') ## Define function Scorecards <- function(recipe) { - ## Parameters for loading data - input.path <- "/esarchive/scratch/nmilders/scorecards_data/test/recipe_scorecards_data_loading_nadia_20231219150442/outputs/" #temp + ## Parameters for loading data files + input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/to_system/" #temp skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") @@ -42,12 +42,28 @@ Scorecards <- function(recipe) { # split = ", | |,")[[1]]) # } + start.months <- sprintf("%02d", start.months) + period <- paste0(start.year, "-", end.year) + + ## Parameters for data aggregation regions <- recipe$Analysis$Workflow$Scorecards$regions for (i in names(regions)){regions[[i]] <- unlist(regions[[i]])} metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) + + if(is.null(recipe$Analysis$Workflow$Scorecards$signif_alpha)){ + alpha <- 0.05 + } else { + alpha <- recipe$Analysis$Workflow$Scorecards$signif_alpha + } + + if (is.null(recipe$Analysis$Workflow$Scorecards$inf_to_na)){ + inf.to.na <- FALSE + } else { + inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf_to_na + } if(is.null(recipe$Analysis$remove_NAs)){ na.rm <- FALSE @@ -57,19 +73,33 @@ Scorecards <- function(recipe) { ncores <- 1 # recipe$Analysis$ncores - ## Parameters for scorecard table - inf.to.na <- recipe$Analysis$Workflow$Scorecards$inf_to_na + ## Paramters for scorecard layout table.label <- recipe$Analysis$Workflow$Scorecards$table_label fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label - legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width - calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff - - ## Load data files - start.months <- sprintf("%02d", start.months) - period <- paste0(start.year, "-", end.year) + columns.width <- recipe$Analysis$Workflow$Scorecards$columns_width + legend.breaks <- recipe$Analysis$Workflow$Scorecards$legend_breaks + legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space + legend.width <- recipe$Analysis$Workflow$Scorecards$legend_width + legend.height <- recipe$Analysis$Workflow$Scorecards$legend_height + label.scale <- recipe$Analysis$Workflow$Scorecards$label_scale + round.decimal <- recipe$Analysis$Workflow$Scorecards$round_decimal + font.size <- recipe$Analysis$Workflow$Scorecards$font_size + if (is.null(recipe$Analysis$Workflow$Scorecards$plot_legend)){ + plot.legend <- TRUE + } else { + plot.legend <- recipe$Analysis$Workflow$Scorecards$plot_legend + } + + ## Define if difference scorecard is to be plotted + if (is.null(recipe$Analysis$Workflow$Scorecards$calculate_diff)){ + calculate.diff <- FALSE + } else { + calculate.diff <- recipe$Analysis$Workflow$Scorecards$calculate_diff + } + ####### SKILL AGGREGATION ####### if(metric.aggregation == 'skill'){ @@ -79,7 +109,7 @@ Scorecards <- function(recipe) { var = var, start.year = start.year, end.year = end.year, - metrics = metrics.load, + metrics = metrics.visualize, ## metrics.load start.months = start.months, forecast.months = forecast.months, inf.to.na = inf.to.na, @@ -130,11 +160,8 @@ Scorecards <- function(recipe) { if(metric.aggregation == 'score'){ ## Comments (Nadia): - ## 1) syear parameter as FALSE instead of NULL for metrics without syear dimension? - ## 2) output files names of statistics not correct, too many _ in name - ## 3) how to load multiple systems? - Loop over sys and ref for metric.aggregation == 'score' - ## Need to test with multiple sdates - + ## syear parameter as FALSE instead of NULL for metrics without syear dimension? + lon_dim <- 'longitude' lat_dim <- 'latitude' time_dim <- 'syear' @@ -226,7 +253,7 @@ Scorecards <- function(recipe) { ## Calculate significance sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, time_dim = time_dim, test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, + alpha = alpha, pval = FALSE, sign = TRUE, ncores = NULL)$sign ## Save metric result in arrays @@ -298,7 +325,7 @@ Scorecards <- function(recipe) { ## Calculate significance sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, time_dim = time_dim, test.type = 'two.sided', - alpha = 0.05, pval = FALSE, sign = TRUE, + alpha = alpha, pval = FALSE, sign = TRUE, ncores = NULL)$sign ## Save metric result in arrays @@ -401,8 +428,6 @@ Scorecards <- function(recipe) { enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') ## Calculate significance of corr - alpha <- 0.05 # Apply 95% confidence - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) @@ -506,12 +531,14 @@ Scorecards <- function(recipe) { } ## close if on score + + ####### PLOT SCORECARDS ########## ## Create simple scorecard tables ## (one system only) ## Metrics input must be in the same order as function SC_spatial_aggregation scorecard_single <- ScorecardsSingle(data = aggregated_metrics, - signif = scorecard_sign, + sign = scorecard_sign, system = system, reference = reference, var = var, @@ -523,9 +550,17 @@ Scorecards <- function(recipe) { metrics = metrics.visualize, table.label = table.label, fileout.label = fileout.label, + plot.legend = plot.legend, + legend.breaks = legend.breaks, legend.white.space = legend.white.space, + legend.width = legend.width, + legend.height = legend.height, + label.scale = label.scale, col1.width = col1.width, col2.width = col2.width, + columns.width = columns.width, + font.size = font.size, + round.decimal = round.decimal, output.path = output.path) ## Create multi system/reference scorecard tables diff --git a/modules/Skill/R/tmp/CRPS.R b/modules/Skill/R/tmp/CRPS.R index c08375c4..9f91be34 100644 --- a/modules/Skill/R/tmp/CRPS.R +++ b/modules/Skill/R/tmp/CRPS.R @@ -126,7 +126,7 @@ CRPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', dat_dim = NU Fair = Fair, ncores = ncores)$output1 - if (return_mean == TRUE) { + if (isTRUE(return_mean)) { crps <- MeanDims(crps, time_dim, na.rm = FALSE) } else { crps <- crps diff --git a/modules/Skill/R/tmp/RPS.R b/modules/Skill/R/tmp/RPS.R index 54ec8440..e15a1754 100644 --- a/modules/Skill/R/tmp/RPS.R +++ b/modules/Skill/R/tmp/RPS.R @@ -252,7 +252,7 @@ RPS <- function(exp, obs, time_dim = 'sdate', memb_dim = 'member', cat_dim = NUL weights = weights, cross.val = cross.val, na.rm = na.rm, ncores = ncores)$output1 - if (return_mean == TRUE) { + if (isTRUE(return_mean)) { rps <- MeanDims(rps, time_dim, na.rm = TRUE) } else { rps <- rps diff --git a/recipes/recipe_scorecards.yml b/recipes/recipe_scorecards.yml index 434426d0..a75ad1d2 100644 --- a/recipes/recipe_scorecards.yml +++ b/recipes/recipe_scorecards.yml @@ -56,6 +56,9 @@ Analysis: metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr # list, don't split cross_validation: yes save: 'all' + Statistics: + metric: cov std n_eff + save: 'all' Probabilities: percentiles: [[1/3, 2/3]] # list, don't split save: 'none' @@ -64,19 +67,30 @@ Analysis: Indicators: index: no # ? Scorecards: - execute: yes # yes/no - regions: + execute: yes # Mandatory, yes/no + regions: # Mandatory, define regions over which to aggregate data Extra-tropical NH: {lon.min: 0, lon.max: 360, lat.min: 30, lat.max: 90} Tropics: {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: 30} - Extra-tropical SH : {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: -90} - start_months: NULL - metric: mean_bias enscorr rpss crpss enssprerr - metric_aggregation: 'score' - table_label: NULL - fileout_label: NULL - col1_width: NULL - col2_width: NULL - calculate_diff: FALSE + Extra-tropical SH : {lon.min: 0, lon.max: 360, lat.min: -90, lat.max: -30} + start_months: NULL + metric: mean_bias enscorr rpss crpss enssprerr # Mandatory, define metrics to visualize in scorecard. + metric_aggregation: 'score' # Mandatory, defines the aggregation level of the metrics. + signif_alpha: 0.05 # Optional, to set alpha for signifiance calculation, default is 0.05. + table_label: NULL # Optional, to add extra information to the table title. + fileout_label: NULL # Optional, to add extra information to the output filename. + col1_width: NULL # Optional, to set the width (cm) of the first table column, default is calculated depending on row names. + col2_width: NULL # Optional, to set the width (cm) of the first table column, default is calculated depending on subrow names. + columns_width: NULL # Optional, to set the width (cm) of all other columns within the table, defualt is 1.2. + plot_legend: TRUE ## Optional, to define is the legend is included in the scorecards image, default is TRUE. + legend_breaks: NULL # Optional, default used legend breaks from modules/Scorecards/R/tmp/Utils.R. + legend_white_space: NULL # Optional, default is automatically calculted depend on column sizes. + legend_width: NULL # Optional, to set the width of the lengend bars, default is 550. + legend_height: NULL # Optional, to set the height of the legend bars, default is 50. + label_scale: NULL # Optional, to set the scale of the legend bar lables, default is 1.4. + round_decimal: NULL # Optional, to round the data shown in the scorecard, default is 2 (decimals). + inf_to_na: TRUE # Optional, to set infinite values to NA, default is FALSE. + font_size: NULL # Optional, to set the font size of the scorecard table values, default is 1.2. + calculate_diff: FALSE # Optional, to calculate difference between two systems or two references, default is FALSE. ncores: 7 remove_NAs: no # bool, don't split Output_format: Scorecards # string, don't split diff --git a/tools/check_recipe.R b/tools/check_recipe.R index f4d711f8..c398c345 100644 --- a/tools/check_recipe.R +++ b/tools/check_recipe.R @@ -567,43 +567,43 @@ check_recipe <- function(recipe) { # Scorecards if ("Scorecards" %in% names(recipe$Analysis$Workflow)) { if(recipe$Analysis$Workflow$Scorecards$execute == TRUE){ - if (is.null(recipe$Analysis$Workflow$Scorecards$metric)) { - error(recipe$Run$logger, - "Parameter 'metric' must be defined under 'Scorecards'.") - error_status <- T - } else { - sc_metrics <- strsplit(recipe$Analysis$Workflow$Scorecards$metric, - ", | |,")[[1]] - if (recipe$Analysis$Workflow$Scorecards$metric_aggregation == 'score') { - if ('rpss' %in% tolower(sc_metrics)) { - if (!('rps_clim_syear' %in% requested_metrics)) { - requested_metrics <- c(requested_metrics, 'rps_clim_syear') - } - if (!('rps_syear' %in% requested_metrics)) { - requested_metrics <- c(requested_metrics, 'rps_syear') + if (is.null(recipe$Analysis$Workflow$Scorecards$metric)) { + error(recipe$Run$logger, + "Parameter 'metric' must be defined under 'Scorecards'.") + error_status <- T + } else { + sc_metrics <- strsplit(recipe$Analysis$Workflow$Scorecards$metric, + ", | |,")[[1]] + if (recipe$Analysis$Workflow$Scorecards$metric_aggregation == 'score') { + if ('rpss' %in% tolower(sc_metrics)) { + if (!('rps_clim_syear' %in% requested_metrics)) { + requested_metrics <- c(requested_metrics, 'rps_clim_syear') + } + if (!('rps_syear' %in% requested_metrics)) { + requested_metrics <- c(requested_metrics, 'rps_syear') + } } - } - if ('crpss' %in% tolower(sc_metrics)) { - if (!('crps_clim_syear' %in% requested_metrics)) { - requested_metrics <- c(requested_metrics, 'crps_clim_syear') + if ('crpss' %in% tolower(sc_metrics)) { + if (!('crps_clim_syear' %in% requested_metrics)) { + requested_metrics <- c(requested_metrics, 'crps_clim_syear') + } + if (!('crps_syear' %in% requested_metrics)) { + requested_metrics <- c(requested_metrics, 'crps_syear') + } } - if (!('crps_syear' %in% requested_metrics)) { - requested_metrics <- c(requested_metrics, 'crps_syear') + if ('enscorr' %in% tolower(sc_metrics)) { + recipe$Analysis$Workflow$Statistics <- c('std', 'cov', 'n_eff') } + recipe$Analysis$Workflow$Skill$metric <- requested_metrics } - if ('enscorr' %in% tolower(sc_metrics)) { - recipe$Analysis$Workflow$Statistics <- c('standard_deviation', 'covariance') + if (!all(tolower(sc_metrics) %in% tolower(requested_metrics))) { + error(recipe$Run$logger, + paste0("All of the metrics requested under 'Scorecards' must ", + "be requested in the 'Skill' section.")) + error_status <- T } - recipe$Analysis$Workflow$Skill$metric <- requested_metrics - } - if (!all(tolower(sc_metrics) %in% tolower(requested_metrics))) { - error(recipe$Run$logger, - paste0("All of the metrics requested under 'Scorecards' must ", - "be requested in the 'Skill' section.")) - error_status <- T } - } - } + } } # --------------------------------------------------------------------- # RUN CHECKS -- GitLab From e26048975b63d852dc79e41d6076159bedc8765f Mon Sep 17 00:00:00 2001 From: vagudets Date: Thu, 11 Jan 2024 10:41:58 +0100 Subject: [PATCH 46/78] Update hub modules --- MODULES | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/MODULES b/MODULES index e2709d66..6654c092 100644 --- a/MODULES +++ b/MODULES @@ -17,6 +17,16 @@ if [[ $BSC_MACHINE == "nord3v2" ]]; then module load PROJ/9.0.0-GCCcore-8.3.0 module load Phantomjs/2.1.1 +elif [[ $HOSTNAME == "bsceshub02.bsc.es" ]]; then + + module purge + module load CDO/1.9.8-foss-2021b + module load R/4.2.1-foss-2021b + module load GEOS/3.11.0-GCC-11.2.0 + module load GDAL/3.5.2-foss-2021b-Python-3.9.6 + module load PROJ/9.1.0-foss-2021b + module load Phantomjs/2.1.1 + else module purge -- GitLab From 45ce374d32e3dd53fe5c4eb2682ff5b8f5568b5e Mon Sep 17 00:00:00 2001 From: vagudets Date: Thu, 11 Jan 2024 15:41:22 +0100 Subject: [PATCH 47/78] Add get_regrid_params(); remove source from scratch --- modules/Loading/R/get_regrid_params.R | 73 +++++++++++++++++++++++++++ modules/Loading/R/load_decadal.R | 11 +--- modules/Loading/R/load_seasonal.R | 3 +- modules/Loading/R/load_tas_tos.R | 2 +- 4 files changed, 77 insertions(+), 12 deletions(-) create mode 100644 modules/Loading/R/get_regrid_params.R diff --git a/modules/Loading/R/get_regrid_params.R b/modules/Loading/R/get_regrid_params.R new file mode 100644 index 00000000..ef08adcd --- /dev/null +++ b/modules/Loading/R/get_regrid_params.R @@ -0,0 +1,73 @@ +#'Read regrid parameters from recipe and returns a list for use with Start() +#' +#'The purpose of this function is to read the recipe and archive configuration +#'data for Auto-S2S workflows, retrieve the regridding parameters for hcst and +#'obs, and return an object that can be the input for 'transform' and +#''transform_params' when the data is loaded using Start(). +#'Requires CDORemapper. +#' +#'@param recipe Auto-S2S configuration recipe as returned by read_yaml() +#'@param archive Auto-S2S exp and obs archive as returned by read_yaml() +#' +#'@return A list containing regridding parameters for fcst and obs +#' +#'@import startR +#'@examples +#'setwd("/esarchive/scratch/vagudets/repos/auto-s2s/") +#'library(yaml) +#'library(startR) +#'recipe <- read_yaml("modules/data_load/recipe_1.yml") +#'archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive +#'regrid_params <- get_regrid_params(recipe, archive) +#' +#'@export +get_regrid_params <- function(recipe, archive) { + + ## TODO: Multi-model case + ## If multi-model, use the first system grid? + ## TODO: 'NULL' entries had to be removed due to bug in Start(). Rewrite when + ## the bug is fixed. + exp.name <- recipe$Analysis$Datasets$System$name + ref.name <- recipe$Analysis$Datasets$Reference$name + exp_descrip <- archive$System[[exp.name]] + reference_descrip <- archive$Reference[[ref.name]] + + if (tolower(recipe$Analysis$Regrid$type) == 'to_reference') { + + regrid_params <- list(fcst.gridtype = reference_descrip$reference_grid, + fcst.gridmethod = recipe$Analysis$Regrid$method, + fcst.transform = CDORemapper, + obs.gridtype = NULL, + obs.gridmethod = NULL, + obs.transform = NULL) + + } else if (tolower(recipe$Analysis$Regrid$type) == 'to_system') { + + regrid_params <- list(fcst.gridtype = NULL, + fcst.gridmethod = NULL, + fcst.transform = NULL, + obs.gridtype = exp_descrip$reference_grid, + obs.gridmethod = recipe$Analysis$Regrid$method, + obs.transform = CDORemapper) + + } else if (tolower(recipe$Analysis$Regrid$type) == 'none') { + + regrid_params <- list(fcst.gridtype = NULL, + fcst.gridmethod = NULL, + fcst.transform = NULL, + obs.gridtype = NULL, + obs.gridmethod = NULL, + obs.transform = NULL) + + } else { + regrid_params <- list(fcst.gridtype = recipe$Analysis$Regrid$type, + fcst.gridmethod = recipe$Analysis$Regrid$method, + fcst.transform = CDORemapper, + obs.gridtype = recipe$Analysis$Regrid$type, + obs.gridmethod = recipe$Analysis$Regrid$method, + obs.transform = CDORemapper) + } + + return(regrid_params) +} + diff --git a/modules/Loading/R/load_decadal.R b/modules/Loading/R/load_decadal.R index 797d6872..9268b090 100644 --- a/modules/Loading/R/load_decadal.R +++ b/modules/Loading/R/load_decadal.R @@ -1,12 +1,5 @@ -# Loading module: -# 1. archive.yml -# 2. recipe.yml -# 3. Load_decadal.R (V) -#setwd('/esarchive/scratch/aho/git/auto-s2s/') - -## TODO: remove paths to personal scratchs -source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") # Load required libraries/funs +source("modules/Loading/R/get_regrid_params.R") source("modules/Loading/R/helper_loading_decadal.R") source("modules/Loading/R/dates2load.R") source("modules/Loading/R/check_latlon.R") @@ -20,7 +13,7 @@ source("modules/Loading/R/compare_exp_obs_grids.R") load_decadal <- function(recipe) { ## - archive <- read_yaml(paste0("conf/archive_decadal.yml"))$esarchive + archive <- read_yaml(paste0("conf/archive_decadal.yml"))[[recipe$Run$filesystem]] # Print Start() info or not DEBUG <- FALSE diff --git a/modules/Loading/R/load_seasonal.R b/modules/Loading/R/load_seasonal.R index 14f34d1c..2caa34a9 100644 --- a/modules/Loading/R/load_seasonal.R +++ b/modules/Loading/R/load_seasonal.R @@ -1,6 +1,5 @@ -## TODO: remove paths to personal scratchs -source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") # Load required libraries/funs +source("modules/Loading/R/get_regrid_params.R") source("modules/Loading/R/dates2load.R") source("modules/Loading/R/get_timeidx.R") source("modules/Loading/R/check_latlon.R") diff --git a/modules/Loading/R/load_tas_tos.R b/modules/Loading/R/load_tas_tos.R index 34f6220c..ea231b56 100644 --- a/modules/Loading/R/load_tas_tos.R +++ b/modules/Loading/R/load_tas_tos.R @@ -1,5 +1,5 @@ -source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") # Load required libraries/funs +source("modules/Loading/R/get_regrid_params.R") source("modules/Loading/R/dates2load.R") source("modules/Loading/R/get_timeidx.R") source("modules/Loading/R/check_latlon.R") -- GitLab From 7fe71264a1ef1be4e8abf481265c56c7c5cef4ed Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 12 Jan 2024 10:36:27 +0100 Subject: [PATCH 48/78] remove TODO --- split.R | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/split.R b/split.R index 9bd95e86..0d443c7c 100755 --- a/split.R +++ b/split.R @@ -29,9 +29,8 @@ arguments <- docopt(doc = doc) recipe <- prepare_outputs(recipe_file = arguments$recipe, uniqueID = !arguments$disable_unique_ID, restructure = FALSE) -# Split recipe into atomic recipes -## TODO: Add autosubmit yes/no to the parameters? +# Split recipe into atomic recipes run_parameters <- divide_recipe(recipe) if (!is.null(recipe$Run$autosubmit) && (recipe$Run$autosubmit)) { -- GitLab From 2368771997846929190dc5d26aae584b978e918d Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 12 Jan 2024 10:36:35 +0100 Subject: [PATCH 49/78] Update NEWS --- NEWS.md | 59 +++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 49 insertions(+), 10 deletions(-) diff --git a/NEWS.md b/NEWS.md index e8cda565..40f97c3a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,21 +1,60 @@ +SUNSET v2.0.0 +============= + +Modules: Loading, Units, Anomalies, Calibration, Downscaling, Indices, Skill, Saving, Visualization, Scorecards + +New features: +- New module for unit conversion, available for temperature, precipitation and pressure variables. +- New Indices module with the following indices available: NAO, Niรฑo1+2, Niรฑo3, Niรฑo3.4 and Niรฑo4. +- New Scorecards module to create Scorecard visualizations of the computed skill metrics. +- New Downscaling module making use of the in-house CSDownscale package. +- Recipe splitting feature: A recipe can be split into 'atomic recipes' and the same workflow can be easily parallelized for multiple forecast systems, references, variables, start dates and regions. +- Option to load, process, save and plot multiple variables in one atomic recipe. +- Possibility to use Autosubmit 4 as a workflow manager to run recipes in parallel. +- New SUNSET launcher script to split a recipe and run it in parallel directly in the cluster with SLURM or with Autosubmit. +- Option to load tas-tos blend by requesting the 'tas-tos' variable. +- For each module there is the possibility to choose whether or not to save the forecast, hindcast and/or observations. +- New skill metrics MSSS and MSE available. +- New use cases with hands-on tutorials. +- GRIB file loading. + +Summary of bugfixes/improvements: + +- The names of the module functions have changed to be the same as the name of the module (e.g. load_datasets() has become Loading()). The old names will be deprecated in a future release. +- New datasets and variables added to the seasonal archive. +- The launcher script and the prepare_outputs() function have a new "disable_unique_ID" option, which removes the numerical identifier that is added to the name of the output folder when it is generated. +- The seasonal and decadal loading functions have been renamed and can now sourced and called from the same Loading module. +- Bugfix in the recipe checker: checks are now completed even when the time horizon is not correct. +- The functions have been adapted to the new s2dv_cube structure in CSTools>5.0.0. +- The metric 'corr' (correlation for each ensemble member) has been renamed to 'corr_individual_members'. +- Datasets saved under 'daily' folders in esarchive can now be loaded along with 'daily_mean' datasets. +- Start date information has been added to the plot names to avoid overwriting plots from different start dates. +- In the Visualization section of the recipe, the user can specify which plots to generate, whether they should be in a single-panel or multi-panel layout, and choose between different projections. +- General improvements to the plots: color scheme, units, titles and subtitles, layout and colorbars. +- Anomalies module can compute anomalies when hindcast and observations do not share the same grid. +- Added module configuration to run SUNSET in the BSC hub. +- Language specification has been added to lubridate functions to ensure they work well in all language locales. + ESS Verification Suite v1.1.0 +============================= Modules: Loading, Anomalies, Calibration, Skill, Saving, Visualization New features: -New module for anomaly computation. -New 'Scorecards' output format (different file names and paths from the default format). -New 'recipe checker' feature in prepare_outputs(): It runs a series of checks on the recipe to detect potential errors, typos, or missing information. +- New module for anomaly computation. +- New 'Scorecards' output format (different file names and paths from the default format). +- New 'recipe checker' feature in prepare_outputs(): It runs a series of checks on the recipe to detect potential errors, typos, or missing information. + Summary of fixes/improvements: -Changed the names of the seasonal systems from the names in /esarchive to the official names in the CDS. -Fixed a bug in the conversion of precipitation units. -Fixed a bug related to the consistency between experiment and observation dates for some systems. -Function parameters have been simplified and uniformized. -Improvements in the logging functionality for longer messages. -Improvements to the plots generated by the Visualization module. -compute_probabilities() now returns the fcst probabilities as well. +- Changed the names of the seasonal systems from the names in /esarchive to the official names in the CDS. +- Fixed a bug in the conversion of precipitation units. +- Fixed a bug related to the consistency between experiment and observation dates for some systems. +- Function parameters have been simplified and uniformized. +- Improvements in the logging functionality for longer messages. +- Improvements to the plots generated by the Visualization module. +- compute_probabilities() now returns the fcst probabilities as well. ESS Verification Suite v1.0.0 ============================= -- GitLab From 28f54118cb740474661526b886a362df9a4184ac Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 12 Jan 2024 10:38:21 +0100 Subject: [PATCH 50/78] Move recipes --- recipe_ecvs_seasonal_oper.yml | 73 ------------ .../{ => examples}/recipe_decadal_split.yml | 0 .../examples/recipe_ecvs_seasonal_oper.yml | 10 +- recipes/{ => examples}/recipe_scorecards.yml | 0 .../recipe_splitting_example.yml | 0 recipes/recipe_scorecards_vic.yml | 106 ------------------ 6 files changed, 5 insertions(+), 184 deletions(-) delete mode 100644 recipe_ecvs_seasonal_oper.yml rename recipes/{ => examples}/recipe_decadal_split.yml (100%) rename recipes/{ => examples}/recipe_scorecards.yml (100%) rename recipes/{ => examples}/recipe_splitting_example.yml (100%) delete mode 100644 recipes/recipe_scorecards_vic.yml diff --git a/recipe_ecvs_seasonal_oper.yml b/recipe_ecvs_seasonal_oper.yml deleted file mode 100644 index 832f36d5..00000000 --- a/recipe_ecvs_seasonal_oper.yml +++ /dev/null @@ -1,73 +0,0 @@ -Description: - Author: nperez - Info: ECVs Oper ESS ECMWF SEAS5 Seasonal Forecast recipe (monthly mean, tas) - -Analysis: - Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal - Variables: - - {name: tas, freq: monthly_mean, units: C} - - {name: prlr, freq: monthly_mean, units: mm, flux: no} - Datasets: - System: - - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s - Multimodel: no # Mandatory, bool: Either yes/true or no/false - Reference: - - {name: ERA5} # Mandatory, str: Reference codename. See docu. - Time: - sdate: '0801' ## MMDD - fcst_year: '2023' # Optional, int: Forecast year 'YYYY' - hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY' - hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY' - ftime_min: 1 # Mandatory, int: First leadtime time step in months - ftime_max: 6 # Mandatory, int: Last leadtime time step in months - Region: - - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40} - Regrid: - method: bilinear # Mandatory, str: Interpolation method. See docu. - type: "to_system" - #type: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/conf/grid_description.txt #'r360x180' # Mandatory, str: to_system, to_reference, or CDO-accepted grid. - Workflow: - Anomalies: - compute: no - cross_validation: no - save: none - Calibration: - method: evmos # Mandatory, str: Calibration method. See docu. - cross_validation: yes - save: none - Skill: - metric: mean_bias EnsCorr rpss crpss bss10 bss90 - save: 'all' - cross_validation: yes - Probabilities: - percentiles: [[1/3, 2/3], [1/10, 9/10]] # frac: Quantile thresholds. - save: 'all' - Indicators: - index: no - Visualization: - plots: skill_metrics forecast_ensemble_mean most_likely_terciles - multi_panel: no - dots: both - ncores: 4 # Optional, int: number of cores, defaults to 1 - remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE - Output_format: scorecards - logo: yes -Run: - Loglevel: INFO - Terminal: yes - filesystem: esarchive - output_dir: /esarchive/scratch/nperez/cs_oper/seasonal/ # replace with the directory where you want to save the outputs - code_dir: /esarchive/scratch/nperez/git/auto-s2s/ # replace with the directory where your code is - autosubmit: yes - # fill only if using autosubmit - auto_conf: - script: /esarchive/scratch/nperez/git/auto-s2s/exec_ecvs_seasonal_oper.R # replace with the path to your script - expid: a68v # replace with your EXPID - hpc_user: bsc32339 # replace with your hpc username - wallclock: 02:00 # hh:mm - processors_per_job: 4 - platform: nord3v2 - email_notifications: yes # enable/disable email notifications. Change it if you want to. - email_address: nuria.perez@bsc.es # replace with your email address - notify_completed: yes # notify me by email when a job finishes - notify_failed: yes # notify me by email when a job fails diff --git a/recipes/recipe_decadal_split.yml b/recipes/examples/recipe_decadal_split.yml similarity index 100% rename from recipes/recipe_decadal_split.yml rename to recipes/examples/recipe_decadal_split.yml diff --git a/recipes/examples/recipe_ecvs_seasonal_oper.yml b/recipes/examples/recipe_ecvs_seasonal_oper.yml index d47fd159..832f36d5 100644 --- a/recipes/examples/recipe_ecvs_seasonal_oper.yml +++ b/recipes/examples/recipe_ecvs_seasonal_oper.yml @@ -5,8 +5,8 @@ Description: Analysis: Horizon: seasonal # Mandatory, str: either subseasonal, seasonal, or decadal Variables: - - {name: tas, freq: monthly_mean} - - {name: prlr, freq: monthly_mean} + - {name: tas, freq: monthly_mean, units: C} + - {name: prlr, freq: monthly_mean, units: mm, flux: no} Datasets: System: - {name: ECMWF-SEAS5.1} # system21_m1 system35c3s @@ -14,14 +14,14 @@ Analysis: Reference: - {name: ERA5} # Mandatory, str: Reference codename. See docu. Time: - sdate: '0701' ## MMDD + sdate: '0801' ## MMDD fcst_year: '2023' # Optional, int: Forecast year 'YYYY' hcst_start: '1993' # Mandatory, int: Hindcast start year 'YYYY' hcst_end: '2016' # Mandatory, int: Hindcast end year 'YYYY' ftime_min: 1 # Mandatory, int: First leadtime time step in months ftime_max: 6 # Mandatory, int: Last leadtime time step in months Region: - - {name: "UE", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40} + - {name: "EU", latmin: 20, latmax: 80, lonmin: -20, lonmax: 40} Regrid: method: bilinear # Mandatory, str: Interpolation method. See docu. type: "to_system" @@ -47,7 +47,7 @@ Analysis: Visualization: plots: skill_metrics forecast_ensemble_mean most_likely_terciles multi_panel: no - projection: lambert_europe + dots: both ncores: 4 # Optional, int: number of cores, defaults to 1 remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE Output_format: scorecards diff --git a/recipes/recipe_scorecards.yml b/recipes/examples/recipe_scorecards.yml similarity index 100% rename from recipes/recipe_scorecards.yml rename to recipes/examples/recipe_scorecards.yml diff --git a/recipes/recipe_splitting_example.yml b/recipes/examples/recipe_splitting_example.yml similarity index 100% rename from recipes/recipe_splitting_example.yml rename to recipes/examples/recipe_splitting_example.yml diff --git a/recipes/recipe_scorecards_vic.yml b/recipes/recipe_scorecards_vic.yml deleted file mode 100644 index fbd2cb90..00000000 --- a/recipes/recipe_scorecards_vic.yml +++ /dev/null @@ -1,106 +0,0 @@ -################################################################################ -## RECIPE DESCRIPTION -################################################################################ - -Description: - Author: V. Agudetse - Info: Test for recipe splitting - -################################################################################ -## ANALYSIS CONFIGURATION -################################################################################ - -Analysis: - Horizon: Seasonal - Variables: # ECVs and Indicators? - - {name: tas, freq: monthly_mean} - Datasets: - System: # multiple systems for single model, split if Multimodel = F - - {name: ECMWF-SEAS5} - Multimodel: False # single option - Reference: - - {name: ERA5} # multiple references for single model? - Time: - sdate: # list, split - - '0101' - - '0201' - - '0301' - - '0401' - - '0501' - - '0601' - - '0701' - - '0801' - - '0901' - - '1001' - - '1101' - - '1201' - fcst_year: - hcst_start: '1993' # single option - hcst_end: '2003' # single option - ftime_min: 1 # single option - ftime_max: 6 # single option - Region: # multiple lists, split? Add region name if length(Region) > 1 - - {name: "global", latmin: -90, latmax: 90, lonmin: 0, lonmax: 359.9} - Regrid: - method: bilinear ## TODO: allow multiple methods? - type: to_system - Workflow: - Anomalies: - compute: yes - cross_validation: no - save: 'none' - Calibration: - method: raw ## TODO: list, split? - save: 'none' - Skill: - metric: mean_bias EnsCorr rps rpss crps crpss EnsSprErr # list, don't split - cross_validation: yes - save: 'all' - Probabilities: - percentiles: [[1/3, 2/3]] # list, don't split - save: 'none' - # Visualization: - # plots: skill_metrics - Indicators: - index: no # ? - Scorecards: - execute: yes # yes/no - regions: - Extra-tropical NH: {lon.min: 0, lon.max: 360, lat.min: 30, lat.max: 90} - Tropics: {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: 30} - Extra-tropical SH : {lon.min: 0, lon.max: 360, lat.min: -30, lat.max: -90} - start_months: NULL - metric: mean_bias enscorr rpss crpss enssprerr - metric_aggregation: 'score' - table_label: NULL - fileout_label: NULL - col1_width: NULL - col2_width: NULL - calculate_diff: FALSE - ncores: 14 - remove_NAs: no # bool, don't split - Output_format: Scorecards # string, don't split - -################################################################################ -## Run CONFIGURATION -################################################################################ -Run: - Loglevel: INFO - Terminal: yes - output_dir: /esarchive/scratch/vagudets/auto-s2s-outputs/ - code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ - autosubmit: yes - # fill only if using autosubmit - auto_conf: - script: /esarchive/scratch/vagudets/repos/auto-s2s/example_scripts/test_scorecards_workflow.R # replace with the path to your script - expid: a6ae # replace with your EXPID - hpc_user: bsc32762 # replace with your hpc username - wallclock: 03:00 # hh:mm - processors_per_job: 14 - platform: nord3v2 - custom_directives: ['#SBATCH --exclusive'] - email_notifications: yes # enable/disable email notifications. Change it if you want to. - email_address: victoria.agudetse@bsc.es # replace with your email address - notify_completed: yes # notify me by email when a job finishes - notify_failed: no # notify me by email when a job fails - -- GitLab From 492f04e9b6b3648c3e9df68196e186f83eac761c Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 12 Jan 2024 11:56:04 +0100 Subject: [PATCH 51/78] Remove Dev_Loading.R --- modules/Loading/Dev_Loading.R | 501 ---------------------------------- 1 file changed, 501 deletions(-) delete mode 100644 modules/Loading/Dev_Loading.R diff --git a/modules/Loading/Dev_Loading.R b/modules/Loading/Dev_Loading.R deleted file mode 100644 index fb456eb3..00000000 --- a/modules/Loading/Dev_Loading.R +++ /dev/null @@ -1,501 +0,0 @@ -## TODO: remove paths to personal scratchs -source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") -# Load required libraries/funs -source("modules/Loading/R/dates2load.R") -source("modules/Loading/R/get_timeidx.R") -source("modules/Loading/R/check_latlon.R") -## TODO: Move to prepare_outputs.R -source("tools/libs.R") -## TODO: remove these two lines when new as.s2dv_cube() is in CSTools -source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R') -source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R') - -## TODO: Source new s2dv_cube version -## TODO: Eliminate dim_var dimension (merge_across_dims?) - -load_datasets <- function(recipe) { - - # ------------------------------------------- - # Set params ----------------------------------------- - - hcst.inityear <- recipe$Analysis$Time$hcst_start - hcst.endyear <- recipe$Analysis$Time$hcst_end - lats.min <- recipe$Analysis$Region$latmin - lats.max <- recipe$Analysis$Region$latmax - lons.min <- recipe$Analysis$Region$lonmin - lons.max <- recipe$Analysis$Region$lonmax - ref.name <- recipe$Analysis$Datasets$Reference$name - exp.name <- recipe$Analysis$Datasets$System$name - - variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]][1] - vars <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]] - store.freq <- recipe$Analysis$Variables$freq - - # get sdates array - ## LOGGER: Change dates2load to extract logger from recipe? - sdates <- dates2load(recipe, recipe$Run$logger) - - idxs <- NULL - idxs$hcst <- get_timeidx(sdates$hcst, - recipe$Analysis$Time$ftime_min, - recipe$Analysis$Time$ftime_max, - time_freq=store.freq) - - if (!(is.null(sdates$fcst))) { - idxs$fcst <- get_timeidx(sdates$fcst, - recipe$Analysis$Time$ftime_min, - recipe$Analysis$Time$ftime_max, - time_freq=store.freq) - } - - ## TODO: Examine this verifications part, verify if it's necessary - # stream <- verifications$stream - # sdates <- verifications$fcst.sdate - - ## TODO: define fcst.name - ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name - - # get esarchive datasets dict: - ## TODO: Adapt to 'filesystem' option in recipe - archive <- read_yaml("conf/archive.yml")$esarchive - exp_descrip <- archive$System[[exp.name]] - - freq.hcst <- unlist(exp_descrip[[store.freq]][variable]) - reference_descrip <- archive$Reference[[ref.name]] - freq.obs <- unlist(reference_descrip[[store.freq]][variable]) - obs.dir <- reference_descrip$src - fcst.dir <- exp_descrip$src - hcst.dir <- exp_descrip$src - fcst.nmember <- exp_descrip$nmember$fcst - hcst.nmember <- exp_descrip$nmember$hcst - - ## TODO: it is necessary? - ##if ("accum" %in% names(reference_descrip)) { - ## accum <- unlist(reference_descrip$accum[store.freq][[1]]) - ##} else { - ## accum <- FALSE - ##} - - var_dir_obs <- reference_descrip[[store.freq]][vars] - var_dir_exp <- exp_descrip[[store.freq]][vars] - - # ----------- - obs.path <- paste0(archive$src, - obs.dir, store.freq, "/$var$", "$var_dir$", - "/$var$_$file_date$.nc") - - hcst.path <- paste0(archive$src, - hcst.dir, store.freq, "/$var$", "$var_dir$", - "$var$_$file_date$.nc") - - fcst.path <- paste0(archive$src, - hcst.dir, store.freq, "/$var$", "$var_dir$", - "/$var$_$file_date$.nc") - - # Define regrid parameters: - #------------------------------------------------------------------- - regrid_params <- get_regrid_params(recipe, archive) - - # Longitude circular sort and latitude check - #------------------------------------------------------------------- - circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max) - - if (recipe$Analysis$Variables$freq == "monthly_mean"){ - split_multiselected_dims = TRUE - } else { - split_multiselected_dims = FALSE - } - - # Load hindcast - #------------------------------------------------------------------- - hcst <- Start(dat = hcst.path, - var = vars, - var_dir = var_dir_exp, - file_date = sdates$hcst, - time = idxs$hcst, - var_dir_depends = 'var', - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$fcst.transform, - transform_params = list(grid = regrid_params$fcst.gridtype, - method = regrid_params$fcst.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat', 'latitude'), - longitude = c('lon', 'longitude'), - ensemble = c('member', 'ensemble')), - ensemble = indices(1:hcst.nmember), - metadata_dims = 'var', # change to just 'var'? - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = split_multiselected_dims, - retrieve = TRUE) - - # Remove var_dir dimension - if ("var_dir" %in% names(dim(hcst))) { - hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected") - } - - if (recipe$Analysis$Variables$freq == "daily_mean") { - # Adjusts dims for daily case, could be removed if startR allows - # multidim split - names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear" - default_dims <- c(dat = 1, var = 1, sday = 1, - sweek = 1, syear = 1, time = 1, - latitude = 1, longitude = 1, ensemble = 1) - default_dims[names(dim(hcst))] <- dim(hcst) - dim(hcst) <- default_dims - # Change time attribute dimensions - default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1) - names(dim(attr(hcst, "Variables")$common$time))[which(names( - dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear" - default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- - dim(attr(hcst, "Variables")$common$time) - dim(attr(hcst, "Variables")$common$time) <- default_time_dims - } - - # Convert hcst to s2dv_cube object - ## TODO: Give correct dimensions to $Dates - ## (sday, sweek, syear instead of file_date) - hcst <- as.s2dv_cube(hcst) - # Adjust dates for models where the time stamp goes into the next month - if (recipe$Analysis$Variables$freq == "monthly_mean") { - hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag) - } - - ## Combine tas and tos data into one variable: tas-tos - if(recipe$Analysis$Variables$name == 'tas tos'){ - #if(recipe$Analysis$Datasets$Reference$name == 'HadCRUT5' || recipe$Analysis$Datasets$Reference$name == 'BEST') { - source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R') - hcst <- mask_tas_tos(input_data = hcst, region = c(lons.min, lons.max,lats.min, lats.max), - grid = 'r360x181', - lon = hcst$coords$longitude, - lat = hcst$coords$latitude, - lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) - - hcst$dims[['var']] <- dim(hcst$data)[['var']] - #} - } - - # Load forecast - #------------------------------------------------------------------- - if (!is.null(recipe$Analysis$Time$fcst_year)) { - # the call uses file_date instead of fcst_syear so that it can work - # with the daily case and the current version of startR not allowing - # multiple dims split - - fcst <- Start(dat = fcst.path, - var = vars, - var_dir = var_dir_exp, - var_dir_depends = 'var', - file_date = sdates$fcst, - time = idxs$fcst, - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$fcst.transform, - transform_params = list(grid = regrid_params$fcst.gridtype, - method = regrid_params$fcst.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat', 'latitude'), - longitude = c('lon', 'longitude'), - ensemble = c('member', 'ensemble')), - ensemble = indices(1:fcst.nmember), - metadata_dims = 'var', - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = split_multiselected_dims, - retrieve = TRUE) - - if ("var_dir" %in% names(dim(fcst))) { - fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected") - } - - if (recipe$Analysis$Variables$freq == "daily_mean") { - # Adjusts dims for daily case, could be removed if startR allows - # multidim split - names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear" - default_dims <- c(dat = 1, var = 1, sday = 1, - sweek = 1, syear = 1, time = 1, - latitude = 1, longitude = 1, ensemble = 1) - default_dims[names(dim(fcst))] <- dim(fcst) - dim(fcst) <- default_dims - # Change time attribute dimensions - default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1) - names(dim(attr(fcst, "Variables")$common$time))[which(names( - dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear" - default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- - dim(attr(fcst, "Variables")$common$time) - dim(attr(fcst, "Variables")$common$time) <- default_time_dims - } - - # Convert fcst to s2dv_cube - fcst <- as.s2dv_cube(fcst) - # Adjust dates for models where the time stamp goes into the next month - if (recipe$Analysis$Variables$freq == "monthly_mean") { - fcst$attrs$Dates[] <- - fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag) - } - - } else { - fcst <- NULL - } - - # Load reference - #------------------------------------------------------------------- - - # Obtain dates and date dimensions from the loaded hcst data to make sure - # the corresponding observations are loaded correctly. - dates <- hcst$attrs$Dates - dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")] - - # Separate Start() call for monthly vs daily data - if (store.freq == "monthly_mean") { - - dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m") - dim(dates_file) <- dim(dates) - - ## tas tos mask - if (recipe$Analysis$Variables$name == 'tas tos'){ - if (recipe$Analysis$Datasets$Reference$name == 'HadCRUT5'){ - vars <- 'tasanomaly' - var_dir_obs <- reference_descrip[[store.freq]][vars] - } - } - - if (recipe$Analysis$Variables$name == 'tas tos'){ - if (recipe$Analysis$Datasets$Reference$name == 'BEST'){ - vars <- 'tas' - var_dir_obs <- reference_descrip[[store.freq]][vars] - } - } - - obs <- Start(dat = obs.path, - var = vars, - var_dir = var_dir_obs, - var_dir_depends = 'var', - file_date = dates_file, - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$obs.transform, - transform_params = list(grid = regrid_params$obs.gridtype, - method = regrid_params$obs.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat','latitude'), - longitude = c('lon','longitude')), - metadata_dims = 'var', - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = TRUE, - retrieve = TRUE) - - } else if (store.freq == "daily_mean") { - - # Get year and month for file_date - dates_file <- sapply(dates, format, '%Y%m') - dim(dates_file) <- dim(dates) - # Set hour to 12:00 to ensure correct date retrieval for daily data - lubridate::hour(dates) <- 12 - lubridate::minute(dates) <- 00 - # Restore correct dimensions - dim(dates) <- dim(dates_file) - - obs <- Start(dat = obs.path, - var = vars, - var_dir = var_dir_obs, - var_dir_depends = 'var', - file_date = sort(unique(dates_file)), - time = dates, - time_var = 'time', - time_across = 'file_date', - merge_across_dims = TRUE, - merge_across_dims_narm = TRUE, - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$obs.transform, - transform_params = list(grid = regrid_params$obs.gridtype, - method = regrid_params$obs.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat','latitude'), - longitude = c('lon','longitude')), - metadata_dims = 'var', - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = TRUE, - retrieve = TRUE) - } - - # Remove var_dir dimension - if ("var_dir" %in% names(dim(obs))) { - obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected") - } - # Adds ensemble dim to obs (for consistency with hcst/fcst) - default_dims <- c(dat = 1, var = 1, sday = 1, - sweek = 1, syear = 1, time = 1, - latitude = 1, longitude = 1, ensemble = 1) - default_dims[names(dim(obs))] <- dim(obs) - dim(obs) <- default_dims - - # Convert obs to s2dv_cube - obs <- as.s2dv_cube(obs) - - ## Combine tas and tos data into one variable: tas-tos - if(recipe$Analysis$Variables$name == 'tas tos'){ - if(recipe$Analysis$Datasets$Reference$name != 'HadCRUT5' & recipe$Analysis$Datasets$Reference$name != 'BEST'){ - source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R') - obs <- mask_tas_tos(input_data = obs, region = c(lons.min, lons.max,lats.min, lats.max), - grid = 'r360x181', - lon = obs$coords$longitude, - lat = obs$coords$latitude, - lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) - - obs$dims[['var']] <- dim(obs$data)[['var']] - } - } - - # Check for consistency between hcst and obs grid - if (!(recipe$Analysis$Regrid$type == 'none')) { - if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) { - lat_error_msg <- paste("Latitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lat_error_msg) - hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1], - "; Last hcst lat: ", hcst$lat[length(hcst$lat)]) - info(recipe$Run$logger, hcst_lat_msg) - obs_lat_msg <- paste0("First obs lat: ", obs$lat[1], - "; Last obs lat: ", obs$lat[length(obs$lat)]) - info(recipe$Run$logger, obs_lat_msg) - stop("hcst and obs don't share the same latitudes.") - } - if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) { - lon_error_msg <- paste("Longitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lon_error_msg) - hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1], - "; Last hcst lon: ", hcst$lon[length(hcst$lon)]) - info(recipe$Run$logger, hcst_lon_msg) - obs_lon_msg <- paste0("First obs lon: ", obs$lon[1], - "; Last obs lon: ", obs$lon[length(obs$lon)]) - info(recipe$Run$logger, obs_lon_msg) - stop("hcst and obs don't share the same longitudes.") - - } - } - - # Remove negative values in accumulative variables - dictionary <- read_yaml("conf/variable-dictionary.yml") - for (var_idx in 1:length(vars)) { - var_name <- vars[var_idx] - if (dictionary$vars[[var_name]]$accum) { - info(recipe$Run$logger, - paste0("Accumulated variable ", var_name, - ": setting negative values to zero.")) - # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data, - # along = "var", - # indices = var_idx, F), 0) - obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0 - hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0 - if (!is.null(fcst)) { - fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0 - } - } - - # Convert prlr from m/s to mm/day - ## TODO: Make a unit conversion function - if (vars[[var_idx]] == "prlr") { - # Verify that the units are m/s and the same in obs and hcst - if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") || - (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) && - ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") || - (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) { - info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.") - obs$data[, var_idx, , , , , , , ] <- - obs$data[, var_idx, , , , , , , ]*86400*1000 - obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day" - hcst$data[, var_idx, , , , , , , ] <- - hcst$data[, var_idx, , , , , , , ]*86400*1000 - hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day" - if (!is.null(fcst)) { - fcst$data[, var_idx, , , , , , , ] <- - fcst$data[, var_idx, , , , , , , ]*86400*1000 - fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day" - } - } - } - } - # Compute anomalies if requested - # Print a summary of the loaded data for the user, for each object - if (recipe$Run$logger$threshold <= 2) { - data_summary(hcst, recipe) - data_summary(obs, recipe) - if (!is.null(fcst)) { - data_summary(fcst, recipe) - } - } - - info(recipe$Run$logger, - "##### DATA LOADING COMPLETED SUCCESSFULLY #####") - - ############################################################################ - # - # CHECKS ON MISSING FILES - # - ############################################################################ - - #obs.NA_dates.ind <- Apply(obs, - # fun=(function(x){ all(is.na(x))}), - # target_dims=c('time', 'latitude', 'longitude'))[[1]] - #obs.NA_dates <- dates_file[obs.NA_dates.ind] - #obs.NA_dates <- obs.NA_dates[order(obs.NA_dates)] - #obs.NA_files <- paste0(obs.dir, store.freq,"/",variable,"_", - # freq.obs,"obs.grid","/",variable,"_",obs.NA_dates,".nc") - # - #if (any(is.na(hcst))){ - # fatal(recipe$Run$logger, - # paste(" ERROR: MISSING HCST VALUES FOUND DURING LOADING # ", - # " ################################################# ", - # " ###### MISSING FILES #### ", - # " ################################################# ", - # "hcst files:", - # hcst.NA_files, - # " ################################################# ", - # " ################################################# ", - # sep="\n")) - # quit(status = 1) - #} - # - #if (any(is.na(obs)) && !identical(obs.NA_dates,character(0))){ - # fatal(recipe$logger, - # paste(" ERROR: MISSING OBS VALUES FOUND DURING LOADING # ", - # " ################################################# ", - # " ###### MISSING FILES #### ", - # " ################################################# ", - # "obs files:", - # obs.NA_files, - # " ################################################# ", - # " ################################################# ", - # sep="\n")) - # quit(status=1) - #} - # - #info(recipe$logger, - # "######### DATA LOADING COMPLETED SUCCESFULLY ##############") - - ############################################################################ - ############################################################################ - - return(list(hcst = hcst, fcst = fcst, obs = obs)) - -} -- GitLab From e64bfede72a8028524d09636b206dd7be3ffc084 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 12 Jan 2024 12:07:15 +0100 Subject: [PATCH 52/78] scorecards significance development --- modules/Scorecards/R/tmp/ScorecardsMulti.R | 27 +++++++- modules/Scorecards/R/tmp/ScorecardsSingle.R | 73 +++++++++---------- modules/Scorecards/R/tmp/WeightedMetrics.R | 29 -------- modules/Scorecards/Scorecards.R | 77 ++++++++++++--------- 4 files changed, 100 insertions(+), 106 deletions(-) diff --git a/modules/Scorecards/R/tmp/ScorecardsMulti.R b/modules/Scorecards/R/tmp/ScorecardsMulti.R index f05275cf..dd9ac257 100644 --- a/modules/Scorecards/R/tmp/ScorecardsMulti.R +++ b/modules/Scorecards/R/tmp/ScorecardsMulti.R @@ -93,6 +93,17 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, data_order <- c('system','reference','metric','time','sdate','region') data <- Reorder(data, data_order) sign <- Reorder(sign, data_order) + + ## Identify metrics loaded + metrics_loaded <- attributes(data)$metrics + + ## Select only the metrics to visualize from data + data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(data)$metrics <- metrics + + sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(sign)$metrics <- metrics + ## Transform data for scorecards by forecast month (types 11 & 12) transformed_data <- SCTransform(data = data, @@ -115,9 +126,15 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, ## Get scorecards table display names from configuration files var.name <- var_dict[[var]]$long_name - for (i in 1:length(recipe$Analysis$Variables)) { - if (recipe$Analysis$Variables[[i]]$name == var) { - var.units <- recipe$Analysis$Variables[[i]]$units + if ('name' %in% names(recipe$Analysis$Variables)){ + if (recipe$Analysis$Variables$name == var) { + var.units <- recipe$Analysis$Variables$units + } + } else { + for (i in 1:length(recipe$Analysis$Variables)) { + if (recipe$Analysis$Variables[[i]]$name == var) { + var.units <- recipe$Analysis$Variables[[i]]$units + } } } if (is.null(var.units)) { @@ -136,6 +153,10 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, reference.name <- c(reference.name, reference.name1) } + if("Multimodel" %in% system ){ + system.name <- c(system.name, "Multimodel") + } + ## Get metric long names metric.names.list <- .met_names(metrics, var.units) diff --git a/modules/Scorecards/R/tmp/ScorecardsSingle.R b/modules/Scorecards/R/tmp/ScorecardsSingle.R index 3eb06537..1e8f93f6 100644 --- a/modules/Scorecards/R/tmp/ScorecardsSingle.R +++ b/modules/Scorecards/R/tmp/ScorecardsSingle.R @@ -104,29 +104,21 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, fileout.label <- "" } - ## Make sure input_data is in correct order for using in functions: + ## Make sure data is in correct order for using in functions: data_order <- c('system', 'reference', 'metric', 'time', 'sdate', 'region') data <- Reorder(data, data_order) sign <- Reorder(sign, data_order) - - ####### PROBLEM HERE ?? ###### - # No attributes in score aggregated dataset - - # ## Identify metrics loaded - # metrics_loaded <- attributes(data)$metrics - # - # ## Select only the metrics to visualize from data - # input_data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) - # attributes(input_data)$metrics <- metrics - # - # input_sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) - # attributes(input_sign)$metrics <- metrics - - input_data <- data #temporary - input_sign <- sign - - ############################ - + + ## Identify metrics loaded + metrics_loaded <- attributes(data)$metrics + + ## Select only the metrics to visualize from data + data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(data)$metrics <- metrics + + sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(sign)$metrics <- metrics + ## Transform data for scorecards by forecast month (types 3 & 4) transformed_data <- SCTransform(data = data, sdate_dim = 'sdate', @@ -148,16 +140,17 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, ## Get scorecards table display names from configuration files var.name <- var_dict[[var]]$long_name - # for (i in 1:length(recipe$Analysis$Variables)) { - # if (recipe$Analysis$Variables[[i]]$name == var) { ## depends on way recipe is written - # var.units <- recipe$Analysis$Variables[[i]]$units - # } - # } - for (i in 1:length(recipe$Analysis$Variables)) { - if (recipe$Analysis$Variables$name == var) { ## TEMPORARY + if ('name' %in% names(recipe$Analysis$Variables)){ + if (recipe$Analysis$Variables$name == var) { var.units <- recipe$Analysis$Variables$units } - } + } else { + for (i in 1:length(recipe$Analysis$Variables)) { + if (recipe$Analysis$Variables[[i]]$name == var) { + var.units <- recipe$Analysis$Variables[[i]]$units + } + } + } if (is.null(var.units)) { var.units <- var_dict[[var]]$units } @@ -188,20 +181,20 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, legend.col.sup <- legend.col.sup[metrics] ## Loop over system and reference for each scorecard plot - for (sys in 1:dim(input_data)['system']) { - for (ref in 1:dim(input_data)['reference']) { + for (sys in 1:dim(data)['system']) { + for (ref in 1:dim(data)['reference']) { ## TO DO: Apply check to each scorecard function ## check dimension 'metric' exists: - if (!("metric" %in% names(dim(input_data)))) { - dim(input_data) <- c(metric = 1, dim(input_data)) + if (!("metric" %in% names(dim(data)))) { + dim(data) <- c(metric = 1, dim(data)) } ## Find position of mean bias metric to calculate breaks breaks_bias <- NULL if ('mean_bias' %in% metrics){ - stopifnot(identical(names(dim(Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected'))), c('metric','time','sdate','region'))) - temp_data <- Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected') + stopifnot(identical(names(dim(Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected'))), c('metric','time','sdate','region'))) + temp_data <- Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected') pos_bias <- which(metrics == 'mean_bias') if(var == 'psl'){ temp_data[pos_bias,,,] <- temp_data[pos_bias,,,]/100 @@ -232,8 +225,8 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, start.year = start.year, end.year = end.year, scorecard.type = 1, fileout.label = fileout.label, output.path = output.path) - data_sc_1 <- Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected') - sign_sc_1 <- Subset(input_sign, c('system', 'reference'), list(sys, ref), drop = 'selected') + data_sc_1 <- Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected') + sign_sc_1 <- Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected') SCPlotScorecard(data = data_sc_1, sign = sign_sc_1, @@ -272,15 +265,15 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, ## (reorder only) ## Scorecard type 2 is same as type 1 for only one region, therefore is ## only plotted if more that one region is requested - if(dim(input_data)['region'] > 1) { + if(dim(data)['region'] > 1) { fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 2, fileout.label = fileout.label, output.path = output.path) new_order <- c('metric', 'region', 'sdate', 'time') - data_sc_2 <- Reorder(Subset(input_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) - sign_sc_2 <- Reorder(Subset(input_sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + data_sc_2 <- Reorder(Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + sign_sc_2 <- Reorder(Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) SCPlotScorecard(data = data_sc_2, sign = sign_sc_2, @@ -362,7 +355,7 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, ## (transformation and reorder) ## Scorecard type 4 is same as type 3 for only one region, therefore is ## only plotted if more that one region is requested - if(dim(input_data)['region'] > 1) { + if(dim(data)['region'] > 1) { fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 4, diff --git a/modules/Scorecards/R/tmp/WeightedMetrics.R b/modules/Scorecards/R/tmp/WeightedMetrics.R index aea23c56..7df20c71 100644 --- a/modules/Scorecards/R/tmp/WeightedMetrics.R +++ b/modules/Scorecards/R/tmp/WeightedMetrics.R @@ -88,35 +88,6 @@ WeightedMetrics <- function(loaded_metrics, regions, metric.aggregation, } ## close loop on reference } ## close loop on system - ## skill aggregation: - if (metric.aggregation == 'score') { - if (all(c("rps", "rps_clim") %in% metrics)) { - ## Calculate RPSS from aggregated RPS and RPS_clim - all_metric_means <- multiApply::Apply(data = all_metric_means, - target_dims = 'metric', - fun = function(x, met) { - res <- 1 - x[which(met == 'rps')] / x[which(met == 'rps_clim')] - c(x, res)}, met = metrics, - output_dims = 'metric', - ncores = ncores)$output1 - ## Define name of newly calculated RPSS metric - metrics <- c(metrics, "rpss_score_aggr") - } - if (all(c("crps", "crps_clim") %in% metrics)) { - ## Calculate CRPSS from aggragated CRPS and CRPS_clim - all_metric_means <- multiApply::Apply(data = all_metric_means, - target_dims = 'metric', - fun = function(x, met) { - res <- 1 - x[which(met == 'crps')] / x[which(met == 'crps_clim')] - c(x, res)}, - met = metrics, - output_dims = 'metric', - ncores = ncores)$output1 - ## Define name of newly calculated CRPSS metric - metrics <- c(metrics, "crpss_score_aggr") - } - ## Add warning in case metric.aggregation == 'score' but 1 of the metrics from each pair is missing - } ## reorder dimensions in array all_metric_means <- s2dv::Reorder(all_metric_means, c('system','reference','metric','time','sdate','region')) diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index af797025..889bdc1e 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -18,7 +18,7 @@ source('modules/Scorecards/R/tmp/SCPlotScorecard.R') Scorecards <- function(recipe) { ## Parameters for loading data files - input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/to_system/" #temp + input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/testing/" #temp skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") @@ -30,17 +30,16 @@ Scorecards <- function(recipe) { end.year <- as.numeric(recipe$Analysis$Time$hcst_end) forecast.months <- recipe$Analysis$Time$ftime_min : recipe$Analysis$Time$ftime_max calib.method <- tolower(recipe$Analysis$Workflow$Calibration$method) - - # NOTE (Eva): This condition needs to be checked, in my case - # (recipe$Analysis$Workflow$Scorecards$start_months = NULL) - start.months <- 1:12 # I added this line - # Needs to be corrected: - # if (recipe$Analysis$Workflow$Scorecards$start_months == 'all') { - # start.months <- 1:12 - # } else { - # start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, - # split = ", | |,")[[1]]) - # } + + if (recipe$Analysis$Workflow$Scorecards$start_months == 'all' || is.null(recipe$Analysis$Workflow$Scorecards$start_months)) { + start.months <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) + } else { + start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, + split = ", | |,")[[1]]) + if(!any(as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))) %in% start.months){ + error(recipe$Run$logger,"Requested start dates for scorecards must be loaded") + } + } start.months <- sprintf("%02d", start.months) period <- paste0(start.year, "-", end.year) @@ -53,7 +52,7 @@ Scorecards <- function(recipe) { metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) - if(is.null(recipe$Analysis$Workflow$Scorecards$signif_alpha)){ + if(is.null(recipe$Analysis$Workflow$Scorecards$signif_alpha)){ alpha <- 0.05 } else { alpha <- recipe$Analysis$Workflow$Scorecards$signif_alpha @@ -73,7 +72,7 @@ Scorecards <- function(recipe) { ncores <- 1 # recipe$Analysis$ncores - ## Paramters for scorecard layout + ## Parameters for scorecard layout table.label <- recipe$Analysis$Workflow$Scorecards$table_label fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width @@ -153,14 +152,11 @@ Scorecards <- function(recipe) { regions = regions, metric.aggregation = metric.aggregation, ncores = ncores) - }## close if on region - } + } ## close if on region + } ## close if on skill ###### SCORE AGGREGATION ###### if(metric.aggregation == 'score'){ - - ## Comments (Nadia): - ## syear parameter as FALSE instead of NULL for metrics without syear dimension? lon_dim <- 'longitude' lat_dim <- 'latitude' @@ -168,7 +164,7 @@ Scorecards <- function(recipe) { memb_dim <- 'ensemble' ## Define arrays to filled with data - scorecard_metrics <- array(data = NA, + aggregated_metrics <- array(data = NA, dim = c(system = length(system), reference = length(reference), time = length(forecast.months), @@ -176,7 +172,7 @@ Scorecards <- function(recipe) { region = length(regions), metric = length(metrics.visualize))) - scorecard_sign <- array(data = NA, + metrics_significance <- array(data = NA, dim = c(system = length(system), reference = length(reference), time = length(forecast.months), @@ -257,8 +253,8 @@ Scorecards <- function(recipe) { ncores = NULL)$sign ## Save metric result in arrays - scorecard_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) - scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) + metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) } ## close if on rpss @@ -329,8 +325,8 @@ Scorecards <- function(recipe) { ncores = NULL)$sign ## Save metric result in arrays - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) - scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) + metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) } ## close if on crpss @@ -423,7 +419,7 @@ Scorecards <- function(recipe) { ## Calculate correlation enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) - + ## Drop unwanted dimensions enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') @@ -453,8 +449,8 @@ Scorecards <- function(recipe) { } ## Save metric result in arrays - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) - scorecard_sign[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) + metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) } ## close if on enscorr @@ -485,7 +481,7 @@ Scorecards <- function(recipe) { mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') ## Save metric result in array - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) } ## close on mean_bias @@ -516,7 +512,7 @@ Scorecards <- function(recipe) { enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') ## Save metric result in array - scorecard_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) } ## close on enssprerr @@ -524,10 +520,11 @@ Scorecards <- function(recipe) { } ## close if on reference } ## close if on system - aggregated_metrics <- scorecard_metrics + #Include metric attributes + attributes(aggregated_metrics)$metrics <- metrics.visualize ## set NAs to False - scorecard_sign[is.na(scorecard_sign)] <- FALSE + metrics_significance[is.na(metrics_significance)] <- FALSE } ## close if on score @@ -538,7 +535,7 @@ Scorecards <- function(recipe) { ## (one system only) ## Metrics input must be in the same order as function SC_spatial_aggregation scorecard_single <- ScorecardsSingle(data = aggregated_metrics, - sign = scorecard_sign, + sign = metrics_significance, system = system, reference = reference, var = var, @@ -568,6 +565,7 @@ Scorecards <- function(recipe) { ## Metrics input must be in the same order as function SC_spatial_aggregation if(length(system) > 1 || length(reference) > 1){ scorecard_multi <- ScorecardsMulti(data = aggregated_metrics, + sign = metrics_significance, system = system, reference = reference, var = var, @@ -575,10 +573,21 @@ Scorecards <- function(recipe) { end.year = end.year, start.months = start.months, forecast.months = forecast.months, - region.names = attributes(regions)$names, + region.names = names(regions), metrics = metrics.visualize, table.label = table.label, fileout.label = fileout.label, + plot.legend = plot.legend, + legend.breaks = legend.breaks, + legend.white.space = legend.white.space, + legend.width = legend.width, + legend.height = legend.height, + label.scale = label.scale, + col1.width = col1.width, + col2.width = col2.width, + columns.width = columns.width, + font.size = font.size, + round.decimal = round.decimal, output.path = output.path) } ## close if -- GitLab From 9a337e10ed40de38534ddb35ca812a393ea5e40f Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 12 Jan 2024 15:51:33 +0100 Subject: [PATCH 53/78] Add use case for sample data --- .../ex0_1_sample_dataset/ex0_1-handson.md | 235 ++++++++++++++++++ .../ex0_1_sample_dataset/ex0_1-recipe.yml | 57 +++++ 2 files changed, 292 insertions(+) create mode 100644 use_cases/ex0_1_sample_dataset/ex0_1-handson.md create mode 100644 use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md new file mode 100644 index 00000000..ef9abbe1 --- /dev/null +++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md @@ -0,0 +1,235 @@ +# Use case 0.1: Loading a sample dataset + +## Goal +Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises. + +## 0. Cloning the SUNSET repository + +The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch//git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command. + +```shell +# Clone the GitLab repository to create a local copy of the code +git clone https://earth.bsc.es/gitlab/es/sunset.git +``` + +## 1. Modifying the recipe + +SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November. + +There is a template file for this hands-on tutorial, which you can open with a text editor: + +```shell +# cd to the main SUNSET directory +# Open the recipe with a text editor such as vim or emacs +vim use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml +``` + +Once you have opened your recipe, it is time to edit the contents. Fill in the blank slots according to your preference, based on the options given in the description. + +```yaml +Description: + Author: <___> + Description: Exercise 1.1: Calibration and skill assessment of MeteoFrance System 7 surface temperature +Analysis: + Horizon: Seasonal + Variables: + name: tas + freq: monthly_mean + units: <___> # Choose your units: C or K + Datasets: + System: + name: Meteo-France-System7 + Multimodel: no + Reference: + name: ERA5 + Time: + sdate: '1101' + fcst_year: '2020' + hcst_start: '1993' + hcst_end: '2016' + ftime_min: <___> # Choose the first time step! A number from 1 to 6 + ftime_max: <___> # Choose the last time step! A number from 1 to 6 + Region: + name: "EU" + latmin: 20 + latmax: 80 + lonmin: -20 + lonmax: 40 + Regrid: + method: bilinear + type: 'r360x181' # options: to_system, to_reference, self-defined grid + Workflow: + Anomalies: + compute: yes + cross_validation: yes + save: 'none' + Calibration: + method: <___> + save: 'none' + Skill: + metric: RPSS, BSS10, BSS90 + cross_validation: yes + save: 'none' + Probabilities: + percentiles: [[1/3, 2/3], [1/10, 9/10]] + save: 'none' + Visualization: + plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + multi_panel: no + projection: cylindrical_equidistant + ncores: 10 + remove_NAs: yes + Output_format: S2S4E +Run: + Loglevel: INFO + Terminal: yes + output_dir: <______> # Path to the directory where you want your outputs to be saved + code_dir: <______> # Path to the directory where your code is +``` + +## 2. Load the required SUNSET modules and read the recipe + +First of all, source the MODULES file to load the environment modules needed to run SUNSET. + +```shell +source MODULES +``` + +Open an R session, by simply typing `R` on the terminal. + +To run SUNSET, we must run the R session from the directory where the code is. To check your working directory, you can run the shell command `pwd`. From the R session, you can use the commands `getwd()` and `setwd()` to see and change your working directory. + +```r +# Load required modules +source("modules/Loading/Loading.R") +source("modules/Units/Units.R") +source("modules/Calibration/Calibration.R") +source("modules/Anomalies/Anomalies.R") +source("modules/Skill/Skill.R") +source("modules/Saving/Saving.R") +source("modules/Visualization/Visualization.R") + +# Read recipe +recipe_file <- "use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml" +recipe <- prepare_outputs(recipe_file) +``` + +The function `prepare_outputs()` creates a unique folder for the logs, data files and plots that result from the execution of your recipe, inside the directory you specified. It also runs a check over the recipe to detect any potential errors, misspellings or missing arguments. At the end of the check, a message is displayed indicating whether or not the recipe passed the check, along with the list of errors and warnings. + +**Questions** + +Read the logs! + +(1) Did your recipe pass the check? Did you get any warnings? + +(2) Where will your outputs be saved? Copy and paste this directory somewhere, so that you can check it later! + +*Tip*: The recipe is now stored as a `list` containing all the information of the original YAML file, plus some extra things! If you want to see any particular element of the recipe from the R session, you can simply access that element in the list. For example: + +```r +# Checking the variable name +recipe$Analysis$Variables$name +# Checking the output directory +recipe$Run$output_dir +``` + +## 3. Load the data and change the units + +The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. + +For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion. + +```r +# Load datasets +data <- Loading(recipe) +# Change units +data <- Units(recipe, data) +``` + +**Questions** + +(1) What is the structure of `data`? What is the class of the objects in `data`? *Tip*: you can use functions like `class()`, `names()` or `str()` to gain information about the structure of the object and its contents. + +```r +class(data) +names(data) +str(data, max.level = 2) +# You can access any of the three objects with the `$` operator: +class(data$hcst) +``` + +(2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects: + +```r +# Check the new units and data of hcst, fcst and/or obs. Are they all the same? +data$hcst$attrs$Variable$metadata$tas$units +data_summary(data$hcst, recipe) +data_summary(data$fcst, recipe) +data_summary(data$obs, recipe) +``` +(3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary! + +## 4. Calibrate the data and compute the anomalies + +SUNSET has a few modules to perform post-processing on the experimental and the reference datasets. The **Calibration** module performs the bias correction method indicated in the recipe, using the `CSTools::CST_Calibration()` function. + +The **Anomalies** module removes the climatologies using functions like `CSTools::CST_Anomaly()` and `s2dv::Clim()`, and also returns the full fields in case they are needed for any future computations. + +```r +# Calibrate the data +data <- Calibration(recipe, data) +# Compute anomalies +data <- Anomalies(recipe, data) +``` +**Questions** + +(1) Verify that you now have anomaly values instead of the original full field. *Tip*: Use `data_summary()` like in the previous example and pay attention to the new values. + +## 5. Evaluate the model skill and compute the probability thresholds + +The **Skill** module returns a list of all the evaluation metrics requested in the recipe, in the shape of multi-dimensional arrays. In this case, we will compute three metrics: + +- **RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology. +- **BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology. + +The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold. +``` +# Compute skill metrics +skill_metrics <- Skill(recipe, data) +# Compute percentiles and probability bins +probabilities <- Probabilities(recipe, data) +``` +**Questions** + +(1) What is the structure of `skill_metrics`? Which metrics were computed? What dimensions do they have? *Tip*: use `str()` and `names()`. + +(2) What is the structure of `probabilities`? Can you identify the probability categories and the percentiles? *Tip*: use `str()`. + +## 6. Plotting the results + +Now, let's visualize the information that was computed! + +The **Visualization** module will generate the three types of maps we requested in the recipe: +- Skill maps to visualize the skill distribution of the model, for each metric. +- The ensemble mean of the calibrated forecast anomalies. +- A map showing the most likely tercile category for each point in the grid. + +With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot. + +```r +# Plot data +Visualization(recipe, data, + skill_metrics = skill_metrics, + probabilities = probabilities, + significance = TRUE) +``` + +Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time. + +**Questions** + +(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest? + +(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score. + +(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain? diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml new file mode 100644 index 00000000..06b1e572 --- /dev/null +++ b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml @@ -0,0 +1,57 @@ +Description: + Author: V. Agudetse + Description: Run SUNSET workflow with CSTools sample data +Analysis: + Horizon: seasonal + Variables: + name: tas + freq: monthly_mean + Datasets: + System: + name: ECMWF-SEAS5.1 + Multimodel: no + Reference: + name: ERA5 + Time: + sdate: '1101' + fcst_year: + hcst_start: '2000' + hcst_end: '2006' + ftime_min: 1 + ftime_max: 3 + Region: + latmin: 27 + latmax: 48 + lonmin: 0 + lonmax: 359 + Regrid: + method: bilinear + type: to_system + Workflow: + Anomalies: + compute: no + cross_validation: + save: + Calibration: + method: bias + save: 'all' + Skill: + metric: RPSS + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3]] + save: 'all' + Indicators: + index: FALSE + Visualization: + plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + ncores: # Optional, int: number of cores, defaults to 1 + remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE + Output_format: S2S4E +Run: + Loglevel: INFO + Terminal: yes + output_dir: /tmp/out-logs/ + code_dir: './' + filesystem: sample + -- GitLab From 62e5c83e6890dfc0b4bba5d18a070169ec97cac0 Mon Sep 17 00:00:00 2001 From: VICTORIA AGUDETSE ROURES Date: Mon, 8 Jan 2024 16:59:08 +0100 Subject: [PATCH 54/78] Add option recipe == 'sample' option with CSTools sample data --- conf/archive.yml | 23 ++++++++ modules/Loading/Loading.R | 3 + modules/Loading/R/load_sample.R | 49 ++++++++++++++++ .../recipe_seasonal_provenance.yml | 57 +++++++++++++++++++ tools/CST_ChangeDimName.R | 39 +++++++++++++ 5 files changed, 171 insertions(+) create mode 100644 modules/Loading/R/load_sample.R create mode 100644 recipes/atomic_recipes/recipe_seasonal_provenance.yml create mode 100644 tools/CST_ChangeDimName.R diff --git a/conf/archive.yml b/conf/archive.yml index dbacf0e7..61f62be2 100644 --- a/conf/archive.yml +++ b/conf/archive.yml @@ -254,3 +254,26 @@ mars: monthly_mean: {"tas":""} calendar: "standard" reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt" + +sample: + src: + System: + ECMWF-SEAS5.1: + name: "ECMWF SEAS5" + institution: "European Centre for Medium-Range Weather Forecasts" + src: + monthly_mean: {"tas":"", "prlr":""} + nmember: + fcst: 15 + hcst: 15 + calendar: "proleptic_gregorian" + time_stamp_lag: "0" + reference_grid: "conf/grid_description/griddes_GRIB_system51_m1.txt" + Reference: + ERA5: + name: "ERA5" + institution: "European Centre for Medium-Range Weather Forecasts" + src: "GRIB_era5_tas/" + monthly_mean: {"tas":"", "prlr":""} + calendar: "standard" + reference_grid: "conf/grid_description/griddes_GRIB_system5_m1.txt" diff --git a/modules/Loading/Loading.R b/modules/Loading/Loading.R index 43dea065..63fee97b 100644 --- a/modules/Loading/Loading.R +++ b/modules/Loading/Loading.R @@ -8,6 +8,9 @@ Loading <- function(recipe) { if (tolower(recipe$Run$filesystem) == "mars") { source("modules/Loading/R/load_GRIB.R") data <- load_GRIB(recipe) + } else if (tolower(recipe$Run$filesystem) == "sample") { + source("modules/Loading/R/load_sample.R") + data <- load_sample(recipe) } else { # Case: esarchive time_horizon <- tolower(recipe$Analysis$Horizon) diff --git a/modules/Loading/R/load_sample.R b/modules/Loading/R/load_sample.R new file mode 100644 index 00000000..e0d906d3 --- /dev/null +++ b/modules/Loading/R/load_sample.R @@ -0,0 +1,49 @@ +## TODO: remove paths to personal scratchs +source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") +# Load required libraries/funs +source("tools/CST_ChangeDimName.R") +source("modules/Loading/R/compare_exp_obs_grids.R") + +load_sample <- function(recipe) { + # Hindcast: + # Change hcst dimension names + hcst <- CST_ChangeDimName(lonlat_temp_st$exp, + original_dimnames = c("dataset", "sdate", "ftime", + "lat", "lon", "member"), + final_dimnames = c("dat", "syear", "time", + "latitude", "longitude", + "ensemble")) + # Add sday and sweek dimensions + hcst <- CST_InsertDim(hcst, posdim = 3, lendim = 1, name = "sday", values = 1) + hcst <- CST_InsertDim(hcst, posdim = 4, lendim = 1, name = "sweek", values = 1) + dim(hcst$attrs$Dates) <- c(sday = 1, sweek = 1, + syear = dim(hcst$attrs$Dates)[['syear']], + time = dim(hcst$attrs$Dates)[['time']]) + # Observations: + # Change obs dimension names + obs <- CST_ChangeDimName(lonlat_temp_st$obs, + original_dimnames = c("dataset", "sdate", "ftime", + "lat", "lon"), + final_dimnames = c("dat", "syear", "time", + "latitude", "longitude")) + # Add sday and sweek dimensions + obs <- CST_InsertDim(obs, posdim = 3, lendim = 1, name = "sday", values = 1) + obs <- CST_InsertDim(obs, posdim = 4, lendim = 1, name = "sweek", values = 1) + dim(obs$attrs$Dates) <- c(sday = 1, sweek = 1, + syear = dim(obs$attrs$Dates)[['syear']], + time = dim(obs$attrs$Dates)[['time']]) + # Add ensemble dimension to obs + obs <- CST_InsertDim(obs, posdim = 7, lendim = 1, name = "ensemble", values = 1) + # Adjust name of 'load_parameters$date' attribute + obs$attrs$load_parameters$dat1$file_date <- obs$attrs$load_parameters$dat1$date + + # Sample fcst NULL + fcst <- NULL + # Check for consistency between hcst and obs grid + compare_exp_obs_grids(hcst, obs) + + info(recipe$Run$logger, + "##### DATA LOADING COMPLETED SUCCESSFULLY #####") + .log_memory_usage(recipe$Run$logger, when = "After loading (sample data)") + return(list(hcst = hcst, fcst = fcst, obs = obs)) +} diff --git a/recipes/atomic_recipes/recipe_seasonal_provenance.yml b/recipes/atomic_recipes/recipe_seasonal_provenance.yml new file mode 100644 index 00000000..196b49c9 --- /dev/null +++ b/recipes/atomic_recipes/recipe_seasonal_provenance.yml @@ -0,0 +1,57 @@ +Description: + Author: V. Agudetse + '': split version +Analysis: + Horizon: seasonal + Variables: + name: tas + freq: monthly_mean + Datasets: + System: + name: ECMWF-SEAS5.1 + Multimodel: no + Reference: + name: ERA5 + Time: + sdate: '1101' + fcst_year: + hcst_start: '2000' + hcst_end: '2006' + ftime_min: 1 + ftime_max: 3 + Region: + latmin: 27 + latmax: 48 + lonmin: 0 + lonmax: 359 + Regrid: + method: bilinear + type: to_system + Workflow: + Anomalies: + compute: no + cross_validation: + save: + Calibration: + method: bias + save: 'all' + Skill: + metric: RPSS + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3]] + save: 'all' + Indicators: + index: FALSE + Visualization: + plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + ncores: # Optional, int: number of cores, defaults to 1 + remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE + Output_format: S2S4E +Run: + Loglevel: INFO + Terminal: yes + output_dir: /tmp/out-logs/ + code_dir: /home/kinow/Development/r/workspace/sunset/ + filesystem: sample + diff --git a/tools/CST_ChangeDimName.R b/tools/CST_ChangeDimName.R new file mode 100644 index 00000000..1ccbbb3d --- /dev/null +++ b/tools/CST_ChangeDimName.R @@ -0,0 +1,39 @@ +## TODO: Documentation + +CST_ChangeDimName <- function(data, original_dimnames, final_dimnames) { + if (!inherits(data, "s2dv_cube")) { + stop("Parameter 'data' must be an object of class 's2dv_cube'") + } + if (!(length(original_dimnames) == length(final_dimnames))) { + stop("The number of dimension names in 'final_dimnames' must be the same + as in 'original_dimnames'") + } + ## TODO: Add check to verify that all original_dimnames are present in the array + for (index in 1:length(original_dimnames)) { + original_name <- original_dimnames[index] + final_name <- final_dimnames[index] + # Step 1: Change dims and data + names(data$dims)[which(names(data$dims) == original_name)] <- final_name + dim(data$data) <- data$dims + # Step 2: Change coords + names(data$coords)[which(names(data$coords) == original_name)] <- final_name + # Step 3: Change attrs + # 3.1 - Dates + if (original_name %in% names(dim(data$attrs$Dates))) { + names(dim(data$attrs$Dates))[which(names(dim(data$attrs$Dates)) + == original_name)] <- final_name + } + # 3.2 - Variable metadata + if (original_name %in% names(data$attrs$Variable$metadata)) { + names(data$attrs$Variable$metadata)[which(names(data$attrs$Variable$metadata) + == original_name)] <- final_name + } + # 3.3 - Source files + if (original_name %in% names(dim(data$attrs$source_files))) { + names(dim(data$attrs$source_files))[which(names(dim(data$attrs$source_files)) + == original_name)] <- final_name + } + } + return(data) +} + -- GitLab From ff0f3ce6739f193755d836d54c027bf009c22fb9 Mon Sep 17 00:00:00 2001 From: vagudets Date: Tue, 16 Jan 2024 15:54:49 +0100 Subject: [PATCH 55/78] Update hands-on 0.1 --- .../ex0_1_sample_dataset/ex0_1-handson.md | 92 ++++++++++--------- .../ex0_1_sample_dataset/ex0_1-recipe.yml | 6 +- 2 files changed, 51 insertions(+), 47 deletions(-) diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md index ef9abbe1..7dc9927f 100644 --- a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md +++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md @@ -1,9 +1,9 @@ # Use case 0.1: Loading a sample dataset ## Goal -Create a SUNSET recipe and use the functions in the suite to reproduce the verification workflow from the previous hands-on exercises. +Load a sample dataset into SUNSET without the need configure an archive of netCDF or GRIB files. -## 0. Cloning the SUNSET repository +## 0. SUNSET software stack The first step to use SUNSET is to create a copy of the code in your local environment. Open a terminal and `cd` to the directory where you would like to store your local copy of SUNSET. For example: `/esarchive/scratch//git/`. If a directory does not exist yet, you can create it with the `mkdir` shell command. @@ -12,24 +12,34 @@ The first step to use SUNSET is to create a copy of the code in your local envir git clone https://earth.bsc.es/gitlab/es/sunset.git ``` -## 1. Modifying the recipe +If you are using BSC infrastructure, all of the software dependencies are already installed within our common environment. However, if you are running SUNSET outside of the BSC facilities, you can install the dependencies by creating a conda environment. A script is provided in the SUNSET repository, and you can install the environment by running the following line from the main folder of the SUNSET repository: -SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we want to evaluate the temperature-at-surface (tas) monthly means, using MeteoFrance System 7 data as our experiment and ERA5 as our reference dataset, for the initialization month of November. +```shell +bash conda_installation/load_sunset.bash +``` + +To run the line above, you should replace `` with the path where you want the conda environment to be installed. For example, `/home//conda-sunset`. + +## 1. The recipe + +SUNSET uses YAML configuration files called 'recipes' to specify which data you want to load and the details of the different steps of the workflow. In this example, we are using a sample dataset which contains data of temperature-at-surface (tas) monthly means, from ECMWF SEAS5 as the experiment and ERA5 as the reference dataset, for the initialization month of November. There is a template file for this hands-on tutorial, which you can open with a text editor: ```shell # cd to the main SUNSET directory # Open the recipe with a text editor such as vim or emacs -vim use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml +vim use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml ``` Once you have opened your recipe, it is time to edit the contents. Fill in the blank slots according to your preference, based on the options given in the description. +NOTE: In this particular example, the sample data is limited, so the system and reference names, variable, forecast times and region cannot be changed. + ```yaml Description: Author: <___> - Description: Exercise 1.1: Calibration and skill assessment of MeteoFrance System 7 surface temperature + Description: Exercise 0.1: Skill assessment of a sample dataset Analysis: Horizon: Seasonal Variables: @@ -38,26 +48,25 @@ Analysis: units: <___> # Choose your units: C or K Datasets: System: - name: Meteo-France-System7 + name: ECMWF-SEAS5.1 Multimodel: no Reference: name: ERA5 Time: sdate: '1101' - fcst_year: '2020' - hcst_start: '1993' - hcst_end: '2016' - ftime_min: <___> # Choose the first time step! A number from 1 to 6 - ftime_max: <___> # Choose the last time step! A number from 1 to 6 + fcst_year: + hcst_start: '2000' + hcst_end: '2006' + ftime_min: 1 + ftime_max: 3 Region: - name: "EU" - latmin: 20 - latmax: 80 - lonmin: -20 - lonmax: 40 + latmin: 27 + latmax: 48 + lonmin: 0 + lonmax: 359 Regrid: method: bilinear - type: 'r360x181' # options: to_system, to_reference, self-defined grid + type: 'to_system' Workflow: Anomalies: compute: yes @@ -67,34 +76,41 @@ Analysis: method: <___> save: 'none' Skill: - metric: RPSS, BSS10, BSS90 + metric: RPSS, BSS10, BSS90 cross_validation: yes save: 'none' Probabilities: - percentiles: [[1/3, 2/3], [1/10, 9/10]] + percentiles: [[1/3, 2/3]] save: 'none' Visualization: - plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + plots: skill_metrics multi_panel: no projection: cylindrical_equidistant ncores: 10 remove_NAs: yes Output_format: S2S4E Run: - Loglevel: INFO - Terminal: yes - output_dir: <______> # Path to the directory where you want your outputs to be saved - code_dir: <______> # Path to the directory where your code is + filesystem: sample # This parameter specifies that we want to load the sample data, rather than data from a filesystem + Loglevel: INFO + Terminal: yes + output_dir: <______> # Path to the directory where you want your outputs to be saved + code_dir: <______> # Path to the directory where your code is ``` ## 2. Load the required SUNSET modules and read the recipe -First of all, source the MODULES file to load the environment modules needed to run SUNSET. +If you are running SUNSET within BSC infrastructure, source the MODULES file to load the environment modules needed to run SUNSET. ```shell source MODULES ``` +Otherwise, activate the conda environment: + +```shell +conda activate +``` + Open an R session, by simply typing `R` on the terminal. To run SUNSET, we must run the R session from the directory where the code is. To check your working directory, you can run the shell command `pwd`. From the R session, you can use the commands `getwd()` and `setwd()` to see and change your working directory. @@ -110,7 +126,7 @@ source("modules/Saving/Saving.R") source("modules/Visualization/Visualization.R") # Read recipe -recipe_file <- "use_cases/ex1_1_single_analysis_terminal/ex1_1-recipe.yml" +recipe_file <- "use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml" recipe <- prepare_outputs(recipe_file) ``` @@ -135,7 +151,7 @@ recipe$Run$output_dir ## 3. Load the data and change the units -The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. +The **Loading** module retrieves the information from the recipe to load the data that has been requested it in. It loads the experiment data for the hindcast period, the reference data for the corresponding period, and the experiment forecast if a forecast year has been requested. In the case of the sample dataset, we have a hindcast and the corresponding observations, but no forecast. For certain variables like temperature, precipitation or sea level pressure, the user can request for specific units to load the data in. The **Units** module will read the original units as stored in the netCDF files and perform any necessary unit converstions to match the request in the recipe. It also verifies that all of the loaded datasets share the same units, even if no specific unit has been requested. For this reason, users are strongly encouraged to run it even if they did not request any unit conversion. @@ -161,10 +177,9 @@ class(data$hcst) (2) Pay attention to the log messages: Did your units get converted? Are the new units what you expect? You can check the metadata of any of the objects in data. SUNSET also provides the `data_summary()` function, which lets you have a quick look at your objects: ```r -# Check the new units and data of hcst, fcst and/or obs. Are they all the same? +# Check the new units and data of the hindcast (hcst) and/or observations (obs). Are they the same? data$hcst$attrs$Variable$metadata$tas$units data_summary(data$hcst, recipe) -data_summary(data$fcst, recipe) data_summary(data$obs, recipe) ``` (3) What are the dimensions of the datasets? Are they consistent with what is requested in the recipe? *Tip*: Check the data summary! @@ -192,7 +207,7 @@ The **Skill** module returns a list of all the evaluation metrics requested in t - **RPSS (Ranked Probability Skill Score)**: This skill score measures how well a forecast predicts the probability of the tercile categories (below normal, normal and above-normal), compared to the climatology. - **BSS10 and BSS90 (Brier Skill Score):** This skill score measures how well a forecast predicts the probability of the 10th percentile and 90th percentile extreme events, compared to the climatology. -The `Probabilities()` function returns the probability values for each requested category for the hindcast and forecast data, as well as the hindcast percentile values corresponding to each threshold. +The `Probabilities()` function returns the probability values for each requested category for the hindcast (and forecast) data, as well as the hindcast percentile values corresponding to each threshold. ``` # Compute skill metrics skill_metrics <- Skill(recipe, data) @@ -209,10 +224,7 @@ probabilities <- Probabilities(recipe, data) Now, let's visualize the information that was computed! -The **Visualization** module will generate the three types of maps we requested in the recipe: -- Skill maps to visualize the skill distribution of the model, for each metric. -- The ensemble mean of the calibrated forecast anomalies. -- A map showing the most likely tercile category for each point in the grid. +The **Visualization** module will generate the maps we requested in the recipe: Skill maps to visualize the skill distribution of the model, for each metric. With the significance option in the `Visualization()` function, you can choose whether or not to shade the grid points that are statistically significant in each skill metric plot. @@ -224,12 +236,4 @@ Visualization(recipe, data, significance = TRUE) ``` -Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are png files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time. - -**Questions** - -(1) Let's take a look at the forecast ensemble mean. What is the sign of the anomalies over Spain? In what regions are the anomalous temperatures strongest? - -(2) Let's take a look at the skill metrics RPSS, BSS10 and BSS90. In what regions and for which metrics is the forecast most skillful? *Tip*: Positive values indicate that the model is a better predictor than the climatology, with 1 being the perfect score. - -(3) Let's take a look at the Most Likely Terciles plots. This plot indicates the probability of the temperature being below normal, near-normal or above normal. What is the most likely category for Spain? +Now, you can `cd` to the the output directory and inspect the contents of the `plots/` subdirectory. The plots are PNG files that can be visualized with the `display` command. They have a descriptive name including the content of the plot, the date and the forecast time. diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml index 06b1e572..bdd7a8d2 100644 --- a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml +++ b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml @@ -1,6 +1,6 @@ Description: Author: V. Agudetse - Description: Run SUNSET workflow with CSTools sample data + '': split version Analysis: Horizon: seasonal Variables: @@ -44,7 +44,7 @@ Analysis: Indicators: index: FALSE Visualization: - plots: skill_metrics, forecast_ensemble_mean, most_likely_terciles + plots: skill_metrics, most_likely_terciles ncores: # Optional, int: number of cores, defaults to 1 remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE Output_format: S2S4E @@ -52,6 +52,6 @@ Run: Loglevel: INFO Terminal: yes output_dir: /tmp/out-logs/ - code_dir: './' + code_dir: filesystem: sample -- GitLab From 99b589f262b5657d2d29777c8e645ceacf8a1093 Mon Sep 17 00:00:00 2001 From: vagudets Date: Tue, 16 Jan 2024 15:57:33 +0100 Subject: [PATCH 56/78] Fix typo in README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5758fbf3..2ae44e8e 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ -SUNSET: SUbseasoNal to decadal climate forecast post-processing and asSEmenT suite +SUNSET: SUbseasoNal to decadal climate forecast post-processing and asSEssmenT suite ====================== This is the Git project for the SUNSET, a collaborative in-house tool developed at BSC-ES for research projects and operational workflows involving subseasonal to seasonal to decadal forecast verification. -- GitLab From a62fa1a0deab64135c5bff75634cd8637d20e1f9 Mon Sep 17 00:00:00 2001 From: vagudets Date: Tue, 16 Jan 2024 16:22:38 +0100 Subject: [PATCH 57/78] add provisional conda environment YAML and script --- conda_installation/environment-sunset.yml | 2 ++ conda_installation/load_sunset.bash | 9 +++++++++ 2 files changed, 11 insertions(+) create mode 100755 conda_installation/load_sunset.bash diff --git a/conda_installation/environment-sunset.yml b/conda_installation/environment-sunset.yml index 0427f523..27dbd8b9 100644 --- a/conda_installation/environment-sunset.yml +++ b/conda_installation/environment-sunset.yml @@ -165,6 +165,7 @@ dependencies: - r-docopt=0.7.1=r42hc72bb7e_3 - r-doparallel=1.0.17=r42hc72bb7e_2 - r-dotcall64=1.0_2=r42h61816a4_2 + - r-dplyr=1.1.2=r42ha503ecb_1 - r-e1071=1.7_13=r42ha503ecb_1 - r-easyncdf=0.1.2=r42hc72bb7e_1 - r-easyverification=0.4.5=r42ha503ecb_0 @@ -187,6 +188,7 @@ dependencies: - r-globals=0.16.2=r42hc72bb7e_1 - r-glue=1.6.2=r42h57805ef_2 - r-gridextra=2.3=r42hc72bb7e_1005 + - r-gridgraphics=0.5_1=r42hc72bb7e_2 - r-gtable=0.3.4=r42hc72bb7e_0 - r-highr=0.10=r42hc72bb7e_1 - r-htmltools=0.5.6=r42ha503ecb_0 diff --git a/conda_installation/load_sunset.bash b/conda_installation/load_sunset.bash new file mode 100755 index 00000000..add332b4 --- /dev/null +++ b/conda_installation/load_sunset.bash @@ -0,0 +1,9 @@ +#!/bin/bash + +prefix=$1 + +conda env create --file environment-sunset.yml --prefix $prefix + +conda activate $prefix + +R -e "options(timeout = 600) ; install.packages('CSTools', repos='https://ftp.cixug.es/CRAN/')" -- GitLab From b77e028cdbdcd2cf87becda821213ef94a7b3b52 Mon Sep 17 00:00:00 2001 From: vagudets Date: Tue, 16 Jan 2024 16:51:12 +0100 Subject: [PATCH 58/78] Correct recipe and hands-on --- use_cases/ex0_1_sample_dataset/ex0_1-handson.md | 5 ++--- use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml | 14 +++++++------- 2 files changed, 9 insertions(+), 10 deletions(-) diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md index 7dc9927f..c1dfe48a 100644 --- a/use_cases/ex0_1_sample_dataset/ex0_1-handson.md +++ b/use_cases/ex0_1_sample_dataset/ex0_1-handson.md @@ -76,11 +76,11 @@ Analysis: method: <___> save: 'none' Skill: - metric: RPSS, BSS10, BSS90 + metric: RPSS BSS10 BSS90 cross_validation: yes save: 'none' Probabilities: - percentiles: [[1/3, 2/3]] + percentiles: [[1/3, 2/3]] save: 'none' Visualization: plots: skill_metrics @@ -232,7 +232,6 @@ With the significance option in the `Visualization()` function, you can choose w # Plot data Visualization(recipe, data, skill_metrics = skill_metrics, - probabilities = probabilities, significance = TRUE) ``` diff --git a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml index bdd7a8d2..662e75cc 100644 --- a/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml +++ b/use_cases/ex0_1_sample_dataset/ex0_1-recipe.yml @@ -29,14 +29,14 @@ Analysis: type: to_system Workflow: Anomalies: - compute: no - cross_validation: - save: + compute: yes + cross_validation: yes + save: 'all' Calibration: method: bias - save: 'all' + save: 'none' Skill: - metric: RPSS + metric: RPSS BSS10 BSS90 save: 'all' Probabilities: percentiles: [[1/3, 2/3]] @@ -44,7 +44,7 @@ Analysis: Indicators: index: FALSE Visualization: - plots: skill_metrics, most_likely_terciles + plots: skill_metrics ncores: # Optional, int: number of cores, defaults to 1 remove_NAs: # Optional, bool: Whether NAs are removed, defaults to FALSE Output_format: S2S4E @@ -52,6 +52,6 @@ Run: Loglevel: INFO Terminal: yes output_dir: /tmp/out-logs/ - code_dir: + code_dir: ./ filesystem: sample -- GitLab From e08f838019356982ab2f9da8561ec5db84cc8548 Mon Sep 17 00:00:00 2001 From: vagudets Date: Tue, 16 Jan 2024 16:51:30 +0100 Subject: [PATCH 59/78] Improve output_conf warning logic --- modules/Visualization/Visualization.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R index a6b6bd75..ae94ed3d 100644 --- a/modules/Visualization/Visualization.R +++ b/modules/Visualization/Visualization.R @@ -46,7 +46,7 @@ Visualization <- function(recipe, "parameters of the requested plotting function, i.e. ", "PlotEquiMap, PlotRobinson or PlotLayout. There could be ", "plotting erros if the list is incomplete.")) - } else { + } else if (!is.null(output_conf)) { warning(paste("Parameter 'output_conf' should be a list.", "Using default configuration.")) output_conf <- NULL -- GitLab From 50c4a4c7c7a8b02dfa304ddf7995658f7612ee57 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 17 Jan 2024 16:22:32 +0100 Subject: [PATCH 60/78] including changes for new VizScorecards function --- modules/Loading/Dev_Loading.R | 501 -------------- modules/Scorecards/R/tmp/ClimPalette.R | 78 +++ modules/Scorecards/R/tmp/ColorBarContinuous.R | 594 +++++++++++++++++ modules/Scorecards/R/tmp/ScorecardsMulti.R | 248 +++---- modules/Scorecards/R/tmp/ScorecardsSingle.R | 248 +++---- modules/Scorecards/R/tmp/Utils.R | 92 --- modules/Scorecards/R/tmp/VizScorecard.R | 619 ++++++++++++++++++ modules/Scorecards/Scorecards.R | 60 +- 8 files changed, 1587 insertions(+), 853 deletions(-) delete mode 100644 modules/Loading/Dev_Loading.R create mode 100644 modules/Scorecards/R/tmp/ClimPalette.R create mode 100644 modules/Scorecards/R/tmp/ColorBarContinuous.R create mode 100644 modules/Scorecards/R/tmp/VizScorecard.R diff --git a/modules/Loading/Dev_Loading.R b/modules/Loading/Dev_Loading.R deleted file mode 100644 index fb456eb3..00000000 --- a/modules/Loading/Dev_Loading.R +++ /dev/null @@ -1,501 +0,0 @@ -## TODO: remove paths to personal scratchs -source("/esarchive/scratch/vagudets/repos/csoperational/R/get_regrid_params.R") -# Load required libraries/funs -source("modules/Loading/R/dates2load.R") -source("modules/Loading/R/get_timeidx.R") -source("modules/Loading/R/check_latlon.R") -## TODO: Move to prepare_outputs.R -source("tools/libs.R") -## TODO: remove these two lines when new as.s2dv_cube() is in CSTools -source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/as.s2dv_cube.R') -source('https://earth.bsc.es/gitlab/external/cstools/-/raw/develop-new_s2dv_cube/R/zzz.R') - -## TODO: Source new s2dv_cube version -## TODO: Eliminate dim_var dimension (merge_across_dims?) - -load_datasets <- function(recipe) { - - # ------------------------------------------- - # Set params ----------------------------------------- - - hcst.inityear <- recipe$Analysis$Time$hcst_start - hcst.endyear <- recipe$Analysis$Time$hcst_end - lats.min <- recipe$Analysis$Region$latmin - lats.max <- recipe$Analysis$Region$latmax - lons.min <- recipe$Analysis$Region$lonmin - lons.max <- recipe$Analysis$Region$lonmax - ref.name <- recipe$Analysis$Datasets$Reference$name - exp.name <- recipe$Analysis$Datasets$System$name - - variable <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]][1] - vars <- strsplit(recipe$Analysis$Variables$name, ", | |,")[[1]] - store.freq <- recipe$Analysis$Variables$freq - - # get sdates array - ## LOGGER: Change dates2load to extract logger from recipe? - sdates <- dates2load(recipe, recipe$Run$logger) - - idxs <- NULL - idxs$hcst <- get_timeidx(sdates$hcst, - recipe$Analysis$Time$ftime_min, - recipe$Analysis$Time$ftime_max, - time_freq=store.freq) - - if (!(is.null(sdates$fcst))) { - idxs$fcst <- get_timeidx(sdates$fcst, - recipe$Analysis$Time$ftime_min, - recipe$Analysis$Time$ftime_max, - time_freq=store.freq) - } - - ## TODO: Examine this verifications part, verify if it's necessary - # stream <- verifications$stream - # sdates <- verifications$fcst.sdate - - ## TODO: define fcst.name - ##fcst.name <- recipe$Analysis$Datasets$System[[sys]]$name - - # get esarchive datasets dict: - ## TODO: Adapt to 'filesystem' option in recipe - archive <- read_yaml("conf/archive.yml")$esarchive - exp_descrip <- archive$System[[exp.name]] - - freq.hcst <- unlist(exp_descrip[[store.freq]][variable]) - reference_descrip <- archive$Reference[[ref.name]] - freq.obs <- unlist(reference_descrip[[store.freq]][variable]) - obs.dir <- reference_descrip$src - fcst.dir <- exp_descrip$src - hcst.dir <- exp_descrip$src - fcst.nmember <- exp_descrip$nmember$fcst - hcst.nmember <- exp_descrip$nmember$hcst - - ## TODO: it is necessary? - ##if ("accum" %in% names(reference_descrip)) { - ## accum <- unlist(reference_descrip$accum[store.freq][[1]]) - ##} else { - ## accum <- FALSE - ##} - - var_dir_obs <- reference_descrip[[store.freq]][vars] - var_dir_exp <- exp_descrip[[store.freq]][vars] - - # ----------- - obs.path <- paste0(archive$src, - obs.dir, store.freq, "/$var$", "$var_dir$", - "/$var$_$file_date$.nc") - - hcst.path <- paste0(archive$src, - hcst.dir, store.freq, "/$var$", "$var_dir$", - "$var$_$file_date$.nc") - - fcst.path <- paste0(archive$src, - hcst.dir, store.freq, "/$var$", "$var_dir$", - "/$var$_$file_date$.nc") - - # Define regrid parameters: - #------------------------------------------------------------------- - regrid_params <- get_regrid_params(recipe, archive) - - # Longitude circular sort and latitude check - #------------------------------------------------------------------- - circularsort <- check_latlon(lats.min, lats.max, lons.min, lons.max) - - if (recipe$Analysis$Variables$freq == "monthly_mean"){ - split_multiselected_dims = TRUE - } else { - split_multiselected_dims = FALSE - } - - # Load hindcast - #------------------------------------------------------------------- - hcst <- Start(dat = hcst.path, - var = vars, - var_dir = var_dir_exp, - file_date = sdates$hcst, - time = idxs$hcst, - var_dir_depends = 'var', - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$fcst.transform, - transform_params = list(grid = regrid_params$fcst.gridtype, - method = regrid_params$fcst.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat', 'latitude'), - longitude = c('lon', 'longitude'), - ensemble = c('member', 'ensemble')), - ensemble = indices(1:hcst.nmember), - metadata_dims = 'var', # change to just 'var'? - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = split_multiselected_dims, - retrieve = TRUE) - - # Remove var_dir dimension - if ("var_dir" %in% names(dim(hcst))) { - hcst <- Subset(hcst, along = "var_dir", indices = 1, drop = "selected") - } - - if (recipe$Analysis$Variables$freq == "daily_mean") { - # Adjusts dims for daily case, could be removed if startR allows - # multidim split - names(dim(hcst))[which(names(dim(hcst)) == 'file_date')] <- "syear" - default_dims <- c(dat = 1, var = 1, sday = 1, - sweek = 1, syear = 1, time = 1, - latitude = 1, longitude = 1, ensemble = 1) - default_dims[names(dim(hcst))] <- dim(hcst) - dim(hcst) <- default_dims - # Change time attribute dimensions - default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1) - names(dim(attr(hcst, "Variables")$common$time))[which(names( - dim(attr(hcst, "Variables")$common$time)) == 'file_date')] <- "syear" - default_time_dims[names(dim(attr(hcst, "Variables")$common$time))] <- - dim(attr(hcst, "Variables")$common$time) - dim(attr(hcst, "Variables")$common$time) <- default_time_dims - } - - # Convert hcst to s2dv_cube object - ## TODO: Give correct dimensions to $Dates - ## (sday, sweek, syear instead of file_date) - hcst <- as.s2dv_cube(hcst) - # Adjust dates for models where the time stamp goes into the next month - if (recipe$Analysis$Variables$freq == "monthly_mean") { - hcst$attrs$Dates[] <- hcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag) - } - - ## Combine tas and tos data into one variable: tas-tos - if(recipe$Analysis$Variables$name == 'tas tos'){ - #if(recipe$Analysis$Datasets$Reference$name == 'HadCRUT5' || recipe$Analysis$Datasets$Reference$name == 'BEST') { - source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R') - hcst <- mask_tas_tos(input_data = hcst, region = c(lons.min, lons.max,lats.min, lats.max), - grid = 'r360x181', - lon = hcst$coords$longitude, - lat = hcst$coords$latitude, - lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) - - hcst$dims[['var']] <- dim(hcst$data)[['var']] - #} - } - - # Load forecast - #------------------------------------------------------------------- - if (!is.null(recipe$Analysis$Time$fcst_year)) { - # the call uses file_date instead of fcst_syear so that it can work - # with the daily case and the current version of startR not allowing - # multiple dims split - - fcst <- Start(dat = fcst.path, - var = vars, - var_dir = var_dir_exp, - var_dir_depends = 'var', - file_date = sdates$fcst, - time = idxs$fcst, - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$fcst.transform, - transform_params = list(grid = regrid_params$fcst.gridtype, - method = regrid_params$fcst.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat', 'latitude'), - longitude = c('lon', 'longitude'), - ensemble = c('member', 'ensemble')), - ensemble = indices(1:fcst.nmember), - metadata_dims = 'var', - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = split_multiselected_dims, - retrieve = TRUE) - - if ("var_dir" %in% names(dim(fcst))) { - fcst <- Subset(fcst, along = "var_dir", indices = 1, drop = "selected") - } - - if (recipe$Analysis$Variables$freq == "daily_mean") { - # Adjusts dims for daily case, could be removed if startR allows - # multidim split - names(dim(fcst))[which(names(dim(fcst)) == 'file_date')] <- "syear" - default_dims <- c(dat = 1, var = 1, sday = 1, - sweek = 1, syear = 1, time = 1, - latitude = 1, longitude = 1, ensemble = 1) - default_dims[names(dim(fcst))] <- dim(fcst) - dim(fcst) <- default_dims - # Change time attribute dimensions - default_time_dims <- c(sday = 1, sweek = 1, syear = 1, time = 1) - names(dim(attr(fcst, "Variables")$common$time))[which(names( - dim(attr(fcst, "Variables")$common$time)) == 'file_date')] <- "syear" - default_time_dims[names(dim(attr(fcst, "Variables")$common$time))] <- - dim(attr(fcst, "Variables")$common$time) - dim(attr(fcst, "Variables")$common$time) <- default_time_dims - } - - # Convert fcst to s2dv_cube - fcst <- as.s2dv_cube(fcst) - # Adjust dates for models where the time stamp goes into the next month - if (recipe$Analysis$Variables$freq == "monthly_mean") { - fcst$attrs$Dates[] <- - fcst$attrs$Dates - seconds(exp_descrip$time_stamp_lag) - } - - } else { - fcst <- NULL - } - - # Load reference - #------------------------------------------------------------------- - - # Obtain dates and date dimensions from the loaded hcst data to make sure - # the corresponding observations are loaded correctly. - dates <- hcst$attrs$Dates - dim(dates) <- hcst$dims[c("sday", "sweek", "syear", "time")] - - # Separate Start() call for monthly vs daily data - if (store.freq == "monthly_mean") { - - dates_file <- format(as.Date(dates, '%Y%m%d'), "%Y%m") - dim(dates_file) <- dim(dates) - - ## tas tos mask - if (recipe$Analysis$Variables$name == 'tas tos'){ - if (recipe$Analysis$Datasets$Reference$name == 'HadCRUT5'){ - vars <- 'tasanomaly' - var_dir_obs <- reference_descrip[[store.freq]][vars] - } - } - - if (recipe$Analysis$Variables$name == 'tas tos'){ - if (recipe$Analysis$Datasets$Reference$name == 'BEST'){ - vars <- 'tas' - var_dir_obs <- reference_descrip[[store.freq]][vars] - } - } - - obs <- Start(dat = obs.path, - var = vars, - var_dir = var_dir_obs, - var_dir_depends = 'var', - file_date = dates_file, - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$obs.transform, - transform_params = list(grid = regrid_params$obs.gridtype, - method = regrid_params$obs.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat','latitude'), - longitude = c('lon','longitude')), - metadata_dims = 'var', - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = TRUE, - retrieve = TRUE) - - } else if (store.freq == "daily_mean") { - - # Get year and month for file_date - dates_file <- sapply(dates, format, '%Y%m') - dim(dates_file) <- dim(dates) - # Set hour to 12:00 to ensure correct date retrieval for daily data - lubridate::hour(dates) <- 12 - lubridate::minute(dates) <- 00 - # Restore correct dimensions - dim(dates) <- dim(dates_file) - - obs <- Start(dat = obs.path, - var = vars, - var_dir = var_dir_obs, - var_dir_depends = 'var', - file_date = sort(unique(dates_file)), - time = dates, - time_var = 'time', - time_across = 'file_date', - merge_across_dims = TRUE, - merge_across_dims_narm = TRUE, - latitude = values(list(lats.min, lats.max)), - latitude_reorder = Sort(), - longitude = values(list(lons.min, lons.max)), - longitude_reorder = circularsort, - transform = regrid_params$obs.transform, - transform_params = list(grid = regrid_params$obs.gridtype, - method = regrid_params$obs.gridmethod), - transform_vars = c('latitude', 'longitude'), - synonims = list(latitude = c('lat','latitude'), - longitude = c('lon','longitude')), - metadata_dims = 'var', - return_vars = list(latitude = 'dat', - longitude = 'dat', - time = 'file_date'), - split_multiselected_dims = TRUE, - retrieve = TRUE) - } - - # Remove var_dir dimension - if ("var_dir" %in% names(dim(obs))) { - obs <- Subset(obs, along = "var_dir", indices = 1, drop = "selected") - } - # Adds ensemble dim to obs (for consistency with hcst/fcst) - default_dims <- c(dat = 1, var = 1, sday = 1, - sweek = 1, syear = 1, time = 1, - latitude = 1, longitude = 1, ensemble = 1) - default_dims[names(dim(obs))] <- dim(obs) - dim(obs) <- default_dims - - # Convert obs to s2dv_cube - obs <- as.s2dv_cube(obs) - - ## Combine tas and tos data into one variable: tas-tos - if(recipe$Analysis$Variables$name == 'tas tos'){ - if(recipe$Analysis$Datasets$Reference$name != 'HadCRUT5' & recipe$Analysis$Datasets$Reference$name != 'BEST'){ - source('/esarchive/scratch/nmilders/gitlab/git_clones/s2s-suite/modules/Loading/R/mask_tas_tos.R') - obs <- mask_tas_tos(input_data = obs, region = c(lons.min, lons.max,lats.min, lats.max), - grid = 'r360x181', - lon = obs$coords$longitude, - lat = obs$coords$latitude, - lon_dim = 'longitude', lat_dim = 'latitude', ncores = NULL) - - obs$dims[['var']] <- dim(obs$data)[['var']] - } - } - - # Check for consistency between hcst and obs grid - if (!(recipe$Analysis$Regrid$type == 'none')) { - if (!isTRUE(all.equal(as.vector(hcst$lat), as.vector(obs$lat)))) { - lat_error_msg <- paste("Latitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lat_error_msg) - hcst_lat_msg <- paste0("First hcst lat: ", hcst$lat[1], - "; Last hcst lat: ", hcst$lat[length(hcst$lat)]) - info(recipe$Run$logger, hcst_lat_msg) - obs_lat_msg <- paste0("First obs lat: ", obs$lat[1], - "; Last obs lat: ", obs$lat[length(obs$lat)]) - info(recipe$Run$logger, obs_lat_msg) - stop("hcst and obs don't share the same latitudes.") - } - if (!isTRUE(all.equal(as.vector(hcst$lon), as.vector(obs$lon)))) { - lon_error_msg <- paste("Longitude mismatch between hcst and obs.", - "Please check the original grids and the", - "regrid parameters in your recipe.") - error(recipe$Run$logger, lon_error_msg) - hcst_lon_msg <- paste0("First hcst lon: ", hcst$lon[1], - "; Last hcst lon: ", hcst$lon[length(hcst$lon)]) - info(recipe$Run$logger, hcst_lon_msg) - obs_lon_msg <- paste0("First obs lon: ", obs$lon[1], - "; Last obs lon: ", obs$lon[length(obs$lon)]) - info(recipe$Run$logger, obs_lon_msg) - stop("hcst and obs don't share the same longitudes.") - - } - } - - # Remove negative values in accumulative variables - dictionary <- read_yaml("conf/variable-dictionary.yml") - for (var_idx in 1:length(vars)) { - var_name <- vars[var_idx] - if (dictionary$vars[[var_name]]$accum) { - info(recipe$Run$logger, - paste0("Accumulated variable ", var_name, - ": setting negative values to zero.")) - # obs$data[, var_idx, , , , , , , ] <- pmax(Subset(obs$data, - # along = "var", - # indices = var_idx, F), 0) - obs$data[, var_idx, , , , , , , ][obs$data[, var_idx, , , , , , , ] < 0] <- 0 - hcst$data[, var_idx, , , , , , , ][hcst$data[, var_idx, , , , , , , ] < 0] <- 0 - if (!is.null(fcst)) { - fcst$data[, var_idx, , , , , , , ][fcst$data[, var_idx, , , , , , , ] < 0] <- 0 - } - } - - # Convert prlr from m/s to mm/day - ## TODO: Make a unit conversion function - if (vars[[var_idx]] == "prlr") { - # Verify that the units are m/s and the same in obs and hcst - if (((obs$attrs$Variable$metadata[[var_name]]$units == "m s-1") || - (obs$attrs$Variable$metadata[[var_name]]$units == "m s**-1")) && - ((hcst$attrs$Variable$metadata[[var_name]]$units == "m s-1") || - (hcst$attrs$Variable$metadata[[var_name]]$units == "m s**-1"))) { - info(recipe$Run$logger, "Converting precipitation from m/s to mm/day.") - obs$data[, var_idx, , , , , , , ] <- - obs$data[, var_idx, , , , , , , ]*86400*1000 - obs$attrs$Variable$metadata[[var_name]]$units <- "mm/day" - hcst$data[, var_idx, , , , , , , ] <- - hcst$data[, var_idx, , , , , , , ]*86400*1000 - hcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day" - if (!is.null(fcst)) { - fcst$data[, var_idx, , , , , , , ] <- - fcst$data[, var_idx, , , , , , , ]*86400*1000 - fcst$attrs$Variable$metadata[[var_name]]$units <- "mm/day" - } - } - } - } - # Compute anomalies if requested - # Print a summary of the loaded data for the user, for each object - if (recipe$Run$logger$threshold <= 2) { - data_summary(hcst, recipe) - data_summary(obs, recipe) - if (!is.null(fcst)) { - data_summary(fcst, recipe) - } - } - - info(recipe$Run$logger, - "##### DATA LOADING COMPLETED SUCCESSFULLY #####") - - ############################################################################ - # - # CHECKS ON MISSING FILES - # - ############################################################################ - - #obs.NA_dates.ind <- Apply(obs, - # fun=(function(x){ all(is.na(x))}), - # target_dims=c('time', 'latitude', 'longitude'))[[1]] - #obs.NA_dates <- dates_file[obs.NA_dates.ind] - #obs.NA_dates <- obs.NA_dates[order(obs.NA_dates)] - #obs.NA_files <- paste0(obs.dir, store.freq,"/",variable,"_", - # freq.obs,"obs.grid","/",variable,"_",obs.NA_dates,".nc") - # - #if (any(is.na(hcst))){ - # fatal(recipe$Run$logger, - # paste(" ERROR: MISSING HCST VALUES FOUND DURING LOADING # ", - # " ################################################# ", - # " ###### MISSING FILES #### ", - # " ################################################# ", - # "hcst files:", - # hcst.NA_files, - # " ################################################# ", - # " ################################################# ", - # sep="\n")) - # quit(status = 1) - #} - # - #if (any(is.na(obs)) && !identical(obs.NA_dates,character(0))){ - # fatal(recipe$logger, - # paste(" ERROR: MISSING OBS VALUES FOUND DURING LOADING # ", - # " ################################################# ", - # " ###### MISSING FILES #### ", - # " ################################################# ", - # "obs files:", - # obs.NA_files, - # " ################################################# ", - # " ################################################# ", - # sep="\n")) - # quit(status=1) - #} - # - #info(recipe$logger, - # "######### DATA LOADING COMPLETED SUCCESFULLY ##############") - - ############################################################################ - ############################################################################ - - return(list(hcst = hcst, fcst = fcst, obs = obs)) - -} diff --git a/modules/Scorecards/R/tmp/ClimPalette.R b/modules/Scorecards/R/tmp/ClimPalette.R new file mode 100644 index 00000000..eed3c802 --- /dev/null +++ b/modules/Scorecards/R/tmp/ClimPalette.R @@ -0,0 +1,78 @@ +#'Generate Climate Color Palettes +#' +#'Generates a colorblind friendly color palette with color ranges useful in +#'climate temperature variable plotting. +#' +#'@param palette A character string of palette. The current choices: +#' \itemize{ +#' \item{'bluered': from blue through white to red.} +#' \item{'redblue': from red through white to blue.} +#' \item{'yellowred': from yellow through orange to red.} +#' \item{'redyellow': from red through orange to yellow.} +#' \item{'purpleorange': from purple through white to orange.} +#' \item{'orangepurple': from orange through white to purple.} +#' } +#'@param n A number indicating how many colors to generate. +#' +#'@return +#'ClimPalette() returns the function that generates the color palette and the +#'attribute 'na_color'.\cr +#'ClimColors() returns a vector of the colors. +#' +#'@examples +#'lims <- seq(-1, 1, length.out = 21) +#' +#'cb <- ColorBarContinuous(lims, color_fun = ClimPalette('redyellow'), plot = FALSE) +#' +#'cols <- ClimColors(20) +#'cb <- ColorBarContinuous(lims, cols, plot = FALSE) +#' +#'@importFrom grDevices colorRampPalette +#'@export +ClimPalette <- function(palette = "bluered") { + if (palette == "bluered") { + colorbar <- colorRampPalette(rev(c("#67001f", "#b2182b", "#d6604d", + "#f4a582", "#fddbc7", "#f7f7f7", + "#d1e5f0", "#92c5de", "#4393c3", + "#2166ac", "#053061"))) + attr(colorbar, 'na_color') <- 'pink' + } else if (palette == "redblue") { + colorbar <- colorRampPalette(c("#67001f", "#b2182b", "#d6604d", + "#f4a582", "#fddbc7", "#f7f7f7", + "#d1e5f0", "#92c5de", "#4393c3", + "#2166ac", "#053061")) + attr(colorbar, 'na_color') <- 'pink' + } else if (palette == "yellowred") { + colorbar <- colorRampPalette(c("#ffffcc", "#ffeda0", "#fed976", + "#feb24c", "#fd8d3c", "#fc4e2a", + "#e31a1c", "#bd0026", "#800026")) + attr(colorbar, 'na_color') <- 'pink' + } else if (palette == "redyellow") { + colorbar <- colorRampPalette(rev(c("#ffffcc", "#ffeda0", "#fed976", + "#feb24c", "#fd8d3c", "#fc4e2a", + "#e31a1c", "#bd0026", "#800026"))) + attr(colorbar, 'na_color') <- 'pink' + } else if (palette == "purpleorange") { + colorbar <- colorRampPalette(c("#2d004b", "#542789", "#8073ac", + "#b2abd2", "#d8daeb", "#f7f7f7", + "#fee0b6", "#fdb863", "#e08214", + "#b35806", "#7f3b08")) + attr(colorbar, 'na_color') <- 'pink' + } else if (palette == "orangepurple") { + colorbar <- colorRampPalette(rev(c("#2d004b", "#542789", "#8073ac", + "#b2abd2", "#d8daeb", "#f7f7f7", + "#fee0b6", "#fdb863", "#e08214", + "#b35806", "#7f3b08"))) + attr(colorbar, 'na_color') <- 'pink' + } else { + stop("Parameter 'palette' must be one of 'bluered', 'redblue', 'yellowred'", + "'redyellow', 'purpleorange' or 'orangepurple'.") + } + colorbar +} + +#'@rdname ClimPalette +#'@export +ClimColors <- function(n, palette = "bluered") { + ClimPalette(palette)(n) +} diff --git a/modules/Scorecards/R/tmp/ColorBarContinuous.R b/modules/Scorecards/R/tmp/ColorBarContinuous.R new file mode 100644 index 00000000..a4ef933f --- /dev/null +++ b/modules/Scorecards/R/tmp/ColorBarContinuous.R @@ -0,0 +1,594 @@ +#'Draws a Continuous Color Bar +#' +#'Generates a color bar to use as colouring function for map plots and +#'optionally draws it (horizontally or vertically) to be added to map +#'multipanels or plots. It is possible to draw triangles at the ends of the +#'colour bar to represent values that go beyond the range of interest. A +#'number of options is provided to adjust the colours and the position and +#'size of the components. The drawn colour bar spans a whole figure region +#'and is compatible with figure layouts.\cr\cr +#'The generated colour bar consists of a set of breaks that define the +#'length(brks) - 1 intervals to classify each of the values in each of the +#'grid cells of a two-dimensional field. The corresponding grid cell of a +#'given value of the field will be coloured in function of the interval it +#'belongs to.\cr\cr +#'The only mandatory parameters are 'var_limits' or 'brks' (in its second +#'format, see below). +#' +#'@param brks Can be provided in two formats: +#'\itemize{ +#' \item{A single value with the number of breaks to be generated +#' automatically, between the minimum and maximum specified in 'var_limits' +#' (both inclusive). Hence the parameter 'var_limits' is mandatory if 'brks' +#' is provided with this format. If 'bar_limits' is additionally provided, +#' values only between 'bar_limits' will be generated. The higher the value +#' of 'brks', the smoother the plot will look.} +#' \item{A vector with the actual values of the desired breaks. Values will +#' be reordered by force to ascending order. If provided in this format, no +#' other parameters are required to generate/plot the colour bar.} +#'} +#' This parameter is optional if 'var_limits' is specified. If 'brks' not +#' specified but 'cols' is specified, it will take as value length(cols) + 1. +#' If 'cols' is not specified either, 'brks' will take 21 as value. +#'@param cols Vector of length(brks) - 1 valid colour identifiers, for each +#' interval defined by the breaks. This parameter is optional and will be +#' filled in with a vector of length(brks) - 1 colours generated with the +#' function provided in 'color_fun' (\code{clim.colors} by default).\cr 'cols' +#' can have one additional colour at the beginning and/or at the end with the +#' aim to colour field values beyond the range of interest represented in the +#' colour bar. If any of these extra colours is provided, parameter +#' 'triangle_ends' becomes mandatory in order to disambiguate which of the +#' ends the colours have been provided for. +#'@param vertical TRUE/FALSE for vertical/horizontal colour bar +#' (disregarded if plot = FALSE). +#'@param subsampleg The first of each subsampleg breaks will be ticked on the +#' colorbar. Takes by default an approximation of a value that yields a +#' readable tick arrangement (extreme breaks always ticked). If set to 0 or +#' lower, no labels are drawn. See the code of the function for details or +#' use 'extra_labels' for customized tick arrangements. +#'@param bar_limits Vector of two numeric values with the extremes of the +#' range of values represented in the colour bar. If 'var_limits' go beyond +#' this interval, the drawing of triangle extremes is triggered at the +#' corresponding sides, painted in 'col_inf' and 'col_sup'. Either of them +#' can be set as NA and will then take as value the corresponding extreme in +#' 'var_limits' (hence a triangle end won't be triggered for these sides). +#' Takes as default the extremes of 'brks' if available, else the same values +#' as 'var_limits'. +#'@param var_limits Vector of two numeric values with the minimum and maximum +#' values of the field to represent. These are used to know whether to draw +#' triangle ends at the extremes of the colour bar and what colour to fill +#' them in with. If not specified, take the same value as the extremes of +#' 'brks'. Hence the parameter 'brks' is mandatory if 'var_limits' is not +#' specified. +#'@param triangle_ends Vector of two logical elements, indicating whether to +#' force the drawing of triangle ends at each of the extremes of the colour +#' bar. This choice is automatically made from the provided 'brks', +#' 'bar_limits', 'var_limits', 'col_inf' and 'col_sup', but the behaviour +#' can be manually forced to draw or not to draw the triangle ends with this +#' parameter. If 'cols' is provided, 'col_inf' and 'col_sup' will take +#' priority over 'triangle_ends' when deciding whether to draw the triangle +#' ends or not. +#'@param col_inf Colour to fill the inferior triangle end with. Useful if +#' specifying colours manually with parameter 'cols', to specify the colour +#' and to trigger the drawing of the lower extreme triangle, or if 'cols' is +#' not specified, to replace the colour automatically generated by ColorBar(). +#'@param col_sup Colour to fill the superior triangle end with. Useful if +#' specifying colours manually with parameter 'cols', to specify the colour +#' and to trigger the drawing of the upper extreme triangle, or if 'cols' is +#' not specified, to replace the colour automatically generated by ColorBar(). +#'@param color_fun Function to generate the colours of the color bar. Must +#' take an integer and must return as many colours. The returned colour vector +#' can have the attribute 'na_color', with a colour to draw NA values. This +#' parameter is set by default to ClimPalette(). +#'@param plot Logical value indicating whether to only compute its breaks and +#' colours (FALSE) or to also draw it on the current device (TRUE). +#'@param draw_ticks Whether to draw ticks for the labels along the colour bar +#' (TRUE) or not (FALSE). TRUE by default. Disregarded if 'plot = FALSE'. +#'@param draw_separators Whether to draw black lines in the borders of each of +#' the colour rectancles of the colour bar (TRUE) or not (FALSE). FALSE by +#' default. Disregarded if 'plot = FALSE'. +#'@param triangle_ends_scale Scale factor for the drawn triangle ends of the +#' colour bar, if drawn at all. Takes 1 by default (rectangle triangle +#' proportional to the thickness of the colour bar). Disregarded if +#' 'plot = FALSE'. +#'@param extra_labels Numeric vector of extra labels to draw along axis of +#' the colour bar. The number of provided decimals will be conserved. +#' Disregarded if 'plot = FALSE'. +#'@param title Title to draw on top of the colour bar, most commonly with the +#' units of the represented field in the neighbour figures. Empty by default. +#'@param title_scale Scale factor for the 'title' of the colour bar. +#' Takes 1 by default. +#'@param label_scale Scale factor for the labels of the colour bar. +#' Takes 1 by default. +#'@param tick_scale Scale factor for the length of the ticks of the labels +#' along the colour bar. Takes 1 by default. +#'@param extra_margin Extra margins to be added around the colour bar, +#' in the format c(y1, x1, y2, x2). The units are margin lines. Takes +#' rep(0, 4) by default. +#'@param label_digits Number of significant digits to be displayed in the +#' labels of the colour bar, usually to avoid too many decimal digits +#' overflowing the figure region. This does not have effect over the labels +#' provided in 'extra_labels'. Takes 4 by default. +#'@param ... Arguments to be passed to the method. Only accepts the following +#' graphical parameters:\cr adj ann ask bg bty cex.lab cex.main cex.sub cin +#' col.axis col.lab col.main col.sub cra crt csi cxy err family fg fig fin +#' font font.axis font.lab font.main font.sub lend lheight ljoin lmitre lty +#' lwd mai mex mfcol mfrow mfg mkh oma omd omi page pch pin plt pty smo srt +#' tck tcl usr xaxp xaxs xaxt xlog xpd yaxp yaxs yaxt ylbias ylog.\cr For more +#' information about the parameters see `par`. +#' +#'@return +#'\item{brks}{ +#' Breaks used for splitting the range in intervals. +#'} +#'\item{cols}{ +#' Colours generated for each of the length(brks) - 1 intervals. +#' Always of length length(brks) - 1. +#'} +#'\item{col_inf}{ +#' Colour used to draw the lower triangle end in the colour +#' bar (NULL if not drawn at all). +#'} +#'\item{col_sup}{ +#' Colour used to draw the upper triangle end in the colour +#' bar (NULL if not drawn at all). +#'} +#' +#'@examples +#'cols <- c("dodgerblue4", "dodgerblue1", "forestgreen", "yellowgreen", "white", +#' "white", "yellow", "orange", "red", "saddlebrown") +#'lims <- seq(-1, 1, 0.2) +#'cb <- ColorBarContinuous(lims, cols, plot = FALSE) +#' +#'@importFrom grDevices col2rgb rgb +#'@import utils +#'@export +ColorBarContinuous <- function(brks = NULL, cols = NULL, vertical = TRUE, + subsampleg = NULL, bar_limits = NULL, var_limits = NULL, + triangle_ends = NULL, col_inf = NULL, col_sup = NULL, + color_fun = ClimPalette(), plot = TRUE, + draw_ticks = TRUE, draw_separators = FALSE, + triangle_ends_scale = 1, extra_labels = NULL, + title = NULL, title_scale = 1, + label_scale = 1, tick_scale = 1, + extra_margin = rep(0, 4), label_digits = 4, ...) { + # Required checks + if ((is.null(brks) || length(brks) < 2) && is.null(bar_limits) && is.null(var_limits)) { + stop("At least one of 'brks' with the desired breaks, 'bar_limits' or ", + "'var_limits' must be provided to generate the colour bar.") + } + + # Check brks + if (!is.null(brks)) { + if (!is.numeric(brks)) { + stop("Parameter 'brks' must be numeric if specified.") + } else if (length(brks) > 1) { + reorder <- sort(brks, index.return = TRUE) + if (!is.null(cols)) { + cols <- cols[reorder$ix[which(reorder$ix <= length(cols))]] + } + brks <- reorder$x + } + } + + # Check bar_limits + if (!is.null(bar_limits)) { + if (!(all(is.na(bar_limits) | is.numeric(bar_limits)) && (length(bar_limits) == 2))) { + stop("Parameter 'bar_limits' must be a vector of two numeric elements or NAs.") + } + } + + # Check var_limits + if (!is.null(var_limits)) { + if (!(is.numeric(var_limits) && (length(var_limits) == 2))) { + stop("Parameter 'var_limits' must be a numeric vector of length 2.") + } else if (anyNA(var_limits)) { + stop("Parameter 'var_limits' must not contain NA values.") + } else if (any(is.infinite(var_limits))) { + stop("Parameter 'var_limits' must not contain infinite values.") + } + } + + # Check cols + if (!is.null(cols)) { + if (!is.character(cols)) { + stop("Parameter 'cols' must be a vector of character strings.") + } else if (any(!sapply(cols, .IsColor))) { + stop("Parameter 'cols' must contain valid colour identifiers.") + } + } + + # Check color_fun + if (!is.function(color_fun)) { + stop("Parameter 'color_fun' must be a colour-generator function.") + } + + # Check integrity among brks, bar_limits and var_limits + if (is.null(brks) || (length(brks) < 2)) { + if (is.null(brks)) { + if (is.null(cols)) { + brks <- 21 + } else { + brks <- length(cols) + 1 + } + } + if (is.null(bar_limits) || anyNA(bar_limits)) { + # var_limits is defined + if (is.null(bar_limits)) { + bar_limits <- c(NA, NA) + } + half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (brks - 1) + bar_limits[which(is.na(bar_limits))] <- c(var_limits[1] - half_width, var_limits[2] + half_width)[which(is.na(bar_limits))] + brks <- seq(bar_limits[1], bar_limits[2], length.out = brks) + } else if (is.null(var_limits)) { + # bar_limits is defined + var_limits <- bar_limits + half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (brks - 1) + brks <- seq(bar_limits[1], bar_limits[2], length.out = brks) + var_limits[1] <- var_limits[1] + half_width / 50 + } else { + # both bar_limits and var_limits are defined + brks <- seq(bar_limits[1], bar_limits[2], length.out = brks) + } + } else if (is.null(bar_limits)) { + if (is.null(var_limits)) { + # brks is defined + bar_limits <- c(head(brks, 1), tail(brks, 1)) + var_limits <- bar_limits + half_width <- 0.5 * (var_limits[2] - var_limits[1]) / (length(brks) - 1) + var_limits[1] <- var_limits[1] + half_width / 50 + } else { + # brks and var_limits are defined + bar_limits <- c(head(brks, 1), tail(brks, 1)) + } + } else { + # brks and bar_limits are defined + # or + # brks, bar_limits and var_limits are defined + if (head(brks, 1) != bar_limits[1] || tail(brks, 1) != bar_limits[2]) { + stop("Parameters 'brks' and 'bar_limits' are inconsistent.") + } + } + + # Check col_inf + if (!is.null(col_inf)) { + if (!.IsColor(col_inf)) { + stop("Parameter 'col_inf' must be a valid colour identifier.") + } + } + + # Check col_sup + if (!is.null(col_sup)) { + if (!.IsColor(col_sup)) { + stop("Parameter 'col_sup' must be a valid colour identifier.") + } + } + + # Check triangle_ends + if (!is.null(triangle_ends) && (!is.logical(triangle_ends) || length(triangle_ends) != 2)) { + stop("Parameter 'triangle_ends' must be a logical vector with two elements.") + } + teflc <- triangle_ends_from_limit_cols <- c(!is.null(col_inf), !is.null(col_sup)) + if (is.null(triangle_ends) && is.null(col_inf) && is.null(col_sup)) { + triangle_ends <- c(FALSE, FALSE) + if (bar_limits[1] >= var_limits[1]) { + triangle_ends[1] <- TRUE + } + if (bar_limits[2] < var_limits[2]) { + triangle_ends[2] <- TRUE + } + } else if (!is.null(triangle_ends) && is.null(col_inf) && is.null(col_sup)) { + triangle_ends <- triangle_ends + } else if (is.null(triangle_ends) && (!is.null(col_inf) || !is.null(col_sup))) { + triangle_ends <- teflc + } else if (any(teflc != triangle_ends)) { + if (!is.null(brks) && length(brks) > 1 && !is.null(cols) && length(cols) >= length(brks)) { + triangle_ends <- teflc + } else if (!is.null(cols)) { + triangle_ends <- teflc + } else { + triangle_ends <- triangle_ends + } + } + if (plot && !is.null(var_limits)) { + if ((bar_limits[1] >= var_limits[1]) && !triangle_ends[1]) { + warning("There are variable values smaller or equal to the lower limit ", + "of the colour bar and the lower triangle end has been ", + "disabled. These will be painted in the colour for NA values.") + } + if ((bar_limits[2] < var_limits[2]) && !triangle_ends[2]) { + warning("There are variable values greater than the higher limit ", + "of the colour bar and the higher triangle end has been ", + "disabled. These will be painted in the colour for NA values.") + } + } + + # Generate colours if needed + if (is.null(cols)) { + cols <- color_fun(length(brks) - 1 + sum(triangle_ends)) + attr_bk <- attributes(cols) + if (triangle_ends[1]) { + if (is.null(col_inf)) col_inf <- head(cols, 1) + cols <- cols[-1] + } + if (triangle_ends[2]) { + if (is.null(col_sup)) col_sup <- tail(cols, 1) + cols <- cols[-length(cols)] + } + attributes(cols) <- attr_bk + } else if ((length(cols) != (length(brks) - 1))) { + stop("Incorrect number of 'brks' and 'cols'. There must be one more break than the number of colours.") + } + + # Check vertical + if (!is.logical(vertical)) { + stop("Parameter 'vertical' must be TRUE or FALSE.") + } + + # Check extra_labels + if (is.null(extra_labels)) { + extra_labels <- numeric(0) + } + if (!is.numeric(extra_labels)) { + stop("Parameter 'extra_labels' must be numeric.") + } else { + if (any(extra_labels > bar_limits[2]) || any(extra_labels < bar_limits[1])) { + stop("Parameter 'extra_labels' must not contain ticks beyond the color bar limits.") + } + } + extra_labels <- sort(extra_labels) + + # Check subsampleg + primes <- function(x) { + # Courtesy of Chase. See http://stackoverflow.com/questions/6424856/r-function-for-returning-all-factors + x <- as.integer(x) + div <- seq_len(abs(x)) + factors <- div[x %% div == 0L] + factors <- list(neg = -factors, pos = factors) + return(factors) + } + remove_final_tick <- FALSE + added_final_tick <- TRUE + if (is.null(subsampleg)) { + subsampleg <- 1 + while (length(brks) / subsampleg > 15 - 1) { + next_factor <- primes((length(brks) - 1) / subsampleg)$pos + next_factor <- next_factor[length(next_factor) - ifelse(length(next_factor) > 2, 1, 0)] + subsampleg <- subsampleg * next_factor + } + if (subsampleg > (length(brks) - 1) / 4) { + subsampleg <- max(1, round(length(brks) / 4)) + extra_labels <- c(extra_labels, bar_limits[2]) + added_final_tick <- TRUE + if ((length(brks) - 1) %% subsampleg < (length(brks) - 1) / 4 / 2) { + remove_final_tick <- TRUE + } + } + } else if (!is.numeric(subsampleg)) { + stop("Parameter 'subsampleg' must be numeric.") + } + subsampleg <- round(subsampleg) + draw_labels <- TRUE + if ((subsampleg) < 1) { + draw_labels <- FALSE + } + + # Check plot + if (!is.logical(plot)) { + stop("Parameter 'plot' must be logical.") + } + + # Check draw_separators + if (!is.logical(draw_separators)) { + stop("Parameter 'draw_separators' must be logical.") + } + + # Check triangle_ends_scale + if (!is.numeric(triangle_ends_scale)) { + stop("Parameter 'triangle_ends_scale' must be numeric.") + } + + # Check draw_ticks + if (!is.logical(draw_ticks)) { + stop("Parameter 'draw_ticks' must be logical.") + } + + # Check title + if (is.null(title)) { + title <- '' + } + if (!is.character(title)) { + stop("Parameter 'title' must be a character string.") + } + + # Check title_scale + if (!is.numeric(title_scale)) { + stop("Parameter 'title_scale' must be numeric.") + } + + # Check label_scale + if (!is.numeric(label_scale)) { + stop("Parameter 'label_scale' must be numeric.") + } + + # Check tick_scale + if (!is.numeric(tick_scale)) { + stop("Parameter 'tick_scale' must be numeric.") + } + + # Check extra_margin + if (!is.numeric(extra_margin) || length(extra_margin) != 4) { + stop("Parameter 'extra_margin' must be a numeric vector of length 4.") + } + + # Check label_digits + if (!is.numeric(label_digits)) { + stop("Parameter 'label_digits' must be numeric.") + } + label_digits <- round(label_digits) + + # Process the user graphical parameters that may be passed in the call + ## Graphical parameters to exclude + excludedArgs <- c("cex", "cex.axis", "col", "lab", "las", "mar", "mgp", "new", "ps") + userArgs <- .FilterUserGraphicArgs(excludedArgs, ...) + + # + # Plotting colorbar + # ~~~~~~~~~~~~~~~~~~~ + # + if (plot) { + pars_to_save <- c('mar', 'cex', names(userArgs), 'mai', 'mgp', 'las', 'xpd') + saved_pars <- par(pars_to_save) + par(mar = c(0, 0, 0, 0), cex = 1) + image(1, 1, t(t(1)), col = rgb(0, 0, 0, 0), axes = FALSE, xlab = '', ylab = '') + # Get the availale space + figure_size <- par('fin') + cs <- par('csi') + # This allows us to assume we always want to plot horizontally + if (vertical) { + figure_size <- rev(figure_size) + } + # pannel_to_redraw <- par('mfg') + # .SwitchToFigure(pannel_to_redraw[1], pannel_to_redraw[2]) + # Load the user parameters + par(new = TRUE) + par(userArgs) + # Set up color bar plot region + margins <- c(0.0, 0, 0.0, 0) + cex_title <- 1 * title_scale + cex_labels <- 0.9 * label_scale + cex_ticks <- -0.3 * tick_scale + spaceticklab <- max(-cex_ticks, 0) + if (vertical) { + margins[1] <- margins[1] + (1.2 * cex_labels * 3 + spaceticklab) * cs + margins <- margins + extra_margin[c(4, 1:3)] * cs + } else { + margins[1] <- margins[1] + (1.2 * cex_labels * 1 + spaceticklab) * cs + margins <- margins + extra_margin * cs + } + if (title != '') { + margins[3] <- margins[3] + (1.0 * cex_title) * cs + } + margins[3] <- margins[3] + sqrt(figure_size[2] / (margins[1] + margins[3])) * + figure_size[2] / 6 * ifelse(title != '', 0.5, 0.8) + # Set side margins + margins[2] <- margins[2] + figure_size[1] / 16 + margins[4] <- margins[4] + figure_size[1] / 16 + triangle_ends_prop <- 1 / 32 * triangle_ends_scale + triangle_ends_cex <- triangle_ends_prop * figure_size[2] + if (triangle_ends[1]) { + margins[2] <- margins[2] + triangle_ends_cex + } + if (triangle_ends[2]) { + margins[4] <- margins[4] + triangle_ends_cex + } + ncols <- length(cols) + # Set up the points of triangles + # Compute the proportion of horiz. space occupied by one plot unit + prop_unit <- (1 - (margins[2] + margins[4]) / figure_size[1]) / ncols + # Convert triangle height to plot inits + triangle_height <- triangle_ends_prop / prop_unit + left_triangle <- list(x = c(1, 1 - triangle_height, 1) - 0.5, + y = c(1.4, 1, 0.6)) + right_triangle <- list(x = c(ncols, ncols + triangle_height, ncols) + 0.5, + y = c(1.4, 1, 0.6)) + # Draw the color squares and title + if (vertical) { + par(mai = c(margins[2:4], margins[1]), + mgp = c(0, spaceticklab + 0.2, 0), las = 1) + d <- 4 + image(1, 1:ncols, t(1:ncols), axes = FALSE, col = cols, + xlab = '', ylab = '') + title(ylab = title, line = cex_title * (0.2 + 0.1), cex.lab = cex_title) + # Draw top and bottom border lines + lines(c(0.6, 0.6), c(1 - 0.5, ncols + 0.5)) + lines(c(1.4, 1.4), c(1 - 0.5, ncols + 0.5)) + # Rotate triangles + names(left_triangle) <- rev(names(left_triangle)) + names(right_triangle) <- rev(names(right_triangle)) + } else { + # The term - cex_labels / 4 * (3 / cex_labels - 1) was found by + # try and error + par(mai = margins, + mgp = c(0, cex_labels / 2 + spaceticklab + - cex_labels / 4 * (3 / cex_labels - 1), 0), + las = 1) + d <- 1 + image(1:ncols, 1, t(t(1:ncols)), axes = FALSE, col = cols, + xlab = '', ylab = '') + title(title, line = cex_title * (0.2 + 0.1), cex.main = cex_title) + # Draw top and bottom border lines + lines(c(1 - 0.5, ncols + 0.5), c(0.6, 0.6)) + lines(c(1 - 0.5, ncols + 0.5), c(1.4, 1.4)) + tick_length <- -0.4 + } + # Draw the triangles + par(xpd = TRUE) + if (triangle_ends[1]) { + # Draw left triangle + polygon(left_triangle$x, left_triangle$y, col = col_inf, border = NA) + lines(left_triangle$x, left_triangle$y) + } + if (triangle_ends[2]) { + # Draw right triangle + polygon(right_triangle$x, right_triangle$y, col = col_sup, border = NA) + lines(right_triangle$x, right_triangle$y) + } + par(xpd = FALSE) + + # Put the separators + if (vertical) { + if (draw_separators) { + for (i in 1:(ncols - 1)) { + lines(c(0.6, 1.4), c(i, i) + 0.5) + } + } + if (draw_separators || is.null(col_inf)) { + lines(c(0.6, 1.4), c(0.5, 0.5)) + } + if (draw_separators || is.null(col_sup)) { + lines(c(0.6, 1.4), c(ncols + 0.5, ncols + 0.5)) + } + } else { + if (draw_separators) { + for (i in 1:(ncols - 1)) { + lines(c(i, i) + 0.5, c(0.6, 1.4)) + } + } + if (draw_separators || is.null(col_inf)) { + lines(c(0.5, 0.5), c(0.6, 1.4)) + } + if (draw_separators || is.null(col_sup)) { + lines(c(ncols + 0.5, ncols + 0.5), c(0.6, 1.4)) + } + } + # Put the ticks + plot_range <- length(brks) - 1 + var_range <- tail(brks, 1) - head(brks, 1) + extra_labels_at <- ((extra_labels - head(brks, 1)) / var_range) * plot_range + 0.5 + at <- seq(1, length(brks), subsampleg) + labels <- brks[at] + # Getting rid of next-to-last tick if too close to last one + if (remove_final_tick) { + at <- at[-length(at)] + labels <- labels[-length(labels)] + } + labels <- signif(labels, label_digits) + if (added_final_tick) { + extra_labels[length(extra_labels)] <- signif(tail(extra_labels, 1), label_digits) + } + at <- at - 0.5 + at <- c(at, extra_labels_at) + labels <- c(labels, extra_labels) + tick_reorder <- sort(at, index.return = TRUE) + at <- tick_reorder$x + if (draw_labels) { + labels <- labels[tick_reorder$ix] + } else { + labels <- FALSE + } + axis(d, at = at, tick = draw_ticks, labels = labels, cex.axis = cex_labels, tcl = cex_ticks) + par(saved_pars) + } + invisible(list(brks = brks, cols = cols, col_inf = col_inf, col_sup = col_sup)) +} diff --git a/modules/Scorecards/R/tmp/ScorecardsMulti.R b/modules/Scorecards/R/tmp/ScorecardsMulti.R index dd9ac257..020a2047 100644 --- a/modules/Scorecards/R/tmp/ScorecardsMulti.R +++ b/modules/Scorecards/R/tmp/ScorecardsMulti.R @@ -237,37 +237,37 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, sign_sc_9 <- Subset(sign, c('system','region'), list(1, reg), drop = 'selected') } - SCPlotScorecard(data = data_sc_9, - sign = sign_sc_9, - row.dim = model, - subrow.dim = 'time', - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = model.name, - subrow.names = forecast.months, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = table.model.name, - subrow.title = 'Forecast Month', - col.title = 'Start date', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = 4, - col2.width = col2.width, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_9, + sign = sign_sc_9, + row_dim = model, + subrow_dim = 'time', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = model.name, + subrow_names = forecast.months, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = table.model.name, + subrow_title = 'Forecast Month', + col_title = 'Start date', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = 4, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) #### Scorecard_type 10 #### @@ -286,37 +286,37 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, data_sc_10 <- Subset(Reorder(data, new_order), c('system','region'), list(1, reg), drop = 'selected') sign_sc_10 <- Subset(Reorder(sign, new_order), c('system','region'), list(1, reg), drop = 'selected') } - SCPlotScorecard(data = data_sc_10, - sign = sign_sc_10, - row.dim = 'time', - subrow.dim = model, - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = forecast.months, - subrow.names = model.name, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = 'Forecast month', - subrow.title = table.model.name, - col.title = 'Start date', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = 4, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_10, + sign = sign_sc_10, + row_dim = 'time', + subrow_dim = model, + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = forecast.months, + subrow_names = model.name, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Forecast month', + subrow_title = table.model.name, + col_title = 'Start date', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = 4, + columns_width = columns.width, + fileout = fileout) #### Scorecard_type 11 #### @@ -334,37 +334,37 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, sign_sc_11 <- Subset(transformed_sign, c('system','region'), list(1, reg), drop = 'selected') } - SCPlotScorecard(data = data_sc_11, - sign = sign_sc_11, - row.dim = model, - subrow.dim = 'time', - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = model.name, - subrow.names = forecast.months, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = table.model.name, - subrow.title = 'Forecast Month', - col.title = 'Target month', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = 4, - col2.width = col2.width, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_11, + sign = sign_sc_11, + row_dim = model, + subrow_dim = 'time', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = model.name, + subrow_names = forecast.months, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = table.model.name, + subrow_title = 'Forecast Month', + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = 4, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) #### Scorecard_type 12 #### @@ -384,37 +384,37 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('system','region'), list(1, reg), drop = 'selected') } - SCPlotScorecard(data = data_sc_12, - sign = sign_sc_12, - row.dim = 'time', - subrow.dim = model, - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = forecast.months, - subrow.names = model.name, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = 'Forecast Month', - subrow.title = table.model.name, - col.title = 'Target month', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = 4, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_12, + sign = sign_sc_12, + row_dim = 'time', + subrow_dim = model, + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = forecast.months, + subrow_names = model.name, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Forecast Month', + subrow_title = table.model.name, + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = 4, + columns_width = columns.width, + fileout = fileout) } ## close loop on region diff --git a/modules/Scorecards/R/tmp/ScorecardsSingle.R b/modules/Scorecards/R/tmp/ScorecardsSingle.R index 1e8f93f6..c5525fd4 100644 --- a/modules/Scorecards/R/tmp/ScorecardsSingle.R +++ b/modules/Scorecards/R/tmp/ScorecardsSingle.R @@ -228,37 +228,37 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, data_sc_1 <- Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected') sign_sc_1 <- Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected') - SCPlotScorecard(data = data_sc_1, - sign = sign_sc_1, - row.dim = 'region', - subrow.dim = 'time', - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = region.names, - subrow.names = forecast.months, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = 'Region', - subrow.title = 'Forecast Month', - col.title = 'Start date', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = col2.width, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_1, + sign = sign_sc_1, + row_dim = 'region', + subrow_dim = 'time', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = region.names, + subrow_names = forecast.months, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Region', + subrow_title = 'Forecast Month', + col_title = 'Start date', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) #### Scorecard_type 2 #### @@ -275,37 +275,37 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, data_sc_2 <- Reorder(Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) sign_sc_2 <- Reorder(Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) - SCPlotScorecard(data = data_sc_2, - sign = sign_sc_2, - row.dim = 'time', - subrow.dim = 'region', - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = forecast.months, - subrow.names = region.names, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = 'Forecast Month', - subrow.title = 'Region', - col.title = 'Start date', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = col2.width, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_2, + sign = sign_sc_2, + row_dim = 'time', + subrow_dim = 'region', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = forecast.months, + subrow_names = region.names, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Forecast Month', + subrow_title = 'Region', + col_title = 'Start date', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) } ## close if @@ -318,37 +318,37 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, data_sc_3 <- Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected') sign_sc_3 <- Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected') - SCPlotScorecard(data = data_sc_3, - sign = sign_sc_3, - row.dim = 'region', - subrow.dim = 'time', - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = region.names, - subrow.names = forecast.months, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = 'Region', - subrow.title = 'Forecast Month', - col.title = 'Target month', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = col2.width, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_3, + sign = sign_sc_3, + row_dim = 'region', + subrow_dim = 'time', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = region.names, + subrow_names = forecast.months, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Region', + subrow_title = 'Forecast Month', + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) #### Scorecard_type 4 #### @@ -365,37 +365,37 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, data_sc_4 <- Reorder(Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) sign_sc_4 <- Reorder(Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) - SCPlotScorecard(data = data_sc_4, - sign = sign_sc_4, - row.dim = 'time', - subrow.dim = 'region', - col.dim = 'metric', - subcol.dim = 'sdate', - legend.dim = 'metric', - row.names = forecast.months, - subrow.names = region.names, - col.names = metric.names, - subcol.names = month.abb[as.numeric(start.months)], - table.title = table.title, - table.subtitle = table.subtitle, - row.title = 'Forecast Month', - subrow.title = 'Region', - col.title = 'Target month', - legend.breaks = legend.breaks, - plot.legend = plot.legend, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round.decimal = round.decimal, - font.size = font.size, - legend.white.space = legend.white.space, - col1.width = col1.width, - col2.width = col2.width, - columns.width = columns.width, - fileout = fileout) + VizScorecard(data = data_sc_4, + sign = sign_sc_4, + row_dim = 'time', + subrow_dim = 'region', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = forecast.months, + subrow_names = region.names, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Forecast Month', + subrow_title = 'Region', + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) } ## close if } ## close loop on ref diff --git a/modules/Scorecards/R/tmp/Utils.R b/modules/Scorecards/R/tmp/Utils.R index 6ba49e8c..caee98e4 100644 --- a/modules/Scorecards/R/tmp/Utils.R +++ b/modules/Scorecards/R/tmp/Utils.R @@ -201,8 +201,6 @@ } - - ## Output file name to save scorecard .Filename <- function(system = NULL, reference = NULL, model = NULL, eval.name = NULL, var = NULL, start.year = NULL, end.year = NULL, scorecard.type = NULL, @@ -230,96 +228,6 @@ return(scorecard_save_path) } -# Scorecards function to assign background color of table cells, -# color of text in table and to bold the text. -# -# It will return a list with 2 arrays: -# (1) metric.color, A 2-dimensional array with character strings containing the -# color codes for each cell background. -# (2) metric.text.color, A 2-dimensional array with character strings -# containing the color codes for each cell text. -.SCTableColors <- function(table, n.col, n.subcol, n.row, n.subrow, - legend.breaks, palette, colorunder, colorsup) { - # Define rows and columns - n.rows <- n.row * n.subrow - n.columns <- n.col * n.subcol - - ## Set table background colors - metric.color <- array(colorunder, c(n.row * n.subrow, n.columns)) - metric.text.color <- array("#2A2A2A", c(n.row * n.subrow , n.columns)) - # metric.text.bold <- array(TRUE, c(n.row * n.subrow , n.columns - 2)) ## Setting all values to bold - - ## Define cell and text colors to show in table - for (i in 1:n.col) { - metric.int <- legend.breaks[[i]] - for (rr in 1:n.rows) { - for (j in 1:n.subcol) { - for (pp in 1:(length(metric.int) - 1)) { - if (is.nan(table[rr,((i - 1) * n.subcol + j)])) { - metric.color[rr,((i - 1) * n.subcol + j)] <- "gray" - } else { - if (table[rr,((i - 1) * n.subcol + j)] >= - metric.int[pp] && table[rr,((i - 1) * n.subcol + j)] <= - metric.int[pp+1]) { - metric.color[rr,((i - 1) * n.subcol + j)] <- palette[[i]][pp] #palette[pp] - } - if (table[rr,((i - 1) * n.subcol + j)] < metric.int[1]) { - metric.color[rr,((i - 1) * n.subcol + j)] <- colorunder[i] - } - if (table[rr,((i - 1) * n.subcol + j)] >= - metric.int[length(metric.int)]) { - metric.color[rr,((i - 1) * n.subcol + j)] <- colorsup[i] - } - } - ## color text in white and bold if background is white or dark blue or dark red: - if (is.nan(table[rr,((i - 1) * n.subcol + j)]) || - (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == 1 && - table[rr,((i - 1) * n.subcol + j)] < metric.int[2]) || - (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == 2 && - table[rr,((i - 1) * n.subcol + j)] < metric.int[3]) || - (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == (length(metric.int) - 1) && - table[rr,((i - 1) * n.subcol + j)] >= metric.int[length(metric.int) - 1]) || - (!is.nan(table[rr,((i - 1) * n.subcol + j)]) && pp == (length(metric.int) - 2) && - table[rr,((i - 1) * n.subcol + j)] >= metric.int[length(metric.int) - 2])) { - metric.text.color[rr,((i - 1) * n.subcol + j)] <- "white" - #metric.text.bold[rr,((i - 1) * n.subcol + j)] <- TRUE - } - } - } - } - } - - return(list(metric.color = metric.color, - metric.text.color = metric.text.color)) - -} - -# Scorecards function to create the color bar legends for the required metrics -# and paste them below the scorecard table -.SCLegend <- function(legend.breaks, palette, colorunder, colorsup, - label.scale, legend.width, legend.height, - legend.white.space, fileout) { - - ## Create color bar legends for each metric - for (i in 1:length(palette)) { - png(filename = paste0(fileout, '_tmpLegend', i, '.png'), width = legend.width, - height = legend.height) - ColorBar(brks = legend.breaks[[i]], cols = palette[[i]], vertical = FALSE, - label_scale = label.scale, col_inf = colorunder[[i]], - col_sup = colorsup[[i]]) - dev.off() - if (i == 1) { - ## Add white space to the left of the first color bar legend - system(paste0('convert ', fileout, '_tmpLegend1.png -background white -splice ', - legend.white.space, 'x0 ', fileout, '_tmpScorecardLegend.png')) - } else { - system(paste0('convert +append ', fileout, '_tmpScorecardLegend.png ', - fileout, '_tmpLegend', i, '.png ', fileout, - '_tmpScorecardLegend.png')) - } - } - unlink(paste0(fileout,'_tmpLegend*.png')) -} # Function to calculate color bar breaks for bias metric .SCBiasBreaks <- function(data){ diff --git a/modules/Scorecards/R/tmp/VizScorecard.R b/modules/Scorecards/R/tmp/VizScorecard.R new file mode 100644 index 00000000..f3b50a6b --- /dev/null +++ b/modules/Scorecards/R/tmp/VizScorecard.R @@ -0,0 +1,619 @@ +#'Function to plot Scorecard tables +#' +#'This function renders a scorecard table from a multidimensional array +#'in HTML style. The structure of the table is based on the assignment of each +#'dimension of the array as a structure element: row, subrow, column or +#'subcolumn. It is useful to present tabular results with colors in a nice way. +#' +#'Note: Module PhantomJS is required. +#' +#'@param data A multidimensional array containing the data to be plotted with +#' at least four dimensions. Each dimension will have assigned a structure +#' element: row, subrow, column and subcolumn. +#'@param sign A multidimensional boolean array with the same dimensions as +#' 'data', indicting which values to be highlighted. If set to NULL no values +#' will be highlighted. +#'@param row_dim A character string indicating the dimension name to show in the +#' rows of the plot. It is set as 'region' by default. +#'@param subrow_dim A character string indicating the dimension name to show in +#' the sub-rows of the plot. It is set as 'time' by default. +#'@param col_dim A character string indicating the dimension name to show in the +#' columns of the plot. It is set as 'metric' by default. +#'@param subcol_dim A character string indicating the dimension name to show in +#' the sub-columns of the plot. It is set as 'sdate' by default. +#'@param legend_dim A character string indicating the dimension name to use for +#' the legend. It is set as 'metric' by default. +#'@param row_names A vector of character strings with row display names. It +#' is set as NULL by default. +#'@param subrow_names A vector of character strings with sub-row display names. +#' It is set as NULL by default. +#'@param col_names A vector of character strings with column display names. It +#' is set as NULL by default. +#'@param subcol_names A vector of character strings with sub-column display +#' names. It is set as NULL by default. +#'@param row_title A character string for the title of the row names. It is set +#' as NULL by default. +#'@param subrow_title A character string for the title of the sub-row names. It +#' is set as NULL by default. +#'@param table_title A character string for the title of the plot. It is set as +#' NULL by default. +#'@param table_subtitle A character string for the sub-title of the plot. It is +#' set as NULL by default. +#'@param legend_breaks A vector of numerics or a list of vectors of numerics, +#' containing the breaks for the legends. If a vector is given as input, then +#' these breaks will be repeated for each 'legend_dim'. A list of vectors can +#' be given as input if the 'legend_dims' require different breaks. This +#' parameter is required even if the legend is not plotted, to define the +#' colors in the scorecard table. It is set as NULL by default. +#'@param plot_legend A logical value to determine if the legend is plotted. It +#' is set as TRUE by default. +#'@param label_scale A numeric value to define the size of the legend labels. +#' It is set as 1.4 by default. +#'@param legend_width A numeric value to define the width of the legend bars. By +#' default it is set to NULL and calculated internally from the table width. +#'@param legend_height A numeric value to define the height of the legend bars. +#' It is set as 50 by default. +#'@param palette A vector of character strings or a list of vectors of +#' character strings containing the colors to use in the legends. If a vector +#' is given as input, then these colors will be used for each legend_dim. A +#' list of vectors can be given as input if different colors are desired for +#' the legend_dims. This parameter must be included even if the legend is +#' not plotted, to define the colors in the scorecard table. +#'@param colorunder A character string or of vector of character strings +#' defining the colors to use for data values with are inferior to the lowest +#' breaks value. This parameter will also plot a inferior triangle in the +#' legend bar. The parameter can be set to NULL if there are no inferior values. +#' If a character string is given this color will be applied to all +#' 'legend_dims'. It is set as NULL by default. +#'@param colorsup A character string or of vector of character strings +#' defining the colors to use for data values with are superior to the highest +#' breaks value. This parameter will also plot a inferior triangle in the +#' legend bar. The parameter can be set to NULL if there are no superior values. +#' If a character string is given this color will be applied to all +#' legend_dims. It is set as NULL by default. +#'@param round_decimal A numeric indicating to which decimal point the data +#' is to be displayed in the scorecard table. It is set as 2 by default. +#'@param font_size A numeric indicating the font size on the scorecard table. +#' Default is 2. +#'@param legend_white_space A numeric value defining the initial starting +#' position of the legend bars, the white space infront of the legend is +#' calculated from the left most point of the table as a distance in cm. The +#' default value is 6. +#'@param columns_width A numeric value defining the width all columns within the +#' table in cm (excluding the first and second columns containing the titles). +#'@param col1_width A numeric value defining the width of the first table column +#' in cm. It is set as NULL by default. +#'@param col2_width A numeric value defining the width of the second table +#' column in cm. It is set as NULL by default. +#'@param fileout A path of the location to save the scorecard plots. By default +#' the plots will be saved to the working directory. +#' +#'@return An image file containing the scorecard. +#'@examples +#'data <- array(rnorm(1000), dim = c('sdate' = 12, 'metric' = 4, 'region' = 3, +#' 'time' = 6)) +#'row_names <- c('Tropics', 'Extra-tropical NH', 'Extra-tropical SH') +#'col_names <- c('Mean bias (K)', 'Correlation', 'RPSS','CRPSS') +#'VizScorecard(data = data, row_names = row_names, col_names = col_names, +#' subcol_names = month.abb[as.numeric(1:12)], +#' row_title = 'Region', subrow_title = 'Forecast Month', +#' col_title = 'Start date', +#' table_title = "Temperature of ECMWF System 5", +#' table_subtitle = "(Ref: ERA5 1994-2016)", +#' fileout = 'test.png') +#' +#'@import kableExtra +#'@importFrom s2dv Reorder +#'@importFrom ClimProjDiags Subset +#'@importFrom CSTools MergeDims +#'@export +VizScorecard <- function(data, sign = NULL, row_dim = 'region', + subrow_dim = 'time', col_dim = 'metric', + subcol_dim = 'sdate', legend_dim = 'metric', + row_names = NULL, subrow_names = NULL, + col_names = NULL, subcol_names = NULL, + row_title = NULL, subrow_title = NULL, + col_title = NULL, table_title = NULL, + table_subtitle = NULL, legend_breaks = NULL, + plot_legend = TRUE, label_scale = 1.4, + legend_width = NULL, legend_height = 50, + palette = NULL, colorunder = NULL, colorsup = NULL, + round_decimal = 2, font_size = 1.1, + legend_white_space = 6, columns_width = 1.2, + col1_width = NULL, col2_width = NULL, + fileout = './scorecard.png') { + + # Input parameter checks + # Check data + if (!is.array(data)) { + stop("Parameter 'data' must be a numeric array.") + } + if (length(dim(data)) != 4) { + stop("Parameter 'data' must have four dimensions.") + } + dimnames <- names(dim(data)) + # Check sign + if (is.null(sign)) { + sign <- array(FALSE, dim = dim(data)) + } else { + if (!is.array(sign)) { + stop("Parameter 'sign' must be a boolean array or NULL.") + } + if (any(sort(names(dim(sign))) != sort(dimnames))) { + stop("Parameter 'sign' must have same dimensions as 'data'.") + } + if (typeof(sign) != 'logical') { + stop("Parameter 'sign' must be an array with logical values.") + } + } + # Check row_dim + if (!is.character(row_dim)) { + stop("Parameter 'row_dim' must be a character string.") + } + if (!row_dim %in% names(dim(data))) { + stop("Parameter 'row_dim' is not found in 'data' dimensions.") + } + # Check row_names + if (is.null(row_names)) { + row_names <- as.character(1:dim(data)[row_dim]) + } + if (length(row_names) != as.numeric(dim(data)[row_dim])) { + stop("Parameter 'row_names' must have the same length of dimension ", + "'row_dim'.") + } + # Check subrow_dim + if (!is.character(subrow_dim)) { + stop("Parameter 'subrow_dim' must be a character string.") + } + if (!subrow_dim %in% names(dim(data))) { + stop("Parameter 'subrow_dim' is not found in 'data' dimensions.") + } + # Check subrow_names + if (is.null(subrow_names)) { + subrow_names <- as.character(1:dim(data)[subrow_dim]) + } + if (length(subrow_names) != as.numeric(dim(data)[subrow_dim])) { + stop("Parameter 'subrow_names' must have the same length of dimension ", + "'subrow_dim'.") + } + # Check col_dim + if (!is.character(col_dim)) { + stop("Parameter 'col_dim' must be a character string.") + } + if (!col_dim %in% names(dim(data))) { + stop("Parameter 'col_dim' is not found in 'data' dimensions.") + } + # Check col_names + if (is.null(col_names)) { + col_names <- as.character(1:dim(data)[col_dim]) + } + if (length(col_names) != as.numeric(dim(data)[col_dim])) { + stop("Parameter 'col_names' must have the same length of dimension ", + "'col_dim'.") + } + # Check subcol_dim + if (!is.character(subcol_dim)) { + stop("Parameter 'subcol_dim' must be a character string.") + } + if (!subcol_dim %in% names(dim(data))) { + stop("Parameter 'subcol_dim' is not found in 'data' dimensions.") + } + # Check subcol_names + if (is.null(subcol_names)) { + subcol_names <- as.character(1:dim(data)[subcol_dim]) + } + if (length(subcol_names) != as.numeric(dim(data)[subcol_dim])) { + stop("Parameter 'subcol_names' must have the same length of dimension ", + "'subcol_dim'.") + } + # Check legend_dim + if (!is.character(legend_dim)) { + stop("Parameter 'legend_dim' must be a character string.") + } + if (!legend_dim %in% names(dim(data))) { + stop("Parameter 'legend_dim' is not found in 'data' dimensions.") + } + # Check row_title + if (is.null(row_title)) { + row_title <- "" + } else { + if (!is.character(row_title)) { + stop("Parameter 'row_title' must be a character string.") + } + } + # Check subrow_title + if (is.null(subrow_title)) { + subrow_title <- "" + } else { + if (!is.character(subrow_title)) { + stop("Parameter 'subrow_title' must be a character string.") + } + } + # Check col_title + if (is.null(col_title)) { + col_title <- "" + } else { + if (!is.character(col_title)) { + stop("Parameter 'col_title' must be a character string.") + } + } + # Check table_title + if (is.null(table_title)) { + table_title <- "" + } else { + if (!is.character(table_title)) { + stop("Parameter 'table_title' must be a character string.") + } + } + # Check table_subtitle + if (is.null(table_subtitle)) { + table_subtitle <- "" + } else { + if (!is.character(table_subtitle)) { + stop("Parameter 'table_subtitle' must be a character string.") + } + } + # Check legend_breaks + if (inherits(legend_breaks, 'list')) { + if (!(length(legend_breaks) == as.numeric(dim(data)[legend_dim]))) { + stop("Parameter 'legend_breaks' must be a list with the same number of ", + "elements as the length of the 'legend_dim' dimension in data.") + } + } else if (is.numeric(legend_breaks)) { + legend_breaks <- rep(list(legend_breaks), as.numeric(dim(data)[legend_dim])) + } else if (is.null(legend_breaks)) { + legend_breaks <- rep(list(seq(-1, 1, 0.2)), as.numeric(dim(data)[legend_dim])) + } else { + stop("Parameter 'legend_breaks' must be a numeric vector, a list or NULL.") + } + # Check plot_legend + if (!inherits(plot_legend, 'logical')) { + stop("Parameter 'plot_legend' must be a logical value.") + } + # Check label_scale + if (any(!is.numeric(label_scale), length(label_scale) != 1)) { + stop("Parameter 'label_scale' must be a numeric value of length 1.") + } + # Check legend_width + if (is.null(legend_width)) { + legend_width <- length(subcol_names) * 46.5 + } else if (any(!is.numeric(legend_width), length(legend_width) != 1)) { + stop("Parameter 'legend_width' must be a numeric value of length 1.") + } + # Check legend_height + if (any(!is.numeric(legend_height), length(legend_height) != 1)) { + stop("Parameter 'legend_height' must be a numeric value of length 1.") + } + # Check colour palette input + if (inherits(palette, 'list')) { + if (!(length(palette) == as.numeric(dim(data)[legend_dim]))) { + stop("Parameter 'palette' must be a list with the same number of ", + "elements as the length of the 'legend_dim' dimension in data.") + } + if (!all(sapply(palette, is.character))) { + stop("Parameter 'palette' must be a list of character vectors.") + } + } else if (is.character(palette)) { + palette <- rep(list(palette), as.numeric(dim(data)[legend_dim])) + } else if (is.null(palette)) { + n <- length(legend_breaks[[1]]) + if (n == 1) { + stop("Parameter 'legend_breaks' can't be of length 1.") + } else if (n == 2) { + colors <- c('#B35806') + } else if (n == 3) { + colors <- c('#8073AC', '#E08214') + } else if (n == 11) { + colors <- c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', + '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08') + } else { + stop("Parameter 'palette' must be provided when 'legend_breaks' ", + "exceed the length of 11.") + } + palette <- rep(list(colors), as.numeric(dim(data)[legend_dim])) + } else { + stop("Parameter 'palette' must be a character vector, a list or NULL.") + } + # Check colorunder + if (is.null(colorunder)) { + colorunder <- rep("#04040E", as.numeric(dim(data)[legend_dim])) + } + if (!is.character(colorunder)) { + stop("Parameter 'colorunder' must be a character string vector.") + } + if (length(colorunder) == 1) { + colorunder <- rep(colorunder, as.numeric(dim(data)[legend_dim])) + } + if (length(colorunder) != as.numeric(dim(data)[legend_dim])) { + stop("Parameter 'colorunder' must be a list with the same number of ", + "elements as the length of the 'legend_dim' dimension in data.") + } + # Check colorsup + if (is.null(colorsup)) { + colorsup <- rep("#730C04", as.numeric(dim(data)[legend_dim])) + } + if (!is.character(colorsup)) { + stop("Parameter 'colorsup' must be a character string vector.") + } + if (length(colorsup) == 1) { + colorsup <- rep(colorsup, as.numeric(dim(data)[legend_dim])) + } + if (length(colorsup) != as.numeric(dim(data)[legend_dim])) { + stop("Parameter 'colorsup' must be a list with the same number of ", + "elements as the length of the 'legend_dim' dimension in data.") + } + # Check round_decimal + if (!is.numeric(round_decimal)) { + stop("Parameter 'round_decimal' must be a numeric value of length 1.") + } + # Check font_size + if (!is.numeric(font_size)) { + stop("Parameter 'font_size' must be a numeric value of length 1.") + } + # Check legend white space + if (!is.numeric(legend_white_space)) { + stop("Parameter 'legend_white_space' must be a numeric value of length 1.") + } + # columns_width + if (!is.numeric(columns_width)) { + stop("Parameter 'columns_width' must be a numeric value.") + } + # Check col1_width + if (is.null(col1_width)) { + if (max(nchar(row_names)) == 1) { + col1_width <- max(nchar(row_names)) + } else { + col1_width <- max(nchar(row_names))/4 + } + } else if (!is.numeric(col1_width)) { + stop("Parameter 'col1_width' must be a numeric value of length 1.") + } + # Check col2_width + if (is.null(col2_width)) { + if (max(nchar(subrow_names)) == 1 ) { + col2_width <- max(nchar(subrow_names)) + } else { + col2_width <- max(nchar(subrow_names))/4 + } + } else if (!is.numeric(col2_width)) { + stop("Parameter 'col2_width' must be a numeric value of length 1.") + } + + # Get dimensions of inputs + n_col_names <- length(col_names) + n_subcol_names <- length(subcol_names) + n_row_names <- length(row_names) + n_subrow_names <- length(subrow_names) + + # Define table size + n_rows <- n_row_names * n_subrow_names + n_columns <- 2 + (n_col_names * n_subcol_names) + + # Column names + row_names_table <- rep("", n_rows) + for (row in 1:n_row_names) { + row_names_table[floor(n_subrow_names/2) + (row - 1) * n_subrow_names] <- row_names[row] + } + + # Define scorecard table titles + column_titles <- c(row_title, subrow_title, rep(c(subcol_names), n_col_names)) + + # Round data + data <- round(data, round_decimal) + + # Define data inside the scorecards table + for (row in 1:n_row_names) { + table_temp <- data.frame(table_column_2 = as.character(subrow_names)) + for (col in 1:n_col_names) { + table_temp <- data.frame(table_temp, + Reorder(data = Subset(x = data, along = c(col_dim, row_dim), + indices = list(col, row), drop = 'selected'), + order = c(subrow_dim, subcol_dim))) + } + if (row == 1) { + table_data <- table_temp + } else { + table_data <- rbind(table_data, table_temp) + } + } + + # All data for plotting in table + table <- data.frame(table_column_1 = row_names_table, table_data) + table_temp <- array(unlist(table[3:n_columns]), dim = c(n_rows, n_columns - 2)) + + # Define colors to show in table + table_colors <- .ScorecardColors(table = table_temp, n_col = n_col_names, + n_subcol = n_subcol_names, n_row = n_row_names, + n_subrow = n_subrow_names, legend_breaks = legend_breaks, + palette = palette, colorunder = colorunder, + colorsup = colorsup) + metric_color <- table_colors$metric_color + metric_text_color <- table_colors$metric_text_color + # metric_text_bold <- table_colors$metric_text_bold + + # Remove temporary table + rm(table_temp) + + # Format values to underline in table + metric_underline <- MergeDims(sign, c(subcol_dim, col_dim), + rename_dim = 'col', na.rm = FALSE) + metric_underline <- MergeDims(metric_underline, c(subrow_dim, row_dim), + rename_dim = 'row', na.rm = FALSE) + metric_underline <- Reorder(metric_underline, c('row', 'col')) + + options(stringsAsFactors = FALSE) + title <- data.frame(c1 = table_title, c2 = n_columns) + subtitle <- data.frame(c1 = table_subtitle, c2 = n_columns) + header.names <- as.data.frame(data.frame(c1 = c("", col_names), + c2 = c(2, rep(n_subcol_names, n_col_names)))) + header.names2 <- as.data.frame(data.frame(c1 = c("", paste0(rep(col_title, n_col_names))), + c2 = c(2, rep(n_subcol_names, n_col_names)))) + title.space <- data.frame(c1 = "\n", c2 = n_columns) + + # Hide NA values in table + options(knitr.kable.NA = '') + + # Create HTML table + table_html_part <- list() + table_html_part[[1]] <- kbl(table, escape = F, col_names = column_titles, align = rep("c", n_columns)) %>% + kable_paper("hover", full_width = FALSE, font_size = 14 * font_size) %>% + add_header_above(header = header.names2, font_size = 16 * font_size) %>% + add_header_above(header = title.space, font_size = 10 * font_size) %>% + add_header_above(header = header.names, font_size = 20 * font_size) %>% + add_header_above(header = title.space, font_size = 10 * font_size) %>% + add_header_above(header = subtitle, font_size = 16 * font_size, align = "left") %>% + add_header_above(header = title.space, font_size = 10 * font_size) %>% + add_header_above(header = title, font_size = 22 * font_size, align = "left") + + for (i in 1:n_col_names) { + for (j in 1:n_subcol_names) { + my_background <- metric_color[, (i - 1) * n_subcol_names + j] + my_text_color <- metric_text_color[, (i - 1) * n_subcol_names + j] + my_underline <- metric_underline[, (i - 1) * n_subcol_names + j] + # my_bold <- metric_text_bold[(i - 1) * n_subcol_names + j] + + table_html_part[[(i - 1) * n_subcol_names + j + 1]] <- + column_spec(table_html_part[[(i - 1) * n_subcol_names + j]], + 2 + n_subcol_names * (i - 1) + j, + background = my_background[1:n_rows], + color = my_text_color[1:n_rows], + underline = my_underline[1:n_rows], + bold = T) # strsplit(toString(bold), ', ')[[1]] + } + } + + # Define position of table borders + column_borders <- NULL + for (i in 1:n_col_names) { + column_spacing <- (n_subcol_names * i) + 2 + column_borders <- c(column_borders, column_spacing) + } + + n_last_list <- n_col_names * n_subcol_names + 1 + + table_html <- column_spec(table_html_part[[n_last_list]], 1, bold = TRUE, + width_min = paste0(col1_width, 'cm')) %>% + column_spec(2, bold = TRUE, width_min = paste0(col2_width, 'cm')) %>% + column_spec(3:n_columns, width_min = paste0(columns_width, 'cm')) %>% + column_spec(c(1, 2, column_borders), border_right = "2px solid black") %>% + column_spec(1, border_left = "2px solid black") %>% + column_spec(n_columns, border_right = "2px solid black") %>% + row_spec(seq(from = 0, to = n_subrow_names * n_row_names, by = n_subrow_names), + extra_css = "border-bottom: 2px solid black", hline_after = TRUE) + if (plot_legend == TRUE) { + # Save the scorecard (without legend) + save_kable(table_html, file = paste0(fileout, '_tmpScorecard.png'), vheight = 1) + + # White space for legend + legend_white_space <- 37.8 * legend_white_space # converting pixels to cm + + # Create and save color bar legend + .ScorecardLegend(legend_breaks = legend_breaks, + palette = palette, + colorunder = colorunder, + colorsup = colorsup, + label_scale = label_scale, + legend_width = legend_width, + legend_height = legend_height, + legend_white_space = legend_white_space, + fileout = fileout) + + # Add the legends below the scorecard table + system(paste0('convert -append ', fileout, '_tmpScorecard.png ', fileout, + '_tmpScorecardLegend.png ', fileout)) + # Remove temporary scorecard table + unlink(paste0(fileout, '_tmpScorecard*.png')) + } + if (plot_legend == FALSE) { + save_kable(table_html, file = fileout) + } +} + +# Scorecards function to create the color bar legends for the required metrics +# and paste them below the scorecard table +.ScorecardLegend <- function(legend_breaks, palette, colorunder, colorsup, + label_scale, legend_width, legend_height, + legend_white_space, fileout) { + + # Create color bar legends for each metric + for (i in 1:length(palette)) { + png(filename = paste0(fileout, '_tmpLegend', i, '.png'), width = legend_width, + height = legend_height) + ColorBarContinuous(brks = legend_breaks[[i]], cols = palette[[i]], vertical = FALSE, + label_scale = label_scale, col_inf = colorunder[[i]], + col_sup = colorsup[[i]]) + dev.off() + if (i == 1) { + # Add white space to the left of the first color bar legend + system(paste0('convert ', fileout, '_tmpLegend1.png -background white -splice ', + legend_white_space, 'x0 ', fileout, '_tmpScorecardLegend.png')) + } else { + system(paste0('convert +append ', fileout, '_tmpScorecardLegend.png ', + fileout, '_tmpLegend', i, '.png ', fileout, + '_tmpScorecardLegend.png')) + } + } + unlink(paste0(fileout,'_tmpLegend*.png')) +} + +# Scorecards function to assign background color of table cells, +# color of text in table and to bold the text. +# +# It will return a list with 2 arrays: +# (1) metric_color, A 2-dimensional array with character strings containing the +# color codes for each cell background. +# (2) metric_text_color, A 2-dimensional array with character strings +# containing the color codes for each cell text. +.ScorecardColors <- function(table, n_col, n_subcol, n_row, n_subrow, + legend_breaks, palette, colorunder, colorsup) { + # Define rows and columns + n_rows <- n_row * n_subrow + n_columns <- n_col * n_subcol + + # Set table background colors + metric_color <- array(colorunder, c(n_row * n_subrow, n_columns)) + metric_text_color <- array("#2A2A2A", c(n_row * n_subrow , n_columns)) + # metric_text_bold <- array(TRUE, c(n_row * n_subrow , n_columns - 2)) # Setting all values to bold + + # Define cell and text colors to show in table + for (i in 1:n_col) { + metric_int <- legend_breaks[[i]] + for (rr in 1:n_rows) { + for (j in 1:n_subcol) { + for (pp in 1:(length(metric_int) - 1)) { + if (is.na(table[rr, ((i - 1) * n_subcol + j)])) { + metric_color[rr, ((i - 1) * n_subcol + j)] <- "gray" + } else { + if (table[rr, ((i - 1) * n_subcol + j)] >= + metric_int[pp] && table[rr, ((i - 1) * n_subcol + j)] <= + metric_int[pp + 1]) { + metric_color[rr, ((i - 1) * n_subcol + j)] <- palette[[i]][pp] # palette[pp] + } + if (table[rr, ((i - 1) * n_subcol + j)] < metric_int[1]) { + metric_color[rr, ((i - 1) * n_subcol + j)] <- colorunder[i] + } + if (table[rr,((i - 1) * n_subcol + j)] >= + metric_int[length(metric_int)]) { + metric_color[rr, ((i - 1) * n_subcol + j)] <- colorsup[i] + } + } + # color text in white and bold if background is white or dark blue or dark red: + if (is.na(table[rr, ((i - 1) * n_subcol + j)]) || + (!is.na(table[rr, ((i - 1) * n_subcol + j)]) && pp == 1 && + table[rr, ((i - 1) * n_subcol + j)] < metric_int[2]) || + (!is.na(table[rr, ((i - 1) * n_subcol + j)]) && pp == 2 && + table[rr, ((i - 1) * n_subcol + j)] < metric_int[3]) || + (!is.na(table[rr, ((i - 1) * n_subcol + j)]) && pp == (length(metric_int) - 1) && + table[rr, ((i - 1) * n_subcol + j)] >= metric_int[length(metric_int) - 1]) || + (!is.na(table[rr, ((i - 1) * n_subcol + j)]) && pp == (length(metric_int) - 2) && + table[rr, ((i - 1) * n_subcol + j)] >= metric_int[length(metric_int) - 2])) { + metric_text_color[rr, ((i - 1) * n_subcol + j)] <- "white" + # metric_text_bold[rr,((i - 1) * n_subcol + j)] <- TRUE + } + } + } + } + } + return(list(metric_color = metric_color, + metric_text_color = metric_text_color)) +} \ No newline at end of file diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 889bdc1e..2e266bbf 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -10,7 +10,13 @@ source('modules/Scorecards/R/tmp/SCTransform.R') source('modules/Scorecards/R/tmp/ScorecardsSingle.R') source('modules/Scorecards/R/tmp/ScorecardsMulti.R') source('modules/Scorecards/R/tmp/ScorecardsSystemDiff.R') -source('modules/Scorecards/R/tmp/SCPlotScorecard.R') +source('modules/Scorecards/R/tmp/VizScorecard.R') + +## Temporary for new ESviz function +source('modules/Scorecards/R/tmp/ColorBarContinuous.R') +source('modules/Scorecards/R/tmp/ClimPalette.R') +.IsColor <- s2dv:::.IsColor +.FilterUserGraphicArgs <- s2dv:::.FilterUserGraphicArgs ## TODO: Change function name to 'Scorecards'? @@ -34,7 +40,7 @@ Scorecards <- function(recipe) { if (recipe$Analysis$Workflow$Scorecards$start_months == 'all' || is.null(recipe$Analysis$Workflow$Scorecards$start_months)) { start.months <- as.numeric(substr(recipe$Analysis$Time$sdate, 1,2)) } else { - start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, + start.months <- as.numeric(strsplit(recipe$Analysis$Workflow$Scorecards$start_months, split = ", | |,")[[1]]) if(!any(as.numeric(substr(recipe$Analysis$Time$sdate, 1,2))) %in% start.months){ error(recipe$Run$logger,"Requested start dates for scorecards must be loaded") @@ -51,7 +57,8 @@ Scorecards <- function(recipe) { metric.aggregation <- recipe$Analysis$Workflow$Scorecards$metric_aggregation metrics.load <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Skill$metric), ", | |,")) metrics.visualize <- unlist(strsplit(tolower(recipe$Analysis$Workflow$Scorecards$metric), ", | |,")) - + ncores <- 1 # recipe$Analysis$ncores + if(is.null(recipe$Analysis$Workflow$Scorecards$signif_alpha)){ alpha <- 0.05 } else { @@ -70,28 +77,57 @@ Scorecards <- function(recipe) { na.rm <- recipe$Analysis$remove_NAs } - ncores <- 1 # recipe$Analysis$ncores - ## Parameters for scorecard layout + table.label <- recipe$Analysis$Workflow$Scorecards$table_label fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width col2.width <- recipe$Analysis$Workflow$Scorecards$col2_width - columns.width <- recipe$Analysis$Workflow$Scorecards$columns_width legend.breaks <- recipe$Analysis$Workflow$Scorecards$legend_breaks - legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space legend.width <- recipe$Analysis$Workflow$Scorecards$legend_width - legend.height <- recipe$Analysis$Workflow$Scorecards$legend_height - label.scale <- recipe$Analysis$Workflow$Scorecards$label_scale - round.decimal <- recipe$Analysis$Workflow$Scorecards$round_decimal - font.size <- recipe$Analysis$Workflow$Scorecards$font_size - + if (is.null(recipe$Analysis$Workflow$Scorecards$plot_legend)){ plot.legend <- TRUE } else { plot.legend <- recipe$Analysis$Workflow$Scorecards$plot_legend } + if(is.null(recipe$Analysis$Workflow$Scorecards$columns_width)){ + columns.width <- 1.2 + } else { + columns.width <- recipe$Analysis$Workflow$Scorecards$columns_width + } + + if(is.null(recipe$Analysis$Workflow$Scorecards$legend_white_space)){ + legend.white.space <- 6 + } else { + legend.white.space <- recipe$Analysis$Workflow$Scorecards$legend_white_space + } + + if(is.null(recipe$Analysis$Workflow$Scorecards$legend_height)){ + legend.height <- 50 + } else { + legend.height <- recipe$Analysis$Workflow$Scorecards$legend_height + } + + if(is.null(recipe$Analysis$Workflow$Scorecards$label_scale)){ + label.scale <- 1.4 + } else { + label.scale <- recipe$Analysis$Workflow$Scorecards$label_scale + } + + if(is.null(recipe$Analysis$Workflow$Scorecards$round_decimal)){ + round.decimal <- 2 + } else { + round.decimal <- recipe$Analysis$Workflow$Scorecards$round_decimal + } + + if(is.null(recipe$Analysis$Workflow$Scorecards$font_size)){ + font.size <- 1.1 + } else { + font.size <- recipe$Analysis$Workflow$Scorecards$font_size + } + ## Define if difference scorecard is to be plotted if (is.null(recipe$Analysis$Workflow$Scorecards$calculate_diff)){ calculate.diff <- FALSE -- GitLab From f309f09542bfb7ba7450a0319fb3062dc5e203dd Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 19 Jan 2024 12:56:51 +0100 Subject: [PATCH 61/78] VizScorecard function corrections --- modules/Scorecards/R/tmp/VizScorecard.R | 128 +++++++++++++----------- 1 file changed, 68 insertions(+), 60 deletions(-) diff --git a/modules/Scorecards/R/tmp/VizScorecard.R b/modules/Scorecards/R/tmp/VizScorecard.R index f3b50a6b..425b799f 100644 --- a/modules/Scorecards/R/tmp/VizScorecard.R +++ b/modules/Scorecards/R/tmp/VizScorecard.R @@ -59,18 +59,19 @@ #' list of vectors can be given as input if different colors are desired for #' the legend_dims. This parameter must be included even if the legend is #' not plotted, to define the colors in the scorecard table. -#'@param colorunder A character string or of vector of character strings -#' defining the colors to use for data values with are inferior to the lowest -#' breaks value. This parameter will also plot a inferior triangle in the -#' legend bar. The parameter can be set to NULL if there are no inferior values. -#' If a character string is given this color will be applied to all -#' 'legend_dims'. It is set as NULL by default. -#'@param colorsup A character string or of vector of character strings -#' defining the colors to use for data values with are superior to the highest -#' breaks value. This parameter will also plot a inferior triangle in the -#' legend bar. The parameter can be set to NULL if there are no superior values. -#' If a character string is given this color will be applied to all -#' legend_dims. It is set as NULL by default. +#'@param colorunder A character string, a vector of character strings or a +#' list with single character string elements defining the colors to use for +#' data values with are inferior to the lowest breaks value. This parameter +#' will also plot a inferior triangle in the legend bar. The parameter can be +#' set to NULL if there are no inferior values. If a character string is given +#' this color will be applied to all 'legend_dims'. It is set as NULL by +#' default. +#'@param colorsup A character string, a vector of character strings or a +#' list with single character string elements defining the colors to use for +#' data values with are superior to the highest breaks value. This parameter +#' will also plot a inferior triangle in the legend bar. The parameter can be +#' set to NULL if there are no superior values. If a character string is given +#' this color will be applied to all legend_dims. It is set as NULL by default. #'@param round_decimal A numeric indicating to which decimal point the data #' is to be displayed in the scorecard table. It is set as 2 by default. #'@param font_size A numeric indicating the font size on the scorecard table. @@ -103,6 +104,7 @@ #' fileout = 'test.png') #' #'@import kableExtra +#'@importFrom RColorBrewer brewer.pal #'@importFrom s2dv Reorder #'@importFrom ClimProjDiags Subset #'@importFrom CSTools MergeDims @@ -306,9 +308,11 @@ VizScorecard <- function(data, sign = NULL, row_dim = 'region', } else if (n == 11) { colors <- c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08') - } else { + } else if (n > 11) { stop("Parameter 'palette' must be provided when 'legend_breaks' ", "exceed the length of 11.") + } else { + colors <- rev(brewer.pal(n-1, "PuOr")) } palette <- rep(list(colors), as.numeric(dim(data)[legend_dim])) } else { @@ -317,30 +321,34 @@ VizScorecard <- function(data, sign = NULL, row_dim = 'region', # Check colorunder if (is.null(colorunder)) { colorunder <- rep("#04040E", as.numeric(dim(data)[legend_dim])) - } - if (!is.character(colorunder)) { - stop("Parameter 'colorunder' must be a character string vector.") } if (length(colorunder) == 1) { colorunder <- rep(colorunder, as.numeric(dim(data)[legend_dim])) } if (length(colorunder) != as.numeric(dim(data)[legend_dim])) { - stop("Parameter 'colorunder' must be a list with the same number of ", - "elements as the length of the 'legend_dim' dimension in data.") + stop("Parameter 'colorunder' must be a character string vector or a list ", + "with the same number of elements as the length of the 'legend_dim' ", + "dimension in data.") + } + if (!is.character(unlist(colorunder))) { + stop("Parameter 'colorunder' must be a character string vector ", + "or a list of character string elements.") } # Check colorsup if (is.null(colorsup)) { colorsup <- rep("#730C04", as.numeric(dim(data)[legend_dim])) } - if (!is.character(colorsup)) { - stop("Parameter 'colorsup' must be a character string vector.") - } if (length(colorsup) == 1) { colorsup <- rep(colorsup, as.numeric(dim(data)[legend_dim])) } if (length(colorsup) != as.numeric(dim(data)[legend_dim])) { - stop("Parameter 'colorsup' must be a list with the same number of ", - "elements as the length of the 'legend_dim' dimension in data.") + stop("Parameter 'colorsup' must be a character string vector or a list ", + "with the same number of elements as the length of the 'legend_dim' ", + "dimension in data.") + } + if (!is.character(unlist(colorsup))) { + stop("Parameter 'colorsup' must be a character string vector ", + "or a list of character string elements.") } # Check round_decimal if (!is.numeric(round_decimal)) { @@ -444,25 +452,25 @@ VizScorecard <- function(data, sign = NULL, row_dim = 'region', options(stringsAsFactors = FALSE) title <- data.frame(c1 = table_title, c2 = n_columns) subtitle <- data.frame(c1 = table_subtitle, c2 = n_columns) - header.names <- as.data.frame(data.frame(c1 = c("", col_names), + header_names <- as.data.frame(data.frame(c1 = c("", col_names), c2 = c(2, rep(n_subcol_names, n_col_names)))) - header.names2 <- as.data.frame(data.frame(c1 = c("", paste0(rep(col_title, n_col_names))), + header_names2 <- as.data.frame(data.frame(c1 = c("", paste0(rep(col_title, n_col_names))), c2 = c(2, rep(n_subcol_names, n_col_names)))) - title.space <- data.frame(c1 = "\n", c2 = n_columns) + title_space <- data.frame(c1 = "\n", c2 = n_columns) # Hide NA values in table options(knitr.kable.NA = '') # Create HTML table table_html_part <- list() - table_html_part[[1]] <- kbl(table, escape = F, col_names = column_titles, align = rep("c", n_columns)) %>% + table_html_part[[1]] <- kbl(table, escape = F, col.names = column_titles, align = rep("c", n_columns)) %>% kable_paper("hover", full_width = FALSE, font_size = 14 * font_size) %>% - add_header_above(header = header.names2, font_size = 16 * font_size) %>% - add_header_above(header = title.space, font_size = 10 * font_size) %>% - add_header_above(header = header.names, font_size = 20 * font_size) %>% - add_header_above(header = title.space, font_size = 10 * font_size) %>% + add_header_above(header = header_names2, font_size = 16 * font_size) %>% + add_header_above(header = title_space, font_size = 10 * font_size) %>% + add_header_above(header = header_names, font_size = 20 * font_size) %>% + add_header_above(header = title_space, font_size = 10 * font_size) %>% add_header_above(header = subtitle, font_size = 16 * font_size, align = "left") %>% - add_header_above(header = title.space, font_size = 10 * font_size) %>% + add_header_above(header = title_space, font_size = 10 * font_size) %>% add_header_above(header = title, font_size = 22 * font_size, align = "left") for (i in 1:n_col_names) { @@ -529,33 +537,6 @@ VizScorecard <- function(data, sign = NULL, row_dim = 'region', } } -# Scorecards function to create the color bar legends for the required metrics -# and paste them below the scorecard table -.ScorecardLegend <- function(legend_breaks, palette, colorunder, colorsup, - label_scale, legend_width, legend_height, - legend_white_space, fileout) { - - # Create color bar legends for each metric - for (i in 1:length(palette)) { - png(filename = paste0(fileout, '_tmpLegend', i, '.png'), width = legend_width, - height = legend_height) - ColorBarContinuous(brks = legend_breaks[[i]], cols = palette[[i]], vertical = FALSE, - label_scale = label_scale, col_inf = colorunder[[i]], - col_sup = colorsup[[i]]) - dev.off() - if (i == 1) { - # Add white space to the left of the first color bar legend - system(paste0('convert ', fileout, '_tmpLegend1.png -background white -splice ', - legend_white_space, 'x0 ', fileout, '_tmpScorecardLegend.png')) - } else { - system(paste0('convert +append ', fileout, '_tmpScorecardLegend.png ', - fileout, '_tmpLegend', i, '.png ', fileout, - '_tmpScorecardLegend.png')) - } - } - unlink(paste0(fileout,'_tmpLegend*.png')) -} - # Scorecards function to assign background color of table cells, # color of text in table and to bold the text. # @@ -616,4 +597,31 @@ VizScorecard <- function(data, sign = NULL, row_dim = 'region', } return(list(metric_color = metric_color, metric_text_color = metric_text_color)) -} \ No newline at end of file +} + +# Scorecards function to create the color bar legends for the required metrics +# and paste them below the scorecard table +.ScorecardLegend <- function(legend_breaks, palette, colorunder, colorsup, + label_scale, legend_width, legend_height, + legend_white_space, fileout) { + + # Create color bar legends for each metric + for (i in 1:length(palette)) { + png(filename = paste0(fileout, '_tmpLegend', i, '.png'), width = legend_width, + height = legend_height) + ColorBarContinuous(brks = legend_breaks[[i]], cols = palette[[i]], vertical = FALSE, + label_scale = label_scale, col_inf = colorunder[[i]], + col_sup = colorsup[[i]]) + dev.off() + if (i == 1) { + # Add white space to the left of the first color bar legend + system(paste0('convert ', fileout, '_tmpLegend1.png -background white -splice ', + legend_white_space, 'x0 ', fileout, '_tmpScorecardLegend.png')) + } else { + system(paste0('convert +append ', fileout, '_tmpScorecardLegend.png ', + fileout, '_tmpLegend', i, '.png ', fileout, + '_tmpScorecardLegend.png')) + } + } + unlink(paste0(fileout,'_tmpLegend*.png')) +} -- GitLab From 8152ca0c1eea5709347d273a7dc5865bed6ab3f8 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 19 Jan 2024 13:03:57 +0100 Subject: [PATCH 62/78] remove testing parameters --- modules/Scorecards/Scorecards.R | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 2e266bbf..16de2a2d 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -24,9 +24,8 @@ source('modules/Scorecards/R/tmp/ClimPalette.R') Scorecards <- function(recipe) { ## Parameters for loading data files - input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/testing/" #temp - skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") - stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") + skill.input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") + stats.input.path <- paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") dir.create(output.path, recursive = T, showWarnings = F) system <- recipe$Analysis$Datasets$System$name -- GitLab From cc2d92cd6403dab70c1dacf35aa319acf4ba5ce4 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Thu, 25 Jan 2024 16:39:50 +0100 Subject: [PATCH 63/78] code fixes to be done --- modules/Scorecards/R/tmp/LoadMetrics.R | 6 ++++++ modules/Scorecards/Scorecards.R | 30 +++++++++++++++----------- 2 files changed, 24 insertions(+), 12 deletions(-) diff --git a/modules/Scorecards/R/tmp/LoadMetrics.R b/modules/Scorecards/R/tmp/LoadMetrics.R index 55030d95..3ba9fa3c 100644 --- a/modules/Scorecards/R/tmp/LoadMetrics.R +++ b/modules/Scorecards/R/tmp/LoadMetrics.R @@ -50,6 +50,10 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, calib_method = NULL, syear = NULL) { + ## TO DO: + ## Include condition for region + ## Change lat lon attributes + ## Remove . from names system <- gsub('.','', system, fixed = T) reference <- gsub('.','', reference, fixed = T) @@ -72,6 +76,8 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, forecast_months = forecast_months, metrics = metrics, calib_method = calib_method, syear = syear) + # attributes(met)$lon <- as.vector(attributes(met)$Variable$dat$lon) + # attributes(met)$lat <- as.vector(attributes(met)$Variable$dat$lat) ## Save metric data as array in reference list by_reference[[reference[ref]]] <- met ## Remove -Inf from crpss data if variable is precipitation diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 16de2a2d..75a22196 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -18,14 +18,19 @@ source('modules/Scorecards/R/tmp/ClimPalette.R') .IsColor <- s2dv:::.IsColor .FilterUserGraphicArgs <- s2dv:::.FilterUserGraphicArgs +## TO DO: +## Apply condition in SingleScorecards/MultiScorecards to only make transformed scorecard when sdate > ftime -## TODO: Change function name to 'Scorecards'? ## Define function Scorecards <- function(recipe) { ## Parameters for loading data files - skill.input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") - stats.input.path <- paste0(recipe$Run$output_dir, "/outputs/Statistics/") + + input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/testing/" #temp + skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") + stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") + # skill.input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") + # stats.input.path <- paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") dir.create(output.path, recursive = T, showWarnings = F) system <- recipe$Analysis$Datasets$System$name @@ -77,7 +82,6 @@ Scorecards <- function(recipe) { } ## Parameters for scorecard layout - table.label <- recipe$Analysis$Workflow$Scorecards$table_label fileout.label <- recipe$Analysis$Workflow$Scorecards$fileout_label col1.width <- recipe$Analysis$Workflow$Scorecards$col1_width @@ -138,17 +142,19 @@ Scorecards <- function(recipe) { if(metric.aggregation == 'skill'){ ## Load data files - loaded_metrics <- LoadMetrics(system = system, + loaded_metrics <- LoadMetrics(input_path = skill.input.path, + system = system, reference = reference, var = var, - start.year = start.year, - end.year = end.year, metrics = metrics.visualize, ## metrics.load - start.months = start.months, - forecast.months = forecast.months, - inf.to.na = inf.to.na, - input.path = skill.input.path) - + period = period, + start_months = start.months, + forecast_months = forecast.months, + calib_method = calib.method, + syear = NULL, + inf_to_na = inf.to.na + ) + ## Spatial Aggregation of metrics if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ -- GitLab From bc3361602bfc0ee7d92a87dfb9601ce97f8c60a7 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Tue, 30 Jan 2024 17:26:35 +0100 Subject: [PATCH 64/78] included changes for skill aggregate --- modules/Scorecards/R/tmp/LoadMetrics.R | 13 +- modules/Scorecards/R/tmp/ScorecardsMulti.R | 254 ++++++++++++-------- modules/Scorecards/R/tmp/ScorecardsSingle.R | 141 ++++++----- modules/Scorecards/R/tmp/WeightedMetrics.R | 14 +- modules/Scorecards/Scorecards.R | 5 +- 5 files changed, 256 insertions(+), 171 deletions(-) diff --git a/modules/Scorecards/R/tmp/LoadMetrics.R b/modules/Scorecards/R/tmp/LoadMetrics.R index 3ba9fa3c..2cecd5c1 100644 --- a/modules/Scorecards/R/tmp/LoadMetrics.R +++ b/modules/Scorecards/R/tmp/LoadMetrics.R @@ -50,9 +50,7 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, calib_method = NULL, syear = NULL) { - ## TO DO: - ## Include condition for region - ## Change lat lon attributes + ## TO DO: Include condition for region ## Remove . from names system <- gsub('.','', system, fixed = T) @@ -76,8 +74,8 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, forecast_months = forecast_months, metrics = metrics, calib_method = calib_method, syear = syear) - # attributes(met)$lon <- as.vector(attributes(met)$Variable$dat$lon) - # attributes(met)$lat <- as.vector(attributes(met)$Variable$dat$lat) + attributes(met)$lon <- as.vector(attributes(met)$Variable$dat$lon) + attributes(met)$lat <- as.vector(attributes(met)$Variable$dat$lat) ## Save metric data as array in reference list by_reference[[reference[ref]]] <- met ## Remove -Inf from crpss data if variable is precipitation @@ -89,6 +87,11 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, all_metrics[[system[sys]]] <- by_reference } ## close loop on system + attributes(all_metrics)$metrics <- metrics + attributes(all_metrics)$start.months <- start.months + attributes(all_metrics)$forecast.months <- forecast.months + + return(all_metrics) } diff --git a/modules/Scorecards/R/tmp/ScorecardsMulti.R b/modules/Scorecards/R/tmp/ScorecardsMulti.R index 020a2047..be3aab71 100644 --- a/modules/Scorecards/R/tmp/ScorecardsMulti.R +++ b/modules/Scorecards/R/tmp/ScorecardsMulti.R @@ -4,6 +4,8 @@ #' and references (types 9 to 12). #'@param data is an array of spatially aggregated metrics containing the #' following dimensions; system, reference, metric, time, sdate, region. +#'@param sign is an array with the same dimensions as data indicting the +#' significance of the metrics, with either true, false or null. #'@param system a vector of character strings defining the systems following the #' archive.yml format from verification suite #'@param reference a vector of character strings defining the references @@ -92,8 +94,7 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, ## Make sure data is in correct order for using in functions: data_order <- c('system','reference','metric','time','sdate','region') data <- Reorder(data, data_order) - sign <- Reorder(sign, data_order) - + ## Identify metrics loaded metrics_loaded <- attributes(data)$metrics @@ -101,18 +102,28 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) attributes(data)$metrics <- metrics - sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) - attributes(sign)$metrics <- metrics + if(!is.null(sign)){ + sign <- Reorder(sign, data_order) + sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(sign)$metrics <- metrics + } ## Transform data for scorecards by forecast month (types 11 & 12) - transformed_data <- SCTransform(data = data, - sdate_dim = 'sdate', - ftime_dim = 'time') - - transformed_sign <- SCTransform(data = sign, - sdate_dim = 'sdate', - ftime_dim = 'time') + if(length(start.months) >= length(forecast.months)){ + + transformed_data <- SCTransform(data = data, + sdate_dim = 'sdate', + ftime_dim = 'time') + + if(!is.null(sign)){ + transformed_sign <- SCTransform(data = sign, + sdate_dim = 'sdate', + ftime_dim = 'time') + } else { + transformed_sign <- NULL + } + } ## Load configuration files if (is.null(recipe$Run$filesystem)) { @@ -231,10 +242,18 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, if(model == 'system'){ data_sc_9 <- Subset(data, c('reference','region'), list(1, reg), drop = 'selected') - sign_sc_9 <- Subset(sign, c('reference','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_9 <- Subset(sign, c('reference','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_9 <- NULL + } } else if(model == 'reference'){ data_sc_9 <- Subset(data, c('system','region'), list(1, reg), drop = 'selected') - sign_sc_9 <- Subset(sign, c('system','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_9 <- Subset(sign, c('system','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_9 <- NULL + } } VizScorecard(data = data_sc_9, @@ -281,10 +300,18 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, if(model == 'system'){ data_sc_10 <- Subset(Reorder(data, new_order), c('reference','region'), list(1, reg), drop = 'selected') - sign_sc_10 <- Subset(Reorder(sign, new_order), c('reference','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_10 <- Subset(Reorder(sign, new_order), c('reference','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_10 <- NULL + } } else if(model == 'reference'){ data_sc_10 <- Subset(Reorder(data, new_order), c('system','region'), list(1, reg), drop = 'selected') - sign_sc_10 <- Subset(Reorder(sign, new_order), c('system','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_10 <- Subset(Reorder(sign, new_order), c('system','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_10 <- NULL + } } VizScorecard(data = data_sc_10, sign = sign_sc_10, @@ -321,100 +348,121 @@ ScorecardsMulti <- function(data, sign, system, reference, var, start.year, #### Scorecard_type 11 #### ## (transformation only) - fileout <- .Filename(model = model, eval.name = eval.filename, var = var, - start.year = start.year, end.year = end.year, scorecard.type = 11, - region = sub(" ", "-", region.names[reg]), - fileout.label = fileout.label, output.path = output.path) + if(length(start.months) >= length(forecast.months)){ - if(model == 'system'){ - data_sc_11 <- Subset(transformed_data, c('reference','region'), list(1, reg), drop = 'selected') - sign_sc_11 <- Subset(transformed_sign, c('reference','region'), list(1, reg), drop = 'selected') - } else if(model == 'reference'){ - data_sc_11 <- Subset(transformed_data, c('system','region'), list(1, reg), drop = 'selected') - sign_sc_11 <- Subset(transformed_sign, c('system','region'), list(1, reg), drop = 'selected') + fileout <- .Filename(model = model, eval.name = eval.filename, var = var, + start.year = start.year, end.year = end.year, scorecard.type = 11, + region = sub(" ", "-", region.names[reg]), + fileout.label = fileout.label, output.path = output.path) + + if(model == 'system'){ + data_sc_11 <- Subset(transformed_data, c('reference','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_11 <- Subset(transformed_sign, c('reference','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_11 <- NULL + } + } else if(model == 'reference'){ + data_sc_11 <- Subset(transformed_data, c('system','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_11 <- Subset(transformed_sign, c('system','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_11 <- NULL + } + } + + VizScorecard(data = data_sc_11, + sign = sign_sc_11, + row_dim = model, + subrow_dim = 'time', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = model.name, + subrow_names = forecast.months, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = table.model.name, + subrow_title = 'Forecast Month', + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = 4, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) } - VizScorecard(data = data_sc_11, - sign = sign_sc_11, - row_dim = model, - subrow_dim = 'time', - col_dim = 'metric', - subcol_dim = 'sdate', - legend_dim = 'metric', - row_names = model.name, - subrow_names = forecast.months, - col_names = metric.names, - subcol_names = month.abb[as.numeric(start.months)], - table_title = table.title, - table_subtitle = table.subtitle, - row_title = table.model.name, - subrow_title = 'Forecast Month', - col_title = 'Target month', - legend_breaks = legend.breaks, - plot_legend = plot.legend, - label_scale = label.scale, - legend_width = legend.width, - legend_height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round_decimal = round.decimal, - font_size = font.size, - legend_white_space = legend.white.space, - col1_width = 4, - col2_width = col2.width, - columns_width = columns.width, - fileout = fileout) - - #### Scorecard_type 12 #### ## (transformation and reorder) - fileout <- .Filename(model = model, eval.name = eval.filename, var = var, - start.year = start.year, end.year = end.year, scorecard.type = 12, - region = sub(" ", "-", region.names[reg]), - fileout.label = fileout.label, output.path = output.path) - - new_order <- c('system', 'reference', 'metric', 'region','sdate', 'time') - - if(model == 'system'){ - data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('reference','region'), list(1, reg), drop = 'selected') - sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('reference','region'), list(1, reg), drop = 'selected') - } else if(model == 'reference'){ - data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('system','region'), list(1, reg), drop = 'selected') - sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('system','region'), list(1, reg), drop = 'selected') + if(length(start.months) >= length(forecast.months)){ + + fileout <- .Filename(model = model, eval.name = eval.filename, var = var, + start.year = start.year, end.year = end.year, scorecard.type = 12, + region = sub(" ", "-", region.names[reg]), + fileout.label = fileout.label, output.path = output.path) + + new_order <- c('system', 'reference', 'metric', 'region','sdate', 'time') + + if(model == 'system'){ + data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('reference','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('reference','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_12 <- NULL + } + } else if(model == 'reference'){ + data_sc_12 <- Subset(Reorder(transformed_data, new_order), c('system','region'), list(1, reg), drop = 'selected') + if(!is.null(sign)){ + sign_sc_12 <- Subset(Reorder(transformed_sign, new_order), c('system','region'), list(1, reg), drop = 'selected') + } else { + sign_sc_12 <- NULL + } + } + + VizScorecard(data = data_sc_12, + sign = sign_sc_12, + row_dim = 'time', + subrow_dim = model, + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = forecast.months, + subrow_names = model.name, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Forecast Month', + subrow_title = table.model.name, + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = 4, + columns_width = columns.width, + fileout = fileout) } - - VizScorecard(data = data_sc_12, - sign = sign_sc_12, - row_dim = 'time', - subrow_dim = model, - col_dim = 'metric', - subcol_dim = 'sdate', - legend_dim = 'metric', - row_names = forecast.months, - subrow_names = model.name, - col_names = metric.names, - subcol_names = month.abb[as.numeric(start.months)], - table_title = table.title, - table_subtitle = table.subtitle, - row_title = 'Forecast Month', - subrow_title = table.model.name, - col_title = 'Target month', - legend_breaks = legend.breaks, - plot_legend = plot.legend, - label_scale = label.scale, - legend_width = legend.width, - legend_height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round_decimal = round.decimal, - font_size = font.size, - legend_white_space = legend.white.space, - col1_width = col1.width, - col2_width = 4, - columns_width = columns.width, - fileout = fileout) } ## close loop on region diff --git a/modules/Scorecards/R/tmp/ScorecardsSingle.R b/modules/Scorecards/R/tmp/ScorecardsSingle.R index c5525fd4..7c76c92c 100644 --- a/modules/Scorecards/R/tmp/ScorecardsSingle.R +++ b/modules/Scorecards/R/tmp/ScorecardsSingle.R @@ -107,8 +107,7 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, ## Make sure data is in correct order for using in functions: data_order <- c('system', 'reference', 'metric', 'time', 'sdate', 'region') data <- Reorder(data, data_order) - sign <- Reorder(sign, data_order) - + ## Identify metrics loaded metrics_loaded <- attributes(data)$metrics @@ -116,17 +115,27 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, data <- Subset(data, along = 'metric', indices = match(metrics, metrics_loaded)) attributes(data)$metrics <- metrics - sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) - attributes(sign)$metrics <- metrics - - ## Transform data for scorecards by forecast month (types 3 & 4) - transformed_data <- SCTransform(data = data, - sdate_dim = 'sdate', - ftime_dim = 'time') + if(!is.null(sign)){ + sign <- Reorder(sign, data_order) + sign <- Subset(sign, along = 'metric', indices = match(metrics, metrics_loaded)) + attributes(sign)$metrics <- metrics + } - transformed_sign <- SCTransform(data = sign, - sdate_dim = 'sdate', - ftime_dim = 'time') + ## Transform data for scorecards by forecast month (types 3 & 4) + if(length(start.months) >= length(forecast.months)){ + + transformed_data <- SCTransform(data = data, + sdate_dim = 'sdate', + ftime_dim = 'time') + + if(!is.null(sign)){ + transformed_sign <- SCTransform(data = sign, + sdate_dim = 'sdate', + ftime_dim = 'time') + } else { + transformed_sign <- NULL + } + } ## Load configuration files if (is.null(recipe$Run$filesystem)) { @@ -226,7 +235,12 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, fileout.label = fileout.label, output.path = output.path) data_sc_1 <- Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected') - sign_sc_1 <- Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected') + + if(!is.null(sign)){ + sign_sc_1 <- Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected') + } else { + sign_sc_1 <- NULL + } VizScorecard(data = data_sc_1, sign = sign_sc_1, @@ -272,8 +286,14 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, fileout.label = fileout.label, output.path = output.path) new_order <- c('metric', 'region', 'sdate', 'time') + data_sc_2 <- Reorder(Subset(data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) - sign_sc_2 <- Reorder(Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + + if(!is.null(sign)){ + sign_sc_2 <- Reorder(Subset(sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + } else { + sign_sc_2 <- NULL + } VizScorecard(data = data_sc_2, sign = sign_sc_2, @@ -311,59 +331,72 @@ ScorecardsSingle <- function(data, sign, system, reference, var, start.year, #### Scorecard_type 3 #### ## (transformation only) - fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, - start.year = start.year, end.year = end.year, scorecard.type = 3, - fileout.label = fileout.label, output.path = output.path) - - data_sc_3 <- Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected') - sign_sc_3 <- Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected') - VizScorecard(data = data_sc_3, - sign = sign_sc_3, - row_dim = 'region', - subrow_dim = 'time', - col_dim = 'metric', - subcol_dim = 'sdate', - legend_dim = 'metric', - row_names = region.names, - subrow_names = forecast.months, - col_names = metric.names, - subcol_names = month.abb[as.numeric(start.months)], - table_title = table.title, - table_subtitle = table.subtitle, - row_title = 'Region', - subrow_title = 'Forecast Month', - col_title = 'Target month', - legend_breaks = legend.breaks, - plot_legend = plot.legend, - label_scale = label.scale, - legend_width = legend.width, - legend_height = legend.height, - palette = palette, - colorunder = legend.col.inf, - colorsup = legend.col.sup, - round_decimal = round.decimal, - font_size = font.size, - legend_white_space = legend.white.space, - col1_width = col1.width, - col2_width = col2.width, - columns_width = columns.width, - fileout = fileout) - + if(length(start.months) >= length(forecast.months)){ + fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, + start.year = start.year, end.year = end.year, scorecard.type = 3, + fileout.label = fileout.label, output.path = output.path) + + data_sc_3 <- Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected') + + if(!is.null(sign)){ + sign_sc_3 <- Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected') + } else { + sign_sc_3 <- NULL + } + + VizScorecard(data = data_sc_3, + sign = sign_sc_3, + row_dim = 'region', + subrow_dim = 'time', + col_dim = 'metric', + subcol_dim = 'sdate', + legend_dim = 'metric', + row_names = region.names, + subrow_names = forecast.months, + col_names = metric.names, + subcol_names = month.abb[as.numeric(start.months)], + table_title = table.title, + table_subtitle = table.subtitle, + row_title = 'Region', + subrow_title = 'Forecast Month', + col_title = 'Target month', + legend_breaks = legend.breaks, + plot_legend = plot.legend, + label_scale = label.scale, + legend_width = legend.width, + legend_height = legend.height, + palette = palette, + colorunder = legend.col.inf, + colorsup = legend.col.sup, + round_decimal = round.decimal, + font_size = font.size, + legend_white_space = legend.white.space, + col1_width = col1.width, + col2_width = col2.width, + columns_width = columns.width, + fileout = fileout) + } #### Scorecard_type 4 #### ## (transformation and reorder) ## Scorecard type 4 is same as type 3 for only one region, therefore is ## only plotted if more that one region is requested - if(dim(data)['region'] > 1) { + if(dim(data)['region'] > 1 & length(start.months) >= length(forecast.months)){ fileout <- .Filename(system = system[sys], reference = reference[ref], var = var, start.year = start.year, end.year = end.year, scorecard.type = 4, fileout.label = fileout.label, output.path = output.path) new_order <- c('metric', 'region', 'sdate', 'time') + data_sc_4 <- Reorder(Subset(transformed_data, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) - sign_sc_4 <- Reorder(Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + + if(!is.null(sign)){ + sign_sc_4 <- Reorder(Subset(transformed_sign, c('system', 'reference'), list(sys, ref), drop = 'selected'), new_order) + } else { + sign_sc_4 + } VizScorecard(data = data_sc_4, sign = sign_sc_4, diff --git a/modules/Scorecards/R/tmp/WeightedMetrics.R b/modules/Scorecards/R/tmp/WeightedMetrics.R index 7df20c71..41758386 100644 --- a/modules/Scorecards/R/tmp/WeightedMetrics.R +++ b/modules/Scorecards/R/tmp/WeightedMetrics.R @@ -53,9 +53,9 @@ WeightedMetrics <- function(loaded_metrics, regions, metric.aggregation, ## Get metric names ## TO DO: check all metric are in the same order for all sys - metrics <- attributes(loaded_metrics[[1]][[1]])$metrics - forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months - start.months <- attributes(loaded_metrics[[1]][[1]])$start.months + metrics <- attributes(loaded_metrics)$metrics + forecast.months <- attributes(loaded_metrics)$forecast.months + start.months <- attributes(loaded_metrics)$start.months all_metric_means <- array(dim = c(metric = length(metrics), time = length(forecast.months), @@ -83,18 +83,20 @@ WeightedMetrics <- function(loaded_metrics, regions, metric.aggregation, latdim = lat_dim_name, na.rm = na.rm, ncores = ncores) + all_metric_means[, , , reg, ref, sys] <- weighted.mean + } ## close loop on region } ## close loop on reference } ## close loop on system - + ## reorder dimensions in array all_metric_means <- s2dv::Reorder(all_metric_means, c('system','reference','metric','time','sdate','region')) ## Add attributes attributes(all_metric_means)$metrics <- metrics - attributes(all_metric_means)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months - attributes(all_metric_means)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months + attributes(all_metric_means)$start.months <- start.months + attributes(all_metric_means)$forecast.months <- forecast.months attributes(all_metric_means)$regions <- regions attributes(all_metric_means)$system.name <- names(loaded_metrics) attributes(all_metric_means)$reference.name <- names(loaded_metrics[[1]]) diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 75a22196..93ad7ed9 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -18,9 +18,6 @@ source('modules/Scorecards/R/tmp/ClimPalette.R') .IsColor <- s2dv:::.IsColor .FilterUserGraphicArgs <- s2dv:::.FilterUserGraphicArgs -## TO DO: -## Apply condition in SingleScorecards/MultiScorecards to only make transformed scorecard when sdate > ftime - ## Define function Scorecards <- function(recipe) { @@ -194,6 +191,8 @@ Scorecards <- function(recipe) { metric.aggregation = metric.aggregation, ncores = ncores) } ## close if on region + metrics_significance <- NULL + } ## close if on skill ###### SCORE AGGREGATION ###### -- GitLab From 0e0c64628debf23776422c5aecc11c0d83030807 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Thu, 1 Feb 2024 18:05:32 +0100 Subject: [PATCH 65/78] working progress --- modules/Scorecards/R/tmp/LoadMetrics.R | 21 ++++++++++++++++++++- modules/Scorecards/Scorecards.R | 10 +++++----- 2 files changed, 25 insertions(+), 6 deletions(-) diff --git a/modules/Scorecards/R/tmp/LoadMetrics.R b/modules/Scorecards/R/tmp/LoadMetrics.R index 2cecd5c1..b4b91128 100644 --- a/modules/Scorecards/R/tmp/LoadMetrics.R +++ b/modules/Scorecards/R/tmp/LoadMetrics.R @@ -72,7 +72,8 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, period = period, start_months = start_months, forecast_months = forecast_months, - metrics = metrics, calib_method = calib_method, + metrics = metrics, + calib_method = calib_method, syear = syear) attributes(met)$lon <- as.vector(attributes(met)$Variable$dat$lon) attributes(met)$lat <- as.vector(attributes(met)$Variable$dat$lat) @@ -129,5 +130,23 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, # (3): Call startR loaded_metrics_start <- do.call(Start, args = args) + + ## If data to load is region not lat lon + # if ("region" %in% rownames(allfiledims)) { + # file_for_att <- ncdf4::nc_open(allfiles[allfiles_exist[1]]) + # region <- ncdf4::ncatt_get(file_for_att, 'region') + # ncdf4::nc_close(file_for_att) + # attributes(array_met_by_sdate)$region <- region + # } else { + # lon <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'longitude', + # unlist = T, drop_var_dim = T) + # lat <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'latitude', + # unlist = T, drop_var_dim = T) + # attributes(array_met_by_sdate)$lon <- lon + # attributes(array_met_by_sdate)$lat <- lat + # } + + + return(loaded_metrics_start) } diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 93ad7ed9..50653c78 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -23,11 +23,11 @@ Scorecards <- function(recipe) { ## Parameters for loading data files - input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/testing/" #temp - skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") - stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") - # skill.input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") - # stats.input.path <- paste0(recipe$Run$output_dir, "/outputs/Statistics/") + # input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/testing/" #temp + # skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") + # stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") + skill.input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") + stats.input.path <- paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") dir.create(output.path, recursive = T, showWarnings = F) system <- recipe$Analysis$Datasets$System$name -- GitLab From c3ea974ea17e3007550d3f497b75ee3881f84556 Mon Sep 17 00:00:00 2001 From: vagudets Date: Fri, 2 Feb 2024 15:44:44 +0100 Subject: [PATCH 66/78] Add for loop to go over individual dates --- .../Units/R/transform_units_precipitation.R | 37 ++++++++++--------- .../atomic_recipes/recipe_system7c3s-prlr.yml | 5 ++- 2 files changed, 23 insertions(+), 19 deletions(-) diff --git a/modules/Units/R/transform_units_precipitation.R b/modules/Units/R/transform_units_precipitation.R index 5cc1e812..d0dd7ffd 100644 --- a/modules/Units/R/transform_units_precipitation.R +++ b/modules/Units/R/transform_units_precipitation.R @@ -96,22 +96,25 @@ transform_units_precipitation <- function(data, original_units, new_units, } -.days_in_month <- function(x, cal) { - if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) { - N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS - if (leap_year(year(x))) { - N_DAYS_IN_MONTHS[2] <- N_DAYS_IN_MONTHS[2] + 1 - } - } else if (cal %in% c('360', '360_day')) { - N_DAYS_IN_MONTHS <- rep(30, 12) - names(N_DAYS_IN_MONTHS) <- month.abb - } else if (cal %in% c('365', '365_day')) { - N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS - } else { - stop("Unknown calendar") - } - month_x <- month(x, label = TRUE, locale = "C") - n_days <- N_DAYS_IN_MONTHS[month_x] - n_days[month_x == "Feb" & leap_year(x)] <- 29L +.days_in_month <- function(dates, cal) { + n_days <- array() + for (x in 1:length(dates)) { + if (cal %in% c('gregorian', 'standard', 'proleptic_gregorian')) { + N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS + if (leap_year(year(dates[x]))) { + N_DAYS_IN_MONTHS[2] <- N_DAYS_IN_MONTHS[2] + 1 + } + } else if (cal %in% c('360', '360_day')) { + N_DAYS_IN_MONTHS <- rep(30, 12) + names(N_DAYS_IN_MONTHS) <- month.abb + } else if (cal %in% c('365', '365_day')) { + N_DAYS_IN_MONTHS <- lubridate:::N_DAYS_IN_MONTHS + } else { + stop("Unknown calendar") + } + month_x <- month(dates[x], label = TRUE, locale = "C") + n_days[x] <- N_DAYS_IN_MONTHS[month_x] + # n_days[month_x == "Feb" & leap_year(x)] <- 29L + } return(n_days) } diff --git a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml index 1cba3c97..590d4499 100644 --- a/recipes/atomic_recipes/recipe_system7c3s-prlr.yml +++ b/recipes/atomic_recipes/recipe_system7c3s-prlr.yml @@ -4,8 +4,9 @@ Description: Analysis: Horizon: Seasonal Variables: - name: prlr + name: prlr freq: monthly_mean + units: mm Datasets: System: name: Meteo-France-System7 @@ -36,7 +37,7 @@ Analysis: method: mse_min save: 'all' Skill: - metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr Corr + metric: RPS RPSS CRPS CRPSS FRPSS BSS10 BSS90 EnsCorr save: 'all' Probabilities: percentiles: [[1/3, 2/3], [1/10, 9/10], [1/4, 2/4, 3/4]] -- GitLab From 77bfdcb26f04f8478d2fb66503cc46e9e5b4cf12 Mon Sep 17 00:00:00 2001 From: vagudets Date: Mon, 5 Feb 2024 11:31:34 +0100 Subject: [PATCH 67/78] Correct HPC specification in use case instructions --- use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md | 2 +- use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md index 7ea0bfa2..56eebb91 100644 --- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md +++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md @@ -29,7 +29,7 @@ You should see a git folder "sunset" under the current directory. Now you have a Since we're going to use Autosubmit to dispatch jobs, we need to have an Autosubmit experiment. Note that SUNSET uses Autosubmit >= 4.0.0. -On the workstation or Nord3, you can create an experiment by the following commands. +On the workstation or the Autosubmit machine, you can create an experiment by the following commands. ```shell module load autosubmit/4.0.0b-foss-2015a-Python-3.7.3 diff --git a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md index 9cd82bdf..3696ffe6 100644 --- a/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md +++ b/use_cases/ex1_3_nino_indices_comparison/ex1_3-handson.md @@ -76,7 +76,7 @@ A complete, ready-to-use sample of this example script can be found in `use_case ## 3. Launching the jobs with the SUNSET Launcher -When working without Autosubmit, the SUNSET Launcher should be run directly from the HPC machine where the jobs will run(for example, Nord3v2). Make sure to ssh to the machine first. You can obtain detailed usage information by running: +The first step is to connect to the HPC machine through `ssh`. When working without Autosubmit, the SUNSET Launcher should be run directly from the HPC machine where the jobs will run (for example, Nord3v2). There is no need to request an interactive session; the launcher script can be called directly from the login node. You can obtain detailed usage information by running: ```shell bash launch_SUNSET.sh --help -- GitLab From 28ff6e29f12d2fd9fba8935d5c91618602502799 Mon Sep 17 00:00:00 2001 From: vagudets Date: Tue, 6 Feb 2024 10:09:15 +0100 Subject: [PATCH 68/78] Fixes in use case 1.2 --- use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md index 56eebb91..999e02f3 100644 --- a/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md +++ b/use_cases/ex1_2_autosubmit_scorecards/ex1_2-handson.md @@ -74,10 +74,11 @@ In its place, we will use the function read_atomic_recipe(). The recipe path is # Load modules source("modules/Loading/Loading.R") source("modules/Anomalies/Anomalies.R") -source("modules/Indices/Indices.R") source("modules/Skill/Skill.R") +source("modules/Saving/Saving.R") -# Read recipe +# Read recipe +## (leave this part as-is! Autosubmit will automatically pass the atomic recipe as a parameter) args = commandArgs(trailingOnly = TRUE) recipe_file <- args[1] recipe <- read_atomic_recipe(recipe_file) @@ -104,7 +105,7 @@ We will start the jobs with the launcher. The SUNSET Launcher is a bash script n 1. Run the recipe checks, split the recipe into atomic recipes and create the directory for the outputs. 2. Modify the Autosubmit configuration of your experiment according to the parameters in the recipe. -The bash script needs two inputs: (1) [recipe](#2-modifying-the-recipe) (2) [R script](#2-modifying-the-recipe). +The bash script needs two inputs: (1) [recipe](#2-modifying-the-recipe) (2) [R script](#3-the-user-defined-script). On your workstation or Nord3 under the SUNSET code directory, run: -- GitLab From 218a2d8338e90ee78faa37a3a56f61d42aa2f1f0 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 9 Feb 2024 16:30:04 +0100 Subject: [PATCH 69/78] corrected LoadMetrics including region condition --- modules/Scorecards/R/tmp/LoadMetrics.R | 241 ++++++++----- modules/Scorecards/R/tmp/WeightedMetrics.R | 7 +- modules/Scorecards/Scorecards.R | 379 +++++++++------------ 3 files changed, 323 insertions(+), 304 deletions(-) diff --git a/modules/Scorecards/R/tmp/LoadMetrics.R b/modules/Scorecards/R/tmp/LoadMetrics.R index b4b91128..4f803c5a 100644 --- a/modules/Scorecards/R/tmp/LoadMetrics.R +++ b/modules/Scorecards/R/tmp/LoadMetrics.R @@ -1,15 +1,7 @@ -#'Scorecards load metrics from verification suite +#' Scorecards load metrics from verification suite #' -#'Scorecards function to load saved data files. This function is created to -#'load NetCDF files that contain 1 metric for each file. It calls startR::Start -#'internally. The NetCDF file is supposed to have the following dimensions: -#''longitude', 'latitude' and 'time'. If 'syear' is also a dimension, you must -#'set the parameter 'syear' different to NULL. +#'@description Scorecards function to load saved data files #' -#'@param input_path A character string indicating the path where metrics output -#' files from verification suite are saved (or any other compatible files) -#'@param metrics A vector of character strings indicating the metrics name to -#' be loaded. #'@param system A vector of character strings defining the names of the #' system names following the archive.yml format from verification suite. #' Accepted system names: 'ECMWF-SEAS5', 'DWD-GFCS2.1', 'CMCC-SPS3.5', @@ -23,40 +15,97 @@ #'@param period A character string indicating the start and end years of the #' reference period (e.g. '1993-203') #'@param start_months A vector indicating the numbers of the start months -#'@param forecast_months A vector indicating the numbers of the forecast months -#'@param syear Used when the NetCDF file contains a metric that has the -#' dimension 'syear'. It can be any value. If there is no 'syear' dimension, -#' you must set it to NULL. It is NULL by default. +#'@param input_path A character string indicating the path where metrics output +#' files from verification suite are saved (or any other compatible files) #' #'@return A is a list by system and reference containing an array of with #' the following dimensions: longitude, latitude, forecast months, metrics, #' start dates. + #'@examples #'\dontrun{ #'loaded_metrics <- LoadMetrics(system = c('ECMWF-SEAS5','DWD-GFCS2.1'), -#' reference = 'ERA5', +#' reference. = 'ERA5', #' var = 'tas', -#' period = '1993-2016', +#' period = '1993-2016' #' metrics = c('mean_bias', 'enscorr', 'rpss', 'crpss', 'enssprerr'), #' start_months = sprintf("%02d", 1:12), -#' forecast_months = 1:6, +#' calib_method = 'raw', #' input_path = '/esarchive/scratch/nmilders/scorecards_data/input_data') #'} #'@import easyNCDF #'@import multiApply -LoadMetrics <- function(input_path, metrics, var, system, reference, - period = NULL, start_months = NULL, - forecast_months = NULL, inf_to_na = FALSE, - calib_method = NULL, syear = NULL) { +#'@export + +system <- 'ECMWF-SEAS5' +reference <- 'ERA5' +var <- 'tas' +period <- '1993-2016' +metrics <- 'rps_syear' +start_months <- 1:2 +input_path <- '/esarchive/scratch/nmilders/scorecards_data/syear/testing/Skill/' +calib_method <- 'raw' +syear <- TRUE +LoadMetrics <- function(input_path, system, reference, var, period, + metrics, start_months, calib_method = NULL, + inf_to_na = FALSE) { + + # Initial checks + ## system + if (!is.character(system)) { + stop("Parameter 'system' must be a character vector with the system names.") + } + ## reference + if (!is.character(reference)) { + stop("Parameter 'reference' must be a character vector with the reference ", + "names.") + } + ## var + if (!is.character(var)) { + stop("Parameter 'var' must be a character vector with the var ", + "names.") + } + if (length(var) > 1) { + warning("Parameter 'var' must be of length one. Only the first value ", + "will be used.") + var <- var[1] + } + ## metrics + if (!is.character(metrics)) { + stop("Parameter 'metrics' cannot be NULL.") + } + ## start_months + if (is.character(start_months)) { + warning("Parameter 'start_months' must be a numeric vector indicating ", + "the starting months.") + start_months <- as.numeric(start_months) + } + if (!is.numeric(start_months)) { + stop("Parameter 'start_months' must be a numeric vector indicating ", + "the starting months.") + } + start_months <- sprintf("%02d", start_months) + ## Check if sdates are continuous or discrete + if (all(diff(as.numeric(start_months)) == 1)) { + consecutive_start_months <- TRUE + } else { + consecutive_start_months <- FALSE + } + ## input_path + if (!is.character(input_path)) { + stop("Parameter 'input_path must be a character string.") + } + if (length(input_path) > 1) { + input_path <- input_path[1] + warning("Parameter 'input_path' has length greater than 1 and only the ", + "first element will be used.") + } - ## TO DO: Include condition for region - ## Remove . from names system <- gsub('.','', system, fixed = T) reference <- gsub('.','', reference, fixed = T) - ## Define empty list to saved data all_metrics <- sapply(system, function(x) NULL) ## Load data for each system for (sys in 1:length(system)) { @@ -65,23 +114,29 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, ## Load data for each reference for (ref in 1:length(reference)) { ## Call function to load metrics data - met <- .loadmetrics(input_path = input_path, # recipe$Run$output, - system = system[sys], - reference = reference[ref], - var = var, - period = period, - start_months = start_months, - forecast_months = forecast_months, - metrics = metrics, - calib_method = calib_method, - syear = syear) - attributes(met)$lon <- as.vector(attributes(met)$Variable$dat$lon) - attributes(met)$lat <- as.vector(attributes(met)$Variable$dat$lat) + met_by_smonth <- NULL + for (met in metrics) { + result <- .loadmetrics(input_path = input_path, + system = system[sys], + reference = reference[ref], + var = var, + period = period, + start_months = start_months, + calib_method = calib_method, + metric = met) + + result_attr <- attributes(result) + met_by_smonth <- abind::abind(met_by_smonth, result, along = length(dim(result)) + 1) + } + attributes(met_by_smonth) <- result_attr[-1] + # names(dim(met_by_smonth)) <- c(names(result_attr$dim), 'metric') + + dim(met_by_smonth) <- c(dim(result), metric = length(metrics)) ## Save metric data as array in reference list - by_reference[[reference[ref]]] <- met + by_reference[[reference[ref]]] <- met_by_smonth ## Remove -Inf from crpss data if variable is precipitation if (inf_to_na) { - by_reference[[reference]][by_reference[[reference]]==-Inf] <- NA + by_reference[[reference]][by_reference[[reference]]==-Inf] <- NA } } ## close loop on reference ## Save reference data in list of system @@ -89,64 +144,76 @@ LoadMetrics <- function(input_path, metrics, var, system, reference, } ## close loop on system attributes(all_metrics)$metrics <- metrics - attributes(all_metrics)$start.months <- start.months - attributes(all_metrics)$forecast.months <- forecast.months - + attributes(all_metrics)$start_months <- start_months return(all_metrics) -} +} ## close function -.loadmetrics <- function(input_path, metrics, var, system = NULL, - reference = NULL, period = NULL, start_months = NULL, - forecast_months = NULL, calib_method = NULL, - syear = NULL) { - # (1) Create the path to the files - directory_path <- paste0(input_path, system, "/", reference, "/", - calib_method, "/", var, "/") - file_path <- paste0(directory_path, "scorecards_", system, "_", reference, - "_", var, "_$var$_", period, "_s", "$smonths$", ".nc") +########################################################### - # (2) Create the startR arguments list - if (is.null(forecast_months)) forecast_months <- 'all' - if (is.null(start_months)) start_months <- 'all' +.loadmetrics <- function(input_path, system, reference, + var, period, start_months, + calib_method, metric) { - args = list(dat = file_path, - var = metrics, - longitude = 'all', - latitude = 'all', - time = forecast_months, - syear = "all", - smonths = start_months, - return_vars = list(longitude = 'dat', - latitude = 'dat', - time = NULL), - metadata_dims = c('dat', 'var'), - retrieve = TRUE) + ## Load data for each start date + allfiles <- sapply(start_months, function(m) { + paste0(input_path, "/", system, "/", reference, "/", calib_method, "/", + var, "/scorecards_", system, "_", reference, "_", + var, "_", metric, "_", period, "_s", m, # mod.pressure, + ".nc")}) + allfiles_exist <- sapply(allfiles, file.exists) - if (is.null(syear)) { - args$syear <- NULL + # Check dims + files_exist_by_month <- seq(1:length(allfiles))[allfiles_exist] + allfiledims <- sapply(allfiles[allfiles_exist], easyNCDF::NcReadDims) + if (length(files_exist_by_month) == 0) { + stop("No files are found.") } - - # (3): Call startR - loaded_metrics_start <- do.call(Start, args = args) - - ## If data to load is region not lat lon - # if ("region" %in% rownames(allfiledims)) { - # file_for_att <- ncdf4::nc_open(allfiles[allfiles_exist[1]]) - # region <- ncdf4::ncatt_get(file_for_att, 'region') - # ncdf4::nc_close(file_for_att) - # attributes(array_met_by_sdate)$region <- region - # } else { - # lon <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'longitude', - # unlist = T, drop_var_dim = T) - # lat <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'latitude', - # unlist = T, drop_var_dim = T) - # attributes(array_met_by_sdate)$lon <- lon - # attributes(array_met_by_sdate)$lat <- lat - # } + num_dims <- numeric(dim(allfiledims)[1]) + for (i in 1:dim(allfiledims)[1]) { + if (length(unique(allfiledims[i,])) > 1) { + warning(paste0("Dimensions of system ", system," with var ", var, + " don't match.")) + } + num_dims[i] <- max(allfiledims[i,]) # We take the largest dimension + } + # dims: [metric, longitude, latitude, time, smonth] + # or [metric, region, time, smonth] + # Loop for file + dim(allfiles) <- c(dat = 1, sdate = length(allfiles)) + array_met_by_sdate <- Apply(data = allfiles, target_dims = 'dat', fun = function(x) { + if (file.exists(x)) { + res <- easyNCDF::NcToArray(x, vars_to_read = metric, unlist = T, + drop_var_dim = T) + names(dim(res)) <- NULL + } else { + res <- array(dim = c(length(metrics), allfiledims[-1,1])) + names(dim(res)) <- NULL + } + res})$output1 - return(loaded_metrics_start) + dim(array_met_by_sdate) <- c(allfiledims[-1,1], + sdate = length(allfiles)) + # Attributes + # Read attributes from the first existing file + if ("region" %in% rownames(allfiledims)) { + file_for_att <- ncdf4::nc_open(allfiles[allfiles_exist[1]]) + region <- ncdf4::ncatt_get(file_for_att, 'region') + ncdf4::nc_close(file_for_att) + attributes(array_met_by_sdate)$region <- region + } else { + lon <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'longitude', + unlist = T, drop_var_dim = T) + lat <- easyNCDF::NcToArray(allfiles[allfiles_exist][1], vars_to_read = 'latitude', + unlist = T, drop_var_dim = T) + attributes(array_met_by_sdate)$lon <- lon + attributes(array_met_by_sdate)$lat <- lat + } + + return(array_met_by_sdate) } + + \ No newline at end of file diff --git a/modules/Scorecards/R/tmp/WeightedMetrics.R b/modules/Scorecards/R/tmp/WeightedMetrics.R index 41758386..9d1630c4 100644 --- a/modules/Scorecards/R/tmp/WeightedMetrics.R +++ b/modules/Scorecards/R/tmp/WeightedMetrics.R @@ -27,8 +27,8 @@ #'@importFrom ClimProjDiags WeightedMean #'@importFrom s2dv Reorder #'@export -WeightedMetrics <- function(loaded_metrics, regions, metric.aggregation, - ncores = NULL, na.rm = TRUE) { +WeightedMetrics <- function(loaded_metrics, regions, forecast.months, + metric.aggregation, ncores = NULL, na.rm = TRUE) { ## Initial checks # loaded_metrics if (any(sapply(loaded_metrics, function(x) { @@ -54,8 +54,7 @@ WeightedMetrics <- function(loaded_metrics, regions, metric.aggregation, ## Get metric names ## TO DO: check all metric are in the same order for all sys metrics <- attributes(loaded_metrics)$metrics - forecast.months <- attributes(loaded_metrics)$forecast.months - start.months <- attributes(loaded_metrics)$start.months + start.months <- attributes(loaded_metrics)$start_months all_metric_means <- array(dim = c(metric = length(metrics), time = length(forecast.months), diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index 50653c78..e3d1abd8 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -22,10 +22,6 @@ source('modules/Scorecards/R/tmp/ClimPalette.R') Scorecards <- function(recipe) { ## Parameters for loading data files - - # input.path <- "/esarchive/scratch/nmilders/scorecards_data/syear/testing/" #temp - # skill.input.path <- paste0(input.path, "Skill/") #paste0(recipe$Run$output_dir, "/outputs/Skill/") - # stats.input.path <- paste0(input.path, "Statistics/") #paste0(recipe$Run$output_dir, "/outputs/Statistics/") skill.input.path <- paste0(recipe$Run$output_dir, "/outputs/Skill/") stats.input.path <- paste0(recipe$Run$output_dir, "/outputs/Statistics/") output.path <- paste0(recipe$Run$output_dir, "/plots/Scorecards/") @@ -146,9 +142,7 @@ Scorecards <- function(recipe) { metrics = metrics.visualize, ## metrics.load period = period, start_months = start.months, - forecast_months = forecast.months, calib_method = calib.method, - syear = NULL, inf_to_na = inf.to.na ) @@ -156,9 +150,9 @@ Scorecards <- function(recipe) { if('region' %in% names(dim(loaded_metrics[[1]][[1]]))){ ### Convert loaded metrics to array for already aggregated data - metrics.dim <- attributes(loaded_metrics[[1]][[1]])$metrics - forecast.months.dim <- attributes(loaded_metrics[[1]][[1]])$forecast.months - start.months.dim <- attributes(loaded_metrics[[1]][[1]])$start.months + metrics.dim <- attributes(loaded_metrics)$metrics + forecast.months.dim <- forecast.months + start.months.dim <- attributes(loaded_metrics)$start_months regions.dim <- regions #list('NAO' = c(lon.min = -80, lon.max = 40, lat.min = 20, lat.max = 80)) aggregated_metrics <- array(dim = c(system = length(loaded_metrics), @@ -177,8 +171,8 @@ Scorecards <- function(recipe) { ## Add attributes attributes(aggregated_metrics)$metrics <- metrics.load - attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics[[1]][[1]])$start.months - attributes(aggregated_metrics)$forecast.months <- attributes(loaded_metrics[[1]][[1]])$forecast.months + attributes(aggregated_metrics)$start.months <- attributes(loaded_metrics)$start_months + attributes(aggregated_metrics)$forecast.months <- forecast.months attributes(aggregated_metrics)$regions <- regions attributes(aggregated_metrics)$system.name <- names(loaded_metrics) attributes(aggregated_metrics)$reference.name <- names(loaded_metrics[[1]]) @@ -188,6 +182,7 @@ Scorecards <- function(recipe) { ## Calculate weighted mean of spatial aggregation aggregated_metrics <- WeightedMetrics(loaded_metrics, regions = regions, + forecast.months = forecast.months, metric.aggregation = metric.aggregation, ncores = ncores) } ## close if on region @@ -220,11 +215,9 @@ Scorecards <- function(recipe) { region = length(regions), metric = length(metrics.visualize))) - + ## Load and aggregated data for each metric for (sys in 1:length(system)){ - # sys_num <- which(system == sys) for (ref in 1:length(reference)){ - # ref_num <- which(refence == ref) for (met in metrics.visualize) { if(met == 'rpss'){ @@ -232,69 +225,61 @@ Scorecards <- function(recipe) { rps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_syear', - calib_method = calib.method, syear = TRUE) + calib_method = calib.method, metric = 'rps_syear') rps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'rps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - rps_syear <- Subset(rps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - rps_clim_syear <- Subset(rps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') + reference = reference[ref], var = var, + period = period, start_months = start.months, + calib_method = calib.method, metric = 'rps_clim_syear') ## Spatially aggregate data - rps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = F) - }, simplify = 'array') - - rps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = rps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(rps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(rps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = F) - }, simplify = 'array') + rps_syear <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_syear)$lon), + lat = as.vector(attributes(rps_syear)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = F) + }, simplify = 'array') + + rps_clim_syear <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = rps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(rps_clim_syear)$lon), + lat = as.vector(attributes(rps_clim_syear)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = F) + }, simplify = 'array') ## Include name of region dimension - names(dim(rps_syear_spatial_aggr))[length(dim(rps_syear_spatial_aggr))] <- 'region' - names(dim(rps_clim_syear_spatial_aggr))[length(dim(rps_clim_syear_spatial_aggr))] <- 'region' + names(dim(rps_syear))[length(dim(rps_syear))] <- 'region' + names(dim(rps_clim_syear))[length(dim(rps_clim_syear))] <- 'region' + ## Calculate significance + sign_rpss <- RandomWalkTest(rps_syear, rps_clim_syear, + time_dim = time_dim, test.type = 'two.sided', + alpha = alpha, pval = FALSE, sign = TRUE, + ncores = NULL)$sign + ## Temporally aggregate data - rps_temp_aggr <- Apply(data = rps_syear_spatial_aggr, + rps_syear <- Apply(data = rps_syear, target_dims = time_dim, fun = 'mean', ncores = ncores)$output1 - rps_clim_temp_aggr <- Apply(data = rps_clim_syear_spatial_aggr, + rps_clim_syear <- Apply(data = rps_clim_syear, target_dims = time_dim, fun = 'mean', ncores = ncores)$output1 ## Calculate RPSS from aggregated RPS and RPS_clim - rpss <- 1 - rps_temp_aggr / rps_clim_temp_aggr - - ## Calculate significance - sign_rpss <- RandomWalkTest(rps_syear_spatial_aggr, rps_clim_syear_spatial_aggr, - time_dim = time_dim, test.type = 'two.sided', - alpha = alpha, pval = FALSE, sign = TRUE, - ncores = NULL)$sign - + rpss <- 1 - rps_syear / rps_clim_syear + ## Save metric result in arrays - aggregated_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'smonths','region')) - metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , ,which(metrics.visualize == met)] <- s2dv::Reorder(data = rpss, order = c('time', 'sdate','region')) + metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_rpss, order = c('time', 'sdate','region')) } ## close if on rpss @@ -304,69 +289,61 @@ Scorecards <- function(recipe) { crps_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_syear', - calib_method = calib.method, syear = TRUE) - + calib_method = calib.method, metric = 'crps_syear') + crps_clim_syear <- .loadmetrics(input_path = skill.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'crps_clim_syear', - calib_method = calib.method, syear = TRUE) - - ## Remove dat and var dimensions - crps_syear <- Subset(crps_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - crps_clim_syear <- Subset(crps_clim_syear, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - + calib_method = calib.method, metric = 'crps_clim_syear') + ## Spatially aggregate data - crps_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - crps_clim_syear_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = crps_clim_syear, - region = regions[[X]], - lon = as.vector(attributes(crps_clim_syear)$Variables$dat1$longitude), - lat = as.vector(attributes(crps_clim_syear)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') + crps_syear <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_syear)$lon), + lat = as.vector(attributes(crps_syear)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + crps_clim_syear <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = crps_clim_syear, + region = regions[[X]], + lon = as.vector(attributes(crps_clim_syear)$lon), + lat = as.vector(attributes(crps_clim_syear)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') ## Include name of region dimension - names(dim(crps_syear_spatial_aggr))[length(dim(crps_syear_spatial_aggr))] <- 'region' - names(dim(crps_clim_syear_spatial_aggr))[length(dim(crps_clim_syear_spatial_aggr))] <- 'region' + names(dim(crps_syear))[length(dim(crps_syear))] <- 'region' + names(dim(crps_clim_syear))[length(dim(crps_clim_syear))] <- 'region' + + ## Calculate significance + sign_crpss <- RandomWalkTest(crps_syear, crps_clim_syear, + time_dim = time_dim, test.type = 'two.sided', + alpha = alpha, pval = FALSE, sign = TRUE, + ncores = NULL)$sign ## Temporally aggregate data - crps_temp_aggr <- Apply(data = crps_syear_spatial_aggr, + crps_syear <- Apply(data = crps_syear, target_dims = time_dim, fun = 'mean', ncores = ncores)$output1 - crps_clim_temp_aggr <- Apply(data = crps_clim_syear_spatial_aggr, + crps_clim_syear <- Apply(data = crps_clim_syear, target_dims = time_dim, fun = 'mean', ncores = ncores)$output1 ## Calculate CRPSS from aggregated CRPS and CRPS_clim - crpss <- 1 - crps_temp_aggr / crps_clim_temp_aggr - - ## Calculate significance - sign_crpss <- RandomWalkTest(crps_syear_spatial_aggr, crps_clim_syear_spatial_aggr, - time_dim = time_dim, test.type = 'two.sided', - alpha = alpha, pval = FALSE, sign = TRUE, - ncores = NULL)$sign + crpss <- 1 - crps_syear / crps_clim_syear ## Save metric result in arrays - aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'smonths','region')) - metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = crpss, order = c('time', 'sdate','region')) + metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_crpss, order = c('time', 'sdate','region')) } ## close if on crpss @@ -375,106 +352,93 @@ Scorecards <- function(recipe) { cov <- .loadmetrics(input_path = stats.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'cov', - calib_method = calib.method, syear = NULL) + calib_method = calib.method, metric = 'cov') std_hcst <- .loadmetrics(input_path = stats.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_hcst', - calib_method = calib.method, syear = NULL) - + calib_method = calib.method, metric = 'std_hcst') + std_obs <- .loadmetrics(input_path = stats.input.path, system = system[sys], - reference = reference[ref], var = var, - period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'std_obs', - calib_method = calib.method, syear = NULL) - + reference = reference[ref], var = var, + period = period, start_months = start.months, + calib_method = calib.method, metric = 'std_obs') + n_eff <- .loadmetrics(input_path = stats.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'n_eff', - calib_method = calib.method, syear = NULL) - + calib_method = calib.method, metric = 'n_eff') + ## Calculate spatial aggregation - cov_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = cov, - region = regions[[X]], - lon = as.vector(attributes(cov)$Variables$dat1$longitude), - lat = as.vector(attributes(cov)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') + cov <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = cov, + region = regions[[X]], + lon = as.vector(attributes(cov)$lon), + lat = as.vector(attributes(cov)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') ## Include name of region dimension - names(dim(cov_spatial_aggr))[length(dim(cov_spatial_aggr))] <- 'region' + names(dim(cov))[length(dim(cov))] <- 'region' - std_hcst_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = std_hcst, - region = regions[[X]], - lon = as.vector(attributes(std_hcst)$Variables$dat1$longitude), - lat = as.vector(attributes(std_hcst)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(std_hcst_spatial_aggr))[length(dim(std_hcst_spatial_aggr))] <- 'region' - - std_obs_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = std_obs, - region = regions[[X]], - lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), - lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(std_obs_spatial_aggr))[length(dim(std_obs_spatial_aggr))] <- 'region' - - n_eff_spatial_aggr <- sapply(X = 1:length(regions), - FUN = function(X) { - WeightedMean(data = n_eff, - region = regions[[X]], - lon = as.vector(attributes(std_obs)$Variables$dat1$longitude), - lat = as.vector(attributes(std_obs)$Variables$dat1$latitude), - londim = lon_dim, - latdim = lat_dim, - na.rm = na.rm) - }, simplify = 'array') - - names(dim(n_eff_spatial_aggr))[length(dim(n_eff_spatial_aggr))] <- 'region' - n_eff_spatial_aggr <- Subset(n_eff_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - + std_hcst <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_hcst, + region = regions[[X]], + lon = as.vector(attributes(std_hcst)$lon), + lat = as.vector(attributes(std_hcst)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_hcst))[length(dim(std_hcst))] <- 'region' + + std_obs <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = std_obs, + region = regions[[X]], + lon = as.vector(attributes(std_obs)$lon), + lat = as.vector(attributes(std_obs)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(std_obs))[length(dim(std_obs))] <- 'region' + + n_eff <- sapply(X = 1:length(regions), + FUN = function(X) { + WeightedMean(data = n_eff, + region = regions[[X]], + lon = as.vector(attributes(n_eff)$lon), + lat = as.vector(attributes(n_eff)$lat), + londim = lon_dim, + latdim = lat_dim, + na.rm = na.rm) + }, simplify = 'array') + + names(dim(n_eff))[length(dim(n_eff))] <- 'region' + ## Calculate correlation - enscorr <- cov_spatial_aggr / (std_hcst_spatial_aggr * std_obs_spatial_aggr) + enscorr <- cov / (std_hcst * std_obs) - ## Drop unwanted dimensions - enscorr <- Subset(enscorr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - ## Calculate significance of corr - t_alpha2_n2 <- qt(p = alpha/2, df = n_eff_spatial_aggr-2, lower.tail = FALSE) - t <- abs(enscorr) * sqrt(n_eff_spatial_aggr-2) / sqrt(1-enscorr^2) + t_alpha2_n2 <- qt(p = alpha/2, df = n_eff-2, lower.tail = FALSE) + t <- abs(enscorr) * sqrt(n_eff-2) / sqrt(1-enscorr^2) sign_corr<- array(data = NA, dim = c(time = length(forecast.months), - smonths = length(start.months), + sdate = length(start.months), region = length(regions))) - for (time in 1:dim(sign_corr)[['time']]){ - for (mon in 1:dim(sign_corr)[['smonths']]){ + for (mon in 1:dim(sign_corr)[['sdate']]){ for (reg in 1:dim(sign_corr)[['region']]){ if (anyNA(c(t[time, mon, reg], t_alpha2_n2[time, mon, reg])) == FALSE @@ -483,14 +447,13 @@ Scorecards <- function(recipe) { } else { sign_corr[time, mon, reg] <- FALSE } - } } } ## Save metric result in arrays - aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'smonths','region')) - metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enscorr, order = c('time', 'sdate','region')) + metrics_significance[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = sign_corr, order = c('time', 'sdate','region')) } ## close if on enscorr @@ -499,29 +462,24 @@ Scorecards <- function(recipe) { mean_bias <- .loadmetrics(input_path = skill.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'mean_bias', - calib_method = calib.method, syear = NULL) + calib_method = calib.method, metric = 'mean_bias') ## Calculate spatial aggregation - mean_bias_spatial_aggr <- sapply(X = 1:length(regions), + mean_bias <- sapply(X = 1:length(regions), FUN = function(X) { WeightedMean(data = mean_bias, region = regions[[X]], - lon = as.vector(attributes(mean_bias)$Variables$dat1$longitude), - lat = as.vector(attributes(mean_bias)$Variables$dat1$latitude), + lon = as.vector(attributes(mean_bias)$lon), + lat = as.vector(attributes(mean_bias)$lat), londim = lon_dim, latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') - names(dim(mean_bias_spatial_aggr))[length(dim(mean_bias_spatial_aggr))] <- 'region' - - ## Drop unwanted dimensions - mean_bias_spatial_aggr <- Subset(mean_bias_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - + names(dim(mean_bias))[length(dim(mean_bias))] <- 'region' + ## Save metric result in array - aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias_spatial_aggr, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = mean_bias, order = c('time', 'sdate','region')) } ## close on mean_bias @@ -530,29 +488,24 @@ Scorecards <- function(recipe) { enssprerr <- .loadmetrics(input_path = skill.input.path, system = system[sys], reference = reference[ref], var = var, period = period, start_months = start.months, - forecast_months = forecast.months, - metrics = 'enssprerr', - calib_method = calib.method, syear = NULL) + calib_method = calib.method, metric = 'enssprerr') ## Calculate spatial aggregation - enssprerr_spatial_aggr <- sapply(X = 1:length(regions), + enssprerr <- sapply(X = 1:length(regions), FUN = function(X) { WeightedMean(data = enssprerr, region = regions[[X]], - lon = as.vector(attributes(enssprerr)$Variables$dat1$longitude), - lat = as.vector(attributes(enssprerr)$Variables$dat1$latitude), + lon = as.vector(attributes(enssprerr)$lon), + lat = as.vector(attributes(enssprerr)$lat), londim = lon_dim, latdim = lat_dim, na.rm = na.rm) }, simplify = 'array') - names(dim(enssprerr_spatial_aggr))[length(dim(enssprerr_spatial_aggr))] <- 'region' - - ## Drop unwanted dimensions - enssprerr_spatial_aggr <- Subset(enssprerr_spatial_aggr, along = c('dat', 'var'), indices = list(1,1) , drop = 'selected') - + names(dim(enssprerr))[length(dim(enssprerr))] <- 'region' + ## Save metric result in array - aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr_spatial_aggr, order = c('time', 'smonths','region')) + aggregated_metrics[sys, ref, , , , which(metrics.visualize == met)] <- s2dv::Reorder(data = enssprerr, order = c('time', 'sdate','region')) } ## close on enssprerr @@ -560,10 +513,10 @@ Scorecards <- function(recipe) { } ## close if on reference } ## close if on system - #Include metric attributes + ## Include metric attributes attributes(aggregated_metrics)$metrics <- metrics.visualize - ## set NAs to False + ## Set NAs to False metrics_significance[is.na(metrics_significance)] <- FALSE } ## close if on score -- GitLab From 691fc577ec0f04c80bb251ffb43488aa26da5480 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 14 Feb 2024 16:54:20 +0100 Subject: [PATCH 70/78] including statistics in plot_skill_metrics.R --- modules/Statistics/Statistics.R | 12 ++---- modules/Visualization/R/plot_skill_metrics.R | 40 +++++++++++++++++++- 2 files changed, 43 insertions(+), 9 deletions(-) diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index 777f5c78..640c7b58 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -11,17 +11,13 @@ compute_statistics <- function(recipe, data, agg = 'global'){ memb_dim <- 'ensemble' ncores <- recipe$Analysis$ncores - # ## Remove unwanted dimensions + ## Remove unwanted dimensions obs_data <- Subset(data$obs$data, along = c('dat', 'sday', 'sweek', 'ensemble'), indices = list(1,1,1,1) , drop = 'selected') hcst_data <- Subset(data$hcst$data, along = c('dat', 'sday', 'sweek'), indices = list(1,1,1) , drop = 'selected') ## Repeat ensemble members for obs obs_data_ens <- InsertDim(data = obs_data, pos = length(dim(obs_data))+1, lendim = 25, name = 'ensemble') - - # obs_data <- adrop(data$obs$data, drop = 9) - # obs_data_ens <- InsertDim(data = obs_data, pos = 9, lendim = 25, name = 'ensemble') - - + statistics_list <- tolower(recipe$Analysis$Workflow$Statistics$metric) statistics_metrics <- list() @@ -94,8 +90,8 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (recipe$Analysis$Workflow$Statistics$save == 'all') { # Save all statistics - save_metrics_scorecards(recipe = recipe, skill = statistics_metrics, ## Not able to save data with these dimensions - data_cube = data$hcst, agg = agg) ## The length of parameter 'order' should be the same with the dimension length of parameter 'data'. + save_metrics_scorecards(recipe = recipe, skill = statistics_metrics, + data_cube = data$hcst, agg = agg) } # Return results diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R index 2698d499..6fed8aed 100644 --- a/modules/Visualization/R/plot_skill_metrics.R +++ b/modules/Visualization/R/plot_skill_metrics.R @@ -58,13 +58,16 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics, "enscorr", "rpss_specs", "bss90_specs", "bss10_specs", "enscorr_specs", "rmsss", "msss") scores <- c("rps", "frps", "crps", "frps_specs", "mse") + statistics <- c("cov", "std_hcst", "std_obs") + # Loop over variables and assign colorbar and plot parameters to each metric for (var in 1:data_cube$dims[['var']]) { + var_name <- data_cube$attrs$Variable$varName[[var]] ## Need to include for statistics plotting to work var_skill <- lapply(skill_metrics, function(x) { ClimProjDiags::Subset(x, along = 'var', indices = var, drop = 'selected')}) - for (name in c(skill_scores, scores, "mean_bias", "enssprerr")) { + for (name in c(skill_scores, scores, statistics, "mean_bias", "enssprerr")) { if (name %in% names(skill_metrics)) { units <- NULL # Define plot characteristics and metric name to display in plot @@ -120,7 +123,42 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics, col_inf <- colorbar[1] col_sup <- colorbar[length(colorbar)] units <- data_cube$attrs$Variable$metadata[[var_name]]$units + } else if (name %in% "cov") { + skill <- var_skill[[name]] + display_name <- "Covariance" + max_value <- max(abs(quantile(skill, 0.02, na.rm = T)), + abs(quantile(skill, 0.98, na.rm = T))) + brks <- max_value * seq(-1, 1, by = 0.2) + colorbar <- clim.colors(length(brks) + 1, diverging_palette) + cols <- colorbar[2:(length(colorbar) - 1)] + col_inf <- colorbar[1] + col_sup <- colorbar[length(colorbar)] + units <- paste0(data_cube$attrs$Variable$metadata[[var_name]]$units, "ยฒ") + } else if (name %in% "std_hcst") { + skill <- var_skill[[name]] + display_name <- "Hindcast Standard Deviation" + max_value <- max(abs(quantile(skill, 0.02, na.rm = T)), + abs(quantile(skill, 0.98, na.rm = T))) + brks <- max_value * seq(-1, 1, by = 0.2) + colorbar <- clim.colors(length(brks) + 1, diverging_palette) + cols <- colorbar[2:(length(colorbar) - 1)] + col_inf <- colorbar[1] + col_sup <- colorbar[length(colorbar)] + units <- data_cube$attrs$Variable$metadata[[var_name]]$units + } else if (name %in% "std_obs") { + skill <- var_skill[[name]] + display_name <- "Observation Standard Deviation" + max_value <- max(abs(quantile(skill, 0.02, na.rm = T)), + abs(quantile(skill, 0.98, na.rm = T))) + brks <- max_value * seq(-1, 1, by = 0.2) + colorbar <- clim.colors(length(brks) + 1, diverging_palette) + cols <- colorbar[2:(length(colorbar) - 1)] + col_inf <- colorbar[1] + col_sup <- colorbar[length(colorbar)] + units <- data_cube$attrs$Variable$metadata[[var_name]]$units } + + # Reorder dimensions skill <- Reorder(skill, c("time", "longitude", "latitude")) # If the significance has been requested and the variable has it, -- GitLab From 13b8e035404d4b9985e29e5a3363235efeeed869 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 16 Feb 2024 17:38:44 +0100 Subject: [PATCH 71/78] corrected statistics calculations --- modules/Scorecards/Scorecards.R | 2 +- modules/Statistics/Statistics.R | 28 +++++++++++++--------------- 2 files changed, 14 insertions(+), 16 deletions(-) diff --git a/modules/Scorecards/Scorecards.R b/modules/Scorecards/Scorecards.R index e3d1abd8..37aa421c 100644 --- a/modules/Scorecards/Scorecards.R +++ b/modules/Scorecards/Scorecards.R @@ -139,7 +139,7 @@ Scorecards <- function(recipe) { system = system, reference = reference, var = var, - metrics = metrics.visualize, ## metrics.load + metrics = metrics.visualize, period = period, start_months = start.months, calib_method = calib.method, diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index 640c7b58..3eff08e2 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -8,26 +8,23 @@ compute_statistics <- function(recipe, data, agg = 'global'){ # recipe: auto-s2s recipe as provided by read_yaml time_dim <- 'syear' - memb_dim <- 'ensemble' ncores <- recipe$Analysis$ncores - - ## Remove unwanted dimensions - obs_data <- Subset(data$obs$data, along = c('dat', 'sday', 'sweek', 'ensemble'), indices = list(1,1,1,1) , drop = 'selected') - hcst_data <- Subset(data$hcst$data, along = c('dat', 'sday', 'sweek'), indices = list(1,1,1) , drop = 'selected') - ## Repeat ensemble members for obs - obs_data_ens <- InsertDim(data = obs_data, pos = length(dim(obs_data))+1, lendim = 25, name = 'ensemble') - + ## Calculate ensemble mean + hcst_data <- Apply(data$hcst$data, target_dims = 'ensemble', fun = 'mean')$output1 + obs_data <- Apply(data$obs$data, target_dims = 'ensemble', fun = 'mean')$output1 + statistics_list <- tolower(recipe$Analysis$Workflow$Statistics$metric) statistics_metrics <- list() for (stat in strsplit(statistics_list, ", | |,")[[1]]) { - # Whether the fair version of the metric is to be computed + if (stat %in% c('cov', 'covariance')) { - covariance <- Apply(data = list(x = obs_data_ens, y = hcst_data), - target_dims = c(time_dim, memb_dim), + ## Calculate covariance + covariance <- Apply(data = list(x = obs_data, y = hcst_data), + target_dims = time_dim, fun = function(x,y){cov(as.vector(x),as.vector(y), use = "everything", method = "pearson")})$output1 @@ -39,9 +36,9 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (stat %in% c('std', 'standard_deviation')) { - ## Calculate standard deviation + ## Calculate standard deviation std_hcst <- Apply(data = hcst_data, - target_dims = c(time_dim, memb_dim), + target_dims = c(time_dim), fun = 'sd')$output1 std_obs <- Apply(data = obs_data, @@ -55,9 +52,9 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (stat %in% c('var', 'variance')) { - ## Calculate standard deviation + ## Calculate variance var_hcst <- (Apply(data = hcst_data, - target_dims = c(time_dim, memb_dim), + target_dims = c(time_dim), fun = 'sd')$output1)^2 var_obs <- (Apply(data = obs_data, @@ -71,6 +68,7 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (stat == 'n_eff') { + ## Calculate degrees of freedom n_eff <- s2dv::Eno(data = obs_data, time_dim = time_dim, na.action = na.pass, ncores = ncores) statistics_metrics[['n_eff']] <- n_eff -- GitLab From 43e346f49bcb6b7c6eb853486e0f16d80e599817 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 16 Feb 2024 18:38:04 +0100 Subject: [PATCH 72/78] including statistics plotting from recipe --- modules/Statistics/Statistics.R | 24 ++++++++++++++---------- modules/Visualization/Visualization.R | 12 ++++++++++++ tools/check_recipe.R | 2 +- 3 files changed, 27 insertions(+), 11 deletions(-) diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index 3eff08e2..4bfa107f 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -9,14 +9,18 @@ compute_statistics <- function(recipe, data, agg = 'global'){ time_dim <- 'syear' ncores <- recipe$Analysis$ncores - + ## Calculate ensemble mean hcst_data <- Apply(data$hcst$data, target_dims = 'ensemble', fun = 'mean')$output1 obs_data <- Apply(data$obs$data, target_dims = 'ensemble', fun = 'mean')$output1 + ## Remove unwanted dimensions + hcst_data <- Subset(hcst_data, along = c('dat', 'sday', 'sweek'), indices = list(1,1,1) , drop = 'selected') + obs_data <- Subset(obs_data, along = c('dat', 'sday', 'sweek'), indices = list(1,1,1) , drop = 'selected') + statistics_list <- tolower(recipe$Analysis$Workflow$Statistics$metric) - statistics_metrics <- list() + statistics <- list() for (stat in strsplit(statistics_list, ", | |,")[[1]]) { @@ -29,7 +33,7 @@ compute_statistics <- function(recipe, data, agg = 'global'){ use = "everything", method = "pearson")})$output1 - statistics_metrics[[ stat ]] <- covariance + statistics[[ stat ]] <- covariance } ## close if on covariance @@ -45,8 +49,8 @@ compute_statistics <- function(recipe, data, agg = 'global'){ target_dims = c(time_dim), fun = 'sd')$output1 - statistics_metrics[['std_hcst']] <- std_hcst - statistics_metrics[['std_obs']] <- std_obs + statistics[['std_hcst']] <- std_hcst + statistics[['std_obs']] <- std_obs } ## close if on std @@ -61,8 +65,8 @@ compute_statistics <- function(recipe, data, agg = 'global'){ target_dims = c(time_dim), fun = 'sd')$output1)^2 - statistics_metrics[['var_hcst']] <- var_hcst - statistics_metrics[['var_obs']] <- var_obs + statistics[['var_hcst']] <- var_hcst + statistics[['var_obs']] <- var_obs } ## close if on variance @@ -71,7 +75,7 @@ compute_statistics <- function(recipe, data, agg = 'global'){ ## Calculate degrees of freedom n_eff <- s2dv::Eno(data = obs_data, time_dim = time_dim, na.action = na.pass, ncores = ncores) - statistics_metrics[['n_eff']] <- n_eff + statistics[['n_eff']] <- n_eff } ## close on n_eff } @@ -88,11 +92,11 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (recipe$Analysis$Workflow$Statistics$save == 'all') { # Save all statistics - save_metrics_scorecards(recipe = recipe, skill = statistics_metrics, + save_metrics_scorecards(recipe = recipe, skill = statistics, data_cube = data$hcst, agg = agg) } # Return results - return(statistics_metrics) + return(statistics) } diff --git a/modules/Visualization/Visualization.R b/modules/Visualization/Visualization.R index a6b6bd75..62075132 100644 --- a/modules/Visualization/Visualization.R +++ b/modules/Visualization/Visualization.R @@ -83,6 +83,18 @@ Visualization <- function(recipe, "parameter 'skill_metrics' is NULL")) } } + + # Plot statistics + if ("statistics" %in% plots) { + if (!is.null(statistics)) { + plot_skill_metrics(recipe, data$hcst, statistics, outdir, + significance, output_conf = output_conf) + } else { + error(recipe$Run$logger, + paste0("The statistics plots have been requested, but the ", + "parameter 'skill_metrics' is NULL")) + } + } # Plot forecast ensemble mean if ("forecast_ensemble_mean" %in% plots) { diff --git a/tools/check_recipe.R b/tools/check_recipe.R index c398c345..e0c44cc0 100644 --- a/tools/check_recipe.R +++ b/tools/check_recipe.R @@ -505,7 +505,7 @@ check_recipe <- function(recipe) { # Visualization if ("Visualization" %in% names(recipe$Analysis$Workflow)) { PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean", - "most_likely_terciles") + "most_likely_terciles", "statistics") # Separate plots parameter and check if all elements are in PLOT_OPTIONS if (is.null(recipe$Analysis$Workflow$Visualization$plots)) { error(recipe$Run$logger, -- GitLab From 86d12d0085524e1caf1a5ea51a5aab103d6d76be Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Fri, 23 Feb 2024 15:51:42 +0100 Subject: [PATCH 73/78] removing SCPlotScorecard function, replaced by VizScorecard --- modules/Scorecards/R/tmp/SCPlotScorecard.R | 478 --------------------- 1 file changed, 478 deletions(-) delete mode 100644 modules/Scorecards/R/tmp/SCPlotScorecard.R diff --git a/modules/Scorecards/R/tmp/SCPlotScorecard.R b/modules/Scorecards/R/tmp/SCPlotScorecard.R deleted file mode 100644 index a6575ea5..00000000 --- a/modules/Scorecards/R/tmp/SCPlotScorecard.R +++ /dev/null @@ -1,478 +0,0 @@ -#'Scorecards function create simple scorecards by region (types 1 & 3) -#' -#'@description This function creates a scorecard for a single system and -#'reference combination, showing data by region and forecast month. -#' -#'@param data A multidimensional array containing spatially aggregated metrics -#' data with dimensions: metric, region, sdate and ftime. -#'@param sign A multidimensional boolean array with the same dimensions as -#' 'data', indicting which values to be highlighted. If set to NULL no values -#' will be highlighted. -#'@param row.dim A character string indicating the dimension name to show in the -#' rows of the plot. -#'@param subrow.dim A character string indicating the dimension name to show in -#' the sub-rows of the plot. -#'@param col.dim A character string indicating the dimension name to show in the -#' columns of the plot. -#'@param subcol.dim A character string indicating the dimension name to show in -#' the sub-columns of the plot. -#'@param legend.dim A character string indicating the dimension name to use for -#' the legend. -#'@param row.names A vector of character strings with row display names. -#'@param subrow.names A vector of character strings with sub-row display names. -#'@param col.names A vector of character strings with column display names. -#'@param subcol.names A vector of character strings with sub-column display -#' names. -#'@param row.title A character string for the title of the row names. -#'@param subrow.title A character string for the title of the sub-row names. -#'@param table.title A character string for the title of the plot. -#'@param table.subtitle A character string for the sub-title of the plot. -#'@param legend.breaks A vector of numerics or a list of vectors of numerics, -#' containing the breaks for the legends. If a vector is given as input, then -#' these breaks will be repeated for each legend.dim. A list of vectors can be -#' given as input if the legend.dims require different breaks. This parameter -#' is required even if the legend is not plotted, to define the colors in the -#' scorecard table. -#'@param plot.legend A logical value to determine if the legend is plotted. -#'@param legend.width A numeric value to define the width of the legend bars. -#'@param legend.height A numeric value to define the height of the legend bars. -#'@param label.scale A numeric value to define the size of the legend labels. -#'@param palette A vector of character strings or a list of vectors of -#' character strings containing the colors to use in the legends. If a vector -#' is given as input, then these colors will be used for each legend.dim. A -#' list of vectors can be given as input if different colors are desired for -#' the legend.dims. This parameter must be included even if the the legend is -#' not plotted, to define the colors in the scorecard table. -#'@param colorunder A character string or of vector of character strings -#' defining the colors to use for data values with are inferior to the lowest -#' breaks value. This parameter will also plot a inferior triangle in the -#' legend bar. The parameter can be set to NULL if there are no inferior values. -#' If a character string is given this color will be applied to all legend.dims. -#'@param colorsup A character string or of vector of character strings -#' defining the colors to use for data values with are superior to the highest -#' breaks value. This parameter will also plot a inferior triangle in the -#' legend bar. The parameter can be set to NULL if there are no superior values. -#' If a character string is given this color will be applied to all legend.dims. -#'@param legend.white.space A numeric value defining the initial starting -#' position of the legend bars, the white space infront of the legend is -#' calculated from the left most point of the table as a distance in cm. -#'@param round.decimal A numeric indicating to which decimal point the data -#' is to be displayed in the scorecard table. Default is 2. -#'@param font.size A numeric indicating the font size on the scorecard table. -#'@param col1.width A numeric value defining the width of the first table column -#' in cm. -#'@param col2.width A numeric value defining the width of the second table -#' column in cm. -#'@param columns.width A numeric value defining the width all columns within the -#' table in cm (excluding the first and second columns containing the titles). -#'@param fileout A path of the location to save the scorecard plots. -#' -#'@return An image file containing the scorecard. -#'@example -#'data <- array(rnorm(1000), dim = c('sdate' = 12, 'metric' = 4, 'region' = 3, -#' 'time' = 6)) -#'row.names <- c('Tropics', 'Extra-tropical NH', 'Extra-tropical SH') -#'col.names <- c('Mean bias (K)', 'Correlation', 'RPSS','CRPSS') -#'SCPlotScorecard(data = data, row.names = row.names, col.names = col.names, -#' subcol.names = month.abb[as.numeric(1:12)], -#' row.title = 'Region', subrow.title = 'Forecast Month', -#' col.title = 'Start date', -#' table.title = "Temperature of ECMWF System 5", -#' table.subtitle = "(Ref: ERA5 1994-2016)", -#' fileout = 'test.png') -#' -#'@import kableExtra -#'@import s2dv -#'@import ClimProjDiags -#'@export -SCPlotScorecard <- function(data, sign = NULL, - row.dim = 'region', subrow.dim = 'time', - col.dim = 'metric', subcol.dim = 'sdate', - legend.dim = 'metric', row.names = NULL, - subrow.names = NULL, col.names = NULL, - subcol.names = NULL, row.title = NULL, - subrow.title = NULL, col.title = NULL, - table.title = NULL, table.subtitle = NULL, - legend.breaks = NULL, plot.legend = TRUE, - label.scale = NULL, legend.width = NULL, - legend.height = NULL, palette = NULL, - colorunder = NULL, colorsup = NULL, - round.decimal = 2, font.size = 1.1, - legend.white.space = NULL, - col1.width = NULL, col2.width = NULL, - columns.width = 1.2, - fileout = './scorecard.png') { - # Input parameter checks - ## Check data - if (!is.array(data)) { - stop("Parameter 'data' must be a numeric array.") - } - ## Check sign - if (is.null(sign)){ - sign <- array(F, dim = dim(data)) - } else { - if (!is.array(sign)) { - stop("Parameter 'sign' must be a boolean array or NULL.") - } - } - ## Check row.dim - if (!is.character(row.dim)) { - stop("Parameter 'row.dim' must be a character string.") - } - if (!row.dim %in% names(dim(data))) { - stop("Parameter 'row.dim' is not found in 'data' dimensions.") - } - ## Check row.names - if (!is.null(row.names)) { - if (length(row.names) != as.numeric(dim(data)[row.dim])) { - stop("Parameter 'row.names' must have the same length of dimension 'row.dims'.") - } - } else { - row.names <- as.character(1:dim(data)[row.dim]) - } - ## Check subrow.dim - if (!is.character(subrow.dim)) { - stop("Parameter 'subrow.dim' must be a character string.") - } - if (!subrow.dim %in% names(dim(data))) { - stop("Parameter 'subrow.dim' is not found in 'data' dimensions.") - } - ## Check subrow.names - if (!is.null(subrow.names)) { - if (length(subrow.names) != as.numeric(dim(data)[subrow.dim])) { - stop("Parameter 'subrow.names' must have the same length of dimension 'subrow.dims'.") - } - } else { - subrow.names <- as.character(1:dim(data)[subrow.dim]) - } - ## Check col.dim - if (!is.character(col.dim)) { - stop("Parameter 'col.dim' must be a character string.") - } - if (!col.dim %in% names(dim(data))) { - stop("Parameter 'col.dim' is not found in 'data' dimensions.") - } - ## Check col.names - if (!is.null(col.names)) { - if (length(col.names) != as.numeric(dim(data)[col.dim])) { - stop("Parameter 'col.names' must have the same length of dimension 'col.dims'.") - } - } else { - col.names <- as.character(1:dim(data)[col.dim]) - } - ## Check subcol.dim - if (!is.character(subcol.dim)) { - stop("Parameter 'subcol.dim' must be a character string.") - } - if (!subcol.dim %in% names(dim(data))) { - stop("Parameter 'subcol.dim' is not found in 'data' dimensions.") - } - ## Check subcol.names - if (!is.null(subcol.names)) { - if (length(subcol.names) != as.numeric(dim(data)[subcol.dim])) { - stop("Parameter 'subcol.names' must have the same length of dimension 'subcol.dims'.") - } - } else { - subcol.names <- as.character(1:dim(data)[subcol.dim]) - } - ## Check legend.dim - if (!is.character(legend.dim)) { - stop("Parameter 'legend.dim' must be a character string.") - } - if (!legend.dim %in% names(dim(data))) { - stop("Parameter 'legend.dim' is not found in 'data' dimensions.") - } - ## Check row.title inputs - if (!is.null(row.title)) { - if (!is.character(row.title)) { - stop("Parameter 'row.title must be a character string.") - } - } else { - row.title <- "" - } - ## Check subrow.title - if (!is.null(subrow.title)) { - if (!is.character(subrow.title)) { - stop("Parameter 'subrow.title must be a character string.") - } - } else { - subrow.title <- "" - } - ## Check col.title - if (!is.null(col.title)) { - if (!is.character(col.title)) { - stop("Parameter 'col.title must be a character string.") - } - } else { - col.title <- "" - } - ## Check table.title - if (!is.null(table.title)) { - if (!is.character(table.title)) { - stop("Parameter 'table.title' must be a character string.") - } - } else { - table.title <- "" - } - ## Check table.subtitle - if (!is.null(table.subtitle)) { - if (!is.character(table.subtitle)) { - stop("Parameter 'table.subtitle' must be a character string.") - } - } else { - table.subtitle <- "" - } - # Check legend.breaks - if (is.vector(legend.breaks) && is.numeric(legend.breaks)) { - legend.breaks <- rep(list(legend.breaks), as.numeric(dim(data)[legend.dim])) - } else if (is.null(legend.breaks)) { - legend.breaks <- rep(list(seq(-1, 1, 0.2)), as.numeric(dim(data)[legend.dim])) - } else if (inherits(legend.breaks, 'list')) { - stopifnot(length(legend.breaks) == as.numeric(dim(data)[legend.dim])) - } else { - stop("Parameter 'legend.breaks' must be a numeric vector, a list or NULL.") - } - ## Check plot.legend - if (!inherits(plot.legend, 'logical')) { - stop("Parameter 'plot.legend' must be a logical value.") - } - ## Check label.scale - if (is.null(label.scale)) { - label.scale <- 1.4 - } else { - if (!is.numeric(label.scale) | length(label.scale) != 1) { - stop("Parameter 'label.scale' must be a numeric value of length 1.") - } - } - ## Check legend.width - if (is.null(legend.width)) { - legend.width <- length(subcol.names) * 46.5 - } else { - if (!is.numeric(legend.width) | length(legend.width) != 1) { - stop("Parameter 'legend.width' must be a numeric value of length 1.") - } - } - if (is.null(legend.height)) { - legend.height <- 50 - } else { - if (!is.numeric(legend.height) | length(legend.height) != 1) { - stop("Parameter 'legend.height' must be a numeric value of length 1.") - } - } - ## Check colour palette input - if (is.vector(palette)) { - palette <- rep(list(palette), as.numeric(dim(data)[legend.dim])) - } else if (is.null(palette)) { - palette <- rep(list(c('#2D004B', '#542789', '#8073AC', '#B2ABD2', '#D8DAEB', - '#FEE0B6', '#FDB863', '#E08214', '#B35806', '#7F3B08')), - as.numeric(dim(data)[legend.dim])) - } else if (inherits(palette, 'list')) { - stopifnot(length(palette) == as.numeric(dim(data)[legend.dim])) - } else { - stop("Parameter 'palette' must be a numeric vector, a list or NULL.") - } - ## Check colorunder - if (is.null(colorunder)) { - colorunder <- rep("#04040E",as.numeric(dim(data)[legend.dim])) - } else if (is.character(colorunder) && length(colorunder) == 1) { - colorunder <- rep(colorunder, as.numeric(dim(data)[legend.dim])) - } else if (is.character(colorunder) && - length(colorunder) != as.numeric(dim(data)[legend.dim])) { - stop("Parameter 'colorunder' must be a numeric vector, a list or NULL.") - } - ## Check colorsup - if (is.null(colorsup)) { - colorsup <- rep("#730C04", as.numeric(dim(data)[legend.dim])) - } else if (is.character(colorsup) && length(colorsup) == 1) { - colorsup <- rep(colorsup,as.numeric(dim(data)[legend.dim])) - } else if (is.character(colorsup) && - length(colorsup) != as.numeric(dim(data)[legend.dim])) { - stop("Parameter 'colorsup' must be a numeric vector, a list or NULL.") - } - ## Check round.decimal - if (is.null(round.decimal)) { - round.decimal <- 2 - } else if (!is.numeric(round.decimal) | length(round.decimal) != 1) { - stop("Parameter 'round.decimal' must be a numeric value of length 1.") - } - ## Check font.size - if (is.null(font.size)) { - font.size <- 1 - } else if (!is.numeric(font.size) | length(font.size) != 1) { - stop("Parameter 'font.size' must be a numeric value of length 1.") - } - ## Check legend white space - if (is.null(legend.white.space)){ - legend.white.space <- 6 - } else { - legend.white.space <- legend.white.space - } - ## Check col1.width - if (is.null(col1.width)) { - if (max(nchar(row.names)) == 1 ) { - col1.width <- max(nchar(row.names)) - } else { - col1.width <- max(nchar(row.names))/4 - } - } else if (!is.numeric(col1.width)) { - stop("Parameter 'col1.width' must be a numeric value of length 1.") - } - ## Check col2.width - if (is.null(col2.width)) { - if (max(nchar(subrow.names)) == 1 ) { - col2.width <- max(nchar(subrow.names)) - } else { - col2.width <- max(nchar(subrow.names))/4 - } - } else if (!is.numeric(col2.width)) { - stop("Parameter 'col2.width' must be a numeric value of length 1.") - } - - - # Get dimensions of inputs - n.col.names <- length(col.names) - n.subcol.names <- length(subcol.names) - n.row.names <- length(row.names) - n.subrow.names <- length(subrow.names) - - # Define table size - n.rows <- n.row.names * n.subrow.names - n.columns <- 2 + (n.col.names * n.subcol.names) - - # Column names - row.names.table <- rep("", n.rows) - for (row in 1:n.row.names) { - row.names.table[floor(n.subrow.names/2) + (row - 1) * n.subrow.names] <- row.names[row] - } - - # Define scorecard table titles - column.titles <- c(row.title, subrow.title, rep(c(subcol.names), n.col.names)) - - # Round data - data <- round(data, round.decimal) - - # Define data inside the scorecards table - for (row in 1:n.row.names) { - table_temp <- data.frame(table_column_2 = as.character(subrow.names)) - for (col in 1:n.col.names) { - table_temp <- data.frame(table_temp, - Reorder(data = Subset(x = data, along = c(col.dim, row.dim), - indices = list(col, row), drop = 'selected'), - order = c(subrow.dim, subcol.dim))) - } - if (row == 1) { - table_data <- table_temp - } else { - table_data <- rbind(table_data, table_temp) - } - } - - # All data for plotting in table - table <- data.frame(table_column_1 = row.names.table, table_data) - table_temp <- array(unlist(table[3:n.columns]), dim = c(n.rows, n.columns - 2)) - - # Define colors to show in table - table_colors <- .SCTableColors(table = table_temp, - n.col = n.col.names, n.subcol = n.subcol.names, - n.row = n.row.names, n.subrow = n.subrow.names, - legend.breaks = legend.breaks, palette = palette, - colorunder = colorunder, colorsup = colorsup) - - metric.color <- table_colors$metric.color - metric.text.color <- table_colors$metric.text.color - # metric.text.bold <- table_colors$metric.text.bold - - # Remove temporary table - rm(table_temp) - - ## Format values to underline in table - metric.underline <- MergeDims(sign, c(subcol.dim, col.dim) , rename_dim = 'col', na.rm =F) - metric.underline <- MergeDims(metric.underline, c(subrow.dim, row.dim) , rename_dim = 'row', na.rm =F) - - options(stringsAsFactors = FALSE) - title <- data.frame(c1 = table.title, c2 = n.columns) - subtitle <- data.frame(c1 = table.subtitle, c2 = n.columns) - header.names <- as.data.frame(data.frame(c1 = c("", col.names), - c2 = c(2, rep(n.subcol.names, n.col.names)))) - header.names2 <- as.data.frame(data.frame(c1 = c("", paste0(rep(col.title, n.col.names))), - c2 = c(2, rep(n.subcol.names, n.col.names)))) - title.space <- data.frame(c1 = "\n", c2 = n.columns) - - # Hide NA values in table - options(knitr.kable.NA = '') - - # Create HTML table - table.html.part <- list() - table.html.part[[1]] <- kbl(table, escape = F, col.names = column.titles, align = rep("c", n.columns)) %>% - kable_paper("hover", full_width = F, font_size = 14 * font.size) %>% - add_header_above(header = header.names2, font_size = 16 * font.size) %>% - add_header_above(header = title.space, font_size = 10 * font.size) %>% - add_header_above(header = header.names, font_size = 20 * font.size) %>% - add_header_above(header = title.space, font_size = 10 * font.size) %>% - add_header_above(header = subtitle, font_size = 16 * font.size, align = "left") %>% - add_header_above(header = title.space, font_size = 10 * font.size) %>% - add_header_above(header = title, font_size = 22 * font.size, align = "left") - - for (i in 1:n.col.names) { - for (j in 1:n.subcol.names) { - my.background <- metric.color[, (i - 1) * n.subcol.names + j] - my.text.color <- metric.text.color[, (i - 1) * n.subcol.names + j] - my.underline <- metric.underline[, (i - 1) * n.subcol.names + j] - # my.bold <- metric.text.bold[(i - 1) * n.subcol.names + j] - - table.html.part[[(i - 1) * n.subcol.names + j + 1]] <- - column_spec(table.html.part[[(i - 1) * n.subcol.names + j]], - 2 + n.subcol.names * (i - 1) + j, - background = my.background[1:n.rows], - color = my.text.color[1:n.rows], - underline = my.underline[1:n.rows], - bold = T) ## strsplit(toString(bold), ', ')[[1]] - } - } - - # Define position of table borders - column.borders <- NULL - for (i in 1:n.col.names) { - column.spacing <- (n.subcol.names * i) + 2 - column.borders <- c(column.borders, column.spacing) - } - - n.last.list <- n.col.names * n.subcol.names + 1 - - table.html <- column_spec(table.html.part[[n.last.list]], 1, bold = TRUE, width_min = paste0(col1.width, 'cm')) %>% - column_spec(2, bold = TRUE, width_min = paste0(col2.width, 'cm')) %>% - column_spec(3:n.columns, width_min = paste0(columns.width, 'cm')) %>% - column_spec(c(1, 2, column.borders), border_right = "2px solid black") %>% - column_spec(1, border_left = "2px solid black") %>% - column_spec(n.columns, border_right = "2px solid black") %>% - row_spec(seq(from = 0, to = n.subrow.names * n.row.names, by = n.subrow.names), - extra_css = "border-bottom: 2px solid black", hline_after = TRUE) - - if (plot.legend == TRUE) { - # Save the scorecard (without legend) - save_kable(table.html, file = paste0(fileout, '_tmpScorecard.png'), vheight = 1) - - # White space for legend - legend.white.space <- 37.8 * legend.white.space ## converting pixels to cm - - # Create and save color bar legend - scorecard_legend <- .SCLegend(legend.breaks = legend.breaks, - palette = palette, - colorunder = colorunder, - colorsup = colorsup, - label.scale = label.scale, - legend.width = legend.width, - legend.height = legend.height, - legend.white.space = legend.white.space, - fileout = fileout) - - # Add the legends below the scorecard table - system(paste0('convert -append ', fileout, '_tmpScorecard.png ', fileout, - '_tmpScorecardLegend.png ', fileout)) - # Remove temporary scorecard table - unlink(paste0(fileout, '_tmpScorecard*.png')) - } - if (plot.legend == FALSE) { - save_kable(table.html, file = fileout) - } -} -- GitLab From 904d1c32190153f643b3fbba98dd271b21c313a9 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 28 Feb 2024 10:50:28 +0100 Subject: [PATCH 74/78] included output_format check for syear metrics, set to Scorecards output_format --- tools/check_recipe.R | 159 +++++++++++++++++++++++-------------------- 1 file changed, 85 insertions(+), 74 deletions(-) diff --git a/tools/check_recipe.R b/tools/check_recipe.R index 4c084b9d..d9f79603 100644 --- a/tools/check_recipe.R +++ b/tools/check_recipe.R @@ -2,11 +2,11 @@ check_recipe <- function(recipe) { # recipe: yaml recipe already read it ## TODO: set up logger-less case info(recipe$Run$logger, paste("Checking recipe:", recipe$recipe_path)) - + # --------------------------------------------------------------------- # ANALYSIS CHECKS # --------------------------------------------------------------------- - + TIME_SETTINGS_SEASONAL <- c("sdate", "ftime_min", "ftime_max", "hcst_start", "hcst_end") TIME_SETTINGS_DECADAL <- c("ftime_min", "ftime_max", "hcst_start", "hcst_end") @@ -15,28 +15,28 @@ check_recipe <- function(recipe) { HORIZONS <- c("subseasonal", "seasonal", "decadal") ARCHIVE_SEASONAL <- "conf/archive.yml" ARCHIVE_DECADAL <- "conf/archive_decadal.yml" - + # Define error status variable error_status <- F - + # Check basic elements in recipe:Analysis: if (!("Analysis" %in% names(recipe))) { error(recipe$Run$logger, "The recipe must contain an element called 'Analysis'.") error_status <- T } - + if (!all(PARAMS %in% names(recipe$Analysis))) { error(recipe$Run$logger, paste0("The element 'Analysis' in the recipe must contain all of ", "the following: ", paste(PARAMS, collapse = ", "), ".")) error_status <- T } - + if (!any(HORIZONS %in% tolower(recipe$Analysis$Horizon))) { error(recipe$Run$logger, paste0("The element 'Horizon' in the recipe must be one of the ", - "following: ", paste(HORIZONS, collapse = ", "), ".")) + "following: ", paste(HORIZONS, collapse = ", "), ".")) error_status <- T } # Check time settings @@ -88,7 +88,7 @@ check_recipe <- function(recipe) { } # Check reference names if (!all(recipe$Analysis$Datasets$Reference$name %in% - names(archive$Reference))) { + names(archive$Reference))) { error(recipe$Run$logger, "The specified Reference name was not found in the archive.") error_status <- T @@ -138,7 +138,7 @@ check_recipe <- function(recipe) { } else { stream <- "fcst" } - + ## TODO: To be implemented in the future # if (length(recipe$Analysis$Time$sdate$fcst_day) > 1 && # tolower(recipe$Analysis$Horizon) != "subseasonal") { @@ -152,7 +152,7 @@ check_recipe <- function(recipe) { # error(recipe$Run$logger, # paste("The element 'fcst_sday' in the recipe should be defined.")) # } - + if (is.null(recipe$Analysis$Time$fcst_year)) { warn(recipe$Run$logger, paste("The element 'fcst_year' is not defined in the recipe.", @@ -168,7 +168,7 @@ check_recipe <- function(recipe) { # } # } # fcst.sdate <- list(stream = stream, fcst.sdate = fcst.sdate) - + # Regrid checks: if (length(recipe$Analysis$Regrid) != 2) { error(recipe$Run$logger, @@ -183,7 +183,7 @@ check_recipe <- function(recipe) { "Only one single Horizon can be specified in the recipe") error_status <- T } - + ## TODO: Refine this # nvar <- length(recipe$Analysis$Variables) # if (nvar > 2) { @@ -191,19 +191,19 @@ check_recipe <- function(recipe) { # "Only two type of Variables can be listed: ECVs and Indicators.") # stop("EXECUTION FAILED") # } - + # remove NULL or None Indicators or ECVs from the recipe: if (!is.null(recipe$Analysis$Variables$Indicators) && !is.list(recipe$Analysis$Variables$Indicators)) { recipe$Analysis$Variables <- recipe$Analysis$Variables[ - -which(names(recipe$Analysis$Variables) == 'Indicators')] + -which(names(recipe$Analysis$Variables) == 'Indicators')] } if (!is.null(recipe$Analysis$Variables$ECVs) && !is.list(recipe$Analysis$Variables$ECVs)) { recipe$Analysis$Variables <- recipe$Analysis$Variables[ - -which(names(recipe$Analysis$Variables) == 'ECVs')] + -which(names(recipe$Analysis$Variables) == 'ECVs')] } - + # Region checks: LIMITS <- c('latmin', 'latmax', 'lonmin', 'lonmax') # Ordinary recipe @@ -212,7 +212,7 @@ check_recipe <- function(recipe) { if (!all(LIMITS %in% names(region))) { error(recipe$Run$logger, paste0("There must be 4 elements in 'Region': ", - paste(LIMITS, collapse = ", "), ".")) + paste(LIMITS, collapse = ", "), ".")) error_status <- T } } @@ -225,35 +225,35 @@ check_recipe <- function(recipe) { } } } - # Atomic recipe + # Atomic recipe } else if (!all(LIMITS %in% names(recipe$Analysis$Region))) { error(recipe$Run$logger, paste0("There must be 4 elements in 'Region': ", paste(LIMITS, collapse = ", "), ".")) error_status <- T } - ## TODO: Implement multiple regions - # nregions <- length(recipe$Analysis$Region) - # for (i in 1:length(recipe$Analysis$Region)) { - # if (!all(limits %in% names(recipe$Analysis$Region[[i]]))) { - # limits <- paste(limits, collapse = " ") - # error(recipe$Run$logger, - # paste0("Each region defined in element 'Region' ", - # "should have 4 elements: ", - # paste(limits, collapse = ", "), ".")) - # error_status <- T - # } - # if (length(recipe$Analysis$Region) > 1) { - # if (!("name" %in% names(recipe$Analysis$Region[[i]]))) { - # error(recipe$Run$logger, - # paste("If multiple regions are requested, each region must", - # "have a 'name'".) - # # are numeric? class list mode list - # } + ## TODO: Implement multiple regions + # nregions <- length(recipe$Analysis$Region) + # for (i in 1:length(recipe$Analysis$Region)) { + # if (!all(limits %in% names(recipe$Analysis$Region[[i]]))) { + # limits <- paste(limits, collapse = " ") + # error(recipe$Run$logger, + # paste0("Each region defined in element 'Region' ", + # "should have 4 elements: ", + # paste(limits, collapse = ", "), ".")) + # error_status <- T + # } + # if (length(recipe$Analysis$Region) > 1) { + # if (!("name" %in% names(recipe$Analysis$Region[[i]]))) { + # error(recipe$Run$logger, + # paste("If multiple regions are requested, each region must", + # "have a 'name'".) + # # are numeric? class list mode list + # } # --------------------------------------------------------------------- # WORKFLOW CHECKS # --------------------------------------------------------------------- - + # Calibration # If 'method' is FALSE/no/'none' or NULL, set to 'raw' ## TODO: Review this check @@ -313,7 +313,7 @@ check_recipe <- function(recipe) { } } } - + # Downscaling if ("Downscaling" %in% names(recipe$Analysis$Workflow)) { downscal_params <- lapply(recipe$Analysis$Workflow$Downscaling, tolower) @@ -329,27 +329,27 @@ check_recipe <- function(recipe) { downscal_params$type <- "none" warn(recipe$Run$logger, paste("Downscaling 'type' is empty in the recipe, setting it to", - "'none'.")) + "'none'.")) } if (!(downscal_params$type %in% DOWNSCAL_TYPES)) { error(recipe$Run$logger, paste0("The type of Downscaling request in the recipe is not ", - "available. It must be one of the following: ", - paste(DOWNSCAL_TYPES, collapse = ", "), ".")) + "available. It must be one of the following: ", + paste(DOWNSCAL_TYPES, collapse = ", "), ".")) error_status <- T } if ((downscal_params$type %in% c("int", "intbc", "intlr", "logreg")) && (is.null(downscal_params$target_grid))) { error(recipe$Run$logger, paste("A target grid is required for the downscaling method", - "requested in the recipe.")) + "requested in the recipe.")) error_status <- T } if (downscal_params$type == "int") { if (is.null(downscal_params$int_method)) { error(recipe$Run$logger, paste("Downscaling type 'int' was requested, but no", - "interpolation method is provided in the recipe.")) + "interpolation method is provided in the recipe.")) error_status <- T } } else if (downscal_params$type %in% @@ -358,63 +358,63 @@ check_recipe <- function(recipe) { error(recipe$Run$logger, paste("Downscaling type", downscal_params$type, "was requested in the recipe, but no", - "interpolation method is provided.")) + "interpolation method is provided.")) error_status <- T } } else if (downscal_params$type == "intbc") { if (is.null(downscal_params$bc_method)) { error(recipe$Run$logger, paste("Downscaling type 'intbc' was requested in the recipe, but", - "no bias correction method is provided.")) + "no bias correction method is provided.")) error_status <- T } else if (!(downscal_params$bc_method %in% BC_METHODS)) { error(recipe$Run$logger, paste0("The accepted Bias Correction methods for the downscaling", - " module are: ", paste(BC_METHODS, collapse = ", "), ".")) + " module are: ", paste(BC_METHODS, collapse = ", "), ".")) error_status <- T } } else if (downscal_params$type == "intlr") { if (length(downscal_params$lr_method) == 0) { error(recipe$Run$logger, paste("Downscaling type 'intlr' was requested in the recipe, but", - "no linear regression method was provided.")) + "no linear regression method was provided.")) error_status <- T } else if (!(downscal_params$lr_method %in% LR_METHODS)) { error(recipe$Run$logger, paste0("The accepted linear regression methods for the", - " downscaling module are: ", - paste(LR_METHODS, collapse = ", "), ".")) + " downscaling module are: ", + paste(LR_METHODS, collapse = ", "), ".")) error_status <- T } } else if (downscal_params$type == "analogs") { if (is.null(downscal_params$nanalogs)) { warn(recipe$Run$logger, paste("Downscaling type is 'analogs, but the number of analogs", - "has not been provided in the recipe. The default is 3.")) + "has not been provided in the recipe. The default is 3.")) } } else if (downscal_params$type == "logreg") { if (is.null(downscal_params$int_method)) { error(recipe$Run$logger, paste("Downscaling type 'logreg' was requested in the recipe, but", - "no interpolation method was provided.")) + "no interpolation method was provided.")) error_status <- T } if (is.null(downscal_params$log_reg_method)) { error(recipe$Run$logger, paste("Downscaling type 'logreg' was requested in the recipe,", - "but no logistic regression method is provided.")) + "but no logistic regression method is provided.")) error_status <- T } else if (!(downscal_params$log_reg_method %in% LOGREG_METHODS)) { error(recipe$Run$logger, paste0("The accepted logistic regression methods for the ", - "downscaling module are: ", - paste(LOGREG_METHODS, collapse = ", "), ".")) + "downscaling module are: ", + paste(LOGREG_METHODS, collapse = ", "), ".")) error_status <- T } } } } - + # Indices if ("Indices" %in% names(recipe$Analysis$Workflow)) { nino_indices <- paste0("nino", c("1+2", "3", "3.4", "4")) @@ -425,9 +425,9 @@ check_recipe <- function(recipe) { "in the recipe.")) error_status <- T } else if (!(recipe$Analysis$Workflow$Anomalies$compute)) { - error(recipe$Run$logger, - paste0("Indices uses Anomalies as input, but the parameter", - "'Anomalies:compute' is set as no/False.")) + error(recipe$Run$logger, + paste0("Indices uses Anomalies as input, but the parameter", + "'Anomalies:compute' is set as no/False.")) error_status <- T } recipe_indices <- tolower(names(recipe$Analysis$Workflow$Indices)) @@ -456,7 +456,7 @@ check_recipe <- function(recipe) { error_status <- T } } - + # Skill AVAILABLE_METRICS <- c("enscorr", "corr_individual_members", "rps", "rps_syear", "rpss", "frps", "frpss", "crps", "crps_syear", @@ -467,9 +467,9 @@ check_recipe <- function(recipe) { "frpss_specs", "bss10_specs", "bss90_specs") if ("Skill" %in% names(recipe$Analysis$Workflow)) { if (is.null(recipe$Analysis$Workflow$Skill$metric)) { - error(recipe$Run$logger, - "Parameter 'metric' must be defined under 'Skill'.") - error_status <- T + error(recipe$Run$logger, + "Parameter 'metric' must be defined under 'Skill'.") + error_status <- T } else { requested_metrics <- strsplit(recipe$Analysis$Workflow$Skill$metric, ", | |,")[[1]] @@ -480,6 +480,14 @@ check_recipe <- function(recipe) { "full list of accepted skill metrics.")) error_status <- T } + if (tolower(recipe$Analysis$Output_format) != 'scorecards') { + if (any(grepl('_syear', requested_metrics))) { + recipe$Analysis$Output_format <- 'scorecards' + warn(recipe$Run$logger, + paste0("'_syear' metrics can only be saved as 'scorecards' ", + "output format. The output format is now 'scorecards'.")) + } + } } # Saving checks SAVING_OPTIONS_SKILL <- c("all", "none") @@ -492,7 +500,7 @@ check_recipe <- function(recipe) { error_status <- T } } - + # Probabilities if ("Probabilities" %in% names(recipe$Analysis$Workflow)) { if (is.null(recipe$Analysis$Workflow$Probabilities$percentiles)) { @@ -517,7 +525,7 @@ check_recipe <- function(recipe) { error_status <- T } } - + # Visualization if ("Visualization" %in% names(recipe$Analysis$Workflow)) { PLOT_OPTIONS <- c("skill_metrics", "forecast_ensemble_mean", @@ -584,9 +592,9 @@ check_recipe <- function(recipe) { if ("Scorecards" %in% names(recipe$Analysis$Workflow)) { if(recipe$Analysis$Workflow$Scorecards$execute == TRUE){ if (is.null(recipe$Analysis$Workflow$Scorecards$metric)) { - error(recipe$Run$logger, - "Parameter 'metric' must be defined under 'Scorecards'.") - error_status <- T + error(recipe$Run$logger, + "Parameter 'metric' must be defined under 'Scorecards'.") + error_status <- T } else { sc_metrics <- strsplit(recipe$Analysis$Workflow$Scorecards$metric, ", | |,")[[1]] @@ -610,7 +618,10 @@ check_recipe <- function(recipe) { if ('enscorr' %in% tolower(sc_metrics)) { recipe$Analysis$Workflow$Statistics <- c('std', 'cov', 'n_eff') } - recipe$Analysis$Workflow$Skill$metric <- requested_metrics + recipe$Analysis$Workflow$Skill$metric <- requested_metrics + } + if (tolower(recipe$Analysis$Output_format) != 'scorecards') { + recipe$Analysis$Output_format <- 'scorecards' } if (!all(tolower(sc_metrics) %in% tolower(requested_metrics))) { error(recipe$Run$logger, @@ -624,11 +635,11 @@ check_recipe <- function(recipe) { # --------------------------------------------------------------------- # RUN CHECKS # --------------------------------------------------------------------- - + ## TODO: These checks should probably go first RUN_FIELDS = c("Loglevel", "Terminal", "output_dir", "code_dir") LOG_LEVELS = c("INFO", "DEBUG", "WARN", "ERROR", "FATAL") - + if (!("Run" %in% names(recipe))) { stop("The recipe must contain an element named 'Run'.") } @@ -668,11 +679,11 @@ check_recipe <- function(recipe) { paste0(LOG_LEVELS, collapse='/'))) error_status <- T } - + # --------------------------------------------------------------------- # AUTOSUBMIT CHECKS # --------------------------------------------------------------------- - + AUTO_PARAMS <- c("script", "expid", "hpc_user", "wallclock", "processors_per_job", "platform", "email_notifications", "email_address", "notify_completed", "notify_failed") @@ -741,7 +752,7 @@ check_recipe <- function(recipe) { error_status <- T } } - + # --------------------------------------------------------------------- # WORKFLOW CHECKS # --------------------------------------------------------------------- @@ -751,7 +762,7 @@ check_recipe <- function(recipe) { #nverifications <- check_number_of_dependent_verifications(recipe) # info(recipe$Run$logger, paste("Start Dates:", # paste(fcst.sdate, collapse = " "))) - + # Return error if any check has failed if (error_status) { error(recipe$Run$logger, "RECIPE CHECK FAILED.") -- GitLab From 46f11ce2ecf5dc2bf76f1cee50b126e0983ab9bb Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 28 Feb 2024 14:52:55 +0100 Subject: [PATCH 75/78] bug fix in saving metrics in S2S4E format --- modules/Skill/Skill.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/Skill/Skill.R b/modules/Skill/Skill.R index cc0da1e1..aad44951 100644 --- a/modules/Skill/Skill.R +++ b/modules/Skill/Skill.R @@ -385,7 +385,7 @@ Skill <- function(recipe, data, agg = 'global') { save_metrics_scorecards(recipe = recipe, skill = skill_metrics[-corr_metric_names], data_cube = data$hcst, agg = agg) } else { - save_metrics_scorecards(recipe = recipe, skill = skill_metrics[-corr_metric_names], + save_metrics(recipe = recipe, skill = skill_metrics[-corr_metric_names], data_cube = data$hcst, agg = agg) } } -- GitLab From a9ec37f85080fb4862d3af44f6e2041db5ce5ff5 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 28 Feb 2024 16:19:07 +0100 Subject: [PATCH 76/78] corrected test-seasonal_NAO.R --- tests/testthat/test-seasonal_NAO.R | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/tests/testthat/test-seasonal_NAO.R b/tests/testthat/test-seasonal_NAO.R index 9582bdfc..4bbfd3b3 100644 --- a/tests/testthat/test-seasonal_NAO.R +++ b/tests/testthat/test-seasonal_NAO.R @@ -214,8 +214,8 @@ TRUE expect_equal( names(skill_metrics), c("mean_bias", "enscorr", - "enscorr_significance", "rps", "rps_clim", "rpss", "rpss_significance", - "crps", "crps_clim", "crpss", "crpss_significance", "enssprerr") + "enscorr_significance", "rps", "rpss", "rpss_significance", + "crps", "crpss", "crpss_significance", "enssprerr") ) expect_equal( class(skill_metrics$rpss), @@ -247,12 +247,22 @@ expect_equal( all(list.files(outputs, recursive = T) %in% c(paste0("Indices/ECMWF-SEAS5/nao/", paste0("nao_", 1993:2000, "0301.nc")), paste0("Indices/ERA5/nao/", paste0("nao_", 1993:2000, "0301.nc")), - "Skill/ECMWF-SEAS5/nao/scorecards_ECMWF-SEAS5_ERA5_nao-skill_1993-2000_s03.nc") + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_crps_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_crpss_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_crpss_significance_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_enscorr_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_enscorr_significance_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_enssprerr_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_mean_bias_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_rps_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_rpss_1993-2000_s03.nc", + "Skill/ECMWF-SEAS5/ERA5/raw/nao/scorecards_ECMWF-SEAS5_ERA5_nao_rpss_significance_1993-2000_s03.nc" + ) ), TRUE) expect_equal( length(list.files(outputs, recursive = T)), -17 +26 ) }) -- GitLab From 527a91e28b46fdbe6e140530f53867c231a65efe Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Wed, 28 Feb 2024 17:41:29 +0100 Subject: [PATCH 77/78] corrected test-decadal_monthly_2.R --- tests/testthat/test-decadal_monthly_2.R | 2 +- .../test-seasonal_monthly_statistics.R | 251 ++++++++++++++++++ 2 files changed, 252 insertions(+), 1 deletion(-) create mode 100644 tests/testthat/test-seasonal_monthly_statistics.R diff --git a/tests/testthat/test-decadal_monthly_2.R b/tests/testthat/test-decadal_monthly_2.R index 9adc16b6..6ee681bb 100644 --- a/tests/testthat/test-decadal_monthly_2.R +++ b/tests/testthat/test-decadal_monthly_2.R @@ -164,7 +164,7 @@ TRUE ) expect_equal( names(skill_metrics), -c("rpss_specs", "enscorr_specs", "frps_specs", "frpss_specs", "bss10_specs", "frps", "frps_clim") +c("rpss_specs", "enscorr_specs", "frps_specs", "frpss_specs", "bss10_specs", "frps") ) expect_equal( class(skill_metrics$rpss_specs), diff --git a/tests/testthat/test-seasonal_monthly_statistics.R b/tests/testthat/test-seasonal_monthly_statistics.R new file mode 100644 index 00000000..d9b7b3c3 --- /dev/null +++ b/tests/testthat/test-seasonal_monthly_statistics.R @@ -0,0 +1,251 @@ +context("Seasonal monthly data") + +source("modules/Loading/Loading.R") +source("modules/Calibration/Calibration.R") +source("modules/Skill/Skill.R") +source("modules/Saving/Saving.R") +source("modules/Visualization/Visualization.R") + +recipe_file <- "tests/recipes/recipe-seasonal_monthly_1.yml" +recipe <- prepare_outputs(recipe_file, disable_checks = F) +archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive + +# Load datasets +suppressWarnings({invisible(capture.output( +data <- Loading(recipe) +))}) + +# Calibrate data +suppressWarnings({invisible(capture.output( +calibrated_data <- Calibration(recipe, data) +))}) + +# Compute skill metrics +suppressWarnings({invisible(capture.output( +skill_metrics <- Skill(recipe, calibrated_data) +))}) + +suppressWarnings({invisible(capture.output( +probs <- Probabilities(recipe, calibrated_data) +))}) + +# Saving +suppressWarnings({invisible(capture.output( +Saving(recipe = recipe, data = calibrated_data, + skill_metrics = skill_metrics, probabilities = probs) +))}) + +# Plotting +suppressWarnings({invisible(capture.output( +Visualization(recipe = recipe, data = calibrated_data, + skill_metrics = skill_metrics, probabilities = probs, + significance = T) +))}) +outdir <- get_dir(recipe = recipe, variable = data$hcst$attrs$Variable$varName) + +# ------- TESTS -------- + +test_that("1. Loading", { + +expect_equal( +is.list(data), +TRUE +) +expect_equal( +names(data), +c("hcst", "fcst", "obs") +) +expect_equal( +class(data$hcst), +"s2dv_cube" +) +expect_equal( +class(data$fcst), +"s2dv_cube" +) +expect_equal( +class(data$obs), +"s2dv_cube" +) +expect_equal( +names(data$hcst), +c("data", "dims", "coords", "attrs") +) +expect_equal( +names(data$hcst), +names(data$fcst) +) +expect_equal( +names(data$hcst), +names(data$obs) +) +expect_equal( +dim(data$hcst$data), +c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 3, longitude = 3, ensemble = 25) +) +expect_equal( +dim(data$fcst$data), +c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 3, longitude = 3, ensemble = 51) +) +expect_equal( +dim(data$hcst$attrs$Dates), +c(sday = 1, sweek = 1, syear = 4, time = 3) +) +expect_equal( +as.vector(drop(data$hcst$data)[1:2,1:2,1,2,3]), +c(293.9651, 295.9690, 290.6771, 290.7957), +tolerance = 0.0001 +) +expect_equal( +mean(data$hcst$data), +290.8758, +tolerance = 0.0001 +) +expect_equal( +range(data$hcst$data), +c(284.7413, 299.6219), +tolerance = 0.0001 +) +expect_equal( +(data$hcst$attrs$Dates)[1], +as.POSIXct("1993-11-30 23:59:59", tz = 'UTC') +) +expect_equal( +(data$hcst$attrs$Dates)[2], +as.POSIXct("1994-11-30 23:59:59", tz = 'UTC') +) +expect_equal( +(data$hcst$attrs$Dates)[5], +as.POSIXct("1993-12-31 23:59:59", tz = 'UTC') +) +expect_equal( +(data$obs$attrs$Dates)[10], +as.POSIXct("1995-01-15 12:00:00", tz = 'UTC') +) + +}) + +test_that("2. Calibration", { + +expect_equal( +is.list(calibrated_data), +TRUE +) +expect_equal( +names(calibrated_data), +c("hcst", "obs", "fcst") +) +expect_equal( +class(calibrated_data$hcst), +"s2dv_cube" +) +expect_equal( +class(calibrated_data$fcst), +"s2dv_cube" +) +expect_equal( +dim(calibrated_data$hcst$data), +c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 3, longitude = 3, ensemble = 25) +) +expect_equal( +dim(calibrated_data$fcst$data), +c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 3, longitude = 3, ensemble = 51) +) +expect_equal( +mean(calibrated_data$fcst$data), +291.6433, +tolerance = 0.0001 +) +expect_equal( +mean(calibrated_data$hcst$data), +290.9006, +tolerance = 0.0001 +) +expect_equal( +as.vector(drop(calibrated_data$hcst$data)[1, , 2, 3, 4]), +c(291.8887, 287.0233, 289.8808), +tolerance = 0.0001 +) +expect_equal( +range(calibrated_data$fcst$data), +c(283.8926, 299.0644), +tolerance = 0.0001 +) + +}) + + +#====================================== +test_that("3. Metrics", { + +expect_equal( +is.list(skill_metrics), +TRUE +) +expect_equal( +names(skill_metrics), +c("rpss", "rpss_significance", "crpss", "crpss_significance", "enscorr", + "enscorr_significance", "corr_individual_members", "corr_individual_members_significance", + "enscorr_specs") +) +expect_equal( +class(skill_metrics$rpss), +"array" +) +expect_equal( +dim(skill_metrics$rpss), +c(var = 1, time = 3, latitude = 3, longitude = 3) +) +expect_equal( +dim(skill_metrics$rpss_significance), +dim(skill_metrics$rpss) +) +expect_equal( +as.vector(skill_metrics$rpss[, , 2, 3]), +c(-0.2918857, -1.4809143, -1.3842286), +tolerance = 0.0001 +) +expect_equal( +as.vector(skill_metrics$rpss_significance[, , 2, 3]), +rep(FALSE, 3) +) + +}) + +test_that("4. Saving", { +outputs <- paste0(recipe$Run$output_dir, "/outputs/") +expect_equal( +all(basename(list.files(outputs, recursive = T)) %in% +c("tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", + "tas_19961101.nc", "tas_20201101.nc", "tas-corr_month11.nc", + "tas-obs_19931101.nc", "tas-obs_19941101.nc", "tas-obs_19951101.nc", + "tas-obs_19961101.nc", "tas-percentiles_month11.nc", "tas-probs_19931101.nc", + "tas-probs_19941101.nc", "tas-probs_19951101.nc", "tas-probs_19961101.nc", + "tas-probs_20201101.nc", "tas-skill_month11.nc")), +TRUE +) +expect_equal( +length(list.files(outputs, recursive = T)), +17 +) + +}) + +test_that("5. Visualization", { +plots <- paste0(recipe$Run$output_dir, "/plots/") +expect_equal( +all(basename(list.files(plots, recursive = T)) %in% +c("crpss-november.png", "enscorr_specs-november.png", "enscorr-november.png", + "forecast_ensemble_mean-20201101.png", "forecast_most_likely_tercile-20201101.png", + "rpss-november.png")), +TRUE +) +expect_equal( +length(list.files(plots, recursive = T)), +6 +) + +}) + +# Delete files +unlink(recipe$Run$output_dir, recursive = T) -- GitLab From 93e5d73f02d8d3aaa61d0757ceb69f719bc54f13 Mon Sep 17 00:00:00 2001 From: Nadia Milders Date: Thu, 29 Feb 2024 12:40:17 +0100 Subject: [PATCH 78/78] statistics unit test --- modules/Statistics/Statistics.R | 12 +- modules/Visualization/R/plot_skill_metrics.R | 2 +- .../recipe-seasonal_monthly_1_statistics.yml | 58 ++++++++ .../recipe-seasonal_monthly_1_tas-tos.yml | 2 +- .../test-seasonal_monthly_statistics.R | 126 +++++------------- 5 files changed, 101 insertions(+), 99 deletions(-) create mode 100644 tests/recipes/recipe-seasonal_monthly_1_statistics.yml diff --git a/modules/Statistics/Statistics.R b/modules/Statistics/Statistics.R index 4bfa107f..26f52de9 100644 --- a/modules/Statistics/Statistics.R +++ b/modules/Statistics/Statistics.R @@ -1,6 +1,6 @@ -compute_statistics <- function(recipe, data, agg = 'global'){ +Statistics <- function(recipe, data, agg = 'global'){ # data$hcst: s2dv_cube containing the hindcast @@ -92,8 +92,14 @@ compute_statistics <- function(recipe, data, agg = 'global'){ if (recipe$Analysis$Workflow$Statistics$save == 'all') { # Save all statistics - save_metrics_scorecards(recipe = recipe, skill = statistics, - data_cube = data$hcst, agg = agg) + if (tolower(recipe$Analysis$Output_format) == 'scorecards') { + save_metrics_scorecards(recipe = recipe, skill = statistics, + data_cube = data$hcst, agg = agg) + } else { + save_metrics(recipe = recipe, skill = statistics, + data_cube = data$hcst, agg = agg) + } + } # Return results diff --git a/modules/Visualization/R/plot_skill_metrics.R b/modules/Visualization/R/plot_skill_metrics.R index 0ab92bc5..26fb4a73 100644 --- a/modules/Visualization/R/plot_skill_metrics.R +++ b/modules/Visualization/R/plot_skill_metrics.R @@ -58,7 +58,7 @@ plot_skill_metrics <- function(recipe, data_cube, skill_metrics, "enscorr", "rpss_specs", "bss90_specs", "bss10_specs", "enscorr_specs", "rmsss", "msss") scores <- c("rps", "frps", "crps", "frps_specs", "mse") - statistics <- c("cov", "std_hcst", "std_obs") + statistics <- c("cov", "std_hcst", "std_obs", "var_hcst", "var_obs", "n_eff") # Loop over variables and assign colorbar and plot parameters to each metric for (var in 1:data_cube$dims[['var']]) { diff --git a/tests/recipes/recipe-seasonal_monthly_1_statistics.yml b/tests/recipes/recipe-seasonal_monthly_1_statistics.yml new file mode 100644 index 00000000..ccfd3cf4 --- /dev/null +++ b/tests/recipes/recipe-seasonal_monthly_1_statistics.yml @@ -0,0 +1,58 @@ +Description: + Author: V. Agudetse + +Analysis: + Horizon: Seasonal + Variables: + name: tas + freq: monthly_mean + Datasets: + System: + name: Meteo-France-System7 + Multimodel: False + Reference: + name: ERA5 + Time: + sdate: '1101' + fcst_year: '2020' + hcst_start: '1993' + hcst_end: '1996' + ftime_min: 1 + ftime_max: 3 + Region: + latmin: 17 + latmax: 20 + lonmin: 12 + lonmax: 15 + Regrid: + method: bilinear + type: to_system + Workflow: + # Anomalies: + # compute: no + # cross_validation: + # save: 'none' + Calibration: + method: mse_min + save: 'all' + Skill: + metric: RPSS CRPSS EnsCorr Corr_individual_members Enscorr_specs + save: 'all' + Statistics: + metric: cov std var n_eff + save: 'all' + Probabilities: + percentiles: [[1/3, 2/3], [1/10, 9/10]] + save: 'all' + Indicators: + index: no + Visualization: + plots: statistics + multi_panel: yes + projection: cylindrical_equidistant + Output_format: scorecards +Run: + Loglevel: INFO + Terminal: yes + output_dir: ./tests/out-logs/ + code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/ #/esarchive/scratch/vagudets/repos/auto-s2s/ diff --git a/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml b/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml index 41048db1..c1404e7d 100644 --- a/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml +++ b/tests/recipes/recipe-seasonal_monthly_1_tas-tos.yml @@ -53,4 +53,4 @@ Run: Loglevel: INFO Terminal: yes output_dir: ./tests/out-logs/ - code_dir: /esarchive/scratch/nmilders/gitlab/git_clones/auto-s2s/ ##/esarchive/scratch/vagudets/repos/auto-s2s/ + code_dir: /esarchive/scratch/vagudets/repos/auto-s2s/ diff --git a/tests/testthat/test-seasonal_monthly_statistics.R b/tests/testthat/test-seasonal_monthly_statistics.R index d9b7b3c3..2db006dc 100644 --- a/tests/testthat/test-seasonal_monthly_statistics.R +++ b/tests/testthat/test-seasonal_monthly_statistics.R @@ -1,12 +1,11 @@ context("Seasonal monthly data") source("modules/Loading/Loading.R") -source("modules/Calibration/Calibration.R") -source("modules/Skill/Skill.R") +source("modules/Statistics/Statistics.R") source("modules/Saving/Saving.R") source("modules/Visualization/Visualization.R") -recipe_file <- "tests/recipes/recipe-seasonal_monthly_1.yml" +recipe_file <- "tests/recipes/recipe-seasonal_monthly_1_statistics.yml" recipe <- prepare_outputs(recipe_file, disable_checks = F) archive <- read_yaml(paste0(recipe$Run$code_dir, "conf/archive.yml"))$archive @@ -15,30 +14,21 @@ suppressWarnings({invisible(capture.output( data <- Loading(recipe) ))}) -# Calibrate data +# Compute statistics suppressWarnings({invisible(capture.output( -calibrated_data <- Calibration(recipe, data) -))}) - -# Compute skill metrics -suppressWarnings({invisible(capture.output( -skill_metrics <- Skill(recipe, calibrated_data) -))}) - -suppressWarnings({invisible(capture.output( -probs <- Probabilities(recipe, calibrated_data) +statistics <- Statistics(recipe, data) ))}) # Saving suppressWarnings({invisible(capture.output( -Saving(recipe = recipe, data = calibrated_data, - skill_metrics = skill_metrics, probabilities = probs) +Saving(recipe = recipe, data = data, + skill_metrics = statistics) ))}) # Plotting suppressWarnings({invisible(capture.output( -Visualization(recipe = recipe, data = calibrated_data, - skill_metrics = skill_metrics, probabilities = probs, +Visualization(recipe = recipe, data = data, + skill_metrics = statistics, significance = T) ))}) outdir <- get_dir(recipe = recipe, variable = data$hcst$attrs$Variable$varName) @@ -125,119 +115,67 @@ as.POSIXct("1995-01-15 12:00:00", tz = 'UTC') }) -test_that("2. Calibration", { - -expect_equal( -is.list(calibrated_data), -TRUE -) -expect_equal( -names(calibrated_data), -c("hcst", "obs", "fcst") -) -expect_equal( -class(calibrated_data$hcst), -"s2dv_cube" -) -expect_equal( -class(calibrated_data$fcst), -"s2dv_cube" -) -expect_equal( -dim(calibrated_data$hcst$data), -c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 4, time = 3, latitude = 3, longitude = 3, ensemble = 25) -) -expect_equal( -dim(calibrated_data$fcst$data), -c(dat = 1, var = 1, sday = 1, sweek = 1, syear = 1, time = 3, latitude = 3, longitude = 3, ensemble = 51) -) -expect_equal( -mean(calibrated_data$fcst$data), -291.6433, -tolerance = 0.0001 -) -expect_equal( -mean(calibrated_data$hcst$data), -290.9006, -tolerance = 0.0001 -) -expect_equal( -as.vector(drop(calibrated_data$hcst$data)[1, , 2, 3, 4]), -c(291.8887, 287.0233, 289.8808), -tolerance = 0.0001 -) -expect_equal( -range(calibrated_data$fcst$data), -c(283.8926, 299.0644), -tolerance = 0.0001 -) - -}) - - #====================================== -test_that("3. Metrics", { +test_that("2. Statistics", { expect_equal( -is.list(skill_metrics), +is.list(statistics), TRUE ) expect_equal( -names(skill_metrics), -c("rpss", "rpss_significance", "crpss", "crpss_significance", "enscorr", - "enscorr_significance", "corr_individual_members", "corr_individual_members_significance", - "enscorr_specs") +names(statistics), +c("cov", "std_hcst", "std_obs", "var_hcst", "var_obs", "n_eff") ) expect_equal( -class(skill_metrics$rpss), +class(statistics$cov), "array" ) expect_equal( -dim(skill_metrics$rpss), +dim(statistics$cov), c(var = 1, time = 3, latitude = 3, longitude = 3) ) expect_equal( -dim(skill_metrics$rpss_significance), -dim(skill_metrics$rpss) +dim(statistics$cov), +dim(statistics$var_hcst) ) expect_equal( -as.vector(skill_metrics$rpss[, , 2, 3]), -c(-0.2918857, -1.4809143, -1.3842286), +as.vector(statistics$cov[, , 2, 3]), +c(1.14846389, 0.05694802, 0.02346492), tolerance = 0.0001 ) expect_equal( -as.vector(skill_metrics$rpss_significance[, , 2, 3]), -rep(FALSE, 3) +as.vector(statistics$var_hcst[, , 2, 3]), +c(0.74897676, 0.14698283, 0.04864656), +tolerance = 0.0001 ) }) -test_that("4. Saving", { +test_that("3. Saving", { outputs <- paste0(recipe$Run$output_dir, "/outputs/") expect_equal( all(basename(list.files(outputs, recursive = T)) %in% -c("tas_19931101.nc", "tas_19941101.nc", "tas_19951101.nc", - "tas_19961101.nc", "tas_20201101.nc", "tas-corr_month11.nc", - "tas-obs_19931101.nc", "tas-obs_19941101.nc", "tas-obs_19951101.nc", - "tas-obs_19961101.nc", "tas-percentiles_month11.nc", "tas-probs_19931101.nc", - "tas-probs_19941101.nc", "tas-probs_19951101.nc", "tas-probs_19961101.nc", - "tas-probs_20201101.nc", "tas-skill_month11.nc")), +c("scorecards_Meteo-France-System7_ERA5_tas_cov_1993-1996_s11.nc", + "scorecards_Meteo-France-System7_ERA5_tas_n_eff_1993-1996_s11.nc", + "scorecards_Meteo-France-System7_ERA5_tas_std_hcst_1993-1996_s11.nc", + "scorecards_Meteo-France-System7_ERA5_tas_std_obs_1993-1996_s11.nc", + "scorecards_Meteo-France-System7_ERA5_tas_var_hcst_1993-1996_s11.nc", + "scorecards_Meteo-France-System7_ERA5_tas_var_obs_1993-1996_s11.nc")), TRUE ) expect_equal( length(list.files(outputs, recursive = T)), -17 +6 ) }) -test_that("5. Visualization", { +test_that("4. Visualization", { plots <- paste0(recipe$Run$output_dir, "/plots/") expect_equal( all(basename(list.files(plots, recursive = T)) %in% -c("crpss-november.png", "enscorr_specs-november.png", "enscorr-november.png", - "forecast_ensemble_mean-20201101.png", "forecast_most_likely_tercile-20201101.png", - "rpss-november.png")), +c("cov-november.png", "n_eff-november.png", "std_hcst-november.png", + "std_obs-november.png", "var_hcst-november.png", "var_obs-november.png" )), TRUE ) expect_equal( -- GitLab

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