Add pipeline changes to run annual metrics for new GCM GLM outputs

.Renviron

@@ -1 +1,2 @@
-R_LIBS_USER="/cxfs/projects/usgs/water/iidd/data-sci/lake-temp/lake-temperature-out/Rlib_3_6":"/opt/ohpc/pub/usgs/libs/gnu8/R/3.6.3/lib64/R/library"
+#R_LIBS_USER="/cxfs/projects/usgs/water/iidd/data-sci/lake-temp/lake-temperature-out/Rlib_3_6":"/opt/ohpc/pub/usgs/libs/gnu8/R/3.6.3/lib64/R/library"
+R_LIBS_USER="/caldera/projects/usgs/water/iidd/datasci/lake-temp/lake-temperature-out/Rlib_3_6":"/home/software/tallgrass/arc/apps/R/3.6.3/lib64/R/library"

1_fetch.yml

@@ -7,6 +7,7 @@ packages:
 
 sources:
   - 1_fetch/src/fetch_sb_item_files.R
+  - 1_fetch/src/copy_caldera_files.R
 
 targets:
   1_fetch:
@@ -28,6 +29,9 @@ targets:
       - 1_fetch/out/temperature_observations.zip
       - 1_fetch/out/pb0_matched_to_observations.zip
       - 1_fetch/out/pgdl_matched_to_observations.zip
+    # climate projections glm3 data release target
+      - 1_fetch/out/pb0_temp_projections.yml
+      - 1_fetch/out/pb0_temp_projections_nml.rds.ind
 
   sb_dl_date:
    command: c(I("2021-04-08"))
@@ -167,3 +171,40 @@ targets:
       sb_id = I("5e774324e4b01d509270e29f"), 
       sb_filename = I("pgdl_matched_to_observations.zip"),
       dummy = sb_dl_date)
+
+  ##-- Access files for GLM3 + GCM projections data release --##
+
+  # TODO: Switch to downloading the NetCDF file(s) from ScienceBase
+  # to use as input instead. That would be less brittle.
+
+  # Identifying what lakes / GLM temp output files to use to for 
+  # lake thermal metrics summaries depends on us changing this 
+  # date to refresh the list of files and hashes. Not the most
+  # ideal process, but the function + practicality of this approach
+  # is a bit of a compromise right now so we can complete this work.
+  caldera_access_date:
+    command: c(I('2022-02-16'))
+
+  # These files from our process-models pipeline are also pushed to ScienceBase
+  #   item `6206d3c2d34ec05caca53071` but since we are running this on Tallgrass
+  #   we have access to Caldera and can skip the need to re-download.
+  pb0_temp_projections_feathers:
+    command: list.files(
+      path = I('../lake-temperature-process-models/3_extract/out'), 
+      pattern = I('GLM_nhdhr_(.*)(GFDL|IPSL|MIROC5|MRI|ACCESS|CNRM).feather'), 
+      full.names = I(TRUE))
+    depends: 
+      - caldera_access_date
+  1_fetch/out/pb0_temp_projections.yml:
+    command: sc_indicate(target_name, data_file = pb0_temp_projections_feathers)
+    depends: 
+      - caldera_access_date
+
+  # These files from our process-models pipeline are also pushed to ScienceBase
+  #   item `6206d3c2d34ec05caca53071` but since we are running this on Tallgrass
+  #   we have access to Caldera and can skip the need to re-download.
+  1_fetch/out/pb0_temp_projections_nml.rds.ind:
+    command: copy_caldera_files(target_name, files_in = I('../lake-temperature-process-models/1_prep/in/nml_list.rds'))
+    depends: 
+      - caldera_access_date
+

1_fetch/src/copy_caldera_files.R

@@ -0,0 +1,16 @@
+
+#' Use `out_dir` if you are copying multiple files and saving
+#' a hash table as the ind. Leave as `NULL` if you are only 
+#' copying and saving one file.
+copy_caldera_files <- function(out_ind, files_in, out_dir = NULL) {
+
+  if(is.null(out_dir)) {
+    files_out <- as_data_file(out_ind)
+  } else {
+    files_out <- file.path(out_dir, basename(files_in))
+  }
+
+  file.copy(from = files_in, to = files_out)
+  sc_indicate(out_ind, data_file = files_out)
+
+}

2_process.yml

@@ -214,3 +214,9 @@ targets:
       "2_process/src/do_obs_lakes_tasks.R")
     depends:
       - morphometry
+
+##-- Munge files for GLM3 + GCM projections data release --##
+
+  # Not much munging required right now
+  glm3_pb0gcm_proj_morphometry:
+    command: nml_to_morphometry(in_ind = '1_fetch/out/pb0_temp_projections_nml.rds.ind')

2_process/src/process_helpers.R

@@ -14,6 +14,10 @@ extract_morphometry <- function(config_fn) {
   return(morphometry_out)
 }
 
+nml_to_morphometry <- function(in_ind) {
+  purrr::map(readRDS(as_data_file(in_ind)), `[`, c("latitude", "longitude", "H", "A"))
+}
+
 unzip_data <- function(target_name, data_file, out_dir) {
   # Sometimes (as is the case with irradiance and clarity), the incoming
   # file has multiple zip files that need to be unpacked and saved

3_summarize.yml

@@ -2,6 +2,7 @@ target_default: 3_summarize
 
 packages:
   - arrow
+  - data.table
   - dplyr
   - readr
   - tidyr
@@ -12,6 +13,7 @@ sources:
   - 2_process/src/calculate_toha.R # Has the benthic area fxn & resample_hypso
   - 3_summarize/src/annual_thermal_metrics.R
   - 3_summarize/src/do_annual_thermal_metric_tasks.R
+  - 3_summarize/src/plot_annual_metric_summaries.R
 
 targets:
   3_summarize:
@@ -25,6 +27,8 @@ targets:
     # multi-state glm2 data release targets
       - 3_summarize/out/annual_metrics_glm2pb0.csv
       - 3_summarize/out/annual_metrics_glm2pball.csv
+    # climate projections glm3 data release target
+      - 3_summarize/out/annual_metrics_glm3pb0_gcms.csv
 
 ##-- MN TOHA Data Release --##
 
@@ -120,3 +124,32 @@ targets:
       '3_summarize/src/annual_thermal_metrics.R')
     depends:
       - temp_ranges
+
+##-- GLM3 + GCM Projections Data Release --##
+
+  # Do all GCMs at once and combine into a single file
+  3_summarize/out/annual_metrics_glm3pb0_gcms.csv:
+    command: do_annual_metrics_multi_lake(
+      final_target = target_name,
+      site_file_yml = "1_fetch/out/pb0_temp_projections.yml",
+      ice_file_yml = I(NULL),
+      n_cores = 79,
+      morphometry = glm3_pb0gcm_proj_morphometry,
+      temp_ranges = temp_ranges,
+      site_file_regex = I("(GLM)_(.*)_(.*).feather"),
+      tmpdir_suffix = I("_proj"),
+      model_id_colname = I("GCM"), 
+      suffix_as_model_id = I(TRUE),
+      '2_process/src/calculate_toha.R',
+      '3_summarize/src/annual_thermal_metrics.R',
+      '3_summarize/src/do_annual_thermal_metric_tasks.R')
+
+  # Verify outputs through some plots:
+  3_summarize/out_plots/annual_metrics_plots_gcms.yml:
+    command: plot_annual_metric_summaries(
+      target_name,
+      in_file = "3_summarize/out/annual_metrics_glm3pb0_gcms.csv",
+      target_dir = I("3_summarize/out_plots"),
+      model_id_colname = I("GCM"))
+
+

3_summarize/src/do_annual_thermal_metric_tasks.R

@@ -1,9 +1,15 @@
 
 do_annual_metrics_multi_lake <- function(final_target, site_file_yml, ice_file_yml, n_cores, morphometry, temp_ranges, ...,
-                                         site_file_regex = NULL, ice_file_regex = NULL, tmpdir_suffix = "") {
+                                         site_file_regex = NULL, ice_file_regex = NULL, tmpdir_suffix = "", 
+                                         model_id_colname = NULL, suffix_as_model_id = TRUE) {
 
   site_files <- get_filenames_from_ind(site_file_yml)
-  ice_files <- get_filenames_from_ind(ice_file_yml)
+
+  if(!is.null(ice_file_yml)) {
+    ice_files <- get_filenames_from_ind(ice_file_yml) 
+  } else {
+    ice_files <- ""
+  }
 
   # Each node on a Yeti normal partition has a max of 20 cores; nodes on Yeti UV partition do not have that same limit
   if(n_cores > 20) message("If using a node on the Yeti normal partition, you need to decrease n_cores to 20 or less")
@@ -25,14 +31,14 @@ do_annual_metrics_multi_lake <- function(final_target, site_file_yml, ice_file_y
 
   temp_ranges_file <- sprintf("temp_ranges.rds")
   saveRDS(temp_ranges, temp_ranges_file)
-
+  
   # Define task table rows
   tasks <- tibble(wtr_filename = site_files) %>% 
     extract(wtr_filename, c('prefix','site_id','suffix'), site_file_regex, remove = FALSE) %>% 
     left_join(extract(tibble(ice_filename = ice_files), ice_filename, c('site_id'), ice_file_regex, remove = FALSE), by = "site_id") %>% 
-    select(site_id, wtr_filename, ice_filename, prefix)
+    select(site_id, wtr_filename, ice_filename, model_id = matches(ifelse(suffix_as_model_id, "suffix", "prefix")))
 
-  model_type <- pull(tasks, prefix) %>% unique()
+  model_type <- pull(tasks, model_id) %>% unique()
 
   # Define task table columns
 
@@ -50,40 +56,95 @@ do_annual_metrics_multi_lake <- function(final_target, site_file_yml, ice_file_y
     } 
   )
 
-  calc_annual_metrics <- create_task_step(
-    step_name = 'calc_annual_metrics',
+  # Depending on how many models per site, this will be a list of one or more steps
+  # Doing multiple here rather than another task table per model so that the morphometry files can be shared
+  calc_annual_metrics <- purrr::map(model_type, function(model_type_i) { create_task_step(
+    step_name = sprintf('calc_annual_metrics_%s', model_type_i),
     target_name = function(task_name, step_name, ...) {
-      sprintf("3_summarize/tmp%s/%s_%s_annual_thermal_metrics.rds.ind", tmpdir_suffix, model_type, task_name)
+      sprintf("3_summarize/tmp%s/%s_%s_annual_thermal_metrics.rds.ind", tmpdir_suffix, model_type_i, task_name)
     },
     command = function(..., task_name, steps) {
-      task_info <- filter(tasks, site_id == task_name)
+      task_info <- filter(tasks, site_id == task_name, model_id == model_type_i)
+      # Return empty string (not `NULL` to avoid warnings) if there is no data for this 
+      # site_id + model_id combo (not all combos were successful runs)
+      if(nrow(task_info) == 0) return("") 
       psprintf("calculate_annual_metrics_per_lake(", 
                "out_ind = target_name,",
                "site_id = I('%s')," = task_name,
                "site_file = '%s'," = task_info$wtr_filename,
-               "ice_file = '%s'," = task_info$ice_filename,
+               "ice_file = %s," = ifelse(is.na(task_info$ice_filename), 'I(NULL)', sprintf("'%s'", task_info$ice_filename)),
                "temp_ranges_file = '%s'," = temp_ranges_file,
                "morphometry_ind = '%s'," = steps[["split_morphometry"]]$target_name,
                # Doesn't actually print to console with `loop_tasks` but let's you see if you are troubleshooting individual files
                "verbose = I(TRUE))" 
       )
     }
-  )
+  )})
+
+  # If there is more than one model type, we should combine them per task
+  # so that each task has the same "final" step that we can use for `final_steps`
+  if(length(calc_annual_metrics) > 1) {
+    combine_model_thermal_metrics <- scipiper::create_task_step(
+      step_name = 'combine_model_thermal_metrics',
+      target_name = function(task_name, step_name, ...){
+        sprintf("3_summarize/tmp%s/%s_annual_thermal_metrics.rds.ind", tmpdir_suffix, task_name)
+      },
+      command = function(..., task_name, steps){
+        calc_annual_metrics_steps <- steps[grepl('calc_annual_metrics_', names(steps))]
+        calc_annual_metrics_targets_str <- paste(sprintf("'%s'", sapply(calc_annual_metrics_steps, `[[`, "target_name")), collapse = ",")
+        psprintf("combine_model_thermal_metrics(", 
+                 "out_ind = target_name,",
+                 "model_id_colname = I('%s')," = model_id_colname,
+                 "%s)" = calc_annual_metrics_targets_str
+        )
+      } 
+    )
+
+    task_steps_list <- c(list(split_morphometry), calc_annual_metrics, list(combine_model_thermal_metrics))
+    final_step_name <- "combine_model_thermal_metrics"
+  } else {
+    task_steps_list <- list(split_morphometry, calc_annual_metrics)
+    final_step_name <- "calc_annual_metrics"
+  }
 
   # Create the task plan
-  task_plan <- create_task_plan(
-    task_names = tasks$site_id,
-    task_steps = list(split_morphometry, calc_annual_metrics),
-    final_steps = c('calc_annual_metrics'),
+  task_plan_all_combos <- create_task_plan(
+    task_names = unique(tasks$site_id),
+    task_steps = task_steps_list,
+    final_steps = final_step_name,
     add_complete = FALSE)
 
+  # Remove any steps with empty commands (there wasn't a matching site_id + model_id combo)
+  task_plan <- lapply(task_plan_all_combos, function(task_def) {
+    missing_command <- sapply(task_def$steps, function(step_def) nchar(step_def$command) == 0)
+    missing_command_target_names <- sapply(task_def$steps[missing_command], `[[`, "target_name")
+
+    # Remove the steps with missing commands
+    task_def$steps[missing_command] <- NULL
+
+    # Also remove in the final step for combining if necessary by removing the filepaths to 
+    # target names that don't exist
+    task_final_step <- task_def$steps[[final_step_name]]
+    if(!is.null(task_final_step)) {
+      command_drop_target_names <- gsub(paste(missing_command_target_names, collapse="|"), "", task_final_step$command)
+      command_drop_target_names <- gsub(",''|'',", "", command_drop_target_names) # Remove leftover string syntax
+      task_def$steps[[final_step_name]]$command <- command_drop_target_names
+    }
+
+    return(task_def)
+  })
+
+  # Need to make sure `task_plan` attributes make it back
+  attributes(task_plan) <- attributes(task_plan_all_combos)
+
+
   # Create the task remakefile
-  task_makefile <- sprintf('3_summarize_%s_metric_tasks.yml', model_type)
+  task_makefile <- sprintf('3_summarize_%s_metric_tasks.yml', paste(model_type, collapse = ".")) # Collapse string if doing tasks for more than one model type
   create_task_makefile(
     task_plan = task_plan,
     makefile = task_makefile,
     sources = c(...),
-    packages = c('tidyverse', 'purrr', 'readr', 'scipiper', 'arrow'),
+    packages = c('tidyverse', 'purrr', 'readr', 'scipiper', 'arrow', 'data.table'),
     final_targets = final_target,
     finalize_funs = 'combine_thermal_metrics',
     as_promises = TRUE,
@@ -92,7 +153,8 @@ do_annual_metrics_multi_lake <- function(final_target, site_file_yml, ice_file_y
   # Build the tasks
   loop_tasks(task_plan = task_plan,
              task_makefile = task_makefile,
-             num_tries = 3,
+             task_names = unique(tasks$site_id),
+             num_tries = 1,
              n_cores = n_cores)
 
   # Clean up files created
@@ -111,6 +173,20 @@ combine_thermal_metrics <- function(target_name, ...) {
   purrr::map(list(...), function(ind) readRDS(as_data_file(ind))) %>% purrr::reduce(bind_rows) %>% readr::write_csv(target_name)
 }
 
+combine_model_thermal_metrics <- function(out_ind, model_id_colname, ...) {
+  data_file <- as_data_file(out_ind)
+  purrr::map(list(...), function(ind) {
+    rds_file <- as_data_file(ind)
+    file_pattern <- "(?<modelid>.*)_(?<sitenum>nhdhr_.*)_annual_thermal_metrics.rds" # based on target_name of `calc_annual_metrics` step
+    readRDS(rds_file) %>% 
+      mutate(model_id = str_match(basename(rds_file), file_pattern)[2]) %>% 
+      select(site_id, !!model_id_colname := model_id, everything())
+  }) %>% 
+    purrr::reduce(bind_rows) %>% 
+    saveRDS(data_file)
+  sc_indicate(ind_file = out_ind, data_file = data_file)
+}
+
 split_and_save_morphometry <- function(out_ind, morphometry_file, site_id) {
   data_file <- as_data_file(out_ind)
   morphometry <- readRDS(morphometry_file)

3_summarize/src/plot_annual_metric_summaries.R

@@ -0,0 +1,30 @@
+# Visualize output
+
+plot_annual_metric_summaries <- function(target_name, in_file, target_dir, model_id_colname) {
+
+  if(!dir.exists(target_dir)) dir.create(target_dir)
+
+  annual_metrics_data <- readr::read_csv(in_file)
+
+  cols2plot <- names(annual_metrics_data)[which(!grepl(sprintf("site_id|GCM|year|_dt|_date|%s", model_id_colname), names(annual_metrics_data)))]
+
+  annual_metrics_long <- annual_metrics_data %>% 
+    select(-matches("_dt|_date")) %>% 
+    pivot_longer(all_of(cols2plot), names_to = "metric")
+
+  plot_groups <- split(cols2plot, ceiling(seq_along(cols2plot)/10))
+  files_out <- purrr::map(plot_groups, function(i) {
+    p <- annual_metrics_long %>% 
+      filter(metric %in% i) %>% 
+      ggplot(aes(x = year, y = value)) + 
+      facet_grid(metric ~ ., scales = "free_y") +
+      geom_point(alpha = 0.2, stroke = 0, shape = 16, size = 2)
+    ggsave(sprintf("%s/%s_THROUGH_%s.png", target_dir, i[1], i[10]),
+           plot = p, width = 5, height = 8)
+  })
+
+  scipiper::sc_indicate(target_name, data_file = unlist(files_out))
+}
+
+
+