Merge pull request #15 from addelany/main

update keywords and model asset items
eco4cast · Nov 7, 2023 · d0a87f2 · d0a87f2
2 parents 773dd18 + a96ff03
commit d0a87f2
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Showing 2 changed files with 11 additions and 10 deletions.
diff --git a/R/build_model.R b/R/build_model.R
@@ -34,11 +34,13 @@ build_model <- function(model_id,
                         collection_name,
                         thumbnail_image_name,
                         table_schema,
-                        table_description) {
+                        table_description,
+                        full_var_df) {
 
 
   preset_keywords <- list("Forecasting", config$project_id)
-  variables_reformat <- paste(var_values, collapse = ", ")
+  #variables_reformat <- paste(var_values, collapse = ", ")
+  variables_reformat <- as.list(var_values)
   site_reformat <- paste(site_values, collapse = ", ")
 
   aws_asset_link <- paste0("s3://anonymous@",
@@ -116,7 +118,7 @@ build_model <- function(model_id,
         "type"= "application/json",
         "title"= "Model Forecast"
       )),
-    "assets"= stac4cast::generate_model_assets(var_values, duration_names, aws_download_path)#,
+    "assets"= stac4cast::generate_model_assets(full_var_df, aws_download_path)#,
     #pull_images(theme_id,model_id,thumbnail_image_name)
   )
 

diff --git a/R/generate_model_assets.R b/R/generate_model_assets.R
@@ -1,12 +1,11 @@
 #' Build the Assets objects for model JSONS
 #'
-#' @param m_vars list of unique model variables
-#' @param m_duration list of unique model duration periods
+#' @param full_var_df dataframe that contains variable column information (name, full name, duration, duration name)
 #' @param aws_path s3 path for accessing model data
 #' @export
 #'
 #'
-generate_model_assets <- function(m_vars, m_duration, aws_path){
+generate_model_assets <- function(full_var_df, aws_path){
 
   metadata_json_asset <- list(
     "1"= list(
@@ -18,20 +17,20 @@ generate_model_assets <- function(m_vars, m_duration, aws_path){
     )
   )
 
-  iterator_list <- 1:length(m_vars)
+  iterator_list <- 1:nrow(full_var_df)
 
   model_data_assets <- purrr::map(iterator_list, function(i)
     list(
       'type'= 'application/x-parquet',
-      'title' = paste0('Database Access for',' ',m_vars[i]),
+      'title' = paste0('Database Access for',' ', full_var_df$var_duration_name[i]),
       'href' = paste0("s3://anonymous@",
                       aws_path,
-                      "/parquet/duration=P1D/variable=", m_vars[i],
+                      "/parquet/duration=", full_var_df$duration[i],"/variable=", full_var_df$variable[i],
                       "/model_id=", m,
                       "?endpoint_override=",config$endpoint),
       'description' = paste0("Use `arrow` for remote access to the database. This R code will return results for this variable and model combination.\n\n### R\n\n```{r}\n# Use code below\n\nall_results <- arrow::open_dataset(",paste0("s3://anonymous@",
                                                                                                                                                                                                                                           aws_path,
-                                                                                                                                                                                                                                          "/parquet/duration=P1D/variable=", m_vars[i],
+                                                                                                                                                                                                                                          "/parquet/duration=", full_var_df$duration[i],"/variable=", full_var_df$variable[i],
                                                                                                                                                                                                                                           "/model_id=", m,
                                                                                                                                                                                                                                           "?endpoint_override=",config$endpoint),")\ndf <- all_results |> dplyr::collect()\n\n```
        \n\nYou can use dplyr operations before calling `dplyr::collect()` to `summarise`, `select` columns, and/or `filter` rows prior to pulling the data into a local `data.frame`. Reducing the data that is pulled locally will speed up the data download speed and reduce your memory usage.\n\n\n")