Update dropbox recipes for ccf water level

Author: YujiaCai23

examples/dropbox/dropbox_spec_ccf_downele.yaml

@@ -7,10 +7,10 @@ target_tz_label: PST
 
 
 data:
-  - name: ccf_ele
+  - name: ccf_elev
     skip: False
     collect:
-      file_pattern: "CC_Water_Levels_2024*.csv"
+      file_pattern: "CC_Water_Levels_2025*.csv"
       wildcard: time_overlap    # time_sharded | time_overlap | data | none. 
             # If time_sharded, indicates non-overlapping timestamps that uses year chunking. 
             # If time_overlap, indicates wildcards are ordered by date 20250531(lexicographic must = chronological)
@@ -20,14 +20,14 @@ data:
       recursive_search: False
       reader: read_ts    # Names, pointers to code etc. To be fleshed out
       reader_args: 
-         names: ["datetime", "up_ele","down_ele"]
+         names: ["datetime", "up_elev","down_elev"]
          na_values: ["(null)", "null", "NULL", ""]
          hint: "resort"
          force_regular: False
       merge_method: "ts_splice" # We will not call read_ts on wildcard, but rather glob and read and creat list of dfs
       merge_args: 
          transition: prefer_first
-      selector: down_ele  # Usually a column name. When read_last_resort_csv is used, use null.
+      selector: down_elev  # Usually a column name. When read_last_resort_csv is used, use null.
     transforms:
       - name: dst_tz      # SCADA is delivered local time. Often this is not needed from better data sources
         args:
@@ -42,13 +42,12 @@ data:
           heartbeat_freq: 60min    
     metadata: 
       station_id: clc
-      structure_id: ccfb
-      agency_id: ccfb
+      agency_id: clc
       processor: dms
       agency: dwr
       source: dwr
       freq: None   # None for irregular, "infer" for infer.
-      param: elevation
+      param: elev
       unit: ft
       subloc: down
       time_zone: ${target_tz}
@@ -58,16 +57,17 @@ data:
                                 # seems like this prevails, but we should prevent conflicts.
 
     output:
+      # repo_data_dir: ${modeling_data}/repo/continuous/proprietary/formatted     # This is a practice directory so you don't go overwriting a real repo while developing the recipe
+                                                  # When omitted the target location is the repo_root as configured in dstore_config.yaml
       repo_name: proprietary_formatted   # This is a pointer to an entry in dstore_config.yaml
       staging:                           # This is where the read and transformed time series will be staged. 
                                          # It will be used to update the repo, but that uses update_repo and may not mean a full replacement
         dir: ${modeling_data}/repo/continuous/proprietary/staging
         write_args:
           float_format: "%0.3f"
-          chunk_years: False
+          chunk_years: True
       reconcile:
-        repo_data_dir: ${modeling_data}/repo/continuous/proprietary/formatted     # This is a practice directory so you don't go overwriting a real repo while developing the recipe
-                                                  # When omitted the target location is the repo_root as configured in dstore_config.yaml
+        
         prefer: staged                            # This is an argument to the update process: 
                                                   #    - staged means "prefer the new stuff we staged"
                                                   #    - repo means "prefer the stuff in the repo, just top off with any new time stamps"

examples/dropbox/dropbox_spec_ccf_upele.yaml

@@ -7,10 +7,10 @@ target_tz_label: PST
 
 
 data:
-  - name: ccf_ele
+  - name: ccf_elev
     skip: False
     collect:
-      file_pattern: "CC_Water_Levels_2024*.csv"
+      file_pattern: "CC_Water_Levels_2025*.csv"
       wildcard: time_overlap    # time_sharded | time_overlap | data | none. 
             # If time_sharded, indicates non-overlapping timestamps that uses year chunking. 
             # If time_overlap, indicates wildcards are ordered by date 20250531(lexicographic must = chronological)
@@ -20,14 +20,14 @@ data:
       recursive_search: False
       reader: read_ts    # Names, pointers to code etc. To be fleshed out
       reader_args: 
-         names: ["datetime", "up_ele", "down_ele"]
+         names: ["datetime", "up_elev", "down_elev"]
          na_values: ["(null)", "null", "NULL", ""]
          hint: "resort"
          force_regular: False
       merge_method: "ts_splice" # We will not call read_ts on wildcard, but rather glob and read and creat list of dfs
       merge_args: 
          transition: prefer_first
-      selector: up_ele  # Usually a column name. When read_last_resort_csv is used, use null.
+      selector: up_elev  # Usually a column name. When read_last_resort_csv is used, use null.
     transforms:
       - name: dst_tz      # SCADA is delivered local time. Often this is not needed from better data sources
         args:
@@ -42,13 +42,12 @@ data:
           heartbeat_freq: 60min    
     metadata: 
       station_id: clc
-      structure_id: ccfb
-      agency_id: ccfb
+      agency_id: clc
       processor: dms
       agency: dwr
       source: dwr
       freq: None   # None for irregular, "infer" for infer.
-      param: elevation
+      param: elev
       unit: ft
       subloc: up
       time_zone: ${target_tz}
@@ -59,15 +58,16 @@ data:
 
     output:
       repo_name: proprietary_formatted    # This is a pointer to an entry in dstore_config.yaml
+      # repo_data_dir: ${modeling_data}/repo/continuous/proprietary/formatted     # This is a practice directory so you don't go overwriting a real repo while developing the recipe
+                                                  # When omitted the target location is the repo_root as configured in dstore_config.yaml
       staging:                           # This is where the read and transformed time series will be staged. 
                                          # It will be used to update the repo, but that uses update_repo and may not mean a full replacement
         dir: ${modeling_data}/repo/continuous/proprietary/staging
         write_args:
           float_format: "%0.3f"
-          chunk_years: False
+          chunk_years: True
       reconcile:
-        repo_data_dir: ${modeling_data}/repo/continuous/proprietary/formatted     # This is a practice directory so you don't go overwriting a real repo while developing the recipe
-                                                  # When omitted the target location is the repo_root as configured in dstore_config.yaml
+       
         prefer: staged                            # This is an argument to the update process: 
                                                   #    - staged means "prefer the new stuff we staged"
                                                   #    - repo means "prefer the stuff in the repo, just top off with any new time stamps"