remove yearisdate

etl/steps/data/garden/climate/2025-02-12/sst_annual.meta.yml

@@ -17,11 +17,4 @@ tables:
         unit: ""
         description_processing: |-
           Annual anomalies of the Oceanic Niño Index (ONI) are calculated by taking the average of the monthly ONI values for a given year.
-      annual_nino_classification:
-        title: Annual El Niño or La Niña classification
-        unit: ""
-        description_processing: |-
-          The annual classification of the El Niño-Southern Oscillation (ENSO) state based on the Oceanic Niño Index (ONI) is determined by the majority of the monthly ONI values for a given year.
-
-
 

etl/steps/data/garden/climate/2025-02-12/sst_annual.py

@@ -21,24 +21,7 @@ def run(dest_dir: str) -> None:
     #
     # Calculate the annual average for the dataset
     tb_annual = tb.groupby(["country", "year"]).mean().reset_index()
-
-    # Classify the year based on nino_classification
-    def classify_year(group):
-        if (group["nino_classification"] == 1).sum() > 6:
-            return 1
-        elif (group["nino_classification"] == 2).sum() > 6:
-            return 2
-        else:
-            return 0
-
-    tb_annual["annual_nino_classification"] = (
-        tb.groupby(["country", "year"]).apply(classify_year).reset_index(drop=True)
-    )
     tb_annual = tb_annual.rename(columns={"oni_anomaly": "annual_oni_anomaly"})
-    tb_annual["annual_nino_classification"] = tb_annual["annual_nino_classification"].copy_metadata(
-        tb["nino_classification"]
-    )
-
     tb_annual = tb_annual.drop(columns={"month", "nino_classification"})
 
     tb_annual = tb_annual.format(["country", "year"])

etl/steps/data/grapher/climate/2025-02-12/sst_by_month.meta.yml

@@ -4,9 +4,6 @@ definitions:
     presentation:
       topic_tags:
         - Climate Change
-    display:
-        yearIsDay: true
-        zeroDay: "1949-01-01"
     processing_level: minor
 
 # Learn more about the available fields:

etl/steps/data/grapher/climate/2025-02-12/sst_by_month.py

@@ -21,7 +21,6 @@ def run(dest_dir: str) -> None:
     # Combine month and year into a single column
     tb["date"] = pd.to_datetime(tb["year"].astype(str) + "-" + tb["month"].astype(str) + "-01")
     tb["date"] = tb["date"] + pd.offsets.Day(14)
-    tb["days_since_1941"] = (tb["date"] - pd.to_datetime("1949-01-01")).dt.days
 
     # Create colour_date column based on decades
     def year_to_decade(year):
@@ -43,13 +42,9 @@ def year_to_decade(year):
     # Create date_as_country column (keep uncommented but might use in the future)
     # tb["date_as_country"] = tb["date"].dt.strftime("%B %Y")
 
-    # Drop the original year and month columns
-    tb = tb.drop(columns=["year", "month", "date"])
+    tb = tb.drop(columns=["year", "month"])
 
-    # Rename the date column to year for grapher purposes
-    tb = tb.rename(columns={"days_since_1941": "year"})
-
-    tb = tb.format(["year", "country"])
+    tb = tb.format(["date", "country"])
     #
     # Save outputs.
     #