feat: calc total diverted tons for county and state

src/wmrc/main.py

@@ -514,7 +514,7 @@ def subscribe(cloud_event: CloudEvent) -> None:
 
 #: Putting this here means you can call the file via `python main.py` and it will run. Useful for pre-GCF testing.
 if __name__ == "__main__":
-    # wmrc_skid = Skid()
-    # wmrc_skid.process()
+    wmrc_skid = Skid()
+    wmrc_skid.process()
 
-    run_validation()
+    # run_validation()

src/wmrc/yearly.py

@@ -57,18 +57,14 @@ def county_summaries(year_df: pd.DataFrame, county_fields: list[str]) -> pd.Data
     counties_df["county_wide_msw_composted"] = composted_df.sum()
     counties_df["county_wide_msw_digested"] = digested_df.sum()
     counties_df["county_wide_msw_landfilled"] = landfilled_df.sum()
+    counties_df["county_wide_msw_diverted_total"] = (
+        counties_df["county_wide_msw_recycled"]
+        + counties_df["county_wide_msw_composted"]
+        + counties_df["county_wide_msw_digested"]
+    )
     counties_df["county_wide_msw_recycling_rate"] = (
-        (
-            counties_df["county_wide_msw_recycled"]
-            + counties_df["county_wide_msw_composted"]
-            + counties_df["county_wide_msw_digested"]
-        )
-        / (
-            counties_df["county_wide_msw_recycled"]
-            + counties_df["county_wide_msw_composted"]
-            + counties_df["county_wide_msw_digested"]
-            + counties_df["county_wide_msw_landfilled"]
-        )
+        counties_df["county_wide_msw_diverted_total"]
+        / (counties_df["county_wide_msw_diverted_total"] + counties_df["county_wide_msw_landfilled"])
         * 100
     )
 
@@ -194,18 +190,14 @@ def statewide_metrics(county_year_df: pd.DataFrame) -> pd.DataFrame:
     statewide_series["statewide_msw_composted"] = in_state_only["county_wide_msw_composted"].sum()
     statewide_series["statewide_msw_digested"] = in_state_only["county_wide_msw_digested"].sum()
     statewide_series["statewide_msw_landfilled"] = in_state_only["county_wide_msw_landfilled"].sum()
+    statewide_series["statewide_msw_diverted_total"] = (
+        statewide_series["statewide_msw_recycled"]
+        + statewide_series["statewide_msw_composted"]
+        + statewide_series["statewide_msw_digested"]
+    )
     statewide_series["statewide_msw_recycling_rate"] = (
-        (
-            statewide_series["statewide_msw_recycled"]
-            + statewide_series["statewide_msw_composted"]
-            + statewide_series["statewide_msw_digested"]
-        )
-        / (
-            statewide_series["statewide_msw_recycled"]
-            + statewide_series["statewide_msw_composted"]
-            + statewide_series["statewide_msw_digested"]
-            + statewide_series["statewide_msw_landfilled"]
-        )
+        statewide_series["statewide_msw_diverted_total"]
+        / (statewide_series["statewide_msw_diverted_total"] + statewide_series["statewide_msw_landfilled"])
         * 100
     )
 

tests/test_yearly.py

@@ -1,17 +1,46 @@
 import pandas as pd
-
 from wmrc import yearly
 
 
 class TestYearlyMetrics:
 
+    def test_county_wide_metrics_happy_path(self):
+        facility_year_df = pd.DataFrame(
+            {
+                "Municipal_Solid_Waste__c": [50, 50],
+                "Combined_Total_of_Material_Recycled__c": [10, 20],
+                "Total_Materials_sent_to_composting__c": [0, 50],
+                "Total_Material_managed_by_ADC__c": [10, 0],
+                "Municipal_Waste_In_State_in_Tons__c": [80, 30],
+                "Cache_County__c": [50, 50],
+                "Utah_County__c": [50, 50],
+            }
+        )
+
+        expected_output = pd.DataFrame(
+            {
+                "county_wide_msw_recycled": [7.5, 7.5],
+                "county_wide_msw_composted": [12.5, 12.5],
+                "county_wide_msw_digested": [2.5, 2.5],
+                "county_wide_msw_landfilled": [55.0, 55.0],
+                "county_wide_msw_diverted_total": [22.5, 22.5],
+                "county_wide_msw_recycling_rate": [22.5 / (22.5 + 55.0) * 100, 22.5 / (22.5 + 55.0) * 100],
+            },
+            index=["Cache_County__c", "Utah_County__c"],
+        )
+
+        output = yearly.county_summaries(facility_year_df, ["Cache_County__c", "Utah_County__c"])
+
+        pd.testing.assert_frame_equal(output, expected_output)
+
     def test_statewide_metrics_happy_path(self):
         input_df = pd.DataFrame(
             {
                 "county_wide_msw_recycled": [10, 10, 20],
                 "county_wide_msw_composted": [0, 0, 50],
                 "county_wide_msw_digested": [10, 0, 0],
                 "county_wide_msw_landfilled": [80, 90, 30],
+                "county_wide_msw_diverted_total": [20, 10, 70],
             },
             index=["foo", "bar", "baz"],
         )
@@ -22,6 +51,7 @@ def test_statewide_metrics_happy_path(self):
                 "statewide_msw_composted": 50,
                 "statewide_msw_digested": 10,
                 "statewide_msw_landfilled": 200,
+                "statewide_msw_diverted_total": 100,
                 "statewide_msw_recycling_rate": 100 / 300 * 100,
             }
         )