added in emissions data

algo.py

@@ -266,14 +266,31 @@ class Algo:
 
         return mapping
     
-    def add_carbon_credit_data(self, mapping, donor):
+    def add_carbon_credit_data(self, mapping, donor, date, weight):
         if not mapping:
             return None
 
         mapping.update({
-            'carbon_credits': None,
             'donor': donor
         })
+        mapping.update({
+            'date': date,
+            'weight': weight,
+            'weight_metric_tonnes': float(weight) * 0.000453592,
+            'distance': 250,
+            'ef': 2.968073544,
+            'mt_lb_mile': 0.0000000809,
+        })
+        baseline_emissions = mapping['weight_metric_tonnes'] * mapping['dry_matter_content'] * mapping['ef']
+        leakage_emissions = mapping['leakage'] * baseline_emissions
+        project_emissions = mapping['distance'] * mapping['mt_lb_mile'] * baseline_emissions
+        total_emissions_reduction = baseline_emissions - leakage_emissions - project_emissions
+        mapping.update({
+            'baseline_emissions': baseline_emissions,
+            'leakage_emissions': leakage_emissions,
+            'project_emissions': project_emissions,
+            'total_emissions_reduction': total_emissions_reduction
+        })
 
         return mapping
 
@@ -298,11 +315,7 @@ class Algo:
                 mapping = self.handle_single_item(input_word)
 
             if mapping:
-                mapping = self.add_carbon_credit_data(mapping, input_donor)
-                mapping.update({
-                    'date': input_date,
-                    'weight': input_weight
-                })
+                mapping = self.add_carbon_credit_data(mapping, input_donor, input_date, input_weight)
                 results.append(mapping)
             
             if stream_results:

run.py

@@ -7,13 +7,12 @@ if __name__ == "__main__":
     algo = Algo(db_conn, True)
     raw_file_name = 'MFB-2023-raw-data.csv'
     input_file_path = f'./raw/{raw_file_name}'
-    # df_input = pd.read_csv(input_file_path)
-    # # input_words = df_input['description'].astype(str).tolist()
+    df_input = pd.read_csv(input_file_path)
 
-    # # make a tuple of description, donor columns
-    # input_data = [(desc, donor, date, weight) for desc, donor, date, weight in zip(df_input['description'].astype(str).tolist(), df_input['donor'].astype(str).tolist(), df_input['date'].astype(str).tolist(), df_input['weight'].astype(str).tolist())]
+    input_data = [(desc, donor, date, weight) for desc, donor, date, weight in zip(df_input['description'].astype(str).tolist(), df_input['donor'].astype(str).tolist(), df_input['date'].astype(str).tolist(), df_input['weight'].astype(str).tolist())]
 
-    # algo.match_words(input_data)
-    algo.match_words([['bananas']])
+    input_data = input_data[:10]
+    algo.match_words(input_data)
+    # algo.match_words([['bananas']])
 
     db_conn.close()