use test data based on recent actual indicator run

Author: nmdefries

google_symptoms/tests/test_date_utils.py

@@ -65,28 +65,44 @@ def test_generate_export_date_missing(self, params_w_no_date, logger, monkeypatc
         expected_num_export_days = (date.today() - FULL_BKFILL_START_DATE.date()).days + 1
         assert num_export_days == expected_num_export_days
 
+    def generate_expected_start_end_dates(self, params_, issue_date):
+        dates_dict = {
+            "2024-06-27": [ '2024-06-02', '2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17', '2024-06-18', '2024-06-19', '2024-06-20', '2024-06-21', '2024-06-22'],
+            "2024-06-28": ['2024-06-03', '2024-06-04', '2024-06-05', '2024-06-06', '2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17', '2024-06-18', '2024-06-19', '2024-06-20', '2024-06-21', '2024-06-22', '2024-06-23'],
+            "2024-06-29": ['2024-06-04', '2024-06-05', '2024-06-06','2024-06-07', '2024-06-08', '2024-06-09', '2024-06-10', '2024-06-11', '2024-06-12', '2024-06-13', '2024-06-14', '2024-06-15', '2024-06-16', '2024-06-17', '2024-06-18', '2024-06-19', '2024-06-20', '2024-06-21', '2024-06-22', '2024-06-23', '2024-06-24'],
+        }
+
+        dates_dict = {
+            datetime.strptime(key, "%Y-%m-%d"): [datetime.strptime(lvalue, "%Y-%m-%d") for lvalue in value]
+            for key, value in dates_dict.items()
+        }
+
+        dates = dates_dict[issue_date]
+
+        return {
+            "export_start_date": min(dates[6:21]),
+            "export_end_date": max(dates[6:21])
+        }
+
     def test_generate_patch_dates(self, params_w_patch, logger, monkeypatch):
         import covidcast
         metadata_df = pd.read_csv(f"{TEST_DIR}/test_data/covid_metadata_missing.csv")
         monkeypatch.setattr(covidcast, "metadata", lambda: metadata_df)
-        max_expected_lag = lag_converter(params_w_patch["validation"]["common"].get("max_expected_lag", {"all": 4}))
+        max_expected_lag = lag_converter(params_w_patch["validation"]["common"]["max_expected_lag"])
         global_max_expected_lag = max(list(max_expected_lag.values()))
-        expected_num_export_days = params_w_patch["validation"]["common"].get("span_length", 14) + global_max_expected_lag
+        num_export_days = params_w_patch["validation"]["common"]["span_length"]
 
         issue_date = datetime.strptime(params_w_patch["patch"]["start_issue"], "%Y-%m-%d")
         end_issue = datetime.strptime(params_w_patch["patch"]["end_issue"], "%Y-%m-%d")
 
         patch_date_dict = generate_patch_dates(params_w_patch)
 
         while issue_date <= end_issue:
-            expected_daterange = generate_query_dates(
-                FULL_BKFILL_START_DATE,
-                issue_date,
-                expected_num_export_days,
-                False
-            )
             # in the patch script the date generated by generate_patch_dates becomes the export_start_date and export_end_date
-            export_start_date, export_end_date = patch_date_dict[issue_date]
-            actual_daterange = generate_query_dates(export_start_date, export_end_date, expected_num_export_days, True)
-            assert set(actual_daterange) == set(expected_daterange)
-            issue_date += timedelta(days=1)
+            patch_settings = patch_date_dict[issue_date]
+            expected_dict = self.generate_expected_start_end_dates(params_w_patch, issue_date)
+            expected_dict["num_export_days"] = num_export_days # unmodified
+
+            assert patch_settings == expected_dict
+
+            issue_date += timedelta(days=1)

google_symptoms/tests/test_patch.py

@@ -55,9 +55,7 @@ def side_effect(*args, **kwargs):
                     df = state_data_gap
                     pattern = re.compile(r'\d{4}-\d{2}-\d{2}')
                     start_date, end_date = re.findall(pattern, args[0])
-                    end_date_w_lag = (datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days=4)).strftime("%Y-%m-%d")
-                    return df[(df["date"] >= start_date) & \
-                              (df["date"] <= end_date_w_lag)]
+                    return df[(df["date"] >= start_date) & (df["date"] <= end_date)]
                 else:
                     return pd.DataFrame()