deepsource

tests/integration/shieldhit/test_averaging.py

@@ -17,14 +17,14 @@ def averaging_bdos_directory(main_dir) -> Generator[Path, None, None]:
 
 
 @pytest.fixture(scope='function')
-def small_stat_bdo_pattern() -> Generator[str, None, None]:
-    """Part of filename denoting small statistics BDO file"""
+def large_stat_bdo_pattern() -> Generator[str, None, None]:
+    """Part of filename denoting large statistics BDO file"""
     yield "_000?.bdo"
 
 
 @pytest.fixture(scope='function')
-def large_stat_bdo_pattern() -> Generator[str, None, None]:
-    """Part of filename denoting large statistics BDO file"""
+def small_stat_bdo_pattern() -> Generator[str, None, None]:
+    """Part of filename denoting small statistics BDO file"""
     yield "_001?.bdo"
 
 
@@ -38,15 +38,14 @@ def test_aggregating_equal_stats(averaging_bdos_directory, small_stat_bdo_patter
     In both sets of data, the same number of primary particles was used
     Therefore we can use simple averaging
     """
-
     for stat_pattern in (small_stat_bdo_pattern, large_stat_bdo_pattern):
         bdo_file_pattern = f"{output_type}{stat_pattern}"
-        input_file_list = list(averaging_bdos_directory.glob(bdo_file_pattern))
+        bdo_file_list = list(averaging_bdos_directory.glob(bdo_file_pattern))
 
-        averaged_estimators = fromfilelist(input_file_list=[str(path) for path in input_file_list])
+        averaged_estimators = fromfilelist(input_file_list=[str(path) for path in bdo_file_list])
         assert len(averaged_estimators.pages) > 0
 
-        list_of_estimators_for_each_file = [fromfile(str(path)) for path in input_file_list]
+        list_of_estimators_for_each_file = [fromfile(str(path)) for path in bdo_file_list]
         assert len(list_of_estimators_for_each_file) > 0
 
         for page_id, page in enumerate(averaged_estimators.pages):
@@ -58,7 +57,7 @@ def test_aggregating_equal_stats(averaging_bdos_directory, small_stat_bdo_patter
             if "mean" in output_type:
                 assert np.average(list_of_entries_to_aggregate) == pytest.approx(page.data)
                 assert np.std(list_of_entries_to_aggregate, ddof=1, axis=0) / np.sqrt(
-                    len(input_file_list)) == pytest.approx(page.error)
+                    len(bdo_file_list)) == pytest.approx(page.error)
             elif "sum" in output_type:
                 assert np.sum(list_of_entries_to_aggregate) == pytest.approx(page.data)
                 assert page.error is None
@@ -68,3 +67,27 @@ def test_aggregating_equal_stats(averaging_bdos_directory, small_stat_bdo_patter
             elif "concat" in output_type:
                 assert np.concatenate(list_of_entries_to_aggregate, axis=1) == pytest.approx(page.data)
                 assert page.error is None
+
+
+@pytest.mark.parametrize(
+    "output_type",
+    ["normalisation-2_aggregation-sum", "normalisation-3_aggregation-mean", "normalisation-5_aggregation-mean"])
+def test_aggregating_weighted_stats(averaging_bdos_directory, small_stat_bdo_pattern, large_stat_bdo_pattern,
+                                    output_type):
+    """
+    For weighted statistics, we need to calculate the weighted average
+    The average from all files, should be approximately the same as from the large statistics file
+    """
+    large_stat_bdo_files = list(averaging_bdos_directory.glob(f"{output_type}{large_stat_bdo_pattern}"))
+    all_bdo_files = list(averaging_bdos_directory.glob(f"{output_type}{small_stat_bdo_pattern}")) + large_stat_bdo_files
+
+    averaged_estimators_all = fromfilelist(input_file_list=[str(path) for path in all_bdo_files])
+    assert len(averaged_estimators_all.pages) > 0
+
+    averaged_estimators_large_stat = fromfilelist(input_file_list=[str(path) for path in large_stat_bdo_files])
+    assert len(averaged_estimators_large_stat.pages) > 0
+
+    for all_stat_pages, large_stat_pages in zip(averaged_estimators_all.pages, averaged_estimators_large_stat.pages):
+        # the small stats should not affect the result by more than 1%
+        assert all_stat_pages.data == pytest.approx(large_stat_pages.data, rel=1e-2)
+        # assert all_stat_pages.error == pytest.approx(large_stat_pages.error, rel=1e-2)