ULTRA l1b stat cull validation test

imap_processing/tests/external_test_data_config.py

@@ -153,6 +153,7 @@
     ("status_test_data_repoint00047.csv", "ultra/data/l1/"),
     ("voltage_culling_results_repoint00047.csv", "ultra/data/l1/"),
     ("validate_high_energy_culling_results_repoint00047_v2.csv", "ultra/data/l1/"),
+    ("validate_stat_culling_results_repoint00047.csv", "ultra/data/l1/"),
     ("de_test_data_repoint00047.csv", "ultra/data/l1/"),
     ("FM45_UltraFM45Extra_TV_Tests_2024-01-22T0930_20240122T093008.CCSDS", "ultra/data/l0/"),
     ("ultra45_raw_sc_rawnrgevnt_19840122_00.csv", "ultra/data/l0/"),

imap_processing/tests/ultra/unit/test_ultra_l1b_culling.py

@@ -844,6 +844,50 @@ def test_get_poisson_stats():
     assert outlier_mask[-1]
 
 
+@pytest.mark.external_test_data
+def test_validate_stat_cull():
+    """Validate that statistical-outlier quality flags match expected results."""
+    # read test data from csv files
+    xspin = pd.read_csv(TEST_PATH / "extendedspin_test_data_repoint00047.csv")
+    results_df = pd.read_csv(
+        TEST_PATH / "validate_stat_culling_results_repoint00047.csv"
+    )
+    de_df = pd.read_csv(TEST_PATH / "de_test_data_repoint00047.csv")
+    de_ds = xr.Dataset(
+        {
+            "de_event_met": ("epoch", de_df.event_times.values),
+            "energy_spacecraft": ("epoch", de_df.energy_spacecraft.values),
+            "quality_outliers": ("epoch", de_df.quality_outliers.values),
+            "quality_scattering": ("epoch", de_df.quality_scattering.values),
+            "ebin": ("epoch", de_df.ebin.values),
+        }
+    )
+    # Use constants from the code to ensure consistency with the actual culling code
+    spin_bin_size = UltraConstants.SPIN_BIN_SIZE
+    spin_tbin_edges = get_binned_spins_edges(
+        xspin.spin_number.values,
+        xspin.spin_period.values,
+        xspin.spin_start_time.values,
+        spin_bin_size,
+    )
+    intervals, _, _ = build_energy_bins()
+    # Get the energy ranges
+    energy_ranges = get_binned_energy_ranges(intervals)
+    mask = np.zeros((len(energy_ranges) - 1, len(spin_tbin_edges) - 1), dtype=bool)
+    flags, con, it, std = flag_statistical_outliers(
+        de_ds, spin_tbin_edges, energy_ranges, mask, 90
+    )
+    expected_qf = results_df.iloc[:, :-3].values.astype(bool)
+    converge = results_df["converge"].values
+    iterations = results_df["iterations"].values
+    std_diff = results_df["std_diff"].values
+    # check that the flags match the expected results
+    np.testing.assert_array_equal(flags, ~expected_qf.astype(bool))
+    np.testing.assert_array_equal(con, converge)
+    np.testing.assert_array_equal(it, iterations)
+    np.testing.assert_allclose(std, std_diff, rtol=1e-10)
+
+
 def test_get_energy_range_flags():
     """Tests get_binned_energy_range_flags function."""
     # Get energy bins used at l1c

imap_processing/ultra/l1b/ultra_l1b_culling.py

@@ -880,7 +880,6 @@ def flag_statistical_outliers(
                 # Select counts that have not been flagged in any channel.
                 counts = count_summary[e_idx, ~combined_mask]
                 std_ratio, _ = get_poisson_stats(counts)
-                std_diff[e_idx] = std_ratio
                 if std_ratio < std_threshold:
                     convergence[e_idx] = True