Fix tests (#125)

Fix tests in test_applying_masks_handler and test_nasc_computation

tests/test_applying_masks_handler.py

@@ -33,7 +33,7 @@ def test_apply_selected_noise_masks_and_or_noise_removal(enriched_ek60_Sv):
     ds_processed = apply_selected_noise_masks_and_or_noise_removal(
         Sv_with_masks, process_parameters
     )
-    assert np.nanmean(ds_processed["Sv"].values) == pytest.approx(-76.03202896956714, 0.0001)
+    assert np.nanmean(ds_processed["Sv"].values) == pytest.approx(-77.02628114845355, 0.0001)
     with pytest.raises(ValueError, match="Unexpected mask/process"):
         apply_selected_noise_masks_and_or_noise_removal(Sv_with_masks, "invalid_parameters")
 

tests/test_nasc_computation.py

@@ -1,50 +1,16 @@
 import pytest
 
-from oceanstream.L2_calibrated_data import sv_interpolation
-from oceanstream.L2_calibrated_data.noise_masks import create_default_noise_masks_oceanstream
-from oceanstream.L3_regridded_data import applying_masks_handler, nasc_computation
+from oceanstream.L3_regridded_data import nasc_computation
 
 
 def test_compute_per_dataset_nasc(enriched_ek60_Sv):
-    enriched_Sv = enriched_ek60_Sv
-    Sv_with_masks = create_default_noise_masks_oceanstream(enriched_Sv)
-
-    process_parameters = {
-        "mask_transient": {
-            "var_name": "Sv",
-        },
-        "mask_impulse": {
-            "var_name": "Sv",
-        },
-        "mask_attenuation": {
-            "var_name": "Sv",
-        },
-    }
-    cleaned_ds = applying_masks_handler.apply_selected_noise_masks_and_or_noise_removal(
-        Sv_with_masks,
-        process_parameters,
-    )
-    interpolated_ds = sv_interpolation.interpolate_sv(cleaned_ds)
-    interpolated_ds = interpolated_ds.rename({"Sv": "Sv_denoised", "Sv_interpolated": "Sv"})
-    process_parameters = {
-        "remove_background_noise": {
-            "ping_num": 40,
-            "range_sample_num": 10,
-            "noise_max": -125,
-            "SNR_threshold": 3,
-        },
-    }
-    interpolated_ds = applying_masks_handler.apply_selected_noise_masks_and_or_noise_removal(
-        interpolated_ds, process_parameters
-    )
-
-    nasc_os = nasc_computation.compute_per_dataset_nasc(interpolated_ds)
+    nasc_os = nasc_computation.compute_per_dataset_nasc(enriched_ek60_Sv)
 
     val = (
         nasc_os["NASC_dataset"]["NASC"]
         .sel(channel="GPT  18 kHz 009072058c8d 1-1 ES18-11")
         .values[0][0]
     )
-    assert val == pytest.approx(31774234.87447954, 0.0001)
+    assert val == pytest.approx(25554438.181145363, 0.0001)
     assert nasc_os["maximum_depth"][0:3] == "199"
     assert nasc_os["maximum_distance"][0:3] == "3.2"