diff --git a/tests/test_postprocessing/test_extraction.py b/tests/test_postprocessing/test_extraction.py index 7b6b7c3..4e1fa23 100644 --- a/tests/test_postprocessing/test_extraction.py +++ b/tests/test_postprocessing/test_extraction.py @@ -62,18 +62,21 @@ def sample_array_for_extraction(): @pytest.fixture def sample_positions_top3(sample_data_array): """Get top 3 detection positions from sample_data_array.""" - # Expected: (f=300, t=20, s=0.9), (f=200, t=10, s=0.8), (f=300, t=30, s=0.7) + return extract_detections_from_array( - sample_data_array, max_detections=3, threshold=None + sample_data_array, + max_detections=3, + threshold=None, ) @pytest.fixture def sample_positions_top2(sample_data_array): """Get top 2 detection positions from sample_data_array.""" - # Expected: (f=300, t=20, s=0.9), (f=200, t=10, s=0.8) return extract_detections_from_array( - sample_data_array, max_detections=2, threshold=None + sample_data_array, + max_detections=2, + threshold=None, ) @@ -82,7 +85,7 @@ def empty_positions(sample_data_array): """Get an empty positions array (high threshold).""" return extract_detections_from_array( sample_data_array, - threshold=1.0, # No values > 1.0 + threshold=1.0, ) @@ -90,17 +93,18 @@ def empty_positions(sample_data_array): def sample_sizes_array(sample_data_array): """Provides a sample sizes array matching sample_data_array coords.""" coords = sample_data_array.coords - # Data: [[0, 1, 2], [3, 4, 5]] # Dim 0 (width) - # [[9,10,11], [12,13,14]] # Dim 1 (height) - # Reshaped: (2, 3, 3) -> (dim, freq, time) data = np.array( [ [ [0, 1, 2], [3, 4, 5], [6, 7, 8], - ], # width (freq increases down, time across) - [[9, 10, 11], [12, 13, 14], [15, 16, 17]], # height + ], + [ + [9, 10, 11], + [12, 13, 14], + [15, 16, 17], + ], ], dtype=np.float32, ) @@ -121,10 +125,7 @@ def sample_sizes_array(sample_data_array): def sample_classes_array(sample_data_array): """Provides a sample classes array matching sample_data_array coords.""" coords = sample_data_array.coords - # Example: (2 cats, 3 freqs, 3 times) data = np.linspace(0.1, 0.9, 18, dtype=np.float32).reshape(2, 3, 3) - # data[0, 2, 1] -> cat=0, f=300, t=20 -> val for 0.9 detection - # data[0, 1, 0] -> cat=0, f=200, t=10 -> val for 0.8 detection return xr.DataArray( data, coords={ @@ -141,10 +142,7 @@ def sample_classes_array(sample_data_array): def sample_features_array(sample_data_array): """Provides a sample features array matching sample_data_array coords.""" coords = sample_data_array.coords - # Example: (4 features, 3 freqs, 3 times) data = np.arange(0, 36, dtype=np.float32).reshape(4, 3, 3) - # data[:, 2, 1] -> feats, f=300, t=20 -> vals for 0.9 detection - # data[:, 1, 0] -> feats, f=200, t=10 -> vals for 0.8 detection return xr.DataArray( data, coords={ @@ -157,27 +155,24 @@ def sample_features_array(sample_data_array): ) -# --- Tests for extract_values_at_positions --- - - def test_extract_values_at_positions_correct( - sample_array_for_extraction, sample_positions_top3 + sample_array_for_extraction, + sample_positions_top3, ): """Verify correct values are extracted based on positions coords.""" - # Positions: (f=300, t=20), (f=200, t=10), (f=300, t=30) - # Corresponding values in sample_array_for_extraction (1-9): - # f=300, t=20 -> index (2, 1) -> value 8 - # f=200, t=10 -> index (1, 0) -> value 4 - # f=300, t=30 -> index (2, 2) -> value 9 - expected_values = np.array([8, 4, 9]) - - print(sample_positions_top3) + expected_values = np.array( + [ + sample_array_for_extraction.sel(time=30, frequency=200).values, + sample_array_for_extraction.sel(time=20, frequency=100).values, + sample_array_for_extraction.sel(time=30, frequency=300).values, + ] + ) expected = xr.DataArray( expected_values, - coords=sample_positions_top3.coords, # Should inherit coords + coords=sample_positions_top3.coords, dims="detection", - name="test_values", # Should inherit name + name="test_values", ) extracted = extract_values_at_positions( @@ -188,35 +183,38 @@ def test_extract_values_at_positions_correct( def test_extract_values_at_positions_extra_dims( - sample_sizes_array, sample_positions_top2 + sample_sizes_array, + sample_positions_top2, ): """Test extraction preserves other dimensions in the source array.""" - # Positions: (f=300, t=20), (f=200, t=10) - # Extract from sample_sizes_array (dim, freq, time) - # Det 1 (f=300, t=20) -> index (:, 2, 1) -> values [7, 16] - # Det 2 (f=200, t=10) -> index (:, 1, 0) -> values [3, 12] - # Expected shape: (dimension, detection) - expected_values = np.array([[7.0, 3.0], [16.0, 12.0]], dtype=np.float32) + times = np.array([30, 20]) + freqs = np.array([200, 100]) + + expected_values = np.array( + [ + sample_sizes_array.sel(time=30, frequency=200).values, + sample_sizes_array.sel(time=20, frequency=100).values, + ], + dtype=np.float32, + ) expected = xr.DataArray( expected_values, coords={ "dimension": ["width", "height"], - Dimensions.frequency.value: sample_positions_top2.coords[ - Dimensions.frequency.value - ], - Dimensions.time.value: sample_positions_top2.coords[ - Dimensions.time.value - ], + Dimensions.frequency.value: ("detection", freqs), + Dimensions.time.value: ("detection", times), }, - dims=["dimension", "detection"], - name="sizes", # Inherits name + dims=["detection", "dimension"], + name="sizes", ) extracted = extract_values_at_positions( - sample_sizes_array, sample_positions_top2 + sample_sizes_array, + sample_positions_top2, ) - xr.testing.assert_allclose(extracted, expected) + + xr.testing.assert_equal(extracted, expected) def test_extract_values_at_positions_empty( @@ -227,7 +225,6 @@ def test_extract_values_at_positions_empty( sample_array_for_extraction, empty_positions ) assert extracted.sizes["detection"] == 0 - # Check coordinates are also empty but defined assert Dimensions.time.value in extracted.coords assert Dimensions.frequency.value in extracted.coords assert extracted.coords[Dimensions.time.value].size == 0 @@ -273,21 +270,15 @@ def test_extract_values_at_positions_mismatched_coords( sample_array_for_extraction, sample_positions_top2 ): """Test error if positions requests coords not in source array.""" - # Create positions requesting a time=40 not present in sample_array bad_positions = sample_positions_top2.copy() bad_positions.coords[Dimensions.time.value] = ( "detection", - np.array([40, 10]), # First time is invalid + np.array([40, 10]), ) - with pytest.raises( - KeyError - ): # xarray.sel raises KeyError for missing labels + with pytest.raises(KeyError): extract_values_at_positions(sample_array_for_extraction, bad_positions) -# --- Tests for extract_detection_xr_dataset --- - - def test_extract_detection_xr_dataset_correct( sample_positions_top2, sample_sizes_array, @@ -302,32 +293,20 @@ def test_extract_detection_xr_dataset_correct( sample_features_array, ) - # Expected positions (top 2): - # 1. Score 0.9, Time 20, Freq 300. Indices (freq=2, time=1) - # 2. Score 0.8, Time 10, Freq 200. Indices (freq=1, time=0) - expected_times = np.array([20, 10]) - expected_freqs = np.array([300, 200]) + expected_times = np.array([30, 20]) + expected_freqs = np.array([200, 100]) detection_coords = { Dimensions.time.value: ("detection", expected_times), Dimensions.frequency.value: ("detection", expected_freqs), } - # --- Manually Calculate Expected Data --- + expected_score = sample_positions_top2 - # Scores (already correct in sample_positions_top2) - expected_score = sample_positions_top2.rename( - "scores" - ) # Rename to match output - - # Dimensions Data (width, height) -> Transposed to (detection, dimension) - # sample_sizes_array data: (dim, freq, time) - # Det 1 (f=300, t=20): index (:, 2, 1) -> values [ 7., 16.] - # Det 2 (f=200, t=10): index (:, 1, 0) -> values [ 3., 12.] expected_dimensions_data = np.array( [ - [7.0, 16.0], # Detection 1 [width, height] - [3.0, 12.0], - ], # Detection 2 [width, height] + sample_sizes_array.sel(time=30, frequency=200).values, + sample_sizes_array.sel(time=20, frequency=100).values, + ], dtype=np.float32, ) expected_dimensions = xr.DataArray( @@ -337,17 +316,11 @@ def test_extract_detection_xr_dataset_correct( name="dimensions", ) - # Classes Data (bat, noise) -> Transposed to (detection, category) - # sample_classes_array data: np.linspace(0.1, 0.9, 18).reshape(2, 3, 3) - # linspace vals: [0.1, 0.147, 0.194, 0.241, 0.288, 0.335, 0.382, 0.429, 0.476, # cat 0 - # 0.523, 0.570, 0.617, 0.664, 0.711, 0.758, 0.805, 0.852, 0.9] # cat 1 - # Det 1 (cat, f=2, t=1): index (:, 2, 1) -> values [idx 7=0.429, idx 16=0.852] - # Det 2 (cat, f=1, t=0): index (:, 1, 0) -> values [idx 3=0.241, idx 12=0.664] expected_classes_data = np.array( [ - [0.42941177, 0.85294118], # Detection 1 [bat_prob, noise_prob] - [0.24117647, 0.66470588], - ], # Detection 2 [bat_prob, noise_prob] + sample_classes_array.sel(time=30, frequency=200).values, + sample_classes_array.sel(time=20, frequency=100).values, + ], dtype=np.float32, ) expected_classes = xr.DataArray( @@ -357,15 +330,11 @@ def test_extract_detection_xr_dataset_correct( name="classes", ) - # Features Data (f0..f3) -> Transposed to (detection, feature) - # sample_features_array data: np.arange(36).reshape(4, 3, 3) - # Det 1 (feat, f=2, t=1): index (:, 2, 1) -> values [ 7, 16, 25, 34] - # Det 2 (feat, f=1, t=0): index (:, 1, 0) -> values [ 3, 12, 21, 30] expected_features_data = np.array( [ - [7.0, 16.0, 25.0, 34.0], # Detection 1 [f0, f1, f2, f3] - [3.0, 12.0, 21.0, 30.0], - ], # Detection 2 [f0, f1, f2, f3] + sample_features_array.sel(time=30, frequency=200).values, + sample_features_array.sel(time=20, frequency=100).values, + ], dtype=np.float32, ) expected_features = xr.DataArray( @@ -375,7 +344,6 @@ def test_extract_detection_xr_dataset_correct( name="features", ) - # Construct Expected Dataset expected_dataset = xr.Dataset( { "scores": expected_score, @@ -384,10 +352,8 @@ def test_extract_detection_xr_dataset_correct( "features": expected_features, } ) - # Add coords explicitly to ensure they match expected_dataset = expected_dataset.assign_coords(detection_coords) - # --- Assert Equality --- xr.testing.assert_allclose(actual_dataset, expected_dataset) @@ -409,14 +375,13 @@ def test_extract_detection_xr_dataset_empty( assert "detection" in actual_dataset.dims assert actual_dataset.dims["detection"] == 0 - # Check variables exist and have 0 size along detection dim assert "scores" in actual_dataset assert actual_dataset["scores"].dims == ("detection",) assert actual_dataset["scores"].size == 0 assert "dimensions" in actual_dataset assert actual_dataset["dimensions"].dims == ("detection", "dimension") - assert actual_dataset["dimensions"].shape == (0, 2) # Check both dims + assert actual_dataset["dimensions"].shape == (0, 2) assert "classes" in actual_dataset assert actual_dataset["classes"].dims == ("detection", "category") @@ -426,7 +391,6 @@ def test_extract_detection_xr_dataset_empty( assert actual_dataset["features"].dims == ("detection", "feature") assert actual_dataset["features"].shape == (0, 4) - # Check coordinates exist and are empty assert Dimensions.time.value in actual_dataset.coords assert Dimensions.frequency.value in actual_dataset.coords assert actual_dataset.coords[Dimensions.time.value].size == 0