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Fixed errors with extraction test

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mbsantiago 2025-04-22 08:50:56 +01:00
parent b82973ca1d
commit ece1a2073d
1 changed files with 60 additions and 96 deletions
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156
tests/test_postprocessing/test_extraction.py
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@ -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